Publications

2020 [ nach oben ]

Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study. Davidson, Padraig; Düking, Peter; Zinner, Christoph; Sperlich, Billy; Hotho, Andreas in Sensors (2020).
[ 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{kobs-etal-2020-submit, 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 }
Weitere Informationen
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.

Anomaly Detection in Beehives using Deep Recurrent Autoencoders. Davidson, Padraig; Steininger, Michael; Lautenschlager, Florian; Kobs, Konstantin; Krause, Anna; Hotho, Andreas (2020). 142–149.
[ 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{10.3389/frai.2020.00071, abstract = {Neural networks have to capture mathematical relationships in order to learn various tasks. They approximate these relations implicitly and therefore often do not generalize well. The recently proposed Neural Arithmetic Logic Unit (NALU) is a novel neural architecture which is able to explicitly represent the mathematical relationships by the units of the network to learn operations such as summation, subtraction or multiplication. Although NALUs have been shown to perform well on various downstream tasks, an in-depth analysis reveals practical shortcomings by design, such as the inability to multiply or divide negative input values or training stability issues for deeper networks. We address these issues and propose an improved model architecture. We evaluate our model empirically in various settings from learning basic arithmetic operations to more complex functions. Our experiments indicate that our model solves stability issues and outperforms the original NALU model in means of arithmetic precision and convergence.}, author = {Schlör, Daniel and Ring, Markus and Hotho, Andreas}, journal = {Frontiers in Artificial Intelligence}, keywords = {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.

Integrating Keywords into BERT4Rec for Sequential Recommendation. Fischer, Elisabeth; Zoller, Daniel; Dallmann, Alexander; Hotho, Andreas (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

Where to Submit? Helping Researchers to Choose the Right Venue. Kobs, Konstantin; Koopmann, Tobias; Zehe, Albin; Fernes, David; Krop, Philipp; Hotho, Andreas (2020). 878–883.
[ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
@article{Kobs_2020, author = {Kobs, Konstantin and Zehe, Albin and Bernstetter, Armin and Chibane, Julian and Pfister, Jan and Tritscher, Julian and Hotho, Andreas}, journal = {{ACM} Transactions on Social Computing}, keywords = {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

Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels. Kobs, Konstantin; Zehe, Albin; Bernstetter, Armin; Chibane, Julian; Pfister, Jan; Tritscher, Julian; Hotho, Andreas in ACM Transactions on Social Computing (2020). 3(2) 1–34.
[ BibTeX ] [ Download ] [ Details ]
 
@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

iNALU: Improved Neural Arithmetic Logic Unit. Schlör, Daniel; Ring, Markus; Hotho, Andreas in Frontiers in Artificial Intelligence (2020). 3 71.
[ BibTeX ] [ Details ]
 
@inproceedings{fischer2020integrating, 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 sequential}, title = {Integrating Keywords into BERT4Rec for Sequential Recommendation}, year = 2020 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/ae0055b6c393efc322a6262ce00f3f5a/hotho
Tags2020 bert4rec embedding kebert4rec keyword myown recommendation sequential

2019 [ nach oben ]

EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification. Hettinger, Lena; Zehe, Albin; Dallmann, Alexander; Hotho, Andreas K. David, K. Geihs, M. Lange, G. Stumme (eds.) (2019). 191–204.
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@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

On the Right Track! Analysing and Predicting Navigation Success in Wikipedia. Koopmann, Tobias; Dallmann, Alexander; Hettinger, Lena; Niebler, Thomas; Hotho, Andreas in HT ’19, C. Atzenbeck, J. Rubart, D. E. Millard (eds.) (2019). 143–152.
[ 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.

Flow-based network traffic generation using Generative Adversarial Networks. Ring, Markus; Schlör, Daniel; Landes, Dieter; Hotho, Andreas in Computers & Security (2019). 82 156–172.
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@inproceedings{Koopmann:2019:RTA:3342220.3343650, address = {New York, NY, USA}, author = {Koopmann, Tobias and Dallmann, Alexander and Hettinger, Lena and Niebler, Thomas and Hotho, Andreas}, booktitle = {Proceedings of the 30th ACM Conference on Hypertext and Social Media}, 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

A Survey of Network-based Intrusion Detection Data Sets. Ring, Markus; Wunderlich, Sarah; Scheuring, Deniz; Landes, Dieter; Hotho, Andreas in Computers & Security (2019). 86 147–167.
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@inproceedings{zehe2019xenophilius, author = {Zehe, Albin and Hettinger, Lena and Ernst, Stefan and Hauptmann, Christian and Hotho, Andreas}, booktitle = {Proceedings of The 13th International Workshop on Semantic Evaluation}, keywords = {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. Wankerl, Sebastian; Götz, Gerhard; Hotho, Andreas in CEUR Workshop Proceedings, R. Jäschke, M. Weidlich (eds.) (2019). (Vol. 2454) 190–194.
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@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

Comparison of System Call Representations for Intrusion Detection. Wunderlich, Sarah; Ring, Markus; Landes, Dieter; Hotho, Andreas 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 ] [ 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

Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks. Zehe, Albin; Hettinger, Lena; Ernst, Stefan; Hauptmann, Christian; Hotho, Andreas (2019).
[ 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

2018 [ nach oben ]

Air Trails -- Urban Air Quality Campaign Exploration Patterns. Becker, Martin; Lautenschlager, Florian; Hotho, Andreas (2018).
[ 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.

ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings. Hettinger, Lena; Dallmann, Alexander; Zehe, Albin; Niebler, Thomas; Hotho, Andreas (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

Analysing Direct Speech in German Novels. Jannidis, Fotis; Konle, Leonard; Zehe, Albin; Hotho, Andreas; Krug, Markus (2018).
[ 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 }
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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.

Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data. Kibanov, Mark; Becker, Martin; Müller, Jürgen; Atzmueller, Martin; Hotho, Andreas; Stumme, Gerd (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.

EveryAware Gears: A Tool to visualize and analyze all types of Citizen Science Data. Lautenschlager, Florian; Becker, Martin; Steininger, Michael; Hotho, Andreas D. Burghardt, S. Chen, G. Andrienko, N. Andrienko, R. Purves, A. Diehl (eds.) (2018).
[ 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.

Accessing Information with Tags: Search and Ranking. Navarro Bullock, Beate; Hotho, Andreas; Stumme, Gerd in Social Information Access: Systems and Technologies, P. Brusilovsky, D. He (eds.) (2018). 310–343.
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@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

Detection of slow port scans in flow-based network traffic. Ring, Markus; Landes, Dieter; Hotho, Andreas in PLOS ONE (2018). 13(9) 1–18.
[ 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

Flow-based Network Traffic Generation using Generative Adversarial Networks. Ring, Markus; Schlör, Daniel; Landes, Dieter; Hotho, Andreas in CoRR (2018). abs/1810.07795
[ 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

Big-Data Helps SDN to Improve Application Specific Quality of Service. Schwarzmann, Susanna; Blenk, Andreas; Dobrijevic, Ognjen; Jarschel, Michael; Hotho, Andreas; Zinner, Thomas; Wamser, Florian in Big Data and Software Defined Networks (2018).
[ 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

Burrows Zeta: Varianten und Evaluation. Schöch, Christof; Calvo, José; Zehe, Albin; Hotho, Andreas (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

Burrows’ Zeta: Exploring and Evaluating Variants and Parameters. Schöch, Christof; Schlör, Daniel; Zehe, Albin; Gebhard, Henning; Becker, Martin; Hotho, Andreas (2018). 274–277.
[ 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

A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels. Zehe, Albin; Schlör, Daniel; Henny-Krahmer, Ulrike; Becker, Martin; Hotho, Andreas (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

2017 [ nach oben ]

MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data. Becker, Martin; Lemmerich, Florian; Singer, Philipp; Strohmaier, Markus; Hotho, Andreas in Data Mining and Knowledge Discovery (2017).
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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

Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time. Dallmann, Alexander; Grimm, Alexander; Pölitz, Christian; Zoller, Daniel; Hotho, Andreas (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. Hotho, A.; Jaeschke, R.; Lerman, K. in Semantic Web (2017). 8(5) 623–624.
[ BibTeX ] [ URL ] [ Download ] [ Details ]
 
@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

Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications. Lemmerich, Florian; Singer, Philipp; Becker, Martin; Espin-Noboa, Lisette; Dimitrov, Dimitar; Helic, Denis; Hotho, Andreas; Strohmaier, Markus Y. Altun, K. Das, T. Mielikäinen, D. Malerba, J. Stefanowski, J. Read, M. vZitnik, M. Ceci, S. Dvzeroski (eds.) (2017). 354–357.
[ 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.

