Web Services Discovery and Recommendation Based on Information Extraction and Symbolic Reputation

This paper shows that the problem of web services representation is crucial and analyzes the various factors that influence on it. It presents the traditional representation of web services considering traditional textual descriptions based on the information contained in WSDL files. Unfortunately, textual web services descriptions are dirty and need significant cleaning to keep only useful information. To deal with this problem, we introduce rules based text tagging method, which allows filtering web service description to keep only significant information. A new representation based on such filtered data is then introduced. Many web services have empty descriptions. Also, we consider web services representations based on the WSDL file structure (types, attributes, etc.). Alternatively, we introduce a new representation called symbolic reputation, which is computed from relationships between web services. The impact of the use of these representations on web service discovery and recommendation is studied and discussed in the experimentation using real world web services

Towards a Secure and Borderless Collaboration between Organizations: An Automated Enforcement Mechanism

During the last decade, organizations have been more and more aware of the benefits of engaging in collaborative activities. To attain a required collaborative objective, they are obligated to share sensitive resources such as data, services, and knowledge. However, sharing sensitive and private resources and exposing them for an external usage may prevent the organizations involved from collaborating. Therefore, this usage requires more preoccupation with security issues. Access control is one of these required security concerns. Several access control models are defined in the literature and this multitude of models creates heterogeneity of access control policies between the collaborating organizations. In this paper, we propose Access Control in Cross-Organizational coLLABoration ACCOLLAB, a solution for automatic mapping between heterogeneous access control policies in cross-organizational collaboration. To carry out this mapping, we suggest a mechanism founded mainly on XACML profiles and on a generic language derivative of XACML we define as Generic-XACML. We also formally prove that the mapping does not affect decision evaluation of policies. Thereby the proposed contribution ACCOLLAB allows each collaborating organization to communicate their access control policies and adopt other’s policies without affecting their existing access control systems

Quantum Machine Learning: A Review and Case Studies

Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist’s perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Learning algorithms. We implement the Quanvolutional Neural Networks (QNNs) on a quantum computer to recognize handwritten digits, and compare its performance to that of its classical counterpart, the Convolutional Neural Networks (CNNs). Additionally, we implement the QSVM on the breast cancer dataset and compare it to the classical SVM. Finally, we implement the Variational Quantum Classifier (VQC) and many classical classifiers on the Iris dataset to compare their accuracies

Découverte et recommandation de services web basées sur l'extraction d'information et la réputation symbolique

International audienceCet article montre que le problème de représentation de services web est crucial et analyse les différents facteurs qui l’influencent. Il discute une représentation classique et en propose deux nouvelles. La première représentation que nous proposons provient du domaine du traitement du langage naturel et est basée sur des règles pour annoter les descriptions de services et ainsi extraire les informations utiles pour l’indexation sémantique de services. La seconde méthode proposée, appelée réputation symbolique, est calculée à partir des relations entre les services considérés et est utilisée pour la recommandation de services web. L’impact de ces représentations pour la découverte et la recommandation est étudié et discuté à la lumière de nos expérimentations utilisant des services web réels

Multiple Representations of Web Services: Discovery, Clustering and Recommendation

International audienceThis paper analyses several web services representations based on web services descriptions and more generally on the content of WSDL files. We introduce also a new representation called symbolic reputation which is computed from relationships between web services. Different implementation issues are discussed and the results considering real world web services are analysed to determine the usefulness of the introduced representations for three main tasks: web services discovery, clustering and recommendation

Représentation de services web : impact sur la découverte et la recommandation

National audienceLes descriptions utilisées par les services web sont en langage naturel, multilingues et inter-domaines. Générer des représentations de services web est donc un défi majeur. Cet article présente une représentation classique et en propose deux nouvelles. L’impact de ces représentations pour la découverte et la recommandation est étudié et discuté à la lumière de nos expérimentations utilisant des services web réels

Découverte et recommandation de services web basées sur l'extraction d'information et la réputation symbolique

International audienceCet article montre que le problème de représentation de services web est crucial et analyse les différents facteurs qui l’influencent. Il discute une représentation classique et en propose deux nouvelles. La première représentation que nous proposons provient du domaine du traitement du langage naturel et est basée sur des règles pour annoter les descriptions de services et ainsi extraire les informations utiles pour l’indexation sémantique de services. La seconde méthode proposée, appelée réputation symbolique, est calculée à partir des relations entre les services considérés et est utilisée pour la recommandation de services web. L’impact de ces représentations pour la découverte et la recommandation est étudié et discuté à la lumière de nos expérimentations utilisant des services web réels