bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Neural Networks and Cognitive Modelling
Authors: Sharkey Amanda J.C.
Published: May 2011

In their heyday, artificial neural networks promised a radically new approach to cognitive modelling. The connectionist approach spawned a number of influential, and controversial, cognitive models. In this article, we consider the main characteristics of the approach, look at the factors leading to its enthusiastic adoption, and discuss the extent to which it differs from earlier computational models. Connectionist cognitive models have made a significant impact on the study of mind. However connectionism is no longer in its prime. Possible reasons for the diminution in its popularity will be identified, together with an attempt to identify its likely future. The rise of connectionist models dates from the publication in 1986 by Rumelhart and McClelland, of an edited work containing a collection of connectionist models of cognition, each trained by exposure to samples of the required tasks. These volumes set the agenda for connectionist cognitive modellers and offered a methodology that subsequently became the standard. Connectionist cognitive models have since been produced in domains including memory retrieval and category formation, and (in language) phoneme recognition, word recognition, speech perception, acquired dyslexia, language acquisition, and (in vision) edge detection, object and shape recognition. More than twenty years later the impact of this work is still apparent.
Association Rule Mining
Authors: Bhatnagar Vasudha , Kochhar Sarabjeet
Published: May 2011

Data mining is a field encompassing study of the tools and techniques to assist humans in intelligently analyzing (mining) mountains of data. Data mining has found successful applications in many fields including sales and marketing, financial crime identification, portfolio management, medical diagnosis, manufacturing process management and health care improvement etc.. Data mining techniques can be classified as either descriptive or predictive techniques. Descriptive techniques summarize / characterize general properties of data, while predictive techniques construct a model from the historical data and use it to predict some characteristics of the future data. Association rule mining, sequence analysis and clustering are key descriptive data mining techniques, while classification and regression are predictive techniques. The objective of this article is to introduce the problem of association rule mining and describe some approaches to solve the problem.
AVI of Surface Flaws on Manufactures II
Authors: Fornarelli Girolamo , Giaquinto Antonio
Published: May 2011

Automatic visual inspection takes a relevant place in defect detection of industrial production. In this field a fundamental role is played by methods for the detection of superficial anomalies on manufactures. In particular, several systems have been proposed in order to reduce the burden of human operators, avoiding the drawbacks due to the subjectivity of judgement criteria (Kwak, Ventura & Tofang-Sazi 2000, Patil, Biradar & Jadhav 2005). Proposed solutions are required to be able to handle and process a large amount of data. For this reason, neural networks-based methods have been suggested for their ability to deal with a wide spread of data (Kumar 2003, Chang, Lin & Jeng 2005, Garcia 2005, Graham, Maas, Donaldson & Carr 2004, Acciani, Brunetti & Fornarelli 2006). Moreover, in many cases these methods must satisfy time constrains of industrial processes, because the inclusion of the diagnosis inside the production process is needed. To this purpose, architectures, based on Cellular Neural Networks (CNNs), revealed successful in the field of real time defect detection, due to the fact that these networks guarantee a hardware implementation and massive parallelism (Bertucco, Fargione, Nunnari & Risitano 2000), (Occhipinti, Spoto, Branciforte & Doddo 2001), (Perfetti & Terzoli 2000). On the basis of these considerations, a method to identify superficial damages and anomalies in manufactures has been given in (Fornarelli & Giaquinto 2007). This method is aimed at the implementation by means of an architecture entirely formed by Cellular Neural Networks, whose synthesis is illustrated in the present work. The suggested solution reveals effective for the detection of defects, as shown by two test cases carried out on an injection pump and a sample textile.
A Comparative Study on E-Note-Taking
Authors: Rashid Shaista , Rigas Dimitris
Published: May 2011

In all walks of life individuals are involved in a cumulative and incremental process of knowledge acquisition. This involves the accessing, processing and understanding of information which can be gained through many different forms. These include, deliberate means by picking up a book or passive by listening to someone. The content of knowledge is translated by individuals and often recorded by the skill of note-taking, which differs in method from one person to another. This article presents an investigation into the techniques to take notes including the most popular Cornell method. A comparative analysis with the Outlining and Mapping methods are carried out stating strengths and weaknesses of each in terms of simplicity, usefulness and effectiveness. The processes of developing such skills are not easy or straightforward and performance is much influenced by cognition. Therefore, such associations regarding cognitive conceptions involve the exploration into note-taking processes encoding and storage, attention and concentration, memory and other stimuli factors such as multimedia. The social changes within education from the traditional manner of study to electronic are being adapted by institutes. This change varies from computerising a sub-component of learning to simulating an entire lecture environment. This has enabled students to explore academia more conveniently however, is still arguable about its feasibility. The article discusses the underlying pedagogical principles, deriving instructions for the development of an e-learning environment. Furthermore, embarking on Tablet PC’s to replace the blackboard in combination with annotation applications is investigated. Semantic analysis into the paradigm shift in e-learning and knowledge management replacing classroom interaction presents its potential in the learning domain. The article concludes with ideas for the design and development of an electronic note-taking platform.
A Comparison of Cooling Schedules for Simulated Annealing
Authors: Fernando Díaz Martín José , Riaño Sierra Jesús M.
Published: May 2011

