bluuebunny/crossref_metadata_2025 huggingface dataset:
A Tool Chain for Constructing QoS-aware Web Services
Authors: Hollunder Bernhard , Al-Moayed Ahmed , Wahl Alexander
Published: October 2011

Web services play a dominant role in service computing and for realizing service-oriented architectures (SOA), which define the architectural foundation for various kinds of distributed applications. In many business domains, Web services must exhibit quality attributes such as robustness, security, dependability, performance, scalability and accounting. As a consequence, there is a high demand to develop, deploy and consume Web services equipped with well-defined quality of service (QoS) attributes – so-called QoS-aware Web services. Currently, there is only limited development support for the creation of QoS-aware Web services, though. In this work we present a tool chain that facilitates development, deployment and testing of QoS-aware Web services. The tool chain has following features: i) integration of standard components such as widely used IDEs, ii) usage of standards and specifications, and iii) support for various application servers and Web services infrastructures.
Measuring and Dealing with the Uncertainty of SOA Solutions
Authors: Chen Yuhui , Gorbenko Anatoliy , Kharchenko Vyachaslav , Romanovsky Alexander
Published: October 2011

The chapter investigates the uncertainty of Web Services performance and the instability of their communication medium (the Internet), and shows the influence of these two factors on the overall dependability of SOA. We present our practical experience in benchmarking and measuring the behaviour of a number of existing Web Services used in e-science and bio-informatics, provide the results of statistical data analysis and discuss the probability distribution of delays contributing to the Web Services response time. The ratio between delay standard deviation and its average value is introduced to measure the performance uncertainty of a Web Service. Finally, we present the results of error and fault injection into Web Services. We summarise our experiments with SOA-specific exception handling features provided by two web service development kits and analyse exception propagation and performance as the major factors affecting fault tolerance (in particular, error handling and fault diagnosis) in Web Services.
Building Web Services with Time Requirements
Authors: Laranjeiro Nuno , Vieira Marco , Madeira Henrique
Published: October 2011

Developing web services with timing requirements is a difficult task, as existing technology does not provide standard mechanisms to support real-time execution, or even to detect and predict timing violations. However, in business-critical environments, an operation that does not conclude on due time may be completely useless, and may result in service abandonment, reputation, or monetary losses. This chapter presents a framework that allows deploying web services with temporal failure detection and prediction capabilities. Detection is based on timing restrictions defined at execution time and historical data is used for failure prediction according to prediction modules. Additional modules can be added to the framework to provide more advanced failure detection and prediction capabilities. The framework enables providers to easily develop and deploy time-aware web services, with the failure detection code decoupled from the application logic, and allows consumers to express their timeliness requirements.
Dependability and Security on Wireless Self-Organized Networks
Authors: Nogueira Michele , Santos Aldri , Pujolle Guy
Published: October 2011

Wireless communication technologies have been improved every day, increasing the dependence of people on distributed systems. Such dependence increases the necessity of guaranteeing dependable and secure services, particularly, for applications related to commercial, financial and medial domains. However, on wireless self-organized network context, providing simultaneously reliability and security is a demanding task due to the network characteristics. This chapter provides an overview of survivability concepts, reviews security threats in wireless self-organized networks (WSONs) and describes existing solutions for survivable service computing on wireless network context. Finally, this chapter presents conclusions and future directions.
Streaming Media Management and Delivery Systems
Authors: Maniar Nipan J.
Published: October 2011

The aim of this chapter is to highlight essential criteria required to set up a streaming media server within Higher Education Institutions. It explores different types of video content and highlights the importance of streaming media technology by differentiating between the traditional Web server software and streaming media server software. This is then followed by the explanation of different streaming media protocols and how the video content gets transported from the streaming media server to students’ computer. The chapter also explores different types of methods used to deliver streaming videos over the Internet, different streaming media software, encoding software, and encoding parameters used for converting high definition/quality videos into streamable quality. Section 3 of this chapter highlights the importance of folder and file naming conventions, exploring essential video metadata which is required to create and manage video files, Digital Rights Management technology to securely deliver video over the internet, and how to publish a video on the Internet.
The Next Step for Use of Streaming Video in Higher Education
Authors: Winnips J.C. , Verheij G.J. , Gommer E.M.
Published: October 2011

