Journal of Information, Technology and Policy

Digital Intelligence and Interactive Experience: Application and Innovation of Visualization Technology in Book Design

Tao Yang — Tue, 30 Sep 2025 00:00:00 +0800

This article explores the transformative impact of visualization technology on book design, focusing on how it redefines spatial interactions and inspires innovative approaches. By examining the evolution of information dissemination media, the study provides theoretical support for future design practices aimed at enhancing the reading experience to align with modern needs. Through methods such as literature review, case studies, and multidisciplinary research integrating psychology, art, mathematics, and computer science, the study reveals limitations in traditional book design, particularly in spatial perception and interactivity. Visualization technology enables advancements in book design, including transitions from 2D to 3D, intelligent interactivity, customized reading experiences, and cross-disciplinary collaboration, which help to overcome these limitations. However, while visualization technology introduces new possibilities, it also poses challenges related to cultural preservation, copyright protection, and data privacy. Future book design is anticipated to be increasingly diverse, intelligent, and interactive, blending various cultural and technological elements. Multidisciplinary collaboration will drive these developments, positioning book design as a crucial tool for knowledge dissemination and cultural expression in the digital era.

Generative AI and Journalism Ethics: Controversies over ChatGPT

Qingtao Liu — Thu, 10 Apr 2025 00:00:00 +0800

The rise of generative artificial intelligence is reshaping the model of news production. While it enhances the efficiency of content output, it also raises profound ethical concerns. Technological tools can rapidly generate standardized news texts, but the mechanized production model may result in a loss of in-depth thinking and value judgment in news reporting, and could even lead to the creation of false or misleading content in the pursuit of traffic benefits. The core contradictions manifest in three aspects: first, the probabilistic nature of algorithm-generated content conflicts with the fundamental requirement for news accuracy, as the phenomenon of hallucination in language models may present fabricated information as fact; second, the ambiguity in assigning responsibility in human-machine collaboration makes it difficult to trace accountability when misinformation spreads; third, media organizations commonly face a transparency paradox, where the covert use of AI tools not only undermines the public’s right to know but also exacerbates the trust crisis.

Research on the Testing Duration of Planning Model

Zhongsheng Li, Shenwang Li — Sat, 11 Oct 2025 00:00:00 +0800

Reliability growth plans are typically described by using reliability growth planning models that generate growth planning curves. The PM2 model, known as the planning model based on projection methodology, is a widely used growth planning model. The PM2 model can always meet the assigned reliability target during any reliability growth testing duration, regardless of changes in the management strategy parameter and/or the fixed effectiveness factor parameter. In fact, these two parameters determine whether reliability growth testing is successful or not and how long it takes. That is, the PM2 model currently in place needs to be modified accordingly. Firstly, a sensitivity analysis is carried out on the three parameters of the PM2 model, followed by a discussion on the correlation between the product of two process management parameters and the duration of testing. Then, a relationship equation for the testing duration is constructed using these two process management parameters. Finally, an extended model for planning reliability growth has been proposed. In addition, the proposed planning model is utilized to examine an illustrated example and a real case. The results have demonstrated that the model is reasonable and reliable, as the testing duration, was removed from the reliability growth planning model.

Asylum Processing Algorithms and Epistemic Violence: A Review of AI’s Role in Refugee Status Determination

Loso Judijanto — Fri, 12 Sep 2025 00:00:00 +0800

The increasing use of Artificial Intelligence (AI) in asylum processing systems has raised significant ethical concerns, particularly regarding its impact on refugee status determination (RSD). AI technologies, such as machine learning and biometric recognition, are increasingly being employed to streamline asylum decision-making. However, there is a growing concern about the biases embedded in these systems and the potential for epistemic violence—the erasure of refugee voices in the decision-making process. This study aims to explore the role of AI algorithms in asylum processing, focusing on how they contribute to epistemic violence in refugee status determination. This research employs a qualitative literature review methodology, analysing a range of academic articles, reports, and case studies published over the past decade. The primary data collection method involves reviewing secondary data from peer-reviewed articles, government reports, and international organisations that focus on AI and asylum processing. Data analysis follows a thematic approach, identifying key trends, challenges, and ethical implications related to the implementation of AI in RSD processes. The results of this review reveal that AI systems often perpetuate biases based on race, gender, and nationality, leading to unfair outcomes for refugees. Additionally, AI’s inability to fully capture the complexity of refugee experiences contributes to epistemic violence, where the unique and personal stories of refugees are reduced to data points. In conclusion, AI has the potential to improve asylum processes but must be applied with caution. Future research should focus on the development of ethical AI frameworks and explore alternative approaches to ensure more inclusive and fair refugee status determination processes.

Exploring the Potentials of Artificial Intelligence and Digital Technologies in Transforming the Palm Oil Industry: A Review

Loso Judijanto — Fri, 01 Aug 2025 00:00:00 +0800

The palm oil industry faces increasing pressure to improve productivity, sustainability, and supply chain transparency amid environmental and economic challenges. This study aims to explore the potential of artificial intelligence (AI) and digital technologies in transforming the palm oil sector by synthesising existing qualitative literature. A qualitative literature review methodology was employed, focusing on secondary data sourced from 80 peer-reviewed academic articles, institutional reports, and relevant industry publications. Data collection involved systematic document retrieval and screening to ensure relevance and credibility. Thematic analysis was conducted to identify key areas where AI and digital tools impact the industry, emphasising precision agriculture, supply chain traceability, environmental monitoring, and labour productivity. The results reveal that AI applications significantly enhance yield optimisation through advanced remote sensing and machine learning algorithms, improve supply chain transparency via blockchain and natural language processing, and support environmental compliance through satellite monitoring and emissions detection. Additionally, AI-driven automation aids labour management, addressing workforce challenges and operational efficiency. Despite these advancements, barriers such as low digital literacy among smallholders and infrastructure limitations persist, limiting widespread adoption. The study concludes that while AI and digital technologies hold transformative potential, comprehensive strategies incorporating technological innovation and capacity building are essential for inclusive sectoral development. Future research should focus on pilot implementations, socio-economic impact assessments, and the development of tailored solutions for smallholder integration to fully harness digital transformation benefits in the palm oil industry.

Research on Optimization of Naive Bayes Algorithm for Spam Filtering

Jian Sun — Sun, 28 Sep 2025 00:00:00 +0800

With the rapid development of the Internet, email as an important communication tool faces increasingly severe spam problems. This paper proposes a series of optimization and improvement solutions targeting several key issues in the application of traditional Naive Bayes algorithm for spam filtering. The research first systematically analyzes the theoretical foundation of Naive Bayes algorithm in text classification and its applicability in spam identification, thoroughly investigating main factors affecting algorithm performance such as data imbalance, feature selection, and high-dimensional sparsity. Building on this foundation, the paper presents comprehensive improvement strategies from four dimensions: data preprocessing, feature selection, model structure, and parameter optimization, including innovative solutions like improved information gain feature selection methods, dynamic weight adjustment mechanisms, and semi-Naive Bayes models. Through comparative experimental validation, the optimized algorithm achieves significant performance enhancement in classification, effectively reducing misjudgment rates while improving identification capability for new types of spam. This research not only provides more effective technical solutions for spam filtering but also offers valuable references for extending Naive Bayes algorithm to other text classification tasks.