LAID - a smart decision room with ambient intelligence for group decision making and argumentation support considering emotional aspects

Decision Making is one of the most important activities of the human being. Nowadays decisions imply to consider many different points of view, so decisions are commonly taken by formal or informal groups of persons. Groups exchange ideas or engage in a process of argumentation and counter-argumentation, negotiate, cooperate, collaborate or even discuss techniques and/or methodologies for problem solving. Group Decision Making is a social activity in which the discussion and results consider a combination of rational and emotional aspects. In this paper we will present a Smart Decision Room, LAID (Laboratory of Ambient Intelligence for Decision Making). In LAID environment it is provided the support to meeting room participants in the argumentation and decision making processes, combining rational and emotional aspects.

Ubiquitous ambient intelligence in a flight decision assistance system

In this paper a new free flight instrument is presented. The instrument named FlyMaster distinguishes from others not only at hardware level, since it is the first one based on a PDA and with an RF interface for wireless sensors, but also at software level once its structure was developed following some guidelines from Ambient Intelligence and ubiquitous and context aware mobile computing. In this sense the software has several features which avoid pilot intervention during flight. Basically, the FlyMaster adequate the displayed information to each flight situation. Furthermore, the FlyMaster has its one way of show information.

Ecotoxicity tests using the green algae chlorella vulgaris — a useful tool in hazardous effluents management

The treatment efficiency of laboratory wastewaters was evaluated and ecotoxicity tests with Chlorella vulgaris were performed on them to assess the safety of their environmental discharge. For chemical oxygen demand wastewaters, chromium (VI), mercury (II) and silver were efficiently removedby chemical treatments.Areduction of ecotoxicitywas achieved; nevertheless, an EC50 (effective concentration that causes a 50% inhibition in the algae growth) of 1.5% (v/v) indicated still high level of ecotoxicity. For chloride determination wastewaters, an efficient reduction of chromium and silver was achieved after treatment. Regarding the reduction of ecotoxicity observed, EC50 increased from 0.059% to 0.5%, only a 0.02% concentration in the aquatic environment would guarantee no effects. Wastewaters containing phenanthroline/iron (II) complex were treated by chemical oxidation. Treatmentwas satisfactory concerning chemical parameters, although an increase in ecotoxicitywas observed (EC50 reduced from 0.31% to 0.21%). The wastes from the kinetic study of persulphate and iodide reaction were treated with sodium bisulphite until colour was removed. Although they did not reveal significant ecotoxicity, only over 1% of the untreated waste produced observable effects over algae. Therefore, ecotoxicity tests could be considered a useful tool not only in laboratory effluents treatment, as shown, but also in hazardous wastewaters management.

Data mining languages for business intelligence

Doctoral Thesis in Information Systems and Technologies Area of Engineering and Manag ement Information Systems

Application of computational intelligence to engineering

Computational Intelligence (CI) includes four main areas: Evolutionary Computation (genetic algorithms and genetic programming), Swarm Intelligence, Fuzzy Systems and Neural Networks. This article shows how CI techniques overpass the strict limits of Artificial Intelligence field and can help solving real problems from distinct engineering areas: Mechanical, Computer Science and Electrical Engineering.

Role of abrasive material on micro-abrasion wear tests

Micro-abrasion wear tests with ball-cratering configuration are widely used. Sources of variability are already studied by different authors and conditions for testing are parameterized by BS EN 1071-6: 2007 standard which refers silicon carbide as abrasive. However, the use of other abrasives is possible and allowed. In this work, ball-cratering wear tests were performed using four different abrasive particles of three dissimilar materials: diamond, alumina and silicon carbide. Tests were carried out under the same conditions on a steel plate provided with TiB2 hard coating. For each abrasive, five different test durations were used allowing understanding the initial wear phenomena. Composition and shape of abrasive particles were investigated by SEM and EDS. Scar areas were observed by optical and electronic microscopy in order to understand the wear effects caused by each of them. Scar geometry and grooves were analyzed and compared. Wear coefficient was calculated for each situation. It was observed that diamond particles produce well-defined and circular wear scars. Different silicon carbide particles presented dissimilar results as consequence of distinct particle shape and size distribution.

Crossing genetic and swarm intelligence algorithms to generate logic circuits

Genetic Algorithms (GAs) are adaptive heuristic search algorithm based on the evolutionary ideas of natural selection and genetic. The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. On the other hand, Particle swarm optimization (PSO) is a population based stochastic optimization technique inspired by social behavior of bird flocking or fish schooling. PSO shares many similarities with evolutionary computation techniques such as GAs. The system is initialized with a population of random solutions and searches for optima by updating generations. However, unlike GA, PSO has no evolution operators such as crossover and mutation. In PSO, the potential solutions, called particles, fly through the problem space by following the current optimum particles. PSO is attractive because there are few parameters to adjust. This paper presents hybridization between a GA algorithm and a PSO algorithm (crossing the two algorithms). The resulting algorithm is applied to the synthesis of combinational logic circuits. With this combination is possible to take advantage of the best features of each particular algorithm.

