Personal computer

A personal computer (PC) is a multi-purpose microcomputer whose size, capabilities, and price make it feasible for individual use. Personal computers are intended to be operated directly by an end user, rather than by a computer expert or technician. Unlike large, costly minicomputers and mainframes, time-sharing by many people at the same time is not used with personal computers. Primarily in the late 1970s and 1980s, the term home computer was also used. The advent of personal computers and the concurrent Digital Revolution have significantly affected the lives of people in all countries. Institutional or corporate computer owners in the 1960s had to write their own programs to do any useful work with the machines. While personal computer users may develop their own applications, usually these systems run commercial software, free-of-charge software ("freeware"), which is most often proprietary, or free and open-source software, which is provided in "ready-to-run", or binary form.

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Measuring the Impact of Model and Input Heterogeneity in Personalized Federated Learning

Freya Behrens

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

The Seamless Peer and Cloud Evolution Framework

Joshua Evan Auerbach, Dario Floreano, Giovanni Iacca, Guillaume Paul Olivier Leclerc

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

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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