Specifying and implementing social Web services operation using commitments

This paper discusses the specification and development of social Web services using commitments. Social Web services establish and maintain networks of contacts, count on their (privileged) contacts when needed, form with other peers strong and long lasting collaborative groups, and know with whom to partner so that ontology reconciliation is minimized. To guarantee the proper execution of these operations, social Web services need to comply with the regulations of the social networks in which they have signed up. This compliance is verified using commitments. Two types of commitments are identified: social and business. The former connect Web services to social networks. And the latter connect social Web services to composite social Web services. A proof-of-concept system to detect commitment violations is, also, discussed in this paper. © 2012 ACM

Impact Analysis of Web Services Substitution on Configurable Compositions

Web services substitution is a promising solution that enables process continuity of SOA-based applications associated with composite Web services (WSs). This chapter proposes an approach that assesses the impact of substitution on the composition and selects the best substitute, from a pool of substitutes, in order to reduce potential conflicts due to different ontologies with other peers in this composition, for example. Two types of impact along with their assessment metrics are defined: local (semantic/policy compatibility matching degree) and global (QoS satisfaction degree). This chapter addresses the selection issue as an optimization problem whose main objective is to minimize the efforts to put into resuming the ongoing composition under some temporal constraints. A set of experiments are conducted as a proof of concept and the findings show that our approach provides the necessary means for achieving Web services substitution with minimal disruption time

Effect of heat treatment time on the dielectric properties of BST

Structural and microstructural properties of sol gel processed BSxT (x = 0.0, 0.05) ceramics have been investigated. Dielectric properties of these ceramics have also been studied as functions of frequency          [1kHz-2MHz] at different measurement temperatures (30-200°C). Heat treatment duration effect on dielectric properties and structural ones has been investigated. The results show that heat treatment and its duration has significant effects on structural, dielectric properties and the average grain size of the samples. Scanning electron microscopy (SEM) analysis show quite regular morphology of the grains and an average grain size which decreases with introduction of Sr, in agreement with structural characterization. Raman analysis has given results which are in accordance of XRD and SEM characterizations. Moreover, the permittivity variations as functions of the frequency, show a decrease in the interval [1kHz-100kHz], followed by an anormal increase in the interval [100kHz-1MHz]. Keywords: Structural, XRD, Raman, Dielectric propertie

A Novel Jamming Attacks Detection Approach Based on Machine Learning for Wireless Communication

Jamming attacks target a wireless network creating an unwanted denial of service. 5G is vulnerable to these attacks despite its resilience prompted by the use of millimeter wave bands. Over the last decade, several types of jamming detection techniques have been proposed, including fuzzy logic, game theory, channel surfing, and time series. Most of these techniques are inefficient in detecting smart jammers. Thus, there is a great need for efficient and fast jamming detection techniques with high accuracy. In this paper, we compare the efficiency of several machine learning models in detecting jamming signals. We investigated the types of signal features that identify jamming signals, and generated a large dataset using these parameters. Using this dataset, the machine learning algorithms were trained, evaluated, and tested. These algorithms are random forest, support vector machine, and neural network. The performance of these algorithms was evaluated and compared using the probability of detection, probability of false alarm, probability of miss detection, and accuracy. The simulation results show that jamming detection based random forest algorithm can detect jammers with a high accuracy, high detection probability and low probability of false alarm