bluuebunny/crossref_metadata_2025 huggingface dataset:
Handling Fuzzy Similarity for Data Classification
Authors: Gelbard Roy , Meged Avichai
Published: May 2011

Representing and consequently processing fuzzy data in standard and binary databases is problematic. The problem is further amplified in binary databases where continuous data is represented by means of discrete ‘1’ and ‘0’ bits. As regards classification, the problem becomes even more acute. In these cases, we may want to group objects based on some fuzzy attributes, but unfortunately, an appropriate fuzzy similarity measure is not always easy to find. The current paper proposes a novel model and measure for representing fuzzy data, which lends itself to both classification and data mining. Classification algorithms and data mining attempt to set up hypotheses regarding the assigning of different objects to groups and classes on the basis of the similarity/distance between them (Estivill-Castro & Yang, 2004) (Lim, Loh & Shih, 2000) (Zhang & Srihari, 2004). Classification algorithms and data mining are widely used in numerous fields including: social sciences, where observations and questionnaires are used in learning mechanisms of social behavior; marketing, for segmentation and customer profiling; finance, for fraud detection; computer science, for image processing and expert systems applications; medicine, for diagnostics; and many other fields. Classification algorithms and data mining methodologies are based on a procedure that calculates a similarity matrix based on similarity index between objects and on a grouping technique. Researches proved that a similarity measure based upon binary data representation yields better results than regular similarity indexes (Erlich, Gelbard & Spiegler, 2002) (Gelbard, Goldman & Spiegler, 2007). However, binary representation is currently limited to nominal discrete attributes suitable for attributes such as: gender, marital status, etc., (Zhang & Srihari, 2003). This makes the binary approach for data representation unattractive for widespread data types. The current research describes a novel approach to binary representation, referred to as Fuzzy Binary Representation. This new approach is suitable for all data types - nominal, ordinal and as continuous. We propose that there is meaning not only to the actual explicit attribute value, but also to its implicit similarity to other possible attribute values. These similarities can either be determined by a problem domain expert or automatically by analyzing fuzzy functions that represent the problem domain. The added new fuzzy similarity yields improved classification and data mining results. More generally, Fuzzy Binary Representation and related similarity measures exemplify that a refined and carefully designed handling of data, including eliciting of domain expertise regarding similarity, may add both value and knowledge to existing databases.
Many-Objective Evolutionary Optimisation
Authors: di Pierro Francesco , Khu Soon-Thiam , Savic Dragan A.
Published: May 2011

Many-objective evolutionary optimisation is a recent research area that is concerned with the optimisation of problems consisting of a large number of performance criteria using evolutionary algorithms. Despite the tremendous development that multi-objective evolutionary algorithms (MOEAs) have undergone over the last decade, studies addressing problems consisting of a large number of objectives are still rare. The main reason is that these problems cause additional challenges with respect to low-dimensional ones. This chapter gives a detailed analysis of these challenges, provides a critical review of the traditional remedies and methods for the evolutionary optimisation of many-objective problems and presents the latest advances in this field.
MREM, Discrete Recurrent Network for Optimization
Authors: Mérida-Casermeiro Enrique , López-Rodríguez Domingo , Ortiz-de-Lazcano-Lobato Juan M.
Published: May 2011

Since McCulloch and Pitts’ seminal work (McCulloch & Pitts, 1943), several models of discrete neural networks have been proposed, many of them presenting the ability of assigning a discrete value (other than unipolar or bipolar) to the output of a single neuron. These models have focused on a wide variety of applications. One of the most important models was developed by J. Hopfield in (Hopfield, 1982), which has been successfully applied in fields such as pattern and image recognition and reconstruction (Sun et al., 1995), design of analogdigital circuits (Tank & Hopfield, 1986), and, above all, in combinatorial optimization (Hopfield & Tank, 1985) (Takefuji, 1992) (Takefuji & Wang, 1996), among others. The purpose of this work is to review some applications of multivalued neural models to combinatorial optimization problems, focusing specifically on the neural model MREM, since it includes many of the multivalued models in the specialized literature.
Neural Control System for Autonomous Vehicles
Authors: García-Córdova Francisco , Guerrero-González Antonio , Marín-García Fulgencio
Published: May 2011

