Search Results (76)

Ports with Mediterranean-like traffic profiles combine dense passenger, cargo, touristic, and local operations in confined waters where many small craft sail without AIS, increasing collision risk. Nature of such traffic in often unpredictable, due to often and sudden course corrections or changes. In such situations, it is possible that larger ships cannot manoeuvre to avoid collisions with small vessels. Hence, it is important to the port authority to develop a fast and adoptable mean to reduce collision risks. We present an end-to-end shore-based framework that detects and tracks vessels from fixed cameras (YOLOv9 + DeepSORT), estimates speed from monocular lateral video with an artificial neural network (ANN), and visualises collision risk in augmented reality (AR) for VTS/port operators. Validation in the Port of Split using laser rangefinder/GPS ground truth yields MAE 1.98 km/h and RMSE 2.18 km/h (0.605 m/s), with relative errors 2.83–21.97% across vessel classes. We discuss limitations (sample size, weather), failure modes, and deployment pathways. The application uses stationary port camera as an input. The core calculations are performed at user’s computer in the building. Mobile application uses wireless communication to show risk assessment at augmented reality smart phone. For training of ANN, we used The Split Port Ship Classification Dataset. Full article

Prompt engineering (PE) has emerged as a transformative paradigm in software engineering (SE), leveraging large language models (LLMs) to support a wide range of SE tasks, including code generation, bug detection, and software traceability. This study conducts a systematic literature review (SLR) combined with a co-citation network analysis of 42 peer-reviewed journal articles to map key research themes, commonly applied PE methods, and evaluation metrics in the SE domain. The results reveal four prominent research clusters: manual prompt crafting, retrieval-augmented generation, chain-of-thought prompting, and automated prompt tuning. These approaches demonstrate notable progress, often matching or surpassing traditional fine-tuning methods in terms of adaptability and computational efficiency. Interdisciplinary collaboration among experts in AI, machine learning, and software engineering is identified as a key driver of innovation. However, several research gaps remain, including the absence of standardized evaluation protocols, sensitivity to prompt brittleness, and challenges in scalability across diverse SE applications. To address these issues, a modular prompt engineering framework is proposed, integrating human-in-the-loop design, automated prompt optimization, and version control mechanisms. Additionally, a conceptual pipeline is introduced to support domain adaptation and cross-domain generalization. Finally, a strategic research roadmap is presented, emphasizing future work on interpretability, fairness, and collaborative development platforms. This study offers a comprehensive foundation and practical insights to advance prompt engineering research tailored to the complex and evolving needs of software engineering. Full article

In the manufacturing supply chain, management reports often begin with concise messages that summarize key inventory insights. Traditionally, human analysts manually crafted these summary messages by sifting through complex data—a process that is both time-consuming and prone to inconsistency. In this research study, we present an AI-based system that automatically generates high-quality inventory insight summaries, referred to as “headline messages,” using real-world inventory data. The proposed system leverages lightweight natural language processing (NLP) and machine learning models to achieve accurate and efficient performance. Historical messages are first clustered using a sentence-translation MiniLM model that provides fast semantic embedding. This is used to derive key message categories and define structured input features for this purpose. Then, an explainable and low-complexity classifier trained to predict appropriate headline messages based on current inventory metrics using minimal computational resources. Through empirical experiments with real enterprise data, we demonstrate that this approach can reproduce expert-written headline messages with high accuracy while reducing report generation time from hours to minutes. This study makes three contributions. First, it introduces a lightweight approach that transforms inventory data into concise messages. Second, the proposed approach mitigates confusion by maintaining interpretability and fact-based control, and aligns wording with domain-specific terminology. Furthermore, it reports an industrial validation and deployment case study, demonstrating that the system can be integrated with enterprise data pipelines to generate large-scale weekly reports. These results demonstrate the application and technological innovation of combining small-scale language models with interpretable machine learning to provide insights. Full article

Optical coherence tomography (OCT) is a leading imaging technique for diagnosing retinal disorders such as age-related macular degeneration and diabetic retinopathy. Its ability to detect structural changes, especially in the optic nerve head, has made it vital for early diagnosis and monitoring. This paper surveys techniques for ocular disease prediction using OCT, focusing on both hand-crafted and deep learning-based feature extractors. While the field has seen rapid growth, a detailed comparative analysis of these methods has been lacking. We address this by reviewing research from the past 20 years, evaluating methods based on accuracy, sensitivity, specificity, and computational cost. Key diseases examined include glaucoma, diabetic retinopathy, cataracts, amblyopia, and macular degeneration. We also assess public OCT datasets widely used in model development. A unique contribution of this paper is the exploration of adversarial attacks targeting OCT-based diagnostic systems and the vulnerabilities of different feature extraction techniques. We propose a practical, robust defense strategy that integrates with existing models and outperforms current solutions. Our findings emphasize the value of combining classical and deep learning methods with strong defenses to enhance the security and reliability of OCT-based diagnostics, and we offer guidance for future research and clinical integration. Full article

