Abstract

During the last decades, Learning Classifier Systems have known many advancements that were highlighting their potential to resolve complex problems. Despite the advantages offered by these algorithms, it is important to tackle other aspects such as the uncertainty to improve their performance. In this paper, we present a new Learning Classifier System (LCS) that deals with uncertainty in the class selection in particular imprecision. Our idea is to integrate the Belief function theory in the sUpervised Classifier System (UCS) for classification purpose. The new approach proved to be efficient to resolve several classification problems.

Abstract

During the few past years, decomposition has shown a high performance in solving Multi-objective Optimization Problems (MOPs) involving more than three objectives, called as Many-objective Optimization Problems (MaOPs). The performance of most of the existing decomposition-based algorithms has been assessed on the widely used DTLZ and WFG unconstrained test problems. However, the number of works that have been devoted to tackle the problematic of constrained many-objective optimization is relatively very small when compared to the number of works handling the unconstrained case. Recently there has been some interest to exploit infeasible isolated solutions when solving Constrained MaOPs (CMaOPs). Motivated by this observation, we firstly propose an IS-update procedure (Isolated Solution-based update procedure) that has the ability to: (1) handle CMaOPs characterized by various types of difficulties and (2) favor the selection of not only infeasible solutions associated to isolated sub-regions but also infeasible solutions with smaller Constraint Violation (CV) values. The IS-update procedure is subsequently embedded within the Multi-Objective Evolutionary Algorithm-based on Decomposition (MOEA/D). The new obtained algorithm, named ISC-MOEA/D (Isolated Solution-based Constrained MOEA/D), has been shown to provide competitive and better results when compared against three recent works on the CDTLZ benchmark problems.

Abstract

In order to cope with certain challenges posed by device capacity and the nature of IoT networks, a lightweight access control model is needed to resolve security and privacy issues. The use of complex encryption algorithms is infeasible due to the volatile nature of IoT environment and pervasive devices with limited resources. In this paper, we present the Trust-ABAC, an access control model for the Internet of Things, in which a coupling between the access control based on attributes and the trust concept is done. We evaluated the performance of Trust-ABAC through an experiment based on a simulation. We used the OMNeT++ simulator to show the efficiency of our model in terms of power consumption, response time and the average number of messages generated by an access request. The obtained results of simulation prove the good scalability of our Trust-ABAC model.

Abstract

In technology-enhanced learning, semantic annotations have been employed to attach semantic metadata to learning materials in order to significantly enhance their accessibility by human users and machines as well. In this paper, we present an ontology-based multi-level semantic representation model that aims to enrich the description of learning objects with semantics regarding their subjects, competencies and instructional roles. More specifically, the proposed model uses three ontologies: a subject domain ontology describing the domain concepts and the relations that are covered by the subject matter being taught, a competency ontology describing the competency-related characteristics of learners and learning resources, and an instructional role ontology specifying the instructional role(s) a learning object can play in an instructional setting. To demonstrate the feasibility of our model, an illustrative example is given that explains how learning object semantics can be represented with different granularities.

Abstract

Abstract

Approximate reasoning aims to manage knowledge imprecision in the inference process. It is a generalization of the Modus Ponens of classical logic. Originally, it is defined in fuzzy logic context, where knowledge are modeled by a quantitative way. We are interested in this paper to approximate reasoning in the symbolic multi-valued logic context. This logic allows presenting imprecise knowledge in a qualitative way, where every predicate is modeled by a multi-set. In order to express imprecision, each multi-set is associated to a scale base of ordered symbolic degrees. In a previous work where a symbolic approximate reasoning has been defined, it has been assumed that all multi-sets of the inference schema have the same scale base. This has the disadvantage to prevent free definition of knowledge. For that, we propose in this paper a new approximate reasoning which can infer with multi-sets having different scale bases. Our solution consists of interfacing all the multi-sets in order to avoid information loss.

Abstract

Knowledge-based systems have nearly become omnipresent in various sectors to facilitate decision-making. Their aim is to get close to human induction. For that, dealing imprecise knowledge is essential since human thinks imprecisely. The principal logics that allow manipulating this kind of knowledge in intelligent systems are fuzzy logic and multi-valued logic. Up to now, according to our knowledge, knowledge-based systems manage separately either fuzzy knowledge or multi-valued knowledge. However, modeling heterogeneous knowledge (fuzzy and multi-valued) in the same inference engine should ensure more flexibility and freedom to the user. In that context, our aim is to allow the use of fuzzy and multi-valued knowledge at once. We propose a new approach to convert fuzzy knowledge into symbolic knowledge by projecting fuzzy inputs over the x-axis that corresponds to the universe of discourse of fuzzy variable. In order to demonstrate its applicability, our proposal is tested within a rule-based system. A numerical example is then provided.

Abstract

This paper explores one of the critical issues, SaaS placement in cloud data centers, for reducing execution time of composite SaaS applications. We adopt a multi-swarm variant of Particle Swarm Optimization (PSO) to propose a service placement method. Also, a cooperative learning strategy is hybridized to the placement algorithm, which makes information of best candidate servers be used more effectively to generate better placement plan. In the proposed method, for each sub-swarm of servers, the worst placement learns from the best servers, so that worst servers can have more excellent exemplars to learn and can find the optimal placement for SaaS components more easily. Experiments show that our solution is efficient in comparison with existing SaaS placement approaches.

Abstract

Cloud service recommendation has become an important technique that helps users decide whether a service satisfies their requirements or not. However, the few existing recommendation systems are not suitable for real world environments and only deal with services hosted in a single cloud, which is simply unrealistic. In addition, a same service may be hosted on more than one cloud and, hence, may have different user ratings that depend on specific conditions of their cloud availability zones. This uncertainty regarding the real quality of the cloud service and users’ satisfaction levels raises a question about how to trust the different users’ ratings in order to recommend the adequate cloud service. Unlike existing solutions, the goal of this work is to propose a cooperative recommender system that aims to resolve two major issues: recommendation of cloud services in multiple clouds and recommendation under uncertainty of users’ ratings. The proposed system will take advantage from a set of powerful techniques and paradigms in order to offer an overlay of cloud recommender entities that cooperate to deliver top-rated services to the user.