IJACSA Volume 8 Issue 10 - thesai.org

IJACSA Volume 8 Issue 10

Copyright Statement: This is an open access publication licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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Paper 1: The Use of Gamification in Higher Education: An Empirical Study

Abstract: The use of gamification in higher education has increased considerably over the past decades. An empirical study was conducted in Hungary with two groups of students to investigate their behaviour while interacting with Kahoot! The results were analyzed based on the technology acceptance model. They indicate that the positive attitude, good experience and ease of availability contributed to improve student performance which strengthened the intention to use the application. Besides these, the perceived utility was positively influenced by the ease of use as consequence.

Author 1: István Varannai

Author 2: Peter Sasvari

Author 3: Anna Urbanovics

Keywords: Gamification; education; Hungary; technology acceptance model; university student

Paper 2: A Secure Mobile Learning Framework based on Cloud

Abstract: With the rising need for highly advanced and digital learning coupled with the growing penetration of smartphones has contributed to the growth of Mobile Learning. According to Ericsson’s forecast, 80% of the world’s population (6.4 billion people) will be Smartphone users by 2021. But the existing Mobile Learning Frameworks has some limitations that need to be addressed for mass adaptation, limitations include device compatibility and security. In this paper we propose a Secure Mobile Learning Framework (SMLF) based on TPM in the cloud. SMLF is supported by three layers Communication Module (CM) which helps in ensuring end to end security. In addition to this we propose a procedure for personalizing mobile learning applications of the student and instructors. We also propose a secure mobile learning protocol in SMLF framework. Proposed SMLF ensures mutual authentication of all the stakeholders, privacy of the message, integrity of the message, and anonymity of the student from the instructor and non-repudiation and is free from known attacks. Our proposed SMLF framework is successfully verified using BAN logic.

Author 1: Mohammad Al Shehri

Keywords: Trusted Platform Module (TPM); Communication Module (CM), anonymity; non-repudiation; personalized; BAN logic

Paper 3: Enhancing Gray Scale Images for Face Detection under Unstable Lighting Condition

Abstract: Facial expression plays a vital role in no verbal communication between human beings. The brain, in a quarter of second, can determine the state of mind and the behaviour of a person using different traits in a stable lighting environment. This is not the case in real applications such as online learning or driver monitoring system where lighting is not stable. It is therefore important to study and improve performance of some image enhancement techniques on face detection under varying lighting conditions in the spatial domain. The study is based on gray scale images. Nine gray scale standards based on colour space separating luminance to other colour components are used. The enhancement techniques compared are: the Global Histogram Equalisation (GHE), the Adaptive Histogram Equalisation (AHE) and Contrast Limited Adaptive Histogram Equalisation (CLAHE). Trials on the Labelled Face in the Wild (LFW) dataset using the Viola Jones Haar like features showed the CLAHE to outperform the GHE and AHE in face detection though the results appeared poor under low lighting condition. This motivated the need to stabilize lighting before applying Histogram Equalization techniques. The novelty in this research is that we have been able to apply the Gamma transform as a lighting stabiliser on the gray scale standard before enhancement. Comparing performance after lighting stabilisation showed AHE to be most appropriate for face detection, as it produced a detection rate of 99.31% and a relative high false positive rate (23.89 %).

Author 1: Mathias A. ONABID

Author 2: DJIMELI TSAMENE Charly

Keywords: Enhancement; AdaBoost; Haar like features; luma; peak signal to noise ratio (PSNR); Adaptive Histogram Equalisation (AHE); Contrast Limited Adaptive Histogram Equalisation (CLAHE); Global Histogram Equalisation (GHE); Gamma transform

Paper 4: Critical Success Factors Plays a Vital Role in ERP Implementation in Developing Countries: An Exploratory Study in Pakistan

Abstract: The capabilities of an Enterprise Resource Planning (ERP) system to integrate all the business functions needed in a single system with a shared database efficiently and effectively has persuaded organizations to adopt them. In enterprise environment, successful ERP implementation has played a vital role for organizational efficiency. In this respect critical success factors (CSFs) have been identified essential for the successful ERP implementation. The purpose of this paper is to identify and analyze CSFs impacting ERP implementation success in Pakistani Small and Medium Sized Enterprises (SMEs). This paper will help Pakistani SME’s on how to obtain better results from ERP implementation focusing on CSFs relevant to them.

Author 1: Naeem Ahmed

Author 2: A. A. Shaikh

Author 3: Muhammad Sarim

Keywords: Information System (IS); Enterprise Resource Planning (ERP) System; ERP implementation; CSFs; Pakistani Small and Medium Sized Enterprises (SMEs); Statistical Package for Social Sciences (SPSS)

Paper 5: A Survey on Smartphones Security: Software Vulnerabilities, Malware, and Attacks

Abstract: Nowadays, the usage of smartphones and their applications have become rapidly popular in people’s daily life. Over the last decade, availability of mobile money services such as mobile-payment systems and app markets have significantly increased due to the different forms of apps and connectivity provided by mobile devices, such as 3G, 4G, GPRS, and Wi-Fi, etc. In the same trend, the number of vulnerabilities targeting these services and communication networks has raised as well. Therefore, smartphones have become ideal target devices for malicious programmers. With increasing the number of vulnerabilities and attacks, there has been a corresponding ascent of the security countermeasures presented by the researchers. Due to these reasons, security of the payment systems is one of the most important issues in mobile payment systems. In this survey, we aim to provide a comprehensive and structured overview of the research on security solutions for smartphone devices. This survey reviews the state of the art on security solutions, threats, and vulnerabilities during the period of 2011-2017, by focusing on software attacks, such those to smartphone applications. We outline some countermeasures aimed at protecting smartphones against these groups of attacks, based on the detection rules, data collections and operating systems, especially focusing on open source applications. With this categorization, we want to provide an easy understanding for users and researchers to improve their knowledge about the security and privacy of smartphones.

Author 1: Milad Taleby Ahvanooey

Author 2: Qianmu Li

Author 3: Mahdi Rabbani

Author 4: Ahmed Raza Rajput

Keywords: Mobile security; malware; adware; malicious attacks

Paper 6: Factors Associated to Online Shopping at the BoP Community in Rural Bangladesh

Abstract: Online shopping is getting popular even in the rural areas of developing countries. However, few research has been conducted to identify the factors associated to online shopping by the poor villagers. Whereas people living at the bottom of the economic pyramid (BoP) has an aggregate purchase power which is a huge market and online shopping has the potentiality in reducing BoP penalty by removing unnecessary middlemen from the supply chain. In this research, we have conducted a field survey on 600 households in the western part of rural Bangladesh to find out current status of online shopping use by the BoP people and the demographic and behavioral factors associated with online shopping. Chi-square test of association and multi-variate logistic regression test have been performed to analyze data. Result shows that cell phone use, computer use, social media use, and mobile money transfer use have significant relationship in online shopping use at the BoP community.

Author 1: Kazi Mozaher Hossein

Author 2: Fumihiko Yokota

Author 3: Mariko Nishikitani

Author 4: Rafiqul Islam

Keywords: Online shopping; BoP; demographic and behavioral factors; Bangladesh

Paper 7: Action Recognition using Key-Frame Features of Depth Sequence and ELM

Abstract: Recently, the rapid development of inexpensive RGB-D sensor, like Microsoft Kinect, provides adequate information for human action recognition. In this paper, a recognition algorithm is presented in which feature representation is generated by concatenating spatial features from human contour of key frames and temporal features from time difference information of a sequence. Then, an improved multi-hidden layers extreme learning machine is introduced as classifier. At last, we test our scheme on the public UTD-MHAD dataset from recognition accuracy and time consumption.

Author 1: Suolan Liu

Author 2: Hongyuan Wang

Keywords: Action recognition; features; key frame; temporal; extreme learning machine

Paper 8: Development of a Mobile GIS Property Mapping Application using Mobile Cloud Computing

Abstract: This study presents the development of a mobile GIS Property mapping application for use by local authorities in developing countries. Attempts to develop property mapping applications especially in developing countries have mostly used GIS desktop productivity software tools that required the digitization of property maps by highly skilled GIS experts. In addition, these applications lacked real time capture of attribute, spatial and image data of properties. A survey was conducted in the Kafue local authority to gather systems requirements for the mobile application. After design and modeling, the developed application was trialed in the field and 10 properties were mapped successfully. The software tools used in this study included Android Studio, Leaflet mapping library, Apache2 web server, PostgresSQL with PostGIS Extensions and OpenstreetMaps and MapBox mobile cloud computing mapping services. The hardware tools used included a laptop computer and a mobile phone running android operating system. The study showed that mobile property mapping applications can be developed by tapping into the computing resources provided by mobile cloud computing. The benefits of this model include real time complete property data capture and the use of non GIS experts in mapping projects.

