The need for technical support for data handling and visualization solutions has increased in tandem with the complexity of today's data and information, that is of multiple sources, huge in size and of different formats. This study focuses on handling and analyzing text-based data. Despite many available text analysis tools, there is a high demand among researchers for easy- to-use tools yet scalable and with incomparable visualization features. Of recent, there has been a significant focus on utilizing VOSviewer, an open-source software for bibliometric analysis. This software is able to analyze a significant amount of data and provide excellent network data mapping. However, there is a lack of existing work in evaluating this sophisticated tool for text analysis. Thus, this article explores the capability of VOSviewer and presents evidence-based implementation of this software for text analysis.

The increasing integration of ChatGPT, a Generative Artificial Intelligence (Gen-AI) model, into educational environments has sparked substantial ethical concerns. This paper addresses the crucial question of whether to impose restrictions or legislate the usage of Gen-AI, with ChatGPT as a pivotal case study. Through systematic literature review and frequency of occurrence analysis, 10 ethical concerns were selected for further analysis using the Analytic Hierarchy Process (AHP). The analysis responses of 10 expert panels show that the top concerns, as revealed by their weights, after meeting the consistency requirement, include copyright, legal, and compliance issues (0.1731), privacy and confidentiality (0.1286), academic integrity (0.1206), incorrect reference and citation practices (0.1111), and safety and security concerns (0.1050). Evaluating the impact of these concerns on the policy alternatives (restriction and legislation), the findings revealed that “Restriction” received a higher weight (0.513712) compared to “Legislation” (0.485887). Notably, copyright, legal, and compliance issues, privacy and confidentiality, and academic integrity emerged as crucial factors influencing the decision between restriction and legislation. This study offers valuable insights for educational institutions and policymakers, suggesting the need for inclusive discussions, pilot programs to assess impacts on critical thinking, development of clear guidelines, flexible regulatory frameworks, awareness campaigns, and potential strategies for ethical and responsible use.

Utilizing the multiple degrees of freedom offered by the data glove for each finger and the hand, a novel online signature verification system using the singular value decomposition (SVD) numerical tool for signature classification and verification is presented. The proposed technique is based on the Singular value decomposition in finding r singular vectors sensing the maximal energy of glove data matrix A, called principal subspace, so the effective dimensionality of A can be reduced. Having modeled the data glove signature through its r-principal subspace, signature authentication is performed by finding the angles between the different subspaces. A demonstration of the data glove is presented as an effective high- bandwidth data entry device for signature verification. This SVD-based signature verification technique is tested and its performance is shown to be able to produce equal error rate (EER) of less than 2.37 percent.

E-commerce reviews are becoming more valued by both customers and companies. The high demand for sentiment analysis is driven by businesses relying on it as a crucial tool to improve product quality and make informed decisions in a fiercely competitive business environment. The purpose of this review paper is to explore and evaluate the applications of the BERT model, a Natural Language Processing (NLP) technique, in sentiment analysis across various fields. The model has been utilized in certain studies for various languages, restaurant businesses, agriculture, Automated Essay Scoring (AES), Twitter, and Google Play. The BERT model's fine-tuning steps involve using pre-trained BERT to perform various language understanding tasks. Text pre-processing is conducted to clean up the data and convert it to numbers before feeding it into BERT, which generates vectors for each input token. We found that BERT outperformed the norm on a range of general language understanding tasks, including sentiment analysis, paraphrase recognition, question-answering, and linguistic acceptability. The detection of neutral reviews and the presence of false reviews in the dataset are two problems that have an impact on the model's accuracy. Training is also slow because it is huge and there are many weights to update. Additional research could be conducted to improve the BERT model's accuracy by constructing a false review categorization model and providing more training to the model in recognizing neutral reviews. Doi: 10.28991/HIJ-2023-04-02-015 Full Text: PDF

Generative artificial intelligence has created a moment in history where human beings have begin to closely interact with artificial intelligence (AI) tools, putting policymakers in a position to restrict or legislate such tools. One particular example of such a tool is ChatGPT which is the first and world's most popular multipurpose generative AI tool. This study aims to put forward a policy-making framework of generative artificial intelligence based on the risk, reward, and resilience framework. A systematic search was conducted, by using carefully chosen keywords, excluding non-English content, conference articles, book chapters, and editorials. Published research were filtered based on their relevance to ChatGPT ethics, yielding a total of 41 articles. Key elements surrounding ChatGPT concerns and motivations were systematically deduced and classified under the risk, reward, and resilience categories to serve as ingredients for the proposed decision-making framework. The decision-making process and rules were developed as a primer to help policymakers navigate decision-making conundrums. Then, the framework was practically tailored towards some of the concerns surrounding ChatGPT in the context of higher education. In the case of the interconnection between risk and reward, the findings show that providing students with access to ChatGPT presents an opportunity for increased efficiency in tasks such as text summarization and workload reduction. However, this exposes them to risks such as plagiarism and cheating. Similarly, pursuing certain opportunities such as accessing vast amounts of information, can lead to rewards, but it also introduces risks like misinformation and copyright issues. Likewise, focusing on specific capabilities of ChatGPT, such as developing tools to detect plagiarism and misinformation, may enhance resilience in some areas (e.g., academic integrity). However, it may also create vulnerabilities in other domains, such as the digital divide, educational equity, and job losses. Furthermore, the finding indicates second-order effects of legislation regarding ChatGPT which have implications both positively and negatively. One potential effect is a decrease in rewards due to the limitations imposed by the legislation, which may hinder individuals from fully capitalizing on the opportunities provided by ChatGPT. Hence, the risk, reward, and resilience framework provides a comprehensive and flexible decision-making model that allows policymakers and in this use case, higher education institutions to navigate the complexities and trade-offs associated with ChatGPT, which have theoretical and practical implications for the future.