Search

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]

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]

Office workers tend to produce paper piles of documents to read or to process sometime later. The information contained in these piles is often lost if it is not transferred to electronic format and connected to knowledge structures. Information that is not part of the knowledge worker's electronic information space is frequently overlooked because it is not proactively provided during actual processes or tasks he is involved in. This paper presents a novel prototype for an intelligent office appliance, which results from an integration of three state-of-the-art office applications/appliances: a workflow system, a document classification system, and a multi-functional peripheral. The resulting system allows for leveraging an office worker's papers to her personal knowledge space in order to realize a pro-active and context-sensitive information support within knowledge-intensive tasks and processes. [ABSTRACT FROM AUTHOR]

To facilitate the transition from paper-based patient record (PPR) to computer-based patient record (CPR), engineers should try to improve the usability of CPR system. From the point of view of a physician, we reviewed PPRs written by 8 Japanese physicians. We revealed the characteristics of PPR to find out about the barrier to transition from PPR to CPR. Our findings may be helpful to the engineers who are aiming to develop a CPR system. [ABSTRACT FROM AUTHOR]

In this paper we describe the design of a hybrid search that combines simple metadata search with a traditional keyword search over unstructured context data. This paradigm provides the inquirer additional options to narrow the search with some semantic aspects through the XML metadata query. We demonstrate a paper search for a case study of the hybrid search, and describe a data integration hub to integrate those data dispersed on the Net. [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]

As location information plays such an important role in pervasive and context-aware computing, location modelling can be cast as a particularly important user modelling problem. Moreover, given the potential sensitivity of personal information about location, it is critical to ensure adequate user control over the use of the location user modelling information. This paper describes MyPlace Locator, a system for modelling people's location, based upon a range of pervasive sensors. A critical feature, the focus of this paper, is the user model control: users can determine the granularity in space and time of the location information released from their model. Users can do this on the level of a single user or a group of users. We describe the interface and report its qualitative evaluation. [ABSTRACT FROM AUTHOR]

One of the main problems of collaborative filtering recommenders is the sparsity of the ratings in the users-items matrix, and its negative effect on the prediction accuracy. This paper addresses this issue applying cross-domain mediation of collaborative user models, i.e., importing and aggregating vectors of users' ratings stored by collaborative systems operating in different application domains. The paper presents several mediation approaches and initial experimental evaluation demonstrating that the mediation can improve the accuracy of the generated predictions. [ABSTRACT FROM AUTHOR]

Intelligent tutoring systems have achieved demonstrable success in supporting formal problem solving. More recently such systems have begun incorporating student explanations of problem solutions. Typically, these natural language explanations are entered with menus, but some ITSs accept open-ended typed inputs. Typed inputs require more work by both developers and students and evaluations of the added value for learning outcomes has been mixed. This paper examines whether typed input can yield more accurate student modeling than menu-based input. This paper examines the application of Knowledge Tracing student modeling to natural language inputs and examines the standard Knowledge Tracing definition of errors. The analyses indicate that typed explanations can yield more predictive models of student test performance than menu-based explanations and that focusing on semantic errors can further improve predictive accuracy. [ABSTRACT FROM AUTHOR]

Information graphics, such as bar charts and line graphs, that appear in popular media generally have a message that they are intended to convey. We have developed a novel plan inference system that uses evidence in the form of communicative signals from the graphic to recognize the graphic designer's intended message. We contend that plan inference research would benefit from examining how each of its evidence sources impacts the system's success. This paper presents such an evidence analysis for the communicative signals that are captured in our plan inference system, and the paper shows how the results of this evidence analysis are informing our research on plan recognition and application systems. [ABSTRACT FROM AUTHOR]

