bluuebunny/crossref_metadata_2025 huggingface dataset:
Web Search Engine Architectures and their Performance Analysis
Authors: Meng Xiannong
Published: May 2011

This chapter surveys various technologies involved in a Web search engine with an emphasis on performance analysis issues. The aspects of a general-purpose search engine covered in this survey include system architectures, information retrieval theories as the basis of Web search, indexing and ranking of Web documents, relevance feedback and machine learning, personalization, and performance measurements. The objectives of the chapter are to review the theories and technologies pertaining to Web search, and help us understand how Web search engines work and how to use the search engines more effectively and efficiently.
Towards a Model for Evaluating Web Retrieval Systems in Non-English Queries
Authors: Lazarinis Fotis
Published: May 2011

As the Web population continues to grow, more non-English users will be amassed online. The purpose of this chapter is to describe the methods and the criteria used for evaluating search engines and to propose a model for evaluating the searching effectiveness of Web retrieval systems in non-English queries. The qualities and weaknesses related to the handling of Greek and Italian queries are evaluated based on this method. The fundamental purpose of the methodology is to establish quality measurements on search engine utilization from the perspective of end users. Application of the proposed evaluation methodology aids users to select the most effective search engine and developers to identify some of the modules of their software that need improvements.
Analytics for Noisy Unstructured Text Data II
Authors: Venkata Subramaniam L. , Roy Shourya
Published: May 2011

The importance of text mining applications is growing proportionally with the exponential growth of electronic text. Along with the growth of internet many other sources of electronic text have become really popular. With increasing penetration of internet, many forms of communication and interaction such as email, chat, newsgroups, blogs, discussion groups, scraps etc. have become increasingly popular. These generate huge amount of noisy text data everyday. Apart from these the other big contributors in the pool of electronic text documents are call centres and customer relationship management organizations in the form of call logs, call transcriptions, problem tickets, complaint emails etc., electronic text generated by Optical Character Recognition (OCR) process from hand written and printed documents and mobile text such as Short Message Service (SMS). Though the nature of each of these documents is different but there is a common thread between all of these—presence of noise. An example of information extraction is the extraction of instances of corporate mergers, more formally MergerBetween(company1,company2,date), from an online news sentence such as: “Yesterday, New-York based Foo Inc. announced their acquisition of Bar Corp.” Opinion(product1,good), from a blog post such as: “I absolutely liked the texture of SheetK quilts.” At superficial level, there are two ways for information extraction from noisy text. The first one is cleaning text by removing noise and then applying existing state of the art techniques for information extraction. There in lies the importance of techniques for automatically correcting noisy text. In this chapter, first we will review some work in the area of noisy text correction. The second approach is to devise extraction techniques which are robust with respect to noise. Later in this chapter, we will see how the task of information extraction is affected by noise.
Basic Cellular Neural Networks Image Processing
Authors: Álvaro Fernández J.
Published: May 2011

Since its seminal publication in 1988, the Cellular Neural Network (CNN) (Chua & Yang, 1988) paradigm have attracted research community’s attention, mainly because of its ability for integrating complex computing processes into compact, real-time programmable analogic VLSI circuits (Rodríguez et al., 2004). Unlike cellular automata, the CNN model hosts nonlinear processors which, from analogic array inputs, in continuous time, generate analogic array outputs using a simple, repetitive scheme controlled by just a few real-valued parameters. CNN is the core of the revolutionary Analogic Cellular Computer, a programmable system whose structure is the so-called CNN Universal Machine (CNN-UM) (Roska & Chua, 1993). Analogic CNN computers mimic the anatomy and physiology of many sensory and processing organs with the additional capability of data and program storing (Chua & Roska, 2002). This article reviews the main features of this Artificial Neural Network (ANN) model and focuses on its outstanding and more exploited engineering application: Digital Image Processing (DIP).
Bio-Inspired Algorithms in Bioinformatics I
Authors: Seoane Fernández José Antonio , Miguélez Rico Mónica
Published: May 2011

Large worldwide projects like the Human Genome Project, which in 2003 successfully concluded the sequencing of the human genome, and the recently terminated Hapmap Project, have opened new perspectives in the study of complex multigene illnesses: they have provided us with new information to tackle the complex mechanisms and relationships between genes and environmental factors that generate complex illnesses (Lopez, 2004; Dominguez, 2006). Thanks to these new genomic and proteomic data, it becomes increasingly possible to develop new medicines and therapies, establish early diagnoses, and even discover new solutions for old problems. These tasks however inevitably require the analysis, filtration, and comparison of a large amount of data generated in a laboratory with an enormous amount of data stored in public databases, such as the NCBI and the EBI. Computer sciences equip biomedicine with an environment that simplifies our understanding of the biological processes that take place in each and every organizational level of live matter (molecular level, genetic level, cell, tissue, organ, individual, and population) and the intrinsic relationships between them. Bioinformatics can be described as the application of computational methods to biological discoveries (Baldi, 1998). It is a multidisciplinary area that includes computer sciences, biology, chemistry, mathematics, and statistics. The three main tasks of bioinformatics are the following: develop algorithms and mathematical models to test the relationships between the members of large biological datasets, analyze and interpret heterogeneous data types, and implement tools that allow the storage, retrieve, and management of large amounts of biological data.
Bio-Inspired Dynamical Tools for Analyzing Cognition
Authors: Bedia Manuel G. , Corchado Juan M. , Castillo Luis F.
Published: May 2011

