Volume 3, Issue 11, November 2011

A Processor Mapping Strategy for Processor Utilization in a Heterogeneous Distributed System

Hidehiro Kanemitsu, Gilhyon Lee, Hidenori Nakazato, Takashige Hoshiai and Yoshiyori Urano

In a distributed system where processors are connected over the network, how to minimize the schedule length is one of major objectives in the field of task scheduling. Conventional task clustering heuristics and processor assignment methods for heterogeneous distributed systems are used to generate the assignment unit for the given set of processors. Such approaches try to use all processors for minimizing the schedule length. However, there is no way to know automatically how many processors are needed to minimize the schedule length. Thus, there is no criterion for maximizing the degree of contribution toward reduction of the schedule length per a processor. In this paper, we propose a method for processor mapping for processor utilization. Our proposal imposes the lower bound for every assignment unit size for limiting the number of required processors. Under the constraint, the processor assignment is performed to minimize the schedule length. In the mapping, the processor, by which the indicative value for the schedule length can be minimized, is selected for the assignment. Experimental results by simulations show that our proposal has advantages of processor utilization over conventional approaches.

Keywords: Processor Mapping, Task Scheduling, Processor Utilization, Hegerogeneouse Distributed Systems

Detection of Learner’s Affective State Based on Mouse Movements

Georgios Tsoulouhas, Dimitrios Georgiou and Alexandros Karakos

In this paper we introduce a new method of detection of the emotional state of a student who attends a lesson online. To be more specific we are occupied with the detection of boredom which can be caused by the presentation of a course through a distance learning environment. The detection method is based on information obtained from the movements of the user's computer mouse. We suggest some metrics which are derived from this information and may be related to the emotional state of the user. Based on these metrics we use data mining methods to classify the results. In order to test the efficiency of the method we carried out an experiment with 136 students in a particular course which consisted of 7 different learning objects. The students were called to attend the lesson and to express periodically their emotional condition (if they feel bored or not) for each learning object separately. The collected data after being processed were applied to the known classification algorithm C4.5 which classifies the values of the metrics according to the user's emotional state.

Keywords: Distance learning, Mining methods and algorithms, Adaptive hypermedia

Deployment of Software Metrics Measurements Techniques within Software Development Process

Zahra Gholami, Nasser Modiri and Sam Jabbehdari

Software quality is an important criterion in producing softwares which increases productivity and results in powerful and invincible softwares. We can say that quality assurance is the main principle and plan in software production. Solution which is suggested for quality assurance and improvement of software is measurement. The result of measurement process is to acquire a set of metrics. Software metrics are continuous usage of techniques based on measurement in software development process and products in order to provide timely and significant management information, with using these techniques in improvement of process and its products. Therefore we need derivation of respective metrics in order to achieve our goal. Considering the importance of software metrics, utilization of international standard software life cycle process model (ISO/IEC 12207) and measurement process of Plan/Do/Check/Act in order to monitor software production cycle is presented in this paper.

Keywords: Software Metrics, Measurement, Software Development Process, ISO/IEC 12207

Global and configurable model for crowd behavioral simulation

Hakim Soussi, Joël Savelli and Marc Neveu

Our purpose in this paper is to describe a generic simple and flexible behavior oriented model that may be used to build various crowd behavioral simulations. We deal here with how behavioral structure of animation is dynamically built, that is how every agent of the scene will adopt a specific behavior (instead of others) in a given situation. We introduce here the conceptual basis of the model that consists in the notions of context and character attribute. By using these tools, a user may define an application and interact with the crowd in an efficient way. We propose a mechanism to select agent behaviors by taking into account their “personality” and the current set of contexts they are submitted to. We also propose a mechanism which allows to define propagating contexts and to propagate them via the agents according to the personality of each one (i.e., according to its personality, the agent can or cannot propagate this context to other agents). To show the efficiency of our model, we separate all possible crowds into three categories and show how to describe them with the elements of the model. We also focus on the realism of crowds. We distinguish macroscopic realism, which comes from the emergent properties and behavior of the crowd, and microscopic realism which depends on the behaviors of a few agents. We show here how macroscopic and microscopic realism may be controlled by using our model.