Participatory sensing, opinions and collective awareness Loreto, Vittorio; Haklay, Mordechai; Hotho, Andreas; Servedio, Vito C. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca (2017). Springer.
[ 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.

Learning Semantic Relatedness From Human Feedback Using Metric Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas (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

Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning. Niebler, Thomas; Becker, Martin; Pölitz, Christian; Hotho, Andreas (2017).
[ 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. Niebler, Thomas; Hahn, Luzian; Hotho, Andreas (2017).
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@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

IP2Vec: Learning Similarities Between IP Addresses. Ring, Markus; Landes, Dieter; Dallmann, Alexander; Hotho, Andreas in 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (2017). 657–666.
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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 }
Weitere Informationen
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.

A Toolset for Intrusion and Insider Threat Detection. Ring, Markus; Wunderlich, Sarah; Grüdl, Dominik; Landes, Dieter; Hotho, Andreas in Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications, I. Palomares Carrascosa, H. K. Kalutarage, Y. Huang (eds.) (2017). 3–31.
[ BibTeX ] [ URL ] [ Download ] [ Details ]
 
@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

Flow-based benchmark data sets for intrusion detection. Ring, Markus; Wunderlich, Sarah; Grüdl, Dominik; Landes, Dieter; Hotho, Andreas (2017). 361–369.
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
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 }
Weitere Informationen
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.

Creation of Flow-Based Data Sets for Intrusion Detection. Ring, Markus; Wunderlich, Sarah; Grüdl, Dominik; Landes, Dieter; Hotho, Andreas in Journal of Information Warfare (2017). 16(4) 41–54.
[ BibTeX ] [ DOI ] [ Download ] [ Details ]
 
@article{ring2017ip2vec, author = {Ring, Markus and Landes, Dieter and Dallmann, Alexander and Hotho, Andreas}, journal = {2017 IEEE International Conference on Data Mining Workshops (ICDMW)}, keywords = {2017 distance embeddings ip ip2vec myown similarity}, pages = {657-666}, title = {IP2Vec: Learning Similarities Between IP Addresses}, type = {Publication}, year = 2017 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/461b0a31f6321f63f142b2a17a4e5eaf/hotho
URNhttp://dx.doi.org/10.1109/ICDMW.2017.93
Tags2017 distance embeddings ip ip2vec myown similarity

A Bayesian Method for Comparing Hypotheses About Human Trails. Singer, Philipp; Helic, Denis; Hotho, Andreas; Strohmaier, Markus in ACM Trans. Web (2017). 11(3) 14:1–.
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Sequential data can be found in many settings, e.g., as sequences of visited websites or as location sequences of travellers. To improve the understanding of the underlying mechanisms that generate such sequences, the HypTrails approach provides for a novel data analysis method. Based on first-order Markov chain models and Bayesian hypothesis testing, it allows for comparing a set of hypotheses, i.e., beliefs about transitions between states, with respect to their plausibility considering observed data. HypTrails has been successfully employed to study phenomena in the online and the offline world. In this talk, we want to give an introduction to HypTrails and showcase selected real-world applications on urban mobility and reading behavior on Wikipedia.
@inproceedings{10.1007/978-3-319-71273-4_30, abstract = {Sequential data can be found in many settings, e.g., as sequences of visited websites or as location sequences of travellers. To improve the understanding of the underlying mechanisms that generate such sequences, the HypTrails approach provides for a novel data analysis method. Based on first-order Markov chain models and Bayesian hypothesis testing, it allows for comparing a set of hypotheses, i.e., beliefs about transitions between states, with respect to their plausibility considering observed data. HypTrails has been successfully employed to study phenomena in the online and the offline world. In this talk, we want to give an introduction to HypTrails and showcase selected real-world applications on urban mobility and reading behavior on Wikipedia.}, address = {Cham}, author = {Lemmerich, Florian and Singer, Philipp and Becker, Martin and Espin-Noboa, Lisette and Dimitrov, Dimitar and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Machine Learning and Knowledge Discovery in Databases}, editor = {Altun, Yasemin and Das, Kamalika and Mielik{ä}inen, Taneli and Malerba, Donato and Stefanowski, Jerzy and Read, Jesse and {\v{Z}}itnik, Marinka and Ceci, Michelangelo and D{\v{z}}eroski, Sa{\v{s}}o}, keywords = {2017 application bayesian data hypotheses hyptrails myown overview sequential}, pages = {354--357}, publisher = {Springer International Publishing}, title = {Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications}, year = 2017 }
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HerausgeberAltun, Yasemin and Das, Kamalika and Mielikäinen, Taneli and Malerba, Donato and Stefanowski, Jerzy and Read, Jesse and vZitnik, Marinka and Ceci, Michelangelo and Dvzeroski, Savso
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/0d73570307d2b656b976b80ac59d97e9/hotho
Tags2017 application bayesian data hypotheses hyptrails myown overview sequential
AbstractSequential data can be found in many settings, e.g., as sequences of visited websites or as location sequences of travellers. To improve the understanding of the underlying mechanisms that generate such sequences, the HypTrails approach provides for a novel data analysis method. Based on first-order Markov chain models and Bayesian hypothesis testing, it allows for comparing a set of hypotheses, i.e., beliefs about transitions between states, with respect to their plausibility considering observed data. HypTrails has been successfully employed to study phenomena in the online and the offline world. In this talk, we want to give an introduction to HypTrails and showcase selected real-world applications on urban mobility and reading behavior on Wikipedia.

Sedentary Behavior among National Elite Rowers during Off-Training—A Pilot Study. Sperlich, Billy; Becker, Martin; Hotho, Andreas; Wallmann-Sperlich, Birgit; Sareban, Mahdi; Winkert, Kay; Steinacker, Jürgen M.; Treff, Gunnar in Frontiers in Physiology (2017). 8 655.
[ BibTeX ] [ Download ] [ Details ]
 
@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--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

Towards Sentiment Analysis on German Literature. Zehe, Albin; Becker, Martin; Jannidis, Fotis; Hotho, Andreas G. Kern-Isberner, J. Fürnkranz, M. Thimm (eds.) (2017). 387–394.
[ 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.

Leveraging User-Interactions for Time-Aware Tag Recommendations. Zoller, Daniel; Doerfel, Stephan; Pölitz, Christian; Hotho, Andreas in CEUR Workshop Proceedings (2017).
[ Abstract ] [ BibTeX ] [ Details ]
 
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--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.