The knowledge about higher brain centres in insects and how they affect the insect’s behaviour has increased significantly in recent years by theoretical and experimental investigations. Nowadays, a large body of evidence suggests that higher brain centres of insects are important for learning, short-term, longterm memory and play an important role for context generalisation (Bazhenof et al., 2001). Related to these subjects, one of the most interesting goals to achieve would be to understand the relationship between sequential memory encoding processes and the higher brain centres in insects in order to develop a general “insect-brain” control architecture to be implemented on simple robots. In this contribution, it is showed a review of the most important and recent results related to spatio-temporal coding and it is suggested the possibility to use continuous recurrent neural networks (CRNNs) (that can be used to model non-linear systems, in particular Lotka-Volterra systems) in order to find out a way to model simple cognitive systems from an abstract viewpoint. After showing the typical and interesting behaviors that emerge in appropriate Lotka- Volterra systems (in particular, winnerless competition processes) next sections deal with a brief discussion about the intelligent systems inspired in studies coming from the biology.
Commonsense Knowledge Representation II
Authors: Ein-Dor Phillip
Published: May 2011

Early attempts to implement systems that understand commonsense knowledge did so for very restricted domains. For example, the Planes system [Waltz, 1978] knew real world facts about a fleet of airplanes and could answer questions about them put to it in English. It had, however, no behaviors, could not interpret the facts, draw inferences from them or solve problems, other than those that have to do with understanding the questions. At the other extreme, SHRDLU (Winograd, 1973) understood situations in its domain of discourse (which it perceived visually), accepted commands in natural language to perform behaviors in that domain and solved problems arising in execution of the commands; all these capabilities were restricted, however, to SHRDLU’s artificial world of colored toy blocks. Thus, in implemented systems it appears that there may be a trade off between the degree of realism of the domain and the number of capabilities that can be implemented. In the frames versus logic debate (see Commonsense Knowledge Representation I - Formalisms in this Encyclopedia), the real problem, in Israel’s (1983) opinion, is not the representation formalism itself, but rather that the facts of the commonsense world have not been formulated, and this is more critical than choice of a particular formalism. A notable attempt to formulate the “facts of the commonsense world” is that of Hayes [1978a, 1978b, 1979] under the heading of naïve physics. This work employs first-order predicate calculus to represent commonsense knowledge of the everyday physical world. The author of this survey has undertaken a similar effort with respect to commonsense business knowledge (Ein-Dor and Ginzberg 1989). Some broader attempts to formulate commonsense knowledge bases are cited in the section Commonsense Knowledge Bases.
Complex Systems Modeling by Cellular Automata
Authors: Kroc Jirí , Sloot Peter M.A.
Published: May 2011

In recent years, the notion of complex systems proved to be a very useful concept to define, describe, and study various natural phenomena observed in a vast number of scientific disciplines. Examples of scientific disciplines that highly benefit from this concept range from physics, mathematics, and computer science through biology and medicine as well as economy, to social sciences and psychology. Various techniques were developed to describe natural phenomena observed in these complex systems. Among these are artificial life, evolutionary computation, swarm intelligence, neural networks, parallel computing, cellular automata, and many others. In this text, we focus our attention to one of them, i.e. ‘cellular automata’. We present a truly discrete modelling universe, discrete in time, space, and state: Cellular Automata (CAs) (Sloot & Hoekstra, 2007, Kroc, 2007, Sloot, Chopard & Hoekstra, 2004). It is good to emphasize the importance of CAs in solving certain classes of problems, which are not tractable by other techniques. CAs, despite theirs simplicity, are able to describe and reproduce many complex phenomena that are closely related to processes such as self-organization and emergence, which are often observed within the above mentioned scientific disciplines.
Decision Making in Intelligent Agents
Authors: Danielson Mats , Ekenberg Love
Published: May 2011

