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Synthesizing Global Negative Association Rules in Multi-Database Mining

Thirunavukkarasu Ramkumar1, Shanmugasundaram Hariharan2, and Shanmugam Selvamuthukumaran1

 1,3Department of Computer Applications, A.V.C. College of Engineering, India

2Department of Computer Science Engineering, TRP Engineering College, India

 
Abstract: Association rule mining has been widely adopted by data mining community for discovering relationship among item-sets that co-occur together frequently. Besides positive association rules, negative association rule mining, which find out negation relationships of frequent item-sets are also important. The importance of negative association rule mining is accounted in customer-driven domains such as market basket analysis for identifying products that conflict with each other. In multi-database mining context, mining negation relation among item-sets and synthesizing global negative association rules from multiple data sources located in different places are having importance in arriving decisions both at strategic and branch levels. This paper made an attempt for synthesizing global negative association rules which are voted by most of the participating data sources while mining multiple data sources. Experimental data are employed to test the theoretical analysis of the proposal using UCI machine learning repository data set. The space and time complexity analysis presented in the paper show the efficiency of the proposed approach.

Keywords: Negation relation, multi-databases, local pattern analysis, rule synthesizing.

Received March 15, 2012; accepted April 15, 2013; published online March 13, 2014

 
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XML Access Control: Mapping XACML Policies to Relational Database Tables

Abd El-Aziz Ahmed Abd El-Aziz1,2 Arputharaj Kannan1

1Department of Information Science and Technology, Anna University, India

2Department of Computer and Information Sciences, ISSR, Cairo University, Egypt

 
Abstract: Although eXtensible Access Control Markup Language (XACML) is recognized as a precise and a complete policy description language, the structure of the current XACML policy is complex. Hence, users need to understand XACML well and write down the securing policy all by hand, which make it difficult to master and use. On the other hand, RDBMS is easy and simple to use by all users and allows hiding the difficulties of XACML by storing XACML policies and rules in relational tables. Hence, it will be easy for users to use and understand the XACML policies and rules. In this paper, we propose a new mapping technique to map XACML policies and rules into relational rules and store them in tables to ease the access control of the XML documents. The implementation of the proposed technique demonstrates a significant access decision time.

 

Keywords: XML security, XACML, relational database, authorization tables, access control..

Received June 21, 2012; accepted April 25, 2013; published online March 13, 2014

  
 
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Parallel Method for Computing Elliptic Curve Scalar Multiplication Based on MOF

Mohammad Anagreh, Azman Samsudin and Mohd Adib Omar
School of Computer Sciences, Universiti Sains Malaysia, Malaysia

 
Abstract: This paper focuses on optimizing the Elliptic Curve Cryptography (ECC) scalar multiplication by optimizing one of the ECC calculations, which is based on the Mutual Opposite Form (MOF) algorithm. A new algorithm is introduced that combines the add-subtract scalar multiplication algorithm with the MOF representation to speed-up the ECC scalar multiplication. The implementation of the algorithm produces an efficient parallel method that improves the computation time. The proposed method is an efficient ECC scalar multiplication that achieves a 90% speed-up compared to the existing methods.
 
Keywords: ECC, MOF, circular buffer, parallel computing

Received March 6, 2012; accepted April 27, 2013; published online March 13, 2014

  

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An Innovative Instructional Method for Teaching Object-Oriented Modelling

Khalid Al-Tahat
Department of Information Technology and Computing, Arab Open University, Jordan

 
Abstract: Object-oriented modelling is considered to be complicated to teach and learn in introductory courses in computer science and software engineering. Animated program visualisation can be significantly used to support teaching object-oriented modelling for beginners. However, there is a lack in instructional methods that support such approach. This paper bridges the gap by introducing a new instructional method for teaching object-oriented modelling using the Unified Modelling Language (UML) with the help of a visualised 3D programming environment; Alice. The proposed instructional method is the first of its kind to introduce teaching software modelling with the support of animated visualisation tool. The basic approach is to extract the UML components from a 3D Alice world, then map these components to build the UML model that represents that world. This approach shows that animated program visualisation has the potential to help in teaching other aspects of software development process besides programming including modelling.