2016 [ nach oben ]

SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails. Becker, Martin; Mewes, Hauke; Hotho, Andreas; Dimitrov, Dimitar; Lemmerich, Florian; Strohmaier, Markus J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks, B. Y. Zhao (eds.) (2016). 17–18.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.
@article{10.3389/fphys.2016.00071, abstract = {Athletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.}, author = {Düking, Peter and Hotho, Andreas and Fuss, Franz Konstantin and Holmberg, Hans-Christer and Sperlich, Billy}, journal = {Frontiers in Physiology}, keywords = {2016 compare data fitness myown sport wearable}, number = 71, title = {Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies}, volume = 7, year = 2016 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/9003aa842adf7a166ef32ae1bfc8c6e7/hotho
URNhttp://dx.doi.org/10.3389/fphys.2016.00071
Tags2016 compare data fitness myown sport wearable
AbstractAthletes adapt their training daily to optimize performance, as well as avoid fatigue, overtraining and other undesirable effects on their health. To optimize training load, each athlete must take his/her own personal objective and subjective characteristics into consideration and an increasing number of wearable technologies (wearables) provide convenient monitoring of various parameters. Accordingly, it is important to help athletes decide which parameters are of primary interest and which wearables can monitor these parameters most effectively. Here, we discuss the wearable technologies available for non-invasive monitoring of various parameters concerning an athlete's training and health. On the basis of these considerations, we suggest directions for future development. Furthermore, we propose that a combination of several wearables is most effective for accessing all relevant parameters, disturbing the athlete as little as possible, and optimizing performance and promoting health.

Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing, DMNLP 2016, co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2016, Riva del Garda, Italy, September 23, 2016. Cellier, Peggy; Charnois, Thierry; Hotho, Andreas; Matwin, Stan; Moens, Marie-Francine; Toussaint, Yannick in CEUR Workshop Proceedings (2016). (Vol. 1646) CEUR-WS.org.
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Social tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.
@article{doerfel2016users, abstract = {Social tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, journal = {ACM Transactions on the Web}, keywords = {2016 WueSFU analysis bibsonomy logs myown social tweb users weblog}, month = {may}, number = 2, pages = {14:1--14:32}, publisher = {ACM}, title = {What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy}, volume = 10, year = 2016 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/95dafc17ef731ee387c6ea52edc01615/hotho
URNhttp://dx.doi.org/10.1145/2896821
Tags2016 WueSFU analysis bibsonomy logs myown social tweb users weblog
AbstractSocial tagging systems have established themselves as an important part in today’s web and have attracted the interest of our research community in a variety of investigations. Henceforth, several aspects of social tagging systems have been discussed and assumptions have emerged on which our community builds their work. Yet, testing such assumptions has been difficult due to the absence of suitable usage data in the past. In this work, we thoroughly investigate and evaluate four aspects about tagging systems, covering social interaction, retrieval of posted resources, the importance of the three different types of entities, users, resources, and tags, as well as connections between these entities’ popularity in posted and in requested content. For that purpose, we examine live server log data gathered from the real-world, public social tagging system BibSonomy. Our empirical results paint a mixed picture about the four aspects. While for some, typical assumptions hold to a certain extent, other aspects need to be reflected in a very critical light. Our observations have implications for the understanding of social tagging systems, and the way they are used on the web. We make the dataset used in this work available to other researchers.

Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods. Dallmann, Alexander; Niebler, Thomas; Lemmerich, Florian; Hotho, Andreas R. West, L. Zia, D. Taraborelli, J. Leskovec (eds.) (2016).
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@inproceedings{hettinger2016significance, author = {Hettinger, Lena and Jannidis, Fotis and Reger, Isabella and Hotho, Andreas}, booktitle = {DH 2016}, keywords = {2016 classification digital genre humanities literature myown significance statistics}, title = {Significance Testing for the Classification of Literary Subgenres}, year = 2016 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/3aaaa74475e7f2e0dddaa4c7201a5a58/hotho
Tags2016 classification digital genre humanities literature myown significance statistics

What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy. Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus in ACM Transactions on the Web (2016). 10(2) 14:1–.
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@inproceedings{hettinger2016classification, author = {Hettinger, Lena and Jannidis, Fotis and Reger, Isabella and Hotho, Andreas}, booktitle = {DHd 2016}, keywords = {2016 classification dh dhd digital genre german humanities myown novels}, title = {Classification of Literary Subgenres}, year = 2016 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/0de901651ae8b602302958aa884cbf0d/hotho
Tags2016 classification dh dhd digital genre german humanities myown novels

Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies. Düking, Peter; Hotho, Andreas; Fuss, Franz Konstantin; Holmberg, Hans-Christer; Sperlich, Billy in Frontiers in Physiology (2016). 7(71)
[ BibTeX ] [ URL ] [ Details ]
 
@inproceedings{zehe2016prediction, author = {Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas and Reger, Isabella and Jannidis, Fotis}, booktitle = {DMNLP@PKDD/ECML}, crossref = {conf/pkdd/2016dmnlp}, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick}, keywords = {2016 endings german happy kallimachos mining myown novels prediction text}, pages = {9-16}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Prediction of Happy Endings in German Novels}, volume = 1646, year = 2016 }
Weitere Informationen
HerausgeberCellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/91296b44b26d5eb74f5be36e23c87cb6/hotho
Tags2016 endings german happy kallimachos mining myown novels prediction text

Significance Testing for the Classification of Literary Subgenres. Hettinger, Lena; Jannidis, Fotis; Reger, Isabella; Hotho, Andreas (2016).
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.
@inproceedings{dallmann2016extracting, abstract = {Semantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.}, author = {Dallmann, Alexander and Niebler, Thomas and Lemmerich, Florian and Hotho, Andreas}, booktitle = {Wiki Workshop@ICWSM}, editor = {West, Robert and Zia, Leila and Taraborelli, Dario and Leskovec, Jure}, keywords = {2016 deepwalk deepwiki myown navigation semantic wikipedia word2vec}, title = {Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods}, year = 2016 }
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HerausgeberWest, Robert and Zia, Leila and Taraborelli, Dario and Leskovec, Jure
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/12ce818c62131f722e6d723574ad2a03/hotho
Tags2016 deepwalk deepwiki myown navigation semantic wikipedia word2vec
AbstractSemantic relatedness between words has been extracted from a variety of sources. In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not only consider the link structure of Wikipedia, but also actual navigation behavior of users. Finally, we analyze if semantics can also be extracted from smaller subsets of the Wikipedia link network. As a result we find that representa- tion learning techniques mostly outperform the investigated co-occurrence counting methods on the Wikipedia network. However, we find that this is not the case for paths sampled from human navigation behavior.

Classification of Literary Subgenres. Hettinger, Lena; Jannidis, Fotis; Reger, Isabella; Hotho, Andreas (2016).
[ BibTeX ] [ URL ] [ Details ]
 
@proceedings{conf/pkdd/2016dmnlp, booktitle = {DMNLP@PKDD/ECML}, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick}, keywords = {2016 data mining myown nlp proceedings workshop}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Proceedings of the Workshop on Interactions between Data Mining and Natural Language Processing, DMNLP 2016, co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML-PKDD 2016, Riva del Garda, Italy, September 23, 2016.}, volume = 1646, year = 2016 }
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HerausgeberCellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/fc28f494f30dffb947607b987a8ec7fe/hotho
Tags2016 data mining myown nlp proceedings workshop

Analyzing Features for the Detection of Happy Endings in German Novels. Jannidis, Fotis; Reger, Isabella; Zehe, Albin; Becker, Martin; Hettinger, Lena; Hotho, Andreas (2016).
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In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.
@article{zoller2016posted, abstract = {In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.}, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, journal = {Journal of Informetrics}, keywords = {2016 altmetrics bibsonomy correlations informetrics myown scientometrics}, number = 3, pages = {732 - 749}, title = {Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy}, volume = 10, year = 2016 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/e9317109a729387f96f25acb6b6a6590/hotho
URNhttp://dx.doi.org/http://dx.doi.org/10.1016/j.joi.2016.03.005
Tags2016 altmetrics bibsonomy correlations informetrics myown scientometrics
AbstractIn social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from users’ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points.

