Special Issue 2017, No. 4A
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Empirical Study of Analysts’ Practices in Packaged Software Implementation at Small Software Enterprises

Issam Jebreen and Ahmad Al-Qerem

Faculty of Information Technology, Zarqa University, Jordan

Abstract: This study investigates the practices of Requirements Engineering (RE) for packaged software implementation, as enacted by Small Packaged Software Vendors (SPSVs). Throughout the study, a focus on the actions carried out by SPSV analysts during RE is maintained, rather than a focus on the actions of client companies. The study confirms assertions in the literature, finding that most contemporary RE practices are unsuitable for SPSVs. The research investigated the means by which SPSVs can adopt, follow and adapt the best possible RE practices for Packaged Software Implementation (PSI), an explanation of the collection of qualitative and quantitative data during an case study in packaged software vendors. The research findings lead to introduced new methods of documentation, was not as concerned as general RE practice with looking for domain constraints or with collecting requirements and viewpoints from multiple sources, was more likely to involve live software demonstrations and screenshots to validate user needs, and was more likely to involve the compilation of a user manual. In PSI, prioritising requirements is not a basic practice; instead, analysts collect requirements in a circular process, with managers then directing analysts regarding which requirements to direct most attention toward. PSI was also found to place emphasis on assessing requirements risks and on considering the relationship between users’ needs and the inter-relationships between software functions, as analysts engaging in PSI do not wish to disrupt functions of their software when making modifications in response to client requests.

Keywords: Requirement engineering; packaged software implementation; ERP; analysts’ practices SMEs.

Received February 15, 2017; accepted May 10, 2017


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An Automatic Grading System Based on Dynamic Corpora

Djamal Bennouar

Department of Computer Science, Bouira University, Algeria

Abstract: Assessment is a key component of the teaching and learning process. In most Algerian Universities, assessing a student’s answer to an open ended question, even if it is a short answer question, is a difficult and time-consuming activity. In order to enhance the learning process quality and the global student evaluation process and to highly reduce the assessment time and difficulties, most Algerian Universities were provided with an e-learning environment as a result of a government initiative. Unfortunately, such environment seems to be rarely used in the student’s assessment process mainly due to the inefficiency of its Automatic Grading Subsystem (AGS) and the underlying corpora. A corpora used in the grading process contains a great number of miscellaneous answers, each one graded by more than two experts. Building efficient corpora for a course is actually a challenge. The underlying subjectivity in grading answers may have a serious impact in the corpus quality . The specific course context defined by a teacher and the time dependent grading strategy may make very difficult the construction of traditional course corpora. This paper presents a short answer AGS which has the capacity to dynamically build an up to date corpus related to each correct reference short answer. The automatically generated corpus is mainly based on a variety of indications specified by the teacher for each reference short answer. The early experiment of the presented AGS has shown its high efficiency for the automatic answers grading in some computer science courses.

Keywords: Architectures for educational technology system, country-specific developments, distance education and e-learning, evaluation methodologies, Computer Aided Assessment (CAA), AGS, Short answer, corpus, Answers predicting, text similarity.


Received February 27, 2017; accepted May 10, 2017

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Euclidean and Geodesic Distance between a Facial Feature Points in Two-Dimensional Face Recognition System

Rachid Ahdid1,2, Said Safi1, and Bouzid Manaut2

1Department of Mathematics and Informatics, Sultan Moulay Slimane University, Morocco

2Poladisciplinary Faculty, Sultan Moulay Slimane University, Morocco

Abstract: In this paper, we present two features extraction methods for two-dimensional face recognition. We have used the facial feature point detection to compute the Euclidean Distance (ED) between all pairs of these points for the first approach of Face Feature Points (ED-FFP) and Geodesic Distance (GD-FFP) in the second one. For a suitable comparison, we have employed three well-known classification techniques: Neural Networks (NN), k-Nearest Neighbor (KNN) and Support Vector Machines (SVM). To test the present methods and evaluate its performance, a series of experiments were performed on two-dimensional face image databases (ORL and Yale). Our results reveal that the extraction of image features is computationally more efficient using GD than ED.

Keywords: Face recognition, landmarks, ED, GD, neural networks, k-nearest neighbor and support vector machines.

