Reviewerly/exHarmony huggingface dataset:
Review of Innovative Immersive Technologies for Healthcare Applications

ABSTRACT Immersive technologies, including virtual reality (VR), augmented reality (AR), and mixed reality (MR), can connect people using enhanced data visualizations to better involve stakeholders as integral members of the process. Immersive technologies have started to change the research on multidimensional genomic data analysis for disease diagnostics and treatments. Immersive technologies are highlighted in some research for health and clinical needs, especially for precision medicine innovation. The use of immersive technology for genomic data analysis has recently received attention from the research community. Genomic data analytics research seeks to integrate immersive technologies to build more natural human-computer interactions that allow better perception engagements. Immersive technologies, especially VR, help humans perceive the digital world as real and give learning output with lower performance errors and higher accuracy. However, there are limited reviews about immersive technologies used in healthcare and genomic data analysis with specific digital health applications. This paper contributes a comprehensive review of using immersive technologies for digital health applications, including patient-centric applications, medical domain education, and data analysis, especially genomic data visual analytics. We highlight the evolution of a visual analysis using VR as a case study for how immersive technologies step, can by step, move into the genomic data analysis domain. The discussion and conclusion summarize the current immersive technology applications' usability, innovation, and future work in the healthcare domain, and digital health data visual analytics.
ColabAR: A Toolkit for Remote Collaboration in Tangible Augmented Reality Laboratories

Current times are accelerating new technologies to provide high-quality education for remote collaboration, as well as hands-on learning. This is particularly important in the case of laboratory-based classes, which play an essential role in STEM education. In this paper, we introduce ColabAR, a toolkit that uses physical proxies to manipulate virtual objects in Tangible Augmented Reality (TAR) laboratories. ColabAR introduces haptic-based customizable interaction techniques to promote remote collaboration between students. Our toolkit provides hardware and software that enable haptic feedback to improve user experience and promote collaboration during learning. Also, we present the architecture of our cloud platform for haptic interaction that supports information sharing between students in a TAR laboratory. We performed two user studies (N=40) to test the effect of our toolkit in enriching local and remote collaborative experiences. Finally, we demonstrated that our TAR laboratory enables students' performance (i.e., lab completion rate, lab scores) to be similar to their performance in an in-person laboratory.
Educational resource information sharing algorithm based on big data association mining and quasi-linear regression analysis

In order to solve the problem that the information sharing level of educational resources is not high in the cloud computing environment, it is required to improve the design of educational resource information sharing algorithm in cloud computing environment. Therefore, an educational resource information sharing algorithm based on big data association mining and quasi-linear regression analysis is proposed. In this algorithm, information fusion and comprehensive measurement index system reconstruction are performed to resource distribution information of the educational resource database based on attribute type and semantic ontology characteristics. The simulation results show that the algorithm performs well in convergence during resource scheduling in educational resource information sharing and can provide average sharing level up to 0.9247, so this algorithm can be adopted to allocate resources in a balance way.
Davies Bouldin Index Algorithm for Optimizing Clustering Case Studies Mapping School Facilities

The lower Davies Bouldin Index (DBI) is considered the best clustering algorithm based on the criteria that yields a cluster set. The purpose of this research is to optimize the clustering results using DBI. The data sources used are the number of villages that have school facilities and the level of education is obtained from the government website (https://www.bps.go.id). The level of education in question is senior high school and vocational high school. The method used is k-means. The results show that from the number of clusters (k = 2, 3, 4, 5, 6) the optimal DBI for (k = 2) is obtained with a value of 0.168 for Measure Type = Mixed Measures. For the value of k = 2, a mapping of areas with L0 (low) = 31 province and L1 (high) = 3 provinces is obtained. The final centroids obtained for each cluster are L0 (315 and 155) and L1 (1710 and 1259). Based on the results of mapping by optimizing k-means and DBI, more than 90% of the villages still have school facilities, especially at the high school and vocational high school levels.
A survey for deep reinforcement learning in markovian cyber–physical systems: Common problems and solutions

