Abstract

The Vehicle Routing Problem (VRP) introduced by Dantzing and Ranser (1959) is a prominent combinatorial optimization problem. Over the last several decades, many variants of the multi-constrained vehicle routing problem have been studied and a class of problems known as rich vehicle routing problem (RVRPs), has been formed. This work is about solving a variant of RVRP with dynamically changing orders and time windows constraints. In the real world application, during the working day, new orders often occur dynamically and need to be integrated into the routes planing. A Genetic Algorithm (GA) with a simple heuristic is proposed to solve the dynamic vehicle routing problem with time windows. The performance is tested on Solomon’s benchmark with different percentage of the orders revealed to the algorithm during operation time.

Abstract

In order to cope with certain challenges posed by IoT environment and device capacity, a Self-Adaptive 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 propose an access control middleware for the Internet of Things. The latter is an extension of the ABAC model in order to take into account the subject behavior and the trust value in the decision making process. In this work, we introduce a dynamic adaptation process of access control rules based on the risk value, the policies and rule sets which can effectively improve the security of IoT applications and produce more efficient access control mechanisms for the Internet of Things.

Abstract

Abstract

In this paper, we present a semantic Web architecture of a competency-based lifelong learning support system which falls within the scope of lifelong learning and attempts to deal with the issue of competency tracking, management, and development in learning networks. In particular, the proposed system aims to track learners’ competencies in learning networks and to provide them with different competency assessment procedures, such as learner positioning, competency gap analysis, and competency profile matching. In addition, it integrates a personalized learning path generator module that enables learners fill the gap between available and expected competencies. This system is currently under construction and will include semantic Web services capabilities for supporting automatic matchmaking tasks.

Abstract

Despite the importance of competency modeling for both individuals and organizations, there is a lack of a comprehensive literature review and a cartography for it. This paper aims to provide an in-depth overview of different approaches to competency modeling reported in the field of technology-enhanced com petency-based learning. This literature review is complemented by an additional overview of related initiatives toward modeling intended learning outcomes, learning opportunities, achieved learning outcome profiles of learners and competency maps. In addition, a cartography illustrating the relationships between some important models is proposed. The main purpose of this work is to provide researchers with a comprehensive review of the current status in this field as well as to highlight ongoing issues and challenges that need to be addressed.

Abstract

When building Knowledge-Based Systems, we are often faced with vague data. The formers are generally modeled and treated using fuzzy logic, which is based on fuzzy set theory, or using symbolic multi-valued logic, which is based on multi-set theory. To provide a unified framework to handle simultaneously both types of information, we propose in this paper a new approach to translate multi-valued knowledge into fuzzy knowledge. For that purpose, we put forward a symbolic–to–fuzzy conversion method to automatically generate fuzzy sets from an initial multi-set. Once unified, handling heterogeneous knowledge become feasible. We apply our proposal in Rule-Based Systems where an approximate reasoning is required in their inference engine. Once new facts are deduced and in order to make the translation completely transparent for the user, we also provide a fuzzy–to–symbolic conversion method. Its purpose is to restore the original knowledge type if they were multi-valued. Our proposal offer a high flexibility to the user to reason regardless to the knowledge type. In addition, it is an alternative to overcome the modeling shortcoming of abstract data by taking advantage of a rigorous mathematical framework of fuzzy logic. A numerical study is finally provided to illustrate the potential application of the proposed methodology.

Abstract

Feature selection is an important pre-processing data mining task, which can reduce the data dimensionality and improve not
only the classification accuracy but also the classifier efficiency. Filters use statistical characteristics of the data as the evaluation
measure rather than using a classification algorithm. On the contrary, the wrapper process is computationally expensive
because the evaluation of every feature subset requires running the classifier on the datasets and computing the accuracy
from the obtained confusion matrix. In order to solve this problem, we propose a hybrid tri-objective evolutionary algorithm
that optimizes two filter objectives, namely the number of features and the mutual information, and one wrapper objective
corresponding to the accuracy. Once the population is classified into different non-dominated fronts, only feature subsets
belonging to the first (best) one are improved using the indicator-based multi-objective local search. Our proposed hybrid
algorithm, named Filter-Wrapper-based Nondominated Sorting Genetic Algorithm-II, is compared against several multiobjective and single-objective feature selection algorithms on eighteen benchmark datasets having different dimensionalities.
Experimental results show that our proposed algorithm gives competitive and better results with respect to existing algorithms.

Abstract

Feature selection and construction are important
pre-processing techniques in data mining. They may allow
not only dimensionality reduction but also classifier accuracy
and efficiency improvement. These two techniques are of great
importance especially for the case of high-dimensional data.
Feature construction for high-dimensional data is still a very
challenging topic. This can be explained by the large search space
of feature combinations, whose size is a function of the number of
features. Recently, researchers have used Genetic Programming
(GP) for feature construction and the obtained results were
promising. Unfortunately, the wrapper evaluation of each feature
subset, where a feature can be constructed by a combination
of features, is computationally intensive since such evaluation
requires running the classifier on the data sets. Motivated by
this observation, we propose, in this paper, a hybrid multiobjective evolutionary approach for efficient feature construction
and selection. Our approach uses two filter objectives and one
wrapper objective corresponding to the accuracy. In fact, the
whole population is evaluated using two filter objectives. However,
only non-dominated (best) feature subsets are improved using an
indicator-based local search that optimizes the three objectives
simultaneously. Our approach has been assessed on six highdimensional datasets and compared with two existing prominent
GP approaches, using three different classifiers for accuracy
evaluation. Based on the obtained results, our approach is shown
to provide competitive and better results compared with two
competitor GP algorithms tested in this study

Abstract

Recently, increasingly large amounts of data are generated from a variety of sources. Existing data processing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on streaming in Big Data, a task referring to the processing of massive volumes of structured/unstructured streaming data. Recently proposed streaming frameworks for Big Data applications help to store, analyze and process the continuously captured data. In this paper, we discuss the challenges of Big Data and we survey existing streaming frameworks for Big Data. We also present an experimental evaluation and a comparative study of the most popular streaming platforms.