Neural networks have been used in a number of robotic applications (Das & Kar, 2006; Fierro & Lewis, 1998), including both manipulators and mobile robots. A typical approach is to use neural networks for nonlinear system modelling, including for instance the learning of forward and inverse models of a plant, noise cancellation, and other forms of nonlinear control (Fierro & Lewis, 1998). An alternative approach is to solve a particular problem by designing a specialized neural network architecture and/or learning rule (Sutton & Barto, 1981). It is clear that biological brains, though exhibiting a certain degree of homogeneity, rely on many specialized circuits designed to solve particular problems. We are interested in understanding how animals are able to solve complex problems such as learning to navigate in an unknown environment, with the aim of applying what is learned of biology to the control of robots (Chang & Gaudiano, 1998; Martínez-Marín, 2007; Montes-González, Santos-Reyes & Ríos- Figueroa, 2006). In particular, this article presents a neural architecture that makes possible the integration of a kinematical adaptive neuro-controller for trajectory tracking and an obstacle avoidance adaptive neuro-controller for nonholonomic mobile robots. The kinematical adaptive neuro-controller is a real-time, unsupervised neural network that learns to control a nonholonomic mobile robot in a nonstationary environment, which is termed Self-Organization Direction Mapping Network (SODMN), and combines associative learning and Vector Associative Map (VAM) learning to generate transformations between spatial and velocity coordinates (García-Córdova, Guerrero-González & García-Marín, 2007). The transformations are learned in an unsupervised training phase, during which the robot moves as a result of randomly selected wheel velocities. The obstacle avoidance adaptive neurocontroller is a neural network that learns to control avoidance behaviours in a mobile robot based on a form of animal learning known as operant conditioning. Learning, which requires no supervision, takes place as the robot moves around a cluttered environment with obstacles. The neural network requires no knowledge of the geometry of the robot or of the quality, number, or configuration of the robot’s sensors. The efficacy of the proposed neural architecture is tested experimentally by a differentially driven mobile robot.
Neural Networks on Handwritten Signature Verification
Authors: Francisco Vargas J. , Ferrer Miguel A.
Published: May 2011

Biometric offers potential for automatic personal identification and verification, differently from other means for personal verification; biometric means are not based on the possession of anything (as cards) or the knowledge of some information (as passwords). There is considerable interest in biometric authentication based on automatic signature verification (ASV) systems because ASV has demonstrated to be superior to many other biometric authentication techniques e.g. finger prints or retinal patterns, which are reliable but much more intrusive and expensive. An ASV system is a system capable of efficiently addressing the task of make a decision whether a signature is genuine or forger. Numerous pattern recognition methods have been applied to signature verification. Among the methods that have been proposed for pattern recognition on ASV, two broad categories can be identified: memory-based and parameter-based methods as a neural network. The Major approaches to ASV systems are the template matching approach, spectrum approach, spectrum analysis approach, neural networks approach, cognitive approach and fractal approach. The proposed article reviews ASV techniques corresponding with approaches that have so far been proposed in the literature. An attempt is made to describe important techniques especially those involving ANNs and assess their performance based on published literature. The paper also discusses possible future areas for research using ASV.
Nonlinear Techniques for Signals Characterization
Authors: Bernardino Alonso Hernández Jesús , Henríquez Rodríguez Patricia
Published: May 2011

The field of nonlinear signal characterization and nonlinear signal processing has attracted a growing number of researchers in the past three decades. This comes from the fact that linear techniques have some limitations in certain areas of signal processing. Numerous nonlinear techniques have been introduced to complement the classical linear methods and as an alternative when the assumption of linearity is inappropriate. Two of these techniques are higher order statistics (HOS) and nonlinear dynamics theory (chaos). They have been widely applied to time series characterization and analysis in several fields, especially in biomedical signals. Both HOS and chaos techniques have had a similar evolution. They were first studied around 1900: the method of moments (related to HOS) was developed by Pearson and in 1890 Henri Poincaré found sensitive dependence on initial conditions (a symptom of chaos) in a particular case of the three-body problem. Both approaches were replaced by linear techniques until around 1960, when Lorenz rediscovered by coincidence a chaotic system while he was studying the behaviour of air masses. Meanwhile, a group of statisticians at the University of California began to explore the use of HOS techniques again. However, these techniques were ignored until 1980 when Mendel (Mendel, 1991) developed system identification techniques based on HOS and Ruelle (Ruelle, 1979), Packard (Packard, 1980), Takens (Takens, 1981) and Casdagli (Casdagli, 1989) set the methods to model nonlinear time series through chaos theory. But it is only recently that the application of HOS and chaos in time series has been feasible thanks to higher computation capacity of computers and Digital Signal Processing (DSP) technology. The present article presents the state of the art of two nonlinear techniques applied to time series analysis: higher order statistics and chaos theory. Some measurements based on HOS and chaos techniques will be described and the way in which these measurements characterize different behaviours of a signal will be analized. The application of nonlinear measurements permits more realistic characterization of signals and therefore it is an advance in automatic systems development.
Particle Swarm Optimization and Image Analysis
Authors: Cagnoni Stefano , Mordonini Monica
Published: May 2011

