bluuebunny/crossref_metadata_2025 huggingface dataset:
A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling
Authors: Özcan Ender , Misir Mustafa , Ochoa Gabriela , Burke Edmund K.
Published: February 2011

Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solution goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study.
Information Systems, Technology Adoption and Innovation Translation
Authors: Tatnall Arthur
Published: February 2011

A new Information System must be adopted before it can be used. New Information Systems can be seen as innovations and viewed through the lens of innovations theory. In this article I will argue that much Information Systems research falls into this category, and that its explanation could benefit from application of innovation theory. One of the difficulties facing any such investigation, however, is that not all of these innovations are adopted in the form in which they were proposed – not all are adopted without change, and many approaches to technological innovation have trouble in explaining this. I will propose that Innovation Translation, informed by Actor-Network Theory, can offer a useful way of examining technological innovations, and particularly in explaining adoptions involving only some parts of the innovation. In this article I give several examples of how I have used Innovation Translation to shed light onto various technological innovations.
Opening the Indonesian Bio-Fuel Box
Authors: Fatimah Yuti Ariani , Yuliar Sonny
Published: February 2011

This article employs the notion of translation introduced in ANT literature to study the current bio-fuel development in Indonesia. Despite the presence of some activities by scientists, businessmen, policy makers, and farmers, diffusion of bio-fuel innovation seems to remain very limited. The article aims at understanding the bio-fuel development trajectory by seeking to disclose a variety of elements that shape the trajectory. We also make use of the notion of ‘qualculation’, to further diagnose the trajectory. Bio-fuel translations generated by three different scientists are described in the article. We show that the translations follow specific patterns of qualculation, namely ‘proliferation’ and ‘rarefaction’. We use this to make sense of the current diffusion of bio-fuel innovation in Indonesia. Beside this contextual result, the article also seeks to contribute to ANT literature by exploring the concept of qualculation in the analysis of technological innovation.
The Role of a Modified Technology Acceptance Model in Explaining Internet Usage in Higher Education in Thailand
Authors: Kripanont Napaporn , Tatnall Arthur
Published: February 2011

This article describes research which modelled adoption of Internet technology by academics in Business Schools in Public Universities in Thailand. It begins by reviewing prominent theories of technology acceptance. Formulation of the research model was based mainly on TAM and its derivatives. Survey methodology was used to collect primary data from academics in Thailand and the survey yielded 455 usable questionnaires. Analysis was performed using Structural Equation Modelling. The study generated a research model: ‘The Internet Acceptance Model’ which demonstrates that only perceived usefulness, perceived ease of use and self-efficacy significantly influence actual usage behaviour. This model is found to be both parsimonious and theoretically justified. Significantly, not only is it capable of specifying general determinants of Internet acceptance and usage in higher education but it can also be used and applied to explain or predict individual behaviour across a broad range of Internet technologies and user groups.
Innovation Translation and Innovation Diffusion
Authors: Tatnall Arthur
Published: February 2011

The process of innovation involves getting new ideas accepted and new technologies adopted and used. A number of approaches to the theorising of technological innovation exist, but this article will compare and contrast just two: Innovation Diffusion and Innovation Translation, giving examples of how each approach is used in different situations. While there are many advantages to use of a Translation approach, it is too simplistic to suggest that Translation always offers a better approach than Diffusion, and the article proposes that perhaps it should not be a case of either one approach or the other but the use of whichever is most appropriate to a particular investigation.
Linux Kernel Developers Embracing Authors Embracing Licenses
Authors: Linden Lars , Saunders Carol
Published: February 2011

In June 2007, with the impending release of a revised version of the GNU General Public License (GPLv3), Linux kernel developers discussed the possibility of changing the license of the Linux kernel from being strictly the GPLv2 to a dual-licensing arrangement of both GPLv2 and GPLv3. We studied a set of Linux Kernel Mailing List (LKML) postings to better understand the relationship among the kernel developers and these licenses. Using Actor-Network Theory, we identify and describe a LKML debate about licensing. Our narrative highlights important actor networks, their interrelationships, and a (failed) process of translation. The details suggest that the conceptualization of a copyright license as a monolithic social force maintaining the Linux community should be tempered with an appreciation of authorship and its distributed nature within Linux development.
Crossing the Chasm or Being Crossed Out
Authors: Rayna Thierry , Striukova Ludmila , Landau Samuel
Published: February 2011

