Publications by Andreas Hotho

2021 [ nach oben ]

The Semantic Web - ISWC 2021 - 20th International Semantic Web Conference, ISWC 2021, Virtual Event, October 24-28, 2021, Proceedings A. Hotho; E. Blomqvist; S. Dietze; A. Fokoue; Y. Ding; P. M. Barnaghi; A. Haller; M. Dragoni; H. Alani in Lecture Notes in Computer Science (2021). (Vol. 12922) Springer.
[ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
@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 = {2021 conference myown semantic web}, 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 }
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HerausgeberHotho, 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
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/d8f94f524d2d0933ab091f4f1d2843a3/hotho
URNhttp://dx.doi.org/10.1007/978-3-030-88361-4
Tags2021 conference myown semantic web

Do Different Deep Metric Learning Losses Lead to Similar Learned Features? K. Kobs; M. Steininger; A. Dulny; A. Hotho in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021). 10644–10654.
[ BibTeX ] [ Details ]
 
@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 = {2021 deepmetriclearning from:konstantinkobs myown}, month = {October}, pages = {10644-10654}, title = {Do Different Deep Metric Learning Losses Lead to Similar Learned Features?}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/06fbf3d9153e21f62256c398c8df69e0/hotho
Tags2021 deepmetriclearning from:konstantinkobs myown

Shared Task on Scene Segmentation @ KONVENS 2021 A. Zehe; L. Konle; S. Guhr; L. Dümpelmann; E. Gius; A. Hotho; F. Jannidis; L. Kaufmann; M. Krug; F. Puppe; N. Reiter; A. Schreiber (2021). 1–21.
[ BibTeX ] [ URL ] [ Details ]
 
@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 = {2021 cls dh from:albinzehe myown nlp scenedetection scenes stss}, pages = {1-21}, title = {Shared Task on Scene Segmentation @ KONVENS 2021}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/abe20fee6aa38fbb47474b4f09146cbe/hotho
Tags2021 cls dh from:albinzehe myown nlp scenedetection scenes stss

Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research T. Koopmann; M. Stubbemann; M. Kapa; M. Paris; G. Buenstorf; T. Hanika; A. Hotho; R. Jäschke; G. Stumme in Scientometrics (2021).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
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 = {2021 from:tobias.koopmann myown regio}, title = {Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/403544c0381c5a42e340c6f288bee105/hotho
URNhttp://dx.doi.org/10.1007/s11192-021-03922-1
Tags2021 from:tobias.koopmann myown regio
AbstractCreation 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.

NeuralPDE: Modelling Dynamical Systems from Data A. Dulny; A. Hotho; A. Krause (2021).
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
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 = {2021 learning model myown network neural pde}, note = {cite arxiv:2111.07671}, title = {NeuralPDE: Modelling Dynamical Systems from Data}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/6d3e77780c5a9420bbf16cb5188343bf/hotho
Tags2021 learning model myown network neural pde
AbstractMany 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.

CoBERT: Scientific Collaboration Prediction via Sequential Recommendation T. Koopmann; K. Kobs; K. Herud; A. Hotho (2021). 45–54.
[ Abstract ] [ BibTeX ] [ DOI ] [ Download ] [ Details ]
 
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 = {2021 collaboration myown recommender}, month = {dec}, pages = {45-54}, title = {CoBERT: Scientific Collaboration Prediction via Sequential Recommendation}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/44eedf4850a325b6c8b81c7196c688a1/hotho
URNhttp://dx.doi.org/10.1109/ICDMW53433.2021.00013
Tags2021 collaboration myown recommender
AbstractCollaborations 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.

Sequential Item Recommendation in the MOBA Game Dota 2 A. Dallmann; J. Kohlmann; D. Zoller; A. Hotho (2021). 10–17.
[ Abstract ] [ BibTeX ] [ DOI ] [ Details ]
 
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 = {2021 Dota game myown recommender}, month = {dec}, pages = {10-17}, title = {Sequential Item Recommendation in the MOBA Game Dota 2}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/fa0b20a0705dcf29df462448bae51282/hotho
URNhttp://dx.doi.org/10.1109/ICDMW53433.2021.00009
Tags2021 Dota game myown recommender
AbstractMultiplayer 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.

Evaluating the multi-task learning approach for land use regression modelling of air pollution A. Dulny; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho in Journal of Physics: Conference Series (2021). 1834(1) 012004.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
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 = {2021 from:msteininger lur myown}, month = {mar}, 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 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/822737650040439929fdd9b863128e39/hotho
URNhttp://dx.doi.org/10.1088/1742-6596/1834/1/012004
Tags2021 from:msteininger lur myown
AbstractAir 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.

Detecting Presence Of Speech In Acoustic Data Obtained From Beehives P. Janetzky; P. Davidson; M. Steininger; A. Krause; A. Hotho F. Font, A. Mesaros, D. P. W. Ellis, E. Fonseca, M. Fuentes, B. Elizalde (Eds.) (2021). 26–30.
[ BibTeX ] [ URL ] [ Details ]
 
@inproceedings{janetzky2021detecting, author = {Janetzky, Pascal and Davidson, Padraig and Steininger, Michael and Krause, Anna and Hotho, Andreas}, booktitle = {Proceedings of the 6th Workshop on Detection and Classification of Acoustic Scenes and Events 2021 {(DCASE} 2021), Online, November 15-19, 2021}, editor = {Font, Frederic and Mesaros, Annamaria and Ellis, Daniel P. W. and Fonseca, Eduardo and Fuentes, Magdalena and Elizalde, Benjamin}, keywords = {2021 audio bee myown we4bee}, pages = {26–30}, title = {Detecting Presence Of Speech In Acoustic Data Obtained From Beehives}, year = 2021 }
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HerausgeberFont, Frederic and Mesaros, Annamaria and Ellis, Daniel P. W. and Fonseca, Eduardo and Fuentes, Magdalena and Elizalde, Benjamin
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/1999c8e7bf7303887445e0d88d8eeb8a/hotho
Tags2021 audio bee myown we4bee

Semi-unsupervised Learning: An In-depth Parameter Analysis P. Davidson; F. Buckermann; M. Steininger; A. Krause; A. Hotho S. Edelkamp, R. Möller, E. Rueckert (Eds.) (2021). 51–66.
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
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{ö}ller, Ralf and Rueckert, Elmar}, keywords = {2021 bidt gm-dgm myown susl unsupervised}, pages = {51–66}, publisher = {Springer International Publishing}, title = {Semi-unsupervised Learning: An In-depth Parameter Analysis}, year = 2021 }
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HerausgeberEdelkamp, Stefan and Möller, Ralf and Rueckert, Elmar
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/e7caa5c1bcdfa25681085ab977388c97/hotho
Tags2021 bidt gm-dgm myown susl unsupervised
AbstractCreating 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.

Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment J. Pfister; K. Kobs; A. Hotho (2021). 816–825.
[ BibTeX ] [ Details ]
 
@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 = {2021 from:janpfister from:konstantinkobs myown}, month = {June}, pages = {816-825}, title = {Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/f2e180c930a93bbd42e3c67cfcfef893/hotho
Tags2021 from:janpfister from:konstantinkobs myown

Semi-Supervised Learning for Grain Size Distribution Interpolation K. Kobs; C. Schäfer; M. Steininger; A. Krause; R. Baumhauer; H. Paeth; A. Hotho (2021). 34–44.
[ BibTeX ] [ Details ]
 
@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 = {2021 from:konstantinkobs myown}, organization = {Springer International Publishing}, pages = {34–44}, title = {Semi-Supervised Learning for Grain Size Distribution Interpolation}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/f7f0922048df23a65a6abe7629d40b05/hotho
Tags2021 from:konstantinkobs myown

Assessing Media Bias in Cross-Linguistic and Cross-National Populations A. Sales; A. Zehe; L. B. Marinho; A. Veloso; A. Hotho; J. Omeliyanenko in Proceedings of the International AAAI Conference on Web and Social Media (2021). 15(1) 561–572.
[ BibTeX ] [ URL ] [ Details ]
 
@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 = {2021 bias from:albinzehe icwsm media myown sentiment}, month = {may}, number = 1, pages = {561-572}, title = {Assessing Media Bias in Cross-Linguistic and Cross-National Populations}, volume = 15, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/66787c3983ae3127e185c04665626f17/hotho
Tags2021 bias from:albinzehe icwsm media myown sentiment

A financial game with opportunities for fraud J. Tritscher; A. Krause; D. Schlör; F. Gwinner; S. von Mammen; A. Hotho in IEE COG 2021 (2021). 2021
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
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{ö}r, Daniel and Gwinner, Fabian and von Mammen, Sebastian and Hotho, Andreas}, journal = {IEE COG 2021}, keywords = {2021 erp fraud from:tritsch game game_design myown}, title = {A financial game with opportunities for fraud}, volume = 2021, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/d159dec3a09f85f34d435ab19be387e9/hotho
Tags2021 erp fraud from:tritsch game game_design myown
AbstractEven 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 M. Steininger; K. Kobs; P. Davidson; A. Krause; A. Hotho in Machine Learning, (A. Appice; S. Escalera; J. A. Gamez; H. Trautmann, Eds.) (2021).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
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 = {2021 data density imbalanced myown regression}, title = {Density-based weighting for imbalanced regression}, year = 2021 }
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HerausgeberAppice, Annalisa and Escalera, Sergio and Gamez, Jose A. and Trautmann, Heike
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/a465795f9b3ed3940e83f332d7deb2bd/hotho
URNhttp://dx.doi.org/10.1007/s10994-021-06023-5
Tags2021 data density imbalanced myown regression
AbstractIn 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.

Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge E. Fischer; D. Zoller; A. Hotho in SIGIR Workshop On eCommerce (2021).
[ BibTeX ] [ Download ] [ Details ]
 
@article{fischer2021comparison, author = {Fischer, Elisabeth and Zoller, Daniel and Hotho, Andreas}, journal = {SIGIR Workshop On eCommerce}, keywords = {2021 myown product recommendation transformer}, month = {July}, title = {Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/ae2441d232b9f0a65bf5d4aa6497b107/hotho
Tags2021 myown product recommendation transformer

A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models A. Dallmann; D. Zoller; A. Hotho (2021).
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@inproceedings{dallmann2021study, author = {Dallmann, Alexander and Zoller, Daniel and Hotho, Andreas}, booktitle = {Fifteenth {ACM} Conference on Recommender Systems}, keywords = {2021 deep evaluation learning myown recommender sampling systems}, month = {sep}, publisher = {{ACM}}, title = {A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/23f589b27e22018936753bb64b33971d/hotho
URNhttp://dx.doi.org/10.1145/3460231.3475943
Tags2021 deep evaluation learning myown recommender sampling systems

Detecting Scenes in Fiction: A new Segmentation Task A. Zehe; L. Konle; L. Dümpelmann; E. Gius; A. Hotho; F. Jannidis; L. Kaufmann; M. Krug; F. Puppe; N. Reiter; A. Schreiber; N. Wiedmer (2021).
[ BibTeX ] [ Download ] [ Details ]
 
@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 = {2021 dataset eacl kallimachos myown scenedetection scenes selected task}, publisher = {ACL}, title = {Detecting Scenes in Fiction: A new Segmentation Task}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/98beaec5a9a324f1f7b7677cf8c74fa9/hotho
Tags2021 dataset eacl kallimachos myown scenedetection scenes selected task

Malware detection on windows audit logs using LSTMs M. Ring; D. Schlör; S. Wunderlich; D. Landes; A. Hotho in Computers & Security (2021). 109 102389.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
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 = {2021 Embeddings LSTM Malware Windows audit logs myown}, pages = 102389, title = {Malware detection on windows audit logs using LSTMs}, volume = 109, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/3ddb12d38e6773b7a382a56780a39e37/hotho
URNhttp://dx.doi.org/https://doi.org/10.1016/j.cose.2021.102389
Tags2021 Embeddings LSTM Malware Windows audit logs myown
AbstractMalware 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.

Anomaly Detection in Beehives: An Algorithm Comparison P. Davidson; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho (2021).
[ BibTeX ] [ Details ]
 
@misc{davidson2021anomaly, author = {Davidson, Padraig and Steininger, Michael and Lautenschlager, Florian and Krause, Anna and Hotho, Andreas}, keywords = {2021 anomaly-detection from:pdavidson honeybees myown we4bee}, title = {Anomaly Detection in Beehives: An Algorithm Comparison}, year = 2021 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/eacd71cd403fab961779674167fcacdb/hotho
Tags2021 anomaly-detection from:pdavidson honeybees myown we4bee

2020 [ nach oben ]

Financial Fraud Detection with Improved Neural Arithmetic Logic Units D. Schlör; M. Ring; A. Krause; A. Hotho (2020). (Vol. Fifth Workshop on MIning DAta for financial applicationS)
[ BibTeX ] [ Details ]
 
@proceedings{schlor2020financial, author = {Schlör, Daniel and Ring, Markus and Krause, Anna and Hotho, Andreas}, keywords = {2020 detection finacial fraud inalu myown}, number = 5, title = {Financial Fraud Detection with Improved Neural Arithmetic Logic Units}, volume = {Fifth Workshop on MIning DAta for financial applicationS}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/400ec7d0f0c48073599917d560280df9/hotho
Tags2020 detection finacial fraud inalu myown

OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning F. Lautenschlager; M. Becker; K. Kobs; M. Steininger; P. Davidson; A. Krause; A. Hotho in Atmospheric Environment (2020). 233 117535.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
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 = {2020 lur myown}, pages = 117535, title = {OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning}, volume = 233, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/990070887f2a7ce3048481cb3fccdd8e/hotho
URNhttp://dx.doi.org/https://doi.org/10.1016/j.atmosenv.2020.117535
Tags2020 lur myown
AbstractTo 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.

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part II U. Brefeld; Élisa Fromont; A. Hotho; A. J. Knobbe; M. H. Maathuis; C. Robardet in Lecture Notes in Computer Science (2020). (Vol. 11907) Springer.
[ BibTeX ] [ URL ] [ Details ]
 
@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 = {2020 ECMLPKDD myown proceedings}, 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 }
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HerausgeberBrefeld, Ulf and Fromont, Élisa and Hotho, Andreas and Knobbe, Arno J. and Maathuis, Marloes H. and Robardet, Céline
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/6b95c3812134a426ca2c821753e3ebe8/hotho
Tags2020 ECMLPKDD myown proceedings

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I U. Brefeld; Élisa Fromont; A. Hotho; A. J. Knobbe; M. H. Maathuis; C. Robardet in Lecture Notes in Computer Science (2020). (Vol. 11906) Springer.
[ BibTeX ] [ URL ] [ Details ]
 
@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 = {2020 ECMLPKDD myown proceedings}, 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 }
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HerausgeberBrefeld, Ulf and Fromont, Élisa and Hotho, Andreas and Knobbe, Arno J. and Maathuis, Marloes H. and Robardet, Céline
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/cef4e33fa58a1adf19a5e874a30fed65/hotho
Tags2020 ECMLPKDD myown proceedings

SimLoss: Class Similarities in Cross Entropy K. Kobs; M. Steininger; A. Zehe; F. Lautenschlager; A. Hotho D. Helic, G. Leitner, M. Stettinger, A. Felfernig, Z. W. Ra’s (Eds.) (2020). 431–439.
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
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 are available at https://github.com/konstantinkobs/SimLoss
@inproceedings{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 are available at https://github.com/konstantinkobs/SimLoss}, address = {Cham}, author = {Kobs, Konstantin and Steininger, Michael and Zehe, Albin and Lautenschlager, Florian 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 = {2020 cross entropy function loss myown}, pages = {431–439}, publisher = {Springer International Publishing}, title = {SimLoss: Class Similarities in Cross Entropy}, year = 2020 }
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HerausgeberHelic, Denis and Leitner, Gerhard and Stettinger, Martin and Felfernig, Alexander and Ra's, Zbigniew W.
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/eb6e8ed0d018b2baae1df7f216cdcacb/hotho
Tags2020 cross entropy function loss myown
AbstractOne 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 are available at https://github.com/konstantinkobs/SimLoss

LM4KG: Improving Common Sense Knowledge Graphs with Language Models J. Omeliyanenko; A. Zehe; L. Hettinger; A. Hotho J. Z. Pan, V. Tamma, C. d’Amato, K. Janowicz, B. Fu, A. Polleres, O. Seneviratne, L. Kagal (Eds.) (2020). 456–473.
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
Language Models (LMs) and Knowledge Graphs (KGs) are both active research areas in Machine Learning and Semantic Web. While LMs have brought great improvements for many downstream tasks on their own, they are often combined with KGs providing additionally aggregated, well structured knowledge. Usually, this is done by leveraging KGs to improve LMs. But what happens if we turn this around and use LMs to improve KGs?
@inproceedings{omeliyanenko2020lm4kg, abstract = {Language Models (LMs) and Knowledge Graphs (KGs) are both active research areas in Machine Learning and Semantic Web. While LMs have brought great improvements for many downstream tasks on their own, they are often combined with KGs providing additionally aggregated, well structured knowledge. Usually, this is done by leveraging KGs to improve LMs. But what happens if we turn this around and use LMs to improve KGs?}, address = {Cham}, author = {Omeliyanenko, Janna and Zehe, Albin and Hettinger, Lena and Hotho, Andreas}, booktitle = {The Semantic Web – ISWC 2020}, editor = {Pan, Jeff Z. and Tamma, Valentina and d'Amato, Claudia and Janowicz, Krzysztof and Fu, Bo and Polleres, Axel and Seneviratne, Oshani and Kagal, Lalana}, keywords = {2020 graph kg knowledge language lm model myown nlp selected semantic}, pages = {456–473}, publisher = {Springer International Publishing}, title = {LM4KG: Improving Common Sense Knowledge Graphs with Language Models}, year = 2020 }
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HerausgeberPan, Jeff Z. and Tamma, Valentina and d'Amato, Claudia and Janowicz, Krzysztof and Fu, Bo and Polleres, Axel and Seneviratne, Oshani and Kagal, Lalana
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/f24522aac1d51bace861cd03d4fb7cf9/hotho
Tags2020 graph kg knowledge language lm model myown nlp selected semantic
AbstractLanguage Models (LMs) and Knowledge Graphs (KGs) are both active research areas in Machine Learning and Semantic Web. While LMs have brought great improvements for many downstream tasks on their own, they are often combined with KGs providing additionally aggregated, well structured knowledge. Usually, this is done by leveraging KGs to improve LMs. But what happens if we turn this around and use LMs to improve KGs?

Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data J. Tritscher; M. Ring; D. Schlr; L. Hettinger; A. Hotho D. Helic, G. Leitner, M. Stettinger, A. Felfernig, Z. W. Ra’s (Eds.) (2020). 422–430.
[ Abstract ] [ BibTeX ] [ Details ]
 
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 = {2020 data datasets evaluation myown tabular xai}, pages = {422–430}, publisher = {Springer International Publishing}, title = {Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data}, year = 2020 }
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HerausgeberHelic, Denis and Leitner, Gerhard and Stettinger, Martin and Felfernig, Alexander and Ra's, Zbigniew W.
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/8b5575da838904117581004f7301f6cb/hotho
Tags2020 data datasets evaluation myown tabular xai
AbstractEvaluating 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.

