Reviewerly/exHarmony huggingface dataset:
Visualization of Weather-Aware Ambient Heat Risks With Global Illumination in Game Engine

In recent years, the risk of heat stroke has been increasing due to global warming and other factors, and the Ministry of the Environment has been using the heat index to alert people in urban areas. Still, citizens need help knowing the detailed risk information for their neighborhoods. The heat index considers the human body's heat balance and is measured with specialized instruments, so comprehensive and high-density measurement is difficult. In this research, by using global illumination (GI) and primary weather data obtained from the Open Weather Map API for each area, we can realistically render the sunlight condition considering the weather and map and visualize the heat index per pixel based on shaded CG. Furthermore, by reconstructing urban 3D geometry from Google Maps, we have developed a system that visualizes the ever-changing heat index distribution for an arbitrary location in real time. The system has shown the possibility of reducing the number of heat stroke patients by using this system
Investigating the Ability of Immersive Virtual Environments to Facilitate Occupant Thermal State Data Collection Involving Face Masks

This study examines the capability of an immersive virtual environment (IVE–based) experimental protocol to support occupant thermal state (sensation, acceptability, and comfort) data collection when participants wear face masks. Specifically, the goal is to see if there is a change in local thermal states due to face covering and would such a change affect overall thermal states. A between-subject experiment was conducted with fifty-four participants (27 masked; 27 unmasked) who were exposed to three-step temperatures (18.3ºC, 23.8ºC, and 29.4ºC) in a climate chamber under both cooling and heating sequences. In masked IVE experiments, participants donned a face mask and viewed the chamber's virtual model on a head-mounted display. In contrast, in unmasked IVE experiments, participants didn't use a face mask. Skin temperatures and overall/local thermal state responses were collected during the experiments. They were then statistically compared between masked IVE and unmasked IVE experiments. The results suggest that forehead temperature was significantly different under all step temperatures in the cooling sequence, with mean forehead temperature being larger in masked IVE than in unmasked IVE experiments. Furthermore, in masked IVE experiments, thermal sensation in the forehead, neck, and upper-back increased while the thermal acceptability in those same skin sites decreased, but this difference was not statistically significant. Also, in masked IVE experiments, the overall thermal sensation increased, whereas both the overall thermal acceptability and comfort decreased when compared with unmasked IVE experiments. Nonetheless, this difference was not statistically significant. To summarize, wearing a face mask didn't affect the participant's overall and local thermal states in IVEs, although few statistical differences were observed in skin temperatures
Visibility Enhancement of Crane Operators Using BIM-Based Diminished Reality

The limited visibility experienced by crane operators in construction sites poses significant challenges, leading to reduced performance and safety concerns. Obstructive elements, such as existing buildings, construction elements, or vehicles, can block the crane operator's field of view, hindering their ability to execute lifting operations with precision and confidence. To address this issue, this study presents a novel approach using Building Information Modelling (BIM)-based diminished reality (DR) to enhance visibility by dynamically removing obstructive objects from the crane operator's perspective in real-time. The research employs a marker-based registration system that effectively aligns BIM data with the physical environment, ensuring realistic and precise DR visualization. Additionally, a semi-automatic selection method that involves minimal intervention from the user is employed to select desired objects. To generate the background, the system utilizes real-time observation data from occluded areas. A validation through a case study demonstrates the practical applicability of the developed system in real-life construction scenarios
Multi-Robot Federated Edge Learning Framework for Efficient Coordination and Information Management in Smart Construction

Smart construction involves a growing array of devices that generate extensive data, capable of enhancing construction efficiency and productivity. Nonetheless, the handling of this diverse and abundant information, along with the geographical spread of construction sites, poses challenges to effective communication and information processing within the management system. Multi-robot systems, as a new type of Internet of Things device, have the potential ability to coordinate workers to complete their work while serving as an edge node for information storage and processing. This paper presents a multi-robot federated edge learning framework that facilitates construction information management and communication. The work demonstrates the role of distributed databases in processing information during project execution, in contrast to centralized information systems. To address the intricacies of construction sites and the wide array of equipment involved, unmanned aerial vehicles and quadruped robots are employed as edge nodes. The formation of a federated edge learning framework ensures the real-time processing of massive data and data privacy issues. The Federated Multi-Robot (FedMR) framework is a global sharing model focused on preserving differential privacy protection. This framework is distributed to multiple edge robots in each round, enabling local real-time processing of robot tasks. The system can accomplish target detection and tracking of workers based on computer vision. Additionally, we collect MiC energy consumption data during the construction process and predict carbon emissions. Based on the implementation and testing of the system, it has been shown to provide structured and reliable information, fast local transmission, and the ability to process information in real-time. The system's ability to coordinate workers and process information makes it a valuable tool in smart construction
A Comparative Study of Deep Learning Models for Symbol Detection in Technical Drawings

