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This paper is concerned with a multi-resolution tool for screening paper formation variations in various frequency regions on production line. A paper web is illuminated by two red diode lasers and the reflected light recorded as two time series of high resolution measurements constitute the input signal to the papermaking process monitoring system. The time series are divided into blocks and each block is analyzed separately. The task is treated as kernel based novelty detection applied to a multi-resolution time series representation obtained from the band-pass filtering of the Fourier power spectrum of the series. The frequency content of each frequency region is characterized by a feature vector, which is transformed using the canonical correlation analysis and then categorized into the inlier or outlier class by the novelty detector. The ratio of outlying data points, significantly exceeding the predetermined value, indicates abnormalities in the paper formation. The tools developed are used for online paper formation monitoring in a paper mill. [ABSTRACT FROM AUTHOR]

Changes in a speaker's emotion are a fundamental component in human communication. Some emotions motivate human actions while others add deeper meaning and richness to human interactions. In this paper, we explore the development of a computing algorithm that uses audio and visual sensors to recognize a speaker's affective state. Within the framework of Multi-stream Hidden Markov Model (MHMM), we analyze audio and visual observations to detect 11 cognitive/emotive states. We investigate the use of individual modality confidence measures as a means of estimating weights when combining likelihoods in the audio-visual decision fusion. Person-independent experimental results from 20 subjects in 660 sequences suggest that the use of stream exponents estimated on training data results in classification accuracy improvement of audio-visual affect recognition. [ABSTRACT FROM AUTHOR]

In this paper we describe a study carried out with SenToy: a tangible interface that has the shape of a doll and is used to capture emotions from its user whilst performing specific gestures. SenToy was used with an application named Fearnot!, which is a virtual storytelling environment, where characters act autonomously and in character, so that stories emerge from those autonomous actions. The integration of SenToy in FearNot! was evaluated in two ways: (1) if users were able to manipulate the tangible interface appropriately, even if engaged in storytelling situation and (2) if the emotions expressed by the users with SenToy were the same as the ones reported to have been felt after the session. The results of the study show that Sentoy can be used to understand how the viewers reacted to the stories portrayed, and at the same time that the emotions that were expressed with SenToy (apart from some exceptions, reported in the paper) are indeed similar as the ones reported to have been felt by the users. Keywords: Affective tangible interface, affective computing, synthetic characters. [ABSTRACT FROM AUTHOR]

Reticular Activating system which has a form of small neural networks in the brain is closely related system with the automatic nervous system. It takes charge of the function that distinguishes between memorizing one and the others, accepts the only selected information and discards the unnecessary things.In this paper, we propose Reticular Activating system which has Knowledge acquisition, selection, storing, reconfiguration and retrieving part. In this paper, On/Off Switching mechanism for flexible memory is specially designed and tested. [ABSTRACT FROM AUTHOR]

Human emotion is related to stimulus and cognitive appraisal. Emotion is very important to entertainment application of virtual reality. Emotion model of 3D virtual characters is a challenging branch of Intelligent Virtual Environment (IVE). A believable 3D character should be provided with emotion and perception. In general, a virtual character is regarded as an autonomous agent with sense, perception, behavior and action. An emotion model of 3D virtual characters on the basis of psychology theory is presented in this paper. Our work is to construct 3D virtual characters that have internal sensor and perception for external stimulus, and express emotion autonomously in real time. Firstly, architecture of a virtual character is set up by cognitive model; Secondly, emotion class is set up by OCC and Plutchik's emotion theory; Thirdly, some new concepts about emotion are presented with a general mathematical model which is relation among emotion, stimulus, motivation variable, personality variable. Fourthly, a perception model of 3D characters by Gibson's theory is introduced. As a result, an emotional animation demo system of 3D virtual character is implemented on PC. [ABSTRACT FROM AUTHOR]

