bluuebunny/crossref_metadata_2025 huggingface dataset:
INVESTMENT DECISION MAKING AMONG GULF INVESTORS: BEHAVIOURAL FINANCE PERSPECTIVE
Authors: Almansour Bashar Yaser , Arabyat Yaser Ahmad
Published: August 2020

The rationality hypothesis has been a very popular topic among the academics. Being a widely accepted hypothesis as part of the traditional finance theories, an investor is deemed a rational agent and makes rational decisions by exhausting all available alternatives. However, recently, new behavioural finance theories have been gaining ground as many empirical findings, which have been left unanswered by the traditional theories, can be explained by these behavioural-approach based theories. This research examined the impact of psychological factors on risk-taking behaviour in investment decisions. In particular, this research considered the possible effects of psychological factors, namely herding, heuristics, prospect, market, self-attribution bias, and familiarity bias, in making investment decisions. The findings in this paper declared that risk-taking behaviour in investment is affected by herding factors, heuristics factors, prospect factors, market factors and self-attribution bias factors. The familiarity bias factors do not significantly affect risk-taking behaviour in financial investment. Keywords: Behavioural finance, Herding, Heuristics, Prospect, Market, Self-attribution bias, Familiarity bias
ESTIMATING THE DETERMINANTS OF VEHICLE LOAN DEFAULT IN MALAYSIA: AN EXPLORATORY STUDY
Authors: Lim Hock-Eam , Yeok Siew Goh
Published: August 2020

As revealed by Malaysia’s bankruptcy statistics, around a quarter of bankruptcy in Malaysia is due to default of vehicle loan. This has led to the tightening up of vehicle loan underwriting and increased vehicle loan rejection rates. The need for a better credit risk scoring model is also raised by the banks. This warrants a study to estimate the determinants of vehicle loan default in Malaysia. This paper estimates the determinants of vehicle loan default probability which could be used to build a loan default prediction or forecasting model for credit risk scoring purposes. Using a simple random sample of 138 car loan borrowers that was provided by an established bank in Malaysia, the descriptive statistical procedures and econometrics modelling were performed to unveil these vehicle loan default determinants. Results of descriptive statistics revealed that more than half of the borrowers were default. Results of logit models further revealed that loan related characteristics are the most important determinants of probability of default. Specifically, the significant determinants of loan default were: areas of residence, vehicle purchase price, length of service, existing relationship with bank, interest rate, and available guarantor. Borrowers who are in high risk of default are characteristically those who reside in rural areas, secure higher vehicle purchase price, have longer length of employment service, are borrowers new to the bank, acquire loans charged with high interest rates, and are without a guarantor. Keywords: Loan default; Vehicle loan; Logit model; Forecasting model.
CONSTRUCTING A SOCIAL ACCOUNTING MATRIX TO ADDRESS INCOME DISTRIBUTION ISSUES IN MALAYSIA
Authors: Yahoo Masoud , A. Rashid Zakariah , Kiaeeha Mohammad Mahdi , Chatri Fatemeh
Published: August 2020

The Malaysian economy has undergone sound growth over the past three decades. During the 1990s, the government designed a vision to become a high-income developed nation by 2020. Strengthening aggregate demand within a developing economy such as Malaysia’s is critical for achieving this target. In quantifying the impact of such measures economists have employed the social accounting matrix as an analytical tool. The challenge associated with the construction of a social accounting matrix for Malaysia, especially when up-to-date databases are not readily available, is the main issue addressed by this paper. The constructed matrix can provide an updated country-wide data source for use in policy analysis and as a database for macroeconomic modelling purposes. The applied database investigates the key economic sectors contributing to Malaysia’s economic growth and income distribution between three household types. This paper presents the first attempt in this direction employing recently published input-output tables and complemented with updated data from different sources. Keywords: Social Accounting Matrix, Government expenditure, Income distribution, Multiplier analysis, Malaysia.
Factors Affecting Employee-based Brand Equity: Evidence From China
Authors: Awan Tahir Mumtaz , Li Xiaolin , Haizhong Wang
Published: August 2020

Employee-based brand equity (EBBE) has been receiving increasing attention in recent years. However, most studies focus on the construct definition and scale development, as well as the effects that EBBE may have on corporate performance. Few studies try to identify the source of EBBE, or in other words, the antecedents of EBBE. The major study of the antecedents of EBBE is King and Grace’s model (2010), which is restricted to the service industry. As this research is cultural context-dependent, it is necessary to test the framework of King and Grace’s model (2010) in other regions or industries. Replication studies are important for the generalizability of strategic management theories as reputed journals such as the Academy of Management Journal (AMJ) and the Strategic Management Journal (SMJ) have proposed. Therefore, this study was designed to test King and Grace’s model (2010) in an eastern cultural context (China) and covers several non-service sector industries. The results confirmed King and Grace’s model to a high extent, which has significant implications for firms in developing countries.   Keywords: Employee-based brand equity, Brand management, China, Employee behavior, Competitive advantage.
AUDIT COMMITTEE ATTRIBUTES AND AUDITOR BRAND NAME OF LISTED COMPANIES IN NIGERIA
Authors: Bala Hussaini , Amran Noor Afza , Shaari Hasnah
Published: August 2020

