bluuebunny/crossref_metadata_2025 huggingface dataset:
Artificial Intelligence Techniques for Modern Energy Applications
Authors: Kalogirou Soteris , Metaxiotis Kostas , Mellit Adel
Published: May 2010

Artificial intelligence (AI) techniques are becoming useful as alternate approaches to conventional techniques or as components of integrated systems. They have been used to solve complicated practical problems in various areas and nowadays are very popular. They are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can learn from examples, are fault tolerant in the sense that they are able to handle noisy and incomplete data, are able to deal with non-linear problems and once trained can perform prediction and generalization at very high speed. AI-based systems are being developed and deployed worldwide in a wide variety of applications, mainly because of their symbolic reasoning, flexibility and explanation capabilities. They have been used in diverse applications in control, robotics, pattern recognition, forecasting, medicine, power systems, manufacturing, optimization, signal processing and social/psychological sciences. They are particularly useful in system modeling such as in implementing complex mappings and system identification. This chapter presents a review of the main AI techniques such as expert systems, artificial neural networks, genetic algorithms, fuzzy logic and hybrid systems, which combine two or more techniques. It also outlines some applications in the energy sector.
Solving Environmental/Economic Dispatch Problem
Authors: Abido M.A.
Published: May 2010

Multiobjective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) problem is proposed and presented in this work. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposed MOPSO technique has been implemented to solve the EED problem with competing and non-commensurable cost and emission objectives. Several optimization runs of the proposed approach have been carried out on a standard test system. The results demonstrate the capabilities of the proposed MOPSO technique to generate a set of well-distributed Pareto-optimal solutions in one single run. The comparison with the different reported techniques demonstrates the superiority of the proposed MOPSO in terms of the diversity of the Pareto optimal solutions obtained. In addition, a quality measure to Pareto optimal solutions has been implemented where the results confirm the potential of the proposed MOPSO technique to solve the multiobjective EED problem and produce high quality nondominated solutions.
Fuel Reduction Effect of the Solar Cell and Diesel Engine Hybrid System with a Prediction Algorithm of Solar Power Generation
Authors: Obara Shin’ya
Published: May 2010

Green energy utilization technology is an effective means of reducing greenhouse gas emissions. We developed the production-of-electricity prediction algorithm (PAS) of the solar cell. In this algorithm, a layered neural network is made to learn based on past weather data and the operation plan of the hybrid system (proposed system) of a solar cell and a diesel engine generator was examined using this prediction algorithm. In addition, system operation without a electricity-storage facility, and the system with the engine generator operating at 25% or less of battery residual quantity was investigated, and the fuel consumption of each system was measured. Numerical simulation showed that the fuel consumption of the proposed system was modest compared with other operating methods. However, there was a significant difference in the prediction error of the electricity production of the solar cell and the actual value, and the proposed system was shown to be not always superior to others. Moreover, although there are errors in the predicted and actual values using PAS, there is no significant influence in the operation plan of the proposed system in almost all cases.
Intelligent Information Systems for Strengthening the Quality of Energy Services in the EU
Authors: Papadopoulou Alexandra G. , Botsikas Andreas , Karakosta Charikleia , Doukas Haris , Psarras John
Published: May 2010

Nowadays, taking into consideration the prevailing situation of price fluctuations, the rapid population increase and the technology’s evolution, the energy efficiency unexploited potential is considered to be extremely significant as a means of partly tackling energy dependence and climate change. This potential can be utilised through the provision of energy services, with the support of intelligent information systems. In particular, up to date several researchers, have proposed energy management tools and methodologies that provide specialized energy management services. However, the majority of the known energy tools are limited to a single equipment type, fuel, or locality. The present paper introduces an intelligent information decision support system, addressed to Energy Service Companies (ESCOs) for assessing an operational unit’s (building or industrial sector) energy behaviour and suggesting the appropriate interventions. Its overall scope is to facilitate the ESCO in reaching a decision quickly and accurately, by simulating the whole unit’s energy behaviour.
Association Rule and Quantitative Association Rule Mining among Infrequent Items
Authors: Zhou Ling , Yau Stephen
Published: May 2010

Association rule mining among frequent items has been extensively studied in data mining research. However, in recent years, there is an increasing demand for mining infrequent items (such as rare but expensive items). Since exploring interesting relationships among infrequent items has not been discussed much in the literature, in this chapter, the authors propose two simple, practical and effective schemes to mine association rules among rare items. Their algorithms can also be applied to frequent items with bounded length. Experiments are performed on the well-known IBM synthetic database. The authors’ schemes compare favorably to Apriori and FP-growth under the situation being evaluated. In addition, they explore quantitative association rule mining in transactional databases among infrequent items by associating quantities of items: some interesting examples are drawn to illustrate the significance of such mining.
A Multi-Methodological Approach to Rare Association Rule Mining
Authors: Koh Yun Sing , Pears Russel
Published: May 2010

