Paper Title, Authors, Abstract (Issue 1, Volume 4, 2010)

Pages

3D Battlefield Modeling and Simulation of War Games
Baki Koyuncu, Erkan Bostanci

Abstract: In this study, a real time 3D virtual simulation software for visualizing the military battlefields was developed. Developed software, named Sandbox, used elevation data stored in DEM format corresponding to the battlefield. Sandbox uses this data to create the platform on which the military units will be added. Different military units can be added in the software. Military units were viewed by both using a recent military symbology, NATO-APP-6A, and 3D models of the real military units. Software uses translation animation for the position updates. Since data transmission between different platforms was considered, developed software extensively uses XML based data. A database was used for long term storage of received reports. Web services were used to transmit and receive reports to/from remote field observers and change the state of the software.
 

Fault Tolerance by Replication of Distributed Database in P2P System using Agent Approach
May Mar Oo, The' The' Soe, Aye Thida

Abstract: TThe term replication in a distributed database refers to the operation of copying and maintaining database objects in more than one location. There are three types of replication in distributed system that are snapshot, transactional and merge. A partial failure may happen when one component in such system fails. This failure may affect the proper operation of other components while at the same time leaving yet other components totally unaffected. An important goal in such systems design is to construct the system in a way that it can automatically recover from partial failures without seriously affecting the overall performance and continue to operate in an acceptable way while repairs are being made. The technologies, architectures, and methodologies traditionally used to develop distributed applications exhibit a variety of limitations and drawbacks when applied to large scale distributed settings (e.g., the Internet). In particular, they fail in providing the desired degree of configurability, scalability, and customizability. To address these issues, researchers are investigating a variety of innovative approaches. The most promising and intriguing ones are those based on the ability of moving code across the nodes of a network, exploiting the notion of mobile code, the agent toolkit can be chosen as a platform for the replication. So, this paper introduced the topic of agent replication for distributed database and examined the issues associated with using agent replication in a multi-agent system as well as the main issues of agent communication, read/write consistency and state synchronization.
 

Image Segmentation Using Discrete Cosine Texture Feature
Chi-Man Pun, Hong-Min Zhu

Abstract: In this paper we propose a computational efficient approach for image segmentation based on texture analysis, a 2D discrete cosine transform (DCT) is utilized to extract texture features in each image block. We first split the input image into MxN blocks, calculate the distances between neighbor blocks by a set of largest energy signatures from DCT for each block. Then we merge blocks with smallest distances to form larger regions. The process will repeat until we got desired number of regions. Experimental results show that our proposed method outperforms the existing image segmentation method, especially on efficiency aspect.
 

A Triple Graph Grammar Mapping of UML 2 Activities into Petri Nets
A. Spiteri Staines

Abstract: Model-to-Model mapping has several advantages over relational mapping. In model-to-model mapping an active correspondence is kept between two pairs of models. This is facilitated if graphical models are used. UML 2 activities are based on Petri net like semantics and substantial literature exists explaining their conversion into Petri nets. This paper explains how UML 2 activities can be formally mapped into Petri nets or Petri net semantics from a theoretical, practical and operational point of view adding on previous work of Triple Graph Grammars (TGGs). UML activity constructs are classified and identified. This is useful for creating a basic set of TGG rules. Generic TGG rules are identified and created. The rules are mainly intended for forward transformation. An example is given illustrating the conversion process. The concepts presented can be elaborated further and even extended to other visual models or notations.
 

Using Edutainment in E-Learning Application: An Empirical Study
Dimitrios Rigas, Khaled Ayad

Abstract: Philosophers and psychologists' arguments in the area of effective learning and HCI demonstrated that humour or entertainment is one of many important factors that help in developing improved learning. Accordingly students' performance increases in learning environment combined with amusement features. This work investigates the role of edutainment using avatars as tool to represent the entertainment attributes in an e-learning framework. The empirical investigation aimed at measuring usability of two experimental interfaces: typical e-learning and multimodal elearning system. The usability of these two environments was analysed by one dependent group of users. The results presented here confirmed that edutainment interface as learning medium persuaded users more than the typical version.
 

