Early phases of space missions rely on diverse engineering tools that often share similar data structures and domains but lack interoperability. As a result, data exchange between tools typically requires manual effort or custom-built converters, limiting efficient collaboration and reuse of design data. This paper proposes the concept of a Semantic Adapter to enable flexible and automated data exchange between heterogeneous engineering tools without tool-specific adaptions. The approach combines OpenAPI, as a standardized way to access and exchange tool data, with a mapping and model transformation algorithm that aligns and converts data between different tool-specific models. The concept was demonstrated and evaluated in a case study involving two concurrent engineering centers collaborating on a joint space mission study. The evaluation shows that the Semantic Adapter enables automated data retrieval and transformation, demonstrating the general feasibility of combining API specifications and model transformations for engineering data exchange. However, results also indicate that automatically writing transformed data to target tools via OpenAPI remains a challenge, especially when dealing with complex, dynamically typed models. The paper concludes by discussing limitations and directions for future work.
The resumption of Tesla's wireless power transfer research by the scientific community has opened up a new paradigm for wireless power networks. Building on the foundation established in previous works, this paper analyzes system energy costs and user service times to determine supply zones per station. Specifically, previous publications introduced two categories of energy providers: one primary source and multiple secondary sources. A cooperation protocol between primary and secondary sources was described to maximize energy savings in the user supply process. In this paper, we analyze system energy costs and user service times across the WEN coverage area to consider future versions of this protocol.
Narrative computer generation is understood as the task of constructing computational models of the way in which humans build stories. In order to imitate human cognition in intelligent systems, it is necessary to have a good knowledge of the principles that explain how human language constructs concepts. In this paper, a new tool in the form of the Interactive Narrative Graph Grammar (INGG) in which a natural language is translated into the internal language of the semantically enriched graph system is proposed. The system oriented for computer games is particularly relevant in the context of collaborative design. It enables cooperation between narrative designers, who may not have a technical background, and level or world creators in game development. By providing an intuitive yet structured way to represent story elements, relationships, and logic, this system serves as a bridge between storytelling and computational implementation.
Cross-disciplinary teams often face the challenges of semantic misalignment and low emotional engagement during the early stages of design. This study proposes an interaction system that integrates visual clustering and physiological sensing to observe semantic alignment and emotional engagement during visual interpretation in team interaction. Although AI image generation tools are increasingly being adopted to facilitate language-based communication, their inherent ambiguity and semantic distortion can lead to misunderstandings and hinder consensus building. At the same time, emotional cues that influence collaboration quality are often embedded in non-verbal interactions; if not detected in time, these can result in withdrawal or breakdowns in communication. To address these issues, this study proposes an interactive system that integrates semantic input, AI image generation, and wearable emotion sensing devices. Using VGG16 for image feature clustering and the Emotion Wings device to record real-time heart rate variability (HRV), the study analyzes the interaction between image style, semantic orientation, and emotional engagement. The results indicate that semantically balanced images help enhance participant engagement and mutual understanding, while semantic bias or character exclusion tend to lead to emotional withdrawal and low consensus.
This paper presents the development and evaluation of a Vietnamese Question Answer Generation (QAG) model specifically designed to generate review questions and exam questions in the Vietnamese subject Marxist-Lenininist Philosophy, helping learners to better manage their knowledge. Through the creation of a new QA dataset dedicated entirely to the Vietnamese subject Marxist-Lenininist Philosophy and the training and fine tuning processes, we have use a small-scale pre-trained language model (PLM) but the performance of generating Vietnamese questions is equivalent to that of LLM models. Our findings show that smaller specialized models can indeed match the capabilities of larger models, providing a more resource-efficient alternative without compromising on quality or efficiency. This study contributes to the broader discourse on the extensibility and adaptability of PLM, providing valuable insights into how to develop specialized models for specific educational purposes.
The construction industry is increasingly embracing Large Language Models (LLMs), to enhance efficiency and decision-making. However, Small and Medium-sized Enterprises (SMEs) face significant barriers, including limited digital literacy, resource constraints, and uncertainty regarding AI's practical application. This study examines an AI upskilling course tailored specifically for SMEs in the building industry, designed around cooperative learning principles to foster knowledge-sharing and enhance AI competencies. Through pre- and post-course evaluations involving 45 participants across diverse professional roles, significant improvements were observed in AI knowledge, prompting skills, and practical application within workflows. Cooperative learning methodologies enabled effective peer-to-peer interaction and collaborative problem-solving, significantly increasing participants' confidence and practical AI capabilities. Nevertheless, notable challenges included varying levels of digital literacy and resistance to adopting AI technologies, highlighting the need for targeted support and ongoing training initiatives. Future courses should include preliminary digital literacy assessments, more extensive hands-on practice, and further tailored content addressing specific industry applications of LLMs. Continuous, peer-driven learning models can sustain AI adoption, ensuring long-term integration and impact. This study contributes insights into AI upskilling strategies, emphasizing cooperative learning as a pivotal approach for helping construction SMEs adopt AI innovations.