Deep Reinforcement Learning (DRL) is increasingly applied in cyber–physical systems for automation tasks. It is important to record the developing trends in DRL’s applications to help researchers overcome common problems using common solutions. This survey investigates trends seen within two applied settings: motor control tasks, and resource allocation tasks. The common problems include intractability of the action space, or state space, as well as hurdles associated with the prohibitive cost of training systems from scratch in the real-world. Real-world training data is sparse and difficult to derive and training in real-world can damage real-world learning systems. Researchers have provided a set of common as well as unique solutions. Tackling the problem of intractability, researchers have succeeded in guiding network training with handcrafted reward functions, auxiliary learning, and by simplifying the state or action spaces before performing transfer learning to more complex systems. Many state-of-the-art algorithms reformulate problems to use multi-agent or hierarchical learning to reduce the intractability of the state or action spaces for a single agent. Common solutions to the prohibitive cost of training include using benchmarks and simulations. This requires a shared feature space common to both simulation and the real world; without that you introduce what is known as the reality gap problem. This is the first survey, to our knowledge, that studies DRL as it is applied in the real world at this scope. It is our hope that the common solutions surveyed become common practice.
Adoption of Artificial Intelligence Along with Gesture Interactive Robot in Musical Perception Education Based on Deep Learning Method

The present work aims to improve students’ interest in music teaching and promote modern teaching. A distributed application system of artificial intelligence gesture interactive robot is designed through deep learning technology and applied to music perception education. First, the user’s gesture instruction data is collected through the double channel convolution neural network (DCCNN). It uses the double-size convolution kernel to extract feature information in the image and collect the video frame’s gesture instruction. Secondly, a two-stream convolutional neural network (two-stream CNN) recognizes the collected gesture instruction data. The spatial and temporal information is extracted from RGB color mode (RGB) images and optical flow images and input into the two-stream CNN to fuse the prediction results of each network as the final detection result. Then, the distributed system used by the interactive robot is introduced. This structure can improve the stability of the interactive systems and reduce the requirements for local hardware performance. Finally, experiments are conducted to test the gesture command acquisition and recognition network, and the performance of the gesture interactive robot in practice. The results indicate that combining convolution kernels of [Formula: see text] and [Formula: see text] can increase the recognition accuracy of DCCNN to 98% and effectively collect gesture instruction data. The gesture recognition accuracy of two-stream CNN after training reaches 90%, higher than the mainstream dynamic gesture recognition algorithm trained with the same data set. Finally, the recognition test of gesture instructions is carried out on the gesture interactive robot reported here. The results show that the recognition accuracy of the gesture interactive robots is more than 90%, meeting the routine interaction needs. Therefore, the interactive gesture robot has good reliability and stability and is applicable to music perception teaching. The research reported here has guiding significance for establishing music teaching with multiple perception modes.
Breast Tumor Detection Using Robust and Efficient Machine Learning and Convolutional Neural Network Approaches

Breast cancer develops when cells in the breast expand and divide uncontrollably, resulting in a lump of tissue known as a tumor. This lump of tissue is called a tumor. After skin cancer, breast cancer is the second most common cancer among women. It is more common in women over the age of 50. Men may also acquire breast cancer, albeit it is uncommon. Each year, approximately 2,600 men in the United States are diagnosed with breast cancer, accounting for less than 1% of all cases. Transgender women are more likely than cisgender men to acquire breast cancer. Additionally, transgender males are less likely than cisgender women to acquire breast cancer. Breast cancer is more common in women over the age of 50, although it can affect anyone at any age. Early detection of a breast tumor may significantly lower the risk of developing breast cancer. A public dataset of breast tumor features was used instead to build models for identifying breast tumors through machine learning and deep learning. Prediction models were built using logistic regression (LR), decision tree (DT), random forest (RF), voting classifier (VC), support vector machine (SVM), and a proprietary convolutional neural network (CNN). These models were used to find critical prognostic indicators linked to breast cancer. The proposed network performs far better, with an average accuracy of 99%. This study has six types of models: LR, RF, SVM, VC, DT, and a custom CNN model. They all had 96% to 99% accuracy in this study. CNN, LR, RF, SVM, VC, and DT achieved 99%, 96%, 98%, 97%, 97%, and 96% F1 score, respectively. There were many machine learning algorithms used in this study that were very accurate, which means that these techniques could be used as alternative prognostic tools in breast tumor detection studies in Asia.
Investor behavior and cryptocurrency market bubbles during the COVID-19 pandemic