Author 1: Victor Neene

Author 2: Monde Kabemba

Keywords: Leaflet; MapBox; mobile cloud computing; OpenstreetMaps; property mapping

Paper 9: The Informative Vector Selection in Active Learning using Divisive Analysis

Abstract: Traditional supervised machine learning techniques require training on large volumes of data to acquire efficiency and accuracy. As opposed to traditional systems Active Learning systems minimizes the size of training data significantly because the selection of the data is done based on a strong mathematical model. This helps in achieving the same accuracy levels of the results as baseline techniques but with a considerably small training dataset. In this paper, the active learning approach has been implemented with a modification into the traditional system of active learning with version space algorithm. The version space concept is replaced with the divisive analysis (DIANA) algorithm and the core idea is to pre-cluster the instances before distributing them into training and testing data. The results obtained by our system have justified our reasoning that pre-clustering instead of the traditional version space algorithm can bring a good impact on the accuracy of the overall system’s classification. Two types of data have been tested, the binary class and multi-class. The proposed system worked well on the multi-class but in case of binary, the version space algorithm results were more accurate.

Author 1: Zareen Sharf

Author 2: Maryam Razzak

Keywords: Active learning; machine learning; pre-clustering; semi-supervised learning

Paper 10: Smart Tourism Architectural Model (Kingdom of Saudi Arabia: A Case Study)

Abstract: The researchers have proposed and implemented a general application architecture model that complies with the demands of the Saudi tourism sector to be used by tourists on their mobile devices. The design architecture aims to improve tourism sector opportunities, facilitate tourists’ guidance in the holy and historical places, fill in the shortage of having multilingual tourists’ guides, cut off cost expenses and build up capacities. It can support KSA to be a tourist attraction in the region. The research project employs the usage of the Quick Response (QR) codes and the Information Communication Technology (ICT) which are capable of converting the smart phones into a tourist guide device. This new system can be considered as a Smart Cicerone (S-Cicerone). The research project has a flexible design that allows tourists, guests and administrators to interact easily with the system in order to use its services and perform a regular system update and management. The system design is based on component-based architecture including Tourist Layer services, Smart Tourism System Layer services and the Administration Layer services. The components are divided into further services and smartly integrated to formulate the main application functions. This project is meant to be implemented in the Kingdom of Saudi Arabia as a pilot project and is also valid for implantation in any other countries.

Author 1: Ahmad H. Al-Omari

Author 2: AbdulSamad Al-Marghirani

Keywords: Smart tourism; smart systems; QR-Code; Saudi tourism; Saudi Vision 2030; S-Cicerone

Paper 11: Role of Expert Systems in Identification and Overcoming of Dengue Fever

Abstract: This paper presents a systematic literature review on expert systems which are used for identification and overcoming of Dengue fever. Dengue is a viral disease produced by Flavivirus. The expansion of Dengue fever is because of uncontrolled population and urbanization without suitable water administration. With the quick technological enhancement, we can identify and overcome Dengue fever by using expert systems. These expert systems require knowledge of the relevant problem and techniques to infer the result in order to make decisions. The literature review provides a comparison of techniques, methodologies, limitations and advantages of different Dengue expert systems. These expert systems facilitate both doctors and patients in Dengue detection. Multiple risk factors can be eliminated by the detection of Dengue fever through expert systems at early stages of Dengue. Furthermore, we find that enhancement of knowledge base improves accuracy of expert systems.

Author 1: Nadeem Ahmed

Author 2: Muhammad Shoaib

Author 3: Adeed Ishaq

Author 4: Abdul Wahab

Keywords: Expert system; rule based; Dengue; health care; disease; fever

Paper 12: Evaluating Urdu to Arabic Machine Translation Tools

Abstract: Machine translation is an active research domain in fields of artificial intelligence. The relevant literature presents a number of machine translation approaches for the translation of different languages. Urdu is the national language of Pakistan while Arabic is a major language in almost 20 different countries of the world comprising almost 450 million people. To the best of our knowledge, there is no published research work presenting any method on machine translation from Urdu to Arabic, however, some online machine translation systems like Google , Bing and Babylon provide Urdu to Arabic machine translation facility. In this paper, we compare the performance of online machine translation systems. The input in Urdu language is translated by the systems and the output in Arabic is compared with the ground truth data of Arabic reference sentences. The comparative analysis evaluates the systems by three performance evaluation measures: BLEU (BiLingual Evaluation Understudy), METEOR (Metric for Evaluation of Translation with Explicit ORdering) and NIST (National Institute of Standard and Technology) with the help of a standard corpus. The results show that Google translator is far better than Bing and Babylon translators. It outperforms, on the average, Babylon by 28.55% and Bing by 15.74%.

Author 1: Maheen Akhter Ayesha

Author 2: Sahar Noor

Author 3: Muhammad Ramzan

Author 4: Hikmat Ullah Khan

Keywords: Natural language processing; machine translation; Urdu-Arabic Corpus; Google; Bing; Babylon; translator; BiLingual Evaluation Understudy (BLEU); National Institute of Standard and Technology (NIST); Metric for Evaluation of Translation with Explicit ORder (METEOR)

Paper 13: A Feature Fusion Approach for Hand Tools Classification

Abstract: The most important functions in objects classification and recognition system are to segment the objects from the input image, extract common features from the objects, and classify these objects as a member of one of the considered object classes. In this paper, we present a new approach for feature-based objects classification. The main idea of the new approach is the fusion of two different feature vectors that are calculated using Fourier descriptors and moment invariants. The fused moment-Fourier feature vector is invariant to image scaling, rotation, and translation. The fused feature vector for a reference object is used for training feed-forward neural network classifier. Classification of some hand tools is used to evaluate the performance of the proposed classification approach. The results show an appreciable increase in the classification accuracy rate with a considerable decrease in the classifier learning time.

Author 1: Mostafa Ibrahim

Author 2: Alaa Ahmed

Keywords: Feature fusion; neural network classifier; invariant features; objects classification

Paper 14: A Survey of Schema Matching Research using Database Schemas and Instances

Abstract: Schema matching is considered as one of the essential phases of data integration in database systems. The main aim of the schema matching process is to identify the correlation between schema which helps later in the data integration process. The main issue concern of schema matching is how to support the merging decision by providing the correspondence between attributes through syntactic and semantic heterogeneous in data sources. There have been a lot of attempts in the literature toward utilizing database instances to detect the correspondence between attributes during schema matching process. Many approaches based on instances have been proposed aiming at improving the accuracy of the matching process. This paper set out a classification of schema matching research in database system exploiting database schema and instances. We survey and analyze the schema matching techniques applied in the literature by highlighting the strengths and the weaknesses of each technique. A deliberate discussion has been reported highlights on challenges and the current research trends of schema matching in database. We conclude this paper with some future work directions that help researchers to explore and investigate current issues and challenges related to schema matching in contemporary databases.

Author 1: Ali A. Alwan

Author 2: Azlin Nordin

Author 3: Mogahed Alzeber

Author 4: Abedallah Zaid Abualkishik

Keywords: Data integration; instance-based schema matching; schema matching; semantic matching; syntactic matching

Paper 15: Lung-Deep: A Computerized Tool for Detection of Lung Nodule Patterns using Deep Learning Algorithms Detection of Lung Nodules Patterns

Abstract: The detection of lung-related disease for radiologists is a tedious and time-consuming task. For this reason, automatic computer-aided diagnosis (CADs) systems were developed by using digital CT scan images of lungs. The detection of lung nodule patterns is an important step for the automatic development of CAD system. Currently, the patterns of lung nodule are detected through domain-expert knowledge of image processing and accuracy is also not up-to-the-mark. Therefore, a computerized CADs tool is presented in this paper to identify six different patterns of lung nodules based on multi-layer deep learning ( known as Lung-Deep) algorithms compare to state-of-the-art systems without using the technical image processing methods. A multilayer combination of the convolutional neural network (CNN), recurrent neural networks (RNNs) and softmax linear classifiers are integrated to develop the Lung-Deep without doing any pre- or post-processing steps. The Lung-Deep system is tested with manually draw radiologist contours on the 1200 images including 3250 nodules by using statistical measures. On this dataset, the higher sensitivity (SE) of 88%, specificity (SP) of 80% and 0.98 of the area under the receiver operating curve (AUC) of 0.98 are obtained compared to other systems. Hence, this proposed lung-deep system is outperformed by integrating different layers of deep learning algorithms to detect six patterns of nodules.

