Reviewerly/exHarmony huggingface dataset:
Embroiderer: Do-It-Yourself Embroidery Aided with Digital Tools

Traditional crafts such as knitting, embroidery and weaving are becoming increasingly popular due to their low cost and high accessibility, creating products with unique value. However, the lack of practical experience and the complexity of the handicraft often limit the participation of handicraft enthusiasts. In this paper, we develop a computer-aided embroidery method, called Embroiderer, which combines design tools to help novice users convert digital images into real embroidery processes. In detail, users can customize, modify and preview their designs in Embroiderer with knitting threads of different colors and thicknesses; the desired designs can be served as craft-aided molds which can be laser-cut or 3D printed with water-soluble materials; thus, users can easily embroider patterns on fabric following instructions from nodes embedded in the molds. Through a series of experiments and design cases, we hope to provide an accessible approach to the hybrid craft process in the pattern transformation of everyday clothing, fabrics or textiles.
Designing Interactive Games based on Intangible Cultural Heritage: A Case Study on Chinese Nuo Culture

The inheritance and promotion of Chinese intangible cultural heritage is an important way to strengthen cultural self-confidence and build a cultural power in the world. As the successors of Chinese cultures, young people must strengthen their cultural self-confidence and take the responsibilities in the construction of the cultural power. Digital games have been used to attract young people’s attention in intangible cultural heritage. In this paper, we gave designed and developed the “Nuo Game”, a puzzle game that combines both virtual content and physical materials, aimed at supporting young people to learn about the meaning of different masks in Nuo culture. The idea behind the game is to involve the player in solving puzzles in the virtual world by wearing different Nuo masks in the real world. In this paper, we depict the aesthetics, mechanics, game story, and technology used in the game based on the four-elements of game design. The integration of virtual and physical content with cultural stories embedded in the gameplay allow the player to learn the basics of Nuo culture in an engaging way.
Avatar Type, Self-Congruence, and Presence in Virtual Reality

Avatars serve as users’ virtual identities and hold a significant role in shaping the user experience within the realm of Virtual Reality (VR). The appearance of individual avatars and the perceived self-congruence within the environment are likely to influence users’ perceived presence in VR. In this paper, we present a study that investigates four types of avatars in VR: anime, human, animal, and item. Participants were asked to choose an avatar before entering a virtual environment (classroom, gallery, café, street, and forest) populated with avatars of different types and to evaluate their perceived self-congruence within the environment and the perceived presence. Our study results showed no significant difference in presence when users use different avatars. However, there is a correlation between users’ perceived self-congruence and social presence. We discuss the findings and provide suggestions for the future use of avatars in VR.
FLOW: Filtering and LSTM-based Optimization for Web Browser Interactions

Web browsers have become an indispensable tool for human-computer interactions across various platforms. However, interactive performance (e.g the immediacy of interaction, the accuracy of interaction data) in browser-based apps can be hindered by hardware limitations and network delays, resulting in unwanted jitter and input signal delays that negatively impact the user experience. To tackle these challenges, we propose a novel approach that leverages effective filtering algorithms and lightweight machine learning models. By integrating the 1 euro filter and Long Short-Term Memory (LSTM) algorithm, we successfully optimize the accuracy of Mediapipe-based gesture interactions and TUIO-based tangible interactions within web browsers. Our findings demonstrate that this combined approach not only enhances input signal accuracy but also ensures seamless and highly responsive web browser interactions.
Developing Canva-Based Learning Media on Maps and Class Layout for Third Graders of Elementary School

This study aimed to develop Canva-Based Learning Media to produce feasible and effective learning media products on Maps and Class Layout for third graders at SDN Dukuh Kupang V Surabaya. The study used the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). Data were collected using a questionnaire instrument and analyzed through validity tests conducted by media experts, content experts, the target audience, and pre-tests and post-tests for students. The results showed that the animated media is categorized as appropriate, with validity results of 90% from media experts, 92,5% from content experts, and 91.25% from participant tests. Furthermore, the pre-test and post-test results showed an 8% improvement, signifying its effectiveness as a learning medium for third graders at SDN Dukuh Kupang V Surabaya.
Abnormality Detection for Medical Images Using Self-Supervision and Negative Samples