Simulated annealing is one of the most important metaheuristics or general-purpose algorithms of combinatorial optimization, whose properties of convergence towards high quality solutions are well known, although with a high computational cost. Due to that, it has been produced a quite number of research works on the convergence speed of the algorithm, especially on the treatment of the temperature parameter, which is known as cooling schedule or strategy. In this article we make a comparative study of the performance of simulated annealing using the most important cooling strategies (Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P., 1983), (Dowsland, K.A., 2001), (Luke, B.T., 1995), (Locatelli, M., 2000). Two classical problems of combinatorial optimization are used in the practical analysis of the algorithm: the travelling salesman problem and the quadratic assignment problem.
The Dempster-Shafer Theory
Authors: Beynon Malcolm J.
Published: May 2011

The initial work introducing Dempster-Shafer (D-S) theory is found in Dempster (1967) and Shafer (1976). Since its introduction the very name causes confusion, a more general term often used is belief functions (both used intermittently here). Nguyen (1978) points out, soon after its introduction, that the rudiments of D-S theory can be considered through distributions of random sets. More furtive comparison has been with the traditional Bayesian theory, where D-S theory has been considered a generalisation of it (Schubert, 1994). Cobb and Shenoy (2003) direct its attention to the comparison of D-S theory and the Bayesian formulisation. Their conclusions are that they have the same expressive power, but that one technique cannot simply take the role of the other. The association with artificial intelligence (AI) is clearly outlined in Smets (1990), who at the time, acknowledged the AI community has started to show interest for what they call the Dempster-Shafer model. It is of interest that even then, they highlight that there is confusion on what type of version of D-S theory is considered. D-S theory was employed in an event driven integration reasoning scheme in Xia et al. (1997), associated with automated route planning, which they view as a very important branch in applications of AI. Liu (1999) investigated Gaussian belief functions and specifically considered their proposed computation scheme and its potential usage in AI and statistics. Huang and Lees (2005) apply a D-S theory model in natural-resource classification, comparing with it with two other AI models. Wadsworth and Hall (2007) considered D-S theory in a combination with other techniques to investigate site-specific critical loads for conservation agencies. Pertinently, they outline its positioning with respect to AI (p. 400); The approach was developed in the AI (artificial intelligence) community in an attempt to develop systems that could reason in a more human manner and particularly the ability of human experts to “diagnose” situations with limited information. This statement is pertinent here, since emphasis within the examples later given is more towards the general human decision making problem and the handling of ignorance in AI. Dempster and Kong (1988) investigated how D-S theory fits in with being an artificial analogy for human reasoning under uncertainty. An example problem is considered, the murder of Mr. White, where witness evidence is used to classify the belief in the identification of an assassin from considered suspects. The numerical analyses presented exposit a role played by D-S theory, including the different ways it can act on incomplete knowledge.
EA Multi-Model Selection for SVM
Authors: Lebrun Gilles , Lezoray Olivier , Charrier Christopher , Cardot Hubert
Published: May 2011

Evolutionary algorithms (EA) (Rechenberg, 1965) belong to a family of stochastic search algorithms inspired by natural evolution. In the last years, EA were used successfully to produce efficient solutions for a great number of hard optimization problems (Beasley, 1997). These algorithms operate on a population of potential solutions and apply a survival principle according to a fitness measure associated to each solution to produce better approximations of the optimal solution. At each iteration, a new set of solutions is created by selecting individuals according to their level of fitness and by applying to them several operators. These operators model natural processes, such as selection, recombination, mutation, migration, locality and neighborhood. Although the basic idea of EA is straightforward, solutions coding, size of population, fitness function and operators must be defined in compliance with the kind of problem to optimize. Multi-class problems with binary SVM (Support Vector Machine) classifiers are commonly treated as a decomposition in several binary sub-problems. An open question is how to properly choose all models for these sub-problems in order to have the lowest error rate for a specific SVM multi-class scheme. In this paper, we propose a new approach to optimize the generalization capacity of such SVM multi-class schemes. This approach consists in a global selection of models for sub-problems altogether and is denoted as multi-model selection. A multi-model selection can outperform the classical individual model selection used until now in the literature, but this type of selection defines a hard optimisation problem, because it corresponds to a search a efficient solution into a huge space. Therefore, we propose an adapted EA to achieve that multi-model selection by defining specific fitness function and recombination operator.
Evolutionary Approaches for ANNs Design
Authors: Azzini Antonia , Tettamanzi Andrea G.B.
Published: May 2011