This paper proposes a cooperative approach that leverages artificial intelligence to support the growing elderly population. It focuses on enhancing mental health, reducing loneliness, and improving overall well-being. It addresses the challenges of elderly isolation through an AI-based virtual companion designed to provide personalized and empathetic support within a collaborative care context. To clarify the underlying structure and manage the complexity of elderly care, the approach incorporates a knowledge mapping technique. This is visualized through a mind map highlighting eight key components: the conversational agent, user interface, health and wellness monitoring, health wallet, social engagement, stakeholders, ethical and privacy strategies and cooperation. These elements work together to offer comprehensive and proactive assistance. The cooperative approach ensures that the system operates effectively within a broader, integrated network of eldercare support.
Early-stage decisions in construction projects are critical for circularity and long-term sustainability, yet often delayed due to fragmented data and limited decision support. This study explores how cooperative digital environments can improve early decision-making in circular construction. Based on a case study with architecture and construction management students, it applies the Recognition-Primed Decision model to examine how teams navigate uncertainty, material reuse, and sustainability trade-offs. Findings highlight the need for structured frameworks, integrated data platforms, and greater trust in digital tools such as large language models. The paper proposes a practice-oriented framework combining visualization, scenario simulation, and interdisciplinary collaboration. Rather than removing uncertainty, it helps professionals manage it through iterative learning and cooperative use of AI tools.
Data videos effectively combine visual representation with narrative techniques to communicate complex information clearly. However, traditional transition methods often struggle to maintain viewer attention, resulting in increased cognitive load and decreased comprehension. This paper presents Adaptive Visual Anchors (AVA), a novel approach that dynamically guides viewer attention by evolving key visual elements throughout data videos. AVA enhances narrative coherence by ensuring persistent semantic and visual continuity, actively directing viewer focus, and minimizing cognitive load. This paper describes the AVA framework and outlines a controlled experimental design that compares AVA-enhanced transitions with standard transition methods. The study seeks to empirically validate AVA's impact on viewer comprehension and cognitive efficiency, thereby contributing valuable insights for optimizing narrative visualization design.
Cooperative skills-such as communication, teamwork, and conflict resolution-are essential in both educational and professional contexts. We present AfterDay Horizon, a two-player cooperative game designed to foster these skills through asymmetric, cross-platform gameplay. Players take on distinct but interdependent roles: the Caretaker (VR) executes survival missions in a post-apocalyptic environment, while the Leader (web) provides strategic support through interactive mini-games. Time-sensitive tasks and role-specific responsibilities promote communication, coordination, and replayability. Experimental results show improvements in survival time, mission success, and task efficiency across repeated playthroughs-indicating strengthened cooperation and joint planning. Participants also reported high satisfaction, suggesting strong engagement and a positive reception of the game's design.
The increasing complexity of graph neural networks (GNNs) necessitates enhanced interpretability to establish trust in real-world deployments. While subgraph-based explanations improve human understanding, current methods face critical limitations: stochastic subgraph sampling induces bias in instance-level interpretations, and labor-intensive manual comparisons hinder efficient analysis. To address these challenges, we propose a classification-driven framework that identifies class-consistent subgraph patterns through graph similarity computation, reducing sampling randomness while providing model insights at the categorical level. Additionally, we develop GEVis, a visual analytics system that supports multi-faceted evaluation of explanation algorithms and model decisions through interactive exploration, enabling efficient discovery of hidden data relationships without requiring specialized expertise. Experimental validation across synthetic, molecular, and textual datasets demonstrates the framework's feasibility, with performance variations across data types aligning with structural complexity differences. Qualitative case studies and quantitative efficiency metrics further confirm GEVis's practical utility in interpretability analysis.
Cross-disciplinary collaboration often faces communication barriers due to differing professional languages and thinking patterns. This study presents 'Emotion Display Wings', a wearable smart vest that visualizes the emotional states of users in real time using heart rate variability (HRV) as its main input. The device provides intuitive and nonverbal feedback through wing movements, enabling team members to perceive and respond to each other's emotions more effectively. Developed through a design thinking approach, the system integrates UX principles and an agile development process to support rapid prototyping and iteration. It helps facilitators identify team issues and improves emotional transparency in collaboration. The results highlight its potential to foster empathy, trust, and dynamic interaction in interdisciplinary teams in the real world and environments driven by innovation.
This research presents an integrated quantitative framework for evaluating football players' adherence to tactical roles compared to their assigned positions. By incorporating multimodal data sources, including Full HD video, wearable sensor information, and match context, the system employs deep learning algorithms such as YOLOv12 for player detection and tracking, mapping positional data onto a standardized 2D pitch model. Quantitative metrics, including individual position deviation, formation stability, zone compliance, and team compactness, are computed and joint into an overall tactical score. The results show that the system can detect subtle tactical deviations and offer real-time alerts, enabling coaches to make prompt strategic adjustments.
The Miro collaboration platform was used by nine student teams for their group project in a Communication Design Research class. Each group used their Miro board extensively, albeit mostly as a repository for collected data. The platform enabled continuous monitoring of all groupwork, but unexpected high volume of content in each board overwhelmed the instructors, hindering timely feedback. While Miro's ease of use and visual versatility were a welcome change from the typical course management system, it did not organically lead to any convergence in teamwork or real-time discussion among learners. Despite the mixed findings and limited generalizability, the case study is a vivid illustration of how East Asian design students engage with collaboration technology, with several recommendations on how to use Miro for class projects in general.
