bluuebunny/crossref_metadata_2025 huggingface dataset:
Introduction
Authors: Sidhu Manjit Singh
Published: May 2010

Computer aided learning (CAL) is a terminology used for delivering educational experiences electronically. CAL materials may use any combination of teaching techniques including question and answer, simulation, multimedia, didacticism (tending to convey information), or problem solving. CAL environments increasingly are using a combination of interactive multimedia and virtual reality (VR) such as text, audio and video, graphics and images, two and three dimensional animations, and simulations in presenting learning materials. Interactive refers to the way the user engages in these environments to enhance his/her learning process.
Hardware and Software for Multimedia Development
Authors: Sidhu Manjit Singh
Published: May 2010

The aforementioned statement is essential to ponder when it comes to multimedia hardware and software consideration. Computers are now making it possible to blend or integrate together multimedia elements such as audio, video, graphics, images and animation into a single learning package. However, blending these multimedia elements together to develop a learning package does not mean that student’s proficiency in the subject matter could be enhanced. Furthermore it is not necessary to convert the entire textbook into a full working multimedia package for students to learn. Selected problems that are difficult to explain to the students from the textbook could be more appropriate and beneficial to the students if shown in the form of motion. On the other hand selecting appropriate multimedia elements and authoring tools could be difficult tasks for a new multimedia author. This is because proper development design process and careful selection of multimedia elements should be used appropriately to develop a high quality and cost effective package that could engage learners in their learning. Understanding the overall development process is an essential part for a multimedia learning package.
Evaluation of TAPS Packages
Authors: Sidhu Manjit Singh
Published: May 2010

The evaluation was carried out to examine the distribution of learning styles (discussed in Chapter 2) of the third year undergraduate engineering students and suggest effective problem solving approaches that could increase the motivation and understanding of slow learners at UNITEN. For this study, a sample target population of 60 third year undergraduate engineering students who had taken the Engineering Mechanics subject was tested. These students were selected based on their second year grade point average (GPA) of less than 2.5 as this study emphasizes on slow learners.
Exploring the Unknown Nature of Data
Authors: Xu Rui , Wunsch II Donald C.
Published: May 2010

To classify objects based on their features and characteristics is one of the most important and primitive activities of human beings. The task becomes even more challenging when there is no ground truth available. Cluster analysis allows new opportunities in exploring the unknown nature of data through its aim to separate a finite data set, with little or no prior information, into a finite and discrete set of “natural,” hidden data structures. Here, the authors introduce and discuss clustering algorithms that are related to machine learning and computational intelligence, particularly those based on neural networks. Neural networks are well known for their good learning capabilities, adaptation, ease of implementation, parallelization, speed, and flexibility, and they have demonstrated many successful applications in cluster analysis. The applications of cluster analysis in real world problems are also illustrated. Portions of the chapter are taken from Xu and Wunsch (2008).
Learning Algorithms for RBF Functions and Subspace Based Functions
Authors: Xu Lei
Published: May 2010

Among extensive studies on radial basis function (RBF), one stream consists of those on normalized RBF (NRBF) and extensions. Within a probability theoretic framework, NRBF networks relates to nonparametric studies for decades in the statistics literature, and then proceeds in the machine learning studies with further advances not only to mixture-of-experts and alternatives but also to subspace based functions (SBF) and temporal extensions. These studies are linked to theoretical results adopted from studies of nonparametric statistics, and further to a general statistical learning framework called Bayesian Ying Yang harmony learning, with a unified perspective that summarizes maximum likelihood (ML) learning with the EM algorithm, RPCL learning, and BYY learning with automatic model selection, as well as their extensions for temporal modeling. This chapter outlines these advances, with a unified elaboration of their corresponding algorithms, and a discussion on possible trends.
Clustering and Visualization of Multivariate Time Series
Authors: Vellido Alfredo , Olier Iván
Published: May 2010

The exploratory investigation of multivariate time series (MTS) may become extremely difficult, if not impossible, for high dimensional datasets. Paradoxically, to date, little research has been conducted on the exploration of MTS through unsupervised clustering and visualization. In this chapter, the authors describe generative topographic mapping through time (GTM-TT), a model with foundations in probability theory that performs such tasks. The standard version of this model has several limitations that limit its applicability. Here, the authors reformulate it within a Bayesian approach using variational techniques. The resulting variational Bayesian GTM-TT, described in some detail, is shown to behave very robustly in the presence of noise in the MTS, helping to avert the problem of data overfitting.
Nonstationary Signal Analysis with Kernel Machines
Authors: Honeine Paul , Richard Cédric , Flandrin Patrick
Published: May 2010

