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In this paper we propose a novel personalized paper search system using the relevance among user's queried keywords and user's behaviors on a searched paper list. The proposed system builds user's individual relevance network from analyzing the appearance frequencies of keywords in the searched papers. The relevance network is personalized by providing weights to the appearance frequencies of keywords according to users' behaviors on the searched list, such as "downloading," "opening," and "no-action." In the experimental section, we demonstrate our method using 100 faculties' search information in the University of Suwon. [ABSTRACT FROM AUTHOR]

This paper is concerned with a multi-resolution tool for analysis of a time series aiming to detect abnormalities in various frequency regions. The task is treated as a kernel based novelty detection applied to a multi-level time series representation obtained from the discrete wavelet transform. Having a priori knowledge that the abnormalities manifest themselves in several frequency regions, a committee of detectors utilizing data dependent aggregation weights is build by combining outputs of detectors operating in those regions. [ABSTRACT FROM AUTHOR]

We study the problem of predicting the popularity of items in a dynamic environment in which authors post continuously new items and provide feedback on existing items. This problem can be applied to predict popularity of blog posts, rank photographs in a photo-sharing system, or predict the citations of a scientific article using author information and monitoring the items of interest for a short period of time after their creation. As a case study, we show how to estimate the number of citations for an academic paper using information about past articles written by the same author(s) of the paper. If we use only the citation information over a short period of time, we obtain a predicted value that has a correlation of r = 0.57 with the actual value. This is our baseline prediction. Our best-performing system can improve that prediction by adding features extracted from the past publishing history of its authors, increasing the correlation between the actual and the predicted values to r = 0.81. [ABSTRACT FROM AUTHOR]

This paper presents way of generation color documents from elements extracted from an original document image. This scheme yields synthetic documents of similar visual information to the original one. This method has several advantages as it allows a far more efficient way of storing and transmitting the information. Its rationale is to decompose a color document into paper and ink texture parameters and textual information. The textual information may be either typed or handwritten and is stored as a compressed monochromatic image. [ABSTRACT FROM AUTHOR]

The techniques for organizing and retrieving the artifacts from digital libraries (DLs) semantically are discussed, which include letting the taxonomies and semantic relations work in tandem to index the artifacts in DLs; integrating the techniques used in natural language processing and taxonomies to help users to start their retrieval processes; and ranking scientific papers on similarity in terms of contents or ranking the relevant papers on multi-factors. These techniques are verified through the design and implementation of a prototype of DLs for scientific paper management. [ABSTRACT FROM AUTHOR]

The tenth year of the annual International Conference on Research in Computational Biology (RECOMB) provides an opportunity to reflect on its history. RECOMB has been held across the world, including 6 different countries spanning 3 continents (Table 1). Over its 10 year history, RECOMB has published 373 papers and 170 individuals have served on its various committees. While there are many new faces in RECOMB each year, a significant number of researchers have participated over many years forming the core of the RECOMB community. [ABSTRACT FROM AUTHOR]

Automatic evaluation of visual print quality is addressed in this study. Due to many complex factors of perceived visual quality its evaluation is divided to separate parts which can be individually evaluated using standardized assessments. Most of the assessments however require active evaluation by trained experts. In this paper one quality assessment, missing dot detection from printed dot patterns, is addressed by defining sufficient hardware for image acquisition and method for detecting and counting missing dots from a test strip. The experimental results are evidence how the human assessment can be automated with the help of machine vision, thus making the test more repeatable and accurate. [ABSTRACT FROM AUTHOR]

Ukiyo-e is one famous traditional woodblock type Japanese drawing. Some pattern printed by special print techniques can only be seen from some special direction. This phenomenon relate to the reflection properties on the surface of Ukiyo-e. In this paper, we propose a method to measure these reflection properties of Ukiyo-e. Fitstly, the normal on the surface and the direction of the fiber in Japanese paper are computed from photos which are taken by a measuring machine named OGM. Then, fit the reflection model to the measured data and the reflection properties of Ukiyo-e can be obtained. Based on these parameters, the the appearance of Ukiyo-e can be rendered on real-time. [ABSTRACT FROM AUTHOR]

