International Encyclopedia of the Social and Behavioral Sciences (vol. 10)

The immune system is a remarkable information processing and self learning system that offers inspiration to build artificial immune system (AIS). The field of AIS has obtained a significant degree of success as a branch of Computational Intelligence since it emerged in the 1990s. This paper surveys the major works in the AIS field, in particular, it explores up-to-date advances in applied AIS during the last few years. This survey has revealed that recent research is centered on four major AIS algorithms: (1) negative selection algorithms; (2) artificial immune networks; (3) clonal selection algorithms; (4) Danger Theory and dendritic cell algorithms. However, other aspects of the biological immune system are motivating computer scientists and engineers to develop new models and problem solving methods. Though an extensive amount of AIS applications has been developed, the success of these applications is still limited by the lack of any exemplars that really stand out as killer AIS applications.

Review Article: Recent Advances in Artificial Immune Systems: Models and Applications

. . . . . . . . ..................... . . . . . . . : i PREFACE................................................................................................ i i i CONTENTS. i v Ch ap t er I . SPEECH: THE GROUND OF BEING AND KNOWING. . . . . . . 1 R i l k e : a r e we he r e perhaps j u s t to say; the f i g u r e o f t h e w a n de r e r ; t he s a n c t i t y o f t h i n g s ; a c t i o n w i t h o u t symbol; t a k i n g t h i n g s to h e a r t . I I . HEIDEGGER: "LANGUAGE". . . . . . . . . ............................... 26 The presence o f l angua ge , l anguage speaks; naming; t h i n g s ; w o r l d ; t h e d i f f e r e n c e ; p a i n ; l i s t e n i n g and r espondi ng in speech. I I I . THE REPRESENTATIONAL MODEL OF LANGUAGE. . . . . . . . 62 Contemporary s t a t e m e n t o f t he p o s i t i o n ; h i s t o r i c a l s t a t e m e n t o f the p o s i t i o n ; on metaphor ; l i s t e n i n g as i n t e g r a l to speech. L I ST OF WORKS CONSULTED . . . . . . . . . . . . . . 97

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Reflections on language

Traditional citation analysis has been widely applied to detect patterns of scientific collaboration, map the landscapes of scholarly disciplines, assess the impact of research outputs, and observe knowledge transfer across domains. It is, however, limited, as it assumes all citations are of similar value and weights each equally. Content-based citation analysis (CCA) addresses a citation’s value by interpreting each one based on its context at both the syntactic and semantic levels. This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications. In addition, we highlight how increased computational capabilities and publicly available full-text resources have opened this area of research to vast possibilities, which enable deeper citation analysis, more accurate citation prediction, and increased knowledge discovery.

http://info.slis.indiana.edu/~dingying/Publication/CCA-final.pdf

Content-Based Citation Analysis: The Next Generation of Citation Analysis

Although generally philosophers have put a high valuation on reason, increasingly the role of emotions in motivating action is being recognized. The essays in this volume explore the emotions from a variety of perspectives, ranging from Aristotelian views of the passions to the new findings of cognitive science, and from such diverse starting points as medieval literature and psychological studies.

Philosophy of the Emotions

Automatic text summarisation has long been studied and used. The growth in the amount of information on the web results in more demands for automatic methods for text summarisation. Designing a system to produce human-quality summaries is difficult and therefore, many researchers have focused on sentence or paragraph extraction, which is a kind of summarisation. In this paper, we introduce a new method to make such extracts. Genetic Algorithm (GA)-based sentence selection is used to make a summary, and once the summary is created, it is evaluated using a fitness function. The fitness function is based on three following factors: Readability Factor (RF), Cohesion Factor (CF) and Topic-Relation Factor (TRF). In this paper, we introduce these factors and discuss the Genetic Algorithm with the specific fitness function. Evaluation results are also shown and discussed in the paper.

http://www-personal.umich.edu/%7Evahed/papers/ijkms07-summ.pdf

Summarising text with a genetic algorithm-based sentence extraction

Recognition of human speech has long been a hot topic among artificial intelligence and signal processing researches. Most of current policies for this subject are based on extraction of precise features of voice signal and trying to make most out of them by heavy computations. But this focus on signal details has resulted in too much sensitivity to noise and as a result, the necessity of complex noise detection and removal algorithms, which composes a trade-off between fast or noise robust recognition. This paper presents a novel approach to speech recognition using fuzzy modeling and decision making that ignores noise instead of its detection and removal. To do so, the speech spectrogram is converted into a fuzzy linguistic description and this description is used instead of precise acoustic features. During the training period, a genetic algorithm finds appropriate definitions for phonemes, and when these definitions are ready, a simple novel operator consisting of low cost functions such as Max, Min, and Average makes the recognition. The approach is tested on a standard speech database and is compared with Hidden Markov model recognition system with MFCC features as a widely used speech recognition approach.

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Recognition of human speech phonemes using a novel fuzzy approach

Emotions are the subject of study in many research areas, including psychology, physiology and arti ficial intelligence. However, there is not yet a general computational definition for emotions and many of the related works in artificial intelligence are based on uncertain assumptions about the origin, fea tures, and applications of emotions. Therefore, we direct our studies toward achieving a better understanding of emotions and generating a computational model that could be a base for further research in the field. As a first step, we present the results of our study into the possible role of emotions in men tal resource management. To do so, we will introduce a model of emotions based on managing mental resources. We have applied it to a general model of mind and the whole model has been implemented in an artificial life simulation environment called Zamin to evaluate its ability to produce emotional behavior and to make better decisions. The results, as discussed in this article, show that a resource management mechanism could be the source of emotion generation in the mind.

Emotional Behavior: A Resource Management Approach

Artificial Immune System algorithms use antibodies which fully specify the solution of an optimization, learning, or pattern recognition problem. By being restricted to fully specified antibodies, an AIS algorithm can not make use of schemata or classes of partial solutions. This paper presents a symbiotic artificial immune system (SymbAIS) algorithm which is an extension of CLONALG algorithm. It uses partially specified antibodies and gradually builds up building blocks of suitable sub-antibodies. The algorithm is compared with CLONALG on multimodal function optimization and combinatorial optimization problems and it is shown that it can solve problems that CLONALG is unable to solve.

An artificial immune system with partially specified antibodies

Persian is a fully cursive handwriting in which each character may take different forms in different parts of the word, characters overlap and there is a wide range of possible styles. These complexities make automatic recognition of Persian a very hard task. This paper presents a novel approach on recognition of such writings systems which is based on the description of input stream by a sequence of fuzzy linguistic terms; representation of character patterns with the same descriptive language; and comparison of inputs with character patterns using a novel elastic pattern matching approach. As there is no general benchmark for recognition of Persian handwriting, the approach has been tested on the set of words in first primary Iranian school books including 1250 words resulting in 78% correct recognition without dictionary and 96% with dictionary.

Ramin Halavati

Papers

Summarising text with a genetic algorithm-based sentence extraction

Recognition of human speech phonemes using a novel fuzzy approach

Emotional Behavior: A Resource Management Approach

An artificial immune system with partially specified antibodies

Recognition of persian online handwriting using elastic fuzzy pattern recognition