Rare association rule mining has received a great deal of attention in the past few years. In this chapter, the authors propose a multi methodological approach to the problem of rare association rule mining that integrates three different strands of research in this area. Firstly, the authors make use of statistical techniques such as the Fisher test to determine whether itemsets co-occur by chance or not. Secondly, they use clustering as a pre-processing technique to improve the quality of the rare rules generated. Their third strategy is to weigh itemsets to ensure upward closure, thus checking unbounded growth of the rule base. Their results show that clustering isolates heterogeneous segments from each other, thus promoting the discovery of rules which would otherwise remain undiscovered. Likewise, the use of itemset weighting tends to improve rule quality by promoting the generation of rules with rarer itemsets that would otherwise not be possible with a simple weighting scheme that assigns an equal weight to all possible itemsets. The use of clustering enabled us to study in detail an important sub-class of rare rules, which we term absolute rare rules. Absolute rare rules are those are not just rare to the dataset as a whole but are also rare to the cluster from which they are derived.
Discovering Interesting Patterns in Numerical Data with Background Knowledge
Authors: Jaroszewicz Szymon
Published: May 2010

The paper presents an approach to mining patterns in numerical data without the need for discretization. The proposed method allows for discovery of arbitrary nonlinear relationships. The approach is based on finding a function of a set of attributes whose values are close to zero in the data. Intuitively such functions correspond to equations describing relationships between the attributes, but they are also able to capture more general classes of patterns. The approach is set in an association rule framework with analogues of itemsets and rules defined for numerical attributes. Furthermore, the user may include background knowledge in the form of a probabilistic model. Patterns which are already correctly predicted by the model will not be considered interesting. Interesting patterns can then be used by the user to update the probabilistic model.
Mining Hidden Association Rules from Real-Life Data
Authors: Balderas Cepeda Marco-Antonio
Published: May 2010

Association rule mining has been a highly active research field over the past decade. Extraction of frequency-related patterns has been applied to several domains. However, the way association rules are defined has limited people’s ability to obtain all the patterns of interest. In this chapter, the authors present an alternative approach that allows us to obtain new kinds of association rules that represent deviations from common behaviors. These new rules are called anomalous rules. To obtain such rules requires that we extract all the most frequent patterns together with certain extension patterns that may occur very infrequently. An approach that relies on anomalous rules has possible application in the areas of counterterrorism, fraud detection, pharmaceutical data analysis and network intrusion detection. They provide an adaption of measures of interest to our anomalous rule sets, and we propose an algorithm that can extract anomalous rules as well. Their experiments with benchmark and real-life datasets suggest that the set of anomalous rules is smaller than the set of association rules. Their work also provides evidence that our proposed approach can discover hidden patterns with good reliability.
Data Extraction, Transformation and Integration Guided by an Ontology
Authors: Reynaud Chantal , Pernelle Nathalie , Rousset Marie-Christine
Published: May 2010

This chapter deals with integration of XML heterogeneous information sources into a data warehouse with data defined in terms of a global abstract schema or ontology. The authors present an approach supporting the acquisition of data from a set of external sources available for an application of interest including data extraction, data transformation and data integration or reconciliation. The integration middleware that the authors propose extracts data from external XML sources which are relevant according to an RDFS+ ontology, transforms returned XML data into RDF facts conformed to the ontology and reconciles RDF data in order to resolve possible redundancies.
Designing Data Marts from XML and Relational Data Sources
Authors: Hachaichi Yasser , Feki Jamel , Ben-Abdallah Hanene
Published: May 2010

Due to the international economic competition, enterprises are ever looking for efficient methods to build data marts/warehouses to analyze the large data volume in their decision making process. On the other hand, even though the relational data model is the most commonly used model, any data mart/ warehouse construction method must now deal with other data types and in particular XML documents which represent the dominant type of data exchanged between partners and retrieved from the Web. This chapter presents a data mart design method that starts from both a relational database source and XML documents compliant to a given DTD. Besides considering these two types of data structures, the originality of our method lies in its being decision maker centered, its automatic extraction of loadable data mart schemas and its genericity.
Ontology-Based Integration of Heterogeneous, Incomplete and Imprecise Data Dedicated to a Decision Support System for Food Safety
Authors: Buche Patrice , Contenot Sandrine , Soler Lydie , Dibie-Barthélemy Juliette , Doussot David , Hignette Gaelle , Ibanescu Liliana
Published: May 2010