There are several ways of building complex distributed software systems, for example in the form of software agents. But regardless of the form, there are some common problems having to do with specification contra execution. One of the problems is the inherent dynamics in the environment many systems are exposed to. The properties of the environment are not known with any precision at the time of construction. This renders a specification of the system incomplete by definition. A traditional software agent is only prepared to handle situations conceived of and implemented at compile-time. Even though it can operate in varying contexts, its decision making abilities are static. One remedy is to prepare the distributed components for a truly dynamic environment, i.e. an environment with changing and somewhat unpredictable conditions. A rational software agent needs both a representation of a decision problem at hand and means for evaluation. AI has traditionally addressed some parts of this problem such as representation and reasoning, but has hitherto to a lesser degree addressed the decision making abilities of independent distributed software components (Ekenberg, 2000a, 2000b). Such decision making often has to be carried out under severe uncertainty regarding several parameters. Thus, methods for independent decision making components should be able to handle uncertainties on the probabilities and utilities involved. They have mostly been studied as means of representation, but are now being developed into functional theories of decision making suitable for dynamic use by software agents and other dynamic distributed components. Such a functional theory will also benefit analytical decision support systems intended to aid humans in their decision making. Thus, the generic term agent below stands for a dynamic software component as well as a human or a group of humans assisted by intelligent software.
Dependency Parsing
Authors: Çakici Ruket
Published: May 2011

Annotated data have recently become more important, and thus more abundant, in computational linguistics . They are used as training material for machine learning systems for a wide variety of applications from Parsing to Machine Translation (Quirk et al., 2005). Dependency representation is preferred for many languages because linguistic and semantic information is easier to retrieve from the more direct dependency representation. Dependencies are relations that are defined on words or smaller units where the sentences are divided into its elements called heads and their arguments, e.g. verbs and objects. Dependency parsing aims to predict these dependency relations between lexical units to retrieve information, mostly in the form of semantic interpretation or syntactic structure. Parsing is usually considered as the first step of Natural Language Processing (NLP). To train statistical parsers, a sample of data annotated with necessary information is required. There are different views on how informative or functional representation of natural language sentences should be. There are different constraints on the design process such as: 1) how intuitive (natural) it is, 2) how easy to extract information from it is, and 3) how appropriately and unambiguously it represents the phenomena that occur in natural languages. In this article, a review of statistical dependency parsing for different languages will be made and current challenges of designing dependency treebanks and dependency parsing will be discussed.
Differential Evolution with Self-Adaptation
Authors: Brest Janez
Published: May 2011

Many practical engineering applications can be formulated as a global optimization problem, in which objective function has many local minima, and derivatives of the objective function are unavailable. Differential Evolution (DE) is a floating-point encoding evolutionary algorithm for global optimization over continuous spaces (Storn & Price, 1997) (Liu & Lampinen, 2005) (Price, Storn & Lampinen, 2005) (Feoktistov, 2006). Nowadays it is used as a powerful global optimization method within a wide range of research areas. Recent researches indicate that self-adaptive DE algorithms are considerably better than the original DE algorithm. The necessity of changing control parameters during the optimization process is also confirmed based on the experiments in (Brest, Greiner, Boškovic, Mernik, Žumer, 2006a). DE with self-adaptive control parameters has already been presented in (Brest et al., 2006a). This chapter presents self-adaptive approaches that were recently proposed for control parameters in DE algorithm.
Evolutionary Algorithms in Discredibility Detection
Authors: Sulc Bohumil , Klimanek David
Published: May 2011

Evolutionary algorithms are well known optimization techniques suitable for solving various kinds of problems (Ruano, 2005). The new application of evolutionary algorithms represents their use in the detection of biased control loop functions caused by controlled variable sensor discredibility (Klimanek, Sulc, 2005). Sensor discredibility occurs when a sensor transmitting values of the controlled variable provides inexact information, however the information is not absolutely faulty yet. Use of discredible sensors in control circuits may cause the real values of controlled variables to exceed the range of tolerated differences, whereas zero control error is being displayed. However, this is not the only negative consequence. Sometimes, sensor discredibility is accompanied with undesirable and hardly recognizable side effects. Most typical is an increase of harmful emission production in the case of combustion control (Sulc, Klimanek, 2005). We have found that evolutionary algorithms are useful tools for solving the particular problem of finding a software-based way (co-called software redundancy) of sensor discredibility detection. Software redundancy is a more economic way than the usual hardware redundancy, which is otherwise necessary in control loop protection against this small, invisible control error occurrence. Namely, the standard genetic algorithm and the simulated annealing algorithm have been successfully applied and tested to minimize the given cost function; by means of these algorithms newly developed method is able to detect controlled variable sensor discredibility. When applied to combustion processes, production of harmful emissions can be kept within accepted limits. Used application of evolutionary algorithms inclusive terminology transfer reflecting this application area can serve as an explanatory case study helping readers in better understanding the way how the evolutionary algorithms operate.
Feature Selection
Authors: Sánchez-Maroño Noelia , Alonso-Betanzos Amparo
Published: May 2011

