Aim/Purpose: This article proposes a methodology for selecting the initial sets for clustering categorical data. The main idea is to combine all the different values of every single criterion or attribute, to form the first proposal of the so-called multiclusters, obtaining in this way the maximum number of clusters for the whole dataset. The multiclusters thus obtained, are themselves clustered in a second step, according to the desired final number of clusters. Background: Popular cluster methods for categorical data, such as the well-known K-Modes, usually select the initial sets by means of some random process. This fact introduces some randomness in the final results of the algorithms. We explore a different application of the clustering methodology for categorical data that overcomes the instability problems and ultimately provides a greater clustering efficiency. Methodology: For assessing the performance of the proposed algorithm and its comparison with K-Modes, we apply both of them to categorical databases where the response variable is known but not used in the analysis. In our examples, that response variable can be identified to the real clusters or classes to which the observations belong. With every data set, we perform a two-step analysis. In the first step we perform the clustering analysis on data where the response variable (the real clusters) has been omitted, and in the second step we use that omitted information to check the efficiency of the clustering algorithm (by comparing the real clusters to those given by the algorithm). Contribution: Simplicity, efficiency and stability are the main advantages of the multicluster method. Findings: The experimental results attained with real databases show that the multicluster algorithm has greater precision and a better grouping effect than the classical K-modes algorithm. Recommendations for Practitioners: The method can be useful for those researchers working with small and medium size datasets, allowing them to detect the underlying structure of the data in an intuitive and reasonable way. Recommendation for Researchers: The proposed algorithm is slower than K-Modes, since it devotes a lot of time to the calculation of the initial combinations of attributes. The reduction of the computing time is therefore an important research topic. Future Research: We are concerned with the scalability of the algorithm to large and complex data sets, as well as the application to mixed data sets with both quantitative and qualitative attributes.

Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

Aim/Purpose: The paper’s objective is to examine the challenges of using the mobile phone to mine location data for effective contact tracing of symptomatic, pre-symptomatic, and asymptomatic individuals and the implications of this technology for public health governance. Background: The COVID-19 crisis has created an unprecedented need for contact tracing across South Africa, requiring thousands of people to be traced and their details captured in government health databases as part of public health efforts aimed at breaking the chains of transmission. Contact tracing for COVID-19 requires the identification of persons who may have been exposed to the virus and following them up daily for 14 days from the last point of exposure. Mining mobile phone location data can play a critical role in locating people from the time they were identified as contacts to the time they access medical assistance. In this case, it aids data flow to various databases designated for COVID-19 work. Methodology: The researchers conducted a review of the available literature on this subject drawing from academic articles published in peer-reviewed journals, research reports, and other relevant national and international government documents reporting on public health and COVID-19. Document analysis was used as the primary research method, drawing on the case studies. Contribution: Contact tracing remains a critical strategy in curbing the deadly COVID-19 pandemic in South Africa and elsewhere in the world. However, given increasing concern regarding its invasive nature and possible infringement of individual liberties, it is imperative to interrogate the challenges related to its implementation to ensure a balance with public governance. The research findings can thus be used to inform policies and practices associated with contact tracing in South Africa. Findings: The study found that contact tracing using mobile phone location data mining can be used to enforce quarantine measures such as lockdowns aimed at mitigating a public health emergency such as COVID-19. However, the use of technology can expose the public to criminal activities by exposing their locations. From a public governance point of view, any exposure of the public to social ills is highly undesirable. Recommendations for Practitioners: In using contact tracing apps to provide pertinent data location caution needs to be exercised to ensure that sensitive private information is not made public to the extent that it compromises citizens’ safety and security. The study recommends the development and implementation of data use protocols to support the use of this technology, in order to mitigate against infringement of individual privacy and other civil liberties. Recommendation for Researchers: Researchers should explore ways of improving digital applications in order to improve the acceptability of the use of contact tracing technology to manage pandemics such as COVID-19, paying attention to ethical considerations. Impact on Society: Since contact tracing has implications for privacy and confidentiality it must be conducted with caution. This research highlights the challenges that the authorities must address to ensure that the right to privacy and confidentiality is upheld. Future Research: Future research could focus on collecting primary data to provide insight on contact tracing through mining mobile phone location data. Research could also be conducted on how app-based technology can enhance the effectiveness of contact tracing in order to optimize testing and tracing coverage. This has the potential to minimize transmission whilst also minimizing tracing delays. Moreover, it is important to develop contact tracing apps that are universally inter-operable and privacy-preserving.

