A new characterization of atomic stable parts for a partial order relation applied to the one-machine scheduling problem

A set E endowed with a partial order relation Ɍ can be decomposed into subsets called “atomic stable parts” for Ɍ, totally ordered. These atomic stable parts are the equivalence classes of an equivalence relation T (®) [1]. In fact if S(x) is the atom containing x (x Є E) and E endowed with the partial order relation Ɍ), then Cl(x) is the equivalence class of x for the equivalence relation T (®) defined by: x,y)Є E 2 , x® y not (x Ɍ y or y Ɍ x); (® is a symmetric relation by construction. Its transitive closure T (®) is an equivalence relation [2]. In this article we propose a new characterization of the atomic stable parts for Ɍ. The approach consists in defining a square matrix B called matrix of “Ranks” from the relation Ɍ whose coefficients are Boolean (bij = 0 or 1) [3] , [4], [5]. This matrix B represents a bipartite graph G. An interpretation of the canonical components of the bipartite graph will allow us to characterize the atomic stable parts of the set E endowed with Ɍ. We indeed show that in the irreducible sub graphs Gi of G (Gi (Si,Ti ;A(Gi)), the subsets Si of E (i=1,….,k) are the atomic stable parts for the partial order relation Ɍ An application is proposed for the temporal decomposition of the one-machine scheduling problem

Interaction in Online System is A Favor Key for Learners’ Success

Nowadays, the online interaction has become more necessary and vital in terms of both socio-cognitive and socio-affective levels. It fully plays an important role in the acquisition of knowledge in online learning. However, the way in which these interactions take place remain little regulated and less efficient. Recognizing this, we have developed an approach used to automatically schedule the interactions in the learning process of learners. These interactions will easily help learners to assimilate the obscure concepts and quite difficult to understand. Our goal is achieved basing on intelligent agents modeling of virtual learners. These agents enter into discussion with learners and are widely involved in clarifying these concepts. To affirm the importance of our study in e-learning, we have given a questionnaire to the learners of the Master in French literature. This latter has confirmed that the majority of the learners choose to collaborate and interact with each other to come over the blockage points in their learning process. Furthermore, the empirical result has shown that our approach contributes effectively in online learning. A significant increase in learner results is well noticed

The Combination between the Individual Factors and the Collective Experience for Ultimate Optimization Learning Path using Ant Colony Algorithm

The approach that we propose in this paper is part of the optimization of the learning path in the e-learning environment. It relates more precisely to the adaptation and the guidance of the learners according to, on one hand, their needs and cognitive abilities and, on the other hand, the collective experience of co-learners. This work is done by an optimizer agent that has the specificity to provide to each learner the best path from the beginning of the learning process to its completion. The optimization in this approach is determined automatically and dynamically, by seeking the path that is more marked by success. This determination is concluding according to the vision of the pedagogical team and the collective experience of the learners. At the same time, we search of the path that is more adapted to the specificities of the learner in terms of preferences, level of knowledge and learner history. This operation is accomplished by exploiting their profile for perfect customization and the adaptation of ant colony algorithm for guidance tends towards maximizing the acquisition of the learner. The design of our work is unitary; it is based on the integration of individual collective factors of the learner. And the results are very conclusive. They show that the proposed approach is able to efficiently select the optimal path and that it participates fully in the satisfaction and success of the learner

Cultuvilate

Treball Final de Grau en Periodisme. Codi: PE0932. Curs acadèmic: 2019/202

A Vehicle Message Scheduling Scheme For Vehicle Trust Management

The trustworthiness of vehicle messages is a major focus in intelligent transportation research. Existing studies focus on enhancing the accuracy of vehicle trustworthiness evaluation, overlooking that the transmission performance may affect the quality of vehicle messages, which are essential for implementing trustworthiness evaluation. This paper studies a novel vehicle message scheduling scheme to regulate vehicles’ transmissions so that incident messages for any part of the road can be reliably and accurately sent to a roadside unit. Through strategically scheduling vehicle transmissions to avoid interference between vehicles while guaranteeing sufficient numbers of vehicles transmitting their sensor data, vehicle messages can reliably yet timely arrive at Road Side Units (RSUs) without missing reporting an event on the road

An Efficient Metric for Physical-layer Jammer Detection in Internet of Things Networks

An active jammer could severely degrade the communication quality for wireless networks. Since all wireless nodes openly access the shared media, the harsh effects are exaggerated by retransmission attempts of affected devices. Fast and precise detection of the jammer is of vital importance for heterogeneous wireless environments such as the Internet of things (IoT). It could activate a series of corrective countermeasures to ensure the robust operation of the network. In this paper, we propose a local, straightforward, and numerical metric called the number of jammed slots (NJS), by which we can quickly detect the presence of a jammer and identify the jammed nodes at the software level in broadcast networks. NJS calculation is carried out by a central node which collects the MAC-layer statuses of all wireless nodes in a periodical fashion. Our simulation results indicate that NJS outperforms current detection methods in terms of accuracy and precision