Artificial neural networks (ANNs) are computational models, loosely inspired by biological neural networks, consisting of interconnected groups of artificial neurons which process information using a connectionist approach. ANNs are widely applied to problems like pattern recognition, classification, and time series analysis. The success of an ANN application usually requires a high number of experiments. Moreover, several parameters of an ANN can affect the accuracy of solutions. A particular type of evolving system, namely neuro-genetic systems, have become a very important research topic in ANN design. They make up the so-called Evolutionary Artificial Neural Networks (EANNs), i.e., biologicallyinspired computational models that use evolutionary algorithms (EAs) in conjunction with ANNs. Evolutionary algorithms and state-of-the-art design of EANN were introduced first in the milestone survey by Xin Yao (1999), and, more recently, by Abraham (2004), by Cantu-Paz and Kamath (2005), and then by Castellani (2006). The aim of this article is to present the main evolutionary techniques used to optimize the ANN design, providing a description of the topics related to neural network design and corresponding issues, and then, some of the most recent developments of EANNs found in the literature. Finally a brief summary is given, with a few concluding remarks.
Evolving Graphs for ANN Development and Simplification
Authors: Rivero Daniel , Periscal David
Published: May 2011

One of the most successful tools in the Artificial Intelligence (AI) world is Artificial Neural Networks (ANNs). This technique is a powerful tool used in many different environments, with many different purposes, like classification, clustering, signal modelization, or regression (Haykin, 1999). Although they are very easy to use, their creation is not a simple task, because the expert has to do much effort and spend much time on it. The development of ANNs can be divided into two parts: architecture development and training and validation. The architecture development determines not only the number of neurons of the ANN, but also the type of the connections among those neurons. The training determines the connection weights for such architecture. The architecture design task is usually performed by means of a manual process, meaning that the expert has to test different architectures to find the one able to achieve the best results. Each architecture trial means training and validating it, which can be a process that needs many computational resources, depending on the complexity of the problem. Therefore, the expert has much participation in the whole ANN development, although techniques for relatively automatic creation of ANNs have been recently developed.
Facial Expression Recognition for HCI Applications
Authors: Dornaika Fadi , Raducanu Bogdan
Published: May 2011

Facial expression plays an important role in cognition of human emotions (Fasel, 2003 & Yeasin, 2006). The recognition of facial expressions in image sequences with significant head movement is a challenging problem. It is required by many applications such as human-computer interaction and computer graphics animation (Cañamero, 2005 & Picard, 2001). To classify expressions in still images many techniques have been proposed such as Neural Nets (Tian, 2001), Gabor wavelets (Bartlett, 2004), and active appearance models (Sung, 2006). Recently, more attention has been given to modeling facial deformation in dynamic scenarios. Still image classifiers use feature vectors related to a single frame to perform classification. Temporal classifiers try to capture the temporal pattern in the sequence of feature vectors related to each frame such as the Hidden Markov Model based methods (Cohen, 2003, Black, 1997 & Rabiner, 1989) and Dynamic Bayesian Networks (Zhang, 2005). The main contributions of the paper are as follows. First, we propose an efficient recognition scheme based on the detection of keyframes in videos where the recognition is performed using a temporal classifier. Second, we use the proposed method for extending the human-machine interaction functionality of a robot whose response is generated according to the user’s recognized facial expression. Our proposed approach has several advantages. First, unlike most expression recognition systems that require a frontal view of the face, our system is viewand texture-independent. Second, its learning phase is simple compared to other techniques (e.g., the Hidden Markov Models and Active Appearance Models), that is, we only need to fit second-order Auto-Regressive models to sequences of facial actions. As a result, even when the imaging conditions change the learned Auto-Regressive models need not to be recomputed. The rest of the paper is organized as follows. Section 2 summarizes our developed appearance-based 3D face tracker that we use to track the 3D head pose as well as the facial actions. Section 3 describes the proposed facial expression recognition based on the detection of keyframes. Section 4 provides some experimental results. Section 5 describes the proposed human-machine interaction application that is based on the developed facial expression recognition scheme.
Finding Multiple Solutions with GA in Multimodal Problems
Authors: Gestal Marcos , Paz Gómez-Carracedo Mari
Published: May 2011

Traditionally, the Evolutionary Computation (EC) techniques, and more specifically the Genetic Algorithms (GAs) (Goldberg & Wang, 1989), have proved to be efficient when solving various problems; however, as a possible lack, the GAs tend to provide a unique solution for the problem on which they are applied. Some non global solutions discarded during the search of the best one could be acceptable under certain circumstances. The majority of the problems at the real world involve a search space with one or more global solutions and multiple local solutions; this means that they are multimodal problems (Harik, 1995) and therefore, if it is desired to obtain multiple solutions by using GAs, it would be necessary to modify their classic functioning outline for adapting them correctly to the multimodality of such problems.
Genetic Algorithms for Wireless Sensor Networks
Authors: Kleinschmidt João H.
Published: May 2011