Aim/Purpose: Blockchain technology (BCT) has emerged as a potential catalyst for transforming government institutions and services, yet the adoption of blockchain in governments faces various challenges, for which previous studies have yet to provide practical solutions. Background: This study aims to identify and analyse barriers, potential solutions, and their relations in implementing BC for governments through a systematic literature review (SLR). The authors grouped the challenges based on the Technology-Organisation-Environment (TOE) framework while exercising a thematic grouping for the solutions, followed by a comprehensive mapping to unveil the relationship between challenges and solutions. Methodology: This study employs the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology, combined with the tollgate method, to improve the quality of selected articles. The authors further administer a three-level approach (open coding, axial coding, and selective coding) to analyse the challenges and solutions from the articles. Contribution: The authors argue that this study enriches the existing literature on BC adoption, particularly in the government context, by providing a comprehensive framework to analyse and address the unique challenges and solutions, thus contributing to the development of new theories and models for future research in BC adoption in government settings and fostering deeper exploration in the field. Findings: The authors have unveiled 40 adoption challenges categorised using the TOE framework. The most prevalent technological challenges include security concerns and integration & interoperability, while cultural resistance, lack of support and involvement, and employees’ capability hinder adoption at the organisational level. Notably, the environmental dimension lacks legal and standard frameworks. The study further unveils 28 potential solutions, encompassing legal frameworks, security and privacy measures, collaboration and governance, technological readiness and infrastructure, and strategic planning and adoption. The authors of the study have further mapped the solutions to the identified challenges, revealing that the establishment of legal frameworks stands out as the most comprehensive solution. Recommendations for Practitioners: Our findings provide a big picture regarding BC adoption for governments around the globe. This study charts the problems commonly encountered by government agencies and presents proven solutions in their wake. The authors endeavour practitioners, particularly those in governments, to embrace our findings as the cornerstone of BC/BCT adoption. These insights can aid practitioners in identifying existing or potential obstacles in adopting BC, pinpointing success factors, and formulating strategies tailored to their organisations. Recommendation for Researchers: Researchers could extend this study by making an in-depth analysis of challenges or solutions in specific types of countries, such as developed and developing countries, as the authors believe this approach would yield more insights. Researchers could also test, validate, and verify the mapping in this study to improve the quality of the study further and thus can be a great aid for governments to adopt BC/BCT fully. Impact on Society: This study provides a comprehensive exploration of BC adoption in the government context, offering detailed explanations and valuable insights that hold significant value for government policymakers and decision-makers, serving as a bedrock for successful implementation by addressing roadblocks and emphasising the importance of establishing a supportive culture and structure, engaging stakeholders, and addressing security and privacy concerns, ultimately enhancing the efficiency and effectiveness of BC adoption in government institutions and services. Future Research: Future research should address the limitations identified in this study by expanding the scope of the literature search to include previously inaccessible sources and exploring alternative frameworks to capture dynamic changes and contextual factors in BC adoption. Additionally, rigorous scrutiny, review, and testing are essential to establish the practical and theoretical validity of the identified solutions, while in-depth analyses of country-specific and regional challenges will provide valuable insights into the unique considerations faced by different governments.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Aim/Purpose: This study aims to investigate the influence of e-government public services on public trust at the local government level, addressing the pressing need to understand the factors shaping citizen perceptions and trust in government institutions. Background: With the proliferation of e-government initiatives worldwide, governments are increasingly turning to digital solutions to enhance public service delivery and promote transparency. However, despite the potential benefits, there remains a gap in understanding how these initiatives impact public trust in government institutions, particularly at the local level. This study seeks to address this gap by examining the relationship between e-government service quality, individual perceptions, and public trust, providing valuable insights into the complexities of citizen-government interactions in the digital age. Methodology: Employing a quantitative approach, this study utilises surveys distributed to users of e-government services in one of the regencies in Indonesia. The sample consists of 278 individuals. Data analysis is conducted using Partial Least Squares Structural Equation Modelling, allowing for the exploration of relationships among variables and their influence on public trust. Contribution: This study provides insights into the factors influencing public trust in e-government services at the local government level, offering a nuanced understanding of the relationship between service quality, individual perceptions, and public trust. Findings: This study emphasises information quality and service quality in e-government-based public services as crucial determinants of individual perception in rural areas. Interestingly, system quality in e-government services has no influence on individual perception. In the individual perception, perceived security and privacy emerge as the strongest antecedent of public trust, highlighting the need to guarantee secure and private services for citizens in rural areas. These findings emphasise the importance of prioritising high-quality information, excellent service delivery, and robust security measures to foster and sustain public trust in e-government services. Recommendations for Practitioners: Practitioners must prioritise enhancing the quality of e-government services due to their significant impact on individual perception, leading to higher public trust. Government agencies must ensure reliability, responsiveness, and the effective fulfilment of user needs. Additionally, upholding high standards of information quality in e-government services by delivering accurate, relevant, and timely information remains crucial. Strengthening security measures through robust protocols such as data encryption and secure authentication becomes essential for protecting user data. With that in mind, the authors believe that public trust in government would escalate. Recommendation for Researchers: Researchers could investigate the relation between system quality in e-government services and individual perception in different rural settings. Longitudinal studies could also elucidate how evolving service quality, information quality, and security measures impact user satisfaction and trust over time. Comparative studies across regions or countries can reveal cultural and contextual differences in individual perceptions, identifying both universal principles and region-specific strategies for e-government platforms. Analysing user behaviour and preferences across various demographic groups can inform targeted interventions. Furthermore, examining the potential of emerging technologies such as blockchain or artificial intelligence in enhancing e-government service delivery, security, and user engagement remains an interesting topic. Impact on Society: This study’s findings have significant implications for fostering public trust in government institutions, ultimately strengthening democracy and citizen-government relations. By understanding how e-government initiatives influence public trust, policymakers can make informed decisions to improve service delivery, enhance citizen engagement, and promote transparency, thus contributing to more resilient and accountable governance structures. Future Research: Future research could opt for longitudinal studies to evaluate the long-term effects of enhancements in service quality, information quality, and security. Cross-cultural investigations can uncover universal principles and contextual differences in user experiences, supporting global e-government strategies in rural areas. Future research could also improve the research model by adding more variables, such as risk aversion or fear of job loss, to gauge individual perceptions.

