Patterns d'Analyse pour l'Ingénierie de Systèmes d'Information à base d'Agents : Une Application au Domaine du Transport

National audienceIntelligent Transport Information Systems may find benefit of using agent-based solutions. Actually, transport information systems require adaptability to varying changes in offers, and unexpected occurring events. Agents and multiagent systems provide such requirements. Unfortunately, agent-based information systems such as other distributed, asynchronous, loose-coupling applications are difficult to design and implement due to lack of best practices to ease development. This paper describes an approach based on software pattern reuse facilitating engineering of such systems. Patterns are generic solutions to frequently occurring problems. Metamodel represents and structures agent concepts. Fourteen analysis patterns have been specified from this metamodel and describe conceptual entities for the design of an agent-based IS application. Reuse support patterns help designers to reuse former patterns during the information system application engineering

Patterns for Agent-Based Information Systems: A Case Study in Transport

International audienceIntelligent Transport Information Systems may find benefit of using agent-based solutions. Actually, transport information systems require adaptability to varying changes in offers, and unexpected occurring events. Agents and multiagent systems provide such requirements. Unfortunately, agent-based information systems such as other distributed, asynchronous, loose coupling applications are difficult to design and implement due to lack of best practices to ease development. This paper describes an approach based on software pattern reuse that facilitates engineering of these systems. Some patterns have been specified for the analysis and design of such information systems and are described here. Implementation patterns for a specific platform are sketched in perspectives of this research

Ambient Intelligence and Pervasive Architecture Designed within the EPI-MEDICS Personal ECG Monitor

International audienc

Méthodologie pour la conception de systèmes d'information pervasifs (application à la pSanté)

Dans ce mémoire, nous présentons et analysons, dans un premier temps des scénarios pour la prise en charge médicale à distance. A partir de ces scénarios, nous proposons un méta-modèle pour l échange de messages au sein d un environnement pervasif qui se compose, d une part, d un modèle de représentation centrée macro-données qui prend en compte l hétérogénéité et la dynamicité structurelle, syntaxique et sémantique des données pour les présenter aux acteurs selon le contexte, et d autre part un modèle de communication qui décrit la connaissance des types d'échange entre les acteurs en fonction des ressources temporelles, humaines et matérielles dont disposent chacun des acteurs. Cette méthodologie nous a permis d implémenter plusieurs infrastructures capables de garantir, dans un délai raisonnable selon le besoin, la prise en charge par un expert des demandes d autres acteurs du système de santé en fournissant, en quasi temps réel, en tous lieux et en tous temps, des données exhaustives concernant le patient et nécessaires à l établissement d un diagnostic fiable.In this document, we present and analyze, initially scenarios for the telemedecine. From these scenarios, we propose a meta-model for the exchange of messages within a pervasif environment which is composed, on the one hand, of a model of centered representation macro-data which takes into account heterogeneity and the structural, syntactic dynamicity and semantics of data to present at the actors according to the context, and on the other hand a model of communication which describes the knowledge of the types of exchange between the actors according to the temporal resources, human and material available to each actor. This methodology allow to implement infrastructures able to guarantee, within a reasonable time according to the need, the catch of load by an expert of the requests of other actors of the system of health while providing, in quasi real time, all places and all times, of the data exhaustive concerning the patient and necessary to the establishment of a reliable diagnosis.VILLEURBANNE-DOC'INSA LYON (692662301) / SudocSudocFranceF

Engineering agent-based information systems: a case study of automatic contract net systems

International audienceIn every business the tender has become an indispensable part to foster the negotiation of new trade agreements. The selection and the attribution are nowadays a long process conducted manually. It is necessary to define criteria for selecting the best offer, evaluate each proposal and negotiate a business contract. In this paper, we present an approach based on agents for the development of an automatic award of contracts (here called Automatic Contract Net Systems). The selection and negotiation are then automatically performed through communication between agents. We focus in this paper on the tendering and selection of the best offer. To facilitate the development of complex systems such as multi-agent systems, we adopt software patterns that will guide the designer in the analysis, design and implementation on an agent-based execution platform

