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Location Fingerprinting (LF) is a promising location technique for many awareness applications in pervasive computing. However, as research on LF systems goes beyond basic methods there is an increasing need for better comparison of proposed LF systems. Developers of LF systems are also lacking good frameworks for understanding different options when building LF systems. This paper proposes a taxonomy to address both of these problems. The proposed taxonomy has been constructed from a literature study of 51 papers and articles about LF. For researchers the taxonomy can also be used as an aid when scoping out future research in the area of LF. [ABSTRACT FROM AUTHOR]

The emerging field of Ecosystem Informatics applies methods from computer science and mathematics to address fundamental and applied problems in the ecosystem sciences. The ecosystem sciences are in the midst of a revolution driven by a combination of emerging technologies for improved sensing and the critical need for better science to help manage global climate change. This paper describes several initiatives at Oregon State University in ecosystem informatics. At the level of sensor technologies, this paper describes two projects: (a) wireless, battery-free sensor networks for forests and (b) rapid throughput automated arthropod population counting. At the level of data preparation and data cleaning, this paper describes the application of linear gaussian dynamic Bayesian networks to automated anomaly detection in temperature data streams. Finally, the paper describes two educational activities: (a) a summer institute in ecosystem informatics and (b) an interdisciplinary Ph.D. program in Ecosystem Informatics for mathematics, computer science, and the ecosystem sciences. [ABSTRACT FROM AUTHOR]

Radio Frequency Identification (RFID) has come under the spotlight as technology supporting ubiquitous society. But now, we face several security problems and challenges in RFID systems. Recent papers have reported that RFID systems have to achieve the following requirements: (1) Indistinguishability, (2) Forward Security, (3) Replay Attack resistance, (4) Tag Killing resistance, and (5) Ownership Transferability. We have to design RFID system that achieves the above-mentioned requirements. The previous security methods achieve only some of them individually, and no RFID system has been constructed that achieves all requirements. In this paper, we propose an RFID security method that achieves all requirements based on a hash function and a symmetric key cryptosystem. In addition, our proposed method provides not only high-security but also high-efficiency. [ABSTRACT FROM AUTHOR]

Recently, a local-search heuristic algorithm called Extremal Optimization (EO) has been successfully applied in some combinatorial optimization problems. However, there are only limited papers studying on the mechanism of EO applied to the numerical optimization problems so far. This paper presents the studies on the applications of EO to numerical constrained optimization problems with a set of popular benchmark problems. To enhance and improve the search performance and efficiency of EO, we developed a novel EO strategy with population based search. The newly developed EO algorithm is named population-based EO (PEO). Additionally, we adopted the adaptive Lévy mutation, which is more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Compared with three state-of-the-art stochastic search methods with six popular benchmark problems, it has been shown that our approach is a good choice to deal with the numerical constrained optimization problems. [ABSTRACT FROM AUTHOR]

This paper discusses the asymptotic convergence of evolutionary algorithms based on finite search space by using the properties of Markov chains and Perron-Frobenius Theorem. First, some convergence results of general square matrices are given. Then, some useful properties of homogeneous Markov chains with finite states are investigated. Finally, the geometric convergence rates of the transition operators, which is determined by the revised spectral of the corresponding transition matrix of a Markov chain associated with the EA considered here, are estimated by combining the acquired results in this paper. [ABSTRACT FROM AUTHOR]

In this paper we propose some extensions of the business process modeling notation (BPMN) to be able to evaluate the overall cost of business process diagrams. The BPMN is very expressive, and a general treatment of this problem is very complex. Therefore, it seems reasonable to define classes of business process diagrams capturing real processes and to develop efficient analysis methods for these classes. In the paper we define some relevant subsets of the BPMN, extend them with the concept of cost, and provide computational models for each class, in most cases reducing them to existing problems for which efficient solutions already exist. [ABSTRACT FROM AUTHOR]

A novel method for automatic occluded targets recognition in SAR images is proposed in this paper. Different SAR occluded targets are simulated based on actual vehicles from the MSTAR database, and are recognized using SVM classifier by grouping recognition based on the targets azimuth angles. It is shown that the proposed method outperforms the typical methods in accuracy at high occlusion, and robustness to occlusion with experiments considering accuracy and confusion matrix. [ABSTRACT FROM AUTHOR]