Wireless sensor networks (WSNs) consist of a large number of low-cost and low-power sensor nodes. Some of the applications of sensor networks are environmental observation, monitoring disaster areas and so on. Distributed evolutionary computing is a poweful tool that can be applied to WSNs, because these networks require algorithms that are capable of learning independent of the operation of other nodes and also capable of using local information (Johnson, Teredesai & Saltarelli, 2005). Evolutionary algorithms must be designed for the resource constraints present in WSNs. This article describes how genetic algorithms can be used in WSNs design in order to satisfy energy conservation and connectivity constraints.
Growing Self-Organizing Maps for Data Analysis
Authors: Delgado Soledad , Gonzalo Consuelo , Martínez Estíbaliz , Arquero Águeda
Published: May 2011

Currently, there exist many research areas that produce large multivariable datasets that are difficult to visualize in order to extract useful information. Kohonen selforganizing maps have been used successfully in the visualization and analysis of multidimensional data. In this work, a projection technique that compresses multidimensional datasets into two dimensional space using growing self-organizing maps is described. With this embedding scheme, traditional Kohonen visualization methods have been implemented using growing cell structures networks. New graphical map displays have been compared with Kohonen graphs using two groups of simulated data and one group of real multidimensional data selected from a satellite scene.
Harmony Search for Multiple Dam Scheduling
Authors: Woo Geem Zong
Published: May 2011

The dam is the wall that holds the water in, and the operation of multiple dams is complicated decisionmaking process as an optimization problem (Oliveira & Loucks, 1997). Traditionally researchers have used mathematical optimization techniques with linear programming (LP) or dynamic programming (DP) formulation to find the schedule. However, most of the mathematical models are valid only for simplified dam systems. Accordingly, during the past decade, some meta-heuristic techniques, such as genetic algorithm (GA) and simulated annealing (SA), have gathered great attention among dam researchers (Chen, 2003) (Esat & Hall, 1994) (Wardlaw & Sharif, 1999) (Kim, Heo & Jeong, 2006) (Teegavarapu & Simonovic, 2002). Lately, another metaheuristic algorithm, harmony search (HS), has been developed (Geem, Kim & Loganathan, 2001) (Geem, 2006a) and applied to various artificial intelligent problems, such as music composition (Geem & Choi, 2007) and Sudoku puzzle (Geem, 2007). The HS algorithm has been also applied to various engineering problems such as structural design (Lee & Geem, 2004), water network design (Geem, 2006b), soil stability analysis (Li, Chi & Chu, 2006), satellite heat pipe design (Geem & Hwangbo, 2006), offshore structure design (Ryu, Duggal, Heyl & Geem, 2007), grillage system design (Erdal & Saka, 2006), and hydrologic parameter estimation (Kim, Geem & Kim, 2001). The HS algorithm could be a competent alternative to existing metaheuristics such as GA because the former overcame the drawback (such as building block theory) of the latter (Geem, 2006a). To test the ability of the HS algorithm in multiple dam operation problem, this article introduces a HS model, and applies it to a benchmark system, then compares the results with those of the GA model previously developed.
Improving the Naïve Bayes Classifier
Authors: Fan Liwei , Leng Poh Kim
Published: May 2011

A Bayesian Network (BN) takes a relationship between graphs and probability distributions. In the past, BN was mainly used for knowledge representation and reasoning. Recent years have seen numerous successful applications of BN in classification, among which the Naïve Bayes classifier was found to be surprisingly effective in spite of its simple mechanism (Langley, Iba & Thompson, 1992). It is built upon the strong assumption that different attributes are independent with each other. Despite of its many advantages, a major limitation of using the Naïve Bayes classifier is that the real-world data may not always satisfy the independence assumption among attributes. This strong assumption could make the prediction accuracy of the Naïve Bayes classifier highly sensitive to the correlated attributes. To overcome the limitation, many approaches have been developed to improve the performance of the Naïve Bayes classifier. This article gives a brief introduction to the approaches which attempt to relax the independence assumption among attributes or use certain pre-processing procedures to make the attributes as independent with each other as possible. Previous theoretical and empirical results have shown that the performance of the Naïve Bayes classifier can be improved significantly by using these approaches, while the computational complexity will also increase to a certain extent.
Knowledge Management Tools and Their Desirable Characteristics
Authors: Ares Juan , García Rafael , Seoane María , Suárez Sonia
Published: May 2011

