bluuebunny/crossref_metadata_2025 huggingface dataset:
Strategies for Next Generation Networks Architectures
Authors: Georgiadou Evangelia M. , Chochliouros Ioannis , Heliotis George , Belesioti Maria
Published: May 2011

As the technological scene of the 21st century changes rapidly, new facts for telecom and networks are coming to the front. Users’ growing demands for enhanced multimedia services on one hand and expanding infrastructure on the other lead to the realization of innovative networks, able to serve more subscribers more efficiently. Past technologies have failed to meet the present and immediate needs for integrated services and applications of real time traffic and high data volumes, high speed Internet, video on demand, and mobile communications everywhere and all the time (Chochliouros & Spiliopoulou, 2003). Globalization and deregulation of the market stimulate increased competition and call for integration of existing switching, optical, satellite, and wireless technologies (Commission of the European Communities, 2006). In the telecom industry new commercial opportunities are introduced. Internet and data services growth, in combination with increased maturity of packet-based technologies, results in the redrawing of traditional telecommunications architectures (Barnes, & Jackson, 2002). High quality, distributed, multiservice networks, with advanced features of flexibility and reliability, are now feasible, accommodating both circuit-switched voice and packet-switched data (Chochliouros & Spiliopoulou, 2005). This key architectural evolution in telecommunication core and access networks is described under the broad term “next generation networking (NGN).” Next generation networks, which are expected to be deployed in the markets over the next years, base their operation on packet transport of all information and services, voice, data, or multimedia. Encapsulation into packets is commonly implemented via the Internet protocol (IP), whereas services become independent of transport details, thus enabling improved functionality at the edge of the network, extreme scalability, and higher availability (European Commission, 2005). Nevertheless, the industry shift from centralized switches to “next generation” distributed, enhanced service platforms arises very important issues. Interoperability with existing networks is implicit, while great challenges appear in the conversion strategies towards implementing and exploiting the new architecture. Conventional communication systems need to evolve smoothly to NGN, through well-defined and carefully- planned transition procedures, in order for true convergence to take place.
Teaching and Learning with Mobile Technologies
Authors: So Hyo-Jeong , Kim Bosung
Published: May 2011

For the past decade, handheld devices with mobile and wireless capabilities have emerged as the next promising generation of technology for teaching and learning. Today, handheld devices such as mobile phones, personal digital assistants (PDAs), portable gaming devices, and tablet PCs have already become pervasive in daily lives of students. With this trend, there has been increased interest as to how mobile technologies can be used to enhance teaching and learning. One of the challenges for educators is to examine how we can use the new innovations of mobile technologies to create learning experiences that are often limited in traditional classroom environments. The purpose of this article, therefore, is to provide innovative and practical applications of mobile technologies in a variety of educational contexts, including from K-12 to higher education and informal learning.
Video Ontology
Authors: Lee Jeongkyu
Published: May 2011

There has been a great deal of interest in the development of ontology to facilitate knowledge sharing and database integration. In general, ontology is a set of terms or vocabularies of interest in a particular information domain, and shows the relationships among them (Doerr, Hunter, & Lagoze, 2003). It includes machine-interpretable definitions of basic concepts in the domain. Ontology is very popular in the fields of natural language processing (NLP) and Web user interface (Web ontology). To take this advantage into multimedia content analysis, several studies have proposed ontology-based schemes (Hollink & Worring, 2005; Spyropoulos, Paliouras, Karkaletsis, Kosmopoulos, Pratikakis, Perantonis, & Gatos, 2005). Modular structure of the ontology methodology is used in a generic analysis scheme to semantically interpret and annotate multimedia content. This methodology consists of domain ontology, core ontology, and multimedia ontology. Domain ontology captures concepts in a particular type of domain, while core ontology is the key building blocks necessary to enable the scalable assimilation of information from diverse sources. Multimedia ontology is used to model multimedia data, such as audio, image, and video. In the multimedia data analysis the meaningful patterns and hidden knowledge are discovered from the database. There are existing tools for managing and searching the discovered patterns and knowledge. However, almost all of the approaches use low-level feature values instead of high-level perceptions, which make a huge gap between machine interpretation and human understanding. For example, if we have to retrieve anomaly from video surveillance systems, low-level feature values cannot represent such semantic meanings. In order to address the problem, the main focus of research has been on the construction and utilization of ontology for specific data domain in various applications. In this chapter, we first survey the state-of-the-art in multimedia ontology, specifically video ontology, and then investigate the methods of automatic generation of video ontology.
The Ethics of Global Communication Online
Authors: Thorseth May
Published: May 2011

