Uma abordagem nebulosa de raciocínio baseado em casos para recomendação de sistemas marítimos de produção de petróleo

Pós-graduação em Ciência da Computação - IBILCE

Epidemiological Models of Directly Transmitted Diseases: An Approach via Fuzzy Sets Theory

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A Dynamic Route Planning Prototype for Cognitive Cities

A software prototype for dynamic route planning in the travel industry for cognitive cities is presented in this paper. In contrast to existing tools, the prototype enhances the travel experience (i.e., sightseeing) by allowing additional flexibility to the user. The theoretical background of the paper strengthens the understanding of the introduced concepts (e.g., cognitive cities, fuzzy logic, graph databases) to comprehend the presented prototype. The prototype applies an instantiation and enhancement of the graph database Neo4j . For didactical reasons and to strengthen the understanding of this prototype a scenario, applied to route planning in the city of Bern (Switzerland) is shown in the paper.

A collaborative filtering approach to mitigate the new user cold start problem.

The new user cold start issue represents a serious problem in recommender systems as it can lead to the loss of new users who decide to stop using the system due to the lack of accuracy in the recommenda- tions received in that first stage in which they have not yet cast a significant number of votes with which to feed the recommender system?s collaborative filtering core. For this reason it is particularly important to design new similarity metrics which provide greater precision in the results offered to users who have cast few votes. This paper presents a new similarity measure perfected using optimization based on neu- ral learning, which exceeds the best results obtained with current metrics. The metric has been tested on the Netflix and Movielens databases, obtaining important improvements in the measures of accuracy, precision and recall when applied to new user cold start situations. The paper includes the mathematical formalization describing how to obtain the main quality measures of a recommender system using leave- one-out cross validation.

Obtaining contradiction measure on intuitionistic fuzzy sets from fuzzy connectives

In a previous paper, we proposed an axiomatic model for measuring self-contradiction in the framework of Atanassov fuzzy sets. This way, contradiction measures that are semicontinuous and completely semicontinuous, from both below and above, were defined. Although some examples were given, the problem of finding families of functions satisfying the different axioms remained open. The purpose of this paper is to construct some families of contradiction measures firstly using continuous t-norms and t-conorms, and secondly by means of strong negations. In both cases, we study the properties that they satisfy. These families are then classified according the different kinds of measures presented in the above paper.

A new dominance intensity method to deal with ordinal information about a DM's preferences within MAVT

Dominance measuring methods are a new approach to deal with complex decision-making problems with imprecise information. These methods are based on the computation of pairwise dominance values and exploit the information in the dominance matrix in dirent ways to derive measures of dominance intensity and rank the alternatives under consideration. In this paper we propose a new dominance measuring method to deal with ordinal information about decision-maker preferences in both weights and component utilities. It takes advantage of the centroid of the polytope delimited by ordinal information and builds triangular fuzzy numbers whose distances to the crisp value 0 constitute the basis for the de?nition of a dominance intensity measure. Monte Carlo simulation techniques have been used to compare the performance of this method with other existing approaches.

Analysis of pattern recognition and dimensionality reduction techniques for odor biometrics

In this paper, we analyze the performance of several well-known pattern recognition and dimensionality reduction techniques when applied to mass-spectrometry data for odor biometric identification. Motivated by the successful results of previous works capturing the odor from other parts of the body, this work attempts to evaluate the feasibility of identifying people by the odor emanated from the hands. By formulating this task according to a machine learning scheme, the problem is identified with a small-sample-size supervised classification problem in which the input data is formed by mass spectrograms from the hand odor of 13 subjects captured in different sessions. The high dimensionality of the data makes it necessary to apply feature selection and extraction techniques together with a simple classifier in order to improve the generalization capabilities of the model. Our experimental results achieve recognition rates over 85% which reveals that there exists discriminatory information in the hand odor and points at body odor as a promising biometric identifier.