This chapter presents an application in the field of food safety using an ontology-based data integration approach. An ontology is a vocabulary used to express the knowledge in a given domain of application. In this chapter, the ontology-based data integration approach permits to homogenize data sources which are heterogeneous in terms of structure and vocabulary. This approach is done in the framework of the Semantic Web, an international initiative which proposes annotating data sources using ontologies in order to manage them more efficiently. In this chapter, the authors explore three ways to integrate data according to a domain ontology: (1) a semantic annotation process to extend local data with Web data which have been semantically annotated according to a domain ontology, (2) a flexible querying system to query uniformly both local data and Web data and (3) an ontology alignment process to find correspondences between data from two sources indexed by distinct ontologies.
On Modeling and Analysis of Multidimensional Geographic Databases
Authors: Bimonte Sandro
Published: May 2010

Data warehouse and OLAP systems are tools to support decision-making. Geographic information systems (GISs) allow memorizing, analyzing and visualizing geographic data. In order to exploit the complex nature of geographic data, a new kind of decision support system has been developed: spatial OLAP (SOLAP). Spatial OLAP redefines main OLAP concepts: dimension, measure and multidimensional operators. SOLAP systems integrate OLAP and GIS functionalities into a unique interactive and flexible framework. Several research tools have been proposed to explore and the analyze spatio-multidimensional databases. This chapter presents a panorama of SOLAP models and an analytical review of research SOLAP tools. Moreover, the authors describe their Web-based system: GeWOlap. GeWOlap is an OLAP-GIS integrated solution implementing drill and cut spatio-multidimensional operators, and it supports some new spatio-multidimensional operators which change dynamically the structure of the spatial hypercube thanks to spatial analysis operators.
Construction and Maintenance of Heterogeneous Data Warehouses
Authors: Badri M. , Boufarès F. , Hamdoun S. , Heiwy V. , Lellahi K.
Published: May 2010

The data necessary to decisional ends are increasingly complex. They have heterogeneous formats and come from distributed sources. They can be classified in three categories: the structured data, the semistructured data and unstructured data. In this work, we are interested in the field of data integration with the aim of construction and maintenance of warehouses whose sources are completely heterogeneous and belonging to the various categories. We propose a formal framework based on the definition of an integration environment. A set of “integration relationships” between the components of the sources is thus defined: an equivalence relation and a strict order relation. These relationships are independent of any data sources modelling. These last can be then heterogeneous and having different models and/ or categories. Two different physical architectures, to create and maintain the warehouses and the materialized views, are given.
Ontology Query Languages for Ontology-Based Databases
Authors: Jean Stéphane , Ameur Yamine Aït , Pierra Guy
Published: May 2010

Current databases and their associated languages allow a user to exploit data according to their logical model. Usually, there is a gap between this logical model and the actual concepts represented. As a consequence, exploiting, exchanging and integrating data stored in databases are difficult. To overcome these problems, several approaches have proposed to extend current databases with ontologies. We called Ontology-Based Databases (OBDB) such databases. However, current database languages such as SQL have not been designed to exploit ontologies. Thus, a new generation of languages we called ontology query languages has emerged. The goal of this chapter is to provide an up to date survey on ontology query languages. We survey languages coming from the Semantic Web community as well as those coming from the database community.
Security in Data Warehouses
Authors: Weippl Edgar R.
Published: May 2010

The last several years have been characterized by global companies building up massive databases containing computer users’ search queries and sites visited; government agencies accruing sensitive data and extrapolating knowledge from uncertain data with little incentive to provide citizens ways of correcting false data; and individuals who can easily combine publicly available data to derive information that – in former times – was not so readily accessible. Security in data warehouses becomes more important as reliable and appropriate security mechanisms are required to achieve the desired level of privacy protection.
Instructional Design for Multimedia and Theory of Learning Styles
Authors: Sidhu Manjit Singh
Published: May 2010

The effectiveness of any instructional programme or instructional material depends upon an appropriate planning or designing, what is called in professional parlance, “Instructional Design”. In general, instructional design is relatively a young discipline (Usha, 2003). In its literal meaning, instruction means a set of events that facilitate learning. On the other hand the word design is a generic term, which means “a creative model”. Instructional design includes several processes such as the use of knowledge, observation, and creativity to plan and create situations that enhance learning opportunities of the individuals. However, to accomplish the aforementioned processes, the instruction has to be planned to be effective and designed in some systematic approach. Learning theories have significant bearing on instructional design, as there is a logical development from learning to instruction (Usha, 2003). Instructional design optimizes learning outcomes while learning theories are the backbone of any instructional design. Instructional design is the articulation or the manifestation of the learning theories, and its main aim is to optimize learning by using the known theories of learning (Usha, 2003).
Technology Assisted Problem Solving Packages
Authors: Sidhu Manjit Singh
Published: May 2010