Keywords: Teaching object-oriented modelling, UML, object-oriented paradigm, animated program visualisation, alice.

 

Received May 8, 2012; accepted April 16, 2013; published online March 13, 2014
  

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Solving the n-Queens Problem using a Tuned Hybr-id Imperialist Competitive Algorithm

Ellips Masehian1, Hossein Akbaripour1, and Nasrin Mohabbati-Kalejahi2
1Industrial Engineering Department, Tarbiat Modares University, Iran
2Faculty of Industrial Engineering, Amirkabir University of Technology, Iran

 
Abstract:The n-queens problem is a classical combinatorial optimization problem which has been proved to be NP-hard. The goal is to place n non-attacking queens on an n×n chessboard. In this paper, the Imperialist Competitive Algorithm (ICA), which is a recent evolutionary metaheuristic method, has been applied for solving the n-queens problem. As another variation, the ICA was combined with a local search method, resulting the Hybrid ICA (HICA). Since, the parameters of heuristic and metaheuristic algorithms have a great influence on the performance of the search, parameter tuning is used for handling the problems in an efficient manner. Hence, a TOPSIS-based parameters tuning is proposed, which not only considers the number of Fitness Function Evaluations (FFE), but also aims to minimize the running time of the presented heuristics. In order to, investigate the performance of the suggested approach, a computational analysis on the n-queens problem was performed. Extensive experimental results showed that the proposed HICA outperformed the basic ICA in terms of average runtimes and average number of FFE. The developed algorithms were also compared to the Cooperative PSO (CPSO) algorithm, which is currently the best algorithm in the literature for finding the first valid solution to the n-queens problem, and the results showed that the HICA dominates the CPSO by evaluating the fitness function fewer times.

 

Keywords: n-queens problem, ICA, local search, parameter tuning, TOPSIS method.

 

Received May 20, 2012; accepted May 13, 2013; published online March 13, 2014  

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On Static Scheduling of Tasks in Real Time Multiprocessor Systems: An Improved GA-Based Approach

Mohammad Ababneh, Salama Hassan, Sulieman Bani-Ahmad
Prince Abdullah Bin Ghazi of Information Technology, Al-Balqa Applied University, Jordan

 
Abstract: Task execution Deadline Time (DL) in real-time systems is a critical constraint. Every task should have a Maximum Computational Time (MCT) that is needed before reaching a given DL time. Scheduling jobs in real-time systems is thus a nondeterministic polynomial NP problem. Three algorithms can be found in literature to solve these problems in a multi processor environment; are the Earliest Deadline First (EDF), Genetic Algorithms (GA), Priority Genetic Algorithms (PGA). In this research, the PGA is introduced and experimentally evaluated against already proposed algorithms in literature. It works just like the GA algorithm introduced in Abraham et al. [1]. However, we do not only consider the DL in sorting the tasks in the first population, but rather, we also include the MCT of individuals in the population to define the priority level of these tasks. We have found that the proposed algorithm has a better average total system utilization, total system tasks visibility compared with Genetic (G) and EDF algorithms. We have also found that this improvement becomes more and more effective with the increase of problem size.
 
Keywords: Task scheduling, multiprocessor systems, GA.

 

            Received December 10, 2012; accepted June 2, 2013; published online March 13, 2014

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Optimum Threshold Parameter Estimation of Wavelet Coefficients using Fisher Discriminant Analysis for Speckle Noise Reduction

Mohammad Motiur Rahman1, Mithun Kumar PK1, and Mohammad Shorif Uddin2
1Department of Computer Science and Engineering, Mawlana Bhashani Science and Technology University, Bangladesh
2Department of Computer Science and Engineering, Jahangirnagar University, Bangladesh

 
Abstract: Optimizing threshold value of wavelet coefficient is an important task in speckle noise reduction in the wavelet domain. Without proper selection of threshold value image information may be lost, which is unwanted. In this paper we proposed optimum threshold parameter using Fisher Discriminant Analysis (FDA) for determining the optimum threshold value of wavelet coefficient for the best speckle noise reduction. It also preserves edges without destroying image information. The method is compared with the several other classical thresholding methods on variety of images and the experimental results confirm significant improvement over existing methods.

