bluuebunny/crossref_metadata_2025 huggingface dataset:
Facilitated Telementoring for K-12 Students and Teachers
Authors: Harris Lisa
Published: May 2010

The Electronic Emissary is a Web-based service and resource center that helps teachers and students with Internet access locate mentors who are experts in various disciplines, then plan and engage in curriculum-based learning. In this way, the interaction that occurs among teachers and students face-to-face in the classroom is supplemented and extended by electronic mail, Web forum, chat, and audio/videoconferencing exchanges that occur among participating teachers, students, and volunteer mentors. These project-based online conversations typically range in length from six weeks to a full academic year, as students’ needs and interests dictate. The Electronic Emissary has been online since February 1993 and on the World Wide Web since December 1995. It serves students and teachers globally, but the majority of its participants to date have been in North America. Emissary-related research has focused upon the nature of telementoring interactions in which K-12 students are active inquirers, the motivations and perceptions of their volunteer subject matter mentors, why some teachers choose to persist in integrating telementoring into curricula despite considerable hindrances, effective telementoring facilitation techniques, and what teachers learn as they help their students to participate in curriculum-oriented telementoring projects. Students exploring complex curriculum-based topics need to actively build deep and sophisticated understanding. One of the most effective ways to do this is by engaging in ongoing dialogue with knowledgeable others, as the students form, refine, and expand their knowledge. Classroom teachers typically serve as the subject matter experts with whom students interact in curriculum-based areas of inquiry. Yet when the issues being explored are multi-disciplinary, technically and conceptually sophisticated, or dependent upon current and highly specialized research and theory, additional expertise must be made directly available to students and teachers longitudinally, and on an as-needed basis. This is what telementoring offers to learners and educators today, and what the Electronic Emissary brings to students and teachers worldwide.
Bridging the 15 Million Person Mentoring Gap
Authors: Oh Caroline Kim , Stroisch Theresa
Published: May 2010

This chapter introduces the history and evolution of iMentor, a NYC-based youth mentoring organization that pioneered the use of guided e-mail communication to enhance the in-person youth mentoring model, and continues to leverage its model and lessons learned to help other groups improve or start up their own mentoring programs. It illustrates how the organization has effectively used technology to add flexibility and structure in its NYC Mentoring Program, engaging a new “class” of program participants: “busy” professionals as mentors and mentees from some of the most economically and geographically isolated communities. It also describes how the organization transformed itself to meet a national demand for its programming by developing and licensing its own mentoring technology platform, iMentor Interactive. The author hopes that by reviewing the experience of iMentor, more mentoring and youth organizations at- large would embrace a thoughtful infusion of technology to positively impact the lives of people they serve. The author, however, strongly believes that the sector must be diligent about adhering to many of the best practices of a good, in-person mentoring program, including screening and monitoring of program participants, providing structure and ongoing assistance.
MobiGlam
Authors: Meawad Fatma , Stubbs Geneen
Published: May 2010

This chapter discusses the principles underpinning the design and the development of a framework, MobiGlam, which supports ubiquitous and scalable access to learning activities. The framework allows full end to end interconnectivity among open source virtual learning environments (VLEs) and Java-enabled mobile devices. Through this framework, interoperability and adaptivity techniques are combined to address the technical, pedagogical, and institutional challenges of mobile learning. The discussed framework achieved a level of flexibility and simplicity that resulted in a wide acceptance of the framework institutionally, allowing its use in various real world settings.
A Ubiquitous and Pervasive Learning Framework
Authors: Carroll Rodger
Published: May 2010

This chapter reports on a learning architecture adopting ubiquity and pervasiveness to support communities of learning practice. The research focused on mobile devices that are capable of voice, text, video/photo interactions, and Web access, and how this can cater for the preferred learning styles of the learners while supporting the workplace learning and the educational environment. The research utilised mobile technology for planned and unplanned learning situations via its capability to send and receive multimedia files, Web objects, and live broadcasting. The information and objects created and gathered using mobile technology are in a digital format. This approach allows for customisation and flexible transferability to future intended audiences, planned learning and assessment activities, and workplace learning activities providing an engaging, learner created platform for the mobile generation.
Schools in Action
Authors: Barajas Mario , Sotiriou Sofoklis , Owen Martin , Lohr Manfred
Published: May 2010

