bluuebunny/crossref_metadata_2025 huggingface dataset:
Intelligent Traffic Sign Classifiers
Authors: Vicen Bueno Raúl , Torijano Gordo Elena , García González Antonio , Rosa Zurera Manuel , Gil Pita Roberto
Published: May 2011

The Artificial Neural Networks (ANNs) are based on the behavior of the brain. So, they can be considered as intelligent systems. In this way, the ANNs are constructed according to a brain, including its main part: the neurons. Moreover, they are connected in order to interact each other to acquire the followed intelligence. And finally, as any brain, it needs having memory, which is achieved in this model with their weights. So, starting from this point of view of the ANNs, we can affirm that these systems are able to learn difficult tasks. In this article, the task to learn is to distinguish between different kinds of traffic signs. Moreover, this ANN learning must be done for traffic signs that are not in perfect conditions. So, the learning must be robust against several problems like rotation, translation or even vandalism. In order to achieve this objective, an intelligent extraction of information from the images is done. This stage is very important because it improves the performance of the ANN in this task.
Interactive Systems and Sources of Uncertainties
Authors: Chen Qiyang , Wang John
Published: May 2011

Today’s e-commerce environment requires that interactive systems exhibit abilities such as autonomy, adaptive and collaborative behavior, and inferential capability. Such abilities are based on the knowledge about users and their tasks to be performed (Raisinghani, Klassen and Schkade, 2001). To adapt users’ input and tasks an interactive system must be able to establish a set of assumptions about users’ profiles and task characteristics, which is often referred as user models. However, to develop a user model an interactive system needs to analyze users’ input and recognize the tasks and the ultimate goals users trying to achieve, which may involve a great deal of uncertainties. Uncertainty refers to a set of values about a piece of assumption that cannot be determined during a dialog session. In fact, the problem of uncertainty in reasoning processes is a complex and difficult one. Information available for user model construction and reasoning is often uncertain, incomplete, and even vague. The propagation of such data through an inference model is also difficult to predict and control. Therefore, the capacity of dealing with uncertainty is crucial to the success of any knowledge management system. Currently, a vigorous debate is in progress concerning how best to represent and process uncertainties in knowledge based systems. This debate carries great importance because it is not only related to the construction of knowledge based system but also focuses on human thinking in which most decisions are made under conditions of uncertainty. This chapter presents and discusses uncertainties in the context of user modeling in interactive systems. Some elementary distinctions between different kinds of uncertainties are introduced. The purpose is to provide an analytical overview and perspective concerning how and where uncertainties arise and the major methods that have been proposed to cope with them.
Learning in Feed-Forward Artificial Neural Networks I
Authors: Belanche Muñoz Lluís A.
Published: May 2011

The view of artificial neural networks as adaptive systems has lead to the development of ad-hoc generic procedures known as learning rules. The first of these is the Perceptron Rule (Rosenblatt, 1962), useful for single layer feed-forward networks and linearly separable problems. Its simplicity and beauty, and the existence of a convergence theorem made it a basic departure point in neural learning algorithms. This algorithm is a particular case of the Widrow-Hoff or delta rule (Widrow & Hoff, 1960), applicable to continuous networks with no hidden layers with an error function that is quadratic in the parameters.
Learning in Feed-Forward Artificial Neural Networks II
Authors: Belanche Muñoz Lluís A.
Published: May 2011

Supervised Artificial Neural Networks (ANN) are information processing systems that adapt their functionality as a result of exposure to input-output examples. To this end, there exist generic procedures and techniques, known as learning rules. The most widely used in the neural network context rely in derivative information, and are typically associated with the Multilayer Perceptron (MLP). Other kinds of supervised ANN have developed their own techniques. Such is the case of Radial Basis Function (RBF) networks (Poggio & Girosi, 1989). There has been also considerable work on the development of adhoc learning methods based on evolutionary algorithms.
Morphological Filtering Principles
Authors: Crespo Jose
Published: May 2011

In the last fifty years, approximately, advances in computers and the availability of images in digital form have made it possible to process and to analyze them in automatic (or semi-automatic) ways. Alongside with general signal processing, the discipline of image processing has acquired a great importance for practical applications as well as for theoretical investigations. Some general image processing references are (Castleman, 1979) (Rosenfeld & Kak, 1982) (Jain, 1989) (Pratt, 1991) (Haralick & Shapiro, 1992) (Russ, 2002) (Gonzalez & Woods, 2006). Mathematical Morphology, which was founded by Serra and Matheron in the 1960s, has distinguished itself from other types of image processing in the sense that, among other aspects, has focused on the importance of shapes. The principles of Mathematical Morphology can be found in numerous references such as (Serra, 1982) (Serra, 1988) (Giardina & Dougherty, 1988) (Schmitt & Mattioli, 1993) (Maragos & Schafer, 1990) (Heijmans, 1994) (Soille, 2003) (Dougherty & Lotufo, 2003) (Ronse, 2005).
Multilogistic Regression by Product Units
Authors: Gutiérrez P. A. , Hervás C. , Martínez-Estudillo F. J. , Carbonero M.
Published: May 2011