This paper examines the effect of audit committee attributes on auditor brand name proxy by the Big 4. The study utilizes 88 listed firms in Nigeria through 440 firm-year observations ranging between the years 2012 to 2016. The data for the study were extracted from the firms’ annual reports and Thompson Reuters DataStream. A panel logistic regression was employed to estimate the model of the study. Consistent with complementary hypothesis the findings demonstrate that audit committee attributes (audit committee independence, audit committee financial accounting experts, audit committee legal expert, female audit committee member and audit committee stock ownership) are positively related to auditor brand name. The findings also support the substitution hypothesis perspectives by revealing an inverse relationship between audit committee meetings, audit committee tenure, audit committee chair and auditor brand name. Our findings offer an initial insight on the effect of audit committee legal expert, and audit committee stock ownership on auditor brand name. Thus, the findings can benefit existing and prospective shareholders who are the direct users of financial reports. This study can also help policy-makers and regulators by allowing them to better recognize the importance of these distinctive audit committee attributes in enhancing the quality of audits, which is one of the most vital elements of improving financial reporting quality. Keywords:Audit committee, Audit quality, Audit size, Gender diversity, Ownership
PERCEIVED BENEFITS AND PERCEIVED RISKS EFFECT ON ONLINE SHOPPING BEHAVIOR WITH THE MEDIATING ROLE OF CONSUMER PURCHASE INTENTION IN PAKISTAN
Authors: Bhatti Anam , Ur Rehman Shafique
Published: August 2020

The purpose of this study is to examine the relationship between different factors including perceived benefits, perceived risks and online shopping behavior with the mediating role of consumer purchase intention. Online shopping states the recent trends of being able to purchase anything from home with a single click. The main focus of this study is to check the influence of four major variables: convenience, product variety, product risk, and privacy risk which were derived from related literature. Data were collected from 380 students from different universities using simple random sampling. PLS-SEM was used in this study and the results showed that benefits had a positive influence and that risk had a negative influence on online shopping behavior.     Keywords: Perceived benefits, perceived risks, consumer purchase intention, online shopping behavior.
GREEN WORK CLIMATE AND PRO-ENVIRONMENTAL BEHAVIOUR AMONG ACADEMICS: THE MEDIATING ROLE OF HARMONIOUS ENVIRONMENTAL PASSION
Authors: Choong Yuen-Onn , Ng Lee-Peng , Tee Chee-Wee , Kuar Lok-Sin , Teoh Sok-Yee , Chen I-Chi
Published: August 2020

Environmental issues such as climate change, pollution, deforestation, and the loss of biodiversity are the concerns of society in different parts of the world today. As human activities have adverse impacts on the environment, developing pro-environmental behaviour in each individual is an essential aspect of protecting mother earth. The primary purpose of this research is to evaluate the influence of green work climate perceptions and harmonious environmental passion on pro-environmental behaviour (PEB). Additionally, the mediating effect of harmonious environmental passion between green work climate and PEB is determined. A total of 167 academics from a private university in Malaysia participated in this study. The results from Partial Least Square – Structural Equation Modelling suggested that green work climate and harmonious environmental passion positively influence PEB. Apart from this, harmonious environmental passion partially mediates the relationship between green work climate and PEB. The results indicate that leaders should actively shape the perception of green work climate within their organisations through policies and procedures that support environmental sustainability efforts, as well as effectively communicate with their employees. Such efforts will lead to greater harmonious environmental passion, in turn, promoting pro-environmental behaviour among members.   Keywords:  Green work climate, harmonious environmental passion, pro-environmental behaviour.
DYSLEXIC CHILDREN’S READING APPLICATION: DESIGN FOR AFFECTION
Authors: Husni Husniza , Jamaludin Zulikha , Aziz Fakhrul Anuar
Published: August 2020

This article presents an interaction design model which considers the affective dimension for a reading application for dyslexic children. The research on the design model for the reading tutor was motivated by the need to offer dyslexic children with an effective and fun learning process. It acknowledges the challenge in designing an acceptable interaction design (IxD) model considering the differences in reading style, preference, and habits of dyslexics. The model was developed based on existing theoretical models combined with the findings from observations and unstructured interviews conducted involving 28, 7 to 14 year old, dyslexic children. A goal-directed design is adopted as the methodology to design such a model. The model’s effectiveness and usefulness is now being tested in a special class for dyslexic children. It is hoped that the outcome could be used to further develop guidelines and standards in designing applications for users with specific impairments.
TRANSFORMING NOUN PHRASE STRUCTURE FORM INTO RULES TO DETECT COMPOUND NOUNS IN MALAY SENTENCES
Authors: Abdul Rahman Suhaimi , Omar Nazlia
Published: August 2020

