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Reducing the number of generated mutants by clustering redundant mutants, reducing the execution time by decreasing the number of generated mutants and reducing the cost of mutation testing are the main goals of this study.

In this study, a method is suggested to identify and prone the redundant mutants. In the method, first, the program source code is analyzed by the developed parser to filter out the effectless instructions; then the remaining instructions are mutated by the standard mutation operators. The single-line mutants are partially executed by the developed instruction evaluator. Next, a clustering method is used to group the single-line mutants with the same results. There is only one complete run per cluster.

The results of experiments on the Java benchmarks indicate that the proposed method causes a 53.51 per cent reduction in the number of mutants and a 57.64 per cent time reduction compared to similar experiments in the MuJava and MuClipse tools.

Developing a classifier that takes the source code of the program and classifies the programs' instructions into effective and effectless classes using a dependency graph; filtering out the effectless instructions reduces the total number of mutants generated; Developing and implementing an instruction parser and instruction-level mutant generator for Java programs; the mutant generator takes instruction in the original program as a string and generates its single-line mutants based on the standard mutation operators in MuJava; Developing a stack-based evaluator that takes an instruction (original or mutant) and the test data and evaluates its result without executing the whole program.

The compliance checking of Building Information Modeling (BIM) models is crucial throughout the lifecycle of construction. The increasing amount and complexity of information carried by BIM models have made compliance checking more challenging, and manual methods are prone to errors. Therefore, this study aims to propose an integrative conceptual framework for automated compliance checking of BIM models, allowing for the identification of errors within BIM models.

This study first analyzed the typical building standards in the field of architecture and fire protection, and then the ontology of these elements is developed. Based on this, a building standard corpus is built, and deep learning models are trained to automatically label the building standard texts. The Neo4j is utilized for knowledge graph construction and storage, and a data extraction method based on the Dynamo is designed to obtain checking data files. After that, a matching algorithm is devised to express the logical rules of knowledge graph triples, resulting in automated compliance checking for BIM models.

Case validation results showed that this theoretical framework can achieve the automatic construction of domain knowledge graphs and automatic checking of BIM model compliance. Compared with traditional methods, this method has a higher degree of automation and portability.

This study introduces knowledge graphs and natural language processing technology into the field of BIM model checking and completes the automated process of constructing domain knowledge graphs and checking BIM model data. The validation of its functionality and usability through two case studies on a self-developed BIM checking platform.

The purpose of this paper is to develop a blockchain-based data capture and transmission system that will collect real-time power consumption data from a household electrical appliance and transfer it securely to a local server for energy analytics such as forecasting.

The data capture system is composed of two current transformer (CT) sensors connected to two different electrical appliances. The CT sensors send the power readings to two Arduino microcontrollers which in turn connect to a Raspberry-Pi for aggregating the data. Blockchain is then enabled onto the Raspberry-Pi through a Java API so that the data are transmitted securely to a server. The server provides real-time visualization of the data as well as prediction using the multi-layer perceptron (MLP) and long short term memory (LSTM) algorithms.

The results for the blockchain analysis demonstrate that when the data readings are transmitted in smaller blocks, the security is much greater as compared with blocks of larger size. To assess the accuracy of the prediction algorithms data were collected for a 20 min interval to train the model and the algorithms were evaluated using the sliding window approach. The mean average percentage error (MAPE) was used to assess the accuracy of the algorithms and a MAPE of 1.62% and 1.99% was obtained for the LSTM and MLP algorithms, respectively.

A detailed performance analysis of the blockchain-based transmission model using time complexity, throughput and latency as well as energy forecasting has been performed.

The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.

We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.

We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.

Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.

Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.

Seekers who visit professional virtual communities (PVCs) are usually motivated by knowledge-seeking, which is a complex cognitive process. How do seekers search for knowledge, and how is their search linked to prior knowledge or PVC situation factors? From the cognitive process and interactional psychology perspectives, this study investigated the three-way interactions between seekers’ expertise, task complexity, and perceptions of PVC features (i.e. knowledge quality and system quality) on knowledge-seeking strategies and resultant outcomes.

A field experiment was conducted with 119 seekers in a PVC using a 2 × 2 factorial design of seekers’ expertise (i.e. expert versus novice) and task complexity (i.e. low versus high).

The study reveals three significant insights: (1) For a high-complexity task, experts adopt an ask-directed searching strategy compared to novices, whereas novices adopt a browsing strategy; (2) For a high-complexity task, experts who perceive a high system quality are more likely than novices to adopt an ask-directed searching strategy; and (3) Task completion time and task quality are associated with the adoption of ask-directed searching strategies, whereas knowledge seekers’ satisfaction is more associated with the adoption of browsing strategy.

