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  Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction. Fischer, Elisabeth; Schlör, Daniel; Zehe, Albin; Hotho, Andreas. 2024, October
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  @misc{fischer2024modeling, author = {Fischer, Elisabeth and Schlör, Daniel and Zehe, Albin and Hotho, Andreas}, keywords = {author:zehe}, month = 10, title = {Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction}, year = 2024 }

  %0 Generic %1 fischer2024modeling %A Fischer, Elisabeth %A Schlör, Daniel %A Zehe, Albin %A Hotho, Andreas %D 2024 %T Modeling and Analyzing the Influence of Non-Item Pages on Sequential Next-Item Prediction %U https://arxiv.org/abs/2408.15953
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  Global Vegetation Modeling with Pre-Trained Weather Transformers. Janetzky, Pascal; Gallusser, Florian; Hentschel, Simon; Hotho, Andreas; Krause, Anna. 2024
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  Accurate vegetation models can produce further insights into the complex inter- action between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecast- ing, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation of the atmosphere’s state can be transferred to model the normalized difference vegetation index (NDVI). Our model globally estimates vegetation activity at a resolution of 0.25◦ while relying only on meteorological data. We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch. Additionally, we compare our results to other recent data-driven NDVI modeling approaches from machine learning and ecology literature. We further provide experimental evidence on how much data and training time is necessary to turn FourCastNet into an effective vegetation model. Code and models will be made available upon publication.

  @misc{janetzky2024global, abstract = {Accurate vegetation models can produce further insights into the complex inter- action between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecast- ing, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation of the atmosphere’s state can be transferred to model the normalized difference vegetation index (NDVI). Our model globally estimates vegetation activity at a resolution of 0.25◦ while relying only on meteorological data. We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch. Additionally, we compare our results to other recent data-driven NDVI modeling approaches from machine learning and ecology literature. We further provide experimental evidence on how much data and training time is necessary to turn FourCastNet into an effective vegetation model. Code and models will be made available upon publication.}, author = {Janetzky, Pascal and Gallusser, Florian and Hentschel, Simon and Hotho, Andreas and Krause, Anna}, keywords = {ecomodelling}, title = {Global Vegetation Modeling with Pre-Trained Weather Transformers}, year = 2024 }

  %0 Generic %1 janetzky2024global %A Janetzky, Pascal %A Gallusser, Florian %A Hentschel, Simon %A Hotho, Andreas %A Krause, Anna %D 2024 %T Global Vegetation Modeling with Pre-Trained Weather Transformers %U https://arxiv.org/abs/2403.18438v1 %X Accurate vegetation models can produce further insights into the complex inter- action between vegetation activity and ecosystem processes. Previous research has established that long-term trends and short-term variability of temperature and precipitation affect vegetation activity. Motivated by the recent success of Transformer-based Deep Learning models for medium-range weather forecast- ing, we adapt the publicly available pre-trained FourCastNet to model vegetation activity while accounting for the short-term dynamics of climate variability. We investigate how the learned global representation of the atmosphere’s state can be transferred to model the normalized difference vegetation index (NDVI). Our model globally estimates vegetation activity at a resolution of 0.25◦ while relying only on meteorological data. We demonstrate that leveraging pre-trained weather models improves the NDVI estimates compared to learning an NDVI model from scratch. Additionally, we compare our results to other recent data-driven NDVI modeling approaches from machine learning and ecology literature. We further provide experimental evidence on how much data and training time is necessary to turn FourCastNet into an effective vegetation model. Code and models will be made available upon publication.
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  Adapting Sequential Recommender Models to Content Recommendation in Chat Data using Non-Item Page-Models. Zehe, Albin; Fischer, Elisabeth; Kaiser, Jonas; Wagner, Toni; Hotho, Andreas. In Proceedings of the Sixth Knowledge-aware and Conversational Recommender Systems Workshop. 2024
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  @inproceedings{zehe2024adapting, author = {Zehe, Albin and Fischer, Elisabeth and Kaiser, Jonas and Wagner, Toni and Hotho, Andreas}, booktitle = {Proceedings of the Sixth Knowledge-aware and Conversational Recommender Systems Workshop}, keywords = {author:zehe}, month = 10, title = {Adapting Sequential Recommender Models to Content Recommendation in Chat Data using Non-Item Page-Models}, year = 2024 }

  %0 Conference Paper %1 zehe2024adapting %A Zehe, Albin %A Fischer, Elisabeth %A Kaiser, Jonas %A Wagner, Toni %A Hotho, Andreas %B Proceedings of the Sixth Knowledge-aware and Conversational Recommender Systems Workshop %D 2024 %T Adapting Sequential Recommender Models to Content Recommendation in Chat Data using Non-Item Page-Models %U https://ceur-ws.org/Vol-3817/
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  PreAdapter: Pre-training Language Models on Knowledge Graphs. Omeliyanenko, Janna; Hotho, Andreas; Schlör, Daniel. In International Semantic Web Conference ISWC 2024, to appear. 2024
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  @article{omeliyanenko2024preadapter, author = {Omeliyanenko, Janna and Hotho, Andreas and Schlör, Daniel}, journal = {International Semantic Web Conference ISWC 2024, to appear}, keywords = {selected}, title = {PreAdapter: Pre-training Language Models on Knowledge Graphs}, year = 2024 }

  %0 Journal Article %1 omeliyanenko2024preadapter %A Omeliyanenko, Janna %A Hotho, Andreas %A Schlör, Daniel %D 2024 %J International Semantic Web Conference ISWC 2024, to appear %T PreAdapter: Pre-training Language Models on Knowledge Graphs
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  Data Generation for Explainable Occupational Fraud Detection. Tritscher, Julian; Wolf, Maximilian; Krause, Anna; Hotho, Andreas; Schlör, Daniel. In 47th German Conference on Artificial Intelligence (KI 2024) - to appear. 2024
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  Occupational fraud, the deliberate misuse of company assets by employees, causes damages of around 5% of yearly company revenue. Recent work therefore focuses on automatically detecting occupational fraud through machine learning on the company data contained within enterprise resource planning systems. Since interpretability of these machine learning approaches is considered a relevant aspect of occupational fraud detection, first works have already integrated post-hoc explainable artificial intelligence approaches into their fraud detectors. While these explainers show promising first results, systematic advancement of explainable fraud detection methods is currently hindered by the general lack of ground truth explanations to evaluate explanation quality and choose suitable explainers. To avoid expensive expert annotations, we propose a data generation scheme based on multi-agent systems to obtain company data with labeled occupational fraud cases and ground truth explanations. Using this data generator, we design a framework that enables the optimization of post-hoc explainers for unlabeled company data. On two datasets, we experimentally show that our framework is able to successfully differentiate between explainers of high and low explanation quality, showcasing the potential of multi-agent-simulations to ensure proper performance of post-hoc explainers.

  @article{tritscher2024generation, abstract = {Occupational fraud, the deliberate misuse of company assets by employees, causes damages of around 5% of yearly company revenue. Recent work therefore focuses on automatically detecting occupational fraud through machine learning on the company data contained within enterprise resource planning systems. Since interpretability of these machine learning approaches is considered a relevant aspect of occupational fraud detection, first works have already integrated post-hoc explainable artificial intelligence approaches into their fraud detectors. While these explainers show promising first results, systematic advancement of explainable fraud detection methods is currently hindered by the general lack of ground truth explanations to evaluate explanation quality and choose suitable explainers. To avoid expensive expert annotations, we propose a data generation scheme based on multi-agent systems to obtain company data with labeled occupational fraud cases and ground truth explanations. Using this data generator, we design a framework that enables the optimization of post-hoc explainers for unlabeled company data. On two datasets, we experimentally show that our framework is able to successfully differentiate between explainers of high and low explanation quality, showcasing the potential of multi-agent-simulations to ensure proper performance of post-hoc explainers.}, author = {Tritscher, Julian and Wolf, Maximilian and Krause, Anna and Hotho, Andreas and Schlör, Daniel}, journal = {47th German Conference on Artificial Intelligence (KI 2024) - to appear}, keywords = {xai}, title = {Data Generation for Explainable Occupational Fraud Detection}, year = 2024 }

  %0 Journal Article %1 tritscher2024generation %A Tritscher, Julian %A Wolf, Maximilian %A Krause, Anna %A Hotho, Andreas %A Schlör, Daniel %D 2024 %J 47th German Conference on Artificial Intelligence (KI 2024) - to appear %T Data Generation for Explainable Occupational Fraud Detection %X Occupational fraud, the deliberate misuse of company assets by employees, causes damages of around 5% of yearly company revenue. Recent work therefore focuses on automatically detecting occupational fraud through machine learning on the company data contained within enterprise resource planning systems. Since interpretability of these machine learning approaches is considered a relevant aspect of occupational fraud detection, first works have already integrated post-hoc explainable artificial intelligence approaches into their fraud detectors. While these explainers show promising first results, systematic advancement of explainable fraud detection methods is currently hindered by the general lack of ground truth explanations to evaluate explanation quality and choose suitable explainers. To avoid expensive expert annotations, we propose a data generation scheme based on multi-agent systems to obtain company data with labeled occupational fraud cases and ground truth explanations. Using this data generator, we design a framework that enables the optimization of post-hoc explainers for unlabeled company data. On two datasets, we experimentally show that our framework is able to successfully differentiate between explainers of high and low explanation quality, showcasing the potential of multi-agent-simulations to ensure proper performance of post-hoc explainers.
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  LL\"aMmlein: Compact and Competitive German-Only Language Models from Scratch. Pfister, Jan; Wunderle, Julia; Hotho, Andreas. 2024
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  @misc{pfister2024llammleincompactcompetitivegermanonly, author = {Pfister, Jan and Wunderle, Julia and Hotho, Andreas}, keywords = {author:pfister}, title = {LL\"aMmlein: Compact and Competitive German-Only Language Models from Scratch}, year = 2024 }

  %0 Generic %1 pfister2024llammleincompactcompetitivegermanonly %A Pfister, Jan %A Wunderle, Julia %A Hotho, Andreas %D 2024 %T LL\"aMmlein: Compact and Competitive German-Only Language Models from Scratch %U https://arxiv.org/abs/2411.11171
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  From Chat to Publication Management: Organizing your related work using BibSonomy \& LLMs. V\"{o}lker, Tom; Pfister, Jan; Koopmann, Tobias; Hotho, Andreas. In Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, of CHIIR ’24, pp. 386–390. Association for Computing Machinery, <conf-loc>, <city>Sheffield</city>, <country>United Kingdom</country>, </conf-loc>, 2024
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  The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search \& Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing \& Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.

  @inproceedings{10.1145/3627508.3638298, abstract = {The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search \& Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing \& Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.}, address = {New York, NY, USA}, author = {V\"{o}lker, Tom and Pfister, Jan and Koopmann, Tobias and Hotho, Andreas}, booktitle = {Proceedings of the 2024 Conference on Human Information Interaction and Retrieval}, keywords = {Publication}, pages = {386–390}, publisher = {Association for Computing Machinery}, series = {CHIIR '24}, title = {From Chat to Publication Management: Organizing your related work using BibSonomy \& LLMs}, year = 2024 }

  %0 Conference Paper %1 10.1145/3627508.3638298 %A V\"{o}lker, Tom %A Pfister, Jan %A Koopmann, Tobias %A Hotho, Andreas %B Proceedings of the 2024 Conference on Human Information Interaction and Retrieval %C New York, NY, USA %D 2024 %I Association for Computing Machinery %P 386–390 %R 10.1145/3627508.3638298 %T From Chat to Publication Management: Organizing your related work using BibSonomy \& LLMs %U https://doi.org/10.1145/3627508.3638298 %X The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search \& Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing \& Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics. %@ 9798400704345
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  BibSonomy Meets ChatLLMs for Publication Management: From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs. Völker, Tom; Pfister, Jan; Koopmann, Tobias; Hotho, Andreas. 2024
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  The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search & Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing & Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.

  @misc{volker2024bibsonomy, abstract = {The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search & Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing & Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.}, author = {Völker, Tom and Pfister, Jan and Koopmann, Tobias and Hotho, Andreas}, keywords = {from:janpf}, note = {cite arxiv:2401.09092Comment: Accepted at 2024 ACM SIGIR CHIIR, For a demo see here http://professor-x.de/demos/bibsonomy-chatgpt/demo.mp4}, title = {BibSonomy Meets ChatLLMs for Publication Management: From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs}, year = 2024 }

  %0 Generic %1 volker2024bibsonomy %A Völker, Tom %A Pfister, Jan %A Koopmann, Tobias %A Hotho, Andreas %D 2024 %R 10.1145/3627508.3638298 %T BibSonomy Meets ChatLLMs for Publication Management: From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs %U http://arxiv.org/abs/2401.09092 %X The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search & Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing & Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.
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  CompTrails: comparing hypotheses across behavioral networks. Koopmann, Tobias; Becker, Martin; Lemmerich, Florian; Hotho, Andreas. In Data Mining and Knowledge Discovery. 2024
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  The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.

  @article{Koopmann2024, abstract = {The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.}, author = {Koopmann, Tobias and Becker, Martin and Lemmerich, Florian and Hotho, Andreas}, journal = {Data Mining and Knowledge Discovery}, keywords = {research:recommender}, month = {01}, title = {CompTrails: comparing hypotheses across behavioral networks}, year = 2024 }

  %0 Journal Article %1 Koopmann2024 %A Koopmann, Tobias %A Becker, Martin %A Lemmerich, Florian %A Hotho, Andreas %D 2024 %J Data Mining and Knowledge Discovery %R 10.1007/s10618-023-00996-8 %T CompTrails: comparing hypotheses across behavioral networks %U https://doi.org/10.1007/s10618-023-00996-8 %X The term Behavioral Networks describes networks that contain relational information on human behavior. This ranges from social networks that contain friendships or cooperations between individuals, to navigational networks that contain geographical or web navigation, and many more. Understanding the forces driving behavior within these networks can be beneficial to improving the underlying network, for example, by generating new hyperlinks on websites, or by proposing new connections and friends on social networks. Previous approaches considered different hypotheses on a single network and evaluated which hypothesis fits best. These hypotheses can represent human intuition and expert opinions or be based on previous insights. In this work, we extend these approaches to enable the comparison of a single hypothesis between multiple networks. We unveil several issues of naive approaches that potentially impact comparisons and lead to undesired results. Based on these findings, we propose a framework with five flexible components that allow addressing specific analysis goals tailored to the application scenario. We show the benefits and limits of our approach by applying it to synthetic data and several real-world datasets, including web navigation, bibliometric navigation, and geographic navigation. Our work supports practitioners and researchers with the aim of understanding similarities and differences in human behavior between environments.
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  Generative Inpainting for Shapley-Value-Based Anomaly Explanation. Tritscher, Julian; Lissmann, Philip; Wolf, Maximilian; Krause, Anna; Hotho, Andreas; Schlör, Daniel. In The World Conference on eXplainable Artificial Intelligence (xAI 2024) - to appear. 2024
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  Feature relevance explanations currently constitute the most used type of explanation in anomaly detection related tasks such as cyber security and fraud detection. Recent works have underscored the importance of optimizing hyperparameters of post-hoc explainers which show a large impact on the resulting explanation quality. In this work, we propose a new method to set the hyperparameter of replacement values within Shapley-value-based post-hoc explainers. Our method leverages ideas from the domain of generative image inpainting, where generative machine learning models are used to replace parts of a given input image. We show that these generative models can also be applied to tabular replacement value generation for Shapley-value-based feature relevance explainers. Experimentally, we train a denoising diffusion probabilistic model for generative inpainting on two tabular anomaly detection datasets from the domains of network intrusion detection and occupational fraud detection, and integrate the generative inpainting model into the SHAP explanation framework. We empirically show that generative inpainting may be used to achieve consistently strong explanation quality when explaining different anomaly detectors on tabular data.

  @article{tritscher2024generative, abstract = {Feature relevance explanations currently constitute the most used type of explanation in anomaly detection related tasks such as cyber security and fraud detection. Recent works have underscored the importance of optimizing hyperparameters of post-hoc explainers which show a large impact on the resulting explanation quality. In this work, we propose a new method to set the hyperparameter of replacement values within Shapley-value-based post-hoc explainers. Our method leverages ideas from the domain of generative image inpainting, where generative machine learning models are used to replace parts of a given input image. We show that these generative models can also be applied to tabular replacement value generation for Shapley-value-based feature relevance explainers. Experimentally, we train a denoising diffusion probabilistic model for generative inpainting on two tabular anomaly detection datasets from the domains of network intrusion detection and occupational fraud detection, and integrate the generative inpainting model into the SHAP explanation framework. We empirically show that generative inpainting may be used to achieve consistently strong explanation quality when explaining different anomaly detectors on tabular data.}, author = {Tritscher, Julian and Lissmann, Philip and Wolf, Maximilian and Krause, Anna and Hotho, Andreas and Schlör, Daniel}, journal = {The World Conference on eXplainable Artificial Intelligence (xAI 2024) - to appear}, keywords = {xai}, month = {04}, title = {Generative Inpainting for Shapley-Value-Based Anomaly Explanation}, year = 2024 }

  %0 Journal Article %1 tritscher2024generative %A Tritscher, Julian %A Lissmann, Philip %A Wolf, Maximilian %A Krause, Anna %A Hotho, Andreas %A Schlör, Daniel %D 2024 %J The World Conference on eXplainable Artificial Intelligence (xAI 2024) - to appear %T Generative Inpainting for Shapley-Value-Based Anomaly Explanation %X Feature relevance explanations currently constitute the most used type of explanation in anomaly detection related tasks such as cyber security and fraud detection. Recent works have underscored the importance of optimizing hyperparameters of post-hoc explainers which show a large impact on the resulting explanation quality. In this work, we propose a new method to set the hyperparameter of replacement values within Shapley-value-based post-hoc explainers. Our method leverages ideas from the domain of generative image inpainting, where generative machine learning models are used to replace parts of a given input image. We show that these generative models can also be applied to tabular replacement value generation for Shapley-value-based feature relevance explainers. Experimentally, we train a denoising diffusion probabilistic model for generative inpainting on two tabular anomaly detection datasets from the domains of network intrusion detection and occupational fraud detection, and integrate the generative inpainting model into the SHAP explanation framework. We empirically show that generative inpainting may be used to achieve consistently strong explanation quality when explaining different anomaly detectors on tabular data.
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  From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs. Völker, Tom; Pfister, Jan; Koopmann, Tobias; Hotho, Andreas. In Proceedings of the 2024 Conference on Human Information Interaction and Retrieval, of CHIIR ’24, pp. 386–390. Association for Computing Machinery, <conf-loc>, <city>Sheffield</city>, <country>United Kingdom</country>, </conf-loc>, 2024
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  The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search \& Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing \& Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.

  @inproceedings{10.1145/3627508.3638298, abstract = {The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search \& Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing \& Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics.}, address = {New York, NY, USA}, author = {Völker, Tom and Pfister, Jan and Koopmann, Tobias and Hotho, Andreas}, booktitle = {Proceedings of the 2024 Conference on Human Information Interaction and Retrieval}, keywords = {author:pfister}, month = {05}, pages = {386–390}, publisher = {Association for Computing Machinery}, series = {CHIIR '24}, title = {From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs}, year = 2024 }

  %0 Conference Paper %1 10.1145/3627508.3638298 %A Völker, Tom %A Pfister, Jan %A Koopmann, Tobias %A Hotho, Andreas %B Proceedings of the 2024 Conference on Human Information Interaction and Retrieval %C New York, NY, USA %D 2024 %I Association for Computing Machinery %P 386–390 %R 10.1145/3627508.3638298 %T From Chat to Publication Management: Organizing your related work using BibSonomy & LLMs %U https://doi.org/10.1145/3627508.3638298 %X The ever-growing corpus of scientific literature presents significant challenges for researchers with respect to discovery, management, and annotation of relevant publications. Traditional platforms like Semantic Scholar, BibSonomy, and Zotero offer tools for literature management, but largely require manual laborious and error-prone input of tags and metadata. Here, we introduce a novel retrieval augmented generation system that leverages chat-based large language models (LLMs) to streamline and enhance the process of publication management. It provides a unified chat-based interface, enabling intuitive interactions with various backends, including Semantic Scholar, BibSonomy, and the Zotero Webscraper. It supports two main use-cases: (1) Explorative Search \& Retrieval - leveraging LLMs to search for and retrieve both specific and general scientific publications, while addressing the challenges of content hallucination and data obsolescence; and (2) Cataloguing \& Management - aiding in the organization of personal publication libraries, in this case BibSonomy, by automating the addition of metadata and tags, while facilitating manual edits and updates. We compare our system to different LLM models in three different settings, including a user study, and we can show its advantages in different metrics. %@ 9798400704345
• 
  Pollice Verso at SemEval-2024 Task 6: The Roman Empire Strikes Back. Kobs, Konstantin; Pfister, Jan; Hotho, Andreas. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), A. K. Ojha, A. S. Do{\u{g}}ru\"oz, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, A. Ros\’a (eds.), pp. 1529–1536. Association for Computational Linguistics, Mexico City, Mexico, 2024
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  We present an intuitive approach for hallucination detection in LLM outputs that is modeled after how humans would go about this task. We engage several LLM {``}experts{''} to independently assess whether a response is hallucinated. For this we select recent and popular LLMs smaller than 7B parameters. By analyzing the log probabilities for tokens that signal a positive or negative judgment, we can determine the likelihood of hallucination. Additionally, we enhance the performance of our {``}experts{''} by automatically refining their prompts using the recently introduced OPRO framework. Furthermore, we ensemble the replies of the different experts in a uniform or weighted manner, which builds a quorum from the expert replies. Overall this leads to accuracy improvements of up to 10.6 p.p. compared to the challenge baseline. We show that a Zephyr 3B model is well suited for the task. Our approach can be applied in the model-agnostic and model-aware subtasks without modification and is flexible and easily extendable to related tasks.

  @inproceedings{kobs-etal-2024-pollice, abstract = {We present an intuitive approach for hallucination detection in LLM outputs that is modeled after how humans would go about this task. We engage several LLM {``}experts{''} to independently assess whether a response is hallucinated. For this we select recent and popular LLMs smaller than 7B parameters. By analyzing the log probabilities for tokens that signal a positive or negative judgment, we can determine the likelihood of hallucination. Additionally, we enhance the performance of our {``}experts{''} by automatically refining their prompts using the recently introduced OPRO framework. Furthermore, we ensemble the replies of the different experts in a uniform or weighted manner, which builds a quorum from the expert replies. Overall this leads to accuracy improvements of up to 10.6 p.p. compared to the challenge baseline. We show that a Zephyr 3B model is well suited for the task. Our approach can be applied in the model-agnostic and model-aware subtasks without modification and is flexible and easily extendable to related tasks.}, address = {Mexico City, Mexico}, author = {Kobs, Konstantin and Pfister, Jan and Hotho, Andreas}, booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)}, editor = {Ojha, Atul Kr. and Do{\u{g}}ru\"oz, A. Seza and Tayyar Madabushi, Harish and Da San Martino, Giovanni and Rosenthal, Sara and Ros\'a, Aiala}, keywords = {author:pfister}, month = {06}, pages = {1529–1536}, publisher = {Association for Computational Linguistics}, title = {Pollice Verso at SemEval-2024 Task 6: The Roman Empire Strikes Back}, year = 2024 }

  %0 Conference Paper %1 kobs-etal-2024-pollice %A Kobs, Konstantin %A Pfister, Jan %A Hotho, Andreas %B Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024) %C Mexico City, Mexico %D 2024 %E Ojha, Atul Kr. %E Do{\u{g}}ru\"oz, A. Seza %E Tayyar Madabushi, Harish %E Da San Martino, Giovanni %E Rosenthal, Sara %E Ros\'a, Aiala %I Association for Computational Linguistics %P 1529--1536 %T Pollice Verso at SemEval-2024 Task 6: The Roman Empire Strikes Back %U https://aclanthology.org/2024.semeval-1.219 %X We present an intuitive approach for hallucination detection in LLM outputs that is modeled after how humans would go about this task. We engage several LLM {``}experts{''} to independently assess whether a response is hallucinated. For this we select recent and popular LLMs smaller than 7B parameters. By analyzing the log probabilities for tokens that signal a positive or negative judgment, we can determine the likelihood of hallucination. Additionally, we enhance the performance of our {``}experts{''} by automatically refining their prompts using the recently introduced OPRO framework. Furthermore, we ensemble the replies of the different experts in a uniform or weighted manner, which builds a quorum from the expert replies. Overall this leads to accuracy improvements of up to 10.6 p.p. compared to the challenge baseline. We show that a Zephyr 3B model is well suited for the task. Our approach can be applied in the model-agnostic and model-aware subtasks without modification and is flexible and easily extendable to related tasks.
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  {O}tterly{O}bsessed{W}ith{S}emantics at {S}em{E}val-2024 Task 4: Developing a Hierarchical Multi-Label Classification Head for Large Language Models. Wunderle, Julia; Schubert, Julian; Cacciatore, Antonella; Zehe, Albin; Pfister, Jan; Hotho, Andreas. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), A. K. Ojha, A. S. Do{\u{g}}ru{\"o}z, H. Tayyar Madabushi, G. Da San Martino, S. Rosenthal, A. Ros{\’a} (eds.), pp. 602–612. Association for Computational Linguistics, Mexico City, Mexico, 2024
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  For our submission for Subtask 1, we developed a custom classification head that is designed to be applied atop of a Large Language Model. We reconstructed the hierarchy across multiple fully connected layers, allowing us to incorporate previous foundational decisions in subsequent, more fine-grained layers. To find the best hyperparameters, we conducted a grid-search and to compete in the multilingual setting, we translated all documents to English.

  @inproceedings{wunderle-etal-2024-otterlyobsessedwithsemantics, abstract = {For our submission for Subtask 1, we developed a custom classification head that is designed to be applied atop of a Large Language Model. We reconstructed the hierarchy across multiple fully connected layers, allowing us to incorporate previous foundational decisions in subsequent, more fine-grained layers. To find the best hyperparameters, we conducted a grid-search and to compete in the multilingual setting, we translated all documents to English.}, address = {Mexico City, Mexico}, author = {Wunderle, Julia and Schubert, Julian and Cacciatore, Antonella and Zehe, Albin and Pfister, Jan and Hotho, Andreas}, booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)}, editor = {Ojha, Atul Kr. and Do{\u{g}}ru{\"o}z, A. Seza and Tayyar Madabushi, Harish and Da San Martino, Giovanni and Rosenthal, Sara and Ros{\'a}, Aiala}, keywords = {author:pfister}, month = {06}, pages = {602–612}, publisher = {Association for Computational Linguistics}, title = {{O}tterly{O}bsessed{W}ith{S}emantics at {S}em{E}val-2024 Task 4: Developing a Hierarchical Multi-Label Classification Head for Large Language Models}, year = 2024 }

  %0 Conference Paper %1 wunderle-etal-2024-otterlyobsessedwithsemantics %A Wunderle, Julia %A Schubert, Julian %A Cacciatore, Antonella %A Zehe, Albin %A Pfister, Jan %A Hotho, Andreas %B Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024) %C Mexico City, Mexico %D 2024 %E Ojha, Atul Kr. %E Do{\u{g}}ru{\"o}z, A. Seza %E Tayyar Madabushi, Harish %E Da San Martino, Giovanni %E Rosenthal, Sara %E Ros{\'a}, Aiala %I Association for Computational Linguistics %P 602--612 %T {O}tterly{O}bsessed{W}ith{S}emantics at {S}em{E}val-2024 Task 4: Developing a Hierarchical Multi-Label Classification Head for Large Language Models %U https://aclanthology.org/2024.semeval-1.90 %X For our submission for Subtask 1, we developed a custom classification head that is designed to be applied atop of a Large Language Model. We reconstructed the hierarchy across multiple fully connected layers, allowing us to incorporate previous foundational decisions in subsequent, more fine-grained layers. To find the best hyperparameters, we conducted a grid-search and to compete in the multilingual setting, we translated all documents to English.
• 
  {S}uper{GLEB}er: {G}erman Language Understanding Evaluation Benchmark. Pfister, Jan; Hotho, Andreas. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), K. Duh, H. Gomez, S. Bethard (eds.), pp. 7897–7916. Association for Computational Linguistics, Mexico City, Mexico, 2024
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  We assemble a broad Natural Language Understanding benchmark suite for the German language and consequently evaluate a wide array of existing German-capable models in order to create a better understanding of the current state of German LLMs. Our benchmark consists of 29 different tasks ranging over different types such as document classification, sequence tagging, sentence similarity, and question answering, on which we evaluate 10 different German-pretrained models, thereby charting the landscape of German LLMs. In our comprehensive evaluation we find that encoder models are a good choice for most tasks, but also that the largest encoder model does not necessarily perform best for all tasks. We make our benchmark suite and a leaderboard publically available at https://supergleber.professor-x.de and encourage the community to contribute new tasks and evaluate more models on it (https://github.com/LSX-UniWue/SuperGLEBer).

  @inproceedings{pfister-hotho-2024-supergleber, abstract = {We assemble a broad Natural Language Understanding benchmark suite for the German language and consequently evaluate a wide array of existing German-capable models in order to create a better understanding of the current state of German LLMs. Our benchmark consists of 29 different tasks ranging over different types such as document classification, sequence tagging, sentence similarity, and question answering, on which we evaluate 10 different German-pretrained models, thereby charting the landscape of German LLMs. In our comprehensive evaluation we find that encoder models are a good choice for most tasks, but also that the largest encoder model does not necessarily perform best for all tasks. We make our benchmark suite and a leaderboard publically available at https://supergleber.professor-x.de and encourage the community to contribute new tasks and evaluate more models on it (https://github.com/LSX-UniWue/SuperGLEBer).}, address = {Mexico City, Mexico}, author = {Pfister, Jan and Hotho, Andreas}, booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)}, editor = {Duh, Kevin and Gomez, Helena and Bethard, Steven}, keywords = {author:pfister}, month = {06}, pages = {7897–7916}, publisher = {Association for Computational Linguistics}, title = {{S}uper{GLEB}er: {G}erman Language Understanding Evaluation Benchmark}, year = 2024 }

  %0 Conference Paper %1 pfister-hotho-2024-supergleber %A Pfister, Jan %A Hotho, Andreas %B Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) %C Mexico City, Mexico %D 2024 %E Duh, Kevin %E Gomez, Helena %E Bethard, Steven %I Association for Computational Linguistics %P 7897--7916 %T {S}uper{GLEB}er: {G}erman Language Understanding Evaluation Benchmark %U https://aclanthology.org/2024.naacl-long.438 %X We assemble a broad Natural Language Understanding benchmark suite for the German language and consequently evaluate a wide array of existing German-capable models in order to create a better understanding of the current state of German LLMs. Our benchmark consists of 29 different tasks ranging over different types such as document classification, sequence tagging, sentence similarity, and question answering, on which we evaluate 10 different German-pretrained models, thereby charting the landscape of German LLMs. In our comprehensive evaluation we find that encoder models are a good choice for most tasks, but also that the largest encoder model does not necessarily perform best for all tasks. We make our benchmark suite and a leaderboard publically available at https://supergleber.professor-x.de and encourage the community to contribute new tasks and evaluate more models on it (https://github.com/LSX-UniWue/SuperGLEBer).
• 
  Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale. Seufert, Michael; Dietz, Katharina; Wehner, Nikolas; Geißler, Stefan; Schüler, Joshua; Wolz, Manuel; Hotho, Andreas; Casas, Pedro; Hoßfeld, Tobias; Feldmann, Anja. In IEEE Transactions on Network and Service Management, pp. 1–1. 2024
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  Network operators require real-time traffic monitoring insights to provide high performance and security to their customers. It has been shown that artificial intelligence and machine learning (ML) can improve the visibility of telemetry systems, especially with encrypted traffic. However, current solutions cannot cope with high traffic rates and volumes in large-scale networks. To realize the ML-driven network intelligence paradigm at terabit scale, we design Marina, a system that spreads monitoring over a highly efficient data plane, which can extract traffic statistics at line rate, and a powerful ML server, which can run monitoring inference using complex ML models. We apply temporal microaggregation into sub-second time slots and extract moment-based statistics. These allow to flexibly obtain accurate ML-based monitoring decisions during the next time slot. To demonstrate the scalability of our design, we implement and evaluate a Marina data plane prototype on a Barefoot Wedge 100BF-65X P4 switch, which can monitor more than 520,000 concurrent flows at full switching capacity of 6.4 Tbps. We validate the analytics capabilities enabled by our Marina implementation for four ML-driven real-time monitoring tasks with a broad set of standard ML models, achieving comparable or better than state-of-the-art results.

