Institute Superieur Of Gestion

Tunis, Tunisia

Institute Superieur Of Gestion

Tunis, Tunisia
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Jouini M.,Institute Superieur Of Gestion | Rabai L.B.A.,Institute Superieur Of Gestion
International Journal of Embedded and Real-Time Communication Systems | Year: 2016

Cloud computing technology is a relatively new concept of offering reliable and virtualized resources, software and hardware on demand to users. It presents a new technology to deliver computing resources as a service. It allows several benefits for example services on demand, provisioning, shared resources and pay per use and suffers from several challenges. In fact, security presents a major obstacle in cloud computing adoption. In this paper, the authors will deal with security problems in cloud computing systems and estimate security breaches using a quantitative security risk assessment model. Finally, the authors use this quantitative model to solve these problems in cloud environments. Copyright © 2016, IGI Global.

Subburayalu S.K.,Ohio State University | Jenhani I.,Institute Superieur Of Gestion | Slater B.K.,Ohio State University
Geoderma | Year: 2014

Data mining from existing County soil surveys can improve the utility of maps for research, management and decision making. Aggregated soil series information in soil survey map units can be disaggregated by following a possibilistic decision tree approach to provide maps at the soil series level. The "overall map unit composition percentage" available in the soil survey tabular data can be treated as a possibility distribution of the prevalence of different soil series within all occurrences of the map unit in the survey area. A case study was conducted in Monroe County in southeastern Ohio. Three different learning approaches including C4.5 decision trees, nonspecificity based possibilistic decision trees and clustering based possibilistic decision trees were applied to the existent County survey, and the efficiency of predicting soils at the series level was assessed using an independent soil series point data set. The results showed an improvement in prediction accuracy by following the clustered possibilistic decision tree approach. The maps were useful in identifying the locations of component soil series within map unit associations, consociations, and complexes. Data mining from existent soil survey maps using the component information available in the tabular data can serve as a guide for disaggregating soil map units to create soil series maps, identifying misplacement of polygon boundaries, identifying presence of inclusions, and correcting mislabeled polygons, when updating soil surveys. © 2013 Elsevier B.V.

Trabelsi E.,Institute Superieur Of Gestion
2013 International Conference on Control, Decision and Information Technologies, CoDIT 2013 | Year: 2013

It is argued in literature that transparency may be detrimental to welfare. [14] suggest reducing the precision of public information or withholding it. The latter seems to be unrealistic. Thus, the issue is not whether central bank should disclose or not its information, but how the central bank should disclose it. We consider a static coordination game (a class of games with multiple pure strategy) in which the private sector receives n semi-public information plus their specific information, and we analyze the impact on the private sector's welfare. The paper consists of three parts: (1) By making assumption that no costs are attached to the provision of private information, we determined the conditions under which the central bank faces a trade-off between enhancing commonality and the use of more precise, but fragmented information. Such intermediate transparent strategies may prevent the bad side of public information from overpowering the good side of it. (2) The latter result is found even in presence of positive externalities. (3) Introducing costs to that framework in equilibrium shows that strategic substitutability between semi-public and private precisions is a very likely outcome. © 2013 IEEE.

Akaichi J.,Institute Superieur Of Gestion
Social Network Analysis and Mining | Year: 2014

Patients are often anxious to quickly discover reliable analysis and concise explanation of their medical images while waiting for the physician decision. The fact of making important choices individually in his own corner may lead the physician to commit errors leading to malpractices and consequently to unforeseeable damages. In order to minimize medical errors by fostering collaboration between physicians and/or patients, we propose in this paper, as a first contribution, a medical social network destined to gather patients’ medical images and physicians’ annotations expressing their medical reviews and advices. The need, to automatically extract information and analyze opinions, becomes obviously a requirement due to the huge number of comments expressing specialists’ recommendations and/or remarks. For this purpose, we propose a second contribution consisting of providing a kind of comments’ summary which extracts the major current terms and relevant words existing on physicians’ reports. Furthermore, this extracted information will present a new and robust input for image indexation enhanced methods. In fact, significant extracted terms will be used later to index images in order to facilitate their search through the underlying social network. To overcome the above challenges, we propose an approach which focuses on algorithms mainly based on statistical methods and external semantic resources destined to filter selected extracted information. © 2014, Springer-Verlag Wien.

