bluuebunny/crossref_metadata_2025_split huggingface dataset:
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.
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.
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.
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.
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.
Bilingual Test as a Test Accommodation to Determine the Mathematics Achievement of Mainstream Students With Limited English Proficiency
Authors: Shanmugam S. Kanageswari Suppiah , Lan Ong Saw 
Published: August 2020

Purpose – This study aims to investigate the validity of using bilingual test to measure the mathematics achievement of students who have limited English proficiency (LEP). The bilingual test and the English-only test consist of 20 computation and 20 word problem multiple-choice questions (from TIMSS 2003 and 2007 released items. The bilingual test consists of items in Malay and English languages. The English teachers classified their students into LEP and non-LEP groups. Methodology – A total of 2,021 LEP and 2,747 non-LEP students from 34 schools were identified. Spiral administration was employed. A total of 2,399 students sat for the bilingual test and 2,369 for the English-only test. The scores were linked using RAGE-RGEQUATE version 3.22. Findings – Findings revealed that in the word problem testlet, the bilingual test was one unit easier for LEP students, probably indicating that the Malay language adaptation assisted them. For the non-LEP students, the bilingual version did not provide any added advantage to them as they did not show elevated achievement score. Significance – This study indicates bilingual test can be an appropriate test accommodation that validly measures both LEP and non-LEP students’ mathematics achievement in countries where English is not the native language but is used as the instructional language.
Motivational Factors in Continuing Education an Academic Achievement of Adult Learners
Authors: Pang Vincent , Lee Pei Ling 
Published: August 2020

Purpose – The purpose of this study is to examine the relationship between motivational factors in continuing education and academic achievement of adult learners. The study is conducted due to a lack of research pertaining to academic achievement among adult learners particularly in Malaysia. Methodology – A random sample of 150 part-time adult learners from a public university in Kota Kinabalu, Sabah, represented 202 part-time adult learners in the areas of social sciences and humanities at the university. A thirty-item questionnaire, adapted from Boshier’s Education Participation Scale(EPS) (1991) and Neill’s The University Students Motivation and Satisfaction Questionnaire 2 (TUSMSQ2) (2004) was used to examine their motivational factors i.e. personal development, career advancement, social pressure, social and communication improvement, and escapism. Self-reported cumulative grade point average (CGPA) was utilized as the measurement of academic achievement. Rasch analysis reaffirmed the content validity of items in the questionnaire. Findings – The findings indicate that personal development and social pressure are the best predictors of academic achievement among adult learners. The motivational factors that are not significant as predictors are career advancement, social and communication improvement, and escapism. Adult learners with higher motivation for personal development (intrinsic motivation) perform better academically. Conversely, adult learners with higher motivation based on social pressure (extrinsic motivation) perform lower academically. Intrinsic motivation seems to be more powerful in enhancing academic achievement of adult learners as compared to extrinsic motivation. Significance – This finding is crucial for program planners and adult educators. They will be able to lead adult learners to be more intrinsically motivated in continuing education. In this way, they will optimize their learning outcomes and develop quality human capital in Malaysia.