bluuebunny/crossref_metadata_2025 huggingface dataset:
A Knowledge Worker Desktop Model (KWDM) Applied to Decision Support System
Authors: Rosenthal-Sabroux Camille , Grundstein Michel , Iafrate Fernando
Published: May 2011

The concept of information system covers two notions: on the one hand, the reality of the evolving organization that collects, communicates, and records information; and, on the over hand, the digital information system, artificial object conceived by humans to acquire, process, store, transmit, and restore the information allowing them to carry out their activities within the context of the organization (Reix, 1995). We shall refer hereinafter to the digital information system. In the first part of this article, we present the knowledge worker desktop’s model (KWDM). This model highlights three types of data: “main-stream” data, shared-data, and source-of- knowledge-data. In the second part, we describe a group decision and negotiation system (GDNS) for operational performance management (OPM) implemented in an entertainment company based in France. This GDNS addresses a zero latency organization problem that is to provide decision makers, both strategic and operational, with the insight they need to interpret multiple and complex operational data, and take immediate decision close to the action. In the third part, in order to validate the KWDM model, we present the methodology that consists to match each system’s component with each model’s element, and the study’s outcomes. This analysis leads to highlight the formalization of the different data flows, the impact of the system on the organization, and to confirm the importance of human factor in the group decision and negotiation process. Furthermore, it opens new, perspectives particularly the influence of the intention, the importance of shared-data system and the role of the system in the organizational learning process to insure the business continuity plan.
Logic Programming Languages for Expert Systems
Authors: Jones James D.
Published: May 2011

“Expert systems” are a significant subset of what is known as “decision support systems” (DSS). This article suggests a different paradigm for expert systems than what is commonly used. Most often, expert systems are developed with a tool called an “expert system shell.” For the more adventurous, an expert system might be developed with Prolog, a language for artificial intelligence. Both Prolog and expert system shells stem from technology that is approximately 30 years old.1 There have been updates to these platforms, such as GUI interfaces, XML interfaces, and other “bells and whistles.” However, the technology is still fundamentally old. As an analogy, the current technology is akin to updating a 30-year-old car with new paint (a gooey interface), new upholstery, GPS, and so forth. However, the car is fundamentally still a 30-year-old car. It may be in far better shape than another 30-year-old car without the updates, but it cannot compete from an engineering perspective with current models.2 Similarly, the reasoning power of current expert system technology cannot compete with the reasoning power of the state of the art in logic programming. These advances that have taken place in the logic programming community since the advent of Prolog and expert system shells include: a well developed theory of multiple forms of negation, an understanding of open domains, and the closed world assumption, default reasoning with exceptions, reasoning with respect to time (i.e., a solution to the frame problem and introspection with regard to previous beliefs), reasoning about actions, introspection, and maintaining multiple views of the world simultaneously (i.e., reasoning with uncertainty). This article examines a family of logic programming languages. This article in conjunction with a companion article this volume, Knowledge Representation That Can Empower Expert Systems, suggest that logic programs employing recent advances in semantics and in knowledge representation provide a more robust framework in which to develop expert systems. The author has successfully applied this paradigm and these ideas to financial applications, security applications, and enterprise information systems.
The Method of Appraising Needs and Possibilities of Activity-Based Costing Implementation
Authors: Januszweski Arkadiusz
Published: May 2011

Changes that occur in the environment of a given company and a constantly growing competition make the managers enhance the management system. The managers consider which management-support methods and tools could appear effective in fighting competition and ensure the company’s survival or development. Introducing a new management method or tool is in general a complex project, incorporating many aspects and frequently calling for considerable changes in the company’s IT system. Taking up the right decision needs thorough consideration of all the factors supporting or rejecting the project. The decision-making process concerning the implementation of a specific method or tool should address the following questions: • Does, in a given company, there exist objective reasons for which the implementation of a given method would be recommended? • What are the possibilities existing in the company which would make the implementation success realistic? • What tangible benefits will be available due to the new method implementation, in other words, what is the effectiveness of the project? To cut a long story short, one shall determine whether the change is needed, whether it is possible, and whether it will be beneficial to the business. Replying to the third question, different kinds of investment project effectiveness evaluation methods are used, including the internal return rate (IRR) or the net present value (NPV). Their application for the management system improvement project, however, faces many difficulties and can be very time consuming. Yet, one shall remember that before an attempt is made at replying to the third question, positive answers to the first two questions must be given.
Mobile Decision Support for Time-Critical Decision Making
Authors: Burstein Frada , Cowie J.
Published: May 2011

