bluuebunny/crossref_metadata_2025 huggingface dataset:
Feed-Forward Artificial Neural Network Basics
Authors: Belanche Muñoz Lluís A.
Published: May 2011

The class of adaptive systems known as Artificial Neural Networks (ANN) was motivated by the amazing parallel processing capabilities of biological brains (especially the human brain). The main driving force was to re-create these abilities by constructing artificial models of the biological neuron. The power of biological neural structures stems from the enormous number of highly interconnected simple units. The simplicity comes from the fact that, once the complex electro-chemical processes are abstracted, the resulting computation turns out to be conceptually very simple. These artificial neurons have nowadays little in common with their biological counterpart in the ANN paradigm. Rather, they are primarily used as computational devices, clearly intended to problem solving: optimization, function approximation, classification, time-series prediction and others. In practice few elements are connected and their connectivity is low. This chapter is focused to supervised feed-forward networks. The field has become so vast that a complete and clearcut description of all the approaches is an enormous undertaking; we refer the reader to (Fiesler & Beale, 1997) for a comprehensive exposition.
Full-Text Search Engines for Databases
Authors: Kovács László , Tikk Domonkos
Published: May 2011

Current databases are able to store several Tbytes of free-text documents. The main purpose of a database from the user’s viewpoint is the efficient information retrieval. In the case of textual data, information retrieval mostly concerns the selection and the ranking of documents. The selection criteria can contain elements that apply to the content or the grammar of the language. In the traditional database management systems (DBMS), text manipulation is restricted to the usual string manipulation facilities, i.e. the exact matching of substrings. Although the new SQL1999 standard enables the usage of more powerful regular expressions, this traditional approach has some major drawbacks. The traditional string-level operations are very costly for large documents as they work without task-oriented index structures. The required full-text management operations belong to text mining, an interdisciplinary field of natural language processing and data mining. As the traditional DBMS engine is inefficient for these operations, database management systems are usually extended with a special full-text search (FTS) engine module. We present here the particular solution of Oracle; there for making the full-text querying more efficient, a special engine was developed that performs the preparation of full-text queries and provides a set of language and semantic specific query operators.
Functional Dimension Reduction for Chemometrics
Authors: Kärnä Tuomas , Lendasse Amaury
Published: May 2011

High dimensional data are becoming more and more common in data analysis. This is especially true in fields that are related to spectrometric data, such as chemometrics. Due to development of more accurate spectrometers one can obtain spectra of thousands of data points. Such a high dimensional data are problematic in machine learning due to increased computational time and the curse of dimensionality (Haykin, 1999; Verleysen & François, 2005; Bengio, Delalleau, & Le Roux, 2006). It is therefore advisable to reduce the dimensionality of the data. In the case of chemometrics, the spectra are usually rather smooth and low on noise, so function fitting is a convenient tool for dimensionality reduction. The fitting is obtained by fixing a set of basis functions and computing the fitting weights according to the least squares error criterion. This article describes a unsupervised method for finding a good function basis that is specifically built to suit the data set at hand. The basis consists of a set of Gaussian functions that are optimized for an accurate fitting. The obtained weights are further scaled using a Delta Test (DT) to improve the prediction performance. Least Squares Support Vector Machine (LS-SVM) model is used for estimation.
Functional Networks
Authors: Fontenla-Romero Oscar , Guijarro-Berdiñas Bertha , Pérez-Sánchez Beatriz
Published: May 2011

Functional networks are a generalization of neural networks, which is achieved by using multiargument and learnable functions, i.e., in these networks the transfer functions associated with neurons are not fixed but learned from data. In addition, there is no need to include parameters to weigh links among neurons since their effect is subsumed by the neural functions. Another distinctive characteristic of these models is that the specification of the initial topology for a functional network could be based on the features of the problem we are facing. Therefore knowledge about the problem can guide the development of a network structure, although on the absence of this knowledge always a general model can be used. In this article we present a review of the field of functional networks, which will be illustrated with practical examples.
Fuzzy Graphs and Fuzzy Hypergraphs
Authors: Bershtein Leonid S. , Bozhenyuk Alexander V.
Published: May 2011