In the realm of educational assessment, the integration of artificial intelligence (AI) offers a promising pathway for the development of robust evaluations. This study explores the application of AI chatbots in crafting and validating examinations tailored to road geometric design, while adhering to the principles of Bloom’s Taxonomy. Utilizing Gemini AI Studio, three distinct exam versions were generated, covering eight crucial topics within road geometric design. A panel of expert chatbots, including Chat GPT 3.5, Claude 3, Sonet, Copilot, Perplexity, and You, assessed the validity of the exam content. These chatbots achieved scores of 9.17 or higher, establishing their proficiency as experts. Subsequent evaluations focused on relevance and wording, revealing high scores for both metrics, indicating the adequacy of the assessment tools. The two remaining versions were administered to student groups enrolled in the Road Construction II course at the Universidad Técnica Particular de Loja. Only 1.2% of students reached Bloom’s Taxonomy level 3, with many questions deemed easy, leading to varying trends in cognitive levels. Comparative analysis of student scores revealed significant discrepancies between a previous “classic” exam. While AI shows potential in crafting valid assessments aligned with Bloom’s Taxonomy, greater human involvement is necessary to ensure high-quality instrument generation. Full article

As artificial intelligence (AI) becomes increasingly embedded in organizational development, understanding how leadership shapes employee responses to AI is critical for fostering workplace innovation. Drawing on trait activation theory, this study develops a theoretical model in which employee AI trust enhances innovative performance through AI crafting. Paternalistic leadership serves as a situational moderator, while the leader’s AI opportunity perception functions as a higher-order moderator. A three-wave survey was conducted with 523 employees from 14 AI-intensive manufacturing firms in China. Results show that the interaction between AI trust and paternalistic leadership positively predicts both AI crafting and innovative performance. In addition, AI crafting mediates the effect of the interaction term on innovative performance. Furthermore, the leader’s AI opportunity perception moderates this interactive effect: when this perception is high, the positive impact of AI trust and paternalistic leadership on AI crafting is significantly stronger; when it is low, the effect weakens. These findings contribute to the literature by clarifying the situational and cognitive conditions under which AI trust promotes innovation, thereby extending trait activation theory to AI-enabled workplaces and offering actionable insights for leadership development in the intelligent era. Full article

Recent breakthroughs in AI-generated content (AIGC) have transformed video creation, empowering systems to translate text, images, or audio into visually compelling stories. Yet reliable evaluation of these machine-crafted videos remains elusive because quality is governed not only by spatial fidelity within individual frames but also by temporal coherence across frames and precise semantic alignment with the intended message. The foundational role of sensor technologies is critical, as they determine the physical plausibility of AIGC outputs. In this perspective, we argue that multimodal large language models (MLLMs) are poised to become the cornerstone of next-generation video quality assessment (VQA). By jointly encoding cues from multiple modalities such as vision, language, sound, and even depth, the MLLM can leverage its powerful language understanding capabilities to assess the quality of scene composition, motion dynamics, and narrative consistency, overcoming the fragmentation of hand-engineered metrics and the poor generalization ability of CNN-based methods. Furthermore, we provide a comprehensive analysis of current methodologies for assessing AIGC video quality, including the evolution of generation models, dataset design, quality dimensions, and evaluation frameworks. We argue that advances in sensor fusion enable MLLMs to combine low-level physical constraints with high-level semantic interpretations, further enhancing the accuracy of visual quality assessment. Full article

Innovative behavior is essential for maintaining an organization’s competitive edge. This study aimed to investigate the impact of job crafting on innovative behavior, focusing on the mediating role of organizational commitment and the moderating effect of psychological ownership. It also explored how the moderating effect of psychological ownership varied between artificial intelligence (AI) and non-AI departments. Data were collected from 457 employees in China’s Internet industry. The results reveal that organizational commitment mediates the relationship between job crafting and innovative behavior. Furthermore, psychological ownership significantly moderates this relationship, with notable differences between AI and non-AI departments. Notably, the mediating role of organizational commitment in the connection between job crafting and innovative behavior is influenced by psychological ownership. These findings underscore the key roles of job crafting, organizational commitment, and psychological ownership in fostering innovative behavior and supporting organizational growth. They also highlight the importance of strategically managing psychological ownership across different departmental contexts to enhance organizational commitment and promote employee innovation. Full article