The wide availability of advanced information and communication technology has made it possible for users to expect a much wider access to decision support. Since the context of decision making is not necessarily restricted to the office desktop, decision support facilities have to be provided through access to technology anywhere, anytime, and through a variety of mediums. The spread of e-services and wireless devices has increased accessibility to data, and in turn, influenced the way in which users make decisions while on the move, especially in time-critical situations. For example, on site decision support for fire weather forecasting during bushfires can include real-time evaluation of quality of local fire weather forecast in terms of accuracy and reliability. Such decision support can include simulated scenarios indicating the probability of fire spreading over nearby areas that rely on data collected locally at the scene and broader data from the regional and national offices. Decision Support Systems (DSS) available on mobile devices, which triage nurses can rely on for immediate, expert advice based on available information, can minimise delay in actions and errors in triage at emergency departments (Cowie & Godley, 2006).
Software Agents
Authors: Stanek Stanislaw , Gawinecki Maciej , Pankowska Malgorzata , Rahimi Shahram
Published: May 2011

The origins of the software agent concept are often traced back to the pioneers of artificial intelligence—John Mc Carthy, the creator of LISP programming language, and Carl Hewitt, the father of distributed artificial intelligence (DAI). Kay (1984, p. 84) states that: …the idea of an agent originated with John McCarthy in the mid-1950s, and the term was coined by Oliver G. Selfridge a few years later, when they were both at the Massachusetts Institute of Technology. They had in view a system that, when given a goal, could carry out the details of the appropriate computer operations and could ask for and receive advice, offered in human terms, when it was stuck. An agent would be a ‘soft robot’ living and doing its business within the computer’s world. Nwana (1996, p. 205), on the other hand, claims that: …software agents have evolved from multi-agent systems (MAS), which in turn form one of three broad areas which fall under DAI, the other two being Distributed Problem Solving (DPS) and Parallel Artificial Intelligence (PAI). (…) The concept of an agent (…) can be traced back to the early days of research into DAI in the 1970s – indeed, to Carl Hewitt’s concurrent Actor model. In this model, Hewitt proposed the concept of a self-contained, interactive and concurrently-executing object which he termed ‘Actor’. This object had some encapsulated internal state and could respond to messages from other similar objects1. The software agent concept meant, in the first place, replacing the idea of an expert, which was at the core of earlier support systems, with the metaphor of an assistant. Until 1990s, decision support systems (DSS) were typically built around databases, models, expert systems, rules, simulators, and so forth. Although they could offer considerable support to the rational manager, whose decision making style would rely on quantitative terms, they had little to offer to managers who were guided by intuition. Software agents promised a new paradigm in which DSS designers would aim to augment the capabilities of individuals and organizations by deploying intelligent tools and autonomous assistants. The concept thus heralded a pivotal change in the way computer support is devised. For one thing, it called for a certain degree of intelligence on the part of the computerized tool; for another, it shifted emphasis from the delivery of expert advice toward providing support for the user’s creativity (King, 1993).
The Use of Simulation as an Experimental Methodology for DMSS Research
Authors: Forgionne Giusseppi , Russell Stephen
Published: May 2011

Decision-making support-system (DMSS) research has been based largely on case and field studies of real organizations or entities and laboratory experiments involving human participants. Each methodology has provided useful results. Case and field studies can identify and describe relevant variables and relationships, suggest theories, and offer conceptual and practical models for evaluation (Benbasat, Goldstein, & Mead, 1987; Fjermestad & Hiltz, 2000; Orlikowski & Baroudi, 1991). Participant-based laboratory experiments can provide initial theory and model hypothesis tests, identify key evaluation assumptions, and offer suggestions for future research (Amaratunga, Baldry, Sarshar, & Newton, 2002; Das, 1983; Patton, 2002). Case and field studies and participant-based laboratory experiments, however, have limitations for decision- making support evaluation. In each setting, it is difficult to acquire and motivate the participants. Real organizations and entities are reluctant to disrupt operations or provide proprietary information. Laboratory participants often consist of students in universitybased studies, and as such frequently have little actual knowledge of the decision situation or incentive to mimic real behavior. Even decision-knowledgeable participants may, for political, psychological, or other reasons, mask real behavior. In addition, case studies, field trials, and laboratory settings may not be representative of the population. By their nature, case and field studies usually involve few organizations or entities. Given the diversity experienced in practice, the few organizations or entities, even if selected judiciously, are unlikely to capture the disparity. While great care may be exercised in selecting sample participants for laboratory experiments, there will be Type I and Type II errors that result from even well-designed experiments (Alavi & Joachimsthaler, 1992).
Using Network Analysis for Understanding How Decisions are Made
Authors: Adam Frédéric
Published: May 2011

