Intern
    Data Science Chair

    Publications by Andreas Hotho

    These publications are hosted by BibSonomy. A searchable overview of my publications can be found here.

    2024[ to top ]
    • CompTrails: comparing hyp...
      CompTrails: comparing hypotheses across behavioral networks T. Koopmann; M. Becker; F. Lemmerich; A. Hotho in Data Mining and Knowledge Discovery (2024).
    • Marina: Realizing ML-Driv...
      Marina: Realizing ML-Driven Real-Time Network Traffic Monitoring at Terabit Scale M. Seufert; K. Dietz; N. Wehner; S. Geißler; J. Schüler; M. Wolz; A. Hotho; P. Casas; T. Hoßfeld; A. Feldmann in IEEE Transactions on Network and Service Management (2024). 1–1.
    2023[ to top ]
    • ConvMOS: climate model ou...
      ConvMOS: climate model output statistics with deep learning M. Steininger; D. Abel; K. Ziegler; A. Krause; H. Paeth; A. Hotho in Data Mining and Knowledge Discovery (2023). 37(1) 136–166.
    • Enhancing Sequential Next...
      Enhancing Sequential Next-Item Prediction through Modelling Non-Item Pages E. Fischer; D. Schlör; A. Zehe; A. Hotho (2023).
    • Long-Term Effects of Perceived Friendship with Intelligent Voice Assistants on Usage Behavior, User Experience, and Social Perceptions C. Wienrich; A. Carolus; A. Markus; Y. Augustin; J. Pfister; A. Hotho in Computers (2023). 12(4)
    • Can Neural Networks Distinguish High-school Level Mathematical Concepts? S. Wankerl; A. Dulny; G. Goetz; A. Hotho (2023).
    • Higher-Order DeepTrails: Unified Approach to *Trails T. Koopmann; J. Pfister; A. Markus; A. Carolus; C. Wienrich; A. Hotho in {CEUR} Workshop Proceedings, M. Leyer, J. Wichmann (Eds.) (2023).
    • TaylorPDENet: Learning PDEs from non-grid Data P. Heinisch; A. Dulny; A. Krause; A. Hotho (2023).
    • Optimizing Medical Servic...
      Optimizing Medical Service Request Processes through Language Modeling and Semantic Search D. Schlör; J. Pfister; A. Hotho in ICMHI 2023 (2023). 136–141.
    • DynaBench: A Benchmark Dataset for Learning Dynamical Systems from Low-Resolution Data A. Dulny; A. Hotho; A. Krause D. Koutra, C. Plant, M. Gomez Rodriguez, E. Baralis, F. Bonchi (Eds.) (2023). 438–455.
    • Evaluating feature relevance XAI in network intrusion detection J. Tritscher; M. Wolf; A. Hotho; D. Schlör in The World Conference on eXplainable Artificial Intelligence (xAI 2023) - to appear (2023).
    • Occupational Fraud Detection through Agent-based Data Generation J. Tritscher; A. Roos; D. Schlör; A. Hotho; A. Krause in The 8th Workshop on MIning DAta for financial applicationS MIDAS 2023 - to appear (2023).
    • Liquor-HGNN: A heterogeneous graph neural network for leakage detection in water distribution networks M. "Schaller; M. "Steininger; A. "Dulny; D. "Schlör; A. "Hotho in LWDA’23: Lernen, Wissen, Daten, Analysen. October 09--11, 2023, Marburg, Germany, (M. Leyer, Ed.) (2023).
    • Swarming Detection in Smart Beehives Using Auto Encoders for Audio Data P. Janetzky; M. Schaller; A. Krause; A. Hotho (2023). 1–5.
    • Feature relevance XAI in anomaly detection: Reviewing approaches and challenges J. Tritscher; A. Krause; A. Hotho in Frontiers in Artificial Intelligence (2023). 6
    • Zero-Shot Clickbait Spoiling by Rephrasing Titles as Questions D. Wangsadirdja; J. Pfister; K. Kobs; A. Hotho (2023). 1090–1095.
    • Towards a Computational A...
      Towards a Computational Analysis of Suspense: Detecting Dangerous Situations A. Zehe; J. Schröter; A. Hotho (2023).
    • CapsKG: Enabling Continual Knowledge Integration in Language Models for Automatic Knowledge Graph Completion J. Omeliyanenko; A. Zehe; A. Hotho; D. Schlör in International Semantic Web Conference ISWC 2023, to appear (2023).
    • Automatic Speech Detection on a Smart Beehive’s Raspberry Pi P. Janetzky; P. Lissmann; A. Hotho; A. Krause (2023).
    2022[ to top ]
    • On Learning Hierarchical Embeddings from Encrypted Network Traffic N. Wehner; M. Ring; J. Sch{{ü}}ler; A. Hotho; T. Hoßfeld; M. Seufert (2022). 1–7.
    • One Graph to Rule them All: Using {NLP} and Graph Neural Networks to analyse Tolkien’s Legendarium V. Perri; L. Qarkaxhija; A. Zehe; A. Hotho; I. Scholtes in {CEUR} Workshop Proceedings, F. Karsdorp, K. L. Nielbo (Eds.) (2022). (Vol. 3290) 291–317.