This paper addresses the process of transforming the noun phrase structure form into a list of rules to detect compound noun words in Malay sentences. Rules are collection of word syntax that are derived from a specific resource (as defined in our study). Comprehension of the concept rule used in a system is important (i.e. using rules to find a list of compound nouns that may exist in a sentence). The noun phrase frame structure is a form that contains a list of noun modifier categories. The list of noun modifier categories is then divided into several sub-categories such as numeral, numeral classifier, appellation, etc. All categories are arranged in sequence based on correct grammar. The noun phrase frame structure is then used to analyse the sentence. The words in the sentence will be arranged according to their suitable noun modifier category as defined by the noun phrase frame structure. In terms of data requirements, we will only focus on examples of sentences that combine two noun phrases.
TECHNICAL CONSIDERATIONS IN DESIGNING HAPTIC APPLICATIONS: A CASE STUDY OF LAPAROSCOPY SURGERY SIMULATION WITH HAPTIC ELEMENTS
Authors: Abdul Rawi Norkhairani , Zaman Halimah Badioze , Ahmad Azlina
Published: August 2020

Simulation has been widely used as a tool for training, especially in high risk areas such as in the aerospace, military and medical fi elds. Surgery is one of the sub areas that has been receiving much attention from researchers due to the ability of simulation to provide a real surgery setting and human organs with appropriate devices to increase the realism. Developing a surgical simulation is a technically complex process since it involves a few components that interact with each other. Thus this necessitates further considerations regarding the issues and challenges in order to produce an accurate and interactive application. Therefore there is a need for a technical solution framework to help a new and novice researcher in this area to get started. This paper discusses the important components of a surgical simulator, together with its issues and challenges. A proposed solution framework, together with the programming or application choices that are available for each of the components, is explained clearly as concluded from the discussion in the previous work. The class structure for the components is shown briefl y to give the new researcher an idea of it. It is hoped that this paper will serve as a foundation for new and novice researchers in haptic development specifi cally and visual informatics generally.
MULTILEVEL KOHONEN NETWORK LEARNING FOR CLUSTERING PROBLEMS
Authors: Shamsuddin Siti Mariyam , Zainal Anazida , Mohd Yusof Norfadzila
Published: August 2020

Clustering is the procedure of recognising classes of patterns that occur in the environment and assigning each pattern to its relevant. Unlike classical statistical methods, self-organising map (SOM) does not require any prior knowledge about the statistical distribution of the patterns in the environment. In this study, an alternative classification of self-organising neural networks, known as multilevel learning, was proposed to solve the task of pattern separation. The performance of standard SOM and multilevel SOM were evaluated with different distance or dissimilarity measures in retrieving similarity between patterns. The purpose of this analysis was to evaluate the quality of map produced by SOM learning using different distance measures in representing a given dataset. Based on the results obtained from both SOM methods, predictions can be made for the unknown samples. The results showed that multilevel SOM learning gives better classification rate for small and medium scale datasets, but not for large scale dataset.
COMPUTER AIDED TOOL SUPPORT (CATS) FOR INFORMATION STRATEGY PLANNING (ISP): GENERATING APPLICATION PORTFOLIO FOR BUSINESS AREA ANALYSIS
Authors: Wahid Juliana , Md Dahalin Zulkhairi , Idrus Mohd Rushdi , Omar Mohd Adan
Published: August 2020

Information Strategy Planning (ISP) is concerned with an organization’s plan to acquire appropriate “IT blueprint” encompassing suitable hardware, software and telecommunication facilities for the utilization of applications required by the business with adequate support structure. The purpose is to determine the enterprise’s business goals and a high-level overview of the enterprise, its functions, data and information needs. This research builds an ISP engine, i.e. the Computer Aided Tool Support (CATS) that has the capability to generate application portfolios that can support the business strategy of the enterprise. Application portfolio is a major part of the ISP deliverables which can signifi cantly aid in formulating suitable strategies for IT supply and IT support structure. CATS will help to facilitate End-User Computing as a tool not only to ease endusers in planning for their IT adoption, but also to ensure that their IT investment is aligned to business goals and strategies. CATS will be able to help the Small Medium Enterprise (SME) companies and other organizations in their information-systems planning so that they can achieve their missions and objectives in a more effi cient way.
FUZZY SUBJECTIVE EVALUATION OF ASIA PACIFIC AIRPORT SERVICES
Authors: Ku-Mahamud Ku Ruhana , Othman Mahmod
Published: August 2020

This paper presents a fuzzy decision-making model to determine the ranking of fourteen Asia Pacific airports based on the services provided to passengers. Airport services were represented by six attributes namely comfort, processing time, convenience, courtesy of staff, information visibility and security. Data for the attributes given by travel experts are in the triangular fuzzy number form. Based on fuzzy set and approximate reasoning, the model allows decision makers to make the best choice in accordance with human thinking and reasoning processes. The use of fuzzy rules which are extracted directly from the input data in making evaluation, contributes to a better decision and is less dependent on experts. Experimental results show that the proposed model is comparable to previous studies. The model is suitable for various fuzzy environments.
IMPROVED SPEAKER-INDEPENDENT EMOTION RECOGNITION FROM SPEECH USING TWO-STAGE FEATURE REDUCTION
Authors: Mohd Nazid Hasrul , Muthusamy Hariharan , Vijean Vikneswaran , Yaacob Sazali
Published: August 2020