Network analysis, a body of research that concentrates on the social networks that connect actors in society, has been found to have many applications in areas where researchers struggle to understand the complex workings of organisations (Nohria, 1992). Social network analysis (SNA) acknowledges that individuals are characterised just as much by their relationships with one another (which is often neglected in traditional research) as by their specific attributes (Knoke & Kuklinski, 1982) and that, beyond individuals, society itself is made of networks (Kilduff & Tsai, 2003). It is the study of the relationships between actors and between clusters of actors in organisations and in society that has been labeled network analysis. These high level observations about network analysis indicate that this orientation has great potential for the study of how managers, groups of managers, and organisations make decisions, following processes that unfold over long periods of time and that are sometimes very hard to fully comprehend without reference to a network approach. This article proposes to investigate the potential application of network analysis to the study of individual and organizational decision making and to leverage its strengths for the design and development of better decision aids.
An Academic Guidance Model to Orient Distance Students
Authors: Vanin Luca , Castelli Stefano , Pepe Alessandro , Addimando Loredana
Published: May 2011

This chapter begins with an analysis of a distance degree in psychological sciences. The structure of the course includes a national centre, faculties of different universities, and technological centres. The technological centres, located both in Italy and abroad, are facilities available to students, and are equipped with all the technology necessary to follow the distance courses (personal computers, satellite connections, internet connections, etc.). These locations constitute actual meeting points for students, teachers and tutors, thus allowing for face-to-face exams and seminars, as well for videoconferencing. The student is guided by a new figure, the e-tutor, who facilitates online learning and communication processes.
Collaborative Knowledge Management (CKM) and Enterprise Knowledge Management
Authors: Vequist IV David G.
Published: May 2011

This article will describe the background, basic infrastructure, and future trends of collaborative knowledge management (CKM) and enterprise knowledge management (KM) systems. CKM is about capturing the information that is in people’s heads and sharing it in a collaborative manner (e.g., an open, application-independent framework that allows all to have access to it) across the enterprise. In order to take advantage of the knowledge that exists in organizations, an authoring process must be undertaken that categorizes and places important information into a shared resource (often referred to as a “knowledge repository”) that can be assessed by all relevant stakeholders. Collaboration should be across departments, various types of employees, and different types of projects within the enterprise. Some of the important topic areas that will be discussed in this article are the focus on the benefits of CKM and enterprise KM systems and the recent attempts to define the return-on-investment (ROI) of sharing knowledge across organizations.
Computer Communication and ICT Attitude and Anxiety Among Higher Education Students
Authors: Ololube Nwachukwu Prince
Published: May 2011

This study integrates literature on computer anxiety and communication apprehension to determine their joint impact upon individual attitudes toward using computer communication (CC). This study introduces a new research domain and its application in identifying computer communication attitude and anxiety from a developing economy’s perspective. This study to the best of my knowledge breaks academic ground because it is a domain that has been under-researched in Sub-Saharan Africa. This study moves forward a new framework that places CC/ICTs attitude and anxiety as an assemblage between the more general framework on computer communication attitude and anxiety with which we are familiar.
Factors Determining Teachers' Beliefs and Perceptions of ICT in Education
Authors: Jimoyiannis Athanassios
Published: May 2011

In this article the factors involved in the uptake of ICT by the teachers and the schools are examined. The focus of our argumentation is that teachers constitute a critical factor in the attempt to integrate technology in the classroom but they are not appropriately prepared for that undertaking. An extensive review of the literature associated with teachers’ views and perceptions of ICT in education is presented. Finally, we conclude debating on the demand of a new curriculum and a new pedagogical framework, both based on and enhanced by the new environments and tolls ICT offer in education.
ICT and the Virtual Organisation
Authors: de Pablos Heredero Carmen
Published: May 2011

The need to be adapted to the changes in the environment, to improve the operational efficiency, and to increase the level of satisfaction in customers has promoted the redesigning of the firm’s organisational processes. Firms tend each time to externalise activities at a different level by making the appearance of new models of organisation possible. The analysis of different theories coming from a variety of literature reviews offers us the opportunity to affirm that being virtual is a characteristic present today in firms at different levels (Shin, 2004). In this article, we offer a definition for the concept of virtual systems. An explanation from the strategic management field is also accompanied. Finally, this analysis attempts to draw an explanatory model of the capacity of value creation in the virtual systems in comparison with traditional alternatives.
ICT Processes for Virtual Academic Research Teams (VART) in Academia
Authors: Lecoure Jason S. , Carroll Wendy R.
Published: May 2011