Particle Swarm Optimization (PSO) is a simple but powerful optimization algorithm, introduced by Kennedy and Eberhart (Kennedy 1995). Its search for function optima is inspired by the behavior of flocks of birds looking for food. Similarly to birds, a set (swarm) of agents (particles) fly over the search space, which is coincident with the function domain, looking for the points where the function value is maximum (or minimum). In doing so, each particle’s motion obeys two very simple difference equations which describe the particle’s position and velocity update. A particle’s motion has a strong random component (exploration) and is mostly independent from the others’; in fact, the only piece of information which is shared among all members of the swarm, or of a large neighborhood of each particle, is the point where the best value for the function has been found so far. Therefore, the search behavior of the swarm can be defined as emergent, since no particle is specifically programmed to achieve the final collective behavior or to play a specific role within the swarm, but just to perform a much simpler local task. This chapter introduces the basics of the algorithm and describes the main features which make it particularly efficient in solving a large number of problems, with particular regard to image analysis and to the modifications that must be applied to the basic algorithm, in order to exploit its most attractive features in a domain which is different from function optimization.
Rule Engines and Agent-Based Systems
Authors: Poggi Agostino , Tomaiuolo Michele
Published: May 2011

Expert systems are successfully applied to a number of domains. Often built on generic rule-based systems, they can also exploit optimized algorithms. On the other side, being based on loosely coupled components and peer to peer infrastructures for asynchronous messaging, multi-agent systems allow code mobility, adaptability, easy of deployment and reconfiguration, thus fitting distributed and dynamic environments. Also, they have good support for domain specific ontologies, an important feature when modelling human experts’ knowledge. The possibility of obtaining the best features of both technologies is concretely demonstrated by the integration of JBoss Rules, a rule engine efficiently implementing the Rete-OO algorithm, into JADE, a FIPA-compliant multi-agent system.
Sequence Processing with Recurrent Neural Networks
Authors: Peng Chun-Cheng , Magoulas George D.
Published: May 2011

Sequence processing involves several tasks such as clustering, classification, prediction, and transduction of sequential data which can be symbolic, non-symbolic or mixed. Examples of symbolic data patterns occur in modelling natural (human) language, while the prediction of water level of River Thames is an example of processing non-symbolic data. If the content of a sequence will be varying through different time steps, the sequence is called temporal or time-series. In general, a temporal sequence consists of nominal symbols from a particular alphabet, while a time-series sequence deals with continuous, real-valued elements (Antunes & Oliverira, 2001). Processing both these sequences mainly consists of applying the current known patterns to produce or predict the future ones, while a major difficulty is that the range of data dependencies is usually unknown. Therefore, an intelligent system with memorising capability is crucial for effective sequence processing and modelling. A recurrent neural network (RNN) is an artificial neural network in which self-loop and backward connections between nodes are allowed (Lin & Lee 1996; Schalkoff, 1997). Comparing to feedforward neural networks, RNNs are well-known for their power to memorise time dependencies and model nonlinear systems. RNNs can be trained from examples to map input sequences to output sequences and in principle they can implement any kind of sequential behaviour. They are biologically more plausible and computationally more powerful than other modelling approaches, such as Hidden Markov Models (HMMs), which have non-continuous internal states, feedforward neural networks and Support Vector Machines (SVMs), which do not have internal states at all. In this article, we review RNN architectures and we discuss the challenges involved in training RNNs for sequence processing. We provide a review of learning algorithms for RNNs and discuss future trends in this area.
State of the Art in Writer's Off-Line Identification
Authors: Travieso González Carlos M. , Romero Carlos F.
Published: May 2011

Today, advances in Computer Science and the proliferation of computers in modern society are an unquestionable fact. Nevertheless, the continuing importance of orthography and the hand-written document are also beyond doubt. The new technologies permit us to work with online information collecting, but there is still a large quantity of information in our society which requires using algorithms for samples off-line. Security in certain applications requires having biometric systems for their identification; in particular, banking checks, wills, postcards, invoices, medical prescriptions, etc, require the identity of the person who has written them to be verified. The only way to do this is with writer recognition techniques. Furthermore, many hand-written documents are vulnerable to possible forgeries, deformations or copies, and generally, to illicit misuse. Therefore, a high percentage of routine work is carried out by experts and professionals in this field, whose task is to certify and to judge the authenticity or falsehood of handwritten documents (for example: wills) in a judicial procedure. Therefore nowadays research on writer identification is an active field. At present, some software tools enable certain characteristics to be displayed and visualised by experts and professionals, but these experts need to devote a great deal of time to such investigations before they are able to draw up conclusions about a given body of writing. Therefore, these tools are not time-saving and nor do they provide a meticulous analysis of the writing. They have to work with graph paper and templates in order to obtain parameters (angles, dimensions of the line, directions, parallelisms, curvatures, alignments, etc.). Moreover, they have to use a magnifying glass and graph paper in order to measure angles and lines. This research aims to lighten this arduous task.
A Survey on Neural Networks in Automated Negotiations
Authors: Papaioannou Ioannis , Roussaki Ioanna , Anagnostou Miltiades
Published: May 2011