Recent progress in computer-aided technologies has had a considerable impact on helping experts with a reliable and fast diagnosis of abnormal samples. In particular, self-supervised and self-distillation techniques have advanced automated out-of-distribution (OOD) detection in the image domain. Further improvements in OOD detection have been observed by including negative samples derived from shifting transformations of natural images. In this work, we study different ways of creating negative samples for medical images and how effective they are when leveraging them in a self-supervised self-distillation framework. We investigate the impact of various types of negative examples by applying different shifting transformations on samples when they are derived from in-distribution training data, an auxiliary dataset, or a combination of both. For the case of the auxiliary dataset, we compare the OOD detection performance when auxiliary samples are extracted from an in-domain or an out-domain. Our approach uses only data belonging to healthy people during the training procedure and does not require any additional information from labels. We demonstrate the efficiency of our technique by comparing abnormality detection performance on diverse medical datasets, setting new benchmarks for pneumonia, polyp, and glaucoma detection from X-ray, colonoscopy, and ophthalmology images.
Training Data Selection to Improve Multi-class Instance Segmentation in Digital Pathology

In digital pathology, the annotation process can be complex and time-consuming, the reason why the available annotations are often imperfect. In this paper, we focus on the detection, segmentation, and classification of multiple instances within histopathology images in the presence of noisy annotations. After highlighting such noise in the training set provided by the Multi-Organ Nuclei Segmentation and Classification Challenge (MoNuSAC 2020), we develop and compare two strategies of model prediction-based filtering, in order to obtain a cleaner dataset for training a deep neural network (HoVer-Net). We show the superiority and positive impact on performance of a filtering method based on analysing model output at instance level rather than pixel level.
Zero-dimensional biomarker based medical action recognition: towards more explainable AI in healthcare

Medical action recognition is an increasingly necessary task as healthcare has shifted towards digital and more remote methods of patient monitoring. To remotely assess a patient with computer-aided diagnosis it is first necessary to identify different actions. In tandem with action recognition, this remote monitoring also requires biomarker identification. This allows meaningful features to be extracted from the patient’s actions, however, each action often requires a different set of features to be monitored. Finally, the mode of data collection requires both portability and accuracy to be used in the healthcare industry. To combine each of these, one of the best solutions for condition-related action recognition is using videos in combination with human pose estimation to create spatio-temporal skeleton data for the patients which allows further analysis to be fast and accurate. By first using manual feature extraction on the skeleton sequences, it is possible to utilise machine learning to both classify different actions and identify latent features as the biomarkers that distinguish each of the action classes. Thus, this new method proposes a new zero-dimensional feature extraction method to classify skeleton sequences and extract medically meaningful, explainable, and objective biomarkers that can be used for the diagnosis and monitoring of patients in a remote setting.
BI-RADS categories and breast lesions classification of mammographic images using artificial intelligence diagnostic model

According to BI-RADS criteria, radiologists evaluate mammography images, and breast lesions are classified as malignant or benign. In this retrospective study, an evaluation was made on 264 mammogram images of 139 patients. First, data augmentation was applied, and then the total number of images was increased to 565. Two computer-aided models were then designed to classify breast lesions and BI-RADS categories. The first of these models is the support vector machine (SVM) based model, and the second is the convolutional neural network (CNN) based model. The SVM-based model could classify BI-RADS categories and malignant-benign discrimination with an accuracy rate of 86.42% and 92.59%, respectively. On the other hand, the CNN-based model showed 79.01% and 83.95% accuracy for BI-RADS categories and malignant benign discrimination, respectively. These results showed that a well-designed machine learning-based classification model can give better results than a deep learning model. Additionally, it can be used as a secondary system for radiologists to differentiate breast lesions and BI-RADS lesion categories.
Stefan Szuman – the Unknown Pioneer of Cognitivism. Genesis of the Object Compared to Neisser’s Cycle of Perception

Urlic Neisser (1928–2012) is widely considered to be one of the leading figures of contemporary psychology. This scholar is also thought to have come up with an original idea concerning the constructive nature of the perception process referred to as Neisser’s cycle of perception (1976, 1978). The thesis has contributed to the development of scientific psychology, but few notice that the idea ascribed to Neisser had been developed much earlier by Stefan Szuman (1889–1972) in his concept known as the genesis of the object (1932). Comparing the two concepts makes one reach the conclusion that they are strikingly similar and that novel ideas originating on the fringes of a given field are not given enough credit.
Efficient Data Curation Using Active Learning for a Video-Based Fire Detection