The aim of this article is to study the phenomenon of chasm that often exists in the diffusion of innovation and to devise a theoretical framework enabling to explain the ability of some firms to cross this chasm, while many others remain unsuccessful. The proposition developed in this article is that the choice of initial market segment has crucial importance as adoption in this segment can lead to a cascade of adoption in the other segments. To illustrate this proposition, three cases studies of an historical leader (Sony), a first mover (Archos) and a newcomer (Apple) in the market for digital audio players are presented.
Higher Education in Further Education in England
Authors: Tummons Jonathan
Published: February 2011

The provision of higher education courses within further education colleges in England poses particular questions for the researcher. This article argues that the complexities of the relationships between colleges where courses are run, and the universities that supply these courses, can be fruitfully explored using actor-network theory. This article provides an actor-network account of one teacher-training course as an example of the ways in which both people and text-based artefacts are coalesced and coordinated so that the course functions across institutional and spatial boundaries. Assessment has been chosen as a specific focus several reasons: it must be performed in certain ways and must conform to particular outcomes that are standardized across colleges; it is an established focus of research; and it is a focus of specific observable activities. The article concludes that assessment processes are regulated and ordered in complex ways for which actor-network theory provides an appropriate conceptual framework.
Aspects of e-Learning in a University
Authors: Manning Karen , Wong Lily , Tatnall Arthur
Published: February 2011

Most universities make use of e-learning facilities to manage and deliver on-line learning. Many universities have adopted an approach to teaching and the delivery of course content that combines traditional face-to-face delivery with online teaching resources: a blended learning approach. Many factors act to determine how online learning is adopted, accepted, and the balance between online and face-to-face delivery is formed. In this paper, the authors suggest that educational technology adoption decisions are made at three levels: strategic decisions are made by the university to implement a particular package, and then individual academics made adoption decisions regarding those aspects of the package they will use in their teaching and how they will use them. They also make a decision on the balance they will have between on-line and face-to-face teaching. This article questions how decisions are made to adopt one e-learning package rather than another. The authors then examine how individual academics relate to this technology once it is adopted and make use of it to deliver some or all of their teaching and determine the appropriate blend.
Actor-Network-Theory in Medical e-Communication – The Role of Websites in Creating and Maintaining Healthcare Corporate Online Identity
Authors: Bielenia-Grajewska Magdalena
Published: February 2011

In this article, an attempt will be made to discuss how websites create and maintain the online identity of medical care providers. To discuss this issue in greater detail, the author has chosen Actor-Network-Theory since an ANT approach makes it possible to study the role of living and nonliving entities in shaping the online identity of healthcare suppliers and to concentrate on the networks and systems within e-healthcare as well as the flows and interrelations constituting it. The primary aim of this research is to show the communicative aspect of healthcare corporate websites by using the selected notions of ANT methodology and their potential implications for corporate identity creation and maintenance.
Electronic Multi-User Randomized Circuit Training For Workout Motivation
Authors: Graves Corey A. , Muldrew Sam , Williams Tiara , Rotich Jerono , Cheek Eric A.
Published: February 2011

The Electronic Multi-User Randomized Circuit Training (EMURCT) system has been developed using pervasive computing concepts to address the issues of monotony and lose of motivation in routine circuit training workouts. EMURCT is implemented with 7 PDA’s (used by 7 trainees using a 7 station circuit) and 1 desktop computer that aids in the synchronization of the PDA’s (i.e. preventing trainees from using the same stations simultaneously). By randomly generating station sequences and round times for each trainee, EMURCT is capable of producing a totally different workout session every time. This has the potential of keeping trainees from getting bored while undertaking a circuit training workout regimen, which could lead to abandonment of training. This approach also supports the physical training concept known as muscle confusion, which some fitness experts believe greatly enhances the physical gains for trainees. Here the development of the EMURCT System is described, and the results of preliminary performance and robustness evaluations are presented.
Autonomic QoS Optimization of Real-Time Internet Audio Using Loss Prediction and Stochastic Control
Authors: Roychoudhuri Lopamudra , Al-Shaer Ehab S.
Published: February 2011

Quality of Internet audio is highly sensitive to packet loss caused by congestion in links. Packet loss for audio is normally rectified by adding redundancy using Forward Error Correction (FEC). Alternatively, path diversity mechanisms are used to improve reliability and thus session quality. To achieve optimized receiver audio quality for transmissions using single or multiple paths, the authors propose a self-adaptive joint Error and Rate Control framework based on packet loss prediction and on-line quality assessment. The Error Control chooses appropriate FEC proactively to preserve quality with optimal bandwidth, using a Markov Decision Process (MDP) and a stochastic inventory control, a novel approach for multimedia error recovery. The Rate Control uses a quality optimization model to determine the optimal dispersion over single or multiple paths. This paper will present results using simulation and Internet experiments to show the superiority of this mechanism over other similar techniques.
A Resource-Aware Dynamic Load-Balancing Parallelization Algorithm in a Farmer-Worker Environment
Authors: Leeman M.
Published: February 2011