The purpose of this chapter is to discuss important ethical aspects of online communication of global scope. We focus particularly on procedural fundamentalism as the most significant threat to free and open communication today. By contrast, it is argued that deliberation models a desirable form of communication, based in both Habermasian discourse ethics, but also rhetoric along with a plurality of communicative styles, as long as they satisfy procedural constraints of deliberation. The importance of judgments that transcend purely private conditions is discussed by reference to reflective judgments aiming at enlarged thinking - to think from the standpoint of everyone else. It is concluded that it is preferable to develop Internet technologies that stimulate imaginative powers in order to make people better informed of knowledge of counterfactual circumstances. Such knowledge may work as an impediment against fundamentalist knowledge.
Inter-Organizational Conflicts in Virtual Alliances
Authors: Yi- Hui Lee Joyce
Published: May 2011

In this chapter we argue that even though conflict has been explored at an intra-organizational level, its effect and role at an inter-organizational level has remained unexplored. Yet, with the increasing number of virtual inter-organizational alliances, attention needs to be given on this issue. This chapter, hence, discusses the factors that contribute to different types of conflicts at an inter-organizational level, which are named as business strategic conflict and cultural conflict. More significantly, two conceptual frameworks of tendencies of business strategic conflict and cultural conflict are illustrated for understanding the course of global virtual alliances. The frameworks are designed to be a foundation of future empirical research. The ethical implications of doing such research are discussed.
Adaptive Mobile Applications
Authors: Kunz Thomas , Gaddah Abdulbaset
Published: May 2011

The convergence of two technological developments has made mobile computing a reality. In the last few years, developed countries spent large amounts of money to install and deploy wireless communication facilities. Originally aimed at telephone services (which still account for the majority of usage), the same infrastructure is increasingly used to transfer data. In parallel, wireless LAN technologies are providing hotspot coverage in many high-traffic locations. The second development is the continuing reduction in size of computer hardware, leading to portable computation devices such as laptops, palmtops, or functionally enhanced cell phones. Given current technology, a user can run a set of applications on a portable device and communicate over a variety of communication links, depending on his/her current location. As will be explained in more detail later on, the mobile computing environment is highly dynamic. Available bandwidth changes by orders of magnitudes, based on the selected wireless access technology. Also, portable devices differ in processing power, memory, display capabilities, and other characteristics. It is generally argued that applications should “adapt” to the current environment, for example by filtering and compressing data or by changing the functionality offered to the user. Some researchers even argue that all future applications, not just the ones intended for execution on mobile devices, will have to be able to adapt to changing requirements and changing implementation environments on time scales from microseconds to years (Kavi, 1999). This article reviews the work on adaptive mobile applications and provides an outlook on future trends. The alternative to adaptive applications is to either implement a single application that is designed for the lowest common denominator (in terms of resource availability) or multiple functionally identical or similar binaries, tuned for specific environments. The former will needlessly sacrifice application features when running in more resource-rich environments. The latter approach is an inferior solution as well, for a number of reasons. The user of a portable device has to install and maintain multiple applications, which is a drain on the limited storage capabilities typically found on those devices. It also potentially results in different user interfaces and causes high software development overheads when developing the “same” mobile application multiple times. Finally, it forces the user to identify the current execution conditions and select the “right” application. The next section will review the motivation for adaptive approaches towards mobile application design. We will then briefly review traditional approaches to adaptive mobile applications, followed by a discussion of mobile middleware that is intended to support adaptive mobile applications. The article finishes with a brief conclusion of the state-of-the-art and identifies areas of future work.
Agent-Oriented Software Engineering
Authors: Taveter Kuldar , Sterling Leon
Published: May 2011