Video-based fire detection is a crucial object detection problem that relies on accurate and reliable data to detect fires. However, collecting and labeling fire-related data can be time-consuming and expensive, making it difficult to obtain sufficient data for training machine learning models. To address this challenge, uncertainty-based active learning techniques can be used to iteratively select the most informative samples for labeling. This can reduce the amount of labeled data needed to achieve high model performance and has the potential to even prune the training data with fewer informative samples. The traditional sampling-based uncertainty estimation methods are computationally expensive. Hence, an efficient prior network-based ensemble distillation State-of-the-Art approach is evaluated on an internal dataset which still requires relatively higher overhead computation making it difficult for production deployment. A biased softmax differencing-based uncertainty approach and a feature-based hard data mining approach are proposed and compared with the distillation approach. The novel approaches are found to have a very low overhead uncertainty estimation time compared to the ensemble distillation approach and traditional sampling techniques. The methods are evaluated in the context of curating the unlabeled pool data and improving the training data. For completeness, the experiments are performed on three different data sizes, and overall, the frame-wise selection strategy is proved to be better than the sequence-wise querying strategy. The Principal Component Analysis (PCA)-based hard data mining outperformed other methods and improved the model performance by 16.33% with AUC2% metric when compared with the random selection of data. The approach even outperformed the main network trained on full data by 7.33%, henceforth improving the training data by using informative 26.39% data. The results indicate that novel data mining provides efficient training and pool data curation
Testing ChatGPT-Aided SPARQL Generation for Semantic Construction Information Retrieval

Recently there has been a strong interest in using semantic technologies to improve information management in the construction domain. Ontologies provide a formalized domain knowledge representation that provides a structured information model to facilitate information management issues such as formalization and integration of construction workflow information and data and enables further applications such as information retrieval and reasoning. SPARQL Protocol And RDF Query Language (SPARQL) queries are the main approaches to conduct the information retrieval from the Resource Description Framework (RDF) format data. However, there is a barrier for end users to develop the SPARQL queries, as it requires proficient skills to code them. This challenge hinders the practical application of ontology-based approaches on construction sites. As a generative language model, ChatGPT has already illustrated its capability to process and generate human-like text, including the capability to generate the SPARQL for domain-specific tasks. However, there are no specific tests evaluating and assessing the SPARQL-generating capability of ChatGPT within the construction domain. Therefore, this paper focuses on exploring the usage of ChatGPT with a case of importing the Digital Construction Ontologies (DiCon) and generating SPARQL queries for specific construction workflow information retrieval. We evaluate the generated queries with metrics including syntactical correctness, plausible query structure, and coverage of correct answers
Creation and Acceptance of Low-Threshold Mobile Training on Sustainability in Construction

Many recent developments in mixed reality applications are exploited for research on improving training in the construction industry. While immersive technologies offer indisputable advantages over classic paper- or multi-media-based training material, access to this kind of technology is still very limited in the academic world and even less widespread in industry. In this paper, the authors follow the current trend of creating low-threshold micro-learning nuggets, which are easily consumable on mobile devices but can be accessed in every web browser. This is essential to reach the construction trade workforces, which for the most part will own a smart or mobile device, but neither specialized equipment, nor will there be time or patience for a lengthy setup phase before learning content consumption. The learning content aims to give construction workers a clear vision of what some of the fundamental components of a sustainable construction site should look like and what role they play in achieving the said vision. The learning content revolves around the initial idea of DGNB certification (German: German Sustainable Building Council), waste management, certification of construction wood, handling of harmful substances and chemicals and some general health and safety regulations that impact the emission of dust, noise and vibration. The paper describes the general approach of the planning, orchestration of learning material, development of the learning nugget, and deployment, as well as a study for acceptance and user experience
An Automated Framework for Ensuring Information Consistency in Price List Tendering Document

