Search

In this paper we present our work about personalized search in digital libraries. Unlike other researches which use content-based methods, we focus on citation-based methods for this purpose. We propose a practical approach to estimate the co-citation relatedness between scientific papers using the Google search engine. We conducted some experiments to evaluate performance of different citation-based methods. The experimental results show that our approach is promising and applicable for personalized search in digital libraries. [ABSTRACT FROM AUTHOR]

Typically, personalized information recommendation services automatically infer the user profile, a structured model of the user interests, from documents that were already deemed relevant by the user. We present an approach based on Word Sense Disambiguation (WSD) for the extraction of user profiles from documents. This approach relies on a knowledge-based WSD algorithm, called JIGSAW, for the semantic indexing of documents: JIGSAW exploits the WordNet lexical database to select, among all the possible meanings (senses) of a polysemous word, the correct one. Semantically indexed documents are used to train a naïve Bayes learner that infers "semantic", sense-based user profiles as binary text classifiers (user-likes and user-dislikes). Two empirical evaluations are described in the paper. In the first experimental session, JIGSAW has been evaluated according to the parameters of the Senseval-3 initiative, that provides a forum where the WSD systems are assessed against disambiguated datasets. The goal of the second empirical evaluation has been to measure the accuracy of the user profiles in selecting relevant documents to be recommended. Performance of classical keyword-based profiles has been compared to that of sense-based profiles in the task of recommending scientific papers. The results show that sense-based profiles outperform keyword-based ones. [ABSTRACT FROM AUTHOR]

The regulation of multiagent systems may be approached from different stand-points. In this paper I will take the perspective of using a certain type of devices, electronic institutions, to regulate agent interactions. Furthermore, in this paper I am concerned with the tasks of design and construction of actual electronic institutions and I will explore some of the empirical aspects that one may encounter in such activities. More specifically, I will focus on those empirical aspects that are characteristic of electronic institutions rather than those that may be typical of multi-agent systems development in general or other types of software engineering. I use three examples of actual electronic institutions that show different and complementary features in order to motivate a number of distinctions that may be used to treat empirical features in a systematic way. [ABSTRACT FROM AUTHOR]

Capturing and understanding mathematics from print form is an important task in translating written mathematical knowledge into electronic form. While the problem of syntactically recognising mathematical formulas from scanned images has received attention, very little work has been done on semantic validation and correction of recognised formulas. We present a first step towards such an integrated system by combining the Infty system with a semantic analyser for matrix expressions. We applied the combined system in experiments on the semantic analysis of matrix images scanned from textbooks. While the first results are encouraging, they also demonstrate many ambiguities one has to deal with when analysing matrix expressions in different contexts. We give a detailed overview of the problems we encountered that motivate further research into semantic validation of mathematical formula recognition. [ABSTRACT FROM AUTHOR]

This paper provides a survey and analysis of applications of Agent Based Simulation (ABS). A framework for describing and assessing the applications is presented and systematically applied. A general conclusion from the study is that even if ABS seems a promising approach to many problems involving simulation of complex systems of interacting entities, it seems as the full potential of the agent concept and previous research and development within ABS often is not utilized. We illustrate this by providing some concrete examples. Another conclusion is that important information of the applications, in particular concerning the implementation of the simulator, was missing in many papers. As an attempt to encourage improvements we provide some guidelines for writing ABS application papers. [ABSTRACT FROM AUTHOR]

Changes and adaptations are always necessary after the deployment of a multiagent system (MAS), as well as of any other type of software systems. Some of these changes may be simply perfective and have local impact only. However, adaptive changes to meet changed situations in the operational environment of the MAS may have global impact on the overall design. In this paper, we analyze the issue of continuous design change/adaptation in a MAS organization, and the specific problem of how to properly model/design a MAS so as to make it ready to adaptation. Following, the paper focuses on the Gaia methodology and analyzes - also with the help of an illustrative example - its suitability in supporting and facilitating adaptive changes in MASs organizations, and its advantages and limitations with this regard over a number of different agent-oriented methodologies. Keywords: Agent Oriented Methodologies, Design for Change, Adaptive Organizations, Methodologies Evaluation. [ABSTRACT FROM AUTHOR]

Semantic Web conferences such as ESWC and ISWC offer prime opportunities to test and showcase semantic technologies. Conference metadata about people, papers and talks is diverse in nature and neither too small to be uninteresting or too big to be unmanageable. Many metadata-related challenges that may arise in the Semantic Web at large are also present here. Metadata must be generated from sources which are often unstructured and hard to process, and may originate from many different players, therefore suitable workflows must be established. Moreover, the generated metadata must use appropriate formats and vocabularies, and be served in a way that is consistent with the principles of linked data. This paper reports on the metadata efforts from ESWC and ISWC, identifies specific issues and barriers encountered during the projects, and discusses how these were approached. Recommendations are made as to how these may be addressed in the future, and we discuss how these solutions may generalize to metadata production for the Semantic Web at large. [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]