Multi-argument fuzzy measures on lattices of fuzzy sets

In this paper, we axiomatically introduce fuzzy multi-measures on bounded lattices. In particular, we make a distinction between four different types of fuzzy set multi-measures on a universe X, considering both the usual or inverse real number ordering of this lattice and increasing or decreasing monotonicity with respect to the number of arguments. We provide results from which we can derive families of measures that hold for the applicable conditions in each case.

Gramáticas lógicas: resolución de problemas en lenguaje natural

Esta tesis tiene por objeto estudiar las posibilidades de realizar en castellano tareas relativas a la resolución de problemas con sistemas basados en el conocimiento. En los dos primeros capítulos se plantea un análisis de la trayectoria seguida por las técnicas de tratamiento del lenguaje natural, prestando especial interés a los formalismos lógicos para la comprensión del lenguaje. Seguidamente, se plantea una valoración de la situación actual de los sistemas de tratamiento del lenguaje natural. Finalmente, se presenta lo que constituye el núcleo de este trabajo, un sistema llamado Sirena, que permite realizar tareas de adquisición, comprensión, recuperación y explicación de conocimiento en castellano con sistemas basados en el conocimiento. Este sistema contiene un subconjunto del castellano amplio pero simple formalizado con una gramática lógica. El significado del conocimiento se basa en la lógica y ha sido implementado en el lenguaje de programación lógica Prolog II vS. Palabras clave: Programación Lógica, Comprensión del Lenguaje Natural, Resolución de Problemas, Gramáticas Lógicas, Lingüistica Computacional, Inteligencia Artificial.---ABSTRACT---The purpose of this thesis is to study the possibi1 ities of performing in Spanish problem solving tasks with knowledge based systems. Ule study the development of the techniques for natural language processing with a particular interest in the logical formalisms that have been used to understand natural languages. Then, we present an evaluation of the current state of art in the field of natural language processing systems. Finally, we introduce the main contribution of our work, Sirena a system that allows the adquisition, understanding, retrieval and explanation of knowledge in Spanish with knowledge based systems. Sirena can deal with a large, although simple» subset of Spanish. This subset has been formalised by means of a logic grammar and the meaning of knowledge is based on logic. Sirena has been implemented in the programming language Prolog II v2. Keywords: Logic Programming, Understanding Natural Language, Problem Solving, Logic Grammars, Cumputational Linguistic, Artificial Intelligence.

Methodology for developing an advanced communications system for the Deaf in a new domain

A methodology for developing an advanced communications system for the Deaf in a new domain is presented in this paper. This methodology is a user-centred design approach consisting of four main steps: requirement analysis, parallel corpus generation, technology adaptation to the new domain, and finally, system evaluation. During the requirement analysis, both the user and technical requirements are evaluated and defined. For generating the parallel corpus, it is necessary to collect Spanish sentences in the new domain and translate them into LSE (Lengua de Signos Española: Spanish Sign Language). LSE is represented by glosses and using video recordings. This corpus is used for training the two main modules of the advanced communications system to the new domain: the spoken Spanish into the LSE translation module and the Spanish generation from the LSE module. The main aspects to be generated are the vocabularies for both languages (Spanish words and signs), and the knowledge for translating in both directions. Finally, the field evaluation is carried out with deaf people using the advanced communications system to interact with hearing people in several scenarios. In this evaluation, the paper proposes several objective and subjective measurements for evaluating the performance. In this paper, the new considered domain is about dialogues in a hotel reception. Using this methodology, the system was developed in several months, obtaining very good performance: good translation rates (10% Sign Error Rate) with small processing times, allowing face-to-face dialogues.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Editorial