Using streaming video for recording and playing back lectures has become a widespread extra feature for lectures in higher education. There is a wide variety in ways of recording these lectures and making them available. The main form is live lectures that are recorded with slides synchronized with the audio and video of the lecturer. These lectures are recorded, placed online via a University portal, students typically study these materials just before an examination of assignments. But many more ways of using weblectures are possible. This chapter describes 5 didactic models of using weblectures. These are: checking back attended lectures, checking back missed lectures, flexible learning, presentation skills, and re-use. The models can help to make decisions for the form in which lectures can be used, and steer technical developments.
Using Video Streaming In an Online, Rich-Media Class to Promote Deep Learning While Educating For Social Change
Authors: Zorn Diane , Parke Kelly
Published: October 2011

In the latter part of this chapter, we outline the theory behind our practice. We discuss the factors that impelled us to rethink ways of creating online, rich-media learning environments, and move toward innovation. We explain the principles, ideas, and concepts that have grounded our approach and inspired us to embrace video streaming, podcasting, and advanced Internet technologies. We unpack a fundamental assumption: deep learning and educating for social change are made possible by an acceptance and understanding of the radical intertwining of learner, educator, technologist, and technology. In sum, we draw on our course to illustrate enactive, online teaching-and-learning.
Effective Online Courses in Business Administration
Authors: Sturges David L.
Published: October 2011

Technologies used to enhance, augment, or replace traditional course content have been widely examined. With few exceptions, study of these technologies focuses on the effects of the technologies in isolation. Only a few discussions have attempted to evaluate multi-technologies and their contribution to effective learning for online students. This chapter looks at the traditional learning styles and creates a model for robust, multi-technology, student learning-centered approach to optimize student learning in online classes in a business school. It finds that a well-designed, multi-technology approach results in better student performance, more satisfied students, and greater cost-benefit for the business school. The results have been adapted into course design to create a new kind of resource for online course deployment.
IFRS Cyber-Guest Lecturers
Authors: Holtzblatt Mark , Tschakert Norbert
Published: October 2011

In early 2011, 117 nations mandate or allow the use of International Financial Reporting Standards (IFRS). On November 14, 2008, the SEC issued its proposed roadmap for U.S. companies to adopt the new international accounting standards. As a result firms nationwide are beginning to prepare for adoption of IFRS. However, many U.S. business schools are still lagging in the teaching of the new standards and attribute their slow movement to the lack of educational materials. While several IFRS textbooks are beginning to appear on the worldwide market and several innovative curriculum developments are occurring, the teaching materials are still considered sparse. This chapter examines an emerging and impressive source of IFRS teaching materials that includes professional and institutional webcasts and online videos. The available IFRS webcasts are first surveyed and then pedagogical strategies are suggested for a variety of accounting courses. This technology based media offers both professors and students alike numerous educational benefits and opportunities. Our experience with these “cyber-guest” lecturers has inspired an innovative Inter-University IFRS Online Video Competition amongst our two universities.
3D Modelling and Artificial Intelligence
Authors: Marichal G. N. , Hernández A. , González E. J. , Acosta L. , Saorin J. L.
Published: October 2011

This paper explains the different interactions between the Artificial Intelligence (AI) techniques and 3D modelling, not only the wide-used application of AI in order to the implementation of 3D environments, but the less explored use of 3D models in order to take advantage from AI techniques. The authors will be especially interested in real Engineering applications, but the techniques presented in this paper are general and can be used in other research fields. Moreover, a particular robotic application is shown.
Adaptive Algorithms for Intelligent Geometric Computing
Authors: Gavrilova M. L.
Published: October 2011

This chapter spans topics from such important areas as Artificial Intelligence, Computational Geometry and Biometric Technologies. The primary focus is on the proposed Adaptive Computation Paradigm and its applications to surface modeling and biometric processing. Availability of much more affordable storage and high resolution image capturing devices have contributed significantly over the past few years to accumulating very large datasets of collected data (such as GIS maps, biometric samples, videos etc.). On the other hand, it also created significant challenges driven by the higher than ever volumes and the complexity of the data, that can no longer be resolved through acquisition of more memory, faster processors or optimization of existing algorithms. These developments justified the need for radically new concepts for massive data storage, processing and visualization. To address this need, the current chapter presents the original methodology based on the paradigm of the Adaptive Geometric Computing. The methodology enables storing complex data in a compact form, providing efficient access to it, preserving high level of details and visualizing dynamic changes in a smooth and continuous manner. The first part of the chapter discusses adaptive algorithms in real-time visualization, specifically in GIS (Geographic Information Systems) applications. Data structures such as Real-time Optimally Adaptive Mesh (ROAM) and Progressive Mesh (PM) are briefly surveyed. The adaptive method Adaptive Spatial Memory (ASM), developed by R. Apu and M. Gavrilova, is then introduced. This method allows fast and efficient visualization of complex data sets representing terrains, landscapes and Digital Elevation Models (DEM). Its advantages are briefly discussed. The second part of the chapter presents application of adaptive computation paradigm and evolutionary computing to missile simulation. As a result, patterns of complex behavior can be developed and analyzed. The final part of the chapter marries a concept of adaptive computation and topology-based techniques and discusses their application to challenging area of biometric computing.
Designing a Computational Model of Learning
Authors: Gibson David
Published: October 2011