Generally, it is difficult to find constant patterns on identifiers in a container image, since the identifiers are not normalized in color, size, and position, etc. and their shapes are damaged by external environmental factors. This paper distinguishes identifier areas from background noises and removes noises by using an ART2-based quantization method and general morphological information on the identifiers such as color, size, ratio of height to width, and a distance from other identifiers. Individual identifier is extracted by applying the 8-directional contour tracking method to each identifier area. This paper proposes a refined ART2-based RBF network and applies it to the recognition of identifiers. Through experiments with 300 container images, the proposed algorithm showed more improved accuracy of recognizing container identifiers than the others proposed previously, in spite of using shorter training time. [ABSTRACT FROM AUTHOR]

In this paper we propose a two-pass classification method and apply it to face recognitions. The method is obtained by integrating together two approaches, the hyper-ellipsoid neural networks (HENN's) and the SVM's with error correcting codes. This method realizes a classification operation in two passes: the first one is to get an intermediate classification result for an input sample by using the HENN's, and the second pass is followed by using the SVM's to re-classify the sample based on both the input data and the intermediate result. Simulations conducted in the paper for applications to face recognition showed that the two-pass method can maintain the advantages of both the HENN's and the SVM's while remedying their disadvantages. Compared with the HENN's and the SVM's, a significant improvement of recognition performance over them has been achieved by the new method. [ABSTRACT FROM AUTHOR]

This paper proposed an automatic recognition system of shipping container identifiers using fuzzy-based noise removal method and ART2-based self-organizing supervised learning algorithm. Generally, identifiers of a shipping container have a feature that the color of characters is black or white. Considering such a feature, in a container image, all areas excepting areas with black or white colors are regarded as noises, and areas of identifiers and noises are discriminated by using a fuzzy-based noise detection method. Noise areas are replaced with a mean pixel value of the whole image and areas of identifiers are extracted by applying the edge detection by Sobel masking operation and the vertical and horizontal block extraction in turn to the noise-removed image. Extracted areas are binarized by using the iteration binarization algorithm, and individual identifiers are extracted by applying 8-directional contour tacking method. This paper proposed an ART2-based self-organizing supervised learning algorithm for the identifier recognition, which creates nodes of the hidden layer by applying ART2 between the input and the hidden layers and improves the performance of learning by applying generalized delta learning and Delta-bar-Delta algorithm between the hidden and the output layers. Experiments using many images of shipping containers showed that the proposed identifier extraction method and the ART2-based self-organizing supervised learning algorithm are more improved compared with the methods previously proposed. [ABSTRACT FROM AUTHOR]

A method based on multi-feature and multi-stage verification is proposed in this paper. The direction and texture features obtained by Gabor filter are used for the first-stage verification. Then the sham dynamic features representing the writing force are used for the second-stage verification. Considering the number of samples for training is limited, the support vector machine (SVM) is selected as the classifier. The experimental results show that this method can not only increase the accuracy, but also satisfy the requirement of speed in real-time system as well. [ABSTRACT FROM AUTHOR]

Increasing the smooth degree of data series is key factor of grey model's precision. In this paper, the more general method is put forward on the basis of summarizing several kinds of ways to improve smooth degree of data series, and a new transformation is represented. The practical application shows the effectiveness and superiority of this method. [ABSTRACT FROM AUTHOR]

A software product-line is a set of products built from a core set of software components. Although software engineers develop software product-lines for various application types, they are most commonly used for embedded systems development, where the variability of hardware features requires variability in the supporting firmware. Feature models are used to represent the variability in these software product-lines. Various feature modeling approaches have been proposed, including feature diagrams, domain specific languages, constraint languages, and the semantic web language OWL. This paper explores a conceptual graph approach to feature modeling in an effort to produce feature models that have a more natural, and more easily expressed mapping to the problem domain. It demonstrates the approach using a standard Graph Product-line problem that has been discussed in various software product-line papers. A conceptual graph feature model is developed for the graph product-line and it is compared to other feature models for this product-line. [ABSTRACT FROM AUTHOR]

