Most Admired Training Transfer Enterprise Model in Agribusiness and Agro-technology Industry: A Conceptual Paper

AbstractThe recognition of training as an important development of human resource in implementing the quality of its human capital needs is no longer a new issue. However, despite several attempts trying to improve the success of training transfer onto the job, majority of the employees attending training had indicated that they had less successfully transferred the knowledge, skills and attitudes they have learnt and even further minimal change in behavior in their job-related performance. In this paper, the qualitative and quantitative investigation was conducted to examine the extent of training transfer knowledge activities (in compiling, gathering, collating and synthesising the employees experience, knowledge, skills and abilities) among executives at selected agribusiness and agro-technology based organizations. Final knowledge on training transfer performance and program were further formulated with feedback from training transfer expert. This paper contributes to the alternate model in favour of innovative and sustainable governance of a holistic agribusiness policy framework

Quality-improved and secure multicast delivery method in mobile IPv6 networks

With widespread deployment of multicast over Wireless Local Area Networks (WLANs), several issues including fixed data rate transmission, multicast key distribution security, and overlapped multicast address have to be addressed for accommodating an efficient multicast scheme for WLANs. The latter problem can be addressed by utilizing Internet Protocol version 6 (IPv6) which provides significantly more address space compare to existing IPv4. However, in multicast IPv6 over WLANs, when a mobile moves to the border of the multicast group, the data are transmitted at the lowest base rate to support more coverage area, leading to poor Quality of Service (QoS). In this paper, a novel multicast data delivery method over WLANs based on IPv6 protocol is proposed to overcome the problem of fixed base rate and security key distribution in WLANs. Specifically, the proposed method dictates a WLAN Access Point (AP) to encapsulate the multicast packets into unicast Medium Access Control (MAC) packets, and subsequently forward them to the mobile host. In addition, the AP is also responsible for updating and distributing security keys whenever a join or leave operation occurs. The results from our test-bed indicate that the proposed method significantly improve the QoS metrics (i.e., throughput and delay) compared to the existing multicast scenario, as well as able to reduce the amount of generated keys in the networks

Intrusion detection based on bidirectional long short-term memory with attention mechanism

With the recent developments in the Internet of Things (IoT), the amount of data collected has expanded tremendously, resulting in a higher demand for data storage, computational capacity, and real-time processing capabilities. Cloud computing has traditionally played an important role in establishing IoT. However, fog computing has recently emerged as a new field complementing cloud computing due to its enhanced mobility, location awareness, heterogeneity, scalability, low latency, and geographic distribution. However, IoT networks are vulnerable to unwanted assaults because of their open and shared nature. As a result, various fog computing-based security models that protect IoT networks have been developed. A distributed architecture based on an intrusion detection system (IDS) ensures that a dynamic, scalable IoT environment with the ability to disperse centralized tasks to local fog nodes and which successfully detects advanced malicious threats is available. In this study, we examined the time-related aspects of network traffic data. We presented an intrusion detection model based on a two-layered bidirectional long short-term memory (Bi-LSTM) with an attention mechanism for traffic data classification verified on the UNSW-NB15 benchmark dataset. We showed that the suggested model outperformed numerous leading-edge Network IDS that used machine learning models in terms of accuracy, precision, recall and F1 score

A many-objective ensemble optimization algorithm for the edge cloud resource scheduling problem

An edge cloud architecture plays a key role in improving the user task computing service system by combining the powerful data processing capability of cloud centres with the low latency of edge computing. Existing methods for maximizing the efficiency of an edge cloud architecture take into account time and task parameters but ignore other factors such as load balancing, cost, and user satisfaction when scheduling resources. In this work, we propose a many-objective resource scheduling model for optimizing the performance of an edge cloud architecture, which takes into account the time spent on task, cost, load balance, user satisfaction, and trust measurement. The resource scheduling model converges to the optimal solution using a novel many-objective ensemble optimization algorithm based on a dynamic selection mechanism. The study also explores the support set convergence of eight evolutionary operators using the ensemble algorithm. The model solutions are dynamically updated with the help of the dynamic integration probability, and then a selection criteria is used to pick the best solutions from the pool of generated solutions. Two simulations on a benchmark dataset are used to verify the usefulness and performance of the designed algorithm. Our approach was able to locate more than half of the best solutions on the benchmark functions, and it also showed to be a better model solution than the some of the popular many-objective algorithms for dealing with the edge cloud resource scheduling problem, according to the results obtained from the simulations