We draw on the perspectives of cognitive process and interactional psychology to explore potential two- and three-way interactions of seekers’ expertise, task complexity, and PVC features on the adoption of knowledge-seeking strategies in a PVC context. Our findings provide deep insights into seekers’ behavior in a PVC, given the popularity of the search for knowledge in PVCs.

This study aims to determine the extent of virtual reality (VR)/augmented reality (AR) adoption in university libraries in Indonesia and identify librarians’ views of library readiness. The assessment centers on two crucial dimensions: library readiness, gauged by commitment and efficacy, and librarian readiness, appraised through cognitive and affective aspects.

The research features a survey-based quantitative method. A sample of 259 librarians from university libraries, all members of the Indonesian Higher Education Library Forum, responded to the online survey.

Indonesian university libraries, especially in Central Java, express strong interest in VR/AR despite low current adoption. Librarians believe in their commitment and efficacy for adoption, with improvements needed in budget procurement and human resources. Librarians are optimistic about innovating academic activities with VR/AR, despite concerns about collaboration, data governance and human resource insecurity. The research underscores the simultaneous influence of librarians’ and libraries’ readiness on VR/AR adoption, emphasizing their interconnectedness and mutual support within library management.

There are practical implications for library institutions in Indonesia in terms of understanding the attendant opportunities and challenges when preparing organizational ecosystems. These findings provide a broad overview of VR/AR adoption in university libraries in Indonesia and contribute to understanding the specific needs and interests of universities in Indonesia considering adopting VR/AR. In addition to preparing adequate infrastructure, libraries should ensure the availability of adequate human resources.

In the Indonesian context, this study, to the best of the authors’ knowledge, makes the first attempt to examine the librarian’s view as part of library management to face the dilemmas that may arise during the adoption of VR/AR despite university libraries’ commitment and efficacy to enable this technology. This research also identifies librarians’ cognitive and affective readiness as individual characteristics and resources able to influence adequate adoption.

The purpose of this paper is to investigate the process by which the level of immersion in virtual reality-based behavioral simulation (VR-BS) impacts on the non-cognitive and cognitive outcomes. The cognitive outcome is measured using the increase in the level of Sharia financial literacy, while the noncognitive outcome is measured using the behavioral intention to use VR-BS.

The method consists of two parts: First, the development of VR-BS, in the context of sharia financial literacy, using the waterfall model. Second, testing the effectiveness of VR-BS using the theory of interactive media effects framework. The participants were 142 students from three secondary schools (two Islamic religious schools and one public school) in Yogyakarta and Central Java, Indonesia. Partial least squares structural equation modeling was used for testing the hypotheses.

VR-BS creates a perceived coolness and vividness, which in turn has an impact on increasing the participants’ engagement. Also, the use of VR has an impact on natural mapping, which increases a user’s engagement through its perceived ease of use. As predicted, the user’s engagement affects VR’s behavior, mediated by the user’s attitude toward VR media. VR’s interactivity, however, does not impact on the cognitive aspect.

The participants were not randomly selected, as the data were collected during the COVID-19 pandemic. As a result, the majority of the participants had never tried VR before this study. The participants, however, were digital natives.

It is implied from the findings that Islamic financial business actors and the relevant government agencies (e.g. the Indonesian Financial Services Authority [OJK], the Ministry of Education, Culture, Research and Technology and the Ministry of Religious Affairs) should collaborate to best prepare the future generation of ummah by using VR-BS in their joint promotion and education programs. The results of the current study reveal that the use of VR-BS may attract people to engage in Islamic financial activities. By engaging in such activities, or at least engaging in real-life simulations/classes/workshops, people may gradually acquire more knowledge about Islamic finance.

As predicted, the user’s engagement has an impact on behavior toward VR-BS, which is mediated by attitude toward VR-BS.

Although the challenges associated with big data are increasing, the question of the most suitable big data analytics (BDA) platform in libraries is always significant. The purpose of this study is to propose a solution to this problem.

The current study identifies relevant literature and provides a review of big data adoption in libraries. It also presents a step-by-step guide for the development of a BDA platform using the Apache Hadoop Ecosystem. To test the system, an analysis of library big data using Apache Pig, which is a tool from the Apache Hadoop Ecosystem, was performed. It establishes the effectiveness of Apache Hadoop Ecosystem as a powerful BDA solution in libraries.