Multi-class pattern recognition has a wide range of applications including handwritten digit recognition (Chiang, 1998), speech tagging and recognition (Athanaselis, Bakamidis, Dologlou, Cowie, Douglas-Cowie & Cox, 2005), bioinformatics (Mahony, Benos, Smith & Golden, 2006) and text categorization (Massey, 2003). This chapter presents a comprehensive and competitive study in multi-class neural learning which combines different elements, such as multilogistic regression, neural networks and evolutionary algorithms. The Logistic Regression model (LR) has been widely used in statistics for many years and has recently been the object of extensive study in the machine learning community. Although logistic regression is a simple and useful procedure, it poses problems when is applied to a real-problem of classification, where frequently we cannot make the stringent assumption of additive and purely linear effects of the covariates. A technique to overcome these difficulties is to augment/replace the input vector with new variables, basis functions, which are transformations of the input variables, and then to use linear models in this new space of derived input features. Methods like sigmoidal feed-forward neural networks (Bishop, 1995), generalized additive models (Hastie & Tibshirani, 1990), and PolyMARS (Kooperberg, Bose & Stone, 1997), which is a hybrid of Multivariate Adaptive Regression Splines (MARS) (Friedman, 1991) specifically designed to handle classification problems, can all be seen as different nonlinear basis function models. The major drawback of these approaches is stating the typology and the optimal number of the corresponding basis functions. Logistic regression models are usually fit by maximum likelihood, where the Newton-Raphson algorithm is the traditional way to estimate the maximum likelihood a-posteriori parameters. Typically, the algorithm converges, since the log-likelihood is concave. It is important to point out that the computation of the Newton-Raphson algorithm becomes prohibitive when the number of variables is large. Product Unit Neural Networks, PUNN, introduced by Durbin and Rumelhart (Durbin & Rumelhart, 1989), are an alternative to standard sigmoidal neural networks and are based on multiplicative nodes instead of additive ones.
Natural Language Processing and Biological Methods
Authors: Bel Enguix Gemma , Jiménez López M. Dolores
Published: May 2011

During the 20th century, biology—especially molecular biology—has become a pilot science, so that many disciplines have formulated their theories under models taken from biology. Computer science has become almost a bio-inspired field thanks to the great development of natural computing and DNA computing. From linguistics, interactions with biology have not been frequent during the 20th century. Nevertheless, because of the “linguistic” consideration of the genetic code, molecular biology has taken several models from formal language theory in order to explain the structure and working of DNA. Such attempts have been focused in the design of grammar-based approaches to define a combinatorics in protein and DNA sequences (Searls, 1993). Also linguistics of natural language has made some contributions in this field by means of Collado (1989), who applied generativist approaches to the analysis of the genetic code. On the other hand, and only from theoretical interest a strictly, several attempts of establishing structural parallelisms between DNA sequences and verbal language have been performed (Jakobson, 1973, Marcus, 1998, Ji, 2002). However, there is a lack of theory on the attempt of explaining the structure of human language from the results of the semiosis of the genetic code. And this is probably the only arrow that remains incomplete in order to close the path between computer science, molecular biology, biosemiotics and linguistics. Natural Language Processing (NLP) –a subfield of Artificial Intelligence that concerns the automated generation and understanding of natural languages— can take great advantage of the structural and “semantic” similarities between those codes. Specifically, taking the systemic code units and methods of combination of the genetic code, the methods of such entity can be translated to the study of natural language. Therefore, NLP could become another “bio-inspired” science, by means of theoretical computer science, that provides the theoretical tools and formalizations which are necessary for approaching such exchange of methodology. In this way, we obtain a theoretical framework where biology, NLP and computer science exchange methods and interact, thanks to the semiotic parallelism between the genetic code and natural language.
Navigation by Image-Based Visual Homing
Authors: Szenher Matthew
Published: May 2011