Stained glass has played important roles in heritage building construction, however, conventional fabrication techniques have become economically prohibitive due to both capital costs and energy inefficiency, as well as high-level artistic and craft skills. To overcome these challenges, this study provides a new design methodology for customized 3D-printed polycarbonate (PC)-based stained-glass window alternatives using a fully open-source toolchain and methodology based on digital fabrication and hybrid crafts. Based on design thinking and open design principles, this procedure involves fabricating an additional insert made of (i) a PC substrate and (ii) custom geometries directly 3D printed on the substrate with PC-based 3D printing feedstock (iii) to be painted after the 3D printing process. This alternative is intended for customizable stained-glass design patterns to be used instead of traditional stained glass or in addition to conventional windows, making stained glass accessible and customizable according to users’ needs. Three approaches are developed and demonstrated to generate customized painted stained-glass geometries according to the different users’ skills and needs using (i) online-retrieved 3D and 2D patterns; (ii) custom patterns, i.e., hand-drawn and digital-drawn images; and (iii) AI-generated patterns. The proposed methodology shows potential for distributed applications in the building and heritage sectors, demonstrating its practical feasibility. Its use makes stained-glass-based products accessible to a broader range of end-users, especially for repairing and replicating existing conventional stained glass and designing new customizable products. The developed custom patterns are 50 times less expensive than traditional stained glass and can potentially improve thermal insulation, paving the way to energy efficiency and economic savings. Full article

Given the significant impact of Generative AI (GenAI) in the workplace, there are surprisingly few empirical studies examining how employees’ GenAI awareness shapes differently oriented job crafting behaviors. In organizations, understanding this is important because GenAI is unlikely to fully replace employees; instead, it requires them to adopt adaptive strategies to work alongside GenAI. If employees engage in avoidance crafting behavior, it could have negative consequences for the organization. Based on sensemaking theory, we develop a theoretical model to explore how employees’ GenAI awareness affects their job crafting behavior, as well as the mediating mechanisms and boundary conditions of its influence. Using self-evaluation data from 316 employees at three time points, the results of our hypothesis testing reveal that when employees perceive high internal Corporate Social Responsibility (CSR), their GenAI awareness triggers harmonious work passion and leads to approach job crafting; conversely, when employees perceive high external Corporate Social Responsibility, their GenAI awareness triggers obsessive work passion and leads to avoidance job crafting. Finally, the theoretical and empirical implications of our findings are discussed. Full article

The question generation system (QGS) for information technology (IT) education, designed to create, evaluate, and improve Multiple-Choice Questions (MCQs) using knowledge graphs (KGs) and large language models (LLMs), encounters three major needs: ensuring the generation of contextually relevant and accurate distractors, enhancing the diversity of generated questions, and balancing the higher-order thinking of questions to match various learning levels. To address these needs, we proposed a multi-agent system named Multi-Examiner, which integrates KGs, domain-specific search tools, and local knowledge bases, categorized according to Bloom’s taxonomy, to enhance the contextual relevance, diversity, and higher-order thinking of automatically generated information technology MCQs. Our methodology employed a mixed-methods approach combining system development with experimental evaluation. We first constructed a specialized architecture combining knowledge graphs with LLMs, then implemented a comparative study generating questions across six knowledge points from K-12 Computer Science Standard. We designed a multidimensional evaluation rubric to assess the semantic coherence, answer correctness, question validity, distractor relevance, question diversity, and higher-order thinking, and conducted a statistical analysis of ratings provided by 30 high school IT teachers. Results showed statistically significant improvements (p < 0.01) with Multi-Examiner outperforming GPT-4 by an average of 0.87 points (on a 5-point scale) for evaluation-level questions and 1.12 points for creation-level questions. The results demonstrated that: (i) overall, questions generated by the Multi-Examiner system outperformed those generated by GPT-4 across all dimensions and closely matched the quality of human-crafted questions in several dimensions; (ii) domain-specific search tools significantly enhanced the diversity of questions generated by Multi-Examiner; and (iii) GPT-4 generated better questions for knowledge points at the “remembering” and “understanding” levels, while Multi-Examiner significantly improved the higher-order thinking of questions for the “evaluating” and “creating” levels. This study contributes to the growing body of research on AI-supported educational assessment by demonstrating how specialized knowledge structures can enhance automated generation of higher-order thinking questions beyond what general-purpose language models can achieve. Full article

