Dans un contexte d’apprentissage de la masso-kinésithérapie en Institut de Formation Initiale, cette étude retrace et analyse les risques pris et encourus par les étudiants de première et deuxième année. Nombre d’entre eux n’ont soit pas conscience des dangers d’un toucher mal exécuté, soit pas envie de se limiter à des gestes plus sécuritaires. Face à cette situation inacceptable, faire appel à leurs capacités sensitives et réflexives n’est pas suffisant. L’hypothèse émise dans cette thèse pour rendre leurs actes moins risqués est qu’ils prennent le temps de suspendre leur toucher à leurs sensations, leur raison, mais également et surtout à leurs émotions. Ainsi, peurs, orgueil et joie, habilement intégrés à leurs désirs, pourraient peut être les mener à un apprentissage du toucher beaucoup plus éthique / First and second year students learning physiotherapy at trainingphysiotherapy-schools take and undergo quite a lot of risks. Some of them are either absolutely not aware of the dangers of the “touch” they perform or don’t feel like being careful. Such a situation is unacceptable, but calling on their sensitive and sensible abilities doesn’t help enough. This is why we suggest “a touch in suspense”, in order to be less dangerous. Sensations, understanding and emotions, especially fear, pride and joy could be linked skillfully to their desires and wishes and thus perhaps lead them to a much more ethical touch

In planning for future electricity supplies certain issues will need to be considered such as increased energy usage, urbanisation, reduction in personnel, global warming and the conservation of natural resources. As the result, some countries have investigated the transformation of their existing power grid to the so-called Smart Grid. The Smart Grid has three main characteristics which are, to some degree, antagonistic. These characteristics are the provision of good power quality, energy cost reduction and improvement in the reliability of the grid. The need to ensure that they can be accomplished together demands much richer Information and Communications Technology (ICT) networks than the current systems available. In this research we have identified the gap in the current proposals for the ICT of the power grid. We have designed and developed an ICT architecture for the neighbourhood sub-Grid level of the electrical network, where monitoring at this level is very underdeveloped because most current grids are controlled centrally and the response of the neighbourhood area is not generally monitored or actively controlled. Our designed ICT architecture, which is based on established architectural principles, can incorporate data from heterogeneous sources. This layered architecture provides both the sensors that can directly measure the electrical activity of the network (e.g. voltage) and also the sensors that measure the environment (e.g. temperature) since these provide information that can be used to anticipate demand and improve control actions. Additionally, we have de-signed a visualisation tool as an interface for a grid operators to facilitate a better comprehension of the behaviour of the neighbourhood level of the Smart Grid. Since we have noticed that energy aware ICT is a prerequisite for an efficient Smart Grid, we have utilised two different approaches to tackle this issue. The first approach was to utilise a cluster-based communication technique for the second layer of the architecture, which comprises Wireless Sensor Networks, where energy limitation is the major problem. Accordingly, we have analysed the energy-aware topology for wireless sensor networks embedded in the mentioned layer. We provide evidence that the proposed topology will bring energy efficiency to the communication network of the Smart Grid. The second approach was to develop a data reduction algorithm to reduce the volume of data prior to data transmission. We demonstrated that our developed data reduction is suitable for Smart Grid applications which can keep the integrity and quality of data. Finally, the work presented in this thesis is based on a real project that is being implemented in the medium voltage power network of the University of Manchester where power grid instrumentation, real data and professionals in the field are available. Since the project is long-term and the environmental sensor networks in particular are not currently installed we have evaluated some of our predictions via simulation. However, where the instrumentation was available, we were able to compare our predictions and our simulations with actual experimental results.

Recommender systems support online customers by suggesting products and services of likely interest to them. Research in recommender systems is now starting to recognise the importance of multiple selection criteria and the role of customer context in improving the recommendation output. This thesis investigates the inclusion of criteria and context information in the recommendation process. Firstly, a novel technique for multi-criteria recommendation is proposed. It assumes that some selection criteria for an item (product or a service) will dominate the overall rating, and that these dominant criteria will be different for different users. Following this assumption, users are clustered based on their criteria preferences, creating a “preference lattice”. The recommendation output for a user is then based on ratings by other users from the same or nearby clusters. Secondly, a context similarity metric for context aware recommendation is presented. This metric can help improve the prediction accuracy in two ways. On the one hand, the metric can guide the aggregation of the feedback from similar context to improve the prediction accuracy. This aggregation is important because the recommendation generation based on prior feedback by similar customers reduces the quantum of feedback used, resulting in a reduction in recommendation quality. On the other hand, the value returned by the context similarity metric can also be used to indicate the importance of the context information in the prediction process for a context aware recommendation.The validation of the two proposed techniques and their applications are conducted in the service domain because the relatively high degree of user involvement attracts users to provide detailed feedback from multiple perspectives, such as from criteria and context perspectives. In particular, hotel services and web services areas are selected due to their different levels of maturity in terms of users’ feedback. For each area, this thesis proposes a different recommendation approach by combining the proposed techniques with a traditional recommendation approach. The thesis concludes with experiments conducted on the datasets from the two aforementioned areas to evaluate the proposed techniques, and to demonstrate the process and the effectiveness of the techniques-based recommendation approaches.