Received February 22, 2017; accepted May 11, 2017 

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Lorentzian Model of Spatially Coherent Noise Field

in Narrowband Direction Finding

Youssef Khmou and Said Safi

 Department of Mathematics and informatics, Sultan Moulay Slimane University, Morocco

Abstract: When studying the radiation coming from far field sources using an array of sensors, besides the internal thermal noise, the received wave field is always perturbed by an external noise field, which can be temporally and spatially coherent to some degree, temporally incoherent and spatially coherent, spatially incoherent and temporally correlated or finally, the incoherence in both domains. Thus treating the received data needs to consider the nature of perturbing field in order to make accurate measurements such as powers of punctual sources, theirs locations and the types of waveforms which can be deterministic or random. In this paper, we study the type of temporally white and spatially coherent noise field; we propose a new spatial coherence function using Lorentz function. After briefly describing some existing models, we numerically study the effect of spatial coherence length on resolving the angular locations of closely radiating sources using spectral techniques which are divided into beam forming and subspace based methods, this study is made comparatively to temporally and spatially white noise with the same power as the proposed one in order to make a precise comparisons.

Keywords: Spatial coherence function, narrowband, direction of arrival, Lorentz function, coherence length.

Received February 10, 2017; accepted May 13, 2017

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A Comparative Analysis of Context-Management

Approaches for the Internet of Things

Farida Retima, Saber Benharzallah, Laid Kahloul, and Okba Kazar

Smart Computer Sciences Laboratory, Biskra University, Batna2 University, Algeria

Abstract: The Internet of Things (IoT) has gained much attention during the last decade. A novel aspect like context management is a fundamental requirement for development of such systems. In literature, there are different approaches enabling the context management for internet of IoT. This paper has both objectives: 1) firstly, it establishes a set of classification criteria: heterogeneity, mobility, the influence of the physical world, scalability, security, privacy, quality of context, autonomous deployment of entities, characterization multi scales, interoperability, context acquisition, context modeling, context reasoning, context distribution, design method, and tools of implementation; 2) secondly, it refers to the previous criteria to make a comparative study between the well known existing approaches.

Keywords: Context management, internet of things, context-awareness, context manager, middleware.

Received February 18, 2017; accepted May 10, 2017

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eLEM: A Novel e-Learner Experience Model

Rawad Hammad, Mohammed Odeh and Zaheer Khan
Faculty of Environment and Technology, University of the West of England, UK

Abstract: Many e-learning artefacts have been developed and promoted based on their ability to enhance learning and e-learner experience. However, there is a lack of precise definition of what the e-learner experience implies and associated models to inform this experience. This paper introduces a novel e-Learner Experience Model (eLEM) along with its roots in: (i) e-learning domain research, and (ii) user experience/usability. It also proposes a definition for the e-learner experience model based on the particularities of e-learning. eLEM has been derived based on a state of the art literature review and consists of a number of constructs along with measures of their effectiveness in evaluating the e-learner experience in an e-learning environment. eLEM has been comprehensively evaluated using a set of sufficient and representative case studies. It has also demonstrated modelling the e-learner’s experience in various contexts and identified four key challenges for further research. Finally, the eLEM has been integrated with the Hybrid e-Learning Framework that is Process-based, Semantically-enriched and Service-oriented enabled (HeLPS) e-learning framework and contributed to validating its process-centric models.

Keywords: e-learner experience, e-learning evaluation, learner modelling, user experience, usability, technology-enhanced learning/e-learning.

Received February 7, 2017; accepted May 10, 2017

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Exploiting Multilingual Wikipedia to Improve Arabic Named Entity Resources

Mariam Biltawi, Arafat Awajan, Sara Tedmori, and Akram Al-Kouz

King Hussein Faculty of Computing Sciences, Princess Sumaya University for Technology, Jordan

Abstract: This paper focuses on the creation of Arabic named entity gazetteers, by exploiting Wikipedia and using the Naïve Bayes classifier to classify the named entities into the three main categories: person, location, and organization. The process of building the gazetteer starts with automatically creating the datasets. The dataset for the training is constructed using only Arabic text, whereas, the testing dataset is derived from an English text using the Stanford name entity recognizer. A Wikipedia title existence check of these English name entities is then performed. Next, if the named entity exists as a Wikipedia page title, a check for Arabic parallel pages is conducted. Finally, the Naïve Bayes classifier is applied to verify or assign new name entity tag to the Arabic name entity. Due to the lack of available resources, the proposed system is evaluated manually by calculating accuracy, recall, and precision. Results show an accuracy of 53%.

Keywords: Arabic name entity resources; naïve bayes classifier; wikipedia.