Graph theory has numerous application to problems in systems analysis, operations research, economics, and transportation. However, in many cases, some aspects of a graph-theoretic problem may be uncertain. For example, the vehicle travel time or vehicle capacity on a road network may not be known exactly. In such cases, it is natural to deal with the uncertainty using the methods of fuzzy sets and fuzzy logic. Hypergraphs (Berge,1989) are the generalization of graphs in case of set of multiarity relations. It means the expansion of graph models for the modeling complex systems. In case of modelling systems with fuzzy binary and multiarity relations between objects, transition to fuzzy hypergraphs, which combine advantages both fuzzy and graph models, is more natural. It allows to realise formal optimisation and logical procedures. However, using of the fuzzy graphs and hypergraphs as the models of various systems (social, economic systems, communication networks and others) leads to difficulties. The graph isomorphic transformations are reduced to redefinition of vertices and edges. This redefinition doesn’t change properties the graph determined by an adjacent and an incidence of its vertices and edges. Fuzzy independent set, domination fuzzy set, fuzzy chromatic set are invariants concerning the isomorphism transformations of the fuzzy graphs and fuzzy hypergraph and allow make theirs structural analysis.
Fuzzy Systems Modeling
Authors: Hoon Joo Young , Chen Guanrong
Published: May 2011

The basic objective of system modeling is to establish an input-output representative mapping that can satisfactorily describe the system behaviors, by using the available input-output data based upon physical or empirical knowledge about the structure of the unknown system.
Genetic Fuzzy Systems Applied to Ports and Coasts Engineering
Authors: Ibáñez Óscar , Castro Alberte
Published: May 2011

Fuzzy Logic (FL) and fuzzy sets in a wide interpretation of FL (in terms in which fuzzy logic is coextensive with the theory of fuzzy sets, that is, classes of objects in which the transition from membership to non membership is gradual rather than abrupt) have placed modelling into a new and broader perspective by providing innovative tools to cope with complex and ill-defined systems. The area of fuzzy sets has emerged following some pioneering works of Zadeh (Zadeh, 1965 and 1973) where the first fundamentals of fuzzy systems were established. Rule based systems have been successfully used to model human problem-solving activity and adaptive behaviour. The conventional approaches to knowledge representation are based on bivalent logic. A serious shortcoming of such approaches is their inability to come to grips with the issue of uncertainty and imprecision. As a consequence, the conventional approaches do not provide an adequate model for modes of reasoning. Unfortunately, all commonsense reasoning falls into this category. The application of FL to rule based systems leads us to fuzzy systems. The main role of fuzzy sets is representing Knowledge about the problem or to model the interactions and relationships among the system variables. There are two essential advantages for the design of rule-based systems with fuzzy sets and logic: • The key features of knowledge captured by fuzzy sets involve handling uncertainty. • Inference methods become more robust and flexible with approximate reasoning methods of fuzzy logic. Genetic Algorithms (GAS) are a stochastic optimization technique that mimics natural selection (Holland, 1975). GAs are intrinsically robust and capable of determining a near global optimal solution. The use of GAS is usually recommended for optimization in high-dimensional, multimodal complex search spaces where deterministic methods normally fail. GAs explore a population of solutions in parallel. The GA is a searching process based on the laws of natural selections and genetics. Generally, a simple GA contains three basic operations: selection, genetic operations and replacement. A typical GA cycle is shown in Fig. 1. In this paper it is shown how a genetic algorithm can be used in order to optimize a fuzzy system which is used in wave reflection analysis at submerged breakwaters.
Hybrid Meta-Heuristics Based System for Dynamic Scheduling
Authors: Madureira Ana Maria
Published: May 2011

The complexity of current computer systems has led the software engineering, distributed systems and management communities to look for inspiration in diverse fields, e.g. robotics, artificial intelligence or biology, to find new ways of designing and managing systems. Hybridization and combination of different approaches seems to be a promising research field of computational intelligence focusing on the development of the next generation of intelligent systems. A manufacturing system has a natural dynamic nature observed through several kinds of random occurrences and perturbations on working conditions and requirements over time. For this kind of environment it is important the ability to efficient and effectively adapt, on a continuous basis, existing schedules according to the referred disturbances, keeping performance levels. The application of Meta-Heuristics to the resolution of this class of dynamic scheduling problems seems really promising. In this article, we propose a hybrid Meta-Heuristic based approach for complex scheduling with several manufacturing and assembly operations, in dynamic Extended Job-Shop environments. Some self-adaptation mechanisms are proposed.
A Hybrid System for Automatic Infant Cry Recognition I
Authors: Reyes-García Carlos Alberto , Zatarain Ramon , Barron Lucia , Reyes-Galaviz Orion Fausto
Published: May 2011

Crying in babies is a primary communication function, governed directly by the brain; any alteration on the normal functioning of the babies’ body is reflected in the cry (Wasz-Höckert, et al, 1968). Based on the information contained in the cry’s wave, the infant’s physical state can be determined; and even pathologies in very early stages of life detected (Wasz-Höckert, et al, 1970). To perform this detection, a Fuzzy Relational Neural Network (FRNN) is applied. The input features are represented by fuzzy membership functions and the links between nodes, instead of weights, are represented by fuzzy relations (Reyes, 1994). This paper, as the first of a two parts document, describes the Infant Cry Recognition System´s architecture as well as the FRNN model. Implementation and testing are reported in the complementary paper.
Information Theoretic Learning
Authors: Erdogmus Deniz , Principe Jose C.
Published: May 2011