Keywords: Behavioral animation, crowd, context, character attributes

Behavioral animation, crowd, context, character attributes

K. Vijayalakshmi, R. Dhanapal and B. Balaji Selva Ganesh

The paper highlights the Critical factors for the Customer preferences in the business markets using the Data mining. The customer purchase patterns approach, using the association rules mining technique, is an effective way of extracting the rules from the raw data and inferring the buying patterns among them. The success of proper implementation of these techniques in the business firms is mixed. This is due to the fact that trends and taste of the customers are highly unpredictable. Hence this implementation requires planning regarding the factors which need to be considered before going for the new innovative ideas. These factors may vary from firm to firm but the general factor for effective implementation of the customer preference is essential. This factor termed as Critical factors of Customer preferences (CFCP) decides the failure or success of the implementation. Marketing efforts usually focus on minimizing churn because the cost of bringing a customer back is usually much greater than the cost of retaining the customer in the first place. The paper highlights those key essential factors which need to be considered before automating the process of searching the mountain of customer’s related data using Data mining to find patterns or a model that helps the business people to predict the behaviors of the customer to achieve their long term goals, vision & mission.

Keywords: Customer preference, Data mining, Churn, Association rule mining, Patterns

Finite Volume Model to Study Calcium Diffusion in Neuron Involving JRYR, JSERCA and JLEAK

Amrita Tripathi and Neeru Adlakha

Calcium dynamics is the highly responsible for intracellular electrical (action potential) and chemical (neurotransmitter) signaling in neuron cell. The Mathematical modeling of calcium dynamics in a neuron leads to the reaction diffusion equation which involves the parameters like diffusion coefficient, free calcium, bound calcium, buffers and bound buffer. Here the parameters like receptors, serca and leak are also incorporated in the model. Appropriate boundary conditions have been framed based on biophysical conditions of the problem. The finite volume method has been employed to obtain the solution. The computer program has been developed using MATLAB 7.11 for the entire problem to compute Ca2+ profiles and study the relationships among various parameters.

Keywords: reaction diffusion equation; JRYR; JSERCA; JLEAK; excess buffer; finite volume method

A Hybrid Ranking Method For Constructing Negative Datasets of Protein-Protein Interactions

Jumail Taliba, Razib M. Othman, Umi K. Hassan and Rosfuzah Roslan

Lack of availability of negative examples in the study of computational Protein-Protein Interaction (PPI) prediction is a crucial problem. This leads to computational methods for creating such examples. Most of these methods rely on the fact that proteins not sharing common information tend not to be interacting. While using this fact as the basis for the selection method for non-PPI pairs may yield a negative dataset with high prediction accuracy, it does come with more bias as it is too selective. Other methods simply use random selection as an alternative for fair selection. However, these approaches do not guarantee the prediction accuracy. A method for constructing non-PPI datasets named AIDNIP is proposed. It is a hybrid of the above approaches. Thus, it can reduce biases of selection, while maintaining prediction accuracies. When compared to the existing methods using a Support Vector Machine-based PPI predictor, the proposed method performs better in several metrics investigated in this study. The Perl code and data used in this study are publically available at https://sites.google.com/a/fsksm.utm.my/aidnip.

Keywords: Non-Interacting Protein Pairs, Negative Datasets, Protein-Protein Interactions Prediction

Kernel Based Nonlinear Weighted Least Squares Regression

Antoni Wibowo and Mohamad Ishak Desa

In this paper, we consider a regression model with heteroscedastic errors in which the prediction of ordinary least squares (OLS) based regressions can be inappropriate. Weighted least squares (WLS) is widely used to handle in the heteroscedastic model by transforming an original model into a new model that satisfies homoscedasticity assumption. However, WLS yields a linear prediction and has no guarantee to avoid the negative effect of multicollinearity. Under this circumstance, we propose a method to overcome these difficulties using the hybridization of WLS regression with kernel method. We use WLS to handle the heteroscedastic errors and use kernel method to perform a nonlinear model and to eliminate the negative effect of multicollinearity in this regression model. Then, we compare the performance of the proposed method with the WLS regression and it gives better results than WLS regression.