This chapter looks at the implementation and evaluation of mobile learning scenarios with locationbased features, done under the project COLLAGE (Collaborative Learning Platform Using Game-like Enhancements). Five scenarios are examined. Evaluating different implementations within the same project gives an appraisal of the technical and pedagogical value of the mobile learning scenarios, but also informs the research community of appropriate evaluation models and evaluation parameters, which are lacking in the game-based mobile learning with location-based components. The project responds to questions about designing mobile learning scenarios that are pedagogically sound and attractive to secondary school students, and presents the pedagogical evaluation of the effectiveness of mobile gamebased learning scenarios in the COLLAGE platform. The project makes a case for a close collaboration with teachers and students in elaborating and validating complex mobile learning scenarios.
Using Technology to Support Quality Learning for School Activities Involving Field Studies
Authors: Stewart K. M. , Thompson K. , Hedberg J. G. , Wong W. Y.
Published: May 2010

The term mobile learning provides an image of active learning, of moving out into the world beyond the confines of the desk, beyond the classroom, beyond the school. The affordances of mobile, networked digital computers can provide learners with seamless access to and between information systems including data capture facilities and global positioning systems in real world settings. Mobile learning has come to represent a fruitful partnership between innovation in pedagogy and innovation in information and communication technologies. This chapter explores this nexus as it appears in emerging practices of a range of classroom teachers who are working to combine their aspirations for high quality student learning with the affordances of mobile technologies.
Personalisation through Technology-Enhanced Learning
Authors: Conole Gráinne
Published: May 2010

There is a growing awareness that one-size-fits-all approaches to school knowledge and organisation are ill-adapted both to individuals’ needs and to the knowledge society at large. To move beyond uniform, mass provision can be described as “personalisation” of education and of public services more widely. This quote is the introduction to an OECD (Organisation for Economic Cooperation and Development) publication (OECD, 2006) that illustrates the growing importance being placed on the concept of personalised learning. This is evident in the increasing reference to this and related terms in national and international policy documents (NSF, 2008; DfES, 2006; Becta, 2008; European Commission, 2008). This chapter will consider the discourse of the concept of personalisation and how it can be supported through technology-enhanced learning. This introduction will look at the policy rhetoric and consider to what extent it is realised in practice. It will describe a range of illustrative examples of how technologies are being used to meet the personalised learning agenda.
eLearning
Authors: Newland Barbara , Papaefthimiou Maria-Christiana
Published: May 2010

Students who have grown up in the digital age have certain expectations for learning in Higher Education (HE). “Using a complex mix of virtual and face-to-face environments, personal and institutional technologies, learners of all ages are developing new working practices around the technologies available to them. Increasingly, they look for flexibility and openness in the institutional policies and provision that support their learning.” (JISC, 2007). The divide between the institutional eLearning provision and the expectations of students who have grown up in a digital world was highlighted through the UK eLearning benchmarking exercise. Institutional eLearning provision and processes within the HE sector were investigated and analysed through this exercise, which was led by the Higher Education Academy (HEA) in collaboration with the Joint Information Systems Committee (JISC). This paper presents the experience of two UK Universities, Bournemouth and Reading, whose participation in the benchmarking exercise provides examples of institutional provision. Subsequent Pathfinder funding enabled them to build on their strengths with projects aimed at narrowing the divide between student expectations of eLearning and institutional provision. The eRes: Innovative eLearning with e-Resources project (Bournemouth) encourages students to use quality e-resources in their learning. The “Driving Institutional Reform: Exploring Change with Technology” (DIRECT) project (Reading) has developed a framework to transform its internal quality management processes.
The Impact of Interactive and Collaborative Learning Activities on the Personalised Learning of Adult Distance Learners
Authors: Hall Richard , Mackenzie Steve , Hall Melanie
Published: May 2010

The adoption across higher education of participatory, collaborative and connective ‘read/write web’ tools and synchronous classrooms has the potential to extend learner engagement and motivation. Embedding these user-centred tools within curriculum practices offers the possibility for a sixth-generation iteration of distance learning that frames a learner-focused pedagogy. This pedagogy is underpinned by problem-based activities that pivot around a cycle of needing/wanting, doing, digesting and feedback. They are supported by a facilitating tutor taking a connectivist approach to stimulate learning. This chapter highlights both the drivers for this sixth-generation iteration and the subsequent development of a model know as SCORE 2.0, or Synchronous Community Orientated Reflective and Experiential 2.0. The impact of this model on two cohorts of adult distance learners is discussed, in order to evaluate opportunities for future pedagogical development.
Personalised Learning
Authors: Doherty Iain , Blake Adam
Published: May 2010