The purpose of this chapter is to bring these smaller self-forming, informal academic research teams into focus to understand the effects of ICT and associated processes which have emerged to support such formations. In addition, the identification of different traits within the small teams will be noted. This chapter will provide insight about virtual academic research teams (VART) based on the literature to date in this area as well as some insights about VARTs from two recent exploratory empirical studies (Carroll & Lecoure, 2005; Lecoure & Carroll, 2004). Specifically, it will address questions relating to the ways in which these teams come about, the different types of virtual research teams that exist, and the possible practices which result in the success these teams.
The Impact of Technological Frames on Knowledge Management Procedures
Authors: Chen Chun-Tsung
Published: May 2011

This article intended to explore technological frames held by organisational group members that implicitly served to shape their interpretations of events to give meaning and deliver actions in knowledge management procedures. The research used the existing technological frame (Orlikowski & Gash, 1994) concept to interpret the social aspect of the problems associated with the introduction and utilisation of information technology in conducting knowledge management systems. This research was carried out in the context of four different industries in Taiwan and four cases based on each industry were chosen.
Inference Tree Use to Design Arguments in Expository Reports
Authors: Mende Jens
Published: May 2011

When they write essays, many students merely attempt ‘to fill pages with material gathered from sources’ (Erion, 2000). Consequently, they produce inane arguments of the form: Adams said this, Brown said that, Cohen said the other, etc. Conclusion: much has been written about this topic. This is unacceptable both in academic ICT courses and subsequently in the ICT profession. In academe, a written argument should ‘make a leap from the raw materials of the library to an informed opinion’ (Fasel, 1963). In the profession, a written argument should similarly make a leap from a present state of affairs to a desired future state. So in both situations, writers should be able to devise a report that contains an argument from available facts towards an intelligent conclusion. This kind of report is called an ‘expository report’ (Trimble, 1975), or an ‘argumentative report’ (Dykeman, 1974).
Interactive Television Accessibility and Usability
Authors: Cuccu Roberto
Published: May 2011

Today, there are more than 70 million people aged 60 and above in the European Union. According to Eurostat, over the next 15 years, the population aged 65 and over will increase by 22%. Many of these citizens will experience dexterity, cognitive, hearing, and sight problems in later life. This means that more than one in seven adults in Europe will have hearing problems. Some 7.4 million people already suffering uncorrectable sight loss will add to the number of European citizens experiencing some form of sensory impairment (Stallard, 2003). Interactive digital television (iTV) is evolving into an enhanced entertainment and information service. There are various degrees of interactivity in digital television: pressing a simple remote control button, sending information back and forth, or servicing providers by means of a return path. If they are to be adopted, interactive facilities need to be usable by viewers, even because, as Jacob Neilsen points out, “increased accessibility for users with disabilities almost invariably leads directly to improved usability for all users” (Slatin & Rush, 2003). Unfortunately, interactive digital television design appears to have been based on the conceptual models of keyboard-based systems, but their users, skills, goals and attitude of interaction differ. The TV audience is more diverse, some having no prior computer experience. It must be realised that iTV is not a PC and therefore cannot be treated as such.
Learning Processes and ITC
Authors: Gallerani Manuela
Published: May 2011

Confronting with the educational emergences defined— in the white paper presented in 1995 by the European commission with the title “Teaching and Learning. Towards a Society of Knowledge”—the Commission identifies three main factors of upheaval: information society, internationalization and the world market, scientific and technological knowledge. These factors involve a modification of the systems of knowledge and work, and, as a consequence, also of educational politics which must promote a personal development of citizens through the development of the necessary competences in dealing with these factors. The consequences that emerge are the reported in the next section. First of all, the society of knowledge is linked with a condition of uncertainty and risk of social exclusion, which determines a great disorientation for the individual. The individua is exposed to infinite cognitive potentialities on one side, but also to a cognitive weakening on the other side. Among these risks, the first is a disorganized and confused fruition of the knowledge resources offered by the symbolic world in which the individual is plunged in. He/she is irreparably depressed when plunged in an infinite net of knowledge which the individual can not reach in a critical way, being also bombarded by pervasive—usually persuasive—information of mass-media pushing him/her toward homologation.
Mobile Learning and an Experience with Blended Mobile Learning
Authors: Pieri Michelle , Diamantini Davide
Published: May 2011