Author 1: Qaisar Abbas

Keywords: Computer-aided diagnosis; lung nodules; patterns detection; deep learning; convolutional neural network; recurrent neural network

Paper 16: Breast Cancer Detection with Mammogram Segmentation: A Qualitative Study

Abstract: Mammography is specialized medical imaging for scanning the breasts. A mammography exam (A Mammogram) helps in the early detection and diagnosis of breast cancer. Mammogram image segmentation is useful in detecting the breast cancer regions, hence, better diagnosis. In this paper, we applied enhanced double thresholding-based approach for Mammograms’ image segmentation. Moreover, we added the borders of the final segmented image as a contour to the original image helping physicians to easily detect the breast cancer into different Mammograms. The result is enhanced wise effect onto breast cancer qualitative detection into Mammograms, helping physicians for better diagnosis. Generalization for our study is possible for not only x-ray based Mammograms, but also for all biomedical images, as an enhanced segmentation way for better visualization, detection, and feature extraction, thus better diagnosis. Moreover, this manual thresholding method has the advantage of not only reducing processing time but also the processing storage area.

Author 1: Samir M. Badawy

Author 2: Alaa A. Hefnawy

Author 3: Hassan E. Zidan

Author 4: Mohammed T. GadAllah

Keywords: Image processing; double thresholding segmentation; breast cancer detection into mammograms

Paper 17: Cloud Computing: Empirical Studies in Higher Education A Literature Review

Abstract: The advent of cloud computing (CC) in recent years has attracted substantial interest from various institutions, especially higher education institutions, which wish to consider the advantages of its features. Many universities have migrated from traditional forms of teaching to electronic learning services, and they rely upon information and communication technology services. The usage of CC in educational environments provides many benefits, such as low-cost services for academics and students. The expanded use of CC comes with significant adoption challenges. Understanding the position of higher education institutions with respect to CC adoption is an essential research area. This paper investigated the current state of CC adoption in the higher education sector in order to enrich the research in this area of interest. Existing limitations and knowledge gaps in current empirical studies are identified. Moreover, suggested areas for further researches will be highlighted for the benefit of other researchers who are interesting in this topic. These researches encourage institutions of education especially in higher education to adopted cloud computing technology.

Author 1: Abusfian Elgelany

Author 2: Weam Gaoud Alghabban

Keywords: Cloud computing; education system; e-learning; information and communication technology (ICT)

Paper 18: Defense Mechanisms against Machine Learning Modeling Attacks on Strong Physical Unclonable Functions for IOT Authentication: A Review

Abstract: Security component in IoT system are very crucial because the devices within the IoT system are exposed to numerous malicious attacks. Typical security components in IoT system performs authentication, authorization, message and content integrity check. Regarding authentication, it is normally performed using classical authentication scheme using crypto module. However, the utilization of the crypto module in IoT authentication is not feasible because of the distributed nature of the IoT system which complicates the message cipher and decipher process. Thus, the Physical Unclonable Function (PUF) is suggested to replace crypto module for IoT authentication because it only utilizes responses from set of challenges instead of cryptographic keys to authenticate devices. PUF can generate large number of challenge-response pairs (CRPs) which is good for authentication because the unpredictability is high. However, with the emergence of machine learning modeling, the CRPs now can be predicted through machine learning algorithms. Various defense mechanisms were proposed to counter machine learning modeling attacks (ML-MA). Although they were experimentally proven to be able to increase resiliency against ML-MA, they caused the generated responses to be instable and incurred high area overhead. Thus, there is a need to design the best defense mechanism which is not only resistant to ML-MA but also produces reliable responses and reduces area overhead. This paper presents an analysis on defense mechanisms against ML-MA on strong PUFs for IoT authentication.

Author 1: Nur Qamarina Mohd Noor

Author 2: Salwani Mohd Daud

Author 3: Noor Azurati Ahmad

Author 4: Nurazean Maarop

Keywords: IoT authentication; machine learning; modeling attack; Physical Unclonable Function; low area defense mechanism

Paper 19: The Ethical and Social Issues of Information Technology: A Case Study

Abstract: The present study is conducted among 283 students from University of Zabol to identify the harm and ethical and social issues in the field of information technology and to classify the immoral practices that students are doing in this field. First various important issues in the field of IT in the social and ethical areas are discussed. Then the cases considered as the most commonly used immoral activities, are selected for evaluation, and the participants ranked these activities according to the method presented in the questionnaire. These activities are examined and analyzed descriptively by SPSS program, reliability of the questionnaire is measured by Cronbach’s alpha coefficient, Bartlett Test of Sphericity and KMO index and the validity of the results is verified using T-test and the results are ranked based on the first performance that happens frequently and the last performance that happens rarely or never. Finally, a set of strategies are presented for preventing ethical abuse in the field of Information Technology so that the challenges are reduced.

Author 1: Ehsan Sargolzaei

Author 2: Mohammad Nikbakht

Keywords: Information technology; ethical and social issues; unethical practices; students

Paper 20: Smart Mobile Healthcare System based on WBSN and 5G

Abstract: The intelligent use of resources enabled by Internet of Things has raised the expectations of the technical as well as the consumer community. However there are many challenges in designing an IoT healthcare system, like security, authentication and exchanging data. The IoT healthcare system, is transforming everyday physical objects, medical devices that surround us into an embedded smart healthcare system. Public healthcare has been paid an increasing attention given the human population and medical expenses exponential growth. It is well known that an effective monitoring healthcare system can detect abnormalities of health conditions in time and make diagnoses according to sensing (WBSN) data. This paper propose a general architecture of a smart mobile IoT healthcare system for monitoring patients risk using a smart phone and 5G. The design of multi-protocol unit for universal connectivity. Web and mobile applications developed to meet the needs of patients, doctors, laboratories analysis and hospitals services. The system advises and alerts in real time the doctors/medical assistants about the changing of vital parameters of the patients, such as body temperature, pulse and Oxygen in Blood etc… and also about important changes on environmental parameters, in order to take preventive measures, save lives in critical care and emergency situations.

Author 1: Farah Nasri

Author 2: Abdellatif Mtibaa

Keywords: IoT; multi-protocol; smart mobile healthcare system; WBSN; android; 5G

Paper 21: A Genetic Algorithm for Optimizing TCM Encoder

Abstract: This article describes a genetic algorithm for the optimization of the Trellis Coded Modulation (TCM) schemes with a view to achieve a higher performance in the multipath fading channel. The use of genetic algorithms is motivated by the fact that they are capable of performing global searches to retrieve an approximate solution to an optimization problem and if the solution is unknown to provide one within a reasonable time lapse. The TCM schemes are indeed optimized by the Rouane and Costello algorithm but the latter has as major disadvantage high requirements in both computation time and memory storage. This is further exacerbated by an increase in the encoder rate, the number of memory piles and the depth of the trellis. We describe a genetic algorithm which is especially well suited to combinatorial optimization, in particular to the optimization of NP-complete problems for which the computation time grows with the complexity of the problem, in a non-polynomial way. Furthermore this opens up the possibility of using the method for the generation of codes for channel characteristics for which no optimization codes are yet known. Simulation results are presented, that show the evolutionary programming algorithm on several generations of populations which only exhibit a medium probability of exchanging genetic information.