Mining Subgroups with Exceptional Transition Behavior. Lemmerich, Florian; Becker, Martin; Singer, Philipp; Helic, Denis; Hotho, Andreas; Strohmaier, Markus B. Krishnapuram, M. Shah, A. J. Smola, C. Aggarwal, D. Shen, R. Rastogi (eds.) (2016). 965–974.
[ BibTeX ] [ URL ] [ Download ] [ Details ]
 
@inproceedings{lemmerich2016mining, author = {Lemmerich, Florian and Becker, Martin and Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, booktitle = {KDD}, crossref = {conf/kdd/2016}, editor = {Krishnapuram, Balaji and Shah, Mohak and Smola, Alexander J. and Aggarwal, Charu and Shen, Dou and Rastogi, Rajeev}, keywords = {2016 WueSFU behavior bericht exceptional hyptrails myown subgroups}, pages = {965-974}, publisher = {ACM}, title = {Mining Subgroups with Exceptional Transition Behavior}, year = 2016 }
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HerausgeberKrishnapuram, Balaji and Shah, Mohak and Smola, Alexander J. and Aggarwal, Charu and Shen, Dou and Rastogi, Rajeev
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/b1a0a3a7ba5e87f1718dfb0ad43249f9/hotho
Tags2016 WueSFU behavior bericht exceptional hyptrails myown subgroups

FolkTrails: Interpreting Navigation Behavior in a Social Tagging System. Niebler, Thomas; Becker, Martin; Zoller, Daniel; Doerfel, Stephan; Hotho, Andreas in CIKM ’16 (2016).
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@inproceedings{conf/www/0003MHDLS16, author = {Becker, Martin and Mewes, Hauke and Hotho, Andreas and Dimitrov, Dimitar and Lemmerich, Florian and Strohmaier, Markus}, booktitle = {WWW (Companion Volume)}, crossref = {conf/www/2016c}, editor = {Bourdeau, Jacqueline and Hendler, Jim and Nkambou, Roger and Horrocks, Ian and Zhao, Ben Y.}, keywords = {2016 big data hyptrails implementation mapreduce myown sparktrails}, pages = {17-18}, publisher = {ACM}, title = {SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails.}, year = 2016 }
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HerausgeberBourdeau, Jacqueline and Hendler, Jim and Nkambou, Roger and Horrocks, Ian and Zhao, Ben Y.
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/651f7159efb176f22e71a73983644f9e/hotho
Tags2016 big data hyptrails implementation mapreduce myown sparktrails

Extracting Semantics from Unconstrained Navigation on Wikipedia. Niebler, Thomas; Schlör, Daniel; Becker, Martin; Hotho, Andreas in KI (2016). 30(2) 163–168.
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@article{journals/ki/NieblerS0H16, author = {Niebler, Thomas and Schlör, Daniel and Becker, Martin and Hotho, Andreas}, journal = {KI}, keywords = {2015 2016 behaviour extracting extraction log myown navigation relatedness semantic user wikipedia}, number = 2, pages = {163-168}, title = {Extracting Semantics from Unconstrained Navigation on Wikipedia}, volume = 30, year = 2016 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/0968a771aa0819dce2fbf7f5c9b94cda/hotho
Tags2015 2016 behaviour extracting extraction log myown navigation relatedness semantic user wikipedia

Creation of Specific Flow-Based Training Data Sets for Usage Behaviour Classification. Otto, Florian; Ring, Markus; Landes, Dieter; Hotho, Andreas (2016). 437.
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we study various partitionings of novels, considering the highly variable concept of "ending". We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.
@misc{jannidis2016analyzing, abstract = {With regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we study various partitionings of novels, considering the highly variable concept of "ending". We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.}, author = {Jannidis, Fotis and Reger, Isabella and Zehe, Albin and Becker, Martin and Hettinger, Lena and Hotho, Andreas}, keywords = {2016 detection dh ending happy myown novel}, note = {cite arxiv:1611.09028}, title = {Analyzing Features for the Detection of Happy Endings in German Novels}, year = 2016 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/d53fbdb472c98d27855e057727afd0cd/hotho
Tags2016 detection dh ending happy myown novel
AbstractWith regard to a computational representation of literary plot, this paper looks at the use of sentiment analysis for happy ending detection in German novels. Its focus lies on the investigation of previously proposed sentiment features in order to gain insight about the relevance of specific features on the one hand and the implications of their performance on the other hand. Therefore, we study various partitionings of novels, considering the highly variable concept of "ending". We also show that our approach, even though still rather simple, can potentially lead to substantial findings relevant to literary studies.

Prediction of Happy Endings in German Novels. Zehe, Albin; Becker, Martin; Hettinger, Lena; Hotho, Andreas; Reger, Isabella; Jannidis, Fotis in CEUR Workshop Proceedings, P. Cellier, T. Charnois, A. Hotho, S. Matwin, M.-F. Moens, Y. Toussaint (eds.) (2016). (Vol. 1646) 9–16.
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.
@inproceedings{niebler2016folktrails, abstract = {Social tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.}, address = {New York, NY, USA}, author = {Niebler, Thomas and Becker, Martin and Zoller, Daniel and Doerfel, Stephan and Hotho, Andreas}, booktitle = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management}, keywords = {2016 WueSFU bericht hyptrails my myown navigation own social tagging}, publisher = {ACM}, series = {CIKM '16}, title = {FolkTrails: Interpreting Navigation Behavior in a Social Tagging System}, year = 2016 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/b3b6e9c496fc336dbfc47169647959a2/hotho
Tags2016 WueSFU bericht hyptrails my myown navigation own social tagging
AbstractSocial tagging systems have established themselves as a quick and easy way to organize information by annotating resources with tags. In recent work, user behavior in social tagging systems was studied, that is, how users assign tags, and consume content. However, it is still unclear how users make use of the navigation options they are given. Understanding their behavior and differences in behavior of different user groups is an important step towards assessing the effectiveness of a navigational concept and of improving it to better suit the users’ needs. In this work, we investigate navigation trails in the popular scholarly social tagging system BibSonomy from six years of log data. We discuss dynamic browsing behavior of the general user population and show that different navigational subgroups exhibit different navigational traits. Furthermore, we provide strong evidence that the semantic nature of the underlying folksonomy is an essential factor for explaining navigation.

Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy. Zoller, Daniel; Doerfel, Stephan; Jäschke, Robert; Stumme, Gerd; Hotho, Andreas in Journal of Informetrics (2016). 10(3) 732–749.
[ BibTeX ] [ Details ]
 
@inproceedings{otto2016creation, author = {Otto, Florian and Ring, Markus and Landes, Dieter and Hotho, Andreas}, booktitle = {ECCWS2016-Proceedings fo the 15th European Conference on Cyber Warfare and Security}, keywords = {2016 classification datasets flow myown network}, organization = {Academic Conferences and publishing limited}, pages = 437, title = {Creation of Specific Flow-Based Training Data Sets for Usage Behaviour Classification}, year = 2016 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/080c088af02e7e5d45c512fa490b1553/hotho
Tags2016 classification datasets flow myown network

2015 [ nach oben ]

MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform. Becker, Martin; Borchert, Kathrin; Hirth, Matthias; Mewes, Hauke; Hotho, Andreas; Tran-Gia, Phuoc in i-KNOW ’15 (2015). 10:1–.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
<p>The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.</p>
@article{10.1371/journal.pone.0136763, abstract = {<p>The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.</p>}, author = {Sîrbu, Alina and Becker, Martin and Caminiti, Saverio and De Baets, Bernard and Elen, Bart and Francis, Louise and Gravino, Pietro and Hotho, Andreas and Ingarra, Stefano and Loreto, Vittorio and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Servedio, Vito D. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca and Van den Bossche, Joris}, journal = {PLoS ONE}, keywords = {2015 air airprobe everyaware for:dmir game myown pollution quality selected}, month = {08}, number = 8, pages = {e0136763}, publisher = {Public Library of Science}, title = {Participatory Patterns in an International Air Quality Monitoring Initiative}, volume = 10, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/f35761dd0fbd9ad8af7c8099e0b6aac4/hotho
URNhttp://dx.doi.org/10.1371/journal.pone.0136763
Tags2015 air airprobe everyaware for:dmir game myown pollution quality selected
Abstract
The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.

Photowalking the city: comparing hypotheses about urban photo trails on Flickr. Becker, Martin; Singer, Philipp; Lemmerich, Florian; Hotho, Andreas; Helic, Denis; Strohmaier, Markus (2015).
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@inproceedings{singer2015hyptrails, address = {Firenze, Italy}, author = {Singer, P. and Helic, D. and Hotho, A. and Strohmaier, M.}, booktitle = {24th International World Wide Web Conference (WWW2015)}, keywords = {2015 WueSFU award bericht bibsonomy compare for:dmir human hypotheses hyptrails myown selected sys:relevantfor:l3s trails web}, month = {May 18 - May 22}, organization = {ACM}, publisher = {ACM}, title = {Hyptrails: A bayesian approach for comparing hypotheses about human trails}, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/5d21e53dc91b35a4a6cb6b9ec858045d/hotho
Tags2015 WueSFU award bericht bibsonomy compare for:dmir human hypotheses hyptrails myown selected sys:relevantfor:l3s trails web

VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories. Becker, Martin; Singer, Philipp; Lemmerich, Florian; Hotho, Andreas; Helic, Denis; Strohmaier, Markus in HT ’15 (2015). 319–320.
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@inproceedings{dallmann2015media, address = {Cyprus, Turkey, September 1-4}, author = {Dallmann, Alexander and Lemmerich, Florian and Zoller, Daniel and Hotho, Andreas}, booktitle = {26th ACM Conference on Hypertext and Social Media}, keywords = {2015 bias for:dmir german media myown news newspaper}, publisher = {ACM}, title = {Media Bias in German Online Newspapers}, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/addfd0d84b4347392dc94a4bec400412/hotho
Tags2015 bias for:dmir german media myown news newspaper

Media Bias in German Online Newspapers. Dallmann, Alexander; Lemmerich, Florian; Zoller, Daniel; Hotho, Andreas (2015).
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@inproceedings{ring2015condist, author = {Ring, Markus and Otto, Florian and Becker, Martin and Niebler, Thomas and Landes, Dieter and Hotho, Andreas}, editor = {ECMLPKDD2015}, keywords = {2015 categorical data for:dmir learning measure myown similarity unsupervised}, title = {ConDist: A Context-Driven Categorical Distance Measure}, year = 2015 }
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HerausgeberECMLPKDD2015
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/a2f9d649f2856677e4d886a3b517404d/hotho
Tags2015 categorical data for:dmir learning measure myown similarity unsupervised

Genre classification on German novels. Hettinger, Lena; Becker, Martin; Reger, Isabella; Jannidis, Fotis; Hotho, Andreas (2015).
[ Abstract ] [ BibTeX ] [ Details ]
 
Today’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.
@inproceedings{vkistowski2015modeling, abstract = {Today’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.}, author = {v. Kistowski, Jóakim and Nikolas, Herbst. and Zoller, Daniel and Kounev, Samuel and Hotho, Andreas}, booktitle = {Proceedings of the 10th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)}, keywords = {2015 extracting for:dmir intensity load modeling myown profile}, title = {Modeling and Extracting Load Intensity Profiles}, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/f449d3cf35941636f96d72aaf620a275/hotho
Tags2015 extracting for:dmir intensity load modeling myown profile
AbstractToday’s system developers and operators face the challenge of creating software systems that make efficient use of dynamically allocated resources under highly variable and dynamic load profiles, while at the same time delivering reliable performance. Benchmarking of systems under these constraints is difficult, as state-of-the-art benchmarking frameworks provide only limited support for emulating such dynamic and highly vari- able load profiles for the creation of realistic workload scenarios. Industrial benchmarks typically confine themselves to workloads with constant or stepwise increasing loads. Alternatively, they support replaying of recorded load traces. Statistical load inten- sity descriptions also do not sufficiently capture concrete pattern load profile variations over time. To address these issues, we present the Descartes Load Intensity Model (DLIM). DLIM provides a modeling formalism for describing load intensity variations over time. A DLIM instance can be used as a compact representation of a recorded load intensity trace, providing a powerful tool for benchmarking and performance analysis. As manually obtaining DLIM instances can be time consuming, we present three different automated extraction methods, which also help to enable autonomous system analysis for self-adaptive systems. Model expressiveness is validated using the presented extraction methods. Extracted DLIM instances exhibit a median modeling error of 12.4% on average over nine different real-world traces covering between two weeks and seven months. Additionally, extraction methods perform orders of magnitude faster than existing time series decomposition approaches.

Automatic Threshold Calculation for the Categorical Distance Measure ConDist. Ring, Markus; Landes, Dieter; Hotho, Andreas in CEUR Workshop Proceedings, R. Bergmann, S. Görg, G. Müller (eds.) (2015). (Vol. 1458) 52–63.
[ Abstract ] [ BibTeX ] [ Details ]
 
Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.
@inproceedings{zoller2015publication, abstract = {Scholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.}, author = {Zoller, Daniel and Doerfel, Stephan and Jäschke, Robert and Stumme, Gerd and Hotho, Andreas}, booktitle = {Proceedings of the 2015 ACM Conference on Web Science}, keywords = {2015 altmetrics bookmarking for:dmir impact myown publication social usage}, title = {On Publication Usage in a Social Bookmarking System}, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/548a7010ee2726f28e04e5c6e5fd6e2d/hotho
Tags2015 altmetrics bookmarking for:dmir impact myown publication social usage
AbstractScholarly success is traditionally measured in terms of citations to publications. With the advent of publication man- agement and digital libraries on the web, scholarly usage data has become a target of investigation and new impact metrics computed on such usage data have been proposed – so called altmetrics. In scholarly social bookmarking sys- tems, scientists collect and manage publication meta data and thus reveal their interest in these publications. In this work, we investigate connections between usage metrics and citations, and find posts, exports, and page views of publications to be correlated to citations.