Deep Learning for Climate Model Output Statistics M. Steininger; D. Abel; K. Ziegler; A. Krause; H. Paeth; A. Hotho in Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020 (2020).
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@article{steininger2020learning, 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 = {2020 climate mos myown}, title = {Deep Learning for Climate Model Output Statistics}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/95f66a3a4be6383baac626383743e2d5/hotho
Tags2020 climate mos myown

NICER — Aesthetic Image Enhancement with Humans in the Loop M. Fischer; K. Kobs; A. Hotho (2020). 357–362.
[ Abstract ] [ BibTeX ] [ Details ]
 
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 = {2020 image-assessment image-editing myown}, pages = {357-362}, title = {NICER — Aesthetic Image Enhancement with Humans in the Loop}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/6e91b22093be37057ead317550ee7589/hotho
Tags2020 image-assessment image-editing myown
AbstractFully- 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.

Towards Predicting the Subscription Status of Twitch.tv Users K. Kobs; M. Potthast; M. Wiegmann; A. Zehe; B. Stein; A. Hotho in Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch (2020).
[ BibTeX ] [ URL ] [ Details ]
 
@article{kobs2020towards, 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 = {2020 myown twitch}, title = {Towards Predicting the Subscription Status of Twitch.tv Users}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/d75a93bc99e9b62711f93daf8ec2f145/hotho
Tags2020 myown twitch

Time Series Forecasting for Self-Aware Systems A. Bauer; M. Züfle; N. Herbst; A. Zehe; A. Hotho; S. Kounev in Proceedings of the IEEE (2020). 1–26.
[ Abstract ] [ BibTeX ] [ DOI ] [ Details ]
 
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{bauer2020series, 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 = {2020 Forecasting myown survey timeseries}, pages = {1-26}, title = {Time Series Forecasting for Self-Aware Systems}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/3fd2fa2bf71d61daaad13a88f344f71c/hotho
URNhttp://dx.doi.org/10.1109/JPROC.2020.2983857
Tags2020 Forecasting myown survey timeseries
AbstractModern 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.

Anomaly Detection in Beehives using Deep Recurrent Autoencoders P. Davidson; M. Steininger; F. Lautenschlager; K. Kobs; A. Krause; A. Hotho (2020). 142–149.
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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 = {2020 anomaly autoencoder beehive detection myown}, number = 9, pages = {142-149}, publisher = {SCITEPRESS – Science and Technology Publications, Lda.}, title = {Anomaly Detection in Beehives using Deep Recurrent Autoencoders}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/553b83221816afd3bd24c508cc9201b2/hotho
Tags2020 anomaly autoencoder beehive detection myown

MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images M. Steininger; K. Kobs; A. Zehe; F. Lautenschlager; M. Becker; A. Hotho in ACM Trans. Spatial Algorithms Syst. (2020). 6(3)
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
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{steininger2020maplur, 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 = {2020 Land-use air deep learning myown pollution regression}, month = {apr}, 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 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/08e6c374ef4f09a05b415748c73d1dd6/hotho
URNhttp://dx.doi.org/10.1145/3380973
Tags2020 Land-use air deep learning myown pollution regression
AbstractLand-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.

Evaluating the multi-task learning approach for land use regression modelling of air pollution A. Dulny; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho (2020).
[ Abstract ] [ BibTeX ] [ Details ]
 
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 = {2020 from:msteininger lur myown}, organization = {IASED}, title = {Evaluating the multi-task learning approach for land use regression modelling of air pollution}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/332a2066e5bd910591b9b1fe4dea0dd3/hotho
Tags2020 from:msteininger lur myown
AbstractAir 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.

HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis A. Zehe; J. Arns; L. Hettinger; A. Hotho (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 = {2020 bert harrymotions harrypotter myown myown:selected nlp proposal-knowledge sentiment sentimentanalysis}, title = {HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/dda4b563d8e9aa69749e08f478f84b38/hotho
Tags2020 bert harrymotions harrypotter myown myown:selected nlp proposal-knowledge sentiment sentimentanalysis

Improving Sentiment Analysis with Biofeedback Data D. Schlör; A. Zehe; K. Kobs; B. Veseli; F. Westermeier; L. Brübach; D. Roth; M. E. Latoschik; A. Hotho (2020). 28–33.
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
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.}, address = {Marseille, France}, author = {Schl{ö}r, Daniel and Zehe, Albin and Kobs, Konstantin and Veseli, Blerta and Westermeier, Franziska and Br{ü}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 = {2020 biofeedback from:albinzehe mutlimodal myown neuralnet sentiment sentimentanalysis}, month = {may}, pages = {28–33}, publisher = {European Language Resources Association (ELRA)}, title = {Improving Sentiment Analysis with Biofeedback Data}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/ad9bb4db2dc7afc8a533fd837a472e02/hotho
Tags2020 biofeedback from:albinzehe mutlimodal myown neuralnet sentiment sentimentanalysis
AbstractHumans 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.

Where to Submit? Helping Researchers to Choose the Right Venue K. Kobs; T. Koopmann; A. Zehe; D. Fernes; P. Krop; A. Hotho (2020). 878–883.
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
Whenever researchers write a paper, the same question occurs: ``Where to submit?'' In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83% for AI papers and 95% in the field of medicine. It is open source and available for testing on https://wheretosubmit.ml.
@inproceedings{kobs2020where, abstract = {Whenever researchers write a paper, the same question occurs: {``}Where to submit?{''} In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83{\%} for AI papers and 95{\%} in the field of medicine. It is open source and available for testing on https://wheretosubmit.ml.}, address = {Online}, author = {Kobs, Konstantin and Koopmann, Tobias and Zehe, Albin and Fernes, David and Krop, Philipp and Hotho, Andreas}, booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020}, keywords = {2020 bert myown nlp paper research venue}, month = {nov}, pages = {878–883}, publisher = {Association for Computational Linguistics}, title = {Where to Submit? Helping Researchers to Choose the Right Venue}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/6fcdcb5345f4bb7b10fb479edb4fd608/hotho
Tags2020 bert myown nlp paper research venue
AbstractWhenever researchers write a paper, the same question occurs: ``Where to submit?'' In this work, we introduce WTS, an open and interpretable NLP system that recommends conferences and journals to researchers based on the title, abstract, and/or keywords of a given paper. We adapt the TextCNN architecture and automatically analyze its predictions using the Integrated Gradients method to highlight words and phrases that led to the recommendation of a scientific venue. We train and test our method on publications from the fields of artificial intelligence (AI) and medicine, both derived from the Semantic Scholar dataset. WTS achieves an Accuracy@5 of approximately 83% for AI papers and 95% in the field of medicine. It is open source and available for testing on https://wheretosubmit.ml.

Integrating Keywords into BERT4Rec for Sequential Recommendation E. Fischer; D. Zoller; A. Dallmann; A. Hotho (2020). (Vol. 12325) 275–282.
[ Abstract ] [ BibTeX ] [ DOI ] [ Download ] [ Details ]
 
A crucial part of recommender systems is to model the user’s preference based on her previous interactions. Different neural networks (e.g., Recurrent Neural Networks), that predict the next item solely based on the sequence of interactions have been successfully applied to sequential recommendation. Recently, BERT4Rec has been proposed, which adapts the BERT architecture based on the Transformer model and training methods used in the Neural Language Modeling community to this task. However, BERT4Rec still only relies on item identifiers to model the user preference, ignoring other sources of information. Therefore, as a first step to include additional information, we propose KeBERT4Rec, a modification of BERT4Rec, which utilizes keyword descriptions of items. We compare two variants for adding keywords to the model on two datasets, a Movielens dataset and a dataset of an online fashion store. First results show that both versions of our model improves the sequential recommending task compared to BERT4Rec.
@inproceedings{fischer2020integrating, abstract = {A crucial part of recommender systems is to model the user’s preference based on her previous interactions. Different neural networks (e.g., Recurrent Neural Networks), that predict the next item solely based on the sequence of interactions have been successfully applied to sequential recommendation. Recently, BERT4Rec has been proposed, which adapts the BERT architecture based on the Transformer model and training methods used in the Neural Language Modeling community to this task. However, BERT4Rec still only relies on item identifiers to model the user preference, ignoring other sources of information. Therefore, as a first step to include additional information, we propose KeBERT4Rec, a modification of BERT4Rec, which utilizes keyword descriptions of items. We compare two variants for adding keywords to the model on two datasets, a Movielens dataset and a dataset of an online fashion store. First results show that both versions of our model improves the sequential recommending task compared to BERT4Rec.}, address = {Cham}, author = {Fischer, Elisabeth and Zoller, Daniel and Dallmann, Alexander and Hotho, Andreas}, booktitle = {KI 2020: Advances in Artificial Intelligence}, keywords = {2020 bert4rec embedding kebert4rec keyword myown recommendation recommender sequential}, pages = {275-282}, publisher = {Springer International Publishing}, title = {Integrating Keywords into BERT4Rec for Sequential Recommendation}, volume = 12325, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/86535744583ac86f6e2c7c08d46a6956/hotho
URNhttp://dx.doi.org/https://doi.org/10.1007/978-3-030-58285-2_23
Tags2020 bert4rec embedding kebert4rec keyword myown recommendation recommender sequential
AbstractA crucial part of recommender systems is to model the user’s preference based on her previous interactions. Different neural networks (e.g., Recurrent Neural Networks), that predict the next item solely based on the sequence of interactions have been successfully applied to sequential recommendation. Recently, BERT4Rec has been proposed, which adapts the BERT architecture based on the Transformer model and training methods used in the Neural Language Modeling community to this task. However, BERT4Rec still only relies on item identifiers to model the user preference, ignoring other sources of information. Therefore, as a first step to include additional information, we propose KeBERT4Rec, a modification of BERT4Rec, which utilizes keyword descriptions of items. We compare two variants for adding keywords to the model on two datasets, a Movielens dataset and a dataset of an online fashion store. First results show that both versions of our model improves the sequential recommending task compared to BERT4Rec.

iNALU: Improved Neural Arithmetic Logic Unit D. Schlör; M. Ring; A. Hotho in Frontiers in Artificial Intelligence (2020). 3 71.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
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{schlor2020inalu, 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 = {2020 arithmetic inalu myown networks neural selected}, pages = 71, title = {iNALU: Improved Neural Arithmetic Logic Unit}, volume = 3, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/393b599f6071bb8d125c8faceee686fa/hotho
URNhttp://dx.doi.org/10.3389/frai.2020.00071
Tags2020 arithmetic inalu myown networks neural selected
AbstractNeural 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.

Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study P. Davidson; P. Düking; C. Zinner; B. Sperlich; A. Hotho in Sensors (2020).
[ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
@article{davidson2020smartwatchderived, author = {Davidson, Padraig and Düking, Peter and Zinner, Christoph and Sperlich, Billy and Hotho, Andreas}, journal = {Sensors}, keywords = {2020 analysis myown rpe smartwatch timeseries}, month = {may}, title = {Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/08716d98cefabc3520f4622d53e59835/hotho
URNhttp://dx.doi.org/10.3390/s20092637
Tags2020 analysis myown rpe smartwatch timeseries

Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels K. Kobs; A. Zehe; A. Bernstetter; J. Chibane; J. Pfister; J. Tritscher; A. Hotho in ACM Transactions on Social Computing (2020). 3(2) 1–34.
[ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
@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 = {2020 deep learning messages myown nlp selected sentiment short twitch}, month = {may}, 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 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/82d2913fbdb827e887988d9b8b8e5c45/hotho
URNhttp://dx.doi.org/10.1145/3365523
Tags2020 deep learning messages myown nlp selected sentiment short twitch

Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part III U. Brefeld; Élisa Fromont; A. Hotho; A. J. Knobbe; M. H. Maathuis; C. Robardet in Lecture Notes in Computer Science (2020). (Vol. 11908) Springer.
[ BibTeX ] [ URL ] [ Details ]
 
@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 = {2020 ECMLPKDD myown proceedings}, 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 }
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HerausgeberBrefeld, Ulf and Fromont, Élisa and Hotho, Andreas and Knobbe, Arno J. and Maathuis, Marloes H. and Robardet, Céline
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/100b285a318e147dafdf963693a5d1cc/hotho
Tags2020 ECMLPKDD myown proceedings

2019 [ nach oben ]

EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification L. Hettinger; A. Zehe; A. Dallmann; A. Hotho K. David, K. Geihs, M. Lange, G. Stumme (Eds.) (2019). 191–204.
[ BibTeX ] [ Download ] [ Details ]
 
@inproceedings{hettinger2019eclaire, 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 = {2019 classification embeddings myown relation semeval10 semeval18 word word_embedding}, pages = {191-204}, publisher = {Gesellschaft für Informatik e.V.}, title = {EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification}, year = 2019 }
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HerausgeberDavid, Klaus and Geihs, Kurt and Lange, Martin and Stumme, Gerd
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/38ab3b669db2139a90729eb40b9ea99a/hotho
Tags2019 classification embeddings myown relation semeval10 semeval18 word word_embedding

Flow-based network traffic generation using Generative Adversarial Networks M. Ring; D. Schlör; D. Landes; A. Hotho in Computers & Security (2019). 82 156–172.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
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 = {2019 GAN flow generation myown network security traffic}, pages = {156 - 172}, title = {Flow-based network traffic generation using Generative Adversarial Networks}, volume = 82, year = 2019 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/931a3b3d44733504dfd0b979d1047648/hotho
URNhttp://dx.doi.org/https://doi.org/10.1016/j.cose.2018.12.012
Tags2019 GAN flow generation myown network security traffic
AbstractFlow-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.

On the Right Track! Analysing and Predicting Navigation Success in Wikipedia T. Koopmann; A. Dallmann; L. Hettinger; T. Niebler; A. Hotho in HT ’19, C. Atzenbeck, J. Rubart, D. E. Millard (Eds.) (2019). 143–152.
[ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
@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}, editor = {Atzenbeck, Claus and Rubart, Jessica and Millard, David E.}, keywords = {2019 behaviour modelling myown navigation path prediction success user web wikipedia}, pages = {143–152}, publisher = {ACM}, series = {HT '19}, title = {On the Right Track! Analysing and Predicting Navigation Success in Wikipedia}, year = 2019 }
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HerausgeberAtzenbeck, Claus and Rubart, Jessica and Millard, David E.
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/48296ebc3f4b528db9df9fd17ccb2944/hotho
URNhttp://dx.doi.org/10.1145/3342220.3343650
Tags2019 behaviour modelling myown navigation path prediction success user web wikipedia

Comparison of System Call Representations for Intrusion Detection. S. Wunderlich; M. Ring; D. Landes; A. Hotho in Advances in Intelligent Systems and Computing, F. Martínez-Álvarez, A. T. Lora, J. A. S. Muñoz, H. Quintián, E. Corchado (Eds.) (2019). (Vol. 951) 14–24.
[ BibTeX ] [ URL ] [ Details ]
 
@inproceedings{wunderlich2019comparison, author = {Wunderlich, Sarah and Ring, Markus and Landes, Dieter and Hotho, Andreas}, booktitle = {CISIS-ICEUTE}, crossref = {conf/cisis-spain/2019}, editor = {Martínez-Álvarez, Francisco and Lora, Alicia Troncoso and Muñoz, José António Sáez and Quintián, Héctor and Corchado, Emilio}, keywords = {2019 call ids imported ml myown system}, pages = {14-24}, publisher = {Springer}, series = {Advances in Intelligent Systems and Computing}, title = {Comparison of System Call Representations for Intrusion Detection.}, volume = 951, year = 2019 }
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HerausgeberMartínez-Álvarez, Francisco and Lora, Alicia Troncoso and Muñoz, José António Sáez and Quintián, Héctor and Corchado, Emilio
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/54b001693b8c11168d0e78e8080a5fd3/hotho
Tags2019 call ids imported ml myown system

Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks A. Zehe; L. Hettinger; S. Ernst; C. Hauptmann; A. Hotho (2019).
[ BibTeX ] [ Download ] [ Details ]
 
@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 = {2019 cnn hyperpartisan myown news nlp nn semeval}, publisher = {Association for Computational Linguistics}, title = {Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks}, year = 2019 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/56cc75c1dc2f220cd4b29198637ae2bc/hotho
Tags2019 cnn hyperpartisan myown news nlp nn semeval

Solving Mathematical Exercises: Prediction of Students’ Success S. Wankerl; G. Götz; A. Hotho in CEUR Workshop Proceedings, R. Jäschke, M. Weidlich (Eds.) (2019). (Vol. 2454) 190–194.
[ BibTeX ] [ URL ] [ Download ] [ Details ]
 
@inproceedings{wankerl2019solving, author = {Wankerl, Sebastian and Götz, Gerhard and Hotho, Andreas}, booktitle = {LWDA}, editor = {Jäschke, Robert and Weidlich, Matthias}, keywords = {2019 imported myown}, pages = {190-194}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Solving Mathematical Exercises: Prediction of Students’ Success}, volume = 2454, year = 2019 }
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HerausgeberJäschke, Robert and Weidlich, Matthias
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/4b52c7a0a1d065191bd8f870859e081f/hotho
Tags2019 imported myown

A Survey of Network-based Intrusion Detection Data Sets M. Ring; S. Wunderlich; D. Scheuring; D. Landes; A. Hotho in Computers & Security (2019). 86 147–167.
[ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
@article{ring2019survey, author = {Ring, Markus and Wunderlich, Sarah and Scheuring, Deniz and Landes, Dieter and Hotho, Andreas}, journal = {Computers & Security}, keywords = {2019 datasets ids imported myown}, pages = {147-167}, publisher = {Elsevier Advanced Technology}, title = {A Survey of Network-based Intrusion Detection Data Sets}, volume = 86, year = 2019 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/2be291cfcc1c1ee976ea683ba4a11e30/hotho
URNhttp://dx.doi.org/10.1016/j.cose.2019.06.005
Tags2019 datasets ids imported myown

Detection of Scenes in Fiction E. Gius; F. Jannidis; M. Krug; A. Zehe; A. Hotho; F. Puppe; J. Krebs; N. Reiter; N. Wiedmer; L. Konle (2019).
[ BibTeX ] [ Download ] [ Details ]
 
@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 = {2019 detection dh dh2019 kallimachos myown nlp scene}, title = {Detection of Scenes in Fiction}, year = 2019 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/64d9994d61561bcb766cfc00bb47cb7f/hotho
Tags2019 detection dh dh2019 kallimachos myown nlp scene

2018 [ nach oben ]

Flow-based Network Traffic Generation using Generative Adversarial Networks. M. Ring; D. Schlör; D. Landes; A. Hotho in CoRR (2018). abs/1810.07795
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
Flow-based data sets are necessary for evaluating network-based intrusion de- tection systems (NIDS). In this work, we propose a novel methodology for gener- ating 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 at- tributes. 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 gener- ated 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{journals/corr/abs-1810-07795, abstract = {Flow-based data sets are necessary for evaluating network-based intrusion de- tection systems (NIDS). In this work, we propose a novel methodology for gener- ating 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 at- tributes. 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 gener- ated 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 = {CoRR}, keywords = {2018 GAN flow ids myown network traffic}, title = {Flow-based Network Traffic Generation using Generative Adversarial Networks.}, volume = {abs/1810.07795}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/ec09bc6976e094dc873d7c0d3391c501/hotho
Tags2018 GAN flow ids myown network traffic
AbstractFlow-based data sets are necessary for evaluating network-based intrusion de- tection systems (NIDS). In this work, we propose a novel methodology for gener- ating 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 at- tributes. 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 gener- ated 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.

ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings L. Hettinger; A. Dallmann; A. Zehe; T. Niebler; A. Hotho (2018).
[ BibTeX ] [ Download ] [ Details ]
 
@inproceedings{hettinger2018semeval, address = {New Orleans, LA, USA}, author = {Hettinger, Lena and Dallmann, Alexander and Zehe, Albin and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of International Workshop on Semantic Evaluation (SemEval-2018)}, keywords = {2018 classification embeddings myown relation scientific semantic senegal w2v word}, title = {ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/6c725d01df9bfe964ad30b0c087737c4/hotho
Tags2018 classification embeddings myown relation scientific semantic senegal w2v word

Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data M. Kibanov; M. Becker; J. Müller; M. Atzmueller; A. Hotho; G. Stumme (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{KBMAHS:18, address = {New York, NY, USA}, author = {Kibanov, Mark and Becker, Martin and Müller, Jürgen and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, booktitle = {Proc. 33rd ACM Symposium On Applied Computing}, keywords = {2018 classification geo knn myown spatial}, publisher = {ACM Press}, title = {{Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data}}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/cb3f8e9e22cfc83cf73337c42116d318/hotho
Tags2018 classification geo knn myown spatial

Accessing Information with Tags: Search and Ranking B. Navarro Bullock; A. Hotho; G. Stumme in Social Information Access: Systems and Technologies, P. Brusilovsky, D. He (Eds.) (2018). 310–343.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.
@inbook{NavarroBullock2018, abstract = {With the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.}, address = {Cham}, author = {Navarro Bullock, Beate and Hotho, Andreas and Stumme, Gerd}, booktitle = {Social Information Access: Systems and Technologies}, editor = {Brusilovsky, Peter and He, Daqing}, keywords = {2018 folkrank folksonomy myown rank search tag}, pages = {310–343}, publisher = {Springer International Publishing}, title = {Accessing Information with Tags: Search and Ranking}, year = 2018 }
Weitere Informationen
HerausgeberBrusilovsky, Peter and He, Daqing
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/df420579b2b03b591c1a454151e4c21a/hotho
URNhttp://dx.doi.org/10.1007/978-3-319-90092-6_9
Tags2018 folkrank folksonomy myown rank search tag
AbstractWith the growth of the Social Web, a variety of new web-based services arose and changed the way users interact with the internet and consume information. One central phenomenon was and is tagging which allows to manage, organize and access information in social systems. Tagging helps to manage all kinds of resources, making their access much easier. The first type of social tagging systems were social bookmarking systems, i.e., platforms for storing and sharing bookmarks on the web rather than just in the browser. Meanwhile, (hash-)tagging is central in many other Social Media systems such as social networking sites and micro-blogging platforms. To allow for efficient information access, special algorithms have been developed to guide the user, to search for information and to rank the content based on tagging information contributed by the users.

Big-Data Helps SDN to Improve Application Specific Quality of Service S. Schwarzmann; A. Blenk; O. Dobrijevic; M. Jarschel; A. Hotho; T. Zinner; F. Wamser in Big Data and Software Defined Networks (2018).
[ Abstract ] [ BibTeX ] [ Details ]
 
This chapter first provides an outline of the current results in the domains of: (a) quality-of-service (QoS) / quality-of-experience (QoE) control and management (CaM) for real-time multimedia services that is supported by software-defined networking (SDN), and (b) big data analytics and methods that are used for QoS/QoE CaM. Then, three specific use case scenarios with respect to video streaming services are presented, so as to illustrate the expected benefits of incorporating big data analytics into SDN-based CaM for the purposes of improving or optimizing QoS/QoE. In the end, we describe our vision and a high-level view of an SDN-based architecture for QoS/QoE CaM that is enriched with big data analytics' functional blocks and summarize corresponding challenges.
@incollection{info3-incollection-2018-2, abstract = {This chapter first provides an outline of the current results in the domains of: (a) quality-of-service (QoS) / quality-of-experience (QoE) control and management (CaM) for real-time multimedia services that is supported by software-defined networking (SDN), and (b) big data analytics and methods that are used for QoS/QoE CaM. Then, three specific use case scenarios with respect to video streaming services are presented, so as to illustrate the expected benefits of incorporating big data analytics into SDN-based CaM for the purposes of improving or optimizing QoS/QoE. In the end, we describe our vision and a high-level view of an SDN-based architecture for QoS/QoE CaM that is enriched with big data analytics' functional blocks and summarize corresponding challenges.}, author = {Schwarzmann, Susanna and Blenk, Andreas and Dobrijevic, Ognjen and Jarschel, Michael and Hotho, Andreas and Zinner, Thomas and Wamser, Florian}, booktitle = {Big Data and Software Defined Networks}, keywords = {2018 big data myown network quality sdn service}, publisher = {IET}, title = {Big-Data Helps SDN to Improve Application Specific Quality of Service}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/dc6f4636d1f670d748a8a586b4b98c0e/hotho
Tags2018 big data myown network quality sdn service
AbstractThis chapter first provides an outline of the current results in the domains of: (a) quality-of-service (QoS) / quality-of-experience (QoE) control and management (CaM) for real-time multimedia services that is supported by software-defined networking (SDN), and (b) big data analytics and methods that are used for QoS/QoE CaM. Then, three specific use case scenarios with respect to video streaming services are presented, so as to illustrate the expected benefits of incorporating big data analytics into SDN-based CaM for the purposes of improving or optimizing QoS/QoE. In the end, we describe our vision and a high-level view of an SDN-based architecture for QoS/QoE CaM that is enriched with big data analytics' functional blocks and summarize corresponding challenges.

Burrows Zeta: Varianten und Evaluation C. Schöch; J. Calvo; A. Zehe; A. Hotho (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{schoch2018burrows, author = {Schöch, Christof and Calvo, José and Zehe, Albin and Hotho, Andreas}, booktitle = {DHd 2018}, keywords = {2018 cligs myown zeta}, title = {Burrows Zeta: Varianten und Evaluation}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/03d6baf7b43a7c8d4c2815c1f04c60d0/hotho
Tags2018 cligs myown zeta

Detection of slow port scans in flow-based network traffic M. Ring; D. Landes; A. Hotho in PLOS ONE (2018). 13(9) 1–18.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
Frequently, port scans are early indicators of more serious attacks. Unfortunately, the detection of slow port scans in company networks is challenging due to the massive amount of network data. This paper proposes an innovative approach for preprocessing flow-based data which is specifically tailored to the detection of slow port scans. The preprocessing chain generates new objects based on flow-based data aggregated over time windows while taking domain knowledge as well as additional knowledge about the network structure into account. The computed objects are used as input for the further analysis. Based on these objects, we propose two different approaches for detection of slow port scans. One approach is unsupervised and uses sequential hypothesis testing whereas the other approach is supervised and uses classification algorithms. We compare both approaches with existing port scan detection algorithms on the flow-based CIDDS-001 data set. Experiments indicate that the proposed approaches achieve better detection rates and exhibit less false alarms than similar algorithms.
@article{10.1371/journal.pone.0204507, abstract = {Frequently, port scans are early indicators of more serious attacks. Unfortunately, the detection of slow port scans in company networks is challenging due to the massive amount of network data. This paper proposes an innovative approach for preprocessing flow-based data which is specifically tailored to the detection of slow port scans. The preprocessing chain generates new objects based on flow-based data aggregated over time windows while taking domain knowledge as well as additional knowledge about the network structure into account. The computed objects are used as input for the further analysis. Based on these objects, we propose two different approaches for detection of slow port scans. One approach is unsupervised and uses sequential hypothesis testing whereas the other approach is supervised and uses classification algorithms. We compare both approaches with existing port scan detection algorithms on the flow-based CIDDS-001 data set. Experiments indicate that the proposed approaches achieve better detection rates and exhibit less false alarms than similar algorithms.}, author = {Ring, Markus and Landes, Dieter and Hotho, Andreas}, journal = {PLOS ONE}, keywords = {2018 ids myown network port scan slow traffic}, month = {09}, number = 9, pages = {1-18}, publisher = {Public Library of Science}, title = {Detection of slow port scans in flow-based network traffic}, volume = 13, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/8af21155d607e9296d511bf8efe60dc3/hotho
URNhttp://dx.doi.org/10.1371/journal.pone.0204507
Tags2018 ids myown network port scan slow traffic
AbstractFrequently, port scans are early indicators of more serious attacks. Unfortunately, the detection of slow port scans in company networks is challenging due to the massive amount of network data. This paper proposes an innovative approach for preprocessing flow-based data which is specifically tailored to the detection of slow port scans. The preprocessing chain generates new objects based on flow-based data aggregated over time windows while taking domain knowledge as well as additional knowledge about the network structure into account. The computed objects are used as input for the further analysis. Based on these objects, we propose two different approaches for detection of slow port scans. One approach is unsupervised and uses sequential hypothesis testing whereas the other approach is supervised and uses classification algorithms. We compare both approaches with existing port scan detection algorithms on the flow-based CIDDS-001 data set. Experiments indicate that the proposed approaches achieve better detection rates and exhibit less false alarms than similar algorithms.