Automated negotiation is a very challenging research field that is gaining momentum in the e-business domain. There are three main categories of automated negotiations, classified according to the participating agent cardinality and the nature of their interaction (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001): the bilateral, where each agent negotiates with a single opponent, the multi-lateral which involves many providers and clients in an auction-like framework and the argumentation/persuasion-based models where the involving parties use more sophisticated arguments to establish an agreement. In all these automated negotiation domains, several research efforts have focused on predicting the behaviour of negotiating agents. This work can be classified in two main categories. The first is based on techniques that require strong a-priori knowledge concerning the behaviour of the opponent agent in previous negotiation threads. The second uses mechanisms that perform well in single-instance negotiations, where no historical data about the past negotiating behaviour of the opponent agent is available. One quite popular tool that can support the latter case is Neural Networks (NNs) (Haykin, 1999). NNs are often used in various real world applications where the estimation or modelling of a function or system is required. In the automated negotiations domain, their usage aims mainly to enhance the performance of negotiating agents in predicting their opponents’ behaviour and thus, achieve better overall results on their behalf. This paper provides a survey of the most popular automated negotiation approaches that are using NNs to estimate elements of the opponent’s behaviour. The rest of this paper is structure as follows. The second section elaborates on the state of the art bilateral negotiation frameworks that are based on NNs. The third section briefly presents the multilateral negotiation solutions that exploit NNs. Finally, in the last section a brief discussion on the survey is provided.
Synthetic Neuron Implementations
Authors: Aunet Snorre
Published: May 2011

Many different synthetic neuron implementations exist, that include a variety of traits associated with biological neurons and our understanding of them. An important motivation behind the studies, modelling and implementations of different synthetic neurons, is that nature has provided the most efficient ways of doing important types of computations, that we are trying to mimick. Whether it is Artificial Neural Networks (ANNs) or other mixed signal systems, technology has always evolved in the direction of lower energy per unit computation ( Mead, 1990 ). Simple Neuron models as threshold elements, or perceptrons, are promising candidates for implementing future signal processing systems, including CMOS and SET ( Schmid & Leblebici, 2003 ), ( Beiu & Ibrahim, 2007 ). In this article a small number of published subthreshold, ultra low power, perceptrons / threshold elements are compared regarding power consumption, operational speed and defect tolerance. The “mirrored” gate operating in subthreshold and combined with redundancy, might be an interesting candidate for implementing artificial neural networks as well as other mixed-signal processing circuitry. Previously unpublished results demonstrate the mirrored gate producing appropriate binary outputs at 180 mV supply voltage, even when a transistor was cut off the supply voltage, for a redundancy factor of 2, using shorted outputs, as in ( Aunet & Hartmann, 2003 ).
ICT Competency of Bangladesh to Face Broadband Diffusion
Authors: Islam Anwarul , Panda K. C.
Published: May 2011

The bull’s eye of Bangladesh has to achieve the millennium development goal and to adapt the globalization, necessitate pursuing the development. Information is playing a driven force in development. As a developing country, Bangladesh has taken keen initiatives to develop its sustainable information infrastructure. Teledensity and overall IT infrastructure is now in a growing stage. Recently, Bangladesh was connected with SEA-ME-WE-4 submarine cable, establishing an optical fiber backbone; its teledensity is changing in rapid pace. But the broadband diffusion in Bangladesh is not on par with other Asian countries, as it is in an embryonic stage in broadband diffusion. This chapter, therefore, tries to show the initiatives taken and the existing condition of Bangladesh to fetch the countrywide broadband diffusion. Efforts have been made in this chapter to unmask the overall development of ICT infrastructure in Bangladesh, so to judge the environment of broadband diffusion in the country.
Small World
Authors: Wilson Diana , O’Reilly Kevin , Murray Dave
Published: May 2011