Received February 7, 2017; accepted May 10, 2017

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Segmentation of Text/Graphic from Handwritten

Mathematical Documents Using Gabor Filter

Yassine Chajri1, Belaid Bouikhalene2, and Abdelkrim Maarir1

1Department of Informatics, Sultan Moulay Slimane University, Morocco

2Department of Mathematics and Computers, Sultan Moulay Slimane University, Morocco

Abstract: Most of handwritten mathematical documents contain graphics in addition to mathematical text. Thus, these documents must be segmented into homogenous areas to facilitate their digitization. Text and graphic segmentation from these documents aims at segmenting the document into two blocks: the first contains the texts and the second includes the graphical objects. In this paper, we focus our interest on document segmentation based on the texture and precisely the frequency methods. These methods are ideal to characterize the texture and allow detecting the frequencies and orientations characteristics. Firstly, we present the main steps of our system (pre-processing, features extraction (using Gabor filter), post-processing and text/graphic segmentation). Secondly, we discuss and interpret the results obtained by our system.

Keywords: Handwrite, mathematical document, segmentation, Gabor filter.

Received February 12, 2017; accepted May 10, 2017

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New Approach for 3D Object Forms Detection Using a New Algorithm of SUSAN Descriptor

Ilhame Agnaou1 and Belaid Bouikhalene2

1Information Processing and Telecommunication Team, Sultan Moulay Slimane University, Morocco

2Department of Mathematics and Computer, Sultan Moulay Slimane University, Morocco

Abstract: This paper was made ​​in the context of object recognition, and in particular, in the detection of 3D objects and their free forms by local descriptors of interest points to identify them. However, it remains to solve several problems in this area that is related to a large amount of information and invariant to scale and angle of view. In this context, our purpose is to make the recognition of a 3D object from the detection of their interest points and extract characteristics of the detection of each object to facilitate his research in a database. For this reason, we will propose a new robust detector to noise that includes criteria for extracting interest points of 3D objects by specifying their free forms, this detector, is based on SUSAN detector using differential measures for comparing it with others.

Keywords: Recognition, detection, 3D objects, detector, descriptor, interest points

Received February 24, 2017; accepted May 10, 2017

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Vehicular Ad-hoc Network Application for Urban

Traffic Management based on Markov Chains


Ahmed Adart1, Hicham Mouncif2, and Mohamed Naimi1

1Faculty of Sciences and Technologies, Sultan Moulay Slimane University, Morocco

 2Polydisciplinary Faculty, Sultan Moulay Slimane University, Morocco

Abstract: Urban traffic management problems have taken an important place in most of transportation research fields, hence the emergence of vehicular ad-hoc network (VANET) as an essential part of the intelligent transportation system (ITS), that intervenes to improve and facilitate traffic management also control as far as improve global driving experience in the future. Indeed, the concept of smart city or city of future becomes a new paradigm for urban planning and management, it considered as a complex system made up of services, citizens and resources. On the other hand, ITS concept is implemented to deal with some problems as though traffic congestion, energy consumption and property damage and human losses caused by transport accidents. In this paper we propose an approach for urban traffic management in smart cities based on markov chains implementing all vanet’s technology units to optimize traffic flow simultaneously with real time monitoring of vehicle in urban area from its starting point to the destination.

Keywords: Vanet, smart city, intelligent transportation system, markov decision process, markov chains.

Received March 1, 2017; accepted May 10, 20

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Enhancing Energy Efficiency of Reactive Routing Protocol in Mobile Ad-Hoc Network with Prediction on Energy Consumption

Mohamed Er-Rouidi1, Houda Moudni1, Hassan Faouzi1, Hicham Mouncif1, and Abdelkrim Merbouha2

1Department of Computer Science, Sultan Moulay Slimane University, Morocco

2Department of Mathematics, Sultan Moulay Slimane University, Morocco

Abstract: Mobile Ad-Hoc Network (MANET) is a decentralized, self-organizing and a dynamic network. These futures make MANET becomes more and more used in many domains. However, this kind of network still suffers from various types of restrictions. Among these restrictions, and the biggest one is the energy consumption. The classical routing protocols proposed by Internet Engineering Task Force (IETF), in its establishment of the routes, searches for the shortest path in terms of the number of hops between the source and destination, while they don't take in consideration the energy level or the lifetime of the intermediate nodes. In this work, we propose a solution called Enhanced Energy-AODV (EE-AODV), which is an enhancement of the Ad-hoc On-demand Distance Vector (AODV) routing protocol. In our proposed solution, we tend to obtain a sufficient result in terms of the stability a lifetime of the different path in the network, by adding the energy consumption among the selection criteria of the AODV routing protocol. The different simulation results show that EE-AODV outperforms EQ-AODV (Energy and QoS supported AODV) and the basic AODV by reducing significantly the energy dissipation, also enhances certain parameters that are affected by the energy issue like Packet Delivery Ratio (PDR) and Normalized Routing Load (NRL).