This is especially true in the domain of engineering where such technology needs much attention. Technology assisted problem solving (TAPS) packages are specialized computer programs developed to work as stand-alone (PC Based) or with Web servers that can supplement student learning; for revision, laboratory experiments, and self-study. In this book the term TAPS is used to represent interactive multimedia CAL in which the student is engaged with a computer tutor in the problem-solving task of the subject matter. TAPS packages offer similar pedagogic values as an experienced human tutor, with the added advantage of guiding students to solve engineering problems on a more flexible mode i.e. a student has the freedom of working on the problem at his/her own pace, repeat all or certain steps, spend more time at each or particular step until they are able to understand, and solve the problem. The objec-tive of these TAPS packages is to improve student’s understanding of the selected engineering problems by guiding and presenting the problem solving steps accordingly. The ultimate goal is to instill a sense of independent learning, encouraging critical thinking, and to promote deep learning. When tutoring a student on solving an engineering problem, a human tutor is expected to gauge the student’s background knowledge, deliver relevant course material at the correct level of detail, and clarify student’s misunderstandings. TAPS packages include the use of the computer to provide most aspects of instruction, which a classroom instructor could provide such as tutorials, questioning, feedback, contingent on answers, analysis and testing. The TAPS packages developed for this project has been customized to anticipate student needs, and have various interactive features built in to allow delivery control, navigation, and feedback More specifically, the packages are designed to assist the student in learning, visualizing, and problem solving in a step-by-step approach.
Challenges and Trends of TAPS Packages in Enhancing Engineering Education
Authors: Sidhu Manjit Singh
Published: May 2010

It can be envisaged that the use of multimedia computer technology as replacement, or supplement to, human educators in engineering education would become widespread in the future. Such technology can be employed to demonstrate and correlate real life application and theory thereby promoting deep learning. Interactive courseware for higher learning institutions may be extremely useful where trained human resources in the engineering education sector are limited. This Chapter discusses the current trends of incorporating new technologies with TAPS packages in the teaching of engineering subjects.
Nature Inspired Methods for Multi-Objective Optimization
Authors: Das Sanjoy , Panigrahi Bijaya K. , Pattnaik Shyam S.
Published: May 2010

This chapter focuses on the concepts of dominance and Pareto-optimality. It then addresses key issues in applying three basic classes of nature inspired algorithms – evolutionary algorithms, particle swarm optimization, and artificial immune systems, to multi-objective optimization problems. As case studies, the most significant multi-objective algorithm from each class is described in detail. Two of these, NSGA-II and MOPSO, are widely used in engineering optimization, while the others show excellent performances. As hybrid algorithms are becoming increasingly popular in optimization, this chapter includes a brief discussion of hybridization within a multi-objective framework.
Deterministic Pattern Mining On Genetic Sequences
Authors: Ferreira Pedro Gabriel , Azevedo Paulo Jorge
Published: May 2010

The recent increase in the number of complete genetic sequences freely available through specialized Internet databases presents big challenges for the research community. One such challenge is the efficient and effective search of sequence patterns, also known as motifs, among a set of related genetic sequences. Such patterns describe regions that may provide important insights about the structural and functional role of DNA and proteins. Two main classes can be considered: probabilistic patterns represent a model that simulates the sequences or part of the sequences under consideration and deterministic patterns that either match or not the input sequences. In this chapter a general overview of deterministic sequence mining over sets of genetic sequences is proposed. The authors formulate an architecture that divides the mining process workflow into a set of blocks. Each of these blocks is discussed individually.
Machine Learning in Natural Language Processing
Authors: Sokolova Marina , Szpakowicz Stan
Published: May 2010

This chapter presents applications of machine learning techniques to traditional problems in natural language processing, including part-of-speech tagging, entity recognition and word-sense disambiguation. People usually solve such problems without difficulty or at least do a very good job. Linguistics may suggest labour-intensive ways of manually constructing rule-based systems. It is, however, the easy availability of large collections of texts that has made machine learning a method of choice for processing volumes of data well above the human capacity. One of the main purposes of text processing is all manner of information extraction and knowledge extraction from such large text. Machine learning methods discussed in this chapter have stimulated wide-ranging research in natural language processing and helped build applications with serious deployment potential.
Machine Learning Applications in Mega-Text Processing
Authors: Sokolova Marina , Szpakowicz Stan
Published: May 2010