The authors consider personalised learning in the context of delivering a specialist postgraduate course – ClinEd 711, ELearning and Clinical Education – at the Faculty of Medical and Health Sciences, University of Auckland. They describe the pedagogical theory underlying the course design and their experience of delivering ClinEd 711 with particular reference to the personalised learning process that the course design facilitated. They present their research results for the student experience of ClinEd 711 and discuss changes made to the course as a result of student feedback. They make reference to the introduction of student-led modules to further personalise the students’ learning experience. ClinEd 711 is a specialist postgraduate course with low student numbers; with this in mind the authors discuss the implications of their pedagogical approach for those educators involved in teaching larger classes. They conclude their paper with a discussion of the role of the educator in personalised learning.
Personalisation and the Online Video Narrative Learning Tools V-ResORT and the ViP
Authors: Joyes Gordon
Published: May 2010

This chapter describes two tools for personalised learning that were outcomes of projects led by the author for use in educational settings. These are the Virtual Resources for Online Research Training (V-ResORT) and the Virtual Interactive Platform (ViP) learning tools. The former was designed to support post graduate research students to develop an understanding of educational research through an exploration of researcher video narratives. The latter was designed to support online communities in sharing and critiquing videos of practice. These tools support the development of a learner identity characterized by proactive participation in construction and reconstruction of knowledge rather than pure consumption. This involves an engagement with communities of practice which it is argued is central to personalised learning.
Using ePortfolios to Evidence Practice Learning for Social Work Students
Authors: Osborne Samantha , Martin Ruben , Frith Louise
Published: May 2010

The University of Kent is piloting the use of ePortfolios in a number of departments; the School of Social Policy, Sociology and Social Research took the opportunity to pilot ePortfolios to investigate whether ePortfolios could improve communication and collaboration between student, placement supervisor and academic tutors whilst Social Work students are out on work-based placement. Social Work students are required to complete two reflective practice documents during each of their two placements during Years 2 and 3 of their degree to assess their competence against a set of National Standards. The chapter will discuss the adoption of a Personalised Learning Environment for recording assessed practice and how the tools provided can enhance the different categories of users’ experiences both in terms of reflective practice and personal development. The chapter gives a background to the pilot and describes the different profiles of each user group which are students, academic staff, practitioners, and other stakeholders. It will also examine to what extent the pilot is in line with government initiatives such as the Leitch Review and Burgess Report and research into the use of ePortfolios for reflection; the issues surrounding the introduction of new technology to non-traditional students and outside organizations; how technology has changed student and practitioner’s perceptions and expectations in the production of a collaborative body of evidence; and the future pedagogical implications of using technology with Social Work students and practitioners.
Virtual Heterarchy
Authors: Pankowska Malgorzata , Sroka Henryk
Published: May 2010

The rapid development of information communication technology (ICT) encourages companies to compete. However, those competitive development goals should enable people to satisfy their own needs and enjoy a better quality of work and life without compromising the quality of life of other people and future generations. Corporate governance models are needed to concentrate on changes of existing rules, customs, practices and rights as the subject matter of governance to be influenced. Governance models must recognize the limitations of the overburdened state and the consequent need to take advantage of existing institutions and structures that promote sustainability. An increasing number of companies are moving into new forms of competition which can be described as information-based competition, knowledge-based competition, technology–based competition and ICT relationship-based competition. However, unlimited supply of information from Internet and other sources, easiness to register and transfer the information, reduced prices of ICT devices result in increase of information processing and its overload. Therefore, information governance model proposed in the chapter seems to be a pattern to deal with information in contemporary common organizations i.e. virtual heterarchical organizations where access to information is democratically permitted. The proposed model is to be an answer to ensure sustainable governance of information i.e. balance, stability and progress of information processing.
Value of Information in Distributed Decision Support Systems
Authors: Sobieska-Karpinska Jadwiga , Hernes Marcin
Published: May 2010

This chapter deals with the analysis of the value of information in a distributed decision support systems. It characterises the basic measures of the value of information, with the stress put to the utility function, the effect of the knowledge discovery techniques in databases on the value of information and multicriteria methods of decisions support. In the chapter, a multi-agent system is presented, which is an example of a distributed decision support system. In the last part of the chapter, the choice methods and consensus methods for increasing the value of information through the eliminate contradiction of information within the system are presented.
Designing Collaborative Infrastructures to get Business Value from Collaboration in Large Scale Networks
Authors: Hawryszkiewycz Igor
Published: May 2010