Author 1: Rekkal Kahina

Author 2: Abdesselam Bassou

Keywords: Trellis Coded Modulation; free distance; genetic algorithm

Paper 22: Development and Implementation of the Balanced Scorecard for a Higher Educational Institution using Business Intelligence Tools

Abstract: The objective of designing a strategy for an institution is to create more value and achieve its vision, with clear and coherent strategies, identifying the conditions in which they are currently, the sector in which they work and the different types of competences that generate, as well as the market in general where they perform, to create this type of conditions requires the availability of strategic information to verify the current conditions, to define the strategic line to follow according to internal and external factors, and in this way decide which methods to use to implement the development of a strategy in the organization. This research project was developed in an institution of higher education where the strategic processes were analyzed from different perspectives i.e. financial, customers, internal processes, and training and learning using business intelligence tools, such as Excel Power BI, Power Pivot, Power Query and a relational database for data repository; which helped having agile and effective information for the creation of the balanced scorecard, involving all levels of the organization and academic units; operating key performance indicators (KPI’s), for operational and strategic decisions. The results were obtained in form of boards of indicators designed to be visualized in the final view of the software constructed with previously described software tools.

Author 1: Alicia Valdez

Author 2: Griselda Cortes

Author 3: Sergio Castaneda

Author 4: Laura Vazquez

Author 5: Jose Medina

Author 6: Gerardo Haces

Keywords: Business intelligence; balanced scorecard; key performance indicators; BI Tools

Paper 23: Developing a New Hybrid Cipher Algorithm using DNA and RC4

Abstract: This paper proposes a new hybrid security algorithm called RC4-DNA-Alg. It combines the symmetric stream cipher RC4 algorithm with DNA-indexing algorithm to provide secured data hiding with high complexity inside steganography framework. While RC4 represent one of the widely used algorithms in network security protocols such as Secure Sockets Layer (SSL), a DNA cryptography considered as a modern branch of cryptography that combines the traditional cryptographic techniques with the power of the genetic material The performance evaluation of the proposed algorithm is measured based on three parameters (conditional entropy, randomness tests and encryption time). The result shows outperformance in security and distorted in hybrid Cipher compared to the native RC4.

Author 1: Rami k. Ahmed

Author 2: Imad J. Mohammed

Keywords: Rivest Cipher 4 (RC4); Secure Sockets Layer (SSL); Deoxyribonucleic acid (DNA); Rivest Cipher 4- Deoxyribonucleic acid-Algorithm (RC4-DNA-Alg)

Paper 24: Evaluating Cancer Treatment Alternatives using Fuzzy PROMETHEE Method

Abstract: The aim of this study is to apply the principle of multi-criteria decision making theories on various types of cancer treatment techniques. Cancer is an abnormal cell that divides in an uncontrolled manner, it is a growth (tumor) that starts when alterations in genes make one cell to grow and multiply rapidly. Eventually, these cells may metastasize to other tissues. The primary factors that influence the comprehensive treatment plan of cancer include, but not limited to genetic factors, patient general health condition, explicit characteristic of cancer, and even purpose of the treatment. Other factors which are also essential include treatment duration, cost of treatment, comfortability, side effects and percentage of survival rate. The latter factors play an important role in the course of treatment and are therefore needed in order to evaluate the several treatment procedures. The outcome of the decision-making theories on these treatment procedures will help the concerned parties such as the patients, oncologists, and the hospital management. The most common cancer treatment techniques were evaluated and compared based on certain criteria using Fuzzy PROMETHEE decision-making theory.

Author 1: Dilber Uzun Ozsahin

Author 2: Berna Uzun

Author 3: Musa Sani Musa

Author 4: Abdulkader Helwan

Author 5: Chidi Nwekwo Wilsona

Author 6: Fatih Veysel Nurçina

Author 7: Niyazi Sentürka

Author 8: Ilker Ozsahin

Keywords: Cancer treatment alternatives; multi criteria decision making; Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE); fuzzy PROMETHEE

Paper 25: Cloud Computing Environment and Security Challenges: A Review

Abstract: Cloud computing exhibits a remarkable potential to offer cost-effective and more flexible services on-demand to the customers over the network. It dynamically increases the capabilities of the organization without training new people, investment in new infrastructure or licensing new software. Cloud computing has grown dramatically in the last few years due to the scalability of resources and appear as a fast-growing segment of the IT industry. The dynamic and scalable nature of cloud computing creates security challenges in their management by examining policy failure or malicious activity. In this paper, we examine the detailed design of cloud computing architecture in which deployment models, service models, cloud components, and cloud security are explored. Furthermore, this study identifies the security challenges in cloud computing during the transfer of data into the cloud and provides a viable solution to address the potential threats. The task of Trusted Third Party (TTP) is introducing that ensure the sufficient security characteristics in the cloud computing. The security solution using the cryptography is specifically as the Public Key Infrastructure (PKI) that operates with Single-Sign-On (SSO) and Lightweight Directory Access Protocol (LDAP) which ensure the integrity, confidentiality, availability, and authenticity involved in communications and data.

Author 1: Muhammad Faheem Mushtaq

Author 2: Urooj Akram

Author 3: Irfan Khan

Author 4: Sundas Naqeeb Khan

Author 5: Asim Shahzad

Author 6: Arif Ullah

Keywords: Cloud computing; deployment models; service models; cloud security; trusted third party; cryptography

Paper 26: Verifying Weak Probabilistic Noninterference

Abstract: Weak probabilistic noninterference is a security property for enforcing confidentiality in multi-threaded programs. It aims to guarantee secure flow of information in the program and ensure that sensitive information does not leak to attackers. In this paper, the problem of verifying weak probabilistic noninterference by leveraging formal methods, in particular algorithmic verification, is discussed. Behavior of multi-threaded programs is modeled using probabilistic Kripke structures and formalize weak probabilistic noninterference in terms of these structures. Then, a verification algorithm is proposed to check weak probabilistic noninterference. The algorithm uses an abstraction technique to compute quotient space of the program with respect to an equivalence relation called weak probabilistic bisimulation and does a simple check to decide whether the security property is satisfied or not. The progress made is demonstrated by a real-world case study. It is expected that the proposed approach constitutes a significant step towards more widely applicable secure information flow analysis.

Author 1: Ali A. Noroozi

Author 2: Jaber Karimpour

Author 3: Ayaz Isazadeh

Author 4: Shahriar Lotfi

Keywords: Confidentiality; secure information flow; noninterference; algorithmic verification; bisimulation

Paper 27: Balanced Active and Reactive Control Applied to a Grid Connected Five Level Inverter

Abstract: This paper presents a balanced active and reactive power control, using a Phase Locked Loop for synchronization, and applied to a grid connected Five Level Inverter. The energy source of the system can be a photovoltaic generator or a wind turbine. We size the passive elements of the system and explain the value of the system architecture using a Five Level Inverter when compared to a classical grid connected system. We also compare the balanced active and reactive power control to an unbalanced active and reactive power control. The simulation results obtained by using Matlab Simulink and Simpowersystems are presented and discussed in this paper.

Author 1: Chabakata MAHAMAT

Author 2: Mickaël PETIT

Author 3: François COSTA

Author 4: Rym MAROUANI

Keywords: Balanced control; grid connected system; multilevel inverter

Paper 28: Redundancy Level Impact of the Mean Time to Failure on Wireless Sensor Network

Abstract: Recently, wireless sensor networks (WSNs) have gained a great attention due to their ability to monitor various environments, such as temperature, pressure sound, etc. They are constructed from a large number of sensor nodes with computation and communication abilities. Most probably, sensors are deployed in an uncontrolled environment and hence their failures are inevitable all times of work. Faulty sensor nodes may cause incorrect sensing data, wrong data computation or even incorrect communication. Achieving a reliable wireless sensor networks is a most needed goal to ensure quality of service whether at deployment time or during normal operation. While Nodes redundancy is considered as an effective solution to overcome nodes failures, it may negatively affect the WSN lifetime. Redundancy may lead to more energy drains of the whole system. In this paper, the impact of redundancy level on the Mean Time to Failure (MTTF) of a clustered based wireless Sensor Networks (WSNs) is investigated. An expression that can be used to determine the most suitable redundancy level that maximizes the network MTTF is derived and evaluated.