    Paper Title, Authors, Abstract (Issue 2, Volume 4, 2010)

Pages

Mathematical Validation of Object-Oriented Class Cohesion Metrics
Jehad Al Dallal

Abstract: Class cohesion is an object-oriented software quality attribute and refers to the extent to which the members of a class are related. Software developers use class cohesion measures to assess the quality of their products and to guide the restructuring of poorly designed classes. Several class cohesion metrics are proposed in the literature, and a few of them are mathematically validated against the necessary properties of class cohesion. Metrics that violate class cohesion properties are not well defined, and their utility as indictors of the relatedness of class members is questionable. The purpose of this paper is to mathematically validate sixteen class cohesion metrics using class cohesion properties. Results show that metrics differ considerably in satisfying the cohesion properties; some of them satisfy all properties, while others satisfy none.
 

Comparing User Satisfaction and Customisation for Variable Size Personalised Menus
Khalid Al-Omar, Dimitrios Rigas

Abstract: This paper reports a comparative empirical investigation of the effects of content size on user satisfaction and customisation of five different personalised menu types: adaptable, adaptive split, adaptive/adaptable highlighted, adaptive/adaptable minimised and mixed-initiative menus. Two independent experiments were conducted, on small menus (17 items) and large menus (29 items) respectively. The experiment was conducted with 60 subjects (30 subjects each on small and large menus) and was tested empirically by four independent groups (15 subjects each). Results show that in small menus, the minimised condition was preferred overall, followed by the adaptable and highlighted types. By contrast, in large menus, the mixed-initiative condition was the most strongly preferred, followed by the minimised approach.
 

Text-Driven Avatars based on Artificial Neural Networks and Fuzzy Logic
Mario Malcangi

Abstract: We discuss a new approach for driving avatars using synthetic speech generated from pure text. Lip and face muscles are controlled by the information embedded in the utterance and its related expressiveness. Rule-based, text-to-speech synthesis is used to generate phonetic and expression transcriptions of the text to be uttered by the avatar. Two artificial neural networks, one for text-to-phone transcription and the other for phone-to-viseme mapping have been trained from phonetic transcription data. Two fuzzy-logic engines were tuned for smoothed control of lip and face movement. Simulations have been run to test neural-fuzzy controls using a parametric speech synthesizer to generate voices and a face synthesizer to generate facial movement. Experimental results show that soft computing affords a good solution for the smoothed control of avatars during the expressive utterance of text.
 

Comparing Effectiveness and Efficiency between Multimodal and Textual Note-Taking Interfaces
Mohamed Sallam, Dimitrios Rigas

Abstract: This paper describes an experimental study conducted to investigate the use of multimodal metaphors in the interface of e-learning applications. This investigation involved two different interface versions of the experimental e-learning tool. In the first interface platform (textual interface), three input modalities, were used to deliver information about note-taking: text, graphic, and image. The second version of the interface application (multimodal interface) offered a combination of multimodal metaphors such as recorded speech, video, and avatar with simple facial expressions to communicate the same information. The aim of the experiment was to measure and compare the level of usability of textual and multimodal interfaces. The usability parameters, which are efficiency, effectiveness, and user? satisfaction were considered in the experiment. The results obtained from this investigation have shown that the multimodal e-learning interface increased the level of usability as users took significantly less time to complete the tasks, performed successfully in a higher number of tasks, and were more satisfied than when using the textual interface. These input modalities could be used to improve the attractiveness of note taking which in turn will be reflected in increasing users? motivation and interest in the learning material presented.
 