    • Semi-unsupervised Learning for Time Series Classification P. Davidson; M. Steininger; A. Huhn; A. Krause; A. Hotho in CoRR (2022). abs/2207.03119
    • Anomaly Detection in Beehives: An Algorithm Comparison P. Davidson; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho A. Ahrens, R. V. Prasad, C. Benavente-Peces, N. Ansari (Eds.) (2022). 1–20.
    • The Impact of Different System Call Representations on Intrusion Detection S. Wunderlich; M. Ring; D. Landes; A. Hotho in Log. J. {IGPL} (2022). 30(2) 239–251.
    • Comparison of Data Representations and Machine Learning Architectures for User Identification on Arbitrary Motion Sequences C. Schell; A. Hotho; M. E. Latoschik in CoRR (2022). abs/2210.00527
    • CLIP knows image aesthetics S. Hentschel; K. Kobs; A. Hotho in Frontiers in artificial intelligence (2022). 5 976235.
    • On Background Bias in Deep Metric Learning K. Kobs; A. Hotho in CoRR (2022). abs/2210.01615
    • Open ERP System Data For Occupational Fraud Detection J. Tritscher; F. Gwinner; D. Schlör; A. Krause; A. Hotho in arxiv (2022).
    • SenPoi at SemEval-2022 Task 10: Point me to your Opinion, SenPoi J. Pfister; S. Wankerl; A. Hotho G. Emerson, N. Schluter, G. Stanovsky, R. Kumar, A. Palmer, N. Schneider, S. Singh, S. Ratan (Eds.) (2022). 1313–1323.
    • Towards Responsible Medical Diagnostics Recommendation Systems D. Schl{{ö}}r; A. Hotho in CoRR (2022). abs/2209.03760
    • Sequential Item Recommendation in the {MOBA} Game Dota 2 A. Dallmann; J. Kohlmann; D. Zoller; A. Hotho in CoRR (2022). abs/2201.08724
    • InDiReCT: Language-Guided...
      InDiReCT: Language-Guided Zero-Shot Deep Metric Learning for Images K. Kobs; M. Steininger; A. Hotho (2022).
    • Do Different Deep Metric Learning Losses Lead to Similar Learned Features? K. Kobs; M. Steininger; A. Dulny; A. Hotho in CoRR (2022). abs/2205.02698
    • Semi-unsupervised Learning for Time Series Classification P. Davidson; M. Steininger; A. Huhn; A. Krause; A. Hotho in Milets@KDD (2022).
    • {LSX} team5 at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity Assessment based on Word- and Sentence Mover{’}s Distance S. Heil; K. Kopp; A. Zehe; K. Kobs; A. Hotho (2022). 1190–1195.
    • Point me to your Opinion, {S}en{P}oi J. Pfister; S. Wankerl; A. Hotho (2022). 1313–1323.
    • {W}ue{D}evils at {S}em{E}val-2022 Task 8: Multilingual News Article Similarity via Pair-Wise Sentence Similarity Matrices D. Wangsadirdja; F. Heinickel; S. Trapp; A. Zehe; K. Kobs; A. Hotho (2022). 1235–1243.
    • NeuralPDE: Modelling Dynamical Systems from Data A. Dulny; A. Hotho; A. Krause in Lecture Notes in Computer Science, R. Bergmann, L. Malburg, S. C. Rodermund, I. J. Timm (Eds.) (2022). (Vol. 13404) 75–89.
    2021[ to top ]
    • NeuralPDE: Modelling Dynamical Systems from Data A. Dulny; A. Hotho; A. Krause (2021).
    • A financial game with opportunities for fraud J. Tritscher; A. Krause; D. Schl{ö}r; F. Gwinner; S. von Mammen; A. Hotho in IEE COG 2021 (2021). 2021
    • Malware detection on windows audit logs using LSTMs M. Ring; D. Schlör; S. Wunderlich; D. Landes; A. Hotho in Computers & Security (2021). 109 102389.
    • Semi-Supervised Learning for Grain Size Distribution Interpolation K. Kobs; C. Schäfer; M. Steininger; A. Krause; R. Baumhauer; H. Paeth; A. Hotho (2021). 34–44.
    • The Semantic Web - {ISWC} 2021 - 20th International Semantic Web Conference, {ISWC} 2021, Virtual Event, October 24-28, 2021, Proceedings A. Hotho; E. Blomqvist; S. Dietze; A. Fokoue; Y. Ding; P. M. Barnaghi; A. Haller; M. Dragoni; H. Alani in Lecture Notes in Computer Science (2021). (Vol. 12922) Springer.
    • Detecting Presence Of Speech In Acoustic Data Obtained From Beehives P. Janetzky; P. Davidson; M. Steininger; A. Krause; A. Hotho F. Font, A. Mesaros, D. P. W. Ellis, E. Fonseca, M. Fuentes, B. Elizalde (Eds.) (2021). 26–30.
    • Density-based weighting f...
      Density-based weighting for imbalanced regression M. Steininger; K. Kobs; P. Davidson; A. Krause; A. Hotho in Machine Learning, (A. Appice; S. Escalera; J. A. Gamez; H. Trautmann, Eds.) (2021).
    • Anomaly Detection in Beehives: An Algorithm Comparison P. Davidson; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho (2021).
    • A financial game with opportunities for fraud J. Tritscher; A. Krause; D. Schl{{ö}}r; F. Gwinner; S. von Mammen; A. Hotho (2021). 1–5.