What would a game or simulation need to have in order to teach a teacher how people learn? This chapter uses a four-part framework of knowledge, learner, assessment, and community (Bransford et al., 2000) to discuss design considerations for building a computational model of learning. A teaching simulation—simSchool—helps illustrate selected psychological, physical, and cognitive models and how intelligence can be represented in software agents. The design discussion includes evolutionary perspectives on artificial intelligence and the role of the conceptual assessment framework (Mislevy et al., 2003) for automating feedback to the simulation user. The purpose of the chapter is to integrate a number of theories into a design framework for a computational model of learning.
Intelligent MAS in System Engineering and Robotics
Authors: Marichal G. Nicolás , González Evelio J.
Published: October 2011

The concept of agent has been successfully used in a wide range of applications such as Robotics, e-commerce, agent-assisted user training, military transport or health-care. The origin of this concept can be located in 1977, when Carl Hewitt proposed the idea of an interactive object called actor. This actor was defined as a computational agent, which has a mail address and a behaviour (Hewitt, 1977). Actors receive messages from other actors and carry out their tasks in a concurrent way. It is difficult that a single agent could be sufficient to carry out a relatively complex task. The usual approach consists of a society of agents - called Multiagent Systems (MAS) -, which communicate and collaborate among them and they are coordinated when pursuing a goal. The purpose of this chapter is to analyze the aspects related to the application of MAS to System Engineering and Robotics, focusing on those approaches that combine MAS with other Artificial Intelligence (AI) techniques.
Rule Engines and Agent-Based Systems
Authors: Poggi Agostino , Tomaiuolo Michele
Published: October 2011

Expert systems are successfully applied to a number of domains. Often built on generic rule-based systems, they can also exploit optimized algorithms. On the other side, being based on loosely coupled components and peer to peer infrastructures for asynchronous messaging, multi-agent systems allow code mobility, adaptability, easy of deployment and reconfiguration, thus fitting distributed and dynamic environments. Also, they have good support for domain specific ontologies, an important feature when modelling human experts’ knowledge. The possibility of obtaining the best features of both technologies is concretely demonstrated by the integration of JBoss Rules, a rule engine efficiently implementing the Rete-OO algorithm, into JADE, a FIPA-compliant multi-agent system.
Higher Order Neural Network Architectures for Agent-Based Computational Economics and Finance
Authors: Seiffertt John , Wunsch II Donald C.
Published: October 2011

As the study of agent-based computational economics and finance grows, so does the need for appropriate techniques for the modeling of complex dynamic systems and the intelligence of the constructive agent. These methods are important where the classic equilibrium analytics fail to provide sufficiently satisfactory understanding. In particular, one area of computational intelligence, Approximate Dynamic Programming, holds much promise for applications in this field and demonstrate the capacity for artificial Higher Order Neural Networks to add value in the social sciences and business. This chapter provides an overview of this area, introduces the relevant agent-based computational modeling systems, and suggests practical methods for their incorporation into the current research. A novel application of HONN to ADP specifically for the purpose of studying agent-based financial systems is presented.
A Bayesian Based Machine Learning Application to Task Analysis
Authors: Lin Shu-Chiang
Published: October 2011

Many task analysis techniques and methods have been developed over the past decades, but identifying and decomposing a user’s task into small task components remains a difficult, impractically time-consuming, and expensive process that involves extensive manual effort (Sheridan, 1997; Liu, 1997; Gramopadhye and Thaker, 1999; Annett and Stanton, 2000; Bridger, 2003; Stammers and Shephard, 2005; Hollnagel, 2006; Luczak et al., 2006; Morgeson et al., 2006). A practical need exists for developing automated task analysis techniques to help practitioners perform task analysis efficiently and effectively (Lin, 2007). This chapter summarizes a Bayesian methodology for task analysis tool to help identify and predict the agents’ subtasks from the call center’s naturalistic decision making’s environment.
Application of Artificial Immune Systems Paradigm for Developing Software Fault Prediction Models
Authors: Catal Cagatay , Banerjee Soumya
Published: October 2011

Artificial Immune Systems, a biologically inspired computing paradigm such as Artificial Neural Networks, Genetic Algorithms, and Swarm Intelligence, embody the principles and advantages of vertebrate immune systems. It has been applied to solve several complex problems in different areas such as data mining, computer security, robotics, aircraft control, scheduling, optimization, and pattern recognition. There is an increasing interest in the use of this paradigm and they are widely used in conjunction with other methods such as Artificial Neural Networks, Swarm Intelligence and Fuzzy Logic. In this chapter, we demonstrate the procedure for applying this paradigm and bio-inspired algorithm for developing software fault prediction models. The fault prediction unit is to identify the modules, which are likely to contain the faults at the next release in a large software system. Software metrics and fault data belonging to a previous software version are used to build the model. Fault-prone modules of the next release are predicted by using this model and current software metrics. From machine learning perspective, this type of modeling approach is called supervised learning. A sample fault dataset is used to show the elaborated approach of working of Artificial Immune Recognition Systems (AIRS).
Artificial Intelligence Techniques for Unbalanced Datasets in Real World Classification Tasks
Authors: Vannucci Marco , Colla Valentina , Cateni Silvia , Sgarbi Mirko
Published: October 2011

In this chapter a survey on the problem of classification tasks in unbalanced datasets is presented. The effect of the imbalance of the distribution of target classes in databases is analyzed with respect to the performance of standard classifiers such as decision trees and support vector machines, and the main approaches to improve the generally not satisfactory results obtained by such methods are described. Finally, two typical applications coming from real world frameworks are introduced, and the uses of the techniques employed for the related classification tasks are shown in practice.
Machine Learning in Morphological Segmentation
Authors: Lezoray O. , Lebrun G. , Meurie C. , Charrier C. , Elmotataz A. , Lecluse M.
Published: October 2011

The segmentation of microscopic images is a challenging application that can have numerous applications ranging from prognosis to diagnosis. Mathematical morphology is a very well established theory to process images. Segmentation by morphological means is based on watershed that considers an image as a topographic surface. Watershed requires input and marker image. The user can provide the latter but far more relevant results can be obtained for watershed segmentation if marker extraction relies on prior knowledge. Parameters governing marker extraction varying from image to image, machine learning approaches are of interest for robust extraction of markers. We review different strategies for extracting markers by machine learning: single classifier, multiple classifier, single classifier optimized by model selection.
A Machine Learning Based Meta-Scheduler for Multi-Core Processors
Authors: Rai Jitendra Kumar , Negi Atul , Wankar Rajeev , Nayak K. D.
Published: October 2011

Sharing resources such as caches and memory buses between the cores of multi-core processors may cause performance bottlenecks for running programs. In this paper, the authors describe a meta-scheduler, which adapts the process scheduling decisions for reducing the contention for shared L2 caches on multi-core processors. The meta-scheduler takes into account the multi-core topology as well as the L2 cache related characteristics of the processes. Using the model generated by the process of machine learning, it predicts the L2 cache behavior, i.e., solo-run-L2-cache-stress, of the programs. It runs in user mode and guides the underlying operating system process scheduler in intelligent scheduling of processes to reduce the contention of shared L2 caches. In these experiments, the authors observed up to 12 percent speedup in individual as well as overall performance, while using meta-scheduler as compared to default process scheduler (Completely Fair Scheduler) of Linux kernel.
Image Processing and Machine Learning Techniques for the Segmentation of cDNA Microarray Images
Authors: Giannakeas Nikolaos , Fotiadis Dimitrios I.
Published: October 2011

Microarray technology allows the comprehensive measurement of the expression level of many genes simultaneously on a common substrate. Typical applications of microarrays include the quantification of expression profiles of a system under different experimental conditions, or expression profile comparisons of two systems for one or more conditions. Microarray image analysis is a crucial step in the analysis of microarray data. In this chapter an extensive overview of the segmentation of the microarray image is presented. Methods already presented in the literature are classified into two main categories:methods which are based on image processing techniques and those which are based on Machine learning techniques. A novel classification-based application for the segmentation is also presented to demonstrate efficiency.
Machine Learning for Clinical Data Processing
Authors: Li Guo-Zheng
Published: October 2011

This chapter introduces great challenges and the novel machine learning techniques employed in clinical data processing. It argues that the novel machine learning techniques including support vector machines, ensemble learning, feature selection, feature reuse by using multi-task learning, and multi-label learning provide potentially more substantive solutions for decision support and clinical data analysis. The authors demonstrate the generalization performance of the novel machine learning techniques on real world data sets including one data set of brain glioma, one data set of coronary heart disease in Chinese Medicine and some tumor data sets of microarray. More and more machine learning techniques will be developed to improve analysis precision of clinical data sets.
Machine Learning Enhancing Adaptivity of Multimodal Mobile Systems
Authors: Esposito Floriana , Basile Teresa M.A. , Di Mauro Nicola , Ferilli Stefano
Published: October 2011