    Paper Title, Authors, Abstract (Issue 3, Volume 4, 2010)

Pages

Fast Algorithms for Preemptive Scheduling of Jobs with Release Times on a Single Processor to Minimize the Number of Late Jobs
Nodari Vakhania

Abstract: We have n jobs with release times and due-dates to be scheduled preemptively on a single-machine that can handle at most one job at a time. Our objective is to minimize the number of late jobs, ones completed after their due-dates. This problem is known to be solvable in time O(n3 log n). Here we present two polynomial-time algorithms with a superior running time. The first algorithm solves optimally in time O(n2) the special case of the problem when job processing times and due-dates are tied so that for each pair of jobs i; j with di > dj , pi pj . This particular setting has real-life applications. The second algorithm runs in time O(n log n) and works for the general version of the problem. As we show, there are strong cases when the algorithm finds an optimal solution.
 

The Influence of Game in E-Learning: An Empirical Study
Dimitrios Rigas, Khaled Ayad

Abstract: A human-computer interface is an attempt to mimic human-human communication. In human-human communication, especially in learning, students interact emotionally either with each other or with their instructor in way that minimizes, to some extent, the formality of the learning arena/environment. In web based learning these emotions are usually not present within many of the types of e-learning environments. Researchers on the other hand have articulated that humour strengthens students? performance in a learning environment combined with amusement features. This mostly happens online were users in front of unadulterated educational screens. In this paper, we empirically investigated the role of edutainment applied avatar as a tool to represent the entertainment attributes in an e-learning framework. The empirical investigation aimed at measuring the usability of four experimental game-based interfaces; each of which is integrated with a combination of different multi-modal features which included; text, earcons, speech, and avatar. These four game-based learning interfaces were introduced in four phases; the first to be introduced consisted of text and speech only (TS), the second, text and earcons only (TE), the third, integrated with text, speech and earcons (TSE) and finally fourth game was with text, speech, earcons and avatar (TSEA). This combination of various multi-modal metaphors with elearning systems were examined to determine the preferable set of multi-modal grouping that entertained and enhanced user's performance. Effectiveness and efficiency of these four environments were analyzed using an independent group of users. The outcomes showed a higher improvement rate in performance of students who learnt with the game interface integrated with the avatar than the other versions.
 

A Testing Theory for Real-Time Systems
Stefan D. Bruda, Chun Dai

Abstract: We develop a testing theory for real-time systems. We keep the usual notion of success or failure (based on finite runs) but we also provide a mechanism of determining the success or failure of infinite runs, using a formalism similar to the acceptance in B¨uchi automata. We present two refinement timed preorders similar to De Nicola and Hennessy’s may and must testing. We then provide alternative, behavioural and languagebased characterizations for these relations to show that the new preorders are extensions of the traditional preorders. Finally we focus on test generation, showing how tests can be automatically generated out of a timed variant of linear-time logic formulae (namely, TPTL), so that a process must pass the generated test if and only if the process satisfies the given temporal logic formula. Beside the obvious use of such an algorithm (to generate tests), our result also establishes a correspondence between timed must testing and timed temporal logic.
 

    Paper Title, Authors, Abstract (Issue 4, Volume 4, 2010)

Pages

Assessment and Evaluation of Different Data Fusion Techniques
A. K. Helmy, A. H. Nasr, Gh. S. El-Taweel

Abstract: Data fusion is a formal framework for combining and utilizing data originating from different sources. It aims at obtaining information of greater quality depending upon the application. There are many data fusion techniques that can be used to produce high-resolution multispectral images from a high-resolution panchromatic (PAN) image and low-resolution multispectral (MS) images, including but not limited to, modified Intensity–hue–saturation, Brovey transform, Principal component analysis, Multiplicative transform, Wavelet resolution merge, High-pass filtering, and Ehlers fusion. One of the major problems associated with a data fusion technique is how to assess the quality of the fused (spatially enhanced) MS images. This paper represents a comprehensive analysis and evaluation of the most commonly used data fusion techniques. The performance of each data fusion method is qualitatively and quantitatively analyzed. Then, the methods are ranked according to the conclusions of the visual analysis and the results from quality budgets. An experiment based on Quickbird images shows that there is inconsistency between different performances measures used to evaluate data fusion techniques.
 