This chapter introduces machine learning for nonstationary signal analysis and classification. It argues that machine learning based on the theory of reproducing kernels can be extended to nonstationary signal analysis and classification. The authors show that some specific reproducing kernels allow pattern recognition algorithm to operate in the time-frequency domain. Furthermore, the authors study the selection of the reproducing kernel for a nonstationary signal classification problem. For this purpose, the kernel-target alignment as a selection criterion is investigated, yielding the optimal time-frequency representation for a given classification problem. These links offer new perspectives in the field of nonstationary signal analysis, which can benefit from recent developments of statistical learning theory and pattern recognition.
Machine Learning in Personalized Anemia Treatment
Authors: Gaweda Adam E.
Published: May 2010

This chapter presents application of reinforcement learning to drug dosing personalization in treatment of chronic conditions. Reinforcement learning is a machine learning paradigm that mimics the trialand- error skill acquisition typical for humans and animals. In treatment of chronic illnesses, finding the optimal dose amount for an individual is also a process that is usually based on trial-and-error. In this chapter, the author focuses on the challenge of personalized anemia treatment with recombinant human erythropoietin. The author demonstrates the application of a standard reinforcement learning method, called Q-learning, to guide the physician in selecting the optimal erythropoietin dose. The author further addresses the issue of random exploration in Q-learning from the drug dosing perspective and proposes a “smart” exploration method. Finally, the author performs computer simulations to compare the outcomes from reinforcement learning-based anemia treatment to those achieved by a standard dosing protocol used at a dialysis unit.
Application to Bankruptcy Prediction in Banks
Authors: Ordieres-Meré Joaquín , González-Marcos Ana , Castejón-Limas Manuel , Martínez-de-Pisón Francisco J.
Published: May 2010

This chapter reports five experiences in successfully applying different data mining techniques in a hotdip galvanizing line. Engineers working in steelmaking have traditionally built mathematical models either for their processes or products using classical techniques. Their need to continuously cut costs down while increasing productivity and product quality is now pushing the industry into using data mining techniques so as to gain deeper insights into their manufacturing processes. The authors’ work was aimed at extracting hidden knowledge from massive data bases in order to improve the existing control systems. The results obtained, though small at first glance, lead to huge savings at such high volume production environment. The effective solutions provided by the use of data mining techniques along these projects encourages the authors to continue applying this data driven approach to frequent hard-to-solve problems in the steel industry.
Software Cost Estimation using Soft Computing Approaches
Authors: Vinaykumar K. , Ravi V. , Carr Mahil
Published: May 2010

Software development has become an essential investment for many organizations. Software engineering practitioners have become more and more concerned about accurately predicting the cost of software products to be developed. Accurate estimates are desired but no model has proved to be successful at effectively and consistently predicting software development cost. This chapter investigates the use of the soft computing approaches in predicting the software development effort. Various statistical and intelligent techniques are employed to estimate software development effort. Further, based on the abovementioned techniques, ensemble models are developed to forecast software development effort. Two types of ensemble models viz., linear (average) and nonlinear are designed and tested on COCOMO’81 dataset. Based on the experiments performed on the COCOMO’81 data, it was observed that the nonlinear ensemble using radial basis function network as arbitrator outperformed all the other ensembles and also the constituent statistical and intelligent techniques. The authors conclude that using soft computing models they can accurately estimate software development effort.
Neural Networks for Modeling the Contact Foot-Shoe Upper
Authors: Rupérez M. J. , Martín J. D. , Monserrat C. , Alcañiz M.
Published: May 2010

Recently, important advances in virtual reality have made possible real improvements in computer aided design, CAD. These advances are being applied to all the fields and they have reached to the footwear design. The majority of the interaction foot-shoe simulation processes have been focused on the interaction between the foot and the sole. However, few efforts have been made in order to simulate the interaction between the shoe upper and the foot surface. To simulate this interaction, flexibility tests (characterization of the relationship between exerted force and displacement) are carried out to evaluate the materials used for the shoe upper. This chapter shows a procedure based on artificial neural networks (ANNs) to reduce the number of flexibility tests that are needed for a comfortable shoe design. Using the elastic parameters of the material as inputs to the ANN, it is possible to find a neural model that provides a unique equation for the relationship between force and displacement instead of a different characteristic curve for each material. Achieved results show the suitability of the proposed approach.
Evolutionary Multi-Objective Optimization of Autonomous Mobile Robots in Neural-Based Cognition for Behavioural Robustness
Authors: On Chin Kim , Teo Jason , Saudi Azali
Published: May 2010