Keywords: FDA, optimum threshold, speckle noise, ultrasound image, wavelet.

Received July 20, 2012; accepted June 4, 2013; published online March 13, 2014

  

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Multi Block based Image Watermarking in Wavelet Domain using Genetic Programming

Almas Abbasi1,2, Woo Chaw Seng1,  Imran Shafiq Ahmad3
 1Faculty of Computer Science and Information Technology, University of Malaya, Malaysia
2Department of Computer Science, COMSATS Institute of Information Technology Islamabad, Pakistan
3School of Computer Science, University of Windsor, Canada

 
Abstract: The increased utilization of internet in sharing and distribution of digital data makes it is very difficult to maintain copyright and ownership of data. Digital watermarking offers a method for authentication and copyright protection. We propose a blind, still image, Genetic Programming (GP) based robust watermark scheme for copyright protection. In this scheme, pseudorandom sequence of real number is used as watermark. It is embedded into perceptually significant blocks of vertical and horizontal sub-band in wavelet domain to achieve robustness. GP is used to structure the watermark for improved imperceptibility by considering the Human Visual System (HVS) characteristics such as luminance sensitivity and self and neighbourhood contrast masking. We also present a GP function which determines the optimal watermark strength for selected coefficients irrespective of the block size. Watermark detection is performed using correlation. Our experiments show that in proposed scheme the watermark resists image processing attack, noise attack, geometric attack and cascading attack. We compare our proposed technique with other two genetic perceptual model based techniques. Comparison results show that our multiblock based technique is approximately 5%, and 23% more robust, then the other two compared techniques.

Keywords: Robust watermark, GP, wavelet domain, digital watermarking, HVS.


Received January 23, 2013; accepted June 4, 2013; published online March 13, 2014

  

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A Real Time Adaptive Resource Allocation Scheme for OFDM Systems using GRBF-Neural Networks and Fuzzy Rule Base System

Atta Rahman1, Ijaz Mansoor Qureshi2, Aqdas Naveed Malik1, Muhammad Tahir Naseem1
1School of Engineering and Applied Sciences, ISRA University, Pakistan
2Department of Electrical Engineering, Air University, Pakistan

 
Abstract: Adaptive Resource Allocation is a prominent and necessary feature of almost all future communication systems. The transmission parameters like power, code rate and modulation scheme are adapted according to the varying channel conditions so that throughput of the OFDM system may be maximized while satisfying certain constraints like Bit Error Rate (BET) and total power at the same time. For real time systems, it is required that the adaptive process should be fast enough to synchronize with Channel State Information (CSI) and Quality of Service (QoS) demand that change rapidly. So in this paper, we have a real time system in which once CSI and QoS is fed in as input, it gives us optimal Modulation Code Pairs (MCPs) and power vectors for different subcarriers. Using a Fuzzy Rule Base System (FRBS) we obtain MCP by giving CSI and QoS and by using Differential Evolution (DE) the power vector is obtained. This becomes an example. A Gaussian Radial Basis Function Neural Network (GRBF-NN) is trained in offline mode using sufficient number of such examples. After training, given QoS and CSI as input GRBF-NN gives Optimum Power Vector (OPV) and FRBS gives optimum MCP immediately. Proposed scheme is compared with various other schemes of same domain and supremacy of the proposed scheme is shown by the simulations.

Keywords: DE, OFDM, FRBS, GRBF-NN, adaptive modulation and coding, MCP.
 

Received January 13, 2013; accepted July 24, 2013; published online March 13, 2014

  

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A Comparative Assessment of the Performance of Ensemble Learning in Customer Churn Prediction

Hossein Abbasimehr, Mostafa Setak, Mohammad Jafar Tarokh
Department of Industrial Engineering, K.N. Toosi University of Tech, Iran

