Engineering Crowdsourced Stream Processing Systems

A crowdsourced stream processing system (CSP) is a system that incorporates crowdsourced tasks in the processing of a data stream. This can be seen as enabling crowdsourcing work to be applied on a sample of large-scale data at high speed, or equivalently, enabling stream processing to employ human intelligence. It also leads to a substantial expansion of the capabilities of data processing systems. Engineering a CSP system requires the combination of human and machine computation elements. From a general systems theory perspective, this means taking into account inherited as well as emerging properties from both these elements. In this paper, we position CSP systems within a broader taxonomy, outline a series of design principles and evaluation metrics, present an extensible framework for their design, and describe several design patterns. We showcase the capabilities of CSP systems by performing a case study that applies our proposed framework to the design and analysis of a real system (AIDR) that classifies social media messages during time-critical crisis events. Results show that compared to a pure stream processing system, AIDR can achieve a higher data classification accuracy, while compared to a pure crowdsourcing solution, the system makes better use of human workers by requiring much less manual work effort

Personalised Visual Art Recommendation by Learning Latent Semantic Representations

In Recommender systems, data representation techniques play a great role as they have the power to entangle, hide and reveal explanatory factors embedded within datasets. Hence, they influence the quality of recommendations. Specifically, in Visual Art (VA) recommendations the complexity of the concepts embodied within paintings, makes the task of capturing semantics by machines far from trivial. In VA recommendation, prominent works commonly use manually curated metadata to drive recommendations. Recent works in this domain aim at leveraging visual features extracted using Deep Neural Networks (DNN). However, such data representation approaches are resource demanding and do not have a direct interpretation, hindering user acceptance. To address these limitations, we introduce an approach for Personalised Recommendation of Visual arts based on learning latent semantic representation of paintings. Specifically, we trained a Latent Dirichlet Allocation (LDA) model on textual descriptions of paintings. Our LDA model manages to successfully uncover non-obvious semantic relationships between paintings whilst being able to offer explainable recommendations. Experimental evaluations demonstrate that our method tends to perform better than exploiting visual features extracted using pre-trained Deep Neural Networks.Comment: Accepted at SMAP202

Systemic formalisation of Cyber-Physical-Social System (CPSS): A systematic literature review

peer reviewedThe notion of Cyber-Physical-Social System (CPSS) is an emerging concept developed as a result of the need to understand the impact of Cyber-Physical Systems (CPS) on humans and vice versa. This paradigm shift from CPS to CPSS was mainly attributed to the increasing use of sensor enabled smart devices and the tight link with the users. The concept of CPSS has been around for over a decade and it has gained an increasing attention over the past few years. The evolution to incorporate human aspects in the CPS research has unlocked a number of research challenges. Particularly human dynamics brings additional complexity that is yet to be explored. The exploration to conceptualise the notion of CPSS has been partially addressed in few scientific literatures. Although its conceptualisation has always been use-case dependent. Thus, there is a lack of generic view as most works focus on specific domains. Furthermore the systemic core and design principles linking it with the theory of systems are loose. This work aims at addressing these issues by first exploring and analysing scientific literatures to understand the complete spectrum of CPSS through a Systematic Literature Review (SLR). Thereby identifying the state-of-the-art perspectives on CPSS regarding definitions, underlining principles and application areas. Subsequently, based on the findings of the SLR, we propose a domain-independent definition and a meta-model for CPSS, grounded in the Theory of Systems. Finally a discussion on feasible future research directions is presented based on the systemic notion and the proposed meta-models

Raisonner sur des connaissances provenant d'une e-communauté

National audienceCe papier présente MKM, un modèle de métaconnaissances pour gérer la fiabilité de connaissances en provenance d'une e-communauté. Le but est d'étendre un système de raisonnement à partir de cas (RàPC), de sorte à pourvoir raisonner sur des connaissances partiellement fiables et non expertes, provenant du Web. La fiabilité des connaissances est formalisée grâce au modèle MKM qui utilise les notions de croyance, confiance, réputation et qualité. Lévaluation de la fiabilité est utilisée pour filtrer les connaissances suffisamment fiable et pour ordonner les réponses fournies par le système de RÀPC, garantissant ainsi une certaine qualité des réponses

ACI : Contribution à la séparation de fluctuations dues à la température dans la réponse d'un capteur de proximité à Courants de foucault

Son bon rapport coût de fabrication/performances fait du Capteur à Courants de Foucault (CCF) un des dispositifs de mesure de proximité les plus utilisés dans l'industrie. Cependant, sa réponse reste sensible aux changements de l'environnement et plus particulièrement à ceux de la température. En se restreignant, dans une première approche, à l'étude des variations de température d'une cible métallique mobile placée en face du capteur, on montre ici que l'ACI (Analyse en Composantes Indépendantes) permet d'extraire ces variations de la mesure de distance capteur-cible fournie par un CCF de proximité multifréquences

Case-Based Reasoning on E-Community Knowledge

International audienceThis paper presents MKM, a meta-knowledge model to manage knowledge reliability, in order to extend a CBR system so that it can reason on partially reliable, non expert, knowledge from the Web. Knowledge reliability is considered from the point of view of the decision maker using the CBR system. It is captured by the MKM model including notions such as belief, trust, reputation and quality, as well as their relationships and rules to evaluate knowledge reliability. We detail both the model and the associated approach to extend CBR. Given a problem to solve for a specific user, reliability estimation is used to filter knowledge with high reliability as well as to rank the results produced by the CBR system, ensuring the quality of results

A Museum App to Trigger Users' Reflection

International audienceThis paper introduces a mobile museum guide that has been designed for the National Gallery in London with the special goal of triggering reflection of the visitor. We also present the results obtained from a first experiment. The underlying postulate is that visitors are more prone to reflection and more interested by the collection in a museum if they can discover it through other facets than those highlighted solely in the museum, and if this discovery is personalised for each of them. The smart guide includes means to personalise a visit by modelling the user preferences and behaviour, and builds recommendations for stories or groups of paintings based on the user profile and reflective topics

Walk the line: Toward an efficient user model for recommendations in museums

International audienceContrary to many application domains, recommending items within a museum is not only a question of preferences. Of course, the visitors expect suggestions that are likely to interest or please them. However, additional factors should be taken into account. Recent works use the visiting styles or the shortest distance between items to adapt the list of recommendations. But, as far as we know, no model of the literature aims at inferring in real time a holistic user model which includes variables such as the crowd tolerance, the distance tolerance, the expected user control, the fatigue, the congestion points, etc. As a work-in-progress, we propose a new representation model which includes psychological, physical and social variables so as to increase user satisfaction and enjoyment. We show how we can infer these characteristics from the user observations (geolocalization over time, moving speed,. . .) and we discuss how we can use them jointly for a sequence recommendation purpose. This work is still in an early stage of development and remains more theoretical than experimental

On the use of an Interoperability Framework in Coopetition Context

The simultaneous cooperation and competition between companies referred to as coopetition in the strategy literature is becoming a recurring theme in the business settings. Companies cooperate with their competitors to gain access to supplementary and complementary resources and capabilities in order to create more value for the customers in order to achieve sustainable value creation and distribution. To coopete, the companies need to be interoperable. Growing globalization, competitiveness and rising environmental awareness are driving many companies to prepare and control their interoperability strategy in order to enhance their ability to interoperate. In this paper, we use an interoperability model called the Maturity Model for Enterprise Interoperability (MMEI) to the coopetition context and we present some initial thoughts on the use of this maturity model in the coopetition context