    • Semi-unsupervised Learning: An In-depth Parameter Analysis P. Davidson; F. Buckermann; M. Steininger; A. Krause; A. Hotho S. Edelkamp, R. M{ö}ller, E. Rueckert (Eds.) (2021). 51–66.
    • Proximity dimensions and the emergence of collaboration: a HypTrails study on German AI research T. Koopmann; M. Stubbemann; M. Kapa; M. Paris; G. Buenstorf; T. Hanika; A. Hotho; R. Jäschke; G. Stumme in Scientometrics (2021).
    • Detecting Scenes in Ficti...
      Detecting Scenes in Fiction: A new Segmentation Task A. Zehe; L. Konle; L. Dümpelmann; E. Gius; A. Hotho; F. Jannidis; L. Kaufmann; M. Krug; F. Puppe; N. Reiter; A. Schreiber; N. Wiedmer (2021).
    • Do Different Deep Metric Learning Losses Lead to Similar Learned Features? K. Kobs; M. Steininger; A. Dulny; A. Hotho in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2021). 10644–10654.
    • Shared Task on Scene Segmentation @ KONVENS 2021 A. Zehe; L. Konle; S. Guhr; L. Dümpelmann; E. Gius; A. Hotho; F. Jannidis; L. Kaufmann; M. Krug; F. Puppe; N. Reiter; A. Schreiber (2021). 1–21.
    • CoBERT: Scientific Collab...
      CoBERT: Scientific Collaboration Prediction via Sequential Recommendation T. Koopmann; K. Kobs; K. Herud; A. Hotho (2021). 45–54.
    • Sequential Item Recommendation in the MOBA Game Dota 2 A. Dallmann; J. Kohlmann; D. Zoller; A. Hotho (2021). 10–17.
    • Learning Mathematical Relations Using Deep Tree Models S. Wankerl; A. Dulny; G. Götz; A. Hotho (2021). 1681–1687.
    • A Case Study on Sampling ...
      A Case Study on Sampling Strategies for Evaluating Neural Sequential Item Recommendation Models A. Dallmann; D. Zoller; A. Hotho (2021).
    • Comparison of Transformer...
      Comparison of Transformer-Based Sequential Product Recommendation Models for the Coveo Data Challenge E. Fischer; D. Zoller; A. Hotho in SIGIR Workshop On eCommerce (2021).
    • Self-Supervised Multi-Task Pretraining Improves Image Aesthetic Assessment J. Pfister; K. Kobs; A. Hotho (2021). 816–825.
    • Assessing Media Bias in Cross-Linguistic and Cross-National Populations A. Sales; A. Zehe; L. B. Marinho; A. Veloso; A. Hotho; J. Omeliyanenko in Proceedings of the International AAAI Conference on Web and Social Media (2021). 15(1) 561–572.
    • Evaluating the multi-task learning approach for land use regression modelling of air pollution A. Dulny; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho in Journal of Physics: Conference Series (2021). 1834(1) 012004.
    2020[ to top ]
    • Anomaly Detection in Beeh...
      Anomaly Detection in Beehives using Deep Recurrent Autoencoders P. Davidson; M. Steininger; F. Lautenschlager; K. Kobs; A. Krause; A. Hotho (2020). 142–149.
    • Evaluating the multi-task learning approach for land use regression modelling of air pollution A. Dulny; M. Steininger; F. Lautenschlager; A. Krause; A. Hotho (2020).
    • LM4KG: Improving Common S...
      LM4KG: Improving Common Sense Knowledge Graphs with Language Models J. Omeliyanenko; A. Zehe; L. Hettinger; A. Hotho J. Z. Pan, V. Tamma, C. d’Amato, K. Janowicz, B. Fu, A. Polleres, O. Seneviratne, L. Kagal (Eds.) (2020). 456–473.
    • Deep Learning for Climate...
      Deep Learning for Climate Model Output Statistics M. Steininger; D. Abel; K. Ziegler; A. Krause; H. Paeth; A. Hotho in Tackling Climate Change with Machine Learning Workshop at NeurIPS 2020 (2020).
    • Towards Predicting the Subscription Status of Twitch.tv Users K. Kobs; M. Potthast; M. Wiegmann; A. Zehe; B. Stein; A. Hotho in Proceedings of ECML-PKDD 2020 ChAT Discovery Challenge on Chat Analytics for Twitch (2020).
    • Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part III U. Brefeld; Élisa Fromont; A. Hotho; A. J. Knobbe; M. H. Maathuis; C. Robardet in Lecture Notes in Computer Science (2020). (Vol. 11908) Springer.
    • Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part II U. Brefeld; Élisa Fromont; A. Hotho; A. J. Knobbe; M. H. Maathuis; C. Robardet in Lecture Notes in Computer Science (2020). (Vol. 11907) Springer.
    • Evaluation of Post-hoc XA...
      Evaluation of Post-hoc XAI Approaches Through Synthetic Tabular Data J. Tritscher; M. Ring; D. Schlr; L. Hettinger; A. Hotho D. Helic, G. Leitner, M. Stettinger, A. Felfernig, Z. W. Ra{{{\’s}}} (Eds.) (2020). 422–430.
    • NICER — Aesthetic Image Enhancement with Humans in the Loop M. Fischer; K. Kobs; A. Hotho (2020). 357–362.
    • Time Series Forecasting for Self-Aware Systems A. {Bauer}; M. {Züfle}; N. {Herbst}; A. {Zehe}; A. {Hotho}; S. {Kounev} in Proceedings of the IEEE (2020). 1–26.
    • Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019, Proceedings, Part I U. Brefeld; Élisa Fromont; A. Hotho; A. J. Knobbe; M. H. Maathuis; C. Robardet in Lecture Notes in Computer Science (2020). (Vol. 11906) Springer.
    • iNALU: Improved Neural Ar...
      iNALU: Improved Neural Arithmetic Logic Unit D. Schlör; M. Ring; A. Hotho in Frontiers in Artificial Intelligence (2020). 3 71.
    • Integrating Keywords into...
      Integrating Keywords into BERT4Rec for Sequential Recommendation E. Fischer; D. Zoller; A. Dallmann; A. Hotho (2020). (Vol. 12325) 275–282.
    • OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning F. Lautenschlager; M. Becker; K. Kobs; M. Steininger; P. Davidson; A. Krause; A. Hotho in Atmospheric Environment (2020). 233 117535.
    • HarryMotions – Classify...
      HarryMotions – Classifying Relationships in Harry Potter based on Emotion Analysis A. Zehe; J. Arns; L. Hettinger; A. Hotho (2020).
    • SimLoss: Class Similariti...
      SimLoss: Class Similarities in Cross Entropy K. Kobs; M. Steininger; A. Zehe; F. Lautenschlager; A. Hotho D. Helic, G. Leitner, M. Stettinger, A. Felfernig, Z. W. Ra{{{\’s}}} (Eds.) (2020). 431–439.
    • MapLUR: Exploring a New Paradigm for Estimating Air Pollution Using Deep Learning on Map Images M. Steininger; K. Kobs; A. Zehe; F. Lautenschlager; M. Becker; A. Hotho in ACM Trans. Spatial Algorithms Syst. (2020). 6(3)
    • Smartwatch-Derived Data and Machine Learning Algorithms Estimate Classes of Ratings of Perceived Exertion in Runners: A Pilot Study P. Davidson; P. Düking; C. Zinner; B. Sperlich; A. Hotho in Sensors (2020).
    • Where to Submit? Helping Researchers to Choose the Right Venue K. Kobs; T. Koopmann; A. Zehe; D. Fernes; P. Krop; A. Hotho (2020). 878–883.
    • Improving Sentiment Analysis with Biofeedback Data D. Schl{ö}r; A. Zehe; K. Kobs; B. Veseli; F. Westermeier; L. Br{ü}bach; D. Roth; M. E. Latoschik; A. Hotho (2020). 28–33.
    • Emote-Controlled: Obtaini...
      Emote-Controlled: Obtaining Implicit Viewer Feedback through Emote based Sentiment Analysis on Comments of Popular Twitch.tv Channels K. Kobs; A. Zehe; A. Bernstetter; J. Chibane; J. Pfister; J. Tritscher; A. Hotho in {ACM} Transactions on Social Computing (2020). 3(2) 1–34.
    • Financial Fraud Detection with Improved Neural Arithmetic Logic Units D. Schlör; M. Ring; A. Krause; A. Hotho (2020). (Vol. Fifth Workshop on MIning DAta for financial applicationS)
    2019[ to top ]
    • Flow-based network traffi...
      Flow-based network traffic generation using Generative Adversarial Networks M. Ring; D. Schlör; D. Landes; A. Hotho in Computers & Security (2019). 82 156–172.
    • Detection of Scenes in Fi...
      Detection of Scenes in Fiction E. Gius; F. Jannidis; M. Krug; A. Zehe; A. Hotho; F. Puppe; J. Krebs; N. Reiter; N. Wiedmer; L. Konle (2019).
    • Solving Mathematical Exer...
      Solving Mathematical Exercises: Prediction of Students’ Success S. Wankerl; G. Götz; A. Hotho in CEUR Workshop Proceedings, R. Jäschke, M. Weidlich (Eds.) (2019). (Vol. 2454) 190–194.
    • A Survey of Network-based Intrusion Detection Data Sets M. Ring; S. Wunderlich; D. Scheuring; D. Landes; A. Hotho in Computers & Security (2019). 86 147–167.
    • Team Xenophilius Lovegood...
      Team Xenophilius Lovegood at SemEval-2019 Task 4: Hyperpartisanship Classification using Convolutional Neural Networks A. Zehe; L. Hettinger; S. Ernst; C. Hauptmann; A. Hotho (2019).
    • EClaiRE: Context Matters!...
      EClaiRE: Context Matters! – Comparing Word Embeddings for Relation Classification L. Hettinger; A. Zehe; A. Dallmann; A. Hotho K. David, K. Geihs, M. Lange, G. Stumme (Eds.) (2019). 191–204.
    • Comparison of System Call Representations for Intrusion Detection. S. Wunderlich; M. Ring; D. Landes; A. Hotho in Advances in Intelligent Systems and Computing, F. Martínez-Álvarez, A. T. Lora, J. A. S. Muñoz, H. Quintián, E. Corchado (Eds.) (2019). (Vol. 951) 14–24.
    • On the Right Track! Analy...
      On the Right Track! Analysing and Predicting Navigation Success in Wikipedia T. Koopmann; A. Dallmann; L. Hettinger; T. Niebler; A. Hotho in HT ’19, C. Atzenbeck, J. Rubart, D. E. Millard (Eds.) (2019). 143–152.