Many scientific disciplines use modelling and simulation processes and techniques in order to implement non-linear mapping between the input and the output variables for a given system under study. Any variable that helps to solve the problem may be considered as input. Ideally, any classifier or regressor should be able to detect important features and discard irrelevant features, and consequently, a pre-processing step to reduce dimensionality should not be necessary. Nonetheless, in many cases, reducing the dimensionality of a problem has certain advantages (Alpaydin, 2004; Guyon & Elisseeff, 2003), as follows: • Performance improvement. The complexity of most learning algorithms depends on the number of samples and features (curse of dimensionality). By reducing the number of features, dimensionality is also decreased, and this may save on computational resources—such as memory and time—and shorten training and testing times. • Data compression. There is no need to retrieve and store a feature that is not required. • Data comprehension. Dimensionality reduction facilitates the comprehension and visualisation of data. • Simplicity. Simpler models tend to be more robust when small datasets are used. There are two main methods for reducing dimensionality: feature extraction and feature selection. In this chapter we propose a review of different feature selection (FS) algorithms, including its main approaches: filter, wrapper and hybrid – a filter/wrapper combination.
Fuzzy Logic Applied to Biomedical Image Analysis
Authors: Castro Alfonso , Arcay Bernardino
Published: May 2011

Ever since Zadeh established the basis of fuzzy logic in his famous article Fuzzy Sets (Zadeh, 1965), an increasing number of research areas have used his technique to solve and model problems and apply it, mainly, to control systems. This proliferation is largely due to the good results in classifying the ambiguous information that is typical of complex systems. Success in this field has been so overwhelming that it can be found in many industrial developments of the last decade: control of the Sendai train (Yasunobu & Miyamoto, 1985), control of air-conditioning systems, washing machines, auto-focus in cameras, industrial robots, etc. (Shaw, 1998) Fuzzy logic has also been applied to computerized image analysis (Bezdek & Keller & Krishnapuram & Pal, 1999) because of its particular virtues: high noise insensitivity and the ability to easily handle multidimensional information (Sutton & Bezdek & Cahoon, 1999), features that are present in most digital images analyses. In fuzzy logic, the techniques that have been most often applied to image analysis have been fuzzy clustering algorithms, ever since Bezdek proposed them in the seventies (Bezdek, 1973). This technique has evolved continuously towards correcting the problems of the initial algorithms and obtaining a better classification: techniques for a better initialization of these algorithms, and algorithms that would allow the evaluation of the solution by means of validity functions. Also, the classification mechanism was improved by modifying the membership function of the algorithm, allowing it to present an adaptative behaviour; recently, kernel functions were applied to the calculation of memberships. (Zhong & Wei & Jian, 2003) At the present moment, applications of fuzzy logic are found in nearly all Computer Sciences fields, it constitutes one of the most promising branches of Artificial Intelligence both from a theoretic and commercial point of view. A proof of this evolution is the development of intelligent systems based on fuzzy logic. This article presents several fuzzy clustering algorithms applied to medical images analysis. We also include the results of a study that uses biomedical images to illustrate the mentioned concepts and techniques.
Granular Computing
Authors: Peters Georg
Published: May 2011

It is well accepted that in many real life situations information is not certain and precise but rather uncertain or imprecise. To describe uncertainty probability theory emerged in the 17th and 18th century. Bernoulli, Laplace and Pascal are considered to be the fathers of probability theory. Today probability can still be considered as the prevalent theory to describe uncertainty. However, in the year 1965 Zadeh seemed to have challenged probability theory by introducing fuzzy sets as a theory dealing with uncertainty (Zadeh, 1965). Since then it has been discussed whether probability and fuzzy set theory are complementary or rather competitive (Zadeh, 1995). Sometimes fuzzy sets theory is even considered as a subset of probability theory and therefore dispensable. Although the discussion on the relationship of probability and fuzziness seems to have lost the intensity of its early years it is still continuing today. However, fuzzy set theory has established itself as a central approach to tackle uncertainty. For a discussion on the relationship of probability and fuzziness the reader is referred to e.g. Dubois, Prade (1993), Ross et al. (2002) or Zadeh (1995). In the meantime further ideas how to deal with uncertainty have been suggested. For example, Pawlak introduced rough sets in the beginning of the eighties of the last century (Pawlak, 1982), a theory that has risen increasing attentions in the last years. For a comparison of probability, fuzzy sets and rough sets the reader is referred to Lin (2002). Presently research is conducted to develop a Generalized Theory of Uncertainty (GTU) as a framework for any kind of uncertainty whether it is based on probability, fuzziness besides others (Zadeh, 2005). Cornerstones in this theory are the concepts of information granularity (Zadeh, 1979) and generalized constraints (Zadeh, 1986). In this context the term Granular Computing was first suggested by Lin (1998a, 1998b), however it still lacks of a unique and well accepted definition. So, for example, Zadeh (2006a) colorfully calls granular computing “ballpark computing” or more precisely “a mode of computation in which the objects of computation are generalized constraints”.
Intelligent Radar Detectors
Authors: Vicen Bueno Raúl , Rosa Zurera Manuel , Jarabo Amores María Pilar , Gil Pita Roberto , de la Mata Moya David
Published: May 2011

