Search Results (149)

Advanced three-dimensional extended reality (XR) technologies are highly suitable for cultural heritage research and education. XR tools enable the creation of realistic virtual or augmented reality applications for curating and disseminating information about cultural artifacts and sites. Developing XR applications for cultural heritage requires interdisciplinary collaboration involving strong teamwork and soft skills to manage user requirements, system specifications, and design cycles. Given the diverse end-users, achieving high precision, accuracy, and efficiency in information management and user experience is crucial. Human–computer interaction (HCI) design and evaluation methods are essential for ensuring usability and return on investment. This article presents ten case studies of cultural heritage software projects, illustrating the interdisciplinary work between computer science and HCI design. Students from institutions such as the State University of New York (USA), Glasgow School of Art (UK), University of Granada (Spain), University of Málaga (Spain), Duy Tan University (Vietnam), Imperial College London (UK), Research University Institute of Communication & Computer Systems (Greece), Technical University of Košice (Slovakia), and Indiana University (USA) contributed to creating, assessing, and improving the usability of these diverse cultural heritage applications. The results include a structured typology of CH XR application scenarios, detailed insights into design and evaluation practices across ten international use cases, and a development framework that supports interdisciplinary collaboration and stakeholder integration in phygital cultural heritage projects. Full article

Understanding how cognitive differences shape visitor behavior in digital heritage experiences is essential for designing inclusive and engaging museum technologies. This study explores the relationship between cognitive level and interaction behavior, affective responses, and sensor-based engagement using a publicly available dataset from a digital museum game. Participants (N = 1000) were categorized into three cognitive levels (Early, Developing, and Advanced), and their data were analyzed across three domains: user interaction behavior, affective and performance states, and sensor-based interaction measures. Our findings suggest that sensor-level interactions are more sensitive indicators of cognitive differences than observable behavior or inferred affect. This work contributes to the heritage HCI field by highlighting the potential for cognitively adaptive systems that personalize the museum experience in real-time, enhancing accessibility, engagement, and learning in cultural settings. Full article

This study systematically investigates the mechanism by which total ionizing dose (TID) affects the lifetime degradation of NMOS devices induced by hot-carrier injection (HCI). Experiments involved Cobalt-60 (Co-60) gamma-ray irradiation to a cumulative dose of 500 krad (Si), followed by 168 h annealing at 100 °C to simulate long-term stability. However, under HCI stress conditions (V_D = 2.7 V, V_G = 1.8 V), irradiated devices show a 6.93% increase in threshold voltage shift (ΔV_th) compared to non-irradiated counterparts. According to the IEC 62416 standard, the lifetime degradation of irradiated devices induced by HCI stress is only 65% of that of non-irradiated devices. Conversely, when the saturation drain current (I_Dsat) degrades by 10%, the lifetime doubles compared to non-irradiated counterparts. Mechanistic analysis demonstrates that partial neutralization of E’ center positive charges at the gate oxide interface by hot electrons weakens the electric field shielding effect, accelerating ΔV_th drift, while interface trap charges contribute minimally to degradation due to annealing-induced self-healing. The saturation drain current shift degradation primarily correlates with electron mobility variations. This work elucidates the multi-physics mechanisms through which TID impacts device reliability and provides critical insights for radiation-hardened design optimization. Full article

This study investigates the biotribological performance of alumina–UHMWPE and alumina–PEEK hip implant couples through finite element simulation (ANSYS v24) and statistical inference (STATA v17). During gait cycle loading simulations, significant disparity in wear behaviour was observed. Alumina–UHMWPE demonstrated superior mechanical resistance, with a wear volume of 0.18481 mm³ and a wear depth of 6.93 × 10⁻⁴ mm compared to alumina–PEEK, which registered higher wear (volume: 8.4006 mm³; depth: 3.15 × 10⁻² mm). Wear distribution analysis indicated alumina–UHMWPE showed an even wear pattern in comparison to the poor, uneven alumina-PEEK high-wear patterns. Statistical comparison validated these findings, wherein alumina–UHMWPE achieved a 27.60 hip joint wear index (HCI) value, which is better than that of alumina–PEEK (35.85 HCI), particularly regarding key parameters like wear depth and volume. This computational–statistical model yields a baseline design for biomaterial choice, demonstrating the potential clinical superiority of alumina–UHMWPE in reducing implant failure risk. While this is a simulation study lacking experimental validation, the results pave the way for experimental and clinical studies for further verification and refinement. The approach enables hip arthroplasty design optimization with maximal efficiency and minimal resource-intensive testing. Full article