Aim/Purpose: This study investigates the drivers of user satisfaction in e-government services and its influence on continued use intention and citizen trust in government. It employs the integration of the Technology Acceptance Model (TAM) and the Information System Success Model (ISSM). Background: Electronic government, transforming citizen-state interactions, has gained momentum worldwide, including in India, where the aim is to leverage technology to improve citizen services, streamline administration, and engage the public. While prior research has explored factors influencing citizen satisfaction with e-government services globally, this area of study has been relatively unexplored in India, particularly in the post-COVID era. Challenges to widespread e-government adoption in India include a large and diverse population, limited digital infrastructure in rural areas, low digital literacy, and weak data protection regulations. Additionally, global declines in citizen trust, attributed to economic concerns, corruption, and information disclosures, further complicate the scenario. This study seeks to investigate the influence of various factors on user satisfaction and continuance usage of e-government services in India. It also aims to understand how these services contribute to building citizens’ trust in government. Methodology: The data were collected by utilizing survey items on drivers of e-government services, user satisfaction, citizen trust, and continuance use intention derived from existing literature on information systems and e-government. Responses from 501 Indian participants, collected using an online questionnaire, were analyzed using PLS-SEM. Contribution: This study makes a dual contribution to the e-government domain. First, it introduces a comprehensive research model that examines factors influencing users’ satisfaction and continuance intention with e-government services. The proposed model integrates the TAM and ISSM. Combining these models allows for a comprehensive examination of e-government satisfaction and continued intention. By analyzing the impact of user satisfaction on continuance intention and citizen trust through an integrated model, researchers and practitioners gain insights into the complex dynamics involved. Second, the study uncovers the effects of residential status on user satisfaction, trust, and continuance intention regarding e-government services. Findings reveal disparities in the influence of system and service quality on user satisfaction across different user segments. Researchers and policymakers should consider these insights when designing e-government services to ensure user satisfaction, continuance intention, and the building of citizen trust. Findings: The findings indicate that the quality of information, service, system, and perceived usefulness play important roles in user satisfaction with e-government services. All hypothesized paths were significant, except for perceived ease of use. Furthermore, the study highlights that user satisfaction significantly impacts citizen trust and continuance use intention. Recommendations for Practitioners: The findings suggest that government authorities should focus on delivering accurate, comprehensive, and timely information in a secure, glitch-free, and user-friendly digital environment. Implementing an interactive and accessible interface, ensuring compatibility across devices, and implementing swift query resolution mechanisms collectively contribute to improving users’ satisfaction. Conducting awareness and training initiatives, providing 24×7 access to online tutorials, helpdesks, technical support, clear FAQs, and integrating AI-driven customer service support can further ensure a seamless user experience. Government institutions should leverage social influence, community engagement, and social media campaigns to enhance user trust. Promotional campaigns, incentive programs, endorsements, and user testimonials should be used to improve users’ satisfaction and continuance intention. Recommendation for Researchers: An integrated model combining TAM and ISSM offers a robust approach for thoroughly analyzing the diverse factors influencing user satisfaction and continuance intention in the evolving digitalization landscape of e-government services. This expansion, aligning with ISSM’s perspective, enhances the literature by demonstrating how user satisfaction impacts continuance usage intention and citizen trust in e-government services in India and other emerging economies. Impact on Society: Examining the factors influencing user satisfaction and continuance intention in e-government services and their subsequent impact on citizen trust carries significant societal implications. The findings can contribute to the establishment of transparent and accountable governance practices, fostering a stronger connection between governments and their citizens. Future Research: There are several promising avenues to explore to enhance future research. Expanding the scope by incorporating a larger sample size could enable a more thorough analysis. Alternatively, delving into the performance of specific e-government services would offer greater precision, considering that this study treats e-government services generically. Additionally, incorporating in-depth interviews and longitudinal studies would yield a more comprehensive understanding of the dynamic evolution of digitalization.