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

Recommender algorithms have been quite successfully employed in a variety of scenarios from filtering applications to recommendations of movies and books at Amazon.com. However, all these algorithms focus on single item recommendations and do not consider any more complex recommendation structures. This paper explores how semantically rich complex recommendation structures, represented as RDF graphs, can be exchanged and shared in a distributed social network. After presenting a motivating scenario we define several annotation ontologies we use in order to describe context information on the user's desktop and show how our ranking algorithm can exploit this information. We discuss how social distributed networks and interest groups are specified using extended FOAF vocabulary, and how members of these interest groups share semantically rich recommendations in such a network. These recommendations transport shared context as well as ranking information, described in annotation ontologies. We propose an algorithm to compute these rankings which exploits available context information and show how rankings are influenced by the context received from other users as well as by the reputation of the members of the social network with whom the context is exchanged. [ABSTRACT FROM AUTHOR]

The paper reports on the development of the formal framework to design strategies for multi-issue non-symmetric meaning negotiations among software agents in a distributed information retrieval system. The advancements of the framework are the following. A resulting strategy compares the contexts of two background domain theories not concept by concept, but the whole context to the other context by accounting the relationships among concepts, the properties, the constraints over properties, and the available instances. It contains the mechanisms for measuring contextual similarity through assessing propositional substitutions and to provide argumentation through generating extra contexts. It uses presuppositions for choosing the best similarity hypotheses and to make the mutual concession to the common sense monotonic. It provides the means to evaluate the possible eagerness to concede through semantic commitments and related notions of knowledgeability and degree of reputation. [ABSTRACT FROM AUTHOR]

Accurate cancer classification is very important to cancer diagnosis and treatment. As molecular information is increasing for the cancer classification, a lot of techniques have been proposed and utilized to classify and predict the cancers from gene expression profiles. In this paper, we propose a method based on speciated evolution for the cancer classification. The optimal combination among several feature-classifier pairs from the various features and classifiers is evolutionarily searched using the deterministic crowding genetic algorithm. Experimental results demonstrate that the proposed method is more effective than the standard genetic algorithm and the fitness sharing genetic algorithm as well as the best single classifier to search the optimal ensembles for the cancer classification. [ABSTRACT FROM AUTHOR]

This paper describes a system capable of semi-automatically filling an XML template from free texts in the clinical domain (practice guidelines). The XML template includes semantic information not explicitly encoded in the text (pairs of conditions and actions/recommendations). Therefore, there is a need to compute the exact scope of conditions over text sequences expressing the required actions. We present in this paper the rules developed for this task. We show that the system yields good performance when applied to the analysis of French practice guidelines. [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]

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]

Usability is a feature of software quality that has traditional significance in the Human Computer Interaction (HCI) community. Recent works that have been proposed by the Software Engineering (SE) community are intended to improve the usability of software applications. This paper combines aspects that are defined in both these communities to produce usable web applications. To achieve this goal, a well-known strategy to improve usability is used: usability patterns. However, many usability patterns and guidelines could only be applied when the final system is implemented. In this work, STATUS patterns have been chosen because they solve usability issues at conceptual level. The main purpose of this paper is to improve the usability of Web Applications automatically generated by OOWS (a model-based web engineering method) applying the STATUS patterns. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

Engineering component interactions is a major challenge in the development of large-scale, open systems. In the realm of multiagent system research, organizational abstractions have been proposed to overcome the complexity of this task. However, the gap between these modeling abstractions, and the constructs provided by todays agent-oriented software frameworks is still rather big. This paper reports on the multiagent programming framework, which provides executable constructs for each of the organizational, ACL-based modeling abstractions of the theory. Setting out from a components and connectors perspective on the elements of the metamodel, their executions semantics is defined and instrumented on top of the JADE platform. Moreover, a systematic reuse approach to the engineering of interactions is put forward. [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 discusses the AgentAssebly architecture and specifically the agent framework platform component of the architecture. An introduction is given to the work covered in the paper and previous work (see [1]) this paper is based upon. Then an in-depth discussion is rendered on the agent framework platform provided by the AgentAssembly architecture. The paper concludes with some remarks on the research presented and future aims of the ongoing research project. Keywords: Agent programming languages, frameworks, and toolkits meta-modeling and meta reasoning. [ABSTRACT FROM AUTHOR]

The traveling salesman problem (TSP) is a very hard optimization problem in the field of operations research. It has been shown to be NP-hard, and is an often-used benchmark for new optimization techniques. This paper pro- poses an improved multi-agent approach for solving large TSP. This proposed approach mainly includes three kinds of agents with different function. The first kind of agent is conformation agent and its function is generating the new solution continuously. The second kind of agent is optimization agent and its function is optimizing the current solutions group. The third kind of agent is refining agent and its function is refining the best solution from the beginning of the trial. At same time, there are many sub-agents in each kind of agent. These sub-agents accomplish the task of its superior agent cooperatively. At the end of this paper, the experimental results have shown that the proposed hybrid approach has good performance with respect to the quality of solution and the speed of computation. [ABSTRACT FROM AUTHOR]

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

This paper suggests an automated method for document classification using an ontology, which expresses terminology information and vocabulary contained in Web documents by way of a hierarchical structure. Ontologybased document classification involves determining document features that represent the Web documents most accurately, and classifying them into the most appropriate categories after analyzing their contents by using at least two pre-defined categories per given document features. In this paper, Web documents are classified in real time not with experimental data or a learning process, but by similar calculations between the terminology information extracted from Web texts and ontology categories. This results in a more accurate document classification since the meanings and relationships unique to each document are determined. Keywords: Document classification, Ontology, Web Page classification. [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]

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]

An optimization algorithm for dynamic Bayesian networks (DBN) based on Bayesian optimization algorithm (BOA) is developed for learning and constructing the DBN structure. In this paper, we first introduce some basic theories and concepts of probability model evolutionary algorithm. Then we describe, the basic mode for constructing DBN diagram and the mechanism of DBN structure learning based on BOA. The DBN structure learning based on BOA consists of two parts. The first part is to obtain the structure and parameters of DBN in terms of a good solution, and the second part is to produce new groups according to the obtained DBN structure. In this paper, the DBN learning is achieved by genetics algorithm based on a greedy mechanism. The DBN inference is performed by a forward-simulation algorithm. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Generalization problem is a key problem in NN society, which can be grouped into two classes: the generalization problem with unlimited size of training sample and that with limited size of training sample. The generalization problem with limited size of training sample is considered in this paper. Similar to margin maximization criterion in SVM, we propose a margin maximization training algorithm for BP network to further improve the generalization ability of BP network. Experimental results show that the margin maximization training algorithm proposed in this paper does improve the performance of BP network, and shows a comparable performance with SVM. [ABSTRACT FROM AUTHOR]

Chaotic noise cancellation has potential application in both secret communication and radar target identification. To solve the problem of parameter estimation in chaotic noise, a novel radial basis function neural network (RBF-NN) -based chaotic time series data modeling method is presented in this paper. Together with the spectral analysis technique, the algorithm combines neural network's ability to approximate any nonlinear function. Based on the flexibility of RBF-NN predictor and classical amplitude spectral analysis technique, this paper proposes a new algorithm for parameter estimation in chaotic noise. Analysis of the proposed algorithm's principle and simulation experiments results are given out, which show the effective of the proposed method. We conclude that the study has potential application in various fields as in secret communication for narrow band interference rejection or attenuation and in radar signal processing for weak target detection and identification in sea clutter. [ABSTRACT FROM AUTHOR]

Electronic product code (EPC) sensor network is a collection of objects for sensing data. It is crucial to ubiquitous society. It can provide an application service based on situation dependency with its properties. The situation dependency is an emerging concept which can collect location-based and personalized information. With the situation dependency, many industries can serve ubiquitous service for independent users. u-Healthcare is one of ubiquitous service to provide seamless medical treatment. The concept of situation dependency is applied in u-Healthcare with EPC sensor network technology. Due to specialized four properties of EPC sensor network which are driven from this paper, the situation dependency is well-established in u-Healthcare service. In this paper, we defined value and architecture of u-Healthcare service and we analyzed application areas of u-Healthcare. [ABSTRACT FROM AUTHOR]

This paper describes the limiting factors for building a virtual Telematics center which integrates ITS (Intelligent Transportation Systems) information collected by the system of each of the three government agencies. It also provides some details into how some of the obstacles and barriers can be solved such as data standardization, system mechanisms and regulations. There are very few papers and case studies regarding real problems and solutions for integrating systems of different organizations in general, and Telematics, in particular. The processes and outcomes of implementing systems in this study can be useful to develop other ITS and Telematics systems. [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]

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]

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]

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]

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]

The www is a dynamic environment and it is difficult to capture the user preferences and interests without interfering with the normal activity of the user. Hence to improve user searching in the World Wide Web, we have proposed a personalized search system that supports user searches by learning about user preferences and by observing responses to prior search experiences aided by a new index called the User Conceptual Index (UCI). This paper models every user's search behavior as a User's Search Behavior (USB) Graph. The main focus of this paper is the analysis of the USB graph and the redesign of the UCI using the results arrived from the analysis. [ABSTRACT FROM AUTHOR]

Data quality is a critical issue both in operational databases and in data warehouse systems. Data quality assessment is a strong requirement regarding the ETL subsystem, since bad data may destroy data warehouse credibility. During the last two decades, research and development efforts in the data quality field have produced techniques for data profiling and cleaning, which focus on detecting and correcting bad values in data. Little efforts have been done considering data quality when it relates to the well-formedness of coarse grained data structures resulting from the assembly of linked data records. This paper proposes a metadata model that supports the structural validation of linked data records, from a data quality point of view. The metamodel is built on top of the CWM standard and it supports the specification of data structure quality rules in a high level of abstraction, as well as by means of very specific fine grained business rules. [ABSTRACT FROM AUTHOR]

This paper presents an implementation of (a simplified version of) the cognitive agent programming language 3APL in the Maude term rewriting language. Maude is based on the mathematical theory of rewriting logic. The language has been shown to be suitable both as a logical framework in which many other logics can be represented, and as a semantic framework, through which programming languages with an operational semantics can be implemented in a rigorous way. We explore the usage of Maude in the context of agent programming languages, and argue that, since agent programming languages such as 3APL have both a logical and a semantic component, Maude is very well suited for prototyping such languages. Further, we show that, since Maude is reflective, 3APL's meta-level reasoning cycle or deliberation cycle can be implemented very naturally in Maude. Moreover, although we have implemented a simplified version of 3APL, we argue that Maude is very well suited for implementing various extensions of this implemented version. An important advantage of Maude, besides the fact that it is well suited for prototyping agent programming languages, is that it can be used for verification as it comes with an LTL model checker. Although this paper does not focus on model checking 3APL, the fact that Maude provides these verification facilities is an important motivation for our effort of implementing 3APL in Maude. [ABSTRACT FROM AUTHOR]

The controlled system is an uncertain nonlinear differential-algebraic subsystem (DASs) in a large-scale system. The problem of robust stabilization for such class of uncertain nonlinear DASs is considered in this paper. The robust stabilization controller is proposed based on backstepping approach using two-layer Artificial Neural Networks (ANN) whose weights are updated on-line. The closed-loop error systems are uniformly ultimately bounded (UUB) and the error of convergence can be made arbitrarily small. Finally, using the design scheme proposed in this paper, a governor controller is designed for one synchronous generator in a multi-machine power systems. The simulation results demonstrate the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper develops a new blind channel identification method in frequency domain. Oversampled signal has the property of spectral redundancy in frequency domain which is corresponding to the cyclostationarity property in time domain. This method exploits the cyclostationarity of oversampled signals to identify possibly non-minimum phase FIR channels. Unlike many existing methods, this method doesn't need EVD or SVD of correlation matrix. Several polynomials are constructed and zeros of channels are identified through seeking for common zeros of those polynomials. It is in the similar spirit of Tong's frequency approach, but this new algorithm is much simpler and computationally more efficient. A sufficient and necessary condition for channel identification is also provided in this paper. This condition is quite similar to Tong's time domain theory but it is derived from a novel point of view. [ABSTRACT FROM AUTHOR]

Most of the algorithms proposed in the literature deal with the problem of digital modulation classification and consider classic probabilistic or decision tree classifiers. In this paper, we compare and analyze the performance of 2 neural network classifiers and 3 support vector machine classifiers (i.e. 1-v-r type, 1-v-1 type and DAG type multi-class classifier). This paper also deals with the modulation classification problems of classifying both analog and digital modulation signals in military and civilian communications applications. A total of 7 statistical signal features are extracted and used to classify 9 modulation signals. It is known that the existing technology is able to classify reliably (accuracy ≥ 90%) only at SNR above 10dB when a large range of modulation types including both digital and analog is being considered. Numerical simulations were conducted to compare performance of classifiers. Results indicated an overall success rate of over 95% at the SNR of 10dB in all classifiers. Especially, it was shown that 3 support vector machine classifiers can achieve the probabilities of correct classification (Pcc) of 96.0%, 97.3% and 97.8% at the SNR of 5dB, respectively. [ABSTRACT FROM AUTHOR]

Currently, there is little quantitative research on macroscopic real estate finance at home and abroad. Seen from the whole system of real estate finance, this paper chooses 14 main indexes to compose an evaluation index system. Based on the evaluation index system, an error -back-propagation BP network model is built to evaluate the growth of real estate finance. Data of real estate financial system from 1997-2005 are used as train and test samples of BP neural network. After training, the BP neural network is used to evaluate and forecast by simulation. Through the good accuracy of evaluation and forecasting, the model is proved to be very efficient. By comparing the growing difference of two adjacent years and analyzing the related macro financial policies in related years, the running effect of related real estate financial policies in related years is gained. So by using the evaluation model of this paper, decision makers can decide to use what kind of macro adjusting and controlling policies to gain anticipated aim of real estate finance in the future. [ABSTRACT FROM AUTHOR]