Computer-Mediated Communication

This book is an anthology of present research trends in Computer-mediated Communications (CMC) from the point of view of different application scenarios. Four different scenarios are considered: telecommunication networks, smart health, education, and human-computer interaction. The possibilities of interaction introduced by CMC provide a powerful environment for collaborative human-to-human, computer-mediated interaction across the globe

Digital Transformation

The amount of literature on Digital Transformation is staggering—and it keeps growing. Why, then, come out with yet another such document? Moreover, any text aiming at explaining the Digital Transformation by presenting a snapshot is going to become obsolete in a blink of an eye, most likely to be already obsolete at the time it is first published. The FDC Initiative on Digital Reality felt there is a need to look at the Digital Transformation from the point of view of a profound change that is pervading the entire society—a change made possible by technology and that keeps changing due to technology evolution opening new possibilities but is also a change happening because it has strong economic reasons. The direction of this change is not easy to predict because it is steered by a cultural evolution of society, an evolution that is happening in niches and that may expand rapidly to larger constituencies and as rapidly may fade away. This creation, selection by experimentation, adoption, and sudden disappearance, is what makes the whole scenario so unpredictable and continuously changing.The amount of literature on Digital Transformation is staggering—and it keeps growing. Why, then, come out with yet another such document? Moreover, any text aiming at explaining the Digital Transformation by presenting a snapshot is going to become obsolete in a blink of an eye, most likely to be already obsolete at the time it is first published. The FDC Initiative on Digital Reality felt there is a need to look at the Digital Transformation from the point of view of a profound change that is pervading the entire society—a change made possible by technology and that keeps changing due to technology evolution opening new possibilities but is also a change happening because it has strong economic reasons. The direction of this change is not easy to predict because it is steered by a cultural evolution of society, an evolution that is happening in niches and that may expand rapidly to larger constituencies and as rapidly may fade away. This creation, selection by experimentation, adoption, and sudden disappearance, is what makes the whole scenario so unpredictable and continuously changing

DIGITISING AGRIFOOD Pathways and Challenges. November 2019

As climate change increasingly poses an existential risk for the Earth, scientists and policymakers turn to agriculture and food as areas for urgent and bold action, which need to return within acceptable Planet Boundaries. The links between agriculture, biodiversity and climate change have become so evident that scientists propose a Great Food Transformation towards a healthy diet by 2050 as a major way to save the planet. Achieving these milestones, however, is not easy, both based on current indicators and on the gloomy state of global dialogue in this domain. This is why digital technologies such as wireless connectivity, the Internet of Things, Arti cial Intelligence and blockchain can and should come to the rescue. This report looks at the many ways in which digital solutions can be implemented on the ground to help the agrifood chain transform itself to achieve more sustainability. Together with the solution, we identify obstacles, challenges, gaps and possible policy recommendations. Action items are addressed at the European Union both as an actor of change at home, and in global governance, and are spread across ten areas, from boosting connectivity and data governance to actions aimed at empowering small farmers and end users

Human-Computer Interaction

In this book the reader will find a collection of 31 papers presenting different facets of Human Computer Interaction, the result of research projects and experiments as well as new approaches to design user interfaces. The book is organized according to the following main topics in a sequential order: new interaction paradigms, multimodality, usability studies on several interaction mechanisms, human factors, universal design and development methodologies and tools

Exploring patient empowerment : presenting an enhanced model for delivery in practice

Patient empowerment evolved as a strategy to address multi-faceted healthcare management issues. Studies over the past decades have provided different patient empowerment frameworks, but even with the emergent frameworks, there is no marked desired result. To date there has been no reliable patient empowerment. This thesis is driven by the ambition to enable greater patient empowerment in our global healthcare services.The methodological approach adopted was a mixed methodology approach based on taxonomical analysis, questionnaire study and focus group discussions. To better understand a patient empowered system, this work explored empowerment, patient empowerment and the role of technology. The thesis built through critical analysis on the knowledge of existing patient empowerment frameworks coupled with technology practice to develop an improved patient empowered system. Through review of existing frameworks and articulation of patients’ demands, weaknesses in current structures to support empowerment are determined.This thesis provides a platform for articulating an improved patient empowerment model, which considered systems theory ideas such as holism and iteration. Further research would propose implementing a trail of this model in practice and exploring with a wider range of stakeholders its potential for integration in the NHS or other health service organisations

The Elements of Big Data Value

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation

Hybrid approaches based on computational intelligence and semantic web for distributed situation and context awareness