Collaboration is playing an increasing role in business especially given an increase in business networking. Such networks are formed to gain business advantage by combining expertise from many businesses or organizational units to quickly create new and competitive products and services. Most processes in business networks now consist of a number of activities whose processes must be coordinated to reach enterprises goals. This chapter addresses ways of supporting such activities using technology and proposes a collaboration infrastructure that encourages collaboration and sharing of knowledge across the activities.
Ethics of Information in Distributed Business Environment
Authors: Burlea Adriana Schiopoiu
Published: May 2010

The aim of this chapter is to examine some of the issues of ethics related to information in DBE. The ethical issue of what is moral to do in order to optimize the use of information in DBE is dealt with. The varied ways of integrating and putting into the practice information in DBE is discussed as well as the great variety of ethical approaches. In the field of ethics of information in DBE we are no longer confronted with “policy vacuum”; we are facing dissipation of ethical responsibility (DER) and this phenomenon leads to difficult and usually late localisation and solving of ethical dilemmas within the system.
XAR
Authors: Ashish Naveen , Mehrotra Sharad
Published: May 2010

The authors present the XAR framework that allows for free text information extraction and semantic annotation. The language underpinning XAR, the authors argue, allows for the inclusion of probabilistic reasoning with the rule language, provides higher level predicates capturing text features and relationships, and defines and supports advanced features such as token consumption and stratified negotiation in the rule language and semantics. The XAR framework also allows the incorporation of semantic information as integrity constraints in the extraction and annotation process. The XAR framework aims to fill in a gap, the authors claim, in the Web based information extraction systems. XAR provides an extraction and annotation framework by permitting the integrated use of hand-crafted extraction rules, machine-learning based extractors, and semantic information about the particular domain of interest. The XAR system has been deployed in an emergency response scenario with civic agencies in North America and in a scenario with an IT department of a county level community clinic.
CRUZAR
Authors: Mínguez Iván , Berrueta Diego , Polo Luis
Published: May 2010

This chapter describes CRUZAR, a Web application that builds custom tourism routes for each visitor to the city of Zaragoza. This application exploits expert knowledge of the tourism domain (captured in rules and ontologies) and consumes a consolidated repository of relevant tourism resources (RDF instances extracted from different legacy databases: historical buildings, museums, public parks, restaurants, cultural events…). User profiles and interests, as well as user-defined constraints, are modeled with an ontology. A semantic matchmaking algorithm is applied to find the most interesting resources for each profile, and a planner organizes the selections into an optimal route. The authors discuss the main challenges and design choices.
An Ontology-Based GeoDatabase Interoperability Platform
Authors: Boucher Serge , Zimányi Esteban
Published: May 2010

This chapter presents an ontology-based platform enabling automatic translation between a large number of geographical formats and data models. It explains the organizational motivations for developing this system, the technologies used, how its architecture and processing components were developed, what it achieves and where it still needs improvement. Since current off-the-shelf description logic reasoners are unable to process the large ontologies involved in this system, this platform uses a custom mapping algorithm that scales gracefully and still computes the required information to effect translation between supported data formats. The authors believe that the lessons learned during this project and discussed in this chapter will prove especially useful to interoperability practitioners contemplating the use of semantic technologies for enabling large-scale integration across organizational boundaries.
A Theoretical Framework Measuring the Usability of Retail Sites
Authors: Vassilopoulou Konstantina , Keeling Kathy K. , Macaulay Linda A.
Published: May 2010

The motivation for this research effort is the failure of usability in Human Computer Interaction (HCI) to consider the needs of users as potential consumers. The hypothesis underlying this research is that it is possible to construct a framework based upon the needs of computer users who are at the same time consumers. The aim of this research is to build a theoretical framework that addresses the needs of a retail site user as a potential consumer. To achieve this we examined the literature relating to usability in HCI as well as Social Attitude Theories. Two empirical studies were conducted using existing sites. The first aimed at incorporating attributes from the fields of Social Attitude Theories and Retail Marketing the second at developing a theoretical framework measuring the usability of retail sites.
Applying the WDP Technique to Usability Inspections in Web Development Organizations
Authors: Conte Tayana , Vaz Verônica T. , Massolar Jobson , Bott Andrew , Mendes Emilia , Travassos Guilherme H.
Published: May 2010

This chapter presents the WDP (Web Design Perspectives-based Usability Evaluation), an inspection technique specifically designed to assess the usability of Web applications. This technique combines Web design perspectives and the heuristic evaluation method proposed by Nielsen (1994b). In addition to describing the components of the WDP technique this chapter also illustrates its use in practice by means of an industrial case study where the technique is applied to inspect a real Web application. In this case study, developers and requirement assessment staff applied the WDP technique to evaluate the usability of modules developed from scratch for a Web application. The results of this case study indicate the feasibility of performing usability inspections with the participation of a software project’s stakeholders, even when stakeholders are not usability experts.
Graphical Models for Representation and Recognition of Human Actions
Authors: Natarajan Pradeep , Nevatia Ramakant
Published: May 2010