Context-aware computing requires software that can adapt to changes in context. When contextual circumstances trigger multiple adaptations, software must also understand the relationships between these adaptations and react according to the rules governing these relationships. Adaptable software needs a means to establish and interpret these rules in order to avoid any undesirable and potentially catastrophic conflicts. This dissertation designs and implements the Adaptation Rules Management API (ArmAPI). ArmAPI has been demonstrated to work with a Context-Oriented Programming variation for Java called ContextJ* to execute conflict-free adaptations in two software applications. ArmAPI allows programmers to define relationship types between adaptations, and transfers these definitions to Prolog facts and rules. The Prolog engine, encapsulated within ArmAPI, then works with imperative algorithms to determine the appropriate adaptations to execute based on the current set of facts, rules, and contextual circumstances. Context represents all of the conditions for all of the entities known to an observing device. In any environment, context represents a large amount of data that can influence a multitude of conflicting adaptations. This research provides an incremental step towards overcoming the problem of adaptation conflict by constructing an API that considers the relationship types of inclusion, exclusion, ordering, conditional dependency, and independence. The API has been validated via two prototypes that provide typical scenarios.

This dissertation is composed of research studies that contribute to three research areas including social context-aware computing, internal context-aware computing, and human behavioral data mining. In social context-aware computing, four studies are conducted. First, mobile phone user calling behavioral patterns are characterized in forms of randomness level where relationships among them are then identified. Next, a study is conducted to investigate the relationship between the calling behavior and organizational groups. Third, a method is presented to quantitatively define mobile social closeness and social groups, which are then used to identify social group sizes and scaling ratio. Last, based on the mobile social grouping framework, the significant role of social ties in communication patterns is revealed. In internal context-aware computing, two studies are conducted where the notions of internal context are intention and situation. For intentional context, the goal is to sense the intention of the user in placing calls. A model is thus presented for predicting future calls envisaged as a call predicted list (CPL), which makes use of call history to build a probabilistic model of calling behavior. As an incoming call predictor, CPL is a list of numbers/contacts that are the most likely to be the callers within the next hour(s), which is useful for scheduling and daily planning. As an outgoing call predictor, CPL is generated as a list of numbers/contacts that are the most likely to be dialed when the user attempts to make an outgoing call (e.g., by flipping open or unlocking the phone). This feature helps save time from having to search through a lengthy phone book. For situational context, a model is presented for sensing the user's situation (e.g., in a library, driving a car, etc.) based on embedded sensors. The sensed context is then used to switch the phone into a suitable alert mode accordingly (e.g., vibrate mode while in a library, handsfree mode while driving, etc.). Inferring (social and internal) context introduces a challenging research problem in human behavioral data mining. Context is determined by the current state of mind (internal), relationship (social), and surroundings (physical). Thus, the current state of context is important and can be derived from the recent behavior and pattern. In data mining research area, therefore, two frameworks are developed for detecting recent patterns, where one is a model-driven approach and the other is a data-driven approach.

