AI for public health: Self-screening for eye diseases

A software-based visual-field testing (perimetry) system is described which incorporates several AI components, including machine learning, an intelligent user interface and pattern discovery. This system has been successfully used for self-screening in several different public environment

Interactive product catalogue with user preference tracking

In the context of m-commerce, small screen size poses serious difficulty for users to browse effectively through a product catalogue, given the limited number of products that may be presented on-screen. Despite the availability of search engines, filters and recommender systems to aid users, these techniques focus on a narrow segment of product offering. The users are thus denied the opportunity to do a more expansive exploration of the products available. This paper describes a novel approach to overcome the constraints of small screen size. Through integration of a product catalogue with a recommender system, an adaptive system has been created that guides users through the process of product browsing. An original technique has been developed to cluster similar positive examples together to identify areas of interest of a user. The performance of this technique has been evaluated and the results proved to be promising

Designing as Construction of Representations: A Dynamic Viewpoint in Cognitive Design Research

This article presents a cognitively oriented viewpoint on design. It focuses on cognitive, dynamic aspects of real design, i.e., the actual cognitive activity implemented by designers during their work on professional design projects. Rather than conceiving de-signing as problem solving - Simon's symbolic information processing (SIP) approach - or as a reflective practice or some other form of situated activity - the situativity (SIT) approach - we consider that, from a cognitive viewpoint, designing is most appropriately characterised as a construction of representations. After a critical discussion of the SIP and SIT approaches to design, we present our view-point. This presentation concerns the evolving nature of representations regarding levels of abstraction and degrees of precision, the function of external representations, and specific qualities of representation in collective design. Designing is described at three levels: the organisation of the activity, its strategies, and its design-representation construction activities (different ways to generate, trans-form, and evaluate representations). Even if we adopt a "generic design" stance, we claim that design can take different forms depending on the nature of the artefact, and we propose some candidates for dimensions that allow a distinction to be made between these forms of design. We discuss the potential specificity of HCI design, and the lack of cognitive design research occupied with the quality of design. We close our discussion of representational structures and activities by an outline of some directions regarding their functional linkages

An Improved Approach of Intention Discovery with Machine Learning for POMDP-based Dialogue Management

An Embodied Conversational Agent (ECA) is an intelligent agent that works as the front end of software applications to interact with users through verbal/nonverbal expressions and to provide online assistance without the limits of time, location, and language. To help to improve the experience of human-computer interaction, there is an increasing need to empower ECA with not only the realistic look of its human counterparts but also a higher level of intelligence. This thesis first highlights the main topics related to the construction of ECA, including different approaches of dialogue management, and then discusses existing techniques of trend analysis for its application in user classification. As a further refinement and enhancement to our prior work on ECA, this thesis research proposes a cohesive framework to integrate emotion-based facial animation with improved intention discovery. In addition, a machine learning technique modelled from Q-learning (Quality-Learning) technique is introduced to support sentiment analysis for the adjustment of policy design in POMDP-based dialogue management. It is anticipated that the proposed research work is going to improve the accuracy of intention discovery while reducing the length of dialogues. Un agent de conversation incorporé (ECA) est un agent intelligent fonctionnant en amont des applications logicielles pour interagir avec les utilisateurs par le biais d\u27expressions verbales / non verbales et pour fournir une assistance en ligne sans limite de temps, de lieu et de langage. Pour aider à améliorer l\u27expérience de l\u27interaction homme-machine, il est de plus en plus nécessaire de doter la CEA de droits non seulement vis-à-vis de ses homologues humains, mais également d\u27un niveau de renseignement supérieur. Cette thèse aborde d’abord les principaux sujets liés à la construction de la CEA, y compris différentes approches de la gestion du dialogue, puis aborde les techniques existantes d’analyse des tendances pour son application à la classification des utilisateurs. Pour affiner et améliorer nos travaux antérieurs sur ECA, cette thèse de recherche propose un cadre cohérent pour intégrer une animation faciale basée sur les émotions avec une découverte de l’intention améliorée. En outre, une technique d\u27apprentissage automatique modélisée à partir de la technique Q-learning (Quality-Learning) est introduite pour prendre en charge l\u27analyse des sentiments afin d\u27ajuster la conception des stratégies dans la gestion du dialogue basée sur POMDP. On s’attend à ce que les travaux de recherche proposés améliorent la précision de la découverte de l’intention tout en réduisant la durée des dialogues