This paper describes an algorithm for dynamically assigning tasks to processing entities in a world where each task has a set of resource or service requirements and each processing entity a set of resources or service capabilities. A task needs to be assigned to a node that offers all required services and the set of tasks is finished within a minimal execution time frame. Dependability and adaptability are inherent to the algorithm so that it accounts for the varying execution time of each task or the failure of a processing node. The algorithm is based on a dependable technique for farmer-worker parallel programs and is enhanced for modeling the time constraints in combination with the required configuration set in a multidimensional resources model. This paper describes how the algorithm is used for dynamically load balancing and parallelizing the nightly tests of a digital television content-processing embedded device.
Systematic Design Principles for Cost-Effective Hard Constraint Management in Dynamic Nonlinear Systems
Authors: Munaga Satyakiran , Catthoor Francky
Published: February 2011

Modern cost-conscious dynamic systems incorporate knobs that allow run-time trade-offs between system metrics of interest. In these systems regular knob tuning to minimize costs while satisfying hard system constraints is an important aspect. Knob tuning is a combinatorial constrained nonlinear dynamic optimization problem with uncertainties and time-linkage. Hiding uncertainties under worst-case bounds, reacting after the fact, optimizing only the present, and applying static greedy heuristics are widely used problem simplification strategies to keep the design complexity and decision overhead low. Applying any of these will result in highly sub-optimal system realizations in the presence of nonlinearities. The more recently introduced System Scenarios methodology can only handle limited form of dynamics and nonlinearities. Existing predictive optimization approaches are far from optimal as they do not fully exploit the predictability of the system at hand. To bridge this gap, the authors propose the combined strategy of dynamic bounding and proactive system conditioning for the predicted likely future. This paper describes systematic principles to design low-overhead controllers for cost-effective hard constraint management. When applied to fine-grain performance scaling mode assignment problem in a video decoder design, proposed concepts resulted in more than 2x energy gains compared to state-of-the-art techniques.
A Generic Adaptation Framework for Mobile Communication
Authors: Sun Hong , Gui Ning , Blondia Chris
Published: February 2011

Today, technologies are providing mobile terminals with much more powerful computational abilities. Such improvement has made it possible to run many complex applications on mobile devices. However, many of these new applications are also resource demanding. Lacking sufficient resources would cause performance failures and impact negatively on the users’ quality of experience. In order to improve this, it is important to provide the users with an easy access to specifying their requirements. It is also crucial to monitor the system resources and make corresponding adaptation immediately according to the user’s specifications. In this paper, the authors propose adaptation strategies that flexibly combine the process of monitoring and adaptation, which provides an easy way to specify user’s requirements. By tuning the quality of service, the applications’ demand on system resources is reduced, thus decreasing the chances of performance failures and improving the users’ quality of experience.
Cognitive Robotics and Multiagency in a Fuzzy Modeling Framework
Authors: Trajkovski Goran , Stojanov Georgi , Collins Samuel , Eidelman Vladimir , Harman Chris , Vincenti Giovanni
Published: February 2011

Fuzzy algebraic structures are a useful and flexible tool for modeling cognitive agents and their societies. In this article we propose a fuzzy algebraic framework where the valuating sets are other than the unit interval (lattices, partially ordered sets or relational structures) . This provides for a flexible organization of the information gathered by the agent (via interactions with the environment and/or other agents) and enables its selected use when different drives are active. Agents (Petitagé, ANNA, POPSICLE and Izbushka), which are instantiations of our model, are also given in order to illustrate the use of this framework, as well as its possible extensions.
Enhancing the Adaptation of BDI Agents Using Learning Techniques
Authors: Airiau Stephane , Padgham Lin , Sardina Sebastian , Sen Sandip
Published: February 2011