The paper first builds a novel affective model based on rough sets, presents the static description of affective space. Meanwhile, the paper creatively combines rough sets with Markov chain, gives the dynamic forecast of human affective change. In this affective model, some concepts and states are defined such as affective description precision and so on. It is a fundamental work to more research. Simulations are done using Matlab software, and simulation results show that this affective model can well simulate the human emotion. The results of this paper are innovative. It is a new research direction of affective computing that rough sets and affective computing infiltrate each other. [ABSTRACT FROM AUTHOR]

In this paper, combined features of global and time-sequence were used as the characteristic parameters for speech emotional recognition. A new method based on formula of MMD (Modified Mahalanobis Distance) was proposed to decrease the estimated errors and simplify the calculation. Four emotions including happiness, anger, surprise and sadness are considered in the paper. 1000 recognizing sentences collected from 10 speakers were used to demonstrate the effectiveness of the new method. The average emotion recognition rate reached at 95%. Comparison with method of MQDF [1] (Modified quadratic discriminant function), Data analysis also displayed that the MMD is better than MQDF. [ABSTRACT FROM AUTHOR]

In this paper, we discuss utilization of data mining techniques in realizing intuitive display for search engines toward fast detection of peculiar WWW pages. A search engine can be regarded as a telescope for WWW because it serves as a means to find relevant information in the huge cyberspace. Most of the current display styles of search engines are, however, just text-based rankings thus are far from intuitive. They are also inadequate for certain activities such as browsing for unexpected Web pages. Detection of peculiar WWW pages is expected to lead to making profits and to stimulating our creativity. Our visualization method DPITT (Detecting Peculiar WWW pages from Image, Topic and Term) is based on several data mining techniques and outperforms that of Google in a problem setting which largely favors Google. In this paper, we mainly present our DPITT and we introduce our latest system GEMVIG. [ABSTRACT FROM AUTHOR]

Ovarian cancer is a primary gynecological cancer which pathological stages include benign, borderline and invasive stages cause death in many countries. In this paper, linear regression, analysis of variance (ANOVA) and support vector machine (SVM) are used to identify the gene markers of ovarian cancer for an authentic cDNA expression datasets among 8 normal ovarian tumors, 6 borderline of cancers, 7 ovarian cancer at stage I and 9 ovarian cancer at stage III samples. First, the linear regression analysis obtains 200 useful genes with largest residuals. Further select 14 genes by ANOVA and Scheffe when P-value is less than 0.000005. Then, we use support vector machine to classify the pathological stages by gene expressions. Five experiments are performed with clustering conditions. In the first clustering experiment, the cluster 1 includes BOT, and other pathological stages are in cluster 2. They have significant differences at BOT stage and can get average accuracy about 95.686% in cross-validation. It is quite precise for classifying pathological stages by gene expressions. The average accuracy of all clustering experiments is about 88.541% in cross-validation. Besides, we also develop a statistical analysis system including linear regression and ANOVA function for gene expression analysis. The experimental results and our analysis system can assist biologists and doctors to research and diagnose ovarian cancer by gene expressions. [ABSTRACT FROM AUTHOR]

This paper describes the effects of adding gene reincarnation to a biologically inspired evolutionary algorithm. When using the biologically inspired part of the algorithm we are able to draw on experience from real life. Reincarnation capabilities, however, must be constructed without any real life experience to guide us. This paper addresses the question ‘can reincarnation be added to a genetic algorithm in such a way as to modify the resulting evolutionary process'? Reincarnation in this context requires that genetic information, saved from earlier generations, be bought back and reintroduced into the population at a later time. A simple algorithm is introduced that selects particular genetic material to add to the storage, performs regular culls of the stored material and inserts some of the stored material back into targeted individuals in later generations. Preliminary experiments show that while much of the reinserted material vanishes without having any obvious evolutionary effect, a small proportion remains for many generations and changes the course of the evolution compared to a genetic algorithm identical in all respects except that it lacks reincarnation. [ABSTRACT FROM AUTHOR]

This paper propose a linearizing scheme based on wavelet networks to reduce nonlinear distortion introduced by a high power amplifier over 256QAM signals. Parameters of the proposed linearizer are estimated by using a hybrid algorithm, namely least square and gradient descent. Computer simulation results confirm that once the 256QAM signals are amplified at an input back off level of 0 dB, there is a reduction of 29 dB spectrum re-growth. In addition proposed linearizing scheme has a low complexity and fast convergence. [ABSTRACT FROM AUTHOR]