Coupling Metrics for Business Process Modeling
Wiem Khlif, Nahla Zaaboub, Hanene Ben-Abdallah

Abstract: Modeling business processes is vital when improving or automating existing business processes, documenting processes properly or comparing business processes. In addition, it is necessary to evaluate the quality of a business process model through a set of quality metrics. One specific categorie of such metrics is coupling which measures the functional and informational dependencies between the tasks/processes in a business process model. Our contribution in this paper consists in adapting object oriented software coupling metrics for business process models. This adaptation is based on correspondences we establish between concepts of the Business Process Modeling Notation and object oriented concepts. The new adapted coupling metrics offer more information about the dependencies among processes and their tasks in terms of data and control. They can be used, for instances to evaluate the transferability effects of errors occurring in a particular task/process. Finally, we validate theoretically the proposed metrics.
 

Minimum Flow in Monotone Parametric Bipartite Networks
Eleonor Ciurea, Mircea Parpalea

Abstract: The algorithm presented in this paper solves the minimum flow problem for a special parametric bipartite network. The algorithm does not work directly in the original network but in the parametric residual network and finds a particular state of the residual network from which the minimum flow and the maximum cut for any of the parameter values are obtained. The approach implements a round-robin algorithm looping over a list of nodes until an entire pass ends without any change of the flow.
 

Homeostasis and Artificial Neuron
Martin Ruzek, Tomas Brandejsky

Abstract: Homeostasis is a property of a system that regulates its internal environment in order to maintain stable condition. This property is typical for biological systems and therefore also for neural cell. This article presents one possible use of the idea of homeostasis in the field of the artificial neural networks. The proposed neuron is a homeostat for which the state of equilibrium means a situation when the level of acceptance of its output reaches its maximum. The neuron is operating with two kinds of information: its input signal (as any artificial neuron), and the input weights of other neurons that are receiving its output. This idea is inspired by the fact that the biological neuron can know which part of its output energy is accepted by other neurons. Several methods of the learning are presented. The main feature of the proposed neuron is the independence of the learning mode; no teacher or higher structure are needed as for example in back-propagation algorithm. Several qualities of the homeostatic neuron, such as stability, speed of learning and independence, are discussed. The results of the first test are presented.
 

An Adaptive Modeling Approach in Collaborative Data and Process-Aware Management Systems
Ion Lungu, Andrei Mihalache

Abstract: Informational systems are used to reflect the business they are supposed to assist. This is the reason why each informational system needs a representation of the business objects that are involved in the processes and also the business rules that are applied to the business objects. Every object is simpler than we can think and also more complex than we can imagine. Objects should be represented as simple as they are, not simpler and not more complicated. Anytime people need to communicate or record information, in any context, it is very useful to create a model. Once the model is implemented into a business application, most of these software platforms are too inflexible to keep pace with the business processes they support and take place in a changing business context. This paper introduces an adaptive approach for enterprise data and process flow modeling in informational systems.
 

Development of a Visualization Tool for XML Documents
Khalil Shihab, Doreen Ying Ying Sim

Abstract: We present the development of a prototype system called Angur, which is designed and built for visualization of XML documents. There two main motivations of this work: firstly is to allow the users to explore and manipulate XML documents and secondly is to display the search results graphically, in two or three dimensions, grouped by topic or category. This prototype employs modern interactive visualization techniques to provide a visual presentation of a set of XML documents. The motivation and evaluation of several design features, such as keyword to concept mapping, explicit clustering, the use of 3-D vs. 2-D, and the relationship of visualization to logical structure are described.
 

Concurrent Differential Evolution Based on MapReduce
Kiyoharu Tagawa, Takashi Ishimizu

Abstract: Multi-core processors, which have more than one Central Processing Unit (CPU), have been introduced widely into personal computers. Therefore, in order to utilize the additional cores, or CPUs, to execute various costly application programs, concurrent implementations of them have been paid to attention. MapReduce is a concurrent programming model and an associated implementation for processing and generating large data sets. This paper has been participated in plenary presentation at the conference of WSEAS and is presenting a further progress of a concurrent implementation of Differential Evolution (DE) based on MapReduce. Especially, through the numerical experiment conducted on a wide range of benchmark problems, the speedup of DE due to the use of multiple cores is demonstrated. Furthermore, the goodness of the proposed concurrent implementation of DE is examined and proved with respect to four categories, namely efficiency, simplicity, portability and scalability.
 