    2018[ to top ]
    • Big-Data Helps SDN to Improve Application Specific Quality of Service S. Schwarzmann; A. Blenk; O. Dobrijevic; M. Jarschel; A. Hotho; T. Zinner; F. Wamser in Big Data and Software Defined Networks (2018).
    • Air Trails -- Urban Air Quality Campaign Exploration Patterns M. Becker; F. Lautenschlager; A. Hotho (2018).
    • Burrows’ Zeta: Exploring and Evaluating Variants and Parameters C. Schöch; D. Schlör; A. Zehe; H. Gebhard; M. Becker; A. Hotho (2018). 274–277.
    • Analysing Direct Speech in German Novels F. Jannidis; L. Konle; A. Zehe; A. Hotho; M. Krug (2018).
    • {Adaptive kNN Using Expected Accuracy for Classification of Geo-Spatial Data} M. Kibanov; M. Becker; J. Müller; M. Atzmueller; A. Hotho; G. Stumme (2018).
    • EveryAware Gears: A Tool to visualize and analyze all types of Citizen Science Data F. Lautenschlager; M. Becker; M. Steininger; A. Hotho D. Burghardt, S. Chen, G. Andrienko, N. Andrienko, R. Purves, A. Diehl (Eds.) (2018).
    • A White-Box Model for Detecting Author Nationality by Linguistic Differences in Spanish Novels A. Zehe; D. Schlör; U. Henny-Krahmer; M. Becker; A. Hotho (2018).
    • Burrows Zeta: Varianten und Evaluation C. Schöch; J. Calvo; A. Zehe; A. Hotho (2018).
    • Flow-based Network Traffic Generation using Generative Adversarial Networks. M. Ring; D. Schlör; D. Landes; A. Hotho in CoRR (2018). abs/1810.07795
    • Accessing Information with Tags: Search and Ranking B. Navarro Bullock; A. Hotho; G. Stumme in Social Information Access: Systems and Technologies, P. Brusilovsky, D. He (Eds.) (2018). 310–343.
    • ClaiRE at SemEval-2018 Ta...
      ClaiRE at SemEval-2018 Task 7: Classification of Relations using Embeddings L. Hettinger; A. Dallmann; A. Zehe; T. Niebler; A. Hotho (2018).
    • Detection of slow port scans in flow-based network traffic M. Ring; D. Landes; A. Hotho in PLOS ONE (2018). 13(9) 1–18.
    2017[ to top ]
    • Learning Word Embeddings ...
      Learning Word Embeddings from Tagging Data: A methodological comparison T. Niebler; L. Hahn; A. Hotho (2017).
    • Flow-based benchmark data...
      Flow-based benchmark data sets for intrusion detection M. Ring; S. Wunderlich; D. Gr{ü}dl; D. Landes; A. Hotho (2017). 361–369.
    • Towards Sentiment Analysi...
      Towards Sentiment Analysis on German Literature A. Zehe; M. Becker; F. Jannidis; A. Hotho G. Kern-Isberner, J. F{ü}rnkranz, M. Thimm (Eds.) (2017). 387–394.
    • Learning Semantic Related...
      Learning Semantic Relatedness from Human Feedback Using Relative Relatedness Learning T. Niebler; M. Becker; C. Pölitz; A. Hotho (2017).
    • Sedentary Behavior among ...
      Sedentary Behavior among National Elite Rowers during Off-Training—A Pilot Study B. Sperlich; M. Becker; A. Hotho; B. Wallmann-Sperlich; M. Sareban; K. Winkert; J. M. Steinacker; G. Treff in Frontiers in Physiology (2017). 8 655.
    • Creation of Flow-Based Da...
      Creation of Flow-Based Data Sets for Intrusion Detection M. Ring; S. Wunderlich; D. Grüdl; D. Landes; A. Hotho in Journal of Information Warfare (2017). 16(4) 41–54.
    • A Toolset for Intrusion a...
      A Toolset for Intrusion and Insider Threat Detection M. Ring; S. Wunderlich; D. Gr{ü}dl; D. Landes; A. Hotho in Data Analytics and Decision Support for Cybersecurity: Trends, Methodologies and Applications, I. Palomares Carrascosa, H. K. Kalutarage, Y. Huang (Eds.) (2017). 3–31.
    • Learning Semantic Related...
      Learning Semantic Relatedness From Human Feedback Using Metric Learning T. Niebler; M. Becker; C. Pölitz; A. Hotho (2017).
    • Improving Session Recomme...
      Improving Session Recommendation with Recurrent Neural Networks by Exploiting Dwell Time A. Dallmann; A. Grimm; C. Pölitz; D. Zoller; A. Hotho (2017).
    • Participatory sensing, op...
      Participatory sensing, opinions and collective awareness V. Loreto; M. Haklay; A. Hotho; V. C. P. Servedio; G. Stumme; J. Theunis; F. Tria (2017). Springer.
    • IP2Vec: Learning Similari...
      IP2Vec: Learning Similarities Between IP Addresses M. Ring; D. Landes; A. Dallmann; A. Hotho in 2017 IEEE International Conference on Data Mining Workshops (ICDMW) (2017). 657–666.
    • Comparing Hypotheses Abou...