This paper presents computer simulations which investigate the effect that different group sizes have on the emergence of compositional structures in languages. The simulations are based on a model that integrates the language game model with the iterated learning model. The simulations show that compositional structures tend to emerge more extensively for larger groups, which has a positive effect on the time in which the languages develop and on communicative success, which may even have an optimal group size. A mathematical analysis of the time of convergence is presented that provides an approximate explanation of the results. The paper concludes that increasing group sizes among humans could not only have triggered the origins of language, but also facilitated the evolution of more complex languages. [ABSTRACT FROM AUTHOR]

In this paper we present a method to generate compact and accuracy-controllable free-form surfaces for web-based surgical simulations. Users can input an important area where high accuracy is required, such as an affected area. The input area can be reflected as meshes with high level of details. By utilizing display tablet interfaces, surgeons are able to draw incision lines or boundaries between organs on an on-screen 3D model, just like they draw lines on paper in surgical planning procedures. Input lines can be reflected as boundaries between patches on free-form surfaces. Practical surgical simulators are also presented to evaluate the efficiency of our framework. [ABSTRACT FROM AUTHOR]

When we meet a emotion keyword in a sentence that expresses a kind of emotion, or a word that does not directly express emotion but carry an attitude clue, it could be the case that the speaker is just stating a truth without any affection; it could be an expression of attitudes or emotive states of the agent but using different ways, or it could be other cases. In this consideration, it seems doubtable to determine the exact communicative emotion function of a sentence just based on the "keywords". This paper endeavors to investigate the collective influence of some factors to the communicative emotion function of a sentence. These factors include emotion keywords, and sentence features such as the mood, negation, etc. we believe that the results will be useful for emotion detection or generation of short sentences. [ABSTRACT FROM AUTHOR]

This paper considers affective interactions to achieve empathic engagement with synthetic characters in virtual learning environments, in order to support and induce the expression of empathy in children. The paper presents FearNot!, a school based virtual learning environment, populated by synthetic characters used for personal, social and health education, specifically bullying issues in schools. An empirical study of 345 children aged 8-11 years who interacted with FearNot! is outlined. The results identify that affective interactions resulting in the expression of empathy were increased when children had high levels of belief and interest in character conversations and if they believed that their interactions had an impact on the characters' behaviour. [ABSTRACT FROM AUTHOR]

To endow the synthetic characters with autonomous behaviors interests several fields of researchers, especially the experts of computer graphics. In this paper, we propose a believable brain architecture to allow the synthetic character to achieve high level of autonomy. There are three new capabilities. Firstly, this architecture is not a simple stimulation-action model, but adds a layer between stimulation and action called agency that plays a role of extraction and synthesis. Secondly, hierarchy Finite State Machines (FSMs) and fuzzy logic techniques are embedded into the synthetic character's brain. Finally, the ability to integrate the emotion expression model into the brain architecture allows the synthetic characters to express their sensibilities such as happy, angry, sorry, etc., which can inspire the users' passion and fuse them into the virtual world. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to Emotion Estimation that assesses the affective content from textual messages. Our main goals are to detect emotion from chat or other dialogue messages and to employ animated agents capable of the emotional reasoning based on the textual interaction. In this paper, the emotion estimation module is applied to a chat system, where avatars associated with chat partners act out the assessed emotions of messages through multiple modalities, including synthetic speech and associated affective gestures. [ABSTRACT FROM AUTHOR]

Emotion is the most abstract semantic structure of images. This paper overviews recent research on emotion semantics image retrieval. First, the paper introduces the general frame of emotion semantics image retrieval and points out the four main research issues: to exact sensitive features from images, to define users' emotion information, to build emotion user model and to individualize the user model. Then several algorithms to solve these four issues are analyzed in detail. After that, some future research topics, including construction of an emotion database, evaluation of the user model and computation of the user model, are discussed, and some resolved strategies are presented elementarily. [ABSTRACT FROM AUTHOR]