Building a system for recognition of human actions from video involves two key problems - 1) designing suitable low-level features that are both efficient to extract from videos and are capable of distinguishing between events 2) developing a suitable representation scheme that can bridge the large gap between low-level features and high-level event concepts, and also handle the uncertainty and errors inherent in any low-level video processing. Graphical models provide a natural framework for representing state transitions in events and also the spatio-temporal constraints between the actors and events. Hidden Markov models(HMMs) have been widely used in several action recognition applications but the basic representation has three key deficiencies: These include unrealistic models for the duration of a sub-event, not encoding interactions among multiple agents directly and not modeling the inherent hierarchical organization of these activities. Several extensions have been proposed to address one or more of these issues and have been successfully applied in various gesture and action recognition domains. More recently, conditionalrandomfields (CRF) are becoming increasingly popular since they allow complex potential functions for modeling observations and state transitions, and also produce superior performance to HMMs when sufficient training data is available. The authors will first review the various extension of these graphical models, then present the theory of inference and learning in them and finally discuss their applications in various domains.
Common Spatial Patterns for Real-Time Classification of Human Actions
Authors: Poppe Ronald
Published: May 2010

We present a discriminative approach to human action recognition. At the heart of our approach is the use of common spatial patterns (CSP), a spatial filter technique that transforms temporal feature data by using differences in variance between two classes. Such a transformation focuses on differences between classes, rather than on modeling each class individually. As a result, to distinguish between two classes, we can use simple distance metrics in the low-dimensional transformed space. The most likely class is found by pairwise evaluation of all discriminant functions, which can be done in real-time. Our image representations are silhouette boundary gradients, spatially binned into cells. We achieve scores of approximately 96% on the Weizmann human action dataset, and show that reasonable results can be obtained when training on only a single subject. We further compare our results with a recent examplarbased approach. Future work is aimed at combining our approach with automatic human detection.
KSM Based Machine Learning for Markerless Motion Capture
Authors: Tangkuampien Therdsak , Suter David
Published: May 2010

A marker-less motion capture system, based on machine learning, is proposed and tested. Pose information is inferred from images captured from multiple (as few as two) synchronized cameras. The central concept of which, we call: Kernel Subspace Mapping (KSM). The images-to-pose learning could be done with large numbers of images of a large variety of people (and with the ground truth poses accurately known). Of course, obtaining the ground-truth poses could be problematic. Here we choose to use synthetic data (both for learning and for, at least some of, testing). The system needs to generalizes well to novel inputs:unseen poses (not in the training database) and unseen actors. For the learning we use a generic and relatively low fidelity computer graphic model and for testing we sometimes use a more accurate model (made to resemble the first author). What makes machine learning viable for human motion capture is that a high percentage of human motion is coordinated. Indeed, it is now relatively well known that there is large redundancy in the set of possible images of a human (these images form som sort of relatively smooth lower dimensional manifold in the huge dimensional space of all possible images) and in the set of pose angles (again, a low dimensional and smooth sub-manifold of the moderately high dimensional space of all possible joint angles). KSM, is based on the KPCA (Kernel PCA) algorithm, which is costly. We show that the Greedy Kernel PCA (GKPCA) algorithm can be used to speed up KSM, with relatively minor modifications. At the core, then, is two KPCA’s (or two GKPCA’s) - one for the learning of pose manifold and one for the learning image manifold. Then we use a modification of Local Linear Embedding (LLE) to bridge between pose and image manifolds.
Real-Time Recognition of Basic Human Actions
Authors: Syrris Vassilis
Published: May 2010

This work describes a simple and computationally efficient, appearance-based approach both for human pose recovery and for real-time recognition of basic human actions. We apply a technique that depicts the differences between two or more successive frames and we use a threshold filter to detect the regions of the video frames where some type of human motion is observed. From each frame difference, the algorithm extracts an incomplete and unformed human body shape and generates a skeleton model which represents it in an abstract way. Eventually, the recognition process is formulated as a time-series problem and handled by a very robust and accurate prediction method (Support Vector Regression). The proposed technique could be employed in applications such as surveillance and security systems.
Action Recognition
Authors: Wei Qingdi , Zhang Xiaoqin , Hu Weiming
Published: May 2010

