Social Media and the Public Sector

{Excerpt} Social media is revolutionizing the way we live, learn, work, and play. Elements of the private sector have begun to thrive on opportunities to forge, build, and deepen relationships. Some are transforming their organizational structures and opening their corporate ecosystems in consequence. The public sector is a relative newcomer. It too can drive stakeholder involvement and satisfaction. Global conversations, especially among Generation Y, were born circa 2004. Beginning 1995 until then, the internet had hosted static, one-way websites. These were places to visit passively, retrieve information from, and perhaps post comments about by electronic mail. Sixteen years later, Web 2.0 enables many-to-many connections in numerous domains of interest and practice, powered by the increasing use of blogs, image and video sharing, mashups, podcasts, ratings, Really Simple Syndication, social bookmarking, tweets, widgets, and wikis, among others. Today, people expect the internet to be user-centric

Automatic Multimedia Creation Enriched with Dynamic Conceptual Data

There is a growing gap between the multimedia production and the context centric multimedia services. The main problem is the under-exploitation of the content creation design. The idea is to support dynamic content generation adapted to the user or display profile. Our work is an implementation of a web platform for automatic generation of multimedia presentations based on SMIL (Synchronized Multimedia Integration Language) standard. The system is able to produce rich media with dynamic multimedia content retrieved automatically from different content databases matching the semantic context. For this purpose, we extend the standard interpretation of SMIL tags in order to accomplish a semantic translation of multimedia objects in database queries. This permits services to take benefit of production process to create customized content enhanced with real time information fed from databases. The described system has been successfully deployed to create advanced context centric weather forecasts

Crowdsourcing for smart engagement apps in an urban context : an explorative study

This paper elaborates on the first results of an ongoing living lab project on ‘smart’ city engagement and offers a theoretical, methodological and empirical contribution to the field of user-driven innovation by describing a crowdsourcing experiment conducted in collaboration with the city of Ghent (Flanders). Our presented living lab approach has a double goal. First, it wants to empower citizens by systematically transforming the relationship(s) between citizens and between citizens (as service users) and local city-related governmental institutes (as service providers) by offering smart city applications. Second, it has the ambition to go beyond reactively studying information systems as change agents and wants to pro-actively improve engineering systems that can contribute to the desired changes in city engagement. Supporting citizens as self-actuating sensors to open up more innovative ways of collecting data is an important boundary of the research within a living lab context. We aim for user-driven innovation by involving citizens in the co-production of new electronic public services. Therefore we choose to go through a co-design process (Sanders & Stappers, 2008) with citizens defining the smart engagement applications that most probably will be developed and implemented in a living lab setting. Today, various innovation companies and organizations envision a central role for the user when looking for innovations. The attention for participation of the user is growing since the 80’s, although that the meaning of the concept ‘participation’ is not stable. Different people have used ‘participation’ in a wide variety of different situations and the widespread use of the term has tended to mean that ‘participation’ is used to refer to a wide variety of different situations by different people (Pateman, 1972). Therefore some point to participation as an empty signifier (Carpentier, 2007). The history and origin (and radicalism) of the concept as related to power issues is fading away under the diversity of its different meanings. Recently different participative methods were developed and are used to learn about users and their needs. Some known user-centered methods within industry are working with living labs (Niitamo, Kulkki, Eriksson, & Hribernik, 2006) and crowdsourcing (Hudson-Smith, Batty, Crooks, & Milton, 2009). Although participative methods were initially mainly focused on handing over the power to the user, currently much more attention is given to usability of applications and market forecasting when in the context of user involvement or co-creation. The analysis of power relations is fading slowly away. In our research the notion of participation is used in two ways: as a political phrase, referring to users who are gaining more power and impact on societal changes, and as a practical phrase referring to the forecasting of the success of urban smart engagement apps. This paper is structured in four parts. The first part of the paper introduces the concepts of engagement and ‘smartness’. The second part of the paper introduces crowdsourcing and also elaborates on the related concepts of ‘Web 2.0”, ‘collective intelligence’ and ‘wisdom of crowds’. The third part of the paper describes our methodology, introduces the online crowdsourcing enabler ‘mijndigitaalideevoorgent’, and presents the first, preliminary results of our crowdscourcing experiment. The fourth and last part of the paper formulates a conclusion and discussion of the results