Belief, Desire, and Intentions (BDI) agents are well suited for complex applications with (soft) real-time reasoning and control requirements. BDI agents are adaptive in the sense that they can quickly reason and react to asynchronous events and act accordingly. However, BDI agents lack learning capabilities to modify their behavior when failures occur frequently. We discuss the use of past experience to improve the agent’s behavior. More precisely, we use past experience to improve the context conditions of the plans contained in the plan library, initially set by a BDI programmer. First, we consider a deterministic and fully observable environment and we discuss how to modify the BDI agent to prevent re-occurrence of failures, which is not a trivial task. Then, we discuss how we can use decision trees to improve the agent’s behavior in a non-deterministic environment.
A Reinforcement Learning Approach to Setting Multi-Objective Goals for Energy Demand Management
Authors: Guo Ying , Zeman Astrid , Li Rongxin
Published: February 2011

In order to cope with the unpredictability of the energy market and provide rapid response when supply is strained by demand, an emerging technology, called energy demand management, enables appliances to manage and defer their electricity consumption when price soars. Initial experiments with our multi-agent, power load management simulator, showed a marked reduction in energy consumption when price-based constraints were imposed on the system. However, these results also revealed an unforeseen, negative effect: that reducing consumption for a bounded time interval decreases system stability. The reason is that price-driven control synchronizes the energy consumption of individual agents. Hence price, alone, is an insufficient measure to define global goals in a power load management system. In this article we explore the effectiveness of a multi-objective, system-level goal which combines both price and system stability. We apply the commonly known reinforcement learning framework, enabling the energy distribution system to be both cost saving and stable.
Virtual Worlds and the Implication for Accountants
Authors: Romero Jorge A.
Published: February 2011

While real world economies may be shaky, economies in virtual worlds keep growing as sites such as Second Life become more and more mainstream. Research firm Gartner Media estimates that by 2011, 80% of Internet users worldwide will be using Second Life (Gartner, 2007). This growing popularity has real world financial implications. On a typical day, Second Life members spend close to $1.5 million on virtual items and virtual real estate transactions (Alter, 2008), and some residents generate six-figure incomes in real world dollars (Hemp, 2006).Second Life is still in an early stage of development, and there are many financial and legal regulatory issues to be resolved. But with these challenges come opportunities; Second Life may be the impetus for a new accounting platform that may bring different practices together and provide new growth opportunities that financial communities have been looking for in the virtual world.
Modeling a Multi-Agents System as a Network
Authors: Araújo Tanya , Louçã Francisco
Published: February 2011

The article presents an empirically oriented investigation on the dynamics of a specific case of a multi-agents system, the stock market. It demonstrates that S&P500 market space can be described using the geometrical and topological characteristics of its dynamics. The authors proposed to measure the coefficient R, an index providing information on the evolution of a manifold describing the dynamics of the market. It indicates the moments of perturbations, proving that the dynamics is driven by shocks and by a structural change. This dynamics has a characteristic dimension, which also allows for a description of its evolution. The consequent description of the market as a network of stocks is useful for the identification of patterns that emerge from multi-agent interaction, and defines our research, as it is derived from a system of measure and it is part of the logic of a defined mathematics.
Merging Education and Business Models to Create and Sustain Transformational Change
Authors: Isenberg Susan
Published: February 2011

In 2004, a large Midwest hospital was losing money, patients, employees, and physicians. A business consultant was hired to engage key employees in a process to improve the quality and efficiency of patient care. The improvement was negligible after the first year, so a 3-man consultancy was added in 2005 to engage all employees in an educational process with the same mission. The author was the hospital director of this experimental change project titled Transformation and worked with both the business and education consultants. The opposing models were serendipitously discovered to be parallel and resulted in positive change. The business model was the application of two Six Sigma models, DMAIC (define, measure, analysis, improvement, control) and Ten Step Kaizen. The education model was proprietary but discovered to be a learning process toward self-direction (Taylor, 1986). Interviews were conducted in this grounded theory study to understand the perceived relationship between the 2005 experiment and current realities. Significant improvements were immediate and sustained over time. The hospital is currently making money and attracting patients, employees, and physicians and the emerged theory posits that merging the models creates transformational change, but sustainability requires empowered leaders to manage the process.
Energy-Efficient Self-Organizing Routing for Wireless Mobile Networks
Authors: Moh Melody , Kukanur Rashmi , Lin Xuquan , Dhar Subhankar
Published: February 2011