ConDist: A Context-Driven Categorical Distance Measure. Ring, Markus; Otto, Florian; Becker, Martin; Niebler, Thomas; Landes, Dieter; Hotho, Andreas ECMLPKDD2015 (ed.) (2015).
[ Abstract ] [ BibTeX ] [ URL ] [ Details ]
 
The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.
@misc{sirbu2015participatory, abstract = {The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.}, author = {Sîrbu, Alina and Becker, Martin and Caminiti, Saverio and De Baets, Bernard and Elen, Bart and Francis, Louise and Gravino, Pietro and Hotho, Andreas and Ingarra, Stefano and Loreto, Vittorio and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Servedio, Vito D. P. and Stumme, Gerd and Theunis, Jan and Tria, Francesca and Van den Bossche, Joris}, keywords = {2015 airprobe everyaware for:dmir myown}, note = {cite arxiv:1503.07730Comment: 17 pages, 6 figures, 1 supplementary file}, title = {Participatory Patterns in an International Air Quality Monitoring Initiative}, year = 2015 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/c58c481d2429a3bc94a68c5790aed27a/hotho
Tags2015 airprobe everyaware for:dmir myown
AbstractThe issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.

Hyptrails: A bayesian approach for comparing hypotheses about human trails. Singer, P.; Helic, D.; Hotho, A.; Strohmaier, M. (2015).
[ BibTeX ] [ URL ] [ Details ]
 
@inproceedings{schwemmlein2015genre, author = {Hettinger, Lena and Becker, Martin and Reger, Isabella and Jannidis, Fotis and Hotho, Andreas}, booktitle = {Proceedings of the 12th International Workshop on Text-based Information Retrieval}, keywords = {2015 classification genre kallimachos lda mining myown text}, title = {Genre classification on German novels}, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/f52dcfd3966c0e241a4211ba869ec865/hotho
Tags2015 classification genre kallimachos lda mining myown text

Participatory Patterns in an International Air Quality Monitoring Initiative. Sîrbu, Alina; Becker, Martin; Caminiti, Saverio; De Baets, Bernard; Elen, Bart; Francis, Louise; Gravino, Pietro; Hotho, Andreas; Ingarra, Stefano; Loreto, Vittorio; Molino, Andrea; Mueller, Juergen; Peters, Jan; Ricchiuti, Ferdinando; Saracino, Fabio; Servedio, Vito D. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca; Van den Bossche, Joris in PLoS ONE (2015). 10(8) e0136763.
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@inproceedings{ring2015automatic, author = {Ring, Markus and Landes, Dieter and Hotho, Andreas}, booktitle = {LWA}, crossref = {conf/lwa/2015}, editor = {Bergmann, Ralph and Görg, Sebastian and Müller, Gilbert}, keywords = {2015 condist distance myown}, pages = {52-63}, publisher = {CEUR-WS.org}, series = {CEUR Workshop Proceedings}, title = {Automatic Threshold Calculation for the Categorical Distance Measure ConDist.}, volume = 1458, year = 2015 }
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HerausgeberBergmann, Ralph and Görg, Sebastian and Müller, Gilbert
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/5f01a06522440fc71daa069baa8b432c/hotho
Tags2015 condist distance myown

Participatory Patterns in an International Air Quality Monitoring Initiative. Sîrbu, Alina; Becker, Martin; Caminiti, Saverio; De Baets, Bernard; Elen, Bart; Francis, Louise; Gravino, Pietro; Hotho, Andreas; Ingarra, Stefano; Loreto, Vittorio; Molino, Andrea; Mueller, Juergen; Peters, Jan; Ricchiuti, Ferdinando; Saracino, Fabio; Servedio, Vito D. P.; Stumme, Gerd; Theunis, Jan; Tria, Francesca; Van den Bossche, Joris (2015).
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@inproceedings{becker2015photowalking, address = {Beijing, China}, author = {Becker, Martin and Singer, Philipp and Lemmerich, Florian and Hotho, Andreas and Helic, Denis and Strohmaier, Markus}, booktitle = {Proceedings of the 7th International Conference on SocialInformatics (to appear)}, keywords = {WueSFU behavior flickr hyptrails mobility myown phototrails trails urban}, month = {dec}, title = {Photowalking the city: comparing hypotheses about urban photo trails on Flickr}, type = {Publication}, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/95dff057c4b86fc4b2307b5405c36193/hotho
TagsWueSFU behavior flickr hyptrails mobility myown phototrails trails urban

Modeling and Extracting Load Intensity Profiles. v. Kistowski, Jóakim; Nikolas, Herbst.; Zoller, Daniel; Kounev, Samuel; Hotho, Andreas (2015).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
Understanding the way people move through urban areas represents an important problem that has implications for a range of societal challenges such as city planning, public transportation, or crime analysis. In this paper, we present an interactive visualization tool called VizTrails for exploring and understanding such human movement. It features visualizations that show aggregated statistics of trails for geographic areas that correspond to grid cells on a map, e.g., on the number of users passing through or on cells commonly visited next. Amongst other features, system allows to overlay the map with the results of SPARQL queries in order to relate the observed trajectory statistics with its geo-spatial context, e.g., considering a city's points of interest. The systems functionality is demonstrated using trajectory examples extracted from the social photo sharing platform Flickr. Overall, VizTrails facilitates deeper insights into geo-spatial trajectory data by enabling interactive exploration of aggregated statistics and providing geo-spatial context.
@inproceedings{Becker:2015:VIV:2700171.2791021, abstract = {Understanding the way people move through urban areas represents an important problem that has implications for a range of societal challenges such as city planning, public transportation, or crime analysis. In this paper, we present an interactive visualization tool called VizTrails for exploring and understanding such human movement. It features visualizations that show aggregated statistics of trails for geographic areas that correspond to grid cells on a map, e.g., on the number of users passing through or on cells commonly visited next. Amongst other features, system allows to overlay the map with the results of SPARQL queries in order to relate the observed trajectory statistics with its geo-spatial context, e.g., considering a city's points of interest. The systems functionality is demonstrated using trajectory examples extracted from the social photo sharing platform Flickr. Overall, VizTrails facilitates deeper insights into geo-spatial trajectory data by enabling interactive exploration of aggregated statistics and providing geo-spatial context.}, address = {New York, NY, USA}, author = {Becker, Martin and Singer, Philipp and Lemmerich, Florian and Hotho, Andreas and Helic, Denis and Strohmaier, Markus}, booktitle = {Proceedings of the 26th ACM Conference on Hypertext \&\#38; Social Media}, keywords = {2016 WueSFU bericht gis hyptrails myown viztails}, pages = {319--320}, publisher = {ACM}, series = {HT '15}, title = {VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories}, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/d8571f0d392b85cf7cb5614f34e103a0/hotho
URNhttp://dx.doi.org/10.1145/2700171.2791021
Tags2016 WueSFU bericht gis hyptrails myown viztails
AbstractUnderstanding the way people move through urban areas represents an important problem that has implications for a range of societal challenges such as city planning, public transportation, or crime analysis. In this paper, we present an interactive visualization tool called VizTrails for exploring and understanding such human movement. It features visualizations that show aggregated statistics of trails for geographic areas that correspond to grid cells on a map, e.g., on the number of users passing through or on cells commonly visited next. Amongst other features, system allows to overlay the map with the results of SPARQL queries in order to relate the observed trajectory statistics with its geo-spatial context, e.g., considering a city's points of interest. The systems functionality is demonstrated using trajectory examples extracted from the social photo sharing platform Flickr. Overall, VizTrails facilitates deeper insights into geo-spatial trajectory data by enabling interactive exploration of aggregated statistics and providing geo-spatial context.