Patterns d'analyse pour l'ingénierie des systèmes multi-agents

National audienceLe paradigme des systèmes multi-agents (SMA) est approprié pour des applications distribuées sans contrôle centralisé et pour lesquelles il est nécessaire qu'un sousensemble des agents collabore afin de résoudre un problème global. Les systèmes multiagents comme toute application distribuée, asynchrone et à faible couplage sont difficiles à concevoir et à développer. Nous proposons de faciliter leur conception par la réutilisation de patterns logiciels. Les patterns constituent des solutions génériques à des problèmes fréquemment rencontrés. Nous avons conçu un métamodèle représentant et structurant les concepts inhérents aux SMA. A partir de ce modèle, douze patterns d'analyse décrivant les éléments conceptuels nécessaires à la spécification d'applications orientées agents ont été conçus, ainsi que des patterns de support d'utilisation facilitant la réutilisation de ces patterns lors de la phase d'analyse du processus d'ingénierie des SMA

Machine Learning for Text Anomaly Detection: A Systematic Review

International audienceAnomaly detection is a common task in various domains, which has attracted significant research efforts in recent years. Existing reviews mainly focus on structured data, such as numerical or categorical data. Several studies treated review of anomaly detection in general on heterogeneous data or concerning a specific domain. However, anomaly detection on unstructured textual data is less treated. In this work, we target textual anomaly detection. Thus, we propose a systematic review of anomaly detection solutions in the text. To do so, we analyze the included papers in our survey in terms of anomaly detection types, feature extraction methods, and machine learning methods. We also introduce a web scrapping to collect papers from digital libraries and propose a clustering method to classify selected papers automatically. Finally, we compare the proposed automatic clustering approach with manual classification, and we show the interest of our contribution

IEcons: A New Consensus Approach Using Multi-Text Representations for Clustering Task

International audienceToday we are able to generate a large set of text representations from the simple Bag-of-word (BOW) to the recent transformers capturing the semantic and the contextual text meaning. It was proven that there is no best text representation for text clustering task. Consequently, some works combined text representations using a consensus clustering approach. Two consensus approach types exist, namely explicit and implicit consensus. In the explicit consensus, also known as ensemble clustering, the consensus function is applied a posterior after obtaining cluster labels from each text representation clustering allowing to capture global mutual information between the partitions of all text representations. On the other hand, implicit consensus uses tensor clustering to optimize the clustering consensus partition that deals with similarity matrices of text representations. In this paper, we propose a new consensus text clustering algorithm named IEcons (Implicit-Explicit consensus) that optimizes explicit and implicit consensus clustering simultaneously through text embeddings and tensor representation of texts through similarity matrices. We compare our algorithm with others from the literature on five different textual datasets using several algorithm performance criteria. The comparison results reveal that our algorithm best suits most situations.</div

Strategic Integration of Context for Fine-Tuning Topic Model Performance

International audienceIssue Tracking Systems software serves as an interface between a company and its customers. Customers can report bugs and seek assistance, among other demands. Reported issues include textual description, along with company defined metadata, aim at simplifying issue treatment by experts. In the context of the rapid growth of customer-reported issues, the manual treatment process becomes tedious and time-consuming. As a result, more and more studies focus on automating parts of this process, using semantic extraction and topic modeling approaches to automatically classify issues. To this end, most approaches consider the issue of textual description along with metadata, which can be a source of uncertainty and misleading in many real-world scenarios. Besides, knowledge from the company experts is often neglected. In this paper, we propose a general taxonomy of information incorporation into topic models. This aims to assemble all existing techniques, to further detect literature gaps. In addition, we propose a technique to incorporate expert knowledge into neural topic models. We evaluate our techniques and others in the literature on a real-world dataset coming from the JIRA software of a French HR management company. Results show a significant increase of more than 22% in classification performances when using expert knowledge, in addition to the issue textual description. The results validate our approach's effectiveness in improving the automatic classification of issues