Transforming pedagogy using mobile Web 2.0

Blogs, wikis, podcasting, and a host of free, easy to use Web 2.0 social software provide opportunities for creating social constructivist learning environments focusing on student-centred learning and end-user content creation and sharing. Building on this foundation, mobile Web 2.0 has emerged as a viable teaching and learning tool, facilitating engaging learning environments that bridge multiple contexts. Today’s dual 3G and wifi-enabled smartphones provide a ubiquitous connection to mobile Web 2.0 social software and the ability to view, create, edit, upload, and share user generated Web 2.0 content. This article outlines how a Product Design course has moved from a traditional face-to-face, studio-based learning environment to one using mobile Web 2.0 technologies to enhance and engage students in a social constructivist learning paradigm. Keywords: m-learning; Web 2.0; pedagogy 2.0; social constructivism; product desig

D1.3 List of available solutions

This report has been submitted by Tempesta Media SL as deliverable D1.3 within the framework of H2020 project "SO-CLOSE: Enhancing Social Cohesion through Sharing the Cultural Heritage of Forced Migrations" Grant No. 870939.This report aims to conduct research on the specific topics and needs of the SO-CLOSE project, addressing the available solutions through a state-of-the-art digital tools analysis, applied in the cultural heritage and migration fields. More specifically the report's scope is:To define proper tools and proceedings for the interview needs -performing, recording, transcription, translation. To analyse potential content gathering tools for the co-creation workshops. To conduct a state-of-the-art sharing tools analysis, applied in the cultural heritage and migration fields, and propose a critically adjusted and innovative digital approach

Intelligence artificielle à la périphérie du réseau mobile avec efficacité de communication