On Publication Usage in a Social Bookmarking System. Zoller, Daniel; Doerfel, Stephan; Jäschke, Robert; Stumme, Gerd; Hotho, Andreas (2015).
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Download ] [ Details ]
 
To optimize the workflow on commercial crowdsourcing platforms like Amazon Mechanical Turk or Microworkers, it is important to understand how users choose their tasks. Current work usually explores the underlying processes by employing user studies based on surveys with a limited set of participants. In contrast, we formulate hypotheses based on the different findings in these studies and, instead of verifying them based on user feedback, we compare them directly on data from a commercial crowdsourcing platform. For evaluation, we use a Bayesian approach called HypTrails which allows us to give a relative ranking of the corresponding hypotheses. The hypotheses considered, are for example based on task categories, monetary incentives or semantic similarity of task descriptions. We find that, in our scenario, hypotheses based on employers as well the the task descriptions work best. Overall, we objectively compare different factors influencing users when choosing their tasks. Our approach enables crowdsourcing companies to better understand their users in order to optimize their platforms, e.g., by incorparting the gained knowledge about these factors into task recommentation systems.
@inproceedings{becker2015microtrails, abstract = {To optimize the workflow on commercial crowdsourcing platforms like Amazon Mechanical Turk or Microworkers, it is important to understand how users choose their tasks. Current work usually explores the underlying processes by employing user studies based on surveys with a limited set of participants. In contrast, we formulate hypotheses based on the different findings in these studies and, instead of verifying them based on user feedback, we compare them directly on data from a commercial crowdsourcing platform. For evaluation, we use a Bayesian approach called HypTrails which allows us to give a relative ranking of the corresponding hypotheses. The hypotheses considered, are for example based on task categories, monetary incentives or semantic similarity of task descriptions. We find that, in our scenario, hypotheses based on employers as well the the task descriptions work best. Overall, we objectively compare different factors influencing users when choosing their tasks. Our approach enables crowdsourcing companies to better understand their users in order to optimize their platforms, e.g., by incorparting the gained knowledge about these factors into task recommentation systems.}, address = {New York, NY, USA}, author = {Becker, Martin and Borchert, Kathrin and Hirth, Matthias and Mewes, Hauke and Hotho, Andreas and Tran-Gia, Phuoc}, booktitle = {Proceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business}, keywords = {analysis crowd crowdsource hyptrails microworker myown sourcing working}, pages = {10:1--10:8}, publisher = {ACM}, series = {i-KNOW '15}, title = {MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform}, year = 2015 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/476c98451e60ba174f94e6cd39718a0d/hotho
URNhttp://dx.doi.org/10.1145/2809563.2809608
Tagsanalysis crowd crowdsource hyptrails microworker myown sourcing working
AbstractTo optimize the workflow on commercial crowdsourcing platforms like Amazon Mechanical Turk or Microworkers, it is important to understand how users choose their tasks. Current work usually explores the underlying processes by employing user studies based on surveys with a limited set of participants. In contrast, we formulate hypotheses based on the different findings in these studies and, instead of verifying them based on user feedback, we compare them directly on data from a commercial crowdsourcing platform. For evaluation, we use a Bayesian approach called HypTrails which allows us to give a relative ranking of the corresponding hypotheses. The hypotheses considered, are for example based on task categories, monetary incentives or semantic similarity of task descriptions. We find that, in our scenario, hypotheses based on employers as well the the task descriptions work best. Overall, we objectively compare different factors influencing users when choosing their tasks. Our approach enables crowdsourcing companies to better understand their users in order to optimize their platforms, e.g., by incorparting the gained knowledge about these factors into task recommentation systems.

2014 [ nach oben ]

Ubicon and its Applications for Ubiquitous Social Computing. Atzmueller, Martin; Becker, Martin; Kibanov, Mark; Scholz, Christoph; Doerfel, Stephan; Hotho, Andreas; Macek, Bjoern-Elmar; Mitzlaff, Folke; Mueller, Juergen; Stumme, Gerd in New Review of Hypermedia and Multimedia (2014). 20(1) 53–77.
[ BibTeX ] [ URL ] [ Download ] [ Details ]
 
@proceedings{cellier2014proceedings, editor = {Cellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie{-}Francine and Toussaint, Yannick}, keywords = {2014 data for:dmir mining myown nlp sys:relevantfor:l3s workshop}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Proceedings of the 1st International Workshop on Interactions between Data Mining and Natural Language Processing co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September 15, 2014}, volume = 1202, year = 2014 }
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HerausgeberCellier, Peggy and Charnois, Thierry and Hotho, Andreas and Matwin, Stan and Moens, Marie-Francine and Toussaint, Yannick
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/cfb7265080d484cfda32e1fbdaff361f/hotho
Tags2014 data for:dmir mining myown nlp sys:relevantfor:l3s workshop

Subjective vs. Objective Data: Bridging the Gap. Becker, Martin; Hotho, Andreas; Mueller, Juergen; Kibanov, Mark; Atzmueller, Martin; Stumme, Gerd (2014).
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ Details ]
 
Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior.
@misc{doerfel2014course, abstract = {Social tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior.}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, keywords = {2014 for:dmir myown share social sys:relevantfor:l3s tagging}, note = {cite arxiv:1401.0629}, title = {Of course we share! Testing Assumptions about Social Tagging Systems}, year = 2014 }
Weitere Informationen
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/988ea3a9b85ec0656e27750e4080325c/hotho
Tags2014 for:dmir myown share social sys:relevantfor:l3s tagging
AbstractSocial tagging systems have established themselves as an important part in today's web and have attracted the interest from our research community in a variety of investigations. The overall vision of our community is that simply through interactions with the system, i.e., through tagging and sharing of resources, users would contribute to building useful semantic structures as well as resource indexes using uncontrolled vocabulary not only due to the easy-to-use mechanics. Henceforth, a variety of assumptions about social tagging systems have emerged, yet testing them has been difficult due to the absence of suitable data. In this work we thoroughly investigate three available assumptions - e.g., is a tagging system really social? - by examining live log data gathered from the real-world public social tagging system BibSonomy. Our empirical results indicate that while some of these assumptions hold to a certain extent, other assumptions need to be reflected and viewed in a very critical light. Our observations have implications for the design of future search and other algorithms to better reflect the actual user behavior.

Proceedings of the 1st International Workshop on Interactions between Data Mining and Natural Language Processing co-located with The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, DMNLP@PKDD/ECML 2014, Nancy, France, September 15, 2014 Cellier, Peggy; Charnois, Thierry; Hotho, Andreas; Matwin, Stan; Moens, Marie-Francine; Toussaint, Yannick in CEUR Workshop Proceedings (2014). (Vol. 1202) CEUR-WS.org.
[ Abstract ] [ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.
@article{atzmueller2014ubicon, abstract = {The combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.}, author = {Atzmueller, Martin and Becker, Martin and Kibanov, Mark and Scholz, Christoph and Doerfel, Stephan and Hotho, Andreas and Macek, Bjoern-Elmar and Mitzlaff, Folke and Mueller, Juergen and Stumme, Gerd}, journal = {New Review of Hypermedia and Multimedia}, keywords = {2014 analytics for:dmir mining myown social sys:relevantfor:l3s ubicon ubiquitous}, number = 1, pages = {53--77}, title = {{Ubicon and its Applications for Ubiquitous Social Computing}}, volume = 20, year = 2014 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/176e4f2816af5fe1630ed65e062900ce/hotho
URNhttp://dx.doi.org/10.1080/13614568.2013.873488
Tags2014 analytics for:dmir mining myown social sys:relevantfor:l3s ubicon ubiquitous
AbstractThe combination of ubiquitous and social computing is an emerging research area which integrates different but complementary methods, techniques and tools. In this paper, we focus on the Ubicon platform, its applications, and a large spectrum of analysis results. Ubicon provides an extensible framework for building and hosting applications targeting both ubiquitous and social environments. We summarize the architecture and exemplify its implementation using four real-world applications built on top of Ubicon. In addition, we discuss several scientific experiments in the context of these applications in order to give a better picture of the potential of the framework, and discuss analysis results using several real-world data sets collected utilizing Ubicon.