Over the past decade, the target environment for software development has complexified dramatically. Software systems must now operate robustly in a dynamic, global, networked environment comprised of distributed diverse technologies, where frequent change is inevitable. There is increasing demand for flexibility and ease of use. Multiagent systems (Wooldridge, 2002) are a potential successor to object-oriented systems, better able to address the new demands on software. In multi-agent systems, heterogeneous autonomous entities (i.e., agents) interact to achieve system goals. In addition to being a technological building block, an agent, also known as an actor, is an important modeling abstraction that can be used at different stages of software engineering. The authors while teaching agent-related subjects and interacting with industry have observed that the agent serves as a powerful anthropomorphic notion readily understood by novices. It is easy to explain to even a nontechnical person that one or more software agents are going to perform a set of tasks on your behalf. We define software engineering as a discipline applied by teams to produce high-quality, large-scale, cost-effective software that satisfies the users’ needs and can be maintained over time. Methods and processes are emerging to place software development on a parallel with other engineering endeavors. Software engineering courses give increasing focus to teaching students how to analyze software designs, emphasizing imbuing software with quality attributes such as performance, correctness, scalability, and security. Agent-oriented software engineering (AOSE) (Ciancarini & Wooldridge, 2001) has become an active research area. Agent-oriented methodologies, such as Tropos (Bresciani, Perini, Giorgini, Giunchiglia, & Mylopoulos, 2004), ROADMAP (Juan & Sterling, 2003), and RAP/AOR (Taveter & Wagner, 2005), use the notion of agent throughout the software lifecycle from analyzing the problem domain to maintaining the functional software system. An agent-oriented approach can be useful even when the resulting system neither consists of nor includes software agents. Some other proposed AOSE methodologies are Gaia (Wooldridge, Jennings, & Kinny, 2000), MESSAGE (Garijo, Gomez-Sanz, & Massonet, 2005), TAO (Silva & Lucena, 2004), and Prometheus (Padgham & Winikoff, 2004). Although none of the AOSE methodologies are yet widely accepted, AOSE is a promising area. The recent book by Henderson-Sellers & Giorgini (2005) contains a good overview of currently available agent-oriented methodologies. AOSE approaches loosely fall into one of two categories. One approach adds agent extensions to an existing objectoriented notation. The prototypical example is Agent UML (Odell, Van Dyke, & Bauer, 2001). The alternate approach builds a custom software methodology around agent concepts such as roles. Gaia (Wooldridge et al., 2000) was the pioneering example. In this article, we address the new paradigm of AOSE for developing both agent-based and traditional software systems.
Architecture Methods and Frameworks Overview
Authors: Shan Tony C. , Hua Winnie W.
Published: May 2011

The e-business models in today’s globalized business world demand ever-increasing flexibility, responsiveness, and agility of information technology (IT) solutions. It is compulsory for the IT group to provide higher levels of services at a lower cost for the business to compete and succeed. The reality to IT is that there is no choice other than to build more complex, flexible, scalable, extensible, innovative, and forward-thinking technical solutions, to satisfy the growing business needs. In large organizations like worldwide financial institutions, virtually thousands, if not millions, of IT applications and systems have been constructed or purchased to provide electronic services for external customers and internal employees in the past years, utilizing heterogeneous technologies and architectures to meet diverse functional requirements from different lines of business. In the banking industry, as an example, the business process generally contains different business sectors in consumer, commercial, small business, wealth management, and capital management. In particular, services are delivered to different channels such as automated teller machines (ATMs), Web browsers, interactive voice response, agent assistance, e-mails, mobile devices, and so on. To effectively manage the architecture assets and rationalize the architecture designs in such a diverse environment, a multi-disciplinary engineering approach is of crucial importance to abstract concerns, divide responsibilities, mitigate risks, encapsulate the complexity, reverse-engineer existing applications, identify reengineering opportunities, and conduct objective technology assessments, which leads to in-depth technical recommendations and rationalization action plans.
Artificial Intelligence Applications in Tourism
Authors: Goh Carey , Mok Henry M.K. , Law Rob
Published: May 2011

The tourism industry has become one of the fastest growing industries in the world, with international tourism flows in year 2006 more than doubled since 1980. In terms of direct economic benefits, United Nations World Tourism Organization (UNWTO, 2007) estimated that the industry has generated US $735 billion through tourism in the year of 2006. Through multiplier effects, World Travel and Tourism Council (WTTC, 2007) estimated that tourism will generate economic activities worth of approximately US $5,390 billion in year 2007 (10.4% of world GDP). Owing to the important economic contribution by the tourism industry, researchers, policy makers, planners, and industrial practitioners have been trying to analyze and forecast tourism demand. The perishable nature of tourism products and services, the information-intensive nature of the tourism industry, and the long lead-time investment planning of equipment and infrastructures all render accurate forecasting of tourism demand necessary (Law, Mok, & Goh, 2007). Past studies have predominantly applied the well-developed econometric techniques to measure and predict the future market performance in terms of the number of tourist arrivals in a specific destination. In this chapter, we aim to present an overview of studies that have adopted artificial intelligence (AI) data-mining techniques in studying tourism demand forecasting. Our objective is to review and trace the evolution of such techniques employed in tourism demand studies since 1999, and based on our observations from the review, a discussion on the future direction of tourism research techniques and methods is then provided. Although the adoption of data mining techniques in tourism demand forecasting is still at its infancy stage, from the review, we identify certain research gaps, draw certain key observations, and discuss possible future research directions.
Attribute Grammars and Their Applications
Authors: Thirunarayan Krishnaprasad
Published: May 2011