Adhesion and wear behaviour of NCD coatings on Si3N4 by micro-abrasion tests

Nanocrystalline diamond (NCD) coatings offer an excellent alternative for tribological applications, preserving most of the intrinsic mechanical properties of polycrystalline CVD diamond and adding to it an extreme surface smoothness. Silicon nitride (Si3N4) ceramics are reported to guarantee high adhesion levels to CVD microcrystalline diamond coatings, but the NCD adhesion to Si3N4 is not yet well established. Micro-abrasion tests are appropriate for evaluating the abrasive wear resistance of a given surface, but they also provide information on thin film/substrate interfacial resistance, i.e., film adhesion. In this study, a comparison is made between the behaviour of NCD films deposited by hot-filament chemical vapour deposition (HFCVD) and microwave plasma assisted chemical vapour deposition (MPCVD) techniques. Silicon nitride (Si3N4) ceramic discs were selected as substrates. The NCD depositions by HFCVD and MPCVD were carried out using H2–CH4 and H2–CH4–N2 gas mixtures, respectively. An adequate set of growth parameters was chosen for each CVD technique, resulting in NCD films having a final thickness of 5 m. A micro-abrasion tribometer was used, with 3 m diamond grit as the abrasive slurry element. Experiments were carried out at a constant rotational speed (80 r.p.m.) and by varying the applied load in the range of 0.25–0.75 N. The wear rate for MPCVD NCD (3.7±0.8 × 10−5 m3N−1m−1) is compatible with those reported for microcrystalline CVD diamond. The HFCVD films displayed poorer adhesion to the Si3N4 ceramic substrates than the MPCVD ones. However, the HFCVD films show better wear resistance as a result of their higher crystallinity according to the UV Raman data, despite evidencing premature adhesion failure.

Influence of the abrasive particles size in the micro-abrasion wear tests of TiAlSiN thin coatings

Ball rotating micro-abrasion tribometers are commonly used to carry out wear tests on thin hard coatings. In these tests, different kinds of abrasives were used, as alumina (Al2O3), silicon carbide (SiC) or diamond. In each kind of abrasive, several particle sizes can be used. Some studies were developed in order to evaluate the influence of the abrasive particle shape in the micro-abrasion process. Nevertheless, the particle size was not well correlated with the material removed amount and wear mechanisms. In this work, slurry of SiC abrasive in distilled water was used, with three different particles size. Initial surface topography was accessed by atomic force microscopy (AFM). Coating hardness measurements were performed with a micro-hardness tester. In order to evaluate the wear behaviour, a TiAlSiN thin hard film was used. The micro-abrasion tests were carried out with some different durations. The abrasive effect of the SiC particles was observed by scanning electron microscopy (SEM) both in the films (hard material) as in the substrate (soft material), after coating perforation. Wear grooves and removed material rate were compared and discussed.

Recomendação de conteúdos multimédia

A quantidade e variedade de conteúdos multimédia actualmente disponíveis cons- tituem um desafio para os utilizadores dado que o espaço de procura e escolha de fontes e conteúdos excede o tempo e a capacidade de processamento dos utilizado- res. Este problema da selecção, em função do perfil do utilizador, de informação em grandes conjuntos heterogéneos de dados é complexo e requer ferramentas específicas. Os Sistemas de Recomendação surgem neste contexto e são capazes de sugerir ao utilizador itens que se coadunam com os seus gostos, interesses ou necessidades, i.e., o seu perfil, recorrendo a metodologias de inteligência artificial. O principal objectivo desta tese é demonstrar que é possível recomendar em tempo útil conteúdos multimédia a partir do perfil pessoal e social do utilizador, recorrendo exclusivamente a fontes públicas e heterogéneas de dados. Neste sen- tido, concebeu-se e desenvolveu-se um Sistema de Recomendação de conteúdos multimédia baseado no conteúdo, i.e., nas características dos itens, no historial e preferências pessoais e nas interacções sociais do utilizador. Os conteúdos mul- timédia recomendados, i.e., os itens sugeridos ao utilizador, são provenientes da estação televisiva britânica, British Broadcasting Corporation (BBC), e estão classificados de acordo com as categorias dos programas da BBC. O perfil do utilizador é construído levando em conta o historial, o contexto, as preferências pessoais e as actividades sociais. O YouTube é a fonte do histo- rial pessoal utilizada, permitindo simular a principal fonte deste tipo de dados - a Set-Top Box (STB). O historial do utilizador é constituído pelo conjunto de vídeos YouTube e programas da BBC vistos pelo utilizador. O conteúdo dos vídeos do YouTube está classificado segundo as categorias de vídeo do próprio YouTube, sendo efectuado o mapeamento para as categorias dos programas da BBC. A informação social, que é proveniente das redes sociais Facebook e Twit- ter, é recolhida através da plataforma Beancounter. As actividades sociais do utilizador obtidas são filtradas para extrair os filmes e séries que são, por sua vez, enriquecidos semanticamente através do recurso a repositórios abertos de dados interligados. Neste caso, os filmes e séries são classificados através dos géneros da IMDb e, posteriormente, mapeados para as categorias de programas da BBC. Por último, a informação do contexto e das preferências explícitas, através da classificação dos itens recomendados, do utilizador são também contempladas. O sistema desenvolvido efectua recomendações em tempo real baseado nas actividades das redes sociais Facebook e Twitter, no historial de vídeos Youtube e de programas da BBC vistos e preferências explícitas. Foram realizados testes com cinco utilizadores e o tempo médio de resposta do sistema para criar o conjunto inicial de recomendações foi 30 s. As recomendações personalizadas são geradas e actualizadas mediante pedido expresso do utilizador.