Symbols are a universal way to convey complex information in technical drawings since they can represent a wide range of elements, including components, materials, or relationships, in a concise and space-saving manner. Therefore, to enable a digital and automatic interpretation of pixel-based drawings, accurate detection of symbols is a crucial step. To enhance the efficiency of the digitization process, current research focuses on automating this symbol detection using deep learning models. However, the ever-increasing repertoire of model architectures poses a challenge for researchers and practitioners alike in retaining an overview of the latest advancements and selecting the most suitable model architecture for their respective use cases. To provide guidance, this contribution conducts a comparative study of prevalent and state-of-the-art model architectures for the task of symbol detection in pixel-based construction drawings. Therefore, this study evaluates six different object detection model architectures, including YOLOv5, YOLOv7, YOLOv8, Swin-Transformer, ConvNeXt, and Faster-RCNN. These models are trained and tested on two distinct datasets from the bridge and residential building domains, both representing substantial sub-sectors of the construction industry. Furthermore, the models are evaluated based on five criteria, i.e., detection accuracy, robustness to data scarcity, training time, inference time, and model size. In summary, our comparative study highlights the performance and capabilities of different deep learning models for symbol detection in construction drawings. Through the comprehensive evaluation and practical insights, this research facilitates the advancement of automated symbol detection by showing the strengths and weaknesses of the model architectures, thus providing users with valuable guidance in choosing the most appropriate model for their real-world applications
Early Detection and Reconstruction of Abnormal Data Using Hybrid VAE-LSTM Framework

Early failure detection and abnormal data reconstruction in sensor data provided by building ventilation control systems are critical for public health. Early detection of abnormal data can help prevent failures in crucial components of ventilation systems, which can result in a variety of issues, from energy wastage to catastrophic outcomes. However, conventional fault detection models ignore valuable features of dynamic fluctuations in indoor air quality (IAQ) measurements and early warning signals of faulty sensor data. This study introduces a hybrid framework for early failure detection and abnormal data reconstruction applying variance analysis and variational autoencoders (VAE) coupled with the long short-term memory network (VAE-LSTM). The periodicity and stable fluctuation of IAQ data are exploited by variance analysis to detect unusual variations before failure occurs. The IAQ dataset which is corrupted by introducing complete failure, bias failure and precision degradation fault is then used to verify the feasibility of the VAE-LSTM model. The results of variance analysis reveal that unusual behavior of the data can be detected as early as 12 hours before failure occurs. The reconstruction performance of the developed method is shown to be superior to other methods under different abnormal data scenarios
Key methods in media linguistics. Useful approaches from early traditions to the state-of-the-art

This paper explores various methods and useful approaches to the study of media. These range from traditional heuristic methods (content analysis, contrastive text and discourse analysis) to computer-based data-mining methods (corpus linguistics) and, finally, to state-of-the-art high-tech research (eye-tracking experiments). After an initial overview of ten different approaches to media studies, some of them are further explained, illustrated with relevant studies, and ultimately evaluated. The paper demonstrates how various methods can be applied in relation to different media texts and media types and how they can be studied from linguistic, cultural and multimodal perspectives.
The Philosophical and Pedagogical Heritage of Yakiv Mamontov: The Concept of Pedagogical Creativity

The article considers Mamontov’s philosophical and pedagogical heritage through the prism of modern approaches to the development of pedagogical creativity. The aim is a deeper understanding of the topic in the context of actualising the issues of organising the activities of the participants of the educational process in the frames of the creation of a new educational paradigm. The philosophical and pedagogical heritage of the teacher has been singled out as an important component of their integral system of views on the meaning of pedagogical creativity and the system of education and upbringing of children and youth. Materials: monographs, educational publications, scientific articles, reviews, are presented and analysed in the article – altogether, 21 works can be attributed to the little-known or unpublished pages of the creative heritage of Mamontov. All these works were considered de visu.A historiographical analysis of the latest works of researchers who studied the pedagogical work of the teacher was carried out and, on this basis, the special contribution of Mamontov to the progress of pedagogical science was revealed. It was concluded that the current creation of a new pedagogical paradigm contributes to a wider acquaintance with the creative work of the teacher and makes prerequisites for considering the concept of pedagogical creativity as a basic component of the advancement of modern education. Through historical-pedagogical knowledge and rethinking, sufficient ideas, forms and methods of organising the educational process are introduced, which contribute to the establishment of the view of Mamontov as a philosopher-thinker, humanist and reformer of pedagogy.
Emotion-Aware Voice Assistants: Design, Implementation, and Preliminary Insights