Attribute grammars are a framework for defining semantics of programming languages in a syntax-directed fashion. In this chapter, we define attribute grammars, and then illustrate their use for language definition, compiler generation, definite clause grammars, design and specification of algorithms, and so forth. Our goal is to emphasize its role as a tool for design, formal specification and implementation of practical systems, so our presentation is example rich.
Automation of American Criminal Justice
Authors: Holland J. William
Published: May 2011

Criminal Justice has been one of the public sectors in the forefront of the move toward automation and digital government. The effect of computerization on American criminal justice has been profound and it has transformed the criminal justice process in many fundamental ways. Starting with President Lyndon Johnson’s government commission, The Challenge of Crime in a Free Society: A Report by the President’s Commission on Law Enforcement and the Administration of Justice, public and private experts in criminal justice and technology laid out the information needs of the criminal justice system and the computer systems to meet those demands. At a time when computerization was minimal throughout the criminal justice system, these task force members developed the blueprint for today’s multilayered automated criminal justice environment (Dallek, 1998, pp. 405-407, 409-411; Challenge of crime in a free society, 1967, pp. 268-271). Among the major recommendations of the commission were the creation of a national directory of offenders’ criminal records, what came to be known as Computerized Criminal History (CCH) and the development of similar directories at the state level. The commission also called for federal coordination of standards for criminal justice information and sharing. Finally, the report urged that a study of fingerprint classification techniques be undertaken with a view to automating much of the fingerprint search and identification effort and that work be intensified to create a national linkage of files on wanted persons and stolen vehicles under the name of the National Crime Information Center (NCIC) (Challenge of crime in a free society, 1967, pp. 255, 268-271; Task force report: Science and technology, 1967, p. 69).
Blended Learning Models
Authors: Graham Charles R.
Published: May 2011

Technological advances and widespread access to information and communication technologies (ICTs) have facilitated the rapid growth of blended learning approaches in both higher education and corporate training contexts. In 2002, the president of Pennsylvania State University expressed his belief that blended learning was “the single greatest unrecognized trend in higher education” (Young, 2002, p. A33). At the same time, the American Society for Training and Development also identified blended learning as one of the top 10 emergent trends in the knowledge delivery industry (Finn, 2002). Since then, the visibility of blended learning environments has increased dramatically in both formal education and corporate training settings. At the third annual Sloan-C Workshop on Blended Learning and Higher Education, Frank Mayadas, the program director for the Alfred P. Sloan Foundation, predicted that “by 2010 you will be hard pressed to find a course that is not blended” (Mayadas, 2006). There is increasing interest in the concept of blended learning as evidenced by greater numbers of books, journal articles, and trade magazine articles that directly address issues related to blended learning. This article will provide an overview of current models of blended learning and provide references to the most recent resources in this emergent area of research and practice.
A Brief Introduction to Sociotechnical Systems
Authors: Whitworth Brian
Published: May 2011

The term sociotechnical was introduced by the Tavistock Institute in the 1950’s for manufacturing cases where the needs of technology confronted those of local communities, for example, longwall mining in English coalmines (see http://www.strategosinc.com/socio-technical.htm). Social needs were opposed to the reductionism of Taylorism, which broke down jobs on say a car assembly line into most ef- ficient elements. Social and technical were seen as separate side-by-side systems which needed to interact positively, for example, a village near a nuclear plant is a social system (with social needs) besides a technical system (with technical needs). The sociotechnical view later developed into a call for ethical computer use by supporters like Mumford (Porra & Hirscheim, 2007). In the modern holistic view the sociotechnical system (STS) is the whole system, not one of two side-by-side systems. To illustrate the contrast, consider a simple case: A pilot plus a plane are two side-by-side systems with different needs, one mechanical (plane) and one human (pilot). Human Computer Interaction (HCI) suggests these systems should interact positively to succeed. However plane plus pilot can also be seen as a single system, with human and mechanical levels. On the mechanical level, the pilot’s body is as physical as the plane, for example, the body of the plane and the body of the pilot both have weight, volume, and so forth. However the pilot adds a human thought level that sits above the plane’s mechanical level, allowing the “pilot + plane” system to strategize and analyze. The sociotechnical concept that will be developed changes the priorities, for example, if a social system sits next to a technical one it is usually secondary, and ethics an afterthought to mechanics, but when a social system sits above a technical one it guides the entire system, that is, the primary factor in system performance.
Challenges of Interoperability in an Ecosystem
Authors: Flügge Barbara , Schmidt Alexander
Published: May 2011