Jouini M.,Institute Superieur Of Gestion | Ben Arfa Rabai L.,Institute Superieur Of Gestion
Procedia Computer Science | Year: 2016

This paper reviews the state of the art in cyber security risk assessment of Cloud Computing systems. We select and examine in detail the quantitative security risk assessment models developed for or applied especially in the context of a Cloud Computing system. We review and then analyze existing models in terms of aim; the stages of risk management addressed; key risk management concepts covered; and sources of probabilistic data. Based on the analysis, we propose as well a comparison between these models to pick out limits and advantages of every presented model. © 2016 The Authors.

Henchiri A.,Institute Superieur Of Gestion | Ennigrou M.,Institute Superieur Of Gestion
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Flexible job shop scheduling problem (FJSP) is an important extension of the classical job shop scheduling problem, where the same operation could be processed on more than one machine and has a processing time depending on the machine used. The objective is to minimize the makespan, i.e., the total duration of the schedule. In this article, we propose a multi-agent model based on the hybridization of the tabu search (TS) method and particle swarm optimization (PSO) in order to solve FJSP. Different techniques of diversification have also been explored in order to improve the performance of our model. Our approach has been tested on a set of benchmarks existing in the literature. The results obtained show that the hybridization of TS and PSO led to promising results. © 2013 Springer-Verlag Berlin Heidelberg.

Cadot J.,Institute Superieur Of Gestion
Australian Journal of Agricultural and Resource Economics | Year: 2013

The present study aims to learn how collateral affects firm performance in the case of newly established wine producers. The issue is to identify the effects of collateral in situations of asymmetric information when the bank is the main financial partner of the entrepreneurs involved. On one hand, the use of collateral may reduce the risk of overinvestment by entrepreneurs and thereby reduce the risk of repayment default. On the other hand, collateral may induce bad performance linked to a reduced monitoring of the investments by the bank. We herein test both hypotheses in two different cases: when the bank monitors the investments and when the bank does not. © 2013 Australian Agricultural and Resource Economics Society Inc. and Wiley Publishing Asia Pty Ltd.

Mili H.,University of Quebec at Montréal | Tremblay G.,University of Quebec at Montréal | Jaoude G.B.,University of Quebec at Montréal | Lefebvre E.,École de Technologie Supérieure of Montreal | And 2 more authors.
ACM Computing Surveys | Year: 2010

Requirements capture is arguably the most important step in software engineering, and yet the most difficult and the least formalized one [Phalp and Shepperd 2000]. Enterprises build information systems to support their business processes. Software engineering research has typically focused on the development process, starting with user requirements-if that-with business modeling often confused with software system modeling [Isoda 2001]. Researchers and practitioners in management information systems have long recognized that understanding the business processes that an information system must support is key to eliciting the needs of its users (see e.g., Eriksson and Penker 2000]), but lacked the tools to model such business processes or to relate such models to software requirements. Researchers and practitioners in business administration have long been interested in modeling the processes of organizations for the purposes of understanding, analyzing, and improving such processes [Hammer and Champy 1993], but their models were often too coarse to be of use to software engineers. The advent of ecommerce and workflow management systems, among other things, has led to a convergence of interests and tools, within the broad IT community, for modeling and enabling business processes. In this article we present an overview of business process modeling languages. We first propose a categorization of the various languages and then describe representative languages from each family. © 2010 ACM.

Smiti A.,Institute Superieur Of Gestion | Elouedi Z.,Institute Superieur Of Gestion
Studies in Computational Intelligence | Year: 2010

Case-Based Reasoning (CBR) suffers, like the majority of systems, from a large storage requirement and a slow query execution time, especially when dealing with a large case base. As a result, there has been a significant increase in the research area of Case Base Maintenance (CBM). This paper proposes a case-base maintenance method based on the machine-learning techniques, it is able to maintain the case bases by reducing its size and preserving maximum competence of the system. The main purpose of our method is to apply clustering analysis to a large case base and efficiently build natural clusters of cases which are smaller in size and can easily use simpler maintenance operations. For each cluster we reduce as much as possible, the size of the cluster. © 2010 Springer-Verlag Berlin Heidelberg.

Jouini M.,Institute Superieur Of Gestion | Rabai L.B.A.,Institute Superieur Of Gestion
ACM International Conference Proceeding Series | Year: 2016

Threat classification is extremely important for individuals and organizations, as it is an important step towards realization of information security. In fact, with the progress of information technologies (IT) security becomes a major challenge for organizations which are vulnerable to many types of insiders and outsiders security threats. The paper deals with threats classification models in order to help managers to define threat characteristics and then protect their assets from them. Existing threats classification models are non complete and present non orthogonal threats classes. The aim of this paper is to suggest a scalable and complete approach that classifies security threat in orthogonal way. © 2016 ACM.

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