 
Abstract: Customer churn is a main concern of most firms in all industries. The aim of customer churn prediction is detecting customers with high tendency to leave a company. Although, many modeling techniques have been used in the field of churn prediction, performance of ensemble methods has not been thoroughly investigated yet. Therefore, in this paper, we perform a comparative assessment of the performance of four popular ensemble methods, i.e., Bagging, Boosting, Stacking, and Voting based on four known base learners, i.e., C4.5 Decision Tree (DT), Artificial Neural Network (ANN), Support Vector Machine (SVM) and Reduced Incremental Pruning to Produce Error Reduction (RIPPER). Furthermore, we have investigated the effectiveness of two different sampling techniques, i.e., oversampling as a representative of basic sampling techniques and Synthetic Minority Over-sampling Technique (SMOTE) as a representative of advanced sampling techniques. Experimental results show that SMOTE doesn’t increase predictive performance. In addition, the results show that the application of ensemble learning has brought a significant improvement for individual base learners in terms of three performance indicators i.e., AUC, sensitivity, and specificity. Particularly, in our experiments, Boosting resulted in the best result among all other methods. Among the four ensemble methods Boosting RIPPER and Boosting C4.5 are the two best methods. These results indicate that ensemble methods can be a best candidate for churn prediction tasks.

 Keywords: Churn prediction, data mining, classification, ensemble learning.

 Received May 19, 2012; accepted January 6, 2013; published online March 13, 2014

  

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Developing an Appliance Real Time Control in Heterogeneous Operating Systems

Dhuha Basheer and Seddeeq Hasan Albana
Computer Sciences Department, University of Mosul, Iraq

 
Abstract: This paper presents a proposed system for controlling the real time application (Video conferencing) in heterogeneous operating systems dealing with transferring streams of audio-video data with real time constraints among heterogeneous operating systems. This system provides integration between multimedia technologies as well as real time technologies via using suitable network protocols. The principle has included reading and analyzing the video data in linux either from a normal video file or from a direct broadcast via using camera and a microphone. The data are scheduled by means of real time technique and transferred to other computers operated with windows for displaying video frames and audio sample. This work achieves lip synchronization rate in video displaying by 95%.

 

Keywords: Multimedia, network, heterogeneous network, real time kernel, operating system.

 Received April 5, 2012; accepted April 10, 2013; published online March 13, 2014

 

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A Deadlock-Free Dynamic Reconfiguration Protocol for Distributed Routing on Interconnection Networks

Mohiadeen Abdul Kadhar  
National College of Engineering, Department of Information Technology, India

 
Abstract: In interconnection networks, reconfiguration protocol is necessary to remap and reconnect the network paths, so that the network remains connected. However, the reconfiguration process brings the deadlock problem and prevention of deadlock is a tedious task in this situation. In existing works, very little work have considered deadlock problem and further, they paid no attention to reduce packet loss rate. In this paper, we propose a Token-Based (TB) robust deadlock-free dynamic reconfiguration protocol. When a device observes topology changes or detects faulty nodes, it triggers the reconfiguration process and it becomes the Reconfiguration Controller (RC). Initially, HELLO message is transmitted by the RC to all devices for which they respond with a network status message. The RC constructs the new routing function based on the received network status messages. To synchronize the old and new routing functions, the RC distributes Reconfiguration Token (RT) in an ordered way. First, it distributes to the devices that surrounds the failed device and then to other devices. Every device holds the packet until it gets packet according to new routing function and then starts the transmission. By simulation, we show efficacy of our reconfiguration protocol. The network evaluation parameter like throughput, latency (delay) and pocket loss are measured in the high and low load scenarios in NS2 network simulator. We compare the results with existing protocol Overlapping Static Reconfiguration (OSR). Based on the simulation results we have proved that the proposed token based reconfiguration protocol is produces better efficiency in all aspects.

 

Keywords: Dynamic reconfiguration, RC, RT, deadlock, interconnection networks, distributed routing.
 

Received August 7, 2012; accepted February 21, 2013; published online March 13, 2014

  

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A Provably Secure Public Key Encryption Scheme Based on Isogeny Star

Weiwei Han
School of Mathematics and Statistics, Guangdong University of Finance and Economics, China

 
Abstract: Public Key Encryption (PKE) scheme based on isogeny star has been proposed to be against the attack of the quantum computer for several years. But, there is no report about provable security PKE scheme based on isogeny star. In this paper, we propose a PKE scheme based on isogeny star and prove the security of the scheme in the random oracle.
 

 Keywords: PKE, isogeny, quantum computer, elliptic curve.

Received December 2, 2011; accepted September 2, 2013; published online March 13, 2014

  

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