Reviewerly/exHarmony huggingface dataset:
Investigating Students’ Engagement, Enjoyment, and Sociability in Virtual Reality-Based Systems: A Comparative Usability Study of Spatial.io, Gather.town, and Zoom

The COVID-19 pandemic has impacted globally on many industries worldwide (e.g., business and tourism). It has also caused unprecedented disruption to Thailand's educational system. For instance, regular face-to-face classrooms had been temporarily stopped and moved to full-time online learning in which educators and students have used commercially available e-learning platforms (e.g., Moodle) and conferencing tools (e.g., Zoom). Although e-learning platforms and conferencing tools have shown benefits for both educators and learners, social interaction is limited on such platforms. Alternative solutions such as Virtual Reality (VR) technologies have shown promise to solve these challenges; however, research is limited in using such VR technologies in higher education in Thailand. To fill these gaps, we conducted a comparative usability study of three different learning platforms (three-dimensional VR – Spatial.io, two-dimensional VR – Gather.town, and Zoom application) to understand differences in usability and user experiences in terms of students' engagement, enjoyment, and their sociability in learning. Findings suggest that due to its game-like environment and user-friendly features and user interface, Gather.town is the most promising alternative for online learning in higher education in Thailand. This study suggests opportunities for using game-like virtual classrooms for students to improve their engagement in learning.
Digital Health Care Industry Ecosystem: Network Analysis

As the need for digital health care based on mobile devices is increasing, with the rapid development of digital technologies, especially in the face of the COVID-19 pandemic, gaining a better understanding of the industrial structure is needed to activate the use of digital health care.The aim of this study was to suggest measures to revitalize the digital health care industry by deriving the stakeholders and major issues with respect to the ecosystem of the industry.A total of 1822 newspaper articles were collected using Big Kings, a big data system for news, for a limited period from 2016 to August 2021, when the mobile health care project was promoted in Korea centered on public health centers. The R and NetMiner programs were used for network analysis.The Korean government and the Ministry of Health and Welfare showed the highest centrality and appeared as major stakeholders, and their common major issues were "reviewing the introduction of telemedicine," "concerns about bankruptcy of local clinics," and "building an integrated platform for precision medicine." In addition, the major stakeholders of medical institutions and companies were Seoul National University Hospital, Kangbuk Samsung Hospital, Ajou University Hospital, Samsung, and Vuno Inc.This analysis confirmed that the issues related to digital health care are largely composed of telemedicine, data, and health care business. For digital health care to develop as a national innovative growth engine and to be institutionalized, the development of a digital health care fee model that can improve the regulatory system and the cost-effectiveness of patient care, centering on the Ministry of Health and Welfare as a key stakeholder, is essential.
Local differential privacy protection for wearable device data

Personal data collected by wearable devices contains rich privacy. It is important to realize the personal privacy protection for user data without affecting the data collection of wearable device services. In order to protect users' personal privacy, a collection scheme based on local differential privacy is proposed for the collected single attribute numerical stream data. At first, the stream data points collected by the wearable device are censored to identify the salient points, and the adaptive Laplacian mechanism is used to add noise to these salient points according to the assigned privacy budget; then the collector reconstructs and fits the stream data curve to the noise-added salient points, so as to protect the personal privacy of the data. This scheme is experimented on the heart rate dataset, and the results show that when the privacy budget is 0.5 (i.e., at higher privacy protection strength), the mean relative error is 0.12, which is 57.78% lower than the scheme of Kim et al. With the satisfaction of user privacy protection, the usability of mean value estimation of wearable device stream data is improved.
Multi-view clustering with constructed bipartite graph in embedding space

Multi-view clustering is a hot topic in the field of machine learning and data mining, which aims to assign objects into clusters with multiple heterogeneous features. Additionally, considerable efforts have been devoted to this issue. However, current methods perform clustering in the original feature space, consequently failing to capture the latency of heterogeneous features. These algorithms assume the independence of feature selection and similarity graph construction, thereby resulting in an undesirable performance of algorithms. Furthermore, the construction of similarity graph only exploits the relations on vertex-level, ignoring the relation of cluster structure. To address these problems, we propose a novel Multi-view Clustering with Constructed Bipartite Graph in Embedding Space (MC2BGES) algorithm by jointly learning the common graph embedding representation among various views and similarity structure of vertices. Distinct from the current algorithms, MC2BGES learns the clustering structure of objects, which is transformed into the bipartite graph partition problem, thereby achieving a better performance. Moreover, the proposed model is formulated as a constrained optimization problem, which can be efficiently solved by an alternating optimization strategy. Extensive experiments on artificial and real-world datasets demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of accuracy and robustness, implying that joint learning is promising for multi-view clustering.
Underwater enhancement based on a self-learning strategy and attention mechanism for high-intensity regions

