EXP-Crowd: A Gamified Crowdsourcing Framework for Explainability

The spread of AI and black-box machine learning models made it necessary to explain their behavior. Consequently, the research field of Explainable AI was born. The main objective of an Explainable AI system is to be understood by a human as the final beneficiary of the model. In our research, we frame the explainability problem from the crowds point of view and engage both users and AI researchers through a gamified crowdsourcing framework. We research whether it's possible to improve the crowds understanding of black-box models and the quality of the crowdsourced content by engaging users in a set of gamified activities through a gamified crowdsourcing framework named EXP-Crowd. While users engage in such activities, AI researchers organize and share AI- and explainability-related knowledge to educate users. We present the preliminary design of a game with a purpose (G.W.A.P.) to collect features describing real-world entities which can be used for explainability purposes. Future works will concretise and improve the current design of the framework to cover specific explainability-related needs

Analyzing user actions within a Web 2.0 portal to improve a collaborative filtering recommendation system

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SEEMP: A Semantic Interoperability Infrastructure for e-government services in the employment sector

This paper presents SEEMP, a marketplace to coordinate and integrate public and private employment services (ESs) around the EU Member States. The need for flexible collaboration in the marketplace gives rise to the issue of interoperability in both data exchange and share of services. SEEMP proposes a mixed approach that relies on the concepts of services and semantics. SEEMP approach combines Software Engineering and Semantic Web methodologies/tools in an infrastructure that allows for a meaningful service-based communication among ESs

Citizen science to monitor light pollution – a useful tool for studying human impacts on the environment

Citizen science, the active participation of the public in scientific research projects, is a rapidly expanding field in open science and open innovation. It provides an integrated model of public knowledge production and engagement with science. As a growing worldwide phenomenon, it is invigorated by evolving new technologies that connect people easily and effectively with the scientific community. Catalysed by citizens’ wishes to be actively involved in scientific processes, as a result of recent societal trends, it also offers contributions to the rise in tertiary education. In addition, citizen science provides a valuable tool for citizens to play a more active role in sustainable development. This book identifies and explains the role of citizen science within innovation in science and society, and as a vibrant and productive science-policy interface. The scope of this volume is global, geared towards identifying solutions and lessons to be applied across science, practice and policy. The chapters consider the role of citizen science in the context of the wider agenda of open science and open innovation, and discuss progress towards responsible research and innovation, two of the most critical aspects of science today

The SEEMP Approach to Semantic Interoperability for E-Employment

SEEMP is a European Project that promotes increased partnership between labour market actors and the development of closer relations between private and public employment services, making optimal use of the various actors’ specific characteristics, thus providing job-seekers and employers with better services. The need for a flexible collaboration gives rise to the issue of interoperability in both data exchange and share of services. SEEMP proposes a solution that relies on the concepts of services and semantics in order to provide a meaningful service-based communication among labour market actors requiring a minimal shared commitment

SEEMP: A marketplace for the Labour Market

Employment Services are an important topic in the agenda of local governments and in the EU due to their social implications, such as sustainability, workforce mobility, workers’ re-qualification paths, training for fresh graduates and students. Many administrations started their own E-Government projects whose imitations emerge as the demand of workers mobility increases. The SEEMP system presented in this paper overcomes this issue in different ways: starting bilateral communications with near-border similar offices, building a federation of the local employment services, and merging isolate trials. The SEEMP approach relies on a distributed semantic service oriented infrastructure able to federate local projects, in order to create geographically aggregated services for employment by leveraging existing local ones. The social and technical aspects of the SEEMP project are presented, showing how the SEEMP system is integrated with National level systems

StarBorn : Towards making in-situ land cover data generation fun with a location-based game

University Research Priority Program: Language and Space, University of ZurichPeer reviewedPublisher PD

Intrinsic Elicitation : A Model and Design Approach for Games Collecting Human Subject Data

Applied games are increasingly used to collect human subject data such as people’s performance or attitudes. Games a ord a motive for data provision that poses a validity threat at the same time: as players enjoy winning the game, they are motivated to provide dishonest data if this holds a strategic in-game advantage. Current work on data collection game design doesn’t address this issue. We therefore propose a theoretical model of why people provide certain data in games, the Rational Game User Model. We derive a design approach for human subject data collection games that we call Intrinsic Elicitation: data collection should be integrated into the game’s mechanics such that honest responding is the necessary, strategically optimal, and least e ortful way to pursue the game’s goal. We illustrate the value of our approach with a sample analysis of the data collection game Urbanology

Growth of nanostructures by cluster deposition : a review

This paper presents a comprehensive analysis of simple models useful to analyze the growth of nanostructures obtained by cluster deposition. After detailing the potential interest of nanostructures, I extensively study the first stages of growth (the submonolayer regime) by kinetic Monte-Carlo simulations. These simulations are performed in a wide variety of experimental situations : complete condensation, growth with reevaporation, nucleation on defects, total or null cluster-cluster coalescence... The main scope of the paper is to help experimentalists analyzing their data to deduce which of those processes are important and to quantify them. A software including all these simulation programs is available at no cost on request to the author. I carefully discuss experiments of growth from cluster beams and show how the mobility of the clusters on the surface can be measured : surprisingly high values are found. An important issue for future technological applications of cluster deposition is the relation between the size of the incident clusters and the size of the islands obtained on the substrate. An approximate formula which gives the ratio of the two sizes as a function of the melting temperature of the material deposited is given. Finally, I study the atomic mechanisms which can explain the diffusion of the clusters on a substrate and the result of their mutual interaction (simple juxtaposition, partial or total coalescence...)Comment: To be published Rev Mod Phys, Oct 99, RevTeX, 37 figure