Faculty of Cognitive Sciences & Human Development

Transformational leadership has two salient characteristics: intellectual stimulation and individualized consideration. Recent studies in this area reveal that the effect of such leadership characteristics on job performance is indirectly affected by empowerment. The nature of this relationship is interesting, but little is known about the mediating effect of empowerment in leadership management models. Therefore, this study was conducted to measure the effect of transformational leadership characteristics and empowerment on service quality by using 110 usable questionnaires gathered from employees working in a city based local authority (CBLAUTHORITY) in Sarawak, Malaysia. Outcomes of a stepwise regression analysis showed that the relationship between empowerment and selected transformational leadership characteristics (i.e. intellectual stimulation and individualized consideration) is positively and significantly correlated with service quality. The result confirms that empowerment does act as a full mediating variable in the leadership model of the organization under study. This paper also discusses implications and limitations, as well as directions for future research.

Transformational leadership has two salient characteristics: intellectual stimulation and individualized consideration. Recent studies in this area reveal that the effect of such leadership characteristics on job performance is indirectly affected by empowerment. The nature of this relationship is interesting, but little is known about the mediating effect of empowerment in leadership management models. Therefore, this study was conducted to measure the effect of transformational leadership characteristics and empowerment on service quality by using 110 usable questionnaires gathered from employees working in a city based local authority (CBLAUTHORITY) in Sarawak, Malaysia. Outcomes of a stepwise regression analysis showed that the relationship between empowerment and selected transformational leadership characteristics (i.e. intellectual stimulation and individualized consideration) is positively and significantly correlated with service quality. The result confirms that empowerment does act as a full mediating variable in the leadership model of the organization under study. This paper also discusses implications and limitations, as well as directions for future research.

The objective of this study is to identify the perceptions of Generation Y employees regarding work-life balance practices. As increasing numbers of Generation Y enter the workforce, questions regarding how they perceive work, family and self-related issues, challenges and coping strategies in balancing personal and professional commitments became issues of interests for employers. This study uses interviews to collect data from six informants in Generation Y and the data were analyzed through content analysis. The results indicate that Generation Y employees do indeed have issues and challenges with balancing work and family. However, unlike the older generation of employees, Generation Y employees perceive managing work-life balance only through segmentation or separation strategies. This particular finding indicates that if organizations want to attract and retain these employees they should adapt a strategy specifically designed to target this employee group.  Keywords: Gen Y; w...

The purpose of this study is to explore the possible influence of women’s entrepreneurial orientations (EO) on their decision to opt out from organizational careers to self-employment. It examines how women account for these transitions, reasons for opting for self-employment and experiences of self-employment. This research employs the qualitative methodology using semi-structured interviews as the main data collection tool. The study revealed that the entrepreneurial orientations of the women under study are commonly associated with the generally successful entrepreneurial behaviour of risk-taking, creative, proactive, competitive, aggressive and confidence. This suggests that enterprises and their women owners may benefit from efforts to increase their levels of entrepreneurial orientation in order to survive the dynamic, fast-paced and complex business environment characterized by shorter life cycles, globalization, and continuous improvements in technology. Hence for the women ...

Kajian ini bertujuan untuk meneroka proses perkembangan yang dialami oleh individu yang terlibat dan berada di tempat kejadian semasa serangan pengganas di Lahad Datu, Sabah pada Mac 2013 yang lalu. Objektif kajian ini adalah untuk meneroka bahagian kehidupan yang berkembang selepas berlakunya trauma serta meneroka proses yang dilalui oleh mangsa serangan tragedi Lahad Datu dalam membentuk makna baru dalam kehidupan berpandukan Model Pembentukan Makna oleh Park (2013). Kajian ini menekankan pengalaman individu selepas berlakunya serangan pengganas dan proses mereka mengatasi trauma dan ketakutan tersebut. Kajian ini melibatkan lima (5) orang responden yang telah ditemu bual dengan menggunakan kaedah temu bual semi struktur. Kajian ini mendapati terdapat aspek kehidupan individu yang berkembang melalui proses – proses tertentu terutama sekali dari aspek kerohanian, perhubungan sosial dan penjagaan kendiri. Sekaligus, ia menunjukkan bahawa objektif kajian ini tercapai.