One of the most important features of a mobile device concerns its flexibility and capability to adapt the functionality it provides to the users. However, the main problems of the systems present in literature are their incapability to identify user needs and, more importantly, the insufficient mappings of those needs to available resources/services. In this paper, we present a two-phase construction of the user model: firstly, an initial static user model is built for the user connecting to the system the first time. Then, the model is revised/adjusted by considering the information collected in the logs of the user interaction with the device/context in order to make the model more adequate to the evolving user’s interests/ preferences/behaviour. The initial model is built by exploiting the stereotype concept, its adjustment is performed exploiting machine learning techniques and particularly, sequence mining and pattern discovery strategies.
Machine Learning for Visual Navigation of Unmanned Ground Vehicles
Authors: Lenskiy Artem A. , Lee Jong-Soo
Published: October 2011

The use of visual information for the navigation of unmanned ground vehicles in a cross-country environment recently received great attention. However, until now, the use of textural information has been somewhat less effective than color or laser range information. This chapter reviews the recent achievements in cross-country scene segmentation and addresses their shortcomings. It then describes a problem related to classification of high dimensional texture features. Finally, it compares three machine learning algorithms aimed at resolving this problem. The experimental results for each machine learning algorithm with the discussion of comparisons are given at the end of the chapter.
A Case Study on Scaffolding Adaptive Feedback within Virtual Learning Environments
Authors: Najjar Mehdi
Published: October 2011

Despite a growing development of virtual laboratories which use the advantages of multimedia and the Internet for distance education, learning by means of such tutorial tools would be more effective if they were specifically tailored to each student needs. The virtual teaching process would be well adapted if an artificial tutor (integrated into the lab) could identify the correct acquired knowledge. The training approach could be more personalised if the tutor is also able to recognise the erroneous learner’s knowledge and to suggest a suitable sequence of pedagogical activities to improve significantly the level of the student. This chapter proposes a knowledge representation model which judiciously serves the remediation process to students’ errors during learning activities via a virtual laboratory. The chapter also presents a domain knowledge generator authoring tool which attempts to offer a user-friendly environment that allows modelling graphically any subject-matter domain knowledge according to the proposed knowledge representation and remediation approach. The model is inspired by artificial intelligence research on the computational representation of the knowledge and by cognitive psychology theories that provide a fine description of the human memory subsystems and offer a refined modelling of the human learning processes. Experimental results, obtained thanks to practical tests, show that the knowledge representation and remediation model facilitates the planning of a tailored sequence of feedbacks that considerably help the learner.
Application of Machine Leaning in Drug Discovery and Development
Authors: Zhang Shuxing
Published: October 2011

Machine learning techniques have been widely used in drug discovery and development, particularly in the areas of cheminformatics, bioinformatics and other types of pharmaceutical research. It has been demonstrated they are suitable for large high dimensional data, and the models built with these methods can be used for robust external predictions. However, various problems and challenges still exist, and new approaches are in great need. In this Chapter, the authors will review the current development of machine learning techniques, and especially focus on several machine learning techniques they developed as well as their application to model building, lead discovery via virtual screening, integration with molecular docking, and prediction of off-target properties. The authors will suggest some potential different avenues to unify different disciplines, such as cheminformatics, bioinformatics and systems biology, for the purpose of developing integrated in silico drug discovery and development approaches.
Artificial Intelligence and Rubble-Mound Breakwater Stability
Authors: Iglesias Rodriguez Gregorio , Castro Ponte Alberte , Carballo Sanchez Rodrigo , Losada Rodriguez Miguel Ángel
Published: October 2011