Purpose Cryptocurrencies lack fundamental values and are often subject to behavioral bias leading to market bubbles. This study aims to investigate the contribution of the coronavirus pandemic to the creation of market bubbles. Design/methodology/approach This study identifies four major cryptocurrency market bubbles by using the Phillips et al. (2016) (hereafter PSY) test. Subsequently, the co-movements of the coronavirus proxies with PSY measurement using the wavelet approach were studied. Findings Short-lived bubbles are detected at the beginning of the studied period, and more extended bubble periods are identified at the end. Besides, the empirical results show evidence of significant negative co-movement between each pandemic proxy and each cryptocurrency bubble measurement. Research limitations/implications Given the complex financial dynamics of the cryptocurrency markets due to some behavioral biases in some circumstances, investors can benefit from the date stamping of the bubbles bursting to make the best trading positions. In the same way, governments could support the healthy development of cryptocurrencies by preventing bubbles during such pandemics. Originality/value The financial bubble is commonly attributed to a change in investor behavior. Because traders and investors think they can resell the asset at a higher price in the future. This study explored the contribution of the COVID-19 pandemic in the creation of these bubbles by date stamping their occurrence and explosive periods. To the best of the authors’ knowledge, this study is the first attempt that explores the contribution of the COVID-19 pandemic to the creation of bubbles caused by a change in the investors’ behavior.
Effects of Augmented Reality-, Virtual Reality-, and Mixed Reality–Based Training on Objective Performance Measures and Subjective Evaluations in Manual Assembly Tasks: A Scoping Review

Objective The present scoping review aims to transform the diverse field of research on the effects of mixed reality-based training on performance in manual assembly tasks into comprehensive statements about industrial needs for and effects of mixed reality-based training. Background Technologies such as augmented and virtual reality, referred to as mixed reality, are seen as promising media for training manual assembly tasks. Nevertheless, current literature shows partly contradictory results, which is due to the diversity of the hardware used, manual assembly tasks as well as methodological approaches to investigate the effects of mixed reality-based training. Method Following the methodological approach of a scoping review, we selected 24 articles according to predefined criteria and analyzed them concerning five key aspects: (1) the needs in the industry for mixed reality-based training, (2) the actual use and classification of mixed reality technologies, (3) defined measures for evaluating the outcomes of mixed reality-based training, (4) findings on objectively measured performance and subjective evaluations, as well as (5) identified research gaps. Results Regarding the improvement of performance and effectiveness through mixed reality-based training, promising results were found particularly for augmented reality-based training, while virtual reality-based training is mostly—but not consistently—as good as traditional training. Application Mixed reality-based training is still not consistently better, but mostly at least as good as traditional training. However, depending on the use case and technology used, the training outcomes in terms of assembly performance and subjective evaluations show promising results of mixed reality-based training.
Psychologically-inspired, unsupervised inference of perceptual groups of GUI widgets from GUI images

Graphical User Interface (GUI) is not merely a collection of individual and unrelated widgets, but rather partitions discrete widgets into groups by various visual cues, thus forming higher-order perceptual units such as tab, menu, card or list. The ability to automatically segment a GUI into perceptual groups of widgets constitutes a fundamental component of visual intelligence to automate GUI design, implementation and automation tasks. Although humans can partition a GUI into meaningful perceptual groups of widgets in a highly reliable way, perceptual grouping is still an open challenge for computational approaches. Existing methods rely on ad-hoc heuristics or supervised machine learning that is dependent on specific GUI implementations and runtime information. Research in psychology and biological vision has formulated a set of principles (i.e., Gestalt theory of perception) that describe how humans group elements in visual scenes based on visual cues like connectivity, similarity, proximity and continuity. These principles are domain-independent and have been widely adopted by practitioners to structure content on GUIs to improve aesthetic pleasantness and usability. Inspired by these principles, we present a novel unsupervised image-based method for inferring perceptual groups of GUI widgets. Our method requires only GUI pixel images, is independent of GUI implementation, and does not require any training data. The evaluation on a dataset of 1,091 GUIs collected from 772 mobile apps and 20 UI design mockups shows that our method significantly outperforms the state-of-the-art ad-hoc heuristics-based baseline. Our perceptual grouping method creates opportunities for improving UI-related software engineering tasks.
A survey on RGB-D datasets