The paper analyzes the prosody features, which includes the intonation, speaking rate, intensity, based on classified emotional speech. As an important feature of voice quality, voice source are also deduced for analysis. With the analysis results above, the paper creates both a CART model and a weight decay neural network model to find acoustic importance towards the emotional speech classification and to disclose whether there is an underlying consistency between acoustic features and speech emotion. The result shows the proposed method can obtain the importance of each acoustic feature through its weight for emotional speech classification and further improve the emotional speech classification. [ABSTRACT FROM AUTHOR]

This paper presents a conception of emotion space modeling using psychological research for reference. Based on this conception, this paper studies the distribution of the seven emotions in spoken Chinese, including joy, anger, surprise, fear, disgust, sadness and neutral, in the two dimensional space of valence and arousal, and analyses the relationship between the dimensional ratings and the prosodic characteristics in terms of F0 maximum, minimum, range and mean. The findings show that the conception of emotion modeling is helpful to describe and distinguish emotions. [ABSTRACT FROM AUTHOR]

Both of the prosody and spectral features are important for emotional speech synthesis. Besides prosody effects, voice quality and articulation parameters are the factors that should be considered to modify in emotional speech synthetic systems. Generally, rules and filters are designed to process these parameters respectively. This paper proves that by modifying spectral envelope, the voice quality and articulation could be adjusted as a whole. Thus, it will not need to modify each of the parameter separately depending on rules. Accordingly, it will make the synthetic system more flexible by designing an automatic spectral envelope model based on some machine learning methods. The perception test in this paper also shows that when prosody and spectral features are all modified, the best emotional synthetic speech will be obtained. [ABSTRACT FROM AUTHOR]

Combining different feature streams to obtain a more accurate experimental result is a well-known technique. The basic argument is that if the recognition errors of systems using the individual streams occur at different points, there is at least a chance that a combined system will be able to correct some of these errors by reference to the other streams. In the emotional speech recognition system, there are many ways in which this general principle can be applied. In this paper, we proposed using feature selection and feature combination to improve the speaker-dependent emotion recognition in Mandarin speech. Five basic emotions are investigated including anger, boredom, happiness, neutral and sadness. Combining multiple feature streams is clearly highly beneficial in our system. The best accuracy recognizing five different emotions can be achieved 99.44% by using MFCC, LPCC, RastaPLP, LFPC feature streams and the nearest class mean classifier. [ABSTRACT FROM AUTHOR]

The paper is focused on the problem of estimating 3D structure and motion of nonrigid object from a monocular video sequence. Many previous methods on this problem utilize the extension technique of factorization based on rank constraint to the tracking matrix, where the 3D shape of nonrigid object is expressed as weighted combination of a set of shape bases. All these solutions are based on the assumption of affine camera model. This assumption will become invalid and cause large reconstruction errors when the object is close to the camera. The main contribution of this paper is that we extend these methods to the general perspective camera model. The proposed algorithm iteratively updates the shape and motion from weak perspective projection to fully perspective projection by refining the scalars corresponding to the projective depths. Extensive experiments on real sequences validate the effectiveness and improvements of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper a new method to classify and retrieve affective images is proposed. First users express the affective semantics of the images with adjective words; process the data got by Semantic Differential method to obtain main factors of affection and establish affective space; extract low-level visual features of image to construct visual feature space; calculate the correlation between affective space and visual feature space with SVMs. The prototype system that embodies trained SVMs has been implemented. The system can classify the images automatically and support the affective image retrieval. The experimental results prove the effectiveness of this method. [ABSTRACT FROM AUTHOR]

To collect data for sign language recognition is not a trivial task. The lack of training data has become a bottleneck in the research of singer independence and large vocabulary recognition. A novel sign language generation algorithm is introduced in this paper. The difference between signers is analyzed briefly and a criterion is introduced to distinguish the same gesture words of different signers. Basing on that criterion we propose a sign word generation method combining the static gesture quantization and Discrete Cosine Transform (DCT), which can generate the new signers' sign words according to the existed signers' sign words. The experimental result shows that not only the data generated are distinct with the training data, they are also demonstrated effective. [ABSTRACT FROM AUTHOR]