Keywords: Ad-hoc, MANET, energy, AODV, routing protocol.

Received March 1, 2017; accepted May 10, 2017

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Arabic Handwritten Script Recognition System

Based on HOG and Gabor Features

 Mohamed Elleuch1, Ansar Hani 2, and Monji Kherallah3

1National School of Computer Science, University of Manouba, Tunisia

2Faculty of Economics and Management of Sfax, University of Sfax, Tunisia

3Faculty of Sciences, University of Sfax, Tunisia

Abstract: Considered as among the most thriving applications in the pattern recognition field, handwriting recognition, despite being quite matured, it still raises so many research questions which are a challenge for the Arabic Handwritten Script. In this paper, we investigate Support Vector Machines (SVM) for Arabic Handwritten Script recognition. The proposed method takes the handcrafted feature as input and proceeds with a supervised learning algorithm. As designed feature, Histogram of Oriented Gradients (HOG) is used to extract feature vectors from textual images. The Multi-class SVM with an RBF kernel was chosen and tested on Arabic Handwritten Database named IFN/ENIT. Performances of the feature extraction method are compared with Gabor filter, showing the effectiveness of the HOG descriptor. We present simulation results so that we will be able to prove that the good functioning on the suggested system based-SVM classifier.

Keywords: SVM, arabic handwritten recognition, handcraft feature, IFN/ENIT, HOG.

Received February 10, 2017; accepted May 10, 2017

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A Hybrid Range-free Localization Algorithm for ZigBee Wireless Sensor Networks

Tareq Alhmiedat1 and Amer Abu Salem2

1Department of Information Technology, University of Tabuk, Saudi Arabia

2Department of Computer Science, Zarqa University, Jordan

Abstract: Localization is one of the key aspects of wireless sensor networks (WSNs) that has attracted significant research interest. A wide variety of proposed approaches regarding the research topic has recently emerged; however, the majority of the existing approaches are limited by at least one of the following restrictions: inaccuracy, high cost, fast energy depletion, inappropriate indoor performance, or the requirement of an additional positioning hardware. In this paper, we present the research and development of a hybrid range-free WSN localization system, using the hop-count and received signal strength (RSS) methods. The proposed system is reliable and efficient indoors in terms of localization accuracy, cost and power consumption. Reference and target nodes have been designed and implemented, while real experiments have been carried out to assess the proposed system’s efficiency.

Keywords: Localization, Tracking, ZigBee, Wireless Sensor Networks (WSNs).

Received February 13, 2017; accepted May 10, 2017


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Improved Hierarchical Classifiers for Multi-Way Sentiment Analysis

Aya Nuseir1, Mahmoud Al-Ayyoub1, Mohammed Al-Kabi2, Ghasan Kanaan3, and Riyad Al-Shalabi3

1Jordan University of Science and Technology, Jordan

2Information Technology Department, Al-Buraimi University College, Oman

3Amman Arab University, Jordan

Abstract: Sentiment Analysis (SA) is field in computational linguistics concerned with determining the sentiment conveyed in a piece of text towards certain entities (such as people, organizations, products, services, events, etc.) using NLP tools. The considered sentiments can be as simple as positive vs. negative. A more fine-grained approach known as Multi-Way Sentiment Analysis (MWSA) is based on ranking systems, such as the 5-star ranking system. In such systems, rankings close to each other can be confusing; thus, some researchers have suggested that using Hierarchical Classifiers (HCs) can yield better results compared with traditional Flat Classifier (FCs). Unlike FCs, which try to address the entire classification problem at once, HCs employ some kind of tree structures where the nodes are simple “core” classifiers customized to address a subset of the classification problem. This study aims to explore extensively the use of HCs to address MWSA by studying six different hierarchies. We compare these hierarchies using four well-known core classifiers (SVM, Decision Tree, Naive Bayes, and KNN) using many measures such as Precision, Recall, F1, Accuracy and Mean Square Error (MSE). The experiments are conducted on the Large Arabic Book Reviews (LABR) dataset, which consists of 63K book reviews in Arabic. The results show that using some of the proposed HCs yield significant improvements in accuracy. Specifically, while the best Accuracy and MSE for FC are 45.77% and 1.61, respective-ly, the best accuracy and MSE for an HC are 72.64% and 0.53, respectively. Also, the results show that, in general, KNN(k-nearest neighbors) benefitted the most from using hierarchical classification.

Keywords: Sentiment Analysis; Arabic Text Processing; Hierarchical Classifiers, Multi-Way Sentiment Analysis.

Received March 1, 2017; accepted May 10, 2017

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