In this chapter we consider from a marketing perspective the political, cultural/social, and economic factors, both micro and macro, affecting the supply/demand nexus of broadband services for the Irish consumer. We chart the development of broadband and its current situation of rollout and uptake, examine the reasons for its continuing poor performance, and offer recommendations on how Ireland may close the gap and perhaps even move ahead. We collated data from a variety of resources, journals and press and trade publications. We attended a ministerial conference on the state of broadband to which many representatives of the telecommunications industry had been invited. We surveyed people with and without the facility, and interviewed key players in the field. We concluded that, although the market is beginning to grow strongly, it is from a low base, and as a result the country still lags behind many of its European counterparts. There is still a lack of competition which is having an adverse effect on both supply and demand of broadband for the Irish domestic consumer. In a small country, the incumbent still holds control of the most profitable areas of the technology, particularly wholesale and selling to other operators. Also, the Irish are still not convinced that broadband is the ‘killer app’ they need. This may change in the future as the technology delivers more of the content-rich multimedia fare that the Irish already enjoy in other formats.
Competition in Broadband Provision and the Digital Divide
Authors: Hu Wei-Min , Prieger James E.
Published: May 2011

This chapter examines the supply of DSL broadband by the incumbent local exchange company (LEC) in five U.S. states in the earlier years of deployment. Our empirical analysis shows that income, other demographics, and cost factors are important determinants of entry and availability. After controlling for other factors, the racial characteristics of the area do not affect DSL provision. Active competition in broadband from competitive LECs reduces deployment of DSL by the incumbent, but potential competition from competitive LECs has the opposite effect. Competition from cable companies also negatively influences the incumbent’s decision to supply DSL. Our objective in gauging the importance of the various factors is to highlight the important drivers of broadband provision for policymakers.
Characteristics of Farm and Rural Internet Use in the USA
Authors: Stenberg Peter L. , Morehart Mitchell
Published: May 2011

The Internet became enmeshed in U.S. businesses management practices over the last decade. During this period access and use of the Internet increased for all regions of the United States, most types of work places, and all income groups. In this study we examine Internet use by farm and rural workers and proprietors using descriptive statistics and market demand analysis. In our market demand analysis approach, the primary methodology we use is categorical dependent variable analysis. The results indicate income is a critical element, though other factors such as age of proprietor and rural-urban location are also significant in market demand determination.
An Empirical Study of the Determinants of Broadband Adoption Among Italian SMEs?
Authors: Colombo Massimo G. , Grilli Luca , Verga Cinzia
Published: May 2011

Why do some small and medium enterprises (SMEs) adopt Internet broadband technologies (high-speed connection and complementary applications) and others do not? This chapter aims at analyzing the issue through an econometric investigation. Relying on the (thin) previous empirical literature on the topic and focusing on a large and representative sample of Italian SMEs, we analyze the determinants of broadband connection and adoption of complementary applications. Results of the econometric analysis reveal that: (i) among firm-specific characteristics, size and the firm’s need to communicate are major determinants both of broadband connection and use of complementary applications, while indicators of firm efficiency and of the competitive pressure suffered from the SME positively affect only the number of used applications, while not exerting any impact on the decision to connect; (ii) among location-specific characteristics, the level of telecommunications infrastructure positively influences both connection and applications use, while the presence within the local labor market of young and skilled workforce makes SMEs adopt more applications; (iii) time-specific variables like those related to the actual and future price of the high-speed Internet connection affect SMEs’ decision to adopt broadband. These findings have important implications for suppliers and policy makers.
Exploring SMEs Adoption of Broadband in the Northwest of England
Authors: Ramdani Boumediene , Kawalek Peter
Published: May 2011

This chapter explores the factors impacting small to medium-sized enterprises’ (SMEs) adoption of broadband. It argues that information and communication technologies (ICTs) are highly differentiated technologies for which there is not necessarily a single adoption model. While most large European companies are connected to broadband, SMEs’ connectivity is lagging behind. The question of why one SME adopts broadband while the other does not is still understudied. Therefore, the purpose of this chapter is to fill this gap by investigating the technological, organizational, and environmental factors impacting SMEs’ adoption of broadband. This chapter starts by highlighting the importance of ICT innovations adoption in general and broadband in particular. Based on the ICT innovations adoption literature, SMEs’ broadband adoption framework will be developed and empirically validated involving nine SMEs’ key decision makers in the northwest of England. Finally, implications for researchers, practitioners, ICTs’ vendors, and policy makers will be discussed.
External Pressures for Adoption of ICT Services Among SMEs
Authors: Ordanini Andrea , Arbore Alessandro
Published: May 2011

This study intends to emphasize the importance that external sources of pressure may have on the level of ICT involvement among small and medium enterprises (SMEs) in Italy. While past research tends to prioritize the role of endogenous conditions for the adoption of information and communication technologies— that is, financial resources, organization conditions, management culture—the high dependence of SMEs on their environment requires paying especial attention to external pressures as well. Both competitive and institutional pressures are proposed and tested through an ordinal regression model on a sample of 285 SMEs. The results suggest both policy and management implications.
Improving Health Services Via Advanced ICT Networks
Authors: Farr Peter , Ellis Isabelle , Royle John
Published: May 2011