Embedding Conditional Knowledge Bases into Question Answering Systems and Java Implementation
Nicolae Tandareanu, Mihaela Colhon, Cristina Zamfir

Abstract: A conditional schema is a graph-based structure which is able to represent conditional knowledge. This structure was introduced in [11]. The inference mechanism corresponding to the conditional schema representations was developed in [12]. In this paper we propose a question answering system that can represent and process conditional knowledge using these mechanisms. In order to accomplish this task we refine the concept of conditional schema by introducing the concepts of XML-conditional structure generated by a conditional schema and XML-conditional knowledge base for such a structure. We describe the architecture of a question answering systems that uses these structures. An implementation by means of Java platform is briefly described.
 

Legal Protection in the Field of Information Technology in the EU
A. Ciurea

Abstract: The information and comunication technologies represent an essential side of the economy and European society. Apparently, their evolution has determined complex consequences also in the legal field, because the access and use of information technology have created new rights and obligations for the beneficiaries of this technical progress. This paper aims at presenting the most important legal consequences that arose from the activities through computer systems, according to EU and Romania regulations.
 

Apples & Oranges? Comparing Unconventional Computers
Ed Blakey

Abstract: Complexity theorists routinely compare—via the preordering induced by asymptotic notation—the efficiency of computers so as to ascertain which offers the most efficient solution to a given problem. Tacit in this statement, however, is that the computers conform to a standard computational model: that is, they are Turing machines, random-access machines or similar. However, whereas meaningful comparison between these conventional computers is well understood and correctly practised, that of non-standard machines (such as quantum, chemical and optical computers) is rarely even attempted and, where it is, is often attempted under the typically false assumption that the conventional-computing approach to comparison is adequate in the unconventional-computing case. We discuss in the present paper a computational-model-independent approach to the comparison of computers’ complexity (and define the corresponding complexity classes). Notably, the approach allows meaningful comparison between an unconventional computer and an existing, digital-computer benchmark that solves the same problem.
 

Clustering of EEG Data using Maximum Entropy Method and LVQ
Yuji Mizuno, Hiroshi Mabuchi, Goutam Chakraborty, Masafumi Matsuhara

Abstract: The study of extracting electroencephalogram (EEG) data as a source of significant information has recently gained attention. However, since EEG data are complex, it is difficult to extract them as a source of intended, significant information. In order to effectively extract EEG data, this paper employs the maximum entropy method (MEM) for frequency analyses and investigates an alpha frequency band and beta frequency band in which features are more apparent. At this time, both the alpha and beta frequency bands are divided further into several sub-bands so as to extract detailed EEG data where the loss of data is small. In addition, learning vector quantization (LVQ) is used for clustering the EEG data with features extracted. In this paper, we will demonstrate the effectiveness of the proposed method by applying it to the EEG data of one subject and two subjects and comparing the results with other related studies. By applying the proposed method further to the EEG data of three subjects, and comparing the results with a related study, the effectiveness of the proposed method will be determined.
 

Using Sequence DNA Chips Data to Mining and Diagnosing Cancer Patients
Zakaria Suliman Zubi, Marim Aboajela Emsaed