The utilization of a multi-objective approach for evolving artificial neural networks that act as the controllers for phototaxis and radio frequency (RF) localization behaviors of a virtual Khepera robot simulated in a 3D, physics-based environment is discussed in this chapter. It explains the comparison performances among the elitism without archive and elitism with archive used in the evolutionary multi-objective optimization (EMO) algorithm in an evolutionary robotics study. Furthermore, the controllers’ moving performances, tracking ability and robustness also have been demonstrated and tested with four different levels of environments. The experimentation results showed the controllers allowed the robots to navigate successfully, hence demonstrating the EMO algorithm can be practically used to automatically generate controllers for phototaxis and RF-localization behaviors, respectively. Understanding the underlying assumptions and theoretical constructs through the utilization of EMO will allow the robotics researchers to better design autonomous robot controllers that require minimal levels of human-designed elements.
Improving Automated Planning with Machine Learning
Authors: Fernández Arregui Susana , Jiménez Celorrio Sergio , de la Rosa Turbides Tomás
Published: May 2010

This chapter reports the last machine learning techniques for the assistance of automated planning. Recent discoveries in automated planning have opened the scope of planners, from toy problems to real-world applications, making new challenges come into focus. The planning community believes that machine learning can assist to address these new challenges. The chapter collects the last machine learning techniques for assisting automated planners classified in: techniques for the improvement of the planning search processes and techniques for the automatic definition of planning action models. For each technique, the chapter provides an in-depth analysis of their domain, advantages and disadvantages. Finally, the chapter draws the outline of the new promising avenues for research in learning for planning systems.
Operating Room Simulation and Agent-Based Optimization
Authors: Peng Q. , Niu Q , Xie Y. , ElMekkawy T.
Published: May 2010

Healthcare systems are characterized by uncertainty, variability, complexity, and human roles. Simulation can test scenarios of changes in processes, resources, and schedules without major physical investment or risk. Agent-based technology can model systems with autonomous and interacting activities. This chapter introduces the method of using simulation and agent-based technologies to enable a better understanding of the patient flow to improve the process performance in healthcare. The proposed method is used to identify the existing problem and to evaluate proposed solutions for the problem of the operating room (OR) at Winnipeg Health Sciences Centre. Issues are identified including patient flows, operation schedules, demand and capacity of the system and the configuration of resources required. An optimum scheduling is proposed for the OR operation to shorten the patient waiting time.
HeCaSe2
Authors: Isern David , Moreno Antonio
Published: May 2010

Clinical guidelines (CGs) contain a set of directions or principles to assist the healthcare practitioner with patient care decisions about appropriate diagnostic, therapeutic, or other clinical procedures for specific clinical circumstances. It is widely accepted that the adoption of guideline-execution engines in daily practice would improve the patient care, by standardising the care procedures. Guideline-based systems constitute part of a knowledge-based decision support system in order to deliver the right knowledge to the right people in the right form at the right time. The automation of the guideline execution process is a basic step towards its widespread use in medical centres. To achieve this general goal, different topics should be tackled, such as the acquisition of clinical guidelines, its formal verification, and finally its execution. This chapter focuses on the execution of CGs and describes the design and implementation of an agent-based platform in which the actors involved in health care coordinate their activities to perform the complex task of guideline enactment.
Time Management and Temporal Personalities
Authors: Wu Dezhi
Published: May 2010

Personal productivity is achieved through an effective time management practice. Due to the advancement of modern telecommunication and information technologies, today’s professionals are taxed with more and more tasks and given shorter time periods to complete them than before. Companies strive to push their new products to the market in order to gain as much profit as possible in the shortest period. Time is regarded as a primary factor to measure both personal and organizational successes. First, time management tactics in practice and some suggested hints for improving time management are introduced. In the existing literature, time management research is scant in that time is a fairly complicated concept with many variables involved in different contexts. In order to provide some theoretical foundation in this area, some empirical time management research is reviewed and four key constructs are identified to measure the quality of individual time management: planning, meeting deadlines, sensing a lack of time control and engaging in procrastination behavior. To further understand how individuals perceive time, psychological aspects of time are also explored, particularly focusing on individuals’ time urgency and time perspective, which indicates individuals’ perceptions on the passage of time and future time. These perceptions support how individuals exhibit meeting their deadlines and engaging in procrastination behaviors, and therefore, can be used to identify whether time managers are effective or not.
Identifying what Constitutes the Quality of Individual Time Management and How Individuals Process Temporal Structure Information
Authors: Wu Dezhi
Published: May 2010