L'intelligence artificielle (AI) et l'informatique à la périphérie du réseau (EC) ont permis de mettre en place diverses applications intelligentes incluant les maisons intelligentes, la fabrication intelligente, et les villes intelligentes. Ces progrès ont été alimentés principalement par la disponibilité d'un plus grand nombre de données, l'abondance de la puissance de calcul et les progrès de plusieurs techniques de compression. Toutefois, les principales avancées concernent le déploiement de modèles dans les dispositifs connectés. Ces modèles sont préalablement entraînés de manière centralisée. Cette prémisse exige que toutes les données générées par les dispositifs soient envoyées à un serveur centralisé, ce qui pose plusieurs problèmes de confidentialité et crée une surcharge de communication importante. Par conséquent, pour les derniers pas vers l'AI dans EC, il faut également propulser l'apprentissage des modèles ML à la périphérie du réseau. L'apprentissage fédéré (FL) est apparu comme une technique prometteuse pour l'apprentissage collaboratif de modèles ML sur des dispositifs connectés. Les dispositifs entraînent un modèle partagé sur leurs données stockées localement et ne partagent que les paramètres résultants avec une entité centralisée. Cependant, pour permettre l' utilisation de FL dans les réseaux périphériques sans fil, plusieurs défis hérités de l'AI et de EC doivent être relevés. En particulier, les défis liés à l'hétérogénéité statistique des données à travers les dispositifs ainsi que la rareté et l'hétérogénéité des ressources nécessitent une attention particulière. L'objectif de cette thèse est de proposer des moyens de relever ces défis et d'évaluer le potentiel de la FL dans de futures applications de villes intelligentes. Dans la première partie de cette thèse, l'accent est mis sur l'incorporation des propriétés des données dans la gestion de la participation des dispositifs dans FL et de l'allocation des ressources. Nous commençons par identifier les mesures de diversité des données qui peuvent être utilisées dans différentes applications. Ensuite, nous concevons un indicateur de diversité permettant de donner plus de priorité aux clients ayant des données plus informatives. Un algorithme itératif est ensuite proposé pour sélectionner conjointement les clients et allouer les ressources de communication. Cet algorithme accélère l'apprentissage et réduit le temps et l'énergie nécessaires. De plus, l'indicateur de diversité proposé est renforcé par un système de réputation pour éviter les clients malveillants, ce qui améliore sa robustesse contre les attaques par empoisonnement des données. Dans une deuxième partie de cette thèse, nous explorons les moyens de relever d'autres défis liés à la mobilité des clients et au changement de concept dans les distributions de données. De tels défis nécessitent de nouvelles mesures pour être traités. En conséquence, nous concevons un processus basé sur les clusters pour le FL dans les réseaux véhiculaires. Le processus proposé est basé sur la formation minutieuse de clusters pour contourner la congestion de la communication et est capable de traiter différents modèles en parallèle. Dans la dernière partie de cette thèse, nous démontrons le potentiel de FL dans un cas d'utilisation réel impliquant la prévision à court terme de la puissance électrique dans un réseau intelligent. Nous proposons une architecture permettant l'utilisation de FL pour encourager la collaboration entre les membres de la communauté et nous montrons son importance pour l'entraînement des modèles et la réduction du coût de communication à travers des résultats numériques.Abstract : Artificial intelligence (AI) and Edge computing (EC) have enabled various applications ranging from smart home, to intelligent manufacturing, and smart cities. This progress was fueled mainly by the availability of more data, abundance of computing power, and the progress of several compression techniques. However, the main advances are in relation to deploying cloud-trained machine learning (ML) models on edge devices. This premise requires that all data generated by end devices be sent to a centralized server, thus raising several privacy concerns and creating significant communication overhead. Accordingly, paving the last mile of AI on EC requires pushing the training of ML models to the edge of the network. Federated learning (FL) has emerged as a promising technique for the collaborative training of ML models on edge devices. The devices train a globally shared model on their locally stored data and only share the resulting parameters with a centralized entity. However, to enable FL in wireless edge networks, several challenges inherited from both AI and EC need to be addressed. In particular, challenges related to the statistical heterogeneity of the data across the devices alongside the scarcity and the heterogeneity of the resources require particular attention. The goal of this thesis is to propose ways to address these challenges and to evaluate the potential of FL in future applications. In the first part of this thesis, the focus is on incorporating the data properties of FL in handling the participation and resource allocation of devices in FL. We start by identifying data diversity measures allowing us to evaluate the richness of local datasets in different applications. Then, we design a diversity indicator allowing us to give more priority to clients with more informative data. An iterative algorithm is then proposed to jointly select clients and allocate communication resources. This algorithm accelerates the training and reduces the overall needed time and energy. Furthermore, the proposed diversity indicator is reinforced with a reputation system to avoid malicious clients, thus enhancing its robustness against poisoning attacks. In the second part of this thesis, we explore ways to tackle other challenges related to the mobility of the clients and concept-shift in data distributions. Such challenges require new measures to be handled. Accordingly, we design a cluster-based process for FL for the particular case of vehicular networks. The proposed process is based on careful clusterformation to bypass the communication bottleneck and is able to handle different models in parallel. In the last part of this thesis, we demonstrate the potential of FL in a real use-case involving short-term forecasting of electrical power in smart grid. We propose an architecture empowered with FL to encourage the collaboration among community members and show its importance for both training and judicious use of communication resources through numerical results

Innovation Gaming: An Immersive Experience Environment Enabling Co-creation

A number of existing innovation paradigms and design approaches such as Open Innovation (Chesbrough, 2003), User Experience (Hassenzahl & Tractinsky, 2006) and User-Centred Design (Von Hippel, 2005), as well as User-Centred Open Innovation Ecosystems (Pallot, 2009a) are promoting distributed collaboration among organisations and user communities. However, project stakeholders are mainly trained for improving their individual skills through learning experience (i.e. practical exercises, role playing game) rather than getting a live user experience through immersive environments (e.g. Virtual Reality, Serious Games) that could unleash their creativity potential. This chapter introduces the findings of a study on serious gaming, which discusses various aspects of games and explores a number of issues related to the use of innovation games for enabling user co-creation in the context of collaborative innovation and experiential Living Labs

Analysis domain model for shared virtual environments

The field of shared virtual environments, which also encompasses online games and social 3D environments, has a system landscape consisting of multiple solutions that share great functional overlap. However, there is little system interoperability between the different solutions. A shared virtual environment has an associated problem domain that is highly complex raising difficult challenges to the development process, starting with the architectural design of the underlying system. This paper has two main contributions. The first contribution is a broad domain analysis of shared virtual environments, which enables developers to have a better understanding of the whole rather than the part(s). The second contribution is a reference domain model for discussing and describing solutions - the Analysis Domain Model