July 2014, No.4
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Using Cellular Automata for Improving KNN Based Spam Filtering

Fatiha Barigou, Bouziane Beldjilali, Baghdad Atmani
Computer Science laboratory, University of Oran (Es-senia), Algeria

 
Abstract: As rapid growth over the Internet nowadays, electronic mail (e-mails) has become a popular communication tool. However, junk mail also, known as Spam has increasingly become a part of life for users as well as internet service providers. To address this problem, many solutions have been proposed in the last decade. Currently, content-based anti-spam filtering methods are an important issue; the spam filtering is considered as a special case of binary text categorization. Many machine learning techniques have been developed and applied to classify email as spam or non-spam. In this paper, we proposed an enhanced K-Nearest Neighbours (KNN) method called Cellular Automaton Combined with KNN (CA-KNN) for spam filtering. In our proposed method, a cellular automaton is used to identify which instances in training set should be selected to classify a new e-mail; CA-KNN selects the nearest neighbours not from the whole training set, but only from a reduced subset selected by a cellular automaton.


Keywords: Spam e-mail filtering, machine learning, k-nearest neighbours, cellular automata, instance selection.
 
 
  Received September 27, 2011; accepted August 18, 2012
 

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Using Artificial Immunity Network for Face Verification

Mehdi Sadeghi1, Keivan Maghooli1, and Mohammad Shahram Moein 2
1Department of Biomedical Engineering, Islamic Azad University, Iran
2Multimedia Research Groupe, Iran Telecommunication Research Center, Iran

 
Abstract: Biometrical systems are of the most interesting research subject matters in the last years. Face biometrics is noteworthy one because of its simple accessibility, easy usage and the ability of better acceptance by persons. The process of facial recognition includes these phases: pre-processing of images, extracting important properties of the face, and finally, the classification of these properties. There are many researches carried out in this area, each of which employed different methods for mentioned phases. According to the previous applications of the methods which have been done by artificial immune network, and to its relatively good results in optimization problems, machine learning, pattern recognition, data search, data clustering and so on, in this research facial verification through classification by aiNET (Artificial Immune Network) has been surveyed. In this article, databank Yale has been used and the statistical properties such as maximum, minimum, variance and energy of wavelet coefficients in different compositions have been examined. In order to validation, we have used the Cross Validation method that its best results in the case of using the Ten-Fold or Leave One Out method, were FAR=2.1 %, FRR=0.9%, and EER=1.8%.


Keywords: Biometrics, feature extraction, face verification, aiNET, artificial intelligence.

  Received December 3, 2011; accepted February 22, 2012
  

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Toward Secure Strong Designated Verifier Signature Scheme from Identity-Based System

Han-Yu Lin
Department of Computer Science and Engineering, National Taiwan Ocean University, Taiwan
 
 
Abstract: A Designated Verifier Signature (DVS) scheme has the property of signer ambiguity which can only convince a designated verifier of the signer’s identity. It is not allowed for the designated verifier to transfer the conviction to any third party. A Strong Designated Verifier Signature (SDVS) scheme further requires the designated verifier’s private key to perform the signature verification, so as to prevent anyone else from validating the signature. However, once the signer’s private key is compromised, the security requirement of signer ambiguity will not be fulfilled. In this paper, we propose an identity-based SDVS scheme which can resist the key-compromise attack. Compared with related works, the proposed scheme also has lower computational costs. Additionally, the security requirement of unforgeability against Existential Forgery on adaptive Chosen-Message Attacks (EF-CMA) is formally proved in the random oracle model.


Keywords: Identity-based, designated verifier, digital signature, key-compromise, bilinear pairing.