To be effective and useful, math search systems must not only maximize precision and recall, but also present the query hits in a form that makes it easy for the user to identify quickly the truly relevant hits. To meet that requirement, the search system must sort the hits according to domain-appropriate relevance criteria, and provide with each hit a query-relevant summary of the hit target. The standard relevance measures in text search, which rely mostly on keyword frequencies and document sizes, turned out to be inadequate in math search. Therefore, alternative relevance measures must be defined, which give more weight to certain types of information than to others and take into account cross-reference statistics. In this paper, new, multidimensional relevance metrics are defined for math search, methods for computing and implementing them are discussed, and comparative performance evaluation results are presented. Query-relevant hit-summary generation is another factor that enables users to quickly determine the relevance of the presented hits. Although the hit title accompanied by a few leading sentences from the target document is simple to produce, this often fails to convey to the user the document's relevant excerpts. This shifts the burden onto the user to pursue many of the hits, and read significant portions of their target documents, to finally locate the wanted documents. Clearly, this task is too time-consuming and should be largely automated. This paper presents query-relevant hit-summary generation methods, outlines implementation strategies, and presents performance evaluation results. [ABSTRACT FROM AUTHOR]

Logiweb is an open source, distributed system for publication of machine checked mathematics. It covers all aspects of electronic publishing: high typographical quality, archival, handling of references to previously published results, and publication of refereed volumes. The present paper is itself produced using Logiweb; and the paper is formally correct in the sense that it has been verified by Logiweb. The paper describes the implementation layers of the Logiweb system as seen by the user: the programming layer, the metalogic layer, the tactic layer, and the object proof layer. [ABSTRACT FROM AUTHOR]

From the very beginning of the development of the Mizar system experiments with using Mizar as a tool for teaching mathematics have been conducted. Numerous organized courses were based on different versions of the system: starting from the first implementation of its processor, through Mizar-MSE, Mizar-4 and PC-Mizar up till its present version. Now Mizar with its mathematical library gives us quite new didactic possibilities. The purpose of this paper is to present a certain course on logic and set theory offered by our Institute for freshman students. The course employs Mizar as the main tool of instruction. In the paper we discuss the organization of this course and describe some examples of students' tasks. Finally, some conclusions and remarks are given. [ABSTRACT FROM AUTHOR]

In previous papers we described the implementation of a system which combines mathematical object generation, transformation and filtering, conjecture generation, proving and disproving for mathematical discovery in non-associative algebra. While the system has generated novel, fully verified theorems, their construction involved a lot of ad hoc communication between disparate systems. In this paper we carefully reconstruct a specification of a sub-process of the original system in a framework for trustable communication between mathematics systems put forth by us. It employs the concept of biform theories that enables the combined formalisation of the axiomatic and algorithmic theories behind the generation process. This allows us to gain a much better understanding of the original system, and exposes clear generalisation opportunities. [ABSTRACT FROM AUTHOR]

This paper describes a theoretical framework for inducing knowledge from incomplete data sets. The general framework can be used with any formalism based on a lattice structure. It is illustrated within two formalisms: the attribute-value formalism and Sowa's conceptual graphs. The induction engine is based on a non-supervised algorithm called default clustering which uses the concept of stereotype and the new notion of default subsumption, inspired by the default logic theory. A validation using artificial data sets and an application concerning the extraction of stereotypes from newspaper articles are given at the end of the paper. [ABSTRACT FROM AUTHOR]

The paper reports how to assist university learners to study TRIZ, which is a series of useful tools, methods and strategies for solving inventive problems systematically, by means of computer games. The aim of this project is to help players to practice and understand their basic TRIZ knowledge and technique effectively and efficiently by playing online mini games. The paper goes through several major tools of TRIZ, Inventive Principles, Trends of Evolution, Contradiction and Engineering Parameters, and finally evaluates their educational and entertainment values to the player in terms of level design, feedback and interaction. [ABSTRACT FROM AUTHOR]

To perform 3D animation with carving on surfaces of 3D models is a new branch of 3D modeling. A set of new methods are carried out in this paper. One based on recursion can solve the image transformation problem with curve constrain. The edge corroding method can gain a relatively external depth image from a 2D image. Also another based on partial resampling is used to paste a 2.5D model to any other 3D model, and has made some good results. The work of this paper has greatly shortened the path from 2D editing to 3D surface animation. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the current state of development of medical education with a special attention to the fields of rehabilitation. Changes in internet technology provide opportunities for rehabilitation. In this work, we apply medical resources and e-learning service technologies to construct a medical aided learning system. There is summarized philosophy at preparing the e-learning tool, presented achieved results and pointed out some practical hints that should be considering at development of medicine based e-learning modules. Then the paper describes implement of the e-learning system for crura rehabilitation. Medical resources associated with the components of the learning system are encapsulated as reusable Learning agent. Integrated rehabilitation knowledge is used within rehabilitation of disabled people and interns. An example of evaluation of crura rehabilitation is demonstrated. Finally, the paper indicates sketched challenges and ways of future development of e-learning in the presented fields. [ABSTRACT FROM AUTHOR]

