Soraya Kouadri

We present an agent-based and context-oriented approach for Web services composition. A Web service is an accessible application that other applications and humans can discover and trigger to satisfy various needs. Due to the complexity of Web services composition, we consider two concepts to reduce this complexity: software agent and context. A software agent is an autonomous entity that acts on behalf of users, whereas context is any information relevant to characterize a situation. During composition, software agents engage conversations with their peers to agree on the Web services that will participate in the composition.

The high degree of dynamism and heterogeneity of the resources involved in a pervasive computing environment makes service adaptation and interoperability a difficult task. We present UBIDEV, a service framework that faces the heterogeneity problem by hiding at the application level the dynamism of the underlying environment. We describe the UBIDEV architecture focusing on the description and the management of services and resources. We also describe how this approach decreases the complexity of the design and development of service-oriented applications. A prototype implementation of a unified messaging system is presented as a validation of the architectural design.

This paper presents our approach for service provisioning in pervasive computing environments. The presented approach is based on the usage of context-aware features and transactions during the discovery and the deployment of composite services. Context ensures that the best service offers are selected to participate in a service composition. Transactions help in improving the reliability and efficiency of the composite services.

One of the main challenges of conventional spectrum estimation methods in cognitive radio applications is the very high sampling rates involved, which imposes significant operating demands upon the <i>analog-to-digital converter</i> (ADC). This has given impetus to employing <i>compressive sensing</i> (CS) techniques, such as the <i>random demodulator</i> (RD) structure to relax the input ADC specification. It has been recently shown the RD spectrum estimation performance for quadrature<i> phased shift keying</i> (PSK) modulated signals can be significantly improved in terms of <i>spectral concentration</i> and signal-to-noise ratio, when signals are precoloured by an <i>autoregressive</i> (AR) filter. This paper presents an extended AR-RD architecture, which provides enhanced CS capability for higher-order digital modulation schemes, including 16 <i>quadrature amplitude modulation</i> (16QAM), 64QAM and <i>binary</i> PSK (BPSK). Quantitative results corroborate the improved CS performance of the AR-RD structure for higher-order modulations schemes, which provides a propitious design trade-off between AR-RD complexity, latency and CS performance.

A social unrest and violent atmosphere can force a vast number of people to flee their country. While governments and international aid organizations need migration data to inform their decisions, the availability of this data is often delayed due to the tediousness to collect and publish this data. Recent studies recognized the increasing usage of social networking platforms amongst refugees to seek help and express their hardship during their journeys. This paper investigates the feasibility of accurately extracting and identifying tweets from Syria refugees. A robust framework has been developed to find, retrieve, clean and classify tweets from Syria. This includes the development of a Random Forest classifier, which automatically determines which tweets are from Syria refugees. Testing the classifier with samples of historical Twitter data produced promising result of 81% correct classification rate. This preliminary study demonstrates the potential that refugees' messages can be accurately identified and extracted from social media data mixed with many unwanted messages, and this enables further works for studying refugee issues and predicting their migration patterns.