Breakwaters are coastal structures constructed to shelter a harbour basin from waves. There are two main types: rubble-mound breakwaters, consisting of various layers of stones or concrete pieces of different sizes (weights), making up a porous mound; and vertical breakwaters, impermeable and monolythic, habitually composed of concrete caissons. This article deals with rubble-mound breakwaters. A typical rubble-mound breakwater consists of an armour layer, a filter layer and a core. For the breakwater to be stable, the armour layer units (stones or concrete pieces) must not be removed by wave action. Stability is basically achieved by weight. Certain types of concrete pieces are capable of achieving a high degree of interlocking, which contributes to stability by impeding the removal of a single unit. The forces that an armour unit must withstand under wave action depend on the hydrodynamics on the breakwater slope, which are extremely complex due to wave breaking and the porous nature of the structure. A detailed description of the flow has not been achieved until now, and it is unclear whether it will be in the future in view of the turbulent phenomena involved. Therefore the instantaneous force exerted on an armour unit is not, at least for the time being, amenable to determination by means of a numerical model of the flow. For this reason, empirical formulations are used in rubble-mound design, calibrated on the basis of laboratory tests of model structures. However, these formulations cannot take into account all the aspects affecting the stability, mainly because the inherent complexity of the problem does not lend itself to a simple treatment. Consequently the empirical formulations are used as a predesign tool, and physical model tests in a wave flume of the particular design in question under the pertinent sea climate conditions are de rigueur, except for minor structures. The physical model tests naturally integrate all the complexity of the problem. Their drawback lies in that they are expensive and time consuming. In this article, Artificial Neural Networks are trained and tested with the results of stability tests carried out on a model breakwater. They are shown to reproduce very closely the behaviour of the physical model in the wave flume. Thus an ANN model, if trained and tested with sufficient data, may be used in lieu of the physical model tests. A virtual laboratory of this kind will save time and money with respect to the conventional procedure.
Decision Support System for Greenhouse Tomato Yield Prediction using Artificial Intelligence Techniques
Authors: Zhang F. , Iliescu D. D. , Hines E. L. , Leeson M. S. , Adams S. R.
Published: October 2011

This chapter introduces a decision support system which is capable of predicting the weekly yields of tomatoes in a greenhouse. The development of this system involves a set of Artificial Intelligence based techniques, namely Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), and Grey System Theory (GST). The prediction was performed by an ANN using a set of optimised input variables, chosen from all available environmental and measured yield parameters. The reduction and optimisation of the inputs was done using either GAs or GST and compared in terms of the ANN’s performance. It was shown that the use of artificial intelligence based methods can offer a promising approach to yield prediction and compared favourably with traditional methods.
Introducing AI and IA into a Non Computer Science Graduate Programme
Authors: Stamatopoulou Ioanna , Fasli Maria , Kefalas Petros
Published: October 2011

As part of designing a new Master’s programme entitled Technology, Innovation and Entrepreneurship there was an apparent need to include a unit involving the use of innovative technologies for the development of innovative products and services. The authors aim was therefore to introduce students to Artificial Intelligence and Intelligent Agents using, however, different from “mainstream” practices since the programme is addressed to students from varying, not necessarily Computer Science related, backgrounds. In this work they report their approach and experience from the entire process -from designing the unit, taking into consideration some inherent restrictions such as coordination of teachers and short duration of the course, to delivering it to a diverse audience that requires different didactic methods to be employed. Their primary aim is to disseminate their ideas and report good practice to fellow educators that may face similar situations or wish to employ any of our techniques.
A Computational Intelligence Approach to Supply Chain Demand Forecasting
Authors: Ampazis Nicholas
Published: October 2011

Estimating customer demand in a multi-level supply chain structure is crucial for companies seeking to maintain their competitive advantage within an uncertain business environment. This work explores the potential of computational intelligence approaches as forecasting mechanisms for predicting customer demand at the first level of organization of a supply chain where products are presented and sold to customers. The computational intelligence approaches that we utilize are Artificial Neural Networks (ANNs), trained with the OLMAM algorithm (Optimized Levenberg-Marquardt with Adaptive Momentum), and Support Vector Machines (SVMs) for regression. The effectiveness of the proposed approach was evaluated using public data from the Netflix movie rental online DVD store in order to predict the demand for movie rentals during the critical, for sales, Christmas holiday season.
Machine Learning and Financial Investing
Authors: Du Jie , Rada Roy
Published: October 2011

This chapter presents the case for knowledge-based machine learning in financial investing. Machine learning here, while it will exploit knowledge, will also rely heavily on the evolutionary computation paradigm of learning, namely reproduction with change and selection of the fit. The chapter will begin with a model for financial investing and then review what has been reported in the literature as regards knowledge-based and machine-learning-based methods for financial investing. Finally, a design of a financial investing system is described which incorporates the key features identified through the literature review. The emerging trend of incorporating knowledge-based methods into evolutionary methods for financial investing suggests opportunities for future researchers.
Problems for Structure Learning Aggregation and Computational Complexity
Authors: Wimberly Frank , Danks David , Glymour Clark , Chu Tianjiao
Published: October 2011