Share GFS – Games, Films & Series: sistema de recomendação

Esta dissertação incide sobre o estudo e análise de uma solução para a criação de um sistema de recomendação para uma comunidade de consumidores de media e no consequente desenvolvimento da mesma cujo âmbito inicial engloba consumidores de jogos, filmes e/ou séries, com o intuito de lhes proporcionar a oportunidade de partilharem experiências, bem como manterem um registo das mesmas. Com a informação adquirida, o sistema reúne condições para proceder a sugestões direcionadas a cada membro da comunidade. O sistema atualiza a sua informação mediante as ações e os dados fornecidos pelos membros, bem como pelo seu feedback às sugestões. Esta aprendizagem ao longo do tempo permite que as sugestões do sistema evoluam juntamente com a mudança de preferência dos membros ou se autocorrijam. O sistema toma iniciativa de sugerir mediante determinadas ações, mas também pode ser invocada uma sugestão diretamente pelo utilizador, na medida em que este não precisa de esperar por sugestões, podendo pedir ao sistema que as forneça num determinado momento. Nos testes realizados foi possível apurar que o sistema de recomendação desenvolvido forneceu sugestões adequadas a cada utilizador específico, tomando em linha de conta as suas ações prévias. Para além deste facto, o sistema não forneceu qualquer sugestão quando o histórico destas tinha provado incomodar o utilizador.

Environmental Decision Support: a Distributed Artificial Intelligence Approach

Decision making in any environmental domain is a complex and demanding activity, justifying the development of dedicated decision support systems. Every decision is confronted with a large variety and amount of constraints to satisfy as well as contradictory interests that must be sensibly accommodated. The first stage of a project evaluation is its submission to the relevant group of public (and private) agencies. The individual role of each agency is to verify, within its domain of competence, the fulfilment of the set of applicable regulations. The scope of the involved agencies is wide and ranges from evaluation abilities on the technical or economical domains to evaluation competences on the environmental or social areas. The second project evaluation stage involves the gathering of the recommendations of the individual agencies and their justified merge to produce the final conclusion. The incorporation and accommodation of the consulted agencies opinions is of extreme importance: opinions may not only differ, but can be interdependent, complementary, irreconcilable or, simply, independent. The definition of adequate methodologies to sensibly merge, whenever possible, the existing perspectives while preserving the overall legality of the system, will lead to the making of sound justified decisions. The proposed Environmental Decision Support System models the project evaluation activity and aims to assist developers in the selection of adequate locations for their projects, guaranteeing their compliance with the applicable regulations.

Negotiation mechanism for self-organized scheduling system with collective intelligence

Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.

Classification Performance of Multilayer Perceptrons with Different Risk Functionals

In the present paper we assess the performance of information-theoretic inspired risks functionals in multilayer perceptrons with reference to the two most popular ones, Mean Square Error and Cross-Entropy. The information-theoretic inspired risks, recently proposed, are: HS and HR2 are, respectively, the Shannon and quadratic Rényi entropies of the error; ZED is a risk reflecting the error density at zero errors; EXP is a generalized exponential risk, able to mimic a wide variety of risk functionals, including the information-thoeretic ones. The experiments were carried out with multilayer perceptrons on 35 public real-world datasets. All experiments were performed according to the same protocol. The statistical tests applied to the experimental results showed that the ubiquitous mean square error was the less interesting risk functional to be used by multilayer perceptrons. Namely, mean square error never achieved a significantly better classification performance than competing risks. Cross-entropy and EXP were the risks found by several tests to be significantly better than their competitors. Counts of significantly better and worse risks have also shown the usefulness of HS and HR2 for some datasets.

Efficient schedulability tests for real-time embedded systems with urgent routines

Task scheduling is one of the key mechanisms to ensure timeliness in embedded real-time systems. Such systems have often the need to execute not only application tasks but also some urgent routines (e.g. error-detection actions, consistency checkers, interrupt handlers) with minimum latency. Although fixed-priority schedulers such as Rate-Monotonic (RM) are in line with this need, they usually make a low processor utilization available to the system. Moreover, this availability usually decreases with the number of considered tasks. If dynamic-priority schedulers such as Earliest Deadline First (EDF) are applied instead, high system utilization can be guaranteed but the minimum latency for executing urgent routines may not be ensured. In this paper we describe a scheduling model according to which urgent routines are executed at the highest priority level and all other system tasks are scheduled by EDF. We show that the guaranteed processor utilization for the assumed scheduling model is at least as high as the one provided by RM for two tasks, namely 2(2√−1). Seven polynomial time tests for checking the system timeliness are derived and proved correct. The proposed tests are compared against each other and to an exact but exponential running time test.