In this paper, we present a face alignment system to deal with various poses and expressions. In addition to global shape model, we use component shape model such as mouth shape model, contour shape model in addition to global shape model to achieve more powerful representation for face components under complex pose and expression variations. Different from 1-D profile texture feature in classical ASM, we use 2-D local texture feature for more accuracy, and in order to achieve high robustness and fast speed it is represented by Haar-wavelet features as in [5]. Extensive experiments are reported to show its effectiveness. [ABSTRACT FROM AUTHOR]

Active Shape Model (ASM) is one of the most powerful statistical tools for face image alignment. In this paper, we propose a novel method, called Bunch-Active Shape Model (Bunch-ASM), based on the standard ASM, to automatically locate facial feature points in Face Recognition. In Bunch-ASM, eyes are localized by a face detector and the matching strategy used in Elastic Bunch Graph Matching (EBGM) is introduced. Experimental results prove that the Bunch-ASM performs much better than the standard ASM and the ASM with iris-refinement. [ABSTRACT FROM AUTHOR]

This article is dedicated to Real-life emotion detection using a corpus of real agent-client spoken dialogs from a medical emergency call center. Emotion annotations have been done by two experts with twenty verbal classes organized in eight macro-classes. Two studies are reported in this paper with the four macro classes: Relief, Anger, Fear and Sadness: the first investigates automatic emotion detection using linguistic information whith a detection score of about 78% and a very good detection of Relief, whereas the second investigates emotion detection with paralinguistic cues with 60% of good detection, Fear being best detected. [ABSTRACT FROM AUTHOR]

This paper investigates how speakers can be classified into native and non-native speakers of a language on the basis of acoustic and perceptually relevant features in their speech. It describes some of the most salient acoustic properties of foreign accent, based on a comparative corpus analysis of native and non-native German and English. These properties include the durational features vowel reduction, consonant cluster reduction and overall speech rate as well as the intonational variables pitch range and pitch movement. The paper further presents an experiment demonstrating that perceptual judgments of foreign accent correlate primarily with the speakers' speech rate. [ABSTRACT FROM AUTHOR]

The vision of Ambient Intelligence (AmI) presumes a plethora of embedded services and devices that all endeavor to support humans in their daily activities as unobtrusively as possible. Hardware gets distributed throughout the environment, occupying even the fabric of our clothing. The environment is equipped with a diversity of sensors, the information of which can be accessed from all over the AmI network. Individual services are distributed over hardware, share sensors with other services and are generally detached from the traditional singleaccess- point computer (see also the paper of Pantic et al. in this volume [51]). [ABSTRACT FROM AUTHOR]

Automatic multimodal recognition of spontaneous emotional expressions is a largely unexplored and challenging problem. In this paper, we explore audio-visual emotion recognition in a realistic human conversation setting—the Adult Attachment Interview (AAI). Based on the assumption that facial expression and vocal expression are at the same coarse affective states, positive and negative emotion sequences are labeled according to Facial Action Coding System. Facial texture in visual channel and prosody in audio channel are integrated in the framework of Adaboost multi-stream hidden Markov model (AdaMHMM) in which the Adaboost learning scheme is used to build component HMM fusion. Our approach is evaluated in AAI spontaneous emotion recognition experiments. [ABSTRACT FROM AUTHOR]

Instinctive computing is a computational simulation of biological and cognitive instincts. It is a meta-program of life, just like universal gravity in nature. It profoundly influences how we look, feel, think, and act. If we want a computer to be genuinely intelligent and to interact naturally with us, we must give computers the ability to recognize, understand, even to have primitive instincts. In this paper, we will review the recent work in this area, the building blocks for the instinctive operating system, and potential applications. The paper proposes a 'bottom-up' approach that is focused on human basic instincts: forage, vigilance, reproduction, intuition and learning. They are the machine codes in human operating systems, where high-level programs, such as social functions can override the low-level instinct. However, instinctive computing has been always a default operation. Instinctive computing is the foundation of Ambient Intelligence as well as Empathic Computing. It is an essential part of Human Computing. [ABSTRACT FROM AUTHOR]