Keywords: Kernel principal component regression, kernel trick, multicollinearity, heteroscedastic, nonlinear regression, weighted least squares

Ranking Discovered Rules from Data Mining by MADM Method

Peyman Gholami, Mohamad Sepehri Rad, Azade Bazle and Nezam Faghih

Data mining techniques, extracting patterns from large databases have become widespread in business. Using these techniques, various rules may be obtained and only a small number of these rules may be selected for implementation due, at least in part, to limitations of budget and resources. Evaluating and ranking the interestingness or usefulness of association rules is important in data mining. This paper proposes entropy, an applicable and useful weighting technique of multiple attribute decision making (MADM) method. Then, utilizing this model, a popular method, technique for order preference by similarity to ideal solution (TOPSIS) method, for prioritizing association rules by considering multiple criteria is proposed. As an advantage, the proposed method is computationally more efficient than previous works. Using an example of market basket analysis, applicability of our method for measuring the ranking of association rules with multiple criteria is illustrated.

Keywords: component ; data mining; association rule; MADM; entropy; ranking; TOPSIS

Accident Detection by Wireless Sensor Network and Sending Rescue Message with GPS

Sukanya Ray, Amnesh Goel and Nidhi Chandra

In the last decade the number of deaths and fatal injuries from traffic accidents has been increasing rapidly but driving remains an essential requirement so, one of the urgent needs of the Vehicular Ad-Hoc Network (VANET) is to provide immediate medication and necessary help after an accident occurs so that the lives can be saved. This paper aims to suggest an Intelligent Transportation System (ITS) for four wheelers by using Wireless Sensor Network (WSN) and sending rescue messages to emergency contacts, which is saved before hand by the driver and to nearby police stations and hospitals so that necessary help can be provided within time.

Keywords: WSN, Four wheeler ITS, SMS, GPS, VANET

Finite Volume Model to Study the Effect of ER flux on Cytosolic Calcium Distribution in Astrocytes

Brajesh Kumar Jha, Neeru Adlakha and M. N. Mehta

Most of the intra-cellular events involved in the initiation and propagation phases of this process has now been identified astrocytes. The control of the spread of intracellular calcium signaling has been demonstrated to occur at several levels including IP3 receptors, intracellular Ca2+ stores like endoplasmic reticulum (ER) . In normal and pathological situations that affect one or several of these steps can be predicted to influence on astrocytic calcium waves. In view of above a mathematical model is developed to study interdependence of all the important parameters like diffusion coefficient and influx over [Ca2+] profile. Model incorporates the ER fluxes. Finite volume method is employed to solve the problem. A program has been developed using in MATLAB 7.5 for the entire problem and simulated on an AMD-Turion 32-bite machine to compute the numerical results. In view of above a mathematical model is developed to study calcium transport between cytosol and ER.

Keywords: Ca2+ profile, ER flux, Astrocytes, FVM

The fuzzy cooperative genetic algorithm (FCoGA): The optimisation of a fuzzy model through incorporation of a cooperative coevolutionary method

Arfian M. Ismail, Hishammuddin Asmuni and Razib M. Othman

Genetic Algorithms (GA) have been widely used to represent parameters in a fuzzy system. However, when a fuzzy system is applied to a complex problem, GA tends to lose their effectiveness because of the representation complexity of the solution. In this paper, an improved method of fuzzy modelling called as Fuzzy Cooperative Genetic Algorithm (FCoGA) is introduced. Cooperative Coevolution (CC) is applied to the GA by subdividing the chromosome into three sub-chromosomes known as species, and thus reducing the representation complexity of the solution. Furthermore, two-level evaluations in the FCoGA, at the species level and cooperative chromosome level, are introduced to improve the performance. To measure the performance of FCoGA, two benchmark datasets namely Wisconsin Breast Cancer Diagnosis (WBCD) and Pima Indian Diabetes (PID) datasets have been used. The experimental results show that FCoGA slightly improves the accuracy rate and maintains comparable effectiveness with other existing study solutions.

Keywords: Cooperative coevolutionary algorithm, Genetic algorithm, Cooperative chromosome, Fuzzy modelling

A Vertical Format to Mine Regular Patterns in Incremental Transactional Databases

G. Vijay Kumar, M. Sreedevi and NVS Pavan Kumar

In real life, transactional databases grow incrementally. The occurrence behaviour of patterns may change significantly when the database is updated. Each time when a small set of transactions added into the database, it is undesirable to mine regular patterns from scratch. So Mining Regular Patterns in incremental transactional databases is an important problem in data mining. Although some efforts done in finding regular patterns in incremental transactional databases, no such method has been proposed yet by using vertical format with one database scan. Therefore, in this paper we proposed a new method called IncVDRP method to generate complete set of regular patterns in incremental transactional databases for a user given regularity threshold. Our experimental result shows our results are quite promising.