Teachers and students perception towards bullying in Batu Pahat district secondary school

This study also attempts to identify the perception of the students and teachers about bullying in secondary schools. Beside that this study also attempts to identify students perception about safety issues at secondary schools. 80 teachers and 480 students from eight secondary schools in Batu Pahat were randomly selected in this study. All information was gathered through Peer Relations Questionnaire - PRQ and The Nature and Prevalence of Bullying in Schools Questionnaire. The alpha cronbach for these two instruments were 0.7010 and 0.8097. Results have shown that there is a different perception about the prevalence of bullying among secondary school students and teachers. Students reported that the overall rate of the bullying prevalence were at moderate level where as teachers reported the bullying prevalence overall rate were at low level. There is no significant on the prevalence of bullying between male and female students but there is a significant difference on the prevalence between verbal bullying and physical bullying. Verbal bullying was seen more frequent compare to physical bullying

From genomes to post-processing of Bayesian inference of phylogeny

Life is extremely complex and amazingly diverse; it has taken billions of years of evolution to attain the level of complexity we observe in nature now and ranges from single-celled prokaryotes to multi-cellular human beings. With availability of molecular sequence data, algorithms inferring homology and gene families have emerged and similarity in gene content between two genes has been the major signal utilized for homology inference. Recently there has been a significant rise in number of species with fully sequenced genome, which provides an opportunity to investigate and infer homologs with greater accuracy and in a more informed way. Phylogeny analysis explains the relationship between member genes of a gene family in a simple, graphical and plausible way using a tree representation. Bayesian phylogenetic inference is a probabilistic method used to infer gene phylogenies and posteriors of other evolutionary parameters. Markov chain Monte Carlo (MCMC) algorithm, in particular using Metropolis-Hastings sampling scheme, is the most commonly employed algorithm to determine evolutionary history of genes. There are many softwares available that process results from each MCMC run, and explore the parameter posterior but there is a need for interactive software that can analyse both discrete and real-valued parameters, and which has convergence assessment and burnin estimation diagnostics specifically designed for Bayesian phylogenetic inference. In this thesis, a synteny-aware approach for gene homology inference, called GenFamClust (GFC), is proposed that uses gene content and gene order conservation to infer homology. The feature which distinguishes GFC from earlier homology inference methods is that local synteny has been combined with gene similarity to infer homologs, without inferring homologous regions. GFC was validated for accuracy on a simulated dataset. Gene families were computed by applying clustering algorithms on homologs inferred from GFC, and compared for accuracy, dependence and similarity with gene families inferred from other popular gene family inference methods on a eukaryotic dataset. Gene families in fungi obtained from GFC were evaluated against pillars from Yeast Gene Order Browser. Genome-wide gene families for some eukaryotic species are computed using this approach. Another topic focused in this thesis is the processing of MCMC traces for Bayesian phylogenetics inference. We introduce a new software VMCMC which simplifies post-processing of MCMC traces. VMCMC can be used both as a GUI-based application and as a convenient command-line tool. VMCMC supports interactive exploration, is suitable for automated pipelines and can handle both real-valued and discrete parameters observed in a MCMC trace. We propose and implement joint burnin estimators that are specifically applicable to Bayesian phylogenetics inference. These methods have been compared for similarity with some other popular convergence diagnostics. We show that Bayesian phylogenetic inference and VMCMC can be applied to infer valuable evolutionary information for a biological case – the evolutionary history of FERM domain.QC 20160201</p