Learning systems depend on three interrelated components: topologies, cost/performance functions, and learning algorithms. Topologies provide the constraints for the mapping, and the learning algorithms offer the means to find an optimal solution; but the solution is optimal with respect to what? Optimality is characterized by the criterion and in neural network literature, this is the least addressed component, yet it has a decisive influence in generalization performance. Certainly, the assumptions behind the selection of a criterion should be better understood and investigated. Traditionally, least squares has been the benchmark criterion for regression problems; considering classification as a regression problem towards estimating class posterior probabilities, least squares has been employed to train neural network and other classifier topologies to approximate correct labels. The main motivation to utilize least squares in regression simply comes from the intellectual comfort this criterion provides due to its success in traditional linear least squares regression applications – which can be reduced to solving a system of linear equations. For nonlinear regression, the assumption of Gaussianity for the measurement error combined with the maximum likelihood principle could be emphasized to promote this criterion. In nonparametric regression, least squares principle leads to the conditional expectation solution, which is intuitively appealing. Although these are good reasons to use the mean squared error as the cost, it is inherently linked to the assumptions and habits stated above. Consequently, there is information in the error signal that is not captured during the training of nonlinear adaptive systems under non-Gaussian distribution conditions when one insists on second-order statistical criteria. This argument extends to other linear-second-order techniques such as principal component analysis (PCA), linear discriminant analysis (LDA), and canonical correlation analysis (CCA). Recent work tries to generalize these techniques to nonlinear scenarios by utilizing kernel techniques or other heuristics. This begs the question: what other alternative cost functions could be used to train adaptive systems and how could we establish rigorous techniques for extending useful concepts from linear and second-order statistical techniques to nonlinear and higher-order statistical learning methodologies?
Learning-Based Planning
Authors: Jiménez Celorrio Sergio , de la Rosa Turbides Tomás
Published: May 2011

Automated Planning (AP) studies the generation of action sequences for problem solving. A problem in AP is defined by a state-transition function describing the dynamics of the world, the initial state of the world and the goals to be achieved. According to this definition, AP problems seem to be easily tackled by searching for a path in a graph, which is a well-studied problem. However, the graphs resulting from AP problems are so large that explicitly specifying them is not feasible. Thus, different approaches have been tried to address AP problems. Since the mid 90’s, new planning algorithms have enabled the solution of practical-size AP problems. Nevertheless, domain-independent planners still fail in solving complex AP problems, as solving planning tasks is a PSPACE-Complete problem (Bylander, 94). How do humans cope with this planning-inherent complexity? One answer is that our experience allows us to solve problems more quickly; we are endowed with learning skills that help us plan when problems are selected from a stable population. Inspire by this idea, the field of learning-based planning studies the development of AP systems able to modify their performance according to previous experiences. Since the first days, Artificial Intelligence (AI) has been concerned with the problem of Machine Learning (ML). As early as 1959, Arthur L. Samuel developed a prominent program that learned to improve its play in the game of checkers (Samuel, 1959). It is hardly surprising that ML has often been used to make changes in systems that perform tasks associated with AI, such as perception, robot control or AP. This article analyses the diverse ways ML can be used to improve AP processes. First, we review the major AP concepts and summarize the main research done in learning-based planning. Second, we describe current trends in applying ML to AP. Finally, we comment on the next avenues for combining AP and ML and conclude.
Multi-Objective Training of Neural Networks
Authors: Cuéllar M. P. , Delgado Miguel , Pegalajar M. C.
Published: May 2011

Traditionally, the application of a neural network (Haykin, 1999) to solve a problem has required to follow some steps before to obtain the desired network. Some of these steps are the data preprocessing, model selection, topology optimization and then the training. It is usual to spend a large amount of computational time and human interaction to perform each task of before and, particularly, in the topology optimization and network training. There have been many proposals to reduce the effort necessary to do these tasks and to provide the experts with a robust methodology. For example, Giles et al. (1995) provides a constructive method to optimize iteratively the topology of a recurrent network. Other methods attempt to reduce the complexity of the network structure by mean of removing unnecessary network nodes and connections like in (Morse, 1994). In the last years, evolutionary algorithms have been shown as promising tools to solve this problem, existing many competitive approaches in the literature. For example, Blanco et al. (2001) proposed a master-slave genetic algorithm to train (master algorithm) and to optimize the size of the network (slave algorithm). For a general view of the problem and the use of evolutionary algorithms for neural network training and optimization, we refer the reader to (Yao, 1999). Although the literature about genetic algorithms and neural networks is very extensive, we would like to remark the recent popularity of multi-objective optimization (Coello et al., 2002, Jin, 2006), specially to solve the problem of simultaneous training and topology optimization of neural networks. These methods have shown to perform suitably for this task in previous works, although most of them are proposed for feedforward models. They attempt to optimize the structure of the network (number of connections, hidden units or layers), while training the network at the same time. Multi-objective algorithms may provide important advantages in the simultaneous training and optimization of neural networks: They may force the search to return a set of optimal networks instead of a single one; they are capable to speed-up the optimization process; they may be preferred to a weight-aggregation procedure to cover the regularization problem in neural networks; and they are more suitable when the designer would like to combine different error measures for the training. A recent review of these techniques may be found in (Jin, 2006).
Privacy-Preserving Estimation
Authors: Saad Al-Ahmadi Mohammad , Sarathy Rathindra
Published: May 2011