Recently, interest in the correlation between 'contract' in transaction governance and 'trust' in relational governance mechanisms has been growing. This study focuses on issues related to the evolution of contract and inter-organisational trust dynamics in transaction governance and uses mixed research method to investigate sectors related to transaction governance in Taiwan's electronics industry. The study finds higher flexibility in contract implementation to be a promoter of trust between two parties in a relationship, thereby promoting project execution efficiency in the case of Taiwanese firms. Organisational management differs between the East and West; therefore, Western firms should understand how various contractual provisions can be used to accommodate different transactions when cooperating with Taiwanese electronics companies.

Transforming a school from traditional teaching and learning to a one-to-one (1:1) classroom, in which a teacher and students have personal digital devices, inevitably requires changes in the way the teacher addresses her role. This study examined the implications of integrating 1:1 computing on teachers’ pedagogical perceptions and the classroom’s educational discourse. A change in pedagogical perceptions during three years of teaching within this model was investigated. The research analyzed data from 14 teachers teaching in a junior high school in the north of Israel collected over the course of three years through interviews and lesson observations. The findings show that the 1:1 computing allows teachers to improve their teaching skills; however, it fails to change their fundamental attitudes in regard to teaching and learning processes. It was further found that the use of a laptop by each student does not significantly improve the classroom’s learning discourse. The computer is perceived as an individual or group learning technology rather than as a tool for conducting learning discourse. An analysis of the data collected shows a great contribution to collaboration among teachers in preparing technology-enhanced lessons. The findings are discussed in terms of Bruner’s (Olson & Bruner, 1996) “folk psychology” and “folk pedagogy” of teachers and “the new learning ecology” framework in 1:1 classroom (Lee, Spires, Wiebe, Hollebrands, & Young, 2015). One of the main recommendations of this research is to reflect on findings from the teaching staff and the school community emphasizing 1:1 technology as a tool for significant pedagogical change. It seems that the use of personal technology per se is not enough for pedagogical changes to take place; the change must begin with teachers’ perceptions and attitudes.

Aim/Purpose: The present study aims to describe existing peer-to-peer, social network-based sharing practices among adult students in teacher colleges. Background: Ubiquitous social network sites open up a wide array of possibilities for peer-to-peer information and knowledge sharing. College instructors are often unaware of such practices that happen behind the scenes. Methodology: An interpretative, qualitative research methodology was used. Thirty-seven Israeli students at a teacher college in Israel participated in either focus group discussions of (N = 29) or in-depth interviews (N = 8). Contribution: Whereas knowledge sharing has been a main focus of research in organizational and information sciences, its relevance to educational settings has thus far been underscored. Recent research shows that peer–to-peer knowledge sharing is wide-spread among teenage students. The current study extends that work to an adult student population. Findings: The findings show that knowledge sharing of this type is a common and even central feature of students’ college life and study behavior. It takes place through a variety of small and larger social network-based peer groups of different formations, including mostly college students but at time also practicing, experienced teachers. Sharing groups are formed on the spot for short term purposes or are stable, continuous over longer time periods. The contents shared are predominantly lesson summaries, material for exams, reading summaries, and lesson plans. They are used immediately or stored for future use, as students have access to vast data bases of stored materials that have been compiled throughout the years by students of previous cohorts. Teacher students mentioned a range of reasons for sharing, and overall regard it very positive. However, some downsides were also acknowledged (i.e., superficial learning, exclusion, attentional overload, and interruptions). Recommendations for Practitioners: College faculty and teaching staff should be cognizant and informed about these widespread peer-based knowledge sharing practices and consider whether perhaps changes in teaching formats and task assignments are required as a result. Future Research: Future research should extend this work to other higher education settings, cultures and countries, and should map the perceptions of higher education teaching staff about peer-to-peer, online knowledge sharing.

