Dokument-ID Dokumenttyp Verfasser/Autoren Herausgeber Haupttitel Abstract Auflage Verlagsort Verlag Erscheinungsjahr Seitenzahl Schriftenreihe Titel Schriftenreihe Bandzahl ISBN Quelle der Hochschulschrift Konferenzname Quelle:Titel Quelle:Jahrgang Quelle:Heftnummer Quelle:Erste Seite Quelle:Letzte Seite URN DOI Abteilungen OPUS4-48333 Wissenschaftlicher Artikel Prasse, Paul; Knaebel, Rene; Machlica, Lukas; Pevny, Tomas; Scheffer, Tobias Joint detection of malicious domains and infected clients Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains. Dordrecht Springer 2019 16 Machine learning 108 8-9 1353 1368 10.1007/s10994-019-05789-z Institut für Informatik und Computational Science OPUS4-48337 Wissenschaftlicher Artikel Cabalar, Pedro; Fandinno, Jorge; Schaub, Torsten H.; Schellhorn, Sebastian Gelfond-Zhang aggregates as propositional formulas Answer Set Programming (ASP) has become a popular and widespread paradigm for practical Knowledge Representation thanks to its expressiveness and the available enhancements of its input language. One of such enhancements is the use of aggregates, for which different semantic proposals have been made. In this paper, we show that any ASP aggregate interpreted under Gelfond and Zhang's (GZ) semantics can be replaced (under strong equivalence) by a propositional formula. Restricted to the original GZ syntax, the resulting formula is reducible to a disjunction of conjunctions of literals but the formulation is still applicable even when the syntax is extended to allow for arbitrary formulas (including nested aggregates) in the condition. Once GZ-aggregates are represented as formulas, we establish a formal comparison (in terms of the logic of Here-and-There) to Ferraris' (F) aggregates, which are defined by a different formula translation involving nested implications. In particular, we prove that if we replace an F-aggregate by a GZ-aggregate in a rule head, we do not lose answer sets (although more can be gained). This extends the previously known result that the opposite happens in rule bodies, i.e., replacing a GZ-aggregate by an F-aggregate in the body may yield more answer sets. Finally, we characterize a class of aggregates for which GZ- and F-semantics coincide. Amsterdam Elsevier 2019 18 Artificial intelligence 274 26 43 10.1016/j.artint.2018.10.007 Institut für Informatik und Computational Science OPUS4-48254 Dissertation Ashouri, Mohammadreza TrainTrap 2020 XIX, 103 Institut für Informatik und Computational Science OPUS4-48259 Wissenschaftlicher Artikel Aguado, Felicidad; Cabalar, Pedro; Fandiño, Jorge; Pearce, David; Perez, Gilberto; Vidal-Peracho, Concepcion Revisiting Explicit Negation in Answer Set Programming New York Cambridge Univ. Press 2019 17 Theory and practice of logic programming 19 5-6 908 924 10.1017/S1471068419000267 Institut für Informatik und Computational Science OPUS4-47028 Wissenschaftlicher Artikel Laskov, Pavel; Gehl, Christian; Krüger, Stefan; Müller, Klaus-Robert Incremental support vector learning: analysis, implementation and applications Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM learning, with the aim of providing a fast, numerically stable and robust implementation. A detailed analysis of convergence and of algorithmic complexity of incremental SVM learning is carried out. Based on this analysis, a new design of storage and numerical operations is proposed, which speeds up the training of an incremental SVM by a factor of 5 to 20. The performance of the new algorithm is demonstrated in two scenarios: learning with limited resources and active learning. Various applications of the algorithm, such as in drug discovery, online monitoring of industrial devices and and surveillance of network traffic, can be foreseen. Cambridge, Mass. MIT Press 2006 28 Journal of machine learning research 7 1909 1936 Institut für Informatik und Computational Science OPUS4-47038 Wissenschaftlicher Artikel Steuer, Ralf; Humburg, Peter; Selbig, Joachim Validation and functional annotation of expression-based clusters based on gene ontology Background: The biological interpretation of large-scale gene expression data is one of the paramount challenges in current bioinformatics. In particular, placing the results in the context of other available functional genomics data, such as existing bio-ontologies, has already provided substantial improvement for detecting and categorizing genes of interest. One common approach is to look for functional annotations that are significantly enriched within a group or cluster of genes, as compared to a reference group. Results: In this work, we suggest the information-theoretic concept of mutual information to investigate the relationship between groups of genes, as given by data-driven clustering, and their respective functional categories. Drawing upon related approaches (Gibbons and Roth, Genome Research 12: 1574-1581, 2002), we seek to quantify to what extent individual attributes are sufficient to characterize a given group or cluster of genes. Conclusion: We show that the mutual information provides a systematic framework to assess the relationship between groups or clusters of genes and their functional annotations in a quantitative way. Within this framework, the mutual information allows us to address and incorporate several important issues, such as the interdependence of functional annotations and combinatorial combinations of attributes. It thus supplements and extends the conventional search for overrepresented attributes within a group or cluster of genes. In particular taking combinations of attributes into account, the mutual information opens the way to uncover specific functional descriptions of a group of genes or clustering result. All datasets and functional annotations used in this study are publicly available. All scripts used in the analysis are provided as additional files. London BioMed Central 2006 12 BMC bioinformatics 7 380 10.1186/1471-2105-7-380 Institut für Informatik und Computational Science OPUS4-41700 Teil eines Buches Kiy, Alexander; Hafer, Jörg; Schumann, Marlen; Enke, Uta Lucke, Ulrike; Schwill, Andreas; Zender, Raphael Digitale Teilnehmerzertifikate und Open Badges verbinden Während Qualifikationen und Kompetenzen, die auf informellem Wege erworben werden, immer mehr Beachtung finden, stellt sowohl deren Darstellung als auch die Anerkennung ein meist unüberwindbares Hindernis für Ausstellende und Erwerbende dar. Vermehrt wird unterdessen von klassisch papiergebundenen auf digitale Teilnahmezertifikate umgestellt, um den Nachweis von Kompetenz- und Qualifikationserwerb zu vereinfachen. In diesem Zusammenhang kann die Verbindung von digitalen Teilnahmezertifikaten und Open Badges einen Mehrwert für die öffentliche Darstellung und Verifikation bieten. Bonn Gesellschaft für Informatik 2016 2 DeLFI 2016 - Die 14. E-Learning Fachtagung Informatik 11.-14. September 2016 Potsdam 978-3-88579-656-5 P-262 285 287 Institut für Informatik und Computational Science OPUS4-13985 Wissenschaftlicher Artikel Sarsakov, Vladimir; Schaub, Torsten H.; Tompits, Hans; Woltran, Stefan A compiler for nested logic programming 2004 3-540- 20721-x Institut für Informatik und Computational Science OPUS4-14297 Wissenschaftlicher Artikel Linke, Thomas; Tompits, Hans; Woltran, Stefan On Acyclic and head-cycle free nested logic programs 2004 3-540-22671-01 Institut für Informatik und Computational Science OPUS4-14299 Wissenschaftlicher Artikel Linke, Thomas; Tompits, Hans; Woltran, Stefan On acyclic and head-cycle free nested logic programs 2004 Institut für Informatik und Computational Science