The purpose of this book is to provide useful user requirements for capturing and designing more extensive temporal structures within the current electronic calendar systems through a series of in-depth user studies. Chapter 5 presents the study results of two in-depth interviews with twenty professionals for identifying what types of temporal structures are being used in personal time management practices and possible design implications to further design the current electronic calendar tools. In order to deepen our understanding of how individual time management quality is related to the various temporal structures, it is necessary to conduct a much larger scale user study to further support the findings reported in Chapter 5. If we could possibly obtain solid support from a larger user study, we would be more confident to state that we need to enhance the design of the current electronic calendar systems through incorporating more extensive temporal structure features.
Theoretical Foundations of Teachers' Professional Development
Authors: Helleve Ingrid
Published: May 2010

To be a professional teacher or teacher educator means to participate in an ongoing learning process. The main concern of teachers is to guide and help students to learn. This means that teaching is by its nature closely connected to personal attitudes and values. Accordingly teaching and teachers’ professional development cannot merely be dictated by policy-makers. Ongoing learning and reflection concerning education has to be built on teachers’ own participation. Recent research shows that teacher educators undergo the same kind of development as teachers do. Throughout this chapter the author argues for a close connection between teachers and teacher educators as a prerequisite for ongoing professional development in education. Possibilities to communicate through online learning communities have made reflective activities through action research between distant educational environments easier to organize and facilitate.
Thinking Things Through
Authors: Cuthell John P.
Published: May 2010

One of the most powerful ways of changing our thinking about how we teach and learn is to experience for ourselves the power of collaborative project-based experiential learning. Few teachers have had the opportunity to learn in this way, and this creates barriers for those who want to change their pedagogy. The Oracle Education Foundation’s Project Learning Institute provides teachers with the experience of collaborative project-based learning, using ThinkQuest® to create their own curriculum project. By collaborating with their peers, tutors and mentors, teachers are able to model the projects, environment and experiences they want for their classes through a blended learning experience. This chapter describes the model of continuing professional development and its impact on schools, pedagogies and professional philosophies.
Computer Technology in Taiwan Kindergartens
Authors: Lee Yu-Yuan
Published: May 2010

Taiwan has become a global high-tech center. The success of becoming the leading country of high-tech and information technology is accredited to the efforts of the government and of all citizens. In addition, Chinese highly value in academic success, and this has contributed to the success of Taiwan. Parents believe the success of life is rooted in a good education, especially in the early years. This chapter presents an overview of early childhood education in Taiwan and explores how Chinese culture had influenced the value of parents’ expectation in education, as well as how children learn through the use of technology. The last part of the chapter discusses the discrepancy between current early childhood education situations and teachers’ professional development.
Technology and its Role in Teacher Education
Authors: McMurtry Zelda , Burkett Candice
Published: May 2010

This chapter discusses technology and its role in teacher education. In order for technology to reach the early childhood classroom, it must first reach the classroom of the teacher education programs. The effect of self-efficacy on preschool teachers use and understanding of technology may influence their instructional applications. Early childhood faculty come from the same career pool as early childhood teachers and may share the same beliefs that practitioners have concerning technology. Teacher education programs have a responsibility to produce professionals who are literate in technology tools and applications.
Using Assistive Technology
Authors: Crow Rene
Published: May 2010

This chapter explores how technology is used with young children with special needs in the United States. It also discusses the legal issues and mandates and the reality of how teachers and schools are dealing with children with special needs in early childhood settings. Information resources and how assistive technology fits into developmentally appropriate practice is included in this discussion.
Technology Integration in Early Childhood and Primary Classrooms
Authors: Grant Michael M. , Mims Clif
Published: May 2010

Calls for increased use of technology in early childhood and primary classrooms have not gone unanswered. However, recent research findings report little technology integration with computers continuing to be unavailable. This descriptive study looked to explore to what extent and in what ways technology is integrated into early childhood and primary classrooms. Findings corroborate previous dated research that trivial technology is being used. Technology use, computer access and styles of pedagogy remain critical in the debate to whether teachers will integrate computers for teaching and learning.
Improving the Performance of Virtual Teams through Team Dynamics
Authors: Shwarts-Asher Daphna
Published: May 2010