The increasing prevalence of voice assistants in daily life has transformed human-technology interaction. However, current voice assistants fall short in their ability to comprehend users’ emotional states through speech and respond empathetically. This limitation leads to suboptimal user experiences and missed opportunities for personalization. To address this gap, this paper presents a groundbreaking prototype of a future voice assistant equipped with the capability to recognize and respond to users’ emotional states. The paper outlines the architecture of a speech-emotion recognition system that utilizes a deep neural network incorporating a sparse attention mechanism, yielding promising results. Furthermore, the paper embarks on an initial exploration of strategies for emotion-based responses, drawing inspiration from human techniques for managing unpleasant emotions. A user study is conducted, exposing participants to emotions such as anger, sadness, and fear, while engaging the voice assistant to elicit happiness. Preliminary findings suggest a tendency toward neutral emotional responses, providing valuable insights into the potential of speech feature analysis as an alternative approach for identifying and responding to emotions.
Low Code Conversation-based Hybrid UI Design Case Study and Reflection

This paper presents a comprehensive case study on the Q-Survey, a chatbot-based qualitative survey tool developed using the Double-Diamond design process. The study delves into the intricacies of balancing user experience (UX) with technical challenges inherent in chatbot development. A significant focus is placed on the role of Low Code (LC) and No Code (NC) tools in facilitating rapid prototype development and testing. While these tools offer agility and ease in the early stages, their limitations become evident as the complexity of the system grows, prompting a reflection on their continued utility in advanced development stages. The Repertory Grid Technique (RGT) is explored as a potential tool for online qualitative surveys, with discussions on its complexity and the potential enhancements using advanced Natural Language Processing (NLP) tools. Through the lens of the Q-Survey and experts’ evaluation of this case, we discuss the broader implications of LC tools in Human-Computer Interaction (HCI) design, emphasizing the need for a structured framework for HCI design with LC. The study concludes with reflections on the current design, potential future directions, and the importance of continuous exploration of LC, especially in the realm of LLMs and coding tools based on LLMs.
From Lab to Virtual: Comparing Real and AI-Generated User Interviews in Home Appliance Evaluation.

This study provides insights into the use of conversational AI, particularly ChatGPT, in household appliance evaluation interviews and how it differs from real user behaviour. Three comparison experiments (real researcher-real user, real researcher-simulated user vs. simulated researcher and simulated user) reveal the differences in the responses of ChatGPT simulated and real users in specific evaluation scenarios, especially in the evaluation of product appearance, GUI, and PUI. The study found that although simulated users agreed with real users in evaluating the core features of smart appliances, there were limitations in certain practical experience aspects and significant differences in SUS, learning ability, and usability scores across experimental settings. The study also explores the advantages and disadvantages of incorporating simulated users into the product evaluation process, concluding that this introduces an innovative approach to product evaluation that, although challenging, demonstrates the great potential of simulated users in future product evaluation.
Creating a “Chinese Virtual Musicscape” —Concept Crafting and the Dynamics of Creative Agents

This research crafts the concept of the Chinese Virtual Musicscape, exploring its unique characteristics and the relationships among its creative agents, such as composers, musicians, and sound technologists, to enhance the understanding and design of virtual musical environments rooted in Chinese culture. The definition of a Chinese Virtual Musicscape is a virtual musical activity shaped by creative agents through their interactions and active selections from the soundscape and cultural aesthetic elements. By exploring the concept of Chinese Virtual Musicscape, and offering a nuanced relationship between the creative agents, this research intends to contribute to the cultivation of virtual musical projects embedded within the Chinese cultural milieu, which the knowledge of this research could be applied as a guide to future interactive designs in Virtual Reality (VR).
Watching Opera at Your Own Ease—A Virtual Character Experience System for Intelligent Opera Facial Makeup

