Online Competency‐Based Assessment (OCBA): From Conceptual Model to Operational Authoring System

Online learning has been growing continuously in the last decade, accelerated by the pandemic Coronavirus, in order to offer learners new possibilities for learning and to guarantee pedagogical continuity. But some aspects, including the learning assessment, are still in development and require profound changes to meet the demands of the 21st century. However, by taking advantage of artificial intelligence techniques and new learning theories, it would be possible to create an assessment system in accordance with the active learning approach. This paper illustrates the models, algorithms and design used to create a formative and summative assessment system that will be mobilized to evaluate learner’s knowledge, skills and competencies

Algorithmes distribués dans les réseaux hétérogènes et autonomes

La diversité croissante des différents agents constituant les réseaux de communication actuels ainsi que la capacité accrue des technologies concurrentes dans l environnement réseau a conduit à la prise en compte d une nouvelle approche distribuée de la gestion du réseau. Dans cet environnement réseau évolué, le besoin en accroissement de la bande passante et en ressources rares, s oppose à la réduction de la consommation énergétique globale.Dans notre travail nous nous intéressons à l application de mécanismes distribués et de méthodes d apprentissages visant à introduire d avantage d autonomie dans les réseaux hétérogènes, mobiles en particulier, tout en améliorant les performances par rapport aux débits et à la qualité de service. Notre étude se concentre principalement sur l élaboration de mécanismes distribués stochastiques et énergétiquement efficaces en profitant des capacités de calcul de tous les agents et entités du réseau. Divers outils de la théorie des jeux nous permettent de modéliser et d étudier différents types de systèmes dont la complexité est induite par la grande taille, l hétérogénéité et le caractère dynamique des interconnexions. Plus spécifiquement, nous utilisons des outils d apprentissage par renforcement pour aborder des questions telles que l attachement distribué des utilisateurs permettant une gestion dynamique, décentralisée et efficace des ressources radio. Nous combinons ensuite les procédures de sélection d accès à des méthodes d optimisation distribuées du type gradient stochastique, pour adresser le problème de coordination des interférences intercellulaires (ICIC) dans les réseaux LTE-A. Cette approche se base sur un contrôle de puissance dynamique conduisant à une réutilisation fractionnaire des fréquences radios. Par ailleurs nous adressons dans les réseaux décentralisés non-hiérarchiques, plus précisément les réseaux tolérants aux délais (DTNs), des méthodes décentralisées liées à la minimisation du délai de transmission de bout en bout. Dans ce cadre nous nous intéressons, en outre des équilibres de Nash, à la notion d équilibre évolutionnairement stables dans différents contextes de jeux évolutionnaires, jeux évolutionnaires décisionnels markoviens et jeux de minorité. Enfin, la majeure partie du travail effectué se rattachant aux tests et validations par simulations,nous présentons plusieurs éléments d implémentations et d intégrations liés à la mise en place de plateformes de simulations et d expérimentationsGrowing diversity of agents in current communication networks and increasing capacitiesof concurrent technologies in the network environment has lead to the considerationof a novel distributed approach of the network management. In this evolvednetwork environment the increasing need for bandwidth and rare channel resources,opposes to reduction of the total energy consumption.This thesis focuses on application of distributed mechanisms and learning methodsto allow for more autonomy in the heterogeneous network, this in order to improveits performances. We are mainly interested in energy efficient stochastic mechanismsthat will operate in a distributed fashion by taking advantage of the computationalcapabilities of all the agents and entities of the network. We rely on application ofGame theory to study different types of complex systems in the distributed wirelessnetworks with dynamic interconnectivity.Specifically, we use the stochastic reinforcement learning tools to address issuessuch as, distributed user-network association that allows achieving an efficient dynamicand decentralized radio resource management. Then, we combine access selectionprocedures with distributed optimization to address the inter-cells interferencescoordination (ICIC) for LTE-advanced networks using dynamic power control and designof fractional frequency reuse mechanisms. Moreover we address in non-hierarchicalnetworks, more precisely in Delay Tolerant Networks (DTNs), decentralized methodsrelated to minimization of the end-to-end communication delay. In this framework weare interested, in addition to Nash equilibrium, to the notion of evolutionary stableequiliria in the different context of Evolutionary Games, Markov Decision EvolutionaryGames and Minority Games. As the major parts of our work includes testing andvalidations by simulations, eventually we present several implementations and integrationsmaterials for edition of simulation platforms and test bedsAVIGNON-Bib. numérique (840079901) / SudocSudocFranceF

A Combined E-Learning Course Recommender System

In this paper, we propose learners course recommender system of the E-Dirassa platform. This system, which distinguishes between new and active learners, adopts three complementary recommendation approaches: content-based recommendation as well as course-based collaborative filtering for active learners, and static profile-based collaborative filtering for new learners. Let us also note the use of multi-criteria decision aid (MDA) techniques for the choice of the textual similarity measure to be used in the first approach of content-based recommendation

Encryption-compression of images based on FMT and AES algorithm, Intern

Abstract The use of the data-processing networks, for the transmission and the transfer of the data, must satisfy two objectives which are: the reduction of the volume of information to free, the maximum possible, the public networks of communication, and the protection in order to guarantee a level of optimum safety. For this we have proposed a new hybrid approach of encryption-compression, which is based on the AES encryption algorithm of the dominant coefficients, in a mixedscale representation, of compression by the Faber-schauder Multi-scale Transform (FMT). The comparison of this approach with other methods of encryption-compression, such as Quadtree-AES and DCT-partialencryption, showed its good performance

Intraday liquidity and trading dynamics around extreme price movements in cryptocurrencies

Covering the 8 most widespread cryptocurrencies in the world across the 16 most active trading platforms from May 2015 to July 2018, we show that intraday extreme price movements (EPMs)in cryptocurrencies are accompanied by a sharp increase in trading volume, spreads and depth. This holds true whether we focus on the Bitcoin on the most active Bitfinex platform only, or extend the analysis across several cryptocurrencies and platforms. Using the logistic regression framework adapted to rare events, we show that the number of trades is the most consistent driver of EPMs, as it is often the case for traditional markets. However, the probability of an EPM varies significantly across platforms, as indicated by the high significance of time-invariant unobservable platform fixed effects. All in all, we expect further platform consolidation but we do not find evidence of obvious market dysfunction when prices move very sharply

Vowel ling and semantic priming effects in Arabic

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