Vers des recommandations plus personnalisées dans les folksonomies

Session 3 : Web socialNational audiencePlusieurs approches ont été proposées dans la littérature pour personnaliser les recommandations dans les folksonomies. Dans ce papier, nous considérons une nouvelle dimension dans les folksonomies comme information supplémentaire pour offrir aux utilisateurs une recommandation plus ciblée et mieux conforme à leurs besoins. Cela passe par un regroupement des utilisateurs ayant des intérêts communs sous forme de structures appelées quadri-concepts. Notre approche, dans laquelle nous répondons également au challenge de cold start, est ensuite évaluée sur deux jeux de données du monde réel, MOVIELENS et BOOKCROSSING. Cette évaluation comprend une mesure de la précision et du rappel, une évaluation sociale ainsi que plusieurs métriques d'évaluation comme la diversité, la couverture ou la scalabilité

mldr.resampling: Efficient Reference Implementations of Multilabel Resampling Algorithms

Resampling algorithms are a useful approach to deal with imbalanced learning in multilabel scenarios. These methods have to deal with singularities in the multilabel data, such as the occurrence of frequent and infrequent labels in the same instance. Implementations of these methods are sometimes limited to the pseudocode provided by their authors in a paper. This Original Software Publication presents mldr.resampling, a software package that provides reference implementations for eleven multilabel resampling methods, with an emphasis on efficiency since these algorithms are usually time-consuming

Data processing for anomaly detection in web-based applications

Web applications are popular attack targets. Misuse detection systems use signature databases to detect known attacks. However, it is difficult to keep the database up to date with the rate of discovery of vulnerabilities. They also cannot detect zero-day attacks. By contrast, anomaly detection systems learn the normal behavior of the system and monitor its activity to detect any deviations from the normal. Any such deviations are flagged as anomalous. This thesis presents an anomaly detection system for web-based applications. The anomaly detection system monitors the attribute value pairs of successful HTTP requests received by webserver applications and automatically creates parameter profiles. It then uses these profiles to detect anomalies in the HTTP requests. Customized profiles help reduce the number of false positives. Automatic learning ensures that the system can be used with different kinds of web application environments, without the necessity for manual configuration. The results of the detection are also visualized, which enable the system administrator to quickly understand the state of the system and respond accordingly.Keywords: anomaly, anomaly detection, web application, security, computer science, computer security, compute

Energy Disaggregation Using Elastic Matching Algorithms

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)In this article an energy disaggregation architecture using elastic matching algorithms is presented. The architecture uses a database of reference energy consumption signatures and compares them with incoming energy consumption frames using template matching. In contrast to machine learning-based approaches which require significant amount of data to train a model, elastic matching-based approaches do not have a model training process but perform recognition using template matching. Five different elastic matching algorithms were evaluated across different datasets and the experimental results showed that the minimum variance matching algorithm outperforms all other evaluated matching algorithms. The best performing minimum variance matching algorithm improved the energy disaggregation accuracy by 2.7% when compared to the baseline dynamic time warping algorithm.Peer reviewedFinal Published versio