Pownell and Bailey (2001) identify four “technological trends” in the relationship between Information and Communication Technologies and educational environments. In the 1960s the first computers, which were very large and extremely expensive were rarely used in the educational area. They were only used to help in administration and in management. In the seventies with the arrival of the personal computer, schools in several countries, as in the United States, introduced computer basic courses to help students learn the use of this new technology. In the nineties the large-scale diffusion of Internet and the World Wide Web lead to a huge number of people who communicated through a computer mediated communication. At last in 2000, extremely small computers were sold on the market and the era of wireless connections began. These two factors in the educational field encouraged the beginning and the development of mobile learning.
A Novel Application of the P2P Technology for Intrusion Detection
Authors: Czirkos Zoltán , Hosszú Gábor
Published: May 2011

The importance of the network security problems come into prominence by the growth of the Internet. This article presents a new kind of software that uses the network itself to protect the hosts and increase their security. The hosts running this software create an application level network (ALN) over the Internet (Hosszú, 2005). Nodes connected to this ALN check their operating systems’ log files to detect intrusion attempts. Information collected this way is then shared over the ALN to increase the security of all peers, which can then make the necessary protection steps, for example, blocking network traffic by their own firewall. Different kinds of security software utilizing the network were also written previously (Snort, 2006). The novelty of Komondor is that its client software entities running in each host create a peer-to-peer (P2P) overlay network (Czirkos, 2006). Organization is automatic; it requires no user interaction. This network model ensures stability, which is important for quick and reliable communication between nodes. By this build-up, the system remains useful over the unstable network.
An Overview of Online Trust Derivatives for E-Commerce Adoption
Authors: Mahmood Omer
Published: May 2011

This chapter identifies various individual and collective online trust transferring techniques that e-merchants can employ for their business growth. The individual sources include, Web user interface, provision of alternate dispute resolution, and availability of transaction insurance. The collective online trust transferring derivatives include trusted referrals, online reputation and use of privacy and security seals. In the next section an overview of each technique is presented. Followed by the overview, a conceptual model is presented which outlines the impact of each trust transferring source on different dynamics of e-business and each dynamic’s characteristics. The proposed model targets to assist the e-businesses to select a right combination of trust derivatives. Further assisting the e businesses in enhancing their brand value based on the behavior of their potential customer base.
University Training on Communities of Practice
Authors: Alessandrini Giuditta , Rosso Giovanni
Published: May 2011

The term communities of practice (CoPs) has been coined by Lave and Wenger (1991) during their quests on apprenticeship from 1988. They considered some studies carried on in very different backgrounds and kinds of culture such as those of Maya midwives in Yucatan, Vai, and Gola tailors in Liberia, U.S. Navy boatswains’ drill-grounds, butchers of some American supermarkets, and among the members of Alcoholics Anonymous Association. The common denominator of these studies that has appeared relevant to Lave and Wenger is the presence of learning mechanisms not surveyed before by others scholars and not connected with the direct interaction between apprentice and master, but with the participation to a practice shared with other actors such as other apprentices, masters and journeyfolks.
Sizing Web Applications for Web Effort Estimation
Authors: Mendes Emilia
Published: May 2011

Surveying and classifying previous work on a particular field brings several benefits, which are: 1) to help organise a given body of knowledge; 2) to provide results that can help identify gaps that need to be filled; 3) to provide a categorisation that can also be applied or adapted to other surveys; and 4) to provide a classification and summary of results that may benefit practitioners and researchers who wish to carry out meta-analyses. This chapter presents a survey literature of size measures (attributes) that have been proposed for Web effort estimation. These measures are classified according to a proposed taxonomy. We also discuss ways in which Web companies can devise their own size measures.
Patterns for Improving the Pragmatic Quality of Web Information Systems
Authors: Kamthan Pankaj
Published: May 2011

The significance of approaching Web information systems (WIS) from an engineering viewpoint is emphasized. A methodology for deploying patterns as means for improving the quality of WIS as perceived by their stakeholders is presented. In doing so, relevant quality attributes and corresponding stakeholder types are identified. The role of a process, feasibility issues, and the challenges in making optimal use of patterns are pointed out. Examples illustrating the use of patterns during macro- and micro-architecture design of a WIS, with the purpose of the improvement of quality attributes, are given.
Towards Quality Web Information Systems Through Precise Model-Driven Development
Authors: Molina Fernando , Lucas Francisco J. , Alvarez Ambrosio Toval , Vara Juan M. , Cáceres Paloma , Marcos Esperanza
Published: May 2011

