A study on the effectiveness factors that influence job performance among employees / Nur Hafizah Ab Hamid and Siti Nabihah Mohamad Jamil

This topic of research is “The Effectiveness Factors That Influence Job Performance among Employees”. This research tries to determine the relationship between factors of personality, teamwork and commitment, and adaptation on workplace effect to job performances among employees. Researchers want to know the performance of employees by specifically in Johor Bahru. This study to identifies and determines factors that have significant influence on job performance. The respondents for this study are 100 employees in Johor Bharu. The researchers had used convenience sampling as a sampling technique. The process of analyzing and interpreting data has presented by figures and tables using method reliability test, frequency, descriptive statistic, correlations and regression analysis. The researchers also have suggestion and recommendation is to helps the companies to increase their employees job performance

Implementing and measuring problem solving agent technology of human resource management system

Agent is one of the software engineering fields with a continuous growing interest. This paper focuses on implementing an agent framework and measuring the agent in human resource management system (HRMS)domain. IAHRMS is the project name where the system (human resource management system) will integrate with an agent and will give its users a new experience in conducting HRMS operation.The agent used breadth-first strategy in order to execute as a helper or tutor to Human Resource people, especially in searching for Human Resource related information, for example; information on how to use the HRMS system and information of employee working in the company.However, this project is limited to employee information only.There are few testing and metrics have been done to ensure the system executes well in real environment

Students’ satisfaction and intention to continue online learning during the Covid-19 pandemic

The Covid-19 pandemic has forced teaching and learning to be conducted online. Without proper preparation, students and academicians face various challenges, which may cause stress and drop out. Thus, this study was conducted to determine factors influencing students’ satisfaction and intention to continue studying online. Three factors were hypothesized to influence satisfaction, namely the lecturer’s performance, students’ interaction, and course content. The study also examined the moderating role of internet connection on the relationship between satisfaction and continuance intention. Using purposive sampling, data were collected from undergraduate and postgraduate students. A total of 305 questionnaires were analyzed using Partial Least Square Structural Equation Modeling (PLS-SEM). The result of the analysis indicated that all three proposed factors are significant in influencing students’ satisfaction, and satisfaction impacts continuance intention. Internet connection on the other hand moderates the relationship between satisfaction and intention. These findings have broadened the knowledge on the factors of students’ satisfaction and continuance intention to study online during the pandemic. This study is among a limited number of studies available exploring the role of internet connection in the context of online learning. The study provides insights to academicians, higher learning institutions and policymakers on the continuance of online learning during and post-pandemic

Formal Analysis of Trust and Reputation for Service Composition in IoT

The exponential growth in the number of smart devices connected to the Internet of Things (IoT) that are associated with various IoT-based smart applications and services, raises interoperability challenges. Service-oriented architecture for IoT (SOA-IoT) solutions has been introduced to deal with these interoperability challenges by integrating web services into sensor networks via IoToptimized gateways to fill the gap between devices, networks, and access terminals. The main aim of service composition is to transform user requirements into a composite service execution. Different methods have been used to perform service composition, which has been classified as trust-based and non-trust-based. The existing studies in this field have reported that trust-based approaches outperform non-trust-based ones. Trust-based service composition approaches use the trust and reputation system as a brain to select appropriate service providers (SPs) for the service composition plan. The trust and reputation system computes each candidate SP’s trust value and selects the SP with the highest trust value for the service composition plan. The trust system computes the trust value from the self-observation of the service requestor (SR) and other service consumers’ (SCs) recommendations. Several experimental solutions have been proposed to deal with trust-based service composition in the IoT; however, a formal method for trust-based service composition in the IoT is lacking. In this study, we used the formal method for representing the components of trustbased service management in the IoT, by using higher-order logic (HOL) and verifying the different behaviors in the trust system and the trust value computation processes. Our findings showed that the presence of malicious nodes performing trust attacks leads to biased trust value computation, which results in inappropriate SP selection during the service composition. The formal analysis has given us a clear insight and complete understanding, which will assist in the development of a robust trust system

Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks

A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results

Invariants generation for method overriding using abstract interpretation / Siti Hafizah Ab. Hamid

Software verification is an important element of software reliability. The significance and importance of verification have been recognized by Bill Gates in his speech in WinHEC 2002. The software verification allows program’s specification to be formally proved to ensure the specification verified the program before its execution time using static analysis. However, in the context of object-oriented program, studies show there is a need to have formal specifications for method overriding because the overriding feature plays important role in allowing program reusability. This thesis develops an abstract formal framework for invariant generation of static analysis for method overriding in object-oriented program using inheritance. It focuses on late bound method in the class invariants generation. There are two main problems arise during the process of generating class invariant which are reverification of class invariant and over-approximation of late binding call. In the context of method overriding, the problem of late binding call happens when the abstract semantic function uses behavioral subtyping that is restricted to the rule of contravariance and covariance. The abstract formal framework using abstract interpretation theory is proposed to overcome the problem above. The framework exploits the capability of abstract interpretation method in making program analysis automated. It also overcomes the problem of generating the invariants for late binding call of method overriding with less restrictions rules of lazy behavioral subtyping method. The use of lazy behavioral subtyping results to the overridden method semantics has a not over approximated invariant. The framework produces two equations for two invariants, which are modular invariants for inheritance and invariants for method overriding. A scenario based evaluation is conducted to validate the invariants and to compare the proposed framework using lazy behavioral subtyping with the framework using behavioral subtyping

The influence of time availability, happiness, and weariness on consumers’ impulse buying tendency amidst Covid-19 partial lockdown in Malaysia

The movement control order (MCO) and conditional movement control order (CMCO), also known as the partial lockdown imposed on Malaysian as a result of Covid-19 pandemic has affected mental health, and consumerism. Spending too much time online, feeling unhappy and weary by staying at home too long may negatively affect buying tendency during the pandemic period. A low online buying tendency may cause firms’ profits to decrease. Therefore, this study aims to examine the influence of consumers’ time availability, state of happiness, and level of weariness on their tendency to buy online during Covid-19 partial lockdown. The study uses a convenience sampling method and collected 236 responses to the survey. The data was analysed using a multiple regression. The findings highlight that time availability does not influence online impulse buying tendency, while happiness, and weariness significantly affect online impulse buying tendency. Specifically, the study found that moderate happiness and a low level of weariness influenced consumers’ tendency to buy online. The study contributes to providing insights for firms and marketers to understand consumers’ psychological states during the pandemic which could be used as a basis to develop strategies on suitable advertising medium and content

DroidbotX: Test Case Generation Tool for Android Applications Using Q-Learning

Android applications provide benefits to mobile phone users by offering operative functionalities and interactive user interfaces. However, application crashes give users an unsatisfactory experience, and negatively impact the application’s overall rating. Android application crashes can be avoided through intensive and extensive testing. In the related literature, the graphical user interface (GUI) test generation tools focus on generating tests and exploring application functions using different approaches. Such tools must choose not only which user interface element to interact with, but also which type of action to be performed, in order to increase the percentage of code coverage and to detect faults with a limited time budget. However, a common limitation in the tools is the low code coverage because of their inability to find the right combination of actions that can drive the application into new and important states. A Q-Learning-based test coverage approach developed in DroidbotX was proposed to generate GUI test cases for Android applications to maximize instruction coverage, method coverage, and activity coverage. The overall performance of the proposed solution was compared to five state-of-the-art test generation tools on 30 Android applications. The DroidbotX test coverage approach achieved 51.5% accuracy for instruction coverage, 57% for method coverage, and 86.5% for activity coverage. It triggered 18 crashes within the time limit and shortest event sequence length compared to the other tools. The results demonstrated that the adaptation of Q-Learning with upper confidence bound (UCB) exploration outperforms other existing state-of-the-art solutions