Action recognition is one of the most active research fields in computer vision. This chapter first reviews the action recognition methods in literature from two aspects: action representation and recognition strategy. Then, a novel method for classifying human actions from image sequences is investigated. In this method, each human action is represented by a sequence of shape context features of human silhouette during the action, and a dominant set-based approach is employed to classify the action to the predefined classes. The dominant set-based approach to classification is compared with K-means, mean shift, and Fuzzy-Cmean approaches.
Distillation
Authors: Cheng Dong Seon , Cristani Marco , Murino Vittorio
Published: May 2010

Image super-resolution is one of the most appealing applications of image processing, capable of retrieving a high resolution image by fusing several registered low resolution images depicting an object of interest. However, employing super-resolution in video data is challenging: a video sequence generally contains a lot of scattered information regarding several objects of interest in cluttered scenes. Especially with hand-held cameras, the overall quality may be poor due to low resolution or unsteadiness. The objective of this chapter is to demonstrate why standard image super-resolution fails in video data, which are the problems that arise, and how we can overcome these problems. In our first contribution, we propose a novel Bayesian framework for super-resolution of persistent objects of interest in video sequences. We call this process Distillation. In the traditional formulation of the image super-resolution problem, the observed target is (1) always the same, (2) acquired using a camera making small movements, and (3) found in a number of low resolution images sufficient to recover high-frequency information. These assumptions are usually unsatisfied in real world video acquisitions and often beyond the control of the video operator. With Distillation, we aim to extend and to generalize the image super-resolution task, embedding it in a structured framework that accurately distills all the informative bits of an object of interest. In practice, the Distillation process: i) individuates, in a semi supervised way, a set of objects of interest, clustering the related video frames and registering them with respect to global rigid transformations; ii) for each one, produces a high resolution image, by weighting each pixel according to the information retrieved about the object of interest. As a second contribution, we extend the Distillation process to deal with objects of interest whose transformations in the appearance are not (only) rigid. Such process, built on top of the Distillation, is hierarchical, in the sense that a process of clustering is applied recursively, beginning with the analysis of whole frames, and selectively focusing on smaller sub-regions whose isolated motion can be reasonably assumed as rigid. The ultimate product of the overall process is a strip of images that describe at high resolution the dynamics of the video, switching between alternative local descriptions in response to visual changes. Our approach is first tested on synthetic data, obtaining encouraging comparative results with respect to known super-resolution techniques, and a good robustness against noise. Second, real data coming from different videos are considered, trying to solve the major details of the objects in motion.
Reducing Cognitive Overload by Meta-Learning Assisted Algorithm Selection
Authors: Fan Lisa , Lei Minxiao
Published: May 2010

With the explosion of available data mining algorithms, a method for helping user to select the most appropriate algorithm or combination of algorithms to solve a given problem and reducing users’ cognitive overload due to the overloaded data mining algorithms is becoming increasingly important. This chapter presents a meta-learning approach to support users automatically selecting most suitable algorithms during data mining model building process. The authors discuss the meta-learning method in detail and present some empirical results that show the improvement that can be achieved with the hybrid model by combining meta-learning method and Rough Set feature reduction. The redundant properties of the dataset can be found. Thus, the ranking process can be sped up and accuracy can be increased by using the reduct of the properties of the dataset. With the reduced searching space, users’ cognitive load is reduced.
Humans and Machines
Authors: Hagège Hélène , Dartnell Christopher , Martin Éric , Sallantin Jean
Published: May 2010

Old and recent theories stress that any understanding of the processes by which humans can learn requires to fully appreciate the relationships between the “nature of learning” and the “learning of nature.” From a constructivist viewpoint, acquiring knowledge is, like any human activity, dissociable neither from its underlying project nor from the knowing subject. The authors of this chapter relate the lessons from philosophy, psychology, didactics and ethics to their work in computational scientific discovery that aims at empowering learning machines with the task of assisting human researchers (Dartnell, Martin, Hagège, & Sallantin, 2008). The chapter concludes with didactical and ethical considerations.
User-Centered Interactive Data Mining
Authors: Zhao Yan , Yao Yiyu
Published: May 2010

While many data mining models concentrate on automation and efficiency, interactive data mining models focus on adaptive and effective communications between human users and computer systems. User requirements and preferences play an important role in human-machine interactions, and guide the selection of knowledge representations, knowledge discovery operations and measurements, combined with explanations of mined patterns. This chapter discusses these fundamental issues based on a usercentered three-layer framework of interactive data mining.
An Efficient and Automatic Iris Recognition System Using ICM Neural Network
Authors: Xu Guangzhu , Ma Yide , Zhang Zaifeng
Published: May 2010