It presents an approach to the automatic generation of pencil sketching with the effects of paper texture. First, proposes a texture mapping of strokes in the aspect of skeleton, the filter algorithm and the standard deviation algorithm for rendering image, the near distances recover algorithm for real-time browsing, and finally implement the Pen-and-Ink Style of pencil sketching Rendering System. In the static rendering, it needn't adjust the threshold for the convenient on rendering and the outlines are more precise and exact. Besides, attaching the graftal and sketching shadow makes the composition of an image more attractive. Through a series of clever image processes, the system finally presents excellent colored pencil style drawings. Because the proposed algorithm is not complicated, the rendering time is quite short compared to other past related studies. Compared with other research works and Photoshop on a set of benchmarks, the system demonstrates its strength in the aspects of full automation, stability of sketching quality and higher visual satisfaction, all achieved in a considerably shorter time. [ABSTRACT FROM AUTHOR]

The storage capacity of the conventional neural network is 0.14 times of the number of neurons (P=0.14N). Due to the huge difficulty in recognizing large number of images or patterns,researchers are looking for new methods at all times. Quantum Neural Network (QNN), which is a young and outlying science built upon the combination of classical neural network and quantum computing,is a candidate to solve this problem.This paper presents Quantum Probability Distribution Network (QPDN) whose elements of the storage matrix are distributed in a probabilistic way on the base of quantum linear superposition and applies QPDN on image recognition. Contrasting to the conventional neural network, the storage capacity of the QPDN is increased by a factor of 2N,where N is the number of neurons. Besides,the case analysis and simulation tests have been carried out for the recognition of images in this paper, and the result indicates that QPDN can recognize the images or patterns effectively and its working process accords with quantum evolvement process. [ABSTRACT FROM AUTHOR]

In this paper we propose a formal approach to the generative design of artefacts. The founding idea, bridging the gap between the domain of architectural artefacts and the field of ontologies, is to represent the notion of species as it exists in the context of generative design by the concept of class existing in the field of formal ontologies. In this paper we propose an artefact design methodology and a system based on it that assists a designer in the building process of artefact 3D models. The implemented system is structured in three conceptual levels: the Design Strategy, the Computational Model, and the 3D graphical rendering system. The Computational model has been defined and developed using the Frame Logic formalism. The proposed methodology is independent of specific modelling environments. In this paper we also present methodological directions to couple artefact representations with 3D rendering systems. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of global stability for a class of neural networks with time-varying delays. A new sufficient condition for global stability is proposed by using some slack matrix variables to express the relationship between the system matrices. The restriction on the derivative of the delay function to be less than unit is removed. A numerical example shows that the result obtained in this paper improves the upper bound of the delay over some existing ones. [ABSTRACT FROM AUTHOR]

In this paper, we first discuss the level of compliance for timetabling research to two important research qualities, namely measurability and reproducibility, analyzing what we believe are the most important contributions in the literature. Secondly, we discuss some practices that, in our opinion, could contribute to the improvement on the two aforementioned qualities for future papers in timetabling research. For the sake of brevity, we restrict our scope to university timetabling problems (exams, courses, or events), and thus we leave out other equally important timetabling problems, such as high-school, employee, and transportation timetabling. [ABSTRACT FROM AUTHOR]

This paper extends a survey on Networked Control Systems (NCSs) published in previous papers. General frameworks and some fundamental issues for the study of NCSs are proposed. A significant emphasis has been on developing control methodologies to study the character of NCSs. This survey paper presents recent NCSs control methodologies, some promising research directions are also covered. [ABSTRACT FROM AUTHOR]

This paper surveys first-order probabilistic languages (FOPLs), which combine the expressive power of first-order logic with a probabilistic treatment of uncertainty. We provide a taxonomy that helps make sense of the profusion of FOPLs that have been proposed over the past fifteen years. We also emphasize the importance of representing uncertainty not just about the attributes and relations of a fixed set of objects, but also about what objects exist. This leads us to Bayesian logic, or BLOG, a language for defining probabilistic models with unknown objects. We give a brief overview of BLOG syntax and semantics, and emphasize some of the design decisions that distinguish it from other languages. Finally, we consider the challenge of constructing FOPL models automatically from data. [ABSTRACT FROM AUTHOR]

We consider some problems about the computation time of evolutionary algorithms in this paper. First, some exact analytic expressions of the mean first hitting times of general evolutionary algorithms in finite search spaces are obtained theoretically by using the properties of Markov chain associated with evolutionary algorithms considered here. Then, by introducing drift analysis and applying Dynkin's Formula, the general upper and lower bounds of the mean first hitting times of evolutionary algorithms are estimated rigorously under some mild conditions listed in the paper. Those results in this paper are commonly useful. Also, the analytic techniques adopted in the paper are widely instructive for analyzing the computation time of evolutionary algorithms in a given search space as long as some specific mathematical arts are introduced accordingly. [ABSTRACT FROM AUTHOR]

This paper presents an approach for solving the driving load forecasting problem based on Cascade Neural Networks with node-decoupled extended Kalman Filtering (CNN-NDEKF). Because of the inherent advantages, hybrid electric vehicles (HEV) are being given more and more attention. The power control strategy of HEVs is the key technology which determines the HEV's efficiency and pollutive emission level. Since the extent of improvement involved with HEV power control strategies greatly depends on the future driving load forecasting, in this paper, we attempt to achieve driving load forecasting using CNN-NDEKF. Instead of forecasting the entire load sequence, we define 5 load levels by a fuzzy logic method and then we forecast the load level. Simulation study is given to illustrate the feasibility of the driving load forecasting approach. [ABSTRACT FROM AUTHOR]

Aiming at improving the convergence performance of conventional BP neural network, this paper presents an improved PSO algorithm instead of gradient descent method to optimize the weights and thresholds of BP network. The strategy of the algorithm is that in each iteration loop, on every dimension d of particle swarm containing n particles, choose the particle whose velocity decreases most quickly to mutate its velocity according to some probability. Simulation results show that the new algorithm is very effective. It is successful to apply the algorithm to gas turbine fault diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we study the Cohen-Grossberg neural networks with mixed time-varying delays. By applying the Lyapunov functional method and combining with the inequality 3abc ≤ a3 + b3 + c3 (a,b,c > 0) technique, a series of new and useful criteria on the existence of equilibrium point and its global asymptotical stability are established. The results obtained in this paper extend and generalize the corresponding results existing in previous literature. [ABSTRACT FROM AUTHOR]

This paper studies a general class of neural networks with time-varying delays and the neuron activations belong to the set of discontinuous monotone increasing functions. The discontinuities in the activations are an ideal model of the situation where the gain of the neuron amplifiers is very high. Because the delay in combination with high-gain nonlinearities is a particularly harmful source of potential instability, in the paper, conditions which ensure the global convergence of the neural network are derived. [ABSTRACT FROM AUTHOR]

Electromagnetic (EM)-based neural network (NN) approaches have recently gained recognition as unconventional and useful methods for radio frequency (RF) components modeling. In this paper, several improvement techniques including a new data preprocessing technique and an improved training algorithm are presented. Comprehensive cases are compared in this paper. The experimental results indicate that with these techniques, the modified model has better performance. [ABSTRACT FROM AUTHOR]

Process models play important role in computer aided pro- cess engineering. Although the structure of these models are a priori known, model parameters should be estimated based on experiments. The accuracy of the estimated parameters largely depends on the information content of the experimental data presented to the parameter identification algorithm. Optimal experiment design (OED) can maximize the confidence on the model parameters. The paper proposes a new additive sequential evolutionary experiment design approach to maximize the amount of information content of experiments. The main idea is to use the identified models to design new experiments to gradually improve the model accuracy while keeping the collected information from previous experiments. This scheme requires an effective optimization algorithm, hence the main contribution of the paper is the incorporation of Evolutionary Strategy (ES) into a new iterative scheme of optimal experiment design (AS-OED). This paper illustrates the applicability of AS-OED for the design of feeding profile for a fed-batch biochemical reactor. [ABSTRACT FROM AUTHOR]

Traditional indexing methods face the difficulty of"curse of dimensionality" at high dimensionality. In this paper, the traditional vector approximation is improved. Firstly, it decreases the dimensions by LLE (locally-linear-embedding). As a result of it, a set of absolute low dimensions are gotten. Then, this paper uses the Gaussian mixture distribution and estimates the distribution through EM (expectation-maximization) method. The original data vectors are replaced by vector approximation. This approach gains higher efficiency and less run time. The experiments show a remarkable reduction of I/O. They also show an improvement on the indexing performance and then speed the image retrieval. [ABSTRACT FROM AUTHOR]

This paper addresses a capacitated production planning (CPP) problem with outsourcing. Specially, the outsourcing problem considered in this paper has the following features: (1) all demands are met by production, inventory, outsourcing, and sale loss without postponement or backlog; (2) The goal of the solution to this problem is to minimize the total cost covering the production, setup, inventory holding, sale loss, and outsourcing activities; (3) different from previous studies which usually involved some special assumptions on the cost function for these activities, the cost function structure we consider in this paper are arbitrary and time-varying; and (4) we consider a general problem situation where the production, inventory, and outsourcing have a capacity limitation. It is likely that there is no solution in the model due to the consideration of such general cost functions and many constraints, hence we employ a Bellman/Ford algorithm for checking feasible solutions of the problem and resulting in a feasible solution region. After that, a proposed algorithm is applied for searching a global optimal solution with in the feasible solution region. [ABSTRACT FROM AUTHOR]

Graphics recognition for raster-based input discovers primitives such as lines, arrowheads, and circles. This paper focuses on graphics recognition of figures in vector-based PDF documents. The first stage consists of extracting the graphic and text primitives corresponding to figures. An interpreter was constructed to translate PDF content into a set of self-contained graphics and text objects (in Java), freed from the intricacies of the PDF file. The second stage consists of discovering simple graphics entities which we call graphemes, e.g., a pair of primitive graphic objects satisfying certain geometric constraints. The third stage uses machine learning to classify figures using grapheme statistics as attributes. A boosting-based learner (LogitBoost in the Weka toolkit) was able to achieve 100% classification accuracy in hold-out-one training/testing using 16 grapheme types extracted from 36 figures from BioMed Central journal research papers. The approach can readily be adapted to raster graphics recognition. Keywords: Graphics Recognition, PDF, Graphemes, Vector Graphics, Machine Learning, Boosting. [ABSTRACT FROM AUTHOR]

The research work in this paper focuses on solving the critical problems in Web Service discovery such as how to locate functionality-desired Web Services and how to select the best one from large numbers of functionality-similar Web Services. The semantic description and quality description of Web Service based on ontology proposed in this paper, provides a consistent description of different kinds of Web Service, which is used as the basis of service matching. Then the matching model of Web Service is discussed in detail, which consists of semantic similarity matching according to functionality of Web Service, and semantic filtering according to quality of Web Service. Using the descriptions and matching approach, the performance of Web Service discovery can be improved by increasing the precision and recall of Web Service searching. [ABSTRACT FROM AUTHOR]

This paper illustrates an algorithm for osteosarcoma segmentation, using vectorial fuzzy-connectedness segmentation, and coming up with a methodology which can be used to segment some distinct tissues of osteosarcoma such as tumor, necrosis and parosteal sarcoma from 3D vectorial images. However, fuzzy-connectedness segmentation can be successfully used only in connected regions. In this paper, some improvements have been made to segment the interested tissues which are distributed in disconnected regions. And the paper speeds up the process of segmentation by segmenting two osteosarcoma tissues simultaneously. The methology has been applied to a medical image analysis system of osteosarcoma segmentation and 3D reconstruction, which has been put into practical use in some hospitals. [ABSTRACT FROM AUTHOR]

Automated timetabling is a research domain that has occupied many researchers over the last 50 years. Several algorithms have proven to be applicable to timetabling but they are nearly all designed to address specific problems. The framework presented in this paper is a step towards a generic semi-automatic timetabling tool. The basis of the framework is an ontology for timetabling that we designed after having investigated different types of timetabling problem. A first step towards solving general problems consists of mapping their data representation to the ontology. That is carried out in such a way that existing data that is already available in databases can further be used throughout the application. In the next step, the tool assists in determining the constraints and objectives of the problem. The semantic components have three sources of information: meta-data about the database, domain knowledge about timetabling problems and external, non-domain-specific knowledge. [ABSTRACT FROM AUTHOR]

Raster map should be corrected after scanned because of the errors caused by paper map deformation. In the paper, the deficiency of the polynomial fitting method is analyzed. The paper introduces an ANN (Artificial Neural Network) correcting method that utilizes the advantage of its function approximation ability. In the paper, two types of ANNs, BP and GRNN, are designed for the correcting. The comparing experiment is done with the same data by the polynomial fitting and ANN methods, utilizing the MALAB. The experiment results show that the ANN methods, especially the GRNN method, performances far better than the polynomial fitting method does. [ABSTRACT FROM AUTHOR]

This paper investigates the fault detection and prediction of rhythmically soniferous products, such as clocks, watches and timers. Such products with fault cannot work steadily or probably cause malfunction. The authors extend the concept of computer audition and establish an architectural model of product fault prediction system based on probabilistic neural networks. The system listens to the product sound by the multimedia technology and the sound features are extracted to detect and predict faults by the neural network. The paper analyzes the reasons of timer faults and the corresponding sound features. Experiments are made in the laboratory to demonstrate the proposed method. The technology is expected to apply in factories in coming years for realizing automatic product test and improving efficiency of product inspection. [ABSTRACT FROM AUTHOR]

In this paper, selecting line segments as matching features in stereo vision, the orientation difference (O.D.) of the line segments is more deeply evaluated than the previous studies and two new constraints i.e. ordering and collinearity are proposed. The O.D. was supposed to be used for an indoor application, reducing candidates without elimination of the actual matches, especially if the line is horizontal or vertical with respect to the floor. The findings of this paper are as follows: 1) Applying a threshold on O.D. would result in a missing probability for the actual matches and this probability can be calculated for any given threshold, 2) The upper limit of O.D. for horizontal and vertical lines is a function of geometric parameters of the system, 3) An optimal tilt angle, whose results is the minimum upper limit for O.D., can be computed, 4) for disambiguation process, ordering and collinearity constraints are proposed. These are applied in a matching algorithm and their effectiveness is investigated on real stereo images. [ABSTRACT FROM AUTHOR]

In this paper, the line segment tracking is considered for an imaging system with translational motions (T.M.), rotational motions (R.M.) and arbitrary motions. Assuming a CCD camera with an arbitrary tilt angle installed on a mobile robot equipped with odometry system, to reduce the search space in the correspondence problem, two constraints are developed. These constraints are location and orientation differences (O.D.) for line segments in consecutive images. The findings of this paper include: 1)The development of the effect of camera tilt on the location constraint for T.M., 2)Illustrating that the upper bound of O.D. for both horizontal and vertical lines with respect to the floor, is a function of tilt in T.M., which can considerably be reduced and thus providing a tight constraint, 3)The development of the location constraint for R.M., 4) The development of the PDF and upper bound of O.D. for R.M., 5) The development of the location and O.D. constraint for arbitrary motions. Furthermore, the efficiency of these developed constraints in a line tracking algorithm was verified. [ABSTRACT FROM AUTHOR]

This paper presents a new description of image edge in the form of asymptote equation utilizing knowledge of differential geometry, and modifies a popular parlance in the traditional image processing. This paper develops the gradient concept and defines a generalized grads operator. The operator is a novel nonlinear transform, which inherits the strongpoint of noise suppression from the classical Gaussian differential filter and has a better edge extraction function. The experimental results show the proposed edge detection algorithm is powerful and effective. [ABSTRACT FROM AUTHOR]

The paper proposes a method to control the motion orbit of a cartoon object based on chaos theory. We first propose an adaptive delaying feedback controlling method which can control the Lorenz system to generate periodic, quasi-periodic and chaotic orbits. Then we apply our method to the topic of cartoon object motion controlling. It can enrich the motion type of the object thus enhances the visual effect of the cartoon video. The experiments validate the controlling results of the proposed method. Our research leads to a new area in which the chaotic theory is applied in the field of multimedia processing. [ABSTRACT FROM AUTHOR]

Recent solutions to object classification have focused on the decomposition of objects into representative parts. However, the vast majority of these methods are based on single visual cue measurements. Psychophysical evidence suggests that humans use multiple visual cues to accomplish recognition. In this paper, we address the problem of integrating multiple visual information for object recognition. Our contribution in this paper is twofold. First, we describe a new probabilistic integration model of multiple visual cues at different spatial locations across the image. Secondly, we use the cue integration framework to classify images of objects by combining two-dimensional and three-dimensional visual cues. Classification results obtained using the method are promising. [ABSTRACT FROM AUTHOR]

In this paper, we propose a memetic EMO algorithm that enhances the similarity of two sets of non-dominated solutions. We employ our algorithm in vehicle routing problems (VRPs) where the demand of customers varies. We consider two periods of different demand in a problem that are Normal Demand Period (NDP) and High Demand Period (HDP). In each period, we can find a set of non-dominated solutions with respect to several objectives such as minimizing total cost for delivery, minimizing maximum cost, minimizing the number of vehicles, minimizing total delay to the date of delivery and so on. Although a set of non-dominated solutions can be searched independently in each period, drivers of vehicles prefer to have similar routes in NDP and HDP in order to reduce their fatigue to drive on a different route. In this paper, we propose a local search that enhance the similarity of routes in NDP and HDP. Simulation results show that the proposed memetic EMO algorithm can find a similar set of non-dominated solutions in HDP to the one in NDP. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach for facial expression analysis and recognition. The main contributions of the paper are as follows. First, we propose an efficient facial expression recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of the AIBO robot. More precisely, the robot is displaying an emotional state in response to the recognized user's facial expression. Experiments using unseen videos demonstrated the effectiveness of the developed method. [ABSTRACT FROM AUTHOR]

The aim of this paper is to introduce a novel, biologically inspired approach to extract visual features relevant for controlling and understanding reach-to-grasp actions. One of the most relevant of such features has been found to be the grip-size defined as the index finger-tip - thumb-tip distance. For this reason, in this paper we focus on this feature. The human visual system is naturally able to recognize many hand configurations - e.g. gestures or different types of grasps - without being affected substantially by the (observer) viewpoint. The proposed computational model preserves this nice ability. It is very likely that this ability may play a crucial role in action understanding within primates (and thus human beings). More specifically, a family of neurons in macaque's ventral premotor area F5 have been discovered which are highly active in correlation with a series of grasp-like movements. This findings triggered a fierce debate about imitation and learning, and inspired several computational models among which the most detailed is due to Oztop and Arbib (MNS model). As a variant of the MNS model, in a previous paper, we proposed the MEP model which relies on an expected perception mechanism. However, both models assume the existence of a mechanism to extract visual features in a viewpoint independent way but neither of them faces the problem of how this mechanism can be achieved in a biologically plausible way. In this paper we propose a neural network model for the extraction of visual features in a viewpoint independent manner, which is based on the work by Poggio and Riesenhuber. [ABSTRACT FROM AUTHOR]

Pattern recognition and classification problems are most popular issue in machine learning, and it seem that they meet their second golden age with bioinformatics. However, the dataset of bioinformatics has several distinctive characteristics compared to the data set in classical pattern recognition and classification research area. One of the most difficulties using this theory in bioinformatics is that raw data of DNA or protein sequences cannot be directly used as input data for machine learning because every sequence has different length of its own code sequences. Therefore, this paper introduces one of the methods to overcome this difficulty, and also argues that the capability of generalization in this method is very poor as showing simple experiments. Finally, this paper suggests different approach to select the fixed number of effective features by using Support Vector Machine, and noise whitening method. This paper also defines the criteria of this suggested method and shows that this method improves the precision of diagnosing abnormal protein sequences with experiment of classifying ovarian cancer data set. [ABSTRACT FROM AUTHOR]

To improve the safety of drivers and walkers in a city, several traffic monitors are usually set on lanes. These traffic monitors can also improve the security of communities. In this paper, we integrate the so-called linear/circular consecutive-k-out-of-n:F systems into our proposed traffic-monitor system. The objective is to find the optimal design of monitors under limited budget for the system. The main purposes of this paper are : (1) to propose a new traffic-monitor system, (2) to present an immune algorithm (IA) for the optimal design of traffic monitors, and (3) to report numerical results of various parameters by the proposed algorithm. It is shown that the proposed immune algorithm performs well for all test problems. [ABSTRACT FROM AUTHOR]

In this paper, we present the results of the Acoustic Event Detection (AED) and Classification (AEC) evaluations carried out in February 2006 by the three participant partners from the CHIL project. The primary evaluation task was AED of the testing portions of the isolated sound databases and seminar recordings produced in CHIL. Additionally, a secondary AEC evaluation task was designed using only the isolated sound databases. The set of meeting-room acoustic event classes and the metrics were agreed by the three partners and ELDA was in charge of the scoring task. In this paper, the various systems for the tasks of AED and AEC and their results are presented. [ABSTRACT FROM AUTHOR]

The aim of this paper is to discuss the possibility of understanding human social interaction in web communities by analogy with a disease propagation model from epidemiology. When an article is submitted by an individual to a social web service, it is potentially influenced by other participants. The submission sometimes starts a long and argumentative chain of articles, but often does not. This complex behavior makes management of server resources difficult and a more theoretical methodology is required. This paper tries to express these complex human dynamics by analogy with infection by a virus. In this first report, by fitting an epidemiological model to Bulletin Board System (BBS) logs in terms of a numerical triple, we show that the analogy is reasonable and beneficial because the analogy can estimate the community size despite the submitter's information alone being observable. [ABSTRACT FROM AUTHOR]

Due to the non-stationarity of EEG signals, online training and adaptation is essential to EEG based brain-computer interface (BCI) systems. Asynchronous BCI offers more natural human-machine interaction, but it is a great challenge to train and adapt an asynchronous BCI online because the user's control intention and timing are usually unknown. This paper proposes a novel motor imagery based asynchronous BCI for controlling a simulated robot in a specifically designed environment which is able to provide user's control intention and timing during online experiments, so that online training and adaptation of motor imagery based asynchronous BCI can be effectively investigated. This paper also proposes an online training method, attempting to automate the process of finding the optimal parameter values of the BCI system to deal with non-stationary EEG signals. Experimental results have shown that the proposed methodfor online training of asynchronous BCI significantly improves the performance. [ABSTRACT FROM AUTHOR]

It has been shown that various geometric vision problems such as triangulation and pose estimation can be solved optimally by minimizing L ∞  error norm. This paper proposes a novel algorithm for sequential estimation. When a measurement is given at a time instance, applying the original batch bi-section algorithm is very much inefficient because the number of seocnd order constraints increases as time goes on and hence the computational cost increases accordingly. This paper shows that, the upper and lower bounds, which are two input parameters of the bi-section method, can be updated through the time sequence so that the gap between the two bounds is kept as small as possible. Furthermore, we may use only a subset of all the given measurements for the L ∞  estimation. This reduces the number of constraints drastically. Finally, we do not have to re-estimate the parameter when the reprojection error of the measurement is smaller than the estimation error. These three provide a very fast L ∞  estimation through the sequence; our method is suitable for real-time or on-line sequential processing under L ∞  optimality. This paper particularly focuses on the triangulation problem, but the algorithm is general enough to be applied to any L ∞  problems. [ABSTRACT FROM AUTHOR]