Job recruitment portals become the main recruitment channel in most of the organizations nowadays because they offer many advantages to recruiters and job applicants. An outstanding recruitment system should be able to filter and recommend the best potential candidates for a job vacancy so that it can avoid hiring of inappropriate individuals or miss out the good candidates. Nevertheless, most of the existing job portals do not cover the unskilled job sectors. Matching unskilled jobs to applicants is challenging because the selection criteria can be very subjective and difficult to specify in terms of professional qualifications. In this paper, Kansei Engineering (KE) Model is applied to find the most prominent personality traits that are preferred by employers in different unskilled job categories in Malaysia. We have identified most prominent 20 Kansei words related to personality traits that are importance to six main industries of unskilled workers. The six unskilled sectors involved are construction, hotel, manufacturing, restaurant, sales and service. 60 employers from the six sectors were interviewed to rank the 50 personality traits identified. Those ranked personality traits can potentially be used for recruitment selection and filtering of unskilled job applicants.

Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. Neural network approaches, while reasonably accurate at classification, give no information about the relationship between the unseen case and the classified items that is useful to biologist. In contrast, in this paper we use a generalized radial basis function (GRBF) neural network architecture that generates fuzzy classification rules that could be used for further knowledge discovery. Our proposed techniques were evaluated using protein sequences with ten classes of super-families downloaded from a public domain database, and the results compared favorably with other standard machine learning techniques.

Background: Discrimination of transcription factor binding sites (TFBS) from background sequences plays a key role in computational motif discovery. Current clustering based algorithms employ homogeneous model for problem solving, which assumes that motifs and background signals can be equivalently characterized. This assumption has some limitations because both sequence signals have distinct properties.

Abstract Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure similarity between an unseen protein sequence and identified protein sequences. Neural network approaches, while reasonably accurate at classification, give no information about the relationship between the unseen case and the classified items that is useful to biologist. In contrast, in this paper we use a ...

ABSTRACT One of the collection joins types in Object Oriented Database (OODB) is collection equi-join. The main feature of collection joins is that they involve collection types. In this paper we present our experience in implementing collection equi-join algorithms by using Message Passing Interface (MPI). In particular, it layouts the fundamental techniques that are used in the implementation and that may be applicable to other collection joins. Two collection equi-joins discussed here are Double Sort- merge and Sort Hash Join. The implementation was done on a clustered environment and employed a data parallelism concept.

Abstract. A protein super-family consists of proteins which share amino acid sequence homology and which may therefore be functionally and structurally re-lated. One of the benefits from this category grouping is that some hint of func-tion may be deduced for individual members ...

Enhancers are indispensable elements in various developmental stages, orchestrating numerous biological processes via the elevation of gene expression with the aid of transcription factors. Enhancer variations have been linked to various onset of genetic diseases, highlighting their equal importance as the coding regions in the genome. Despite the first enhancer, SV40 been discovered four decades ago, the progress in enhancer identification and characterization is still in its infancy. As more genome sequences are made available, especially from that of the non-human primates, we can finally study the enhancer landscape of these primates that differs evolutionarily from that of human. One interesting genome to investigate is that of the proboscis monkey as it is deemed as one of the most ancient primates alive to date with unique morphological and dietary characteristics; it is also one of the IUCN endangered species with the strong demands for immediate conservation. In this review, we provide some justifications and considerations of selecting the proboscis monkey as a model for enhancer landscape discovery. It is hoped that more conservation research and protective measures can come in time to prevent this species from extinction.