The aim of this chapter is to understanding how virtual teams operate in organizations. Qualitative data was collected by interviewing 20 virtual team managers and members of 20 different organizations. A semi-structured interview format was used to collect extensive information of the characteristics of the organizations, what projects virtual team participated in, how virtual teams operate and the difficulties virtual teams face. Using a comprehensive literature review and interviews summaries, a model, suggesting that team dynamics can increase the teams’ output, was developed, and propositions that are raised by the model are discussed. The virtual team is a common way of working nowadays, and with the growing use of Internet applications and firms’ globalization the use of virtual teams will expand in the future. Thus, this chapter provides new directions for future research in the field of virtual teams.
The Impact of Culture on the Application of the SECI Model
Authors: Haag Markus , Duan Yanqing , Mathews Brian
Published: May 2010

The concept of culture and its relationship with Nonaka’s SECI model, a widely used model of organizational knowledge creation, is discussed in this chapter. Culture, in various forms, is argued to impact on the SECI model and the model itself is embedded in a certain context. This context determines the characteristics of the knowledge creation modes as described by SECI and therefore makes the model either more, or less, pertinent in a given context. This is regardless of whether that context is primarily determined by national culture, organizational culture or other factors. Differences in emphases in a given contextual environment on either tacit or explicit knowledge also impacts on knowledge creation as defined by SECI. Finally, it is emphasized that being conscious of the cultural situatedness of the SECI model can lead to a more adequate use of the model for organizational knowledge creation.
Knowledge, Culture, and Cultural Impact on Knowledge Management
Authors: Harorimana Deogratias
Published: May 2010

This chapter offers a taste of the ingredients for further debates that continue to emerge from within knowledge management communities. The author has identified the nuts and bolts of the debate encountered by managers who find themselves faced with high costs involved in breaking cultural barriers, and offers suggestions as to how these can be overcome. From an academic perspective, the author argues that successful knowledge creation and management comes from the combination of two schools of thought – social and technological - and that any considerations that sideline either of these will be wrong or may be hard to justify, when related to the claim of best practice and/or the rationale of quality delivery of the business case. The chapter argues that current organisational practices involving a strong emphasis on team work and the ability to use technologies dominate business operations hence, it is equally important to unblock the human factors that are likely to hinder people’s interaction within a team as it is to keep to the minimum physical barriers and systems that may impede this exercise.
Strategising Impression Management in Corporations
Authors: Kamau Caroline
Published: May 2010

Impression management is a powerful psychological phenomenon with much unexplored potential in corporate settings. Employees or corporations can deploy impression management strategies in order to manipulate others’ perceptions of them. Cultural knowledge is powerful capital in impression management, yet this has not been sufficiently explored in previous literature. This chapter argues that impression-motivated employees or corporations need to perform a three-step knowledge audit: (i) knowing what their impression deficits are; (ii) knowing what impression management strategy is needed to address that deficit, based on the taxonomy of impression management strategies tabulated here; (iii) knowing what societal (e.g. collectivist culture or individualist culture) or organization-specific cultural adjustments are needed. A cultural knowledge base can thus be created through cross-cultural training of and knowledge transfer by expatriates. Multinational corporations can also benefit from utilising the knowledge presented in this chapter in their international public relations efforts.
Toward a Living Systems Framework for Unifying Technology and Knowledge Management, Organizational, Cultural and Economic Change
Authors: Bond Peter L.
Published: May 2010

This chapter raises difficult questions regarding the validity and motive for prolonging current forms of economic development and competition in the face of the much heralded global environmental crisis threatened by humankind’s success as a species. In response, a living systems theoretical framework is introduced that provides many elements of a possible new paradigm of economic development one that closes the gap between the social and natural sciences. New forms of explanation for organization and culture are developed from the perspective of complexity science to produce a synthesis of knowledge management and new philosophical, sociological, anthropological, and, distinctively, biological perspectives of technology, which effectively reconciles the practices of technology, knowledge and cultural change management.
Strengthening Knowledge Transfer between the University and Enterprise
Authors: Pineda José L. , Zapata Laura Esther , Ramírez Jacobo
Published: May 2010

In today’s world, where uncertainty and the rapidity of technological changes predominate, companies need to generate and adopt knowledge continuously in order to build a sustainable competitive advantage. In this context, analyzing the collaborative relationships existing between the university and firms is relevant. The aim of this chapter is to explore the role of the university as a generator and disseminator of knowledge, as well as the difficulties it faces in making the results of its research available to the business world. The collaboration efforts between the academic and business worlds are assessed in order to ultimately propose the review of teaching, continuing education, and consulting as knowledge dissemination channels. This research project has been conducted in the context of a Mexican university. Besides the findings of the current and future research projects, the matter of the question is the redefinition of the university and its role in society. In business schools in particular, the pending issue is to discuss the basic aim of academic research in management.
Mentoring and the Transfer of Organizational Memory within the Context of an Aging Workforce
Authors: Dunham Annette H. , Burt Christopher D.B.
Published: May 2010

