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Abstract Research and development are central to national and civilizational advances. Research and development in any nation are measured according to research outcomes, such as publications. Publications are becoming the primary source for evaluating academic staff and academic institutions. Knowledge is the most important resource in universities. Useful knowledge can improve the performance of workers in the organization. Knowledge sharing, or the exchange of information between workers, is important for academics and practitioners. Knowledge acquisition, knowledge sharing and knowledge utilization are three types of knowledge management. Higher education in Saudi Arabia seeks to enhance scientific research as a key component of developing the nation and its universities. In recent years, higher education in Saudi Arabia has initiated multiple projects on enhancing the research productivity of academic staff in Saudi universities. Saudi higher education is seeking to automate all research activities to enhance research performance. This study examines the impact of using knowledge sharing to improve scholarly publications from academic staff in Saudi universities. In addition, this research identifies the factors that lead to enhanced levels of knowledge sharing in Saudi universities. The researchers interviewed 22 academic staff from Taibah University and reviewed papers and academic institutions' websites to identify the important indicators for measuring research productivity. Responses to an online questionnaire from 374 academic staff in Saudi universities were used to determine the relation between knowledge sharing and scholarly publication and to examine the factors that enhance knowledge sharing in Saudi universities. This study used SmartPLS to analyse the results. The results show that computer-mediated communication and nature of knowledge are the primary factors that positively affect knowledge sharing in Saudi universities, and scholarly publications are the primary indicator for measuring research productivity. In addition, knowledge sharing and computer-mediated communication significantly enhance research productivity at Saudi universities. Highlights • Research productivity and development are critical for developing and improving rankings of universities. • It is one of the primary indicators for measuring the performance of academic staff at educational institutions. • The results of this study indicate that scholarly publication is the most important indicator for measuring the level of research productivity in Saudi universities. • The knowledge sharing is one of the most important factors in enhancing communication and sharing knowledge between academic staff at Saudi universities. [ABSTRACT FROM AUTHOR]

The importance of scholarly publications to an institute of higher learning cannot be exaggerated. Publication leads to the creation of new knowledge, increases an institution's reputation, stimulates modernization and innovation, enhance the quality of academic staff and improves the economic status of the institution. Although there has been a satisfactory level of scholarly publication production among the academic staff of Universiti Teknologi Malaysia (UTM), the targets for the key performance indicators have not been achieved as desired by the institution. It is therefore significant to thoroughly understand the influencing factors for research productivity among the university staffs. The aim of this study is to investigate the factors associated with scholarly publication productivity at UTM by determining the influence of three factors, namely personal, environmental and behavioral is associated with scholarly publication productivity among academic staff. The study uses a descriptive survey to collect all relevant data. The target population consists of academic staff of UTM and particularly those from the middle management level. The results show that all three factors had a positive influence on the scholarly publication productivity among the targeted population. [ABSTRACT FROM AUTHOR]

Email is a convenient means of communication throughout the entire world today. The increased popularity of email spam in both text and images requires a real-time protection mechanism for the media flow. The previous approach has been limited by the adaptive nature of unsolicited email spam. This research introduces an email detection system that is designed based on an improvement in the negative selection algorithm. Furthermore, particle swarm optimization (PSO) was implemented to improve the random detector generation in the negative selection algorithm (NSA). The algorithm generates detectors in the random detector generation phase of the negative selection algorithm. The combined NSA–PSO uses a local outlier factor (LOF) as the fitness function for the detector generation. The detector generation process is terminated when the expected spam coverage is reached. A distance measure and a threshold value are employed to enhance the distinctiveness between the non-spam and spam detectors after the detector generation. The implementation and evaluation of the models are analyzed. The results show that the accuracy of the proposed NSA–PSO model is better than the accuracy of the standard NSA model. The proposed model with the best accuracy is further used to differentiate between spam and non-spam in a network that is developed based on a client–server network for spam detection. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a new model for a route planning system based on multi-agent reinforcement learning (MARL) algorithms is proposed. The combined Q-value based dynamic programming (QVDP) with Boltzmann distribution was used to solve vehicle delay's problems by studying the weights of various components in road network environments such as weather, traffic, road safety, and fuel capacity to create a priority route plan for vehicles. The important part of the study was to use a multi-agent system (MAS) with learning abilities which in order to make decisions about routing vehicles between Malaysia's cities. The evaluation was done using a number of case studies that focused on road networks in Malaysia. The results of these experiments indicated that the travel durations for the case studies predicted by existing approaches were between 0.00 and 12.33% off from the actual travel times by the proposed method. From the experiments, the results illustrate that the proposed approach is a unique contribution to the field of computational intelligence in the route planning system. [Copyright &y& Elsevier]