The Artificial Neural Networks (ANNs) are based on the behaviour 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 the presence or not of a reflected signal called target in a Radar environment dominated by clutter. The clutter involves all the signals reflected from other objects in a Radar environment that are not the desired target. Moreover, the noise is considered in this environment because it always exists in all the communications systems we can work with.
A Longitudinal Analysis of Labour Market Data with SOM
Authors: Rousset Patrick , Giret Jean-Francois
Published: May 2011

The aim of this paper is to present a typology of career paths in France drawn up with the Kohonen algorithm and its extension to a clustering method of life history analysis based on the use of Self Organizing Maps (SOMs). Several methods have previously been presented for transforming qualitative into quantitative information so as to be able to apply clustering algorithms such as SOMs based on the Euclidean distance. Our approach consists in performing quantitative encoding on labor market situation proximities across time. Using SOMs, the preservation of the topology also makes it possible to check whether this new method of encoding preserves the particularities of the life history according to our economic approach to careers. Lastly, this quantitative encoding preprocessing, which can be easily applied to analysis methods of life history, completes the set of methods extending the use of SOM to qualitative data.
Microarray Information and Data Integration Using SAMIDI
Authors: Gómez Juan M. , Colomo Ricardo , Ruano Marcos , García Ángel
Published: May 2011

Technological advances in high-throughput techniques and efficient data gathering methods, coupled computational biology efforts, have resulted in a vast amount of life science data often available in distributed and heterogeneous repositories. These repositories contain information such as sequence and structure data, annotations for biological data, results of complex computations, genetic sequences and multiple bio-datasets. However, the heterogeneity of these data, have created a need for research in resource integration and platform independent processing of investigative queries, involving heterogeneous data sources. When processing huge amounts of data, information integration is one of the most critical issues, because it’s crucial to preserve the intrinsic semantics of all the merged data sources. This integration would allow the proper organization of data, fostering the analysis and access the information to accomplish critical tasks, such as the processing of micro-array data to study protein function and medical researches in making detailed studies of protein structures to facilitate drug design (Ignacimuthu, 2005). Furthermore, DNA micro-array research community urgently requires technology to allow up-to-date micro-array data information to be found, accessed and delivered in a secure framework (Sinnot, 2007). Several research disciplines, such as Bioinformatics, where information integration is critical, could benefit from harnessing the potential of a new approach: the Semantic Web (SW). The SW term was coined by Berners-Lee, Hendler and Lassila (2001) to describe the evolution of a Web that consisted of largely documents for humans to read towards a new paradigm that included data and information for computers to manipulate. The SW is about adding machine-understandable and machine-processable metadata to Web resource through its key-enabling technology: ontologies (Fensel, 2002). Ontologies are a formal explicit and shared specification of a conceptualization. The SW was conceived as a way to solve the need for data integration on the Web. This article expounds SAMIDI, a Semantics-based Architecture for Micro-array Information and Data Integration. The most remarkable innovation offered by SAMIDI is the use of semantics as a tool for leveraging different vocabularies and terminologies and foster integration. SAMIDI is composed of a methodology for the unification of heterogeneous data sources from the analysis of the requirements of the unified data set and a software architecture.
Mobile Robots Navigation, Mapping, and Localization Part I
Authors: Hee Lee Gim , Ang Jr. Marcelo H.
Published: May 2011

The development of autonomous mobile robots is continuously gaining importance particularly in the military for surveillance as well as in industry for inspection and material handling tasks. Another emerging market with enormous potential is mobile robots for entertainment. A fundamental requirement for autonomous mobile robots in most of its applications is the ability to navigate from a point of origin to a given goal. The mobile robot must be able to generate a collision-free path that connects the point of origin and the given goal. Some of the key algorithms for mobile robot navigation will be discussed in this article.
NLP Techniques in Intelligent Tutoring Systems
Authors: Boonthum-Denecke Chutima , Levinstein Irwin B. , McNamara Danielle S. , Magliano Joseph P. , Millis Keith K.
Published: May 2011

