Geometric deep learning: going beyond Euclidean data

Many scientific fields study data with an underlying structure that is a non-Euclidean space. Some examples include social networks in computational social sciences, sensor networks in communications, functional networks in brain imaging, regulatory networks in genetics, and meshed surfaces in computer graphics. In many applications, such geometric data are large and complex (in the case of social networks, on the scale of billions), and are natural targets for machine learning techniques. In particular, we would like to use deep neural networks, which have recently proven to be powerful tools for a broad range of problems from computer vision, natural language processing, and audio analysis. However, these tools have been most successful on data with an underlying Euclidean or grid-like structure, and in cases where the invariances of these structures are built into networks used to model them. Geometric deep learning is an umbrella term for emerging techniques attempting to generalize (structured) deep neural models to non-Euclidean domains such as graphs and manifolds. The purpose of this paper is to overview different examples of geometric deep learning problems and present available solutions, key difficulties, applications, and future research directions in this nascent field

Virtual Reality Games for Motor Rehabilitation

This paper presents a fuzzy logic based method to track user satisfaction without the need for devices to monitor users physiological conditions. User satisfaction is the key to any product’s acceptance; computer applications and video games provide a unique opportunity to provide a tailored environment for each user to better suit their needs. We have implemented a non-adaptive fuzzy logic model of emotion, based on the emotional component of the Fuzzy Logic Adaptive Model of Emotion (FLAME) proposed by El-Nasr, to estimate player emotion in UnrealTournament 2004. In this paper we describe the implementation of this system and present the results of one of several play tests. Our research contradicts the current literature that suggests physiological measurements are needed. We show that it is possible to use a software only method to estimate user emotion

Towards the 3D Web with Open Simulator

Continuing advances and reduced costs in computational power, graphics processors and network bandwidth have led to 3D immersive multi-user virtual worlds becoming increasingly accessible while offering an improved and engaging Quality of Experience. At the same time the functionality of the World Wide Web continues to expand alongside the computing infrastructure it runs on and pages can now routinely accommodate many forms of interactive multimedia components as standard features - streaming video for example. Inevitably there is an emerging expectation that the Web will expand further to incorporate immersive 3D environments. This is exciting because humans are well adapted to operating in 3D environments and it is challenging because existing software and skill sets are focused around competencies in 2D Web applications. Open Simulator (OpenSim) is a freely available open source tool-kit that empowers users to create and deploy their own 3D environments in the same way that anyone can create and deploy a Web site. Its characteristics can be seen as a set of references as to how the 3D Web could be instantiated. This paper describes experiments carried out with OpenSim to better understand network and system issues, and presents experience in using OpenSim to develop and deliver applications for education and cultural heritage. Evaluation is based upon observations of these applications in use and measurements of systems both in the lab and in the wild.Postprin

Hardware acceleration of reaction-diffusion systems:a guide to optimisation of pattern formation algorithms using OpenACC

Reaction Diffusion Systems (RDS) have widespread applications in computational ecology, biology, computer graphics and the visual arts. For the former applications a major barrier to the development of effective simulation models is their computational complexity - it takes a great deal of processing power to simulate enough replicates such that reliable conclusions can be drawn. Optimizing the computation is thus highly desirable in order to obtain more results with less resources. Existing optimizations of RDS tend to be low-level and GPGPU based. Here we apply the higher-level OpenACC framework to two case studies: a simple RDS to learn the ‘workings’ of OpenACC and a more realistic and complex example. Our results show that simple parallelization directives and minimal data transfer can produce a useful performance improvement. The relative simplicity of porting OpenACC code between heterogeneous hardware is a key benefit to the scientific computing community in terms of speed-up and portability