Abstract: Email spam is an increasing problem that not only affects normal users of internet but also causes a major problem for companies and organizations. Earlier techniques have been impaired by the adaptive nature of unsolicited email spam. Inspired by adaptive algorithm, this paper introduces a modified machine learning technique of the human immune system called negative selection algorithm (NSA). A local selection differential evolution (DE) generates detectors at the random detector generation phase of NSA; code named NSA–DE. Local outlier factor (LOF) is implemented as fitness function to maximize the distance of generated spam detectors from the non-spam space. The problem of overlapping detectors is also solved by calculating the minimum and maximum distance of two overlapped detectors in the spam space. From the experiments, the results show that the detection accuracy of NSA–DE is 83.06% while the standard negative selection algorithm is 68.86% at 7000 generated detectors. [Copyright &y& Elsevier]

Abstract: Sometimes in travel planning, finding the best route to the road transportation network by considering the environmental conditions that are affecting the actual time travel of the travellers are vital especially in handling the logistic operations in supply chain management (SCM). Furthermore, the policy strategy is needed in order to influence the managers or drivers to find the optimum and the most effective route for a trip plan in supporting the logistic operations of SCM. In this paper we analyze the effectiveness of the coordination model of the environmental conditions that are affecting for the travelling time based on multi-agent system for a road transportation network for supply chain management. A number of experimental cases have been used to evaluate the proposed approach transportation network problems in some Malaysian cities. Finally, experimental results affirmed that the proposed approach is practical and efficient. [Copyright &y& Elsevier]

Abstract: This paper presented a new prediction model of pressure–volume–temperature (PVT) properties of crude oil systems using type-2 fuzzy logic systems. PVT properties are very important in the reservoir engineering computations, and its accurate determination is important in the primary and subsequent development of an oil field. Earlier developed models are confronted with several limitations especially in uncertain situations coupled with their characteristics instability during predictions. In this work, a type-2 fuzzy logic based model is presented to improve PVT predictions. In the formulation used, the value of a membership function corresponding to a particular PVT properties value is no longer a crisp value; rather, it is associated with a range of values that can be characterized by a function that reflects the level of uncertainty. In this way, the model will be able to adequately model PVT properties. Comparative studies have been carried out and empirical results show that Type-2 FLS approach outperforms others in general and particularly in the area of stability, consistency and the ability to adequately handle uncertainties. Another unique advantage of the newly proposed model is its ability to generate, in addition to the normal target forecast, prediction intervals without extra computational cost. [Copyright &y& Elsevier]

Abstract: This paper presented a new prediction model of pressure–volume–temperature (PVT) properties of crude oil systems using sensitivity based linear learning method (SBLLM). PVT properties are very important in the reservoir engineering computations. The accurate determination of these properties, such as bubble-point pressure and oil formation volume factor, is important in the primary and subsequent development of an oil field. Earlier developed models are confronted with several limitations especially their instability and inconsistency during predictions. In this paper, a sensitivity based linear learning method (SBLLM) prediction model for PVT properties is presented using three distinct databases while comparing forecasting performance, using several kinds of evaluation criteria and quality measures, with neural network and the three common empirical correlations. In the formulation used, sensitivity analysis coupled with a linear training algorithm for each of the two layers is employed which ensures that the learning curve stabilizes soon and behaves homogenously throughout the entire process operation. In this way, the model will be able to adequately model PVT properties faster with high stability and consistency. Empirical results from simulations demonstrated that the proposed SBLLM model produced good generalization performance, with high stability and consistency, which are requisites of good prediction models in reservoir characterization and modeling. [Copyright &y& Elsevier]