True e-enabled collaboration has been assessed for many years. With the growing reach of companies’ business and cross-border trade, the entire ecosystem enterprises are embedded in is playing a crucial role to succeed. As ICT is a key driver for deploying true interoperability and integration among the participants of the ecosystem, actors with a lack of ICT knowledge, equipment, and implementation represent the vulnerable parts within the ecosystem. This article aims at providing an overview of challenges limiting business partners in an ecosystem to truly e-collaborate. Furthermore, it describes the key elements of e-enabled collaboration and interoperability ranging from the technical and business oriented to cross-organizational and cultural aspects.
Chief Knowledge Officers
Authors: Herschel Richard T.
Published: May 2011

Knowledge management (KM) refers to a range of practices used by organizations to identify, create, represent, and distribute knowledge for reuse, awareness, and learning across the organization. KM typically takes the form of programs that are tied to organizational objectives and are intended to lead to the achievement of specific outcomes such as shared intelligence, improved performance, competitive advantage, or higher levels of innovation. Knowledge management focuses on developing and maintaining intellectual capital across the organization. It attempts to bring under one set of practices various strands of thought and practice relating to: • Harnessing the effective use of data, information, and know-how in a knowledge-based organization and economy • The idea of the learning organization • Various enabling organizational practices such as communities of practice and corporate yellow page directories for accessing key personnel and expertise • Various enabling technologies such as knowledge bases and expert systems, help desks, corporate intranets and extranets, and content management systems (Wikipedia, 2007). Beginning in the 1990s, the person responsible for directing and coordinating these activities for organizations was oftentimes designated the chief knowledge office (CKO).
Combination of Forecasts in Data Mining
Authors: Chan Chi Kin
Published: May 2011

The traditional approach to forecasting involves choosing the forecasting method judged most appropriate of the available methods and applying it to some specific situations. The choice of a method depends upon the characteristics of the series and the type of application. The rationale behind such an approach is the notion that a “best” method exists and can be identified. Further that the “best” method for the past will continue to be the best for the future. An alternative to the traditional approach is to aggregate information from different forecasting methods by aggregating forecasts. This eliminates the problem of having to select a single method and rely exclusively on its forecasts. Considerable literature has accumulated over the years regarding the combination of forecasts. The primary conclusion of this line of research is that combining multiple forecasts leads to increased forecast accuracy. This has been the result whether the forecasts are judgmental or statistical, econometric or extrapolation. Furthermore, in many cases one can make dramatic performance improvements by simply averaging the forecasts.
Cross-Cultural Research in MIS
Authors: Karahanna Elena , Evaristo Roberto , Srite Mark
Published: May 2011

“Globalization of business highlights the need to understand the management of organizations that span different nations and cultures” (Srite et al., 2003, p. 31). In these multinational and transcultural organizations, there is a growing call for utilizing information technology (IT) to achieve efficiencies, coordination, and communication. However, cultural differences between countries may have an impact on the effectiveness and efficiency of IT deployment. Despite its importance, the effect of cultural factors has received limited attention from information systems’ (IS) researchers. In a review of cross-cultural research specifically focused on the MIS area (Evaristo, Karahanna, & Srite, 2000), a very limited number of studies were found that could be classi- fied as cross-cultural. Additionally, even though many of the studies found provided useful insights, raised interesting questions, and generally contributed toward the advancement of the state of the art in its field, with few exceptions, no study specifically addressed equivalency issues central to measurement in cross-cultural research. It is this methodological issue of equivalency that is the focus of this article.
Designing Agents with Negotiation Capabilities
Authors: Polgar Jana
Published: May 2011

Agents are viewed as the next significant software abstraction, and it is expected they will become as ubiquitous as graphical user interfaces are today. Agents are specialized programs designed to provide services to their users. Multiagent systems have a key capability to reallocate tasks among the members, which may result in significant savings and improvements in many domains, such as resource allocation, scheduling, e-commerce, and so forth. In the near future, agents will roam the Internet, selling and buying information and services. These agents will evolve from their present day form - simple carriers of transactions - to efficient decision makers. It is envisaged that the decisionmaking processes and interactions between agents will be very fast (Kephart, 1998). The importance of automated negotiation systems is increasing with the emergence of new technologies supporting faster reasoning engines and mobile code. A central part of agent systems is a sophisticated reasoning engine that enables the agents to reallocate their tasks, optimize outcomes, and negotiate with other agents. The negotiation strategy used by the reasoning engine also requires high-level inter-agent communication protocols, and suitable collaboration strategies. Both of these sub-systems – a reasoning engine and a negotiation strategy - typically result in complicated agent designs and implementations that are difficult to maintain. Activities of a set of autonomous agents have to be coordinated. Some could be mobile agents, while others are static intelligent agents. We usually aim at decentralized coordination, which produces the desired outcomes with minimal communication. Many different types of contract protocols (cluster, swaps, and multiagent, as examples) and negotiation strategies are used. The evaluation of outcomes is often based on marginal cost (Sandholm, 1993) or game theory payoffs (Mass-Colell, 1995). Agents based on constraint technology use complex search algorithms to solve optimization problems arising from the agents’ interaction. In particular, coordination and negotiation strategies in the presence of incomplete knowledge are good candidates for constraint-based implementations.
Energy Management in Wireless Networked Embedded Systems
Authors: Manimaran G.
Published: May 2011