Organizational memory, the knowledge gained from organizational experience, has significant potential for competitive advantage. Many authors in the knowledge management and human resource management literatures consider mentoring to be a particularly effective method of transferring organizational memory. In addition, older workers are often considered ideal mentors in organizations because of their experience and alleged willingness to pass on their knowledge to less experienced employees. There is an associated assumption that these workers also anticipate and experience positive outcomes when mentoring others. This chapter considers whether these assumptions hold up in the workplaces of the 21st century, particularly within Western countries. Individualistic cultural norms and some discriminatory practices towards older workers, along with a changing career contract that no longer guarantees employment in one organization for life, may discourage knowledge sharing in organizations. This chapter discusses the constraints and motivations that may operate when older experienced workers consider mentoring others. It considers relevant global and organizational cultural characteristics that may influence mentoring to transfer knowledge, and accordingly suggests strategies for those eager to capitalise on the knowledge experienced employees possess.
Overcoming Reticence to Aid Knowledge Creation Between Universities and Business
Authors: Philpott Elly , Beaumont-Kerridge John
Published: May 2010

This chapter argues the case for a proactive process to facilitate knowledge creation between universities and small to medium size enterprises (SMEs). Cultural issues dictating reticence of engagement are discussed as well as the inhibitors that prevent the free interchange of knowledge. The chapter shows how reticence can be overcome by serving the needs of both parties and how knowledge created through successful interaction can be measured. The knowledge creation process itself is analysed in the context of Nonaka’s SECI model. The chapter concludes with recommendations for the reader on areas for public investment to enhance the knowledge transfer process and provides lessons learned for the measurement of knowledge transfer at these interfaces. The outcomes are of value to those interested in the continuing applicability of Nonaka’s work outside of the heavy industrial context as well as to those interested in the traditional problems associated with knowledge transfer between universities and SMEs.
Outsourcing Opportunities
Authors: Solli-Sæther Hans , Gottschalk Petter
Published: May 2010

In this chapter on outsourcing opportunities, a common conceptual understanding of important issues of the business practice of our interest is established. First, we take a look at how researchers have defined the term outsourcing. An updated outsourcing definition will serve as a common platform for understanding the sourcing universe presented in the following section. Next, we describe in more detail the building blocks of the sourcing universe – insourcing, information technology (IT) and information systems (IS) outsourcing, business process outsourcing, transformational outsourcing, and global outsourcing. At the end of the chapter, we discuss some key aspects of the important outsourcing decision.
Maturity in Outsourcing Relationships
Authors: Solli-Sæther Hans , Gottschalk Petter
Published: May 2010

Stages of growth models have been used widely in both organizational research and information technology management research. According to King and Teo (1997), these models describe a wide variety of phenomena – the organizational life cycle, product life cycle, biological growth, stages of growth in information systems, growth model for integration between business planning and information systems planning, electronic commerce evolution, stages of knowledge management technology, and a number of other interesting developments in time perspectives. These models assume that predictable patterns (conceptualized in terms of stages or levels) exist in the growth of organizations and organizational parts, the sales levels of products, and the growth of living organisms. These stages are (1) sequential in nature, (2) occur as a hierarchical progression that is not easily reversed, and (3) evolve a broad range of organizational activities and structures. This chapter starts with an introduction to stages of growth models. In the following sections we present the three-stage model for the evolution of IT outsourcing relationships (Gottschalk & Solli-Sæther, 2006). The three stages are labelled cost stage, resource stage, and partnership stage respectively. Theory-based benchmark variables for measuring maturity in IT outsourcing relationships are presented, followed by the stage hypothesis and a description of how benchmark variables are used to indicate characteristics at each stage of growth. Finally in this chapter, we present results from an exploratory study testing the stage model. The purpose of this chapter is to develop a framework for improved understanding of the current situation in an IT outsourcing relationship in terms of a specific stage, to develop strategies for moving to a higher stage in the future, and to learn from earlier stage experience.
Dynamically Reconfigurable Hardware for Evolving Bio-Inspired Architectures
Authors: Upegui Andres
Published: May 2010