Iris recognition has been shown to be very accurate for human identification. In this chapter, an efficient and automatic iris recognition system using Intersecting Cortical Model (ICM) neural network is presented which includes two parts mainly. The first part is image preprocessing which has three steps. First, iris location is implemented based on local areas. Then the localized iris area is normalized into a rectangular region with a fixed size. At last the iris image enhancement is implemented. In the second part, the ICM neural network is used to generate iris codes and the Hamming Distance between two iris codes is calculated to measure the dissimilarity. In order to evaluate the performance of the proposed algorithm, CASIA v1.0 iris image database is used and the recognition results show that the system has good performance.
An Analysis of Internal Representations for Two Artificial Neural Networks that Classify Musical Chords
Authors: Yaremchuk Vanessa
Published: May 2010

Cognitive informatics is a field of research that is primarily concerned with the information processing of intelligent agents; it can be characterised in terms of an evolving notion of information (Wang, 2007). When it originated six decades ago, conventional accounts of information were concerned about using probability theory and statistics to measure the amount of information carried by an external signal. This, in turn, developed into the notion of modern informatics which studied information as “properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed” (Wang, 2007, p. iii). The current incarnation of cognitive informatics recognised that both information theory and modern informatics defined information in terms of factors that were external to brains, and has replaced this with an emphasis on exploring information as an internal property. This emphasis on the internal processing of information raises fundamental questions about how such information can be represented. One approach to answering such questions — and for proposing new representational accounts — would be to train a brain-like system to perform an intelligent task, and then to analyse its internal structure to determine the types of representations that the system had developed to perform this intelligent behaviour. The logic behind this approach is that when artificial neural networks Cognitive informatics is a field of research that is primarily concerned with the information processing of intelligent agents; it can be characterised in terms of an evolving notion of information (Wang, 2007). When it originated six decades ago, conventional accounts of information were concerned about using probability theory and statistics to measure the amount of information carried by an external signal. This, in turn, developed into the notion of modern informatics which studied information as “properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed” (Wang, 2007, p. iii). The current incarnation of cognitive informatics recognised that both information theory and modern informatics defined information in terms of factors that were external to brains, and has replaced this with an emphasis on exploring information as an internal property. This emphasis on the internal processing of information raises fundamental questions about how such information can be represented. One approach to answering such questions — and for proposing new representational accounts — would be to train a brain-like system to perform an intelligent task, and then to analyse its internal structure to determine the types of representations that the system had developed to perform this intelligent behaviour. The logic behind this approach is that when artificial neural networks
Scaling Behavior of Maximal Repeat Distributions in Genomic Sequences
Authors: Wang J. D. , Ng Ka-Lok
Published: May 2010

The maximal repeat distribution analysis is redone by plotting the relative frequency of maximal repeat patterns against the rank of the appearance. In almost all of the cases, the rank plots give a better coefficient of determination values than the authors’ previous work, i.e. frequency plot was used. A randomized version is repeated for the maximal repeat study; it is found that rank plot regression analysis did not support scaling behavior; hence, the validity of the findings is not due to an artifact.
Beyond Open Source
Authors: Feller Joseph , Finnegan Patrick , Hayes Jeremy
Published: May 2010

Researchers have argued that competitive necessities will require open source software companies to participate in cooperative business networks in order to offer the complete product / service (whole product) demanded by customers. It is envisaged that these business networks will enhance the business models of participant firms by supplementing their value adding activities and increasing responsiveness to customers. However, while such propositions have intuitive appeal, there is a paucity of empirical research on such networks. This study examines Zea Partners, a network of small open source companies cooperating to deliver the ‘whole product’ in the area of Content Management Systems (CMS). It investigates how network participation augments the business models of the participant companies, and identifies the agility challenges faced by the business network. The chapter concludes that reconciling the coordination needs of OSS networks with the operational practices of participant firms is of crucial importance if such networks are to achieve adaptive efficiency to deliver whole products in a ‘bazaarfriendly’ manner.
Patterns Relevant to the Temporal Data-Context of an Alarm of Interest
Authors: Kordic Savo , Lam Chiou Peng , Xiao Jitian , Li Huaizhong
Published: May 2010