Almost all autonomous robots need to navigate. We define navigation as do Franz & Mallot (2000): “Navigation is the process of determining and maintaining a course or trajectory to a goal location” (p. 134). We allow that this definition may be more restrictive than some readers are used to - it does not for example include problems like obstacle avoidance and position tracking - but it suits our purposes here. Most algorithms published in the robotics literature localise in order to navigate (see e.g. Leonard & Durrant- Whyte (1991a)). That is, they determine their own location and the position of the goal in some suitable coordinate system. This approach is problematic for several reasons. Localisation requires a map of available landmarks (i.e. a list of landmark locations in some suitable coordinate system) and a description of those landmarks. In early work, the human operator provided the robot with a map of its environment. Researchers have recently, though, developed simultaneous localisation and mapping (SLAM) algorithms which allow robots to learn environmental maps while navigating (Leonard & Durrant-Whyte (1991b)). Of course, autonomous SLAM algorithms must choose which landmarks to map and sense these landmarks from a variety of different positions and orientations. Given a map, the robot has to associate sensed landmarks with those on the map. This data association problem is difficult in cluttered real-world environments and is an area of active research. We describe in this chapter an alternative approach to navigation called visual homing which makes no explicit attempt to localise and thus requires no landmark map. There are broadly two types of visual homing algorithms: feature-based and image-based. The featurebased algorithms, as the name implies, attempt to extract the same features from multiple images and use the change in the appearance of corresponding features to navigate. Feature correspondence is - like data association - a difficult, open problem in real-world environments. We argue that image-based homing algorithms, which provide navigation information based on whole-image comparisons, are more suitable for real-world environments in contemporary robotics.
Nelder-Mead Evolutionary Hybrid Algorithms
Authors: Das Sanjoy
Published: May 2011

Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular as they are derivativefree techniques, are not as prone to getting trapped in local minima, and can be tailored specifically to suit any given problem. The performance of evolutionary algorithms can be improved further by adding a local search component to them. The Nelder-Mead simplex algorithm (Nelder & Mead, 1965) is a simple local search algorithm that has been routinely applied to improve the search process in evolutionary algorithms, and such a strategy has met with great success. In this article, we provide an overview of the various strategies that have been adopted to hybridize two wellknown evolutionary algorithms - genetic algorithms (GA) and particle swarm optimization (PSO).
Neural/Fuzzy Computing Based on Lattice Theory
Authors: Kaburlasos Vassilis G.
Published: May 2011

Computational Intelligence (CI) consists of an evolving collection of methodologies often inspired from nature (Bonissone, Chen, Goebel & Khedkar, 1999, Fogel, 1999, Pedrycz, 1998). Two popular methodologies of CI include neural networks and fuzzy systems. Lately, a unification was proposed in CI, at a “data level”, based on lattice theory (Kaburlasos, 2006). More specifically, it was shown that several types of data including vectors of (fuzzy) numbers, (fuzzy) sets, 1D/2D (real) functions, graphs/trees, (strings of) symbols, etc. are partially(lattice)-ordered. In conclusion, a unified cross-fertilization was proposed for knowledge representation and modeling based on lattice theory with emphasis on clustering, classification, and regression applications (Kaburlasos, 2006). Of particular interest in practice is the totally-ordered lattice (R,=) of real numbers, which has emerged historically from the conventional measurement process of successive comparisons. It is known that (R,=) gives rise to a hierarchy of lattices including the lattice (F,=) of fuzzy interval numbers, or FINs for short (Papadakis & Kaburlasos, 2007). This article shows extensions of two popular neural networks, i.e. fuzzy-ARTMAP (Carpenter, Grossberg, Markuzon, Reynolds & Rosen 1992) and self-organizing map (Kohonen, 1995), as well as an extension of conventional fuzzy inference systems (Mamdani & Assilian, 1975), based on FINs. Advantages of the aforementioned extensions include both a capacity to rigorously deal with nonnumeric input data and a capacity to introduce tunable nonlinearities. Rule induction is yet another advantage.
Representing Non-Rigid Objects with Neural Networks
Authors: García-Rodríguez José , Flórez-Revuelta Francisco , García-Chamizo Juan Manuel
Published: May 2011

Self-organising neural networks try to preserve the topology of an input space by means of their competitive learning. This capacity has been used, among others, for the representation of objects and their motion. In this work we use a kind of self-organising network, the Growing Neural Gas, to represent deformations in objects along a sequence of images. As a result of an adaptive process the objects are represented by a topology representing graph that constitutes an induced Delaunay triangulation of their shapes. These maps adapt the changes in the objects topology without reset the learning process.
A Roadmap on Updates
Authors: Zacarías Flores Fernando , Zacarías Flores Dionicio , Cuapa Canto Rosalba , Guzmán Muñoz Luis Miguel
Published: May 2011

Updates, is a central issue in relational databases and knowledge databases. In the last years, it has been well studied in the non-monotonic reasoning paradigm. Several semantics for logic program updates have been proposed (Brewka, Dix, & Knonolige 1997), (De Schreye, Hermenegildo, & Pereira, 1999) (Katsumo & Mendelzon, 1991). However, recently a set of proposals has been characterized to propose mechanisms of updates based on logic and logic programming. All these mechanisms are built on semantics based on structural properties (Eiter, Fink, Sabattini & Thompits, 2000) (Leite, 2002) (Banti, Alferes & Brogi, 2003) (Zacarias, 2005). Furthermore, all these semantic ones coincide in considering the AGM proposal as the standard model in the update theory, for their wealth in properties. The AGM approach, introduced in (Alchourron, Gardenfors & Makinson, 1985) is the dominating paradigm in the area, but in the context of monotonic logic. All these proposals analyze and reinterpret the AGM postulates under the Answer Set Programming (ASP) such as (Eiter, Fink, Sabattini & Thompits, 2000). However, the majority of the adapted AGM and update postulates are violated by update programs, as shown in (De Schreye, Hermenegildo, & Pereira, 1999).
Shortening Automated Negotiation Threads via Neural Nets
Authors: Roussaki Ioanna , Papaioannou Ioannis , Anagnostou Miltiades
Published: May 2011