Machine learning methods to find graphical models of genetic regulatory networks from cDNA microarray data have become increasingly popular in recent years. We provide three reasons to question the reliability of such methods: (1) a major theoretical challenge to any method using conditional independence relations; (2) a simulation study using realistic data that confirms the importance of the theoretical challenge; and (3) an analysis of the computational complexity of algorithms that avoid this theoretical challenge. We have no proof that one cannot possibly learn the structure of a genetic regulatory network from microarray data alone, nor do we think that such a proof is likely. However, the combination of (i) fundamental challenges from theory, (ii) practical evidence that those challenges arise in realistic data, and (iii) the difficulty of avoiding those challenges leads us to conclude that it is unlikely that current microarray technology will ever be successfully applied to this structure learning problem.
Emotions, Diffusive Emotional Control and the Motivational Problem for Autonomous Cognitive Systems
Authors: Gros C.
Published: October 2011

All self-active living beings need to solve the motivational problem—the question of what to do at any moment of their life. For humans and non-human animals at least two distinct layers of motivational drives are known, the primary needs for survival and the emotional drives leading to a wide range of sophisticated strategies, such as explorative learning and socializing. Part of the emotional layer of drives has universal facets, being beneficial in an extended range of environmental settings. Emotions are triggered in the brain by the release of neuromodulators, which are, at the same time, are the agents for meta-learning. This intrinsic relation between emotions, meta-learning and universal action strategies suggests a central importance for emotional control for the design of artificial intelligences and synthetic cognitive systems. An implementation of this concept is proposed in terms of a dense and homogeneous associative network (dHan).
Embodying Cognition
Authors: Casacuberta David , Ayala Saray , Vallverdú Jordi
Published: October 2011

After several decades of success in different areas and numerous effective applications, algorithmic Artificial Intelligence has revealed its limitations. If in our quest for artificial intelligence we want to understand natural forms of intelligence, we need to shift/move from platform-free algorithms to embodied and embedded agents. Under the embodied perspective, intelligence is not so much a matter of algorithms, but of the continuous interactions of an embodied agent with the real world. In this paper we adhere to a specific reading of the embodied view usually known as enactivism, to argue that 1) It is a more reasonable model of how the mind really works; 2) It has both theoretical and empirical benefits for Artificial Intelligence and 3) Can be easily implemented in simple robotic sets like Lego Mindstorms (TM). In particular, we will explore the computational role that morphology can play in artificial systems. We will illustrate our ideas presenting several Lego Mindstorms robots where morphology is critical for the robot’s behaviour.
Brain-Like Processing and Classification of Chemical Data
Authors: Schmuker Michael , Schneider Gisbert
Published: October 2011

The purpose of the olfactory system is to encode and classify odorants. Hence, its circuits have likely evolved to cope with this task in an efficient, quasi-optimal manner. In this chapter the authors present a three-step approach that emulate neurocomputational principles of the olfactory system to encode, transform and classify chemical data. In the first step, the original chemical stimulus space is encoded by virtual receptors. In the second step, the signals from these receptors are decorrelated by correlation-dependent lateral inhibition. The third step mimics olfactory scent perception by a machine learning classifier. The authors observed that the accuracy of scent prediction is significantly improved by decorrelation in the second stage. Moreover, they found that although the data transformation they propose is suited for dimensionality reduction, it is more robust against overdetermined data than principal component scores. The authors successfully used our method to predict bioactivity of drug-like compounds, demonstrating that it can provide an effective means to connect chemical space with biological activity.
Non-Visual Programming, Perceptual Culture and Mulsemedia
Authors: Hayhoe Simon
Published: October 2011

The study found that programmers who had been introduced to, and educated using a range of visual, audio and / or tactile devices, whether early or late blind, could adapt to produce code with GUIs, but programmers who were educated using only tactile and audio devices preferred to shun visual references in their work.
Haptic Rendering of HTML Components and 2D Maps Included in Web Pages
Authors: Kaklanis Nikolaos , Moustakas Konstantinos , Tsovaras Dimitrios
Published: October 2011

This chapter describes an interaction technique wherein web pages are parsed so as to automatically generate a corresponding 3D virtual environment with haptic feedback. The automatically created 3D scene is composed of “hapgets” (haptically-enhanced widgets), which are three dimensional widgets providing a behavior that is analogous to the behavior of the original HTML components but are also enhanced with haptic feedback. Moreover, for each 2D map included in a web page a corresponding multimodal (haptic-aural) map is automatically generated. The proposed interaction technique enables the haptic navigation through the internet as well as the haptic exploration of conventional 2D maps for the visually impaired users. A rendering engine of web pages that was developed according to the proposed interaction technique is also presented.
Entertainment Media Arts with Multi-Sensory Interaction
Authors: Imura Masataka , Yoshimoto Shunsuke
Published: October 2011