Real-time systems have undergone an evolution in the last several years in terms of their number and variety of applications, as well as in complexity. A natural result of these advances, coupled with those in sensor techniques and networking, have led to the rise of a new class of applications that fall into the distributed real-time embedded systems category (Loyall, Schantz, Corman, Paunicka, & Fernandez, 2005; Report, 2006). Recent technological advancements in device scaling have been instrumental in enabling the mass production of such devices at reduced costs. As a result, applications with a number of internetworked embedded systems have become prominent. At the same time, there has been a need to move from stand-alone real-time unit into a network of units that collaborate to achieve a real-time functionality. Extensive research has been carried out to achieve real-time guarantees over a set of nodes distributed over wired networks (Siva Ram Murthy & Manimaran, 2001). However, there exist a number of realtime applications in domains, such as industrial processing, military, robotics and tracking, that require the nodes to communicate over the wireless medium where the application dynamics prevent the existence of a wired communication infrastructure. These applications present challenges beyond those of traditional embedded or networked systems, since they involve many heterogeneous nodes and links, shared and constrained resources, and are deployed in dynamic environments where resource contention is dynamic and communication channel is noisy (Report, 2006, Loyall et al., 2005). Hence, resource management in embedded realtime networks requires efficient algorithms and strategies that achieve competing requirements, such as time sensitive energy-efficient reliable message delivery. In what follows, we discuss some applications in this category, and discuss their requirements and the research challenges.
Faculty Competencies and Incentives for Teaching in E-Learning Environments
Authors: Dooley Kim E. , Pesl Murphrey Theresa , Lindner James R. , Murphy Timothy H.
Published: May 2011

In 2001, Michele Bunn offered her readers timeless and timely issues in distance education. There were predictions that virtual universities would shift from a teacher-centered to a student-centered learning environment. Although emerging technologies have allowed for more student-centered approaches, university instructors remain a key factor in the success or failure of distance education efforts in university settings. Shifts in technological advances over the past five years have made the term “distance education” less accurate. Learners are not necessarily located away from campuses, but instead choose the flexibility to learn asynchronously. Therefore, we will use the term e-learning, rather than distance education. Timeless issues impacting e-learning typically include the core values of universities in regard to strategic planning, faculty competence, and incentives. Administrative decision-making determines relevant timely issues that tend to fall into three categories: (1) student-related issues, (2) instructional issues, and (3) organizational issues (Bunn, 2001). These three areas will frame our discussion.
Formal Development of Reactive Agent-Based Systems
Authors: Kefalas P. , Holcombe M. , Eleftherakis G. , Gheorghe M.
Published: May 2011

Recent advances in both the testing and verification of software based on formal specifications have reached a point where the ideas can be applied in a powerful way in the design of agent-based systems. The software engineering research has highlighted a number of important issues: the importance of the type of modelling technique used; the careful design of the model to enable powerful testing techniques to be used; the automated verification of the behavioural properties of the system; and the need to provide a mechanism for translating the formal models into executable software in a simple and transparent way. An agent is an encapsulated computer system that is situated in some environment and that is capable of flexible, autonomous action in that environment in order to meet its design objectives (Jennings, 2000). There are two fundamental concepts associated with any dynamic or reactive system (Holcombe & Ipate, 1998): the environment, which could be precisely or ill-specified or even completely unknown and the agent that will be responding to environmental changes by changing its basic parameters and possibly affecting the environment as well. Agents, as highly dynamic systems, are concerned with three essential factors: a set of appropriate environmental stimuli or inputs, a set of internal states of the agent, and a rule that relates the two above and determines what the agent state will change to if a particular input arrives while the agent is in a particular state. One of the challenges that emerges in intelligent agent engineering is to develop agent models and agent implementations that are “correct.” The criteria for “correctness” are (Ipate & Holcombe, 1998): the initial agent model should match the requirements, the agent model should satisfy any necessary properties in order to meet its design objectives, and the implementation should pass all tests constructed using a complete functional test-generation method. All the above criteria are closely related to stages of agent system development, i.e., modelling, validation, verification, and testing.
Image Compression Concepts Overview
Authors: Liew Alan Wee-Chung , Law Ngai-Fong
Published: May 2011