During the last few years, reconfigurable computing devices have experienced an impressive development in their resource availability, speed, and configurability. Currently, commercial FPGAs offer the possibility of self-reconfiguring by partially modifying their configuration bit-string, providing high architectural flexibility, while guaranteeing high performance. On the other hand, we have bio-inspired hardware, a large research field taking inspiration from living beings in order to design hardware systems, which includes diverse approaches like evolvable hardware, neural hardware, and fuzzy hardware. Living beings are well known for their high adaptability to environmental changes, featuring very flexible adaptations at several levels. Bio-inspired hardware systems require such flexibility to be provided by the hardware platform on which the system is implemented. Even though some commercial FPGAs provide enhanced reconfigurability features such as partial and dynamic reconfiguration, their utilization is still in the early stages and they are not well supported by FPGA vendors, thus making their inclusion difficult in existing bio-inspired systems. This chapter presents a set of methodologies and architectures for exploiting the reconfigurability advantages of current commercial FPGAs in the design of bio-inspired hardware systems. Among the presented architectures are neural networks, spiking neuron models, fuzzy systems, cellular automata and Random Boolean Networks.
A Self-Organizing Neural Network to Approach Novelty Detection
Authors: Albertini Marcelo Keese , Fernandes de Mello Rodrigo
Published: May 2010

Machine learning is a field of artificial intelligence which aims at developing techniques to automatically transfer human knowledge into analytical models. Recently, those techniques have been applied to time series with unknown dynamics and fluctuations in the established behavior patterns, such as humancomputer interaction, inspection robotics and climate change. In order to detect novelties in those time series, techniques are required to learn and update knowledge structures, adapting themselves to data tendencies. The learning and updating process should integrate and accommodate novelty events into the normal behavior model, possibly incurring the revaluation of long-term memories. This sort of application has been addressed by the proposal of incremental techniques based on unsupervised neural networks and regression techniques. Such proposals have introduced two new concepts in time-series novelty detection. The first defines the temporal novelty, which indicates the occurrence of unexpected series of events. The second measures how novel a single event is, based on the historical knowledge. However, current studies do not fully consider both concepts of detecting and quantifying temporal novelties. This motivated the proposal of the self-organizing novelty detection neural network architecture (SONDE) which incrementally learns patterns in order to represent unknown dynamics and fluctuation of established behavior. The knowledge accumulated by SONDE is employed to estimate Markov chains which model causal relationships. This architecture is applied to detect and measure temporal and nontemporal novelties. The evaluation of the proposed technique is carried out through simulations and experiments, which have presented promising results.
A Review on Evolutionary Prototype Selection
Authors: García Salvador , Cano José Ramón , Herrera Francisco
Published: May 2010

Evolutionary algorithms have been successfully used in different data mining problems. Given that the prototype selection problem could be seen as a combinatorial problem, evolutionary algorithms have been used to solve it with promising results. This chapter presents an evolutionary data mining application known as evolutionary prototype selection. Various approaches have been proposed in the literature following two strategies on the use of evolutionary algorithms: general evolutionary models and models specific to prototype selection problem. In this chapter, the authors review the representative evolutionary prototype selection algorithms proposed, give their description and analyze their performance in terms of efficiency and effectiveness. They study their performance considering different sizes of the data sets, and analyze their behavior when the database scales up. The results are statistically contrasted in order to argue the benefits and drawbacks of each model.
Evolutionary Based Adaptive User Interfaces in Complex Supervisory Tasks
Authors: Yen Gary G.
Published: May 2010

In this chapter, the author proposes a novel idea based on evolutionary algorithm for adaptation of the user interface in complex supervisory tasks. Under the assumption that the user behavior is stationary and that the user has limited cognitive and motor abilities, the author has shown that a combination of genetic algorithm for constrained optimization and probabilistic modeling of the user may evolve the adaptive interface to the level of personalization. The non-parametric statistics has been employed to evaluate the feasibility of the ranking approach. The method proposed is flexible and easy to use in various problem domains. The author has tested the method with an automated user and a group of real users in an air traffic control environment. The automated user, implemented for initial tests, is built under the same assumptions as a real user. In the second step, the author has exploited the adaptive interface through a group of real users and collected subjective ratings using questionnaires. The author has shown that the proposed method can effectively improve human-computer interaction and his approach is pragmatically a valid design for the interface adaptation in complex environments.
Ant Colony Programming
Authors: Boryczka Mariusz
Published: May 2010

Automatic programming is the method in which a computer program is constructed automatically based on the specification of goals which are to be realized. This chapter describes one of the methods for automatic function approximation (as a form of automatic programming) – ant colony programming (ACP). It is based on ant colony system (ACS) as a new method for solving approximation problems. While solving these problems by ACP two approaches are used: the expression approach and the program approach. Several improvements of this method are presented, including the elimination of introns, the use of a structure similar to the candidate list introduced in ACS, and parameter-tuning. The chapter first describes ACS and introduces the problem of symbolic regression. Then, ACP is defined. After that, improvements of ACP are presented. The main objective of the chapter is to give an overview of the published results of studies carried out on ACP, while at the same time present a new idea in the process of parameter-tuning.
The SYNCROMAX Solution for Air Traffic Flow Management in Brazil
Authors: Timoszczuk Antonio Pedro , Pizzo Walter Nogueira , Staniscia Giacomo Feres , Siewerdt Eno
Published: May 2010