It can be inferred from the literature that libraries and librarians have not taken the possibility of big data services in libraries very seriously. Also, the literature suggests that there is no significant effort made to establish any BDA architecture in libraries. This study establishes the Apache Hadoop Ecosystem as a possible solution for delivering BDA services in libraries.

The present work suggests adapting the idea of providing various big data services in a library by developing a BDA platform, for instance, providing assistance to the researchers in understanding the big data, cleaning and curation of big data by skilled and experienced data managers and providing the infrastructural support to store, process, manage, analyze and visualize the big data.

The study concludes that Apache Hadoops’ Hadoop Distributed File System and MapReduce components significantly reduce the complexities of big data storage and processing, respectively, and Apache Pig, using Pig Latin scripting language, is very efficient in processing big data and responding to queries with a quick response time.

According to the study, there are significantly fewer efforts made to analyze big data from libraries. Furthermore, it has been discovered that acceptance of the Apache Hadoop Ecosystem as a solution to big data problems in libraries are not widely discussed in the literature, although Apache Hadoop is regarded as one of the best frameworks for big data handling.

Drawing from emotional geography framework promoted by Hargreaves (2000), our research sought to depict the emotional geography of two faculty members who engaged in a virtual teacher professional development (VTPD) sessions during the COVID-19 pandemic. This study aimed to focus on capturing participants’ emotional closeness or distance while they were engaging in VTPD.

This study employed narrative inquiry by exploring three-dimensional narrative inquiry space: temporality, personal-social interaction and place (Clandinin and Connelly, 2000). Following this step, the participants were interviewed online through Zoom meetings and WhatsApp to capture critical incidents of their emotional experience. All collected data were transcribed, and some data from Bahasa Indonesia were translated into English. Member checking was also done several times to ensure the accuracy of the data as well as to avoid misinterpretation. The data were analysed inductively to generate coding categories using systemic functional linguistics (SFL) language appraisal (Martin and White, 2007) and emotional geography parameter (Hargreaves, 2001b).

The findings of the study revealed that both participants experienced greater positive feeling than negative ones. The participants experienced positive feelings such as seriousness, happiness, successfulness and satisfaction. They also experienced negative feelings such as insecurity, unhappiness, dissatisfaction and impatience. Such positive and negative feelings create closeness and distance among participants, mentor and workshop organiser. This study indicates that maintaining positive feelings is a passport to succeed in VTPD.

The study has two limitations. First, its findings cannot be overgeneralised since the analysis was restricted to data gathered from a small number of participants. Second, the scope of investigation was limited in virtual situations.

The present study empirically showed that faculty members need to engage in constructing or maintaining positive emotional bond with the mentor and other participants and create conducive situations to understand their own and others’ emotions (Mayer, 2011). Practically, a mentor in VTPD may ask faculty members to voice and share their emotional experience as an evaluation tool to make VTPD programmes more successful. Future participants can benefit from these findings by engaging in emotional understanding and building a conducive situation during VTPD to develop their academic competence, agency and identity.

While previous research into VTPD in the context of higher education mainly focused on designs, attention to pedagogy of online teacher learning environments, trends toward innovation in teacher collaboration and communities of practice in online settings, the present study specifically looked into how participants emotionally engaged in VTPD, which is inevitably linked to physical, moral, sociocultural, professional and political geographies.

Social welfare scholarship has not paid adequate attention to the phenomenon of single-parent women in Asia, especially in terms of their economic and social vulnerabilities. This study aims to explore the strategies employed by women who are single parents to secure their families from socioeconomic issues. It also delves into the experiences and viewpoints of families regarding the social institutions they turn to for assistance during times of hardship – whether it be the state, market, or informal networks.

The data were collected through 33 semi-structured interviews, with informants selected using the purposive sampling technique in South Sumatra Province, Indonesia, from February 2023 to July 2023. This number was obtained based on informants who were included in the criteria the researchers determined, which included “being a female single parent” and “being over the age of 17,” which is the minimum age for Indonesians to marry. Others included “having dependents to support, be they children and/or the respondent’s family” and occupying the main role as “household head” – all of which defined whether someone belongs to the “vulnerable group.”

The study highlights the significance of informal support for single mothers facing economic hardship. Still, overreliance on it can lead to concerns about the sustainability of the everyday social safety net they receive. Social exclusion is also problematic due to societal assumptions about divorce and widowhood. The last highlight is how 'deskilling' among single parents has complicated the challenges women face to re-enter the workforce.

This study’s outcomes provide crucial insights into analyzing the patterns of single-parent families in Indonesia and serve as a framework for further research on the resilience of single parents in developing countries.