In the artificial intelligence domain, an emerging research field that rapidly gains momentum is Automated Negotiations (Fatima, Wooldridge, & Jennings, 2007) (Buttner, 2006). In this framework, building intelligent agents (Silva, Romão, Deugo, & da Silva, 2001) adequate for participating in negotiations and acting autonomously on behalf of their owners is a very challenging research topic (Saha, 2006) (Jennings, Faratin, Lomuscio, Parsons, Sierra, & Wooldridge, 2001). In automated negotiations, three main items need to be specified (Faratin, Sierra, & Jennings, 1998) (Rosenschein, & Zlotkin, 1994): (i) the negotiation protocol & model, (ii) the negotiation issues, and (iii) the negotiation strategies that the agents will employ. According to (Walton, & Krabbe, 1995), “Negotiation is a form of interaction in which a group of agents, with conflicting interests and a desire to cooperate try to come to a mutually acceptable agreement on the division of scarce resources”. These resources do not only refer to money, but also include other parameters, over which the agents’ owners are willing to negotiate, such as product quality features, delivery conditions, guarantee, etc. (Maes, Guttman, & Moukas, 1999) (Sierra, 2004). In this framework, agents operate following predefined rules and procedures specified by the employed negotiation protocol (Rosenschein, & Zlotkin, 1994), aiming to address the requirements of their human or corporate owners as much as possible. Furthermore, the negotiating agents use a reasoning model based on which their responses to their opponent’s offers are formulated (Muller, 1996). This policy is widely known as the negotiation strategy of the agent (Li, Su, & Lam, 2006). This paper elaborates on the design of negotiation strategies for autonomous agents. The proposed strategies are applicable in cases where the agents have strict deadlines and they negotiate with a single party over the value of a single parameter (single-issue bilateral negotiations). Learning techniques based on MLP and GR Neural Networks (NNs) are employed by the client agents, in order to predict their opponents’ behaviour and achieve a timely detection of unsuccessful negotiations. The proposed NN-assisted strategies have been evaluated and turn out to be highly effective with regards to the duration reduction of the negotiation threads that cannot lead to agreements. The rest of the paper is structured as follows. In the second section, the basic principles of the designed negotiation framework are presented, while the formal problem statement is provided. The third section elaborates on the NN-assisted strategies designed and provides the configuration details of the NNs employed. The fourth section presents the experiments conducted, while the fifth section summarizes and evaluates the results of these experiments. Finally, in the last section, conclusions are drawn and future research plans are exposed.
Solar Radiation Forecasting Model
Authors: Onur Hocaoglu Fatih , Nezih Gerek Ömer , Kurban Mehmet
Published: May 2011

The prediction of hourly solar radiation data has important consequences in many solar applications (Markvart, Fragaki & Ross, 2006). Such data can be regarded as a time series and its prediction depends on accurate modeling of the stochastic process. The computation of the conditional expectation, which is in general non-linear, requires the knowledge of the high order distribution of the samples. Using a finite data, such distributions can only be estimated or fit into a pre-set stochastic model. Methods like Auto-Regressive (AR) prediction, Fourier Analysis (Dorvlo, 2000) Markov chains (Jain & Lungu, 2002) (Muselli, Poggi, Notton & Louche, 2001) and ARMA model (Mellit, Benghanem, Hadj Arab, & Guessoum, 2005) for designing the non-linear signal predictors are examples to this approach. The neural network (NN) approach also provides a good to the problem by utilizing the inherent adaptive nature (Elminir, Azzam, Younes, 2007). Since NNs can be trained to predict results from examples, they are able to deal with non-linear problems. Once the training is complete, the predictor can be set to a fixed value for further prediction at high speed. A number of researchers have worked on prediction of global solar radiation data (Kaplanis, 2006) (Bulut & Buyukalaca, 2007). In these works, the data is treated in its raw form as a 1-D time series, therefore the inter-day dependencies are not exploited. This article introduces a new and simple approach for hourly solar radiation forecasting. First, the data are rendered in a matrix to form a 2-D image-like model. As a first attempt to test the 2-D model efficiency, optimal linear image prediction filters (Gonzalez, 2002) are constructed. In order to take into account the adaptive nature for complex and non-stationary time series, NNs are also applied to the forecasting problem and results are discussed.
A Study of the Performance Effect of Genetic Operators
Authors: Deng Pi-Sheng
Published: May 2011