In the recent years, researchers are focusing to improve the accuracy of speech emotion recognition. Generally, high emotion recognition accuracies were obtained for two-class emotion recognition, but multi-class emotion recognition is still a challenging task . The main aim of this work is to propose a two-stage feature reduction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) for improving the accuracy of the speech emotion recognition (ER) system. Short-term speech features were extracted from the emotional speech signals. Experiments were carried out using four different supervised classifi ers with two different emotional speech databases. From the experimental results, it can be inferred that the proposed method provides better accuracies of 87.48% for speaker dependent (SD) and gender dependent (GD) ER experiment, 85.15% for speaker independent (SI) ER experiment, and 87.09% for gender independent (GI) experiment.
KNOWLEDGE AUDIT FRAMEWORK : A CASE STUDY OF THE MALAYSIAN ELECTRICITY SUPPLY INDUSTRY
Authors: Mohd Drus Sulfeeza , Mohamed Shariff Siti Salbiah , Othman Marini
Published: August 2020

Literature has concurred that knowledge audit promotes efficient management and exploitation of organizational knowledge. Due to the distinctive business environment of the electricity supply industry, the existing knowledge audit frameworks may not be suitable to be adopted ‘as-they-are’ by the industry. In this context, this research has proposed a knowledge audit framework that is specifically tailored to the environment of the electricity supply industry. Two main electricity companies in Malaysia were selected as the case companies for this research. The proposed knowledge audit framework aims to present a holistic way of assessing and analyzing the current knowledge environment of the electricity supply industry in Malaysia. The outcomes of the knowledge audit exercise could be used in developing a knowledge strategy that is aligned with the business strategies of the electricity companies.
HASHTAG AND HIGHEST SCORED TERMS FOR EXPANDING QUERY
Authors: Wibowo Wahyu Catur , Widodo Widodo
Published: August 2020

Communicating in short messages, such as using microblogs, was becoming more popular currently. Twitter https://twitter.com supports microblogs and retrieval of the blogs by users. To retrieve Twitter documents, we need specific strategies due to its specific characteristics. One new strategy for improving the effectiveness of twitter document retrieval is using the query expansion technique. This paper elaborates query expansion in twitter document retrieval by using the hashtag. We compared the effectiveness of query expansion in four different scenarios: the baseline result using no query expansion, highest scoredterm in terms of frequency-inverse document frequency (tfidf), maximum hashtag occurance, and combination of the highest scored-term and the maximum hashtag. The results show that the combination of the maximum term in tfidf and the maximum hashtag performs better in retrieving relevant documents than the baseline.
NEURAL NETWORK-BASED DOUBLE ENCRYPTION FOR JPEG2000 IMAGES
Authors: Memon Qurban Ali
Published: August 2020

The JPEG2000 is the more efficient next generation coding standard than the current JPEG standard. It can code files witless visual loss, and the file format is less likely to be affected by system file or bit errors. On the encryption side, the current 128-bit image encryption schemes are reported to be vulnerable to brute force. So there is a need for stronger schemes that not only utilize the efficient coding structure of the JPEG2000, but also apply stronger encryption with better key management. This research investigated a two-layer 256-bit encryption technique proposed for the JPEG2000 compatible images. In the first step, the technique used a multilayer neural network with a 128-bit key to generate single layer encrypted sequences. The second step used a cellular neural network with a different 128-bit key to finally generate a two-layer encrypted image. The projected advantages were compatible with the JPEG2000, 256-bit long key, managing each 128-bit key at separate physical locations, and flexible to opt for a single or a two-layer encryption. In order to test the proposed encryption technique for robustness, randomness tests on random sequences, correlation and histogram tests on encrypted images were conducted. The results show that random sequences pass the NIST statistical tests and the 0/1 balancedness test; the bit sequences are decorrelated, and the histogram of the resulting encrypted images is fairly uniform with the statistical properties of those of the white noise.
AN IMPROVED ARTIFICIAL DENDRITE CELL ALGORITHM FOR ABNORMAL SIGNAL DETECTION
Authors: Mohamad Mohsin Mohamad Farhan , Abu Bakar Azuraliza , Hamdan Abdul Razak , Abdul Wahab Mohd Helmy
Published: August 2020