Author 1: Alaa E. S. Ahmed

Author 2: Mostafa E. A. Ibrahim

Keywords: Wireless sensor network; reliability; clustering; fault tolerant; Mean Time to Failure (MTTF)

Paper 29: Bi-Objective Task Scheduling in Cloud Computing using Chaotic Bat Algorithm

Abstract: Cloud computing is a technology for providing services over the Internet. It gives approach to renting IT infrastructures on a short-term pay- per-usage basis. One of the service provider’s goals is to use the resources efficiently and gain maximum profit. Cloud processes a huge amount of data, so tasks scheduling is a vital role in the cloud computing. The purpose of this paper is to propose a method based on chaos theory and bat algorithm for task scheduling in Cloud computing. Task scheduling is a core and challenging issue in cloud computing. The nature of the scheduling issue is as an NP-Hard problem and because of the success of heuristic algorithms in optimization and NP-Hard problems, the authors use a newly inspired bat algorithm and chaos theory to scheduling the tasks in the cloud. In this method, bat or candidate solutions are represented by a one-dimensional array. The fitness function is calculated based on makespan and energy consumption. The results show that the proposed method can schedule the received tasks in proper time than other compared heuristic algorithms, also the proposed method has better performance in term of makespan and energy consumption than compared methods.

Author 1: Fereshteh Ershad Farkar

Author 2: Ali Asghar Pourhaji Kazem

Keywords: Cloud computing; scheduling; chaos theory; bat algorithm

Paper 30: Tagging Urdu Sentences from English POS Taggers

Abstract: Being a global language, English has attracted a majority of researchers and academia to work on several Natural Language Processing (NLP) applications. The rest of the languages are not focused as much as English. Part-of-speech (POS) Tagging is a necessary component for several NLP applications. An accurate POS Tagger for a particular language is not easy to construct due to the diversity of that language. The global language English, POS Taggers are more focused and widely used by the researchers and academia for NLP processing. In this paper, an idea of reusing English POS Taggers for tagging non-English sentences is proposed. On exemplary basis, Urdu sentences are processed to tagged from 11 famous English POS Taggers. State-of-the-art English POS Taggers were explored from the literature, however, 11 famous POS Taggers were being input to Urdu sentences for tagging. A famous Google translator is used to translate the sentences across the languages. Data from twitter.com is extracted for evaluation perspective. Confusion matrix with kappa statistic is used to measure the accuracy of actual Vs predicted tagging. The two best English POS Taggers which tagged Urdu sentences were Stanford POS Tagger and MBSP POS Tagger with an accuracy of 96.4% and 95.7%, respectively. The system can be generalized for multi-lingual sentence tagging.

Author 1: Adnan Naseem

Author 2: Muazzama Anwar

Author 3: Salman Ahmed

Author 4: Qadeem Akhtar Satti

Author 5: Faizan Rasul Hashmi

Author 6: Tahira Malik

Keywords: Standford part-of-speech (POS) tagger; Google translator; Urdu POS tagging; kappa statistic

Paper 31: Energy-Aware Virtual Network Embedding Approach for Distributed Cloud

Abstract: Network virtualization has caught the attention of many researchers in recent years. It facilitates the process of creating several virtual networks over a single physical network. Despite this advantage, however, network virtualization suffers from the problem of mapping virtual links and nodes to physical network in most efficient way. This problem is called virtual network embedding (“VNE”). Many researches have been proposed in an attempt to solve this problem, which have many optimization aspects, such as improving embedding strategies in a way that preserves energy, reducing embedding cost and increasing embedding revenue. Moreover, some researchers have extended their algorithms to be more compatible with the distributed clouds instead of a single infrastructure provider (“ISP”). This paper proposes energy aware particle swarm optimization algorithm for distributed clouds. This algorithm aims to partition each virtual network request (“VNR”) to sub-graphs, using the Heavy Clique Matching technique (“HCM”) to generate a coarsened graph. Each coarsened node in the coarsened graph is assigned to a suitable data center (“DC”). Inside each DC, a modified particle swarm optimization algorithm is initiated to find the near optimal solution for the VNE problem. The proposed algorithm was tested and evaluated against existing algorithms using extensive simulations, which shows that the proposed algorithm outperforms other algorithms.

Author 1: Amal S. Alzahrani

Author 2: Ashraf A. Shahin

Keywords: Distributed virtual network embedding; energy consumption; particle swarm optimization; network virtualization; virtual network embedding; virtual network request; virtual network partitioning

Paper 32: A Tri-Level Industry-Focused Learning Approach for Software Engineering Management

Abstract: Most engineering classes in higher education rely heavily on the traditional lecture format, despite the fact that a number of investigations have shown that lectures, even when given by good lecturers, have limited success in helping students make sense of the engineering practices they are learning. Recently, the Software Engineering Body of Knowledge (SWEBOK) highlighted the importance of professional practice in producing high quality engineering programs. The integration of industry links in teaching pedagogy is essential. In this paper, the authors introduce a new industry-oriented tri-level teaching approach in order to offer students the opportunity to be involved in industry projects and gain important work experience during the academic period. To prioritize industry hands-on activities for students and shape the traditional classroom toward an industry environment, three entities are involved in this approach: industry guest speakers, teachers, and students. Traditionally, guest lecturing is centered on the speaker, who delivers a presentation and follows with a short question and answer session. Students are often passive learners in this process. A blended learning approach was therefore integrated between all entities to allow more flexible learning opportunities, wherein students participated in each step of guest lecturing, including preparation, questions, and reflection. A software project management case study was introduced to measure students’ performance and satisfaction.

Author 1: Anis Zarrad

Author 2: Yassin Daadaa

Keywords: Component; software project management; education; student rubric assessment; student learning outcomes; industry activities; guest lecturing approach

Paper 33: Design and Implementation of a Communication System and Device Aimed at the Inclusion of People with Oral Communication Disabilities

Abstract: Disability is part of human condition; it discriminates people who have this complication. The present work was carried out due to this and an experience in our research center. A prototype was designed and build that allows eye signals to be sent to a mobile device, where through a computer system, it was possible to generate an appropriate dialogue mechanism to respond to this challenge. The results allow us to open up an area of opportunity for a contribution in the inclusion of people with disabilities.

Author 1: Máximo López Sánchez

Author 2: Juan Gabriel González Serna

Author 3: José Luis Molina Salgado

Author 4: Melisa Hernández Salinas

Keywords: Communication; system; disabilities; device; oral

Paper 34: FFD Variants for Virtual Machine Placement in Cloud Computing Data Centers

Abstract: Virtualization technology is used to efficiently utilize the resources of a Cloud datacenter by running multiple virtual machines (VMs) on a single physical machine (PM) as if each VM is a standalone PM. Efficient placement/consolidation of VMs into PMs can reduce number of active PMs which consequently reduces resource wastage and power consumption. Therefore, VM placement algorithms need to be optimized to reduce the number of PMs required for VM Placements. In this paper, two heuristic based Vector Bin Packing algorithms called FFDmean and FFDmedian are proposed for VM placement. These algorithms use First Fit Decreasing (FFD) technique. FFD preprocesses VMs by sorting all VMs in descending order of their sizes. Since a VM is multidimensional therefore, it is difficult to decide on its size. For this, FFDmean and FFDmedian use measures of central tendency, i.e. mean and median as heuristics, respectively, in order to estimate the size of a VM. The goal of these algorithms is to utilize the PM resources efficiently so that the number of required PMs for accommodation of all VMs can be reduced. CloudSim toolkit is used to carry out the cloud simulation and experiments. Algorithms are compared over three metrics, i.e. hosts used, power consumption and resource utilization efficiency. The results reveal that FFDmean and FFDmedian remarkably outperformed two existing algorithms called Dot-Product and L2 in all three metrics when PM resources were limited.

Author 1: Aneeba Khalil Soomro

Author 2: Mohammad Arshad Shaikh

Author 3: Hameedullah Kazi

Keywords: Cloud computing; virtual machine placement; virtualization; first fit decreasing; first fit decreasing (FFD)

Paper 35: Implementation of a Hierarchical Hybrid Intrusion Detection Mechanism in Wireless Sensors Network

Abstract: During the last years, Wireless Sensor Networks (WSNs) have attracted considerable attention within the scientific community. The applications based on Wireless Sensor Networks, whose areas include, agriculture, military, hospitality management, etc. are growing swiftly. Yet, they are vulnerable to various security threats, like Denial Of Service (DOS) attacks. Such issues can affect and absolutely degrade the performances and cause a dysfunction of the network and its components. However, key management, authentication and secure routing protocols aren’t able to offer the required security for WSNs. In fact, all they can offer is a first line of defense especially against outside attacks. Therefore, the implementation of a second line of defense, which is the Intrusion Detection System (IDS), is deemed necessary as part of an integrated approach, to secure the network against malicious and abnormal behaviors of intruders, hence the goal of this paper. This allows improving security and protecting all resources related to a WSN. Recently, different detection methods have been proposed to develop an effective intrusion detection system for WSNs. In this regard, we proposed an integral mechanism which is an hybrid Intrusion Detection approach based on anomaly, detection using support vector machine (SVM), specifications based technique, signature and clustering algorithm to decrease the consumption of resources, by reducing the amount of information forwarded. So, our aim is to protect WSN, without disturbing networks performances through a good management of their resources, especially the energy.

