Ordinateur personnel

Résumé

L'ordinateur personnel (en anglais : personal computer ou PC) se confond aujourd'hui avec le micro-ordinateur ou ordinateur individuel : c'est un ordinateur destiné à l'usage d'une personne, de prix accessible et dont les dimensions sont assez réduites pour tenir sur un bureau. La première machine appelée micro-ordinateur est le Micral N, breveté en 1973 par le Français François Gernelle. Toutefois, à cette époque, on pouvait déjà considérer comme ordinateurs personnels les mini-ordinateurs diffusés au cours des années 1960, ainsi que le premier ordinateur de bureau Olivetti Programma 101 commercialisé en 1965. Quant aux premiers ordinateurs personnels grand public, ils sont apparus à la fin des années 1970 et les premiers ayant fait l'objet d'une certaine popularité ont été mis en marché en 1977. Depuis le début de l'année 2013, on assiste à un recul des ventes d'ordinateurs personnels (portables ou de bureau) au bénéfice des tablettes tactiles et des smartphones. vignette|Olivett

Source officielle

https://fr.wikipedia.org/wiki/Ordinateur_personnel

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Federated Learning is explicitly designed for learning a global model from distributed, possibly sensitive non-i.i.d. data at different clients. In reality, this scenario is not always legible since in some cases the heterogeneous needs of clients cannot be packaged into a single global model, e.g. in next-word prediction on mobile phones where different clients might express themselves differently. Thus, in personalized federated learning the goal is to learn a personal model for each client individually by training a global model to be easy to adapt locally. Such a formulation is also known in meta-learning, where the goal is to learn a global model that can be easily adapted to different tasks rather than clients. In this project, we examine the differences and similarities between algorithms from the two fields. We furthermore develop a benchmarking dataset that captures different aspects of heterogeneity between the clients. On this we evaluate both when personalization in federated learning is actually beneficial over individual training and compare which algorithms from meta-learning and personalized federated learning fare best.

2021

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Evolutionary algorithms are increasingly being applied to problems that are too computationally expensive to run on a single personal computer due to costly fitness function evaluations and/or large numbers of fitness evaluations. Here, we introduce the Seamless Peer And Cloud Evolution (SPACE) framework, which leverages bleeding edge web technologies to allow the computational resources necessary for running large scale evolutionary experiments to be made available to amateur and professional researchers alike, in a scalable and cost-effective manner, directly from their web browsers. The SPACE framework accomplishes this by distributing fitness evaluations across a heterogeneous pool of cloud compute nodes and peer computers. As a proof of concept, this framework has been attached to the RoboGen open-source platform for the co-evolution of robot bodies and brains, but importantly the framework has been built in a modular fashion such that it can be easily coupled with other evolutionary computation systems.

ACM Press2016

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Identifying a Motivational Profile for Older Adults Towards Increased Physical Activity

Yuan Lu, Pearl Pu Faltings

Personalizing behavior change (BC) strategies to motivate increased physical activity is especially important for the diverse older adult population. However, there is a lack of knowledge about how to profile older users to most effectively personalize BC solutions. Self-awareness and social awareness are BC strategies commonly used in commercially available applications to promote physical activities. Through a randomized controlled trial (N = 53), we studied the effect of some personal factors on the physical activity of older adults under these two strategies. For this purpose, each BC strategy was implemented in a mobile application. Based on the statistical analysis of the measured step data and the collected questionnaire data, we identified a list of personal factors to personalize each BC strategy towards improved physical activity. Hereby we suggest how to create effective motivational profiles and provide design recommendations to personalize these BC strategies toward increased physical activity for older users.

2021

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