In dendrite cell algorithm (DCA), the abnormality of a data point is determined by comparing the multi-context antigen value (MCAV) with anomaly threshold. The limitation of the existing threshold is that the value needs to be determined before mining based on previous information and the existing MCAV is inefficient when exposed to extreme values. This causes the DCA fails to detect new data points if the pattern has distinct behavior from previous information and affects detection accuracy. This paper proposed an improved anomaly threshold solution for DCA using the statistical cumulative sum (CUSUM) with the aim to improve its detection capability. In the proposed approach, the MCAV were normalized with upper CUSUM and the new anomaly threshold was calculated during run time by considering the acceptance value and min MCAV. From the experiments towards 12 benchmark and two outbreak datasets, the improved DCA is proven to have a better detection result than its previous version in terms of sensitivity, specificity, false detection rate and accuracy.
EXAMINING THE INFORMATION DISSEMINATION PROCESS ON SOCIAL MEDIA DURING THE MALAYSIA 2014 FLOODS USING SOCIAL NETWORK ANALYSIS (SNA)
Authors: Olanrewaju Abdus-Samad Temitope , Ahmad Rahayu
Published: August 2020

This article is based on a study which examined the information dissemination process on the social media during the Malaysia 2014 floods by employing the Social Network Analysis. Specifically, the study analyzed the type of network structure formed and its density, the influential people involved, and the kind of information shared during the flood. The data was collected from a non-governmental organization fan page (NGOFP) and a significant civilian fan page (ICFP) on Facebook using NodeXL. The two datasets contained 296 posts which generated different network structures based on the state of the flood, information available, and the needs of the information. Through content analysis, five common themes emerged from the information exchanges for both fan pages which helped in providing material and psychological support to the flood victims. However, only 5% of the networks’ population served as information providers, and this prompted the need for more active participation especially from organizations with certified information. Based on the findings presented and elaborated, this article concluded by stating the implications and recommendations of the study conducted.
FUZZY DISCRETIZATION TECHNIQUE FOR BAYESIAN FLOOD DISASTER MODEL
Authors: Ahmad Azami Nor Idayu , Yusoff Nooraini , Ku-Mahamud Ku Ruhana
Published: August 2020

The use of Bayesian Networks in the domain of disaster management has proven its efficiency in developing the disaster model and has been widely used to represent the logical relationships between variables. Prior to modelling the correlation between the flood factors, it was necessary to discretize the continuous data due to the weakness of the Bayesian Network to handle such variables. Therefore, this paper aimed to propose a data discretization technique and compare the existing discretization techniques to produce a spatial correlation model. In particular, the main contribution of this paper was to propose a fuzzy discretization method for the Bayesian-based flood model. The performance of the model is based on precision, recall, F-measure, and the receiver operating characteristic area. The experimental results demonstrated that the fuzzy discretization method provided the best measurements for the correlation model. Consequently, the proposed fuzzy discretization technique facilitated the data input for the flood model and was able to help the researchers in developing effective early warning systems in the future. In addition, the results of correlation were prominent in disaster management to provide reference that may help the government, planners, and decision-makers to perform actions and mitigate flood events.
NOISY IMAGE CLASSIFICATION USING HYBRID DEEP LEARNING METHODS
Authors: Roy Sudipta Singha , Ahmed Mahtab , Akhand Muhammad Aminul Haque
Published: August 2020

In real-world scenario, image classification models degrade in performance as the images are corrupted with noise, while these models are trained with preprocessed data. Although deep neural networks (DNNs) are found efficient for image classification due to their deep layer-wise design to emulate latent features from data, they suffer from the same noise issue. Noise in image is common phenomena in real life scenarios and a number of studies have been conducted in the previous couple of decades with the intention to overcome the effect of noise in the image data. The aim of this study was to investigate the DNN-based better noisy image classification system. At first, the autoencoder (AE)-based denoising techniques were considered to reconstruct native image from the input noisy image. Then, convolutional neural network (CNN) is employed to classify the reconstructed image; as CNN was a prominent DNN method with the ability to preserve better representation of the internal structure of the image data. In the denoising step, a variety of existing AEs, named denoising autoencoder (DAE), convolutional denoising autoencoder  (CDAE)  and  denoising  variational  autoencoder (DVAE) as well as two hybrid AEs (DAE-CDAE and DVAE- CDAE) were used. Therefore, this study considered five hybrid models for noisy image classification termed as: DAE-CNN, CDAE-CNN,   DVAE-CNN,   DAE-CDAE-CNN   and   DVAE- CDAE-CNN. The proposed hybrid classifiers were validated by experimenting over two benchmark datasets (i.e. MNIST and CIFAR-10) after corrupting them with noises of various proportions. These methods outperformed some of the existing eminent methods attaining satisfactory recognition accuracy even when the images were corrupted with 50% noise though these models were trained with 20% noise in the image. Among the proposed methods, DVAE-CDAE-CNN was found to be better than the others while classifying massive noisy images, and DVAE-CNN was the most appropriate for regular noise. The main significance of this work is the employment of the hybrid model with the complementary strengths of AEs and CNN in noisy image classification. AEs in the hybrid models enhanced the proficiency of CNN to classify highly noisy data even though trained with low level noise.
AN EMPIRICAL STUDY ON PREDICTORS OF GREEN SUSTAINABLE SOFTWARE PRACTICES IN MALAYSIAN ELECTRONIC INDUSTRIES
Authors: Anthony Jnr Bokolo , Abdul Majid Mazlina , Romli Awanis
Published: August 2020