Images acquired during underwater activities suffer from environmental properties of the water, such as turbidity and light attenuation. These phenomena cause color distortion, blurring, and contrast reduction. In addition, irregular ambient light distribution causes color channel unbalance and regions with high-intensity pixels. Recent works related to underwater image enhancement, and based on deep learning approaches, tackle the lack of paired datasets generating synthetic ground-truth. In this paper, we present a self-supervised learning methodology for underwater image enhancement based on deep learning that requires no paired datasets. The proposed method estimates the degradation present in underwater images. Besides, an autoencoder reconstructs this image, and its output image is degraded using the estimated degradation information. Therefore, the strategy replaces the output image with the degraded version in the loss function during the training phase. This procedure misleads the neural network that learns to compensate the additional degradation. As a result, the reconstructed image is an enhanced version of the input image. Also, the algorithm presents an attention module to reduce high-intensity areas generated in enhanced images by color channel unbalances and outlier regions. Furthermore, the proposed methodology requires no ground-truth. Besides, only real underwater images were used to train the neural network, and the results indicate the effectiveness of the method in terms of color preservation, color cast reduction, and contrast improvement.
Managing multiple perspectives in the collaborative design process of a team health information technology

We need to design technologies that support the work of health care teams; designing such solutions should integrate different clinical roles. However, we know little about the actual collaboration that occurs in the design process for a team-based care solution. This study examines how multiple perspectives were managed in the design of a team health IT solution aimed at supporting clinician information needs during pediatric trauma care transitions. We focused our analysis on four co-design sessions that involved multiple clinicians caring for pediatric trauma patients. We analyzed design session transcripts using content analysis and process coding guided by Détienne's (2006) co-design framework. We expanded upon Détienne (2006) three collaborative activities to identify specific themes and processes of collaboration between care team members engaged in the design process. The themes and processes describe how team members collaborated in a team health IT design process that resulted in a highly usable technology.
WACPN: A Neural Network for Pneumonia Diagnosis

Community-acquired pneumonia (CAP) is considered a sort of pneumonia developed outside hospitals and clinics. To diagnose community-acquired pneumonia (CAP) more efficiently, we proposed a novel neural network model. We introduce the 2-dimensional wavelet entropy (2d-WE) layer and an adaptive chaotic particle swarm optimization (ACP) algorithm to train the feed-forward neural network. The ACP uses adaptive inertia weight factor (AIWF) and Rossler attractor (RA) to improve the performance of standard particle swarm optimization. The final combined model is named WE-layer ACP-based network (WACPN), which attains a sensitivity of 91.87±1.37%, a specificity of 90.70±1.19%, a precision of 91.01±1.12%, an accuracy of 91.29±1.09%, F1 score of 91.43±1.09%, an MCC of 82.59±2.19%, and an FMI of 91.44±1.09%. The AUC of this WACPN model is 0.9577. We find that the maximum deposition level chosen as four can obtain the best result. Experiments demonstrate the effectiveness of both AIWF and RA. Finally, this proposed WACPN is efficient in diagnosing CAP and superior to six state-of-the-art models. Our model will be distributed to the cloud computing environment.
Domain-Agnostic Document Authentication Against Practical Recapturing Attacks

Recapturing attack can be employed as a simple but effective anti-forensic tool for digital document images. Inspired by the document inspection process that compares a questioned document against some known samples, we proposed a document recapture detection scheme by employing a Siamese network to compare and extract distinct features in a recaptured document image. The proposed algorithm takes advantage of both metric learning and image forensic techniques, and forms triplets by considering some important factors in document authentication, e.g., document types, resolutions, and content in each image patch. After training with our triplet selection strategy, the resulting feature embedding clusters the genuine samples near the reference while pushing the recaptured samples apart. In the experiment, we consider practical settings under domain differences, such as the variations in printing/imaging devices, substrates, recapturing channels, and document types. To evaluate the robustness of different approaches, we benchmark some popular off-the-shelf machine learning-based approaches, a state-of-the-art document image detection scheme, and the proposed schemes with different network backbones under various experimental protocols. Experimental results show that the proposed scheme consistently outperforms the state-of-the-art approaches under different experimental settings. Specifically, under the most challenging scenario in our experiment, i.e., evaluation across different types of documents (produced by different manufacturers, devices, and substrates), we have achieved 6.92% APCER (Attack Presentation Classification Error Rate) and 8.51% BPCER (Bona Fide Presentation Classification Error Rate) by the proposed network with ResNeXt101 backbone at 5.00% BPCER decision threshold.
Automatic Parking Path Planning of Tracked Vehicle Based on Improved A* and DWA Algorithms