This chapter describes an innovative broadband initiative that connects a group of general practices, medical specialists, hospitals, and other health providers in rural areas of Australia through a managed virtual private network. It provides secure connectivity for a variety of mission-critical healthcare delivery applications—for example, transmission of pathology and radiology test results direct to clinicians. The medical practices involved are small-medium enterprises (SMEs) and the key aspects of ICTs for them are the impact on costs, productivity, and customer service. The formal evaluation process examined the domains of appropriateness, efficiency, and cost-effectiveness. Being the first such health network of its kind in Australia, the project encountered challenges and, by overcoming these, has been guiding government policy in respect to e-health. Initially funded from March 2005 via a Commonwealth Government grant, the GoldHealth network moved into a sustainable mode in July 2006. This chapter provides insights into GoldHealth and should be a useful guide to any similar broadband network initiatives for the health sector elsewhere in the world.
Introduction to Fuzzy Data Mining Methods
Authors: Feil Balazs , Abonyi Janos
Published: May 2011

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.
From User Requirements to Evaluation Strategies of Flexible Queries in Databases
Authors: Mouaddib Noureddine , Raschia Guillaume , Voglozin W. Amenel , Ughetto Laurent
Published: May 2011

This chapter presents a discussion on fuzzy querying. It deals with the whole process of fuzzy querying, from the query formulation to its evaluation. Mainly, it advocates the use of index structures in the evaluation of fuzzy queries. First, various ways of introducing flexibility in querying processes are discussed, especially the most represented in the literature, which are based on rankings of the answers or which are using user-oriented fuzzy labels in the queries. Current methods for evaluating fuzzy queries are also reviewed. Then, properties of access methods are given in the context of fuzzy querying. Last, SaintEtiQ, the method developed in our team, is briefly presented.
FSQL and SQLf
Authors: Urrutia Angélica , Tineo Leonid , Gonzalez Claudia
Published: May 2011

Actually, FSQL and SQLf are the main fuzzy logic based proposed extensions to SQL. It would be very interesting to integrate them with a standard for fuzzy databases. The issue is what to take from one or other proposal. In this chapter, we analyze FSQL and SQLf making a comparison in several ways: approach direction, fuzzy components, system architecture, satisfaction degree, evaluation mechanisms, and experimental performance. We observe that there are powerful and interesting features in both proposals that could be mixed in a unified language for fuzzy relational databases.
Hierarchical Fuzzy Sets to Query Possibilistic Databases
Authors: Thomopoulos Rallou , Buche Patrice , Haemmerlé Ollivier
Published: May 2011

Within the framework of flexible querying of possibilistic databases, based on the fuzzy set theory, this chapter focuses on the case where the vocabulary used both in the querying language and in the data is hierarchically organized, which occurs in systems that use ontologies. We give an overview of previous works concerning two issues: first, flexible querying of imprecise data in the relational model and, second, the introduction of fuzziness in hierarchies. Concerning the latter point, we develop an aspect where there is a lack of study in current literature: fuzzy sets whose definition domains are hierarchies. Hence, we propose the concept of hierarchical fuzzy set and present its properties. We present its application in the MIEL flexible querying system for the querying of two imprecise relational databases, including user interfaces and experimental results.
Towards a Fuzzy Object-Relational Database Model
Authors: Barranco Carlos D. , Campaña Jesús R. , Medina Juan M.
Published: May 2011

This chapter introduces a fuzzy object-relational database model including fuzzy extensions of the basic object-relational databases constructs, the user-defined data types, and the collection types. The fuzzy extensions of these constructs focus on two main flexible aspects, a way to flexibly compare complex data types and an extension of collection types allowing partial membership of its elements. Collection operators are also adapted to consider flexibly comparable domains for its elements. Such a fuzzy object-relational database model, and its implementation in a fuzzy object-relational database management system, provides an easy and effective way to manage a great amount of complex fuzzy data in object-relational databases for emerging fuzzy applications. As a sample of the proposal advantages, an application for dominant color based image retrieval, which is built on an object-relational database management system implementing the proposed fuzzy database model, is introduced.
Fuzzy Inclusion Dependencies in Fuzzy Databases
Authors: Sharma Awadhesh Kumar , Goswami A. , Gupta D. K.
Published: May 2011

In this chapter, the concept of fuzzy inclusion dependencies (FIDas) in fuzzy databases is introduced and inference rules on such FIDas are derived. These FIDas may arise while putting efforts to integrate fuzzy relational databases into a fuzzy relational multidatabase. Since the concept of FIDas itself is new, no work has been done as yet for its discovery in fuzzy relational databases. Hence, an algorithm has been proposed for the discovery of FIDas that may exist between two given fuzzy relations stored in one or more fuzzy relational databases. The existence of such an FIDa will indicate that the two relations are relevant for integration during the course of integration of fuzzy databases.
An Integrative Framework for the Study of Information Security Management Research
Authors: D’Arcy John , Hovav Anat
Published: May 2011