Until now, the most web-based communication is based on texts or graphics in a collaborative learning context. However, texts have many limitations as a means of communication. The use of multimedia is very useful for effective communication because it helps to deliver nonverbal messages as tacit knowledge. User participation and communication techniques, which are emphasized in Web 2.0, are important for facilitating knowledge sharing. The latest User Created Content (UCC) technique, such as YouTube, Video Blog and Pandora TV, has many advantages for communication and knowledge sharing. In this paper, an adaptive QoS architecture is proposed to use Educational User Created Contents as multimedia and realtime multimedia pedagogically. This paper contributes towards an understanding of how to support adaptive QoS for learning environments in a web-based learning environment utilizing UCC. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the usability of multimodal feedback for enhancing immersion in augmented reality (AR) based product design environments, introduced in the paper [1]. We study what effects occur when providing multi-sensory feedback to users. First of all, we compare design environments between conventional desktop based design environments and AR based design environments which support multimodal feedback. For a usability test for visual feedback, we test the degree of immersion for showing hands which are occluded by augmented virtual objects, and test the suitability of mapping between content and users' input. In sound feedback, also we test the degree of immersion and suitability of mapping between background-sound/effect sound mapping and corresponding events in the scenario. Lastly, as a test for tactile feedback, we evaluate the effect of the physical entity for virtual objects by registering the object to physical tangible objects, and evaluate the degree of immersion for vibration w.r.t events in scenario. Our analyzed results from usability tests and comments could be useful for enhancing immersion in conventional AR based product design as well as AR based learning and teaching contents for edutainment. [ABSTRACT FROM AUTHOR]

This paper introduces an immersive game interface called a well-tep. A well-tep is made based on a stepper which is sporting goods for home envi-ronment and used for moving a game character back and forward in the game. To operate the well-tep, a game player only needs to walk on the well-tep and use two hand-held switches to change direction. Spatial sound is applied to items which a game character will face in the game. An item emits sound diffe-rently according to where a character stares. The paper explains how they work in the game and potential uses for a well-tep. [ABSTRACT FROM AUTHOR]

An investor will in general designate a goal in the investment process. The traditional constant proportion portfolio insurance (CPPI) strategy considers only the floor constraint but not the goal aspect. This paper proposes a goal-directed (GD) strategy to express an investor's goal-directed trading behavior and combines it with the portfolio insurance perspective to form a piecewise linear goal-directed CPPI (GDCPPI) strategy. This piecewise linear GDCPPI strategy shows that there is a wealth position M at the intersection of the linear GD strategy and linear CPPI strategy. This M position guides investors to apply the CPPI strategy or GD strategy depending on whether the current wealth is less or greater thanM, respectively. In addition, we extend the piecewise linear GDCPPI strategy to a piecewise nonlinear GDCPPI strategy with a minimum function. These piecewise GDCPPI strategies when applying the minimum function can fully maintain the features of the CPPI strategy and the GD strategy without considering the explicitM. This minimum function in fact can obtain the concept of the explicit M, but it operates the M implicitly. Furthermore, we argue that the piecewise nonlinear GDCPPI strategy owns a larger solution space and it can then outperform the piecewise linear GDCPPI strategy in terms of the return rate performance measure. This paper performs some experiments using the Brownian, GA and forest GP techniques to prove with statistical significance that the piecewise nonlinear GDCPPI strategy can outperform the piecewise linear GDCPPI strategy and that there are some data-driven techniques that can find better piecewise linear GDCPPI strategies than strategies based on the Brownian technique. [ABSTRACT FROM AUTHOR]