Many Intelligent Tutoring Systems (ITSs) aim to help students become better readers. The computational challenges involved are (1) to assess the students’ natural language inputs and (2) to provide appropriate feedback and guide students through the ITS curriculum. To overcome both challenges, the following non-structural Natural Language Processing (NLP) techniques have been explored and the first two are already in use: word-matching (WM), latent semantic analysis (LSA, Landauer, Foltz, & Laham, 1998), and topic models (TM, Steyvers & Griffiths, 2007). This article describes these NLP techniques, the iSTART (Strategy Trainer for Active Reading and Thinking, McNamara, Levinstein, & Boonthum, 2004) intelligent tutor and the related Reading Strategies Assessment Tool (R-SAT, Magliano et al., 2006), and how these NLP techniques can be used in assessing students’ input in iSTART and R-SAT. This article also discusses other related NLP techniques which are used in other applications and may be of use in the assessment tools or intelligent tutoring systems.
Ontologies for Education and Learning Design
Authors: Lama Manuel , Sánchez Eduardo
Published: May 2011

In the last years, the growing of the Internet have opened the door to new ways of learning and education methodologies. Furthermore, the appearance of different tools and applications has increased the need for interoperable as well as reusable learning contents, teaching resources and educational tools (Wiley, 2000). Driven by this new environment, several metadata specifications describing learning resources, such as IEEE LOM (LTCS, 2002) or Dublin Core (DCMI, 2004), and learning design processes (Rawlings et al., 2002) have appeared. In this context, the term learning design is used to describe the method that enables learners to achieve learning objectives after a set of activities are carried out using the resources of an environment. From the proposed specifications, the IMS (IMS, 2003) has emerged as the de facto standard that facilitates the representation of any learning design that can be based on a wide range of pedagogical techniques. The metadata specifications are useful solutions to describe educational resources in order to favour the interoperability and reuse between learning software platforms. However, the majority of the metadata standards are just focused on determining the vocabulary to represent the different aspects of the learning process, while the meaning of the metadata elements is usually described in natural language. Although this description is easy to understand for the learning participants, it is not appropriate for software programs designed to process the metadata. To solve this issue, ontologies (Gómez-Pérez, Fernández-López, and Corcho, 2004) could be used to describe formally and explicitly the structure and meaning of the metadata elements; that is, an ontology would semantically describe the metadata concepts. Furthermore, both metadata and ontologies emphasize that its description must be shared (or standardized) for a given community. In this paper, we present a short review of the main ontologies developed in last years in the Education field, focusing on the use that authors have given to the ontologies. As we will show, ontologies solve issues related with the inconsistencies of using natural language descriptions and with the consensous for managing the semantics of a given specification.
Protein Structure Prediction by Fusion,Bayesian Methods
Authors: Akkaladevi Somasheker , Katangur Ajay K. , Luo Xin
Published: May 2011

Prediction of protein secondary structure (alpha-helix, beta-sheet, coil) from primary sequence of amino acids is a very challenging and difficult task, and the problem has been approached from several angles. A protein is a sequence of amino acid residues and can thus be considered as a one dimensional chain of ‘beads’ where each bead correspond to one of the 20 different amino acid residues known to occur in proteins. The length of most protein sequence ranges from 50 residues to about 1000 residues but longer proteins are also known, e.g. myosin, the major protein of muscle fibers, consists of 1800 residues (Altschul et al. 1997). Many techniques were used many researchers to predict the protein secondary structure, but the most commonly used technique for protein secondary structure prediction is the neural network (Qian et al. 1988). This chapter discusses a new method combining profile-based neural networks (Rost et al. 1993b), Simulated Annealing (SA) (Akkaladevi et al. 2005; Simons et al. 1997), Genetic algorithm (GA) (Akkaladevi et al. 2005) and the decision fusion algorithms (Akkaladevi et al. 2005). Researchers used the neural network (Hopfield 1982) combined with GA and SA algorithms, and then applied the two decision fusion methods; committee method and the correlation methods and obtained improved results on the prediction accuracy (Akkaladevi et al. 2005). Sequence profiles of amino acids are fed as input to the profile-based neural network. The two decision fusion methods improved the prediction accuracy, but noticeably one method worked better in some cases and the other method for some other sequence profiles of amino acids as input (Akkaladevi et al. 2005). Instead of compromising on some of the good solutions that could have generated from either approach, a combination of these two approaches is used for obtaining better prediction accuracy. This criterion is the basis for the Bayesian inference method (Anandalingam et al. 1989; Schmidler et al. 2000; Simons et al. 1997). The results obtained show that the prediction accuracy improves by more than 2% using the combination of the decision fusion approach and the Bayesian inference method.
Robust Learning Algorithm with LTS Error Function
Authors: Rusiecki Andrzej
Published: May 2011