Recent years have seen the arrival of the Internet as the platform that supports most areas within organizations, a fact which has led to the appearance of specific methodologies and tools for the construction of Web information systems (WIS). However, an absence of functionalities for the verification and validation (V&V) has been detected in the methodologies and tools of the models which have been built. This chapter presents one of these methodologies for WIS development (MIDAS) and shows how it has been completed with the definition of a strategy for the formal specification of its models with V&V objectives. This will contribute to increasing the quality of the models used in WIS development. The plug-in architecture which integrates this formal approach within CASE tools for WIS development is also shown.
Adaptive Algorithms for Intelligent Geometric Computing
Authors: Gavrilova M. L.
Published: May 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.
An AI Walk from Pharmacokinetics to Marketing
Authors: Martín-Guerrero José D. , Soria-Olivas Emilio , Lisboa Paulo J.G. , Serrano-López Antonio J.
Published: May 2011

This work is intended for providing a review of reallife practical applications of Artificial Intelligence (AI) methods. We focus on the use of Machine Learning (ML) methods applied to rather real problems than synthetic problems with standard and controlled environment. In particular, we will describe the following problems in next sections: • Optimization of Erythropoietin (EPO) dosages in anaemic patients undergoing Chronic Renal Failure (CRF). • Optimization of a recommender system for citizen web portal users. • Optimization of a marketing campaign. The choice of these problems is due to their relevance and their heterogeneity. This heterogeneity shows the capabilities and versatility of ML methods to solve real-life problems in very different fields of knowledge. The following methods will be mentioned during this work: • Artificial Neural Networks (ANNs): Multilayer Perceptron (MLP), Finite Impulse Response (FIR) Neural Network, Elman Network, Self-Oganizing Maps (SOMs) and Adaptive Resonance Theory (ART). • Other clustering algorithms: K-Means, Expectation- Maximization (EM) algorithm, Fuzzy C-Means (FCM), Hierarchical Clustering Algorithms (HCA). • Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH). • Support Vector Regression (SVR). • Collaborative filtering techniques. • Reinforcement Learning (RL) methods.
Combining Classifiers and Learning Mixture-of-Experts
Authors: Xu Lei , Amari Shun-ichi
Published: May 2011

Expert combination is a classic strategy that has been widely used in various problem solving tasks. A team of individuals with diverse and complementary skills tackle a task jointly such that a performance better than any single individual can make is achieved via integrating the strengths of individuals. Started from the late 1980’ in the handwritten character recognition literature, studies have been made on combining multiple classifiers. Also from the early 1990’ in the fields of neural networks and machine learning, efforts have been made under the name of ensemble learning or mixture of experts on how to learn jointly a mixture of experts (parametric models) and a combining strategy for integrating them in an optimal sense. The article aims at a general sketch of two streams of studies, not only with a re-elaboration of essential tasks, basic ingredients, and typical combining rules, but also with a general combination framework (especially one concise and more useful one-parameter modulated special case, called a-integration) suggested to unify a number of typical classifier combination rules and several mixture based learning models, as well as max rule and min rule used in the literature on fuzzy system.
Commonsense Knowledge Representation I
Authors: Ein-Dor Phillip
Published: May 2011

Significant advances in artificial intelligence, including machines that play master level chess, or make medical diagnoses, highlight an intriguing paradox. While systems can compete with highly qualified experts in many fields, there has been much less progress in constructing machines that exhibit simple commonsense, the kind expected of any normally intelligent child. As a result, commonsense has been identified as one of the most difficult and important problems in AI (Doyle, 1984; Waltz, 1982).
Computer Vision for Wave Flume Experiments
Authors: Ibáñez Óscar , Ramón Rabuñal Dopico Juan
Published: May 2011