Performance of genetic algorithms (GAs) is mainly determined by several factors. Not only the genetic operators affect the performance of a GA with varying degrees, but also the parameter settings for genetic operators interact in a complicated manner with each other in influencing a GA’s performance. Though many studies have been conducted for this cause, they failed to converge to consistent conclusions regarding the importance of different genetic operators and their parameter settings on the performance of GAs. Actually, optimizing the combinations of different strategies and parameters for different problem types is an NPcomplete problem in itself, and is still an open research problem for GAs (Mitchell, 1996). Recognizing the intrinsic difficulties in finding universally optimal parameter configurations for different classes of problems, we advocate the experience-based approach to discovering generalized guiding rules for different problem domains. To this end, it is necessary for us to gain a better understanding about how different genetic operators and their parameter combinations affect a GA’s behavior. In this research, we systematically investigate, through a series of experiments, the effect of GA operators and the interaction among GA operators on the performance of the GA-based batch selection system as proposed in Deng (2007). This paper intends to serve as an initial inquiry into the research of useful design guidelines for configuring GA-based systems.
A 2D Positioning Application in PET Using ANNs
Authors: Mateo Fernando , Aliaga Ramón J. , Martínez Jorge D. , Ma Monzó José , Gadea Rafael
Published: May 2011

Positron Emission Tomography (PET) is a radiotracer imaging technique based on the administration (typically by injection) of compounds labelled with positron emitting radionuclides to a patient under study. When the radio-isotope decays, it emits a positron, which travels a short distance before annihilating with an electron. This annihilation produces two high-energy (511 keV) gamma photons propagating in nearly opposite directions, along an imaginary line called Line of Response (LOR). In PET imaging, the photons emitted by the decaying isotope are detected with gamma cameras. These cameras consist of a lead collimator to ensure that all detected photons are propagated along parallel paths, a crystal scintillator to convert high-energy photons to visible light, photo-multiplier tubes (PMT) to transform light signals into electric signals, and associated electronics to determine the position of each incident photon from the light distribution in the crystal (Ollinger & Fessler, 1997). We have researched on how Artificial Neural Networks (henceforth ANNs or NNs) could be used for bias-corrected position estimation. Small-scale ANNs like the ones considered in this work can be easily implemented in hardware, due to their highly parallelizable structure. Therefore, we have tried to take advantage of the capabilities of ANNs for modelling the real detector response.
Visualizing Cancer Databases Using Hybrid Spaces
Authors: Valdés Julio J. , Barton Alan J.
Published: May 2011

According to the World Health Organization(WHO), the directing and coordinating authority for health within the United Nations system http://www.who.int/ cancer/en/, from a total of 58 million deaths in 2005, cancer accounts for 7.6 million (or 13%) of all deaths worldwide. This places cancer as one of the leading causes of death in the world, with lung cancer (the main cancer leading to mortality) accounting for 1.3 million deaths per year. Thus the importance of understanding the mechanisms of lung cancer is clear. One approach is through the rapid quantification of the gene expression levels of samples of healthy and diseased lung tissue. This new field blending the knowledge from biologists, computer scientists and mathematicians is known as Bioinformatics and is yielding large quantities of data of a very high dimensional nature that needs to be understood.
Wave Reflection at Submerged Breakwaters
Authors: Castro Ponte Alberte , Iglesias Gregorio , Taveira Pinto Francisco , Carballo Rodrigo
Published: May 2011

Several types of structures are used in Coastal Engineering with the aim of preventing shoreline erosion, such as groynes, detached breakwaters, submerged breakwaters, etc. Submerged breakwaters have the advantage of their minimal visual impact, which has made them ever more popular (Chang & Liou, 2007). When the incoming waves impinge on a submerged breakwater, a process of energy transformation occurs. Many laboratory and numerical studies have been carried out in order to investigate this process (Kobayashi & Wurjanto, 1989) (Losada, Losada & Martin, 1995) (Losada, Silva & Losada, 1996) (Liu, Lin, Hsu, Chang, Losada, Vidal & Sakakiyama, 2000). The energy of the incident wave is transformed as follows: (i) one part of this energy is transmitted above the crest of the structure and ? in the case of permeable submerged breakwaters ? through its interior; (ii) another part is dissipated by wave breaking and by friction with the structure during the transmission process and finally, (iii) the remaining energy is reflected seaward. The reflection level is related with the scour in front of the structure. Therefore, a good knowledge about the reflection process may be helpful in order to avoid or at least mitigate the possible problems in the structure foundations. However, due to the complexity of the problem, the influence of all the relevant parameters (the structure slope and submergence, the water depth, the wave period and height, etc.) is not entirely understood yet and new approaches are needed. In this work, an Artificial Neural Network (ANN) has been applied to a series of results obtained from a previous study of Taveira-Pinto (2001), in which several physical models were tested. Once trained and validated, the ANN has been used to estimate the wave reflection coefficient.
Broadband Policy, Market Competition, and User Adoption in Taiwan
Authors: Liu Yu-li
Published: May 2011