  @article{10480735, abstract = {Network operators require real-time traffic monitoring insights to provide high performance and security to their customers. It has been shown that artificial intelligence and machine learning (ML) can improve the visibility of telemetry systems, especially with encrypted traffic. However, current solutions cannot cope with high traffic rates and volumes in large-scale networks. To realize the ML-driven network intelligence paradigm at terabit scale, we design Marina, a system that spreads monitoring over a highly efficient data plane, which can extract traffic statistics at line rate, and a powerful ML server, which can run monitoring inference using complex ML models. We apply temporal microaggregation into sub-second time slots and extract moment-based statistics. These allow to flexibly obtain accurate ML-based monitoring decisions during the next time slot. To demonstrate the scalability of our design, we implement and evaluate a Marina data plane prototype on a Barefoot Wedge 100BF-65X P4 switch, which can monitor more than 520,000 concurrent flows at full switching capacity of 6.4 Tbps. We validate the analytics capabilities enabled by our Marina implementation for four ML-driven real-time monitoring tasks with a broad set of standard ML models, achieving comparable or better than state-of-the-art results.}, author = {Seufert, Michael and Dietz, Katharina and Wehner, Nikolas and Geißler, Stefan and Schüler, Joshua and Wolz, Manuel and Hotho, Andreas and Casas, Pedro and Hoßfeld, Tobias and Feldmann, Anja}, journal = {IEEE Transactions on Network and Service Management}, keywords = {traffic}, month = {03}, pages = {1-1}, title = {Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale}, year = 2024 }

  %0 Journal Article %1 10480735 %A Seufert, Michael %A Dietz, Katharina %A Wehner, Nikolas %A Geißler, Stefan %A Schüler, Joshua %A Wolz, Manuel %A Hotho, Andreas %A Casas, Pedro %A Hoßfeld, Tobias %A Feldmann, Anja %D 2024 %J IEEE Transactions on Network and Service Management %P 1-1 %R 10.1109/TNSM.2024.3382393 %T Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale %X Network operators require real-time traffic monitoring insights to provide high performance and security to their customers. It has been shown that artificial intelligence and machine learning (ML) can improve the visibility of telemetry systems, especially with encrypted traffic. However, current solutions cannot cope with high traffic rates and volumes in large-scale networks. To realize the ML-driven network intelligence paradigm at terabit scale, we design Marina, a system that spreads monitoring over a highly efficient data plane, which can extract traffic statistics at line rate, and a powerful ML server, which can run monitoring inference using complex ML models. We apply temporal microaggregation into sub-second time slots and extract moment-based statistics. These allow to flexibly obtain accurate ML-based monitoring decisions during the next time slot. To demonstrate the scalability of our design, we implement and evaluate a Marina data plane prototype on a Barefoot Wedge 100BF-65X P4 switch, which can monitor more than 520,000 concurrent flows at full switching capacity of 6.4 Tbps. We validate the analytics capabilities enabled by our Marina implementation for four ML-driven real-time monitoring tasks with a broad set of standard ML models, achieving comparable or better than state-of-the-art results.

• 
  TaylorPDENet: Learning PDEs from non-grid Data. Heinisch, Paul; Dulny, Andrzej; Krause, Anna; Hotho, Andreas. 2023
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  Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models. Previous approaches assume the measurements to be distributed on a grid. However, for real-world applications like weather prediction, the observations are taken from arbitrary locations within the spatial domain. In this paper, we propose TaylorPDENet - a novel machine learning method that is designed to overcome this challenge. Our algorithm uses the multidimensional Taylor expansion of a dynamical system at each observation point to estimate the spatial derivatives to perform predictions. TaylorPDENet is able to accomplish two objectives simultaneously: accurately forecast the evolution of a complex dynamical system and explicitly reconstruct the underlying differential equation describing the system. We evaluate our model on a variety of advection-diffusion equations with different parameters and show that it performs similarly to equivalent approaches on grid-structured data while being able to process unstructured data as well.

  @misc{heinisch2023taylorpdenet, abstract = {Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models. Previous approaches assume the measurements to be distributed on a grid. However, for real-world applications like weather prediction, the observations are taken from arbitrary locations within the spatial domain. In this paper, we propose TaylorPDENet - a novel machine learning method that is designed to overcome this challenge. Our algorithm uses the multidimensional Taylor expansion of a dynamical system at each observation point to estimate the spatial derivatives to perform predictions. TaylorPDENet is able to accomplish two objectives simultaneously: accurately forecast the evolution of a complex dynamical system and explicitly reconstruct the underlying differential equation describing the system. We evaluate our model on a variety of advection-diffusion equations with different parameters and show that it performs similarly to equivalent approaches on grid-structured data while being able to process unstructured data as well.}, author = {Heinisch, Paul and Dulny, Andrzej and Krause, Anna and Hotho, Andreas}, keywords = {from:martinr}, note = {cite arxiv:2306.14511}, title = {TaylorPDENet: Learning PDEs from non-grid Data}, year = 2023 }

  %0 Generic %1 heinisch2023taylorpdenet %A Heinisch, Paul %A Dulny, Andrzej %A Krause, Anna %A Hotho, Andreas %D 2023 %T TaylorPDENet: Learning PDEs from non-grid Data %U http://arxiv.org/abs/2306.14511 %X Modeling data obtained from dynamical systems has gained attention in recent years as a challenging task for machine learning models. Previous approaches assume the measurements to be distributed on a grid. However, for real-world applications like weather prediction, the observations are taken from arbitrary locations within the spatial domain. In this paper, we propose TaylorPDENet - a novel machine learning method that is designed to overcome this challenge. Our algorithm uses the multidimensional Taylor expansion of a dynamical system at each observation point to estimate the spatial derivatives to perform predictions. TaylorPDENet is able to accomplish two objectives simultaneously: accurately forecast the evolution of a complex dynamical system and explicitly reconstruct the underlying differential equation describing the system. We evaluate our model on a variety of advection-diffusion equations with different parameters and show that it performs similarly to equivalent approaches on grid-structured data while being able to process unstructured data as well.
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  Optimizing Medical Service Request Processes through Language Modeling and Semantic Search. Schlör, Daniel; Pfister, Jan; Hotho, Andreas. In 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), of ICMHI 2023, pp. 136–141. Association for Computing Machinery, Kyoto, Japan, 2023
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  Medical service requests are a crucial part of the workflow in hospitals and healthcare organizations. However, the process of requesting medical services can be time consuming and can require physicians and medical personnel to navigate complex interfaces and enter detailed information about the requested service. In this paper, we propose a system that uses machine learning techniques such as large language models and semantic search to optimize the process of requesting medical services. Our approach enables physicians to request medical services using natural language rather than navigating complex interfaces, allowing for more efficient and flexible interactions with hospital information systems. We evaluate our approach on real-world data and discuss the implications of our work for the future of digital health care. Our results suggest that our approach has the potential to streamline the process of requesting medical services and reduce the time and manual effort required in the daily hospital routine.

  @inproceedings{10.1145/3608298.3608324, abstract = {Medical service requests are a crucial part of the workflow in hospitals and healthcare organizations. However, the process of requesting medical services can be time consuming and can require physicians and medical personnel to navigate complex interfaces and enter detailed information about the requested service. In this paper, we propose a system that uses machine learning techniques such as large language models and semantic search to optimize the process of requesting medical services. Our approach enables physicians to request medical services using natural language rather than navigating complex interfaces, allowing for more efficient and flexible interactions with hospital information systems. We evaluate our approach on real-world data and discuss the implications of our work for the future of digital health care. Our results suggest that our approach has the potential to streamline the process of requesting medical services and reduce the time and manual effort required in the daily hospital routine.}, address = {New York, NY, USA}, author = {Schlör, Daniel and Pfister, Jan and Hotho, Andreas}, booktitle = {2023 the 7th International Conference on Medical and Health Informatics (ICMHI)}, keywords = {app_medical}, pages = {136–141}, publisher = {Association for Computing Machinery}, series = {ICMHI 2023}, title = {Optimizing Medical Service Request Processes through Language Modeling and Semantic Search}, year = 2023 }

  %0 Conference Paper %1 10.1145/3608298.3608324 %A Schlör, Daniel %A Pfister, Jan %A Hotho, Andreas %B 2023 the 7th International Conference on Medical and Health Informatics (ICMHI) %C New York, NY, USA %D 2023 %I Association for Computing Machinery %P 136–141 %R 10.1145/3608298.3608324 %T Optimizing Medical Service Request Processes through Language Modeling and Semantic Search %U https://doi.org/10.1145/3608298.3608324 %X Medical service requests are a crucial part of the workflow in hospitals and healthcare organizations. However, the process of requesting medical services can be time consuming and can require physicians and medical personnel to navigate complex interfaces and enter detailed information about the requested service. In this paper, we propose a system that uses machine learning techniques such as large language models and semantic search to optimize the process of requesting medical services. Our approach enables physicians to request medical services using natural language rather than navigating complex interfaces, allowing for more efficient and flexible interactions with hospital information systems. We evaluate our approach on real-world data and discuss the implications of our work for the future of digital health care. Our results suggest that our approach has the potential to streamline the process of requesting medical services and reduce the time and manual effort required in the daily hospital routine. %@ 9798400700712
• 
  Can Neural Networks Distinguish High-school Level Mathematical Concepts?. Wankerl, Sebastian; Dulny, Andrzej; Goetz, Gerhard; Hotho, Andreas. In ICDM. 2023
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  Processing symbolic mathematics algorithmically is an important field of research. It has applications in computer algebra systems and supports researchers as well as applied mathematicians in their daily work. Recently, exploring the ability of neural networks to grasp mathematical concepts has received special attention. One particularly complex task for neural networks is to understand the relation of two mathematical expressions to each other. Despite the advances in learning mathematical relationships, previous studies are limited by small-scale datasets, relatively simple formula construction by few axiomatic rules and even artifacts in the data. With this work, we aim at overcoming these limitations and provide a deeper insight into the representation power of neural networks for classifying mathematical relations. We introduce a novel data generation algorithm to allow for more complex formula compositions and fully include mathematical fields up to high-school level. We research several tree-based and sequential neural architectures for classifying mathematical relations and conduct a systematic analysis of the models against rule-based as well as neural baselines with a focus on varying data set complexity, generalization abilities, and understanding of syntactical patterns. Our findings highlight the effectiveness of tree-structured models for this task and show the potential of deep learning models to distinguish high-school level mathematical concepts.

  @conference{wankerl2023neural, abstract = {Processing symbolic mathematics algorithmically is an important field of research. It has applications in computer algebra systems and supports researchers as well as applied mathematicians in their daily work. Recently, exploring the ability of neural networks to grasp mathematical concepts has received special attention. One particularly complex task for neural networks is to understand the relation of two mathematical expressions to each other. Despite the advances in learning mathematical relationships, previous studies are limited by small-scale datasets, relatively simple formula construction by few axiomatic rules and even artifacts in the data. With this work, we aim at overcoming these limitations and provide a deeper insight into the representation power of neural networks for classifying mathematical relations. We introduce a novel data generation algorithm to allow for more complex formula compositions and fully include mathematical fields up to high-school level. We research several tree-based and sequential neural architectures for classifying mathematical relations and conduct a systematic analysis of the models against rule-based as well as neural baselines with a focus on varying data set complexity, generalization abilities, and understanding of syntactical patterns. Our findings highlight the effectiveness of tree-structured models for this task and show the potential of deep learning models to distinguish high-school level mathematical concepts.}, author = {Wankerl, Sebastian and Dulny, Andrzej and Goetz, Gerhard and Hotho, Andreas}, booktitle = {ICDM}, keywords = {from:martinr}, title = {Can Neural Networks Distinguish High-school Level Mathematical Concepts?}, year = 2023 }

  %0 Generic %1 wankerl2023neural %A Wankerl, Sebastian %A Dulny, Andrzej %A Goetz, Gerhard %A Hotho, Andreas %B ICDM %D 2023 %T Can Neural Networks Distinguish High-school Level Mathematical Concepts? %X Processing symbolic mathematics algorithmically is an important field of research. It has applications in computer algebra systems and supports researchers as well as applied mathematicians in their daily work. Recently, exploring the ability of neural networks to grasp mathematical concepts has received special attention. One particularly complex task for neural networks is to understand the relation of two mathematical expressions to each other. Despite the advances in learning mathematical relationships, previous studies are limited by small-scale datasets, relatively simple formula construction by few axiomatic rules and even artifacts in the data. With this work, we aim at overcoming these limitations and provide a deeper insight into the representation power of neural networks for classifying mathematical relations. We introduce a novel data generation algorithm to allow for more complex formula compositions and fully include mathematical fields up to high-school level. We research several tree-based and sequential neural architectures for classifying mathematical relations and conduct a systematic analysis of the models against rule-based as well as neural baselines with a focus on varying data set complexity, generalization abilities, and understanding of syntactical patterns. Our findings highlight the effectiveness of tree-structured models for this task and show the potential of deep learning models to distinguish high-school level mathematical concepts.
• 
  Higher-Order DeepTrails: Unified Approach to *Trails. Koopmann, Tobias; Pfister, Jan; Markus, André; Carolus, Astrid; Wienrich, Carolin; Hotho, Andreas. In Lernen, Wissen, Daten, Analysen {(LWDA)} Conference Proceedings, Marburg, Germany, October 9-11, 2023, Vol. 3630 of {CEUR} Workshop Proceedings, M. Leyer, J. Wichmann (eds.), pp. 372–386. CEUR-WS.org, 2023
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  @inproceedings{DBLP:conf/lwa/Koopmann23, author = {Koopmann, Tobias and Pfister, Jan and Markus, André and Carolus, Astrid and Wienrich, Carolin and Hotho, Andreas}, booktitle = {Lernen, Wissen, Daten, Analysen {(LWDA)} Conference Proceedings, Marburg, Germany, October 9-11, 2023}, editor = {Leyer, Michael and Wichmann, Johannes}, keywords = {author:pfister}, pages = {372–386}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Higher-Order DeepTrails: Unified Approach to *Trails}, volume = 3630, year = 2023 }

  %0 Conference Paper %1 DBLP:conf/lwa/Koopmann23 %A Koopmann, Tobias %A Pfister, Jan %A Markus, André %A Carolus, Astrid %A Wienrich, Carolin %A Hotho, Andreas %B Lernen, Wissen, Daten, Analysen {(LWDA)} Conference Proceedings, Marburg, Germany, October 9-11, 2023 %D 2023 %E Leyer, Michael %E Wichmann, Johannes %I CEUR-WS.org %P 372--386 %T Higher-Order DeepTrails: Unified Approach to *Trails %U https://ceur-ws.org/Vol-3630/LWDA2023-paper33.pdf %V 3630
• 
  Towards a Computational Analysis of Suspense: Detecting Dangerous Situations. Zehe, Albin; Schröter, Julian; Hotho, Andreas. 2023
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  Suspense is an important tool in storytelling to keep readers engaged and wanting to read more. However, it has so far not been studied extensively in Computational Literary Studies. In this paper, we focus on one of the elements authors can use to build up suspense: dangerous situations. We introduce a corpus of texts annotated with dangerous situations, distinguishing between 7 types of danger. Additionally, we annotate parts of the text that describe fear experienced by a character, regardless of the actual presence of danger. We present experiments towards the automatic detection of these situations, finding that unsupervised baseline methods can provide valuable signals for the detection, but more complex methods are necessary for further analysis. Not unexpectedly, the description of danger and fear often relies heavily on the context, both local (e.g., situations where danger is only mentioned, but not actually present) and global (e.g., "storm" being used in a literal sense in an adventure novel, but metaphorically in a romance novel).

  @misc{zehe2023towards, abstract = {Suspense is an important tool in storytelling to keep readers engaged and wanting to read more. However, it has so far not been studied extensively in Computational Literary Studies. In this paper, we focus on one of the elements authors can use to build up suspense: dangerous situations. We introduce a corpus of texts annotated with dangerous situations, distinguishing between 7 types of danger. Additionally, we annotate parts of the text that describe fear experienced by a character, regardless of the actual presence of danger. We present experiments towards the automatic detection of these situations, finding that unsupervised baseline methods can provide valuable signals for the detection, but more complex methods are necessary for further analysis. Not unexpectedly, the description of danger and fear often relies heavily on the context, both local (e.g., situations where danger is only mentioned, but not actually present) and global (e.g., "storm" being used in a literal sense in an adventure novel, but metaphorically in a romance novel).}, author = {Zehe, Albin and Schröter, Julian and Hotho, Andreas}, keywords = {suspense}, note = {cite arxiv:2305.06818 Comment: 4 pages + references & appendix; work in progress}, title = {Towards a Computational Analysis of Suspense: Detecting Dangerous Situations}, year = 2023 }

  %0 Generic %1 zehe2023towards %A Zehe, Albin %A Schröter, Julian %A Hotho, Andreas %D 2023 %T Towards a Computational Analysis of Suspense: Detecting Dangerous Situations %U http://arxiv.org/abs/2305.06818 %X Suspense is an important tool in storytelling to keep readers engaged and wanting to read more. However, it has so far not been studied extensively in Computational Literary Studies. In this paper, we focus on one of the elements authors can use to build up suspense: dangerous situations. We introduce a corpus of texts annotated with dangerous situations, distinguishing between 7 types of danger. Additionally, we annotate parts of the text that describe fear experienced by a character, regardless of the actual presence of danger. We present experiments towards the automatic detection of these situations, finding that unsupervised baseline methods can provide valuable signals for the detection, but more complex methods are necessary for further analysis. Not unexpectedly, the description of danger and fear often relies heavily on the context, both local (e.g., situations where danger is only mentioned, but not actually present) and global (e.g., "storm" being used in a literal sense in an adventure novel, but metaphorically in a romance novel).
• 
  Automatic Speech Detection on a Smart Beehive’s Raspberry Pi. Janetzky, Pascal; Lissmann, Philipp; Hotho, Andreas; Krause, Anna. In LWDA 2023 - Learning, Knowledge, Data, and Analysis. 2023
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  @inproceedings{janetzky2023automatic, author = {Janetzky, Pascal and Lissmann, Philipp and Hotho, Andreas and Krause, Anna}, booktitle = {LWDA 2023 - Learning, Knowledge, Data, and Analysis}, keywords = {author:LISSMANN}, title = {Automatic Speech Detection on a Smart Beehive's Raspberry Pi}, year = 2023 }

  %0 Conference Paper %1 janetzky2023automatic %A Janetzky, Pascal %A Lissmann, Philipp %A Hotho, Andreas %A Krause, Anna %B LWDA 2023 - Learning, Knowledge, Data, and Analysis %D 2023 %T Automatic Speech Detection on a Smart Beehive's Raspberry Pi
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  CapsKG: Enabling Continual Knowledge Integration in Language Models for Automatic Knowledge Graph Completion. Omeliyanenko, Janna; Zehe, Albin; Hotho, Andreas; Schlör, Daniel. In International Semantic Web Conference ISWC 2023, to appear. 2023
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  Automated completion of knowledge graphs is a popular topic in the Semantic Web community that aims to automatically and continuously integrate new appearing knowledge into knowledge graphs using artificial intelligence. Recently, approaches that leverage implicit knowledge from language models for this task have shown promising re- sults. However, by fine-tuning language models directly to the domain of knowledge graphs, models forget their original language representation and associated knowledge. An existing solution to address this issue is a trainable adapter, which is integrated into a frozen language model to extract the relevant knowledge without altering the model itself. How- ever, this constrains the generalizability to the specific extraction task and by design requires new and independent adapters to be trained for new knowledge extraction tasks. This effectively prevents the model from benefiting from existing knowledge incorporated in previously trained adapters. In this paper, we propose to combine the benefits of adapters for knowl- edge graph completion with the idea of integrating capsules, introduced in the field of continual learning. This allows the continuous integra- tion of knowledge into a joint model by sharing and reusing previously trained capsules. We find that our approach outperforms solutions using traditional adapters, while requiring notably fewer parameters for con- tinuous knowledge integration. Moreover, we show that this architecture benefits significantly from knowledge sharing in low-resource situations, outperforming adapter-based models on the task of link prediction.

  @article{noauthororeditor, abstract = {Automated completion of knowledge graphs is a popular topic in the Semantic Web community that aims to automatically and continuously integrate new appearing knowledge into knowledge graphs using artificial intelligence. Recently, approaches that leverage implicit knowledge from language models for this task have shown promising re- sults. However, by fine-tuning language models directly to the domain of knowledge graphs, models forget their original language representation and associated knowledge. An existing solution to address this issue is a trainable adapter, which is integrated into a frozen language model to extract the relevant knowledge without altering the model itself. How- ever, this constrains the generalizability to the specific extraction task and by design requires new and independent adapters to be trained for new knowledge extraction tasks. This effectively prevents the model from benefiting from existing knowledge incorporated in previously trained adapters. In this paper, we propose to combine the benefits of adapters for knowl- edge graph completion with the idea of integrating capsules, introduced in the field of continual learning. This allows the continuous integra- tion of knowledge into a joint model by sharing and reusing previously trained capsules. We find that our approach outperforms solutions using traditional adapters, while requiring notably fewer parameters for con- tinuous knowledge integration. Moreover, we show that this architecture benefits significantly from knowledge sharing in low-resource situations, outperforming adapter-based models on the task of link prediction.}, author = {Omeliyanenko, Janna and Zehe, Albin and Hotho, Andreas and Schlör, Daniel}, journal = {International Semantic Web Conference ISWC 2023, to appear}, keywords = {graph}, title = {CapsKG: Enabling Continual Knowledge Integration in Language Models for Automatic Knowledge Graph Completion}, year = 2023 }

  %0 Journal Article %1 noauthororeditor %A Omeliyanenko, Janna %A Zehe, Albin %A Hotho, Andreas %A Schlör, Daniel %D 2023 %J International Semantic Web Conference ISWC 2023, to appear %T CapsKG: Enabling Continual Knowledge Integration in Language Models for Automatic Knowledge Graph Completion %X Automated completion of knowledge graphs is a popular topic in the Semantic Web community that aims to automatically and continuously integrate new appearing knowledge into knowledge graphs using artificial intelligence. Recently, approaches that leverage implicit knowledge from language models for this task have shown promising re- sults. However, by fine-tuning language models directly to the domain of knowledge graphs, models forget their original language representation and associated knowledge. An existing solution to address this issue is a trainable adapter, which is integrated into a frozen language model to extract the relevant knowledge without altering the model itself. How- ever, this constrains the generalizability to the specific extraction task and by design requires new and independent adapters to be trained for new knowledge extraction tasks. This effectively prevents the model from benefiting from existing knowledge incorporated in previously trained adapters. In this paper, we propose to combine the benefits of adapters for knowl- edge graph completion with the idea of integrating capsules, introduced in the field of continual learning. This allows the continuous integra- tion of knowledge into a joint model by sharing and reusing previously trained capsules. We find that our approach outperforms solutions using traditional adapters, while requiring notably fewer parameters for con- tinuous knowledge integration. Moreover, we show that this architecture benefits significantly from knowledge sharing in low-resource situations, outperforming adapter-based models on the task of link prediction.
• 
  Large Language Models and Knowledge Graphs: Opportunities and Challenges. Pan, Jeff Z.; Razniewski, Simon; Kalo, Jan-Christoph; Singhania, Sneha; Chen, Jiaoyan; Dietze, Stefan; Jabeen, Hajira; Omeliyanenko, Janna; Zhang, Wen; Lissandrini, Matteo; Biswas, Russa; de Melo, Gerard; Bonifati, Angela; Vakaj, Edlira; Dragoni, Mauro; Graux, Damien. In Transactions on Graph Data and Knowledge, Vol. 1, Issue 1, pp. 2:1–2:38. Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik, Dagstuhl, Germany, 2023
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  @article{pan_et_al:TGDK.1.1.2, address = {Dagstuhl, Germany}, annote = {Keywords: Large Language Models, Pre-trained Language Models, Knowledge Graphs, Ontology, Retrieval Augmented Language Models}, author = {Pan, Jeff Z. and Razniewski, Simon and Kalo, Jan-Christoph and Singhania, Sneha and Chen, Jiaoyan and Dietze, Stefan and Jabeen, Hajira and Omeliyanenko, Janna and Zhang, Wen and Lissandrini, Matteo and Biswas, Russa and de Melo, Gerard and Bonifati, Angela and Vakaj, Edlira and Dragoni, Mauro and Graux, Damien}, journal = {Transactions on Graph Data and Knowledge}, keywords = {selected}, number = 1, pages = {2:1–2:38}, publisher = {Schloss Dagstuhl – Leibniz-Zentrum f{\"u}r Informatik}, title = {Large Language Models and Knowledge Graphs: Opportunities and Challenges}, volume = 1, year = 2023 }

  %0 Journal Article %1 pan_et_al:TGDK.1.1.2 %A Pan, Jeff Z. %A Razniewski, Simon %A Kalo, Jan-Christoph %A Singhania, Sneha %A Chen, Jiaoyan %A Dietze, Stefan %A Jabeen, Hajira %A Omeliyanenko, Janna %A Zhang, Wen %A Lissandrini, Matteo %A Biswas, Russa %A de Melo, Gerard %A Bonifati, Angela %A Vakaj, Edlira %A Dragoni, Mauro %A Graux, Damien %C Dagstuhl, Germany %D 2023 %I Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik %J Transactions on Graph Data and Knowledge %N 1 %P 2:1--2:38 %R 10.4230/TGDK.1.1.2 %T Large Language Models and Knowledge Graphs: Opportunities and Challenges %U https://drops.dagstuhl.de/entities/document/10.4230/TGDK.1.1.2 %V 1 %Z Keywords: Large Language Models, Pre-trained Language Models, Knowledge Graphs, Ontology, Retrieval Augmented Language Models
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  Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages. Fischer, Elisabeth; Schlör, Daniel; Zehe, Albin; Hotho, Andreas. 2023
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  @inproceedings{fischer2023enhancing, author = {Fischer, Elisabeth and Schlör, Daniel and Zehe, Albin and Hotho, Andreas}, keywords = {author:zehe}, title = {Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages}, year = 2023 }

  %0 Conference Paper %1 fischer2023enhancing %A Fischer, Elisabeth %A Schlör, Daniel %A Zehe, Albin %A Hotho, Andreas %D 2023 %T Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages
• 
  Optimizing Medical Service Request Processes through Language Modeling and Semantic Search. Schl\"{o}r, Daniel; Pfister, Jan; Hotho, Andreas. In 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), of ICMHI 2023, pp. 136–141. Association for Computing Machinery, Kyoto, Japan, 2023
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  Medical service requests are a crucial part of the workflow in hospitals and healthcare organizations. However, the process of requesting medical services can be time consuming and can require physicians and medical personnel to navigate complex interfaces and enter detailed information about the requested service. In this paper, we propose a system that uses machine learning techniques such as large language models and semantic search to optimize the process of requesting medical services. Our approach enables physicians to request medical services using natural language rather than navigating complex interfaces, allowing for more efficient and flexible interactions with hospital information systems. We evaluate our approach on real-world data and discuss the implications of our work for the future of digital health care. Our results suggest that our approach has the potential to streamline the process of requesting medical services and reduce the time and manual effort required in the daily hospital routine.

  @inproceedings{10.1145/3608298.3608324, abstract = {Medical service requests are a crucial part of the workflow in hospitals and healthcare organizations. However, the process of requesting medical services can be time consuming and can require physicians and medical personnel to navigate complex interfaces and enter detailed information about the requested service. In this paper, we propose a system that uses machine learning techniques such as large language models and semantic search to optimize the process of requesting medical services. Our approach enables physicians to request medical services using natural language rather than navigating complex interfaces, allowing for more efficient and flexible interactions with hospital information systems. We evaluate our approach on real-world data and discuss the implications of our work for the future of digital health care. Our results suggest that our approach has the potential to streamline the process of requesting medical services and reduce the time and manual effort required in the daily hospital routine.}, address = {New York, NY, USA}, author = {Schl\"{o}r, Daniel and Pfister, Jan and Hotho, Andreas}, booktitle = {2023 the 7th International Conference on Medical and Health Informatics (ICMHI)}, keywords = {author:pfister}, pages = {136–141}, publisher = {Association for Computing Machinery}, series = {ICMHI 2023}, title = {Optimizing Medical Service Request Processes through Language Modeling and Semantic Search}, year = 2023 }

  %0 Conference Paper %1 10.1145/3608298.3608324 %A Schl\"{o}r, Daniel %A Pfister, Jan %A Hotho, Andreas %B 2023 the 7th International Conference on Medical and Health Informatics (ICMHI) %C New York, NY, USA %D 2023 %I Association for Computing Machinery %P 136–141 %R 10.1145/3608298.3608324 %T Optimizing Medical Service Request Processes through Language Modeling and Semantic Search %U https://doi.org/10.1145/3608298.3608324 %X Medical service requests are a crucial part of the workflow in hospitals and healthcare organizations. However, the process of requesting medical services can be time consuming and can require physicians and medical personnel to navigate complex interfaces and enter detailed information about the requested service. In this paper, we propose a system that uses machine learning techniques such as large language models and semantic search to optimize the process of requesting medical services. Our approach enables physicians to request medical services using natural language rather than navigating complex interfaces, allowing for more efficient and flexible interactions with hospital information systems. We evaluate our approach on real-world data and discuss the implications of our work for the future of digital health care. Our results suggest that our approach has the potential to streamline the process of requesting medical services and reduce the time and manual effort required in the daily hospital routine. %@ 9798400700712
• 
  Occupational Fraud Detection through Agent-based Data Generation. Tritscher, Julian; Roos, Alexander; Schlör, Daniel; Hotho, Andreas; Krause, Anna. In The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023. 2023
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  Occupational fraud is an increasing concern for enterprises that is estimated to cause losses of around 5% of company revenue each year. With the increasing data tracked by companies through enterprise resource planning systems, recent research has taken interest in the automated detection of occupational fraud. Automated detection is however hindered by the unavailability of labeled fraud cases which require known occupational frauds within company data and costly expert annotation. Even despite the existence of anomaly detection methods that can be trained on unsupervised data, selecting the ideal preprocessing techniques, the most suitable model, and the optimal hyperparameters necessitates the availability of labeled data for evaluation purposes. To alleviate this issue, we propose to use simulation through multi-agent systems for generating business processes according to best practices from economics and creating labeled synthetic data that closely matches a given unlabeled real-world dataset. We extend an existing simulation by incorporating functionality for including, tracking and automatic labeling of occupational fraud cases. Using this simulation, we propose a framework that decides on important design choices for fraud detection models in enterprise resource planning data and does not require labeled real-world data. We demonstrate in multiple experiments that the framework can aid automated occupational fraud detection through data generation.

