Machine learning algorithms for spatial data. Case studies: Environmental pollution, natural hazards, renewable resources

This paper presents general problems and approaches for the spatial data analysis using machine learning algorithms. Machine learning is a very powerful approach to adaptive data analysis, modelling and visualisation. The key feature of the machine learning algorithms is that they learn from empirical data and can be used in cases when the modelled environmental phenomena are hidden, nonlinear, noisy and highly variable in space and in time. Most of the machines learning algorithms are universal and adaptive modelling tools developed to solve basic problems of learning from data: classification/pattern recognition, regression/mapping and probability density modelling. In the present report some of the widely used machine learning algorithms, namely artificial neural networks (ANN) of different architectures and Support Vector Machines (SVM), are adapted to the problems of the analysis and modelling of geo-spatial data. Machine learning algorithms have an important advantage over traditional models of spatial statistics when problems are considered in a high dimensional geo-feature spaces, when the dimension of space exceeds 5. Such features are usually generated, for example, from digital elevation models, remote sensing images, etc. An important extension of models concerns considering of real space constrains like geomorphology, networks, and other natural structures. Recent developments in semi-supervised learning can improve modelling of environmental phenomena taking into account on geo-manifolds. An important part of the study deals with the analysis of relevant variables and models' inputs. This problem is approached by using different feature selection/feature extraction nonlinear tools. To demonstrate the application of machine learning algorithms several interesting case studies are considered: digital soil mapping using SVM, automatic mapping of soil and water system pollution using ANN; natural hazards risk analysis (avalanches, landslides), assessments of renewable resources (wind fields) with SVM and ANN models, etc. The dimensionality of spaces considered varies from 2 to more than 30. Figures 1, 2, 3 demonstrate some results of the studies and their outputs. Finally, the results of environmental mapping are discussed and compared with traditional models of geostatistics.

ARCHITECTING USER-CENTRIC PRIVACY-AS-A-SET-OF-SERVICES: DIGITAL IDENTITY RELATED PRIVACY FRAMEWORK

AbstractDigitalization gives to the Internet the power by allowing several virtual representations of reality, including that of identity. We leave an increasingly digital footprint in cyberspace and this situation puts our identity at high risks. Privacy is a right and fundamental social value that could play a key role as a medium to secure digital identities. Identity functionality is increasingly delivered as sets of services, rather than monolithic applications. So, an identity layer in which identity and privacy management services are loosely coupled, publicly hosted and available to on-demand calls could be more realistic and an acceptable situation. Identity and privacy should be interoperable and distributed through the adoption of service-orientation and implementation based on open standards (technical interoperability). Ihe objective of this project is to provide a way to implement interoperable user-centric digital identity-related privacy to respond to the need of distributed nature of federated identity systems. It is recognized that technical initiatives, emerging standards and protocols are not enough to guarantee resolution for the concerns surrounding a multi-facets and complex issue of identity and privacy. For this reason they should be apprehended within a global perspective through an integrated and a multidisciplinary approach. The approach dictates that privacy law, policies, regulations and technologies are to be crafted together from the start, rather than attaching it to digital identity after the fact. Thus, we draw Digital Identity-Related Privacy (DigldeRP) requirements from global, domestic and business-specific privacy policies. The requirements take shape of business interoperability. We suggest a layered implementation framework (DigldeRP framework) in accordance to model-driven architecture (MDA) approach that would help organizations' security team to turn business interoperability into technical interoperability in the form of a set of services that could accommodate Service-Oriented Architecture (SOA): Privacy-as-a-set-of- services (PaaSS) system. DigldeRP Framework will serve as a basis for vital understanding between business management and technical managers on digital identity related privacy initiatives. The layered DigldeRP framework presents five practical layers as an ordered sequence as a basis of DigldeRP project roadmap, however, in practice, there is an iterative process to assure that each layer supports effectively and enforces requirements of the adjacent ones. Each layer is composed by a set of blocks, which determine a roadmap that security team could follow to successfully implement PaaSS. Several blocks' descriptions are based on OMG SoaML modeling language and BPMN processes description. We identified, designed and implemented seven services that form PaaSS and described their consumption. PaaSS Java QEE project), WSDL, and XSD codes are given and explained.

Religions in Play. Games, Rituals, and Virtual Worlds

Often dismissed as "not serious," the notion of play has nevertheless been at the center of classical theories of religion and ritual (Huizinga, Caillois, Turner, Staal, etc.). What can be retained of these theories for the contemporary study of religions? Can a study of "play" or "game" bring new perspectives for the study of religions? The book deals with the history of games and their relation to religions, the links between divination and games, the relations between sport and ritual, the pedagogical functions of games in religious education, and the interaction between games, media and religions. Richly illustrated, the book contributes to the study of religions, to ritual, game and media studies, and addresses an academic as well as a general public.

Video Gaming in Children Improves Performance on a Virtual Reality Trainer but Does Not yet Make a Laparoscopic Surgeon.

Background: In children, video game experience improves spatial performance, a predictor of surgical performance. This study aims at comparing laparoscopic virtual reality (VR) task performance of children with different levels of experience in video games and residents. Participants and methods: A total of 32 children (8.4 to 12.1 years), 20 residents, and 14 board-certified surgeons (total n = 66) performed several VR and 2 conventional tasks (cube/spatial and pegboard/fine motor). Performance between the groups was compared (primary outcome). VR performance was correlated with conventional task performance (secondary outcome). Results: Lowest VR performance was found in children with low video game experience, followed by those with high video game experience, residents, and board-certified surgeons. VR performance correlated well with the spatial test and moderately with the fine motor test. Conclusions: The use of computer games can be considered not only as pure entertainment but may also contribute to the development of skills relevant for adequate performance in VR laparoscopic tasks. Spatial skills are relevant for VR laparoscopic task performance.

Personality traits and virtual reality performance.

BACKGROUND: Surgeons' personalities have been described as different from those of the general population, but this was based on small descriptive studies limited by the choice of evaluation instrument. Furthermore, although the importance of the human factor in team performance has been recognized, the effect of personality traits on technical performance is unknown. This study aimed to compare surgical residents' personality traits with those of the general population and to evaluate whether an association exists between their personality traits and technical performance using a virtual reality (VR) laparoscopy simulator. METHODS: In this study, 95 participants (54 residents with basic, 29 with intermediate laparoscopic experience, and 12 students) underwent personality assessment using the NEO-Five Factor Inventory and performed five VR tasks of the Lap Mentor? basic tasks module. The residents' personality traits were compared with those of the general population, and the association between VR performance and personality traits was investigated. RESULTS: Surgical residents showed personality traits different from those of the general population, demonstrating lower neuroticism, higher extraversion and conscientiousness, and male residents showed greater openness. In the multivariable analysis, adjusted for gender and surgical experience, none of the personality traits was found to be an independent predictor of technical performance. CONCLUSIONS: Surgical residents present distinct personality traits that differ from those of the general population. These traits were not found to be associated with technical performance in a virtual environment. The traits may, however, play an important role in team performance, which in turn is highly relevant for optimal surgical performance.

Personalized modeling for drug concentration prediction using Support Vector Machine

Building a personalized model to describe the drug concentration inside the human body for each patient is highly important to the clinical practice and demanding to the modeling tools. Instead of using traditional explicit methods, in this paper we propose a machine learning approach to describe the relation between the drug concentration and patients' features. Machine learning has been largely applied to analyze data in various domains, but it is still new to personalized medicine, especially dose individualization. We focus mainly on the prediction of the drug concentrations as well as the analysis of different features' influence. Models are built based on Support Vector Machine and the prediction results are compared with the traditional analytical models.