Clinical Decision Support Systems (CDSSs) have become indispensable in medical decision-making. The heterogeneity and vast volume of medical data require firm attention to data management and integration strategies. On the other hand, CDSS functionality must be enhanced through improved human–computer interaction (HCI) principles. This study investigates the bidirectional relationship between data management practices (specifically data entry management, data transformation, and data integration) and HCI principles within CDSSs. Through a novel framework and practical case studies, we demonstrate how high-quality data entry, driven by controlled workflows and automated technologies, is crucial for system usability and reliability. We explore the transformative positive impact of robust data management techniques, including standardization, normalization, and advanced integration solutions, on the HCI elements and overall system performance. Conversely, we illustrate how effective HCI design improves data quality by reducing cognitive load, minimizing errors, and fostering user engagement. The findings reveal a synergistic relationship between HCI and data science, providing actionable insights for designing intuitive and efficient CDSSs. This research bridges the gap between technical and human-centric approaches, advancing CDSS usability, decision accuracy, and clinician trust for better patient outcomes. Full article

Sentiment analysis is pivotal in advancing human–computer interaction (HCI) systems as it enables emotionally intelligent responses. While existing models show potential for HCI applications, current conversational datasets exhibit critical limitations in real-world deployment, particularly in capturing domain-specific emotional dynamics and context-sensitive behavioral patterns—constraints that hinder semantic comprehension and adaptive capabilities in task-driven HCI scenarios. To address these gaps, we present Multi-HM, the first multimodal emotion recognition dataset explicitly designed for human–machine consultation systems. It contains 2000 professionally annotated dialogues across 10 major HCI domains. Our dataset employs a five-dimensional annotation framework that systematically integrates textual, vocal, and visual modalities while simulating authentic HCI workflows to encode pragmatic behavioral cues and mission-critical emotional trajectories. Experiments demonstrate that Multi-HM-trained models achieve state-of-the-art performance in recognizing task-oriented affective states. This resource establishes a crucial foundation for developing human-centric AI systems that dynamically adapt to users’ evolving emotional needs. Full article

Emotion recognition based on Electroencephalogram (EEG) signals plays a vital role in affective computing and human–computer interaction (HCI). However, noise, artifacts, and signal distortions present challenges that limit classification accuracy and robustness. To address these issues, we propose ECA-ResDNN, a novel hybrid model designed to leverage the frequency, spatial, and temporal characteristics of EEG signals for improved emotion recognition. Unlike conventional models, ECA-ResDNN integrates an Efficient Channel Attention (ECA) mechanism within a residual neural network to enhance feature selection in the frequency domain while preserving essential spatial information. A Deep Neural Network further extracts temporal dependencies, improving classification precision. Additionally, a hybrid loss function that combines cross-entropy loss and fuzzy set loss enhances the model’s robustness to noise and uncertainty. Experimental results demonstrate that ECA-ResDNN significantly outperforms existing approaches in both accuracy and robustness, underscoring its potential for applications in affective computing, mental health monitoring, and intelligent human–computer interaction. Full article