From genomes to post-processing of Bayesian inference of phylogeny

Life is extremely complex and amazingly diverse; it has taken billions of years of evolution to attain the level of complexity we observe in nature now and ranges from single-celled prokaryotes to multi-cellular human beings. With availability of molecular sequence data, algorithms inferring homology and gene families have emerged and similarity in gene content between two genes has been the major signal utilized for homology inference. Recently there has been a significant rise in number of species with fully sequenced genome, which provides an opportunity to investigate and infer homologs with greater accuracy and in a more informed way. Phylogeny analysis explains the relationship between member genes of a gene family in a simple, graphical and plausible way using a tree representation. Bayesian phylogenetic inference is a probabilistic method used to infer gene phylogenies and posteriors of other evolutionary parameters. Markov chain Monte Carlo (MCMC) algorithm, in particular using Metropolis-Hastings sampling scheme, is the most commonly employed algorithm to determine evolutionary history of genes. There are many softwares available that process results from each MCMC run, and explore the parameter posterior but there is a need for interactive software that can analyse both discrete and real-valued parameters, and which has convergence assessment and burnin estimation diagnostics specifically designed for Bayesian phylogenetic inference. In this thesis, a synteny-aware approach for gene homology inference, called GenFamClust (GFC), is proposed that uses gene content and gene order conservation to infer homology. The feature which distinguishes GFC from earlier homology inference methods is that local synteny has been combined with gene similarity to infer homologs, without inferring homologous regions. GFC was validated for accuracy on a simulated dataset. Gene families were computed by applying clustering algorithms on homologs inferred from GFC, and compared for accuracy, dependence and similarity with gene families inferred from other popular gene family inference methods on a eukaryotic dataset. Gene families in fungi obtained from GFC were evaluated against pillars from Yeast Gene Order Browser. Genome-wide gene families for some eukaryotic species are computed using this approach. Another topic focused in this thesis is the processing of MCMC traces for Bayesian phylogenetics inference. We introduce a new software VMCMC which simplifies post-processing of MCMC traces. VMCMC can be used both as a GUI-based application and as a convenient command-line tool. VMCMC supports interactive exploration, is suitable for automated pipelines and can handle both real-valued and discrete parameters observed in a MCMC trace. We propose and implement joint burnin estimators that are specifically applicable to Bayesian phylogenetics inference. These methods have been compared for similarity with some other popular convergence diagnostics. We show that Bayesian phylogenetic inference and VMCMC can be applied to infer valuable evolutionary information for a biological case – the evolutionary history of FERM domain.QC 20160201</p

Discipline problems among secondary school students in Johor Bahru, Malaysia

The indiscipline problem in schools is ranked as a major problem among students of primary and secondary schools in Malaysia. Disruptive behavior is a concern to schools and parents and to fellow pupils, whose education may be adversely affected.Objective:The objectives of this study were to identify the level of students discipline problems and dominant factors attributing to the students discipline problems among the secondary school students in Johor in Malaysia.Material and Methods:The study was carried out to 90 discipline teachers from several secondary schools around Johor using questionnaires. The data collected were analyzed by Statistical Packages for Social Science (SPSS) in forms of frequency, percentage and mean value. The findings showed that the level of discipline problems among students was quitehigh especially for absenteeism problem.Results and Discussion:The results also showed that the students with family problems,always hung out with friends and others faced high level of discipline problems compared to students with no such problems. Some of the students with records in discipline problems showed that they did not faced any difficulties in learning as they passed in their examinations and the discipline problems did not depend on parents’ education background because it was not necessary for the students to have discipline Conclusions: Lastly, some recommendations also had been put forward as guidance to the research organization and future researches

The effects of various modes of absenteeism problem in school on the academic performance of students in secondary schools

This article highlights some of the major research findings regarding the problem of school discipline such as truancy and demonstrates why it is important that schools and communities work to prevent and reduce absenteeism. Beside that this article demonstrated the types of truant and activities done during truant against academic achievement among the lower secondary students.. The respondents for this study were 80 students from form 1, 2 and 3. They were randomly chosen as respondents through simple random sampling. The data collected is analyzed by using the Statistical Package for Social Science for Windows (SPSS 11.5) to find the mean, frequency and standard deviation by using descriptive statistics. The findings showed that the causes of truant are at the medium level and types of truant are at the low level. Activities done during truant such as helping the family, joining the negative groups, crime are at the low level and working part-time together with loafing are at the medium leve