This chapter presents applications of machine learning techniques to problems in natural language processing that require work with very large amounts of text. Such problems came into focus after the Internet and other computer-based environments acquired the status of the prime medium for text delivery and exchange. In all cases which the authors discuss, an algorithm has ensured a meaningful result, be it the knowledge of consumer opinions, the protection of personal information or the selection of news reports. The chapter covers elements of opinion mining, news monitoring and privacy protection, and, in parallel, discusses text representation, feature selection, and word category and text classification problems. The applications presented here combine scientific interest and significant economic potential.
Applications of Evolutionary Neural Networks for Sales Forecasting of Fashionable Products
Authors: Yu Yong , Choi Tsan-Ming , Au Kin-Fan , Sun Zhan-Li
Published: May 2010

The evolutionary neural network (ENN), which is the hybrid combination of evolutionary computation and neural network, is a suitable candidate for topology design, and is widely adopted. An ENN approach with a direct binary representation to every single neural network connection is proposed in this chapter for sales forecasting of fashionable products. In this chapter, the authors will first explore the details on how an evolutionary computation approach can be applied in searching for a desirable network structure for establishing the appropriate sales forecasting system. The optimized ENN structure for sales forecasting is then developed. With the use of real sales data, the authors compare the performances of the proposed ENN forecasting scheme with several traditional methods which include artificial neural network (ANN) and SARIMA. The authors obtain the conditions in which their proposed ENN outperforms other methods. Insights regarding the applications of ENN for forecasting sales of fashionable products are generated. Finally, future research directions are outlined.
Support Vector Machine based Hybrid Classifiers and Rule Extraction thereof
Authors: Farquad M. A.H. , Ravi V. , Bapi Raju S.
Published: May 2010

Support vector machines (SVMs) have proved to be a good alternative compared to other machine learning techniques specifically for classification problems. However just like artificial neural networks (ANN), SVMs are also black box in nature because of its inability to explain the knowledge learnt in the process of training, which is very crucial in some applications like medical diagnosis, security and bankruptcy prediction etc. In this chapter a novel hybrid approach for fuzzy rule extraction based on SVM is proposed. This approach handles rule-extraction as a learning task, which proceeds in two major steps. In the first step the authors use labeled training patterns to build an SVM model, which in turn yields the support vectors. In the second step extracted support vectors are used as input patterns to fuzzy rule based systems (FRBS) to generate fuzzy “if-then” rules. To study the effectiveness and validity of the extracted fuzzy rules, the hybrid SVM+FRBS is compared with other classification techniques like decision tree (DT), radial basis function network (RBF) and adaptive network based fuzzy inference system. To illustrate the effectiveness of the hybrid developed, the authors applied it to solve a bank bankruptcy prediction problem. The dataset used pertain to Spanish, Turkish and US banks. The quality of the extracted fuzzy rules is evaluated in terms of fidelity, coverage and comprehensibility.
Statistical Machine Learning Approaches for Sports Video Mining Using Hidden Markov Models
Authors: Fan Guoliang , Ding Yi
Published: May 2010

This chapter summarizes the authors’ recent research on the hidden Markov model (HMM)-based machine learning approaches to sports video mining. They will advocate the concept of semantic space that provides explicit semantic modeling at three levels, high-level semantics, mid-level semantic structures, and low-level visual features. Sports video mining is formulated as two related statistical inference problems. One is from low-level features to mid-level semantic structures, and the other is from midlevel semantic structures to high-level semantics. The authors assume that a sport video is composed of a series of consecutive play shots each of which contains variable-length frames and can be labelled with certain mid-level semantic structures. In this chapter, the authors present several HMM–based approaches to the first inference problem where the hidden states are directly associated with mid-level semantic structures at the shot level and observations are the visual features extracted from frames in a shot. Specifically, they will address three technical issues about HMMs: (1) how to enhance the observation model to handle variable-length frame-wise observations; (2) how to capture the interaction between multiple semantic structures in order to improve the overall mining performance; (3) how to optimize the model structure and to learn model parameters simultaneously. This work is the first step toward the authors’ long-term goal that is to develop a general sports video mining framework with explicit semantic modeling and direct semantic computing.
A Survey of Bayesian Techniques in Computer Vision
Authors: Blasco José , Aleixos Nuria , Gómez-Sanchis Juan , Guerrero Juan F. , Moltó Enrique
Published: May 2010