Keywords: Incremental transactional database, Regular patterns, regularity threshold, Vertical format

ENFD: An Expert System tool for Fault Detection on Computer Network

Effat mirzaei and Chitra Dadkhah

One of the important tasks of computer network administrator is diagnostic faults occurred in computer networks. Design an automated system is necessary because the absence of computer network manager in the organization and detect the faults quickly. So this paper aims to design an ENFD system (Expert Network Fault Detection) based on expert knowledge for detecting the hardware and software problems in computer networks. The knowledge of ENFD system has been classified in four categories (client, server, network hardware and firewall) based on expert opinion and has been represented with If-then method, which is compatible with the problem nature, in knowledge base of system. The ENFD system will suggest an appropriate solution for solving the detected problem. The ENFD system is independent on the topology of computer networks and it is easily usable for users who have not a lot of information on computer networks. In the absent of specialists when problems is occur, computer network management could consult the ENFD system for solving the problem. The ENFD system is capable to explain the cause of the problems has occurred for increasing the accurate of its inference. The ENFD system designed and tested with prolog programming language.

Keywords: expert system, computer network, fault detection, decision tree

S.pombe Protein-Protein Interaction Prediction by using Topological Ontology of S.cerevisiae Protein Network

Praveen Kumar Singh and Madhvi Shakya

Traditionally, protein interactions have been studied individually by genetic, biochemical and biophysical techniques. Availability of genomic information and protein interaction maps has created an opportunity to improve computational methods. Proposed method is novel approach of graph comparison method used to find protein interaction networks of Schizosaccharomyces pombe from that of Saccharomyces cerevisiae. Topological properties of a protein network can be described through topological ontology by using ontological entities – nodes (proteins) and edge (interaction). Unknown protein interaction of S. pombe is predicted by topological similarity search in two steps. First is the identification of similar nodes (orthologous proteins) through BLAST. Second similarity search is done by ontological instances which measure functional similarity based on the binary interactions and groupwise similarity search. Proposed method predicts potential protein interaction by topological similarity, which solves the problem of understanding the biological behavior of protein interaction network within different organism.

Keywords: Binary interaction, Functional conservation, Orthologous groups, Topological similarity

SOAR Agents

S. Srinivasan, Nitin Bansal, Vivek Jaglan and Sujit Kumar Singh

This paper intends to investigate Soar architecture to work on the full range of tasks like an intelligent agent from a highly routine to difficult and unexpected problems . Also to understand how Soar agent uses appropriate forms of knowledge such as procedural , declarative and episodic also to apply all possible methods by interacting with outside world and at the same time how Soar agent acquires knowledge by performing such tasks . Our intention is to find out all the capabilities that Soar agent possesses . The world is very complex place for general intelligent agents to act . The aim of our work is to study various environments under which the Soar agent performs . Also our focus is to study Memory ,knowledge and knowledge representation that are handled by Soar agent . Ultimately various parameters are considered for evaluating soar agent . The design of Soar can be seen as an investigation of all such capabilities , environments , knowledge and knowledge representations.

Keywords: BDI, subgoal and chunking

Visual Reading Patterns on Arabic Interfaces: Insights from Eye Tracking

Remya P. George, Rasiya Anwar and Sunitha Jeyasekhar

This paper describes visual reading patterns of Arabic script exhibited by web users in their interaction with Arabic interfaces. Eye gaze of participants has been extracted from experiments which investigated different contexts. Patterns on electronic newspapers, eLearning Module, forums and search engines are examined and implications for design are discussed. We have found differences in the reading patterns exhibited by the readers compared to the studies in English interfaces because of variations in the direction of Arabic scripts as well as cultural factors.

Keywords: Arabic Interfaces, Eye Tracking, Visual Reading Patterns

Virtualization Technology in Green IT with Cloud Computing Infrastructure

Afshin Rezakhani Roozbahani, Nasser Modiri and Ali Mohammadi

The importance of IT is increased in ten years. One of the most important scopes in IT is virtualization. This technology helps to creating Green IT. In this paper we get help from cloud computing infrastructure to ready collaborative platform in Internet with placing servers that play the role of virtual machines servers. With this, Green IT can implement in high level and the management of virtualization is increased. With this approach, the concept of Green IT developed faster than before and created a new platform to better implementation of virtualization.

Keywords: BDI, subgoal and chunking