Feedforward neural networks (FFNs) are often considered as universal tools and find their applications in areas such as function approximation, pattern recognition, or signal and image processing. One of the main advantages of using FFNs is that they usually do not require, in the learning process, exact mathematical knowledge about input-output dependencies. In other words, they may be regarded as model-free approximators (Hornik, 1989). They learn by minimizing some kind of an error function to fit training data as close as possible. Such learning scheme doesn’t take into account a quality of the training data, so its performance depends strongly on the fact whether the assumption, that the data are reliable and trustable, is hold. This is why when the data are corrupted by the large noise, or when outliers and gross errors appear, the network builds a model that can be very inaccurate. In most real-world cases the assumption that errors are normal and iid, simply doesn’t hold. The data obtained from the environment are very often affected by noise of unknown form or outliers, suspected to be gross errors. The quantity of outliers in routine data ranges from 1 to 10% (Hampel, 1986). They usually appear in data sets during obtaining the information and pre-processing them when, for instance, measurement errors, long-tailed noise, or results of human mistakes may occur. Intuitively we can define an outlier as an observation that significantly deviates from the bulk of data. Nevertheless, this definition doesn’t help in classifying an outlier as a gross error or a meaningful and important observation. To deal with the problem of outliers a separate branch of statistics, called robust statistics (Hampel, 1986, Huber, 1981), was developed. Robust statistical methods are designed to act well when the true underlying model deviates from the assumed parametric model. Ideally, they should be efficient and reliable for the observations that are very close to the assumed model and simultaneously for the observations containing larger deviations and outliers. The other way is to detect and remove outliers before the beginning of the model building process. Such methods are more universal but they do not take into account the specific type of modeling philosophy (e.g. modeling by the FFNs). In this article we propose new robust FFNs learning algorithm based on the least trimmed squares estimator.
Statistical Modelling of Highly Inflective Languages
Authors: Sepesy Maucec Mirjam , Kacic Zdravko
Published: May 2011

A language model is a description of language. Although grammar has been the prevalent tool in modelling language for a long time, interest has recently shifted towards statistical modelling. This chapter refers to speech recognition experiments, although statistical language models are applicable over a wide-range of applications: machine translation, information retrieval, etc. Statistical modelling attempts to estimate the frequency of word sequences.
Support Vector Machines
Authors: Angulo Cecilio , Gonzalez-Abril Luis
Published: May 2011

Support Vector Machines -- SVMs -- are learning machines, originally designed for bi-classification problems, implementing the well-known Structural Risk Minimization (SRM) inductive principle to obtain good generalization on a limited number of learning patterns (Vapnik, 1998). The optimization criterion for these machines is maximizing the margin between two classes, i.e. the distance between two parallel hyperplanes that split the vectors of each one of the two classes, since larger is the margin separating classes, smaller is the VC dimension of the learning machine, which theoretically ensures a good generalization performance (Vapnik, 1998), as it has been demonstrated in a number of real applications (Cristianini, 2000). In its formulation is applicable the kernel trick, which improves the capacity of these algorithms, learning not being directly performed in the original space of data but in a new space called feature space; for this reason this algorithm is one of the most representative of the called Kernel Machines (KMs). Main theory was originally developed on the sixties and seventies by V. Vapnik and A. Chervonenkis (Vapnik et al., 1963, Vapnik et al., 1971, Vapnik, 1995, Vapnik, 1998), on the basis of a separable binary classification problem, however generalization in the use of these learning algorithms did not take place until the nineties (Boser et al., 1992). SVMs has been used thoroughly in any kind of learning problems, mainly in classification problems, although also in other problems like regression (Schölkopf et al., 2004) or clustering (Ben-Hur et al., 2001). The fields of Optic Character Recognition (Cortes et al., 1995) and Text Categorization (Sebastiani, 2002) were the most important initial applications where SVMs were used. With the extended application of new kernels, novel applications have taken place in the field of Bioinformatics, concretely many works are related with the classification of data in Genetic Expression (Microarray Gene Expression) (Brown et al., 1997) and detecting structures between proteins and their relationship with the chains of DNA (Jaakkola et al., 2000). Other applications include image identification, voice recognition, prediction in time series, etc. A more extensive list of applications can be found in (Guyon, 2006).
2D-PAGE Analysis Using Evolutionary Computation
Authors: Mesejo Pablo , Fernández-Blanco Enrique , Martínez-Feijóo Diego , Blanco Francisco J.
Published: May 2011

This paper presents the preliminary studies for the creation of a new tool to assist in medical diagnostic. The tool will help in the analysis of 2D-PAGE images. In order to create a 2D-PAGE image of an ideal patient—the patient could be healthy or ill—the tool will help us in the creation of an image that facilitates and speeds up future diagnostics. The creation of a master image has motivated the development of a tool to alignment gel images. The tool will make easier the correspondence among the proteins into the ideal image and the ones of a new image. Due to the fact that image registering process is quite complex, we use the Intel’s library OpenCV which provides functions to calculate optical flow and translation vectors. This library introduces into the project a set of variables unknown by the facultative. To solve this, an automatic selection of values for this set of variables is necessary. This last task is made with the Evolutionary Computation technique called Particle Swarm Optimization (Kennedy, R. & Eberhart, J. 1995)
Bridging the Digital Divide through Broadband Deployment
Authors: Radhakumari Challa
Published: May 2011