This study presents a comparative analysis of several multimodal large language models (LLMs) for no-reference image quality assessment, with a particular focus on images containing authentic distortions. We evaluate three models developed by OpenAI and three models from Claude.AI, comparing their performance in estimating image quality without reference images. Our results demonstrate that these LLMs outperform traditional methods based on hand-crafted features. However, more advanced deep learning models, especially those based on deep convolutional networks, surpass LLMs in performance. Notably, we make a unique contribution by publishing the processed outputs of the LLMs, providing a transparent and direct comparison of their quality assessments based solely on the predicted quality scores. This work underscores the potential of multimodal LLMs in image quality evaluation, while also highlighting the continuing advantages of specialized deep learning approaches. Full article

Embodied Conversational Agents (ECAs) serve as digital twins (DTs), visually and behaviorally mirroring human counterparts in various roles, including healthcare coaching. While existing research primarily focuses on single-coach ECAs, our work explores the benefits of multi-coach virtual health sessions, where users engage with specialized diet, physical, and cognitive coaches simultaneously. ECAs require verbal relational cues—such as empowerment, affirmation, and empathy—to foster user engagement and adherence. Our study integrates Generative AI to automate the embedding of these cues into coaching dialogues, ensuring the advice remains unchanged while enhancing delivery. We employ ChatGPT to generate empathetic and collaborative dialogues, comparing their effectiveness against manually crafted alternatives. Using three participant cohorts, we analyze user perception of the helpfulness of AI-generated versus human-generated relational cues. Additionally, we investigate whether AI-generated dialogues preserve the original advice’s semantics and whether human or automated validation better evaluates their lexical meaning. Our findings contribute to the automation of digital health coaching. Comparing ChatGPT- and human-generated dialogues for helpfulness, users rated human dialogues as more helpful, particularly for working alliance and affirmation cues, whereas AI-generated dialogues were equally effective for empowerment. By refining relational cues in AI-generated dialogues, this research paves the way for automated virtual health coaching solutions. Full article

The Industrial Revolution (IR) involves a centuries-long process of economic and societal transformation driven by industrial and technological innovation. From agrarian, craft-based societies to modern systems powered by Artificial Intelligence (AI), each IR has brought significant societal advancements yet raised concerns about future implications. As we transition from the Fourth Industrial Revolution (IR4.0) to the emergent Fifth Industrial Revolution (IR5.0), similar questions arise regarding human employment, technological control, and adaptation. During all these shifts, a recurring theme emerges as we fear the unknown and bring a concern that machines may replace humans’ hard and soft skills. Therefore, comprehensive preparation, critical discussion, and future-thinking policies are necessary to successfully navigate any industrial revolution. While IR4.0 emphasized cyber-physical systems, IoT (Internet of Things), and AI-driven automation, IR5.0 aims to integrate these technologies, keeping human, emotion, intelligence, and ethics at the center. This paper critically examines this transition by highlighting the technological foundations, socioeconomic implications, challenges, and opportunities involved. We explore the role of AI, blockchain, edge computing, and immersive technologies in shaping IR5.0, along with workforce reskilling strategies to bridge the potential skills gap. Learning from historic patterns will enable us to navigate this era of change and mitigate any uncertainties in the future. Full article

This study examines the influence of leaders’ artificial intelligence symbolization on job-crafting behaviors, highlighting both positive and negative consequences in Chinese small and medium-sized firms. This research utilizes signaling theory to investigate the impact of leaders’ visible adoption of AI on employees’ readiness for change, perceived threats, and job-crafting behaviors. This study examines the moderating influence of organizational support to understand its amplifying and decreasing effects. This work utilizes Python-based statistical tools to provide a novel approach for evaluating behavioral data in social science research. The results reveal that leaders’ AI symbolization significantly improves employees’ readiness for change and promotes proactive job crafting. Conversely, symbolic actions may exacerbate perceived risks, adversely affecting job-crafting behaviors. Organizational support is essential to enhancing the beneficial impacts of AI symbolization on change readiness while alleviating its adverse consequences on perceived threats. These results show how crucial symbolic leadership is for getting people to use new technology and making staff more flexible in SMEs that use AI. By offering organizational training and resources, leaders may optimize favorable results and mitigate adverse effects. This study highlights its significance regarding change readiness, perceived threats, and job crafting. Furthermore, it underscores Python’s (3.9) potential as a groundbreaking tool for enhancing behavioral research in the age of AI. Full article