Data mining has evolved from a need to make sense of the enormous amounts of data generated by organizations. But data mining comes with its own cost, including possible threats to the confidentiality and privacy of individuals. This chapter presents a background on privacy-preserving data mining (PPDM) and the related field of statistical disclosure limitation (SDL). We then focus on privacy-preserving estimation (PPE) and the need for a data-centric approach (DCA) to PPDM. The chapter concludes by presenting some possible future trends.
Ranking Functions
Authors: Huber Franz
Published: May 2011

Ranking functions have been introduced under the name of ordinal conditional functions in Spohn (1988; 1990). They are representations of epistemic states and their dynamics. The most comprehensive and up to date presentation is Spohn (manuscript).
Robots in Education
Authors: Ali Yousuf Muhammad
Published: May 2011

The new paradigm in engineering education demands hands-on training of the students using technology oriented projects. The roots of this approach can be traced back to the work of Seymour Papert in 1970s when he built a programmable turtle with a reflective light sensor (Papert, 1971). His ideas ultimately lead to the educational theory of constructionism (Papert, 1986 and Harel & Papert, 1991). According to this theory, students learn very effectively when they are involved in the creation of an external object that lives in the real world. Learners use this object to think with, and to relate ideas of, their subject of inquiry (Bourgoin, 1990). From an educational point of view, the theory of Papert can be linked to the constructivist theory of Jean Piaget (Paiget, 1972). According to this theory, learning comes from an active process of knowledge construction. This knowledge can be gained through real life experiences and linked to a learners’ previous knowledge. The concept of turtle was evolved further at MIT and became the famous Programmable Brick by Fred Martin who also developed new learning environments and methodologies based on this concept (Martin, 1988 and Martin 1994). The unusual idea put forward by the Brick, at least at the time of its invention, was the incorporation of the “design” work into the learning process. Students were not only users in this case, but were actively involved in the design process, while solving their problems (Martin, 1996a). The ‘Brick’ was later adopted and incorporated by the LEGO MINDSTORMS kit (RCX in 1998 and NXT made available in 2006). The use of the name “MINDSTROMS” can also be traced back to the book by Seymour Papert (Papert 1980). Versions of these Bricks for economically challenged communities have also been proposed recently (Sipitakiat, et al, 2004). The active learning methodology (Harmin and Toth, 2006) uses this philosophy of involving students in their own learning through class discussions and group problem solving and proves to be effective at least in certain cases. Robots have become a major player in this area and have been employed in improving the quality and level of student learning, ranging from primary schools to graduate level. As pointed out by Resnick and Martin (Resnick and Martin, 1990), “Creatures built from Electronic Bricks fall on the fuzzy boundary between animals and machines, forcing students to come to terms with how machines can be like animals, and vice versa”. In engineering courses incorporating connectionism approach, the students are asked to design and program a robot for a specific task. They also work in small teams and help and learn from each other. However it is important to know what is currently available to an educator so that he/she can develop the required skills, abilities, attitudes and values in students. In this article we identify some of the major research centres working in the area of education utilizing robots and discuss some of the robotic kits now available to educators. We also comment on the famous robotic competitions worldwide.
Stationary Density of Stochastic Search Processes
Authors: Berrones Arturo , Peña Dexmont , Sánchez Ricardo
Published: May 2011

The optimization of a cost function which has a number of local minima is a relevant subject in many important fields. For instance, the determination of the weights of learning machines depends in general on the solution of global optimization tasks (Haykin, 1999). A feature shared by almost all of the most common deterministic and stochastic algorithms for continuous non – linear optimization is that their performance is strongly affected by their starting conditions. Depending on the algorithm, the correct selection of an initial point or set of points have direct consequences on the efficiency, or even on the possibility to find the global minima. Of course, adequate selection of seeds implies prior knowledge on the structure of the optimization task. In the absence of prior information, a natural choice is to draw seeds from a uniform density defined over the search space. Knowledge on the problem can be gained through the exploration of this space. In this contribution is presented a method to estimate probability densities that describe the asymptotic behavior of general stochastic search processes over continuously differentiable cost functions. The relevance of such densities is that they give a description of the residence times over the different regions of the search space, after an infinitely long exploration. The preferred regions are those which minimize the cost globally, which is reflected in the asymptotic densities. In first instance, the resulting densities can be used to draw populations of points that are consistent with the global properties of the associated optimization tasks.
Statistical Simulations on Perceptron-Based Adders
Authors: Aunet Snorre , Otnes Berge Hans Kristian
Published: May 2011

In this article we compare a number of full-adder (1- bit addition) cells regarding minimum supply voltage and yield, when taking statistical simulations into account. According to the ITRS Roadmap two of the most important challenges for future nanoelectronics design are reducing power consumption and increasing manufacturability (ITRS, 2005). We use subthreshold CMOS, which is regarded by many as the most promising ultra low power circuit technique. It is also shown that a minimum redundancyfactor as low as 2 is sufficient to make circuits maintain full functionality under the presence of defects. This is, to our knowledge, the lowest redundancy reported for comparable circuits, and builds on a method suggested a few years ago (Aunet & Hartmann, 2003). A standard Full-Adder (FA) and an FA based on perceptrons exploiting the “mirrored gate”, implemented in a standard 90 nm CMOS technology, are shown not to withstand statistical mismatch and process variations for supply voltages below 150 mV. Exploiting a redundancy scheme tolerating “open” faults, with gate-level redundancy and shorted outputs, shows that the same two FAs might produce adequate Sum and Carry outputs at the presence of a defect PMOS for supply voltages above 150 mV, for a redundancy factor of 2 (Aunet & Otnes Berge, 2007). Two additional perceptrons do not tolerate the process variations, according to simulations. Simulations suggest that the standard FA has the lowest power consumption. Power consumption varies more than an order of magnitude for all subthreshold FAs, due to the statistical variations
Stochastic Approximation Monte Carlo for MLP Learning
Authors: Liang Faming
Published: May 2011

Over the past several decades, multilayer perceptrons (MLPs) have achieved increased popularity among scientists, engineers, and other professionals as tools for knowledge representation. Unfortunately, there is no a universal architecture which is suitable for all problems. Even with the correct architecture, frustrating problems of connection weights training still remain due to the rugged nature of the energy landscape of MLPs. The energy function often refers to the sum-of-square error function for conventional MLPs and the negative logposterior density function for Bayesian MLPs. This article presents a Monte Carlo method that can be used for MLP learning. The main focus is on how to apply the method to train connection weights for MLPs. How to apply the method to choose the optimal architecture and to make predictions for future values will also be discussed, but within the Bayesian framework.
Stream Processing of a Neural Classifier I
Authors: Martínez-Zarzuela M. , Díaz Pernas F. J. , González Ortega D. , Díez Higuera J. F. , Antón Rodríguez M.
Published: May 2011

An Artificial Neural Network (ANN) is a computational structure inspired by the study of biological neural processing. Although neurons are considered as very simple computation units, inside the nervous system, an incredible amount of widely inter-connected neurons can process huge amounts of data working in a parallel fashion. There are many different types of ANNs, from relatively simple to very complex, just as there are many theories on how biological neural processing works. However, execution of ANNs is always a heavy computational task. Important kinds of ANNs are those devoted to pattern recognition such as Multi-Layer Perceptron (MLP), Self-Organizing Maps (SOM) or Adaptive Resonance Theory (ART) classifiers (Haykin, 2007). Traditional implementations of ANNs used by most of scientists have been developed in high level programming languages, so that they could be executed on common Personal Computers (PCs). The main drawback of these implementations is that though neural networks are intrinsically parallel systems, simulations are executed on a Central Processing Unit (CPU), a processor designed for the execution of sequential programs on a Single Instruction Single Data (SISD) basis. As a result, these heavy programs can take hours or even days to process large input data. For applications that require real-time processing, it is possible to develop small ad-hoc neural networks on specific hardware like Field Programmable Gate Arrays (FPGAs). However, FPGA-based realization of ANNs is somewhat expensive and involves extra design overheads (Zhu & Sutton, 2003). Using dedicated hardware to do machine learning was typically expensive; results could not be shared with other researchers and hardware became obsolete within a few years. This situation has changed recently with the popularization of Graphics Processing Units (GPUs) as low-cost and high-level programmable hardware platforms. GPUs are being increasingly used for speeding up computations in many research fields following a Stream Processing Model (Owens, Luebke, Govindaraju, Harris, Krüger, Lefohn & Purcell, 2007). This article presents a GPU-based parallel implementation of a Fuzzy ART ANN, which can be used both for training and testing processes. Fuzzy ART is an unsupervised neural classifier capable of incremental learning, widely used in a universe of applications as medical sciences, economics and finance, engineering and computer science. CPU-based implementations of Fuzzy ART lack efficiency and cannot be used for testing purposes in real-time applications. The GPU implementation of Fuzzy ART presented in this article speeds up computations more than 30 times with respect to a CPU-based C/C++ development when executed on an NVIDIA 7800 GT GPU.
Supervised Learning of Fuzzy Logic Systems
Authors: Mohammadian M.
Published: May 2011