In this paper we propose a novel feature extracting method based on spatiotemporal slice analyzing. To date, video features are focused on the character of every single video frame. With our method, the video content is no longer represented with every single frame. The temporal variation of visual information is taken as an important feature of video in our method. We examined this kind of feature with experiments in this paper. The experiment results show that the proposed feature is effective and robust for variant video content and format. [ABSTRACT FROM AUTHOR]

Over the years, multimedia applications are getting increasingly more complex and large in scale. Multimedia Web Service is identified as one of the possible solutions to meet the challenges. The advantages of using Web Services are ease of application development, adaptive to changes, fault tolerance and etc. In the paper, a sample tracking application will be discussed and developed using multimedia Web Services (multimediaWS) approach. Throughout the paper, we will suggest some general rules on designing the multimediaWS as well as evaluate the pros and cons of using multimediaWS for multimedia application. [ABSTRACT FROM AUTHOR]

Business services are deliveries of capabilities to consumers. The way such capabilities are selected, combined and delivered makes for the flexibility in services provision compared to, for example, manufacturing of tangible goods. The coordination (‘orchestration’) of services is an essential requirement for the delivery of more complex services. However, current technologies for web service orchestration assume a procedural ‘program-like’ approach that as we argue in this paper reduces the flexibility to adapt the composite service in response to changing requirements. This paper proposes that service orchestration should be carried out at the business level, preserving the business semantics and transformed if required to specific orchestration execution models such as BPEL4WS, using MDA techniques. Keywords: Service composition, Service orchestration, Service semantics, IDEF0, MDA, BPEL4WS. [ABSTRACT FROM AUTHOR]

The theory of regions and the algorithms for synthesizing a Petri net model from a transition system, which are based on this theory, have interesting practical applications – in particular in the design of electronic circuits. In this paper, we show that this theory can be also applied for mining the underlying process from the user interactions with a document management system. To this end, we combine an algorithm that we called activity mining with such Petri net synthesis algorithms. We present the basic idea of this approach, show some first results, and compare them with classical process mining techniques. The main benefit is that, in combination, the activity mining algorithm and the synthesis algorithms do not need a log of the activities, which is not available when the processes are supported by a document management system only. [ABSTRACT FROM AUTHOR]

Effective automatic summarization usually requires simulating human reasoning such as abstraction or relevance reasoning. In this paper we describe a solution for this type of reasoning in the particular case of surveillance of the behavior of a dynamic system using sensor data. The paper first presents the approach describing the required type of knowledge with a possible representation. This includes knowledge about the system structure, behavior, interpretation and saliency. Then, the paper shows the inference algorithm to produce a summarization tree based on the exploitation of the physical characteristics of the system. The paper illustrates how the method is used in the context of automatic generation of summaries of behavior in an application for basin surveillance in the presence of river floods. [ABSTRACT FROM AUTHOR]

This paper reports a snapshot of our on-going experiments in which a common target we call a-tiny-island-in-a-huge-lake is explored with different methods ranging from a data-mining technique to an artificial immune system. Our implicit interest is a network intrusion detection, and we assume data floating in the huge lake are normal while ones found on the tiny island are abnormal. Our goal here is twofold. One is to know (i) whether or not it is possible to train a system using just normal data alone. The other is to study (ii) a limit of the size of the detectable area, when we decrease the size of the island eventually shrinking to zero, equivalently so-called a-needle-in-a-haystack which is still an open and worth while tuckling problem. To learn these two issues, a fuzzy rule extraction system - one with fixed triangle/trapezoid membership functions for our first goal, and for the second goal with Gaussian membership functions whose shape is adaptively determined for the second goal, are exploited in this paper. [ABSTRACT FROM AUTHOR]

Neural networks (NNs) have been widely used in the power industry for applications such as fault classification, protection, fault diagnosis, relaying schemes, load forecasting, power generation and optimal power flow etc. Most of NNs are built upon the environment of real numbers. However, it is well known that in computations related to electric power systems, such as load-flow analysis and fault level estimation etc., complex numbers are extensively involved. The reactive power drawn from a substation, the impedance, busbar voltages and currents are all expressed in complex numbers. Hence, NNs in the complex domain must be adopted for these applications. This paper proposes the complex-valued neural network (CVNN) and a new fuzzy clustering complex-valued neural network (FC-CVNN) to estimate busbar voltages in a load-flow problem. The aim of this paper is to present a comparative study of estimation busbar voltages in load-flow analysis using the conventional neural network (real-valued neural network, RVNN), the CVNN and the new FC-CVNN. The results suggest that a new proposed FC-CVNN and CVNN architecture can generalize better than ordinary RVNN and the FC-CVNN is also learn faster. [ABSTRACT FROM AUTHOR]