Organizations gain competitive advantages and benefits through e-Business Intelligence (e-BI) technologies at all levels of business operations. E-BI gathers, processes, and analyzes tremendous relevant data to help enterprises make better decisions. Data mining, which utilizes methods and tools from various fields to extract useful knowledge from large amount of data, provides significant support to e-BI/BI applications. This paper presents an overview of a data mining approach: Multiple Criteria Mathematical Programming (MCMP); describes a real-life application using MCMP; and explains how business users at different levels can benefit from the results of MCMP. Then three application models were presented for efficient implementation of e-BI/BI by MCMP models. [ABSTRACT FROM AUTHOR]

Neuroscience becomes a strategic growing field for both academic institutions and industrial companies because of their profound impact on human health, clinical therapy, and basic research such as brain informatics and cognitive science. To track activities of the rapidly developing field and observe new trends, neuroscientists must keep up-to-date with all the relevant information on the internet. In this paper, we present an ontology-based mining system, which is able to probe Competitive Intelligence in Neuroscience by using a well-defined ontology. It is able to support decision making by searching neuroscientific discoveries semantically and tracking new trends statistically. The experiments performed on 15,433,668 MEDLINE articles yield evidence for the feasibility and validity of our system. [ABSTRACT FROM AUTHOR]

Recent advances in computing, communications, digital storage technologies, and high-throughput data-acquisition technologies, make it possible to gather and store incredible volumes of data. It creates unprecedented opportunities for large-scale knowledge discovery from database. Data mining (DM) technology has emerged as a means of performing this discovery. It is a useful tool in many fields such as marketing, decision making, etc. There are countless researchers working on designing efficient data mining techniques, methods, and algorithms. Unfortunately, most data mining researchers pay much attention to technique problems for developing data mining models and methods, while little to basic issues of data mining. What is data mining? What is the product of a data mining process? What are we doing in a data mining process? What is the rule we should obey in a data mining process? In this paper, we will address these questions and propose our answers based on a conceptual data mining model. Our answer would be "data mining is a process of knowledge transformation". It is consistent with the process of human knowledge understanding. Based on analysis of the user-driven and "data-driven" data mining approaches proposed by many other researchers, a conceptual knowledge transformation model and a conceptual domain-oriented data-driven data mining (3DM) model are proposed. It integrates user-driven data mining and data-driven data mining into one system. Some future works for developing such a 3DM data mining system are proposed. [ABSTRACT FROM AUTHOR]

Intelligence concerns many aspects of human mental activity and is considered difficult to be defined clearly. Apart from its relation to mental activity, however, it is possible to discuss intelligence formally based on information it deals with. This paper defines intelligence as the capability to upgrading information and notes that there have been four important phases in the progress of intelligence. These are: (1) language acquisition, (2) knowledge discovery, (3) conceptualization and (4) granulation. These phases are discussed in this paper. [ABSTRACT FROM AUTHOR]

Understanding the processing of information in our cortex is a significant part of understanding how the brain works and of understanding intelligence itself, arguably one of the greatest problems in science today. In particular, our visual abilities are computationally amazing and we are still far from imitating them with computers. Thus, visual cortex may well be a good proxy for the rest of the cortex and indeed for intelligence itself. But despite enormous progress in the physiology and anatomy of the visual cortex, our understanding of the underlying computations remains fragmentary. This position paper is based on the very recent, surprising realization that we may be on the verge of developing an initial quantitative theory of visual cortex, faithful to known physiology and able to mimic human performance in difficult recognition tasks, outperforming current computer vision systems. The proof of principle was provided by a preliminary model that, spanning several levels from biophysics to circuitry to the highest system level, describes information processing in the feedforward pathway of the ventral stream of primate visual cortex. The thesis of this paper is that - finally - neurally plausible computational models are beginning to provide powerful new insights into the key problem of how the brain works, and how to implement learning and intelligence in machines. [ABSTRACT FROM AUTHOR]

