A Framework for Research Supervision

One of the main challenges that are encountered in research development is the management of research activities. Supervisors often have difficulties in managing schedules, issues and supervision of different research activities. This is compounded by students’ poor research skills. Consequently, in this paper, we propose a knowledge management framework to point out, track, and monitor various research supervision activities. The proposed framework consists of two layers, abstract and detail. The abstract layer consists of six stages which are; basement stage, review stage, data collection stage, data analysis stage, development stage, and testing and validation stage. These stages, according to our framework, are mandatory; in other words, any research must go through the stages. To complete the task of each stage, a number of steps are defined, which constitute the detail layer. A supervisor is able to pick up appropriate steps (and not all suggested steps) from the detail layer since the complexity varies from one research to another. We discuss the results of our findings in conceiving the framework. Keywords: research development, development stage, research activities, supervision managemen

An Investigation on Measuring Accuracy of Explicit Knowledge Sources in Universities

At present, e-libraries contain huge volumes of articles that may be irrelevant or inaccurate to academics’ research areas. The academics may spend extensive time and efforts to retrieve knowledge contents from these articles. Thus, universities need to manage their articles’ libraries effectively to deliver the most suitable explicit contents based on academics’ research areas. Consequently, in this paper, we identify potential factors that could contribute to candidate relevant and accurate articles that meet employees need. These factors work on measuring the accuracy of articles to identify the most relevant articles from the huge resources of online articles. Therefore, the search time and efforts could be reduced through classifying articles based on the contents’ knowledge using practical measurement factors. To address the objectives of this research, quantitative and qualitative studies are made to collect data using questionnaire survey and interview of experts in knowledge management. The results of the data analysis are used to identify the relevant factors and to compute the accuracy of articles based on these factors. Keywords: knowledge management, explicit knowledge, knowledge measurement

An Automated Negotiation-based Framework via Multi-Agent System for the Construction Domain

In this paper, we propose an automated multi-agent negotiation framework for decision making in the construction domain. It enables software agents to conduct negotiations and autonomously make decisions. The proposed framework consists of two types of components, internal and external. Internal components are integrated into the agent architecture while the external components are blended within the environment to facilitate the negotiation process. The internal components are negotiation algorithm, negotiation style, negotiation protocol, and solution generators. The external components are the negotiation base and the conflict resolution algorithm. We also discuss the decision making process flow in such system. There are three main processes in decision making for specific projects, which are propose solutions, negotiate solutions and handling conflict outcomes (conflict resolution). We finally present the proposed architecture that enables software agents to conduct automated negotiation in the construction domain

A Bayesian Hau-Kashyap Approach for Hepatitis Disease Detection

World Health Organization reported that viral hepatitis affects 400 million people globally. Every year, 610 million people are newly infected. In this research, we integrate a Bayesian theory and Hau-Kashyap approach for detecting hepatitis and displaying the result of calculation process. The basic idea of the Bayesian theory is using the known prior probability and conditional probability density parameter based on the Bayes theorem to calculate the corresponding posterior probability and then obtain the posterior probability to infer and make decisions. Bayesian methods combine present knowledge, prior probabilities, with additional knowledge derived from new data, the likelihood function. Hau-Kashyap presented an alternative Dempster-Shafer combination rule, and the alternative combination rule is that with the use of this alternative rule, the intersection conflict is put into the union. In this chapter, we get basic possibility assignment value from Bayesian probability. The result reveals that a Bayesian Hau-Kashyap approach has successfully identified the existence of hepatitis

Multi-perspective evaluation of integrated active cooling systems using fuzzy decision making model

As global median temperatures continue to rise, the demand for active cooling systems (ACs) is increasing. These systems are particularly prevalent in developed countries for maintaining comfort during hot weather. Various ACs technologies are available, and assessing their performance in multi-perspective settings is necessary to determine the best option for intended usage. This requires an evaluation platform for assessment. This paper presents a novel multi-criteria decision-making (MCDM) model based on a new integrated 2-tuple linguistic Pythagorean fuzzy-weighted zero-inconsistency (2 TLP-FWZIC) and modified 2-tuple linguistic Pythagorean fuzzy multi-attributive border approximation area comparison (2TLPF-MABAC). The former is used to determine the importance of assessment criteria, while the latter is employed for selecting the optimal ACs using the obtained weights. The first-level weighting results reveal that performance criteria were predominantly favored for assessment, with ‘energy performance’ acquiring the most significant weight (0.2487) among all performance criteria. In terms of ACs selection results, among the 20 tested and assessed systems, the ‘geothermal borehole electricity-based ACs’ obtained the highest score value (0.1296), while the ‘window packaged electricity-based ACs’ had the lowest score (-0.0515). The robustness of the results was confirmed through sensitivity analysis