  @article{tritscher2023occupational, abstract = {Occupational fraud is an increasing concern for enterprises that is estimated to cause losses of around 5% of company revenue each year. With the increasing data tracked by companies through enterprise resource planning systems, recent research has taken interest in the automated detection of occupational fraud. Automated detection is however hindered by the unavailability of labeled fraud cases which require known occupational frauds within company data and costly expert annotation. Even despite the existence of anomaly detection methods that can be trained on unsupervised data, selecting the ideal preprocessing techniques, the most suitable model, and the optimal hyperparameters necessitates the availability of labeled data for evaluation purposes. To alleviate this issue, we propose to use simulation through multi-agent systems for generating business processes according to best practices from economics and creating labeled synthetic data that closely matches a given unlabeled real-world dataset. We extend an existing simulation by incorporating functionality for including, tracking and automatic labeling of occupational fraud cases. Using this simulation, we propose a framework that decides on important design choices for fraud detection models in enterprise resource planning data and does not require labeled real-world data. We demonstrate in multiple experiments that the framework can aid automated occupational fraud detection through data generation.}, author = {Tritscher, Julian and Roos, Alexander and Schlör, Daniel and Hotho, Andreas and Krause, Anna}, journal = {The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023}, keywords = {mas}, title = {Occupational Fraud Detection through Agent-based Data Generation}, year = 2023 }

  %0 Journal Article %1 tritscher2023occupational %A Tritscher, Julian %A Roos, Alexander %A Schlör, Daniel %A Hotho, Andreas %A Krause, Anna %D 2023 %J The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023 %T Occupational Fraud Detection through Agent-based Data Generation %X Occupational fraud is an increasing concern for enterprises that is estimated to cause losses of around 5% of company revenue each year. With the increasing data tracked by companies through enterprise resource planning systems, recent research has taken interest in the automated detection of occupational fraud. Automated detection is however hindered by the unavailability of labeled fraud cases which require known occupational frauds within company data and costly expert annotation. Even despite the existence of anomaly detection methods that can be trained on unsupervised data, selecting the ideal preprocessing techniques, the most suitable model, and the optimal hyperparameters necessitates the availability of labeled data for evaluation purposes. To alleviate this issue, we propose to use simulation through multi-agent systems for generating business processes according to best practices from economics and creating labeled synthetic data that closely matches a given unlabeled real-world dataset. We extend an existing simulation by incorporating functionality for including, tracking and automatic labeling of occupational fraud cases. Using this simulation, we propose a framework that decides on important design choices for fraud detection models in enterprise resource planning data and does not require labeled real-world data. We demonstrate in multiple experiments that the framework can aid automated occupational fraud detection through data generation.
• 
  Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data. Janetzky, Pascal; Schaller, Melanie; Krause, Anna; Hotho, Andreas. In 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1–5. 2023
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  —Swarming is the natural mechanism by which bee colonies reproduce, but for beekeepers it is a challenge. Precision beekeeping can aid their work through early notifications about impending swarms. In this work, we focus on identifying swarms and their early indicators in audio data captured from a smart beehive. The challenge with such domain-specific data is the low availability of labelled samples, the strong label imbalance, and the recording of undesired sources. We approach this challenge through a two-step setup: First, we use an auto encoder network to detect sounds from mechanical sources and then use it to clean data. Secondly, on the cleaned data we then employ a second network to identify event-related bee sounds. Using spectrogram features, our networks are able to reach a balanced accuracy score of more than 99 % in the detection of special bee events. The findings of this initial study can serve as the starting point for further research on handling imbalanced data collections from smart, remote sensor environments that also contain undesired signals.

  @inproceedings{10180253, abstract = {—Swarming is the natural mechanism by which bee colonies reproduce, but for beekeepers it is a challenge. Precision beekeeping can aid their work through early notifications about impending swarms. In this work, we focus on identifying swarms and their early indicators in audio data captured from a smart beehive. The challenge with such domain-specific data is the low availability of labelled samples, the strong label imbalance, and the recording of undesired sources. We approach this challenge through a two-step setup: First, we use an auto encoder network to detect sounds from mechanical sources and then use it to clean data. Secondly, on the cleaned data we then employ a second network to identify event-related bee sounds. Using spectrogram features, our networks are able to reach a balanced accuracy score of more than 99 % in the detection of special bee events. The findings of this initial study can serve as the starting point for further research on handling imbalanced data collections from smart, remote sensor environments that also contain undesired signals.}, author = {Janetzky, Pascal and Schaller, Melanie and Krause, Anna and Hotho, Andreas}, booktitle = {2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP)}, keywords = {author:KRAUSE}, pages = {1-5}, title = {Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data}, year = 2023 }

  %0 Conference Paper %1 10180253 %A Janetzky, Pascal %A Schaller, Melanie %A Krause, Anna %A Hotho, Andreas %B 2023 30th International Conference on Systems, Signals and Image Processing (IWSSIP) %D 2023 %P 1-5 %R 10.1109/IWSSIP58668.2023.10180253 %T Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data %X —Swarming is the natural mechanism by which bee colonies reproduce, but for beekeepers it is a challenge. Precision beekeeping can aid their work through early notifications about impending swarms. In this work, we focus on identifying swarms and their early indicators in audio data captured from a smart beehive. The challenge with such domain-specific data is the low availability of labelled samples, the strong label imbalance, and the recording of undesired sources. We approach this challenge through a two-step setup: First, we use an auto encoder network to detect sounds from mechanical sources and then use it to clean data. Secondly, on the cleaned data we then employ a second network to identify event-related bee sounds. Using spectrogram features, our networks are able to reach a balanced accuracy score of more than 99 % in the detection of special bee events. The findings of this initial study can serve as the starting point for further research on handling imbalanced data collections from smart, remote sensor environments that also contain undesired signals.
• 
  Feature relevance XAI in anomaly detection: Reviewing approaches and challenges. Tritscher, Julian; Krause, Anna; Hotho, Andreas. In Frontiers in Artificial Intelligence, Vol. 6. 2023
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  With complexity of artificial intelligence systems increasing continuously in past years, studies to explain these complex systems have grown in popularity. While much work has focused on explaining artificial intelligence systems in popular domains such as classification and regression, explanations in the area of anomaly detection have only recently received increasing attention from researchers. In particular, explaining singular model decisions of a complex anomaly detector by highlighting which inputs were responsible for a decision, commonly referred to as local post-hoc feature relevance, has lately been studied by several authors. In this paper, we systematically structure these works based on their access to training data and the anomaly detection model, and provide a detailed overview of their operation in the anomaly detection domain. We demonstrate their performance and highlight their limitations in multiple experimental showcases, discussing current challenges and opportunities for future work in feature relevance XAI for anomaly detection.

  @article{tritscher2023feature, abstract = {With complexity of artificial intelligence systems increasing continuously in past years, studies to explain these complex systems have grown in popularity. While much work has focused on explaining artificial intelligence systems in popular domains such as classification and regression, explanations in the area of anomaly detection have only recently received increasing attention from researchers. In particular, explaining singular model decisions of a complex anomaly detector by highlighting which inputs were responsible for a decision, commonly referred to as local post-hoc feature relevance, has lately been studied by several authors. In this paper, we systematically structure these works based on their access to training data and the anomaly detection model, and provide a detailed overview of their operation in the anomaly detection domain. We demonstrate their performance and highlight their limitations in multiple experimental showcases, discussing current challenges and opportunities for future work in feature relevance XAI for anomaly detection.}, author = {Tritscher, Julian and Krause, Anna and Hotho, Andreas}, journal = {Frontiers in Artificial Intelligence}, keywords = {xai}, month = {02}, title = {Feature relevance XAI in anomaly detection: Reviewing approaches and challenges}, volume = 6, year = 2023 }

  %0 Journal Article %1 tritscher2023feature %A Tritscher, Julian %A Krause, Anna %A Hotho, Andreas %D 2023 %J Frontiers in Artificial Intelligence %R 10.3389/frai.2023.1099521 %T Feature relevance XAI in anomaly detection: Reviewing approaches and challenges %U https://www.frontiersin.org/articles/10.3389/frai.2023.1099521/full %V 6 %X With complexity of artificial intelligence systems increasing continuously in past years, studies to explain these complex systems have grown in popularity. While much work has focused on explaining artificial intelligence systems in popular domains such as classification and regression, explanations in the area of anomaly detection have only recently received increasing attention from researchers. In particular, explaining singular model decisions of a complex anomaly detector by highlighting which inputs were responsible for a decision, commonly referred to as local post-hoc feature relevance, has lately been studied by several authors. In this paper, we systematically structure these works based on their access to training data and the anomaly detection model, and provide a detailed overview of their operation in the anomaly detection domain. We demonstrate their performance and highlight their limitations in multiple experimental showcases, discussing current challenges and opportunities for future work in feature relevance XAI for anomaly detection.
• 
  Long-Term Effects of Perceived Friendship with Intelligent Voice Assistants on Usage Behavior, User Experience, and Social Perceptions. Wienrich, Carolin; Carolus, Astrid; Markus, André; Augustin, Yannik; Pfister, Jan; Hotho, Andreas. In Computers, Vol. 12, Issue 4. 2023
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  Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human–AI interactions.

  @article{wienrich2023longterm, abstract = {Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human–AI interactions.}, author = {Wienrich, Carolin and Carolus, Astrid and Markus, André and Augustin, Yannik and Pfister, Jan and Hotho, Andreas}, journal = {Computers}, keywords = {from:janpf}, number = 4, title = {Long-Term Effects of Perceived Friendship with Intelligent Voice Assistants on Usage Behavior, User Experience, and Social Perceptions}, volume = 12, year = 2023 }

  %0 Journal Article %1 wienrich2023longterm %A Wienrich, Carolin %A Carolus, Astrid %A Markus, André %A Augustin, Yannik %A Pfister, Jan %A Hotho, Andreas %D 2023 %J Computers %N 4 %R 10.3390/computers12040077 %T Long-Term Effects of Perceived Friendship with Intelligent Voice Assistants on Usage Behavior, User Experience, and Social Perceptions %U https://www.mdpi.com/2073-431X/12/4/77 %V 12 %X Social patterns and roles can develop when users talk to intelligent voice assistants (IVAs) daily. The current study investigates whether users assign different roles to devices and how this affects their usage behavior, user experience, and social perceptions. Since social roles take time to establish, we equipped 106 participants with Alexa or Google assistants and some smart home devices and observed their interactions for nine months. We analyzed diverse subjective (questionnaire) and objective data (interaction data). By combining social science and data science analyses, we identified two distinct clusters—users who assigned a friendship role to IVAs over time and users who did not. Interestingly, these clusters exhibited significant differences in their usage behavior, user experience, and social perceptions of the devices. For example, participants who assigned a role to IVAs attributed more friendship to them used them more frequently, reported more enjoyment during interactions, and perceived more empathy for IVAs. In addition, these users had distinct personal requirements, for example, they reported more loneliness. This study provides valuable insights into the role-specific effects and consequences of voice assistants. Recent developments in conversational language models such as ChatGPT suggest that the findings of this study could make an important contribution to the design of dialogic human–AI interactions.
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  Multi-Task Transformer Visualization to build Trust for Clinical Outcome Prediction. Antweiler, Dario; Gallusser, Florian; Fuchs, Georg. In 2023 Workshop on Visual Analytics in Healthcare (VAHC), pp. 21–26. 2023
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  Clinical decision support systems based on machine learning are a rising application in healthcare. Early detection of deteriorating conditions provide the opportunity for medical intervention in hospital patients. Recent approaches increasingly rely on Large Language Models such as BERT, because patient data is often in the form of structured temporal data. These models are notoriously hard to interpret and therefore to trust, while precisely trust is an essential principle for technology in healthcare. We develop a visual analytics system to inspect, compare, and explain pre-trained transformer models for a given clinical outcome prediction task. The work is developed on the basis of a large hospital patient dataset and prediction tasks for acute kidney injury and heart failure. Discussion with healthcare professionals confirms that our system can lead to a faster decision process and improved modeling results.

  @inproceedings{10356804, abstract = {Clinical decision support systems based on machine learning are a rising application in healthcare. Early detection of deteriorating conditions provide the opportunity for medical intervention in hospital patients. Recent approaches increasingly rely on Large Language Models such as BERT, because patient data is often in the form of structured temporal data. These models are notoriously hard to interpret and therefore to trust, while precisely trust is an essential principle for technology in healthcare. We develop a visual analytics system to inspect, compare, and explain pre-trained transformer models for a given clinical outcome prediction task. The work is developed on the basis of a large hospital patient dataset and prediction tasks for acute kidney injury and heart failure. Discussion with healthcare professionals confirms that our system can lead to a faster decision process and improved modeling results.}, author = {Antweiler, Dario and Gallusser, Florian and Fuchs, Georg}, booktitle = {2023 Workshop on Visual Analytics in Healthcare (VAHC)}, keywords = {clinical_outcome_prediction}, month = 10, pages = {21-26}, title = {Multi-Task Transformer Visualization to build Trust for Clinical Outcome Prediction}, year = 2023 }

  %0 Conference Paper %1 10356804 %A Antweiler, Dario %A Gallusser, Florian %A Fuchs, Georg %B 2023 Workshop on Visual Analytics in Healthcare (VAHC) %D 2023 %P 21-26 %R 10.1109/VAHC60858.2023.00010 %T Multi-Task Transformer Visualization to build Trust for Clinical Outcome Prediction %X Clinical decision support systems based on machine learning are a rising application in healthcare. Early detection of deteriorating conditions provide the opportunity for medical intervention in hospital patients. Recent approaches increasingly rely on Large Language Models such as BERT, because patient data is often in the form of structured temporal data. These models are notoriously hard to interpret and therefore to trust, while precisely trust is an essential principle for technology in healthcare. We develop a visual analytics system to inspect, compare, and explain pre-trained transformer models for a given clinical outcome prediction task. The work is developed on the basis of a large hospital patient dataset and prediction tasks for acute kidney injury and heart failure. Discussion with healthcare professionals confirms that our system can lead to a faster decision process and improved modeling results.
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  Pointer Networks: A Unified Approach to Extracting {G}erman Opinions. Wunderle, Julia; Pfister, Jan; Hotho, Andreas. In Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023), M. Georges, A. Herygers, A. Friedrich, B. Roth (eds.), pp. 127–138. Association for Computational Lingustics, Ingolstadt, Germany, 2023
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  @inproceedings{wunderle-etal-2023-pointer, address = {Ingolstadt, Germany}, author = {Wunderle, Julia and Pfister, Jan and Hotho, Andreas}, booktitle = {Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023)}, editor = {Georges, Munir and Herygers, Aaricia and Friedrich, Annemarie and Roth, Benjamin}, keywords = {from:janpf}, month = {09}, pages = {127–138}, publisher = {Association for Computational Lingustics}, title = {Pointer Networks: A Unified Approach to Extracting {G}erman Opinions}, year = 2023 }

  %0 Conference Paper %1 wunderle-etal-2023-pointer %A Wunderle, Julia %A Pfister, Jan %A Hotho, Andreas %B Proceedings of the 19th Conference on Natural Language Processing (KONVENS 2023) %C Ingolstadt, Germany %D 2023 %E Georges, Munir %E Herygers, Aaricia %E Friedrich, Annemarie %E Roth, Benjamin %I Association for Computational Lingustics %P 127--138 %T Pointer Networks: A Unified Approach to Extracting {G}erman Opinions %U https://aclanthology.org/2023.konvens-main.13
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  Zero-Shot Clickbait Spoiling by Rephrasing Titles as Questions. Wangsadirdja, Dirk; Pfister, Jan; Kobs, Konstantin; Hotho, Andreas. In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), pp. 1090–1095. Association for Computational Linguistics, Toronto, Canada, 2023
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  In this paper, we describe our approach to the clickbait spoiling task of SemEval 2023. The core idea behind our system is to leverage pre-trained models capable of Question Answering (QA) to extract the spoiler from article texts based on the clickbait title without any task-specific training. Since oftentimes, these titles are not phrased as questions, we automatically rephrase the clickbait titles as questions in order to better suit the pretraining task of the QA-capable models. Also, to fit as much relevant context into the model's limited input size as possible, we propose to reorder the sentences by their relevance using a semantic similarity model. Finally, we evaluate QA as well as text generation models (via prompting) to extract the spoiler from the text.Based on the validation data, our final model selects each of these components depending on the spoiler type and achieves satisfactory zero-shot results. The ideas described in this paper can easily be applied in fine-tuning settings.

  @inproceedings{wangsadirdja-etal-2023-jack, abstract = {In this paper, we describe our approach to the clickbait spoiling task of SemEval 2023. The core idea behind our system is to leverage pre-trained models capable of Question Answering (QA) to extract the spoiler from article texts based on the clickbait title without any task-specific training. Since oftentimes, these titles are not phrased as questions, we automatically rephrase the clickbait titles as questions in order to better suit the pretraining task of the QA-capable models. Also, to fit as much relevant context into the model's limited input size as possible, we propose to reorder the sentences by their relevance using a semantic similarity model. Finally, we evaluate QA as well as text generation models (via prompting) to extract the spoiler from the text.Based on the validation data, our final model selects each of these components depending on the spoiler type and achieves satisfactory zero-shot results. The ideas described in this paper can easily be applied in fine-tuning settings.}, address = {Toronto, Canada}, author = {Wangsadirdja, Dirk and Pfister, Jan and Kobs, Konstantin and Hotho, Andreas}, booktitle = {Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)}, keywords = {from:janpf}, month = {07}, pages = {1090–1095}, publisher = {Association for Computational Linguistics}, title = {Zero-Shot Clickbait Spoiling by Rephrasing Titles as Questions}, year = 2023 }

  %0 Conference Paper %1 wangsadirdja-etal-2023-jack %A Wangsadirdja, Dirk %A Pfister, Jan %A Kobs, Konstantin %A Hotho, Andreas %B Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023) %C Toronto, Canada %D 2023 %I Association for Computational Linguistics %P 1090--1095 %T Zero-Shot Clickbait Spoiling by Rephrasing Titles as Questions %U https://aclanthology.org/2023.semeval-1.150 %X In this paper, we describe our approach to the clickbait spoiling task of SemEval 2023. The core idea behind our system is to leverage pre-trained models capable of Question Answering (QA) to extract the spoiler from article texts based on the clickbait title without any task-specific training. Since oftentimes, these titles are not phrased as questions, we automatically rephrase the clickbait titles as questions in order to better suit the pretraining task of the QA-capable models. Also, to fit as much relevant context into the model's limited input size as possible, we propose to reorder the sentences by their relevance using a semantic similarity model. Finally, we evaluate QA as well as text generation models (via prompting) to extract the spoiler from the text.Based on the validation data, our final model selects each of these components depending on the spoiler type and achieves satisfactory zero-shot results. The ideas described in this paper can easily be applied in fine-tuning settings.
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  Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks. Schaller, Melanie; Steininger, Michael; Dulny, Andrzej; Schlör, Daniel; Hotho, Andreas. In LWDA’23: Lernen, Wissen, Daten, Analysen. October 09--11, 2023, Marburg, Germany, M. Leyer (ed.). 2023
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  @article{schaller2023liquorhgnn, author = {Schaller, Melanie and Steininger, Michael and Dulny, Andrzej and Schlör, Daniel and Hotho, Andreas}, editor = {Leyer, M.}, journal = {LWDA'23: Lernen, Wissen, Daten, Analysen. October 09–11, 2023, Marburg, Germany}, keywords = {author:manli}, month = {Oktober}, title = {Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks}, year = 2023 }

  %0 Journal Article %1 schaller2023liquorhgnn %A Schaller, Melanie %A Steininger, Michael %A Dulny, Andrzej %A Schlör, Daniel %A Hotho, Andreas %D 2023 %E Leyer, M. %J LWDA'23: Lernen, Wissen, Daten, Analysen. October 09--11, 2023, Marburg, Germany %T Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks
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  ConvMOS: climate model output statistics with deep learning. Steininger, Michael; Abel, Daniel; Ziegler, Katrin; Krause, Anna; Paeth, Heiko; Hotho, Andreas. In Data Mining and Knowledge Discovery, Vol. 37, Issue 1, pp. 136–166. 2023
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  Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.

  @article{steininger2023convmos, abstract = {Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.}, author = {Steininger, Michael and Abel, Daniel and Ziegler, Katrin and Krause, Anna and Paeth, Heiko and Hotho, Andreas}, journal = {Data Mining and Knowledge Discovery}, keywords = {author:KRAUSE}, number = 1, pages = {136–166}, title = {ConvMOS: climate model output statistics with deep learning}, volume = 37, year = 2023 }

  %0 Journal Article %1 steininger2023convmos %A Steininger, Michael %A Abel, Daniel %A Ziegler, Katrin %A Krause, Anna %A Paeth, Heiko %A Hotho, Andreas %D 2023 %J Data Mining and Knowledge Discovery %N 1 %P 136--166 %R 10.1007/s10618-022-00877-6 %T ConvMOS: climate model output statistics with deep learning %U https://doi.org/10.1007/s10618-022-00877-6 %V 37 %X Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.
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  Evaluating feature relevance XAI in network intrusion detection. Tritscher, Julian; Wolf, Maximilian; Hotho, Andreas; Schlör, Daniel. In The World Conference on eXplainable Artificial Intelligence (xAI 2023). 2023
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  As machine learning models become increasingly complex, there is a growing need for explainability to understand and trust the decision-making processes. In the domain of network intrusion detection, post-hoc feature relevance explanations have been widely used to provide insight into the factors driving model decisions. However, recent research has highlighted challenges with these methods when applied to anomaly detection, which can vary in importance and impact depending on the application domain. In this paper, we investigate the challenges of post-hoc feature relevance explanations for network intrusion detection, a critical area for ensuring the security and integrity of computer networks. To gain a deeper understanding of these challenges for the application domain, we quantitatively and qualitatively investigate the popular feature relevance approach SHAP when explaining different network intrusion detection approaches. We conduct experiments to jointly evaluate detection quality and explainability, and explore the impact of replacement data, a commonly overlooked hyperparameter of post-hoc feature relevance approaches. We find that post-hoc XAI can provide high quality explanations, but requires a careful choice of its replacement data as default settings and common choices do not transfer across different detection models. Our study showcases the viability of post-hoc XAI for network intrusion detection systems, but highlights the need for rigorous evaluations of produced explanations.

  @article{tritscher2023evaluating, abstract = {As machine learning models become increasingly complex, there is a growing need for explainability to understand and trust the decision-making processes. In the domain of network intrusion detection, post-hoc feature relevance explanations have been widely used to provide insight into the factors driving model decisions. However, recent research has highlighted challenges with these methods when applied to anomaly detection, which can vary in importance and impact depending on the application domain. In this paper, we investigate the challenges of post-hoc feature relevance explanations for network intrusion detection, a critical area for ensuring the security and integrity of computer networks. To gain a deeper understanding of these challenges for the application domain, we quantitatively and qualitatively investigate the popular feature relevance approach SHAP when explaining different network intrusion detection approaches. We conduct experiments to jointly evaluate detection quality and explainability, and explore the impact of replacement data, a commonly overlooked hyperparameter of post-hoc feature relevance approaches. We find that post-hoc XAI can provide high quality explanations, but requires a careful choice of its replacement data as default settings and common choices do not transfer across different detection models. Our study showcases the viability of post-hoc XAI for network intrusion detection systems, but highlights the need for rigorous evaluations of produced explanations.}, author = {Tritscher, Julian and Wolf, Maximilian and Hotho, Andreas and Schlör, Daniel}, journal = {The World Conference on eXplainable Artificial Intelligence (xAI 2023)}, keywords = {xai}, title = {Evaluating feature relevance XAI in network intrusion detection}, year = 2023 }

  %0 Journal Article %1 tritscher2023evaluating %A Tritscher, Julian %A Wolf, Maximilian %A Hotho, Andreas %A Schlör, Daniel %D 2023 %J The World Conference on eXplainable Artificial Intelligence (xAI 2023) %T Evaluating feature relevance XAI in network intrusion detection %X As machine learning models become increasingly complex, there is a growing need for explainability to understand and trust the decision-making processes. In the domain of network intrusion detection, post-hoc feature relevance explanations have been widely used to provide insight into the factors driving model decisions. However, recent research has highlighted challenges with these methods when applied to anomaly detection, which can vary in importance and impact depending on the application domain. In this paper, we investigate the challenges of post-hoc feature relevance explanations for network intrusion detection, a critical area for ensuring the security and integrity of computer networks. To gain a deeper understanding of these challenges for the application domain, we quantitatively and qualitatively investigate the popular feature relevance approach SHAP when explaining different network intrusion detection approaches. We conduct experiments to jointly evaluate detection quality and explainability, and explore the impact of replacement data, a commonly overlooked hyperparameter of post-hoc feature relevance approaches. We find that post-hoc XAI can provide high quality explanations, but requires a careful choice of its replacement data as default settings and common choices do not transfer across different detection models. Our study showcases the viability of post-hoc XAI for network intrusion detection systems, but highlights the need for rigorous evaluations of produced explanations.
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  Can Neural Networks Distinguish High-school Level Mathematical Concepts?. Wankerl, Sebastian; Dulny, Andrzej; Götz, Gerhard; Hotho, Andreas. In Accepted at ICDM. 2023
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  @conference{wankerl2023neural, author = {Wankerl, Sebastian and Dulny, Andrzej and Götz, Gerhard and Hotho, Andreas}, booktitle = {Accepted at ICDM}, keywords = {author:wankerl}, title = {Can Neural Networks Distinguish High-school Level Mathematical Concepts?}, year = 2023 }

  %0 Generic %1 wankerl2023neural %A Wankerl, Sebastian %A Dulny, Andrzej %A Götz, Gerhard %A Hotho, Andreas %B Accepted at ICDM %D 2023 %T Can Neural Networks Distinguish High-school Level Mathematical Concepts?
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  DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data. Dulny, Andrzej; Hotho, Andreas; Krause, Anna. In Machine Learning and Knowledge Discovery in Databases: Research Track, D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, F. Bonchi (eds.), pp. 438–455. Springer Nature Switzerland, Cham, 2023
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  Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at https://professor-x.de/dynabench.

  @inproceedings{10.1007/978-3-031-43412-9_26, abstract = {Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at https://professor-x.de/dynabench.}, address = {Cham}, author = {Dulny, Andrzej and Hotho, Andreas and Krause, Anna}, booktitle = {Machine Learning and Knowledge Discovery in Databases: Research Track}, editor = {Koutra, Danai and Plant, Claudia and Gomez Rodriguez, Manuel and Baralis, Elena and Bonchi, Francesco}, keywords = {selected}, pages = {438–455}, publisher = {Springer Nature Switzerland}, title = {DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data}, year = 2023 }

  %0 Conference Paper %1 10.1007/978-3-031-43412-9_26 %A Dulny, Andrzej %A Hotho, Andreas %A Krause, Anna %B Machine Learning and Knowledge Discovery in Databases: Research Track %C Cham %D 2023 %E Koutra, Danai %E Plant, Claudia %E Gomez Rodriguez, Manuel %E Baralis, Elena %E Bonchi, Francesco %I Springer Nature Switzerland %P 438--455 %T DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data %U http://arxiv.org/abs/2306.05805 %X Previous work on learning physical systems from data has focused on high-resolution grid-structured measurements. However, real-world knowledge of such systems (e.g. weather data) relies on sparsely scattered measuring stations. In this paper, we introduce a novel simulated benchmark dataset, DynaBench, for learning dynamical systems directly from sparsely scattered data without prior knowledge of the equations. The dataset focuses on predicting the evolution of a dynamical system from low-resolution, unstructured measurements. We simulate six different partial differential equations covering a variety of physical systems commonly used in the literature and evaluate several machine learning models, including traditional graph neural networks and point cloud processing models, with the task of predicting the evolution of the system. The proposed benchmark dataset is expected to advance the state of art as an out-of-the-box easy-to-use tool for evaluating models in a setting where only unstructured low-resolution observations are available. The benchmark is available at https://professor-x.de/dynabench. %@ 978-3-031-43412-9

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  Detecting Anomalies in Transaction Data. Technical Report (PhD dissertation), . Schlör, Daniel. PhD dissertation. Universität Würzburg, 2022
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  @phdthesis{Schloer2022, author = {Schlör, Daniel}, keywords = {from:daschloer}, school = {Universität Würzburg}, title = {Detecting Anomalies in Transaction Data}, type = {Doctoral Thesis}, year = 2022 }

  %0 Thesis %1 Schloer2022 %A Schlör, Daniel %D 2022 %R 10.25972/OPUS-29856 %T Detecting Anomalies in Transaction Data
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  CLIP knows image aesthetics. Hentschel, Simon; Kobs, Konstantin; Hotho, Andreas. In Frontiers in artificial intelligence, Vol. 5, p. 976235. 2022
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  Most Image Aesthetic Assessment (IAA) methods use a pretrained ImageNet classification model as a base to fine-tune. We hypothesize that content classification is not an optimal pretraining task for IAA, since the task discourages the extraction of features that are useful for IAA, e.g., composition, lighting, or style. On the other hand, we argue that the Contrastive Language-Image Pretraining (CLIP) model is a better base for IAA models, since it has been trained using natural language supervision. Due to the rich nature of language, CLIP needs to learn a broad range of image features that correlate with sentences describing the image content, composition, environments, and even subjective feelings about the image. While it has been shown that CLIP extracts features useful for content classification tasks, its suitability for tasks that require the extraction of style-based features like IAA has not yet been shown. We test our hypothesis by conducting a three-step study, investigating the usefulness of features extracted by CLIP compared to features obtained from the last layer of a comparable ImageNet classification model. In each step, we get more computationally expensive. First, we engineer natural language prompts that let CLIP assess an image's aesthetic without adjusting any weights in the model. To overcome the challenge that CLIP's prompting only is applicable to classification tasks, we propose a simple but effective strategy to convert multiple prompts to a continuous scalar as required when predicting an image's mean aesthetic score. Second, we train a linear regression on the AVA dataset using image features obtained by CLIP's image encoder. The resulting model outperforms a linear regression trained on features from an ImageNet classification model. It also shows competitive performance with fully fine-tuned networks based on ImageNet, while only training a single layer. Finally, by fine-tuning CLIP's image encoder on the AVA dataset, we show that CLIP only needs a fraction of training epochs to converge, while also performing better than a fine-tuned ImageNet model. Overall, our experiments suggest that CLIP is better suited as a base model for IAA methods than ImageNet pretrained networks.