ICT in higher education in Portugal. Call computer assisted language learning

Este artículo pretende explorar el Aprendizaje de Lenguas Asistido por Ordenador (CALL, Computer-Assisted Language Learning) en Portugal. Para ello, se centrará en el nivel de educación superior. La escasa explotación del CALL en Portugal ha sido ampliamente estudiado en varios informes, por ejemplo, el informe encargado por la UE titulado The Impact of Information and Communications Technologies on the Teaching of Foreign Languages and on the Role of Teachers of Foreign Languages (2002: 5): "The use and employment of ICT in FLT and FLL is far from satisfactory, as ICT resources are traditionally reserved for '(computer) science' subjects, and rarely assigned to art subjects. A general lack of appropriate training of language teachers in meaningful uses of ICT tends to strengthen this trend". Este artículo abordará dichas necesidades centrándose en los resultados de proyectos europeos como POOLS. Analizaremos cuestiones relativas a los materiales en línea que pueden utilizarse para desarrollar contenidos de clases de lengua, hacienda uso de las ventajas del e-Learning.G.I. HUM 767 (ayudas a Grupos de Investigación de la Junta de Andalucía) / Editorial Comares (colección interlingua

Factors affecting e-Learning effectiveness in a higher learning institution in Malaysia

The purpose of this research was to investigate factors that influence the effectiveness of the e-learning system in a higher learning institution. The participants were students randomly selected from diploma and degree programs. The main instrument was a questionnaire that was distributed to the students. The researchers collected 205 completed questionnaires out of a total of 300. Four factors were chosen as independent variables namely: reaction and satisfaction,learning outcome and achievement, familiarity with online learning technology, and participation and interaction. It was found that the effectiveness of the e-learning system was significantly affected by reaction and satisfaction, learning outcome and achievement, and familiarity with online learning technology. The participation and interaction factor had no apparent effect on the effectiveness of the e-learning system. Therefore, it is recommended that higher learning institutions interested in introducing e-learning should focus on students’ reaction and satisfaction towards the system.E-learning should focus on learning outcomes and achievement. It is also recommended that institutions first look into the issue of familiarity with online learning technology among students before introducing the e-learning system so as to determine whether students are comfortable with the online learning tools

The Aftermath

Multiple activity, comprehensive lesson plan includes background information, grading rubric, information on associated learning standards and assessment, as well as links to additional external resources. Activity explores the concepts of a hurricane's impact on the environment, society, and economics of a given community. Students map the potential storm surge and flooding on a topographic map and locate and report on past hurricanes in a specific geographical region. Educational levels: Middle school, High school

Reinforcement learning for qualitative group behaviours applied to non-player computer game characters

This thesis investigates how to train the increasingly large cast of characters in modern commercial computer games. Modern computer games can contain hundreds or sometimes thousands of non-player characters that each should act coherently in complex dynamic worlds, and engage appropriately with other non-player characters and human players. Too often, it is obvious that computer controlled characters are brainless zombies portraying the same repetitive hand-coded behaviour. Commercial computer games would seem a natural domain for reinforcement learning and, as the trend for selling games based on better graphics is peaking with the saturation of game shelves with excellent graphics, it seems that better artificial intelligence is the next big thing. The main contribution of this thesis is a novel style of utility function, group utility functions, for reinforcement learning that could provide automated behaviour specification for large numbers of computer game characters. Group utility functions allow arbitrary functions of the characters’ performance to represent relationships between characters and groups of characters. These qualitative relationships are learned alongside the main quantitative goal of the characters. Group utility functions can be considered a multi-agent extension of the existing programming by reward method and, an extension of the team utility function to be more generic by replacing the sum function with potentially any other function. Hierarchical group utility functions, which are group utility functions arranged in a tree structure, allow character group relationships to be learned. For illustration, the empirical work shown uses the negative standard deviation function to create balanced (or equal performance) behaviours. This balanced behaviour can be learned between characters, groups and also, between groups and single characters. Empirical experiments show that a balancing group utility function can be used to engender an equal performance between characters, groups, and groups and single characters. It is shown that it is possible to trade some amount of quantitatively measured performance for some qualitative behaviour using group utility functions. Further experiments show how the results degrade as expected when the number of characters and groups is increased. Further experimentation shows that using function approximation to approximate the learners’ value functions is one possible way to overcome the issues of scale. All the experiments are undertaken in a commercially available computer game engine. In summary, this thesis contributes a novel type of utility function potentially suitable for training many computer game characters and, empirical work on reinforcement learning used in a modern computer game engine