A Review of Norms and Normative Multiagent Systems

Norms and normative multiagent systems have become the subjects of interest for many researchers. Such interest is caused by the need for agents to exploit the norms in enhancing their performance in a community. The term norm is used to characterize the behaviours of community members. The concept of normative multiagent systems is used to facilitate collaboration and coordination among social groups of agents. Many researches have been conducted on norms that investigate the fundamental concepts, definitions, classification, and types of norms and normative multiagent systems including normative architectures and normative processes. However, very few researches have been found to comprehensively study and analyze the literature in advancing the current state of norms and normative multiagent systems. Consequently, this paper attempts to present the current state of research on norms and normative multiagent systems and propose a norm’s life cycle model based on the review of the literature. Subsequently, this paper highlights the significant areas for future work

Decarbonizing the global electricity sector through demand-side management : a systematic critical review of policy responses

This paper provides an up-to-date and comprehensive systematic literature review (SLR) of the existing research on long-term electricity decarbonization which is dominated by the global scenarios of Integrated Assessment Models. The aim is to synthesize and extend current understanding on the existing supply-side solutions and demand-side technological options despite the broader range of co-benefits and the latter’s lesser risk. We achieve this by adopting a two-step systematic literature review approach to analyse and review SLR datasets consisting of 103 empirical studies conducted in Asia, Europe, and North America countries in economics and environmental economics from 1994 to 2018 and published in Web of Science and Scopus indexed journals. We find that demand-side policy studies are predominantly carried out in Asia, Europe, and North America. The US contributes more than one-quarter of the studies reviewed, most of which were published after US withdrawal from the Paris Agreement. Three types of Demand-Side Management (DSM) are identified namely energy efficiency, energy conservation, and demand response policies. The corresponding policy instruments can be categorised into six basic categories. We further found that these instruments are not always implemented for emissions reduction. In addition, energy-saving is found to be the reason for DSM implementation. The findings suggest that demand-side solutions through policies need to be fully exploited to achieve carbon emission targets from the electricity sector or energy sector in general

An adaptive protection of flooding attacks model for complex network environments

Currently, online organizational resources and assets are potential targets of several types of attack, the most common being flooding attacks. We consider the Distributed Denial of Service (DDoS) as the most dangerous type of flooding attack that could target those resources. The DDoS attack consumes network available resources such as bandwidth, processing power, and memory, thereby limiting or withholding accessibility to users. The Flash Crowd (FC) is quite similar to the DDoS attack whereby many legitimate users concurrently access a particular service, the number of which results in the denial of service. Researchers have proposed many different models to eliminate the risk of DDoS attacks, but only few efforts have been made to differentiate it from FC flooding as FC flooding also causes the denial of service and usually misleads the detection of the DDoS attacks. In this paper, an adaptive agent-based model, known as an Adaptive Protection of Flooding Attacks (APFA) model, is proposed to protect the Network Application Layer (NAL) against DDoS flooding attacks and FC flooding traffics. The APFA model, with the aid of an adaptive analyst agent, distinguishes between DDoS and FC abnormal traffics. It then separates DDoS botnet from Demons and Zombies to apply suitable attack handling methodology. There are three parameters on which the agent relies, normal traffic intensity, traffic attack behavior, and IP address history log, to decide on the operation of two traffic filters. We test and evaluate the APFA model via a simulation system using CIDDS as a standard dataset. The model successfully adapts to the simulated attack scenarios' changes and determines 303,024 request conditions for the tested 135,583 IP addresses. It achieves an accuracy of 0.9964, a precision of 0.9962, and a sensitivity of 0.9996, and outperforms three tested similar models. In addition, the APFA model contributes to identifying and handling the actual trigger of DDoS attack and differentiates it from FC flooding, which is rarely implemented in one model

Towards the Development of Smart and Sustainable Transportation System for Foodservice Industry: Modelling Factors Influencing Customer’s Intention to Adopt Drone Food Delivery (DFD) Services

While the attempts to deploy drones in the foodservice industry focus on the technical aspects, research studies from behavioral perspectives that support service substantially are still in their infancy. When new technology-based services are introduced, it is crucial to examine and understand consumers’ perceptions by identifying a set of actions that influence acceptance and fulfilling their target. Therefore, service providers of drone food delivery (DFD) services need to identify significant factors that influence potential consumers to use drone delivery. Although a few existing models are significant, these models lack a comprehensive basic theory that addresses factors which influence consumers’ intention and behavior. To overcome this limitation and propose a more comprehensive model, relevant research studies from the domain of drone delivery services and other emerging technology such as IoT, Autonomous Vehicles, and Mobile Banking are identified, reviewed, and analyzed, and ten potential factors are subsequently extracted. This study’s data were collected from 209 participants who regularly order food online for delivery and were analyzed using SPSS. Descriptive statistics, reliability, Pearson correlation, regression analysis, r-squared, and standardized beta coefficient analyses are carried out to present the study’s findings. The results show that there is a significant relationship between behavioral intention and the user behavior of DFD. Although the participants in this study are yet to experience drone technology in the foodservice domain, the identified factors explain around 32.9% of the variation in the use behavior of DFD services. In the early stage of adoption, it is highly recommended for stakeholders to conduct marketing campaigns through media channels such as television and different social media platforms to bring awareness of this technology