Aim/Purpose: Use of the term “rigor” is ubiquitous in the research community. But do we actually know what it means, and how it applies to transdisciplinary research? Background: Too often, rigor is presumed to mean following an established research protocol scrupulously. Unfortunately, that frequently leads to research with little or no impact. Methodology: We identify a sample of 62 articles with “rigor” in the title and analyze their content in order to capture the range of perspectives on rigor. We then analyze how these findings might apply to informing science. Contribution: This paper offers an approach to defining rigor that is theory based and appropriate for transdisciplinary research. Findings: Rigor definitions tend to fall into one of two categories: criteria-based and compliance-based. Which is appropriate depends on the research context. Even more variation was found with respect to relevance, which is often used as a catch-all for research characteristics that aren’t associated with rigor. Recommendations for Practitioners: Recognize that when researchers are referring to rigor and relevance, they of-ten mean these to apply to other researchers rather than to practice. When funding research, it is important to understand who the rigor and relevance are directed towards. Recommendations for Researchers: When using the term “rigor”, think carefully about which meaning is intended and be transparent about that meaning in your writing. Impact on Society: A great deal of public money is invested in achieving research rigor. Society should be aware of what it is buying with that funding. Future Research: Developing a better understanding of research fitness and the factors that contribute to it.

Aim/Purpose: Nigeria’s university education goes through incessant strikes by the Academic Staff Union of Universities (ASUU). This strike has led to shared emotion on micro-blogging sites like Twitter. This study analyzed selected historical tweets from the “ASUU” to understand citizens’ opinions. Background: The researchers conducted sentiment analysis and topic modelling to understand Twitter users’ opinions on the strike. Methodology: The researchers used the Valence Aware Dictionary for Sentiment Reasoning (VADER) technique for sentiment analysis, and the Latent Dirichlet allocation (LDA) was used for topic modelling. A total of 10,000 tweets were first extracted for the study. After data cleaning, 1323 tweets were left. Contribution: To the researcher’s best knowledge, no published study has presented a sentiment analysis on the topic of the ASUU strike using the Twitter dataset. This research will fill this gap by providing a sentiment analysis and drawing out subjects by exploring the tweets on the phrase “ASUU.” Findings: The sentiment analysis result using VADER returned 567 tweets as ‘Negative,’ with the remaining 544 and 212 categorized as Positive and Neutral. The result of the LDA returned six topics, all comprising seven keywords. The topics were the solution to the strike, ASUU strike effect, strike Call-off, appeal to ASUU, student protest and student appeal. Recommendation for Researchers: Researchers can use this study’s findings to compare with other contexts of opinion mining. Practitioners may also use the research to understand better the attitudes of their staff and students about the strikes to create actionable solutions before the suspension of the strike. Future Research: Future studies can collect information from other social networking and blogging sites.

Aim/Purpose: Cyberbullying (CB) is an ongoing phenomenon that affects youth in negative ways. Using online news articles to provide information to schools can help with the development of comprehensive cyberbullying prevention campaigns, and in restoring faith in news reporting. The inclusion of online news also allows for increased awareness of cybersafety issues for youth. Background: CB is an inherent problem of information delivery and security. Textual analysis provides input into prevention and training efforts to combat the issue. Methodology: Text extraction and text analysis methods of term and concept extraction; text link analysis and sentiment analysis are performed on a body of news articles. Contribution: News articles are determined to be a major source of information for comprehensive cyberbullying prevention campaigns. Findings: Online news articles are relatively neutral in their sentiment; terms and topic extraction provide fertile ground for information presentation and context. Recommendation for Researchers: Researchers should seek support for research projects that extract timely information from online news articles. Future Research: Refinement of the terms and topics analytic model, as well as a system development approach for information extraction of online CB news.