  @article{PMID:36504688, abstract = {Most Image Aesthetic Assessment (IAA) methods use a pretrained ImageNet classification model as a base to fine-tune. We hypothesize that content classification is not an optimal pretraining task for IAA, since the task discourages the extraction of features that are useful for IAA, e.g., composition, lighting, or style. On the other hand, we argue that the Contrastive Language-Image Pretraining (CLIP) model is a better base for IAA models, since it has been trained using natural language supervision. Due to the rich nature of language, CLIP needs to learn a broad range of image features that correlate with sentences describing the image content, composition, environments, and even subjective feelings about the image. While it has been shown that CLIP extracts features useful for content classification tasks, its suitability for tasks that require the extraction of style-based features like IAA has not yet been shown. We test our hypothesis by conducting a three-step study, investigating the usefulness of features extracted by CLIP compared to features obtained from the last layer of a comparable ImageNet classification model. In each step, we get more computationally expensive. First, we engineer natural language prompts that let CLIP assess an image's aesthetic without adjusting any weights in the model. To overcome the challenge that CLIP's prompting only is applicable to classification tasks, we propose a simple but effective strategy to convert multiple prompts to a continuous scalar as required when predicting an image's mean aesthetic score. Second, we train a linear regression on the AVA dataset using image features obtained by CLIP's image encoder. The resulting model outperforms a linear regression trained on features from an ImageNet classification model. It also shows competitive performance with fully fine-tuned networks based on ImageNet, while only training a single layer. Finally, by fine-tuning CLIP's image encoder on the AVA dataset, we show that CLIP only needs a fraction of training epochs to converge, while also performing better than a fine-tuned ImageNet model. Overall, our experiments suggest that CLIP is better suited as a base model for IAA methods than ImageNet pretrained networks.}, author = {Hentschel, Simon and Kobs, Konstantin and Hotho, Andreas}, journal = {Frontiers in artificial intelligence}, keywords = {from:hotho}, pages = 976235, title = {CLIP knows image aesthetics}, volume = 5, year = 2022 }

  %0 Journal Article %1 PMID:36504688 %A Hentschel, Simon %A Kobs, Konstantin %A Hotho, Andreas %D 2022 %J Frontiers in artificial intelligence %P 976235 %R 10.3389/frai.2022.976235 %T CLIP knows image aesthetics %U https://europepmc.org/articles/PMC9732445 %V 5 %X Most Image Aesthetic Assessment (IAA) methods use a pretrained ImageNet classification model as a base to fine-tune. We hypothesize that content classification is not an optimal pretraining task for IAA, since the task discourages the extraction of features that are useful for IAA, e.g., composition, lighting, or style. On the other hand, we argue that the Contrastive Language-Image Pretraining (CLIP) model is a better base for IAA models, since it has been trained using natural language supervision. Due to the rich nature of language, CLIP needs to learn a broad range of image features that correlate with sentences describing the image content, composition, environments, and even subjective feelings about the image. While it has been shown that CLIP extracts features useful for content classification tasks, its suitability for tasks that require the extraction of style-based features like IAA has not yet been shown. We test our hypothesis by conducting a three-step study, investigating the usefulness of features extracted by CLIP compared to features obtained from the last layer of a comparable ImageNet classification model. In each step, we get more computationally expensive. First, we engineer natural language prompts that let CLIP assess an image's aesthetic without adjusting any weights in the model. To overcome the challenge that CLIP's prompting only is applicable to classification tasks, we propose a simple but effective strategy to convert multiple prompts to a continuous scalar as required when predicting an image's mean aesthetic score. Second, we train a linear regression on the AVA dataset using image features obtained by CLIP's image encoder. The resulting model outperforms a linear regression trained on features from an ImageNet classification model. It also shows competitive performance with fully fine-tuned networks based on ImageNet, while only training a single layer. Finally, by fine-tuning CLIP's image encoder on the AVA dataset, we show that CLIP only needs a fraction of training epochs to converge, while also performing better than a fine-tuned ImageNet model. Overall, our experiments suggest that CLIP is better suited as a base model for IAA methods than ImageNet pretrained networks.
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  Semi-unsupervised Learning for Time Series Classification. Davidson, Padraig; Steininger, Michael; Huhn, André; Krause, Anna; Hotho, Andreas. In Milets@KDD. arXiv, 2022
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  Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is collected in large amounts any given second. The collected data is rich in information, as one can detect accidents (e.g. cars) in real time, or assess injury/sickness over a given time span (e.g. health devices). Due to its chaotic nature and massive amounts of datapoints, timeseries are hard to label manually. Furthermore new classes within the data could emerge over time (contrary to e.g. handwritten digits), which would require relabeling the data. In this paper we present SuSL4TS, a deep generative Gaussian mixture model for semi-unsupervised learning, to classify time series data. With our approach we can alleviate manual labeling steps, since we can detect sparsely labeled classes (semi-supervised) and identify emerging classes hidden in the data (unsupervised). We demonstrate the efficacy of our approach with established time series classification datasets from different domains.

  @article{davidson2022semiunsupervised, abstract = {Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is collected in large amounts any given second. The collected data is rich in information, as one can detect accidents (e.g. cars) in real time, or assess injury/sickness over a given time span (e.g. health devices). Due to its chaotic nature and massive amounts of datapoints, timeseries are hard to label manually. Furthermore new classes within the data could emerge over time (contrary to e.g. handwritten digits), which would require relabeling the data. In this paper we present SuSL4TS, a deep generative Gaussian mixture model for semi-unsupervised learning, to classify time series data. With our approach we can alleviate manual labeling steps, since we can detect sparsely labeled classes (semi-supervised) and identify emerging classes hidden in the data (unsupervised). We demonstrate the efficacy of our approach with established time series classification datasets from different domains.}, author = {Davidson, Padraig and Steininger, Michael and Huhn, André and Krause, Anna and Hotho, Andreas}, journal = {Milets@KDD}, keywords = {timeseries-classification}, publisher = {arXiv}, title = {Semi-unsupervised Learning for Time Series Classification}, year = 2022 }

  %0 Journal Article %1 davidson2022semiunsupervised %A Davidson, Padraig %A Steininger, Michael %A Huhn, André %A Krause, Anna %A Hotho, Andreas %D 2022 %I arXiv %J Milets@KDD %R 10.48550/ARXIV.2207.03119 %T Semi-unsupervised Learning for Time Series Classification %U https://arxiv.org/abs/2207.03119 %X Time series are ubiquitous and therefore inherently hard to analyze and ultimately to label or cluster. With the rise of the Internet of Things (IoT) and its smart devices, data is collected in large amounts any given second. The collected data is rich in information, as one can detect accidents (e.g. cars) in real time, or assess injury/sickness over a given time span (e.g. health devices). Due to its chaotic nature and massive amounts of datapoints, timeseries are hard to label manually. Furthermore new classes within the data could emerge over time (contrary to e.g. handwritten digits), which would require relabeling the data. In this paper we present SuSL4TS, a deep generative Gaussian mixture model for semi-unsupervised learning, to classify time series data. With our approach we can alleviate manual labeling steps, since we can detect sparsely labeled classes (semi-supervised) and identify emerging classes hidden in the data (unsupervised). We demonstrate the efficacy of our approach with established time series classification datasets from different domains.
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  NeuralPDE: Modelling Dynamical Systems from Data. Dulny, Andrzej; Hotho, Andreas; Krause, Anna. In {KI} 2022: Advances in Artificial Intelligence - 45th German Conference on AI, Trier, Germany, September 19-23, 2022, Proceedings, Vol. 13404 of Lecture Notes in Computer Science, R. Bergmann, L. Malburg, S. C. Rodermund, I. J. Timm (eds.), pp. 75–89. Springer, 2022
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  @inproceedings{DBLP:conf/ki/DulnyHK22, author = {Dulny, Andrzej and Hotho, Andreas and Krause, Anna}, booktitle = {{KI} 2022: Advances in Artificial Intelligence - 45th German Conference on AI, Trier, Germany, September 19-23, 2022, Proceedings}, editor = {Bergmann, Ralph and Malburg, Lukas and Rodermund, Stephanie C. and Timm, Ingo J.}, keywords = {from:hotho}, pages = {75–89}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {NeuralPDE: Modelling Dynamical Systems from Data}, volume = 13404, year = 2022 }

  %0 Conference Paper %1 DBLP:conf/ki/DulnyHK22 %A Dulny, Andrzej %A Hotho, Andreas %A Krause, Anna %B {KI} 2022: Advances in Artificial Intelligence - 45th German Conference on AI, Trier, Germany, September 19-23, 2022, Proceedings %D 2022 %E Bergmann, Ralph %E Malburg, Lukas %E Rodermund, Stephanie C. %E Timm, Ingo J. %I Springer %P 75--89 %R 10.1007/978-3-031-15791-2\_8 %T NeuralPDE: Modelling Dynamical Systems from Data %U https://doi.org/10.1007/978-3-031-15791-2 %V 13404
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  A sensor-driven approach for analyzing a beehive’s state. Davidson, Padraig; Hotho, Andreas; Krause, Anna. 2022
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  Assessing a colony’s state is one of the key components in apiculture. In recent years precision apiculture has emerged as a field of study to quantify the analysis of beehives with objective sensors (e.g. temperature within the beehive). One objective of analysis is the study of semantically consecutive time spans, sometimes covering several months. A common task for example could be detecting the different breeding periods in a year. A change in semantics is called a change point, which can be detected by rule-based knowledge (for example from the beekeeper), or an automated procedure called change point detection (CPD). Detecting the onset of succinct periods enables the beekeeper to provide specific care for each period. Within these longer time spans, short term events can occur, mostly specific to the semantic context. For example swarming can only occur in the summer breed rearing period. On the other hand, the death of a colony is more likely to happen during the winter seasons. These events are also known as anomalies, as they deviate from the expected normal behavior during that time span. Like for CPD, anomalies can be detected via certain rules, or automatically detected using an anomaly detection algorithm. In this study we investigate the use of machine learning algorithms to analyze a beehive’s state. With the data from multiple precision apiculture projects (BeeConnected, we4bee, HOBOS), we evaluate different algorithms to this end. This also enables us to not only study behavior at different locations but also with different sensor setups. We study the usage of single and multiple temperature sensors, but also integrate a scale for our purposes, and evaluate which combination of sensors yields the best results from an algorithmic standpoint. Besides quality of detection, we can also assess the applicability of specific sensors for beekeepers, keeping ease of use in mind. We hope our studies support beekeepers to gain new insight into their colonies for a better understanding. This also creates a feedback loop with the algorithms to improve prediction quality in the future.

  @presentation{davidson2022sensordriven, abstract = {Assessing a colony’s state is one of the key components in apiculture. In recent years precision apiculture has emerged as a field of study to quantify the analysis of beehives with objective sensors (e.g. temperature within the beehive). One objective of analysis is the study of semantically consecutive time spans, sometimes covering several months. A common task for example could be detecting the different breeding periods in a year. A change in semantics is called a change point, which can be detected by rule-based knowledge (for example from the beekeeper), or an automated procedure called change point detection (CPD). Detecting the onset of succinct periods enables the beekeeper to provide specific care for each period. Within these longer time spans, short term events can occur, mostly specific to the semantic context. For example swarming can only occur in the summer breed rearing period. On the other hand, the death of a colony is more likely to happen during the winter seasons. These events are also known as anomalies, as they deviate from the expected normal behavior during that time span. Like for CPD, anomalies can be detected via certain rules, or automatically detected using an anomaly detection algorithm. In this study we investigate the use of machine learning algorithms to analyze a beehive’s state. With the data from multiple precision apiculture projects (BeeConnected, we4bee, HOBOS), we evaluate different algorithms to this end. This also enables us to not only study behavior at different locations but also with different sensor setups. We study the usage of single and multiple temperature sensors, but also integrate a scale for our purposes, and evaluate which combination of sensors yields the best results from an algorithmic standpoint. Besides quality of detection, we can also assess the applicability of specific sensors for beekeepers, keeping ease of use in mind. We hope our studies support beekeepers to gain new insight into their colonies for a better understanding. This also creates a feedback loop with the algorithms to improve prediction quality in the future.}, author = {Davidson, Padraig and Hotho, Andreas and Krause, Anna}, keywords = {bee_connected}, title = {A sensor-driven approach for analyzing a beehive’s state}, year = 2022 }

  %0 Generic %1 davidson2022sensordriven %A Davidson, Padraig %A Hotho, Andreas %A Krause, Anna %D 2022 %T A sensor-driven approach for analyzing a beehive’s state %U https://sfe2gfomeeting.sciencesconf.org %X Assessing a colony’s state is one of the key components in apiculture. In recent years precision apiculture has emerged as a field of study to quantify the analysis of beehives with objective sensors (e.g. temperature within the beehive). One objective of analysis is the study of semantically consecutive time spans, sometimes covering several months. A common task for example could be detecting the different breeding periods in a year. A change in semantics is called a change point, which can be detected by rule-based knowledge (for example from the beekeeper), or an automated procedure called change point detection (CPD). Detecting the onset of succinct periods enables the beekeeper to provide specific care for each period. Within these longer time spans, short term events can occur, mostly specific to the semantic context. For example swarming can only occur in the summer breed rearing period. On the other hand, the death of a colony is more likely to happen during the winter seasons. These events are also known as anomalies, as they deviate from the expected normal behavior during that time span. Like for CPD, anomalies can be detected via certain rules, or automatically detected using an anomaly detection algorithm. In this study we investigate the use of machine learning algorithms to analyze a beehive’s state. With the data from multiple precision apiculture projects (BeeConnected, we4bee, HOBOS), we evaluate different algorithms to this end. This also enables us to not only study behavior at different locations but also with different sensor setups. We study the usage of single and multiple temperature sensors, but also integrate a scale for our purposes, and evaluate which combination of sensors yields the best results from an algorithmic standpoint. Besides quality of detection, we can also assess the applicability of specific sensors for beekeepers, keeping ease of use in mind. We hope our studies support beekeepers to gain new insight into their colonies for a better understanding. This also creates a feedback loop with the algorithms to improve prediction quality in the future.
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  Anomaly Detection in Beehives: An Algorithm Comparison. Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. In Sensor Networks, A. Ahrens, R. V. Prasad, C. Benavente-Peces, N. Ansari (eds.), pp. 1–20. Springer International Publishing, Cham, 2022
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  Sensor-equipped beehives allow monitoring the living conditions of bees. Machine learning models can use the data of such hives to learn behavioral patterns and find anomalous events. One type of event that is of particular interest to apiarists for economical reasons is bee swarming. Other events of interest are behavioral anomalies from illness and technical anomalies, e.g. sensor failure. Beekeepers can be supported by suitable machine learning models which can detect these events.

  @inproceedings{10.1007/978-3-031-17718-7_1, abstract = {Sensor-equipped beehives allow monitoring the living conditions of bees. Machine learning models can use the data of such hives to learn behavioral patterns and find anomalous events. One type of event that is of particular interest to apiarists for economical reasons is bee swarming. Other events of interest are behavioral anomalies from illness and technical anomalies, e.g. sensor failure. Beekeepers can be supported by suitable machine learning models which can detect these events.}, address = {Cham}, author = {Davidson, Padraig and Steininger, Michael and Lautenschlager, Florian and Krause, Anna and Hotho, Andreas}, booktitle = {Sensor Networks}, editor = {Ahrens, Andreas and Prasad, RangaRao Venkatesha and Benavente-Peces, C{\'e}sar and Ansari, Nirwan}, keywords = {author:KRAUSE}, pages = {1–20}, publisher = {Springer International Publishing}, title = {Anomaly Detection in Beehives: An Algorithm Comparison}, year = 2022 }

  %0 Conference Paper %1 10.1007/978-3-031-17718-7_1 %A Davidson, Padraig %A Steininger, Michael %A Lautenschlager, Florian %A Krause, Anna %A Hotho, Andreas %B Sensor Networks %C Cham %D 2022 %E Ahrens, Andreas %E Prasad, RangaRao Venkatesha %E Benavente-Peces, C{\'e}sar %E Ansari, Nirwan %I Springer International Publishing %P 1--20 %T Anomaly Detection in Beehives: An Algorithm Comparison %X Sensor-equipped beehives allow monitoring the living conditions of bees. Machine learning models can use the data of such hives to learn behavioral patterns and find anomalous events. One type of event that is of particular interest to apiarists for economical reasons is bee swarming. Other events of interest are behavioral anomalies from illness and technical anomalies, e.g. sensor failure. Beekeepers can be supported by suitable machine learning models which can detect these events. %@ 978-3-031-17718-7
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  Towards Explainable Occupational Fraud Detection. Tritscher, Julian; Schlör, Daniel; Gwinner, Fabian; Krause, Anna; Hotho, Andreas. In Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022, Vol. Communications in Computer and Information Science, Issue 1753, pp. 79–96. 2022
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  @article{tritscher2022towards, author = {Tritscher, Julian and Schlör, Daniel and Gwinner, Fabian and Krause, Anna and Hotho, Andreas}, journal = {Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022}, keywords = {xai}, number = 1753, pages = {79-96}, title = {Towards Explainable Occupational Fraud Detection}, volume = {Communications in Computer and Information Science}, year = 2022 }

  %0 Journal Article %1 tritscher2022towards %A Tritscher, Julian %A Schlör, Daniel %A Gwinner, Fabian %A Krause, Anna %A Hotho, Andreas %D 2022 %J Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022 %N 1753 %P 79-96 %R https://doi.org/10.1007/978-3-031-23633-4_7 %T Towards Explainable Occupational Fraud Detection %U https://link.springer.com/chapter/10.1007/978-3-031-23633-4_7 %V Communications in Computer and Information Science %@ 978-3-031-23632-7
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  One Graph to Rule them All: Using {NLP} and Graph Neural Networks to analyse Tolkien’s Legendarium. Perri, Vincenzo; Qarkaxhija, Lisi; Zehe, Albin; Hotho, Andreas; Scholtes, Ingo. In Proceedings of the Computational Humanities Research Conference 2022, {CHR} 2022, Antwerp, Belgium, December 12-14, 2022, Vol. 3290 of {CEUR} Workshop Proceedings, F. Karsdorp, K. L. Nielbo (eds.), pp. 291–317. CEUR-WS.org, 2022
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  @inproceedings{perri2022graph, author = {Perri, Vincenzo and Qarkaxhija, Lisi and Zehe, Albin and Hotho, Andreas and Scholtes, Ingo}, booktitle = {Proceedings of the Computational Humanities Research Conference 2022, {CHR} 2022, Antwerp, Belgium, December 12-14, 2022}, editor = {Karsdorp, Folgert and Nielbo, Kristoffer L.}, keywords = {author:zehe}, pages = {291–317}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {One Graph to Rule them All: Using {NLP} and Graph Neural Networks to analyse Tolkien's Legendarium}, volume = 3290, year = 2022 }

  %0 Conference Paper %1 perri2022graph %A Perri, Vincenzo %A Qarkaxhija, Lisi %A Zehe, Albin %A Hotho, Andreas %A Scholtes, Ingo %B Proceedings of the Computational Humanities Research Conference 2022, {CHR} 2022, Antwerp, Belgium, December 12-14, 2022 %D 2022 %E Karsdorp, Folgert %E Nielbo, Kristoffer L. %I CEUR-WS.org %P 291--317 %T One Graph to Rule them All: Using {NLP} and Graph Neural Networks to analyse Tolkien's Legendarium %U http://ceur-ws.org/Vol-3290/long\_paper2171.pdf %V 3290
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  The Impact of Different System Call Representations on Intrusion Detection. Wunderlich, Sarah; Ring, Markus; Landes, Dieter; Hotho, Andreas. In Log. J. {IGPL}, Vol. 30, Issue 2, pp. 239–251. 2022
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  @article{DBLP:journals/igpl/WunderlichRLH22, author = {Wunderlich, Sarah and Ring, Markus and Landes, Dieter and Hotho, Andreas}, journal = {Log. J. {IGPL}}, keywords = {from:hotho}, number = 2, pages = {239–251}, title = {The Impact of Different System Call Representations on Intrusion Detection}, volume = 30, year = 2022 }

  %0 Journal Article %1 DBLP:journals/igpl/WunderlichRLH22 %A Wunderlich, Sarah %A Ring, Markus %A Landes, Dieter %A Hotho, Andreas %D 2022 %J Log. J. {IGPL} %N 2 %P 239--251 %R 10.1093/jigpal/jzaa058 %T The Impact of Different System Call Representations on Intrusion Detection %U https://doi.org/10.1093/jigpal/jzaa058 %V 30
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  Do Different Deep Metric Learning Losses Lead to Similar Learned Features?. Kobs, Konstantin; Steininger, Michael; Dulny, Andrzej; Hotho, Andreas. In CoRR, Vol. abs/2205.02698. 2022
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  @article{DBLP:journals/corr/abs-2205-02698, author = {Kobs, Konstantin and Steininger, Michael and Dulny, Andrzej and Hotho, Andreas}, journal = {CoRR}, keywords = {from:hotho}, title = {Do Different Deep Metric Learning Losses Lead to Similar Learned Features?}, volume = {abs/2205.02698}, year = 2022 }

  %0 Journal Article %1 DBLP:journals/corr/abs-2205-02698 %A Kobs, Konstantin %A Steininger, Michael %A Dulny, Andrzej %A Hotho, Andreas %D 2022 %J CoRR %R 10.48550/arXiv.2205.02698 %T Do Different Deep Metric Learning Losses Lead to Similar Learned Features? %U https://doi.org/10.48550/arXiv.2205.02698 %V abs/2205.02698
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  On Learning Hierarchical Embeddings from Encrypted Network Traffic. Wehner, Nikolas; Ring, Markus; Sch{\"{u}}ler, Joshua; Hotho, Andreas; Ho{\ss}feld, Tobias; Seufert, Michael. In 2022 {IEEE/IFIP} Network Operations and Management Symposium, {NOMS} 2022, Budapest, Hungary, April 25-29, 2022, pp. 1–7. {IEEE}, 2022
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  @inproceedings{DBLP:conf/noms/WehnerRSHHS22, author = {Wehner, Nikolas and Ring, Markus and Sch{\"{u}}ler, Joshua and Hotho, Andreas and Ho{\ss}feld, Tobias and Seufert, Michael}, booktitle = {2022 {IEEE/IFIP} Network Operations and Management Symposium, {NOMS} 2022, Budapest, Hungary, April 25-29, 2022}, keywords = {from:hotho}, pages = {1–7}, publisher = {{IEEE}}, title = {On Learning Hierarchical Embeddings from Encrypted Network Traffic}, year = 2022 }

  %0 Conference Paper %1 DBLP:conf/noms/WehnerRSHHS22 %A Wehner, Nikolas %A Ring, Markus %A Sch{\"{u}}ler, Joshua %A Hotho, Andreas %A Ho{\ss}feld, Tobias %A Seufert, Michael %B 2022 {IEEE/IFIP} Network Operations and Management Symposium, {NOMS} 2022, Budapest, Hungary, April 25-29, 2022 %D 2022 %I {IEEE} %P 1--7 %R 10.1109/NOMS54207.2022.9789896 %T On Learning Hierarchical Embeddings from Encrypted Network Traffic %U https://doi.org/10.1109/NOMS54207.2022.9789896
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  Sequential Item Recommendation in the {MOBA} Game Dota 2. Dallmann, Alexander; Kohlmann, Johannes; Zoller, Daniel; Hotho, Andreas. In CoRR, Vol. abs/2201.08724. 2022
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  @article{DBLP:journals/corr/abs-2201-08724, author = {Dallmann, Alexander and Kohlmann, Johannes and Zoller, Daniel and Hotho, Andreas}, journal = {CoRR}, keywords = {selected}, title = {Sequential Item Recommendation in the {MOBA} Game Dota 2}, volume = {abs/2201.08724}, year = 2022 }

  %0 Journal Article %1 DBLP:journals/corr/abs-2201-08724 %A Dallmann, Alexander %A Kohlmann, Johannes %A Zoller, Daniel %A Hotho, Andreas %D 2022 %J CoRR %R 10.48550/arXiv.2201.08724 %T Sequential Item Recommendation in the {MOBA} Game Dota 2 %U https://arxiv.org/abs/2201.08724 %V abs/2201.08724
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  On Background Bias in Deep Metric Learning. Kobs, Konstantin; Hotho, Andreas. In CoRR, Vol. abs/2210.01615. 2022
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  @article{DBLP:journals/corr/abs-2210-01615, author = {Kobs, Konstantin and Hotho, Andreas}, journal = {CoRR}, keywords = {from:hotho}, title = {On Background Bias in Deep Metric Learning}, volume = {abs/2210.01615}, year = 2022 }

  %0 Journal Article %1 DBLP:journals/corr/abs-2210-01615 %A Kobs, Konstantin %A Hotho, Andreas %D 2022 %J CoRR %R 10.48550/arXiv.2210.01615 %T On Background Bias in Deep Metric Learning %U https://doi.org/10.48550/arXiv.2210.01615 %V abs/2210.01615
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  Personalization through User Attributes for Transformer-based Sequential Recommendation. Fischer, Elisabeth; Dallmann, Alexander; Hotho, Andreas. 2022
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  @article{fischer2022personalization, author = {Fischer, Elisabeth and Dallmann, Alexander and Hotho, Andreas}, keywords = {selected}, title = {Personalization through User Attributes for Transformer-based Sequential Recommendation}, year = 2022 }

  %0 Journal Article %1 fischer2022personalization %A Fischer, Elisabeth %A Dallmann, Alexander %A Hotho, Andreas %D 2022 %T Personalization through User Attributes for Transformer-based Sequential Recommendation %U https://link.springer.com/chapter/10.1007/978-3-031-22192-7_2
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  Towards Responsible Medical Diagnostics Recommendation Systems. Schlör, Daniel; Hotho, Andreas. In CoRR, Vol. abs/2209.03760. 2022
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  @article{DBLP:journals/corr/abs-2209-03760, author = {Schlör, Daniel and Hotho, Andreas}, journal = {CoRR}, keywords = {app_medical}, title = {Towards Responsible Medical Diagnostics Recommendation Systems}, volume = {abs/2209.03760}, year = 2022 }

  %0 Journal Article %1 DBLP:journals/corr/abs-2209-03760 %A Schlör, Daniel %A Hotho, Andreas %D 2022 %J CoRR %R 10.48550/arXiv.2209.03760 %T Towards Responsible Medical Diagnostics Recommendation Systems %U https://doi.org/10.48550/arXiv.2209.03760 %V abs/2209.03760
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  Comparison of Data Representations and Machine Learning Architectures for User Identification on Arbitrary Motion Sequences. Schell, Christian; Hotho, Andreas; Latoschik, Marc Erich. In CoRR, Vol. abs/2210.00527. 2022
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  @article{DBLP:journals/corr/abs-2210-00527, author = {Schell, Christian and Hotho, Andreas and Latoschik, Marc Erich}, journal = {CoRR}, keywords = {from:hotho}, title = {Comparison of Data Representations and Machine Learning Architectures for User Identification on Arbitrary Motion Sequences}, volume = {abs/2210.00527}, year = 2022 }

  %0 Journal Article %1 DBLP:journals/corr/abs-2210-00527 %A Schell, Christian %A Hotho, Andreas %A Latoschik, Marc Erich %D 2022 %J CoRR %R 10.48550/arXiv.2210.00527 %T Comparison of Data Representations and Machine Learning Architectures for User Identification on Arbitrary Motion Sequences %U https://doi.org/10.48550/arXiv.2210.00527 %V abs/2210.00527
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  ConvMOS: Climate Model Output Statistics with Deep Learning. Steininger, Michael; Abel, Daniel; Ziegler, Katrin; Krause, Anna; Paeth, Heiko; Hotho, Andreas. In Data Mining and Knowledge Discovery, P. Cellier; K. Dembczynski; A. Zimmermann; E. Devijver (eds.). Springer, 2022
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  Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.

  @article{steininger2022convmos, abstract = {Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts.}, author = {Steininger, Michael and Abel, Daniel and Ziegler, Katrin and Krause, Anna and Paeth, Heiko and Hotho, Andreas}, editor = {Cellier, Peggy and Dembczynski, Krzysztof and Zimmermann, Albrecht and Devijver, Emilie}, journal = {Data Mining and Knowledge Discovery}, keywords = {from:msteininger}, month = 10, publisher = {Springer}, title = {ConvMOS: Climate Model Output Statistics with Deep Learning}, year = 2022 }

  %0 Journal Article %1 steininger2022convmos %A Steininger, Michael %A Abel, Daniel %A Ziegler, Katrin %A Krause, Anna %A Paeth, Heiko %A Hotho, Andreas %D 2022 %E Cellier, Peggy %E Dembczynski, Krzysztof %E Zimmermann, Albrecht %E Devijver, Emilie %I Springer %J Data Mining and Knowledge Discovery %R 10.1007/s10618-022-00877-6 %T ConvMOS: Climate Model Output Statistics with Deep Learning %X Climate models are the tool of choice for scientists researching climate change. Like all models they suffer from errors, particularly systematic and location-specific representation errors. One way to reduce these errors is model output statistics (MOS) where the model output is fitted to observational data with machine learning. In this work, we assess the use of convolutional Deep Learning climate MOS approaches and present the ConvMOS architecture which is specifically designed based on the observation that there are systematic and location-specific errors in the precipitation estimates of climate models. We apply ConvMOS models to the simulated precipitation of the regional climate model REMO, showing that a combination of per-location model parameters for reducing location-specific errors and global model parameters for reducing systematic errors is indeed beneficial for MOS performance. We find that ConvMOS models can reduce errors considerably and perform significantly better than three commonly used MOS approaches and plain ResNet and U-Net models in most cases. Our results show that non-linear MOS models underestimate the number of extreme precipitation events, which we alleviate by training models specialized towards extreme precipitation events with the imbalanced regression method DenseLoss. While we consider climate MOS, we argue that aspects of ConvMOS may also be beneficial in other domains with geospatial data, such as air pollution modeling or weather forecasts. %@ 1573-756X
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  {LSX} team5 at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover{’}s Distance. Heil, Stefan; Kopp, Karina; Zehe, Albin; Kobs, Konstantin; Hotho, Andreas. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pp. 1190–1195. Association for Computational Linguistics, Seattle, United States, 2022
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  This paper introduces our submission for the SemEval 2022 Task 8: Multilingual News Article Similarity. The task of the competition consisted of the development of a model, capable of determining the similarity between pairs of multilingual news articles. To address this challenge, we evaluated the Word Mover{'}s Distance in conjunction with word embeddings from ConceptNet Numberbatch and term frequencies of WorldLex, as well the Sentence Mover{'}s Distance based on sentence embeddings generated by pretrained transformer models of Sentence-BERT. To facilitate the comparison of multilingual articles with Sentence-BERT models, we deployed a Neural Machine Translation system. All our models achieve stable results in multilingual similarity estimation without learning parameters.

  @inproceedings{heil2022lsxteam5, abstract = {This paper introduces our submission for the SemEval 2022 Task 8: Multilingual News Article Similarity. The task of the competition consisted of the development of a model, capable of determining the similarity between pairs of multilingual news articles. To address this challenge, we evaluated the Word Mover{'}s Distance in conjunction with word embeddings from ConceptNet Numberbatch and term frequencies of WorldLex, as well the Sentence Mover{'}s Distance based on sentence embeddings generated by pretrained transformer models of Sentence-BERT. To facilitate the comparison of multilingual articles with Sentence-BERT models, we deployed a Neural Machine Translation system. All our models achieve stable results in multilingual similarity estimation without learning parameters.}, address = {Seattle, United States}, author = {Heil, Stefan and Kopp, Karina and Zehe, Albin and Kobs, Konstantin and Hotho, Andreas}, booktitle = {Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)}, keywords = {multilingual}, month = {07}, pages = {1190–1195}, publisher = {Association for Computational Linguistics}, title = {{LSX} team5 at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover{'}s Distance}, year = 2022 }

  %0 Conference Paper %1 heil2022lsxteam5 %A Heil, Stefan %A Kopp, Karina %A Zehe, Albin %A Kobs, Konstantin %A Hotho, Andreas %B Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) %C Seattle, United States %D 2022 %I Association for Computational Linguistics %P 1190--1195 %T {LSX} team5 at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover{'}s Distance %U https://aclanthology.org/2022.semeval-1.168 %X This paper introduces our submission for the SemEval 2022 Task 8: Multilingual News Article Similarity. The task of the competition consisted of the development of a model, capable of determining the similarity between pairs of multilingual news articles. To address this challenge, we evaluated the Word Mover{'}s Distance in conjunction with word embeddings from ConceptNet Numberbatch and term frequencies of WorldLex, as well the Sentence Mover{'}s Distance based on sentence embeddings generated by pretrained transformer models of Sentence-BERT. To facilitate the comparison of multilingual articles with Sentence-BERT models, we deployed a Neural Machine Translation system. All our models achieve stable results in multilingual similarity estimation without learning parameters.
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  {W}ue{D}evils at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices. Wangsadirdja, Dirk; Heinickel, Felix; Trapp, Simon; Zehe, Albin; Kobs, Konstantin; Hotho, Andreas. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pp. 1235–1243. Association for Computational Linguistics, Seattle, United States, 2022
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  We present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively. For each news article sentence, it searches the most similar sentence from the other article and computes an average score. Further, a convolutional neural network calculates a total similarity score for the article pairs on these matrices. Finally, a random forest regressor merges the previous results to a final score that can optionally be extended with a publishing date score.