Currently, sustainability is a pertinent issue that should be considered in the software development process; hence it is imperative to recognize how environmental-friendly practices can be applied in the electronic industries that develop and deploy software products. However, sustainability is not fully considered when electronic industries implement modern software systems. Additionally, software developers in electronic industries believe that software is environmental friendly mainly because it is virtual. Conversely, the life cycle process and approaches applied to implement, deploy and maintain software do possess social and environmental impacts that are usually not accounted for by electronic industries. Therefore this study identified the predictors that determine sustainable software practice applications in electronics industries by presenting a model to facilitate sustainable software products development. The identified predictors influence sustainable software practices applications which correlate to environmental, technical, economic, social and individual dimensions of sustainability in electronics industries. Based on the identified predicators, this research developed a set of indicators for survey questions and collected data from 133 respondents from Information Technology (IT), software, environmental and electronic- based industries. The survey data aimed to verify each of the identified predictors that influence sustainable software practice applications. Descriptive and inferential statistical results from the survey data show that each of the predictors is significant and do influence sustainable software development. The finding from this study provides insights to electronic industries in implementing sustainable software practice applications.
SMART FOG COMPUTING FOR EFFICIENT SITUATIONS MANAGEMENT IN SMART HEALTH ENVIRONMENTS
Authors: Achouri Mounir , Alti Adel , Derdour Makhlouf , Laborie Sébastien , Roose Philippe
Published: August 2020

Ontologies are considered a backbone for supporting advanced situation management in various smart domains, particularly smart health. It plays a vital role in understanding user context in order to determine patients’ safety, situation identification accuracy, and provide personalized comfort. The smart health domain contains a huge number of different types of context profiles related to interactive devices, linked health objects, and smart-home. The key role of context profiles is to deduce urgent situations that are needed to run adaptation components on a specific smarthealth Fog. Existing platforms and middlewares lack support to efficiently analyze a large number of heterogeneous specific profiles and continuous context changing in near real time. In this paper, we focus on data and dissemination of information from services related to the field of e-health. This paper aims to provide a new generic user situation-aware profile ontology (GUSP-Onto) for a semantic description of heterogeneous users’ profiles with efficient patients’ situation management and health multimedia information dissemination related to smart health services. Based on the users’ situation management ontology, a two-layered architecture was proposed. The first layer is used to achieve a quality diagnosis of urgent situations including a smart fog computing enhanced with semantic profile modeling that offers efficient situation management. The second layer allows a more in-depth situation analysis for patients and enhanced rich services using cloud computing that provides good scalability. The most innovative of this architecture is the potential benefits from the semantic representation to conduct emergency situation knowledge reasoning and ultimately realize early service selection and adaptation process. The experimental results show a decreased time response and an enhanced accuracy of the proposed approach.
EXTENSIONS TO THE K-AMH ALGORITHM FOR NUMERICAL CLUSTERING
Authors: Seman Ali , Mohd Sapawi Azizian
Published: August 2020

The k-AMH algorithm has been proven efficient in clustering categorical datasets. It can also be used to cluster numerical values with minimum modification to the original algorithm. In this paper, we present two algorithms that extend the k-AMH algorithm to the clustering of numerical values. The original k-AMH algorithm for categorical values uses a simple matching dissimilarity measure, but for numerical values it uses Euclidean distance. The first extension to the k-AMH algorithm, denoted k-AMH Numeric I, enables it to cluster numerical values in a fashion similar to k-AMH for categorical data. The second extension, k-AMH Numeric II, adopts the cost function of the fuzzy k-Means algorithm together with Euclidean distance, and has demonstrated performance similar to that of k-AMH Numeric I. The clustering performance of the two algorithms was evaluated on six real-world datasets against a benchmark algorithm, the fuzzy k-Means algorithm. The results obtained indicate that the two algorithms are as efficient as the fuzzy k-Means algorithm when clustering numerical values. Further, on an ANOVA test, k-AMH Numeric I obtained the highest accuracy score of 0.69 for the six datasets combined with p-value less than 0.01, indicating a 95% confidence level. The experimental results prove that the k-AMH Numeric I and k-AMH Numeric II algorithms can be effectively used for numerical clustering. The significance of this study lies in that the k-AMH numeric algorithms have been demonstrated as potential solutions for clustering numerical objects.
SIMILARITY DISTANCE MEASURE AND PRIORITIZATION ALGORITHM FOR TEST CASE PRIORITIZATION IN SOFTWARE PRODUCT LINE TESTING
Authors: Abd Halim Shahliza , Abang Jawawi Dayang Norhayati , Sahak Muhammad
Published: August 2020