 
Received Jun 27, 2012; accepted February 19, 2013
 
  

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Analyzing the Point Multiplication Operation of Elliptic Curve Cryptosystem over Prime Field
for Parallel Processing

Arumugam Sakthivel1 and Raju Nedunchezhian2
1Centre for Cryptography and Network Security, Adithya Institute of Technology, India
2Department of Information Technology, Sri Ramakrishna Engineering College, India
 
 
Abstract: The Elliptic Curve Cryptosystem shortly called as (ECC) is one of the asymmetric key cryptosystems which provides a high security for wireless applications compared to other asymmetric key cryptosystem. The implementation of this algorithm over prime field Zp has a set of point operations which are point addition, point subtraction, point multiplication, point division, point inversion and point doubling. In these operations, the time complexity of the point multiplication is higher than any other time complexity of ECC point operations. So, it is necessary to find out an alternative implementation for point multiplication to take minimum amount of clock cycles, to reduce power consumption and to support the software scheduling for parallel processing on arithmetic operations during execution. Considering this, the proposed implementation is very useful to perform encryption or decryption on texts and also for analysing the strength of encryption or decryption computation.


Keywords: ECC, asymmetric key cryptosystem, time complexity, clock cycles, software scheduling, parallel processing.

 
Received January 31, 2012; accepted February 20, 2013
 
  

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ASCII Based GUI System for Arabic Scripted Languages: A Case of Urdu

Bacha Rehman, Zahid Halim, and Mustaq Ahmad
 Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute of Engineering Science and Technology, Pakistan
 
 
Abstract: From its advent, computer science is facing language-based problems; as English is considered being the most widely used communication language. Arabic Scripted Languages (ASLs) including Urdu have been considered less in this aspect. To rectify this problem, it is decided to build up the idea of interface designing for ASL. All the algorithms and methods are used to develop a Graphical User Interface (GUI) for Urdu language, which belongs to the family of ASLs.  This work presents a novel idea of designing an ASL interface for desktop application e.g., databases, applied for Urdu language. American Standard Code for Information Interchange (ASCII) codes been used for mapping the keystrokes to the Urdu character’s images using Phonetic keyboard styles. Four fonts are created. The Urdu characters contained in these fonts are mapped through ASCII codes. The main goal achieved is to develop algorithm for Urdu desktop controls which are the primary entity for its interface designing. Proposed algorithm facilitates the ordinary user to work in any ASL applications. Two implementations of the proposed method have been applied in this paper. This work presents a set of methods for new researchers to investigate these techniques for further improvement and for other ASLs.


Keywords: Urdu, GUI, dictionary, ASCII, interface design, Arabic scripted language.

 
Received March 12, 2012; accepted February 22, 2013

  

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Association Rule Mining and Load Balancing Strategy in Grid Systems

Sarra Senhadji, Salim Khiat, and Hafida Belbachir
Department of Computing, University of Sciences and Technology of Oran Mohamed Boudiaf, Algeria
 
 
Abstract: The parallel and distributed systems represent one of the important solutions proposed to ameliorate the performance of the sequential association rule mining algorithms. However, parallelization and distribution process is not trivial and still facing many problems of synchronization, communication and workload balancing. Our study is limited to the workload balancing problem. In this paper we propose a dynamic load balancing strategy of association rule mining algorithm under a grid environment. This strategy is built upon a hierarchical grid model with three levels Super Coordinator, Coordinator, processing nodes. The main objective of our strategy is to ameliorate the performances of the distributed association rule mining algorithm (APRIORI).


Keywords: Association rule mining, load balancing, grid computer, apriori.

 
Received May 21, 2012; accepted January 28, 2013

  

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Wiimote Squash: Comparing DTW and WFM Techniques for 3D Gesture Recognition

Muhammad Tahir1, Tahir Mustafa Madani2, Sheikh Ziauddin3, Muhammad Arshad Awan4, Rana Waqar Hussain5, and Saher Khalid6
1Faculty of Computing and Information Technology, King Abdulaziz University, Kingdom of Saudi Arabia
2Department of Information Technology, University Technology Petronas, Malaysia
3Department of Computer Science, COMSATS Institute of Information Technology, Pakistan
4Department of Computer Science, Yonsei University, South Korea
5Department of Software Engineering, Telenor, Pakistan
6President Cell, Ministry of Information and Broadcasting, Pakistan
 

Abstract: This paper compares Dynamic Time Warping (DTW) and Waveform Matching (WFM), the two gesture recognition techniques, applied on a specific Squash game application we have developed. Our application gets accelerometer readings by moving the Nintendo™ Wiimote in a 3D space. This application manipulates the Wiimote gestures in the form of signals. These signals are the effect of the movements, the positioning and the orientations of Squash racket (actually Wiimote) on the computer screen. We implemented five Squash shots (i.e. 3D uni-stroke gestures) in order to compare DTW and WFM. The results indicate that there is not a significant difference in the detection of gestures by both techniques (DTW and WFM) but there exists a strong degree of association between both techniques.