Author 1: Lamyaa Moulad

Author 2: Hicham Belhadaoui

Author 3: Mounir Rifi

Keywords: Wireless Sensor Networks (WSNs); Intrusion Detection System (IDS); anomalies; specification-based detection; Denial Of Service (DOS) attacks; hybrid intrusion detection system; support vector machine(SVM); false alarm; detection rate

Paper 36: A Novel Design for XOR Gate used for Quantum-Dot Cellular Automata (QCA) to Create a Revolution in Nanotechnology Structure

Abstract: Novel digital technologies always lead to high density and very low power consumption. One of these concepts is Quantum-dot Cellular Automata (QCA), which is one of the new emerging nanotechnology-based on Coulomb repulsion. This article presents three architectures of logical “XOR” gate, a novel structure of two inputs “XOR” gate, which is used as a module to implement four inputs “XOR” gate and eight inputs “XOR” gate using QCA technique. The two inputs, four inputs, and eight inputs QCA “XOR” gate architectures are built using 10, 35, and 90 Cells on 0.008 µm2, 0.036 µm2 and 0.114 µm2 of areas, respectively. The proposed “XOR” gate structure provides an improvement in terms of circuit complexity, area, latency and type of cross wiring compared to other previous architectures. These proposed architectures of “XOR” gate are evaluated and simulated using the QCADesigner tool version 2.0.3.

Author 1: Radhouane Laajimi

Author 2: Ali Ajimi

Author 3: Lamjed Touil

Author 4: Ali Newaz Bahar

Keywords: QCA exclusive-OR; XOR gate; quantum-dot cellular automata (QCA); nanotechnology; majority gate; unique structure; QCA designer

Paper 37: Word-Based Grammars for PPM

Abstract: The Prediction by Partial Matching (PPM) compression algorithm is considered one of the most efficient methods for compressing natural language text. Despite the advances of the PPM method for the English language to predict upcoming symbols or words, more research is required to devise better compression methods for other languages, such as Arabic due, for example, to the rich morphological nature of the Arabic text, where a word can take many different forms. In this paper, we propose a new method that achieves the best compression rates not only for Arabic text but also for other languages that use Arabic script in their writing system such as Persian. Our word-based method constructs a context-free grammar (CFG) for the text and this grammar is then encoded using PPM to achieve excellent compression rates.

Author 1: Nojood O. Aljehane

Author 2: William J. Teahan

Keywords: Component; context-free grammar (CFG); grammar-base; word-based; Preprocessing; Prediction by Partial Matching (PPM); encoding

Paper 38: Sentiment Summerization and Analysis of Sindhi Text

Abstract: Text corpus is important for assessment of language features and variation analysis. Machine learning techniques identify the language terms, features, text structures and sentiment from linguistic corpus. Sindhi language is one of the oldest languages of the world having proper script and complete grammar. Sindhi is remained less resourced language computationally even in this digital era. Viewing this problem of Sindhi language, Sindhi NLP toolkit is developed to solve the Sindhi NLP and computational linguistics problems. Therefore, this research work may be an addition to NLP. This research study has developed an own Sindhi sentimentally structured and analyzed corpus on the basis of accumulated results of Sindhi sentiment analysis tool. Corpus is normalized and analyzed for language features and variation analysis using DTM and TF-IDF techniques. DTM and TF-IDF analysis is performed using n-gram model. The supervised machine learning model is formulated using SVMs and K-NN techniques to perform analysis on Sindhi sentiment analysis corpus dataset. Precision, recall and f-score show better performance of machine learning technique than other techniques. Cross validation techniques is used with 10 folds to validate and evaluate data set randomly for supervised machine learning analysis. Research study opens doors for linguists, data analysts and decision makers to work more for sentiment summarization and visual tracking.

Author 1: Mazhar Ali

Author 2: Asim Imdad Wagan

Keywords: Sindhi NLP; sentiment structurization; sentiment analysis; supervised analysis

Paper 39: QRishing: A User Perspective

Abstract: QR Code offers more benefits and features than its predecessor, Barcode, which make it more popular. However, there is no doubt that behind the features and conveniences offered by QR Code, it turns that the QR Code can be utilized to perform QRishing. This study proposes a model based on Technology Acceptance Model (TAM) combined with Perceived Security, Trust, Perceived Behavioral Control, Self-Efficacy and Perceived Risk based on previous research. Data obtained from 300 respondents are then analyzed with Structural Equation Modeling (SEM). The results show that Attitude, Perceive Security and Perceived Risk affect the individual to scan QR Code.

Author 1: Ari Kusyanti

Author 2: Ali Arifin

Keywords: QR code; perceived risk; perceived privacy; trust; Structural Equation Modeling (SEM)

Paper 40: Impact of External Disturbance and Discontinuous Input on the Redundant Manipulator Robot Behaviour using the Linear Parameter Varying Modelling Approach

Abstract: This paper is concerned with the synthesis of dynamic model of the redundant manipulator robot based on Linear Parameter Varying approach. To evaluate its behavior and in presence of external disturbance several motions profiles are developed using a new algorithm which produce smooth trajectories in optimal time. The main advantages of this proposed approach are its robustness and its simplicity with respect to the flexibility structure, to the motion profile and mass load variations. Numerical simulations with several tasks show that in presence of mass load variation the desired trajectory is more efficiently followed by the LPV model than the dynamic model of the studied mechanism. Its performances are ensured using the smoothest trajectory designed by the Eighth-degree polynomial profile than the Fifth-degree polynomial one and the trapezoidal one.

Author 1: Sameh Zribi

Author 2: Hatem Tlijani

Author 3: Jilani Knani

Author 4: Vicenç Puig

Keywords: Redundant manipulator robot; flexible structure; linear parameter varying approach; discontinuous torque; external disturbance

Paper 41: A Novel Algorithm to Improve Resolution for Very Few Samples

Abstract: This paper presents a new technic to improve resolution and direction of arrival (DOA) estimation of two closed source, in array processing, when only few samples of received signal are available. In these conditions, the detection of sources (targets) is more arduous, and even breaks down. To overcome these problems, a new algorithm is proposed. It combines spatial smooth method to widen the spatial resolution, bootstrap technique to estimate increased sample size, and a high resolution technique which is Multiple Signal Classification (MUSIC) to estimate DOA. Through different simulations, performance and effectiveness of the proposed approach, referred to as Spatial Smooth and Bootstrapped technique “SSBoot’’, are demonstrated.

Author 1: Sidi Mohamed Hadj Irid

Author 2: Samir Kameche

Keywords: Direction of arrival (DOA) estimation; Bootstrap; Multiple Signal Classification (MUSIC); resolution; spatial smoothing; array processing; Uniform Linear Array (ULA)

Paper 42: Modeling House Price Prediction using Regression Analysis and Particle Swarm Optimization Case Study : Malang, East Java, Indonesia

Abstract: House prices increase every year, so there is a need for a system to predict house prices in the future. House price prediction can help the developer determine the selling price of a house and can help the customer to arrange the right time to purchase a house. There are three factors that influence the price of a house which include physical conditions, concept and location. This research aims to predict house prices based on NJOP houses in Malang city with regression analysis and particle swarm optimization (PSO). PSO is used for selection of affect variables and regression analysis is used to determine the optimal coefficient in prediction. The result from this research proved combination regression and PSO is suitable and get the minimum prediction error obtained which is IDR 14.186.