The Bayesian approach to classification is intended to solve questions concerning how to assign a class to an observed pattern using probability estimations. Red, green and blue (RGB) or hue, saturation and lightness (HSL) values of pixels in digital colour images can be considered as feature vectors to be classified, thus leading to Bayesian colour image segmentation. Bayesian classifiers are also used to sort objects but, in this case, reduction of the dimensionality of the feature vector is often required prior to the analysis. This chapter shows some applications of Bayesian learning techniques in computer vision in the agriculture and agri-food sectors. Inspection and classification of fruit and vegetables, robotics, insect identification and process automation are some of the examples shown. Problems related with the natural variability of colour, sizes and shapes of biological products, and natural illuminants are also discussed. Moreover, implementations that lead to real-time implementation are explained.
Counting the Hidden Defects in Software Documents
Authors: Padberg Frank
Published: May 2010

The author uses neural networks to estimate how many defects are hidden in a software document. Input for the models are metrics that get collected when effecting a standard quality assurance technique on the document, a software inspection. For inspections, the empirical data sets typically are small. The author identifies two key ingredients for a successful application of neural networks to small data sets: Adapting the size, complexity, and input dimension of the networks to the amount of information available for training; and using Bayesian techniques instead of cross-validation for determining model parameters and selecting the final model. For inspections, the machine learning approach is highly successful and outperforms the previously existing defect estimation methods in software engineering by a factor of 4 in accuracy on the standard benchmark. The author’s approach is well applicable in other contexts that are subject to small training data sets.
Economic Efficiency and the Canadian Health Care System
Authors: Sadanand Asha B.
Published: May 2010

In this chapter the authors examine the compatibility of the objectives of universality and public funding which are two important pillars of the Canadian healthcare system, with the objectives of cost effectiveness and more generally economic efficiency. The authors note that under some very innocuous conditions, markets and other economic based mechanisms such as second price auctions are characterized by economic efficiency and cost effectiveness. For the particular case of healthcare, some additional features that must be considered in the design of the mechanism are that healthcare services and products are valuable if, when taken together they constitute the components of a needed procedure, and otherwise they are worthless to the individual; and timely completion of procedures is what is valued, delays and waiting not only prolong suffering but may eventually prove to be more costly to the system if the condition worsens. They recommend a market-based mechanism, encompassing these features, that utilizes mobile agents representing patients and their medical needs. In order to incorporate the basic goals of universality and public funding, the agents will participate in virtual auctions using a needs based ranking as the currency for making bids.
What are Temporal Structures?
Authors: Wu Dezhi
Published: May 2010

Temporal structure, a key notion in this book, is defined as a patterned organization of time, used by humans to help them manage, comprehend or coordinate their use of time. The objective of this chapter is to provide a theoretical overview for understanding the role temporal structures play in personal time management practices. This chapter discusses how temporal structures are aligned with personal temporal constraints and how to understand the impact of these structures on personal productivity. This chapter first introduces the concept of temporal structure and its relationship to individual time management practices. Individual temporal experiences contain many different forms of temporal structures, which can be either explicit or implicit. A good example of explicit temporal structures is a deadline, which most individuals write on their personal calendar tools. An example of implicit temporal structure is an informal project meeting with a small group, which is not publically announced and is only known internally.
Individual Time Management Profiles
Authors: Wu Dezhi
Published: May 2010

Today’s users have many options in choosing electronic calendar tools. They can adopt free online calendar tools for personal use, such as GoogleTM, YahooTM, or MSNTM online calendars, which can be synchronized with their mobile devices (e.g., iPhone, Blackberry, PDA, etc.). At work, electronic calendars tools, such as OutlookTM and GroupWise, are often mandated by employers. Individual users can readily record meetings, appointments, or tasks with their electronic calendar tools, which usually provide easy recording, editing, repeating, and archiving functions. Despite the many advanced features and benefits provided by current electronic calendar tools, some people still keep traditional paper-based calendars. This chapter presents selected personal profiles regarding personal time management and describes the reasons why each individual chose her or his current calendar tools. These profiles were selected from a set of field interviews conducted with professionals in the United States. Four identified basic needs for choosing different calendar tools are (1) portability, (2) ability to gain an overview, (3) ability to better coordinate between multiple tools via convenient synchronization features, and (4) collaborative scheduling. Users’ comments about their calendar tool experiences give valuable insights for enhancing the current electronic calendar tool design.
Reflections Concerning Technology
Authors: Gibbons Andew Neil
Published: May 2010

The philosophy of technology underlies how people view and use technology. The historic relationship of humans to technology is important to explore when developing a context for current technology applications. Within such a context, this chapter outlines the core components, philosophy and debate contributing to sessions run with early childhood education student teachers and early childhood educators.
Technology in Preschool Education in Mexico
Authors: López Jorge , López María Eugenia
Published: May 2010