Conventionally modelling and simulation of complex nonlinear systems has been to construct a mathematical model and examine the system’s evolution or its control. This kind of approach can fail for many of the very large non-linear and complex systems being currently studied. With the invention of new advanced high-speed computers and the application of artificial intelligence paradigms new techniques have become available. Particularly neural networks and fuzzy logic for nonlinear modelling and genetic algorithms [Goldberg, D. (1989)] and evolutionary algorithms for optimisation methods have created new opportunities to solve complex systems [Bai, Y., Zhuang H. and Wang, D. (2006)]. This paper considers issues in design of multi-layer and hierarchical fuzzy logic systems. It proposes a decomposition technique for complex systems into hierarchical and multi-layered fuzzy logic sub-systems. The learning of fuzzy rules and internal parameters in a supervised manner is performed using genetic algorithms. The decomposition of complex nonlinear systems into hierarchical and multi-layered fuzzy logic sub-systems reduces greatly the number of fuzzy rules to be defined and improves the learning speed for such systems. In this paper a method for combining subsystems to create a hierarchical and multilayer fuzzy logic system is also described. Application areas considered are - the prediction of interest rate, unemployment rate predication and electricity usage prediction. Genetic Algorithms can be used as a tool for design and generation of fuzzy rules for a fuzzy logic system. This automatic design and generation of fuzzy rules, via genetic algorithms, can be categorised into two learning techniques namely, supervised and unsupervised. In supervised learning there are two distinct phases to the operation. In the first phase each individual is assessed based on the input signal that is propagated through the system producing output respond. The actual respond produced is then compared with a desired response, generating error signals that are then used as the fitness for the individual in the population of genetic algorithms. Supervised learning has successfully applied to solve some difficult problems. In this paper design and development of a genetic algorithm based supervised learning for fuzzy models with application to several problems is considered. A hybrid integrated architecture incorporating fuzzy logic and genetic algorithm can generate fuzzy rules that can be used in a fuzzy logic system for modelling, control and prediction
Web-Based Assessment System Applying Many-Valued Logic
Authors: Encheva Sylvia , Tumin Sharil
Published: May 2011

The issue of rewarding partially correct answers has been addressed by many authors (Guzman, E. & Conejo, R., 2004, Gardner-Medwin, A.R. 1995, Huffman, D, Goldberg, F., & Michlin, M. 2003). Intelligent systems have been designed to assign scores related to the importance of missing or incorrect part of an answer. Such systems are meant to facilitate the process of knowledge assessment. While trying to be efficient in evaluating students’ responses these systems operate with the answers to a single question addressing learning a new term, understanding a new concept or mastering a new skill. However, experimental practice shows that asking several questions about the same item results in inconsistent and/or incomplete feedback, i.e. some of the answers are correct while others are partially correct or even incorrect. A large number of computer based systems and thus automated assessment systems lack the ability to reason with inconsistent information. Such a situation occurs when, f. ex. a student answers to two questions about one item and one of the answers is correct and the other one is incorrect or missing. Reasoning by applying classical logic cannot solve the problem because the presence of contradiction leads to trivialization, i. e. anything follows from ‘correct and incorrect’ and thus all inconsistencies are treated as equally bad (Priest, 2001). In this paper we discuss how to assess students’ understanding of new terms and concepts, shortly after they have been introduced in a subject. Application of many-valued logic allows the system to give meaningful responses in the presence of inconsistencies. Decision making rules, an intelligent agent is applying for assessing students’ understanding of new terms and concepts are presented. Such rules distinguish between students’ hesitation in the process of giving an answer and lack of knowledge. We propose use of the generalized Lukasiewicz’s logic in a Web-based assessment system as a way of resolving problems with inconsistent and/or incomplete input.
South Africa
Authors: Beneke Justin Henley
Published: May 2011

South Africa has fallen behind its international peers both developing and developed markets in the race to rollout broadband services. In fact, even within the African continent, it is neither the broadband leader nor progressive in comparison to its Northern African counterparts. This chapter explores the development of broadband services in South Africa, as well as touching on the challenges faced in bringing this phenomenon into the mainstream. Reasons for the lack of diffusion and adoption of such services point to high end user costs of the service, a very limited geographical footprint of both fixedline and mobile broadband infrastructure, as well as a lack of computer literacy and an understanding of what broadband is able to offer. The chapter looks at possible solutions, including introducing a greater degree of competition into the market to facilitate downward pressure on prices, as well as providing cost-based access to international submarine fiber cables and the unbundling of the local loop to further this objective.
Australia's Government Policy and Broadband Internet Access
Authors: Fan Qiuyan
Published: May 2011

