Recovering component dependencies hidden by frameworks - experiences from analyzing OSGi and Qt

In this paper we present experiences we made in the identification and analysis of software dependencies in the context of two popular software development frameworks: OSGi and Qt. Both frameworks are designed for minimizing coupling between software entities by enabling late binding. In case of OSGi, it is the service-oriented bundle concept, in Qt it is the signals and slots mechanisms, that hide dependencies from the programmer at development-time. However, in order to keep an overview of the system it is mandatory to reveal a bigger picture in terms of components and their connections as they are going to exist during runtime. In this paper, we elaborate on the experiences we made in dependency identification and analysis in the context of an industrial and an academic project. In particular, we show how the framework mechanisms of Qt and OSGi can be analyzed in order to reveal a system's component dependencies without having to execute the software

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Resilience The 2nd International Workshop on Modelling of Physical Economic and Social Systems for Resilience Assessment

JRC Directorate E – Space, Security and Migration has organized the 2nd international workshop on Modelling of Physical, Economic and Social Systems for Resilience Assessment in Ispra that will consist in more than ten sessions for three days of full immersion into this topic. Interest in resilience has been rising rapidly during the last twenty years, both among policy makers and academia, as a response to increasing concern about the potential effect of shocks to individuals, civil infrastructure, regions, countries and social, economic and political institutions. The objective of the workshop is to bring together the scientific community and policy makers towards developing better policies and practices incorporating the element of resilience in various fields. This workshop has been organized in close collaboration with NIST and Colorado State University who organized in Washington on 19-21 October 2016 the 1st International workshop on the same subject. This is a follow-up of several similar events in this field. The JRC already organized a higher level event, the JRC-EPSC annual conference "Building a Resilient Europe in a Globalised World" in September 2015. These workshops aimed at identifying more strategic needs and provide an outlook of future actions. In addition, the JRC organized the first plenary session during the IDRC Davos 2016 conference entitled “Implementing resilience in a world of interconnectedness and emerging challenges” in which the JRC, NIST, Rotterdam city, the Dutch authorities and researchers from Japan presented their views and best practices on resilience implementation. Such an event constitutes an excellent opportunity for positioning JRC among the top institutions in resilience modelling with the capability to influence and steer the work of this community in close collaboration with recognized institutions around the globe.JRC.E.2-Technology Innovation in Securit

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic