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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]

The Lost Cosmonaut is an interactive narrative based on digitally enhanced paper. This technology uses an electronic pen to mediate between paper and computer. Thus any actions of the pen on the paper can be captured and manipulated by a computer as well as we can map digitally controlled events onto paper. The story in this narrative environment reveals itself partially through written text and images on the paper surface just as any other printed story. However, additional information in form of digitally controlled outputs such as sound, light and projections can be accessed through interaction with pen and paper. Furthermore the audience is not only supposed to read and otherwise perceive information, we also want them to actively produce content for this environment by writing onto the paper. By doing so they also add content to the database containing the digital output at the same time. Hence we produce a complex multimedia environment that works on three levels: On paper, in a digitally controlled visual and acoustic environment and in the combination of both worlds. Last but not least this environment is an open system, which grows as a collaborative effort over time as each user adds his own entries to paper and database. We argue that using paper as an integrated part of a digital environment is a best-of-both-world approach that opens up new possibilities for producing and perceiving narrative. [ABSTRACT FROM AUTHOR]

Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBASCuBA: Subspace Clustering Based Analysis.) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers. [ABSTRACT FROM AUTHOR]

We present a framework for generating paper-cutting effects on mesh models. Our system involves the construction of model defined paper-cutting patterns, extraction of the medial axis (MA) from the mesh models and position location for patterns on the mesh surface based on MA. Patch sets for placing patterns on the mesh surface are determined by the region growing algorithm. After patch sets parameterization, patterns are drawn on the parameterized domain and mapped onto the mesh surface by inverse sampling. [ABSTRACT FROM AUTHOR]

SimStudent is a machine-learning agent that learns cognitive skills by demonstration. It was originally developed as a building block of the Cognitive Tutor Authoring Tools (CTAT), so that the authors do not have to build a cognitive model by hand, but instead simply demonstrate solutions for SimStudent to automatically generate a cognitive model. The SimStudent technology could then be used to model human students' performance as well. To evaluate the applicability of SimStudent as a tool for modeling real students, we applied SimStudent to a genuine learning log gathered from classroom experiments with the Algebra I Cognitive Tutor. Such data can be seen as the human students' "demonstrations" of how to solve problems. The results from an empirical study show that SimStudent can indeed model human students' performance. After training on 20 problems solved by a group of human students, a cognitive model generated by SimStudent explained 82% of the problem-solving steps performed correctly by another group of human students. [ABSTRACT FROM AUTHOR]

Active Learning in the classroom domain presents an interesting case for integrating physical and digital affordances. Traditional physical handouts and transparencies are giving way to new digital slides and PCs, but the fully digital systems still lag behind the physical artifacts in many aspects such as readability and tangibility. To better understand the interplay between physical and digital affordances in this domain, we developed PaperCP, a paper-based interface for a Tablet PC-based classroom interaction system (Classroom Presenter), and deployed it in an actual university course. This paper reports on an exploratory experiment studying the use of the system in a real-world scenario. The experiment confirms the feasibility of the paper interface in supporting student-instructor communication for Active Learning. We also discuss the challenges associated with creating a physical interface such as print layout, the use of pen gestures, and logistical issues. [ABSTRACT FROM AUTHOR]

This paper presents the advantage that knowledge-intensive activities, such as Scientific Conference Management, can take by the exploitation of expert components in the key tasks. Typically, in this domain the task of scheduling the activities and resources or the assignment of reviewers to papers is performed manually leading therefore to time-consuming procedures with high degree of inconsistency due to many parameters and constraints to be considered. The proposed systems, evaluated on real conference datasets, show good results compared to manual scheduling and assignment, in terms of both accuracy and reduction of runtime. [ABSTRACT FROM AUTHOR]

Finding the right material on the Web could be a worthwhile result. Users waste too much time to discover the useful information. Recommender system can provide some shortcuts to the user, but if the recommendation is based on people's opinion, one question remains — how relevant is a user's opinion? This paper presents a model to define the user's relevance opinion in a recommender system. This metric aims to help the target user to decide in what recommendation he should focus his attention. Beyond the model, we present a real experiment using an e-government database. Keywords: User modeling, Authority, Recommender System. [ABSTRACT FROM AUTHOR]

In this chapter, we proudly introduce eight awarded papers, selected from the papers presented in the 20th annual conference of Japan Society for Artificial Intelligence (JSAI2006). The conference was held at Tower Hall Funabori located in the east part of Tokyo from June 7 until June 9, 2006. 276 papers were presented in about 60 sessions and over 500 people participated in the conference. Sessions vary from the fundamental issues to the state-of-the-art applications. Session for regular papers are as follows; Logic and learning, Reinforcement learning and agent learning, Agent theory, Auction/Game/Economics, Agent learning, Agent planning, Agent simulation and interaction, Genetic algorithm, Image processing, Information extraction and classification, Clustering/self-organization, Classifi- cation learning, Text mining, Graph mining, Mining algorithm, Web mining, Pre- and post-processing for data mining, Practices of data mining, Cognitive modeling, Language processing and dialogue, Robot/sensor network, Web information system, Semantic Web, Knowledge modeling and knowledge sharing, Support of knowledge management, knowledge modeling/ontology,Web service, Human interface and communication support, Education support, Learning support environment, and Musical and auditory information processing. [ABSTRACT FROM AUTHOR]

Vertical Search Engines (VSEs), which usually work on specific domains, are designed to answer complex queries of professional users. VSEs usually have large repositories of structured instances. Traditional instance ranking methods do not consider the categories that instances belong to. However, users of different interests usually care only the ranking list in their own communities. In this paper we design a ranking algorithm -ZRank, to rank the classified instances according to their importances in specific categories. To test our idea, we develop a scientific paper search engine-CPaper. By employing instance classifying and ranking algorithms, we discover some helpful facts to users of different interests. [ABSTRACT FROM AUTHOR]

Collaborative tagging has grown on the Internet as a new paradigm for web information discovering, filtering and retrieval. In the physical world, we use visual tags: labels readable by smartphones with cameras. While visual tags are usually related to a web site address, collaborative tagging, instead, provides updated, recommended information contributed and shared by users. In this paper we investigate the combination of collaborative tagging systems with visual tags. We present a prototype of a semiautomatic system generating visual tags which gather information from collaborative tagging. The user can interact with a list of relevant tags (built by clustering closely related tags) that can be further encoded in a visual tag, according to user's preferences. The user experience is enriched by retrieving multimedia content linked to the selected tags, present on the web. We finally show a case study illustrating our approach. [ABSTRACT FROM AUTHOR]

In this paper, we discuss importance and usefulness of interpersonal network in a community support system. We built a scheduling support system for an academic conference. Our system supports information exchange among participants and information discovery with generating participants' interpersonal network. This system was used in an academic conference called JSAI2003 involving 276 active users. The analysis of the networks reveals that interpersonal networks can promote information exchange among people by indicating existence of people to the others, and that it can also support information discovery by recommendation. [ABSTRACT FROM AUTHOR]

In this chapter, we introduce five awarded papers, selected from the 19th annual conference of JSAI (Japan Society for Artificial Intelligence) 2005. These five papers are truly excellent, for they were selected out of over 290 papers, the rate of which was less than 2%, voted by the total number of approximately seventy reviewers. As has been often mentioned, the research of artificial intelligence branches to many fields; among them, we could observe such tendency that empirical methods were rather prevalent compared with theoretical issues. However, we contend that the following five papers were both sound in theory and practical in feasibility. We show the synopses of the five papers, adding short comments for the award for each. [ABSTRACT FROM AUTHOR]

On behalf of the program committee (PC) of JSAI 2003, I would like to thank all the chair persons, discussants, and attentive audience who contributed to select these awarded papers. [ABSTRACT FROM AUTHOR]

The research community plays a very important role in helping researchers undertake new research topics. The authors propose a community mining system that helps to find communities of researchers by using bibliography data. The basic concept of this system is to provide interactive visualization of communities both local and global communities. We implemented this concept using actual bibliography data and present a case study using the proposed system. [ABSTRACT FROM AUTHOR]

We develop a novel user-centric modeling technology, which can dynamically describe and update a person's expertise profile. In an enterprise environment, the technology can enhance employees' collaboration and productivity by assisting in finding experts, training employees, etc. Instead of using the traditional search methods, such as the keyword match, we propose to use relational and evolutionary graph models, which we call ExpertiseNet, to describe and find experts. These ExpertiseNets are used for mining, retrieval, and visualization. We conduct experiments by building ExpertiseNets for researchers from a research paper collection. The experiments demonstrate that expertise mining and matching are more efficiently achieved based on the proposed relational and evolutionary graph models. [ABSTRACT FROM AUTHOR]

In order to discover services in business-driven Web service composition conveniently, accurately, and efficiently, this paper proposes a business process-oriented service discovery approach. In order to link business property with services, this paper proposes a business related description model of operations. Based on the business related relation model proposed in this paper, a relation-driven discovering algorithm for business process-oriented service discovery is presented. Compared with other discovering approach, the proposed discovering approach considers the business relation between operations and can solve the problem of discovering services with meaningful business relation. The experimentation shows the better performance of this algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we suggest a postal RFID application model that can be used for real time trace and tracking system implementation of parcel processing and pallet management. This paper also shows the tag recognition performance of parcels by speed and mounting tag material such as thin cans, water bottles, and paper using an implemented postal RFID system to find the best solution for RFID adaptation in postal logistics. [ABSTRACT FROM AUTHOR]

Adaptive Hypermedia builds upon the annotation and adaptation of content. As manual annotation has proven to be the main bottleneck, all means for supporting it by reusing automatically generated metadata are helpful. In this paper we discuss two issues. The first is the integration of a generic AH authoring environment MOT into a semantic desktop environment. In this setup, the semantic desktop environment provides a rich source of automatically generated meta-data, whilst MOT provides a convenient way to enhance this meta-data manually, as needed for an adaptive course environment. Secondly, we also consider the issue of source schema heterogeneity, especially during the automatic metadata generation process, as semantic desktop metadata are generated through a lot of different tools and at different times, so that schemas are overlapping and evolving. Explicitly taking into account all versions of these schemas would require a combinatorial explosion of generation rules. This paper investigates a solution to this problem based on malleable schemas, which allow metadata generation rules to flexibly match different versions of schemas, and can thus cope with the heterogeneous and evolving desktop environment. [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 tracking 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]

It is not easy to develop a game that applies physical laws. In this paper we proposed a physics editor to make it easy to produce a physics game. The physics editor uses XML for processing attribute values of each object. This paper presents the architecture of the physics editor, and describes its detailed components. The physics editor provides an efficient method that is easily applied to physical attributes of all objects in the game. In this paper, we showed the process of a car's creation, using the physics editor. The physics editor that is presented in this paper automatically creates physical objects, but it is limited to the rigid body for car racing. [ABSTRACT FROM AUTHOR]

This edited volume contains the papers selected for presentation at the Second Workshop on Data Mining for Biomedical Applications (BioDM'07) held in Nanjing, China on 22nd of May 2007. The workshop was held in conjunction with the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2007), a leading international conference in the areas of data mining and knowledge discovery. The aim of this workshop was to provide a forum for discussing research topics related to biomedical applications where data mining techniques were found to be necessary and/or useful. [ABSTRACT FROM AUTHOR]

Organisations want nowadays more controlled processes and easily usable solutions, but achieving them requires winning many challenges. Radio Frequency Identification (RFID) based technology has been proposed for improving mobile solution workflows and usability. NFC (Near Field Communication) technology is based on integrating RFID on mobile devices. This paper identifies how NFC based systems could be used to improve mobile solution workflows and usability. The results of this study are based on the implementation of six pilot cases in 2005-2007. This paper concludes that use of NFC provides both advantages and disadvantages and, therefore, to estimate the potential of NFC, its pros and cons have to be weighed up for each case in question. This paper also concludes that only a comprehensive solution achieves the full potential of NFC from users' point of view. Considering mobile solution usability, this paper concludes that NFC based solutions are easy to use, but the small and limited keyboard of mobile devices poses challenges to the design of these solutions. NFC use poses new organizational challenges like need for the rest of the system to catch up and feeling of distrust from the user side. [ABSTRACT FROM AUTHOR]

This paper presents a multi-dependency language modeling approach to information retrieval. The approach extends the basic KL-divergence retrieval approach by introducing the hybrid dependency structure, which includes syntactic dependency, syntactic proximity dependency and co-occurrence dependency, to describe dependencies between terms. Term and dependency language models are constructed for both document and query. The relevant between a document and a query is then evaluated by using the KL-divergence between their corresponding models. The new dependency retrieval model has been compared with other traditional retrieval models. Experiment results indicate that it produces significant improvements in retrieval effectiveness. [ABSTRACT FROM AUTHOR]

Various organizations use the RFID technology for linking, tracking and identifying objects in their operative context. Nevertheless, the RFID components cannot yet be considered as mobile, intelligent and communicating elements of the organization's information infrastructure. They are not always used for accomplishing the linkage between the physical world and the adopted Information Technologies (IT) solutions. Therefore, they are not used for enabling organizations to automatically monitor, decide, and take actions. Heterogeneous networked devices and services installed within the organizations, often work independently instead of collaborating for offering a better quality of the daily activities to the end-user. This paper discusses the adoption of a Smart Ubiquitous Platform (SUP) for monitoring and managing the documents circulation by using the RFID technology. Advantages of the adoption of RFID are discussed too. [ABSTRACT FROM AUTHOR]

This paper presents the integration of linguistic knowledge in learning semantic user profiles able to represent user interests in a more effective way with respect to classical keyword-based profilesThis research was partially funded by the EC under the 6th FP IST IP VIKEF No. 507173, Priority 2.3.1.7 Semantic Based Knowledge Systems (www.vikef.net) and under the DELOS NoE on Digital Libraries, Priority: Technology-enhanced Learning and Access to Cultural Heritage - Contract n. G038-507618 (www.delos.info). The work was supported also by the research project "Modelli avanzati di personalizzazione per biblioteche elettroniche", year 2005, funded by the University of Bari.. Semantic profiles are obtained by integrating a naïve Bayes approach for text categorization with a word sense disambiguation strategy based on the WordNet lexical database (Section 2). Semantic profiles are exploited by the "conference participant advisor" service in order to suggest papers to be read and talks to be attended by a conference participant. Experiments on a real dataset show the effectiveness of the service (Section 3). [ABSTRACT FROM AUTHOR]

In Service-Oriented Architectures (SOAs), software systems are decomposed into independent units, namely services, that interact with one another through message exchanges. To promote reuse and evolvability, these interactions are explicitly described right from the early phases of the development lifecycle. Up to now, emphasis has been placed on capturing structural aspects of service interactions. Gradually though, the description of behavioral dependencies between service interactions is gaining increasing attention as a means to push forward the SOA vision. This paper deals with the description of these behavioral dependencies during the analysis and design phases. The paper outlines a set of requirements that a language for modeling service interactions at this level should fulfill, and proposes a language whose design is driven by these requirements. [ABSTRACT FROM AUTHOR]

This paper extends the upright face detection framework proposed by Viola et al. 2001 to handle in-plane rotated faces. These haar-like features work inefficiently on rotated faces, so this paper proposes a new set of ±26.565 ° haar-like features which can be calculated quickly to represent the features of rotated faces. Unlike previous face detection techniques in training quantities of samples to build different rotated detectors, with these new features, we address to build different rotated detectors by rotating an upright face detector directly so as to achieve in-plane rotated face detection. This approach is selected because of its computational efficiency, simplicity and training time saving. This proposed method is tested on CMU-MIT rotated test data and yields good results in accuracy and maintains speed advantage. [ABSTRACT FROM AUTHOR]

There are several researches on 2D gaze tracking techniques for the 2D screen for the Human-Computer Interaction. However, the researches for the gaze-based interaction to the stereo images or contents are not reported. The 3D display techniques are emerging now for the reality service. Moreover, the 3D interaction techniques are much more needed in the 3D contents service environments. This paper addresses the gaze-based 3D interaction techniques. It is well known that there are characteristics of eye vergence according to the depth of object to be focused by human. This paper presents our experimental research results about the characteristics of eye vergence according to stereo image depth. Our research result is evaluated and applied to the gaze-based 3D interaction with the parallax barrier stereo image display system. [ABSTRACT FROM AUTHOR]

In this paper, a framework for individualizing the learning material structure in adaptive learning system is introduced. It aims to utilize the learning characteristics and provide a personalized learning environment that exploit pedagogical model and fuzzy logic techniques. The learning material consists of 4 structures; 1) theory, 2) examples 3) exercises and 4) activities. The pedagogical model and learning characteristics are based on the student's personality factor (Myers-Briggs Type Indicator (MBTI)), whilst the fuzzy logic techniques are used to classify the structure of learning material which is based on student's personality factors. This paper tend to illustrate the construction of fuzzy set and fuzzy rules to find the best rules based on combination of two approaches; learning style approach and fuzzy logic approach for adapting the content to the user, allowing a learning system to dynamically adapt the choice of possible learning structure through the learning material based on the user's personality factor. [ABSTRACT FROM AUTHOR]

Due to the advance of e-community, collaborative learning becomes popular on the Web. Among the learning models for collaborative learning on the Web, there are seminar-based model and project-based model. The objective of this paper is to compare the two learning models by analyzing learners' activities. Among learners' activities, we focus on learners' interaction level, their collaborability, and their accountability. Also, we propose new assessment items for evaluating learners fairly. In this paper, the existing evaluation items in traditional classroom-based collaborative learning are firstly analyzed in order to develop new evaluation criteria for the Web. Based on these analyzed items, we propose new items for assessing learners' activities in Web-based collaborative learning. After two Web-based classes, each of which has a different learning model, are performed, the proposed items are applied to compare the two learning models in terms of learners' interaction level, collaborability, and accountability. By performing this research, we can understand learners' activities clearer and can develop a better instructional technique to make improved academic achievement, improved attendance, and increased self-confidence in collaborative e-learning. [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]

Research on e-learner community building has attracted much attention for its effectiveness in sharing the learning experience and resources among geographically dispersed e-learners. While collaborative filtering proves its success as one of the most efficient methods in finding similar users in e-commerce domain, it does meet special challenges in e-learning areas. In this paper, we incorporate multi-agent techniques into collaborative filtering and propose a novel community building scheme. By doing so, we manage to collect useful information from the learner behaviors and thus increase the scalability and flexibility of traditional collaborative filtering methods. The experiment on a standard benchmark shows that our scheme has reasonable community building quality and e-learners can make better recommendations to each other inside the community. [ABSTRACT FROM AUTHOR]

This paper discusses the mechanical design and simulation of a novel omnidirectional wheel based on Reuleaux-triangles. The main feature of our omniwheel is that the point of contact of the wheel with the floor is always kept at the same distance from the center of rotation by mechanical means. This produces smooth translational movement on a flat surface, even when the profile of the complete wheel assembly has gaps between the passive rollers. The grip of the wheel with the floor is also improved. The design described in this paper is ideal for hard surfaces, and can be scaled to fit small or large vehicles. This is the first design for an omnidirectional wheel without circular profile, yet capable of rolling smoothly on a hard surface. [ABSTRACT FROM AUTHOR]

During the early stages of architectural training, tangibility plays an important role in developing spatial awareness. In such contexts, tangible user interfaces are believed to provide a significant advantage as they combine the affordances of both the physical and the digital world. We introduce CubeExplorer, a hybrid 3D conceptual aid that combines physical interaction and digital modeling in an effort to complement conventional architectural space-training tools (such as physical materials and digital CAD programs). Using a digital pen as an input mechanism, CubeExplorer lets users perform subtractive 3D geometric operations on a simple paper based cube model while observing the resulting model on a display. The tangibility of the model simplifies navigation and command execution, while the digital interface makes it easy for users to explore multiple alternative designs. To evaluate the potential of such an approach, we conducted a user study in a normal classroom environment where students were provided with physical (wooden block), hybrid (CubeExplorer), and virtual (FormZ) interfaces to complete the same assignment. Our evaluation showed that CubeExplorer combined the advantages of both digital and tangible media. The advantages of CubeExplorer over digital media were substantiated in a follow-up study comparing CubeExplorer and SketchUp in a similar building task. [ABSTRACT FROM AUTHOR]

Ubiquitous interaction places the user in the centre of dynamic configurations of technology, where work not necessarily is performed through a single personal computer, but supported by a multiplicity of technologies and physical devices. This paper presents an activity-theoretically based framework for analyzing ubiquitous substitution, i.e. a set of mediators that are or can be continuously substituted with the purpose of highlighting expected and indented uses, and the conflicts encountered when attempting substitution between them. The paper develops a four-leveled analysis of such mediators, and point towards a minimalist approach to design of ubiquitous interaction. [ABSTRACT FROM AUTHOR]

Authoring-by-Aggregation is an authoring paradigm for creating new Learning Resources by composing several smaller ones. However finding suitable Learning Resources that can be reused in a particular course is a time-consuming task. The user has to query a repository and review the resulting list of Learning Resources whether they are applicable. In this paper we present a new method for narrowing the result set by taking the aggregation context into account. Context features are used as additional query attributes, leading to more precise query results. This paper also presents an improved Authoring-by-Aggregation process and a corresponding implementation, which facilitates the creation of new Learning Resources. [ABSTRACT FROM AUTHOR]

Recognizing that trust states are mental states, this paper presents a formal analysis of the dynamics of trust in terms of the functional roles and representation relations for trust states. This formal analysis is done both in a logical framework and in a mathematical framework based on integral and differential equations. Furthermore, the paper presents formal specifications of a number of relevant dynamic properties of trust. The specifications provided were used to perform automated formal analysis of empirical and simulated data from two case studies, one involving two experiments with humans, and one involving simulation experiments in the context of an economic game. [ABSTRACT FROM AUTHOR]

This paper considers how the interaction of quantification and dependent anaphora may be analysed in a dynamic semantics. It discusses a simple theory of the creation and accessibility of dependencies based on a dynamic semantics for distributivity and some basic assumptions on number agreement in discourse. This theory forms a partial defence of the line of semantics set out by van den Berg 1996. I argue, however, that it is essential to quantified anaphora to contextualise the labelling of antecedents. [ABSTRACT FROM AUTHOR]

This paper analyzes complex collective behaviors of a multiagent system, which consists of interacting agents with evolutionary learning capabilities. The interaction and learning of the agents are modeled by the concept of Connected Replicator Dynamics expanded from evolutionary Game Theory.The dynamic learning system we analyze shows various behavioral and decision changes including bifurcation of chaos in the sense of physical sciences.The main contributions of the paper are summarized as follows: (1) In amultiagent system, the emergence of chaotic behaviors is general and essential, even if each agent does not have chaotic properties; and (2) However,asimple controlling agent with the Keep-It-Simple-Stupid (KISS) principle, or a sheep-dog agent, is able to domesticate or tame the complex behaviors. [ABSTRACT FROM AUTHOR]

In this paper, a load balancing method using ring network in the Grid database is proposed. Past research proposed to solve unbalanced load problem. But, past techniques can not be applied as the Grid database has a number of systems and user's request always changes dynamically. The proposed method connects each node having the same replicated data through ring network. If workload overflows in some node, user's request is transferred to a linked node which has the target data. And, this node stops another request from processing until the workload has significantly decreased. Then, to stop request forwarding from a previous node, it changes the link structure by sending a message to the previous node. Through performance evaluation, this paper shows that the proposed method has increased performance and is more suitable to the Grid database than the existing methods. [ABSTRACT FROM AUTHOR]

Clustering a peer-to-peer (P2P) network into distinct semantic clusters can efficiently improve the search efficiency and enhance scalability of the network. This paper considers P2P systems in which peers employ taxonomy hierarchy to describe the contents of their objects, and presents a taxonomy-based approach for constructing semantics-based super-peer networks. By dynamically clustering peers in taxonomy-based semantic space based on the semantics of their data and organizing the clusters into semantic routing overlays, an efficient query-routing algorithm can be used among these clusters. Preliminary evaluation indicates that our approach achieves a competitive trade-off between search latencies and overheads, and load-balancing among super-peers is well maintained. [ABSTRACT FROM AUTHOR]

With the increasing popularity of self-organized communication systems, distributed trust and reputation systems in particular have received increasing attention. By formalizing trust relationships, trust between parties within the community can be derived by analyzing the trust paths linking the parties together. This paper treats trust network as an emergent property. The emergence interpretation concerns both the maintenance and usage of trust network inference. Specifically, in P2P system, trust network is maintained by individual agents at micro level, and it is used (i.e., queried) as one entity at macro level. In this paper, we first discuss micro level activities, that is, we classifies trust into functional trust and referral trust to facilitate trust inference, and extend the referral trust to include factors of similarity and truthfulness, so that our approach can accommodate the personalized feature of reputation rating, and reduce trust inference error effectively; then we discuss macro level properties of trust network. Specifically, we investigate the emergence of network structural properties of trust and reputation system in terms of efficiency and cost. That is, efficiency measures how well information propagates over trust system, and cost measures how expensive it is to build this system. Preliminary simulation results show the performance improvement of P2P community and the emergence of economical small-world trust network, namely relatively high efficiency and low cost. [ABSTRACT FROM AUTHOR]

The sub-block-based image retrieval method utilizes global and local image features to retrieve trademarks through separating individual an images into some blocks. This paper proposes a sub-block-based trademark image retrieval algorithm under the polar coordinate system. Experiment results show that our algorithm can keep excellent invariance in image translation, scaling and rotation and the retrieved results can satisfy human visual perception very well. [ABSTRACT FROM AUTHOR]

This paper proposes a new persistent storage to efficiently manage OWL Web ontologies. The Semantic Web is recognized as a next direction for progress of the current Web. To realize the Semantic Web, various technologies have been developed. Especially, OWL (Web Ontology Language) is the most important and state-of-the-art technology toward the ideal Semantic Web. However, most of research is being focused on designing and building OWL documents (OWL Ontology). One of the most important issues is how to efficiently and persistently store a very large OWL data into database management systems. In this paper, we propose a persistent storage system to achieve the purpose. This paper describes the performance evaluation result on the load time. The experiment result explicitly shows that our proposal provides more enhanced performance comparing with the existing systems. [ABSTRACT FROM AUTHOR]

This paper presents a middleware based on SOA (Service-Oriented Architecture) for context-awareness in a home network with ubiquitous computing. The context-awareness middleware addresses design issues such as heterogeneity, dynamicity, and extensibility issues of ubiquitous computing environment. We also suggest a context model supporting semantic level interoperability of context. In this paper, first we model context metadata ontology as well as context information ontology for the context-awareness middleware. Then we explain a service-oriented context-awareness middleware based on the context model which is introduced in this paper. [ABSTRACT FROM AUTHOR]

One of the particulars to be considered for effective servicing of wireless Internet to mobile terminal is the information of terminal environment. It is because time cost is required in producing mobile contents to suit environment of the connected terminal. In other words, in order to provide mobile web service by considering terminal characteristics, methods to minimize response time and server capacity in accordance with transcoding time in server are required. In this paper, response time to occur in the course of mobile web contents servicing was analyzed and, as a solution for this, Pre-Service method was introduced. Also, this paper suggests two methods in Pre-Service process for selecting optimize content in Cache. One is Device Profile Aware Selection using mobile device information. The other is Content Aware Selection using component information of requested content. Performance level of each method proposed was compared through experiment and the result of analysis was described. [ABSTRACT FROM AUTHOR]

Now, the study of UWB signal's propagation attenuation model concentrates mainly on residential indoor environment. In this paper, the monitor of the personnel and equipments in tunnel of coal mine is regarded as application background, the environment in coal mine is looked as a kind of special indoor environment. On the basis of residential indoor UWB propagation model, the tunnel of coal mine is divided into near-field region and far-field region according to Fresnel theory. The propagation mode of UWB signal is mainly multimode in near-field region and mainly waveguide in far-field region. Considering the additional ullage factors that caused by the factors of tunnel, such as personnel and coal transports and slicers etc at the same time, this paper puts forward the UWB signal propagation attenuation model in coal mine. Aiming at the parameter of symbol BER, comparison and analysis that based on the existing residential indoor propagation attenuation model and the model of this paper is done. [ABSTRACT FROM AUTHOR]

In this paper, a speech emotion recognition agent for mobile communication service is proposed. The proposed system can recognize five emotional states - neutral, happiness, sadness, anger, and annoyance from the speech captured by a cellular phone in real time and then it calculates the degree of affection such as love, truthfulness, weariness, trick, friendship of the person who you are interesting to know through the mobile phone. In general, a speech acquired by a cellular phone contains noise due to the mobile network and environmental noise. Thus it can causes serious performance degradation due to the distortion in emotional features of the query speech. In order to alleviate the effect of these noises, we adopt a MA (Moving Average) filter which has relatively simple structure and low computational complexity. Then a feature optimization method is implemented to further improve and stabilize the system performance. For a practical application, we create an agent that can measure the degree of affection from the person who you want to know on the mobile phone. Two pattern classification methods, k-NN and SVM with probability estimates, are compared for estimating the degree of affection. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 72.5% over five emotional states and it shows the feasibility of the agent for mobile communication services. [ABSTRACT FROM AUTHOR]

This paper attempts to make a summary of current models used to evaluate the performance of IEEE 802.11 DCF and develops a new modeling to analyze the packet delay. The main contribution of this paper is that we found the backoff process of DCF can be considered as a Bernoulli Trials process. The new modeling based on the Bernoulli Trials can be a standard to analyze and validate other analytical methods. It can avoid confusion and conflicting produced by different simulation and calculating methods. In contrast to traditional work in the literature, we use our modeling to calculate the average backoff windows and average packet delay. At the same time, verify the analytical results by Monte Carlo and OPNET. The conclusion shows that the new modeling and analysis are comprehensible, simple and accurate. [ABSTRACT FROM AUTHOR]