This chapter charts Brazil’s participation and strategy in dealing with Air Traffic Flow Management (ATFM). First a review of ATFM concepts is provided, where the demand and capacity balancing problem is defined. Afterwards the Brazilian air traffic scenario is laid out and a short history is presented. Finally, the SYNCROMAX system architecture is presented as defined for it’s first implementation phase. Internal details to the system are given and finally current directions indicate a higher level of decision making tools required in the future, in order to face the growing air navigation requirements.
Cooperative Control for Ground Traffic at Airports
Authors: Galvão França Felipe Maia , Mora-Camino Félix
Published: May 2010

With the sustained increase in air transportation, resulting in increased operational costs, potential danger with conflictive traffic conditions and delays for passengers and airlines, ground traffic has become a critical issue for many airports. In this communication the ground traffic at an airport is considered to be composed of three dependent flows: aircraft, passenger vehicles and servicing vehicles. It is assumed in this study that each type of vehicles belongs to a common pool which attends every arriving or departing aircraft. The objective here is to propose a global control structure based on cooperation between the different agents responsible for the management of each fleet to reduce overall aircraft traffic delays at airside.
Critical Review and Analysis of Air-Travel Demand
Authors: Karlaftis Matthew G.
Published: May 2010

Demand forecasting may be the most critical factor in the development of airports and airline networks. This chapter reviews various approaches used to forecast air travel and airport demand forecasting. It classifies existing methods according to the modeling approach used to evaluate the available data; then, the forecasting approaches are viewed in relation to data requirements. Finally, a new matrix classification scheme is introduced that combines both the data available and the technique used to evaluate this data in a more concise and manner.
Real-Time Non-Destructive Evaluation of Airport Pavements Using Neural Network Based Models
Authors: Kasthurirangan Gopalakrishnan
Published: May 2010

Nondestructive test (NDT) and evaluation methods are well-suited for characterizing materials and determining structural integrity of airfield pavement systems. The Heavy Weight Deflectometer (HWD) test is one of the most widely used NDT impulse device for assessing the structural condition of airport pavements in a non-destructive manner. Through inverse analysis of HWD deflection data (more commonly referred to as backcalculation), the structural stiffness parameters of the individual airport pavement layers are, in general, determined using iterative optimization routines. In recent years, Artificial Neural Networks (ANN) aided inverse analysis has emerged as a successful alternative for predicting pavement layer moduli from HWD deflection data in real-time. Especially, the use of Finite Element (FE) based pavement modeling results for training the ANN aided inverse analysis is considered to be accurate in realistically characterizing the non-linear stress-sensitive response of underlying pavement layers. The development of an effective tool for real-time backcalculation of flexible airfield pavement layer moduli based on HWD test data is discussed in this Chapter. The ANN-based backcalculation tool is validated using actual field data acquired from a full-scale, state-of-the-art airport pavement test facility.
Advances in Data Processing for Airlines Revenue Management
Authors: Félix Mora-Camino  , Luiz Gustavo Zelaya Cruz
Published: May 2010

In this communication advances in data processing techniques applied to Airlines Revenue Management are displayed. The general introduction presents a brief review of Airlines Revenue Management. The first of the paper introduces the problem of updating the probability distributions of demand for reservations. This updating process, facing the stochastic nature of demand for travel, is a cornerstone for the design of an efficient on-line decision support system to control the reservation process for a flight by an airline. The considered problem is formulated as a dual geometric problem to which an unconstrained nonconvex, primal geometric problem is associated. A genetic algorithm optimization approach is proposed to solve the primal geometric problem, and then the classical geometric primal-dual transformations provide the solution to the initial problem. Then, the second part of the paper considers the design of a new Decision Support System for improving the reservation control process of airlines. A new recursive Dynamic Programming model for maximum expected revenue evaluation is defined, which, contrarily to other approaches, takes explicitly into account daily booking request arrivals. A practical Backward Dynamic Programming algorithm is established, leading to the design of an on-line optimisation module for Revenue Management. In this study two cases are considered. The first one considers that fare classes are not physically confined and the obtained results are extended in the second case to cover the situations where confinement of fare classes (Business Class and Economy Class) is applied.