In a field of application of virtual reality technologies, lots of multi-sensory entertainments have been developed. Researchers have been trying not only to develop the haptic, tactile, olfactory, and taste displays individually, but also to represent subtle sensations with combinations of multiple displays. In this chapter, we will introduce three entertainment applications which utilize multi-sensory stimulation for representing and improving the reality of virtual worlds. (1) Haptic Canvas: An entertainment system with dilatant fluid based haptic device, (2) Fragra: An entertainment system with a hand-mounted olfaction display, and (3) Invisible: An entertainment system that reproduce presence of virtual creatures by indirect information.
Cross-Modal Semantic-Associative Labelling, Indexing and Retrieval of Multimodal Data
Authors: Zhu Meng , Badii Atta
Published: October 2011

Digitalised multimedia information today is typically represented in different modalities and distributed through various channels. The use of such a huge amount of data is highly dependent on effective and efficient cross-modal labelling, indexing and retrieval of multimodal information. In this Chapter, we mainly focus on the combining of the primary and collateral modalities of the information resource in an intelligent and effective way in order to provide better multimodal information understanding, classification, labelling and retrieval. Image and text are the two modalities we mainly talk about here. A novel framework for semantic-based collaterally cued image labelling had been proposed and implemented, aiming to automatically assign linguistic keywords to regions of interest in an image. A visual vocabulary was constructed based on manually labelled image segments. We use Euclidean distance and Gaussian distribution to map the low-level region-based image features to the high-level visual concepts defined in the visual vocabulary. Both the collateral content and context knowledge were extracted from the collateral textual modality to bias the mapping process. A semantic-based high-level image feature vector model was constructed based on the labelling results, and the performance of image retrieval using this feature vector model appears to outperform both content-based and text-based approaches in terms of its capability for combining both perceptual and conceptual similarity of the image content.
The MultiPlasticity of New Media
Authors: Mura Gianluca
Published: October 2011

Interaction systems with the user need complex and suitable conceptual and multisensorial new media definition. This study analyzes social and conceptual evolutions of digital media and proposes an interactive mixed-space media model which communicates the information contents and enhances the user experience between interactive space of physical objects and online virtual space. Its feedback gives information through user performance among its multisensorial interfaces. The research widens previous research publications, and gives precisely a definition of a fuzzy logic cognitive and emotional perception level to the metaplastic multimedia model. It augments the interaction quality within its conceptual media space through an action-making loop and gives as a result new contents of information within its metaplastic metaspace configurations.
Challenges of Civil Military Cooperation / Coordination in Humanitarian Relief
Authors: Heaslip Graham
Published: October 2011

The term civil military coordination (CIMIC) suggests the seamless division of labor between aid workers and international military forces. The images of humanitarian organizations distributing food and medicines under the protection of military forces, or aid workers and military working together to construct refugee camps, set up field hospitals, provide emergency water and sanitation, et cetera, has become more frequent. The media coverage from crises such as New Orleans, Kosovo, the tsunami in Asia, Pakistan, Liberia, Sierra Leone, Chad, and more recently Haiti and Japan, has heightened the expectation of a smooth interaction between humanitarian organizations and military forces. Due to fundamental differences between international military forces, humanitarian and development organizations (in terms of the principles and doctrines guiding their work, their agendas, operating styles, and roles), the area of civil military coordination in disaster relief has proven to be more difficult than other interagency relationships. This chapter will identify the many factors that render integration and collaboration problematic between diverse organizations, and especially so between civilian and military agencies. The chapter will conclude with proposals to improve CIMIC within disaster relief.
Developing and Maintaining Trust in Hastily Formed Relief Networks
Authors: Tatham Peter , Kovács Gyöngyi
Published: October 2011

Although there is a vast body of academic and practitioner literature championing the importance of trust in long-term business relationships, relatively little has been written discussing the development and maintenance of trust in networks that are formed at short notice and that often operate for a limited period of time. However, some models of trust and trusting behavior in such “hastily formed relief networks” (HFRN) do exist, and the aim of this chapter is to consider the theoretical application of one of the most prominent examples – that known as “swift trust” – to a post-disaster humanitarian logistics scenario. Presented from the perspective of a HFRN, this chapter presents a discussion of the practical application of the swift trust model.
Model-Driven Engineering, Services and Interactive Real-Time Applications
Authors: Costa Luis , Loughran Neil , Grønmo Roy
Published: October 2011

Model-driven software engineering (MDE) has the basic assumption that the development of software systems from high-level abstractions along with the generation of low-level implementation code can improve the quality of the systems and at the same time reduce costs and improve time to market. This chapter provides an overview of MDE, state of the art approaches, standards, resources, and tools that support different aspects of model-driven software engineering: language development, modeling services, and real-time applications. The chapter concludes with a reflection over the main challenges faced by projects using the current MDE technologies, pointing out some promising directions for future developments.