This chapter provides a summary relating to the functioning of two projects in the two Southern States of India, Andhra Pradesh and Kerala, to show how through broadband deployment in rural areas the digital divide can be bridged. By focusing on the implementation of the two projects, the chapter illustrates their contribution in practically using the broadband technologies in overcoming the hurdles to bridging the digital divide, and highlights the critical success factors as identified during the functioning of the projects which helped the states in achieving their goals. The chapter also reveals through its analysis that the accessibility of services through broadband technology have brought an opportunity to the citizens to become a part of the current knowledge revolution, besides bringing about a great technological transformation to the areas where it is implemented, and thus contributed to bridging the digital divide. The chapter is finally concluded by proving that moving from a manual to electronic process with broadband technology as an enabler; the States set an example, which will serve as a set of guidelines for application of similar projects in other geographical settings.
The Internet's Impact on the Law and Economics Of the US's Motion Picture Industry
Authors: Levin Stanford L. , Meisel John B. , Sullivan Timothy S.
Published: May 2011

This chapter describes the far-reaching effects of broadband Internet access on the motion picture industry. It first provides a summary of the effects on the industry’s business model: the Internet (particularly when combined with broadband connections) provides a new window for the movie studios to utilize in releasing their product. It next examines the ways that legal, political, and cultural environments are already influencing the industry’s search for a new business model to replace the old. Finally, we draw on lessons from the music industry to predict how the industry will ultimately incorporate broadband technology into a new business model. The authors believe that the motion picture industry provides an excellent case study of broadband’s effects on a mature industry.
Explaining Patterns of Broadband Development in OECD Countries
Authors: Ferreruela Inmaculada Cava
Published: May 2011

The aim of this chapter is to provide some insights about the explaining patterns of broadband deployment and adoption. This problem is addressed by examining them in the light of the results of an exhaustive cross-national empirical analysis that uses a comprehensive panel data set from the 30 OECD countries with more than 40 features. The results suggest that technological competition and the low cost of deploying infrastructures on one side, and the predisposition to use new technologies as well as some social indicators on the other, appear to be the key drivers for broadband deployment and adoption, respectively.
Evaluation of Quantified Statements Using Gradual Numbers
Authors: Liétard Ludovic , Rocacher Daniel
Published: May 2011

This chapter is devoted to the evaluation of quantified statements which can be found in many applications as decision making, expert systems, or flexible querying of relational databases using fuzzy set theory. Its contribution is to introduce the main techniques to evaluate such statements and to propose a new theoretical background for the evaluation of quantified statements of type “Q X are A” and “Q B X are A.” In this context, quantified statements are interpreted using an arithmetic on gradual numbers from Nf, Zf, and Qf. It is shown that the context of fuzzy numbers provides a framework to unify previous approaches and can be the base for the definition of new approaches.
Applying Fuzzy Data Mining to Tourism Area
Authors: Carrasco R. A. , Araque F. , Salguero A. , Vila M. A.
Published: May 2011

Soaring is a recreational activity and a competitive sport where individuals fly un-powered aircrafts known as gliders. The soaring location selection process depends on a number of factors, resulting in a complex decision-making task. In this chapter, we propose the use of an extension of the FSQL language for fuzzy queries as one of the techniques of data mining that can be used to solve the problem of offering a better place for soaring given the environmental conditions and customer characteristics. The FSQL language is an extension of the SQL language that permits us to write flexible conditions in our queries to a fuzzy or traditional database. After doing a process of clustering and characterization of a large customer database in a data warehouse, we are able of classify the next clients in a cluster and offer an answer according to it.
Applying Fuzzy Logic in Dynamic Causal Mining
Authors: Wang Yi
Published: May 2011

This chapter applies fuzzy logic to a dynamic causal mining (DCM) algorithm and argues that DCM, a combination of association mining and system dynamics for discovering causality patterns, needs a potentially more substantive approach for the user to understand the nature of extracted rules and information in a variety of contexts. Furthermore, the author hopes that the use of fuzzy logic will not only assist the user to make better decisions, but also assist in a better understanding of future behaviour of the target system.
Fuzzy Imputation Method for Database Systems
Authors: Peláez J. I. , Doña J. M. , La Red D.
Published: May 2011

Missing data is often an actual problem in real data sets, and different imputation techniques are normally used to alleviate this problem. Imputation is a method to fill in missing data with plausible values to produce a complete data set. In this chapter, we analyze the performance of the different traditional data imputation methods. A new fuzzy imputation approach is proposed using ordered weighted average operators and the majority concept. In order to form the majority concept, we propose the use of neat OWA operators and linguistic quantifiers with two fusion strategies for aggregation operators.