A number of academic studies that focus on various aspects of information security management (ISM) have emerged in recent years. This body of work ranges from the technical, economic, and behavioral aspects of ISM to the effect of industry standards, regulations, and best practices. The purpose of this chapter is to review the current state of ISM research, while providing an integrative framework for future studies. Using the proposed framework as a guide, we identify areas of depth within current ISM literature and areas where research is underdeveloped. Finally, we call for a more comprehensive approach to ISM research that considers multiple dimensions of our framework and their interrelationships.
Cryptography for Information Security
Authors: Al-Hamdani Wasim A.
Published: May 2011

This chapter introduces cryptography from information security phase rather than from deep mathematical and theoretical aspects, along with cryptography application in information security. The chapters introduce classical cryptography, block, stream cipher, and public key family, and with the most advance systems as elliptic curve, digital signature, cryptography key managements, and the last part of the chapter are the cryptography application in protocols, communications, e-mails, and the most advance research topics.
Government Web Sites as Public Forums
Authors: Liddell Jr. Pearson , Moore Robert S. , Moore Melissa , Eshee William D. , Liddell Gloria J.
Published: May 2011

Public managers who are the gatekeepers regarding the informational content of communication systems have to be especially cognizant of which resources to provide on public owned systems. At times, online solutions provided by the government may involve referring citizens to for-profit firms through hyperlinks on governmentowned Web sites (Sellitto & Burgess, 2005). The inclusion of one firm over another on government Government Web Sites as Public Forums Web sites has been noted to be a real concern for public managers (Menzel, 1998). In this article, we employ a legal perspective to examine the ramifications of public information strategies that allow firms to have hyperlinks embedded within the content of public information systems. This perspective allows the public information manager to make informed decisions when developing government portal strategies.
E-Census 2006 in New Zealand
Authors: Paynter John , Peko Gabrielle
Published: May 2011

A census is an official count. It can be contrasted with sampling in which information is only obtained from a subset of a population. As such, it is a ethod used for accumulating statistical data, and it is also vital to democracy (voting). Census data is also commonly used for research, business marketing, and planning purposes. In New Zealand a census is held every five years. It is a snapshot on the chosen day when the number of people and dwellings (houses, flats, apartments) counted. Everyone in the country on that day is asked to complete census forms. There are two census forms. The blue individual form must be completed by everyone in your household on census day. The brown dwelling form must be completed by one person in our household. For the 2006 census an option was introduced to complete the forms on the Internet. Other initiatives included sending text messages about this process, amongst other things to the enumerators (collectors) whose job it is to collate the information in the field.
Using Partial Least Squares in Digital Government Research
Authors: Gil-Garcia J. Ramon
Published: May 2011

Digital government is a complex socio-technical phenomenon, which is affected by technical, managerial, institutional, and environmental factors (Dawes & Pardo, 2002; Fountain, 2001; Gant, 2003; Garson, 2000; Heeks, 2005; Kraemer, King, Dunkle et al., 1989; Landsbergen & Wolken, 2001; Laudon, 1985; Rocheleau, 2003). Recent studies have greatly contributed to developing the necessary knowledge about e-government benefits and success factors (Barrett & Greene, 2000; Dawes, 1996; Gil-García & Pardo, 2005; Heeks, 2003; Holmes, 2001; O’Looney, 2002; Rocheleau, 1999; West, 2005; Zhang et al., 2002). However, an important portion of this research has used a single measure of e-government and relatively simple assumptions about the relationships between information technologies and organizational, institutional, and contextual factors (Gil-García, 2005b).
Digital Convergence and Cybersecurity Policy
Authors: Buenger Anthony W.
Published: May 2011

Digital convergence constitutes the full realization of the Information Age and provides the foundation to link cultural, personal, business, governmental, and economic affairs into a rapidly expanding global digital world called cyberspace. However, this linking of people around the globe is challenging the government to actively work with private industry to ensure its critical infrastructures and associated information is adequately protected. The purpose of this chapter is to explain how digital convergence is affecting the public sector and the need for a cybersecurity policy that includes the active involvement of both the public and private sectors. Digital convergence has made incredible inroads thanks to rapidly developing technologies such as the ubiquitous Internet, seemingly endless bandwidth (including wireless), and rapid advances in computer processing power that are all responsible for the processing, transporting, and storing of digital information throughout cyberspace. Moreover, these technologies have brought about the collision of three colossal industrial segments within the private sector—(a) computing, (b) consumer electronics, and (c) telecommunications providers—and are providing a multitude of compatible services via various digital devices (Figure 1). Without a doubt, the explosion of digital convergence has produced a flourishing multimedia, multidevice, and multitasking environment (Baker & Green, 2004). A significant impact of a converged society is the empowerment of individuals (consumers) and organizations to collaborate and compete on a global scale. Most importantly, however, these highly mobile and perpetually connected consumers are putting information at a greater risk as they have access to this information outside of its traditionally protected network boundaries in an environment where this information is increasingly vital to the nation’s critical infrastructure assets. The government must be able to effectively secure the information flowing throughout cyberspace.
Radio Frequency Identification (RFID) Technology
Authors: Wyld David C.
Published: May 2011