Compared to wheeled vehicles, tracked vehicles have unique advantages in disaster relief and engineering sites. The working environment of tracked vehicles is mostly in fixed-point conditions, so high-precision automatic stopping is essential for tracked vehicles. Improved A* and dynamic window approach are proposed in this article to enhance the accuracy and speed of automatic parking of tracked vehicles. The parking trajectory is significantly optimized within low deviations. Furthermore, a prototype is designed to verify the working conditions of fixed-point parking to improve the work efficiency of engineering rescue. The simulation results based on the robot operating system show that both single- and two-step parking methods can reach the parking position without collision. More importantly, simulated and experimental parking positions and angles have considerable accuracy within acceptable limits. This study has essential guidance and application value for the high-precision automatic parking of tracked vehicles for various extreme application scenarios.
A Radar-Based Human Activity Recognition Using a Novel 3-D Point Cloud Classifier

This article provides a new benchmark dataset for 3-D point cloud classification in which the manually labeled human activity data exceeds 100 point clouds per frame and is capable of meeting the training needs for data-intensive learning approaches. In this study, a case study is considered for evaluating the benchmark using a deep long short-term memory (LSTM) neural network, which demonstrated a significant performance improvement over the state-of-the-art human activity recognition (HAR) area. To date, numerous types of collection devices have been used in the recognition of human activities. However, due to the scarcity of training data, the task of 3-D point cloud labeling has not yet made significant progress. To overcome this challenge, it is aimed to deduce this data requirements gap, allowing deep-learning methods to reach their full potential in 3-D point cloud tasks. The dataset used for this process is comprised of dense point clouds acquired with the static ground sensor by the NodeNs company-supported multiple input multiple output (MIMO) radar (NodeNs ZERO 60 GHz IQ radar). It contains multiple types of human being data ranging from one to four individuals and encompasses a range of human action scenarios, including standing, sitting, picking up, falling, and walking. Furthermore, it also investigated sensor locations and requirements for human being data collection that is from a single subject to multiple subjects, as well as identified and analyzed various sensing devices and applications that collect activity data. In this regard, a thorough study is conducted on several benchmark datasets, examining sensors, characteristics, activity categories, and other data. Finally, it compares and analyzes the activity recognition methods used in several benchmark datasets based on the current study. Unlike existing devices, the new NodeNs sensor provides more accessible and straightforward point cloud data to capture human movement information. Depending on an advanced detection algorithm to process point cloud data, it achieved more than 95% accuracy on the benchmark dataset.
Trust and knowledge sharing among hospitals during COVID-19: the compound effect of four barriers to organisational trust for knowledge sharing

Purpose COVID-19 highlighted the potential value of improving knowledge sharing (KS) processes among hospital estates and facilities management (HEFM) departments. Organisational trust (OT) is a recognised predictor of KS interactions, but the interplay of impersonal and interpersonal OT components is yet to be investigated fully. In response to recent calls, this study aims to explore the effect of organisational features on personal trust and OT components required for KS episodes, in the context of the English National Health Service (NHS). Design/methodology/approach A qualitative, exploratory grounded theory approach was selected, using primary data from 22 semi-structured interviews and secondary data from grey literature. A model of trust for KS among employees from geographically distributed units with pooled interdependence was synthesised from a review of the literature and used to connect the organisational features to different trust mechanisms. Findings This study identifies four organisational features with a compound barrier-effect on impersonal-based OT, interpersonal-based OT and personal trust for KS interactions: lack of professional development, inappropriate reward and incentive systems, reorganisations/organisational change and benchmarking. Research limitations/implications This study sought to generate theory about the interplay of organisational barriers and trust components required for KS, not to describe HEFM KS across the entire NHS. Future studies with more comprehensive data collections can build on this exploratory study by quantitatively testing the compound barrier effect of the organisational features. Practical implications Practitioners can benefit from the insights into the barriers inhibiting trust mechanisms required for effective KS processes. These can inform policymakers in English and potentially other health-care systems in designing enhanced collaborative arrangements, which are required as future crises, e.g. pandemics and climate change hazards, will require increasingly complex solutions. Originality/value This study addresses the interplay between personal trust, impersonal OT and interpersonal OT for KS by identifying the compound barrier effects of underlying organisational barriers common to personal trust and OT.
The blockchain challenge for Sweden's housing and mortgage markets