      Comparing Hypotheses About Sequential Data: A Bayesian Approach and Its Applications F. Lemmerich; P. Singer; M. Becker; L. Espin-Noboa; D. Dimitrov; D. Helic; A. Hotho; M. Strohmaier Y. Altun, K. Das, T. Mielik{ä}inen, D. Malerba, J. Stefanowski, J. Read, M. {\v{Z}}itnik, M. Ceci, S. D{\v{z}}eroski (Eds.) (2017). 354–357.
    • Mining social semantics o...
      Mining social semantics on the social web A. Hotho; R. Jaeschke; K. Lerman in Semantic Web (2017). 8(5) 623–624.
    • A Bayesian Method for Comparing Hypotheses About Human Trails P. Singer; D. Helic; A. Hotho; M. Strohmaier in ACM Trans. Web (2017). 11(3) 14:1–14:29.
    • MixedTrails: Bayesian hyp...
      MixedTrails: Bayesian hypothesis comparison on heterogeneous sequential data M. Becker; F. Lemmerich; P. Singer; M. Strohmaier; A. Hotho in Data Mining and Knowledge Discovery (2017).
    • Leveraging User-Interacti...
      Leveraging User-Interactions for Time-Aware Tag Recommendations D. Zoller; S. Doerfel; C. Pölitz; A. Hotho in {CEUR} Workshop Proceedings (2017).
    2016[ to top ]
    • Mining Subgroups with Exc...
      Mining Subgroups with Exceptional Transition Behavior F. Lemmerich; M. Becker; P. Singer; D. Helic; A. Hotho; M. Strohmaier B. Krishnapuram, M. Shah, A. J. Smola, C. Aggarwal, D. Shen, R. Rastogi (Eds.) (2016). 965–974.
    • Significance Testing for ...
      Significance Testing for the Classification of Literary Subgenres L. Hettinger; F. Jannidis; I. Reger; A. Hotho (2016).
    • Extracting Semantics from...
      Extracting Semantics from Unconstrained Navigation on Wikipedia T. Niebler; D. Schlör; M. Becker; A. Hotho in KI (2016). 30(2) 163–168.
    • SparkTrails: A MapReduce ...
      SparkTrails: A MapReduce Implementation of HypTrails for Comparing Hypotheses About Human Trails. M. Becker; H. Mewes; A. Hotho; D. Dimitrov; F. Lemmerich; M. Strohmaier J. Bourdeau, J. Hendler, R. Nkambou, I. Horrocks, B. Y. Zhao (Eds.) (2016). 17–18.
    • 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. P. Cellier; T. Charnois; A. Hotho; S. Matwin; M.-F. Moens; Y. Toussaint in CEUR Workshop Proceedings (2016). (Vol. 1646) CEUR-WS.org.
    • Prediction of Happy Endings in German Novels A. Zehe; M. Becker; L. Hettinger; A. Hotho; I. Reger; F. Jannidis in CEUR Workshop Proceedings, P. Cellier, T. Charnois, A. Hotho, S. Matwin, M.-F. Moens, Y. Toussaint (Eds.) (2016). (Vol. 1646) 9–16.
    • Classification of Literar...
      Classification of Literary Subgenres L. Hettinger; F. Jannidis; I. Reger; A. Hotho (2016).
    • FolkTrails: Interpreting ...
      FolkTrails: Interpreting Navigation Behavior in a Social Tagging System T. Niebler; M. Becker; D. Zoller; S. Doerfel; A. Hotho in CIKM ’16 (2016).
    • Extracting Semantics from...
      Extracting Semantics from Random Walks on Wikipedia: Comparing learning and counting methods A. Dallmann; T. Niebler; F. Lemmerich; A. Hotho R. West, L. Zia, D. Taraborelli, J. Leskovec (Eds.) (2016).
    • Comparison of non-invasive individual monitoring of the training and health of athletes with commercially available wearable technologies P. Düking; A. Hotho; F. K. Fuss; H.-C. Holmberg; B. Sperlich in Frontiers in Physiology (2016). 7(71)
    • Analyzing Features for the Detection of Happy Endings in German Novels F. Jannidis; I. Reger; A. Zehe; M. Becker; L. Hettinger; A. Hotho (2016).
    • Posted, Visited, Exported...
      Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy D. Zoller; S. Doerfel; R. Jäschke; G. Stumme; A. Hotho in Journal of Informetrics (2016). 10(3) 732–749.
    • Creation of Specific Flow-Based Training Data Sets for Usage Behaviour Classification F. Otto; M. Ring; D. Landes; A. Hotho (2016). 437.
    • What Users Actually do in...
      What Users Actually do in a Social Tagging System: A Study of User Behavior in BibSonomy S. Doerfel; D. Zoller; P. Singer; T. Niebler; A. Hotho; M. Strohmaier in ACM Transactions on the Web (2016). 10(2) 14:1–14:32.
    2015[ to top ]
    • Genre classification on German novels L. Hettinger; M. Becker; I. Reger; F. Jannidis; A. Hotho (2015).
    • VizTrails: An Information...
      VizTrails: An Information Visualization Tool for Exploring Geographic Movement Trajectories M. Becker; P. Singer; F. Lemmerich; A. Hotho; D. Helic; M. Strohmaier in HT ’15 (2015). 319–320.
    • ConDist: A Context-Driven...
      ConDist: A Context-Driven Categorical Distance Measure M. Ring; F. Otto; M. Becker; T. Niebler; D. Landes; A. Hotho ECMLPKDD2015 (Ed.) (2015).