EveryAware Gears: A Tool to visualize and analyze all types of Citizen Science Data F. Lautenschlager; M. Becker; M. Steininger; A. Hotho D. Burghardt, S. Chen, G. Andrienko, N. Andrienko, R. Purves, A. Diehl (Eds.) (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{lautenschlager2018everyaware, author = {Lautenschlager, Florian and Becker, Martin and Steininger, Michael and Hotho, Andreas}, booktitle = {Proceedings of VGI Geovisual Analytics Workshop, colocated with BDVA 2018}, editor = {Burghardt, Dirk and Chen, Siming and Andrienko, Gennady and Andrienko, Nathalia and Purves, Ross and Diehl, Alexandra}, keywords = {2018 everyaware myown project:p2map tool}, month = {oct}, publisher = {KOPS}, title = {EveryAware Gears: A Tool to visualize and analyze all types of Citizen Science Data}, year = 2018 }
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HerausgeberBurghardt, Dirk and Chen, Siming and Andrienko, Gennady and Andrienko, Nathalia and Purves, Ross and Diehl, Alexandra
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/a9cc8089901f1e28d4eb61fde4ea0dfb/hotho
Tags2018 everyaware myown project:p2map tool

Air Trails -- Urban Air Quality Campaign Exploration Patterns M. Becker; F. Lautenschlager; A. Hotho (2018).
[ BibTeX ] [ Details ]
 
@article{becker2018air, author = {Becker, Martin and Lautenschlager, Florian and Hotho, Andreas}, keywords = {2018 airtrails exploration hyptrails mobility myown pattern project:p2map}, title = {Air Trails – Urban Air Quality Campaign Exploration Patterns}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/7c7fe63b1608268c84999a7968ae29f0/hotho
Tags2018 airtrails exploration hyptrails mobility myown pattern project:p2map

Burrows’ Zeta: Exploring and Evaluating Variants and Parameters C. Schöch; D. Schlör; A. Zehe; H. Gebhard; M. Becker; A. Hotho (2018). 274–277.
[ BibTeX ] [ URL ] [ Details ]
 
@inproceedings{schoech2018zeta, author = {Schöch, Christof and Schlör, Daniel and Zehe, Albin and Gebhard, Henning and Becker, Martin and Hotho, Andreas}, booktitle = {DH}, keywords = {2018 cligs myown zeta}, pages = {274-277}, title = {Burrows’ Zeta: Exploring and Evaluating Variants and Parameters}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/3889b7bb3acabb9ac9101c511e79fc91/hotho
Tags2018 cligs myown zeta

A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels A. Zehe; D. Schlör; U. Henny-Krahmer; M. Becker; A. Hotho (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{zehe2018whitebox, author = {Zehe, Albin and Schlör, Daniel and Henny-Krahmer, Ulrike and Becker, Martin and Hotho, Andreas}, booktitle = {DH}, keywords = {2018 author classification cligs myown nationality}, organization = {ADHO}, title = {A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/f96df49d9be6200fe469f023d0e0cc23/hotho
Tags2018 author classification cligs myown nationality

Analysing Direct Speech in German Novels F. Jannidis; L. Konle; A. Zehe; A. Hotho; M. Krug (2018).
[ BibTeX ] [ Details ]
 
@inproceedings{jannidis2018analysing, author = {Jannidis, Fotis and Konle, Leonard and Zehe, Albin and Hotho, Andreas and Krug, Markus}, booktitle = {DHd 2018}, keywords = {2018 directspeech kallimachos myown}, title = {Analysing Direct Speech in German Novels}, year = 2018 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/53ddfdc7eeb50ebef874b9977b3bda4f/hotho
Tags2018 directspeech kallimachos myown

2017 [ nach oben ]

Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning T. Niebler; M. Becker; C. Pölitz; A. Hotho (2017).
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@inproceedings{niebler2017learning, author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas}, booktitle = {Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks co-located with 16th International Semantic Web Conference (ISWC 2017)}, keywords = {2017 feedback human learning metric myown relatedness relative semantics}, title = {Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning}, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/a6da777b0717135aab66fc4ac0c07902/hotho
Tags2017 feedback human learning metric myown relatedness relative semantics

Participatory sensing, opinions and collective awareness V. Loreto; M. Haklay; A. Hotho; V. C. P. Servedio; G. Stumme; J. Theunis; F. Tria (2017). Springer.
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@book{loreto2017participatory, editor = {Loreto, Vittorio and Haklay, Mordechai and Hotho, Andreas and Servedio, Vito C. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca}, keywords = {2017 everyaware myown participatory sensing}, publisher = {Springer}, title = {Participatory sensing, opinions and collective awareness}, year = 2017 }
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HerausgeberLoreto, Vittorio and Haklay, Mordechai and Hotho, Andreas and Servedio, Vito C. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/4dca15ffae60fc62e68ab32e6fa2cc1b/hotho
Tags2017 everyaware myown participatory sensing

Learning Semantic Relatedness From Human Feedback Using Metric Learning T. Niebler; M. Becker; C. Pölitz; A. Hotho (2017).
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. For this, we argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost. We rigorously test our approach on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We find that our method can significantly improve semantic relatedness measures by learning from additional information, such as explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for ontology and recommendation engineers, but also for any other researchers and practitioners of Semantic Web techniques.
@misc{niebler2017learning, abstract = {Assessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. For this, we argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost. We rigorously test our approach on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We find that our method can significantly improve semantic relatedness measures by learning from additional information, such as explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for ontology and recommendation engineers, but also for any other researchers and practitioners of Semantic Web techniques.}, author = {Niebler, Thomas and Becker, Martin and Pölitz, Christian and Hotho, Andreas}, keywords = {2017 learning metric myown relatedness semantic}, note = {cite arxiv:1705.07425Comment: Under review at ISWC 2017}, title = {Learning Semantic Relatedness From Human Feedback Using Metric Learning}, year = 2017 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/38ec3d4e41af2acda32f86c37ac89ad0/hotho
Tags2017 learning metric myown relatedness semantic
AbstractAssessing the degree of semantic relatedness between words is an important task with a variety of semantic applications, such as ontology learning for the Semantic Web, semantic search or query expansion. To accomplish this in an automated fashion, many relatedness measures have been proposed. However, most of these metrics only encode information contained in the underlying corpus and thus do not directly model human intuition. To solve this, we propose to utilize a metric learning approach to improve existing semantic relatedness measures by learning from additional information, such as explicit human feedback. For this, we argue to use word embeddings instead of traditional high-dimensional vector representations in order to leverage their semantic density and to reduce computational cost. We rigorously test our approach on several domains including tagging data as well as publicly available embeddings based on Wikipedia texts and navigation. Human feedback about semantic relatedness for learning and evaluation is extracted from publicly available datasets such as MEN or WS-353. We find that our method can significantly improve semantic relatedness measures by learning from additional information, such as explicit human feedback. For tagging data, we are the first to generate and study embeddings. Our results are of special interest for ontology and recommendation engineers, but also for any other researchers and practitioners of Semantic Web techniques.

Mining social semantics on the social web A. Hotho; R. Jaeschke; K. Lerman in Semantic Web (2017). 8(5) 623–624.
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@article{wrro115871, author = {Hotho, A. and Jaeschke, R. and Lerman, K.}, journal = {Semantic Web}, keywords = {2017 mining myown semantic social web}, month = {April}, note = {\copyright 2017 IOS Press and the authors. This is an author produced version of a paper subsequently published in Semantic Web. Uploaded in accordance with the publisher's self-archiving policy.}, number = 5, pages = {623–624}, publisher = {IOS Press}, title = {Mining social semantics on the social web}, volume = 8, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/dfa668b67165e8f52def015f9324caef/hotho
Tags2017 mining myown semantic social web

MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data M. Becker; F. Lemmerich; P. Singer; M. Strohmaier; A. Hotho in Data Mining and Knowledge Discovery (2017).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.
@article{Becker2017, abstract = {Sequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.}, author = {Becker, Martin and Lemmerich, Florian and Singer, Philipp and Strohmaier, Markus and Hotho, Andreas}, journal = {Data Mining and Knowledge Discovery}, keywords = {2017 bayesian hyptrails mixed mixedtrails myown}, month = {jul}, title = {MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data}, year = 2017 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/2a095f6c9f638bc9df3b886d5f96cbfa/hotho
URNhttp://dx.doi.org/10.1007/s10618-017-0518-x
Tags2017 bayesian hyptrails mixed mixedtrails myown
AbstractSequential traces of user data are frequently observed online and offline, e.g., as sequences of visited websites or as sequences of locations captured by GPS. However, understanding factors explaining the production of sequence data is a challenging task, especially since the data generation is often not homogeneous. For example, navigation behavior might change in different phases of browsing a website or movement behavior may vary between groups of users. In this work, we tackle this task and propose MixedTrails , a Bayesian approach for comparing the plausibility of hypotheses regarding the generative processes of heterogeneous sequence data. Each hypothesis is derived from existing literature, theory, or intuition and represents a belief about transition probabilities between a set of states that can vary between groups of observed transitions. For example, when trying to understand human movement in a city and given some data, a hypothesis assuming tourists to be more likely to move towards points of interests than locals can be shown to be more plausible than a hypothesis assuming the opposite. Our approach incorporates such hypotheses as Bayesian priors in a generative mixed transition Markov chain model, and compares their plausibility utilizing Bayes factors. We discuss analytical and approximate inference methods for calculating the marginal likelihoods for Bayes factors, give guidance on interpreting the results, and illustrate our approach with several experiments on synthetic and empirical data from Wikipedia and Flickr. Thus, this work enables a novel kind of analysis for studying sequential data in many application areas.

