The evolution of smart devices has led to the transformation of many physical spaces to the so-called smart environments collectively termed as Cyber-Physical-Social System (CPSS). In CPSS users co-exist with different stakeholders influencing each other while being influenced by different environmental factors. Additionally, these environments often have their own desired goals and corresponding set of rules in place expecting people to behave in certain ways. Hence, in such settings classical approaches to personalisation which solely optimise for user satisfaction are often encumbered by competing objectives and environmental constraints which are yet to be addressed jointly. In this work we set out to (i) formalise the general problem of personalisation in CPSS from a multi-stakeholder perspective taking into account the full environmental complexity, (ii) extend the general formalisation to the case of exhibition areas and propose a personalised Multi-stakeholder aware Recommendation and Guidance method on a case study of National Gallery, London.

This is a concept paper that introduces the Cognitive Internet of Things and particularly the role that human users play in it. The Cognitive Internet of Things, which forms a Collective AI, based on autonomous software agents, with things that can sense, think and act. We address three specific IoT challenges from a human-centred and social way: interaction, user experience, and human models. We have made an extended literature review from 2015 since today, however we present here only a few most current human-centred IoT related works. The keywords we searched for, among others were "Cognitive IoT", "Collective Artificial Intelligence", "Social IoT", "smart/cognitive things", 'CIoT design/usability', etc. The asset of the paper is to provide three open research questions for future investigation pertaining to natural human-agent interaction, design and user experience, and human modeling, and identify literature gaps

Risk management in the field of information security is most often handled individually by enterprises, taking only a limited view on the influential factors coming from their providers, clients or more globally from their environment. This approach becomes less appropriate in the case of networked enterprises, which tend to form ecosystems with complex influence links. A more holistic approach is needed to take these into account, leading to systemic risk management, i.e. risk management on the entire system formed by the networked enterprises, to avoid perturbations of the ecosystem due to local, individual, decision-making. In this paper, we propose a new meta-model for Information System Security Risk Management (ISSRM), comprising systemic elements as defined in the General Systems Theory. We discuss the design of this new model, highlighting in particular how risk management can be related to a problem-solving approach and the important concepts that are instantiated when taking a systemic approach to ISSRM.

When personalities clash, teams operate less effectively. Personality differences affect face-to-face collaboration and may lower trust in virtual teams. For relatively short-lived assignments, like those of online crowdsourcing, personality matching could provide a simple, scalable strategy for effective team formation. However, it is not clear how (or if) personality differences affect teamwork in this novel context where the workforce is more transient and diverse. This study examines how personality compatibility in crowd teams affects performance and individual perceptions. Using the DISC personality test, we composed 14 five-person teams (N=70) with either a harmonious coverage of personalities (balanced) or a surplus of leader-type personalities (imbalanced). Results show that balancing for personality leads to significantly better performance on a collaborative task. Balanced teams exhibited less conflict and their members reported higher levels of satisfaction and acceptance. This work demonstrates a simple personality matching strategy for forming more effective teams in crowdsourcing contexts.

In this paper the authors propose a crowd coordination mechanism to increase the quality of articles produced in wiki systems. Wikis constitute promising social digital ecosystems for collaborative knowledge creation on the Web. However, as a result of the purely self-coordinated manner that they function, they cannot ensure the quality of the produced articles - an issue that affects their reliability and acceptance. The authors show that wiki article quality optimization can be formulated as a resource allocation problem. Contributors are selected from the wiki community crowd according to their skills, and matched to the articles they can improve the most. A model of the English Wikipedia is given, parameterized and validated from recent field studies results. Experimental results were obtained with simulation systems implementing this model and on a series of scenarios, which include an analysis of the impact of using semantic relations between wiki domains. The obtained results indicate that the proposed mechanism can lead to the production of wiki articles of higher quality, compared to the respective results achieved by the fully self-coordinated wiki.

The main purpose of this paper is to present a new approach for High-Stake Decision Support Systems. We introduce the challenge of the exploitation of heterogeneous community-based knowledge for decision-aid in critical situations. Knowledge shared in e-communities can be very rich, but is also inherently chaotic and questionable. Such uncontrolled knowledge is not usually considered in High-Stake decision support systems, which traditionally rely on expert data. In this paper, we present the new concept of High-Stake Community-Contributed Knowledge Base (HSCCKB) and propose an associated knowledge model. This model constitutes to the theoretical background for the design of HSCCKB-based decision-support systems, by defining the specific knowledge characteristics that should be considered in the reasoning process.