Abstract

Presently, Artificial Intelligence (AI) has seen a significant shift in focus towards the design and development of interpretable or explainable intelligent systems. This shift was boosted by the fact that AI and especially the Machine Learning (ML) field models are, currently, more complex to understand due to the large amount of the treated data. However, the interchangeable misuse of XAI concepts mainly “interpretability” and “explainability” was a hindrance to the establishment of common grounds for them. Hence, given the importance of this domain, we present an overview on XAI, in this paper, in which we focus on clarifying its misused concepts. We also present the interpretability levels, some taxonomies of the literature on XAI techniques as well as some recent XAI applications.

Abstract

In the recent year, Internet of Things (IoT) has been adopted in several real-world applications such as smart transportation, smart city, retail, agriculture, smart factory, etc. to make human life more reliable. The integration of social networking concepts into the IoT led to the rise of a new paradigm: the Social Internet of Things (SIoT). In the SIoT environment, the objects are capable of establishing in an autonomous way many social relationships anywhere and anytime with other trusted objects. However, in such environment, objects may provide dishonest recommendations due to malicious reasons such as bad mouthing, ballot stuffing, random opinion, etc. In order to cater these challenges, we propose a new fuzzy logic-based model to filter dishonest recommendations and estimate their trust level based on (1) their values and sending time and the place coordinates and (2) the social relationship parameters of the recommenders. Results prove that our proposed approach is able to detect 100% of the fake Sybil attack and achieves 100% of Recognition Proportion, Sensitivity, Specificity, Accuracy and F1 score and gets 0% of False Negative and False Positive Proportions in presence of up to 90% dishonest recommendations.

Abstract

The efforts to successfully complete projects lead to the development of various project management (PM) standards, best practices and guidelines, issued by different organisational bodies. These PM practices, when appropriately implemented, lead to a better project performance. However, studies on how to adopt and adapt such practices according to management needs, remain limited. In this paper, we are going to focus on how to map the project requirements with the suitable PM practices to support the PM decision making. To respond this question, we put forward an in-depth comparative study amongst the well-established PM practices considering a set of features to pick out their challenges, limits as well as their applicability. Through this comparison, we extend the discussion of PM practices features by contrasting them to three distinct categories of requirements which are technical, contextual and behavioural. This analysis allows us to map each category to its corresponding practice(s). Our finding provides comprehensive recommendation guidelines to both practitioners and researchers in order to improve their decision making in line with the project environment.

Abstract

Multi-Objective Evolutionary Algorithms (MOEAs) is actually one of the most attractive and active research field in computer science. Significant research has been conducted in handling complex multi-objective optimization problems within this research area. The Non-Dominated Sorting Genetic Algorithm (NSGA-II) has garnered significant attention in various domains, emphasizing its specific popularity. However, the complexity of this algorithm is found to be O(MN2) with M objectives and N solutions, which is considered computationally demanding. In this paper, we are proposing a new variant of NSGA-II termed (Efficient-NSGA-II) based on our recently proposed quick non-dominated sorting algorithm with quasi-linear average time complexity; thereby making the NSGA-II algorithm efficient from a computational cost viewpoint. Experiments demonstrate that the improved version of the algorithm is indeed much faster than the previous one. Moreover, comparisons results against other multi-objective algorithms on a variety of benchmark problems show the effectiveness and the efficiency of this multi-objective version.

Abstract

Internet of vehicles (IoV) can be considered as a superset of vehicular ad-hoc networks (VANETs). It extends VANET’s structure, applications and scale. Unlike, the traditional intelligent transportation system (ITS), IoV focus more on information interactions between vehicles, roadside units (RSU) and humans. The principal aim is to make people obtain road traffic information easily and in real-time, to ensure the travel convenience, and to increase the travel comfort. The goal behind the Internet of vehicles is essentially to be used in urban traffic environment to ensure network access for passengers and drivers. The environment of the IoV is the combination of different wireless network environment as well as road conditions. Despite its continuing expansion, the IOV contains different radio access technologies that lead to a heterogeneous network, and make it more crucial than the VANET. These drawbacks pose numerous challenges, especially the routing one. In IoV environment, the routing protocol must cope with events such as link failure and to find the best route to propagate the data toward the desired destination. In this paper, we mainly focus on surveying the IoV routing protocols, hence we present and compare unicast, multicast and broadcast protocols.

Abstract

Abstract

Abstract

As in software development projects, the most critical activity in
Serious Game (SG) design process is the requirements specification due to SG's
multidisciplinary and characteristics complexity. In the literature, specific
design methodologies with requirements specification strategies are still needed
to achieve an engaged SG. This paper proposes an approach that assists
designers and design stakeholders when specifying required SG features and
their relationships. We shaped this approach into three stages with three
abstraction levels based on both characteristics taxonomy model and the SG
design challenges we propose in this work. We practiced the proposed process
in specifying an SG for health safety environment training for workers in fuel
storage sites. The feedback shows that such a strategy would be highly
beneficial for the participatory design process since it reduces game features'
complexity and thus their understanding by the design team members. It also
promotes game design artifacts evaluation and allows effective processing of
the game requirement changes.

Abstract

This paper models a transit control system for the management of traffic perturbations of public transport. The transit system data is voluminous and highly dynamic. Moreover, the transit domain has a remarkable lack of intelligent systems to monitor and maintain better performance. Consequently, realizing an intelligent transit control system has become a consistent need. The modeling of the system addresses a problem of optimizing performance measures based on key performance indicators. Its objective is to find the optimal control action in disturbance cases. The solution consists in combining all performance measures in a single measure by using fuzzification without neglecting the space and time requirements of the traffic. To model and implement our system we used a multi-agent approach. The experiments performed were based on real network traffic data. The obtained results demonstrate the relevance of the proposed fuzzy approach in our optimization problem and show the advantage of the multi-agent system in the modeling of our control system. We prove that the proposed control system achieves better results than certain existing fuzzy approaches and is able to manage disturbances with a better performance than the existing solutions.