Keywords: Human computer interaction, 3D gestures, wiimote, squash game, dynamic time warping, waveform matching.g.

  Received February 20, 2012; accepted March 12, 2013
  

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AARI: Automatic Arabic Readability Index

Abdel-Karim Al-Tamimi1, Manar Jaradat2, Nuha Aljarrah2, and Sahar Ghanem1
1Computer Engineering Department, Yarmouk University, Jordan
2Software Engineering Department, Jordan University of Science and Technology, Jordan

 
Abstract: Text readability refers to the ability of the reader to understand and comprehend a given text. In this research, we present our approach to develop an automatic readability index for the Arabic language: Automatic Arabic Readability Index (AARI), using factor analysis. Our results are based on more than 1196 Arabic texts extracted from the Jordanian curriculum in the subjects of: Arabic language, Islamic religion, natural sciences, and national and social education for the elementary classes (first grade through tenth grade). We conduct a comparison study to support our model using cluster analysis and Support Vector Machines (SVM). In order to facilitate the usage of our Arabic readability index, we developed two applications to compute the Arabic text readability: a standalone application and an add-on for Microsoft Word text processer. Through our presented research results and developed tools, we aim to improve the overall readability of Arabic texts, especially those targeted towards the younger generations.

Keywords: Readability index, Arabic language, factor analysis, cluster analysis, SVM, text mining.

  Received March 17, 2013; accepted March 12, 2013
  

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An Ensemble Multi-Label Feature Selection Algorithm Based on Information Entropy

Shining Li, Zhenhai Zhang, JiaqiDuan
School of Computer Science, North Western Polytechnical University, China

 
Abstract: In multi-label classification, feature selection is able to remove redundant and irrelevant features, which makes the classifiers faster and improves the prediction performance of the classifiers. Currently most of feature selection algorithms in multi-label classification are dependent on the concrete classifier, which leads to high computation complexity. Hence this paper proposes an ensemble multi-label feature selection algorithm based on information entropy (EMFSIE), which is independent on any concrete classifiers. Its core idea consists of: 1). we employs the information gain to evaluate the correlation between the feature and the label set; 2). to filter out useful features more effectively, we calculate the information gain in an ensemble framework and filter out useful features according to the threshold value determined by the effective factor. We validate EMFSIE on four datasets from two domains using four different multi-label classifiers. The experimental resultsand their analysis show preliminarily that EMFSIE can not only remove more than 70% of original features, which makes the classifiers faster, but also keep the prediction performance of the classifiers as good as before, even enhance the prediction performance on three datasets underthe two-tailed paired t-tests at 0.05 significance level.


Keywords: Data mining, ensembles, feature extraction, feature selection, information entropy,multi-label classification.

  Received October 28, 2012; accepted March 13, 2013
  

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Inter-Communication Classification for Multi-View Face Recognition

Chouaib Moujahdi1, Sanaa Ghouzali1,2,  Abdul Wadood4,   Mounia Mikram1,3,  Mohammed Rziza1
1LRIT (associated unit with the CNRST, URAC 29), Mohammed V-Agdal University, Morocco
2Information Technology Department, King Saud University, Saudi Arabia
3The School of Information Sciences, Rabat, Morocco
4Computer Engineering Department, King Saud University, Saudi Arabia

 
Abstract: In this paper we present a new multi-view face recognition approach. Besides the recognition performance gain and the computation time reduction, our main objective is to deal with the variability of the face pose (multi-view) in the same class (identity). Several new methods were applied on face images to calculate our biometric templates. The Laplacian Smoothing Transform (LST) and Discriminant Analysis via Support Vectors (SVDA) have been used for the feature extraction and selection. For the classification, we have developed a new inter-communication technique using a model for the automatic pose estimation of the head in a face image. Experimental results conducted on UMIST database show that an average improvement for face recognition performance has been obtained in comparison with several multi-view face recognition techniques in the literature. Moreover, the system maintains a very acceptable running time and a high performance even in uncontrolled conditions.