The instant deployment without relying on an existing infrastructure makes mobile ad hoc networks (MANET) a striking choice for many dynamic situations. An efficient MANET protocol may be applied to other important emerging wireless technologies, such as wireless mesh and sensor networks. This article proposes a hierarchical routing scheme that is scalable, energy efficient, and self-organizing. The new algorithm that is discussed in this article is the Dynamic Leader Set Generation (DLSG). This algorithm dynamically selects leader nodes based on traffic demand, locality, and residual energy level, and de-selects them based on residual energy. Therefore, energy consumption and traffic load are balanced throughout the network, and the network reorganizes itself around the dynamically selected leader nodes. Time, space, and message complexities are formally analyzed and implementation issues are also addressed. Incorporating the IEEE 802.11 medium access control mechanism and including the power saving mode, performance evaluation is carried out by simulating DLSG and four existing hierarchical routing algorithms. It shows that DLSG successfully extends network lifetime by 20 to 50% while achieving a comparable level of network performance.
Improving Business Intelligence
Authors: Miller Dorothy
Published: February 2011

Business Intelligence has never been examined with the same rigor as demanded for any other organization investments. Although global investment in Business Intelligence has reached over 6 billion dollars, business managers continue to follow tradition and leave the management of business intelligence to the technocrats. In this paper, the author proposes that a critical need exists to apply the same six sigma methods, which have worked for the rest of the organization to business intelligence operations and products. This proven structured approach, including the associated rigor and metrics, can be customized and integrated into a program which will allow effective management of business intelligence. The proposed Six Sigma program for business intelligence will ensure that an organization can gain control, improve understanding of operations and products, and improve the value of this crucial organization investment.
TagClusters
Authors: Chen Ya-Xi , Santamaría Rodrigo , Butz Andreas , Therón Roberto
Published: February 2011

Many online communities use TagClouds, an aesthetic and easy to understand visualization, to represent popular tags collaboratively generated by their users. However, due to the free nature of tagging, such collaborative tags have linguistic problems and limitations, such as high semantic density. Moreover, the alphabetical order of TagClouds poorly supports a hierarchical exploration among tags. This paper presents an exploration to support semantic understanding of collaborative tags beyond TagClouds. Based on the results of the authors’ survey of practical usages of collaborative tags, they developed a visualization named TagClusters, in which tags are clustered into different groups, with font size representing tag popularity and the spatial distance indicating the semantic similarity between tags. The subgroups in each group and the overlap between groups are highlighted, illustrating the underlying hierarchical structure and semantic relations between groups. The authors conducted a comparative evaluation with TagClouds and TagClusters based on the same tag set. The results confirmed the advantage of TagClusters in facilitating browsing, comparing and comprehending semantic relations between tags.
Magnet Mail
Authors: Castro Paulo , Lopes Adriano
Published: February 2011

Magnet Mail (MM) is a visualization system for emails based on a zoomable interface and on manipulation of objects. Users are able to search, analyze and understand relations among email messages as long as they provide searching keywords to do so, as well as interact with graphical objects in the display in a pro-active manner. The underlying concept is a magnet metaphor that relates user interaction, searching keywords and relations among emails. In this paper, the authors present a prototype that interacts with a mass-market email system and most of its graphical implementation relies on the Piccolo toolkit.
An Assistant Interface to Design and Produce a Pop-Up Card
Authors: Okamura Sosuke , Igarashi Takeo
Published: February 2011

This article describes an assistant interface to design and produce pop-up cards. A pop-up card is a piece of folded paper from which a three-dimensional structure pops up when opened. The authors propose an interface to assist the user in the design and production of a pop-up card. During the design process, the system examines whether the parts protrude from the card or whether the parts collide with one another when the card is closed. The user can concentrate on the design activity because the error occurrence and the error resolution are continuously fed to the user in real time. The authors demonstrate the features of their system by creating two pop-up card examples and perform an informal preliminary user study, showing that automatic protrusion and collision detection are effective in the design process.
The Theoretical Framework of Cognitive Informatics
Authors: Wang Yingxu
Published: February 2011

Cognitive Informatics (CI) is a transdisciplinary enquiry of the internal information processing mechanisms and processes of the brain and natural intelligence shared by almost all science and engineering disciplines. This article presents an intensive review of the new field of CI. The structure of the theoretical framework of CI is described encompassing the Layered Reference Model of the Brain (LRMB), the OAR model of information representation, Natural Intelligence (NI) vs. Artificial Intelligence (AI), Autonomic Computing (AC) vs. imperative computing, CI laws of software, the mechanism of human perception processes, the cognitive processes of formal inferences, and the formal knowledge system. Three types of new structures of mathematics, Concept Algebra (CA), Real-Time Process Algebra (RTPA), and System Algebra (SA), are created to enable rigorous treatment of cognitive processes of the brain as well as knowledge representation and manipulation in a formal and coherent framework. A wide range of applications of CI in cognitive psychology, computing, knowledge engineering, and software engineering has been identified and discussed.
Towards Cognitive Machines
Authors: Kinsner Witold
Published: February 2011