Like many other governments in the world, the Australian government has taken a multi-faceted approach to promoting broadband Internet access. This chapter provides an in-depth analysis of the impact of policy issues on broadband Internet access in Australia. The primary goal of this chapter is to develop a holistic understanding of Australia’s national approaches pertaining to broadband Internet access. This research has clearly indicated that the state of broadband Internet access is closely related to the government’s policy and regulatory framework. The government has been basing its actions on market forces as a principal driver for broadband Internet connectivity. However, market forces only play their roles in improving broadband Internet access in the major cities and have little effect in regional and rural Australia. The research has indicated that the regulatory competition regime, by and large, has failed to address concerns of market dominance and market power in the telecommunications sector as is evidenced by a relatively lower price-performance ratio of broadband services in Australia.
Broadband for the Mass Market
Authors: Saunders Roger
Published: May 2011

This chapter suggests there is no new application to stimulate adoption of broadband by the mass market. Many new applications have been introduced but have not created the desired growth. One application that could have mass market attraction is Voice over Internet Protocol (VoIP), and it is the most likely killer application. But failure by major communications carriers to develop VoIP is slowing broadband penetration to this larger market segment. This resistance results from the risk to current carrier call revenue from VoIP, and that infrastructure to support high-speed broadband may not generate adequate revenue in the short term to satisfy shareholders or financial markets. No international standards have yet been set, and VoIP between the Internet platforms is not fully integrated. Also the multitude of broadband packages offered by the various competing carriers creates confusion in the mass market which, as a result, defers purchase decisions.
Digital Divide and Broadband Access in an Italian Region
Authors: Ferro Enrico , Gil-Garcia J. Ramon , Helbig Natalie
Published: May 2011

Reducing the digital divide in order to build an information society for all is one of the top priorities for European policymakers. A better understanding of the determinants of broadband access at the individual level represents a key starting point for any e-inclusion policy. Based on a review of the literature on digital divide and broadband access, we document different approaches to understanding the digital divide and argue that these perspectives can also help to understand broadband access. Combining the digital divide and broadband literature provides a systematic and theory-based approach to the selection and inclusion of variables in different models. This chapter presents the results of a survey conducted in an Italian region. We provide some implications of our findings and argue that policymakers should explore the relationship between IT skills acquisition, broadband access, and Internet use in order to develop more effective policies and programs.
Improving Broadband Access in Rural Areas
Authors: Skogseid Ingjerd
Published: May 2011

The chapter explores the characteristics of rural broadband infrastructure development. Taking the existing installed base into consideration, small rural communities can initiate bottom-up cultivation of broadband infrastructure. Such initiatives are important contributions to overcoming the disparity in broadband access. In effect they aggregate demand by creating a larger total market for suppliers than the individual needs of the actors. The proposal is to use descriptive clusters as a way to reveal the installed base. A set of questions and answers will be defined to reveal the installed base. This can be used to acquire an overview of the types of resources available and the choices that need to be made. The use of descriptive clusters places emphasis on the local context and culture. With a bottom-up strategy, questions must be answered in relation to the local context. The responses and lessons learned may vary from one location to the next, making blueprint implementations impossible.
Social, Political and Ethical Responsibility in Broadband Adoption and Diffusion
Authors: Schulz Axel , Stahl Bernd Carsten , Rogerson Simon
Published: May 2011

There is considerable interest worldwide in broadband diffusion, with research focusing on aspects such as the provision of broadband in remote areas and the socio-economic factors that determine the likelihood of adoption. This chapter identifies the policies and initiatives used to encourage broadband awareness, availability, and adoption. Using the case study of a local broadband initiative in remote and rural Germany, the chapter asks the question of who can and should be responsible for broadband provision, and how such responsibility ascriptions are realized.
Developing a Dynamic View of Broadband Adoption
Authors: Daly Herbert , Ortiz Adriana , Dwivedi Yogesh K. , Paul Ray J. , Santos Javier , Sarriegi Jose M.
Published: May 2011

The widespread domestic use of broadband Internet technology has been recognized to have a positive influence on national economies and improve the life of citizens. Despite substantial investment to develop the infrastructure, many countries have experienced slow adoption rates for broadband. This chapter develops a view of UK broadband adoption using dynamic modeling techniques based on an existing statistical study. The contrasting approaches to modeling are compared. Principles of a dynamic modeling system are introduced and an appropriate form for broadband adoption chosen. The process of building a dynamic model based on an existing static model of broadband adoption is presented. Finally, the new perspective of the dynamic model is explored using the causal loop analysis technique.
The Uses and Gratifications of Broadband Internet
Authors: Vermaas Karianne , van de Wijngaert Lidwien
Published: May 2011