The Knowledge Management (KM) is a recent discipline that was born under the idea of explicitly managing the whole existing knowledge of a given organisation (Wiig, 1995) (Wiig et al., 1997). More specifically, the KM involves providing the people concerned with the right information and knowledge at the most suitable level for them, when and how best suit them; in such way, these people will have all the necessary ingredients for choosing the best option when faced with a specific problem (Rodríguez, 2002). As the knowledge, together with the ability for its best management, has turned into the key factor for the organizations to stand out, it is desirable to determine and develop the support instruments for the generation of such value within the organisations. This situation has been commonly accepted by several authors as (Brooking, 1996) (Davenport & Prusak, 2000) (Huang et al., 1999) (Liebowitz & Beckman, 1998) (Nonaka & Takeuchi, 1995) and (Wiig, 1993) among others. Technological tools should be available for diminishing the communication distance and for providing a common environment where the knowledge might accessible for being stored or shared. As KM is a very recent discipline, there are few commercial software tools that deal with those aspects necessary for its approach. Most of the tools classified as KM-related are mere tools for managing documents, which is unsuitable for the correct management of the organisations knowledge. Bearing such problem in mind, the present work approaches the establishment of a KM support software tool based on the own definition of KM and on the existing tools. For achieving this, section 2 presents the market analysis that was performed for studying the existing KM tools, where not only their characteristics were analysed, but also the future needs of the knowledge workers. Following this study, the functionality that a KM support tool should have and the proposal for the best approach to that functionality were identified.
Knowledge-Based Systems
Authors: Hopgood Adrian A.
Published: May 2011

The tools of artificial intelligence (AI) can be divided into two broad types: knowledge-based systems (KBSs) and computational intelligence (CI). KBSs use explicit representations of knowledge in the form of words and symbols. This explicit representation makes the knowledge more easily read and understood by a human than the numerically derived implicit models in computational intelligence. KBSs include techniques such as rule-based, modelbased, and case-based reasoning. They were among the first forms of investigation into AI and remain a major theme. Early research focused on specialist applications in areas such as chemistry, medicine, and computer hardware. These early successes generated great optimism in AI, but more broad-based representations of human intelligence have remained difficult to achieve (Hopgood, 2003; Hopgood, 2005).
Modal Logics for Reasoning about Multiagent Systems
Authors: Shilov Nikolay V. , Garanina Natalia
Published: May 2011

It becomes evident in recent years a surge of interest to applications of modal logics for specification and validation of complex systems. It holds in particular for combined logics of knowledge, time and actions for reasoning about multiagent systems (Dixon, Nalon & Fisher, 2004; Fagin, Halpern, Moses & Vardi, 1995; Halpern & Vardi, 1986; Halpern, van der Meyden & Vardi, 2004; van der Hoek & Wooldridge, 2002; Lomuscio, & Penczek, W., 2003; van der Meyden & Shilov, 1999; Shilov, Garanina & Choe, 2006; Wooldridge, 2002). In the next paragraph we explain what are logics of knowledge, time and actions from a viewpoint of mathematicians and philosophers. It provides us a historic perspective and a scientific context for these logics. For mathematicians and philosophers logics of actions, time, and knowledge can be introduced in few sentences. A logic of actions (ex., Elementary Propositional Dynamic Logic (Harel, Kozen & Tiuryn, 2000)) is a polymodal variant of a basic modal logic K (Bull & Segerberg, 2001) to be interpreted over arbitrary Kripke models. A logic of time (ex., Linear Temporal Logic (Emerson, 1990)) is a modal logic with a number of modalities that correspond to “next time”, “always”, “sometimes”, and “until” to be interpreted in Kripke models over partial orders (discrete linear orders for LTL in particular). Finally, a logic of knowledge or epistemic logic (ex., Propositional Logic of Knowledge (Fagin, Halpern, Moses & Vardi, 1995; Rescher, 2005)) is a polymodal variant of another basic modal logic S5 (Bull & Segerberg, 2001) to be interpreted over Kripke models where all binary relations are equivalences.
Multi-Objective Evolutionary Algorithms
Authors: Das Sanjoy , Panigrahi Bijaya K.
Published: May 2011

Real world optimization problems are often too complex to be solved through analytical means. Evolutionary algorithms, a class of algorithms that borrow paradigms from nature, are particularly well suited to address such problems. These algorithms are stochastic methods of optimization that have become immensely popular recently, because they are derivative-free methods, are not as prone to getting trapped in local minima (as they are population based), and are shown to work well for many complex optimization problems. Although evolutionary algorithms have conventionally focussed on optimizing single objective functions, most practical problems in engineering are inherently multi-objective in nature. Multi-objective evolutionary optimization is a relatively new, and rapidly expanding area of research in evolutionary computation that looks at ways to address these problems. In this chapter, we provide an overview of some of the most significant issues in multi-objective optimization (Deb, 2001).
A New Self-Organizing Map for Dissimilarity Data
Authors: Ho-Phuoc Tien , Guerin-Dugue Anne
Published: May 2011