The productivity of chemical plants and petroleum refineries depends on the performance of alarm systems. Alarm history collected from distributed control systems (DCS) provides useful information about past plant alarm system performance. However, the discovery of patterns and relationships from such data can be very difficult and costly. Due to various factors such as a high volume of alarm data (especially during plant upsets), huge amounts of nuisance alarms, and very large numbers of individual alarm tags, manual identification and analysis of alarm logs is usually a labor-intensive and time-consuming task. This chapter describes a data mining approach for analyzing alarm logs in a chemical plant. The main idea of the approach is to investigate dependencies between alarms effectively by considering the temporal context and time intervals between different alarm types, and then employing a data mining technique capable of discovering patterns associated with these time intervals. A prototype has been implemented to allow an active exploration of the alarm grouping data space relevant to the tags of interest.
Intrusion Detection Using Machine Learning
Authors: Mazid Mohammed M. , Shawkat Ali A. B.M. , Tickle Kevin S.
Published: May 2010

Intrusion detection has received enormous attention from the beginning of computer network technology. It is the task of detecting attacks against a network and its resources. To detect and counteract any unauthorized activity, it is desirable for network and system administrators to monitor the activities in their network. Over the last few years a number of intrusion detection systems have been developed and are in use for commercial and academic institutes. But still there have some challenges to be solved. This chapter will provide the review, demonstration and future direction on intrusion detection. The authors’ emphasis on Intrusion Detection is various kinds of rule based techniques. The research aims are also to summarize the effectiveness and limitation of intrusion detection technologies in the medical diagnosis, control and model identification in engineering, decision making in marketing and finance, web and text mining, and some other research areas.
A Re-Ranking Method of Search Results Based on Keyword and User Interest
Authors: Xu Ming , Yang Hong-Rong , Zheng Ning
Published: May 2010

It is a pivotal task for a forensic investigator to search a hard disk to find interesting evidences. Currently, most search tools in digital forensic field, which utilize text string match and index technology, produce high recall (100%) and low precision. Therefore, the investigators often waste vast time on huge irrelevant search hits. In this chapter, an improved method for ranking of search results was proposed to reduce human efforts on locating interesting hits. The K-UIH (the keyword and user interest hierarchies) was constructed by both investigator-defined keywords and user interest learnt from electronic evidence adaptive, and then the K-UIH was used to re-rank the search results. The experimental results indicated that the proposed method is feasible and valuable in digital forensic search process.
On the Mining of Cointegrated Econometric Models
Authors: van Velsen J.L. , Choenni R.
Published: May 2010

The authors describe a process of extracting a cointegrated model from a database. An important part of the process is a model generator that automatically searches for cointegrated models and orders them according to an information criterion. They build and test a non-heuristic model generator that mines for common factor models, a special kind of cointegrated models. An outlook on potential future developments is given.
Introduction to Clustering
Authors: Greenlaw Raymond , Kantabutra Sanpawat
Published: May 2010

This chapter provides the reader with an introduction to clustering algorithms and applications. A number of important well-known clustering methods are surveyed. The authors present a brief history of the development of the field of clustering, discuss various types of clustering, and mention some of the current research directions in the field of clustering. Algorithms are described for top-down and bottom-up hierarchical clustering, as are algorithms for K-Means clustering and for K-Medians clustering. The technique of representative points is also presented. Given the large data sets involved with clustering, the need to apply parallel computing to clustering arises, so they discuss issues related to parallel clustering as well. Throughout the chapter references are provided to works that contain a large number of experimental results. A comparison of the various clustering methods is given in tabular format. They conclude the chapter with a summary and an extensive list of references.
Financial Data Mining Using Flexible ICA-GARCH Models
Authors: Yu Philip L.H. , Wu Edmond H.C. , Li W.K.
Published: May 2010

As a data mining technique, independent component analysis (ICA) is used to separate mixed data signals into statistically independent sources. In this chapter, we apply ICA for modeling multivariate volatility of financial asset returns which is a useful tool in portfolio selection and risk management. In the finance literature, the generalized autoregressive conditional heteroscedasticity (GARCH) model and its variants such as EGARCH and GJR-GARCH models have become popular standard tools to model the volatility processes of financial time series. Although univariate GARCH models are successful in modeling volatilities of financial time series, the problem of modeling multivariate time series has always been challenging. Recently, Wu, Yu, & Li (2006) suggested using independent component analysis (ICA) to decompose multivariate time series into statistically independent time series components and then separately modeled the independent components by univariate GARCH models. In this chapter, we extend this class of ICA-GARCH models to allow more flexible univariate GARCH-type models. We also apply the proposed models to compute the value-at-risk (VaR) for risk management applications. Backtesting and out-of-sample tests suggest that the ICA-GARCH models have a clear cut advantage over some other approaches in value-at-risk estimation.