Aim/Purpose: To explore the incidence of addiction potential within the Iranian public working population, describing how many Iranian public employees fall within the diagnostic categories of low, moderate, and high addiction potential. Also, to investigate the predicting role of occupational variables such as perceived stress, job security, and job satisfaction on addiction potential and belonging to low, moderate, and high addiction potential diagnostic categories. Background: Substance addiction among employees can lead to several negative consequences at the individual and organizational levels. Also, it is the fourth cause of death in Iran. However, few studies have been conducted on the topic among employees, and non among Iranian employees. Methodology: The study participants were 430 employees working in governmental offices of the North Khorasan province, Iran. Descriptive statistical analysis and multiple linear regression analysis were conducted to explore the incidence of addiction potential within the analyzed population and to investigate whether occupational variables such as perceived stress, job security, and job satisfaction predicted low, moderate, or high addiction potential. Contribution: This paper suggests that perceived stress might act as a risk factor for developing addiction, whereas job security and job satisfaction might be protective factors against the likelihood of addiction development. Findings: More than half of the sample showed moderate to high addiction potential. Perceived stress was positively related to addiction potential. Job security and job satisfaction were negatively related to addiction potential. Recommendation for Researchers: When addressing the topic of substance addiction, researchers should focus on the preventative side of investigating it; that is, addiction risk rather than already unfolded addiction. Also, researchers should be mindful of the cultural context in which studies are conducted. Future Research: Future research might investigate other relevant occupational predictors in relation to employee addiction potential, such as leadership style, work-life balance, and worktime schedule, or expand on the relevant causal chain by including personality traits such as neuroticism.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Qinghua WU,Weitong LI, Vol.E107-D, No.11, pp.1422-1432
Multi-focus image fusion involves combining partially focused images of the same scene to create an all-in-focus image. Aiming at the problems of existing multi-focus image fusion algorithms that the benchmark image is difficult to obtain and the convolutional neural network focuses too much on the local region, a fusion algorithm that combines local and global feature encoding is proposed. Initially, we devise two self-supervised image reconstruction tasks and train an encoder-decoder network through multi-task learning. Subsequently, within the encoder, we merge the dense connection module with the PS-ViT module, enabling the network to utilize local and global information during feature extraction. Finally, to enhance the overall efficiency of the model, distinct loss functions are applied to each task. To preserve the more robust features from the original images, spatial frequency is employed during the fusion stage to obtain the feature map of the fused image. Experimental results demonstrate that, in comparison to twelve other prominent algorithms, our method exhibits good fusion performance in objective evaluation. Ten of the selected twelve evaluation metrics show an improvement of more than 0.28%. Additionally, it presents superior visual effects subjectively.
Publication Date: 2024/11/01

In my knowledge graph class yesterday we talked about the SPARQL query language and I illustrated it with DBpedia queries, including an example getting data about the movie Double Indemnity. I had brought a google assistant device and used it to compare its answers to those from DBpedia. When I asked the Google assistant “Who […]

The post Why does Google think Raymond Chandler starred in Double Indemnity? appeared first on UMBC ebiquity.

'What's going on?' Within the context of our critically-informed teaching practice, we see moments of deep learning and reflexivity in classroom discussions and assessments. Yet, these moments of criticality are interspersed with surface learning and reflection. We draw on dichotomous, linear developmental, and messy explanations of learning processes to empirically explore the learning journeys of 20 international Chinese and 42 domestic New Zealand students. We find contradictions within our own data, and between our findings and the extant literature. We conclude that expressions of surface learning and reflection are considerably more complex than they first appear. Moreover, developing critical reflexivity is a far more subtle, messy, and emotional experience than previously understood. We present the theoretical and pedagogical significance of these findings when we consider the implications for the learning process and the practice of management education.

There is a growing consensus that cooperative goal structures are more effective at motivating groups than competitive goal structures. However, such results are based largely on studies conducted in highly-controlled settings where participants were provided with the necessary resources to accomplish their assigned task. In an attempt to extend the boundary conditions of current theoretical predictions, we undertook a field experiment within a base-of-the-pyramid setting where resource scarcity is extremely high. Specifically, we collected data on 44 communities within rural Sri Lanka who were tasked with contributing a portion of their resources to the construction of a school building; 24 were assigned to a competition condition and 20 to a cooperation condition. The results of our field experiment, and subsequent follow-up interviews and focus groups, collectively suggest that competitive goal structures generally lead to higher levels of motivation within a resource scarce environment. However, our results also suggest that cooperative goal structures can be highly motivating when groups are unfamiliar with one another, as cooperating with unfamiliar groups can provide access to valuable and rare knowledge within such settings.

Studies suggest that category-spanning organizations receive lower evaluation and perform worse than organizations focused on a single category. We propose that (1) these effects are contingent on clients' theory of value and that as clients expect more sophisticated services, they tend to value category spanners more positively and (2) the evaluation of producers mediates the relationship between category spanning and performance. We test our hypotheses using original data on corporate legal services in three markets (London, New York City, and Paris) over the decade 2000-2010. We find that (1) category spanners receive a better evaluation, and more so when their categorical combination is more inclusive and (2) evaluation mediates significantly the relationship between category spanning and performance. This study enriches our understanding of how audiences apprehend a whole market category system and why organizations span categories.

This paper investigates how "dual directors" enable firms that undertake corporate spinoffs to manage their post-spinoff relationships with the firms they divest, as well as the performance implications of dual directors serving simultaneously on these companies' boards. While the presence of dual directors is positively associated with the average stock market returns of parent and spinoff firms, their presence is increasingly positively associated with parent firm performance but increasingly negatively associated with spinoff firm performance as the share of sales a spinoff firm makes to its parent firm rises. These findings show that while dual directors give a parent firm power over its spinoff firm, dual directors only exercise that power at the spinoff firm's expense when that company is highly dependent on its parent firm.