Numerous attempts are being made to develop machines that could act not only autonomously, but also in an increasingly intelligent and cognitive manner. Such cognitive machines ought to be aware of their environments, which include not only other machines, but also human beings. Such machines ought to understand the meaning of information in more human-like ways by grounding knowledge in the physical world and in the machines’ own goals. The motivation for developing such machines range from self-evidenced practical reasons such as the expense of computer maintenance, to wearable computing in health care, and gaining a better understanding of the cognitive capabilities of the human brain. To achieve such an ambitious goal requires solutions to many problems, ranging from human perception, attention, concept creation, cognition, consciousness, executive processes guided by emotions and value, and symbiotic conversational human-machine interactions. An important component of this cognitive machine research includes multiscale measures and analysis. This article presents definitions of cognitive machines, representations of processes, as well as their measurements, measures, and analysis. It provides examples from current research, including cognitive radio, cognitive radar, and cognitive monitors.
Neo-Symbiosis
Authors: Griffith Douglas , Greitzer Frank L.
Published: February 2011

The purpose of this article is to re-address the vision of human-computer symbiosis as originally expressed by J.C.R. Licklider nearly a half-century ago and to argue for the relevance of this vision to the field of cognitive informatics. We describe this vision, place it in some historical context relating to the evolution of human factors research, and observe that the field is now in the process of re-invigorating Licklider’s vision. A central concept of this vision is that humans need to be incorporated into computer architectures. We briefly assess the state of the technology within the context of contemporary theory and practice, and we describe what we regard as this emerging field of neo-symbiosis. Examples of neo-symbiosis are provided, but these are nascent examples and the potential of neo-symbiosis is yet to be realized. We offer some initial thoughts on requirements to define functionality of neo-symbiotic systems and discuss research challenges associated with their development and evaluation. Methodologies and metrics for assessing neo-symbiosis are discussed.
Cognitive Modelling Applied to Aspects of Schizophrenia and Autonomic Computing
Authors: Flax Lee
Published: February 2011

We give an approach to cognitive modelling, which allows for richer expression than the one based simply on the firing of sets of neurons. The object language of the approach is first-order logic augmented by operations of an algebra, PSEN. Some operations useful for this kind of modelling are postulated: combination, comparison, and inhibition of sets of sentences. Inhibi-tion is realised using an algebraic version of AGM belief contraction (Gärdenfors, 1988). It is shown how these operations can be realised using PSEN. Algebraic modelling using PSEN is used to give an account of an explanation of some signs and symptoms of schizophrenia due to Frith (1992) as well as a proposal for the cognitive basis of autonomic computing. A brief discussion of the computability of the operations of PSEN is also given.
Toward Theoretical Foundations of Autonomic Computing
Authors: Wang Yingxu
Published: February 2011

Autonomic computing (AC) is an intelligent computing approach that autonomously carries out robotic and interactive applications based on goal- and inference-driven mechanisms. This article attempts to explore the theoretical foundations and technical paradigms of AC. It reviews the historical development that leads to the transition from imperative computing to AC. It surveys transdisciplinary theoretical foundations for AC such as those of behaviorism, cognitive informatics, denotational mathematics, and intelligent science. On the basis of this work, a coherent framework toward AC may be established for both interdisciplinary theories and application paradigms, which will result in the development of new generation computing architectures and novel information processing systems.
AURELLIO
Authors: Najjar Mehdi , Mayers André
Published: February 2011

Encouraging results of the last few years in the field of knowledge representation within virtual learning environments confirm that artificial intelligence research in this topic finds it very beneficial to integrate the knowledge that psychological research has accumulated on understanding the cognitive mechanism of human learning and all the positive results obtained in computational modeling theories. This article introduces a novel cognitive and computational knowledge representation approach inspired by cognitive theories that explain the human cognitive activity in terms of memory subsystems and their processes, and whose aim is to suggest formal computational models of knowledge that offer efficient and expressive representation structures for virtual learning. Practical studies both contribute to validate the novel approach and permit to draw general conclusions.
Development of an Ontology for an Industrial Domain
Authors: Chan Christine W.
Published: February 2011