  @inproceedings{wangsadirdja2022wuedevils, abstract = {We present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively. For each news article sentence, it searches the most similar sentence from the other article and computes an average score. Further, a convolutional neural network calculates a total similarity score for the article pairs on these matrices. Finally, a random forest regressor merges the previous results to a final score that can optionally be extended with a publishing date score.}, address = {Seattle, United States}, author = {Wangsadirdja, Dirk and Heinickel, Felix and Trapp, Simon and Zehe, Albin and Kobs, Konstantin and Hotho, Andreas}, booktitle = {Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)}, keywords = {multilingual}, month = {07}, pages = {1235–1243}, publisher = {Association for Computational Linguistics}, title = {{W}ue{D}evils at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices}, year = 2022 }

  %0 Conference Paper %1 wangsadirdja2022wuedevils %A Wangsadirdja, Dirk %A Heinickel, Felix %A Trapp, Simon %A Zehe, Albin %A Kobs, Konstantin %A Hotho, Andreas %B Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) %C Seattle, United States %D 2022 %I Association for Computational Linguistics %P 1235--1243 %T {W}ue{D}evils at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices %U https://aclanthology.org/2022.semeval-1.175 %X We present a system that creates pair-wise cosine and arccosine sentence similarity matrices using multilingual sentence embeddings obtained from pre-trained SBERT and Universal Sentence Encoder (USE) models respectively. For each news article sentence, it searches the most similar sentence from the other article and computes an average score. Further, a convolutional neural network calculates a total similarity score for the article pairs on these matrices. Finally, a random forest regressor merges the previous results to a final score that can optionally be extended with a publishing date score.
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  Point me to your Opinion, {S}en{P}oi. Pfister, Jan; Wankerl, Sebastian; Hotho, Andreas. In Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022), pp. 1313–1323. Association for Computational Linguistics, Seattle, United States, 2022
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  Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70{\%}. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.

  @inproceedings{pfister-etal-2022-senpoi, abstract = {Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70{\%}. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.}, address = {Seattle, United States}, author = {Pfister, Jan and Wankerl, Sebastian and Hotho, Andreas}, booktitle = {Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)}, keywords = {from:janpf}, month = {07}, pages = {1313–1323}, publisher = {Association for Computational Linguistics}, title = {Point me to your Opinion, {S}en{P}oi}, year = 2022 }

  %0 Conference Paper %1 pfister-etal-2022-senpoi %A Pfister, Jan %A Wankerl, Sebastian %A Hotho, Andreas %B Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) %C Seattle, United States %D 2022 %I Association for Computational Linguistics %P 1313--1323 %T Point me to your Opinion, {S}en{P}oi %U https://aclanthology.org/2022.semeval-1.183 %X Structured Sentiment Analysis is the task of extracting sentiment tuples in a graph structure commonly from review texts. We adapt the Aspect-Based Sentiment Analysis pointer network BARTABSA to model this tuple extraction as a sequence prediction task and extend their output grammar to account for the increased complexity of Structured Sentiment Analysis. To predict structured sentiment tuples in languages other than English we swap BART for a multilingual mT5 and introduce a novel Output Length Regularization to mitigate overfitting to common target sequence lengths, thereby improving the performance of the model by up to 70{\%}. We evaluate our approach on seven datasets in five languages including a zero shot crosslingual setting.
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  Towards Responsible Medical Diagnostics Recommendation Systems. Schl{\"{o}}r, Daniel; Hotho, Andreas. In CoRR, Vol. abs/2209.03760. 2022
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  @article{DBLP:journals/corr/abs-2209-03760, author = {Schl{\"{o}}r, Daniel and Hotho, Andreas}, journal = {CoRR}, keywords = {from:hotho}, title = {Towards Responsible Medical Diagnostics Recommendation Systems}, volume = {abs/2209.03760}, year = 2022 }

  %0 Journal Article %1 DBLP:journals/corr/abs-2209-03760 %A Schl{\"{o}}r, Daniel %A Hotho, Andreas %D 2022 %J CoRR %R 10.48550/arXiv.2209.03760 %T Towards Responsible Medical Diagnostics Recommendation Systems %U https://doi.org/10.48550/arXiv.2209.03760 %V abs/2209.03760
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  Towards Responsible Medical Diagnostics Recommendation Systems. Schlör, Daniel; Hotho, Andreas. 2022
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  The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, which previously required paperwork and telephone arrangements, are now integrated into IT solutions and require physicians and medical staff to interact with appropriate interfaces and tools. Although this shift to digital data management and process support has benefited patient care in many ways, it requires physicians to accurately capture all relevant information digitally for billing and documentation purposes, which takes a lot of time away from actual patient care work. However, systematic collection of healthcare data over a long period of time offers opportunities to improve this process and support medical staff by introducing recommender systems. Based on a practical working example, in this position paper, we will outline the design of a responsible recommender system in the medical context from a technical, application driven perspective and discuss potential design choices and criteria with a specific focus on accountability, safety, and fairness.

  @misc{schlor2022towards, abstract = {The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, which previously required paperwork and telephone arrangements, are now integrated into IT solutions and require physicians and medical staff to interact with appropriate interfaces and tools. Although this shift to digital data management and process support has benefited patient care in many ways, it requires physicians to accurately capture all relevant information digitally for billing and documentation purposes, which takes a lot of time away from actual patient care work. However, systematic collection of healthcare data over a long period of time offers opportunities to improve this process and support medical staff by introducing recommender systems. Based on a practical working example, in this position paper, we will outline the design of a responsible recommender system in the medical context from a technical, application driven perspective and discuss potential design choices and criteria with a specific focus on accountability, safety, and fairness.}, author = {Schlör, Daniel and Hotho, Andreas}, keywords = {selected}, note = {cite arxiv:2209.03760Comment: 4th FAccTRec Workshop on Responsible Recommendation}, title = {Towards Responsible Medical Diagnostics Recommendation Systems}, year = 2022 }

  %0 Generic %1 schlor2022towards %A Schlör, Daniel %A Hotho, Andreas %D 2022 %T Towards Responsible Medical Diagnostics Recommendation Systems %U http://arxiv.org/abs/2209.03760 %X The early development and deployment of hospital and healthcare information systems have encouraged the ongoing digitization of processes in hospitals. Many of these processes, which previously required paperwork and telephone arrangements, are now integrated into IT solutions and require physicians and medical staff to interact with appropriate interfaces and tools. Although this shift to digital data management and process support has benefited patient care in many ways, it requires physicians to accurately capture all relevant information digitally for billing and documentation purposes, which takes a lot of time away from actual patient care work. However, systematic collection of healthcare data over a long period of time offers opportunities to improve this process and support medical staff by introducing recommender systems. Based on a practical working example, in this position paper, we will outline the design of a responsible recommender system in the medical context from a technical, application driven perspective and discuss potential design choices and criteria with a specific focus on accountability, safety, and fairness.
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  InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images. Kobs, Konstantin; Steininger, Michael; Hotho, Andreas. 2022
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  Common Deep Metric Learning (DML) datasets specify only one notion of similarity, e.g., two images in the Cars196 dataset are deemed similar if they show the same car model. We argue that depending on the application, users of image retrieval systems have different and changing similarity notions that should be incorporated as easily as possible. Therefore, we present Language-Guided Zero-Shot Deep Metric Learning (LanZ-DML) as a new DML setting in which users control the properties that should be important for image representations without training data by only using natural language. To this end, we propose InDiReCT (Image representations using Dimensionality Reduction on CLIP embedded Texts), a model for LanZ-DML on images that exclusively uses a few text prompts for training. InDiReCT utilizes CLIP as a fixed feature extractor for images and texts and transfers the variation in text prompt embeddings to the image embedding space. Extensive experiments on five datasets and overall thirteen similarity notions show that, despite not seeing any images during training, InDiReCT performs better than strong baselines and approaches the performance of fully-supervised models. An analysis reveals that InDiReCT learns to focus on regions of the image that correlate with the desired similarity notion, which makes it a fast to train and easy to use method to create custom embedding spaces only using natural language.

  @misc{kobs2022indirect, abstract = {Common Deep Metric Learning (DML) datasets specify only one notion of similarity, e.g., two images in the Cars196 dataset are deemed similar if they show the same car model. We argue that depending on the application, users of image retrieval systems have different and changing similarity notions that should be incorporated as easily as possible. Therefore, we present Language-Guided Zero-Shot Deep Metric Learning (LanZ-DML) as a new DML setting in which users control the properties that should be important for image representations without training data by only using natural language. To this end, we propose InDiReCT (Image representations using Dimensionality Reduction on CLIP embedded Texts), a model for LanZ-DML on images that exclusively uses a few text prompts for training. InDiReCT utilizes CLIP as a fixed feature extractor for images and texts and transfers the variation in text prompt embeddings to the image embedding space. Extensive experiments on five datasets and overall thirteen similarity notions show that, despite not seeing any images during training, InDiReCT performs better than strong baselines and approaches the performance of fully-supervised models. An analysis reveals that InDiReCT learns to focus on regions of the image that correlate with the desired similarity notion, which makes it a fast to train and easy to use method to create custom embedding spaces only using natural language.}, author = {Kobs, Konstantin and Steininger, Michael and Hotho, Andreas}, keywords = {selected}, note = {cite arxiv:2211.12760Comment: Accepted to WACV 2023}, title = {InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images}, year = 2022 }

  %0 Generic %1 kobs2022indirect %A Kobs, Konstantin %A Steininger, Michael %A Hotho, Andreas %D 2022 %T InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images %U http://arxiv.org/abs/2211.12760 %X Common Deep Metric Learning (DML) datasets specify only one notion of similarity, e.g., two images in the Cars196 dataset are deemed similar if they show the same car model. We argue that depending on the application, users of image retrieval systems have different and changing similarity notions that should be incorporated as easily as possible. Therefore, we present Language-Guided Zero-Shot Deep Metric Learning (LanZ-DML) as a new DML setting in which users control the properties that should be important for image representations without training data by only using natural language. To this end, we propose InDiReCT (Image representations using Dimensionality Reduction on CLIP embedded Texts), a model for LanZ-DML on images that exclusively uses a few text prompts for training. InDiReCT utilizes CLIP as a fixed feature extractor for images and texts and transfers the variation in text prompt embeddings to the image embedding space. Extensive experiments on five datasets and overall thirteen similarity notions show that, despite not seeing any images during training, InDiReCT performs better than strong baselines and approaches the performance of fully-supervised models. An analysis reveals that InDiReCT learns to focus on regions of the image that correlate with the desired similarity notion, which makes it a fast to train and easy to use method to create custom embedding spaces only using natural language.
• 
  SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi. Pfister, Jan; Wankerl, Sebastian; Hotho, Andreas. In Proceedings of the 16th International Workshop on Semantic Evaluation, SemEval@NAACL 2022, Seattle, Washington, United States, July 14-15, 2022, G. Emerson, N. Schluter, G. Stanovsky, R. Kumar, A. Palmer, N. Schneider, S. Singh, S. Ratan (eds.), pp. 1313–1323. Association for Computational Linguistics, 2022
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  @inproceedings{DBLP:conf/semeval/PfisterWH22, author = {Pfister, Jan and Wankerl, Sebastian and Hotho, Andreas}, booktitle = {Proceedings of the 16th International Workshop on Semantic Evaluation, SemEval@NAACL 2022, Seattle, Washington, United States, July 14-15, 2022}, editor = {Emerson, Guy and Schluter, Natalie and Stanovsky, Gabriel and Kumar, Ritesh and Palmer, Alexis and Schneider, Nathan and Singh, Siddharth and Ratan, Shyam}, keywords = {from:hotho}, pages = {1313–1323}, publisher = {Association for Computational Linguistics}, title = {SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi}, year = 2022 }

  %0 Conference Paper %1 DBLP:conf/semeval/PfisterWH22 %A Pfister, Jan %A Wankerl, Sebastian %A Hotho, Andreas %B Proceedings of the 16th International Workshop on Semantic Evaluation, SemEval@NAACL 2022, Seattle, Washington, United States, July 14-15, 2022 %D 2022 %E Emerson, Guy %E Schluter, Natalie %E Stanovsky, Gabriel %E Kumar, Ritesh %E Palmer, Alexis %E Schneider, Nathan %E Singh, Siddharth %E Ratan, Shyam %I Association for Computational Linguistics %P 1313--1323 %R 10.18653/v1/2022.semeval-1.183 %T SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi %U https://doi.org/10.18653/v1/2022.semeval-1.183
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  Open ERP System Data For Occupational Fraud Detection. Tritscher, Julian; Gwinner, Fabian; Schlör, Daniel; Krause, Anna; Hotho, Andreas. In arxiv. 2022
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  @article{tritscher2022erpData, author = {Tritscher, Julian and Gwinner, Fabian and Schlör, Daniel and Krause, Anna and Hotho, Andreas}, journal = {arxiv}, keywords = {author:hotho}, title = {Open ERP System Data For Occupational Fraud Detection}, year = 2022 }

  %0 Journal Article %1 tritscher2022erpData %A Tritscher, Julian %A Gwinner, Fabian %A Schlör, Daniel %A Krause, Anna %A Hotho, Andreas %D 2022 %J arxiv %T Open ERP System Data For Occupational Fraud Detection %U https://arxiv.org/abs/2206.04460

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  Detecting Presence Of Speech In Acoustic Data Obtained From Beehives. Janetzky, Pascal; Davidson, Padraig; Steininger, Michael; Krause, Anna; Hotho, Andreas. In DCASE Workshop. 2021
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  @article{janetzkydetecting, author = {Janetzky, Pascal and Davidson, Padraig and Steininger, Michael and Krause, Anna and Hotho, Andreas}, journal = {DCASE Workshop}, keywords = {from:msteininger}, title = {Detecting Presence Of Speech In Acoustic Data Obtained From Beehives}, year = 2021 }

  %0 Journal Article %1 janetzkydetecting %A Janetzky, Pascal %A Davidson, Padraig %A Steininger, Michael %A Krause, Anna %A Hotho, Andreas %D 2021 %J DCASE Workshop %T Detecting Presence Of Speech In Acoustic Data Obtained From Beehives
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  NeuralPDE: Modelling Dynamical Systems from Data. Dulny, Andrzej; Hotho, Andreas; Krause, Anna. 2021
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  Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs). Modelling such dynamical systems using Neural Networks is an emerging research field. However, current methods are restricted in various ways: they require prior knowledge about the governing equations, and are limited to linear or first-order equations. In this work we propose NeuralPDE, a model which combines convolutional neural networks (CNNs) with differentiable ODE solvers to model dynamical systems. We show that the Method of Lines used in standard PDE solvers can be represented using convolutions which makes CNNs the natural choice to parametrize arbitrary PDE dynamics. Our model can be applied to any data without requiring any prior knowledge about the governing PDE. We evaluate NeuralPDE on datasets generated by solving a wide variety of PDEs, covering higher orders, non-linear equations and multiple spatial dimensions.

  @misc{dulny2021neuralpde, abstract = {Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs). Modelling such dynamical systems using Neural Networks is an emerging research field. However, current methods are restricted in various ways: they require prior knowledge about the governing equations, and are limited to linear or first-order equations. In this work we propose NeuralPDE, a model which combines convolutional neural networks (CNNs) with differentiable ODE solvers to model dynamical systems. We show that the Method of Lines used in standard PDE solvers can be represented using convolutions which makes CNNs the natural choice to parametrize arbitrary PDE dynamics. Our model can be applied to any data without requiring any prior knowledge about the governing PDE. We evaluate NeuralPDE on datasets generated by solving a wide variety of PDEs, covering higher orders, non-linear equations and multiple spatial dimensions.}, author = {Dulny, Andrzej and Hotho, Andreas and Krause, Anna}, keywords = {from:hotho}, note = {cite arxiv:2111.07671}, title = {NeuralPDE: Modelling Dynamical Systems from Data}, year = 2021 }

  %0 Generic %1 dulny2021neuralpde %A Dulny, Andrzej %A Hotho, Andreas %A Krause, Anna %D 2021 %T NeuralPDE: Modelling Dynamical Systems from Data %U http://arxiv.org/abs/2111.07671 %X Many physical processes such as weather phenomena or fluid mechanics are governed by partial differential equations (PDEs). Modelling such dynamical systems using Neural Networks is an emerging research field. However, current methods are restricted in various ways: they require prior knowledge about the governing equations, and are limited to linear or first-order equations. In this work we propose NeuralPDE, a model which combines convolutional neural networks (CNNs) with differentiable ODE solvers to model dynamical systems. We show that the Method of Lines used in standard PDE solvers can be represented using convolutions which makes CNNs the natural choice to parametrize arbitrary PDE dynamics. Our model can be applied to any data without requiring any prior knowledge about the governing PDE. We evaluate NeuralPDE on datasets generated by solving a wide variety of PDEs, covering higher orders, non-linear equations and multiple spatial dimensions.
• 
  The Semantic Web - {ISWC} 2021 - 20th International Semantic Web Conference, {ISWC} 2021, Virtual Event, October 24-28, 2021, Proceedings. Hotho, Andreas; Blomqvist, Eva; Dietze, Stefan; Fokoue, Achille; Ding, Ying; Barnaghi, Payam M.; Haller, Armin; Dragoni, Mauro; Alani, Harith. In Vol. 12922 of Lecture Notes in Computer Science. Springer, 2021
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  @proceedings{DBLP:conf/semweb/2021, editor = {Hotho, Andreas and Blomqvist, Eva and Dietze, Stefan and Fokoue, Achille and Ding, Ying and Barnaghi, Payam M. and Haller, Armin and Dragoni, Mauro and Alani, Harith}, keywords = {from:hotho}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {The Semantic Web - {ISWC} 2021 - 20th International Semantic Web Conference, {ISWC} 2021, Virtual Event, October 24-28, 2021, Proceedings}, volume = 12922, year = 2021 }

  %0 Conference Proceedings %1 DBLP:conf/semweb/2021 %B Lecture Notes in Computer Science %D 2021 %E Hotho, Andreas %E Blomqvist, Eva %E Dietze, Stefan %E Fokoue, Achille %E Ding, Ying %E Barnaghi, Payam M. %E Haller, Armin %E Dragoni, Mauro %E Alani, Harith %I Springer %R 10.1007/978-3-030-88361-4 %T The Semantic Web - {ISWC} 2021 - 20th International Semantic Web Conference, {ISWC} 2021, Virtual Event, October 24-28, 2021, Proceedings %U https://doi.org/10.1007/978-3-030-88361-4 %V 12922 %@ 978-3-030-88360-7
• 
  Malware detection on windows audit logs using LSTMs. Ring, Markus; Schlör, Daniel; Wunderlich, Sarah; Landes, Dieter; Hotho, Andreas. In Computers & Security, Vol. 109, p. 102389. 2021
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  Malware is a constant threat and is continuously evolving. Security systems try to keep up with the constant change. One challenge that arises is the large amount of logs generated on an operating system and the need to clarify which information contributes to the detection of possible malware. This work aims at the detection of malware using neural networks based on Windows audit log events. Neural networks can only process continuous data, but Windows audit logs are sequential and textual data. To address these challenges, we extract features out of the audit log events and use LSTMs to capture sequential effects. We create different subsets of features and analyze the effects of additional information. Features describe for example the action-type of windows audit log events, process names or target files that are accessed. Textual features are represented either as one-hot encoding or embedding representation, for which we compare three different approaches for representation learning. Effects of different feature subsets and representations are evaluated on a publicly available data set. Results indicate that using additional information improves the performance of the LSTM-model. While different representations lead to similar classification results, analysis of the latent space shows differences more precisely where FastText seems to be the most promising representation.

  @article{RING2021102389, abstract = {Malware is a constant threat and is continuously evolving. Security systems try to keep up with the constant change. One challenge that arises is the large amount of logs generated on an operating system and the need to clarify which information contributes to the detection of possible malware. This work aims at the detection of malware using neural networks based on Windows audit log events. Neural networks can only process continuous data, but Windows audit logs are sequential and textual data. To address these challenges, we extract features out of the audit log events and use LSTMs to capture sequential effects. We create different subsets of features and analyze the effects of additional information. Features describe for example the action-type of windows audit log events, process names or target files that are accessed. Textual features are represented either as one-hot encoding or embedding representation, for which we compare three different approaches for representation learning. Effects of different feature subsets and representations are evaluated on a publicly available data set. Results indicate that using additional information improves the performance of the LSTM-model. While different representations lead to similar classification results, analysis of the latent space shows differences more precisely where FastText seems to be the most promising representation.}, author = {Ring, Markus and Schlör, Daniel and Wunderlich, Sarah and Landes, Dieter and Hotho, Andreas}, journal = {Computers & Security}, keywords = {author:hotho}, pages = 102389, title = {Malware detection on windows audit logs using LSTMs}, volume = 109, year = 2021 }

  %0 Journal Article %1 RING2021102389 %A Ring, Markus %A Schlör, Daniel %A Wunderlich, Sarah %A Landes, Dieter %A Hotho, Andreas %D 2021 %J Computers & Security %P 102389 %R https://doi.org/10.1016/j.cose.2021.102389 %T Malware detection on windows audit logs using LSTMs %U https://www.sciencedirect.com/science/article/pii/S0167404821002133 %V 109 %X Malware is a constant threat and is continuously evolving. Security systems try to keep up with the constant change. One challenge that arises is the large amount of logs generated on an operating system and the need to clarify which information contributes to the detection of possible malware. This work aims at the detection of malware using neural networks based on Windows audit log events. Neural networks can only process continuous data, but Windows audit logs are sequential and textual data. To address these challenges, we extract features out of the audit log events and use LSTMs to capture sequential effects. We create different subsets of features and analyze the effects of additional information. Features describe for example the action-type of windows audit log events, process names or target files that are accessed. Textual features are represented either as one-hot encoding or embedding representation, for which we compare three different approaches for representation learning. Effects of different feature subsets and representations are evaluated on a publicly available data set. Results indicate that using additional information improves the performance of the LSTM-model. While different representations lead to similar classification results, analysis of the latent space shows differences more precisely where FastText seems to be the most promising representation.
• 
  Semi-unsupervised Learning: An In-depth Parameter Analysis. Davidson, Padraig; Buckermann, Florian; Steininger, Michael; Krause, Anna; Hotho, Andreas. In KI 2021: Advances in Artificial Intelligence, S. Edelkamp, R. M{\"o}ller, E. Rueckert (eds.), pp. 51–66. Springer International Publishing, Cham, 2021
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  Creating datasets for supervised learning is a very challenging and expensive task, in which each input example has to be annotated with its expected output (e.g. object class). By combining unsupervised and semi-supervised learning, semi-unsupervised learning proposes a new paradigm for partially labeled datasets with additional unknown classes. In this paper we focus on a better understanding of this new learning paradigm and analyze the impact of the amount of labeled data, the number of augmented classes and the selection of hidden classes on the quality of prediction. Especially the number of augmented classes highly influences classification accuracy, which needs tuning for each dataset, since too few and too many augmented classes are detrimental to classifier performance. We also show that we can improve results on a large variety of datasets when using convolutional networks as feature extractors while applying output driven entropy regularization instead of a simple weight based L2 norm.

  @inproceedings{10.1007/978-3-030-87626-5_5, abstract = {Creating datasets for supervised learning is a very challenging and expensive task, in which each input example has to be annotated with its expected output (e.g. object class). By combining unsupervised and semi-supervised learning, semi-unsupervised learning proposes a new paradigm for partially labeled datasets with additional unknown classes. In this paper we focus on a better understanding of this new learning paradigm and analyze the impact of the amount of labeled data, the number of augmented classes and the selection of hidden classes on the quality of prediction. Especially the number of augmented classes highly influences classification accuracy, which needs tuning for each dataset, since too few and too many augmented classes are detrimental to classifier performance. We also show that we can improve results on a large variety of datasets when using convolutional networks as feature extractors while applying output driven entropy regularization instead of a simple weight based L2 norm.}, address = {Cham}, author = {Davidson, Padraig and Buckermann, Florian and Steininger, Michael and Krause, Anna and Hotho, Andreas}, booktitle = {KI 2021: Advances in Artificial Intelligence}, editor = {Edelkamp, Stefan and M{\"o}ller, Ralf and Rueckert, Elmar}, keywords = {author:hotho}, pages = {51–66}, publisher = {Springer International Publishing}, title = {Semi-unsupervised Learning: An In-depth Parameter Analysis}, year = 2021 }

  %0 Conference Paper %1 10.1007/978-3-030-87626-5_5 %A Davidson, Padraig %A Buckermann, Florian %A Steininger, Michael %A Krause, Anna %A Hotho, Andreas %B KI 2021: Advances in Artificial Intelligence %C Cham %D 2021 %E Edelkamp, Stefan %E M{\"o}ller, Ralf %E Rueckert, Elmar %I Springer International Publishing %P 51--66 %T Semi-unsupervised Learning: An In-depth Parameter Analysis %U https://link.springer.com/chapter/10.1007/978-3-030-87626-5_5 %X Creating datasets for supervised learning is a very challenging and expensive task, in which each input example has to be annotated with its expected output (e.g. object class). By combining unsupervised and semi-supervised learning, semi-unsupervised learning proposes a new paradigm for partially labeled datasets with additional unknown classes. In this paper we focus on a better understanding of this new learning paradigm and analyze the impact of the amount of labeled data, the number of augmented classes and the selection of hidden classes on the quality of prediction. Especially the number of augmented classes highly influences classification accuracy, which needs tuning for each dataset, since too few and too many augmented classes are detrimental to classifier performance. We also show that we can improve results on a large variety of datasets when using convolutional networks as feature extractors while applying output driven entropy regularization instead of a simple weight based L2 norm. %@ 978-3-030-87626-5
• 
  Shared Task on Scene Segmentation @ KONVENS 2021. Zehe, Albin; Konle, Leonard; Guhr, Svenja; Dümpelmann, Lea; Gius, Evelyn; Hotho, Andreas; Jannidis, Fotis; Kaufmann, Lucas; Krug, Markus; Puppe, Frank; Reiter, Nils; Schreiber, Annekea. In Shared Task on Scene Segmentation @ KONVENS 2021, pp. 1–21. 2021
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  @inproceedings{zehe2021shared, author = {Zehe, Albin and Konle, Leonard and Guhr, Svenja and Dümpelmann, Lea and Gius, Evelyn and Hotho, Andreas and Jannidis, Fotis and Kaufmann, Lucas and Krug, Markus and Puppe, Frank and Reiter, Nils and Schreiber, Annekea}, booktitle = {Shared Task on Scene Segmentation @ KONVENS 2021}, keywords = {author:zehe}, pages = {1-21}, title = {Shared Task on Scene Segmentation @ KONVENS 2021}, year = 2021 }

  %0 Conference Paper %1 zehe2021shared %A Zehe, Albin %A Konle, Leonard %A Guhr, Svenja %A Dümpelmann, Lea %A Gius, Evelyn %A Hotho, Andreas %A Jannidis, Fotis %A Kaufmann, Lucas %A Krug, Markus %A Puppe, Frank %A Reiter, Nils %A Schreiber, Annekea %B Shared Task on Scene Segmentation @ KONVENS 2021 %D 2021 %P 1-21 %T Shared Task on Scene Segmentation @ KONVENS 2021 %U http://ceur-ws.org/Vol-3001/paper1.pdf
• 
  Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research. Koopmann, Tobias; Stubbemann, Maximilian; Kapa, Matthias; Paris, Michael; Buenstorf, Guido; Hanika, Tom; Hotho, Andreas; Jäschke, Robert; Stumme, Gerd. In Scientometrics. 2021
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  Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.

  @article{koopmann2021proximity, abstract = {Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.}, author = {Koopmann, Tobias and Stubbemann, Maximilian and Kapa, Matthias and Paris, Michael and Buenstorf, Guido and Hanika, Tom and Hotho, Andreas and Jäschke, Robert and Stumme, Gerd}, journal = {Scientometrics}, keywords = {regio}, title = {Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research}, year = 2021 }

  %0 Journal Article %1 koopmann2021proximity %A Koopmann, Tobias %A Stubbemann, Maximilian %A Kapa, Matthias %A Paris, Michael %A Buenstorf, Guido %A Hanika, Tom %A Hotho, Andreas %A Jäschke, Robert %A Stumme, Gerd %D 2021 %J Scientometrics %R 10.1007/s11192-021-03922-1 %T Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research %U https://doi.org/10.1007/s11192-021-03922-1 %X Creation and exchange of knowledge depends on collaboration. Recent work has suggested that the emergence of collaboration frequently relies on geographic proximity. However, being co-located tends to be associated with other dimensions of proximity, such as social ties or a shared organizational environment. To account for such factors, multiple dimensions of proximity have been proposed, including cognitive, institutional, organizational, social and geographical proximity. Since they strongly interrelate, disentangling these dimensions and their respective impact on collaboration is challenging. To address this issue, we propose various methods for measuring different dimensions of proximity. We then present an approach to compare and rank them with respect to the extent to which they indicate co-publications and co-inventions. We adapt the HypTrails approach, which was originally developed to explain human navigation, to co-author and co-inventor graphs. We evaluate this approach on a subset of the German research community, specifically academic authors and inventors active in research on artificial intelligence (AI). We find that social proximity and cognitive proximity are more important for the emergence of collaboration than geographic proximity.
• 
  Semi-Supervised Learning for Grain Size Distribution Interpolation. Kobs, Konstantin; Schäfer, Christian; Steininger, Michael; Krause, Anna; Baumhauer, Roland; Paeth, Heiko; Hotho, Andreas. In Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10--15, 2021, Proceedings, Part VI, pp. 34–44. Springer International Publishing, 2021
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  @inproceedings{kobs2021semi, author = {Kobs, Konstantin and Schäfer, Christian and Steininger, Michael and Krause, Anna and Baumhauer, Roland and Paeth, Heiko and Hotho, Andreas}, booktitle = {Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10–15, 2021, Proceedings, Part VI}, keywords = {author:KRAUSE}, organization = {Springer International Publishing}, pages = {34–44}, title = {Semi-Supervised Learning for Grain Size Distribution Interpolation}, year = 2021 }

  %0 Conference Paper %1 kobs2021semi %A Kobs, Konstantin %A Schäfer, Christian %A Steininger, Michael %A Krause, Anna %A Baumhauer, Roland %A Paeth, Heiko %A Hotho, Andreas %B Pattern Recognition. ICPR International Workshops and Challenges: Virtual Event, January 10--15, 2021, Proceedings, Part VI %D 2021 %P 34--44 %T Semi-Supervised Learning for Grain Size Distribution Interpolation
• 
  A financial game with opportunities for fraud. Tritscher, Julian; Krause, Anna; Schl{\"o}r, Daniel; Gwinner, Fabian; von Mammen, Sebastian; Hotho, Andreas. In IEE COG 2021, Vol. 2021. 2021
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  Even though companies store large amounts of business data in enterprise resource planning (ERP) systems, obtaining data for financial fraud detection is prohibitively difficult due to privacy concerns and companies protecting trade secrets. One possible solution is game-based generation of synthetic ERP data, which poses the challenge of designing an environment that generates realistic ERP data and allows players to commit many different types of fraud. In this work, we design a multiplayer game that allows players to cooperatively run a fictional company, while simultaneously challenging them to maximize their personal gain. We introduce an approach for letting players explore fraud scenarios through emergent gameplay and present a prototype that may be primed with information from real world ERP systems to generate realistic data.