Gene libraries are a biological mechanism for generating combinatorial diversity in the immune system. However, they also bias the antibody creation process, so that they can be viewed as a way of guiding lifetime learning mechanisms. In this paper we examine the implications of this view, by examining coverage, avoidance of self, clustering and diversity. We show how gene libraries may improve both computational expense and performance, and present an analysis which suggests how they might do it. We suggest that gene libraries: provide combinatorial efficiency; improve coverage; reduce the cost of negative selection; and allow targeting of fixed antigen populations. Keywords: gene libraries, artificial immune systems, antibodies, diversity, Baldwin effect, lifetime learning. [ABSTRACT FROM AUTHOR]

The Digital Database for Screening Mammography (DDSM) is an invaluable resource for digital mammography research. However, there are two particular shortcomings that can pose a significant barrier to many of those who may want to use the resource: 1) the actual mammographic image data is encoded using a non-standard lossless variant of the JPEG image format; 2) although detailed metadata is provided, it is not in a form that permits it to be searched, manipulated or reasoned over by standard tools. This paper describes web services that will allow both humans and computers to query for, and obtain, mammograms from the DDSM in a standard and well-supported image file format. Further, this paper describes how these and other services can be used within grid-based workflows, allowing digital mammography researchers to make use of distributed computing facilities. [ABSTRACT FROM AUTHOR]

In this paper the problem of matching a query with imprecise or missing data is analyzed for geographic information systems, and an approach for the relaxation query constraints is proposed. This approach, similarly with the 9-intersection matrix between two sets of points presented in [1] [2] [3], proposes the representation of two symbolic graphical objects (SGO) in terms of interior, boundary, and exterior points, applied to each of the configurations between two objects. The paper distinguishes the different configurations considering the results obtained from the 9 intersections, not only by whether results are null or not null. This approach allows a more detailed similarity graph to be defined. The aim of the proposed methodology is to relax geographical query constraints, in order to obtain meaningful answers for imprecise or missing data. In this paper the polyline-polygon case is discussed in detail. Keywords: Geographical query languages, relaxing constraints, spatial operators. [ABSTRACT FROM AUTHOR]

This paper is about how to efficiently evaluate partially-dimensional range queries, which are often used in many actual applications. If the existing multidimensional indices are employed to evaluate partially-dimensional range queries, then a great deal of information that is irrelevant to the queries also has to be read from disk. A modification of R*-tree is described in this paper to ameliorate such a situation. Discussions and experiments indicate that the proposed modification can clearly improve the performance of partially-dimensional range queries, especially for large datasets. [ABSTRACT FROM AUTHOR]

This paper presents a method for discovering and detecting shill bids in combinatorial auctions. Combinatorial auctions have been studied very widely. The Generalized Vickrey Auction (GVA) is one of the most important combinatorial auctions because it can satisfy the strategy-proof property and Pareto efficiency. As Yokoo et al. pointed out, false-name bids and shill bids pose an emerging problem for auctions, since on the Internet it is easy to establish different e-mail addresses and accounts for auction sites. Yokoo et al. proved that GVA cannot satisfy the false-name-proof property. Moreover, they proved that there is no auction protocol that can satisfy all three of the above major properties. Their approach concentrates on designing new mechanisms. As a new approach against shill-bids, in this paper, we propose a method for finding shill bids with the GVA in order to avoid them. Our algorithm can judge whether there might be a shill bid from the results of the GVA's procedure. However, a straightforward way to detect shill bids requires an exponential amount of computing power because we need to check all possible combinations of bidders. Therefore, in this paper we propose an improved method for finding a shill bidder. The method is based on winning bidders, which can dramatically reduce the computational cost. The results demonstrate that the proposed method successfully reduces the computational cost needed to find shill bids. The contribution of our work is in the integration of the theory and detecting fraud in combinatorial auctions. [ABSTRACT FROM AUTHOR]

The paper presents a fuzzy database management system, and a fuzzy method for dominant colour description of images, on which an image retrieval system is built. The paper shows the suitability of the fuzzy database management system for this kind of applications when the images are characterized by fuzzy data. The synergy of these two introduced components, improves traditional image retrieval systems in three aspects: natural and automatic image description, a natural and easy query language, and high performance in query resolution. [ABSTRACT FROM AUTHOR]

Ontology mapping is one of the important problems for the development of Semantic Web. Establishing such mappings has been the focus of a variety of research originating from diverse communities. In this paper, we propose a Composite Approach for Ontology Mapping (ACAOM) for semi-automatic ontology mapping based on the combination of the name and instance methods. We conclude that the combination is a promising method. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a processor architecture that is suitable for next generation embedded applications, especially for personal information devices such as smart phones, PDAs, and handheld computers. Latest high performance embedded processors are developed to achieve high clock speed. Because increasing performance makes design more difficult and induces large overhead, architectural evolution in embedded processor field is necessary. Among more enhanced processor types, out-of-order superscalar cannot be a candidate for embedded applications due to its excessive complexity and relatively low performance gain compared to its overhead. Therefore, new architecture with moderate complexity must be designed. In this paper, we developed a low-cost SMT architecture model and compared its performance to other architectures including scalar, superscalar and multiprocessor. Because current personal information devices have a tendency to execute multiple tasks simultaneously, SMT or CMP can be a good choice. And our simulation result shows that the efficiency of SMT is the best among the architectures considered. [ABSTRACT FROM AUTHOR]

This paper discusses an adaptive modulation technique combined with space-frequency block coded OFDM(SFBC OFDM) over frequency selective channels and evaluates the performance in terms of the outdated channel state information(CSI) in mobile environments. This paper employs the Alamouti's diversity scheme in multiple input multiple output OFDM (MIMO OFDM) and an adaptive modulation with enhanced performance. Adaptive modulation scheme shows very attractive performance when the CSI is perfect. The CSI for bit loading in MIMO OFDM can be obtained from the singular value decomposition(SVD) of MIMO channel. But, SFBC OFDM system based on Alamouti's diversity scheme does not require the SVD process. Through various simulations, the performance of SFBC OFDM employing adaptive modulation is compared with that of fixed modulation. Also, in adaptive modulation scheme, the effects of the outdated CSI under mobile environments are shown [ABSTRACT FROM AUTHOR]

In this paper we describe a framework for the application of semantic granularities to the Semantic Web. Given a data source and an ontology formalizing qualities which describe the source, we define a dynamic granularity system for the navigation of the repository according to different levels of detail, i.e., granularities. Semantic granularities summarize the degree of informativeness of the qualities, taking into account both the individuals populating the repository, which concur in the definition of the implicit semantics, and the ontology schema, which gives the formal semantics. The method adapts and extends to ontologies existing natural language processing techniques for topics generalization. [ABSTRACT FROM AUTHOR]

The growing development of web-based reputation systems in the 21st century will have a powerful social and economic impact on both business entities and individual customers, because it makes transparent quality assessment on products and services to achieve customer assurance in the distributed web-based Reputation Systems. The web-based reputation systems will be the foundation for web intelligence in the future. Trust and Reputation help capture business intelligence through establishing customer trust relationships, learning consumer behavior, capturing market reaction on products and services, disseminating customer feedback, buyers' opinions and end-user recommendations. It also reveals dishonest services, unfair trading, biased assessment, discriminatory actions, fraudulent behaviors, and un-true advertising. The continuing development of these technologies will help in the improvement of professional business behavior, sales, reputation of sellers, providers, products and services. Given the importance of reputation in this paper, we propose ontology for reputation. In the business world we can consider the reputation of a product or the reputation of a service or the reputation of an agent. In this paper we propose ontology for these entities that can help us unravel the components and conceptualize the components of reputation of each of the entities. [ABSTRACT FROM AUTHOR]

Service-Oriented computing can be characterized as the motivating force for interoperable web applications. Businesses that embark on service-oriented technology are inclined to an effective interoperation with other businesses. This interoperation not only can expand businesses' markets but also allow the provision of quality services to citizens for carrying out their everyday transactions. However for an effective utilization of service-oriented computing a variety of other technologies must be involved namely semantics, pragmatics, effective search techniques, intelligent agents and pervasive services. In this paper we focus in one of the phases of service-oriented computing, the web service discovery, and propose an effective pragmatic and pervasive methodology that will enable potential business partners to discover the web servicesThe term web services, as in this paper, is used to refer to semantic web services./processes of their interest. We are particularly emphasizing on the importance of pragmatics and their application during web service discovery, because only a pragmatic consideration can provide complete and unambiguous meaning to web service usage. [ABSTRACT FROM AUTHOR]

When using electronic services, people are often asked to provide personal information. This raises many privacy issues. To gain the trust of the user, service providers can use privacy policy languages such as P3P to declare the purpose and usage of this personal information. User agents can compare these policies to privacy preferences of a user and warn the user if his privacy is threatened. This paper extends two languages: P3P and APPEL. It makes it possible to refer to certified data and credentials. This allows service providers to define the minimal level of assurance. It is also shown how different ways of disclosure (exact, blurred, verifiably encrypted...) can be specified to achieve more privacy friendly policies. Last, the paper describes a privacy agent that makes use of the policies to automate privacy friendly information disclosure. [ABSTRACT FROM AUTHOR]

Wireless PAN Mesh Network (WMN) is currently going to be standardized and enhanced to take full advantages of the flexible and heterogeneous networks. Although the standard (802.15.5) is under-construction, WMNs are expected to become popular as they have the ability to connect all kinds of current networks. So far, there is no applied architecture which is efficient enough to completely solve routing and security problems in WMN. To assist IEEE P805.15 in routing and security aspects, in this paper, we propose an adaptive algorithm for detecting bogus nodes when they attempt to intrude into the network by attacking routing protocol. In addition, a procedure to find the most optimal path between two nodes is presented along with adaptive pre-conditions for WMNs. We also show that our algorithm is robust according to the mobility of the nodes and it is easy to implement in currently proposed architectures. It can work with many kinds of wireless networks as well as can reduce computational costs. Keywords: Wireless PAN Mesh Networks, Security, Intrusion Detection, Clustering, Optimal Path, Routing, Attack on Routing Protocol. [ABSTRACT FROM AUTHOR]

This paper presents WorkflowNet2BPEL4WS a tool to automatically map a graphical workflow model expressed in terms of Workflow Nets (WF-nets) onto BPEL. The Business Process Execution Language for Web Services (BPEL) has emerged as the de-facto standard for implementing processes and is supported by an increasing number of systems (cf. the IBM WebSphere Choreographer and the Oracle BPEL Process Manager). While being a powerful language, BPEL is difficult to use. Its XML representation is very verbose and only readable for the trained eye. It offers many constructs and typically things can be implemented in many ways, e.g., using links and the flow construct or using sequences and switches. As a result only experienced users are able to select the right construct. Some vendors offer a graphical interface that generates BPEL code. However, the graphical representations are a direct reflection of the BPEL code and not easy to use by end-users. Therefore, we provide a mapping from WF-nets to BPEL. This mapping builds on the rich theory of Petri nets and can also be used to map other languages (e.g., UML, EPC, BPMN, etc.) onto BPEL. To evaluate WorkflowNet2BPEL4WS we used more than 100 processes modeled using Protos (the most widely used business process modeling tool in the Netherlands), automatically converted these into CPN Tools, and applied our mapping. The results of these evaluation are very encouraging and show the applicability of our approach. Keywords: BPEL4WS, Petri nets, workflow management, business process management. [ABSTRACT FROM AUTHOR]

OLAP provides an efficient way for business data analysis. However, most up-to-date OLAP tools often make the analysts lost in the sea of data while the analysts usually focus their interest on a subset of the whole dataset. Unfortunately, OLAP operators are usually not capsulated within the subset. What's more, the users' interests often arise in an impromptu way after the user getting some information from the data. In this paper, we give the definition of users' interests and propose the user-defined virtual cubes to solve this problem. At the same time, we present an algorithm to answer the queries upon virtual cube. All the OLAP operators will be encapsulated within this virtual cube without superfluous information retrieved. Experiments show the effectiveness and efficiency of the virtual cube mechanism. [ABSTRACT FROM AUTHOR]

DoS(Denial of Service) / DDoS(Distributed Denial of Service) attacks threaten Internet security nowadays. However, the current Internet protocol and backbone network do not support traceback to know attacker's real location. Many methods to defend DoS/DDoS attack have been proposed. However these kinds of methods cause network overhead because they use many packets to reconstruct an attack path. In this paper we propose effective probability marking methods and a pushback algorithm to reduce network overhead. [ABSTRACT FROM AUTHOR]

Distributed computing systems are continuously increasing in complexity and cost of managing, and system management tasks require significantly higher levels of autonomic management. In distributed computing, system management is changing from a conventional central administration, to autonomic computing. However, most existing research focuses on healing after a problem has already occurred. In order to solve this problem, an inference model is required to recognize operating environments and predict error occurrence. In this paper, we proposed a hybrid inference model - ID3, Fuzzy Logic, FNN and Bayesian Network - through four algorithms supporting self-healing in autonomic computing. This inference model adopts a selective healing model, according to system situations for self-diagnosing and prediction of problems using four algorithms. Therefore, correction of error prediction becomes possible. In this paper, a hybrid inference model is adopted to evaluate the proposed model in a self-healing system. In addition, inference is compared with existing research and the effectiveness is demonstrated by experiment. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate the feasibility of distributed control of shared resources in user-managed networks. This paradigm has become possible with the advent of broadband wireless networking technologies such as IEEE 802.11. One of the most popular applications in these networks is peer-to-peer (P2P) file exchange. Node cooperation can optimize the usage of shared "external" accesses to the Internet (set of links between the user network and the Internet). In a previous paper, we evaluated different agent-oriented distributed control schema, based on the concept of credit limits, on ideal mesh networks subject to uniform traffic. Each node in the mesh network chooses to behave as a cooperator or a defector. Cooperators may assist in file exchange, whereas defectors try to get advantage of network resources without providing help in return. Before this paper was completed, we observed that popular P2P protocols like eMule, Kazaa and BitTorrent were evolving towards the same credit-oriented strategies we previously proposed. Now, we realistically model both user network traffic and topology, and evaluate a new advanced agent-based distributed control scheme. The simulation results in this paper confirm on realistic networks the main conclusion in our previous research: autonomous node agents become cooperators in their permanent state when they take decisions from local information, checking that file exchange services offered to neighbor nodes do not exceed appropriate credit limits. Keywords: P2P, agents, network management, mesh networks. [ABSTRACT FROM AUTHOR]

This paper presents a consensus-based algorithm for resolving inconsistency in Web-based recommendation systems. In such systems it is possible that a user have his own usage path (the way of usage of the system) and system can recommend individual usage path for new user. The problem of determining a new path for a new user is the subject of this paper. An algorithm for solving this problem will be described below and some results of the experiments with this algorithm will be presented. [ABSTRACT FROM AUTHOR]

Trust plays a fundamental role in multi-agent systems in which tasks are delegated or agents must rely on others to perform actions that they themselves cannot do. Dating from the mid-1980s, the Belief-Desire-Intention architecture (BDI) is the longest-standing model of intelligent agency used in multi-agent systems. Part of the attraction of BDI is that it is amenable to logical formalisms such as Wooldridge's Logic Of Rational Agents (LORA). In a previous paper the present authors introduced a model of trust, here named the Ability-Belief-Commitment-Desire (ABCD) model, that could be implemented within the BDI framework. This paper explores the definition of the ABCD model within the LORA formalism. [ABSTRACT FROM AUTHOR]

In this paper we model the concept of "coarseness", typically used in texture image descriptions, by means of fuzzy sets. Specifically, we relate representative measures of this kind of texture with its presence degree. To obtain these "presence degrees", we collect assessments from polls filled by human subjects, performing an aggregation of these assessments by means of OWA operators. Using this collected data, and some statistics as reference set, the membership function corresponding to the fuzzy set "coarseness" is modelled. Keywords: Image features, texture features, fuzzy texture, visual coarseness. [ABSTRACT FROM AUTHOR]

The paper deals with experience management in computer-mediated environments. It particularly focuses on complex tasks whose support relies more on experience than on knowledge. The presented approach is based on the "use traces" concept to investigate the "activity reflexivity" as a first step in experience management, and experience sharing and reusing as possible applications. The paper also outlines the framework supporting Trace-Based Systems creation. Traces, trace models and trace life cycle are formally defined. The main parts of the framework architecture: collecting, transformation, visualization, and query systems are also detailed. [ABSTRACT FROM AUTHOR]

Camera Calibration (CC) is required in many Photogrammetry and Computer-Vision applications, where 3D information is extracted from images and CC is also employed for pose determination of imaging sensors. In this paper, a novel implicit-CC model (ICC) based on Adaptive Neuro Fuzzy Inference System has been introduced. The ICC is particularly useful for back-projection in the applications that do not require internal and external camera calibration parameters in addition to the expert knowledge. The ICC supports multi-view back-projection in intelligent-photogrammetry. In this paper, the back-projection performance of the ICC has been compared with the Modified Direct-Linear-Transformation (MDLT) on real-images in order to evaluate the success of the proposed ICC. Extensive simulation results show that the ICC achieves a better performance than the MDLT in the 3D reconstruction of scene. [ABSTRACT FROM AUTHOR]

Clustering is one branch of unsupervised machine learning theory, which has a wide variety of applications in pattern recognition, image processing, economics, document categorization, web mining, etc. Today, we constantly face how to handle a large number of similar data items, which drives many researchers to contribute themselves to this field. Support vector machine provides a new pathway for clustering, however, it behaves bad in handling massive data. As an emergent theory, artificial immune system can effectively recognize antigens and produce the memory antibodies. This mechanism is constantly used to achieve representative or feature data from raw data. A combinational clustering method is proposed in this paper based on artificial immune system and support vector machine. Experimentation in functionality and performance is done in detail. Finally a more challenging application in elevator industry is conducted. The results strongly indicate that this combinational clustering in this paper is of feasibility and of practice. Indexed Terms: Data clustering, combinational clustering, artificial immune system, support vector machine. [ABSTRACT FROM AUTHOR]

In this study we address the multi-product, multi-period, multi-mode distribution planning problem. The objective of this paper is to present a real distribution planning problem in which rail/road transportation is integrated within a whole focus on supply chain management. However, in real world problems, practical situations are often not well-defined and thus can not be described precisely. Therefore fuzzy mathematical programming becomes a valuable extension of traditional crisp optimization models. This paper also illustrates how a fuzzy linear programming approach be used to model and solve the multi-mode transportation problem. [ABSTRACT FROM AUTHOR]

With the growth of ontology scale and complexity, the query performance of Ontology Repository Management System (ORMS) becomes more and more important. The paper proposes materialized relations technique which speeds up query processing in ORMS by making the implicit derived relations of ontology explicit. Here the selection of materialized relations is a key problem, because the materialized relations technique trades off required inference time against maintenance cost and storage space. However, the problem has not been discussed formally before. So the paper proposes a QSS model to describe the queries set of ontology formally and gives the benefit evaluation model and the selection algorithm of materialized relations based on QSS model. The method in this paper not only considers the benefit in query response of the materialization technique, but also the storage and maintenance cost of it. In the end, an application case is introduced to prove the selection method of materialized relations is effective. [ABSTRACT FROM AUTHOR]

We discuss issues raised by applying von Neumann kernels to graphs with multiple communities. Depending on the parameter setting, Kandola et al.'s von Neumann kernels can identify not only nodes related to a given node but also the most important nodes in a graph. However, when von Neumann kernels are biased towards importance, top-ranked nodes are the important nodes in the dominant community of the graph irrespective of the communities where the target node belongs. To solve this "topic-drift" problem, we apply von Neumann kernels to the weighted graphs (community graph), which are derived from a generative model of links. [ABSTRACT FROM AUTHOR]

The discovery of interesting regions in spatial datasets is an important data mining task. In particular, we are interested in identifying disjoint, contiguous regions that are unusual with respect to the distribution of a given class; i.e. a region that contains an unusually low or high number of instances of a particular class. This paper centers on the discussion of techniques, methodologies, and algorithms to discover such regions. A measure of interestingness and a supervised clustering framework are introduced for this purpose. Moreover, three supervised clustering algorithms are proposed in the paper: an agglomerative hierarchical supervised clustering named SCAH, an agglomerative, grid-based clustering method named SCHG, and lastly an algorithm named SCMRG which searches a multi-resolution grid structure top down for interesting regions. Finally, experimental results of applying the proposed framework and algorithms to the problem of identifying hotspots in spatial datasets are discussed. [ABSTRACT FROM AUTHOR]

Application of kernel methods to link analysis is presented. Novel kernels based on directed graph Laplacians are proposed and their application as measures of relatedness between nodes in a directed graph is presented. The kernels express relatedness and take into account the global importance of the nodes in a citation graph. Limitations of existing kernels are given with a discussion how they are addressed by directed Laplacian kernels. Links between the kernels and PageRank ranking algorithm are also presented. The proposed kernels are evaluated on a dataset of scientific bibliographic citations. [ABSTRACT FROM AUTHOR]

It is increasingly recognized that business models offer an abstraction that is useful not only in the exploration of new business networks but also for the design and redesign of operational business processes. Among others, they can be used as input for a risk analysis that is crucial in cross-organizational business process design. However, the notion of value object is up till now not clearly defined. In this paper we investigate the notion of value, value objects and the activities involved when transferring value objects between business actors. We illustrate the proposed value object model by applying it on the well-known conference case. [ABSTRACT FROM AUTHOR]

CE-ants (Cross Entropy ants) is a distributed, robust and adaptive swarm intelligence system for dealing with path management in communication networks. This paper focuses on strategies for adjusting the overhead generated by the CE-ants as the state of the network changes. The overhead is in terms of number of management packets (ants) generated, and the adjustments are done by controlling the generation rate of ants traversing the network. The self-tuned strategies proposed in this paper detect state changes implicitly by monitoring parameters and ant rates in the management system. Rate adaptation is done both in the network nodes and in the peering points of the virtual paths. The results are promising, and compared to fixed rate strategies the self-tuned strategies show a significant saving (70-85%) in number of packets, and has similar (even slightly better) data packet delay and service availability. The rate adaptation in network nodes provides fast restoration with short path detection times and hence also high service availability. The implicit self-tuned ant rate in the path endpoints improves the convergence time on link state events without flooding the network with management packets in steady state when these are not required. Keywords: Cross Entropy, swarm intelligence, CE-ants, network management, restoration time, service availability. [ABSTRACT FROM AUTHOR]

This paper deals with the application of segment procedure neural networks to predict harm status of horsetail-pine worm. A novel procedure neural networks is proposed to solve those problems which are related to certain distinct segments of procedure. It is indicated that this model is a generalized form of the known procedure neural networks, and it owns all properties of the known model. This paper also presents learning algorithms for the segment procedure neural networks. Horsetail-pine worm forecast is a hard work for forest experts, but it is a typical segment procedure problem. In this paper a segment procedure neural networks is applied to deal with this issue, and some simulation experiment results are presented. Keywords: Neural networks, topological structure, procedure neural networks, segment, algorithm, learning, prediction. [ABSTRACT FROM AUTHOR]

The classical rough set theory based on complete information systems stems from the observation that objects with the same characteristics are indiscernible according to available information. With respect to upper-approximation and lower-approximation defined on an indiscernibility relation it classifies objects into different equivalent classes. But in some cases such a rigid indiscernibility relation is far from applications in the real world. Therefore, several generalizations of the rough set theory have been proposed some of which extend the indiscernibility relation using more general similarity or tolerance relations. For example, Kryszkiewicz [4] studied a tolerance relation, and Stefanowski [7] explored a non-symmetric, similarity relation and valued tolerance relation. Unfortunately, All the extensions mentioned above have their inherent limitations. In this paper, after discussing several extension models based on rough sets for incomplete information, a concept of constrained dissymmetrical similarity relation is introduced as a new extension of the rough set theory, the upper-approximation and the lower-approximation defined on constrained similarity relation are proposed as well. Furthermore, we present the comparison between the performance of these extended relations. Analysis of results shows that this relation works effectively in incomplete information and generates rational object classification. Keywords: Rough sets, incomplete information, constrained dissymmetrical similarity relation. [ABSTRACT FROM AUTHOR]

Quality management is, not only for digital libraries, an important task in which many dimensions and different aspects have to be considered. The following paper gives a short overview on DBLP in which the data acquisition and maintenance process underlying DBLP is discussed from a quality point of view. The paper finishes with a new approach to identify erroneous person names. [ABSTRACT FROM AUTHOR]

CiteSeer began as the first search engine for scientific literature to incorporate Autonomous Citation Indexing, and has since grown to be a well-used, open archive for computer and information science publications, currently indexing over 730,000 academic documents. However, CiteSeer currently faces significant challenges that must be overcome in order to improve the quality of the service and guarantee that CiteSeer will continue to be a valuable, up-to-date resource well into the foreseeable future. This paper describes a new architectural framework for CiteSeer system deployment, named CiteSeer Plus. The new framework supports distributed indexing and storage for load balancing and fault-tolerance as well as modular service deployment to increase system flexibility and reduce maintenance costs. In order to facilitate novel approaches to information extraction, a blackboard framework is built into the architecture. [ABSTRACT FROM AUTHOR]