This study introduces FRIDA, a novel framework that integrates Requirements Engineering (RE) and DevOps practices into Robotic Process Automation (RPA), with a focus on optimizing human–computer interaction (HCI). The framework was designed using the PICO (Population, Intervention, Comparison, and Outcome) methodology and evaluated through a case study in an automation-driven department. Key results include an 83% reduction in processing time, an 81.25% decrease in error rates, and an 80% reduction in manual tasks, alongside improved compliance and scalability. The integration of RE and DevOps ensures a structured approach to requirement management and continuous delivery, while the emphasis on HCI enhances user-friendliness and adoption. FRIDA represents a significant advancement in RPA development, offering a robust, user-centric solution for optimizing automated processes. Future research should explore its application across diverse industries and the integration of advanced technologies like AI and machine learning to further enhance RPA capabilities. Full article

This paper investigates the integration of emotional, cognitive, and interactional processes in the design of educational technologies through the lens of Human–Computer Interaction (HCI). While previous studies have focused on cognitive and interactional engagement, emotional engagement remains underdeveloped in many tools, limiting learning effectiveness. To bridge this gap, this study proposes a theoretical holistic framework integrating usability, emotional intelligence, and adaptive interaction. Through a qualitative analysis, we examine educational platforms—including Duolingo, Khan Academy, and Google Classroom—alongside simulation-based systems such as EduTechRPGs. The study applies Cognitive Load Theory, Emotional Intelligence Theory, and Self-Determination Theory to assess their effectiveness. The findings highlight the importance of designing emotionally intelligent, scalable, and adaptive learning environments, and the proposed framework integrates psychological principles to boost engagement, motivation, and learning outcomes. This study contributes to a learner-centered HCI approach, ensuring that educational technologies support both cognitive and emotional development. Future research should validate the proposed framework empirically and explore interdisciplinary approaches to optimize educational technology. This study highlights the role of HCI in creating meaningful digital learning experiences by integrating psychology, cognitive science, and user experience design. Full article

As technology-mediated social interaction in virtual environments prevails, recent Human–Computer Interaction (HCI) studies have suggested incorporating biosensory information cues that reveal users’ inner states to facilitate social information sharing and augment copresent experience. Physiological synchrony is believed to be engaged in several important processes of copresent experience. However, what impact different biosensory cues have on physiological synchrony and users’ copresent experience remains underinvestigated. This study selected a virtual reality (VR) electronic dance music setting and integrated five different biosignals, namely, power of electromyography (pEMG), galvanic skin response (GSR), heart rate (HR), respiration effort (RE), and oxyhemoglobin saturation by pulse oximetry (SpO₂). A non-randomized controlled experiment with 67 valid participants and five baseline data providers revealed that GSR enhanced physiological synchrony significantly. However, semi-structure interviews with 10 participants indicated that RE and HR provided the strongest user-perceived copresence, which implies an intriguing gap between quantitative and qualitative analysis results. Five design implications were further generated and discussed in details for the future design and development of virtual copresent experience based on biosensory information cues. Full article

Behavioral changes are critical for addressing sustainability challenges, which have become increasingly urgent due to the growing impact of global greenhouse gas emissions on ecosystems and human livelihoods. However, translating awareness into meaningful action requires practical tools to bridge this gap. Mobile applications, utilizing strategies from human–computer interaction (HCI) such as gamification, nudging, and persuasive technologies, have proven to be powerful in promoting sustainable behaviors. To support designers in developing effective apps of this kind, theory-based design guidelines were created, drawing on established theories and design approaches aimed at shaping and encouraging virtuous user behaviors fostering sustainability. To make these guidelines more accessible and enhance their usability during the design phase, this study presents their transformation into the SBAM card deck, a deck of 11 design cards. The SBAM cards aim to simplify theoretical concepts, stimulate creativity, and provide structured support for design discussions, helping designers generate solutions tailored to specific project contexts. This study also evaluates the effectiveness of the SBAM cards in the design process through two workshops with design students. Results show that the cards enhance ideation, foster creativity, and improve designers’ perceived self-efficacy compared to the exploitation of the same design guidelines information presented in traditional textual formats. This paper discusses the SBAM cards design and evaluation methodology, findings, and implications, offering insights into how the SBAM design cards can bridge the gap between theory and practice in sustainability-focused mobile app development. To ensure broader accessibility, the SBAM cards have been made available to the public through a dedicated website. Full article