Author 1: Adyan Nur Alfiyatin

Author 2: Ruth Ema Febrita

Author 3: Hilman Taufiq

Author 4: Wayan Firdaus Mahmudy

Keywords: House prediction; regression analysis; particle swarm optimization

Paper 43: Area k-Coverage Optimization Protocol for Heterogeneous Dense Wireless Sensor Networks

Abstract: Detecting redundant nodes and scheduling their activity is mandatory to prolong the lifetime of a densely-deployed wireless sensor network. Provided that the redundancy check and the scheduling phases both help to preserve the coverage ratio and guarantee energy efficiency. However, most of the solutions usually proposed in the literature, tend to allocate a large number of unnecessary neighbor (re)discovery time slots in the dutycycle of the active nodes. Such a shortcoming is detrimental to battery power conservation. In this paper, we propose a crossing points-based heuristic to fast detect redundant nodes even in heterogeneous networks; then, an integer linear program and a local exclusion based strategy to respectively, formulate and solve the sensing unit scheduling problem. Simulations show that the resulting localized asynchronous protocol outperforms some state-of-the-art solutions with respect to coverage preservation and network lifetime enhancement.

Author 1: Hervé Gokou Diédié

Author 2: Boko Aka

Author 3: Michel Babri

Keywords: Coverage; optimization; wireless sensor network; scheduling; GRASP

Paper 44: A Novel Hybrid Quicksort Algorithm Vectorized using AVX-512 on Intel Skylake

Abstract: The modern CPU’s design, which is composed of hierarchical memory and SIMD/vectorization capability, governs the potential for algorithms to be transformed into efficient implementations. The release of the AVX-512 changed things radically, and motivated us to search for an efficient sorting algorithm that can take advantage of it. In this paper, we describe the best strategy we have found, which is a novel two parts hybrid sort, based on the well-known Quicksort algorithm. The central partitioning operation is performed by a new algorithm, and small partitions/arrays are sorted using a branch-free Bitonicbased sort. This study is also an illustration of how classical algorithms can be adapted and enhanced by the AVX-512 extension. We evaluate the performance of our approach on a modern Intel Xeon Skylake and assess the different layers of our implementation by sorting/partitioning integers, double floatingpoint numbers, and key/value pairs of integers. Our results demonstrate that our approach is faster than two libraries of reference: the GNU C++ sort algorithm by a speedup factor of 4, and the Intel IPP library by a speedup factor of 1.4.

Author 1: Berenger Bramas

Keywords: Quicksort; Bitonic; sort; vectorization; SIMD; AVX-512; Skylake

Paper 45: Evaluating Dependency based Package-level Metrics for Multi-objective Maintenance Tasks

Abstract: Role of packages in organization and maintenance of software systems has acquired vital importance in recent research of software quality. With an advancement in modularization approaches of object oriented software, packages are widely considered as re-usable and maintainable entities of objectoriented software architectures, specially to avoid complicated dependencies and insure software design of well identified services. In this context, recently research study of H. Abdeen on automatic optimization of package dependencies provide composite frame of metrics for package quality and overall source code modularization. There is an opportunity to conduct comprehensive empirical analysis over proposed metrics for assessing their usefulness and application for fault-prediction, design flaw detection, identification of source code anomalies and architectural erosion. In this paper, we examine impact of these dependency optimization based metrics in wide spectrum of software quality for single package and entire software modularization. Our experimental work is conducted over open source software systems through statistical methodology based on cross validation fault-prediction and correlation.We conclude with empirical evidence that dependency based package modularization metrics provide more accurate view for predicting fault-prone packages and improvement of overall software structure. Thus, application of these metrics can help the developers and software practitioners to insure proactive management of the source code dependencies and avoid design flaws during software development.

Author 1: Mohsin Shaikh

Author 2: Akhtar Hussain Jalbani

Author 3: Adil Ansari

Author 4: Ahmed Ali

Author 5: Kashif Memon

Keywords: Software quality; package-level metrics; software modularization; fault-prediction

Paper 46: Emotion Recognition based on EEG using LSTM Recurrent Neural Network

Abstract: Emotion is the most important component in daily interaction between people. Nowadays, it is important to make the computers understand user’s emotion who interacts with it in human-computer interaction (HCI) systems. Electroencephalogram (EEG) signals are the main source of emotion in our body. Recently, emotion recognition based on EEG signals have attracted many researchers and many methods were reported. Different types of features were extracted from EEG signals then different types of classifiers were applied to these features. In this paper, a deep learning method is proposed to recognize emotion from raw EEG signals. Long-Short Term Memory (LSTM) is used to learn features from EEG signals then the dense layer classifies these features into low/high arousal, valence, and liking. DEAP dataset is used to verify this method which gives an average accuracy of 85.65%, 85.45%, and 87.99% with arousal, valence, and liking classes, respectively. The proposed method introduced high average accuracy in comparison with the traditional techniques.

Author 1: Salma Alhagry

Author 2: Aly Aly Fahmy

Author 3: Reda A. El-Khoribi

Keywords: Electroencephalogram; emotion; emotion recognition; deep learning; long-short term memory

Paper 47: Framework for Managing Uncertain Distributed Categorical Data

Abstract: In recent years, data has become uncertain due to the flourishing advanced technologies that participate continuously and increasingly in producing large amounts of incomplete data. Often, many modern applications where uncertainty occurs are distributed in nature, e.g., distributed sensor networks, information extraction, data integration, social network, etc. Consequently, even though the data uncertainty has been studied in the past for centralized behavior, it is still a challenging issue to manage uncertainty over the data in situ. In this paper, we propose a framework to managing uncertain categorical data over distributed environments that is built upon a hierarchical indexing technique based on inverted index, and a distributed algorithm to efficiently process queries on uncertain data in distributed environment. Leveraging this indexing technique, we address two kinds of queries on the distributed uncertain databases 1) a distributed probabilistic thresholds query, where its answers satisfy the probabilistic threshold requirement; and 2) a distributed top-k-queries, optimizing, the transfer of the tuples from the distributed sources to the coordinator site and the time treatment. Extensive experiments are conducted to verify the effectiveness and efficiency of the proposed method in terms of communication costs and response time.

Author 1: Adel Benaissa

Author 2: Mustapha Yahmi

Author 3: Yassine Jamil

Keywords: Distributed uncertain data; Top-k query; threshold query; indexing; categorical data

Paper 48: Accuracy Based Feature Ranking Metric for Multi-Label Text Classification

Abstract: In many application domains, such as machine learning, scene and video classification, data mining, medical diagnosis and machine vision, instances belong to more than one categories. Feature selection in single label text classification is used to reduce the dimensionality of datasets by filtering out irrelevant and redundant features. The process of dimensionality reduction in multi-label classification is a different scenario because here features may belong to more then one classes. Label and instance space is rapidly increasing by the grandiose of Internet, which is challenging for Multi-Label Classification (MLC). Feature selection is crucial for reduction of data in MLC. Method adaptation and data set transformation are two techniques used to select features in multi label text classification. In this paper, we present dataset transformation technique to reduce the dimensionality of multi-label text data. We used two model transformation approaches: Binary Relevance, and Label Power set for transformation of data from multi-label to single label. The Process of feature selection is done using filter approach which utilizes the data to decide the importance of features without applying learning algorithm. In this paper we used a simple measure (ACC2) for feature selection in multi-label text data. We used problem transformation approach to apply single label feature selection measures on multi-label text data; did the comparison of ACC2 with two other feature selection methods, information gain (IG) and Relief measure. Experimentation is done on three bench mark datasets and their empirical evaluation results are shown. ACC2 is found to perform better than IG and Relief in 80% cases of our experiments.

Author 1: Muhammad Nabeel Asim

Author 2: Abdur Rehman

Author 3: Umar Shoaib

Keywords: Binary relevance (BR); label powerset (LP); ACC2; information gain (IG); Relief-F (RF)

Paper 49: Design of a Microstrip Patch Antenna with High Bandwidth and High Gain for UWB and Different Wireless Applications

Abstract: We propose square shape patch antenna in this research work. Focus of the work is to obtain large bandwidth with compact ground plane for wireless applications. The proposed antenna is designed using dielectric material of FR4 having height of 1.6 mm and having r of 4.4. We simulated the proposed antenna in CST Microwave Studio. Simulation results show that the proposed antenna achieved bandwidth from 2.33 GHz to 12.4 GHz with radiation efficiency more than 90% in ultrawideband range. The proposed antenna covers the range of ultra wideband from 3.1 GHz to 10.6 GHz, the range of local area network, wide area network, and also covers the range of satellite communications (for both uplink and downlink).