Image compression aims to produce a new image representation that can be stored and transmitted efficiently. It is a core technology for multimedia processing and has played a key enabling role in many commercial products, such as digital camera and camcorders. It facilitates visual data transmission through the Internet, contributes to the advent of digital broadcast system, and makes possible the storage on VCD and DVD. Despite a continuing increase in capacity, efficient transmission and storage of images still present the utmost challenge in all these systems. Consequently, fast and efficient compression algorithms are in great demand. The basic principle for image compression is to remove any redundancy in image representation. For example, simple graphic images such as icons and line drawings can be represented more efficiently by considering differences among neighbor pixels, as the differences always have lower entropy value than the original images (Shannon, 1948). These kinds of techniques are often referred to as lossless compression. It tries to exploit statistical redundancy in an image so as to provide a concise representation in which the original image can be reconstructed perfectly. However, statistical compression techniques alone cannot provide high compression ratio. To improve image compressibility, lossy compression is often used so that visually important image features are preserved while some fine details are removed or not represented perfectly. This type of compression is often used for natural images where the loss of some details is generally unnoticeable to viewers. This articles deals with image compression. Specifi- cally, it is concern with compression of natural color images because they constitute the most important class of digital image. First, the basic principle and methodology of natural image compression is described. Then, several major natural image compression standards, namely JPEG, JPEG-LS, and JPEG 2000 are discussed.
An Inclusive IS&T Work Climate
Authors: Major Debra A. , Morganson Valerie L.
Published: May 2011

Employees develop perceptions regarding which behaviors are expected, supported, and rewarded in their organization through a series of workplace events, practices, and procedures; these beliefs comprise a workplace climate (Schneider, Wheeler, & Cox, 1992). An inclusive workplace climate is one in which everyone has a sense of belonging, is invited to participate in decisions, and feels that their input matters (Hayes, Bartle, & Major, 2002; Major, Davis, Sanchez-Hucles, Germano, & Mann, 2006). For an inclusive climate to exist, all organizational members should feel equally welcome in the IT work environment and feel free to make suggestions regardless of their gender or ethnicity. Moreover, all organizational members should feel that their contributions have an impact (Major et al., 2006). Rather than simply tolerating diversity, organizations with an inclusive climate embrace it and capitalize upon it. In an IT sample, inclusive climate was positively associated with job satisfaction, organizational and career commitment, and intentions to remain with one’s employer (Major et al., 2003). In contrast, exclusion is associated with turnover, reduced organizational commitment and decreased job satisfaction (Greenhaus, Parasuraman, & Wormley, 1990). Research has highlighted the role of three contributors to inclusive climate: (1) strong supervisor/subordinate relationships, (2) supportive coworkers, and (3) a supportive culture (Margolis & Fisher, 2003; Major, Davis, Sanchez-Hucles, & Mann, 2003). The current article briefly reviews social factors that have hindered the realization of a gender and minority inclusive IT climate and draws upon these three contributors to identify strategic levers to guide managers and researchers toward fostering inclusion in the IT workforce.
Increasing the Accuracy of Predictive Algorithms
Authors: Kotsiantis Sotiris , Kanellopoulos Dimitris , Pintelas Panayotis
Published: May 2011

In classification learning, the learning scheme is presented with a set of classified examples from which it is expected tone can learn a way of classifying unseen examples (see Table 1). Formally, the problem can be stated as follows: Given training data {(x1, y1)…(xn, yn)}, produce a classifier h: X- >Y that maps an object x ? X to its classification label y ? Y. A large number of classification techniques have been developed based on artificial intelligence (logic-based techniques, perception-based techniques) and statistics (Bayesian networks, instance-based techniques). No single learning algorithm can uniformly outperform other algorithms over all data sets. The concept of combining classifiers is proposed as a new direction for the improvement of the performance of individual machine learning algorithms. Numerous methods have been suggested for the creation of ensembles of classi- fiers (Dietterich, 2000). Although, or perhaps because, many methods of ensemble creation have been proposed, there is as yet no clear picture of which method is best.
Indicators and Measures of E-Government
Authors: Amoretti Francesco , Musella Fortunato
Published: May 2011