We are in the midst of what may become one of the true technological transformations of our time. RFID (radio frequency identification) is by no means a new technology. RFID is fundamentally based on the study of electromagnetic waves and radio, pioneered in the 19th century work of Faraday, Maxwell, and Marconi. The idea of using radio frequencies to reflect waves from objects dates back as far as 1886 to experiments conducted by Hertz. Radar was invented in 1922, and its practical applications date back to World War II, when the British used the IFF (Identify Friend or Foe) system to identify enemy aircraft (Landt, 2001). Stockman (1948) laid out the basic concepts for RFID. However, it would take decades of development before RFID technology would become a reality. Since 2000, significant improvements in functionality, decreases in both size and costs, and agreements on communication standards have combined to make RFID technology viable for commercial and governmental purposes. Today, RFID is positioned as an alternative way to identify objects with the ubiquitous bar code.
Government Innovation Through Knowledge Management
Authors: Luna-Reyes Luis Felipe
Published: May 2011

Contemporary organizations face the challenge of growing and advancing in a complex and changing environment (Johannessen, Olaisen, & Olsen, 2001; Malhotra, 2000). In order to accomplish this objective, private organizations continuously innovate to attract customers (Johannessen et al.). Competition has been accelerated by information technology, which allows the appearance of new business models, introducing new competitors in the business arena (Rayport, 2001). Under these circumstances, it appears that innovation is one of the most valuable activities for any organization (Nonaka, 1996). Furthermore, the management of intangible assets such as knowledge is one of the critical factors to promote innovation and sustainable competitive advantage (Davenport, 2001; De Long & Fahey, 2000; Malhotra; Nonaka).
Probability Association Approach in Automatic Image Annotation
Authors: Xu Feng , Zhang Yu-Jin
Published: May 2011

Content-based image retrieval (CBIR) has wide applications in public life. Either from a static image database or from the Web, one can search for a specific image, generally browse to make an interactive choice, and search for a picture to go with a broad story or to illustrate a document. Although CBIR has been well studied, it is still a challenging problem to search for images from a large image database because of the well-acknowledged semantic gap between low-level features and high-level semantic concepts. An alternative solution is to use keyword-based approaches, which usually associate images with keywords by either manually labeling or automatically extracting surrounding text from Web pages. Although such a solution is widely adopted by most existing commercial image search engines, it is not perfect. First, manual annotation, though precise, is expensive and difficult to extend to large-scale databases. Second, automatically extracted surrounding text might by incomplete and ambiguous in describing images, and even more, surrounding text may not be available in some applications. To overcome these problems, automated image annotation is considered as a promising approach in understanding and describing the content of images.
Online Analytical Processing and Data-Cube Technologies
Authors: Fu Lixin , Hu Wen-Chen
Published: May 2011

Since the late ’80s and early ’90s, database technologies have evolved to a new level of applications: online analytical processing (OLAP), where executive management can make quick and effective strategic decisions based on knowledge in terms of queries against large amounts of stored data. Some OLAP systems are also regarded as decision support systems (DSSs) or executive information systems (EIS). The traditional, well-established online transactional processing (OLTP) systems such as relational database management systems (RDBMS) mainly deal with mission-critical daily transactions. Typically, there are a large number of short, simple queries such as lookups, insertions, and deletions. The main focus is transaction throughput, consistency, concurrency, and failure recovery issues. OLAP systems, on the other hand, are mainly analytical and informational. OLAP systems are usually closely coupled with data warehouses, which can contain very large data sets that may include historical data as well as data integrated from different departments and geographical locations. So the sizes of data warehouses are usually significantly larger than common OLTP systems. In addition, the workloads of OLAP are quite different from those of traditional transaction systems: The queries are unpredictable and much more complicated. For example, an OLAP query could be, “For each type of car and each manufacturer, list market share change in terms of car sales between the first quarter of 2005 and the first quarter of 2006.” The purpose of these queries is not for the daily operational maintenance of data; instead, it is for deeper knowledge from data used for decision support.