The Self-Organizing Map (Kohonen, 1997) is an effective and a very popular tool for data clustering and visualization. With this method, the input samples are projected into a low dimension space while preserving their topology. The samples are described by a set of features. The input space is generally a high dimensional space Rd. 2D or 3D maps are very often used for visualization in a low dimension space (2 or 3). For many applications, usually in psychology, biology, genetic, image and signal processing, such vector description is not available; only pair-wise dissimilarity data is provided. For instance, applications in Text Mining or ADN exploration are very important in this field and the observations are usually described through their proximities expressed by the “Levenshtein”, or “String Edit” distances (Levenshtein, 1966). The first approach consists of the transformation of a dissimilarity matrix into a true Euclidean distance matrix. A straightforward strategy is to use “Multidimensional Scaling” techniques (Borg & Groenen, 1997) to provide a feature space. So, the initial vector SOM algorithm can be naturally used. If this transformation involves great distortions, the initial vector model for SOM is no longer valid, and the analysis of dissimilarity data requires specific techniques (Jain & Dubes, 1988; Van Cutsem, 1994) and Dissimilarity Self Organizing Map (DSOM) is a new one. Consequently, adaptation of the Self-Organizing Map (SOM) to dissimilarity data is of a growing interest. During this last decade, different propositions emerged to extend the vector SOM model to pair-wise dissimilarity data. The main motivation is to cope with large proximity databases for data mining. In this article, we present a new adaptation of the SOM algorithm which is compared with two existing ones.
Inside the Microcosm
Authors: Vialle Pierre , Epinette Olivier , Segard Olivier
Published: May 2011

The objective of this chapter is to highlight critical elements affecting the diffusion of broadband wireless Internet at a hotspot location, through a case study. The research deals with a wireless Internet services project in the main Paris airports, and comprises two components. First, we analyze the emergence of a value chain for a new service, based on the cooperation of several firms. In particular, we show how different actors can or cannot position themselves on this value chain, according to their resources and capabilities. Second, we explore the perceptions and attitudes of business passengers in order to better understand the potential adoption and use of hotspot services, and provide a preliminary framework of analysis. The research is drawn from a qualitative survey, made up of in-depth interviews of potential suppliers (airline companies, service providers, airport managers) and business passengers.
The Adoption of Broadband Internet in Australia and Canada
Authors: Middleton Catherine , Chang Shanton
Published: May 2011

Broadband Internet connectivity is seen as a means to increase the efficiency and competitiveness of an economy. But despite ongoing efforts to promote broadband in Australia, uptake has been much slower than expected. This chapter aims to identify areas that have been holding up the broadband development in Australia. In examining multiple areas for attention (competition, user characteristics and behaviors, applications, network characteristics, and pricing), we refer to the experience of Canada, a leader in broadband deployment, to show the differences in each area. The chapter outlines objectives for the development of a more user-friendly broadband environment in Australia, which would encourage broadband adoption. Although both countries discussed here have their own policy agendas and some unique circumstances related to broadband deployment, the chapter provide valuable insights for policy makers and industry leaders in Australia, and in other countries which are struggling to develop widespread broadband deployment.
Introduction and Trends to Fuzzy Logic and Fuzzy Databases
Authors: Galindo José
Published: May 2011

This chapter presents an introduction to fuzzy logic and to fuzzy databases. With regard to the first topic, we have introduced the main concepts in this field to facilitate the understanding of the rest of the chapters to novel readers in fuzzy subjects. With respect to the fuzzy databases, this chapter gives a list of six research topics in this fuzzy area. All these topics are briefly commented on, and we include references to books, papers, and even to other chapters of this handbook, where we can find some interesting reviews about different subjects and new approaches with different goals. Finally, we give a historic summary of some fuzzy models, and we conclude with some future trends in this scientific area.
Flexible Querying Techniques Based on CBR
Authors: De Tré Guy , Demoor Marysa , Callens Bert , Gosseye Lise
Published: May 2011

In case-based reasoning (CBR), a new untreated case is compared to cases that have been treated earlier, after which data from the similar cases (if found) are used to predict the corresponding unknown data values for the new case. Because case comparisons will seldom result in an exact-similarity matching of cases and the conventional CBR approaches do not efficiently deal with such imperfections, more advanced approaches that adequately cope with these imperfections can help to enhance CBR. Moreover, CBR in its turn can be used to enhance flexible querying. In this chapter, we describe how fuzzy set theory can be used to model a gradation in similarity of the cases and how the inevitable uncertainty that occurs when predictions are made can be handled using possibility theory resulting in what we call flexible CBR. Furthermore, we present how and under which conditions flexible CBR can be used to enhance flexible querying of regular databases.
Customizable Flexible Querying in Classical Relational Databases
Authors: Bordogna Bordogna , Psaila Guiseppe
Published: May 2011