Automated Essay Scoring (AES) is a service or software that can predictively grade essay based on a pre-trained computational model. It has gained a lot of research interest in educational institutions as it expedites the process and reduces the effort of human raters in grading the essays as close to humans' decisions. Despite the strong appeal, its implementation varies widely according to researchers' preferences. This critical review examines various AES development milestones specifically on different methodologies and attributes used in deriving essay scores. To generalize existing AES systems according to their constructs, we attempted to fit all of them into three frameworks which are content similarity, machine learning and hybrid. In addition, we presented and compared various common evaluation metrics in measuring the efficiency of AES and proposed Quadratic Weighted Kappa (QWK) as standard evaluation metric since it corrects the agreement purely by chance when estimate the degree of agreement between two raters. In conclusion, the paper proposes hybrid framework standard as the potential upcoming AES framework as it capable to aggregate both style and content to predict essay grades Thus, the main objective of this study is to discuss various critical issues pertaining to the current development of AES which yielded our recommendations on the future AES development.

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to date. One of the most efficacious treatment for naïve or pre-treated HIV patients is with the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is lifelong , the emergence of HIV-1 strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r 2 = 0.998, q 10CV 2 = 0.721, q external_test 2 = 0.754) and a boosted K* algorithm (r 2 = 0.987, q 10CV 2 = 0.721, q external_test 2 = 0.758) to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top ranked compounds were further evaluated for their target engagement activity using molecular docking studies and their potential as INSTIs evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.

Epigenetic marks like chromatin remodelers and histone marks are eminent indicator of enhancers' activity. K-mer is a simple representation of DNA sequences that has been useful for computational epigenetic marks prediction. While many studies have been utilizing k-mer as feature of epigenetic marks prediction, no comprehensive studies have been done to show sophistication of k-mers feature of epigenetic sequences with learning models. This study performs a comprehensive evaluation of diverse learning models using k-mer feature to draw comparison between different learning models and employs blended ensemble learning technique to improve overall performance. Our results show that each learning model ranked quite differently the important of different k-mers for discriminative purpose. The blended ensemble increases the performance of enhancer classification significantly compared with using individual classifiers.

Genome annotation is an essential task for understanding and analyzing the whole genome and its function. We have sequenced the complete proboscis Monkey (Nasalis larvatus) genome due to its importance for medical and evolutionary studies. We have performed an initial annotation of the genes genome using the MAKER gene annotation pipeline. 3084 genes were predicted from chromosome 18 of the genome using six eukaryotic model species. Intergenic regions possibly enriched with enhancers are then predicted using five different tools: DeepBind, LS-GKM, GMFR-CNN, CSI-ANN and iEnhancer-2L. These tools find the enhancers of the complex intergenic regions based on epigenetic features, in which intergenic regions are seen as a potential region for enhancers with a certain epigenetic features bound to it. Empirical results demonstrate competitive performance using different prediction tools with multiple epigenetic features to predict the enhancers for chromosome 18 in proboscis monkey. Based ...

Unravelling gene expression has become a critical procedure in bioinformatics world today and required continuous efforts to form a complete picture of enhancers. Enhancers are explicit patterns of gene expression that bound by activators to stimulate transcription. It could reside in upstream or downstream thousands of base pairs away without any fixed position. Therefore, the identification task of enhancers is extremely challenging. The inclusion of gaps in motif identification improved the overall accuracy and sensitivity, however, this feature is not fully utilised in deep learning method yet. Deep learning, is a powerful machine learning technique that has been actively used in image recognition and this technique has begun to shed light in bioinformatics. The expressiveness of deep learning enables higher feature learning from lower level ones. As a result, an integration of gapped motif feature representation (GMFR) and deep learning approach called deep convolutional neural...

Computational DNA motif discovery is important because it allows for speedy and cost effective analysis of sequences enriched with DNA motifs, performs large scale comparative studies, and tests hypotheses on biological problems. In this work, we provide a comprehensive survey on DNA motif discovery using genetic algorithm (GA). According to the ways of how the solution domain are encoded, we categorize existing GA-based motif discovery techniques into search for consensus and search by position (matrix). Within each category, we make distinctive algorithmic comparisons based on model representations, fitness functions, genetic operators, data post-processing, as well as the experimental results. Moreover, we discuss the strengths and weaknesses of different approaches with recommendations for practical use. This survey paper is useful as guideline for practitioners who would like to design GA solutions for DNA motif prediction in the future.