RGB-D data is essential for solving many problems in computer vision. Hundreds of public RGB-D datasets containing various scenes, such as indoor, outdoor, aerial, driving, and medical, have been proposed. These datasets are useful for different applications and are fundamental for addressing classic computer vision tasks, such as monocular depth estimation. This paper reviewed and categorized image datasets that include depth information. We gathered 231 datasets that contain accessible data and grouped them into three categories: scene/objects, body, and medical. We also provided an overview of the different types of sensors, depth applications, and we examined trends and future directions of the usage and creation of datasets containing depth data, and how they can be applied to investigate the development of generalizable machine learning models in the monocular depth estimation field.
Decisions for Blockchain Adoption and Information Sharing in a Low Carbon Supply Chain

Enterprises in low-carbon supply chains have been exploring blockchain technology in order to make carbon data transparent. However, there is still some opaque information in the market, such as the value-added service efficiency. How do supply chain members make decisions between information sharing and blockchain adoption? This study considers blockchain adoption and information sharing in a low-carbon supply chain with a single manufacturer and a single retailer. The retailer has private information about value-added services and decides how to share it with the manufacturer. We examine six combined strategies comprised of blockchain scenarios and information sharing formats (no sharing, voluntary sharing, and mandatory sharing). The results indicate that supply chain members prefer blockchain technology under no sharing and voluntary sharing. Under mandatory sharing, supply chain members have incentives to participate in blockchain when the value-added service efficiency exceeds a threshold value. While the manufacturer prefers to obtain the value-added service information, the retailer decides to share information depending on the value-added service efficiency. Besides, supply chain members’ attitude toward the sharing contract also depends on the value-added service efficiency.
Electrodermal activity for measuring cognitive and emotional stress level

Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions - Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.
Conditional Anonymous Certificateless Public Auditing Scheme Supporting Data Dynamics for Cloud Storage Systems

Cloud computing provides users with convenient data storage services, which simultaneously poses various security concerns, the integrity of outsourced data has been termed as one of the most concerning security issues. Certificateless public auditing not only enables a third-party auditor (TPA) to check data integrity, but also avoids complex certificates management and inherent drawbacks of key escrow. Up to date, a few certificateless public auditing schemes have been proposed, without providing fast data dynamics or protecting the identity privacy of users. In this paper, we propose a lightweight conditional anonymous certificateless public auditing (CACPA) scheme, supporting much faster data dynamics in cloud storage systems. Based on a homomorphic hash function, we design a certificateless signature and integrate it into the construction of CACPA, reducing the computational costs of TPA substantially. CACPA achieves conditional identity privacy preservation, anyone cannot infer the real identity of a user based on outsourced data, only the private key generator (PKG) can revoke the users when some misbehaviors occur. We provide security analysis of CACPA, and conduct performance evaluation demonstrating the lightweight advantages of CACPA, and therefore it is suitable for auditors with resource-constrained mobile devices.
Contrastive Attributed Network Anomaly Detection with Data Augmentation

Attributed networks are a type of graph structured data used in many real-world scenarios. Detecting anomalies on attributed networks has a wide spectrum of applications such as spammer detection and fraud detection. Although this research area draws increasing attention in the last few years, previous works are mostly unsupervised because of expensive costs of labeling ground truth anomalies. Many recent studies have shown different types of anomalies are often mixed together on attributed networks and such invaluable human knowledge could provide complementary insights in advancing anomaly detection on attributed networks. To this end, we study the novel problem of modeling and integrating human knowledge of different anomaly types for attributed network anomaly detection. Specifically, we first model prior human knowledge through a novel data augmentation strategy. We then integrate the modeled knowledge in a Siamese graph neural network encoder through a well-designed contrastive loss. In the end, we train a decoder to reconstruct the original networks from the node representations learned by the encoder, and rank nodes according to its reconstruction error as the anomaly metric. Experiments on five real-world datasets demonstrate that the proposed framework outperforms the state-of-the-art anomaly detection algorithms.