Ant colony optimisation (ACO), a constructive metaheuristic inspired by the foraging behaviour of ants, has frequently been applied to shop scheduling problems such as the job shop, in which a collection of operations (grouped into jobs) must be scheduled for processing on different machines. In typical ACO applications solutions are generated by constructing a permutation of the operations, from which a deterministic algorithm can generate the actual schedule. An alternative approach is to assign each machine one of a number of alternative dispatching rules to determine its individual processing order. This representation creates a substantially smaller search space biased towards good solutions. A previous study compared the two alternatives applied to a complex real-world instance and found that the new approach produced better solutions more quickly than the original. This paper considers its application to a wider set of standard benchmark job shop instances. More detailed analysis of the resultant search space reveals that, while it focuses on a smaller region of good solutions, it also excludes the optimal solution. Nevertheless, comparison of the performance of ACO algorithms using the different solution representations shows that, using this solution space, ACO can find better solutions than with the typical representation. Hence, it may offer a promising alternative for quickly generating good solutions to seed a local search procedure which can take those solutions to optimality. [ABSTRACT FROM AUTHOR]

This paper proposes the replacement of mutation and crossover operators of the NSGA II with a variant of differential evolution (DE). The resulting algorithm, termed NSGAII-DE, is tested on three test problems, and shown to be comparable to NSGA II. The algorithm is subsequently applied to two real world problems: (i.) a mass rapid transit scheduling problem and (ii.) the optimization of inspection frequencies for power substations. For both the real world problems, NSGAII-DE is found to have generated better results based on comparative studies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a modification to particle swarm optimization in order to speed up the optimization process. The modification is applied to the constriction coefficient, an important parameter that controls the convergence rate. To validate the proposed strategy, we carried out a number of experiments on a wide range of 25 standard test problems. The obtained results show that the proposed strategy significantly improves the performance of the selected PSO algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we apply a theoretical swarm-generating technique to a system implementing cluster-based construction. The technique, known as swarm engineering consists of two stages. In the first stage, which is top down, the global goal is expressed in such a way that specific conditions may be developed which, when satisfied, guarantees achievement of the global goal. The second step, which is bottom up, concerns the design of specific agents. These agents, once built in accordance with the conditions from the top-down step, will provably lead to the global goal. We develop parts of this theory and apply them to the cluster-based construction problem. [ABSTRACT FROM AUTHOR]

This paper presents in-formation flocking, a novel information visualization technique that extends the original information flocking concept with dynamic and data-driven visual formation behavior generation. This approach extends the emergent swarming properties of a decentralized multi-agent system in order to represent complex time-varying datasets through visually-recognizable formations and motion typologies. In-formation flocking is capable of representing volatile and inherently chaotic time-varying datasets while sustaining a comprehensible representation at a global level as well as revealing more detailed patterns in subsets of the data. This paper demonstrates the capabilities of in-formation flocking to historical stock market data. [ABSTRACT FROM AUTHOR]

Self-assembly and self-replication are the main common thematic features of living organism. The life of most living organisms bases on biomolecular systems such as DNA and RNA. Self-replication and assembly are emergent properties of such systems. These biomolecular systems have common thematic features that enable them from self-replicating and self-assembling themselves. In this paper, I try to generalize these common thematic features to generate a generalized model of the complement-based self-replicated, self-assembled systems. In this model, I explained the main requirements for achieving these systems in terms of basic system components and how these components interact with each other through self-assembling rule set and complement-based replication rule set generating systems with potential to be self-replicated machines. After generating this model, I have applied this model to DNA and simulated its basics. [ABSTRACT FROM AUTHOR]

This paper addresses the topic of how architectural visual experience can be represented and utilised by a software system. The long-term aim is to equip an artificial agent with the ability to make sensible decisions about aesthetics and proportions. The focus of the investigation is on the feature of line distributions extracted from digital images of house façades. It is demonstrated how the dimensionality reduction method isomap can be applied to calculate non-linear "streetmanifolds" where each point on the manifold corresponds to a house façade. Through interpolation between manifold points and the application of an inverse Hough transform, basic structure plans for new house façades are obtained. If the interpolated points are close to the manifold it can be argued that the new plans reflect the character of the surrounding streetscape. The method is also demonstrated using basic examples which can be represented by circles. [ABSTRACT FROM AUTHOR]