2011 - 2012The research work focuses on Situation Awareness and Context Awareness topics. Specifically, Situation Awareness involves being aware of what is happening in the vicinity to understand how information, events, and one’s own actions will impact goals and objectives, both immediately and in the near future. Thus, Situation Awareness is especially important in application domains where the information flow can be quite high and poor decisions making may lead to serious consequences. On the other hand Context Awareness is considered a process to support user applications to adapt interfaces, tailor the set of application-relevant data, increase the precision of information retrieval, discover services, make the user interaction implicit, or build smart environments. Despite being slightly different, Situation and Context Awareness involve common problems such as: the lack of a support for the acquisition and aggregation of dynamic environmental information from the field (i.e. sensors, cameras, etc.); the lack of formal approaches to knowledge representation (i.e. contexts, concepts, relations, situations, etc.) and processing (reasoning, classification, retrieval, discovery, etc.); the lack of automated and distributed systems, with considerable computing power, to support the reasoning on a huge quantity of knowledge, extracted by sensor data. So, the thesis researches new approaches for distributed Context and Situation Awareness and proposes to apply them in order to achieve some related research objectives such as knowledge representation, semantic reasoning, pattern recognition and information retrieval. The research work starts from the study and analysis of state of art in terms of techniques, technologies, tools and systems to support Context/Situation Awareness. The main aim is to develop a new contribution in this field by integrating techniques deriving from the fields of Semantic Web, Soft Computing and Computational Intelligence. From an architectural point of view, several frameworks are going to be defined according to the multi-agent paradigm. Furthermore, some preliminary experimental results have been obtained in some application domains such as Airport Security, Traffic Management, Smart Grids and Healthcare. Finally, future challenges is going to the following directions: Semantic Modeling of Fuzzy Control, Temporal Issues, Automatically Ontology Elicitation, Extension to other Application Domains and More Experiments. [edited by author]XI n.s

Assessing the impacts of digital government transformation in the EU

This report presents the results of the conceptual and empirical work conducted as part of the JRC research on “Exploring Digital Government Transformation: understanding public sector innovation in a data-driven society” conducted within the framework of the “European Location Interoperability Solutions for eGovernment (ELISE)" Action of the ISA2 Programme on Interoperability solutions for public administrations, businesses and citizens, coordinated by DIGIT. Building on the systematisation of the state of the art carried out in the previous phase of the research, the report presents an original conceptual framework for assessing the impacts of Digital Government transformation in the EU and discusses the results of case studies carried out using an experimental or quasi-experimental approach to test and validate it, carried out in different policy areas in various EU countries. The report concludes outlining the final proposal of DigiGov F 2.0, which defines the dimensions and elements of analysis for assessing the effects that can be generated by digital innovation in the public sector and the impacts they have at social, economic and political levels in different policy-cycle phases and governance contexts.JRC.B.6-Digital Econom

Consensus-Based Data Management within Fog Computing For the Internet of Things

The Internet of Things (IoT) infrastructure forms a gigantic network of interconnected and interacting devices. This infrastructure involves a new generation of service delivery models, more advanced data management and policy schemes, sophisticated data analytics tools, and effective decision making applications. IoT technology brings automation to a new level wherein nodes can communicate and make autonomous decisions in the absence of human interventions. IoT enabled solutions generate and process enormous volumes of heterogeneous data exchanged among billions of nodes. This results in Big Data congestion, data management, storage issues and various inefficiencies. Fog Computing aims at solving the issues with data management as it includes intelligent computational components and storage closer to the data sources. Often, an IoT-enabled infrastructure is shared among many users with various requirements. Sharing resources, sharing operational costs and collective decision making (consensus) among many stakeholders is frequently neglected. This research addresses an essential requirement for adaptive, autonomous and consensus-based Fog computational solutions which are able to support distributed and in-network schemes and policies. These network schemes and policies need to meet the requirements of many users. In this work, innovative consensus-based computational solutions are investigated. These proposed solutions aim to correlate and organise data for effective management and decision making in Fog. Instead of individual decision making, the algorithms aim to aggregate several decisions into a consensus decision representing a collective agreement, benefiting from the individuals variant knowledge and meeting multiple stakeholders requirements. In order to validate the proposed solutions, hybrid research methodology is involved that includes the design of a test-bed and the execution of several experiments. In order to investigate the effectiveness of the paradigm, three experiments were designed and validated. Real-life sensor data and synthetic statistical data was collected, processed and analysed. Bayesian Machine Learning models and Analytics were used to consolidate the design and evaluate the performance of the algorithms. In the Fog environment, the first scenario tests the Aggregation by Distribution algorithm. The solution contribute in achieving a notable efficiency of data delivery obtained with a minimal loss in precision. The second scenario validates the merits of the approach in predicting the activities of high mobility IoT applications. The third scenario tests the applications related to smart home IoT. All proposed Consensus algorithms use statistical analysis to support effective decision making in Fog and enable data aggregation for optimal storage, data transmission, processing and analytics. The final results of all experiments showed that all the implemented consensus approaches surpass the individual ones in different performance terms. Formal results also showed that the paradigm is a good fit in many IoT environments and can be suitable for different scenarios when applying data analysis to correlate data with the design. Finally, the design demonstrates that Fog Computing can compete with Cloud Computing in terms of accuracy with an added preference of locality