The last nine years have seen major change in the Mexican educational system as sweeping reforms across all levels have been implemented. In particular the early years of education have become the focus of legislation to increase quality, open access, and improve curriculum. How technology shaped these sweeping reform efforts will change the future of Mexico in a global community. Early childhood education must support the use of technology if the population is to move into a techno-society.
Monitoring Students' Activity and Performance in Online Higher Education
Authors: Lera-López Fernando , Faulin Javier , Juan Angel A. , Cavaller Victor
Published: May 2010

In this chapter the authors first explain the recently created European Higher Education Area and its implications over instructors’ and students’ roles. They also analyze how e-learning management systems are contributing to modify higher education around the world, and which are the benefits and the challenges associated to their use. In this new educational scenario, the authors discuss the importance of monitoring students’ and groups’ activity and performance, and some of the monitoring tools already available in the most popular learning management systems are reviewed. Then, after identifying the informational necessities of online instructors and students, the authors propose a data-analysis model to assist instructors by providing them with easy-to-understand and updated visual reports. Instructors can use these reports to classify students and groups according to their activity and learning outcomes, track their evolution, and identify those who might need immediate guidance or assistance.
Improvement of Self-Assessment Effectiveness by Activity Monitoring and Analysis
Authors: Burdescu Dumitru Dan , Mihaescu Marian Cristian
Published: May 2010

Self-assessment is one of the crucial activities within e-learning environments that provide learners with feedback regarding their level of accumulated knowledge. From this point of view, the authors think that guidance of learners in self-assessment activity must be an important goal of e-learning environment developers. The scope of the chapter is to present a recommender software system that runs along the e-learning platform. The recommender software system improves the effectiveness of self-assessment activities. The activities performed by learners represent the input data and the machine learning algorithms are used within the business logic of the recommender software system that runs along the e-learning platform. The output of the recommender software system is represented by advice given to learners in order to improve the effectiveness of self-assessment process. The methodology for obtaining improvement of self-assessment is based on embedding knowledge management into the business logic of the e-learning platform. Naive Bayes Classifier is used as machine learning algorithm for obtaining the resources (e.g., questions, chapters, and concepts) that need to be further accessed by learners. The analysis is accomplished for disciplines that are well structured according to a concept map. The input data set for the recommender software system is represented by student activities that are monitored within Tesys e-learning platform. This platform has been designed and implemented within Multimedia Applications Development Research Center at Software Engineering Department, University of Craiova. Monitoring student activities is accomplished through various techniques like creating log files or adding records into a table from a database. The logging facilities are embedded in the business logic of the e-learning platform. The e-learning platform is based on a software development framework that uses only open source software. The software architecture of the e-learning platform is based on MVC (model-view-controller) model that ensures the independence between the model (represented by MySQL database), the controller (represented by the business logic of the platform implemented in Java) and the view (represented by WebMacro which is a 100% Java open-source template language).
Creating Competitive Advantage in Scottish Family Businesses
Authors: Seaman Claire , Graham Stuart
Published: May 2010

This chapter seeks to consider both the role that knowledge transfer may have in family businesses and the different manners in which knowledge transfer may take place within this diverse environment. The economic, social and community importance of family businesses within Scotland is considered, alongside the different manner in which family businesses commonly operate and the implications for knowledge transfer. The importance of knowledge transfer in the creation of competitive advantage within a family business environment and the relatively limited nature of research in this area are explored, highlighting the need for further research both to support the on-going development of a strategy for family businesses in Scotland and to facilitate future development of high quality knowledge transfer. Key to all of this, however, is an increased understanding of what is meant by knowledge transfer and the breadth of ways in which it happens.
The Impact of Culture on University–Industry Knowledge Interaction in the Chinese MNC Context
Authors: Hong Jianzhong , Heikkinen Johanna , Salila Mia
Published: May 2010

Recent studies on university–industry collaboration have paid a growing attention to complementary knowledge interaction, which is of crucial importance for networked learning and knowledge co-creation needed in today’s rapidly changing markets and for gaining global competitiveness. The existent studies concentrate on the transfer of knowledge from the university to the company, and the impact of culture is examined with a focus on fundamentally different cultures between two types of organizations (i.e., between universities and firms). The studies, however, remain highly fragmented in cultural exploration on one level, and are primarily concerned with one-way technology and knowledge transfer. Research on more interactive knowledge interaction (e.g., collaborative knowledge creation) and especially in the Chinese context is seriously lacking. This chapter explores university–industry knowledge interaction in a broad sense, focusing on the development of a conceptual view on the understanding and analysis of the cultural impact in the Chinese MNC context. The chapter is an early work in process and it is theoretical in nature. It clarifies and elaborates key concepts and perspectives, and suggests implications for future research and practice regarding effective knowledge co-creation involving dissimilar cultures.
Exploring the Links between Structural Capital, Knowledge Sharing, Innovation Capability and Business Competitiveness
Authors: Sáenz Josune , Aramburu Nekane , Rivera Olga
Published: May 2010