Leveraging User-Interactions for Time-Aware Tag Recommendations D. Zoller; S. Doerfel; C. Pölitz; A. Hotho in CEUR Workshop Proceedings (2017).
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@inproceedings{zoller2017leveraging, author = {Zoller, Daniel and Doerfel, Stephan and Pölitz, Christian and Hotho, Andreas}, booktitle = {Proceedings of the Workshop on Temporal Reasoning in Recommender Systems}, keywords = {2017 interaction myown recommendation tag user}, series = {{CEUR} Workshop Proceedings}, title = {Leveraging User-Interactions for Time-Aware Tag Recommendations}, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/2d8fcb8ac8112dbf5aca43d8c961b7ef/hotho
Tags2017 interaction myown recommendation tag user

A Toolset for Intrusion and Insider Threat Detection M. Ring; S. Wunderlich; D. Grüdl; D. Landes; A. Hotho in Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications, I. Palomares Carrascosa, H. K. Kalutarage, Y. Huang (Eds.) (2017). 3–31.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
Company data are a valuable asset and must be protected against unauthorized access and manipulation. In this contribution, we report on our ongoing work that aims to support IT security experts with identifying novel or obfuscated attacks in company networks, irrespective of their origin inside or outside the company network. A new toolset for anomaly based network intrusion detection is proposed. This toolset uses flow-based data which can be easily retrieved by central network components. We study the challenges of analysing flow-based data streams using data mining algorithms and build an appropriate approach step by step. In contrast to previous work, we collect flow-based data for each host over a certain time window, include the knowledge of domain experts and analyse the data from three different views. We argue that incorporating expert knowledge and previous flows allow us to create more meaningful attributes for subsequent analysis methods. This way, we try to detect novel attacks while simultaneously limiting the number of false positives.
@inbook{Ring2017, abstract = {Company data are a valuable asset and must be protected against unauthorized access and manipulation. In this contribution, we report on our ongoing work that aims to support IT security experts with identifying novel or obfuscated attacks in company networks, irrespective of their origin inside or outside the company network. A new toolset for anomaly based network intrusion detection is proposed. This toolset uses flow-based data which can be easily retrieved by central network components. We study the challenges of analysing flow-based data streams using data mining algorithms and build an appropriate approach step by step. In contrast to previous work, we collect flow-based data for each host over a certain time window, include the knowledge of domain experts and analyse the data from three different views. We argue that incorporating expert knowledge and previous flows allow us to create more meaningful attributes for subsequent analysis methods. This way, we try to detect novel attacks while simultaneously limiting the number of false positives.}, address = {Cham}, author = {Ring, Markus and Wunderlich, Sarah and Gr{ü}dl, Dominik and Landes, Dieter and Hotho, Andreas}, booktitle = {Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications}, editor = {Palomares Carrascosa, Iv{á}n and Kalutarage, Harsha Kumara and Huang, Yan}, keywords = {2017 flow myown network security toolset}, pages = {3–31}, publisher = {Springer International Publishing}, title = {A Toolset for Intrusion and Insider Threat Detection}, year = 2017 }
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HerausgeberPalomares Carrascosa, Iván and Kalutarage, Harsha Kumara and Huang, Yan
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/87a19e38f7d15abf2e9588b99c3a40f9/hotho
URNhttp://dx.doi.org/10.1007/978-3-319-59439-2_1
Tags2017 flow myown network security toolset
AbstractCompany data are a valuable asset and must be protected against unauthorized access and manipulation. In this contribution, we report on our ongoing work that aims to support IT security experts with identifying novel or obfuscated attacks in company networks, irrespective of their origin inside or outside the company network. A new toolset for anomaly based network intrusion detection is proposed. This toolset uses flow-based data which can be easily retrieved by central network components. We study the challenges of analysing flow-based data streams using data mining algorithms and build an appropriate approach step by step. In contrast to previous work, we collect flow-based data for each host over a certain time window, include the knowledge of domain experts and analyse the data from three different views. We argue that incorporating expert knowledge and previous flows allow us to create more meaningful attributes for subsequent analysis methods. This way, we try to detect novel attacks while simultaneously limiting the number of false positives.

Learning Word Embeddings from Tagging Data: A methodological comparison T. Niebler; L. Hahn; A. Hotho (2017).
[ BibTeX ] [ Download ] [ Details ]
 
@inproceedings{niebler2017learning_2, author = {Niebler, Thomas and Hahn, Luzian and Hotho, Andreas}, booktitle = {Proceedings of the LWDA}, keywords = {2017 bibsonomy citeulike delicious embeddings glove line myown relatedness semantic tag word2vec}, title = {Learning Word Embeddings from Tagging Data: A methodological comparison}, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/43fb590e432f966dd664d7ddfd8f73aa/hotho
Tags2017 bibsonomy citeulike delicious embeddings glove line myown relatedness semantic tag word2vec

A Bayesian Method for Comparing Hypotheses About Human Trails P. Singer; D. Helic; A. Hotho; M. Strohmaier in ACM Trans. Web (2017). 11(3) 14:1–14:29.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful, for example, for improving underlying network structures, predicting user clicks, or enhancing recommendations. In this work, we present a method called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our method utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to calculate the evidence of the data under them. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method, and to compare the relative plausibility of hypotheses, we employ Bayes factors. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including Web site navigation, business reviews, and online music played. Our work expands the repertoire of methods available for studying human trails.
@article{singer2017bayesian, abstract = {When users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful, for example, for improving underlying network structures, predicting user clicks, or enhancing recommendations. In this work, we present a method called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our method utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to calculate the evidence of the data under them. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method, and to compare the relative plausibility of hypotheses, we employ Bayes factors. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including Web site navigation, business reviews, and online music played. Our work expands the repertoire of methods available for studying human trails.}, address = {New York, NY, USA}, author = {Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, journal = {ACM Trans. Web}, keywords = {2017 hyptrails long myown tweb version}, month = {jun}, number = 3, pages = {14:1–14:29}, publisher = {ACM}, title = {A Bayesian Method for Comparing Hypotheses About Human Trails}, volume = 11, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/97a9db024c1e5bc67394a0ffee9b254d/hotho
URNhttp://dx.doi.org/10.1145/3054950
Tags2017 hyptrails long myown tweb version
AbstractWhen users interact with the Web today, they leave sequential digital trails on a massive scale. Examples of such human trails include Web navigation, sequences of online restaurant reviews, or online music play lists. Understanding the factors that drive the production of these trails can be useful, for example, for improving underlying network structures, predicting user clicks, or enhancing recommendations. In this work, we present a method called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our method utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to calculate the evidence of the data under them. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method, and to compare the relative plausibility of hypotheses, we employ Bayes factors. We demonstrate the general mechanics and applicability of HypTrails by performing experiments with (i) synthetic trails for which we control the mechanisms that have produced them and (ii) empirical trails stemming from different domains including Web site navigation, business reviews, and online music played. Our work expands the repertoire of methods available for studying human trails.

Sedentary Behavior among National Elite Rowers during Off-Training—A Pilot Study B. Sperlich; M. Becker; A. Hotho; B. Wallmann-Sperlich; M. Sareban; K. Winkert; J. M. Steinacker; G. Treff in Frontiers in Physiology (2017). 8 655.
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The aim of this pilot study was to analyze the off-training physical activity (PA) profile in national elite German U23 rowers during 31 days of their preparation period. The hours spent in each PA category (i.e. sedentary: <1.5 MET; light physical activity: 1.5–3 MET; moderate physical activity: 3–6 MET and vigorous intense physical activity: >6 MET) were calculated for every valid day (i.e. > 480 min of wear time). The off-training PA during 21 weekdays and 10 weekend days of the final 11-wk preparation period was assessed by a wrist-worn multisensory device (Microsoft Band II (MSBII)). A total of 11 rowers provided valid data (i.e. > 480 min/day) for 11.6 week days and 4.8 weekend days during the 31 days observation period. The average sedentary time was 11.63±1.25 hours per day during the week and 12.49±1.10 hours per day on the weekend, with a tendency to be higher on the weekend compared to weekdays (p = 0.06; d = 0.73). The average time in light, moderate and vigorous PA during the weekdays was 1.27±1.15, 0.76±0.37, 0.51±0.44 hours per day and 0.67±0.43, 0.59±0.37, 0.53±0.32 hours per weekend day. Light physical activity was higher during weekdays compared to the weekend (p = 0.04; d = 0.69) Based on our pilot study of eleven national elite rowers we conclude that rowers display a considerable sedentary off-training behavior of more than 11.5 hours/day.
@article{sperlich2017sedentary, abstract = {The aim of this pilot study was to analyze the off-training physical activity (PA) profile in national elite German U23 rowers during 31 days of their preparation period. The hours spent in each PA category (i.e. sedentary: <1.5 MET; light physical activity: 1.5–3 MET; moderate physical activity: 3–6 MET and vigorous intense physical activity: >6 MET) were calculated for every valid day (i.e. > 480 min of wear time). The off-training PA during 21 weekdays and 10 weekend days of the final 11-wk preparation period was assessed by a wrist-worn multisensory device (Microsoft Band II (MSBII)). A total of 11 rowers provided valid data (i.e. > 480 min/day) for 11.6 week days and 4.8 weekend days during the 31 days observation period. The average sedentary time was 11.63±1.25 hours per day during the week and 12.49±1.10 hours per day on the weekend, with a tendency to be higher on the weekend compared to weekdays (p = 0.06; d = 0.73). The average time in light, moderate and vigorous PA during the weekdays was 1.27±1.15, 0.76±0.37, 0.51±0.44 hours per day and 0.67±0.43, 0.59±0.37, 0.53±0.32 hours per weekend day. Light physical activity was higher during weekdays compared to the weekend (p = 0.04; d = 0.69) Based on our pilot study of eleven national elite rowers we conclude that rowers display a considerable sedentary off-training behavior of more than 11.5 hours/day.}, author = {Sperlich, Billy and Becker, Martin and Hotho, Andreas and Wallmann-Sperlich, Birgit and Sareban, Mahdi and Winkert, Kay and Steinacker, Jürgen M. and Treff, Gunnar}, journal = {Frontiers in Physiology}, keywords = {2017 behavior health microsoft myown national row rower sendentary sport wearable}, pages = 655, title = {Sedentary Behavior among National Elite Rowers during Off-Training—A Pilot Study}, volume = 8, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/c8cdba2bc2c14098d39b38f7edefade0/hotho
URNhttp://dx.doi.org/10.3389/fphys.2017.00655
Tags2017 behavior health microsoft myown national row rower sendentary sport wearable
AbstractThe aim of this pilot study was to analyze the off-training physical activity (PA) profile in national elite German U23 rowers during 31 days of their preparation period. The hours spent in each PA category (i.e. sedentary: <1.5 MET; light physical activity: 1.5–3 MET; moderate physical activity: 3–6 MET and vigorous intense physical activity: >6 MET) were calculated for every valid day (i.e. > 480 min of wear time). The off-training PA during 21 weekdays and 10 weekend days of the final 11-wk preparation period was assessed by a wrist-worn multisensory device (Microsoft Band II (MSBII)). A total of 11 rowers provided valid data (i.e. > 480 min/day) for 11.6 week days and 4.8 weekend days during the 31 days observation period. The average sedentary time was 11.63±1.25 hours per day during the week and 12.49±1.10 hours per day on the weekend, with a tendency to be higher on the weekend compared to weekdays (p = 0.06; d = 0.73). The average time in light, moderate and vigorous PA during the weekdays was 1.27±1.15, 0.76±0.37, 0.51±0.44 hours per day and 0.67±0.43, 0.59±0.37, 0.53±0.32 hours per weekend day. Light physical activity was higher during weekdays compared to the weekend (p = 0.04; d = 0.69) Based on our pilot study of eleven national elite rowers we conclude that rowers display a considerable sedentary off-training behavior of more than 11.5 hours/day.