  @article{tritscherfinancial, abstract = {Even though companies store large amounts of business data in enterprise resource planning (ERP) systems, obtaining data for financial fraud detection is prohibitively difficult due to privacy concerns and companies protecting trade secrets. One possible solution is game-based generation of synthetic ERP data, which poses the challenge of designing an environment that generates realistic ERP data and allows players to commit many different types of fraud. In this work, we design a multiplayer game that allows players to cooperatively run a fictional company, while simultaneously challenging them to maximize their personal gain. We introduce an approach for letting players explore fraud scenarios through emergent gameplay and present a prototype that may be primed with information from real world ERP systems to generate realistic data.}, author = {Tritscher, Julian and Krause, Anna and Schl{\"o}r, Daniel and Gwinner, Fabian and von Mammen, Sebastian and Hotho, Andreas}, journal = {IEE COG 2021}, keywords = {research_imbalanced_data}, title = {A financial game with opportunities for fraud}, volume = 2021, year = 2021 }

  %0 Journal Article %1 tritscherfinancial %A Tritscher, Julian %A Krause, Anna %A Schl{\"o}r, Daniel %A Gwinner, Fabian %A von Mammen, Sebastian %A Hotho, Andreas %D 2021 %J IEE COG 2021 %T A financial game with opportunities for fraud %U https://ieee-cog.org/2021/assets/papers/paper_273.pdf %V 2021 %X Even though companies store large amounts of business data in enterprise resource planning (ERP) systems, obtaining data for financial fraud detection is prohibitively difficult due to privacy concerns and companies protecting trade secrets. One possible solution is game-based generation of synthetic ERP data, which poses the challenge of designing an environment that generates realistic ERP data and allows players to commit many different types of fraud. In this work, we design a multiplayer game that allows players to cooperatively run a fictional company, while simultaneously challenging them to maximize their personal gain. We introduce an approach for letting players explore fraud scenarios through emergent gameplay and present a prototype that may be primed with information from real world ERP systems to generate realistic data.
• 
  Density-based weighting for imbalanced regression. Steininger, Michael; Kobs, Konstantin; Davidson, Padraig; Krause, Anna; Hotho, Andreas. In Machine Learning, A. Appice; S. Escalera; J. A. Gamez; H. Trautmann (eds.). 2021
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  In many real world settings,imbalanced data impedes model performance of learning algorithms, like neural networks, mostly for rare cases. This is especially problematic for tasks focusing on these rare occurrences. For example, when estimating precipitation, extreme rainfall events are scarce but important considering their potential consequences. While there are numerous well studied solutions for classification settings, most of them cannot be applied to regression easily. Of the few solutions for regression tasks, barely any have explored cost-sensitive learning which is known to have advantages compared to sampling-based methods in classification tasks. In this work, we propose a sample weighting approach for imbalanced regression datasets called DenseWeight and a cost-sensitive learning approach for neural network regression with imbalanced data called DenseLoss based on our weighting scheme. DenseWeight weights data points according to their target value rarities through kernel density estimation (KDE). DenseLoss adjusts each data point’s influence on the loss according to DenseWeight, giving rare data points more influence on modeltraining compared to common data points. We show on multiple differently distributed datasets that DenseLoss significantly improves model performance for rare data points through its density-based weighting scheme. Additionally, we compare DenseLoss to the state-of-the-art method SMOGN, finding that our method mostly yields better performance. Our approach provides more control over model training as it enables us to actively decide on the trade-off between focusing on common or rare cases through a single hyperparameter, allowing the training of better models for rare data points.

  @article{steininger2021densitybased, abstract = {In many real world settings,imbalanced data impedes model performance of learning algorithms, like neural networks, mostly for rare cases. This is especially problematic for tasks focusing on these rare occurrences. For example, when estimating precipitation, extreme rainfall events are scarce but important considering their potential consequences. While there are numerous well studied solutions for classification settings, most of them cannot be applied to regression easily. Of the few solutions for regression tasks, barely any have explored cost-sensitive learning which is known to have advantages compared to sampling-based methods in classification tasks. In this work, we propose a sample weighting approach for imbalanced regression datasets called DenseWeight and a cost-sensitive learning approach for neural network regression with imbalanced data called DenseLoss based on our weighting scheme. DenseWeight weights data points according to their target value rarities through kernel density estimation (KDE). DenseLoss adjusts each data point’s influence on the loss according to DenseWeight, giving rare data points more influence on modeltraining compared to common data points. We show on multiple differently distributed datasets that DenseLoss significantly improves model performance for rare data points through its density-based weighting scheme. Additionally, we compare DenseLoss to the state-of-the-art method SMOGN, finding that our method mostly yields better performance. Our approach provides more control over model training as it enables us to actively decide on the trade-off between focusing on common or rare cases through a single hyperparameter, allowing the training of better models for rare data points.}, author = {Steininger, Michael and Kobs, Konstantin and Davidson, Padraig and Krause, Anna and Hotho, Andreas}, editor = {Appice, Annalisa and Escalera, Sergio and Gamez, Jose A. and Trautmann, Heike}, journal = {Machine Learning}, keywords = {selected}, title = {Density-based weighting for imbalanced regression}, year = 2021 }

  %0 Journal Article %1 steininger2021densitybased %A Steininger, Michael %A Kobs, Konstantin %A Davidson, Padraig %A Krause, Anna %A Hotho, Andreas %D 2021 %E Appice, Annalisa %E Escalera, Sergio %E Gamez, Jose A. %E Trautmann, Heike %J Machine Learning %R 10.1007/s10994-021-06023-5 %T Density-based weighting for imbalanced regression %U https://doi.org/10.1007/s10994-021-06023-5 %X In many real world settings,imbalanced data impedes model performance of learning algorithms, like neural networks, mostly for rare cases. This is especially problematic for tasks focusing on these rare occurrences. For example, when estimating precipitation, extreme rainfall events are scarce but important considering their potential consequences. While there are numerous well studied solutions for classification settings, most of them cannot be applied to regression easily. Of the few solutions for regression tasks, barely any have explored cost-sensitive learning which is known to have advantages compared to sampling-based methods in classification tasks. In this work, we propose a sample weighting approach for imbalanced regression datasets called DenseWeight and a cost-sensitive learning approach for neural network regression with imbalanced data called DenseLoss based on our weighting scheme. DenseWeight weights data points according to their target value rarities through kernel density estimation (KDE). DenseLoss adjusts each data point’s influence on the loss according to DenseWeight, giving rare data points more influence on modeltraining compared to common data points. We show on multiple differently distributed datasets that DenseLoss significantly improves model performance for rare data points through its density-based weighting scheme. Additionally, we compare DenseLoss to the state-of-the-art method SMOGN, finding that our method mostly yields better performance. Our approach provides more control over model training as it enables us to actively decide on the trade-off between focusing on common or rare cases through a single hyperparameter, allowing the training of better models for rare data points.
• 
  A financial game with opportunities for fraud. Tritscher, Julian; Krause, Anna; Schlör, Daniel; Gwinner, Fabian; Von Mammen, Sebastian; Hotho, Andreas. In 2021 IEEE Conference on Games (CoG), pp. 1–5. 2021
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  @inproceedings{Tritscher:2021aa, author = {Tritscher, Julian and Krause, Anna and Schlör, Daniel and Gwinner, Fabian and Von Mammen, Sebastian and Hotho, Andreas}, booktitle = {2021 IEEE Conference on Games (CoG)}, keywords = {from:daschloer}, pages = {1-5}, title = {A financial game with opportunities for fraud}, year = 2021 }

  %0 Conference Paper %1 Tritscher:2021aa %A Tritscher, Julian %A Krause, Anna %A Schlör, Daniel %A Gwinner, Fabian %A Von Mammen, Sebastian %A Hotho, Andreas %B 2021 IEEE Conference on Games (CoG) %D 2021 %P 1-5 %R 10.1109/CoG52621.2021.9619070 %T A financial game with opportunities for fraud %U https://downloads.hci.informatik.uni-wuerzburg.de/Tritscher2021aa.pdf
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  Anomaly Detection in Beehives: An Algorithm Comparison. Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. In International Conference on Sensor Networks, Vol. 1674 of Communications in Computer and Information Science, A. Ahrens, R. Prasad, C. Benavente-Peces, N. Ansari (eds.), pp. 1–20. Springer, 2021
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  @inproceedings{davidson2021anomaly, author = {Davidson, Padraig and Steininger, Michael and Lautenschlager, Florian and Krause, Anna and Hotho, Andreas}, booktitle = {International Conference on Sensor Networks}, editor = {Ahrens, A. and Prasad, R.V. and Benavente-Peces, C. and Ansari, N.}, keywords = {author:hotho}, pages = {1–20}, publisher = {Springer}, series = {Communications in Computer and Information Science}, title = {Anomaly Detection in Beehives: An Algorithm Comparison}, volume = 1674, year = 2021 }

  %0 Conference Paper %1 davidson2021anomaly %A Davidson, Padraig %A Steininger, Michael %A Lautenschlager, Florian %A Krause, Anna %A Hotho, Andreas %B International Conference on Sensor Networks %D 2021 %E Ahrens, A. %E Prasad, R.V. %E Benavente-Peces, C. %E Ansari, N. %I Springer %P 1--20 %R https://doi.org/10.1007/978-3-031-17718-7_1 %T Anomaly Detection in Beehives: An Algorithm Comparison %V 1674
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  Assessing Media Bias in Cross-Linguistic and Cross-National Populations. Sales, Allan; Zehe, Albin; Marinho, Leandro Balby; Veloso, Adriano; Hotho, Andreas; Omeliyanenko, Janna. In Proceedings of the International AAAI Conference on Web and Social Media, Vol. 15, Issue 1, pp. 561–572. 2021
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  @article{sales2021assessing, author = {Sales, Allan and Zehe, Albin and Marinho, Leandro Balby and Veloso, Adriano and Hotho, Andreas and Omeliyanenko, Janna}, journal = {Proceedings of the International AAAI Conference on Web and Social Media}, keywords = {author:zehe}, month = {05}, number = 1, pages = {561-572}, title = {Assessing Media Bias in Cross-Linguistic and Cross-National Populations}, volume = 15, year = 2021 }

  %0 Journal Article %1 sales2021assessing %A Sales, Allan %A Zehe, Albin %A Marinho, Leandro Balby %A Veloso, Adriano %A Hotho, Andreas %A Omeliyanenko, Janna %D 2021 %J Proceedings of the International AAAI Conference on Web and Social Media %N 1 %P 561-572 %T Assessing Media Bias in Cross-Linguistic and Cross-National Populations %U https://ojs.aaai.org/index.php/ICWSM/article/view/18084 %V 15
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  Detecting Scenes in Fiction: A new Segmentation Task. Zehe, Albin; Konle, Leonard; Dümpelmann, Lea; Gius, Evelyn; Hotho, Andreas; Jannidis, Fotis; Kaufmann, Lucas; Krug, Markus; Puppe, Frank; Reiter, Nils; Schreiber, Annekea; Wiedmer, Nathalie. In Proceedings of the 16th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. ACL, 2021
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  @inproceedings{zehe2021detecting, author = {Zehe, Albin and Konle, Leonard and Dümpelmann, Lea and Gius, Evelyn and Hotho, Andreas and Jannidis, Fotis and Kaufmann, Lucas and Krug, Markus and Puppe, Frank and Reiter, Nils and Schreiber, Annekea and Wiedmer, Nathalie}, booktitle = {Proceedings of the 16th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers}, keywords = {author:zehe}, publisher = {ACL}, title = {Detecting Scenes in Fiction: A new Segmentation Task}, year = 2021 }

  %0 Conference Paper %1 zehe2021detecting %A Zehe, Albin %A Konle, Leonard %A Dümpelmann, Lea %A Gius, Evelyn %A Hotho, Andreas %A Jannidis, Fotis %A Kaufmann, Lucas %A Krug, Markus %A Puppe, Frank %A Reiter, Nils %A Schreiber, Annekea %A Wiedmer, Nathalie %B Proceedings of the 16th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers %D 2021 %I ACL %T Detecting Scenes in Fiction: A new Segmentation Task
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  Automating the optical identification of abrasive wear on electrical contact pins. Buckermann, Florian; Klement, Nils; Beyer, Oliver; Hütten, Andreas; Hammer, Barbara. In at - Automatisierungstechnik, Vol. 69, Issue 10, pp. 903–914. 2021
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  The automation of quality control in manufacturing has made great strides in recent years, in particular following new developments in machine learning, specifically deep learning, which allow to solve challenging tasks such as visual inspection or quality prediction. Yet, optimum quality control pipelines are often not obvious in specific settings, since they do not necessarily align with (supervised) machine learning tasks. In this contribution, we introduce a new automation pipeline for the quantification of wear on electrical contact pins. More specifically, we propose and test a novel pipeline which combines a deep network for image segmentation with geometric priors of the problem. This task is important for a judgement of the quality of the material and it can serve as a starting point to optimize the choices of materials based on its automated evaluation.

  @article{BuckermannKlementBeyerHüttenHammer+2021+903+914, abstract = {The automation of quality control in manufacturing has made great strides in recent years, in particular following new developments in machine learning, specifically deep learning, which allow to solve challenging tasks such as visual inspection or quality prediction. Yet, optimum quality control pipelines are often not obvious in specific settings, since they do not necessarily align with (supervised) machine learning tasks. In this contribution, we introduce a new automation pipeline for the quantification of wear on electrical contact pins. More specifically, we propose and test a novel pipeline which combines a deep network for image segmentation with geometric priors of the problem. This task is important for a judgement of the quality of the material and it can serve as a starting point to optimize the choices of materials based on its automated evaluation.}, author = {Buckermann, Florian and Klement, Nils and Beyer, Oliver and Hütten, Andreas and Hammer, Barbara}, journal = {at - Automatisierungstechnik}, keywords = {from:fbuckermann}, number = 10, pages = {903–914}, title = {Automating the optical identification of abrasive wear on electrical contact pins}, volume = 69, year = 2021 }

  %0 Journal Article %1 BuckermannKlementBeyerHüttenHammer+2021+903+914 %A Buckermann, Florian %A Klement, Nils %A Beyer, Oliver %A Hütten, Andreas %A Hammer, Barbara %D 2021 %J at - Automatisierungstechnik %N 10 %P 903--914 %R doi:10.1515/auto-2021-0021 %T Automating the optical identification of abrasive wear on electrical contact pins %U https://doi.org/10.1515/auto-2021-0021 %V 69 %X The automation of quality control in manufacturing has made great strides in recent years, in particular following new developments in machine learning, specifically deep learning, which allow to solve challenging tasks such as visual inspection or quality prediction. Yet, optimum quality control pipelines are often not obvious in specific settings, since they do not necessarily align with (supervised) machine learning tasks. In this contribution, we introduce a new automation pipeline for the quantification of wear on electrical contact pins. More specifically, we propose and test a novel pipeline which combines a deep network for image segmentation with geometric priors of the problem. This task is important for a judgement of the quality of the material and it can serve as a starting point to optimize the choices of materials based on its automated evaluation.
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  Evaluating the multi-task learning approach for land use regression modelling of air pollution. Dulny, Andrzej; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. In Journal of Physics: Conference Series, Vol. 1834, Issue 1, p. 012004. {IOP} Publishing, 2021
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  Air pollution has been linked to several health problems including heart disease, stroke and lung cancer. Modelling and analyzing this dependency requires reliable and accurate air pollutant measurements collected by stationary air monitoring stations. However, usually only a low number of such stations are present within a single city. To retrieve pollution concentrations for unmeasured locations, researchers rely on land use regression (LUR) models. Those models are typically developed for one pollutant only. However, as results in different areas have shown, modelling several related output variables through multi-task learning can improve the prediction results of the models significantly. In this work, we compared prediction results from single-task and multi-task learning multilayer perceptron models on measurements taken from the OpenSense dataset and the London Atmospheric Emissions Inventory dataset. LUR features were generated from OpenStreetMap using OpenLUR and used to train hard parameter sharing multilayer perceptron models. The results show multi-task learning with sufficient data significantly improves the performance of a LUR model.

  @article{dulny2021evaluating, abstract = {Air pollution has been linked to several health problems including heart disease, stroke and lung cancer. Modelling and analyzing this dependency requires reliable and accurate air pollutant measurements collected by stationary air monitoring stations. However, usually only a low number of such stations are present within a single city. To retrieve pollution concentrations for unmeasured locations, researchers rely on land use regression (LUR) models. Those models are typically developed for one pollutant only. However, as results in different areas have shown, modelling several related output variables through multi-task learning can improve the prediction results of the models significantly. In this work, we compared prediction results from single-task and multi-task learning multilayer perceptron models on measurements taken from the OpenSense dataset and the London Atmospheric Emissions Inventory dataset. LUR features were generated from OpenStreetMap using OpenLUR and used to train hard parameter sharing multilayer perceptron models. The results show multi-task learning with sufficient data significantly improves the performance of a LUR model.}, author = {Dulny, Andrzej and Steininger, Michael and Lautenschlager, Florian and Krause, Anna and Hotho, Andreas}, journal = {Journal of Physics: Conference Series}, keywords = {from:msteininger}, month = {03}, number = 1, organization = {IOP Publishing}, pages = {012004}, publisher = {{IOP} Publishing}, title = {Evaluating the multi-task learning approach for land use regression modelling of air pollution}, volume = 1834, year = 2021 }

  %0 Journal Article %1 dulny2021evaluating %A Dulny, Andrzej %A Steininger, Michael %A Lautenschlager, Florian %A Krause, Anna %A Hotho, Andreas %D 2021 %I {IOP} Publishing %J Journal of Physics: Conference Series %N 1 %P 012004 %R 10.1088/1742-6596/1834/1/012004 %T Evaluating the multi-task learning approach for land use regression modelling of air pollution %U https://doi.org/10.1088/1742-6596/1834/1/012004 %V 1834 %X Air pollution has been linked to several health problems including heart disease, stroke and lung cancer. Modelling and analyzing this dependency requires reliable and accurate air pollutant measurements collected by stationary air monitoring stations. However, usually only a low number of such stations are present within a single city. To retrieve pollution concentrations for unmeasured locations, researchers rely on land use regression (LUR) models. Those models are typically developed for one pollutant only. However, as results in different areas have shown, modelling several related output variables through multi-task learning can improve the prediction results of the models significantly. In this work, we compared prediction results from single-task and multi-task learning multilayer perceptron models on measurements taken from the OpenSense dataset and the London Atmospheric Emissions Inventory dataset. LUR features were generated from OpenStreetMap using OpenLUR and used to train hard parameter sharing multilayer perceptron models. The results show multi-task learning with sufficient data significantly improves the performance of a LUR model.
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  Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment. Pfister, Jan; Kobs, Konstantin; Hotho, Andreas. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 816–825. 2021
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  @inproceedings{Pfister_2021_CVPR, author = {Pfister, Jan and Kobs, Konstantin and Hotho, Andreas}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, keywords = {author:PFISTER}, month = {06}, pages = {816-825}, title = {Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment}, year = 2021 }

  %0 Conference Paper %1 Pfister_2021_CVPR %A Pfister, Jan %A Kobs, Konstantin %A Hotho, Andreas %B Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops %D 2021 %P 816-825 %T Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment
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  Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge. Fischer, Elisabeth; Zoller, Daniel; Hotho, Andreas. In SIGIR Workshop On eCommerce. 2021
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  @article{fischer2021comparison, author = {Fischer, Elisabeth and Zoller, Daniel and Hotho, Andreas}, journal = {SIGIR Workshop On eCommerce}, keywords = {author:hotho}, month = {07}, title = {Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge}, year = 2021 }

  %0 Journal Article %1 fischer2021comparison %A Fischer, Elisabeth %A Zoller, Daniel %A Hotho, Andreas %D 2021 %J SIGIR Workshop On eCommerce %T Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge
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  A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models. Dallmann, Alexander; Zoller, Daniel; Hotho, Andreas. In Fifteenth {ACM} Conference on Recommender Systems. {ACM}, 2021
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  @inproceedings{dallmann2021study, author = {Dallmann, Alexander and Zoller, Daniel and Hotho, Andreas}, booktitle = {Fifteenth {ACM} Conference on Recommender Systems}, keywords = {selected}, month = {09}, publisher = {{ACM}}, title = {A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models}, year = 2021 }

  %0 Conference Paper %1 dallmann2021study %A Dallmann, Alexander %A Zoller, Daniel %A Hotho, Andreas %B Fifteenth {ACM} Conference on Recommender Systems %D 2021 %I {ACM} %R 10.1145/3460231.3475943 %T A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models
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  Do Different Deep Metric Learning Losses Lead to Similar Learned Features?. Kobs, Konstantin; Steininger, Michael; Dulny, Andrzej; Hotho, Andreas. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10644–10654. 2021
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  @article{noauthororeditor, author = {Kobs, Konstantin and Steininger, Michael and Dulny, Andrzej and Hotho, Andreas}, journal = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}, keywords = {core-ml}, month = 10, pages = {10644-10654}, title = {Do Different Deep Metric Learning Losses Lead to Similar Learned Features?}, year = 2021 }

  %0 Journal Article %1 noauthororeditor %A Kobs, Konstantin %A Steininger, Michael %A Dulny, Andrzej %A Hotho, Andreas %D 2021 %J Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) %P 10644-10654 %T Do Different Deep Metric Learning Losses Lead to Similar Learned Features?
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  The {F}airy{N}et Corpus - Character Networks for {G}erman Fairy Tales. Schmidt, David; Zehe, Albin; Lorenzen, Janne; Sergel, Lisa; D{\"u}ker, Sebastian; Krug, Markus; Puppe, Frank. In Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature, pp. 49–56. Association for Computational Linguistics, Punta Cana, Dominican Republic (online), 2021
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  This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.

  @inproceedings{schmidt2021fairynet, abstract = {This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.}, address = {Punta Cana, Dominican Republic (online)}, author = {Schmidt, David and Zehe, Albin and Lorenzen, Janne and Sergel, Lisa and D{\"u}ker, Sebastian and Krug, Markus and Puppe, Frank}, booktitle = {Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature}, keywords = {networks}, month = 11, pages = {49–56}, publisher = {Association for Computational Linguistics}, title = {The {F}airy{N}et Corpus - Character Networks for {G}erman Fairy Tales}, year = 2021 }

  %0 Conference Paper %1 schmidt2021fairynet %A Schmidt, David %A Zehe, Albin %A Lorenzen, Janne %A Sergel, Lisa %A D{\"u}ker, Sebastian %A Krug, Markus %A Puppe, Frank %B Proceedings of the 5th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature %C Punta Cana, Dominican Republic (online) %D 2021 %I Association for Computational Linguistics %P 49--56 %R 10.18653/v1/2021.latechclfl-1.6 %T The {F}airy{N}et Corpus - Character Networks for {G}erman Fairy Tales %U https://aclanthology.org/2021.latechclfl-1.6 %X This paper presents a data set of German fairy tales, manually annotated with character networks which were obtained with high inter rater agreement. The release of this corpus provides an opportunity of training and comparing different algorithms for the extraction of character networks, which so far was barely possible due to heterogeneous interests of previous researchers. We demonstrate the usefulness of our data set by providing baseline experiments for the automatic extraction of character networks, applying a rule-based pipeline as well as a neural approach, and find the neural approach outperforming the rule-approach in most evaluation settings.
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  Learning Mathematical Relations Using Deep Tree Models. Wankerl, Sebastian; Dulny, Andrzej; Götz, Gerhard; Hotho, Andreas. In 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1681–1687. 2021
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  To the present day, computer algebra systems used for calculating mathematical relations on a symbolic level are mainly rule-based systems. Only recently, deep learning has been applied to the task of symbolic mathematics. Researchers succeeded in developing neural networks which can do symbolic calculations of the equivalence of multiple pairing of equations. However, the generator used to create mathematical terms in previous research has several drawbacks and as with all deep learning tasks, the quality of the data used for training the models has a significant impact on the quality of the results. In this work, we propose a new generator for polynomials. We use it to train several recursive neural networks to recognize the equivalence, derivative, or variable substitution between pairs of polynomials for the first time. Our results indicate that these mathematical relations are identifiable with the help of deep learning.

  @inproceedings{9680206, abstract = {To the present day, computer algebra systems used for calculating mathematical relations on a symbolic level are mainly rule-based systems. Only recently, deep learning has been applied to the task of symbolic mathematics. Researchers succeeded in developing neural networks which can do symbolic calculations of the equivalence of multiple pairing of equations. However, the generator used to create mathematical terms in previous research has several drawbacks and as with all deep learning tasks, the quality of the data used for training the models has a significant impact on the quality of the results. In this work, we propose a new generator for polynomials. We use it to train several recursive neural networks to recognize the equivalence, derivative, or variable substitution between pairs of polynomials for the first time. Our results indicate that these mathematical relations are identifiable with the help of deep learning.}, author = {Wankerl, Sebastian and Dulny, Andrzej and Götz, Gerhard and Hotho, Andreas}, booktitle = {2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)}, keywords = {treemodels}, month = 12, pages = {1681-1687}, title = {Learning Mathematical Relations Using Deep Tree Models}, year = 2021 }

  %0 Conference Paper %1 9680206 %A Wankerl, Sebastian %A Dulny, Andrzej %A Götz, Gerhard %A Hotho, Andreas %B 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) %D 2021 %P 1681-1687 %R 10.1109/ICMLA52953.2021.00268 %T Learning Mathematical Relations Using Deep Tree Models %U https://ieeexplore.ieee.org/document/9680206 %X To the present day, computer algebra systems used for calculating mathematical relations on a symbolic level are mainly rule-based systems. Only recently, deep learning has been applied to the task of symbolic mathematics. Researchers succeeded in developing neural networks which can do symbolic calculations of the equivalence of multiple pairing of equations. However, the generator used to create mathematical terms in previous research has several drawbacks and as with all deep learning tasks, the quality of the data used for training the models has a significant impact on the quality of the results. In this work, we propose a new generator for polynomials. We use it to train several recursive neural networks to recognize the equivalence, derivative, or variable substitution between pairs of polynomials for the first time. Our results indicate that these mathematical relations are identifiable with the help of deep learning.
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  Sequential Item Recommendation in the MOBA Game Dota 2. Dallmann, Alexander; Kohlmann, Johannes; Zoller, Daniel; Hotho, Andreas. In 2021 International Conference on Data Mining Workshops (ICDMW), pp. 10–17. 2021
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  Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds of thousands of players every year. Despite the large player base, it is still important to attract new players to prevent the community of a game from becoming inactive. Entering MOBA games is, however, often demanding, requiring the player to learn numerous skills at once. An important factor of success is buying the correct items which forms a complex task depending on various in-game factors such as already purchased items, the team composition, or available resources. A recommendation system can support players by reducing the mental effort required to choose a suitable item, helping, e.g., newer players or players returning to the game after a longer break, to focus on other aspects of the game. Since Sequential Item Recommendation (SIR) has proven to be effective in various domains (e.g. e-commerce, movie recommendation or playlist continuation), we explore the applicability of well-known SIR models in the context of purchase recommendations in Dota 2. To facilitate this research, we collect, analyze and publish Dota-350k, a new large dataset based on recent Dota 2 matches. We find that SIR models can be employed effectively for item recommendation in Dota 2. Our results show that models that consider the order of purchases are the most effective. In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.

  @inproceedings{9679858, abstract = {Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds of thousands of players every year. Despite the large player base, it is still important to attract new players to prevent the community of a game from becoming inactive. Entering MOBA games is, however, often demanding, requiring the player to learn numerous skills at once. An important factor of success is buying the correct items which forms a complex task depending on various in-game factors such as already purchased items, the team composition, or available resources. A recommendation system can support players by reducing the mental effort required to choose a suitable item, helping, e.g., newer players or players returning to the game after a longer break, to focus on other aspects of the game. Since Sequential Item Recommendation (SIR) has proven to be effective in various domains (e.g. e-commerce, movie recommendation or playlist continuation), we explore the applicability of well-known SIR models in the context of purchase recommendations in Dota 2. To facilitate this research, we collect, analyze and publish Dota-350k, a new large dataset based on recent Dota 2 matches. We find that SIR models can be employed effectively for item recommendation in Dota 2. Our results show that models that consider the order of purchases are the most effective. In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.}, author = {Dallmann, Alexander and Kohlmann, Johannes and Zoller, Daniel and Hotho, Andreas}, booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)}, keywords = {from:hotho}, month = 12, pages = {10-17}, title = {Sequential Item Recommendation in the MOBA Game Dota 2}, year = 2021 }

  %0 Conference Paper %1 9679858 %A Dallmann, Alexander %A Kohlmann, Johannes %A Zoller, Daniel %A Hotho, Andreas %B 2021 International Conference on Data Mining Workshops (ICDMW) %D 2021 %P 10-17 %R 10.1109/ICDMW53433.2021.00009 %T Sequential Item Recommendation in the MOBA Game Dota 2 %X Multiplayer Online Battle Arena (MOBA) games such as Dota 2 attract hundreds of thousands of players every year. Despite the large player base, it is still important to attract new players to prevent the community of a game from becoming inactive. Entering MOBA games is, however, often demanding, requiring the player to learn numerous skills at once. An important factor of success is buying the correct items which forms a complex task depending on various in-game factors such as already purchased items, the team composition, or available resources. A recommendation system can support players by reducing the mental effort required to choose a suitable item, helping, e.g., newer players or players returning to the game after a longer break, to focus on other aspects of the game. Since Sequential Item Recommendation (SIR) has proven to be effective in various domains (e.g. e-commerce, movie recommendation or playlist continuation), we explore the applicability of well-known SIR models in the context of purchase recommendations in Dota 2. To facilitate this research, we collect, analyze and publish Dota-350k, a new large dataset based on recent Dota 2 matches. We find that SIR models can be employed effectively for item recommendation in Dota 2. Our results show that models that consider the order of purchases are the most effective. In contrast to other domains, we find RNN-based models to outperform the more recent Transformer-based architectures on Dota-350k.
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  CoBERT: Scientific Collaboration Prediction via Sequential Recommendation. Koopmann, Tobias; Kobs, Konstantin; Herud, Konstantin; Hotho, Andreas. In 2021 International Conference on Data Mining Workshops (ICDMW), pp. 45–54. 2021
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  Collaborations are an Important factor for scientific success, as the joint work leads to results individual scientists cannot easily reach. Recommending collaborations automatically can alleviate the time consuming and tedious search for potential collaborators. Usually, such recommendation systems rely on graph structures modeling co-authorship of papers and content-based relations such as similar paper keywords. Models are then trained to estimate the probability of links between certain authors in these graphs.In this paper, we argue that the order of papers is crucial for reliably predicting future collaborations, which is not considered by graph-based recommendation systems. We thus propose to reformulate the task of collaboration recommendation as a sequential recommendation task. Here, we aim to predict the next co-author in a chronologically sorted sequence of an author’s collaborators. We introduce CoBERT, a BERT4Rec inspired model, that predicts the sequence’s next co-author and thus a potential collaborator. Since the order of co-authors of a single paper is not that important compared to the overall paper order, we leverage positional embeddings encoding paper positions instead of co-author positions in the sequence. Additionally, we inject content features about every paper and their co-authors. We evaluate CoBERT on two datasets consisting of papers from the field of Artificial Intelligence and the journal PlosOne. We show that CoBERT can outperform graph-based methods and BERT4Rec when predicting the co-authors of the next paper. We make our code and data available.