So, any thought of disobeying them must be destroyed – along with anyone daring to spread the idea that the oath is to the Constitution, not to a regime and its unlawful orders.

President Donald Trump laid out a bold vision for America’s future—one where freedom of speech is non-negotiable, and censorship from both government and big tech is crushed.

U.S. President-elect Donald Trump on Tuesday named Elon Musk and former Republican presidential candidate Vivek Ramaswamy to lead a newly created Department of Government Efficiency, rewarding two of Trump’s well known supporters from the private sector.

Musk and Ramaswamy “will pave the way for my Administration to dismantle Government Bureaucracy, slash excess regulations, cut wasteful expenditures, and restructure Federal Agencies,“ Trump said in a statement.

Trump said the new department “will provide advice and guidance from outside of government,“ signaling the entity would operate outside the confines of government.

However, it would work with the White House and Office of Management & Budget to “drive large scale structural reform, and create an entrepreneurial approach” to government never seen before.

Trump said their work would conclude by July 4, 2026, making it a “gift” to the country on the 250th anniversary of the signing of the Declaration of Independence.

Musk, ranked by Forbes as the richest person in the world, already stood to benefit from Trump’s victory, with the billionaire entrepreneur expected to wield extraordinary influence to help his companies and secure favorable government treatment.

Musk gave millions of dollars to support Trump’s presidential campaign and made public appearances with him. Trump had said he would offer Musk a role in his administration promoting government efficiency.

He has many links to Washington, opens new tab and his lineup of companies includes electric car company Tesla (TSLA.O), opens new tab, social media platform X and rocket company SpaceX.

“This will send shockwaves through the system, and anyone involved in government waste, which is a lot of people!” Musk said, according to Trump’s statement, which called the new government initiative “potentially ‘The Manhattan Project’ of our time,“ referring to the U.S. plan to build the atomic bomb that helped end World War Two.

Ramaswamy is the founder of a pharmaceutical company who ran for the Republican presidential nomination against Trump and then threw his support behind the former president after dropping out.

“We will not go gently, @elonmusk,“ Ramaswamy said on X.

Musk reposted the announcement from Trump on his X account and added comments such as that, “The merch will be (fire),“ using three fire emojis, and, “People have no idea how much this will move the needle!”

He also posted: “Threat to democracy? Nope, threat to BUREAUCRACY!!!”

The acronym of the new department - DOGE - coincides with the name of the cryptocurrency dogecoin that Musk promotes.

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SHAH ALAM: A total of 387 cases of child abuse were recorded by the Selangor police from January to October, said state police chief Datuk Hussein Omar Khan.

He said that of the total, 139 victims were aged between 0 and 1 year, 96 were between two and five years old, while the and remaining victims were aged up to 18 years.

“Childcare providers were the main perpetrators of these crimes, followed by biological parents, teachers and stepparents,“ he said.

He made these comments to the press after officiating the second Child Interview Centre (CIC) under the Sexual, Abuse and Child Investigation Division (D11) of the Criminal Investigation Department (CID) at the Selangor police headquarters in Seksyen 11 police station today.

Hussein said police investigations found that most child abuse cases were caused by negligence, such as leaving babies or young children alone, which posed risks to the victims and led to neglect.

He also noted that there had been a trend of increasing reports of child abuse cases, partly due to growing awareness of violence against children among the public and various organisations.

“Some people are now coming forward and bravely making reports, thanks to numerous awareness programmes and initiatives by the Royal Malaysia Police (PDRM) in the community to provide information,“ he said.

Regarding the second CIC, Hussein said that RM180,000 had been allocated to refurbish an existing premises at the Seksyen 11 Police Station for this purpose.

He said the establishment of the second CIC, which has been operational since March 5, was in response to the increasing number of child-related cases that require interviews each year, with an average of 400 to 500 cases annually.

“The establishment of this CIC takes into account the rising number of cases, with 875 children already interviewed this year alone, involving various cases such as abuse, neglect and sexual offences.

“Given current needs, we are also planning to expand this service. Both CIC facilities are currently located in Shah Alam, so there is a need to extend them to Kuala Selangor, Sabak Bernam, Hulu Selangor or the southern part of the state,“ he said.

Hussein also said that the first CIC, established in 2014 and located in Seksyen 7, serves the police districts (IPD) of South Klang, North Klang, Gombak, Shah Alam, Hulu Selangor, Kuala Selangor, Kajang and KLIA.