This paper investigates how blockchain technology may alter the defining mechanisms of residential real estate transactions in Sweden. It departs from the assumption that blockchain applications do not merely change complex socio-economic, organisational and governance arrangements of mature housing and mortgage markets. Rather, they may trigger resistance among incumbents in the current organisational model, which will drive incremental change. This analysis shows that housing transactions in Sweden are long and complex endeavours, underpinned by exclusivity, uncertainty and information inequalities. A public-private consortium led by Lantmäteriet – a Swedish government agency – has pioneered the use of blockchain technology for the conveyance of a house to address these deficiencies. This project has not moved beyond the proof-of-concept phase, while the socio-technical organisation of transactions has evolved. Insights from institutional and evolutionary approaches help conceptualise how technologically induced and qualitatively different structures and functional effects may change the way housing and mortgage markets will work, thereby highlighting both disruptive potentialities and limitations of blockchain applications for the legacy design underpinning the current Swedish housing transaction system. Following a forensic empirical approach, this paper dissects the organisational architecture, key practices and the linked network of actor groups running Sweden's property and mortgage markets. Results illustrate that understanding concrete blockchain-induced changes and their unintended consequences requires closer scholarly attention at the scale of actor networks, the types of knowledge and technologies, and the micro-geographies in which they are embedded. Such in-depth understanding enables better-informed assessments of the blockchain challenge for complex housing and mortgage markets.
Constrained Reinforcement Learning for Vehicle Motion Planning with Topological Reachability Analysis

Rule-based traditional motion planning methods usually perform well with prior knowledge of the macro-scale environments but encounter challenges in unknown and uncertain environments. Deep reinforcement learning (DRL) is a solution that can effectively deal with micro-scale unknown and uncertain environments. Nevertheless, DRL is unstable and lacks interpretability. Therefore, it raises a new challenge: how to combine the effectiveness and overcome the drawbacks of the two methods while guaranteeing stability in uncertain environments. In this study, a multi-constraint and multi-scale motion planning method is proposed for automated driving with the use of constrained reinforcement learning (RL), named RLTT, and comprising RL, a topological reachability analysis used for vehicle path space (TPS), and a trajectory lane model (TLM). First, a dynamic model of vehicles is formulated; then, TLM is developed on the basis of the dynamic model, thus constraining RL action and state space. Second, macro-scale path planning is achieved through TPS, and in the micro-scale range, discrete routing points are achieved via RLTT. Third, the proposed motion planning method is designed by combining sophisticated rules, and a theoretical analysis is provided to guarantee the efficiency of our method. Finally, related experiments are conducted to evaluate the effectiveness of the proposed method; our method can reduce 19.9% of the distance cost in the experiments as compared to the traditional method. Experimental results indicate that the proposed method can help mitigate the gap between data-driven and traditional methods, provide better performance for automated driving, and facilitate the use of RL methods in more fields.
The SECI model and darknet markets: Knowledge creation in criminal organizations and communities of practice

This study examines darknet markets through the lens of a business theory on knowledge management. Taking epistemological and ontological dimensions into consideration, this study uses Nonaka's (1991) SECI model as a theoretical framework to identify and describe how tacit and explicit knowledge is created and shared on Silk Road, Pandora and Agora darknet markets, and how people affect this process. By studying this process, insights can be obtained into darknet market criminal organizations and communities of practice and their impact on the continuity and resilience of illicit darknet markets. This project used data from the Internet Archive collection of publicly available darknet market scrapes between 2011 and 2015 from Branwen et al. (2015) . We observed instances of the SECI model (socialization, externalization, combination, and internalization) on darknet markets in both criminal organizations and communities of practice. Darknet market leaders and groups facilitated both knowledge creation and sharing. This study is the first to test the SECI model on darknet markets. The study provides an understanding of the complexity and resilience of darknet markets, as well as valuable information to help guide law enforcement agencies efforts to stop the illicit trade of goods and services.