  @inproceedings{koopmann2021cobert, abstract = {Collaborations are an Important factor for scientific success, as the joint work leads to results individual scientists cannot easily reach. Recommending collaborations automatically can alleviate the time consuming and tedious search for potential collaborators. Usually, such recommendation systems rely on graph structures modeling co-authorship of papers and content-based relations such as similar paper keywords. Models are then trained to estimate the probability of links between certain authors in these graphs.In this paper, we argue that the order of papers is crucial for reliably predicting future collaborations, which is not considered by graph-based recommendation systems. We thus propose to reformulate the task of collaboration recommendation as a sequential recommendation task. Here, we aim to predict the next co-author in a chronologically sorted sequence of an author’s collaborators. We introduce CoBERT, a BERT4Rec inspired model, that predicts the sequence’s next co-author and thus a potential collaborator. Since the order of co-authors of a single paper is not that important compared to the overall paper order, we leverage positional embeddings encoding paper positions instead of co-author positions in the sequence. Additionally, we inject content features about every paper and their co-authors. We evaluate CoBERT on two datasets consisting of papers from the field of Artificial Intelligence and the journal PlosOne. We show that CoBERT can outperform graph-based methods and BERT4Rec when predicting the co-authors of the next paper. We make our code and data available.}, author = {Koopmann, Tobias and Kobs, Konstantin and Herud, Konstantin and Hotho, Andreas}, booktitle = {2021 International Conference on Data Mining Workshops (ICDMW)}, keywords = {selected}, month = 12, pages = {45-54}, title = {CoBERT: Scientific Collaboration Prediction via Sequential Recommendation}, year = 2021 }

  %0 Conference Paper %1 koopmann2021cobert %A Koopmann, Tobias %A Kobs, Konstantin %A Herud, Konstantin %A Hotho, Andreas %B 2021 International Conference on Data Mining Workshops (ICDMW) %D 2021 %P 45-54 %R 10.1109/ICDMW53433.2021.00013 %T CoBERT: Scientific Collaboration Prediction via Sequential Recommendation %X Collaborations are an Important factor for scientific success, as the joint work leads to results individual scientists cannot easily reach. Recommending collaborations automatically can alleviate the time consuming and tedious search for potential collaborators. Usually, such recommendation systems rely on graph structures modeling co-authorship of papers and content-based relations such as similar paper keywords. Models are then trained to estimate the probability of links between certain authors in these graphs.In this paper, we argue that the order of papers is crucial for reliably predicting future collaborations, which is not considered by graph-based recommendation systems. We thus propose to reformulate the task of collaboration recommendation as a sequential recommendation task. Here, we aim to predict the next co-author in a chronologically sorted sequence of an author’s collaborators. We introduce CoBERT, a BERT4Rec inspired model, that predicts the sequence’s next co-author and thus a potential collaborator. Since the order of co-authors of a single paper is not that important compared to the overall paper order, we leverage positional embeddings encoding paper positions instead of co-author positions in the sequence. Additionally, we inject content features about every paper and their co-authors. We evaluate CoBERT on two datasets consisting of papers from the field of Artificial Intelligence and the journal PlosOne. We show that CoBERT can outperform graph-based methods and BERT4Rec when predicting the co-authors of the next paper. We make our code and data available.

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  iNALU: Improved Neural Arithmetic Logic Unit. Schlör, Daniel; Ring, Markus; Hotho, Andreas. In Frontiers in Artificial Intelligence, Vol. 3, p. 71. 2020
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  Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.

  @article{10.3389/frai.2020.00071, abstract = {Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.}, author = {Schlör, Daniel and Ring, Markus and Hotho, Andreas}, journal = {Frontiers in Artificial Intelligence}, keywords = {inalu}, pages = 71, title = {iNALU: Improved Neural Arithmetic Logic Unit}, volume = 3, year = 2020 }

  %0 Journal Article %1 10.3389/frai.2020.00071 %A Schlör, Daniel %A Ring, Markus %A Hotho, Andreas %D 2020 %J Frontiers in Artificial Intelligence %P 71 %R 10.3389/frai.2020.00071 %T iNALU: Improved Neural Arithmetic Logic Unit %U https://www.frontiersin.org/article/10.3389/frai.2020.00071 %V 3 %X Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.
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  Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part III. Brefeld, Ulf; Fromont, Élisa; Hotho, Andreas; Knobbe, Arno J.; Maathuis, Marloes H.; Robardet, Céline. In Vol. 11908 of Lecture Notes in Computer Science. Springer, 2020
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  @proceedings{brefeld2020machine1, booktitle = {ECML/PKDD (3)}, editor = {Brefeld, Ulf and Fromont, Élisa and Hotho, Andreas and Knobbe, Arno J. and Maathuis, Marloes H. and Robardet, Céline}, keywords = {from:hotho}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part III}, volume = 11908, year = 2020 }

  %0 Conference Proceedings %1 brefeld2020machine1 %B ECML/PKDD (3) %D 2020 %E Brefeld, Ulf %E Fromont, Élisa %E Hotho, Andreas %E Knobbe, Arno J. %E Maathuis, Marloes H. %E Robardet, Céline %I Springer %T Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part III %U http://dblp.uni-trier.de/db/conf/pkdd/pkdd2019-3.html %V 11908 %@ 978-3-030-46133-1
• 
  Deep Learning for Climate Model Output Statistics. Steininger, Michael; Abel, Daniel; Ziegler, Katrin; Krause, Anna; Paeth, Heiko; Hotho, Andreas. In Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020. 2020
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  @article{steininger2020deep, author = {Steininger, Michael and Abel, Daniel and Ziegler, Katrin and Krause, Anna and Paeth, Heiko and Hotho, Andreas}, journal = {Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020}, keywords = {from:msteininger}, title = {Deep Learning for Climate Model Output Statistics}, year = 2020 }

  %0 Journal Article %1 steininger2020deep %A Steininger, Michael %A Abel, Daniel %A Ziegler, Katrin %A Krause, Anna %A Paeth, Heiko %A Hotho, Andreas %D 2020 %J Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020 %T Deep Learning for Climate Model Output Statistics %U https://www.climatechange.ai/papers/neurips2020/7
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  Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data. Tritscher, Julian; Ring, Markus; Schlör, Daniel; Hettinger, Lena; Hotho, Andreas. In Foundations of Intelligent Systems, D. Helic, G. Leitner, M. Stettinger, A. Felfernig, Z. W. Raś (eds.), pp. 422–430. Springer International Publishing, Cham, 2020
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  Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.

  @inproceedings{tritscher2020evaluation, abstract = {Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.}, address = {Cham}, author = {Tritscher, Julian and Ring, Markus and Schlör, Daniel and Hettinger, Lena and Hotho, Andreas}, booktitle = {Foundations of Intelligent Systems}, editor = {Helic, Denis and Leitner, Gerhard and Stettinger, Martin and Felfernig, Alexander and Raś, Zbigniew W.}, keywords = {xai}, pages = {422–430}, publisher = {Springer International Publishing}, title = {Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data}, year = 2020 }

  %0 Conference Paper %1 tritscher2020evaluation %A Tritscher, Julian %A Ring, Markus %A Schlör, Daniel %A Hettinger, Lena %A Hotho, Andreas %B Foundations of Intelligent Systems %C Cham %D 2020 %E Helic, Denis %E Leitner, Gerhard %E Stettinger, Martin %E Felfernig, Alexander %E Raś, Zbigniew W. %I Springer International Publishing %P 422--430 %T Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data %X Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data. %@ 978-3-030-59491-6
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  NICER — Aesthetic Image Enhancement with Humans in the Loop. Fischer, Michael; Kobs, Konstantin; Hotho, Andreas. In ACHI 2020: The Thirteenth International Conference on Advances in Computer-Human Interactions, pp. 357–362. 2020
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  Fully- or semi-automatic image enhancement software helps users to increase the visual appeal of photos and does not require in-depth knowledge of manual image editing. However, fully-automatic approaches usually enhance the image in a black-box manner that does not give the user any control over the optimization process, possibly leading to edited images that do not subjectively appeal to the user. Semi-automatic methods mostly allow for controlling which pre-defined editing step is taken, which restricts the users in their creativity and ability to make detailed adjustments, such as brightness or contrast. We argue that incorporating user preferences by guiding an automated enhancement method simplifies image editing and increases the enhancement’s focus on the user. This work thus proposes the Neural Image Correction & Enhancement Routine (NICER), a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process that is interactive and user-centered. NICER iteratively adjusts image editing parameters in order to maximize an aesthetic score based on image style and content. Users can modify these parameters at any time and guide the optimization process towards a desired direction. This interactive workflow is a novelty in the field of human-computer interaction for image enhancement tasks. In a user study, we show that NICER can improve image aesthetics without user interaction and that allowing user interaction leads to diverse enhancement outcomes that are strongly preferred over the unedited image. We make our code publicly available to facilitate further research in this direction.

  @inproceedings{fischer2020nicer, abstract = {Fully- or semi-automatic image enhancement software helps users to increase the visual appeal of photos and does not require in-depth knowledge of manual image editing. However, fully-automatic approaches usually enhance the image in a black-box manner that does not give the user any control over the optimization process, possibly leading to edited images that do not subjectively appeal to the user. Semi-automatic methods mostly allow for controlling which pre-defined editing step is taken, which restricts the users in their creativity and ability to make detailed adjustments, such as brightness or contrast. We argue that incorporating user preferences by guiding an automated enhancement method simplifies image editing and increases the enhancement’s focus on the user. This work thus proposes the Neural Image Correction & Enhancement Routine (NICER), a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process that is interactive and user-centered. NICER iteratively adjusts image editing parameters in order to maximize an aesthetic score based on image style and content. Users can modify these parameters at any time and guide the optimization process towards a desired direction. This interactive workflow is a novelty in the field of human-computer interaction for image enhancement tasks. In a user study, we show that NICER can improve image aesthetics without user interaction and that allowing user interaction leads to diverse enhancement outcomes that are strongly preferred over the unedited image. We make our code publicly available to facilitate further research in this direction.}, author = {Fischer, Michael and Kobs, Konstantin and Hotho, Andreas}, journal = {ACHI 2020: The Thirteenth International Conference on Advances in Computer-Human Interactions}, keywords = {image-assessment}, pages = {357-362}, title = {NICER — Aesthetic Image Enhancement with Humans in the Loop}, year = 2020 }

  %0 Conference Paper %1 fischer2020nicer %A Fischer, Michael %A Kobs, Konstantin %A Hotho, Andreas %D 2020 %J ACHI 2020: The Thirteenth International Conference on Advances in Computer-Human Interactions %P 357-362 %T NICER — Aesthetic Image Enhancement with Humans in the Loop %X Fully- or semi-automatic image enhancement software helps users to increase the visual appeal of photos and does not require in-depth knowledge of manual image editing. However, fully-automatic approaches usually enhance the image in a black-box manner that does not give the user any control over the optimization process, possibly leading to edited images that do not subjectively appeal to the user. Semi-automatic methods mostly allow for controlling which pre-defined editing step is taken, which restricts the users in their creativity and ability to make detailed adjustments, such as brightness or contrast. We argue that incorporating user preferences by guiding an automated enhancement method simplifies image editing and increases the enhancement’s focus on the user. This work thus proposes the Neural Image Correction & Enhancement Routine (NICER), a neural network based approach to no-reference image enhancement in a fully-, semi-automatic or fully manual process that is interactive and user-centered. NICER iteratively adjusts image editing parameters in order to maximize an aesthetic score based on image style and content. Users can modify these parameters at any time and guide the optimization process towards a desired direction. This interactive workflow is a novelty in the field of human-computer interaction for image enhancement tasks. In a user study, we show that NICER can improve image aesthetics without user interaction and that allowing user interaction leads to diverse enhancement outcomes that are strongly preferred over the unedited image. We make our code publicly available to facilitate further research in this direction. %@ 978-1-61208-761-0
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  Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part II. Brefeld, Ulf; Fromont, Élisa; Hotho, Andreas; Knobbe, Arno J.; Maathuis, Marloes H.; Robardet, Céline. In Vol. 11907 of Lecture Notes in Computer Science. Springer, 2020
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  @proceedings{brefeld2020machine2, booktitle = {ECML/PKDD (2)}, editor = {Brefeld, Ulf and Fromont, Élisa and Hotho, Andreas and Knobbe, Arno J. and Maathuis, Marloes H. and Robardet, Céline}, keywords = {from:hotho}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part II}, volume = 11907, year = 2020 }

  %0 Conference Proceedings %1 brefeld2020machine2 %B ECML/PKDD (2) %D 2020 %E Brefeld, Ulf %E Fromont, Élisa %E Hotho, Andreas %E Knobbe, Arno J. %E Maathuis, Marloes H. %E Robardet, Céline %I Springer %T Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part II %U http://dblp.uni-trier.de/db/conf/pkdd/pkdd2019-2.html %V 11907 %@ 978-3-030-46147-8
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  Financial Fraud Detection with Improved Neural Arithmetic Logic Units. Schlör, Daniel; Ring, Markus; Krause, Anna; Hotho, Andreas. Vol. Fifth Workshop on MIning DAta for financial applicationS. 2020
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  @proceedings{schlr2020financial, author = {Schlör, Daniel and Ring, Markus and Krause, Anna and Hotho, Andreas}, keywords = {inalu}, number = 5, title = {Financial Fraud Detection with Improved Neural Arithmetic Logic Units}, volume = {Fifth Workshop on MIning DAta for financial applicationS}, year = 2020 }

  %0 Conference Proceedings %1 schlr2020financial %A Schlör, Daniel %A Ring, Markus %A Krause, Anna %A Hotho, Andreas %D 2020 %N 5 %T Financial Fraud Detection with Improved Neural Arithmetic Logic Units %V Fifth Workshop on MIning DAta for financial applicationS
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  Improving Sentiment Analysis with Biofeedback Data. Schlör, Daniel; Zehe, Albin; Kobs, Konstantin; Veseli, Blerta; Westermeier, Franziska; Brübach, Larissa; Roth, Daniel; Latoschik, Marc Erich; Hotho, Andreas. In Proceedings of the Workshop on peOple in laNguage, vIsiOn and the miNd (ONION). 2020
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  Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.

  @inproceedings{schlor2020improving, abstract = {Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.}, author = {Schlör, Daniel and Zehe, Albin and Kobs, Konstantin and Veseli, Blerta and Westermeier, Franziska and Brübach, Larissa and Roth, Daniel and Latoschik, Marc Erich and Hotho, Andreas}, booktitle = {Proceedings of the Workshop on peOple in laNguage, vIsiOn and the miNd (ONION)}, keywords = {author:zehe}, title = {Improving Sentiment Analysis with Biofeedback Data}, year = 2020 }

  %0 Conference Paper %1 schlor2020improving %A Schlör, Daniel %A Zehe, Albin %A Kobs, Konstantin %A Veseli, Blerta %A Westermeier, Franziska %A Brübach, Larissa %A Roth, Daniel %A Latoschik, Marc Erich %A Hotho, Andreas %B Proceedings of the Workshop on peOple in laNguage, vIsiOn and the miNd (ONION) %D 2020 %T Improving Sentiment Analysis with Biofeedback Data %U https://downloads.hci.informatik.uni-wuerzburg.de/2018-ieeevr-lugrin-vr-teacher-training/2020-onion-sentiment-eeg-preprint.pdf %X Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.
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  LM4KG: Improving Common Sense Knowledge Graphs with Language Models. Omeliyanenko, Janna; Zehe, Albin; Hettinger, Lena; Hotho, Andreas. In International Semantic Web Conference. Springer, 2020
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  @inproceedings{omeliyanenko2020reweight, author = {Omeliyanenko, Janna and Zehe, Albin and Hettinger, Lena and Hotho, Andreas}, booktitle = {International Semantic Web Conference}, keywords = {graph}, organization = {Springer}, title = {LM4KG: Improving Common Sense Knowledge Graphs with Language Models}, year = 2020 }

  %0 Conference Paper %1 omeliyanenko2020reweight %A Omeliyanenko, Janna %A Zehe, Albin %A Hettinger, Lena %A Hotho, Andreas %B International Semantic Web Conference %D 2020 %T LM4KG: Improving Common Sense Knowledge Graphs with Language Models
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  Towards Predicting the Subscription Status of Twitch.tv Users. Kobs, Konstantin; Potthast, Martin; Wiegmann, Matti; Zehe, Albin; Stein, Benno; Hotho, Andreas. In Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch. 2020
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  @article{kobstowards, author = {Kobs, Konstantin and Potthast, Martin and Wiegmann, Matti and Zehe, Albin and Stein, Benno and Hotho, Andreas}, journal = {Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch}, keywords = {author:zehe}, title = {Towards Predicting the Subscription Status of Twitch.tv Users}, year = 2020 }

  %0 Journal Article %1 kobstowards %A Kobs, Konstantin %A Potthast, Martin %A Wiegmann, Matti %A Zehe, Albin %A Stein, Benno %A Hotho, Andreas %D 2020 %J Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch %T Towards Predicting the Subscription Status of Twitch.tv Users %U http://ceur-ws.org/Vol-2661/paper1.pdf
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  Anomaly Detection in Beehives using Deep Recurrent Autoencoders. Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Kobs, Konstantin; Krause, Anna; Hotho, Andreas. In Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020), pp. 142–149. SCITEPRESS – Science and Technology Publications, Lda., 2020
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  @inproceedings{davidson2020anomaly, author = {Davidson, Padraig and Steininger, Michael and Lautenschlager, Florian and Kobs, Konstantin and Krause, Anna and Hotho, Andreas}, booktitle = {Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020)}, keywords = {author:hotho}, number = 9, pages = {142-149}, publisher = {SCITEPRESS – Science and Technology Publications, Lda.}, title = {Anomaly Detection in Beehives using Deep Recurrent Autoencoders}, year = 2020 }

  %0 Conference Paper %1 davidson2020anomaly %A Davidson, Padraig %A Steininger, Michael %A Lautenschlager, Florian %A Kobs, Konstantin %A Krause, Anna %A Hotho, Andreas %B Proceedings of the 9th International Conference on Sensor Networks (SENSORNETS 2020) %D 2020 %I SCITEPRESS – Science and Technology Publications, Lda. %N 9 %P 142-149 %T Anomaly Detection in Beehives using Deep Recurrent Autoencoders
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  SimLoss: Class Similarities in Cross Entropy. Kobs, Konstantin; Steininger, Michael; Zehe, Albin; Lautenschlager, Florian; Hotho, Andreas. 2020
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  One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an inherent structure. For instance, classifying an image of a rose as "violet" is better than as "truck". We introduce SimLoss, a drop-in replacement for CCE that incorporates class similarities along with two techniques to construct such matrices from task-specific knowledge. We test SimLoss on Age Estimation and Image Classification and find that it brings significant improvements over CCE on several metrics. SimLoss therefore allows for explicit modeling of background knowledge by simply exchanging the loss function, while keeping the neural network architecture the same. Code and additional resources can be found at https://github.com/konstantinkobs/SimLoss.

  @misc{kobs2020simloss, abstract = {One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an inherent structure. For instance, classifying an image of a rose as "violet" is better than as "truck". We introduce SimLoss, a drop-in replacement for CCE that incorporates class similarities along with two techniques to construct such matrices from task-specific knowledge. We test SimLoss on Age Estimation and Image Classification and find that it brings significant improvements over CCE on several metrics. SimLoss therefore allows for explicit modeling of background knowledge by simply exchanging the loss function, while keeping the neural network architecture the same. Code and additional resources can be found at https://github.com/konstantinkobs/SimLoss.}, author = {Kobs, Konstantin and Steininger, Michael and Zehe, Albin and Lautenschlager, Florian and Hotho, Andreas}, keywords = {author:zehe}, note = {cite arxiv:2003.03182Comment: This paper is going to be published in the proceedings of the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS)}, title = {SimLoss: Class Similarities in Cross Entropy}, year = 2020 }

  %0 Generic %1 kobs2020simloss %A Kobs, Konstantin %A Steininger, Michael %A Zehe, Albin %A Lautenschlager, Florian %A Hotho, Andreas %D 2020 %T SimLoss: Class Similarities in Cross Entropy %U http://arxiv.org/abs/2003.03182 %X One common loss function in neural network classification tasks is Categorical Cross Entropy (CCE), which punishes all misclassifications equally. However, classes often have an inherent structure. For instance, classifying an image of a rose as "violet" is better than as "truck". We introduce SimLoss, a drop-in replacement for CCE that incorporates class similarities along with two techniques to construct such matrices from task-specific knowledge. We test SimLoss on Age Estimation and Image Classification and find that it brings significant improvements over CCE on several metrics. SimLoss therefore allows for explicit modeling of background knowledge by simply exchanging the loss function, while keeping the neural network architecture the same. Code and additional resources can be found at https://github.com/konstantinkobs/SimLoss.
• 
  Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I. Brefeld, Ulf; Fromont, Élisa; Hotho, Andreas; Knobbe, Arno J.; Maathuis, Marloes H.; Robardet, Céline. In Vol. 11906 of Lecture Notes in Computer Science. Springer, 2020
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  @proceedings{brefeld2020machine3, booktitle = {ECML/PKDD (1)}, editor = {Brefeld, Ulf and Fromont, Élisa and Hotho, Andreas and Knobbe, Arno J. and Maathuis, Marloes H. and Robardet, Céline}, keywords = {from:hotho}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, title = {Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I}, volume = 11906, year = 2020 }

  %0 Conference Proceedings %1 brefeld2020machine3 %B ECML/PKDD (1) %D 2020 %E Brefeld, Ulf %E Fromont, Élisa %E Hotho, Andreas %E Knobbe, Arno J. %E Maathuis, Marloes H. %E Robardet, Céline %I Springer %T Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I %U http://dblp.uni-trier.de/db/conf/pkdd/pkdd2019-1.html %V 11906 %@ 978-3-030-46150-8
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  HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis. Zehe, Albin; Arns, Julia; Hettinger, Lena; Hotho, Andreas. In 5th SwissText & 16th KONVENS Joint Conference. 2020
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  @inproceedings{zehe2020harrymotions, author = {Zehe, Albin and Arns, Julia and Hettinger, Lena and Hotho, Andreas}, booktitle = {5th SwissText & 16th KONVENS Joint Conference}, keywords = {author:zehe}, title = {HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis}, year = 2020 }

  %0 Conference Paper %1 zehe2020harrymotions %A Zehe, Albin %A Arns, Julia %A Hettinger, Lena %A Hotho, Andreas %B 5th SwissText & 16th KONVENS Joint Conference %D 2020 %T HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis
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  Evaluation of post-hoc XAI approaches through synthetic tabular data. Tritscher, Julian; Ring, Markus; Schlör, Daniel; Hettinger, Lena; Hotho, Andreas. In International Symposium on Methodologies for Intelligent Systems. Springer, 2020
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  Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.

  @article{tritscher2020evaluation, abstract = {Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.}, author = {Tritscher, Julian and Ring, Markus and Schlör, Daniel and Hettinger, Lena and Hotho, Andreas}, booktitle = {International Symposium on Methodologies for Intelligent Systems}, keywords = {xai}, note = {Accepted but not published}, organization = {Springer}, title = {Evaluation of post-hoc XAI approaches through synthetic tabular data}, year = 2020 }

  %0 Journal Article %1 tritscher2020evaluation %A Tritscher, Julian %A Ring, Markus %A Schlör, Daniel %A Hettinger, Lena %A Hotho, Andreas %B International Symposium on Methodologies for Intelligent Systems %D 2020 %T Evaluation of post-hoc XAI approaches through synthetic tabular data %X Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.
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  Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data. Tritscher, Julian; Ring, Markus; Schlr, Daniel; Hettinger, Lena; Hotho, Andreas. In Foundations of Intelligent Systems, D. Helic, G. Leitner, M. Stettinger, A. Felfernig, Z. W. Ra{\’{s}} (eds.), pp. 422–430. Springer International Publishing, Cham, 2020
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  Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.

  @inproceedings{tritscher2020evaluation, abstract = {Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data.}, address = {Cham}, author = {Tritscher, Julian and Ring, Markus and Schlr, Daniel and Hettinger, Lena and Hotho, Andreas}, booktitle = {Foundations of Intelligent Systems}, editor = {Helic, Denis and Leitner, Gerhard and Stettinger, Martin and Felfernig, Alexander and Ra{\'{s}}, Zbigniew W.}, keywords = {from:hotho}, month = {04}, pages = {422–430}, publisher = {Springer International Publishing}, title = {Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data}, year = 2020 }

  %0 Conference Paper %1 tritscher2020evaluation %A Tritscher, Julian %A Ring, Markus %A Schlr, Daniel %A Hettinger, Lena %A Hotho, Andreas %B Foundations of Intelligent Systems %C Cham %D 2020 %E Helic, Denis %E Leitner, Gerhard %E Stettinger, Martin %E Felfernig, Alexander %E Ra{\'{s}}, Zbigniew W. %I Springer International Publishing %P 422--430 %T Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data %X Evaluating the explanations given by post-hoc XAI approaches on tabular data is a challenging prospect, since the subjective judgement of explanations of tabular relations is non trivial in contrast to e.g. the judgement of image heatmap explanations. In order to quantify XAI performance on categorical tabular data, where feature relationships can often be described by Boolean functions, we propose an evaluation setting through generation of synthetic datasets. To create gold standard explanations, we present a definition of feature relevance in Boolean functions. In the proposed setting we evaluate eight state-of-the-art XAI approaches and gain novel insights into XAI performance on categorical tabular data. We find that the investigated approaches often fail to faithfully explain even basic relationships within categorical data. %@ 978-3-030-59491-6
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  OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning. Lautenschlager, Florian; Becker, Martin; Kobs, Konstantin; Steininger, Michael; Davidson, Padraig; Krause, Anna; Hotho, Andreas. In Atmospheric Environment, Vol. 233, p. 117535. 2020
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  To assess the exposure of citizens to pollutants like NOx or particulate matter in urban areas, land use regression (LUR) models are a well established method. LUR models leverage information about environmental and anthropogenic factors such as cars, heating, or industry to predict air pollution in areas where no measurements have been made. However, existing approaches are often not globally applicable and require tedious hyper-parameter tuning to enable high quality predictions. In this work, we tackle these issues by introducing OpenLUR, an off-the-shelf approach for modeling air pollution that (i) works on a set of novel features solely extracted from the globally and openly available data source OpenStreetMap and (ii) is based on state-of-the-art machine learning featuring automated hyper-parameter tuning in order to minimize manual effort. We show that our proposed features are able to outperform their counterparts from local and closed sources, and illustrate how automated hyper parameter tuning can yield competitve results while alleviating the need for expert knowledge in machine learning and manual effort. Importantly, we further demonstrate the potential of the global availability of our features by applying cross-learning across different cities in order to reduce the need for a large amount of training samples. Overall, OpenLUR represents an off-the-shelf approach that facilitates easily reproducible experiments and the development of globally applicable models.

  @article{lautenschlager2020openlur, abstract = {To assess the exposure of citizens to pollutants like NOx or particulate matter in urban areas, land use regression (LUR) models are a well established method. LUR models leverage information about environmental and anthropogenic factors such as cars, heating, or industry to predict air pollution in areas where no measurements have been made. However, existing approaches are often not globally applicable and require tedious hyper-parameter tuning to enable high quality predictions. In this work, we tackle these issues by introducing OpenLUR, an off-the-shelf approach for modeling air pollution that (i) works on a set of novel features solely extracted from the globally and openly available data source OpenStreetMap and (ii) is based on state-of-the-art machine learning featuring automated hyper-parameter tuning in order to minimize manual effort. We show that our proposed features are able to outperform their counterparts from local and closed sources, and illustrate how automated hyper parameter tuning can yield competitve results while alleviating the need for expert knowledge in machine learning and manual effort. Importantly, we further demonstrate the potential of the global availability of our features by applying cross-learning across different cities in order to reduce the need for a large amount of training samples. Overall, OpenLUR represents an off-the-shelf approach that facilitates easily reproducible experiments and the development of globally applicable models.}, author = {Lautenschlager, Florian and Becker, Martin and Kobs, Konstantin and Steininger, Michael and Davidson, Padraig and Krause, Anna and Hotho, Andreas}, journal = {Atmospheric Environment}, keywords = {pollution}, month = {07}, pages = 117535, title = {OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning}, volume = 233, year = 2020 }

  %0 Journal Article %1 lautenschlager2020openlur %A Lautenschlager, Florian %A Becker, Martin %A Kobs, Konstantin %A Steininger, Michael %A Davidson, Padraig %A Krause, Anna %A Hotho, Andreas %D 2020 %J Atmospheric Environment %P 117535 %R https://doi.org/10.1016/j.atmosenv.2020.117535 %T OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning %U http://www.sciencedirect.com/science/article/pii/S1352231020302703 %V 233 %X To assess the exposure of citizens to pollutants like NOx or particulate matter in urban areas, land use regression (LUR) models are a well established method. LUR models leverage information about environmental and anthropogenic factors such as cars, heating, or industry to predict air pollution in areas where no measurements have been made. However, existing approaches are often not globally applicable and require tedious hyper-parameter tuning to enable high quality predictions. In this work, we tackle these issues by introducing OpenLUR, an off-the-shelf approach for modeling air pollution that (i) works on a set of novel features solely extracted from the globally and openly available data source OpenStreetMap and (ii) is based on state-of-the-art machine learning featuring automated hyper-parameter tuning in order to minimize manual effort. We show that our proposed features are able to outperform their counterparts from local and closed sources, and illustrate how automated hyper parameter tuning can yield competitve results while alleviating the need for expert knowledge in machine learning and manual effort. Importantly, we further demonstrate the potential of the global availability of our features by applying cross-learning across different cities in order to reduce the need for a large amount of training samples. Overall, OpenLUR represents an off-the-shelf approach that facilitates easily reproducible experiments and the development of globally applicable models.
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  Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study. Davidson, Padraig; Düking, Peter; Zinner, Christoph; Sperlich, Billy; Hotho, Andreas. In Sensors, Vol. 20, Issue 9. 2020
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  @article{davidson2020smartwatchderived, author = {Davidson, Padraig and Düking, Peter and Zinner, Christoph and Sperlich, Billy and Hotho, Andreas}, journal = {Sensors}, keywords = {timeseries-classification}, month = {05}, number = 9, title = {Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study}, volume = 20, year = 2020 }

  %0 Journal Article %1 davidson2020smartwatchderived %A Davidson, Padraig %A Düking, Peter %A Zinner, Christoph %A Sperlich, Billy %A Hotho, Andreas %D 2020 %J Sensors %N 9 %R 10.3390/s20092637 %T Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study %U https://www.mdpi.com/1424-8220/20/9/2637 %V 20
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  Improving Sentiment Analysis with Biofeedback Data. Schl{\"o}r, Daniel; Zehe, Albin; Kobs, Konstantin; Veseli, Blerta; Westermeier, Franziska; Br{\"u}bach, Larissa; Roth, Daniel; Latoschik, Marc Erich; Hotho, Andreas. In Proceedings of LREC2020 Workshop ``People in language, vision and the mind’’ (ONION2020), pp. 28–33. European Language Resources Association (ELRA), Marseille, France, 2020
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  Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.