“The second CIC caters to the IPDs of Petaling Jaya, Subang Jaya, Sabak Bernam, Kuala Langat, Sungai Buloh, Sepang, Serdang and Ampang Jaya,“ he added, noting that the centre conducts interviews with children under the age of 16, as referred by investigating officers from the 16 IPDs.

ALOR SETAR: An officer of the Malaysian Maritime Enforcement Agency (MMEA) was fined RM25,000 after pleading guilty at the Sessions Court here today to five charges of accepting bribes amounting to RM2,300 two years ago.

Judge N. Priscilla Hemamalini imposed a fine of RM5,000 for each charge faced by Muhamad Abdul Hadi Abdullah, 35 and the court ordered the accused to be jailed for five months for each charge if he failed to pay the fine.

According to all the charges, the accused, who holds the rank of Senior Maritime Officer at the MMEA Kedah and Perlis Headquarters, received money amounting to RM2,300 with no reply from the owner of LGH Maju Trading Company, Lim Kian Chong, who knew that he had an official working relationship with the individual.

The money was received by the accused through five money transfers from the Maybank account of a middleman, a woman, which was then deposited into the accused’s RHB Bank account and all the offences were committed at RHB Bank Bhd Langkawi Island Branch on Jan 2, April 10, May 11, July 7 and Oct 8, 2022.

The charge was filed under Section 165 of the Penal Code (Act 574) which carries a jail term of up to two years or a fine or both.

The Malaysian Anti-Corruption Commission (MACC) officers Abd Muntaqim Abdul Aziz and Mohd Syahzada Azad Sanusi led the prosecution while the accused was not represented.

KUALA LUMPUR: Malaysia’s goal of reaching 50% electric vehicle (EV) adoption by 2040 and 80% by 2050 in terms of total industry volume (TIV) is within reach, according to Deputy Investment, Trade, and Industry Minister Liew Chin Tong.

Liew said that the target – in accordance with the National Energy Transition Roadmap – aligns with the global shift towards sustainable transport.

“According to the International Energy Agency in the Global EV Outlook, globally in 2018, only 2% of total global sales was from EV, but by 2022, it was 14%, and by 2023, 18% of total global sales of cars comes from electric vehicles. In China this year, there were several months that EV overtook internal combustion cars, ICE cars. So these are all possible,” he told reporters at E-Mobility Asia 2024 (EMA 2024) today.

To achieve the target, Liew said that Malaysia needs to work together to develop a national effort to electrify its vehicles as much as possible.

He added that this is necessary to reduce national oil consumption and create more opportunities for various forms of manufacturing, including crossings of semiconductor and automotive industries.

Additionally, he said that the government is hoping that Malaysia will not just manufacture parts of the cars, but it is hoping that there will be horizontal crossing between the automotive industry and the semiconductor industry.

“So that one day, we are also known for designing chips for the automotive industry. That is one of our aspirations,” he remarked.

Liew said that another aspiration is to take advantage of the electrification of mobility, so that through this transition, Malaysia can reduce its overall national petroleum consumption.

“In most of our discussions, we are talking about shifting the burden of who pays for the petroleum consumption in this country. To address the question of the RON95 subsidy, I think E-Mobility has a big role to play. Electrification has a big role to play,” he added.

The event, EMA 2024 unveils electromobility and sustainable solutions as the way forward to reduce global emissions and tackle climate change.

China’s electric car manufacturers BYD, Chery and GWM are showcasing their latest models at the event, while Malaysia’s Eclimo is unveiling its new bikes.

EMA 2024 comes as EV demand surges in Southeast Asia and amid the global outlook that more than one in four vehicles on the road will be electric by 2035 according to the International Energy Agency.

Liew officiated the opening of the event that has drawn stakeholder and industry support including the state-owned Malaysia Automotive, Robotics & IoT Institute, and Electric Vehicle Association of Malaysia as strategic partners.

GOODYEAR is proud to be the official tyre sponsor of the Tokyo Auto Salon Kuala Lumpur 2024, happening from 8 – 10 November 2024 at MITEC, Kuala Lumpur. Known as the world’s premier customised car show, this event promises to showcase the latest in automotive technology, design, and more, drawing car enthusiasts from across the region.

Event Details

Date: 8 – 10 November 2024

Time: 10:00 am – 10:00 pm

Venue: MITEC, Kuala Lumpur

At the Goodyear booth, attendees can explore the latest in high-performance tyre technology and see how Goodyear is driving innovation in tyre performance and quality. This event offers automotive fans the perfect chance to engage with Goodyear and witness the exceptional standards that Goodyear tyres bring to every journey.

Don’t miss this exciting opportunity to connect with industry leaders and fellow car enthusiasts!

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