Taiwan has been making great efforts to promote the fulfillment of its National Information Infrastructure (NII), with broadband network technology and its application having rapidly developed in recent years. In relation to the two major forms of broadband network technology, cable modem and ADSL, two multiple system operators—namely, Eastern Multimedia Company and China Network System—provide broadband network via cable modem, and HiNet and Seednet are the major ADSL service providers. Three new fixed networks have offered ADSL services as well since they began their operations. There are 5.25 million households (HHs), or 72.11% of the household population in Taiwan, using the Internet, and 4.74 million households, or 65.05% of the HH population, using broadband (TWNIC, 2006). This chapter analyzes the broadband adoption, the competition among providers of broadband, and relevant policies in Taiwan. The research methods adopted include literature reviews, in-depth interviews, and secondary analysis of previous surveys conducted by TWNIC. Since Taiwan’s broadband penetration is the fifth highest in the world, the discussions of the major factors contributing to broadband deployment in this chapter might provide some experiences from which other countries can learn.
Employing Content Validity Approach for Improving the Content of Broadband Adoption Survey Instrument
Authors: Dwivedi Yogesh K. , Lal Banita , Khoumbati Khalil
Published: May 2011

Studies on broadband adoption are just beginning to emerge and are exploratory in nature. Progress has been made to develop conceptual models to understand the adoption of broadband from the consumer perspective. However, in order to test the conceptual model of broadband adoption, a reliable survey instrument is yet to be developed and validated. Therefore, the overall aim of this research is to validate content of the broadband adoption survey instrument from the household consumer’s perspective. The objectives of this chapter are: first, to ensure that the identified constructs and their respective items adequately cover relevant dimensions of factors that affect consumers in the domain of broadband adoption; second, to determine whether the identified constructs and their respective items adequately cover relevant dimensions; and third, to conduct a pre- and pilot test on the resulting survey instruments in order to obtain feedback leading to improvements in the final questionnaire. The initial items for each construct were identified from both the technology adoption literature and exploratory studies on broadband adoption. Validation of the identified items was then performed employing a variation of a quantitative approach to content validity. The findings obtained from the content validation are then presented and subsequently discussed. Finally, emphasizing the limitations of content validation concludes the chapter.
Broadband User Behavior Characterization
Authors: Marques Neto Humberto T. , Rocha Leonardo C.D. , Guerra Pedro H.C. , Almeida Jussara M. , Meira Jr. Wagner , Almeida Virgilio A.F.
Published: May 2011

This chapter presents a broadband user behavior characterization from an Internet service provider standpoint. Understanding these user behavior patterns is important to the development of more efficient applications for broadband users. Our characterization divides the users into two categories: residential and small-office/home-office (SOHO). It employs four characterization criteria: session arrival process, session duration, number of bytes transferred within a session, and user request patterns. Our results show that both residential and SOHO session interarrival times are exponentially distributed, and point out that a typical SOHO user session is longer and transfers a larger volume of data. Our analysis also uncovers two main groups of session request patterns within each user category: (i) sessions that comprise traditional Internet services, such as WWW services, e-mail, and instant messenger, and (ii) sessions that comprise peer-to-peer file sharing applications, basically. We also analyzed and classified the e-business services most commonly accessed by users, which did not vary significantly across the user categories.
On the Versatility of Fuzzy Sets for Modeling Flexible Queries
Authors: Bosc P , Hadjali A , Pivert O
Published: May 2011

The idea of extending the usual Boolean queries with preferences has become a hot topic in the database community. One of the advantages of this approach is to deliver discriminated answers rather than flat sets of elements. Fuzzy sets are a natural means to represent preferences, and many works have been undertaken to define queries where fuzzy predicates can be introduced inside user queries. The objective of this chapter is to illustrate the expressiveness of fuzzy sets with the division operator in the context of regular databases. Like other operators, the regular division is not flexible at all and small variations in the data may lead to totally different results. To counter this behavior, a variety of extended division operators founded on fuzzy sets are suggested. All of them obey a double requirement: to have a clear meaning from a user point of view and to deliver a resulting relation which is a quotient.
Query Expansion by Taxonomy
Authors: Andreasen Troels , Bulskov Henrik
Published: May 2011