During the past several decades, a number of attempts have been made to contain oil slicks (or any surface contaminants) in the open sea by means of a floating barrier. Many of those attempts were not very successful especially in the presence of waves and currents. The relative capabilities of these booms have not been properly quantified for lack of standard analysis or testing procedure (Hudon, 1992). In this regard, more analysis and experimental programs to identify important boom effectiveness parameters are needed. To achieve the desirable performance of floating booms in the open sea, it is necessary to investigate the static and dynamic responses of individual boom sections under the action of waves; this kind of test is usually carried out in a wave flume, where open sea conditions can be reproduced at a scale. Traditional methods use capacitance or conductivity gauges (Hughes, 1993) to measure the waves. One of these gauges only provides the measurement at one point; further, it isn’t able to detect the interphase between two or more fluids, such as water and a hydrocarbon. An additional drawback of conventional wave gauges is their cost. Other experiments such as velocity measurements, sand concentration measurements, bed level measurements, breakwater’s behaviour, etc… and the set of traditional methods or instruments used in those experiments which goes from EMF, ADV for velocity measurements to pressure sensors, capacity wires, acoustic sensors, echo soundings for measuring wave height and sand concentration, are common used in wave flume experiments. All instruments have an associate error (Van Rijn, Grasmeijer & Ruessink, 2000), and an associate cost (most of them are too expensive for a lot of laboratories that can not afford pay those amount of money), certain limitations and some of them need a large term of calibration. This paper presents another possibility for wave flume experiments, computer vision, which used a cheap and affordable technology (common video cameras and pc’s), it is calibrated automatically (once we have developed the calibration task), is a non-intrusive technology and its potential uses could takes up all kind experiments developed in wave flumes. Are artificial vision’s programmers who can give computer vision systems all possibilities inside the visual field of a video camera. Most experiments conducted in wave flumes and new ones can be carried out programming computer vision systems. In fact, in this paper, a new kind of wave flume experiment is presented, a kind of experiment that without artificial vision technology it couldn’t be done.
Data Mining Fundamental Concepts and Critical Issues
Authors: Wang John , Chen Qiyang , Yao James
Published: May 2011

Data mining is the process of extracting previously unknown information from large databases or data warehouses and using it to make crucial business decisions. Data mining tools find patterns in the data and infer rules from them. The extracted information can be used to form a prediction or classification model, identify relations between database records, or provide a summary of the databases being mined. Those patterns and rules can be used to guide decision making and forecast the effect of those decisions, and data mining can speed analysis by focusing attention on the most important variables.
EC Techniques in the Structural Concrete Field
Authors: Pérez Juan L. , González-Fonteboa Belén , Martínez-Abella Fernando
Published: May 2011

Throughout the last decades, one of society’s concerns has been the development of new tools to optimize every aspect of daily life. One of the mechanisms that can be applied to this effect is what is nowadays called Artificial Intelligence (AI). This branch of science enables the design of intelligent systems, meaning that they display features that can be associated to human intelligence, search methods being one of the most remarkable. Amongst these, Evolutionary Computation (EC) stands out. This technique is based on the modelling of certain traits of nature, especially the capacity shown by living beings to adapt to their environment, using as a starting point Darwin’s Theory of Evolution following the principle of natural selection (Darwin, 1859). These models search for solutions in an automatized way. As a result, a series of search techniques which solve problems in an automatized and parallel way has arisen. The most successful amongst these are Genetic Algorithms (GA) and, more recently, Genetic Programming (GP). The main difference between them is rooted on the way solutions are coded, which implies certain changes in their processing, even though the operation in both systems is similar. Like most disciplines, the field of Civil Engineering is no stranger to optimization methods, which are applied especially to construction, maintenance or rehabilitation processes (Arciszewski and De Jong, 2001) (Shaw, Miles and Gray, 2003) (Kicinger, Arciszewski and De Jong, 2005). For instance, in Structural Engineering in general and in Structural Concrete in particular, there are a number of problems which are solved simultaneously through theoretical studies, based on physical models, and experimental benchmarks which sanction and adjust the former, where a large amount of factors intervene. In these cases, techniques based on Evolutionary Computation are capable of optimizing constructive processes while accounting for structural safety levels. In this way, for each particular case, the type of materials, their amount, their usage, etc. can be determined, leading to an optimal development of the structure and thus minimizing manufacturing costs (Rabuñal , Varela, Dorado, González and Martínez, 2005).
Ensemble of ANN for Traffic Sign Recognition
Authors: Paz Sesmero Lorente M. , Manuel Alonso-Weber Juan , Gutiérrez Sánchez Germán , Ledezma Espino Agapito , Sanchis de Miguel Araceli
Published: May 2011