In this chapter, we present the Soft-SQL project whose goal is to define a rich extension of SQL aimed at effectively exploiting flexibility offered by fuzzy sets theory to solve practical issues when querying classic relational databases. The Soft-SQL language is based on previous approaches that introduced soft conditions on tuples in the classical relational database model. We retain the main features of these approaches and focus on the need to provide tools allowing users to directly specify the context dependent semantics of soft conditions. To this end, Soft-SQL provides a command (named CREATE TERM-SET) to define the semantics of linguistic values with respect to a context represented by a linguistic variable (Zadeh, 1975); the SELECT command is extended in order to support soft predicates based on the user defined term sets, the semantics of grouping and aggregation can be modified, and finally, the clauses in the SELECT command can be combined effectively.
Fabric Database and Fuzzy Logic Models for Evaluating Fabric Performance
Authors: Chen Yan , Rong Graham H. , Chen Jianhua
Published: May 2011

A Web-based fabric database is introduced in terms of its physical structure, software system architecture, basic and intelligent search engines, and various display methods for search results. A fuzzy linear clustering method is used to predict fabric drape coefficient from fabric mechanical and structural properties. Experimental data indicate that fuzzy linear clustering is quite effective for this purpose. A hybrid method combining fuzzy linear clustering with K-nearest neighbor is also applied for the prediction of the fabric drape coefficient with improved prediction accuracy. The study also reveals that the fuzzy linear clustering method can also be used for predicting fabric tailorability with good prediction accuracy. Mathematical principles of fuzzy comprehensive evaluation are summarized and a typical application for assessing fabric comfort is exhibited. Through the fuzzy calculation, a single numerical value is produced to express female preferences for six fabric types for use in blouses, slacks, and underpants with respect to fabric property changes in an incremental-wear trial. Finally, a neuro-fuzzy computing technique for evaluating nonwoven fabric softness is presented. The combinational use of the fuzzy logic models (CANFIS) and the neural network method makes a significant step toward launching a fabric database application for neural network computing as a routine laboratory evaluation.
Fuzzy Decision-Tree-Based Analysis of Databases
Authors: Beynon Malcolm
Published: May 2011

The general fuzzy decision tree approach encapsulates the benefits of being an inductive learning technique to classify objects, utilising the richness of the data being considered, as well as the readability and interpretability that accompanies its operation in a fuzzy environment. This chapter offers a description of fuzzy decision tree based research, including the exposition of small and large fuzzy decision trees to demonstrate their construction and practicality. The two large fuzzy decision trees described are associated with a real application, namely, the identification of workplace establishments in the United Kingdom that pay a noticeable proportion of their employees less than the legislated minimum wage. Two separate fuzzy decision tree analyses are undertaken on a low-pay database, which utilise different numbers of membership functions to fuzzify the continuous attributes describing the investigated establishments. The findings demonstrate the sensitivity of results when there are changes in the compactness of the fuzzy representation of the associated data.
Distributed Denial of Service Attacks in Networks
Authors: Kiran Tupakula Udaya
Published: May 2011

In this chapter we discuss Distributed Denial of Service (DDoS) attacks in networks such as the Internet, which have become significantly prevalent over the recent years. We explain how DDoS attacks are performed and consider the ideal solution characteristics for defending against the DDoS attacks in the Internet. Then we present different research directions and thoroughly analyse some of the important techniques that have been recently proposed. Our analysis confirms that none of the proposed techniques can efficiently and completely counteract the DDoS attacks. Furthermore, as networks become more complex, they become even more vulnerable to DoS attacks when some of the proposed techniques are deployed in the Internet. The gap between the tools that can generate DDoS attacks and the tools that can detect or prevent DDoS attacks continues to increase. Finally, we briefly outline some best practices that the users are urged to follow to minimise the DoS attacks in the Internet.
Edge-to-Edge Network Monitoring to Detect Service Violations and DoS Attacks
Authors: Habib Ahsan
Published: May 2011

This chapter develops a distributed monitoring scheme that uses edge-to-edge measurements to identify congested links and capture the misbehaving flows that violate service-level-agreements and inject excessive traffic that leads into denial of service (DoS) attacks. The challenge of this problem is to develop low overhead schemes that do not involve core routers in any measurement to achieve scalability. The main contribution of this work is overlay-based network monitoring schemes for efficient and scalable network monitoring. This monitoring scheme uses edge-to-edge measurements of delay, loss, and throughput to infer the internal characteristics of a network domain. The analytical and experimental results show that a network domain can be monitored with O(n) probes, where n is the number of edge routers. Upon detection of an attack, the routers regulate misbehaving flows to stop it. We introduce a new way to measure communication and computation overhead among monitoring schemes. This comparative study shows that core-assisted network monitoring has higher communication and computation overhead comparing to edge-to-edge network monitoring scheme.
Human Factors in Information Security and Privacy
Authors: Proctor Robert W. , Schultz E. Eugene , Vu Kim-Phuong L.
Published: May 2011