The use of taxonomies and ontologies as a foundation for enhancing textual information base access has recently gained increased attention in the field of information retrieval. The objective is to provide a domain model of an application domain where key concepts are organized and related. If queries and information base objects can be mapped to this, then the ontology may provide a valuable basis for a means of query evaluation that matches conceptual content rather than just strings, words, and numbers. This chapter presents an overview of the use of taxonomies and ontologies in querying with a special emphasis on similarity derived from the ontology. The notion of ontology is briefly introduced and similarity is surveyed. The former can be considered a generalization of taxonomy, while the latter can be seen as an interpretation where aspects of formal reasoning are ignored and replaced by measures reflecting how close concepts are connected, thereby significantly enhancing performance. In turn, similarity measures can be used in conceptual querying. Queries can be expanded with similar concepts, thereby causing query evaluation to be based on concepts from the domain model rather than on words in the query.
Fuzzy Spatial Data Types for Spatial Uncertainty Management in Databases
Authors: Schneider Markus
Published: May 2011

Spatial database systems and geographical information systems are currently only able to support geographical applications that deal with crisp spatial objects, that is, objects whose extent, shape, and boundary are precisely determined. Examples are land parcels, school districts, and state territories. However, many new, emerging applications are interested in modeling and processing geographic data that are inherently characterized by spatial vagueness or spatial indeterminacy. This requires novel concepts due to the lack of adequate approaches and systems. In this chapter, we focus on an important kind of spatial vagueness called spatial fuzziness. Spatial fuzziness captures the property of many spatial objects in reality that do not have sharp boundaries and interiors or whose boundaries and interiors cannot be precisely determined. We will designate this kind of entities as fuzzy spatial objects. Examples are polluted areas, temperature zones, and lakes. We propose an abstract, formal, and conceptual model of so-called fuzzy spatial data types (that is, a fuzzy spatial algebra) introducing fuzzy points, fuzzy lines, and fuzzy regions in the two-dimensional Euclidean space. This chapter provides a definition of their structure and semantics, which is supposed to serve as a specification of their implementation. Furthermore, we introduce fuzzy spatial set operations like fuzzy union, fuzzy intersection, and fuzzy difference, as well as fuzzy topological predicates as they are useful in fuzzy spatial joins and fuzzy spatial selections. We also sketch implementation strategies for the whole type system and show their integration into databases. An outlook on future research challenges rounds out the chapter.
Fuzzy Classification on Relational Databases
Authors: Meier Andreas , Schindler Günter , Werro Nicolas
Published: May 2011

In practice, information systems are based on very large data collections mostly stored in relational databases. As a result of information overload, it has become increasingly difficult to analyze huge amounts of data and to generate appropriate management decisions. Furthermore, data are often imprecise because they do not accurately represent the world or because they are themselves imperfect. For these reasons, a context model with fuzzy classes is proposed to extend relational database systems. More precisely, fuzzy classes and linguistic variables and terms, together with appropriate membership functions, are added to the database schema. The fuzzy classification query language (fCQL) allows the user to formulate unsharp queries that are then transformed into appropriate SQL statements using the fCQL toolkit so that no migration of the raw data is needed. In addition to the context model with fuzzy classes, fCQL and its implementation are presented here, illustrated by concrete examples.
Incremental Discovery of Fuzzy Functional Dependencies
Authors: Wang Shyue-Liang , Shen Ju-Wen , Hong Tuzng-Pei
Published: May 2011

Mining functional dependencies (FDs) from databases has been identified as an important database analysis technique. It has received considerable research interest in recent years. However, most current data mining techniques for determining functional dependencies deal only with crisp databases. Although various forms of fuzzy functional dependencies (FFDs) have been proposed for fuzzy databases, they emphasized conceptual viewpoints and only a few mining algorithms are given. In this research, we propose methods to validate and incrementally search for FFDs from similarity-based fuzzy relational databases. For a given pair of attributes, the validation of FFDs is based on fuzzy projection and fuzzy selection operations. In addition, the property that FFDs are monotonic in the sense that r1 ? r2 implies FDa(r1) ? FDa(r2) is shown. An incremental search algorithm for FFDs based on this property is then presented. Experimental results showing the behavior of the search algorithm are discussed.
Fuzzy Outranking Methods Including Fuzzy PROMETHEE
Authors: Beynon Malcolm
Published: May 2011

Outranking methods are a family of techniques concerned with ranking the preference for alternatives based on the criteria values that describe them. The breadth of applications taking inference from such preference ranking analysis includes the areas of business, health, environment, marketing, and public services. In the context of databases, the ranking issue is closely associated with data retrieval, including the ranking of matches to queries. This chapter describes the rudiments of fuzzy outranking methods, with particular attention to one such approach, namely fuzzy PROMETHEE, compounded further with the different structures of defined fuzziness of the criteria values also considered. Alternative fuzzy PROMETHEE approaches are described, with one used in two real-life applications. The results presented, with emphasis on their graphical representation, offer insights into the appropriate application of such fuzzy outranking methods.
A Formal Verification Centred Development Process for Security Protocols
Authors: Coffey Tom
Published: May 2011