To achieve the goal of creating products for a specific market segment, implementation of Software Product Line (SPL) is required to fulfill specific needs of customers by managing a set of common features and exploiting the variabilities between the products. Testing product-by-product is not feasible in SPL due to the combinatorial explosion of product number, thus, Test Case Prioritization (TCP) is needed to select a few test cases which could yield high number of faults. Among the most promising TCP techniques is similarity-based TCP technique which consists of similarity distance measure and prioritization algorithm. The goal of this paper is to propose an enhanced string distance and prioritization algorithm which could reorder the test cases resulting to higher rate of fault detection. Comparative study has been done between different string distance measures and prioritization algorithms to select the best techniques for similarity-based test case prioritization. Identified enhancements have been implemented to both techniques for a better adoption of prioritizing SPL test cases. Experiment has been done in order to identify the effectiveness of enhancements done for combination of both techniques. Result shows the effectiveness of the combination where it achieved highest average fault detection rate, attained fastest execution time for highest number of test cases and accomplished 41.25% average rate of fault detection. The result proves that the combination of both techniques improve SPL testing effectiveness compared to other existing techniques.
ANALYSIS OF USER REQUIREMENTS FOR A MOBILE AUGMENTED REALITY APPLICATION TO SUPPORT LITERACY DEVELOPMENT AMONGST HEARING-IMPAIRED CHILDREN
Authors: Al-Megren Shiroq , Almutairi Aziza
Published: August 2020

Literacy is fundamental for children’s growth and development, as it impacts their educational, societal, and vocational progress. However, the mapping of language to printed text is different for children with hearing impairments. When reading, a hearing-impaired child maps text to sign language (SL) which is a visual language that can benefit from technological advancements, such as augmented reality (AR). There exist several efforts that utilise AR for the purpose of advancing the educational needs of people who are hearing impaired for different SLs. Nevertheless, only a few directly elicit the visual needs of children who are hearing impaired. This study aims to address this gap in the literature with a series of user studies to elicit user requirements for the development of an AR application that supports the literacy development of Arab children who are hearing impaired. Three instruments were utilised in these user studies, each targeting a different group of literacy influencers: questionnaires issued to parents of children with hearing impairments, interviews with teachers, and observations of children who were deaf or hard of hearing. The findings indicated that the parents and teachers preferred Arabic SL (ArSL), pictures, and videos, whereas the children struggled with ArSL and preferred finger-spelling. These preferences highlighted the importance of integrating various resources to strengthen the written Arabic and ArSL literacy of Arab children. The findings have contributed to the literature on the preferences of Arab children who are hearing impaired, their educators, and parents. They also showed the importance of establishing requirements elicited directly from intended users who are disabled to proactively support their learning process. The results of the study were used in the preliminary development of Word & Sign, an AR mobile application intended to aid Arab children who are hearing impaired in their linguistic development.
AN IMPROVED ACTION KEY FRAMES EXTRACTION ALGORITHM FOR COMPLEX COLOUR VIDEO SHOT SUMMARIZATION
Authors: Mizher Manar Abduljabbar Ahmad , Choo Ang Mei , Sheikh Abdullah Siti Norul Huda , Ng Kok Weng
Published: August 2020

Key frame extraction is one of the critical techniques in computer vision fields such as video search, video identification and video forgeries detection. The extracted key frames should be sufficient key frames that preserve main actions in a video with compact representation. The objective of this work is to improve our previous action key frames extraction algorithm (AKF) by adapting a threshold which selects action key frames as final key frames. The threshold adaptation was achieved by using the mean value, the standard deviation, and the L1-norm instead of the comparison of user summaries evaluation method to obtain a fully automatic video summarisation algorithm, and by eliminating the conditions in selecting the final key frames to reduce the complexity of the algorithm. We have validated our proposed Improved AKF on complex colour video shots instead of the simple grey level video shots. The Improved AKF algorithm was able to extract a compact number of action key frames by preventing redundant key frames, reduce processing complexity, and preserve sufficient information about the main actions in a video shot. We then evaluated the Improved AKF algorithm with the-state-of-the-art algorithms in terms of compression ratio using Paul videos and Shih-Tang dataset. The evaluation results showed that the Improved AKF algorithm achieved better compression ratio and retained sufficient information in the extracted action key frames under different testing video shots. Therefore, the improved AKF algorithm is a suitable technique for applications in computer vision fields such as passive object-based video authentication systems
HEART RATE READING PATTERNS OF MOMENT OF INFORMATION OVERLOAD DURING ONLINE INFORMATION SEARCHING
Authors: Abdullah Natrah , Mustapar Nur Amirah
Published: August 2020

User are confronted with information overload during searching for the information in virtual library. Studies claim that information overload leads to the changes in physiological signal of an individual which later result in decreased efficiency of information processing. There is a strong perception that when something changes, there is a moment at which the change occurs. The primary purpose of this research is to detect the existence of moment of the changes occur during searching in virtual library, which focusing on the pattern reflected in the physiological data that can potentially be used as indicator of a signal of information overload. This study adopts user testing methods and methods from psychophysiology. This paper presents result from quantitative analysis through graphs and tables. The results indicate that heart rate measurement is the best measure compare to other physiological measurement and the underlying pattern of the signal of information overload is presenting in a form of matrix. The recommendation of the future work is made which is the patterns can be used to design an application which monitor the information load among the individuals.
AN EXPLORATORY STUDY FOR INVESTIGATING THE ISSUES AND CURRENT PRACTICES OF SERVICE-ORIENTED ARCHITECTURE ADOPTION
Authors: Irwan Hamzah Mohd Hamdi , Baharom Fauziah , Mohd Haslina
Published: August 2020