Many measures that enhance information security and privacy exist. Because these measures involve humans in various ways, their effectiveness depends on the human factor. This chapter reviews basic components of information security and privacy with an emphasis on human factors issues. It provides an overview of empirical investigations that have been conducted regarding the usability of security and privacy measures. These studies show that users have difficulty interacting with complex interfaces and that users’ performance can be improved by incorporating human factors principles into the designs. The authors conclude by discussing how human factors analyses can lead to the design of usable systems for information security and privacy assurance.
Key Issues in E-Government and Public Administration
Authors: Joseph Rhoda C. , Kitlan David P.
Published: May 2011

Information and communication technologies (ICTs) are key elements supporting the growth of e-government initiatives. Public administration refers to the products and procedures that the government implements to interact with its constituents: citizens, businesses, employees, and other governments. To address the needs of these different constituents, a wide variety of government services are necessary. This chapter examines the impact of e-government on public administration from both the constituent and service perspectives. The chapter presents a holistic view of both challenges and advantages of implementing e-government in the area of public administration.
Limitations of Evolutionary Approaches to E-Government
Authors: Sandoval-Almazán Rodrigo , Gil-Garcia J. Ramon
Published: May 2011

Information and communication technologies (ICTs) have the potential to improve the quality of the overall citizen experience when interacting with government, including information and services (Bourquard, 2003; Dawes, Pardo, & DiCaterino, 1999; Garson, 2004; Gartner, 2000; Grönlund, 2001). State and local governments are increasingly using ICTs in their operational tasks as well as their provision of public services (Holden, Norris & Fletcher, 2003; Moon, 2002; West, 2005). Many of these governments have created Websites and portals, which provide information about the government agencies and, in some cases, electronic transactions such as tax payment systems, online communities, job search, licensing, and vehicle registration, among others. Through the incorporation of these new features and applications, technological and organizational sophistication have been systematically added to e-government initiatives throughout the last few years (Holden, Norris & Fletcher, 2003; Moon, 2002; West, 2005). However, there are few systematic approaches to evaluate the quality and impact of these initiatives.
Public Sector Participation in Open Communities
Authors: Baker Andrea B. , Gil-Garcia J. Ramon , Canestraro Donna , Costello Jim , Werthmuller Derek
Published: May 2011

As new Internet-based products, services, and resources are developed, private companies and government agencies are exploring the use of these open standards and open source software for their daily operations. One of the main advantages of the open paradigm is interoperability and re-usability of code. Another significant advantage is data longevity, which means that the data created by these products are not constrained by future technology or vendor changes. They will be compatible with new document formats, applications, or specific pieces of software. However, there are also challenges associated with open standards and open software, particularly for public sector organizations. Issues such as technical training and support services can be a major concern for government agencies. Another issue that must be explored is associated with participating in online development communities and how this is constrained by the current legal framework and personnel practices.
Institutional Theory and E-Government Research
Authors: Hassan Shahidul , Gil-Garcia J. Ramon
Published: May 2011

Recent developments in institutional theory are highly promising for the study of e-government. Scholars in various disciplines, such as economics (North, 1999; Rutherford, 1999), sociology (Brinton & Nee, 1998), and political science (March & Olsen, 1989; Peters, 2001), have used institutional approaches to understand diverse social and organizational phenomena. Insights gained from these studies can be valuable for guiding research in e-government. In fact, there are some initial efforts in information systems and e-government research that have applied institutional theory and proved useful in generating new insights about how information technologies are adopted (Teo, Wei, & Benbasat, 2003; Tingling & Parent, 2002), designed and developed (Butler, 2003; Klein, 2000; Laudon, 1985), implemented (Robey & Holmstrom, 2001), and used (Fountain, 2001) in organizations. In this chapter, we provide a brief overview of some of these initial studies to highlight the usefulness of institutional theory in e-government research. We also suggest some opportunities for future research in e-government using institutional theory. This chapter does not capture all the essential theoretical and empirical issues related to using institutional theory in information systems and e-government research. Instead, it is a brief review and a good starting point to explore the potential of institutional theory. We hope that e-government scholars find it interesting and useful. The chapter is organized in five sections, including this introduction. The second section provides a brief overview of institutional theory in various disciplinary traditions, with an emphasis on institutional theory in sociology. Then the chapter identifies various patterns of the use of institutional theory in information systems and e-government research. Based on our analysis of the current state of the art, the fourth section suggests some opportunities for future research. Finally, the fifth section provides some final comments.
Roaming-Agent Protection for E-Commerce
Authors: Guan Sheng-Uei
Published: May 2011

There has been a lot of research done in the area of intelligent agents. Some of the literature (Guilfoyle, 1994; Johansen, Marzullo, & Lauvset, 1999) only proposes certain features of intelligent agents, while some of it attempts to define a complete agent architecture. Unfortunately, there is no standardization in the various proposals, resulting in vastly different agent systems. Efforts are being made to standardize some aspects of agent systems so that different systems can interoperate with each other.