This piece of research is focused on providing a review of the software solutions that exist when it comes to mechanisms that govern the management of intellectual property. It takes a deeper look at requirements within the university transfer office domain. Universities are a genuine source of knowledge. They have been identified not just as knowledge hubs but also as the spaces where innovations are born. These innovations then make their way into the market through the different industries they serve, becoming products that gain the attention of actual consumers. Given the magnitude of the innovations being developed in different universities around the world, it is imperative that mechanisms for the safety of this knowledge also be put into place. The world has evolved into a knowledge economy, where knowledge is an asset and something that can create profitability. This means that not protecting the knowledge that is being created can only lead to a loss in the future. Managing intellectual property, therefore, is not only a matter of procedure but one of great importance. Solutions that are easily accessible, cost-effective, and time-effective are essential. Thus, the goal of this article is to provide an overview of existing software (SW) solutions suitable for managing technology and knowledge transfer at universities based on requirements from the technology transfer office at university and specified using the model of the whole process from inventor until patent office. University Technology Transfer (TT) is a bit different in comparison to TT at companies. This gap is shown in the article using modelling of process, states, and class diagrams of a university Technology Transfer Office (TTO). Based on process definition and TTO responsibilities, a review of available SW solutions is done for 10 selected examples, as well as a related literature analysis. Findings and implications are summarized at the end of article in the context of specific needs of a university TTO, while major implications are shown as a problem of priority definition of every university TTO, namely, in the sense of value of SW solutions for intellectual property (IP) management, reporting possibilities, and representing IP and know-how. [ABSTRACT FROM AUTHOR]

Cross-lingual sentiment classification aims to utilize annotated sentiment resources in one language (typically English) for sentiment classification of text documents in another language. Most existing research works rely on automatic machine translation services to directly project information from one language to another. However, due to the existence of differing linguistic terms and writing styles between different languages, translated data cannot cover all vocabularies which exist in the original data. Further, different term distribution between translated data and original data can lead to low performance in cross-lingual sentiment classification. To overcome these problems, we propose a new model which uses labelled data from multiple source languages in a multi-view semi-supervised learning approach so as to incorporate unlabelled data from the target language into the learning process. The proposed model was applied to book review datasets in four different languages. Experiments have shown that our model can effectively improve the cross-lingual sentiment classification performance in comparison with some baseline methods. [ABSTRACT FROM AUTHOR]

Deciding the signal length is an important challenge for one-class time-series classification (OCTSC). This paper aims to develop an OCTSC algorithm that does not require model retraining for different signal lengths. For this purpose, a distance-based one-class time-series classification approach using local cluster balance (OCLCB) is proposed. OCLCB extracts feature vectors, namely, local cluster balance (LCB), from the clustering results of sliding windows. K-means clustering is applied to the sliding windows extracted from the training signal. Then, the local prototype (LP) is calculated as the average of the local cluster balance (LCB) in the training data. Unseen scores are computed as the distance metrics between LP and LCBs in the testing data. Since the sliding window size is independent of the entire signal size, OCLCB does not need to retrain the model. This aspect gives the benefit of reducing the parameter tuning costs. The source code is uploaded at https://github.com/ToshiHayashi/OCLCB. [ABSTRACT FROM AUTHOR]

In scholars' recommender systems, acquisition knowledge for construction profiles is crucial because profiles provide fundamental information for accurate recommendation. Despite the availability of various knowledge resources, identification and collecting extensive knowledge in an unobtrusive manner is not straightforward. In order to capture scholars' knowledge, some questions must be answered: what knowledge resource is appropriate for profiling, how knowledge items can be unobtrusively captured, and how heterogeneity among different knowledge resources should be resolved. To address these issues, we first model the scholars' academic behavior and extract different knowledge items, diffused over the Web including mediated profiles in digital libraries, and then integrate those heterogeneous knowledge items by Wikipedia. Additionally, we analyze the correlation between knowledge items and partition the scholars' research areas for multi-disciplinary profiling. Compared to the state-of-the-art, the result of empirical evaluation shows the efficiency of our approach in terms of completeness and accuracy. [ABSTRACT FROM AUTHOR]