    • Participatory Patterns in an International Air Quality Monitoring Initiative A. Sîrbu; M. Becker; S. Caminiti; B. De Baets; B. Elen; L. Francis; P. Gravino; A. Hotho; S. Ingarra; V. Loreto; A. Molino; J. Mueller; J. Peters; F. Ricchiuti; F. Saracino; V. D. P. Servedio; G. Stumme; J. Theunis; F. Tria; J. Van den Bossche (2015).
    • Media Bias in German Onli...
      Media Bias in German Online Newspapers A. Dallmann; F. Lemmerich; D. Zoller; A. Hotho (2015).
    • Hyptrails: A bayesian app...
      Hyptrails: A bayesian approach for comparing hypotheses about human trails P. Singer; D. Helic; A. Hotho; M. Strohmaier (2015).
    • Automatic Threshold Calcu...
      Automatic Threshold Calculation for the Categorical Distance Measure ConDist. M. Ring; D. Landes; A. Hotho in CEUR Workshop Proceedings, R. Bergmann, S. Görg, G. Müller (Eds.) (2015). (Vol. 1458) 52–63.
    • Modeling and Extracting Load Intensity Profiles J. v. Kistowski; H. Nikolas; D. Zoller; S. Kounev; A. Hotho (2015).
    • MicroTrails: Comparing Hy...
      MicroTrails: Comparing Hypotheses About Task Selection on a Crowdsourcing Platform M. Becker; K. Borchert; M. Hirth; H. Mewes; A. Hotho; P. Tran-Gia in i-KNOW ’15 (2015). 10:1–10:8.
    • Participatory Patterns in...
      Participatory Patterns in an International Air Quality Monitoring Initiative A. Sîrbu; M. Becker; S. Caminiti; B. De Baets; B. Elen; L. Francis; P. Gravino; A. Hotho; S. Ingarra; V. Loreto; A. Molino; J. Mueller; J. Peters; F. Ricchiuti; F. Saracino; V. D. P. Servedio; G. Stumme; J. Theunis; F. Tria; J. Van den Bossche in PLoS ONE (2015). 10(8) e0136763.
    • Photowalking the city: co...
      Photowalking the city: comparing hypotheses about urban photo trails on Flickr M. Becker; P. Singer; F. Lemmerich; A. Hotho; D. Helic; M. Strohmaier (2015).
    • On Publication Usage in a Social Bookmarking System D. Zoller; S. Doerfel; R. Jäschke; G. Stumme; A. Hotho (2015).
    2014[ to top ]
    • Proceedings of the 1st In...
      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 P. Cellier; T. Charnois; A. Hotho; S. Matwin; M.- }Francine Moens; Y. Toussaint in {CEUR} Workshop Proceedings (2014). (Vol. 1202) CEUR-WS.org.
    • Subjective vs. Objective Data: Bridging the Gap M. Becker; A. Hotho; J. Mueller; M. Kibanov; M. Atzmueller; G. Stumme (2014).
    • {Ubicon and its Applications for Ubiquitous Social Computing} M. Atzmueller; M. Becker; M. Kibanov; C. Scholz; S. Doerfel; A. Hotho; B.-E. Macek; F. Mitzlaff; J. Mueller; G. Stumme in New Review of Hypermedia and Multimedia (2014). 20(1) 53–77.
    • HypTrails: A Bayesian App...
      HypTrails: A Bayesian Approach for Comparing Hypotheses about Human Trails on the Web P. Singer; D. Helic; A. Hotho; M. Strohmaier (2014).
    • Of course we share! Testi...
      Of course we share! Testing Assumptions about Social Tagging Systems S. Doerfel; D. Zoller; P. Singer; T. Niebler; A. Hotho; M. Strohmaier (2014).
    • 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 D. Jannach; J. Freyne; W. Geyer; I. Guy; A. Hotho; B. Mobasher in {CEUR} Workshop Proceedings (2014). (Vol. 1271) CEUR-WS.org.
    • The social distributional...
      The social distributional hypothesis: a pragmatic proxy for homophily in online social networks F. Mitzlaff; M. Atzmueller; A. Hotho; G. Stumme in Social Network Analysis and Mining (2014). 4(1)
    • The sixth {ACM} RecSys workshop on recommender systems and the social web D. Jannach; J. Freyne; W. Geyer; I. Guy; A. Hotho; B. Mobasher (2014). 395.
    • Evaluating Assumptions about Social Tagging - {A} Study of User Behavior in BibSonomy S. Doerfel; D. Zoller; P. Singer; T. Niebler; A. Hotho; M. Strohmaier T. Seidl, M. Hassani, C. Beecks (Eds.) (2014). 18–19.
    • How Social is Social Tagg...
      How Social is Social Tagging? S. Doerfel; D. Zoller; P. Singer; T. Niebler; A. Hotho; M. Strohmaier in WWW 2014 (2014).
    • Folksonomies P. Singer; T. Niebler; A. Hotho; M. Strohmaier in Encyclopedia of Social Network Analysis and Mining (2014). 542–547.
    2013[ to top ]
    • {Semantics of User Interaction in Social Media} F. Mitzlaff; M. Atzmueller; G. Stumme; A. Hotho in Complex Networks IV, G. Ghoshal, J. Poncela-Casasnovas, R. Tolksdorf (Eds.) (2013). (Vol. 476)
    • Computing Semantic Relate...