This second issue of Knowledge Management Research & Practice (KMRP) continues the international nature of the first issue, with papers from authors based on four different continents. There are five regular papers, plus the first of what is intended to be an occasional series of 'position papers' from respected figures in the knowledge management field, who have specific issues they wish to raise from a personal standpoint. The first two regular papers are both based on case studies. The first is 'Aggressively pursuing knowledge management over two years: a case study a US government organization' by Jay Liebowitz. Liebowitz is well known to both academics and practictioners as an author on knowledge management and knowledge based systems. Government departments in many Western countries must soon face up to the problems that will occur as the 'baby boomer' generation reaches retirement age over the next decade. This paper describes how one particular US government organization has attempted to address this situation (and others) through the introduction of a knowledge management initiative. The second case study paper is 'Knowledge creation through the synthesizing capability of networked strategic communities: case study on new product development in Japan' by Mitsuru Kodama. This paper looks at the importance of strategic communities - communities that have strategic relevance and support - in knowledge management. Here, the case study organization is Nippon Telegraph and Telephone Corporation (NTT), a Japanese telecommunication firm. The third paper is 'Knowledge management and intellectual capital: an empirical examination of current practice in Australia' by Albert Zhou and Dieter Fink. This paper reports the results of a survey carried out in 2001, exploring the practices relating to knowledge management and intellectual capital in Australia and the relationship between them. The remaining two regular papers are conceptual in nature. The fourth is 'The enterprise knowledge dictionary' by Stuart Galup, Ronald Dattero and Richard Hicks. Galup, Dattero and Hicks propose the concept of an enterprise knowledge dictionary and its associated knowledge management system architecture as offering the appropriate form of information technology to support various different types of knowledge sources, while behaving as a single source from the user's viewpoint. The fifth and final regular paper is 'Community of practice and metacapabilities' by Geri Furlong and Leslie Johnson. This paper looks at the role of communities of practice in learning in organizations. Its emphasis is on metacapabilities - the properties required to learn, develop and apply skills. This discussion takes work on learning and core competences to a higher level. Finally, this issue includes a position paper 'Innovation as an objective of knowledge management. Part I: the landscape of management' by Dave Snowden. Snowden has been highly visible in the knowledge management community thanks to his role as the Director of IBM Global Services' Canolfan Cynefin Centre. He has helped many government and private sector organizations to consider their knowledge management problems and strategies. This, the first of two-part paper, is inspired by the notion of complexity. In it, Snowden calls for what he sees as a 20th century emphasis on designed systems for knowledge management to be consigned to history, and replaced by a 21st century emphasis on emergence. Letters to the editor on this, or any other topic related to knowledge management research and practice, are welcome. We trust that you will find the contributions stimulating, and again invite you to contribute your own paper(s) to future issues of KMRP.

MP-OWA: the most preferred OWA operator

In practical term any result obtained using an ordered weighted averaging (OWA) operator heavily depends upon the method to determine the weighting vector. Several approaches for obtaining the associated weights have been suggested in the literature, in which none of them took into account the preference of alternatives. This paper presents a method for determining the OWA weights when the preferences of alternatives across all the criteria are considered. An example is given to illustrate this method and an application in internet search engine shows the use of this new OWA operator.

Computer automation of land evaluation procedures

This study was concerned with the computer automation of land evaluation. This is a broad subject with many issues to be resolved, so the study concentrated on three key problems: knowledge based programming; the integration of spatial information from remote sensing and other sources; and the inclusion of socio-economic information into the land evaluation analysis. Land evaluation and land use planning were considered in the context of overseas projects in the developing world. Knowledge based systems were found to provide significant advantages over conventional programming techniques for some aspects of the land evaluation process. Declarative languages, in particular Prolog, were ideally suited to integration of social information which changes with every situation. Rule-based expert system shells were also found to be suitable for this role, including knowledge acquisition at the interview stage. All the expert system shells examined suffered from very limited constraints to problem size, but new products now overcome this. Inductive expert system shells were useful as a guide to knowledge gaps and possible relationships, but the number of examples required was unrealistic for typical land use planning situations. The accuracy of classified satellite imagery was significantly enhanced by integrating spatial information on soil distribution for Thailand data. Estimates of the rice producing area were substantially improved (30% change in area) by the addition of soil information. Image processing work on Mozambique showed that satellite remote sensing was a useful tool in stratifying vegetation cover at provincial level to identify key development areas, but its full utility could not be realised on typical planning projects, without treatment as part of a complete spatial information system.