This chapter concerns the correct and reliable design of modern security protocols. It discusses the importance of formal verification of security protocols prior to their release by publication or implementation. A discussion on logic-based verification of security protocols and its automation provides the reader with an overview of the current state-of-the-art of formal verification of security protocols. The authors propose a formal verification centred development process for security protocols. This process provides strong confidence in the correctness and reliability of the designed protocols. Thus, the usage of weak security protocols in communication systems is prevented. A case-study on the development of a security protocol demonstrates the advantages of the proposed approach. The case-study concludes with remarks on the performance of automated logic-based verification and presents an overview of formal verification results of a range of modern security protocols.
Aspect-Oriented Analysis of Security in Distributed Virtual Environment
Authors: Yang Li , Ege Raimund K. , Luo Lin
Published: May 2011

This chapter describes our approach to handle security in a complex Distributed Virtual Environment (DVE). The modules of such an environment all need to be concerned about security. An object-oriented model of a DVE allows us to capture security in an aspect-oriented fashion as a crosscutting concern among the multiple modules. As DVEs become more complex in content, distribution, and capabilities, the security requirement emerges as a key issue in their design and implementation. In order to control the software complexity, our strategy is to model and analyze the impact of security concerns on the functional model of DVEs via an aspect-oriented technique. This approach is appropriate to be applied at both the system design and modeling stages and provides guidance during the implementation stage. This chapter illustrates an aspect-oriented approach to the impact analysis of security concerns upon the functionalities of DVEs. A design-level security model for DVEs is provided to show how to weave security concerns into the models of DVE designs seamlessly.
Formal Analysis and Design of Authentication Protocols
Authors: Ahmed Shaikh Siraj
Published: May 2011

The purpose of this chapter is to introduce the reader to the research area of formal analysis of authentication protocols. It briefly introduces the basic notions of cryptography and its use in authentication protocols. The chapter looks at the Needham-Schroeder (1978) protocol as an example of an authentication protocol, and examines the history of the protocol as a stimulus to the formal analysis of such protocols. We then introduce the process algebra CSP (Hoare, 1985) to model authentication protocols and present Schneider’s (1998) rank function approach to analysing such protocols. The chapter concludes by describing related ongoing work in this area of research and highlight some of the challenges posed by the problem of analysing and designing protocols.
Assurance for Temporal Compatibility Using Contracts
Authors: Tilak Omkar J.
Published: May 2011

Software realization of a large-scale Distributed Computing System (DCS) is achieved through the Componentbased Software Development (CBSD) approach. A DCS consists of many autonomous components that interact with each other to coordinate each system activity. The need for such coordination, along with requirements such as heterogeneity, scalability, security, and availability, considerably increases the complexity of code in a distributed system. This chapter depicts a formal method to specify component interactions involving temporal constraints. Using the component interactions, various types of temporal interaction compatibility classes are defined. A simple case study is presented that indicates the benefits of the component interaction specifications discussed in this chapter.
Security-Efficient Identity Management Using Service Provisioning (Markup Language)
Authors: Gupta Manish
Published: May 2011

Enterprises are increasingly interested in new and cost effective technologies to leverage existing investments in IT and extend capabilities to reduce costs and improve security and productivity. User account and password management has long been a major expense for organizations. As a means to improve the integration of applications hosted both internal and external to the enterprise, enterprises are turning to automated account provisioning systems for providing secure and efficient accesses to wide range of systems. Provisioning and Identity Management are key technologies for enhancing operational efficiencies and gaining improvements in productivity and security. With tremendous growth in the number of information systems and user accounts in any enterprise, standards are key and necessary for solutions to interoperate and integrate with existing solutions. SPML is such a standard that has recently gained immense adoption from organizations. This shift can have major impact on enterprise’s information architecture. In this chapter, we outline the concepts of identity provisioning, service provisioning and how SPML can be leveraged to provide enterprise identity provisioning to both internal and external applications. The paper suggests come common security and business benefits that SPML can introduce. The paper also presents detailed discourse on architecture of SPML and improvises an implementation scenario to effect the functioning of different features and protocols of the standard.
The Last Line of Defense
Authors: Taylor Art
Published: May 2011

With the rise of the Internet, computer systems appear to be more vulnerable than ever from security attacks. Much attention has been focused on the role of the network in security attacks, but evidence suggests that the computer server and its operating system deserve closer examination since it is ultimately the operating system and its core defense mechanisms of authentication and authorization which are compromised in an attack. This chapter provides an exploratory and evaluative discussion of the authentication and authorization features of two widely used server operating systems: Windows and Linux.
Bioterrorism and Biosecurity
Authors: Pradhan M.
Published: May 2011

Information Technology can be used at all levels to counter attack bioterrorism. This article gives an overview of use of Information Technology for Biosecurity measures. Biosecurity measures involve information with respect to identifying living organisms, security for information retrieval from those databases that store laboratory details, medical, and clinical data, and finally security measures for retrieval using wireless technology. Also, this chapter gives an understanding of different Bioinformatics techniques and tools that are widely used for biosecurity measures.