Cell pattern formation has a crucial role in both artificial and natural development. This paper presents an artificial development model for cell pattern generation based on the cellular automata (CA) paradigm. Cellular growth is controlled by a genome consisting of an artificial regulatory network (ARN) and a series of structural genes. The genome was evolved by a Genetic Algorithm (GA) in order to produce 2D cell patterns through the selective activation and inhibition of genes. Morphogenetic gradients were used to provide cells with positional information that constrained cellular replication. After a genome was evolved, a single cell in the middle of the CA lattice was allowed to reproduce until a cell pattern was formed. The model was applied to the problem of growing a French flag pattern. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of enhancing the quality of speech signals, which has received growing attention in the last few decades. Many different approaches have been proposed in the literature under various configurations and operating hypotheses. The aim of this paper is to give an overview of the main classes of noise reduction algorithms proposed to-date, focusing on the case of additive independent noise. In this context, we first distinguish between single and multi channel solutions, with the former generally shown to be based on statistical estimation of the involved signals whereas the latter usually employ adaptive procedures (as in the classical adaptive noise cancellation scheme). Within these two general classes, we distinguish between certain sub-families of algorithms. Subsequently, the impact of nonlinearity on the speech enhancement problem is highlighted: the lack of perfect linearity in related processes and the non-Gaussian nature of the involved signals are shown to have motivated several researchers to propose a range of efficient nonlinear techniques for speech enhancement. Finally, the paper summarizes (in tabular form) for comparative purposes, the general features, list of operating assumptions, the relative advantages and drawbacks, and the various types of non-linear techniques for each class of speech enhancement strategy. [ABSTRACT FROM AUTHOR]

In this paper we give a brief overview of speaker recognition with special emphasis on nonlinear predictive models, based on neural nets. Main challenges and possibilities for nonlinear feature extraction are described, and experimental results of several strategies are provided. This paper is presented as a starting point for the non-linear model for speaker recognition. [ABSTRACT FROM AUTHOR]

Different design approaches contributed to what we see today as the prevalent design paradigm for Human Computer Interaction; though they have been mostly applied to the visual aspect of interaction. In this paper we presented a proposal for sound design strategies that can be used in applications involving affective interaction. For testing our approach we propose the sonification of the Affective Diary, a digital diary with focus on emotions, affects, and bodily experience of the user. We applied results from studies in music and emotion to sonic interaction design. This is one of the first attempts introducing different physics-based models for the real-time complete sonification of an interactive user interface in portable devices. [ABSTRACT FROM AUTHOR]

In this paper we describe a system allowing users to express themselves through their full-body movement and gesture and to control in real-time the generation of an audio-visual feedback. The systems analyses in real-time the user's full-body movement and gesture, extracts expressive motion features and maps the values of the expressive motion features onto real-time control of acoustic parameters for rendering a music performance. At the same time, a visual feedback generated in real-time is projected on a screen in front of the users with their coloured silhouette, depending on the emotion their movement communicates. Human movement analysis and visual feedback generation were done with the EyesWeb software platform and the music performance rendering with pDM. Evaluation tests were done with human participants to test the usability of the interface and the effectiveness of the design. [ABSTRACT FROM AUTHOR]

Recently, there has been considerable interest in the automated recognition of affect from written and spoken language. In this paper, we investigate how information on a speaker's affect may be inferred from lexical features using statistical methods. Dictionaries of affect offer great promise to affect sensing since they contain information on the affective qualities of single words or phrases that may be employed to estimate the emotional tone of the corresponding dialogue turn. We investigate to what extent such information may be extracted from general-purpose dictionaries in comparison to specialized dictionaries of affect. In addition, we report on results obtained for a dictionary that was tailored to our corpus. [ABSTRACT FROM AUTHOR]