Service-Oriented Architecture (SOA) is an approach that can be used to integrate different services across the operating system, platform, language, and network which offers some benefits. However, many organizations failed to fully utilize the SOA because the adoption processes are still immature. Therefore, an exploratory study was conducted which aims to investigate the current issues and practices of SOA adoption, the use of maturity levels for assessing the SOA adoption, and the importance of IT and business benefits in the SOA adoption. Thus, the Grounded Theory approach was adapted in the study which involved seven (7) software development companies in Malaysia. In this study, fourteen (14) SOA practitioners with at least three years of experience in the SOA development were interviewed. The collected data was analyzed through three main coding stages which are open, axial and selective coding. The theory emerged from this study reveals the SOA adoption issues, current practices, maturity levels, IT and Business benefits. The study managed to identify five main issues in SOA adoption which are knowledge, infrastructures, costing, readiness, and documentation issues. The study also portrays five practices related to technology, framework, platform, standard, and tools. On the other hand, the results show five IT and business benefits consecutively. The findings from the study have lead to the theories formulation on the SOA adoption which may assist researchers and SOA assessors to continuously improve the quality and maturity of SOA adoption in the future.
A HYBRID ANT COLONY OPTIMIZATION ALGORITHM FOR SOLVING A HIGHLY CONSTRAINED NURSE ROSTERING PROBLEM
Authors: Ramli Razamin , Abd Rahman Rosshairy , Rohim Nurdalila
Published: August 2020

Distribution of work shifts and off days to nurses in a duty roster is a crucial task. In hospital wards, much effort is spent trying to produce workable and quality rosters for their nurses. However, there are cases, such as mandatory working days per week and balanced distribution of shift types that could not be achieved in the manually generated rosters, which are still being practiced. Hence, this study focused on solving those issues arising in nurse rostering problems (NRPs) strategizing on a hybrid of Ant Colony Optimization (ACO) algorithm with a hill climbing technique. The hybridization with the hill climbing is aiming at fine-tuning the initial solution or roster generated by the ACO algorithm to achieve better rosters. The hybrid model is developed with the goal of satisfying the hard constraints, while minimizing the violation of soft constraints in such a way that fulfill hospital’s rules and nurses’ preferences. The real data used for this highly constrained NRPs was obtained from a large Malaysian hospital. Specifically, three main phases were involved in developing the hybrid model, which are generating an initial roster, updating the roster through the ACO algorithm, and implementing the hill climbing to further search for a refined solution. The results show that at a larger value of pheromone, the chance of obtaining a good solution was found with only small penalty values. This study has proven that the hybrid ACO is able to solve NRPs with good potential solutions that fulfilled all the four important criteria, which are coverage, quality, flexibility, and cost. Subsequently, the hybridmodel is also beneficial to the hospital’s management whereby nurses can be scheduled with balanced distribution of shifts, which fulfil their preferences as well.
China and the Emerging Global Energy System
Authors: Vinodan C.
Published: August 2020

Energy and resource security have become critical issues on the economic and strategic agenda in the Asian region as demand and dependence on imported supplies grow. Energy security cuts across many sectors–economic, environmental and national security. Recent increases in energy prices, climate change and a steady escalation in global energy demand –expected to rise by nearly 60% over the next 20 years–have led energy policy-makers across the world to engage in a wide ranging debate over how best to address their country’s future energy requirements. Regional powers, most notably China, have responded with nationalistic strategies to secure control over energy and commodity supplies. China is trying to shift its role from a passive recipient to an active innovator of international energy rules; from an onlooker to an active participant in international energy affairs; and from a receiver to a contributor of international energy policy. Major Asian powers are now redefining their foreign policy to meet the growing energy needs. This is most evident from the grand strategy adopted by China since 1991. China is destined to become a significant player in key energy- and resource-exporting regions, such as the Middle East, Central Asia, Africa, and Latin America. China’s new involvement in these regions could have a powerful impact on the strategic goals of other major power in the world. Asian stability is central to the global prosperity and security; thus, the potential for conflict driven by energy competition and resource insecurity must become a conscious and carefully crafted dimension of energy strategies of all leading players in the region.
The Internationalization of Firms: Some Lessons from ASEAN Firms
Authors: Mohamad Mohamad Hanapi
Published: August 2020

In the last 50 years the debate on the development of international business remained unsettled, especially that concerning the establishment of multinational firms from developing countries. Using the Ownership Locational Internalization (OLI) Model this paper examined the formation of multinational firms from ASEAN countries. We found positive similarities in the advancement of the firm’s specific ownership advantages such as skills, management know-how, R&D and technological capabilities. Unlike the firms from developed countries, the firms from developing countries adopted local elements in their products and services.