Creation of Flow-Based Data Sets for Intrusion Detection M. Ring; S. Wunderlich; D. Grüdl; D. Landes; A. Hotho in Journal of Information Warfare (2017). 16(4) 41–54.
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
Publicly available labelled data sets are necessary for evaluating anomaly-based Intrusion Detection Systems (IDS). However, existing data sets are often not up-to-date or not yet published because of privacy concerns. This paper identifies requirements for good data sets and proposes an approach for their generation. The key idea is to use a test environment and emulate realistic user behaviour with parameterised scripts on the clients. Comprehensive logging mechanisms provide additional information which may be used for a better understanding of the inner dynamics of an IDS. Finally, the proposed approach is used to generate the flow-based CIDDS-002 data set.
@article{ring2017creation, abstract = {Publicly available labelled data sets are necessary for evaluating anomaly-based Intrusion Detection Systems (IDS). However, existing data sets are often not up-to-date or not yet published because of privacy concerns. This paper identifies requirements for good data sets and proposes an approach for their generation. The key idea is to use a test environment and emulate realistic user behaviour with parameterised scripts on the clients. Comprehensive logging mechanisms provide additional information which may be used for a better understanding of the inner dynamics of an IDS. Finally, the proposed approach is used to generate the flow-based CIDDS-002 data set.}, author = {Ring, Markus and Wunderlich, Sarah and Grüdl, Dominik and Landes, Dieter and Hotho, Andreas}, journal = {Journal of Information Warfare}, keywords = {2017 dataset detection intrusion myown}, number = 4, pages = {41-54}, title = {Creation of Flow-Based Data Sets for Intrusion Detection}, volume = 16, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/4cdd67ec1dee90095658f31654edf3ae/hotho
Tags2017 dataset detection intrusion myown
AbstractPublicly available labelled data sets are necessary for evaluating anomaly-based Intrusion Detection Systems (IDS). However, existing data sets are often not up-to-date or not yet published because of privacy concerns. This paper identifies requirements for good data sets and proposes an approach for their generation. The key idea is to use a test environment and emulate realistic user behaviour with parameterised scripts on the clients. Comprehensive logging mechanisms provide additional information which may be used for a better understanding of the inner dynamics of an IDS. Finally, the proposed approach is used to generate the flow-based CIDDS-002 data set.

Towards Sentiment Analysis on German Literature A. Zehe; M. Becker; F. Jannidis; A. Hotho G. Kern-Isberner, J. Fürnkranz, M. Thimm (Eds.) (2017). 387–394.
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Sentiment Analysis is a Natural Language Processing-task that is relevant in a number of contexts, including the analysis of literature. We report on ongoing research towards enabling, for the first time, sentence-level Sentiment Analysis in the domain of German novels. We create a labelled dataset from sentences extracted from German novels and, by adapting existing sentiment classifiers, reach promising F1-scores of 0.67 for binary polarity classification.
@inproceedings{zehe2017towards, abstract = {Sentiment Analysis is a Natural Language Processing-task that is relevant in a number of contexts, including the analysis of literature. We report on ongoing research towards enabling, for the first time, sentence-level Sentiment Analysis in the domain of German novels. We create a labelled dataset from sentences extracted from German novels and, by adapting existing sentiment classifiers, reach promising F1-scores of 0.67 for binary polarity classification.}, address = {Cham}, author = {Zehe, Albin and Becker, Martin and Jannidis, Fotis and Hotho, Andreas}, booktitle = {KI 2017: Advances in Artificial Intelligence}, editor = {Kern-Isberner, Gabriele and F{ü}rnkranz, Johannes and Thimm, Matthias}, keywords = {2017 analysis dh myown sentiment}, pages = {387&ndash;394}, publisher = {Springer International Publishing}, title = {Towards Sentiment Analysis on German Literature}, year = 2017 }
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HerausgeberKern-Isberner, Gabriele and Fürnkranz, Johannes and Thimm, Matthias
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/e874dcdf3abdd25dfae4e704da8bd3cc/hotho
Tags2017 analysis dh myown sentiment
AbstractSentiment Analysis is a Natural Language Processing-task that is relevant in a number of contexts, including the analysis of literature. We report on ongoing research towards enabling, for the first time, sentence-level Sentiment Analysis in the domain of German novels. We create a labelled dataset from sentences extracted from German novels and, by adapting existing sentiment classifiers, reach promising F1-scores of 0.67 for binary polarity classification.

Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time A. Dallmann; A. Grimm; C. Pölitz; D. Zoller; A. Hotho (2017).
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Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation. These approaches use RNNs to predict the next item in a user session based on the previ- ously visited items. While some approaches consider additional item properties, we argue that item dwell time can be used as an implicit measure of user interest to improve session-based item recommen- dations. We propose an extension to existing RNN approaches that captures user dwell time in addition to the visited items and show that recommendation performance can be improved. Additionally, we investigate the usefulness of a single validation split for model selection in the case of minor improvements and find that in our case the best model is not selected and a fold-like study with different validation sets is necessary to ensure the selection of the best model.
@misc{dallmann2017improving, abstract = {Recently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation. These approaches use RNNs to predict the next item in a user session based on the previ- ously visited items. While some approaches consider additional item properties, we argue that item dwell time can be used as an implicit measure of user interest to improve session-based item recommen- dations. We propose an extension to existing RNN approaches that captures user dwell time in addition to the visited items and show that recommendation performance can be improved. Additionally, we investigate the usefulness of a single validation split for model selection in the case of minor improvements and find that in our case the best model is not selected and a fold-like study with different validation sets is necessary to ensure the selection of the best model.}, author = {Dallmann, Alexander and Grimm, Alexander and Pölitz, Christian and Zoller, Daniel and Hotho, Andreas}, keywords = {2017 myown recommender}, note = {cite arxiv:1706.10231Comment: 6 pages, 3 figures, submission to DLRS workshop}, title = {Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time}, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/55ba70ec3bcb1fe6bc6c76fb180fd019/hotho
Tags2017 myown recommender
AbstractRecently, Recurrent Neural Networks (RNNs) have been applied to the task of session-based recommendation. These approaches use RNNs to predict the next item in a user session based on the previ- ously visited items. While some approaches consider additional item properties, we argue that item dwell time can be used as an implicit measure of user interest to improve session-based item recommen- dations. We propose an extension to existing RNN approaches that captures user dwell time in addition to the visited items and show that recommendation performance can be improved. Additionally, we investigate the usefulness of a single validation split for model selection in the case of minor improvements and find that in our case the best model is not selected and a fold-like study with different validation sets is necessary to ensure the selection of the best model.

Flow-based benchmark data sets for intrusion detection M. Ring; S. Wunderlich; D. Grüdl; D. Landes; A. Hotho (2017). 361–369.
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@inproceedings{ring2017flow, author = {Ring, Markus and Wunderlich, Sarah and Gr{ü}dl, Dominik and Landes, Dieter and Hotho, Andreas}, booktitle = {Proceedings of the 16th European Conference on Cyber Warfare and Security}, keywords = {myown 2017 flow intrusion detection benchmark citedby:scholar:count:4 citedby:scholar:timestamp:2018-1-31}, pages = {361&ndash;369}, title = {Flow-based benchmark data sets for intrusion detection}, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/d1698de969084bab64003c673558b548/hotho
Tagsmyown 2017 flow intrusion detection benchmark citedby:scholar:count:4 citedby:scholar:timestamp:2018-1-31

Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications F. Lemmerich; P. Singer; M. Becker; L. Espin-Noboa; D. Dimitrov; D. Helic; A. Hotho; M. Strohmaier Y. Altun, K. Das, T. Mielikäinen, D. Malerba, J. Stefanowski, J. Read, M. vZitnik, M. Ceci, S. Dvzeroski (Eds.) (2017). 354–357.
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