Diversity is the Optimal Education Strategy: A Mathematical Proof”,

Abstract To enhance learning, it is desirable to also let students learn from each other, e.g., by working in groups. It is known that such groupwork can improve learning, but the effect strongly depends on how we divide students into groups. In this paper, we describe how to optimally divide students into groups so as to optimize the resulting learning. It turns out that the largest gain is attained when each of the resulting groups is a representative sample for the student population as a whole -i.e., when we have diversity

Calidad de servicio en computación en la nube: técnicas de modelado y sus aplicaciones

Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management

Améliorations aux systèmes à initiative partagée humain-ordinateur pour l'optimisation des systèmes linéaires

La programmation linéaire permet d’effectuer l’optimisation de la gestion des réseaux de création de valeur. Dans la pratique, la taille de ces problèmes demande l’utilisation d’un ordinateur pour effectuer les calculs nécessaires, et l’algorithme du simplexe, entre autres, permet d’accomplir cette tâche. Ces solutions sont cependant construites sur des modèles approximatifs et l’humain est généralement méfiant envers les solutions sorties de « boîtes noires ». Les systèmes à initiative partagée permettent une synergie entre, d’une part, l’intuition et l’expérience d’un décideur humain et, d’autre part, la puissance de calcul de l’ordinateur. Des travaux précédents au sein du FORAC ont permis l’application de cette approche à la planification tactique des opérations des réseaux de création de valeur. L’approche permettrait l’obtention de solutions mieux acceptées. Elle a cependant une interface utilisateur limitée et contraint les solutions obtenues à un sous-espace de l’ensemble des solutions strictement optimales. Dans le cadre de ce mémoire, les principes de conception d’interface humain-machine sont appliqués pour concevoir une interface graphique plus adaptée à l’utilisateur type du système. Une interface basée sur le modèle de présentation de données de l’outil Logilab, à laquelle sont intégrées les interactivités proposées par Hamel et al. est présentée. Ensuite, afin de permettre à l’expérience et à l’intuition du décideur humain de compenser les approximations faites lors de la modélisation du réseau de création de valeur sous forme de problème linéaire, une tolérance quant à l’optimalité des solutions est introduite pour la recherche interactive de solutions alternatives. On trouvera un nouvel algorithme d’indexation des solutions à combiner et une nouvelle heuristique de combinaison convexe pour permettre cette flexibilité. Afin d‘augmenter la couverture de l’espace solutions accessible au décideur humain, un algorithme de recherche interactive de solution basé sur le simplexe est introduit. Cet algorithme présente une stabilité similaire à la méthode de Hamel et al., mais ses performances en temps de calcul sont trop basses pour offrir une interactivité en temps réel sur de vrais cas industriels avec les ordinateurs présentement disponibles.Une seconde approche d’indexation complète de l’espace solutions est proposée afin de réduire les temps de calcul. Les nouveaux algorithmes « Linear Redundancyless Recursive Research » (Recherche linéaire récursive sans redondance, LRRR) pour la cartographie et l’indexation de l’espace solutions et « N-Dimension Navigation Direction » (direction de navigation à n-dimensions, NDND) pour l’exploration interactive de celui-ci sont présentés. Ces algorithmes sont justes et rapides, mais ont cependant un coût mémoire au-delà de la capacité des ordinateurs contemporains. Finalement, d’autres pistes d’exploration sont présentées, notamment l’exploitation des méthodes du point intérieur et de l’algorithme de Karmarkar ainsi qu’une ébauche d’approche géométrique

Analysis of Android Device-Based Solutions for Fall Detection

Falls are a major cause of health and psychological problems as well as hospitalization costs among older adults. Thus, the investigation on automatic Fall Detection Systems (FDSs) has received special attention from the research community during the last decade. In this area, the widespread popularity, decreasing price, computing capabilities, built-in sensors and multiplicity of wireless interfaces of Android-based devices (especially smartphones) have fostered the adoption of this technology to deploy wearable and inexpensive architectures for fall detection. This paper presents a critical and thorough analysis of those existing fall detection systems that are based on Android devices. The review systematically classifies and compares the proposals of the literature taking into account different criteria such as the system architecture, the employed sensors, the detection algorithm or the response in case of a fall alarms. The study emphasizes the analysis of the evaluation methods that are employed to assess the effectiveness of the detection process. The review reveals the complete lack of a reference framework to validate and compare the proposals. In addition, the study also shows that most research works do not evaluate the actual applicability of the Android devices (with limited battery and computing resources) to fall detection solutions.Ministerio de Economía y Competitividad TEC2013-42711-

Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)

1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020 Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student’s research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option

Measuring joint movement through garment-integrated wearable sensing

University of Minnesota Ph.D. dissertation. April 2015. Major: Computer Science. Advisor: Lucy Dunne. 1 computer file (PDF); xv, 154 pages.Wearable technology is generally interpreted as electronic devices with passive and/or active electronic components worn on the human body. A further sub-set of wearable technology includes devices that are equipped with sensing abilities for body movements or biosignals and computational power that allows for further analysis. Wearable devices can be distinguished by different levels of wearability: wearable devices integrated into clothing, which are an integral part of the clothes; and wearable devices put on as an accessory. This thesis introduces a novel approach to truly wearable sensing of body movement through novel garment-integrated sensors. It starts from an initial investigation of garment movement in order to quantify the effect that garment movement has on sensor accuracy in garment-integrated sensors; continues with the development and detailed characterization of garment-integrated sensors that use a stitched technique to create comfortable, soft sensors capable of sensing stretch and bend; and ends with a final evaluation of the proposed wearable solution for the specific case of knee joint monitoring in both the stretch and bend modalities

Multi-Agent Reinforcement Learning in Large Complex Environments

Multi-agent reinforcement learning (MARL) has seen much success in the past decade. However, these methods are yet to find wide application in large-scale real world problems due to two important reasons. First, MARL algorithms have poor sample efficiency, where many data samples need to be obtained through interactions with the environment to learn meaningful policies, even in small environments. Second, MARL algorithms are not scalable to environments with many agents since, typically, these algorithms are exponential in the number of agents in the environment. This dissertation aims to address both of these challenges with the goal of making MARL applicable to a variety of real world environments. Towards improving sample efficiency, an important observation is that many real world environments already, in practice, deploy sub-optimal or heuristic approaches for generating policies. A useful possibility that arises is how to best use such approaches as advisors to help improve reinforcement learning in multi-agent domains. In this dissertation, we provide a principled framework for incorporating action recommendations from online sub-optimal advisors in multi-agent settings. To this end, we propose a general model for learning from external advisors in MARL and show that desirable theoretical properties such as convergence to a unique solution concept, and reasonable finite sample complexity bounds exist, under a set of common assumptions. Furthermore, extensive experiments illustrate that these algorithms: can be used in a variety of environments, have performances that compare favourably to other related baselines, can scale to large state-action spaces, and are robust to poor advice from advisors. Towards scaling MARL, we explore the use of mean field theory. Mean field theory provides an effective way of scaling multi-agent reinforcement learning algorithms to environments with many agents, where other agents can be abstracted by a virtual mean agent. Prior work has used mean field theory in MARL, however, they suffer from several stringent assumptions such as requiring fully homogeneous agents, full observability of the environment, and centralized learning settings, that prevent their wide application in practical environments. In this dissertation, we extend mean field methods to environments having heterogeneous agents, and partially observable settings. Further, we extend mean field methods to include decentralized approaches. We provide novel mean field based MARL algorithms that outperform previous methods on a set of large games with many agents. Theoretically, we provide bounds on the information loss experienced as a result of using the mean field and further provide fixed point guarantees for Q-learning-based algorithms in each of these environments. Subsequently, we combine our work in mean field learning and learning from advisors to show that we can achieve powerful MARL algorithms that are more suitable for real world environments as compared to prior approaches. This method uses the recently introduced attention mechanism to perform per-agent modelling of others in the locality, in addition to using the mean field for global responses. Notably, in this dissertation, we show applications in several real world multi-agent environments such as the Ising model, the ride-pool matching problem, and the massively multi-player online (MMO) game setting (which is currently a multi-billion dollar market)