Strategic market position of the European Crime Prevention Network

The activities and tasks of the European Crime Prevention Network (EUCPN), established in 2001, have significantly expanded over the past two decades. In view of the further implementation of its multiannual strategy, the EUCPN has commissioned a study into its current and future strategic market position, conducted with the financial support of the EU’s Internal Security Fund – Police. This book reflects the results. Whilst the EUCPN proves a well-equipped, versatile and multipurpose network in the EU crime prevention area, consolidation and further boosting are due. Key suggestions are to enhance outputs and visibility, to intensify existing partnerships, to broaden target and beneficiary audiences, including at local levels, to implement practice-oriented, multi-language and multimedia approaches, and to focus on the implementation, monitoring, coordination and evaluation of crime prevention policies or strategies, including through cooperation with academia

Towards critical event monitoring, detection and prediction for self-adaptive future Internet applications

The Future Internet (FI) will be composed of a multitude of diverse types of services that offer flexible, remote access to software features, content, computing resources, and middleware solutions through different cloud delivery models, such as IaaS, PaaS and SaaS. Ultimately, this means that loosely coupled Internet services will form a comprehensive base for developing value added applications in an agile way. Unlike traditional application development, which uses computing resources and software components under local administrative control, FI applications will thus strongly depend on third-party services. To maintain their quality of service, those applications therefore need to dynamically and autonomously adapt to an unprecedented level of changes that may occur during runtime. In this paper, we present our recent experiences on monitoring, detection, and prediction of critical events for both software services and multimedia applications. Based on these findings we introduce potential directions for future research on self-adaptive FI applications, bringing together those research directions

KnowledgePro windows: The order of merit?

The producers of KnowledgePro look set with their latest release of KPWIN (KnowledgePro Windows) to fulfil Richard Hale‐Shaw's prophecy that it will become ‘one of the most powerful visual development environments’ (Hale‐Shaw 1992). Comparisons are drawn in this paper between the KPWIN family of products and other authoring tools. The conclusion is that KPWIN is worthy of being included in any courseware developer's tool set. Reasons for preferring a tool from the KnowledgePro family of products for courseware development over three main competitors ‐ Authorware, Toolbook and Visual Basic ‐ are explained, and the merits of KPWIN and KPWIN++ (a version that generates C++ code) are examined

Spatio-Temporal Sentiment Hotspot Detection Using Geotagged Photos

We perform spatio-temporal analysis of public sentiment using geotagged photo collections. We develop a deep learning-based classifier that predicts the emotion conveyed by an image. This allows us to associate sentiment with place. We perform spatial hotspot detection and show that different emotions have distinct spatial distributions that match expectations. We also perform temporal analysis using the capture time of the photos. Our spatio-temporal hotspot detection correctly identifies emerging concentrations of specific emotions and year-by-year analyses of select locations show there are strong temporal correlations between the predicted emotions and known events.Comment: To appear in ACM SIGSPATIAL 201

A comparative study on face recognition techniques and neural network

In modern times, face recognition has become one of the key aspects of computer vision. There are at least two reasons for this trend; the first is the commercial and law enforcement applications, and the second is the availability of feasible technologies after years of research. Due to the very nature of the problem, computer scientists, neuro-scientists and psychologists all share a keen interest in this field. In plain words, it is a computer application for automatically identifying a person from a still image or video frame. One of the ways to accomplish this is by comparing selected features from the image and a facial database. There are hundreds if not thousand factors associated with this. In this paper some of the most common techniques available including applications of neural network in facial recognition are studied and compared with respect to their performance.Comment: 8 page