  @inproceedings{schlor-etal-2020-improving, abstract = {Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level.}, address = {Marseille, France}, author = {Schl{\"o}r, Daniel and Zehe, Albin and Kobs, Konstantin and Veseli, Blerta and Westermeier, Franziska and Br{\"u}bach, Larissa and Roth, Daniel and Latoschik, Marc Erich and Hotho, Andreas}, booktitle = {Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020)}, keywords = {author:zehe}, month = {05}, pages = {28–33}, publisher = {European Language Resources Association (ELRA)}, title = {Improving Sentiment Analysis with Biofeedback Data}, year = 2020 }

  %0 Conference Paper %1 schlor-etal-2020-improving %A Schl{\"o}r, Daniel %A Zehe, Albin %A Kobs, Konstantin %A Veseli, Blerta %A Westermeier, Franziska %A Br{\"u}bach, Larissa %A Roth, Daniel %A Latoschik, Marc Erich %A Hotho, Andreas %B Proceedings of LREC2020 Workshop ``People in language, vision and the mind'' (ONION2020) %C Marseille, France %D 2020 %I European Language Resources Association (ELRA) %P 28--33 %T Improving Sentiment Analysis with Biofeedback Data %U https://www.aclweb.org/anthology/2020.onion-1.5 %X Humans frequently are able to read and interpret emotions of others by directly taking verbal and non-verbal signals in human-to-human communication into account or to infer or even experience emotions from mediated stories. For computers, however, emotion recognition is a complex problem: Thoughts and feelings are the roots of many behavioural responses and they are deeply entangled with neurophysiological changes within humans. As such, emotions are very subjective, often are expressed in a subtle manner, and are highly depending on context. For example, machine learning approaches for text-based sentiment analysis often rely on incorporating sentiment lexicons or language models to capture the contextual meaning. This paper explores if and how we further can enhance sentiment analysis using biofeedback of humans which are experiencing emotions while reading texts. Specifically, we record the heart rate and brain waves of readers that are presented with short texts which have been annotated with the emotions they induce. We use these physiological signals to improve the performance of a lexicon-based sentiment classifier. We find that the combination of several biosignals can improve the ability of a text-based classifier to detect the presence of a sentiment in a text on a per-sentence level. %@ 979-10-95546-70-2
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  Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels. Kobs, Konstantin; Zehe, Albin; Bernstetter, Armin; Chibane, Julian; Pfister, Jan; Tritscher, Julian; Hotho, Andreas. In {ACM} Transactions on Social Computing, Vol. 3, Issue 2, pp. 1–34. Association for Computing Machinery ({ACM}), 2020
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  @article{Kobs_2020, author = {Kobs, Konstantin and Zehe, Albin and Bernstetter, Armin and Chibane, Julian and Pfister, Jan and Tritscher, Julian and Hotho, Andreas}, journal = {{ACM} Transactions on Social Computing}, keywords = {selected}, month = {05}, number = 2, pages = {1–34}, publisher = {Association for Computing Machinery ({ACM})}, title = {Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels}, volume = 3, year = 2020 }

  %0 Journal Article %1 Kobs_2020 %A Kobs, Konstantin %A Zehe, Albin %A Bernstetter, Armin %A Chibane, Julian %A Pfister, Jan %A Tritscher, Julian %A Hotho, Andreas %D 2020 %I Association for Computing Machinery ({ACM}) %J {ACM} Transactions on Social Computing %N 2 %P 1--34 %R 10.1145/3365523 %T Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels %U https://doi.org/10.1145%2F3365523 %V 3
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  Evaluating the multi-task learning approach for land use regression modelling of air pollution. Dulny, Andrzej; Steininger, Michael; Lautenschlager, Florian; Krause, Anna; Hotho, Andreas. In International Conference on Frontiers of Artificial Intelligence and Machine Learning. IASED, 2020
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  Air pollution has been linked to several health problems including heart disease, stroke and lung cancer. Modelling and analyzing this dependency requires reliable and accurate air pollutant measurements collected by stationary air monitoring stations. However, usually only a low number of such stations are present within a single city. To retrieve pollution concentrations for unmeasured locations, researchers rely on land use regression (LUR) models. Those models are typically developed for one pollutant only. However, as results in different areas have shown, modelling several related output variables through multi-task learning can improve the prediction results of the models significantly. In this work, we compared prediction results from single-task and multi-task learning multilayer perceptron models on measurements taken from the OpenSense dataset and the London Atmospheric Emissions Inventory dataset. LUR features were generated from OpenStreetMap using OpenLUR and used to train hard parameter sharing multilayer perceptron models. The results show multi-task learning with sufficient data significantly improves the performance of a LUR model.

  @inproceedings{dulny2020evaluating, abstract = {Air pollution has been linked to several health problems including heart disease, stroke and lung cancer. Modelling and analyzing this dependency requires reliable and accurate air pollutant measurements collected by stationary air monitoring stations. However, usually only a low number of such stations are present within a single city. To retrieve pollution concentrations for unmeasured locations, researchers rely on land use regression (LUR) models. Those models are typically developed for one pollutant only. However, as results in different areas have shown, modelling several related output variables through multi-task learning can improve the prediction results of the models significantly. In this work, we compared prediction results from single-task and multi-task learning multilayer perceptron models on measurements taken from the OpenSense dataset and the London Atmospheric Emissions Inventory dataset. LUR features were generated from OpenStreetMap using OpenLUR and used to train hard parameter sharing multilayer perceptron models. The results show multi-task learning with sufficient data significantly improves the performance of a LUR model.}, author = {Dulny, Andrzej and Steininger, Michael and Lautenschlager, Florian and Krause, Anna and Hotho, Andreas}, booktitle = {International Conference on Frontiers of Artificial Intelligence and Machine Learning}, keywords = {from:msteininger}, organization = {IASED}, title = {Evaluating the multi-task learning approach for land use regression modelling of air pollution}, year = 2020 }

  %0 Conference Paper %1 dulny2020evaluating %A Dulny, Andrzej %A Steininger, Michael %A Lautenschlager, Florian %A Krause, Anna %A Hotho, Andreas %B International Conference on Frontiers of Artificial Intelligence and Machine Learning %D 2020 %T Evaluating the multi-task learning approach for land use regression modelling of air pollution %X Air pollution has been linked to several health problems including heart disease, stroke and lung cancer. Modelling and analyzing this dependency requires reliable and accurate air pollutant measurements collected by stationary air monitoring stations. However, usually only a low number of such stations are present within a single city. To retrieve pollution concentrations for unmeasured locations, researchers rely on land use regression (LUR) models. Those models are typically developed for one pollutant only. However, as results in different areas have shown, modelling several related output variables through multi-task learning can improve the prediction results of the models significantly. In this work, we compared prediction results from single-task and multi-task learning multilayer perceptron models on measurements taken from the OpenSense dataset and the London Atmospheric Emissions Inventory dataset. LUR features were generated from OpenStreetMap using OpenLUR and used to train hard parameter sharing multilayer perceptron models. The results show multi-task learning with sufficient data significantly improves the performance of a LUR model.
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  MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images. Steininger, Michael; Kobs, Konstantin; Zehe, Albin; Lautenschlager, Florian; Becker, Martin; Hotho, Andreas. In ACM Trans. Spatial Algorithms Syst., Vol. 6, Issue 3. Association for Computing Machinery, New York, NY, USA, 2020
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  Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data. Progress within this paradigm will alleviate the need for experts to adapt models to the local characteristics of the available data sources and thus facilitate the generalizability of air pollution models to new areas on a global scale. In order to illustrate the feasibility of the DOG paradigm for LUR, we introduce a deep learning model called MapLUR. It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering. We compare our model to state-of-the-art baselines like linear regression, random forests and multi-layer perceptrons using a large data set of modeled NO2 concentrations in Central London. Our results show that MapLUR significantly outperforms these approaches even though they are provided with manually tailored features. Furthermore, we illustrate that the automatic feature extraction inherent to models based on the DOG paradigm can learn features that are readily interpretable and closely resemble those commonly used in traditional LUR approaches.

  @article{10.1145/3380973, abstract = {Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data. Progress within this paradigm will alleviate the need for experts to adapt models to the local characteristics of the available data sources and thus facilitate the generalizability of air pollution models to new areas on a global scale. In order to illustrate the feasibility of the DOG paradigm for LUR, we introduce a deep learning model called MapLUR. It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering. We compare our model to state-of-the-art baselines like linear regression, random forests and multi-layer perceptrons using a large data set of modeled NO2 concentrations in Central London. Our results show that MapLUR significantly outperforms these approaches even though they are provided with manually tailored features. Furthermore, we illustrate that the automatic feature extraction inherent to models based on the DOG paradigm can learn features that are readily interpretable and closely resemble those commonly used in traditional LUR approaches.}, address = {New York, NY, USA}, author = {Steininger, Michael and Kobs, Konstantin and Zehe, Albin and Lautenschlager, Florian and Becker, Martin and Hotho, Andreas}, journal = {ACM Trans. Spatial Algorithms Syst.}, keywords = {pollution}, month = {04}, number = 3, publisher = {Association for Computing Machinery}, title = {MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images}, volume = 6, year = 2020 }

  %0 Journal Article %1 10.1145/3380973 %A Steininger, Michael %A Kobs, Konstantin %A Zehe, Albin %A Lautenschlager, Florian %A Becker, Martin %A Hotho, Andreas %C New York, NY, USA %D 2020 %I Association for Computing Machinery %J ACM Trans. Spatial Algorithms Syst. %N 3 %R 10.1145/3380973 %T MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images %U https://doi.org/10.1145/3380973 %V 6 %X Land-use regression (LUR) models are important for the assessment of air pollution concentrations in areas without measurement stations. While many such models exist, they often use manually constructed features based on restricted, locally available data. Thus, they are typically hard to reproduce and challenging to adapt to areas beyond those they have been developed for. In this paper, we advocate a paradigm shift for LUR models: We propose the Data-driven, Open, Global (DOG) paradigm that entails models based on purely data-driven approaches using only openly and globally available data. Progress within this paradigm will alleviate the need for experts to adapt models to the local characteristics of the available data sources and thus facilitate the generalizability of air pollution models to new areas on a global scale. In order to illustrate the feasibility of the DOG paradigm for LUR, we introduce a deep learning model called MapLUR. It is based on a convolutional neural network architecture and is trained exclusively on globally and openly available map data without requiring manual feature engineering. We compare our model to state-of-the-art baselines like linear regression, random forests and multi-layer perceptrons using a large data set of modeled NO2 concentrations in Central London. Our results show that MapLUR significantly outperforms these approaches even though they are provided with manually tailored features. Furthermore, we illustrate that the automatic feature extraction inherent to models based on the DOG paradigm can learn features that are readily interpretable and closely resemble those commonly used in traditional LUR approaches.
• 
  Time Series Forecasting for Self-Aware Systems. {Bauer}, A.; {Züfle}, M.; {Herbst}, N.; {Zehe}, A.; {Hotho}, A.; {Kounev}, S. In Proceedings of the IEEE, pp. 1–26. 2020
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  Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are required. However, selecting a suitable forecasting method for a given scenario is a challenging task. According to the ``No-Free-Lunch Theorem,'' there is no general forecasting method that always performs best. Thus, manual feature engineering remains to be a mandatory expert task to avoid trial and error. Furthermore, determining the expected time-to-result of existing forecasting methods is a challenge. In this article, we extensively assess the state-of-the-art in time series forecasting. We compare existing methods and discuss the issues that have to be addressed to enable their use in a self-aware computing context. To address these issues, we present a step-by-step approach to fully automate the feature engineering and forecasting process. Then, following the principles from benchmarking, we establish a level-playing field for evaluating the accuracy and time-to-result of automated forecasting methods for a broad set of application scenarios. We provide results of a benchmarking competition to guide in selecting and appropriately using existing forecasting methods for a given self-aware computing context. Finally, we present a case study in the area of self-aware data-center resource management to exemplify the benefits of fully automated learning and reasoning processes on time series data.

  @article{9069193, abstract = {Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are required. However, selecting a suitable forecasting method for a given scenario is a challenging task. According to the ``No-Free-Lunch Theorem,'' there is no general forecasting method that always performs best. Thus, manual feature engineering remains to be a mandatory expert task to avoid trial and error. Furthermore, determining the expected time-to-result of existing forecasting methods is a challenge. In this article, we extensively assess the state-of-the-art in time series forecasting. We compare existing methods and discuss the issues that have to be addressed to enable their use in a self-aware computing context. To address these issues, we present a step-by-step approach to fully automate the feature engineering and forecasting process. Then, following the principles from benchmarking, we establish a level-playing field for evaluating the accuracy and time-to-result of automated forecasting methods for a broad set of application scenarios. We provide results of a benchmarking competition to guide in selecting and appropriately using existing forecasting methods for a given self-aware computing context. Finally, we present a case study in the area of self-aware data-center resource management to exemplify the benefits of fully automated learning and reasoning processes on time series data.}, author = {{Bauer}, A. and {Züfle}, M. and {Herbst}, N. and {Zehe}, A. and {Hotho}, A. and {Kounev}, S.}, journal = {Proceedings of the IEEE}, keywords = {author:zehe}, pages = {1-26}, title = {Time Series Forecasting for Self-Aware Systems}, year = 2020 }

  %0 Journal Article %1 9069193 %A {Bauer}, A. %A {Züfle}, M. %A {Herbst}, N. %A {Zehe}, A. %A {Hotho}, A. %A {Kounev}, S. %D 2020 %J Proceedings of the IEEE %P 1-26 %R 10.1109/JPROC.2020.2983857 %T Time Series Forecasting for Self-Aware Systems %X Modern distributed systems and Internet-of-Things applications are governed by fast living and changing requirements. Moreover, they have to struggle with huge amounts of data that they create or have to process. To improve the self-awareness of such systems and enable proactive and autonomous decisions, reliable time series forecasting methods are required. However, selecting a suitable forecasting method for a given scenario is a challenging task. According to the ``No-Free-Lunch Theorem,'' there is no general forecasting method that always performs best. Thus, manual feature engineering remains to be a mandatory expert task to avoid trial and error. Furthermore, determining the expected time-to-result of existing forecasting methods is a challenge. In this article, we extensively assess the state-of-the-art in time series forecasting. We compare existing methods and discuss the issues that have to be addressed to enable their use in a self-aware computing context. To address these issues, we present a step-by-step approach to fully automate the feature engineering and forecasting process. Then, following the principles from benchmarking, we establish a level-playing field for evaluating the accuracy and time-to-result of automated forecasting methods for a broad set of application scenarios. We provide results of a benchmarking competition to guide in selecting and appropriately using existing forecasting methods for a given self-aware computing context. Finally, we present a case study in the area of self-aware data-center resource management to exemplify the benefits of fully automated learning and reasoning processes on time series data.
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  Integrating Keywords into BERT4Rec for Sequential Recommendation. Fischer, Elisabeth; Zoller, Daniel; Dallmann, Alexander; Hotho, Andreas. In KI 2020: Advances in Artificial Intelligence. 2020
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  @inproceedings{fischer2020integrating, author = {Fischer, Elisabeth and Zoller, Daniel and Dallmann, Alexander and Hotho, Andreas}, booktitle = {KI 2020: Advances in Artificial Intelligence}, keywords = {selected}, title = {Integrating Keywords into BERT4Rec for Sequential Recommendation}, year = 2020 }

  %0 Conference Paper %1 fischer2020integrating %A Fischer, Elisabeth %A Zoller, Daniel %A Dallmann, Alexander %A Hotho, Andreas %B KI 2020: Advances in Artificial Intelligence %D 2020 %T Integrating Keywords into BERT4Rec for Sequential Recommendation %U http://dblp.uni-trier.de/db/conf/ki/ki2020.html#FischerZDH20
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  Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels. Kobs, Konstantin; Zehe, Albin; Bernstetter, Armin; Chibane, Julian; Pfister, Jan; Tritscher, Julian; Hotho, Andreas. In ACM Transactions on Social Computing. 2020
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  @article{kobs2020emotecontrolled, author = {Kobs, Konstantin and Zehe, Albin and Bernstetter, Armin and Chibane, Julian and Pfister, Jan and Tritscher, Julian and Hotho, Andreas}, journal = {ACM Transactions on Social Computing}, keywords = {sentiment-analysis}, title = {Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels}, year = 2020 }

  %0 Journal Article %1 kobs2020emotecontrolled %A Kobs, Konstantin %A Zehe, Albin %A Bernstetter, Armin %A Chibane, Julian %A Pfister, Jan %A Tritscher, Julian %A Hotho, Andreas %D 2020 %J ACM Transactions on Social Computing %R 10.1145/3365523 %T Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels
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  Where to Submit? Helping Researchers to Choose the Right Venue. Kobs, Konstantin; Koopmann, Tobias; Zehe, Albin; Fernes, David; Krop, Philipp; Hotho, Andreas. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pp. 878–883. Association for Computational Linguistics, Online, 2020
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  @inproceedings{kobs-etal-2020-submit, address = {Online}, author = {Kobs, Konstantin and Koopmann, Tobias and Zehe, Albin and Fernes, David and Krop, Philipp and Hotho, Andreas}, booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings}, keywords = {author:zehe}, month = 11, pages = {878–883}, publisher = {Association for Computational Linguistics}, title = {Where to Submit? Helping Researchers to Choose the Right Venue}, year = 2020 }

  %0 Conference Paper %1 kobs-etal-2020-submit %A Kobs, Konstantin %A Koopmann, Tobias %A Zehe, Albin %A Fernes, David %A Krop, Philipp %A Hotho, Andreas %B Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings %C Online %D 2020 %I Association for Computational Linguistics %P 878--883 %T Where to Submit? Helping Researchers to Choose the Right Venue %U https://www.aclweb.org/anthology/2020.findings-emnlp.78
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  The German and International AI Network Data Set. Stubbemann, Maximilian; Koopmann, Tobias. Zenodo, 2020
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  @article{koopmann-germanai_2020, author = {Stubbemann, Maximilian and Koopmann, Tobias}, keywords = {regio}, month = {03}, publisher = {Zenodo}, title = {The German and International AI Network Data Set}, year = 2020 }

  %0 Journal Article %1 koopmann-germanai_2020 %A Stubbemann, Maximilian %A Koopmann, Tobias %D 2020 %I Zenodo %R 10.5281/zenodo.3693604 %T The German and International AI Network Data Set

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  Classification of text-types in german novels. Schlör, D; Schöch, C; Hotho, A. In Digital Humanities 2019: Conference Abstracts. 2019
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  @inproceedings{schlor2019classification, author = {Schlör, D and Schöch, C and Hotho, A}, booktitle = {Digital Humanities 2019: Conference Abstracts}, keywords = {from:daschloer}, title = {Classification of text-types in german novels}, year = 2019 }

  %0 Conference Paper %1 schlor2019classification %A Schlör, D %A Schöch, C %A Hotho, A %B Digital Humanities 2019: Conference Abstracts %D 2019 %R https://doi.org/10.34894/OMLKRN %T Classification of text-types in german novels
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  Solving Mathematical Exercises: Prediction of Students’ Success. Wankerl, Sebastian; Götz, Gerhard; Hotho, Andreas. In LWDA, Vol. 2454 of CEUR Workshop Proceedings, R. Jäschke, M. Weidlich (eds.), pp. 190–194. CEUR-WS.org, 2019
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  @inproceedings{wankerl2019solving, author = {Wankerl, Sebastian and Götz, Gerhard and Hotho, Andreas}, booktitle = {LWDA}, editor = {Jäschke, Robert and Weidlich, Matthias}, keywords = {from:hotho}, pages = {190-194}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Solving Mathematical Exercises: Prediction of Students’ Success}, volume = 2454, year = 2019 }

  %0 Conference Paper %1 wankerl2019solving %A Wankerl, Sebastian %A Götz, Gerhard %A Hotho, Andreas %B LWDA %D 2019 %E Jäschke, Robert %E Weidlich, Matthias %I CEUR-WS.org %P 190-194 %T Solving Mathematical Exercises: Prediction of Students’ Success %U http://ceur-ws.org/Vol-2454/paper_84.pdf %V 2454
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  EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification. Hettinger, Lena; Zehe, Albin; Dallmann, Alexander; Hotho, Andreas. In INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft, K. David, K. Geihs, M. Lange, G. Stumme (eds.), pp. 191–204. Gesellschaft für Informatik e.V., Bonn, 2019
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  @inproceedings{mci/Hettinger2019, address = {Bonn}, author = {Hettinger, Lena and Zehe, Albin and Dallmann, Alexander and Hotho, Andreas}, booktitle = {INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft}, editor = {David, Klaus and Geihs, Kurt and Lange, Martin and Stumme, Gerd}, keywords = {classification}, pages = {191-204}, publisher = {Gesellschaft für Informatik e.V.}, title = {EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification}, year = 2019 }

  %0 Conference Paper %1 mci/Hettinger2019 %A Hettinger, Lena %A Zehe, Albin %A Dallmann, Alexander %A Hotho, Andreas %B INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für Gesellschaft %C Bonn %D 2019 %E David, Klaus %E Geihs, Kurt %E Lange, Martin %E Stumme, Gerd %I Gesellschaft für Informatik e.V. %P 191-204 %T EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification
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  Classification of text-types in german novels. Schl{\"o}r, D; Sch{\"o}ch, C; Hotho, A. In Digital Humanities 2019: Conference Abstracts. 2019
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  @inproceedings{schlor2019classification, author = {Schl{\"o}r, D and Sch{\"o}ch, C and Hotho, A}, booktitle = {Digital Humanities 2019: Conference Abstracts}, keywords = {from:daschloer}, title = {Classification of text-types in german novels}, year = 2019 }

  %0 Conference Paper %1 schlor2019classification %A Schl{\"o}r, D %A Sch{\"o}ch, C %A Hotho, A %B Digital Humanities 2019: Conference Abstracts %D 2019 %R https://doi.org/10.34894/OMLKRN %T Classification of text-types in german novels
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  Detection of Scenes in Fiction. Gius, Evelyn; Jannidis, Fotis; Krug, Markus; Zehe, Albin; Hotho, Andreas; Puppe, Frank; Krebs, Jonathan; Reiter, Nils; Wiedmer, Nathalie; Konle, Leonard. In Proceedings of Digital Humanities 2019. 2019
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  @inproceedings{gius2019detection, author = {Gius, Evelyn and Jannidis, Fotis and Krug, Markus and Zehe, Albin and Hotho, Andreas and Puppe, Frank and Krebs, Jonathan and Reiter, Nils and Wiedmer, Nathalie and Konle, Leonard}, booktitle = {Proceedings of Digital Humanities 2019}, keywords = {author:zehe}, title = {Detection of Scenes in Fiction}, year = 2019 }

  %0 Conference Paper %1 gius2019detection %A Gius, Evelyn %A Jannidis, Fotis %A Krug, Markus %A Zehe, Albin %A Hotho, Andreas %A Puppe, Frank %A Krebs, Jonathan %A Reiter, Nils %A Wiedmer, Nathalie %A Konle, Leonard %B Proceedings of Digital Humanities 2019 %D 2019 %T Detection of Scenes in Fiction
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  Flow-based network traffic generation using Generative Adversarial Networks. Ring, Markus; Schlör, Daniel; Landes, Dieter; Hotho, Andreas. In Computers & Security, Vol. 82, pp. 156–172. 2019
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  Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major challenge lies in the fact that GANs can only process continuous attributes. However, flow-based data inevitably contain categorical attributes such as IP addresses or port numbers. Therefore, we propose three different preprocessing approaches for flow-based data in order to transform them into continuous values. Further, we present a new method for evaluating the generated flow-based network traffic which uses domain knowledge to define quality tests. We use the three approaches for generating flow-based network traffic based on the CIDDS-001 data set. Experiments indicate that two of the three approaches are able to generate high quality data.

  @article{RING2019156, abstract = {Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major challenge lies in the fact that GANs can only process continuous attributes. However, flow-based data inevitably contain categorical attributes such as IP addresses or port numbers. Therefore, we propose three different preprocessing approaches for flow-based data in order to transform them into continuous values. Further, we present a new method for evaluating the generated flow-based network traffic which uses domain knowledge to define quality tests. We use the three approaches for generating flow-based network traffic based on the CIDDS-001 data set. Experiments indicate that two of the three approaches are able to generate high quality data.}, author = {Ring, Markus and Schlör, Daniel and Landes, Dieter and Hotho, Andreas}, journal = {Computers & Security}, keywords = {selected}, pages = {156 - 172}, title = {Flow-based network traffic generation using Generative Adversarial Networks}, volume = 82, year = 2019 }

  %0 Journal Article %1 RING2019156 %A Ring, Markus %A Schlör, Daniel %A Landes, Dieter %A Hotho, Andreas %D 2019 %J Computers & Security %P 156 - 172 %R https://doi.org/10.1016/j.cose.2018.12.012 %T Flow-based network traffic generation using Generative Adversarial Networks %U http://www.sciencedirect.com/science/article/pii/S0167404818308393 %V 82 %X Flow-based data sets are necessary for evaluating network-based intrusion detection systems (NIDS). In this work, we propose a novel methodology for generating realistic flow-based network traffic. Our approach is based on Generative Adversarial Networks (GANs) which achieve good results for image generation. A major challenge lies in the fact that GANs can only process continuous attributes. However, flow-based data inevitably contain categorical attributes such as IP addresses or port numbers. Therefore, we propose three different preprocessing approaches for flow-based data in order to transform them into continuous values. Further, we present a new method for evaluating the generated flow-based network traffic which uses domain knowledge to define quality tests. We use the three approaches for generating flow-based network traffic based on the CIDDS-001 data set. Experiments indicate that two of the three approaches are able to generate high quality data.
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  A survey of network-based intrusion detection data sets. Ring, Markus; Wunderlich, Sarah; Scheuring, Deniz; Landes, Dieter; Hotho, Andreas. In Comput. Secur., Vol. 86, pp. 147–167. 2019
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  @article{ring2019survey, author = {Ring, Markus and Wunderlich, Sarah and Scheuring, Deniz and Landes, Dieter and Hotho, Andreas}, journal = {Comput. Secur.}, keywords = {datasets}, pages = {147-167}, title = {A survey of network-based intrusion detection data sets}, volume = 86, year = 2019 }

  %0 Journal Article %1 ring2019survey %A Ring, Markus %A Wunderlich, Sarah %A Scheuring, Deniz %A Landes, Dieter %A Hotho, Andreas %D 2019 %J Comput. Secur. %P 147-167 %T A survey of network-based intrusion detection data sets %U http://dblp.uni-trier.de/db/journals/compsec/compsec86.html#RingWSLH19 %V 86
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  Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks. Zehe, Albin; Hettinger, Lena; Ernst, Stefan; Hauptmann, Christian; Hotho, Andreas. In Proceedings of The 13th International Workshop on Semantic Evaluation. Association for Computational Linguistics, 2019
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  @inproceedings{zehe2019xenophilius, author = {Zehe, Albin and Hettinger, Lena and Ernst, Stefan and Hauptmann, Christian and Hotho, Andreas}, booktitle = {Proceedings of The 13th International Workshop on Semantic Evaluation}, keywords = {neuralnets}, publisher = {Association for Computational Linguistics}, title = {Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks}, year = 2019 }

  %0 Conference Paper %1 zehe2019xenophilius %A Zehe, Albin %A Hettinger, Lena %A Ernst, Stefan %A Hauptmann, Christian %A Hotho, Andreas %B Proceedings of The 13th International Workshop on Semantic Evaluation %D 2019 %I Association for Computational Linguistics %T Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks
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  Comparison of System Call Representations for Intrusion Detection. Wunderlich, Sarah; Ring, Markus; Landes, Dieter; Hotho, Andreas. 2019
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  @article{wunderlich2019comparison, author = {Wunderlich, Sarah and Ring, Markus and Landes, Dieter and Hotho, Andreas}, keywords = {from:nosebrain}, title = {Comparison of System Call Representations for Intrusion Detection}, year = 2019 }

  %0 Journal Article %1 wunderlich2019comparison %A Wunderlich, Sarah %A Ring, Markus %A Landes, Dieter %A Hotho, Andreas %D 2019 %T Comparison of System Call Representations for Intrusion Detection
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  On the Right Track! Analysing and Predicting Navigation Success in Wikipedia. Koopmann, Tobias; Dallmann, Alexander; Hettinger, Lena; Niebler, Thomas; Hotho, Andreas. In Proceedings of the 30th ACM Conference on Hypertext and Social Media, of HT ’19, pp. 143–152. ACM, Hof, Germany, 2019
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  @inproceedings{Koopmann:2019:RTA:3342220.3343650, address = {New York, NY, USA}, author = {Koopmann, Tobias and Dallmann, Alexander and Hettinger, Lena and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of the 30th ACM Conference on Hypertext and Social Media}, keywords = {author:hotho}, pages = {143–152}, publisher = {ACM}, series = {HT '19}, title = {On the Right Track! Analysing and Predicting Navigation Success in Wikipedia}, year = 2019 }

  %0 Conference Paper %1 Koopmann:2019:RTA:3342220.3343650 %A Koopmann, Tobias %A Dallmann, Alexander %A Hettinger, Lena %A Niebler, Thomas %A Hotho, Andreas %B Proceedings of the 30th ACM Conference on Hypertext and Social Media %C New York, NY, USA %D 2019 %I ACM %P 143--152 %R 10.1145/3342220.3343650 %T On the Right Track! Analysing and Predicting Navigation Success in Wikipedia %U http://doi.acm.org/10.1145/3342220.3343650 %@ 978-1-4503-6885-8

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  ClaiRE at SemEval-2018 Task 7 - Extended Version. Hettinger, Lena; Dallmann, Alexander; Zehe, Albin; Niebler, Thomas; Hotho, Andreas. 2018
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  In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.

  @misc{hettinger2018claire, abstract = {In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, keywords = {extraction}, note = {cite arxiv:1804.05825Comment: This is the extended version for our work: ClaiRE at SemEval-2018 Task 7}, title = {ClaiRE at SemEval-2018 Task 7 - Extended Version}, year = 2018 }

  %0 Generic %1 hettinger2018claire %A Hettinger, Lena %A Dallmann, Alexander %A Zehe, Albin %A Niebler, Thomas %A Hotho, Andreas %D 2018 %T ClaiRE at SemEval-2018 Task 7 - Extended Version %U http://arxiv.org/abs/1804.05825 %X In this paper we describe our post-evaluation results for SemEval-2018 Task 7 on classification of semantic relations in scientific literature for clean (subtask 1.1) and noisy data (subtask 1.2). Due to space limitations we publish an extended version of Hettinger et al. (2018) including further technical details and changes made to the preprocessing step in the post-evaluation phase. Due to these changes Classification of Relations using Embeddings (ClaiRE) achieved an improved F1 score of 75.11% for the first subtask and 81.44% for the second.