Author 1: Zain Ul Abedin

Author 2: Zahid Ullah

Keywords: High bandwidth, patch antenna, low profile, linear polarization

Paper 50: RGBD Human Action Recognition using Multi-Features Combination and K-Nearest Neighbors Classification

Abstract: In this paper, we present a novel system to analyze human body motions for action recognition task from two sets of features using RGBD videos. The Bag-of-Features approach is used for recognizing human action by extracting local spatialtemporal features and shape invariant features from all video frames. These feature vectors are computed in four steps: Firstly, detecting all interest keypoints from RGB video frames using Speed-Up Robust Features and filters motion points using Motion History Image and Optical Flow, then aligned these motion points to the depth frame sequences. Secondly, using a Histogram of orientation gradient descriptor for computing the features vector around these points from both RGB and depth channels, then combined these feature values in one RGBD feature vector. Thirdly, computing Hu-Moment shape features from RGBD frames; fourthly, combining the HOG features with Hu-moments features in one feature vector for each video action. Finally, the k-means clustering and the multi-class K-Nearest Neighbor is used for the classification task. This system is invariant to scale, rotation, translation, and illumination. All tested, are utilized on a dataset that is available to the public and used often in the community. By using this new feature combination method improves performance on actions with low movement and reach recognition rates superior to other publications of the dataset.

Author 1: Rawya Al-Akam

Author 2: Dietrich Paulus

Keywords: RGBD videos; feature extraction; K-means clustering; KNN (K-Nearest Neighbor)

Paper 51: Analyzing the Diverse Impacts of Conventional Distributed Energy Resources on Distribution System

Abstract: In recent years, the rapid boost in energy demand around the globe has put power system in stress. To fulfill the energy demands and confine technical losses, researchers are eager to investigate the diverse impacts of Distributed Generation (DG) on the parameters of distribution network. DG is becoming even more attractive to power producing companies, utilities and consumers due to production of energy near to load centers. Reduction in power losses, better voltage profile and less environmental impact are the benefits of DG. Besides renewable energy resources, conventional energy resources are also a viable option for DG. This research aims to analyze the impact of localized synchronous and induction generators on distributions network. The main objectives are to find optimal type, size and location of DG in distribution network to have better impact on voltage profile and reduction in power losses. Using worldwide recognized software tool ETAP and Kohat road electricity distribution network as a test case. Results depicted that at certain buses, positive impacts on voltage profile were recorded while almost 20% of power losses were decreased when synchronous generator as DG unit was injected in distribution network. Injecting induction generator as DG unit, the results showed increase in power losses due to absorption of reactive power, while improving voltage profile by injecting active power.

Author 1: Muhammad Aamir Aman

Author 2: Sanaullah Ahmad

Author 3: Azzam ul Asar

Author 4: Babar Noor

Keywords: Electric power system; distributed generation; voltage profile; power losses; synchronous generator; induction generator

Paper 52: Text Summarization Techniques: A Brief Survey

Abstract: In recent years, there has been a explosion in the amount of text data from a variety of sources. This volume of text is an invaluable source of information and knowledge which needs to be effectively summarized to be useful. Text summarization is the task of shortening a text document into a condensed version keeping all the important information and content of the original document. In this review, the main approaches to automatic text summarization are described. We review the different processes for summarization and describe the effectiveness and shortcomings of the different methods.

Author 1: Mehdi Allahyari

Author 2: Seyedamin Pouriyeh

Author 3: Mehdi Assefi

Author 4: Saeid Safaei

Author 5: Elizabeth D. Trippe

Author 6: Juan B. Gutierrez

Author 7: Krys Kochut

Keywords: Text summarization; extractive summary; abstractive summary knowledge bases; topic models

Paper 53: Aggregation Operator for Assignment of Resources in Distributed Systems

Abstract: In distributed processing systems it is often necessary to coordinate the allocation of shared resources that should be assigned to processes in the modality of mutual exclusion; in such cases, the order in which the shared resources will be assigned to processes that require them must be decided; in this paper we propose an aggregation operator (which could be used by a shared resources manager module) that will decide the order of allocation of the resources to the processes considering the requirements of the processes (shared resources) and the state of the distributed nodes where the processes operate (their computational load).

Author 1: David L la Red Martínez

Keywords: Aggregation operators; concurrency control; communication between groups of processes; mutual exclusion; operating systems; processor scheduling

Paper 54: Feature Weight Optimization Mechanism for Email Spam Detection based on Two-step Clustering Algorithm and Logistic Regression Method

Abstract: This research proposed an improved filtering spam technique for suspected emails, messages based on feature weight and the combination of two-step clustering and logistic regression algorithm. Unique, important features are used as the optimum input for a hybrid proposed approach. This study adopted a spam detector model based on distance measure and threshold value. The aim of this model was to study and select distinct features for email filtering using feature weight method as dimension reduction. Two-step clustering algorithm was used to generate a new feature called “Label” to cluster and differentiate the diversity emails and group them based on the inter samples similarity. Thereby the spam filtering process was simplified using the Logistic regression classifier in order to distinguish the hidden patterns of spam and non-spam emails. Experimental design was conducted based on the UCI spam dataset. The outcome of the findings shows that the results of the email filtering are promising compared to other modern spam filtering methods.

Author 1: Ahmed Hamza Osman

Author 2: Hani Moetque Aljahdali

Keywords: Two-step clustering; spam filtering; classification; detection; feature weight; logistic regression

Paper 55: A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Abstract: The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event detection.

Author 1: Pushpa D

Keywords: Abnormal event; detection; event detection; object detection; machine learning; video surveillance

Paper 56: Validation of Semantic Discretization based Indian Weighted Diabetes Risk Score (IWDRS)

Abstract: The objective of this research study is to validate Indian Weighted Diabetes Risk Score (IWDRS). The IWDRS is derived by applying the novel concept of semantic discretization based on Data Mining techniques. 311 adult participants (age > 18 years), who have been tested for diabetes using the biochemical test in pathology laboratory according to World Health Organization (WHO) guidelines, were selected for this study. These subjects were not included for deriving IWDRS tool. IWDRS is calculated for all 311 subjects. Prediction parameters, such as sensitivity and specificity are evaluated along with other performance parameters for an optimal cut-off score for IWDRS. The IWDRS tool is validated and found to be highly sensitive in diagnosing diabetes positive cases at the same time it is almost equally specific for identifying diabetes negative cases as well. The result of IWDRS is compared with the results of another two similar studies conducted for the Indian population and found it better. At optimal cut-off score IWDRS>=294, the prediction accuracy is 82.32%, while sensitivity and specificity is 82.22% and 82.44%, respectively.

Author 1: Omprakash Chandrakar

Author 2: Jatinderkumar R. Saini

Author 3: Lal Bihari Barik

Keywords: Data mining; indian weighted diabetes risk score; semantic discretization; type-2 diabetes risk score

Paper 57: Integrated Framework to Study Efficient Spectral Estimation Techniques for Assessing Spectral Efficiency Analysis

Abstract: The advanced network applications enable software driven spectral analysis of non-stationary signal or processes which precisely involves domain analysis with the purpose of decomposing a complex signal coefficients into simpler forms. However, the proper estimation of power coefficients over frequency components of a random signal leads to provide very useful information required in various fields of study. The complex design constraints associated with conventional parametric models such as Dynamic Average Model, Autoregressive MA, etc. for multidimensional spectral estimation using adaptive filters leads to a situation where higher computational complexities generate significant overhead on the systems. Therefore, the proposed study aims to formulate an efficient framework intended to derive a fast algorithm for processing Adaptive Capon and Phase Estimator (APES). The proposed method is applied to a non-stationary signal which is random. Further, the adaptive estimation of power spectra along with more accurate spectral efficiency has been identified in case of APES. An extensive performance evaluation followed by a comparative analysis has been performed by obtaining the values from different spectral estimation techniques, such as APES, PSC, ASC, and CAPON. Moreover, the framework ensures that unlike others, APES is subjected to attain superior signal quality regarding Power Spectral Density (PSD) and Signal to Noise Ratio (SNR) while achieving very less amount of Mean Square Error (MSE). It also exhibits comparatively low convergence speed and computational complexity as compared to its legacy versions.

Author 1: Kantipudi MVV Prasad

Author 2: H.N. Suresh

Keywords: Amplitude and phase estimation; ASC; capon spectral estimator; spectral estimation; PSC