The objective of the study in this chapter is to identify a small number of relatively homogeneous groups of Dutch Internet users (both broadband and narrowband), based on their usage pattern. Using individual and behavioral characteristics, we further investigate the nature of the different groups. The Uses and Gratifications approach is employed as a starting point for an online questionnaire (N=2404) that was conducted. Cluster-analysis and logistic regression were used for data analysis. We were able to identify five clusters of different Internet users based on patterns of behavior. Results show that the Internet users in our sample consist of a large group that more or less conducts the same online activities. Results also show that narrowband and wideband users differ in their Internet behavior.
A Survey on the Adoption and Usage of Broadband Internet
Authors: Gholami Roya , Lim John , Lee Sang-Yong Tom
Published: May 2011

In spite of the increasing significance of broadband Internet, there are not many research papers explicitly addressing issues pertaining to its adoption and postadoption. Previous research on broadband has mainly focused on the supply side aspect at the national level, ignoring the importance of the demand side which may involve looking more deeply into the use, as well as factors impacting organizational and individual uptake. In an attempt to fill this gap, the current study empirically verifies an integrated theoretical model comprising the theory of planned behavior and the IS continuance model to examine factors influencing broadband Internet adoption and postadoption behavior of some 1,500 organizations in Singapore. Overall, strong support for the integrated model has been manifested by our results, providing insight into influential factors. At the adoption stage, perceived behavioral control has the greatest impact on behavioral intention. Our findings also suggest that, as compared to attitude, subjective norms and perceived behavioral control more significantly affect the broadband Internet adoption decision. At the postadoption stage, intention is no longer the only determinant of broadband Internet continuance; rather, initial usage was found to significantly affect broadband Internet continuance.
Broadband Diffusion to SMEs in the UK
Authors: Oni Oluwasola , Papazafeiropoulou Anastasia
Published: May 2011

Broadband is a relatively new technology and its adoption in the United Kingdom has been an issue due to its perceived benefits for businesses and more so for small/medium size enterprises (SMEs). In this chapter we argue that previous research focuses on home uses of broadband, particularly for educational purposes with little attention to its adoption by SMEs. We argue that the existing diffusion of innovation theories are inadequate for the study of broadband diffusion and we propose a more sociotechnical approach for that purpose. This study can be useful for SMEs considering adoption of new technologies such as broadband as well as policy makers that seek to apply effective technological adoption policies.
Broadband for Health in Developing Countries
Authors: Srivastava Aradhana
Published: May 2011

This chapter highlights the major issues in the use of broadband technologies in health care in developing countries. The use of Internet technologies in the health sector has immense potential in developing countries, especially in the context of public health programs. Some of the main uses of information and communication technologies (ICT) in health include remote consultations and diagnosis, information dissemination and networking between health providers, user groups, and forums, Internet-based disease surveillance and identification of target groups for health interventions, facilitation of health research and support to health care delivery, and administration. The technology has immense potential, but is also constrained by lack of policy direction, problems with access to technology, and lack of suitable infrastructure in developing nations. However, given its crucial role in public health, comprehensive efforts are required from all concerned stakeholders if universal e-health is to become a reality.
Adoption of Broadband Services
Authors: Falch Morten
Published: May 2011

Broadband is seen as a key infrastructure for developing the information society. For this reason many Governments are actively engaged in stimulating investments in broadband infrastructures and use of broadband services. This chapter compares a wide range of broadband strategies in the most successful markets for broadband. This is done through analysis of national policies in three European countries—Denmark, Sweden, and Germany—and the U.S., Japan, and South Korea. We concluded that successful implementation of broadband depends on the kind of policy measures to be taken at the national level. Many countries have provided active support for stimulating diffusion of broadband and national variants of this type of policies in different countries are important for an explanation of national differences in adoption of broadband.
Diffusion of Broadband Access in Latin America
Authors: Rovalo Arturo Robles , González Claudio Feijóo , Gómez-Barroso José Luis
Published: May 2011

The “geographic” digital divide is obvious when comparing more developed countries to the rest. Its first and most obvious sign is the difference in the diffusion of broadband accesses. However, it is clear that there are also lines of separation in smaller geographic ranges: between countries of a same area, inside each country and, sometimes, in each specific region. This chapter shows this situation by studying the broadband access diffusion in Latin America on a three level basis (regional, national, and local). At the national level, a few explanatory variables of the different situations presented by the countries chosen for the study are researched. Additionally, a description of the environment (market and public action) where this diffusion is occurring is also included.