Abstract: Deoxyribonucleic acid (DNA) micro-arrays present a powerful means of observing thousands of gene terms levels at the same time. They consist of high dimensional datasets, which challenge conventional clustering methods. The data’s high dimensionality calls for Self Organizing Maps (SOMs) to cluster DNA micro-array data. The DNA micro-array dataset are stored in huge biological databases for several purposes [1]. The proposed methods are based on the idea of selecting a gene subset to distinguish all classes, it will be more effective to solve a multi-class problem, and we will propose a genetic programming (GP) based approach to analyze multi-class micro-array datasets. This biological dataset will be derived from multiple biological databases. The procedure responsible for extracting datasets called DNA-Aggregator. We will design a biological aggregator, which aggregates various datasets via DNA micro-array community-developed ontology based upon the concept of semantic Web for integrating and exchanging biological data. Our aggregator is composed of modules that retrieve the data from various biological databases. It will also enable queries by other applications to recognize the genes. The genes will be categorized in groups based on a classification method, which collects similar expression patterns. Using a clustering method such as k-mean is required either to discover the groups of similar objects from the biological database to characterize the underlying data distribution.
 

Supporting Requirements Engineering with Different Petri Net Classes
A. Spiteri Staines

Abstract: This paper considers how Petri net main classes or categories can be used to support systems and software requirements engineering processes. In general Petri nets are classifiable into four main categories which are i) elementary nets , ii) Normal Petri nets , iii) higher order nets and iv) timed Petri nets or Petri nets with time. Apart from some major fundamental differences, each category has a specific use for systems engineering and software engineering and thus they can clearly help with requirements engineering issues. In this work the main differences between these categories are briefly explained. It is also shown how these Petri net classes can be made to fit in a semi structured approach. This is very useful for the analysis and design of a whole range of system types. A simple case study of a vending machine is used for illustrating this work.
 

An Intelligent Web-based GRA/Cointegration analysis for Systematic Risk
Shu Ling Lin, Shun Jyh Wu

Abstract: A new intelligent web-based grey relational analysis (GRA)/cointegration analysis is proposed to examine the effects of cross-border bank M&As on the systematic risk that took place in the American, Asia, Europe, Africa and Middle East of banks in this paper. The potential diversification gains that arise from geographic or cross-border diversification are studied using a database that includes deals and bank stock return information for 114 cross-border M&As during 1998-2005. Cointegration analysis is first developed to obtain the relationship between financial variables and web-based GRA is then applied to establish the ranking and clustering of all acquirer events. The findings have important regulatory policy implications in that, the potential diversification gains have obtained in home country. Consequently, regulators in home countries may be less concerned with a rise in systematic risk following cross-border M&As, and no need to impose barriers to restrict the cross-border M&As activity. Grey relational analysis is demonstrated to be well developed to the clustering and ranking of cross-border M&As events. This study suggests that the proposed intelligent web-based GRA/cointegration analysis is effective and robust.
 

Intelligent Web-Based Fuzzy and Grey Models for Hourly Wind Speed Forecast
Shun Jyh Wu, Shu Ling Lin

Abstract: An intelligent web-based fuzzy model is developed for the forecast of hourly wind speed. The hourly wind speed of three meteorological stations in Taiwan are forecasted and compared. The selected sites of Taiwan meteorological stations are Lan-Yu, Tung-Chi-Tao, and Wuci, whose wind speeds are the highest among 25 areas during the period of 1971-2000. An intelligent time series model and GM(1,1) are developed and used to forecast the randomly distributed wind speed data. Hourly records of wind speed are first used to establish intelligent fuzzy linguistic functions, and then fuzzy relational matrix is developed to form the time series relationship. Effects of interval number are studied. For the same order of the intelligent fuzzy model, the model with higher interval number provides better prediction of the hourly wind speed with lower RMSE. On the other hand, GM(1,1) gives higher RMSE for all the three site. The present results demonstrate the benefits and the robustness of the intelligent model.
 

Obtaining Thin Layers of ZnO with Magnetron Sputtering Method
Chitanu Elena, Ionita Gheorghe

Abstract: This paper presents research results on the obtaining of ZnO thin layers using a method of physical vapor deposition, namely magnetron sputtering. They used two types of ZnO targets sintered and non sintered. Deposit layers was done by magnetron sputtering method in argon atmosphere. Rigorous characterization of the deposited layers was performed by analysis of electron microscopy SEM and HRTEM.