Of course we share! Testing Assumptions about Social Tagging Systems. Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus (2014).
[ BibTeX ] [ URL ] [ Details ]
 
@inproceedings{doerfel2014evaluating, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Proceedings of the 16th {LWA} Workshops: KDML, {IR} and FGWM, Aachen, Germany, September 8-10, 2014.}, editor = {Seidl, Thomas and Hassani, Marwan and Beecks, Christian}, keywords = {2014 behavior bibsonomy for:dmir myown navigation sys:relevantfor:l3s tagging}, pages = {18--19}, publisher = {CEUR-WS.org}, title = {Evaluating Assumptions about Social Tagging - {A} Study of User Behavior in BibSonomy}, year = 2014 }
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HerausgeberSeidl, Thomas and Hassani, Marwan and Beecks, Christian
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/4b2e73c82b5a84e1959ad66aaad4a235/hotho
Tags2014 behavior bibsonomy for:dmir myown navigation sys:relevantfor:l3s tagging

Evaluating Assumptions about Social Tagging - A Study of User Behavior in BibSonomy. Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus T. Seidl, M. Hassani, C. Beecks (eds.) (2014). 18–19.
[ BibTeX ] [ Download ] [ Details ]
 
@inproceedings{doerfel2014social, address = {New York, NY, USA}, author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Proceedings of the 23rd International World Wide Web Conference}, keywords = {2014 WWW analyis behavior for:dmir log myown social sys:relevantfor:l3s tagging}, publisher = {ACM}, series = {WWW 2014}, title = {How Social is Social Tagging?}, year = 2014 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/11fab5468dd4b4e3db662ea5e68df8e0/hotho
Tags2014 WWW analyis behavior for:dmir log myown social sys:relevantfor:l3s tagging

How Social is Social Tagging?. Doerfel, Stephan; Zoller, Daniel; Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus in WWW 2014 (2014).
[ BibTeX ] [ URL ] [ DOI ] [ Details ]
 
@inproceedings{jannach2014sixth, author = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, booktitle = {Eighth {ACM} Conference on Recommender Systems, RecSys '14, Foster City, Silicon Valley, CA, {USA} - October 06 - 10, 2014}, keywords = {2014 for:dmir introduction myown recommender social sys:relevantfor:l3s workshop}, pages = 395, title = {The sixth {ACM} RecSys workshop on recommender systems and the social web}, year = 2014 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/5773f799bec72240eda5e6cfb6a03d7b/hotho
URNhttp://dx.doi.org/10.1145/2645710.2645786
Tags2014 for:dmir introduction myown recommender social sys:relevantfor:l3s workshop

The sixth ACM RecSys workshop on recommender systems and the social web. Jannach, Dietmar; Freyne, Jill; Geyer, Werner; Guy, Ido; Hotho, Andreas; Mobasher, Bamshad (2014). 395.
[ Abstract ] [ BibTeX ] [ URL ] [ Download ] [ 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 e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. 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 website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.
@misc{singer2014hyptrails, 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 e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. 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 website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.}, author = {Singer, Philipp and Helic, Denis and Hotho, Andreas and Strohmaier, Markus}, keywords = {2014 bayesian comparing for:dmir hypotheses myown semantic social sys:relevantfor:l3s}, note = {cite arxiv:1411.2844}, title = {HypTrails: A Bayesian Approach for Comparing Hypotheses about Human Trails on the Web}, year = 2014 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/07a19041ef1bfd5cef707e03d1510d5e/hotho
Tags2014 bayesian comparing for:dmir hypotheses myown semantic social sys:relevantfor:l3s
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 e.g., improving underlying network structures, predicting user clicks or enhancing recommendations. In this work, we present a general approach called HypTrails for comparing a set of hypotheses about human trails on the Web, where hypotheses represent beliefs about transitions between states. Our approach utilizes Markov chain models with Bayesian inference. The main idea is to incorporate hypotheses as informative Dirichlet priors and to leverage the sensitivity of Bayes factors on the prior for comparing hypotheses with each other. For eliciting Dirichlet priors from hypotheses, we present an adaption of the so-called (trial) roulette method. 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 website navigation, business reviews and online music played. Our work expands the repertoire of methods available for studying human trails on the Web.

Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th ACM Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014 Jannach, Dietmar; Freyne, Jill; Geyer, Werner; Guy, Ido; Hotho, Andreas; Mobasher, Bamshad in CEUR Workshop Proceedings (2014). (Vol. 1271) CEUR-WS.org.
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@proceedings{jannach2014proceedings, editor = {Jannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad}, keywords = {2014 for:dmir myown proceedings recommender social sys:relevantfor:l3s workshop}, publisher = {CEUR-WS.org}, series = {{CEUR} Workshop Proceedings}, title = {Proceedings of the 6th Workshop on Recommender Systems and the Social Web (RSWeb 2014) co-located with the 8th {ACM} Conference on Recommender Systems (RecSys 2014), Foster City, CA, USA, October 6, 2014}, volume = 1271, year = 2014 }
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HerausgeberJannach, Dietmar and Freyne, Jill and Geyer, Werner and Guy, Ido and Hotho, Andreas and Mobasher, Bamshad
BibSonomy-Posthttps://www.bibsonomy.org/bibtex/09cb7c63e60bd3c5e6773c9c871a8aba/hotho
Tags2014 for:dmir myown proceedings recommender social sys:relevantfor:l3s workshop

The social distributional hypothesis: a pragmatic proxy for homophily in online social networks. Mitzlaff, Folke; Atzmueller, Martin; Hotho, Andreas; Stumme, Gerd in Social Network Analysis and Mining (2014). 4(1)
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Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the
@article{noKey, abstract = {Applications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the}, author = {Mitzlaff, Folke and Atzmueller, Martin and Hotho, Andreas and Stumme, Gerd}, journal = {Social Network Analysis and Mining}, keywords = {2014 distributional for:dmir hypothesis myown pragmatic proxy social sys:relevantfor:l3s}, number = 1, publisher = {Springer Vienna}, title = {The social distributional hypothesis: a pragmatic proxy for homophily in online social networks}, volume = 4, year = 2014 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/5b268a7c5308af783c3028573ffcd0c0/hotho
URNhttp://dx.doi.org/10.1007/s13278-014-0216-2
Tags2014 distributional for:dmir hypothesis myown pragmatic proxy social sys:relevantfor:l3s
AbstractApplications of the Social Web are ubiquitous and have become an integral part of everyday life: Users make friends, for example, with the help of online social networks, share thoughts via Twitter, or collaboratively write articles in Wikipedia. All such interactions leave digital traces; thus, users participate in the creation of heterogeneous, distributed, collaborative data collections. In linguistics, the

HypTrails: A Bayesian Approach for Comparing Hypotheses about Human Trails on the Web. Singer, Philipp; Helic, Denis; Hotho, Andreas; Strohmaier, Markus (2014).
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@incollection{singer2014folksonomies, author = {Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus}, booktitle = {Encyclopedia of Social Network Analysis and Mining}, keywords = {2014 characterization folksonomy for:dmir myown}, pages = {542--547}, publisher = {Springer}, title = {Folksonomies}, year = 2014 }
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BibSonomy-Posthttps://www.bibsonomy.org/bibtex/84d9498b73de976d8d550c6761d4be0d/hotho
Tags2014 characterization folksonomy for:dmir myown

Folksonomies. Singer, Philipp; Niebler, Thomas; Hotho, Andreas; Strohmaier, Markus in Encyclopedia of Social Network Analysis and Mining (2014). 542–547.