Keywords: Face recognition, Multi-view, Inter-communication, LST, SVDA, Pose estimation.

  Received July 26, 2012; accepted March 19, 2013
  

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ANN and Rule Based Method for English to Arabic Machine Translation

Marwan Akeel and Ravi Mishra
Department of Computer Engineering, Indian Institute of Technology (BHU), India

 
Abstract: Machine Translation is the process by which computer software is used to translate a text from one natural language into another language with or without minimal human intervention. This definition involves accounting for the grammatical structure of each language and using rules and grammars to transfer the grammatical structure of the source language into the target language. This paper presents an English into Arabic Machine Translation (MT) system for translating simple well-structured English sentences into well-structured Arabic sentences using a rule-based approach and feed-forward back-propagation Artificial Neural Network (ANN). Our system is able to translate sentences having gerunds, infinitives, prepositions and prepositional objects, direct objects, indirect objects, etc. Neural network works as bilingual dictionary, which does not only store the meaning of English word in Arabic but it also stores linguistic features attached to the word. The performance is evaluated by different MT evaluation methods. The n-gram blue score achieved by the system is 0.7143, METEOR score achieved is 0.8734 and 0.8928 on F-measure.


Keywords: MT, neural network, back-propagation, rule based translation, English-Arabic machine translation system.

  Received April 21, 2012; accepted March 20, 2013
  

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Consistent Integration between Object Oriented and Coloured Petri Nets Models

Bassam Atieh Rajabi and Sai Peck Lee
Faculty of Computer Science and Information Technology, University of Malaya, Malaysia
 
 
Abstract: Unified Modeling Language (UML) is easier to understand and communicate using graphical notations, but lacks techniques for model validation and verification especially if these diagrams are updated. Formal approaches like Coloured Petri Nets (CPNs) are based on strong mathematical notations and proofs as basis for executable modeling languages. Transforming UML diagrams to executable models that are ready for analysis is significant, and providing an automated technique that can transform these diagrams to a mathematical model such as CPNs avoids the redundancy of writing specifications. The use of UML diagrams in modeling Object Oriented Diagrams (OODs) leads to a large number of interdependent diagrams. It is necessary to preserve the diagrams consistency since they are updated continuously. This research proposes a new structure for the mutual integration between OODs and CPNs modeling languages to support model changes, the proposed integration suggest a new structure (Object Oriented Coloured Petri Nets (OOCPN)) to include set of rules to check and maintain the consistency and integrity of the OOCPN model based on OODs relations.


Keywords: CPNs, Consistency Rules, OODs, OOCPN, UML.
 
Received September 13, 2012; accepted March 24, 2013

  

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Static and Dynamic Features for Writer Identification Based on Multi-fractals

Aymen Chaabouni1, Houcine Boubaker1, Monji Kherallah1, Haikal El-Abed2, and Adel Alimi1
1REsearch Groups on Intelligent Machines, University of Sfax, Tunisia
2Institute for Communications Technology, Technische Universität Braunschweig, Germany

 
Abstract: Writer identification still remains as a challenge area in the field of off-line handwriting recognition because only an image of the handwriting is available. Consequently, some information on the dynamic of writing, which is valuable for identification of writer, is unavailable in the off-line approaches, contrary to the on-line ones where temporal and spatial information about the writing are available. In this paper we present a new method for writer identification based on Multi-fractal features for both types of presented approaches. This method consists to extract the multi-fractal dimensions from the images of words and their on-line signals. In order to enhance the performance of our writer identification system, we have combined both on-line and off-line approaches. In this way, our work consists to take advantage of static and dynamic representations of handwriting, in order to identify the writer in realistic conditions. The tests are performed on the writing of 110 writers from the ADAB database. The obtained results show the effectiveness of the proposed writer identification method.


Keywords: Online and offline features, writer identification, multi-fractal.

  Received March 31, 2012; accepted March 27, 2013
  

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