Effective cost estimation for tendering plays a critical role in the building construction process, enabling efficient investment management and ensuring successful execution of the construction phase. Traditional cost estimation procedure involves manual information processing to extract and match technical data from textual description construction resources. This activity requires practitioner deep experience and manual effort, often resulting in errors and, in the worst scenario, judicial disputes. In response to the increasing demand for structured information and automated processes, this study addresses the need for Public Administrations to achieve better control over the data contained in public tendering documents provided to practitioners. To fulfill this objective, a framework is proposed to automatically retrieve information from these documents, serving as a support tool to map items within the documents, highlight missing data, and critical semantic ambiguity. The designed framework aims to develop a tool for automatically identifying similarities between work items and their corresponding elementary resource items in Price List tendering documents. By leveraging the information retrieval NLP technique of cosine similarity through TF-IDF, a methodology was developed to support and facilitate practitioners' activities. Finally, the framework was tested on four case studies extracted from Lombardy Regional Italian price list documents showing that the resulting support tool is able to automate the analysis process and efficiently reveal inconsistency. The model successfully extracted and correctly matched the elementary resource to the corresponding work query in 75% of the cases where the elementary resource was present in the list. Additionally, the model proved to be a valuable tool in helping practitioners identify missing resources
A Systematic Literature Review to Identify a Methodological Approach for Use in the Modelling and Forecasting of Capital Expenditure of Hyperscale Data Centres

The theme of ‘Managing the digital transformation of the construction industry’ emphasises the importance of considering various dimensions of digitalisation and optimising the built environment. This review aims to present methodological approaches from existing literature that elucidate location-related factors impacting the capital cost of data centres. These findings facilitate adjustments to historical cost data when estimating total costs for new data centres. A systematic literature review method was employed to ensure an objective and comprehensive synthesis. In conjunction with Bayes's theory, this review identifies that a Delphi methodology is the most suitable methodological approach for forecasting and modelling capital expenditure for hyper-scale data centres. The methodology enables collective decision-making and consensus building, recognising the stakeholder's pivotal role in shaping the future of data centres. These findings offer valuable insights for researchers and practitioners in forming a methodological approach for further investigations into the location-related factors impacting the capital cost of data centres. Embracing this knowledge allows us to align research and practice, ensuring that these practices become integral to shaping the future of data centres and the digitalisation and optimisation of the built environment
Localizing and Visualizing the Degree of People Crowding with an Omnidirectional Camera by Different Times

The Corona Disaster increased the demand for information on the degree of human crowding, as it was essential to balance avoiding restricting behavior and reducing the risk of crowding. Although there are many technologies for detecting people using monitoring cameras, the number of cameras installed in a wide area is costly, and coverage is limited. In this study, we propose a method to qualitatively visualize the distribution of people by using images captured by a moving omnidirectional camera from the viewpoint of facility management during regular security patrols. Omnidirectional images are used for both 3D modeling of the target space based on SfM (structure from motion) and person detection/tracking by machine learning. The distribution of people is visualized qualitatively by obtaining the positions of the extracted people on the 3D model of the site and mapping them. The parallel software processing of visitor observation and mapping is expected to be highly cost-effective in terms of implementation and operation. On the other hand, although there are time deviations in the mapping depending on the location, the visualization and the updated time show their usefulness in understanding the distribution of congestion
Bayes Theory as a Methodological Approach to Assess the Impact of Location Variables of Hyperscale Data Centres: Testing a Concept

The theme of ’The Impact of Engineering Practices on a Sustainable Built Environment’ emphasises the importance of considering various dimensions of resilient infrastructure. Selecting the location for a Hyperscale Data Centre is a crucial process that involves assessing the impact of various location variables. To determine the viability of a location, it is essential to identify the potential risks associated with each variable. This paper presents a proprietary methodological approach that includes a Delphi study to identify risks, a Likert scoring system to assess prior probabilities, and a Bayesian theory-based decision tree to assess the impact through risk prediction. The paper's contributions are significant, and the proposed methodology makes it possible to predict the risk level of each location variable by identifying the appropriate contingency percentage. The study's findings indicate that the paper's proposed approach is an effective way to mitigate the risks associated with selecting a location for a Hyperscale Data Centre. Embracing this knowledge allows us to align research and practise with the conference’s call to studying the resilience of buildings and infrastructure to natural disasters and climate change, and developing strategies for adaptation and mitigation, ensuring that these practises become integral to shaping the future of Data Centres