Although the question of measurement is crucial when defining any concept, little attention has been devoted to a comprehensive view of information and communication technologies (ICTs) applications, spanning qualitative and quantitative assessments. Due to the lack of a clear definition of e-government, many differences can be noted in the way in which digital policies have been interpreted by academics and practitioners. Coined by the U.S. programme for reinventing government under the Clinton administration (National Performance Review), the term e-government refers to a public sector reorganisation which aims at increasing the efficiency of the public administration and reducing its budget through the use of new technologies. In the words of Douglas Holmes (2001), e-government is “the use of information technology, in particular the Internet, to deliver public services in a much more convenient, customer oriented, cost effective and altogether different and better way. It affects an agency’s dealing with citizens, business and other public agencies as well as its internal business processes and employees” (p. 2). Yet many definitions go beyond the role of e-government in improving the provision of public services. Indeed, the label e-government supports other definitions, not necessarily limited to the computerisation of the public administration (Osborne & Gaebler, 1992). The concept of e-government seems to contain both the redesigning of public services system and a wider transformation of the relationship between private and public actors, so that the restructuring of public administration–influenced by the ideal of a new public management–is combined with the renewal of the democratic decision-making process. Digital policies are presumed to be a key element in improving online service quality and other factors, casting a new role for the citizen-costumer. At the same time, although e-government is becoming a catch-all concept, from an analytical point of view, official reports produced by international actors show a significant convergence in the way in which this is evaluated and measured. Diffusion of e-government practices are often closely related, and limited, to features of public administration Web sites, with reference to dimensions of openness and interactivity (La Porte, Demchak, & De Jong, 2002). Other studies focus exclusively on how citizens and businesses perceive the quality of public e-service, with reference to customer satisfaction, benefits conceived in terms of value and utility of services offered and opportunity of use as strategic factors for performance efficacy and efficiency (Graafland-Essers & Ettedgui, 2003; Stowers, 2004). Only recently a new approach has taken shape, which concentrates more attention on socio-political aspects of the intensive use of new technologies.
Information Technology in Franchising
Authors: Chen Ye-Sho , Hua Grace , Justis Bob
Published: May 2011

Franchising is “a business opportunity by which the owner (producer or distributor) of a service or a trademarked product grants exclusive rights to an individual for the local distribution and/or sale of the service or product, and in return receives a payment or royalty and conformance to quality standards. The individual or business granting the business rights is called the franchisor, and the individual or business granted the right to operate in accordance with the chosen method to produce or sell the product or service is called the franchisee.” (Justis & Judd, 2002) Information technology (IT) has been widely used in today’s businesses. In his best seller, Business @ the Speed of Thought, Bill Gates (1999) wrote: “Information Technology and business are becoming inextricably interwoven. I don’t think anybody can talk meaningfully about one without talking about the other.” Thus, to see how IT is used in franchising, one needs to know how franchising really works. The objective of this paper is to propose an attention-based IT infrastructure that can cultivate the relationship building between the franchisors and their franchisees which will ultimately lead to the success of the franchise organizations.
Innovations for Online Collaborative Learning in Mathematics
Authors: Nason Rodney , Woodruff Earl
Published: May 2011

The field of computer-supported collaborative learning (CSCL) has been growing in a number of areas and across a number of subjects (Koschmann, 1996; Koschmann, Hall, & Miyake, 2002; Wasson, Baggetun, Hoppe, & Ludvigsen, 2003). One of the most promising pedagogical advances, however, for online collaborative learning that has emerged in recent years is Scardamalia and Bereiter’s (1996) notion of knowledge-building communities. Unfortunately, establishing and maintaining knowledge-building communities in CSCL environments such as Knowledge Forum® in the domain of mathematics has been found to be a rather intractable problem (Bereiter, 2002b; Nason, Brett, & Woodruff, 1996). In this chapter, we begin by identifying two major reasons why computer-supported knowledge-building communities in mathematics have been difficult to establish and maintain. 1. The inability of most “textbook” math problems to elicit ongoing discourse and other knowledge-building activity 2. Limitations inherent in most CSCL environments’ math representational tools Therefore, in this chapter, we argue that if mathematics education is to exploit the potentially powerful new ways of learning mathematics being provided by online knowledgebuilding communities, then the following innovations need to be designed and integrated into CSCL environments: 1. authentic mathematical problems that involve students in the production of mathematical models that can be discussed, critiqued, and improved, and 2. comprehension-modeling tools that (a) enable students to adequately represent mathematical problems and to translate within and across representation modes during problem solving, and (b) facilitate online student- student and teacher-student hypermedia-mediated discourse.