Volume 27 - 2017 - Special issue CARI 2016

Special Issue for CARI 2016 Editors : Eric Badouel, Nabil Gmati, and Moussa Lo,

1. XPath bipolar queries and evaluation

Tchoupé Tchendji, Maurice ; Nguefack, Brice.
The concept of bipolar queries (also call preferences queries) emerged in the Relational Databasescommunity, allowing users to get much more relevant responses to their requests, expressed via queries say withpreferences. Such requests usually have two parts: the first is used to express the strict constraints and thesecond, preferences or wishes. Any response to a query with preferences must necessarily satisfy the first partand preferably the latter. However, if there is at least a satisfactory answer of the second part, those satisfyingonly the first part will be excluded from the final result: they are dominated. In this paper, we explore an approachof importation of this concept in a XML Database via XPath language. To do this, we propose PrefSXPathlanguage, an extension of XPath in order to express XPath queries with structural preferences, then we presenta query evaluation algorithm of PrefSXPath using automata

2. Unfolding through processes to compute the complete prefix of Petri nets

Sogbohossou, Médésu ; Vianou, Antoine.
The partial-order technique of the unfolding implicitly represents state-space of a Petri net (PN), by in particular preserving the concurrency relations between the events. That makes it possible to contain state-space explosion problem in case of strong concurrency. A complete prefix of unfolding is used to cover all the state-space of a bounded PN: its computation according to the classical approach is based on the concept of adequate order, taking directly into account only safe PN. In this paper, a new approach independent of the concept of adequate order and faithful to the partial-order semantics, consists in creating the events of the unfolding in the context of a single process at the same time. The results of the tests are conclusive for safe and nonsafe PN. Some solutions are presented to improve compactness of the prefix obtained.

3. Arabic topic identification based on empirical studies of topic models

Naili, Marwa ; Chaibi, Anja,  ; Ghézala, Henda, .
This paper focuses on the topic identification for the Arabic language based on topic models. We study the Latent Dirichlet Allocation (LDA) as an unsupervised method for the Arabic topic identification. Thus, a deep study of LDA is carried out at two levels: Stemming process and the choice of LDA hyper-parameters. For the first level, we study the effect of different Arabic stemmers on LDA. For the second level, we focus on LDA hyper-parameters α and β and their impact on the topic identification. This study shows that LDA is an efficient method for Arabic topic identification especially with the right choice of hyper-parameters. Another important result is the high impact of the stemming algorithm on topic identification.

4. Management of Low-density Sensor-Actuator Network in a Virtual Architecture

Kengne Tchendji , Vianney ; Paho Nana , Blaise.
Wireless sensor networks (WSN) face many implementation’s problems such as connectivity, security, energy saving, fault tolerance, interference, collision, routing problems, etc. In this paper, we consider a low-density WSN where the distribution of the sensors is poor, and the virtual architecture introduced by Wadaa and al which provides a powerful and fast partitioning of the network into a set of clusters. In order to effectively route the information collected by each sensor node to the base station (sink node, located at the center of the network), we propose a technique based on multiple communication frequencies in order to avoid interferences during the communications. Secondly, we propose an empty clusters detection algorithm, allowing to know the area actually covered by the sensors after the deployment, and therefore, giving the possibility to react accordingly. Finally, we also propose a strategy to allow mobile sensors (actuators) to move in order to: save the WSN’s […]

5. Non-parametric kernel-based bit error probability estimation in digital communication systems: An estimator for soft coded QAM BER computation

Poda , Pasteur ; Saoudi , Samir ; Chonavel , Thierry ; GUILLOUD , Frédéric ; Tapsoba , Théodore , .
The standard Monte Carlo estimations of rare events probabilities suffer from too much computational time. To make estimations faster, kernel-based estimators proved to be more efficient for binary systems whilst appearing to be more suitable in situations where the probability density function of the samples is unknown. We propose a kernel-based Bit Error Probability (BEP) estimator for coded M-ary Quadrature Amplitude Modulation (QAM) systems. We defined soft real bits upon which an Epanechnikov kernel-based estimator is designed. Simulation results showed, compared to the standard Monte Carlo simulation technique, accurate, reliable and efficient BEP estimates for 4-QAM and 16-QAM symbols transmissions over the additive white Gaussian noise channel and over a frequency-selective Rayleigh fading channel.

6. Social Information Retrieval and Recommendation: state- of-the-art and future research

Gorrab, Abir ; Kboubi, Ferihane ; Ghézala, Henda, .
The explosion of web 2.0 and social networks has created an enormous and rewarding source of information that has motivated researchers in different fields to exploit it. Our work revolves around the issue of access and identification of social information and their use in building a user profile enriched with a social dimension, and operating in a process of personalization and recommendation. We study several approaches of Social IR (Information Retrieval), distinguished by the type of incorporated social information. We also study various social recommendation approaches classified by the type of recommendation. We then present a study of techniques for modeling the social user profile dimension, followed by a critical discussion. Thus, we propose our social recommendation approach integrating an advanced social user profile model.