Peking Opera is a signature form of Chinese opera, an essential aspect of the country's rich cultural heritage with a deep historical significance. Contemporary efforts to safeguard and pass down this tradition, especially the art of Peking Opera facial makeup, are highly prioritized. With the advancement of technology, it has become a trend to integrate traditional facial makeup with modern virtual interactive design. The primary objective of this study is to create an immersive virtual character experience system by utilizing face capture technology, custom filters, and an interactive user interface. This project reveals a significant piece of Chinese culture in detail and offers users an engaging means to experience its charm through technology and entertainment. The research culminated in a comprehensive website housing a full range of virtual interactive experiences, focusing on the digitization and revitalization of Peking Opera culture and engagement with younger audiences.
TAPE: Tangible Augmented Previz Environment for Filmmaking

This paper presents TAPE, a tangible augmented previsualization environment for filmmaking. Using tangible interfaces and augmented reality technology, TAPE combines physical props and virtual elements, allowing users to manipulate and position objects within and beyond a tabletop space to visualize and refine their film concepts. The paper describes the overall design of the TAPE system and presents key features of the integration of TUI and AR technology. The system was evaluated in a between-subjects user study with film professionals and students, demonstrating its potential to enhance the creative process and facilitate collaboration.
Research on the strategy of travel service system for visually impaired people based on service design thinking

Object: we focus on utilizing service design thinking to enhance the quality of travel services for visually impaired individuals. Our approach involves conducting empathy experiments to gain insights into users' perceptions and expectations and developing relevant service design strategies based on the findings.Problem: As China's travel industry recovers rapidly, the market is becoming more segmented, and visually impaired travel is gradually gaining attention. Visually impaired travelers have different perceptions, needs, and experiences than sighted travelers. Domestic visually impaired travel is in its early stages, and travelers' experience is inadequate. Some services need quality improvement, and there are no corresponding service countermeasures.Approach: To conduct an empathy experiment, we developed tools that simulate physiological responses and identified four key service situations during travel to design travel simulation tasks. Using role simulation, we had eight sighted people and ourselves wear these simulation tools to finish the travel simulation tasks and obtain Service Demand Points. We then used situational simulation to have eight visually impaired individuals complete the same tasks. After completing the tasks, the visually impaired participants were asked to rate demand points for travel services using a Likert scale. The empathy experiment was validated by excluding demand points with mean scores below 0.Result: After conducting empathy experiments and validations, we identified 16 points of opportunity for designing travel services for visually impaired individuals. We then created a blueprint of service design for a visually impaired travel service and developed optimization strategies in three key areas: travel experience, interaction, and inclusiveness.Discover and Implication: Through the research, the potential needs of visually impaired travelers were discovered. A new service system was redefined and constructed, using the system framework as a guide to provide a comprehensive solution that included the detailed development of the service process and service content.Limitation and Potential: The study mainly focuses on the travel needs of visually impaired individuals. However, there is limited research on other stakeholders, resulting in the service strategies being centered around visually impaired travelers. The service optimization strategies presented in the study highlight the importance of meeting the needs of visually impaired travelers and provide direction for improving travel services for this group.
Research on the Reconstruction of Ming Dynasty History Based on AIGC

The purpose of this paper is to explore a new perspective for experiencing historical culture through AI technology. The specific content is divided into three parts: The first part introduces that we use Ming Dynasty ancient paintings as the original input images, use the Stable Diffusion model to transform the images, and then use the 3D Morphable Face Model (3DMM) based methods to output a single character image into a 3D face model. After integrating from 3D software and game engine, we obtain a complete and controllable 3D character, As a participant and experiential carrier in the reconstruction of Ming Dynasty history. The second part introduces the use of images and text as inputs, through the Stable Diffusion model to generate scene backgrounds and related items related to Ming Dynasty history. The final section introduces the historical and cultural rules as a framework presented in a game mechanism, allowing the experience to perceive the immersion of historical and cultural, rather than simply conveying historical knowledge or appreciating historical artifacts. By employing AIGC, this research reconstructs 3D characters and scenes from the ancient Ming Dynasty in China, creating a historical immersive experience co-authored by humans and AI in the form of a game. Specifically, AIGC creates assets to restore 3D characters and scenes from the ancient Ming Dynasty in China. The creators use game gameplay to restore historical rules, and players participate in interactive experiences. This approach facilitates an objective assessment of history by allowing participants to deeply engage with historical narratives, prompting critical thinking and influencing the future. Empirical studies find that this method enables participants to actively immerse themselves in historical stories, triggering thoughtful reflections on history.