      Computing Semantic Relatedness from Human Navigational Paths: A Case Study on Wikipedia P. Singer; T. Niebler; M. Strohmaier; A. Hotho in International Journal on Semantic Web and Information Systems (IJSWIS) (2013). 9(4) 41–70.
    • Proceedings of the Fifth ACM RecSys Workshop on Recommender Systems and the Social Web co-located with the 7th ACM Conference on Recommender Systems (RecSys 2013), Hong Kong, China, October 13, 2013. B. Mobasher; D. Jannach; W. Geyer; J. Freyne; A. Hotho; S. S. Anand; I. Guy in CEUR Workshop Proceedings (2013). (Vol. 1066) CEUR-WS.org.
    • Informationelle Selbstbestimmung Im Web 2.0 Chancen Und Risiken Sozialer Verschlagwortungssysteme S. Doerfel; A. Hotho; A. Kartal-Aydemir; A. Roßnagel; G. Stumme (2013). Vieweg + Teubner Verlag.
    • Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations N. Landia; S. Doerfel; R. Jäschke; S. S. Anand; A. Hotho; N. Griffiths in cs.IR (2013). 1310.1498
    • How Tagging Pragmatics Influence Tag Sense Discovery in Social Annotation Systems T. Niebler; P. Singer; D. Benz; C. Körner; M. Strohmaier; A. Hotho in Advances in Information Retrieval, P. Serdyukov, P. Braslavski, S. Kuznetsov, J. Kamps, S. Rüger, E. Agichtein, I. Segalovich, E. Yilmaz (Eds.) (2013). (Vol. 7814) 86–97.
    • A Generic Platform for Ub...
      A Generic Platform for Ubiquitous and Subjective Data M. Becker; J. Mueller; A. Hotho; G. Stumme (2013). New York, NY, USA.
    • Ubiquitous Social Media Analysis Third International Workshops, MUSE 2012, Bristol, UK, September 24, 2012, and MSM 2012, Milwaukee, WI, USA, June 25, 2012, Revised Selected Papers M. Atzmueller; A. Chin; D. Helic; A. Hotho (2013). Imprint: Springer, Berlin, Heidelberg.
    • Exploiting Structural Consistencies with Stacked Conditional Random Fields P. Kluegl; M. Toepfer; F. Lemmerich; A. Hotho; F. Puppe in Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics (2013). 30 111–125.
    • Awareness and Learning in...
      Awareness and Learning in Participatory Noise Sensing M. Becker; S. Caminiti; D. Fiorella; L. Francis; P. Gravino; M. (Muki) Haklay; A. Hotho; V. Loreto; J. Mueller; F. Ricchiuti; V. D. P. Servedio; A. Sîrbu; F. Tria in PLoS ONE (2013). 8(12) e81638.
    • Tag Recommendations for SensorFolkSonomies J. Mueller; S. Doerfel; M. Becker; A. Hotho; G. Stumme (2013). (Vol. 1066)
    2012[ to top ]
    • Modeling and Mining Ubiquitous Social Media M. Atzmueller; A. Chin; D. Helic; A. Hotho in Lecture Notes in Computer Science (2012). (Vol. 7472) Springer Verlag, Heidelberg, Germany.
    • Stacked Conditional Random Fields Exploiting Structural Consistencies P. Klügl; M. Toepfer; F. Lemmerich; A. Hotho; F. Puppe P. L. Carmona, J. S. Sánchez, A. Fred (Eds.) (2012). 240–248.
    • The challenge of recommender systems challenges. A. Said; D. Tikk; A. Hotho P. Cunningham, N. J. Hurley, I. Guy, S. S. Anand (Eds.) (2012). 9–10.
    • Leveraging publication metadata and social data into FolkRank for scientific publication recommendation S. Doerfel; R. Jäschke; A. Hotho; G. Stumme in RSWeb ’12 (2012). 9–16.
    • Collective Information Ex...
      Collective Information Extraction with Context-Specific Consistencies. P. Klügl; M. Toepfer; F. Lemmerich; A. Hotho; F. Puppe in Lecture Notes in Computer Science, P. A. Flach, T. D. Bie, N. Cristianini (Eds.) (2012). (Vol. 7523) 728–743.
    • RSWeb ’12: Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web B. Mobasher; D. Jannach; W. Geyer; A. Hotho (2012). ACM, Dublin, Ireland.
    • Challenges in Tag Recommendations for Collaborative Tagging Systems R. Jäschke; A. Hotho; F. Mitzlaff; G. Stumme in Recommender Systems for the Social Web, J. J. Pazos Arias, A. Fernández Vilas, R. P. Díaz Redondo (Eds.) (2012). (Vol. 32) 65–87.
    • Datenschutz im Web 2.0 am Beispiel des sozialen Tagging-Systems BibSonomy. B. Krause; H. Lerch; A. Hotho; A. Roßnagel; G. Stumme in Informatik Spektrum (2012). 35(1) 12–23.
    • Face-to-Face Contacts at a Conference: Dynamics of Communities and Roles M. Atzmueller; S. Doerfel; A. Hotho; F. Mitzlaff; G. Stumme in {Modeling and Mining Ubiquitous Social Media} (2012). (Vol. 7472)
    • Extending FolkRank with content data N. Landia; S. S. Anand; A. Hotho; R. J{ä}schke; S. Doerfel; F. Mitzlaff in RSWeb ’12 (2012). 1–8.