This article presents a method for ontology construction and its application in developing an ontology in the domain of natural gas pipeline operations. Both the method, as well as the application ontology developed, contribute to the infrastructure of Semantic Web that provides semantic foundation for supporting information processing by autonomous software agents. This article presents the processes of knowledge acquisition and ontology construction for developing a knowledge-based decision support system for monitoring and control of natural gas pipeline operations. Knowledge on the problem domain was acquired and analyzed using the Inferential Modeling Technique; then the analyzed knowledge was organized into an application ontology and represented in the Knowledge Modeling System. Since an ontology is an explicit specification of a conceptualization that provides a comprehensive foundation specification of knowledge in a domain, it provides semantic clarifications for autonomous software agents that process information on the Internet.
The OAR Model of Neural Informatics for Internal Knowledge Representation in the Brain
Authors: Wang Yingxu
Published: February 2011

The cognitive models of information representation are fundamental research areas in cognitive informatics, which attempts to reveal the mechanisms and potential of the brain in learning and knowledge representation. Because memory is the foundation of all forms of natural intelligence, a generic model of memory, particularly the long-term memory, may explain the fundamental mechanism of internal information representation and the forms of learning results. This article presents the Object-Attribute-Relation (OAR) model to formally represent the structures of internal information and knowledge acquired and learned in the brain. The neural informatics model of human memory is introduced with particular focus on the long-term memory. Then, the OAR model that explains the mechanisms of internal knowledge and information representation in the brain is formally described, and the physical and physiological meanings of this model are explained. Based on the OAR model, knowledge structures and learning mechanisms are rigorously explained. Further, the magnitude of human memory capacity is rigorously estimated on the basis of OAR, by which the memory capacity is derived to be in the order of 108,432 bits.
Constructivist Learning During Software Development
Authors: Rajlich Václav , Xu Shaochun
Published: February 2011

This article explores the non-monotonic nature of the programmer learning that takes place during incremental program development. It uses a constructivist learning model that consists of four fundamental cognitive activities: absorption that adds new facts to the knowledge, denial that rejects facts that do not fit in, reorganization that reorganizes the knowledge, and expulsion that rejects obsolete knowledge. A case study of an incremental program development illustrates the application of the model and demonstrates that it can explain the learning process with episodes of both increase and decrease in the knowledge. Implications for the documentation systems are discussed in the conclusions.
A Unified Approach To Fractal Dimensions
Authors: Kinsner Witold
Published: February 2011

Many scientific papers treat the diversity of fractal dimensions as mere variations on either the same theme or a single definition. There is a need for a unified approach to fractal dimensions for there are fundamental differences between their definitions. This paper presents a new description of three essential classes of fractal dimensions based on: (1) morphology, (2) entropy, and (3) transforms, all unified through the generalized-entropy-based Rényi fractal dimension spectrum. It discusses practical algorithms for computing 15 different fractal dimensions representing the classes. Although the individual dimensions have already been described in the literature, the unified approach presented in this paper is unique in terms of (1) its progressive development of the fractal dimension concept, (2) similarity in the definitions and expressions, (3) analysis of the relation between the dimensions, and (4) their taxonomy. As a result, a number of new observations have been made, and new applications discovered. Of particular interest are behavioral processes (such as dishabituation), irreversible and birth-death growth phenomena (e.g., diffusion-limited aggregates (DLAs), dielectric discharges, and cellular automata), as well as dynamical non-stationary transient processes (such as speech and transients in radio transmitters), multi-fractal optimization of image compression using learned vector quantization with Kohonen’s self-organizing feature maps (SOFMs), and multi-fractal-based signal denoising.
A Selective Sparse Coding Model with Embedded Attention Mechanism
Authors: Li Qingyong , Shi Zhiping , Shi Zhongzhi
Published: February 2011

Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics, but a typical scene contains many different patterns (corresponding to neurons in cortex) competing for neural representation because of the limited processing capacity of the visual system. We propose an attention-guided sparse coding model (AGSC). This model includes two modules: non-uniform sampling module simulating the process of retina and data-driven attention module based on the response saliency (RS). Our experiment results show that the model notably decreases the number of coefficients that may be activated and retains the main vision information at the same time. It provides a way to improve the coding efficiency for sparse coding model and to achieve good performance in both population sparseness and lifetime sparseness.
Interactive Classification Using a Granule Network
Authors: Zhao Yan , Yao Yiyu
Published: February 2011