The aim of this chapter is to analyze the degree of influence of different organizational enablers (i.e., “structural capital”) on knowledge sharing, as well as the influence of the latter and other structural capital components on innovation capability, both from a theoretical and empirical perspective. Additionally, the relevance of different innovation capability dimensions (i.e., ideation, project management, and timeliness and cost efficiency) on business competitiveness will be examined. For these relationships to be tested, an empirical study has been carried out among Spanish manufacturing firms with more than 50 employees and with R&D activities. To this end, a questionnaire has been designed and submitted to the CEOs of the companies making up the target population of the research. Structural equation modelling (SEM) based on partial least squares (PLS) has then been applied in order to test the main hypotheses of the research.
Outsourcing Performance
Authors: Solli-Sæther Hans , Gottschalk Petter
Published: May 2010

For the vendor an IT outsourcing relationship is successful if it generates profit for the company and if it strengthens the company’s value proposition in terms of complementary competencies such as IT personnel development, methodology development and dissemination, and customer relationship management (Levina & Ross, 2003). For the client an IT outsourcing relationship is successful if it generates profit for the company and if it contributes to achievement of outsourcing objectives as exemplified by Lee and Kim (1999). Managing successful IT outsourcing relationships means to us that source firm and sourcing firm both achieve their objectives in a joint effort. Achieving objectives is a matter of outsourcing outcome. In this chapter we start by presenting the vendor’s value proposition because outsourcing outcomes are dependent on the vendor’s ability to create value for both parties in the relationship. Satisfaction based on successful exploration and exploitation of the vendor value proposition plays an important role in building other important assets for the client company. The following two sections in this chapter discuss outsourcing opportunities and threats in a client perspective. Next, we present a method for developing quantitative performance measurements. Although decisions to outsource should be a part of the overall business strategy, control and objective measurement of service quality is important for any company as this may influence the overall relationship. At the end of this chapter, we take a look at how to measure the success of IT outsourcing relationships.
Outsourcing Governance
Authors: Solli-Sæther Hans , Gottschalk Petter
Published: May 2010

The overall objective of this chapter is to concentrate on the important issues of strategy, structure, and management of IT outsourcing arrangements. First, we take a look at the broader issue of governance. Learning that IT outsourcing governance includes not only information and IT assets, but also such aspects as human, financial, physical, intellectual property, and relationship assets, we present in the next section the interaction approach as a model that focuses both on short-term episodes and general long-term relationships in dyadic buyer-supplier ventures. Then we discuss how appropriate governance structures – including management control systems and the development of trust – may work to reduce risk and decrease failure. We continue by presenting the important partnering process, where management can take actions when building and sustaining outsourcing relationships. Important stakeholder groups are presented as they may have distinct expectations and goals in outsourcing and for outsourcing relationships. Hard and soft sides of outsourcing management are presented, as both sides are keys to success. Finally, using theoretical perspectives described earlier and experience earned from several business case studies, we present a governance model for successful management of IT outsourcing relationships.
A Lyapunov Theory-Based Neural Network Approach for Face Recognition
Authors: Ang Li-Minn , Lim King Hann , Seng Kah Phooi , Chin Siew Wen
Published: May 2010

This chapter presents a new face recognition system comprising of feature extraction and the Lyapunov theory-based neural network. It first gives the definition of face recognition which can be broadly divided into (i) feature-based approaches, and (ii) holistic approaches. A general review of both approaches will be given in the chapter. Face features extraction techniques including Principal Component Analysis (PCA) and Fisher’s Linear Discriminant (FLD) are discussed. Multilayered neural network (MLNN) and Radial Basis Function neural network (RBF NN) will be reviewed. Two Lyapunov theory-based neural classifiers: (i) Lyapunov theory-based RBF NN, and (ii) Lyapunov theory-based MLNN classifiers are designed based on the Lyapunov stability theory. The design details will be discussed in the chapter. Experiments are performed on two benchmark databases, ORL and Yale. Comparisons with some of the existing conventional techniques are given. Simulation results have shown good performance for face recognition using the Lyapunov theory-based neural network systems.