Utilisateur (informatique)

Résumé

En informatique, le terme utilisateur (anciennement un opérateur ou un informaticien avec possibilité de feminisation) est employé pour désigner une qui utilise un système informatisé (ordinateur ou robot) mais qui n'est pas nécessairement informaticien (par opposition au programmeur par exemple). L'utilisateur peut aussi être une machine automatique (essentiellement représenté par un bot informatique) pouvant disposer de différents degrés d'autonomie. Les différents niveaux d'utilisateurs

L'utilisateur humain qui n'a aucune compétence en informatique, qui utilise le système dans le cadre de son temps de loisir, celui-ci peut alors avoir un comportement proche d'une entrée aléatoire. C'est aussi ce type d'utilisateur qu'il faut convaincre dans le cadre de diffusion commerciale d'un système vers le grand public. Il peut adopter des comportements d'utilisateur au sens commercial.

L'utilisateur professionnel qui aborde le système dans le cadre de contraintes liées à so

Source officielle

https://fr.wikipedia.org/wiki/Utilisateur_(informatique)

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Publications associées (43)

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Concepts associés (43)

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Cours associés (23)

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Séances de cours associées (45)

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The world today is full of information systems which make huge quantities of information available. This incredible amount of information is clearly overwhelming Internet endusers. As a consequence, intelligent tools to identify worthwhile information are needed, in order to fully assist people in finding the right information. Moreover, most systems are ultimately used, not just to provide information, but also to solve problems. Encouraged by the growing popular success of Internet and the enormous business potential of electronic commerce, e-catalogs have been consolidated as one of the most relevant types of information systems. Nearly all currently available electronic catalogs are offering tools for extracting product information based on key-attribute filtering methods. The most advanced electronic catalogs are implemented as recommender systems using collaborative filtering techniques. This dissertation focuses on strategies for coping with the difficulty of building intelligent catalogs which fully support the user in his purchase decision-making process, while maintaining the scalability of the whole system. The contributions of this thesis lie on a mixed-initiative system which is inspired by observations on traditional commerce activities. Such a conversational model consists basically of a dialog between the customer and the system, where the user criticizes proposed products and the catalog suggests new products accordingly. Constraint satisfaction techniques are analyzed in order to provide a uniform framework for modeling electronic catalogs for configurable products. Within the same framework, user preferences and optimization constraints are also easily modeled. Searching strategies for proposing the adequate products according to criteria are described in detail. Another dimension of this dissertation faces the problem of scalability, i.e., the problem of supporting hundreds, or thousands of users simultaneously using intelligent electronic catalogs. Traditional wisdom would presume that in order to provide full assistance to users in complex tasks, the business logic of the system must be complex, thus preventing scalability. SmartClient is a software architectural model that uses constraint satisfaction problems for representing solution spaces, instead of traditional models which represent solution spaces by collections of single solutions. This main idea is supported by the fact that constraint solvers are extreme in their compactness and simplicity, while providing sophisticated business logic. Different SmartClient architecture configurations are provided for different uses and architectural requirements. In order to illustrate the use of constraint satisfaction techniques for complex electronic catalogs with the SmartClient architecture, a commercial Internet-based application for travel planning, called reality, has been successfully developed. Travel planning is a particularly appropriate domain for validating the results of this research, since travel information is dynamic, travel planning problems are combinatorial, and moreover, complex user preferences and optimization constraints must be taken into consideration.

EPFL2003

Chargement

CoPainter: Interactive AI drawing experience

Pawel Golinski

Google AI Experiments [3] is a set of simple experiments that showcase innova- tive uses of AI in UX. As part of this collection, Google shared Quick Draw, where user draws simple sketches and intelligent agent tries to guess what it depicts, and Sketch- RNN interactive demo, an experience where user starts a sketch and recurrent neural network model completes it [4]. These experiences provide fun and engaging drawing activities which have potential to be used in more educational context. This report describes CoPainter - an interactive AI drawing experience which aims to provide an educational activity based on learning by teaching paradigm - the application asks the user to draw a painting of some object in cooperation with an intel- ligent agent (which can either be a robot or an abstract entity). The activity is based on Sketch-RNN model mentioned above. Main contributions of this project are porting the Sketch-RNN web app experience to Qt, which enables integration with CoWriter code- base and makes it possible to collect more data on user activities. An additional draw in turns game mode is also introduced. The activity may possibly be extended to enable it to be used in diagnosing and treating dysgraphia, which is briefly mentioned in this re- port and is aim of further research on this project.

2019

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We present a new algorithm for realtime face tracking on commodity RGB-D sensing devices. Our method requires no user-specific training or calibration, or any other form of manual assistance, thus enabling a range of new applications in performance-based facial animation and virtual interaction at the consumer level. The key novelty of our approach is an optimization algorithm that jointly solves for a detailed 3D expression model of the user and the corresponding dynamic tracking parameters. Realtime performance and robust computations are facilitated by a novel subspace parameterization of the dynamic facial expression space. We provide a detailed evaluation that shows that our approach significantly simplifies the performance capture workflow, while achieving accurate facial tracking for realtime applications.

Assoc Computing Machinery2013

Chargement

EPFL2003