Visual complexity has significant research value across various fields, influencing both first impressions and user experiences. In the context of human–computer interactions, studies on visual complexity in user interface design have evolved progressively alongside technological advancements, and the quantification of visual complexity has been demonstrated to be significant. However, existing research has primarily focused on web interfaces. In contrast, mobile interfaces have less maneuverable space and rely on finger interactions, necessitating more targeted studies. Additionally, while model research has gradually emerged in recent years, there are still issues regarding the influence weight of each visual element in the mobile interface that need to be addressed. This study employed a multiple linear regression analysis to develop a measurement model based on the visual elements used in mobile GUI design, addressing the weighting of each metric from the designer’s perspective. Different from other studies, this study focused on the independent visual elements of the mobile phone interface. This measurement model offers designers intuitive and quantitative reference metrics, enhancing the efficiency of design iterations. Furthermore, it lays the groundwork for the development of visual-complexity measurement tools. Full article

Despite the advantages of fin field-effect transistors (FinFETs), there are hidden issues such as electric field enhancement and exacerbated self-heating effects, which will intensify device aging effects. Due to the escalating costs associated with aging protection at the device process level, there is an urgent need to reduce the impact of aging on circuit performance from the circuit design perspective. This study focuses on the specific structure of the strong-arm latch comparator and conducts a detailed aging analysis. Based on the quasi-static approximation (QSA) model, the threshold voltage shift under operational stress is simulated. It is concluded that both the hot carrier injection (HCI) effect and negative bias temperature instability (NBTI) effect play equally non-negligible roles. Furthermore, aging tests were conducted based on 14 nm FinFET devices, validating the substantial HCI effects induced by short-duration pulses. Simultaneously, the test results suggest that the aging effect becomes more remarkable with increasing current. An improved circuit is proposed to reduce the HCI effect by reducing the current pulse by the way of pre-charging, which effectively reduces the threshold voltage shift of the latch comparator input transistors. Full article

Craft-based methodologies have emerged as a vital human-computer interaction (HCI) approach, bridging digital and physical materials in interactive system design. This study, born from a collaboration between two research networks focused on affective design and interaction design, investigates how diverse professionals use craft-based approaches to transform design processes. Through carefully curated workshops, participants from varied backgrounds worked to identify specific problems, select technologies, and consider contextual factors within a creative framework. The workshops served as a platform for observing participant behaviors and goals in real-world settings, with researchers systematically collecting data through material engagement and visual problem-solving exercises. Drawing inspiration from concepts like Chindogu (Japanese “unuseless” inventions), the research demonstrates how reframing interaction design through craft-based methodologies can lead to more intuitive and contextually aware solutions. The findings highlight how interdisciplinary collaboration and sustainable and socially responsible design principles generate innovative solutions that effectively address user requirements. This integration of creative frameworks with physical and digital materials advances our understanding of meaningful technological interactions while establishing more holistic approaches to interactive system design that can inform future research directions in the field. Full article

Practice and Research Optimization Environment in Python (PyPROE) is a GUI-based, integrated framework designed to improve the user experience in both learning and research on engineering design optimization. Traditional optimization programs require either coding or creating complex input files, and often involve a variety of applications in sequence to arrive at the solution, which presents a steep learning curve. PyPROE addresses these challenges by providing an intuitive, user-friendly Graphical User Interface (GUI) that integrates key steps in design optimization into a seamless workflow through a single application. This integration reduces the potential for user error, lowers the barriers to entry for learners, and allows students and researchers to focus on core concepts rather than software intricacies. PyPROE’s human-centered design simplifies the learning experience and enhances productivity by automating data transfers between function modules. This automation allows users to dedicate more time to solving engineering problems rather than dealing with disjointed tools. Benchmarking and user surveys demonstrate that PyPROE offers significant usability improvements, making complex engineering optimization accessible to a broader audience. Full article