A Computação Ubíqua está revolucionando a interação do ser-humano com os dispositivos computacionais ao disponibilizar tecnologias pouco intrusivas ao cotidiano das pessoas. A Computação Ciente de Contexto, um dos temas de pesquisa em Computação Ubíqua, tem contribuído para a construção de aplicações capazes de customizar-se e adaptar-se às necessidades do usuário sem a intervenção explícita deste. Suportar o desenvolvimento de aplicações Cientes de Contexto é um dos desafios da Computação Ubíqua. Desenvolvido no mesmo grupo de pesquisa que este trabalho está inserido, o Serviço Web Context Kernel, gerencia informações de contexto e explora as especificações e tecnologias da Web como plataforma de intercâmbio para a informação contextual. O trabalho aqui reportado teve como objetivo investigar a utilização de Serviços Web por meio do emprego da infra-estrutura Context Kernel na integração de aplicações em cenários de trabalho colaborativo e de aprendizado eletrônico. Como resultado, foi elaborado um conjunto de lições aprendidas provenientes do estudo e do emprego das especificações para Serviços Web. Outros resultados foram: as especificações de informações de contexto de grupo e de metadados educacionais em dimensões de contexto e exemplos de utilização do Context Kernel para tornar aplicações cientes de contexto. / Ubiquitous computing has became a revolution in terms of the user-computer interaction by providing technologies that seamlessly integrate themselves to people?s everyday life. Context-aware computing, which is an important research theme in ubiquitous computing, has been contributing for the building of applications that are capable both to customize and adapt themselves aiming to support a user without disturb him. A great challenge in ubiquitous computing has been the support to build context-aware aplications. For that reason, the it has been developed the Context Kernel Web Service, which is a service infrastructure that makes use of Web-based specifications and technologies in order to promote the management and the interchange of context information. In this work the usage of Web Services was analyzed by using the Context Kernel to integrate applications in scenarios of cooperative work and e-learning. As a result, a list of lessons learned was ellaborated with respect to the use and the study of Web Services specifications for context-aware computing. Other results include a context information specifications for metadata about group and educational resources as well as examples of how to use the Context Kernel to leverage applications context-aware.

abstract: Humans have an excellent ability to analyze and process information from multiple domains. They also possess the ability to apply the same decision-making process when the situation is familiar with their previous experience. Inspired by human's ability to remember past experiences and apply the same when a similar situation occurs, the research community has attempted to augment memory with Neural Network to store the previously learned information. Together with this, the community has also developed mechanisms to perform domain-specific weight switching to handle multiple domains using a single model. Notably, the two research fields work independently, and the goal of this dissertation is to combine their capabilities. This dissertation introduces a Neural Network module augmented with two external memories, one allowing the network to read and write the information and another to perform domain-specific weight switching. Two learning tasks are proposed in this work to investigate the model performance - solving mathematics operations sequence and action based on color sequence identification. A wide range of experiments with these two tasks verify the model's learning capabilities. / Dissertation/Thesis / Masters Thesis Computer Science 2020

Video streaming is used daily by people around the world, plays a big role in many Internet users daily lives, and are today responsible for the majority of the Internet traffic. As 360 video streaming services become increasingly popular and each such user session requires much higher bandwidths than traditional video streaming, optimized solutions for this type of video is becoming increasingly important. One method that has been proposed to reduce the bandwidth usage is the usage of proxy servers. In this thesis, we evaluate custom-adapted prefetching policies that tries to improve the users Quality of Experience (QoE). Defining a prefetching policy for something adaptive as 360 video brings challenges that need to be simulated before release in the real world. Without proper testing the prefetch policy can do more harm than good by flooding the network with unnecessary amount of transmissions. Prior research has shown that the QoE of HTTP-based adaptive streaming (HAS) clients can be improved with content-aware prefetching (e.g., Krishnamoothi et al. 2013). However, there have been limited prior work adapting and evaluating such policies in the context of 360 streaming. This thesis presents a simulation-based evaluation of proxy-assisted 360solutions that includes custom designed prefetching polices. The main contributions of the thesis are as follows:First, we implement four types of proxy-assisted prefetching policies and simulate these under two scenarios with different networks conditions. One scenario simulate a network environment with a bottleneck located between the client and proxy while the other scenario simulates a network environment with the bottleneck located between the proxy and server. The cooperation between the client and proxy is evaluated for each scenario and prefetching policy. Second, we evaluate the proxy-assisted prefetching policies in comparison with baselines and each other, in regards of their ability to improve the viewers QoE. Our results show that the bottleneck location has major impact on proxy-performance and that simple prefetching policies can enable clients to download bigger loads of data, which have a significant effect on viewers QoE. Considering that 360 videos require much higher bandwidth then traditional video streaming, service providers may consider integrating prefetching policies for 360 video streaming.

After the introduction of CUDA by Nvidia, the GPUs became devices capable of accelerating any general purpose computation. GPUs are designed as parallel processors which posses huge computation power. Modern supercomputers are often equipped with GPU accelerators. Sometimes the performance or the memory capacity of a single GPU is not enough for a scientific application. The application needs to be scaled into multiple GPUs. During the computation there is need for the GPUs to exchange partial results. This communication represents computation overhead. For this reason it is important to research the methods of the effective communication between GPUs. This means less CPU involvement, lower latency, shared system buffers. Inter-node and intra-node communication is examined. The main focus is on GPUDirect technologies from Nvidia and CUDA-Aware MPI. Subsequently k-Wave toolbox for simulating the propagation of acoustic waves is introduced. This application is accelerated by using CUDA-Aware MPI.

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