“Machine Learning (ML) is the subfield of Artificial Intelligence conceived with the bold objective to develop computational methods that would implement various forms of learning, in particular mechanisms capable of inducing knowledge form examples or data” (Kubat, Bratko & Michalski, 1998, p. 3). The simplest and best-understood ML task is known as supervised learning. In supervised learning, each example consists of a vector of features (x) and a class (y). The goal of the learning algorithm is, given a set of examples and their classes, find a function, f, that can be applied to assign the correct class to new examples. When the function f takes values from a discrete set of classes {C1, .…., CK,}, f is called a classifier (Dietterich, 2002). In the last decades it has been proved that learning tasks in which the unknown function f takes more than two values (multi-class learning problems) the better approach is to decompose the problem into multiple two-class classification problems (Ou & Murphey, 2007) (Dietterich, & Bakiri, 1995) (Massulli & Valentini, 2000). This article describes the implementation of a system whose main task is to classify prohibition road signs into several categories. In order to reduce the learning problem complexity and to improve the classification performance, the system is composed by a collection (ensemble) of independent binary classifiers. In the proposed approach, each binary classifier is a singleoutput neural network (NN) trained to distinguish a particular road sign kind from the others. The proposed system is a part of a Driver Support System (DSS) supported by the Spanish Government under project TRA2004-07441-C03-C02. For this reason, one of the main system requirements is that it should be implemented in hardware in order to use it aboard a vehicle for real time categorization. In order to fulfill this constraint, a reduction in the number of features that describe the instances must be performed. As consequence if we have k generic road sign types we will use k binary NN and k feature selection process will be executed.
Fuzzy Logic Estimator for Variant SNR Environments
Authors: Alsina Pagès Rosa Maria , Mateo Segura Clàudia , Socoró Carrié Joan-Claudi
Published: May 2011

The acquisition system is one of the most sensitive stages in a Direct Sequence Spread Spectrum (DS-SS) receiver (Peterson, Ziemer & Borth, 1995), due to its critical position in order to demodulate the received information. There are several schemes to deal with this problem, such as serial search and parallel algorithms (Proakis, 1995). Serial search algorithms have slow convergence time but their computational load is very low; on the other hand, parallel systems converge very quickly but their computational load is very high. In our system, the acquisition scheme used is the multiresolutive structure presented in (Moran, Socoró, Jové, Pijoan & Tarrés, 2001), which combines quick convergence and low computational load. The decisional system that evaluates the acquisition stage is a key process in the overall system performance, being a drawback of the structure. This becomes more important when dealing with time-varying channels, where signal to noise ratio (called SNR) is not a constant parameter. Several factors contribute to the performance of the acquistion system (Glisic & Vucetic, 1997): channel distorsion and variations, noise and interference, uncertainty about the code phase, and data randomness. The existence of all these variables led us to think about the possibility of using fuzzy logic to solve this complex acquisition estimation (Zadeh, 1973). A fuzzy logic acquisition estimator had already been tested and used in our research group to control a serial search algorithm (Alsina, Morán & Socoró, 2005) with encouraging results, and afterwards in the multiresolutive scheme (Alsina, Mateo & Socoró, 2007), and other applications to this field can be found in bibliography as (Bas, Pérez & Lagunas, 2001) or (Jang, Ha, Seo, Lee & Lee, 1998). Several previous works have been focused in the development of acquisition systems for non frequency selective channels with fast SNR variations (Moran, Socoró, Jové, Pijoan & Tarrés, 2001) (Mateo & Alsina, 2004).
Fuzzy Rule Interpolation
Authors: Kovács Szilveszter
Published: May 2011

The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming the completeness of the fuzzy rule base. If there are some rules missing i.e. the rule base is “sparse”, observations may exist which hit no rule in the rule base and therefore no conclusion can be obtained. One way of handling the “fuzzy dot” knowledge representation in case of sparse fuzzy rule bases is the application of the Fuzzy Rule Interpolation (FRI) methods, where the derivable rules are deliberately missing. Since FRI methods can provide reasonable (interpolated) conclusions even if none of the existing rules fires under the current observation. From the beginning of 1990s numerous FRI methods have been proposed. The main goal of this article is to give a brief but comprehensive introduction to the existing FRI methods.
IA Algorithm Acceleration Using GPUs
Authors: Seoane Antonio , Jaspe Alberto
Published: May 2011

Graphics Processing Units (GPUs) have been evolving very fast, turning into high performance programmable processors. Though GPUs have been designed to compute graphics algorithms, their power and flexibility makes them a very attractive platform for generalpurpose computing. In the last years they have been used to accelerate calculations in physics, computer vision, artificial intelligence, database operations, etc. (Owens, 2007). In this paper an approach to general purpose computing with GPUs is made, followed by a description of artificial intelligence algorithms based on Artificial Neural Networks (ANN) and Evolutionary Computation (EC) accelerated using GPU.