Highlighting the recurrence of topics usage in candidates speeches is a key feature to identify the main ideas of each candidate during a political campaign.In this paper, we present a method combining standard topic modeling with signature mining for analyzing topic recurrence in speeches of Clinton and Trump during the 2016 American presidential campaign.The results show that the method extracts automatically the main ideas of each candidate and, in addition, provides information about the evolution of these topics during the campaign.

This paper presents a new model to detect and explain customer defection in a grocery retail context. This new model analyzes the evolution of each customer basket content. It therefore provides actionable knowledge for the retailer at an individual scale. In addition, this model is able to identify customers that are likely to defect in the future months.

Detection of web attacks is an important issue in current defense-in-depth security framework. In this paper, we propose a novel general framework for adaptive and online detection of web attacks. The general framework can be based on any online clustering methods. A detection model based on the framework is able to learn online and deal with "concept drift" in web audit data streams. Str-DBSCAN that we extended DBSCAN to streaming data as well as StrAP are both used to validate the framework. The detection model based on the framework automatically labels the web audit data and adapts to normal behavior changes while identifies attacks through dynamical clustering of the streaming data. A very large size of real HTTP Log data collected in our institute is used to validate the framework and the model. The preliminary testing results demonstrated its effectiveness.

Monitoring patients in intensive care units is a critical task. Simple condition detection is generally insufficient to diagnose a patient and may generate many false alarms to the clinician operator. Deeper knowledge is needed to discriminate among alarms those that necessitate urgent therapeutic action. We propose an intelligent monitoring system that makes use of many artificial intelligence techniques: artificial neural networks for temporal abstraction, temporal reasoning, model based diagnosis, decision rule based system for adaptivity and machine learning for knowledge acquisition. To tackle the difficulty of taking context change into account, we introduce a pilot aiming at adapting the system behavior by reconfiguring or tuning the parameters of the system modules. A prototype has been implemented and is currently experimented and evaluated. Some results, showing the benefits of the approach, are given.

Article Free Access Share on A user level program transformation tool Authors: François Bodin IRISA-INRIA, Campus de Beaulieu, 35042 Rennes, France IRISA-INRIA, Campus de Beaulieu, 35042 Rennes, FranceView Profile , Yann Mével IRISA-INRIA, Campus de Beaulieu, 35042 Rennes, France IRISA-INRIA, Campus de Beaulieu, 35042 Rennes, FranceView Profile , René Quiniou IRISA-INRIA, Campus de Beaulieu, 35042 Rennes, France IRISA-INRIA, Campus de Beaulieu, 35042 Rennes, FranceView Profile Authors Info & Claims ICS '98: Proceedings of the 12th international conference on SupercomputingJuly 1998Pages 180–187https://doi.org/10.1145/277830.277868Published:13 July 1998Publication History 16citation393DownloadsMetricsTotal Citations16Total Downloads393Last 12 Months24Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF