bluuebunny/crossref_metadata_2025 huggingface dataset:
Introduction to Computer Forensics in the Age of Information Warfare
Authors: Kidd Terry T.
Published: May 2011

The rapid expansion and dramatic advances in information technology in recent years have without question generated tremendous benefits to business and organizations. At the same time, this expansion has created significant, unprecedented risks to organization operations. Computer security has, in turn, become much more important as organizations utilize information systems and security measures to avoid data tampering, fraud, disruptions in critical operations, and inappropriate disclosure of sensitive information. Such use of computer security is essential in minimizing the risk of malicious attacks from individuals and groups. To be effective in ensuring accountability, management and information technology security personnel must be able to evaluate information systems security and offer recommendations for reducing security risks to an acceptable level. To do so, they must possess the appropriate resources, skills, and knowledge.
Knowledge Management with Partners in a Dynamic Information Environment
Authors: Kettunen Juha , Hautala Jouni , Kantola Mauri
Published: May 2011

The purpose of this article is to analyse the interaction between the dynamic information environment (IE) and the operational environment to promote the cooperation of the higher education institution with business life and enhance the external impact of the institution on its environment. The dynamic IEs are analysed to manage the information systems and internal processes in an educational institution and their cooperating partners. This article is organised as follows: The article introduces the approach of IEs and operational environments. Then the concept of a dynamic IE is used to analyse the information systems used in the core internal processes of the higher education institution. The study also describes the partnership in a dynamic IE. Thereafter, a short case study is presented about partnership using the electronic Optima platform at the Turku University of Applied Sciences (TUAS). Finally, the results of the study are summarised in the concluding section.
Narrative Learning Environments
Authors: Dettori Giuliana
Published: May 2011

Narrative, in the form of stories and narrations, is a natural mode of communication and expression, familiar to children from a very early age and frequently used also by adults. For this reason, it has often been informally employed, both in and outside school, to facilitate understanding and raise learners’ interest, therefore supporting learning in both its cognitive and motivational aspects. For a long time, however, narrative was not an object of interest for the educational research. Its first systematic analyses were worked out within humanities studies, characterizing it in several different ways. Some of such definitions already highlight characteristics that appear crucial for its use in education. Ricoeur (1981), for instance, describes it as a sequence of events connected with each other by cause-effect relations supporting the construction of a meaningful totality out of a set of scattered events.
Network Management Resource Costs
Authors: Pattinson Colin
Published: May 2011

The reliance on computer communications networks for business and commerce, education, entertainment and many other applications demands these resources are managed effectively. In this context, “management” refers to ensuring security and performance, recovering from faults and accounting for utilisation. Each of these activities requires knowledge of the network configuration, and information about the networked devices. It is on the basis of this knowledge that management decisions to change the configuration and network behaviour are taken. Hence the manager requires as full and accurate a set of information about the network under their control as it is possible to get. Typically, this information resides across the network and is transferred (to the network manager) to assist the network manager’s decision making.
A New Algorithm for Minimizing Tree Pattern Queries
Authors: Chen Yangjun
Published: May 2011

XML employs a tree-structured model for representing data. Queries in XML query languages, for example, XPath (World Wide Web Consortium, 1999), XQuery (World Wide Web Consortium, 2001), XML-QL (Deutch, Fernandex, Florescu, Levy, & Suciu, 1999), and Quilt (Chamberlin, Clark, Florescu, & Stefanescu 1999; Chamberlin, Robie, & Florescu, 2000), typically specify patterns of selection predicates on multiple elements that have some specified tree structured relationships. For instance, the following XPath expression: a[b[c and //d]]/b[c and e//d] asks for any node of type b that is a child of some node of type a. In addition, the b-node is the parent of some c-node and some e-node, as well as an ancestor of some d-node. In general, such an expression can be represented by a tree structure as shown in Figure 1(a). In such a tree pattern, the nodes are types from S ? {*} (* is a wildcard, matching any node type), and edges are parent-child or ancestor-descendant relationships. Among all the nodes of a query Q, one is designated as the output node, denoted by output(Q), corresponding to the output of the query.
Online Learning's Future in the Workplace with Augmented Reality
Authors: Ira Katherine , Berge Zane
Published: May 2011

Augmented reality is a technology system that enhances one’s perceptions and experiences by overlaying digitized images onto real world environments. By looking through the display lenses of special eyewear, the seamless, information-rich, composite view that can be seen enhances understanding of the real world. As the technology that enables augmented reality evolves, auditory and haptic information overlays will be added, making the user’s experience far more vivid. Augmented reality has enormous potential for workplace e-learning and productivity improvement. Prototypes are currently in research and development for military, K-12, and university application. It is anticipated that advances in technology will make augmented reality a common learning tool within the next decade (Azuma, Baillot, Reinhold, Feiner, Julier, & MacIntyre, 2001).
The Open Catalogue of Manuscripts in the Malatestiana Library
Authors: Cartelli A. , Daltri A. , Errani P. , Palma M. , Zanfini P.
Published: May 2011

Many experiences laying on the Internet use have been carried out in Paleography during last years and radical changes in the methods usually adopted in that discipline have been introduced. Furthermore specially planned information systems for bibliographical data management are more and more used and are influencing everyday work and study processes. In the authors’ opinion the proposal for the introduction of special information systems and especially of the Open Catalogue of manuscripts have their roots in the revision of Paleography research and study methods, in the connections the discipline will settle with other sciences (i.e., IS, IT and ICT disciplines) and in the creation of new roles for the users of the information the discipline will produce.
Pattern-Based Identification in P2P Systems
Authors: Richly Gábor , Hosszú Gábor , Kovács Ferenc
Published: May 2011

This article presents a novel approach to search in shared audio file storages such as P2P based systems. The proposed method is based on the recognition of specific patterns in the audio contents in such a way extending the searching possibility from the description based model to the content based model. The importance of the real-time pattern recognition algorithms that are used on audio data for content-based searching in streaming media is rapidly growing (Liu, Wang, & Chen, 1998). The main problem of such algorithms is the optimal selection of the reference patterns (soundprints) used in the recognition procedure. The proposed method is based on distance maximization and is able to quickly choose the pattern that later will be used as reference by the pattern recognition algorithms (Richly, Kozma, Kovács, & Hosszú, 2001). The presented method called EMESE (experimental media-stream recognizer) is an important part of a lightweight content-searching method, which is suitable for the investigation of the networkwide shared file storages. The experimental measurement data shown in the article demonstrate the efficiency of the proposed procedure.
Quality Assurance View of a Management Information System
Authors: Kettunen Juha , Kantola Ismo
Published: May 2011

The purpose of this chapter is to describe the management information system (MIS) which integrates strategic management and quality assurance. Institutional quality audits have shown that quality assurance is quite often separate from the general management system of HEIs. By contrast, a well-functioning quality assurance system produces evaluative information about results and processes and then management uses this information to develop processes. Management at each institutional level determines the corrective action. While providing support for strategic management, themanagement information system can also contribute to continuous improvement, which is the core of quality assurance.
Supporting Self-Regulated Learning with ICT
Authors: Dettori Giuliana , Persico Donatella
Published: May 2011

In this chapter we investigate whether ICT tools can support the practice and development of SRL and, if so, under what conditions. More specifically, we discuss what features of such environments are likely to favour SRL.
Video Object Segmentation
Authors: Ong Ee Ping , Lin Weisi
Published: May 2011

Video object segmentation aims to extract different video objects from a video (i.e., a sequence of consecutive images). It has attracted vast interests and substantial research effort for the past decade because it is a prerequisite for visual content retrieval (e.g., MPEG-7 related schemes), object-based compression and coding (e.g., MPEG-4 codecs), object recognition, object tracking, security video surveillance, traffic monitoring for law enforcement, and many other applications. Video object segmentation is a nonstandardized but indispensable component for an MPEG4/7 scheme in order to successfully develop a complete solution. In fact, in order to utilize MPEG-4 object-based video coding, video object segmentation must first be carried out to extract the required video object masks. Video object segmentation is an even more important issue in military applications such as real-time remote missile/vehicle/soldier’s identification and tracking. Other possible applications include home/office/warehouse security where monitoring and recording of intruders/foreign objects, alarming the personnel concerned or/and transmitting the segmented foreground objects via a bandwidth-hungry channel during the appearance of intruders are of particular interest. Thus, it can be seen that fully automatic video object segmentation tool is a very useful tool that has very wide practical applications in our everyday life where it can contribute to improved efficiency, time, manpower, and cost savings.
Virtual Reality and Virtual Environments in Education
Authors: Sala Nicoletta
Published: May 2011

Virtual reality (2007), or VR, is defined in the Encyclopædia Britannica as: the use of computer modeling and simulation that enables a person to interact with an artificial threedimensional (3D) visual or other sensory environment. VR applications immerse the user in a computergenerated environment that simulates reality through the use of interactive devices, which send and receive information and are worn as goggles, headsets, gloves, or body suits.
Web-Based Course Management Systems (WCMS) Acceptance with College Students in Estonia
Authors: Ifinedo Princely
Published: May 2011

The objective of this article is to present the results of a study that investigates the acceptance of WCMS among college students in Estonia. The country is an emerging economy in the Baltic region of Europe. Estonia has made remarkable progress with respect to the use of ICT products in enhancing education at all levels (The Tiger Leap Foundation, 1997). Recently, Estonia joined forces with a pan-European e-learning project called the UNIVe (Estonian eUniversity, 2004; Ifinedo, 2005). Among other goals, the project aims at “increasing the availability of quality education for students and other people willing to learn …and, educating lecturers of universities to compile and practice quality and efficient e-courses” (Ifinedo, 2006).
Evaluation of the Effectiveness of Small and Medium Sized Businesses Web Sites in a Business to Business Context
Authors: Stockdale Rosemary , Lin Chad
Published: May 2011

Many small and medium sized businesses (SMEs) have set up their own Web sites, as part of their business strategies, to improve their competitiveness and responsiveness. Careful evaluation and adoption of Web sites by SMEs can assist them in improving the effectiveness of their venture into e-commerce. This chapter discusses and analyses the effectiveness of SME business to business Web sites from a user perspective. An effective method of evaluating a Web site can contribute to the development of more quality Web sites and greater realization of benefits. Therefore, an established evaluation instrument (eQual) is used to assess 80 Web sites in terms of usability, information quality, and interaction and service. The analysis shows that although a significant number of sites reflect little understanding of the attributes of good design or potential benefits to be gained from Web sites, there are examples of competent and effective Web site use.
Web Accessibility
Authors: Moreno Carlos García
Published: May 2011

This chapter faces the Web accessibility issue from the perspective of Web Information Systems Quality, which is the main topic of the handbook. The closed relation between both issues (and also standard Web technologies) will be explained. It will be proven that Web accessibility does not imply any extra effort; it is a consequence of good development and design practices. In order to achieve this objective the basic topics related to Web accessibility will be defined and necessary introductory information will be exposed, in order to set the basis for the understanding of the points of discussion, which form the main content of the chapter. Also, some benefits of Web accessibility will be proven and the myths related to Web accessibility will be refuted. By the end of the chapter, the future trends and the newest standard Web technologies that have been designed, taking into account Web accessibility needs, will be introduced.
New Approaches to Portletization of Web Applications
Authors: Bellas Fernando , Paz Iñaki , Pan Alberto , Díaz Óscar
Published: May 2011

Portlets are interactive Web mini-applications that can be plugged into a portal. This chapter focuses on “portletizing” existing Web applications, that is, wrapping them as portlets, without requiring any modification. After providing some background on portlet technology, we discuss two kinds of approaches to portletization: automatic and annotation-based. Automatic approaches make use of heuristics to automatically choose the fragments of the Web application pages to be displayed into the space available in the portlet’s window. In turn, in annotation-based approaches, it is the portal administrator who annotates each page of the portletized Web application to specify which fragments should be displayed. Annotation-based approaches also allow to supplement the functionality of the original Web application. Each approach is explained by using a sample scenario based on the same Web application. We also pinpoint the advantages and shortcomings of each approach, and outline future trends in portletization.
A Web Metadata Based-Model for Information Quality Prediction
Authors: Barros Ricardo , Xexéo Geraldo , Pinheiro Wallace A. , de Souza Jano
Published: May 2011

Currently, in the Web environment, users have to deal with an enormous amount of information. In a Web search, they often receive useless, replicated, outdated, or false data, which, at first, they have no means to assess. Web search engines provide good examples of these problems: As reply from these mechanisms, users usually find links to replicated or conflicting information. Further, in these cases, information is spread out among heterogeneous and unrelated data sources, that normally present different information quality approaches. This chapter addresses those issues by proposing a Web Metadata-Based Model to evaluate and recommend Web pages based on their information quality, as predicted by their metadata. We adopt a fuzzy theory approach to obtain the values of quality dimensions from metadata values and to evaluate the quality of information, taking advantage of fuzzy logic’s ability to capture humans’ imprecise knowledge and deal with different concepts.
Restrictive Methods and Meta Methods for Thematically Focused Web Exploration
Authors: Sizov Sergej , Siersdorfer Stefan
Published: May 2011

This chapter addresses the problem of automatically organizing heterogeneous collections of Web documents for the generation of thematically-focused expert search engines and portals. As a possible application scenario for our techniques, we consider a focused Web crawler that aims to populate topics of interest by automatically categorizing newly-fetched documents. A higher accuracy of the underlying supervised (classification) and unsupervised (clustering) methods is achieved by leaving out uncertain documents rather than assigning them to inappropriate topics or clusters with low confidence. We introduce a formal probabilistic model for ensemble-based meta methods and explain how it can be used for constructing estimators and for quality-oriented tuning. Furthermore, we provide a comprehensive experimental study of the proposed meta methodology and realistic use-case examples.
Philosophy of Architecture Design in Web Information Systems
Authors: Shan Tony C. , Hua Winnie W.
Published: May 2011

This article defines a comprehensive set of guiding principles, called philosophy of architecture design (PAD), as a means of coping with the architecture design complexity and managing the architectural assets of Web information systems in a service-oriented paradigm. This coherent model comprises a multidimensional collection of key guiding principles and criteria in system analysis, modeling, design, development, testing, deployment, operations, management, and governance. The systematic framework provides a multidisciplinary view of the design tenets, idioms, principles, and styles (TIPS) in the IT architecting practices for engineering process and quality assurance. There are 26 constituent elements defined in the scheme, the names of which form an array of A-Z using the first letter. The characteristics and applicability of all 26 dimensions in the PAD model are articulated in detail. Recommendations and future trends are also presented in the context. This overarching model has been extensively leveraged in one format or another to design a wide range of Web-based systems in various industry sectors.
Web Information Resources Vis-à-Vis Traditional Information Services
Authors: D’Ambra John D. , Mistillis Nina
Published: May 2011

This chapter considers the change in information seeking behaviour of tourists as a result of the increased use of the World Wide Web as an information resource in the context of information services provided by visitor information centres (VICs). The theoretical approach adopts the model of expectation-disconfirmation effects on Web customer satisfaction. The chapter proposes that visitor information centres are analogous to an information system and that the user experience of visiting the centre can partially be explained by users perception of the information quality of information resources used at the centre and a prior use of the Web. The research proposition explored in the reported research is that a priori usage of the Web may influence tourists’ perceptions of the information services provided by visitor information centres. In order to investigate this proposition a survey was conducted at the Sydney visitor information centre resulting in 519 responses. The analysis of the data collected, using structural equation modeling, found that perceived information quality of staff and brochures used at the centre explained 63% of the variance of the user experience at the centre, a prior use of the Web did not explain any of the variance. The implications for VICs’ strategic information resource management to meet visitor needs are discussed.
Active Learning with SVM
Authors: Jiang Jun , Ip Horace H.S.
Published: May 2011

Jiang, Jun; IP Horace H. S. With the increasing demand of multimedia information retrieval, such as image and video retrieval from the Web, there is a need to find ways to train a classifier when the training dataset is combined with a small number of labelled data and a large number of unlabeled one. Traditional supervised or unsupervised learning methods are not suited to solving such problems particularly when the problem is associated with data in a high-dimension space. In recent years, many methods have been proposed that can be broadly divided into two groups: semi-supervised and active learning (AL). Support Vector Machine (SVM) has been recognized as an efficient tool to deal with high-dimensionality problems, a number of researchers have proposed algorithms of Active Learning with SVM (ALSVM) since the turn of the Century. Considering their rapid development, we review, in this chapter, the state-of-the-art of ALSVM for solving classification problems.
Adaptive Business Intelligence
Authors: Michalewicz Zbigniew
Published: May 2011

Since the computer age dawned on mankind, one of the most important areas in information technology has been that of “decision support.” Today, this area is more important than ever. Working in dynamic and ever-changing environments, modern-day managers are responsible for an assortment of far reaching decisions: Should the company increase or decrease its workforce? Enter new markets? Develop new products? Invest in research and development? The list goes on. But despite the inherent complexity of these issues and the ever-increasing load of information that business managers must deal with, all these decisions boil down to two fundamental questions: What is likely to happen in the future? What is the best decision right now? Whether we realize it or not, these two questions pervade our everyday lives — both on a personal and professional level. When driving to work, for instance, we have to make a traffic prediction before we can choose the quickest driving route. At work, we need to predict the demand for our product before we can decide how much to produce. And before investing in a foreign market, we need to predict future exchange rates and economic variables. It seems that regardless of the decision being made or its complexity, we first need to make a prediction of what is likely to happen in the future, and then make the best decision based on that prediction. This fundamental process underpins the basic premise of Adaptive Business Intelligence.
Advanced Cellular Neural Networks Image Processing
Authors: Fernández J. Álvaro
Published: May 2011

Since its introduction to the research community in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm has become a fruitful soil for engineers and physicists, producing over 1,000 published scientific papers and books in less than 20 years (Chua & Roska, 2002), mostly related to Digital Image Processing (DIP). This Artificial Neural Network (ANN) offers a remarkable ability of integrating complex computing processes into compact, real-time programmable analogic VLSI circuits as the ACE16k (Rodríguez et al., 2004) and, more recently, into FPGA devices (Perko et al., 2000). CNN is the core of the revolutionary Analogic Cellular Computer (Roska et al., 1999), a programmable system based on the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing biological organs (Chua & Roska, 2002). This article continues the review started in this Encyclopaedia under the title Basic Cellular Neural Network Image Processing.
AI and Ideas by Statistical Mechanics
Authors: Ingber Lester
Published: May 2011

A briefing (Allen, 2004) demonstrates the breadth and depth complexity required to address real diplomatic, information, military, economic (DIME) factors for the propagation/evolution of ideas through defined populations. An open mind would conclude that it is possible that multiple approaches may be required for multiple decision makers in multiple scenarios. However, it is in the interests of multiple decision-makers to as much as possible rely on the same generic model for actual computations. Many users would have to trust that the coded model is faithful to process their inputs. Similar to DIME scenarios, sophisticated competitive marketing requires assessments of responses of populations to new products. Many large financial institutions are now trading at speeds barely limited by the speed of light. They colocate their servers close to exchange floors to be able to turn quotes into orders to be executed within msecs. Clearly, trading at these speeds require automated algorithms for processing and making decisions. These algorithms are based on "technical" information derived from price, volume and quote (Level II) information. The next big hurdle to automated trading is to turn "fundamental" information into technical indicators, e.g., to include new political and economic news into such algorithms.
ANN Development with EC Tools
Authors: Rivero Daniel , Rabuñal Juan
Published: May 2011

Among all of the Artificial Intelligence techniques, Artificial Neural Networks (ANNs) have shown to be a very powerful tool (McCulloch & Pitts, 1943) (Haykin, 1999). This technique is very versatile and therefore has been succesfully applied to many different disciplines (classification, clustering, regression, modellization, etc.) (Rabuñal & Dorado, 2005). However, one of the greatest problems when using ANNs is the great manual effort that has to be done in their development. A big myth of ANNs is that they are easy to work with and their development is almost automatically done. This development process can be divided into two parts: architecture development and training and validation. As the network architecture is problem-dependant, the design process of this architecture used to be manually performed, meaning that the expert had to test different architectures and train them until finding the one that achieved best results after the training process. The manual nature of the described process determines its slow performance although the training part is completely automated due to the existence of several algorithms that perform this part. With the creation of Evolutionary Computation (EC) tools, researchers have worked on the application of these techniques to the development of algorithms for automatically creating and training ANNs so the whole process (or, at least, a great part of it) can be automatically performed by computers and therefore few human efforts has to be done in this process
Artificial Intelligence and Education
Authors: Sánchez Eduardo , Lama Manuel
Published: May 2011

Governments and institutions are facing the new demands of a rapidly changing society. Among many significant trends, some facts should be considered (Silverstein, 2006): (1) the increment of number and type of students; and (2) the limitations imposed by educational costs and course schedules. About the former, the need of a continuous update of knowledge and competences in an evolving work environment requires life-long learning solutions. An increasing number of young adults are returning to classrooms in order to finish their graduate degrees or attend postgraduate programs to achieve an specialization on a certain domain. About the later, due to the emergence of new types of students, budget constraints and schedule conflicts appear. Workers and immigrants, for instance, are relevant groups for which educational costs and job incompatible schedules could be the key factor to register into a course or to give up a program after investing time and effort on it. In order to solve the needs derived from this social context, new educational approaches should be proposed: (1) to improve and extend the online learning courses, which would reduce student costs and allows to cover the educational needs of a higher number of students, and (2) to automate learning processes, then reducing teacher costs and providing a more personalized educational experience anytime, anywhere. As a result of this context, in the last decade an increasing interest on applying computer technologies in the field of Education has been observed. On this regard, the paradigms of the Artificial Intelligence (AI) field are attracting an special attention to solve the issues derived from the introduction of computers as supporting resources of different learning strategies. In this paper we review the state-of-art of the application of Artificial Intelligence techniques in the field of Education, focusing on (1) the most popular educational tools based on AI, and (2) the most relevant AI techniques applied on the development of intelligent educational systems.
Automatic Classification of Impact-Echo Spectra I
Authors: Salazar Addisson , Serrano Arturo
Published: May 2011

We investigate the application of artificial neural networks (ANNs) to the classification of spectra from impact-echo signals. In this paper we provide analyses from simulated signals and the second part paper details results of lab experiments. The data set for this research consists of sonic and ultrasonic impact-echo signal spectra obtained from 100 3D-finite element models. These spectra, along with a categorization of the materials among homogeneous and defective classes depending on the kind of material defects, were used to develop supervised neural network classifiers. Four levels of complexity were proposed for classification of materials as: material condition, kind of defect, defect orientation and defect dimension. Results from Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks with Linear Discriminant Analysis (LDA), and k-Nearest Neighbours (kNN) algorithms (Duda, Hart, & Stork, 2000), (Bishop C.M., 2004) are compared. Suitable results for LDA and RBF were obtained. The impact-echo is a technique for non-destructive evaluation based on monitoring the surface motion resulting from a short-duration mechanical impact. It has been widely used in applications of concrete structures in civil engineering. Cross-sectional resonant modes in impact-echo signals have been analyzed in elements of different shapes, such as, circular and square beams, beams with empty ducts or cement fillings, etc. In addition, frequency analyses of the displacement of the fundamental frequency to lower values for detection of cracks have been studied (Sansalone & Street, 1997), (Carino, 2001). The impact-echo wave propagation can be analyzed from transient and stationary behaviour. The excitation signal (the impact) produces a short transient stage where the first P (normal stress), S (shear stress) and Rayleigh (superficial) waves arrive to the sensors; afterward the wave propagation phenomenon becomes stationary and a manifold of different mixtures of waves including various changes of S-wave to P-wave propagation mode and viceversa arrive to the sensors. Patterns of waveform displacements in this latter stage are known as the resonant modes of the material. The spectra of impact-echo signals provide of information for classification based on resonant modes the inspected materials. The classification tree approached in this paper has four levels from global to detailed classes with up to 12 classes in the lowest level. The levels are: (i) Material condition: homogeneous, one defect, multiple defects, (ii) Kind of defect: homogeneous, hole, crack, multiple defects, (iii) Defect orientation: homogeneous, hole in axis X or axis Y, crack in planes XY, ZY, or XZ, multiple defects, and (iv) Defect dimension: homogeneous, passing through and half passing through types of holes and cracks of level iii, multiple defects. Some examples of defective models are in Figure 1.
Automatic Classification of Impact-Echo Spectra II
Authors: Salazar Addisson , Serrano Arturo
Published: May 2011

We study the application of artificial neural networks (ANNs) to the classification of spectra from impact-echo signals. In this paper we focus on analyses from experiments. Simulation results are covered in paper I. Impact-echo is a procedure from Non-Destructive Evaluation where a material is excited by a hammer impact which produces a response from the material microstructure. This response is sensed by a set of transducers located on material surface. Measured signals contain backscattering from grain microstructure and information of flaws in the material inspected (Sansalone & Street, 1997). The physical phenomenon of impact-echo corresponds to wave propagation in solids. When a disturbance (stress or displacement) is applied suddenly at a point on the surface of a solid, such as by impact, the disturbance propagates through the solid as three different types of stress waves: a P-wave, an S-wave, and an R-wave. The P-wave is associated with the propagation of normal stress and the S-wave is associated with shear stress, both of them propagate into the solid along spherical wave fronts. In addition, a surface wave, or Rayleigh wave (R-wave) travels throughout a circular wave front along the material surface (Carino, 2001). After a transient period where the first waves arrive, wave propagation becomes stationary in resonant modes of the material that vary depending on the defects inside the material. In defective materials propagated waves have to surround the defects and their energy decreases, and multiple reflections and diffraction with the defect borders become reflected waves (Sansalone, Carino, & Hsu, 1998). Depending on the observation time and the sampling frequency used in the experiments we may be interested in analyzing the transient or the stationary stage of the wave propagation in impact- echo tests. Usually with high resolution in time, analyzes of wave propagation velocity can give useful information, for instance, to build a tomography of a material inspected from different locations. Considering the sampling frequency that we used in the experiments (100 kHz), a feature extracted from the signal as the wave propagation velocity is not accurate enough to discern between homogeneous and different kind of defective materials. The data set for this research consists of sonic and ultrasonic impact-echo signal (1-27 kHz) spectra obtained from 84 parallelepiped-shape (7x5x22cm. width, height and length) lab specimens of aluminium alloy series 2000. These spectra, along with a categorization of the quality of materials among homogeneous, one-defect and multiple-defect classes were used to develop supervised neural network classifiers. We show that neural networks yield good classifications (
Biometric Security Technology
Authors: Faundez-Zanuy Marcos
Published: May 2011

The word biometrics comes from the Greek words “bios” (life) and “metrikos” (measure). Strictly speaking, it refers to a science involving the statistical analysis of biological characteristics. Thus, we should refer to biometric recognition of people, as those security applications that analyze human characteristics for identity verification or identification. However, we will use the short term “biometrics” to refer to “biometric recognition of people”. Biometric recognition offers a promising approach for security applications, with some advantages over the classical methods, which depend on something you have (key, card, etc.), or something you know (password, PIN, etc.). A nice property of biometric traits is that they are based on something you are or something you do, so you do not need to remember anything neither to hold any token.
Chaotic Neural Networks
Authors: Del-Moral-Hernandez Emilio
Published: May 2011

Artificial Neural Networks have proven, along the last four decades, to be an important tool for modelling of the functional structures of the nervous system, as well as for the modelling of non-linear and adaptive systems in general, both biological and non biological (Haykin, 1999). They also became a powerful biologically inspired general computing framework, particularly important for solving non-linear problems with reduced formalization and structure. At the same time, methods from the area of complex systems and non-linear dynamics have shown to be useful in the understanding of phenomena in brain activity and nervous system activity in general (Freeman, 1992; Kelso, 1995). Joining these two areas, the development of artificial neural networks employing rich dynamics is a growing subject in both arenas, theory and practice. In particular, model neurons with rich bifurcation and chaotic dynamics have been developed in recent decades, for the modelling of complex phenomena in biology as well as for the application in neuro-like computing. Some models that deserve attention in this context are those developed by Kazuyuki Aihara (1990), Nagumo and Sato (1972), Walter Freeman (1992), K. Kaneko (2001), and Nabil Farhat (1994), among others. The following topics develop the subject of Chaotic Neural Networks, presenting several of the important models of this class and briefly discussing associated tools of analysis and typical target applications.
CNS Tumor Prediction Using Gene Expression Data Part I
Authors: Islam Atiq , Iftekharuddin Khan M. , George E. Olusegun , Russomanno David J.
Published: May 2011

Automated diagnosis and prognosis of tumors of the central nervous system (CNS) offer overwhelming challenges because of heterogeneous phenotype and genotype behavior of tumor cells (Yang et al. 2003, Pomeroy et al. 2002). Unambiguous characterization of these tumors is essential for accurate prognosis and therapy. Although the present imaging techniques help to explore the anatomical features of brain tumors, they do not provide an effective means of early detection. Currently, the histological examination of brain tumors is widely used for an accurate diagnosis; however, the tumor classification and grading based on histological appearance does not always guarantee absolute accuracy (Yang et al., 2003, Pomeroy et al., 2002). In many cases, it may not be sufficient to detect the detailed changes in the molecular level using a histological examination (Yang et al. 2003) since such examination may not allow accurate prediction of therapeutic responses or prognosis. If the biopsy sample is too small, the problems are aggravated further. Toward achieving a more reliable diagnosis and prognosis of brain tumors, gene expression measures from microarrays are the center of attention to many researchers who are working on tumor prediction schemes. Our proposed tumor prediction scheme is discussed in two chapters in this volume. In part I (this chapter), we use an analysis of variance (ANOVA) model for characterizing the Affymetrix gene expression data from CNS tumor samples (Pomeroy et al. 2002) while in part II we discuss the prediction of tumor classes based on marker genes selected using the techniques developed in this chapter. In this chapter, we estimate the tumor-specific gene expression measures based on the ANOVA model and exploit them to locate the significantly differentially expressed marker genes among different types of tumor samples. We also provide a novel visualization method to validate the marker gene selection process.
Complex-Valued Neural Networks
Authors: Nitta Tohru
Published: May 2011

The usual real-valued artificial neural networks have been applied to various fields such as telecommunications, robotics, bioinformatics, image processing and speech recognition, in which complex numbers (two dimensions) are often used with the Fourier transformation. This indicates the usefulness of complex-valued neural networks whose input and output signals and parameters such as weights and thresholds are all complex numbers, which are an extension of the usual real-valued neural networks. In addition, in the human brain, an action potential may have different pulse patterns, and the distance between pulses may be different. This suggests that it is appropriate to introduce complex numbers representing phase and amplitude into neural networks. Aizenberg, Ivaskiv, Pospelov and Hudiakov (1971) (former Soviet Union) proposed a complex-valued neuron model for the first time, and although it was only available in Russian literature, their work can now be read in English (Aizenberg, Aizenberg & Vandewalle, 2000). Prior to that time, most researchers other than Russians had assumed that the first persons to propose a complex-valued neuron were Widrow, McCool and Ball (1975). Interest in the field of neural networks started to grow around 1990, and various types of complex- valued neural network models were subsequently proposed. Since then, their characteristics have been researched, making it possible to solve some problems which could not be solved with the real-valued neuron, and to solve many complicated problems more simply and efficiently.
Conditional Hazard Estimating Neural Networks
Authors: Eleuteri Antonio , Taktak Azzam , Damato Bertil , Douglas Angela , Coupland Sarah
Published: May 2011

Survival analysis is used when we wish to study the occurrence of some event in a population of subjects and the time until the event of interest. This time is called survival time or failure time. Survival analysis is often used in industrial life-testing experiments and in clinical follow-up studies. Examples of application include: time until failure of a light bulb, time until occurrence of an anomaly in an electronic circuit, time until relapse of cancer, time until pregnancy. In the literature we find many different modeling approaches to survival analysis. Conventional parametric models may involve too strict assumptions on the distributions of failure times and on the form of the influence of the system features on the survival time, assumptions which usually extremely simplify the experimental evidence, particularly in the case of medical data (Cox & Oakes, 1984). In contrast, semiparametric models do not make assumptions on the distributions of failures, but instead make assumptions on how the system features influence the survival time (the usual assumption is the proportionality of hazards); furthermore, these models do not usually allow for direct estimation of survival times. Finally, non-parametric models usually only allow for a qualitative description of the data on the population level. Neural networks have recently been used for survival analysis; for a survey on the current use of neural networks, and some previous attempts at neural network survival modeling we refer to (Bakker & Heskes, 1999), (Biganzoli et al., 1998), (Eleuteri et al., 2003), (Lisboa et al., 2003), (Neal, 2001), (Ripley & Ripley, 1998), (Schwarzer et al. 2000). Neural networks provide efficient parametric estimates of survival functions, and, in principle, the capability to give personalised survival predictions. In a medical context, such information is valuable both to clinicians and patients. It helps clinicians to choose appropriate treatment and plan follow-up efficiently. Patients at high risk could be followed up more frequently than those at lower risk in order to channel valuable resources to those who need them most. For patients, obtaining information about their prognosis is also extremely valuable in terms of planning their lives and providing care for their dependents. In this article we describe a novel neural network model aimed at solving the survival analysis problem in a continuous time setting; we provide details about the Bayesian approach to modeling, and a sample application on real data is shown.
Discovering Mappings Between Ontologies
Authors: Sorathia Vikram , Maitra Anutosh
Published: May 2011

Knowledge Representation is important part of AI. The purpose is to reveal best possible representation of the Universe of Discourse (UoD) by capturing entities, concepts and relations among them. With increased understanding of various scientific and technological disciplines, it is possible to derive rules that governs the behaviour and outcome of the entities in the UoD. In certain cases, it is not possible to establish any explicit rule, yet through experience or observation, some experts can define rules from their tacit knowledge in specific domain. Knowledge representation techniques are focused on techniques that allows externalization of implicit and explicit knowledge of expert(s) with a goal of reuse in absence of physical presence of such expertise. To ease this task, two parallel dimensions have developed over period of time. One dimension is focused on investigating more efficient methods that best suit the knowledge representation requirement resulting in theories and tools that allows capturing the domain knowledge (Brachman & Levesque, 2004). Another development has taken place in harmonization of tools and techniques that allows standard based representation of knowledge (Davies, Studer, & Warren, 2006). Various languages are proposed for representation of the knowledge. Reasoning and classification algorithms are also realized. As an outcome of standardization process, standards like DAML-OIL (Horrocks & Patel- Schneider, 2001), RDF (Manola & Miller, 2004) and OWL(Antoniou & Harmelen, 2004) are introduced. Capturing the benefit of both developments, the tooling is also came in to existence that allows creation of knowledgebase. As a result of these developments, the amount of publicly shared knowledge is continuously increasing. At the time of this writing, a search engine like Swoogle (Ding et al., 2004)-developed to index publicly available Ontologies, is handling over 2,173,724 semantic web documents containing 431,467,096 triples. While the developments are yielding positive results by such a huge amount of knowledge available for reuse, it have become difficult to select and reuse required knowledge from this vast pool. The concepts and their relations that are important to the given problem could have already been defined in multiple Ontologies with different perspectives with specific level of details. It is very likely that to get complete representation of the knowledge, multiple Ontologies must be utilized. This requirement has introduced a new discipline within the domain of knowledge representation that is focused on investigation of techniques and tools that allows integration of multiple shared Ontologies.
Emerging Applications in Immersive Technologies
Authors: Davis Darryl N. , Chapman Paul M.
Published: May 2011

The world of Virtual Environments and Immersive Technologies (Sutherland, 1965) (Kalawsky, 1993) are evolving quite rapidly. As the range and complexity of applications increases, so does the requirement for intelligent interaction. The now relatively simple environments of the OZ project (Bates, Loyall & Reilly, 1992) have been superseded by Virtual Theatres (Doyle & Hayes-Roth, 1997) (Giannachi, 2004), Tactical Combat Air (Jones, Tambe, Laird & Rosenbloom, 1993) training prototypes and Air Flight Control Simulators (Wangermann & Stengel, 1998). This article presents a brief summary of present and future technologies and emerging applications that require the use of AI expertise in the area of immersive technologies and virtual environments. The applications are placed within a context of prior research projects.