Over the last decade, numerous papers have investigated the use of Genetic Programming (GP) for creating financial trading strategies. Typically, in the literature, the results are inconclusive but the investigators always suggest the possibility of further improvements, leaving the conclusion regarding the effectiveness of GP undecided. In this paper, we discuss a series of pretests aimed at giving more clear-cut answers as to whether GP can be effective with the training data at hand. Precisely, pretesting allows us to distinguish between a failure due to the market being efficient or due to GP being inefficient. The basic idea here is to compare GP with several variants of random searches and random trading behaviors having well-defined characteristics. In particular, if the outcomes of the pretests reveal no statistical evidence that GP possesses a predictive ability superior to a random search or a random trading behavior, then this suggests to us that there is no point in investing further resources in GP. The analysis is illustrated with GP-evolved strategies for nine markets exhibiting various trends. [ABSTRACT FROM AUTHOR]

This paper is a practical guide to the application of instance-based machine learning techniques to the solution of a financial problem. A broad class of instance-based families is considered for classification using the WEKA software package. The problem selected for analysis is a common one in financial and econometric work: the use of publicly available economic data to forecast future changes in a stock market index. This paper examines various stages in the analysis of this problem including: identification of the problem, considerations in obtaining and preprocessing data, model and parameter selection, and interpretation of results. Finally, the paper offers suggestions of areas of future study for applying instance-based machine learning in the setting of solving financial problems. [ABSTRACT FROM AUTHOR]

To improve the management of the Employment Time Guarantee Fund (Fundo de Garantia do Tempo de Serviço - FGTS), a study in Brazil is conducted to analyze past data and anticipate the future trends of this fund. In this paper, Nonlinear Principal Component Analysis (NLPCA) - with the Artificial Neural Network architecture and Back-Propagation algorithm - is used to reduce the data dimension in describing various causes of withdrawals from the FGTS. With the analysis of the properties of these withdrawals, the paper discusses the correlation between the policy of free treatment of AIDS patients and their withdrawal from the plan. Nonlinear time series corresponding to each cause of withdrawal over 75 months - from 1994 to 2000 - are collected from the administrator of the FGTS. Using NLPCA, 17 small quantity time series (Group 1) are combined into one variable and then combined with other 7 middle quantity series (Group 2) to form another variable. Finally, four combined time series (Group 3) are formed which can well represent features of the total of 27 kinds of withdrawals with respect to their different causes. As a criterion for dimension reducing, the coefficient of correlation between the output of Group 1 and the sum of 17 is 0.8486 and that between Group 2 and sum of 8 is 0.9765. [ABSTRACT FROM AUTHOR]

There have been many studies on the design of P2P systems for effective keyword search. This paper proposes and tests technique to reduce network traffic due to many inverted lists forwarded in carrying out query containing multi-keywords in DHT base structured p2p network. Many of inverted lists, forwarded inter-medium, are discarded regardless of search result. This paper proposes Distance and Smart-bloom filter to diminish those unrelated inverted lists. Distance can correctly distinguish document not containing a certain keyword. Smart-bloom filter with low false positive rate can sort out document with a high chance of including certain keyword among inverted lists selected by distance. Ultimately large amount of unrelated inverted lists can be diminished. The performance of Distance and Smart-bloom filter was tested through simulation and the traffic was decreased by 67%. [ABSTRACT FROM AUTHOR]

The paper proposes the framework for investigation of prospective adaptive and cooperative defense mechanisms against the Internet attacks. The approach suggested is based on the multi-agent modeling and simulation. According to the approach the defense and attack systems are represented as interacting teams of intelligent agents that act under some adaptation criterion. They adjust their configuration and behavior in compliance with the network conditions and attack (defense) severity. The paper represents the architecture and software implementation of simulation environment that combines discrete-event simulation, multi-agent approach and packet-level simulation of various Internet protocols. The environment allows to simulate complex attack and defense scenarios. The paper describes the experiments aimed on the investigation of adaptive "Distributed Denial of Service" attacks and defense mechanisms. [ABSTRACT FROM AUTHOR]

Distributed decision making and learning procedures operate with distributed data sources. Intelligent access to distributed data sources is one of the important requirements to any distributed learning and decision making systems. In many such applications, input data arrive from spatially distributed sensors structured in sensor networks. An increasing interest to what is called intelligent sensors for which agent-based technology seems to be rather attractive implementation paradigm forms a marked trend in the sensor network area. Indeed, the latter provides a natural mapping from the set of nodes of intelligent sensor network to the set of collaborating agents and, at the same time, enriches the network nodes by the methods of collaboration developed under multi-agent approach and provides the network nodes with the capability to communicate in terms of high level language. The paper proposes a typical (reusable) architecture of the intelligent sensor agents as well as typical protocols supporting interaction of sensors with other software components of agent-based network intended for distributed learning and classification. The main solutions proposed in the paper are demonstrated via prototyping of two case studies. [ABSTRACT FROM AUTHOR]

As modern applications tend to stretch between large, ever-growing datasets and increasing demand for meaningful content at the user end, more elaborate and sophisticated knowledge extraction technologies are needed. Towards this direction, the inherently contradicting technologies of deductive software agents and inductive data mining have been integrated, in order to address knowledge intensive problems. However, there exists no generalized evaluation methodology for assessing the efficiency of such applications. On the one hand, existing data mining evaluation methods focus only on algorithmic precision, ignoring overall system performance issues. On the other hand, existing systems evaluation techniques are insufficient, as the emergent intelligent behavior of agents introduce unpredictable factors of performance. In this paper, we present a generalized methodology for performance evaluation of intelligent agents that employ knowledge models produced through data mining. The proposed methodology consists of concise steps for selecting appropriate metrics, defining measurement methodologies and aggregating the measured performance indicators into thorough system characterizations. The paper concludes with a demonstration of the proposed methodology to a real world application, in the Supply Chain Management domain. [ABSTRACT FROM AUTHOR]

Peer-to-Peer (P2P) networks are gaining increasing popularity in many distributed applications such as file-sharing, network storage, web caching, sear- ching and indexing of relevant documents and P2P network-threat analysis. Many of these applications require scalable analysis of data over a P2P network. This paper starts by offering a brief overview of distributed data mining applications and algorithms for P2P environments. Next it discusses some of the privacy concerns with P2P data mining and points out the problems of existing privacy-preserving multi-party data mining techniques. It further points out that most of the nice assumptions of these existing privacy preserving techniques fall apart in real-life applications of privacy-preserving distributed data mining (PPDM). The paper offers a more realistic formulation of the PPDM problem as a multi-party game and points out some recent results. [ABSTRACT FROM AUTHOR]

Nowadays, market environments can be expressed in terms of increased competition, the diverse demands of customers, and the rapid innovation of IT technologies and so forth. These environmental changes have left companies facing a highly competitive market, and company revenues are gradually decreasing. Virtual enterprise is considered as the one of the alternatives available to facilitate adjustment to the new digital economy and save companies from falling into a red ocean. The contents of this research include the definition of virtual enterprise, and the characteristics and technologies for embodying it in a company. The paper suggests a new flow model for a decision support system in a virtual enterprise set-up, assesses critical success factors and weights as per the factors from case studies. From the previous analysis, paper defines the needs of collaborative activities in virtual enterprise and suggests the ubiquitous virtual enterprise reference model. [ABSTRACT FROM AUTHOR]

This paper proposes the solution of compatibility problem between heterogeneous by converting automatically CAD data into proper data for the PCB production process by using IC information. It also proposes a method for data conversion agent by analyzing data of each IC item designed by CAD from the assembling process. This paper has a purpose of maximizing work efficiency with NC Programming automation and contributing to maximize productivity by NC program conversion of different Mounters and Mounter Data & Line Balance optimization. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) systems can be applied to various areas. A RFID system is composed of tag, reader and host computer. The reader has an antenna for transferring the energy and sending/receiving of data. Cables are used for communication between reader of RFID system and host computer. If a RFID system is implemented in the existing building or gate, the data communication line should be installed in the wall of building or inside of the wall, and this causes some problems. We suggest a solution to this problem, as ID data from tag is transferred to host computer by wireless communication in the reader. In addition, TCP/IP communication can be used for recognizing ID data in long distance. In this paper, we show a device that sends ID Data from reader to the receiver over wireless network using CC1020 chip and a device that sends ID data from the receiver to the host computer over internet network using W3100A chip. From this, the paper explains the system transferring ID data to the host computer over wireless and Internet network. There are main circuits for each device and block diagram for programming shown in the picture. The system contains a security agent for security managements and services. In addition, the actual data waveform and experimental device are shown in the picture as well as the result of identification in the host computer. [ABSTRACT FROM AUTHOR]

From both technical and economic viewpoints, DiffServ IP QoS architecture is accepted as a more practical solution in Internet world because the other IntServ architecture is more complex and has scalability problem. This paper presents a dynamic DiffServ Model called Indirect DiffServ to support dynamic QoS requirements of SIP-based ASP multimedia applications in broadband access networks. An operational model of QoS-enabled broadband access networks to support dynamic QoS for SIP is firstly proposed. Then the procedures to establish QoS-enabled SIP sessions of ASP applications are presented. The signaling interfaces for dynamic provisioning of DiffServ QoS are designed to be based on COPS and COPS-PR protocols. The inter-layer mapping of QoS attributes between SIP and underlying layers is also discussed in this paper. The underling broadband access networks are assumed to support largely-deployed Metro Ethernet 802.1 D/Q QoS. [ABSTRACT FROM AUTHOR]

This paper presents a parallel and distributed approach to ensemble learning of Fuzzy ARTMAP classifiers based on the multi-agent platform. Neural networks have been used successfully in a broad range of non-linear problems that are difficult to solve using traditional techniques. Training a neural network for practical applications is often time consuming thus extensive research work is being carried out to accelerate this process. Fuzzy ARTMAP (FAM) is one of the fastest neural network architectures given its ability to produce neurons on demand to represent new classification categories. FAM can adapt to the input data without having to specify an arbitrary structure. However, FAM is vulnerable to noisy data which can rapidly degrade network performance. Due to its fast learning features, FAM is sensitive to the sequence of input sample presentations. In this paper we propose a parallel and distributed approach to ensemble learning for FAM networks as a means to improve the over-all performance of the classifier and increase its resilience to noisy data. We use the multi-agent platform to distribute the computational load of the ensemble to several hosts. The multi-agent platform is a robust environment that can support large-scale neural network ensembles. Our approach also demonstrates the feasibility of large-scale ensembles. The experimental results show that ensemble learning substantially improved the performance of fuzzy ARTMAP classifiers. [ABSTRACT FROM AUTHOR]

Recent research topics in healthcare including intelligent decision support services, expert medical services and autonomous management are based on multi-agent systems. The cooperation of these software agents provides efficient monitoring, analyzing, and managing the data of patient where abnormal patterns are detected to have an advance treatment and prevent loss of life. In this paper, a framework for ubiquitous healthcare based on multi-agent is presented. This paper proposes a mobile agent for diagnosis support in ubiquitous healthcare. The expert mobile agent (EMA) classifies the data of patient by using neuro-fuzzy algorithm for consultation report. A pre-processing method based on the profile of an expert is used to filter the data from the history of patient. Result of neuro-fuzzy from cross-validation test shows a high accurate classification in data compared to other highly accurate classifiers. [ABSTRACT FROM AUTHOR]

This research is intended to design agent for the security requirement to estimate by empirical study. This paper is intended to help the design agent and progress management based on the ISO/IEC 15408. There are many defects that cause the security requirement problems during the web development. This paper remove the risk of the lifecycle and progress management that check the risk items in the web security requirements and also manage the schedule and quality problems. For projects in similar domains, it is possible to remove security risk items and to manage progress by using agent web security lifecycle and progress milestone, which can greatly improve the software process. The analysis is made based on the types required for making use of the defect data. In this case, additional measures must be taken besides merely recording defects. And the theory is apply to the agent web security access control system for the security requirement. [ABSTRACT FROM AUTHOR]

In this paper, we suggested a mobile collaboration framework based on distributed object group framework (DOGF). This paper focuses on the use of this framework to support mobile collaboration. Therefore, we improved the existing work and apply it to the construction of u-healthcare agent services. For supporting mobile collaboration, we divided into two agent types such as the stationary and the moving-typed agents according to the function of mobile devices. The data collected by sensors attached on arbitrary spaces can be shared by 2-typed agents or a home server, and exchanged with each other using the Push and Pull methods. For managing this information, the DOGF provides functions of object group management, storing information and security services to our mobile collaboration framework via defined application interfaces. The agent for executing service functions of mobile devices and an integrated monitoring system on home server are implemented by using TMO scheme. And we used the TMOSM for interactions between distributed components. Finally, we showed via GUI the executablility of healthcare application supporting for medical work in hospitals on our mobile collaboration framework. [ABSTRACT FROM AUTHOR]

This paper is researched to design agent for the progress management. And this paper is intended to help the design agent and progress management based on the project management. Also, there are many defects that cause the domain problems during the project development. This paper remove the risk of the lifecycle and progress management that check the risk items in the process and also manage the progress and quality problems. Therefore, we are use of the incremental method for the remove of risk items. The analysis is made based on the types required for making use of the defect data. In this case, additional measures must be taken besides merely recording defects. And the theory is apply to the agent web security access control system for the security requirement. This paper provides an design of intelligence agent for the progress management. And the process can also be managed by using lifecycle and progress milestone, which can greatly improve the software process. [ABSTRACT FROM AUTHOR]

This paper presents a framework for controlling intelligent robots connected by communication networks. This framework provides novel methods to control coordinated systems using higher-order mobile agents. Higher-order mobile agents are hierarchically structured agents that can contain other mobile agents. The combination of the higher-order mobile agent and mobile multi-robots open a new horizon of efficient use of mobile robot resources. Instead of physical movement of multi-robots, mobile software agents can migrate from one robot to another so that they can find the most suitably equipped and/or the most suitably located robots to perform their task. Thus the framework presented here provides efficient use of robot resources. In this paper, we focus on the energy saving. We have demonstrated the efficiency by numerical experiments. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate a control method for robotic agent, operating in ubiquitous network. It is difficult for people to fulfill their tasks under dynamically changing environment. Therefore, robotic agent performs tasks in stead of people. The ubiquitous network makes it possible to connect between robotic agent and people. Because robotic agent should fulfill tasks according to the received commands from people, the adaptive control method for the agent is needed in order to do the given tasks properly. This paper introduces an adaptive tracking control method for robotic agent based on the radial based functions network (RBFN). When some commands are received through networks, the proposed method can make robotic agent possible to perform tasks under dynamically changing environment. Experimental results show that the proposed control method based on RBFN is adaptable to the environment changes and is more robust than the conventional PID control method and the neuro-control method based on the multilayer perceptron. [ABSTRACT FROM AUTHOR]

The entire path deletion methods (EPDM) explore the K-th network transformation in order to prevent the predetermined K-1 number of path(s) from being re-searched in optimum path finding agent system. But, they show a critical drawback for the application in practical traffic network because loops, in which the same node and link are reappeared without limitation, can't be constrained. The purpose of this paper is to develop a method to selectively control loop-paths by applying link-label and to design the method to be utilized for analysis of intermodal transportation networks. For the fist purpose, this paper takes advantage of the link-label technique in optimum path agent. For the second purpose, the link-mode transformation technique is adopted to treat all modes passing through the same link as each separate link-feature is generated. [ABSTRACT FROM AUTHOR]

The paper is based on FieldExchange - a computer system responsible for monitoring and supporting resource re-distribution decision making in BT's Operational Resource Management units. This paper considers the problem of resource allocation in the service industries approached from an agent-based perspective. The problem is formulated as a centralized/distributed planning problem. The paper describes the context of this solution, the general model and solution and four specific implementations with results and discussion. [ABSTRACT FROM AUTHOR]

Resource management is one of the key research issues for multi-agent systems. Aside from increasing the system reliability, the load balancer is also able to schedule incoming tasks to the available machines. This paper introduces a load balancing strategy algorithm which can optimize the utilization of several different resources (CPU, memory, etc.) by migrating mobile agents and their tasks to the best alternative nodes. In the course of the research, a few scenarios were simulated and analyzed. In the paper the scenarios along with initial experiment results are presented. [ABSTRACT FROM AUTHOR]

Web services selection is based on QoS and trust. As one of the important attributes of QoS, reputation is commonly used to assess the trustworthiness of the web services and minimize the threats of transactions. However, most existing reputation models of web services are all based on the subjective user ratings. These systems are easily attacked by malicious raters. This paper presents a novel reputation model named WSrep, in WSrep, the reputation integrates user ratings and a significant objective factor-credibility of QoS advertisements which is an objective view of the past behaviors of a given service. Other contributions of the paper include a customer measurable QoS model, a Bayesian learning model for building the credibility, and a set of experiments to show the benefits of our approach. [ABSTRACT FROM AUTHOR]

The paper contains a description of the implementation and performance evaluation of the agent-based population learning algorithm used to train the feed-forward artificial neural networks. The goal of the research was to evaluate efficiency of the agent-based approach and to establish experimentally which different factors representing the A-team structure and topology affect the performance of the analyzed agent-based algorithm. The paper includes a general overview of the JABAT environment used to deploy the ANN training algorithm, a description of different agents employed and their roles, as well as the computational experiment plan and the discussion of the performance evaluation results. [ABSTRACT FROM AUTHOR]

Some research has been done on probabilistic extension of description logics such as P-CLASSIC and P-$\mathcal {SHOQ}$ which focus on the statistical information. For example, in those kind of probabilistic DL, we can express such kind of uncertainty that the probability a randomly chosen individual in concept C is also in concept D is 90 percent. This kind of statistical knowledge is certain which means the author of this statement is sure about it. In this paper, we will describe a new kind of probabilistic description logic Pr$\mathcal{SH}$ which could let user express the uncertain knowledge(i.e. degrees of belief). For example, if the user is not sure about that concept C is subsumed by concept D, he could describe it with Pr$\mathcal{SH}$ such as the probability that concept C is subsumed by concept D is 90 percent.Furthermore, user could make use of the uncertain knowledge to infer some implicit knowledge by the extension of tableau-algorithm of $\mathcal {SH}$ which will be also introduced in this paper. [ABSTRACT FROM AUTHOR]

Group Nearest Neighbor query is a relatively prevalent application in spatial databases and overlay network. Unlike the traditional KNN queries, GNN queries maintain several query points and allow aggregate operations among them. Our paper proposes a novel approach for dealing with difference operation of GNN queries on multiple query points. Difference nearest neighbor (DNN) plays an important role on statistical analysis and engineer computation. Seldom existing approaches consider DNN queries. In our paper, we use the properties of hyperbola to efficiently solve DNN queries. A hyperbola divides the query space into several subspaces. Such properties can help us to prune the search spaces. However, the computation cost using hyperbola is not desirable since it is difficult to estimate spaces using curves. Therefore, we adopt asymptotes of hyperbola to simplify the hyperbola-based pruning strategy to reduce the computation cost and the search space. Our experimental results show that the proposed approaches can efficiently solve DNN queries. [ABSTRACT FROM AUTHOR]

The problem of optimally scheduling tasks onto heterogeneous resources in grids, minimizing the makespan of these tasks, has proved to be NP-complete. There is no best scheduling algorithm for all grid computing systems. An alternative is to select an appropriate scheduling algorithm to use in a given grid environment because of the characteristics of the tasks, machines and network connectivity. In this paper a survey is presented on the problem and the different aspects of job scheduling in grids such as (a) fault-tolerance; (b) security; and (c) simulation of grid job scheduling strategies are discussed. This paper also presents a discussion on the future research topics and the challenges of job scheduling in grids. [ABSTRACT FROM AUTHOR]

Solving overshoot and undershoot problems existed in the spline interpolation of empirical mode decomposition (EMD), improving this method and extracting trend of time series with it are the main tasks of this paper. A method is devised by using simple means of successive extrema instead from the envelope average to form the mean envelope. In this way, only one spline interpolation is required rather than two during the course of sifting process of EMD. It is easier to implement, those problems can be alleviated and EMD method is improved. How to get the successive extrema of series and how to realize trend extraction are also expounded in the paper. Experimental results show that the improved EMD method is better at trend extraction than the original one. [ABSTRACT FROM AUTHOR]