Classification is one of the main tasks in machine learning, data mining, and pattern recognition. Compared with the extensively studied automation approaches, the interactive approaches, centered on human users, are less explored. This article studies interactive classification at three levels: At the philosophical level, the motivations and a process-based framework of interactive classification are proposed. At the technical level, a granular computing (GrC) model is suggested for re-examining not only existing classification problems but also interactive classification problems. At the application level, an interactive classification system (ICS) using a granule network as the search space, is introduced. ICS allows multi-strategies for granule tree construction and enhances the understanding and interpretation of the classification process. Interactive classification is complementary to the existing classification methods.
A Cognitive Approach to the Mechanism of Intelligence
Authors: Zhong Yi X.
Published: February 2011

An attempt was made in the article to propose a new approach to the intelligence research, namely the cognitive approach that tries to explore in depth the core mechanism of intelligence formation of intelligent systems from the cognitive viewpoint. It is discovered, as result, that the mechanism of intelligence formation in general case is implemented by a sequence of transformations conversing the information to knowledge and further to intelligence (i.e., the intelligent strategy, the embodiment of intelligence in a narrower sense). It is also discovered that the three major approaches to AI that exist, the structural simulation approach, the functional simulation approach, and the behavior simulation approach, can all be harmoniously unified within the framework of the cognitive approach. These two discoveries, as well as the related background, will be reported here in the article.
A Novel Plausible Model for Visual Perception
Authors: Shi Zhiwei , Shi Zhongzhi , Hu Hong
Published: February 2011

Traditionally, how to bridge the gap between low-level visual features and high-level semantic concepts has been a tough task for researchers. In this article, we propose a novel plausible model, namely cellular Bayesian networks (CBNs), to model the process of visual perception. The new model takes advantage of both the low-level visual features, such as colors, textures, and shapes, of target objects and the interrelationship between the known objects, and integrates them into a Bayesian framework, which possesses both firm theoretical foundation and wide practical applications. The novel model successfully overcomes some weakness of traditional Bayesian Network (BN), which prohibits BN being applied to large-scale cognitive problem. The experimental simulation also demonstrates that the CBNs model outperforms purely Bottom-up strategy 6% or more in the task of shape recognition. Finally, although the CBNs model is designed for visual perception, it has great potential to be applied to other areas as well.
3D Object Classification Based on Volumetric Parts
Authors: Xing Weiwei , Liu Weibin , Yuan Baozong
Published: February 2011

This article proposes a 3D object classification approach based on volumetric parts, where Superquadricbased Geon (SBG) description is implemented for representing the volumetric constituents of 3D object. In the approach, 3D object classification is decomposed into the constrained search on interpretation tree and the similarity measure computation. First, a set of integrated features and corresponding constraints are presented, which are used for defining efficient interpretation tree search rules and evaluating the model similarity. Then a similarity measure computation algorithm is developed to evaluate the shape similarity of unknown object data and the stored models. By this classification approach, both whole and partial matching results with model shape similarity ranks can be obtained; especially, focus match can be achieved, in which different key parts can be labeled and all the matched models with corresponding key parts can be obtained. Some experiments are carried out to demonstrate the validity and efficiency of the approach for 3D object classification.
Formal Modeling and Specification of Design Patterns Using RTPA
Authors: Wang Yingxu , Huang Jian
Published: February 2011

Software patterns are recognized as an ideal documentation of expert knowledge in software design and development. However, its formal model and semantics have not been generalized and matured. The traditional UML specifications and related formalization efforts cannot capture the essence of generic patterns precisely, understandably, and essentially. A generic mathematical model of patterns is presented in this article using real-time process algebra (RTPA). The formal model of patterns are more readable and highly generic, which can be used as the meta model to denote any design patterns deductively, and can be translated into code in programming languages by supporting tools. This work reveals that a pattern is a highly complicated and dynamic structure for software design encapsulation, because of its complex and flexible internal associations between multiple abstract classes and instantiations. The generic model of patterns is not only applicable to existing patterns’ description and comprehension, but also useful for future patterns’ identification and formalization.
Artificial Neural Networks that Classify Musical Chords
Authors: Yaremchuk Vanessa , Dawson Michael R.W.
Published: February 2011

An artificial neural network was trained to classify musical chords into four categories—major, dominant seventh, minor, or diminished seventh—independent of musical key. After training, the internal structure of the network was analyzed in order to determine the representations that the network was using to classify chords. It was found that the first layer of connection weights in the network converted the local representations of input notes into distributed representations that could be described in musical terms as circles of major thirds and on circles of major seconds. Hidden units then were able to use this representation to organize stimuli geometrically into a simple space that was easily partitioned by output units to classify the stimuli. This illustrates one potential contribution of artificial neural networks to cognitive informatics: the discovery of novel forms of representation in systems that can accomplish intelligent tasks.