Monitoring natural environments is currently a hot research topic, as the early detection of an anomaly or the presence of contaminants in such a setting allows for mitigating environmental damage. In this regard, these tasks have been carried out using nodes that executed code sequentially, mainly due to their inability to execute multiple threads. However, in this article, we propose the collaborative use of sensor nodes within a network, so that a single node can be multipurpose and provide service by monitoring different sets of variables for the same environment. To this end, we propose using autonomic computing, a self-management mode for nodes that allows them to interact with each other and execute different codes as needed. After testing the system, it has been confirmed that this type of operation generates significant energy savings for each node in the network and that, ultimately, it is possible to work with a smaller number of nodes, which implies consequent economic savings.
A novel knowledge management (KM) approach aims to offer a distributed, decentralized, bottom-up, affordable, knowledge-worker-centric application prioritizing personalization, mobility, generativity, and entropy reduction; its mission is to serve a knowledge-co-creating community characterized by highly diverse individual abilities, contexts, means, and ends facing increasingly volatile, uncertain, complex, and ambiguous futures. This paper reflects on its longitudinal academic design science research undertaking (DSR) recently complemented by a start-up project. With an initial focus on a Personal KM System (PKMS), the publications and developments have evolved towards a digital platform and community for knowledge co-creation. The digital twin concept is utilized and extended to introduce and visualize the envisioned system which substitutes the traditional document-centric paradigm with a meme-based approach to ease authorship via smart-manufacturing methodologies. Memetics affords a metaphor of knowledge as living organisms which not only changes the symmetries of connecting people and knowledge but also the logics and logistics for creating a more just digital future for all.
This paper introduces the Socio-Technical Maintenance Equity Framework (STMEF), a normative and operational model for enhancing data-driven decision-making in facility management. Existing Computer-Aided Facility Management (CAFM) systems predominantly rely on asset-centric indicators, often overlooking the socio-cultural dimensions that shape building use, maintenance behavior, and service equity. Drawing on interdisciplinary insights and a large-scale CAFM dataset from Denmark, the framework integrates technical maintenance data with contextual indicators, such as task diversity, trade recurrence, and access failures, and simulates the incorporation of public socio-demographic attributes. The framework enables predictive, equity-aware prioritization while embedding ethical safeguards, feedback mechanisms, and participatory governance. The framework is tested through spatial load profiling, cross-trade clustering, and scenario-based analysis. Findings demonstrate that facility management systems can be adapted to recognize buildings as socio-technical environments and to support context-aware interventions that improve both performance and fairness. The paper contributes an approach to aligning predictive maintenance with the lived realities of occupants.
Architecture has the potential to stimulate thought and engagement by challenging conventional forms and functions, which is rooted in Rancière’s concept of “aesthetic dissensus.” This paper investigates how architectural forms that deviate from expected norms within a specific urban or cultural context affect social integration in public spaces. Although significant attention has been directed toward functional performance in public architecture, there is limited understanding of how formal deviations influence appropriation. Using a two-phase approach, we first examine architectural aesthetics using computer vision algorithms after conducting a literature review on Rancièrean dissensus in design. We then merge spatial analysis with crowdsourced social data and behavioral heat maps from computer vision to monitor user interactions and establish an “appropriation index” for the chosen architectural space. These computational methods objectively measure physical engagement, while crowdsourced social data assesses public sentiment and participation. The findings propose a technique for correlating formal deviations with inclusivity and new strategies for architects to create socially engaging environments. This paper demonstrates how aesthetic dissensus can foster place appropriation in public architecture by combining computational analysis with social data.
Snoring has a significant effect on human health, and it is a prevalent issue worldwide. Therefore, this study aims to establish a machine learning algorithm to detect snoring. The acoustic features of snoring are extracted based on Mel-Frequency Cepstral Coefficients (MFCCs) and Zero-Crossing Rate (ZCR). The extracted features are inputs for the cascaded model, where the first layer, the Random Forest (RF) model, is used to evaluate the features’ importance and extract the most important features. The refined feature set and the RF’s prediction result are then put into the second layer, the Support Vector Machine (SVM) classifier with a Radial Basis Function (RBF) kernel, where the SVM learns when the RF’s prediction results are trustworthy, while maintaining the ability to make independent decisions using its advantages in modeling non-linear signals. Therefore, this cascaded model structure fully utilizes the strengths of both the RF and the SVM. The accuracy of the model reached above 95
A good prediction of the target customer group is very important for developers and sellers of flats or apartments. In this paper, a system, which predicts a group of people who would be interested in the available flat based on its design and localization, is proposed. The described model extends the typical linear process of designing/building/selling a flat into a cooperative one, allowing potential buyers to provide their preferences regarding a place to live. The model includes a specially built website for data collection, where floor layouts augmented with the additional information are assessed by four different groups of people, as well as four multi-class classifiers trained on the dataset obtained from the collaborative assessments of flats.
