Reviewerly/exHarmony huggingface dataset:
Implementation of immersive virtual reality influences outcomes of exercise test

Background: Exercise tests can be used to assess functional limitations, to assess the ability to perform a rehabilitation program or determine the outcome of interventions. Cycle ergometers is a common exercise testing modes. However, stationary cycling exercise tolerance is highly dependent on patient motivation. Therefore, the test may end prematurely, before reaching a cardiopulmonary endpoint. A method of increasing patient motivation may be to perform exercise in a virtual reality (VR). VR may work as a distractor from exercise fatigue. Methods: This research was designed as a cross-sectional, randomly assigned study aimed to assess the influence of applying immersive virtual reality during an incremental exercise test on a cycle ergometer in healthy individuals. The study enrolled 70 healthy volunteers aged 22–24 years. The participants performed two types of tests: traditional and within virtual reality. Both tests consisted of incremental exercise, in which work rate was increased by 25 watts every three minutes. The VR research station consisted of a HTC Vive Pro Goggle starter kit along with VR health care (aerobic exercise) and VR cycling software. An ankle mounted HTC Vive Tracker device was used to link the pedal speed with the VR images. Heart rate (HR) was continuously recorded using a Polar H10 monitor. All tests were performed on a Lode Excalibur Sport PFM cycle ergometer. Results: Data analysis revealed significant differences between tests (p
Cybersecurity During COVID-19

Presents the introductory editorial for this issue of the publication.
Trends and Effects of Learning through AR-Based Education in S-Korea

The purpose of this study is to help teachers of AR-based education by reviewing the literature on the types, learning effects and limitations of AR technology used in Korean AR-based education. Therefore, this thesis examines the literature focusing on thesis and articles of Korean thesis, and defines the definition of AR, technical elements, implementation method, utilization media, usage and trends, educational status, and educational effects and limitations. As a result, the classes using AR-based education could be easily immersed by learners and the presence of learning occurred. In addition, it is possible to take advantage of the AR, enabling immediate training using GPS and collaborative training using the network. AR-based education is becoming increasingly popular according to the use of digital textbooks through AR technology and AR education contents using mobile devices. However, there are limitations to AR implementation media such as qualitative differences in AR educational contents, high price, motion sickness and dizziness of HMD devices. Therefore, in order to achieve AR-based education in educational institutions and educational sites, this paper presents the development of AR implementation media suitable for the level of learners, provides flexible AR curriculum guidelines, industry-academic cooperation between companies for technology development, and inspection / censorship of educational contents. It suggests that a procedure should be provided.
Introduction to Selenium

Before Selenium, testing the functionality of a web application was done manually, which took many hours. The testing tended to rely on different scenarios. Each scenario was considered a test case to enact the behavior of the web app before its implementation. These test cases were deployed on various browsers to affirm any issues in the source code.
Multimodal and Multidimensional Interactive Teaching Mode of Comprehensive English Under the Background of Big Data

As the core basic course of English major, Comprehensive English also needs corresponding transformation and innovation in the context of big data. Multimodal and multidimensional interactive teaching has the conditionality of depending on the sustainable development of modern technology. Therefore, taking the innovation of Comprehensive English teaching as the starting point, making full use of all kinds of information technology in big data era, fully exploring the multidimensional interactive channels between teachers and students in the background of big data and developing multimodal interactive teaching, can further meet the needs of Comprehensive English teaching and students’ learning in the context of big data. This paper mainly explores the multimodal and multidimensional interactive teaching mode of Comprehensive English in big data. Through multimodal sensory integration, the creation of multimodal interactive practice activities, the improvement of teaching methods and means, and the creation of diversified evaluation system, a more scientific and complete multimodal multidimensional interactive mode is established to further promote the Comprehensive English multi-modal interactive teaching coverage and coverage quality, and finally improve the teaching effect.
A novel downscaling procedure for compositional data in the Aitchison geometry with application to soil texture data

Abstract In this work, we present a novel downscaling procedure for compositional quantities based on the Aitchison geometry. The method is able to naturally consider compositional constraints, i.e. unit-sum and positivity, accounting for the scale invariance and relative scale of these data. We show that the method can be used in a block sequential Gaussian simulation framework in order to assess the variability of downscaled quantities. Finally, to validate the method, we test it first in an idealized scenario and then apply it for the downscaling of digital soil maps on a more realistic case study. The digital soil maps for the realistic case study are obtained from SoilGrids, a system for automated soil mapping based on state-of-the-art spatial predictions methods.
Load Prediction Energy Efficient VM Consolidation Policy in Multimedia Cloud

Multimedia cloud computing is a dynamic development on could computing by upgrading various kind of media services including text, image, video on the Internet. The most significant challenges of the rising popularity of multimedia cloud computing are energy utilization and green cloud computing. To enhance the efficiency of energy on multimedia cloud data centers, we enhance the virtual machine consolidation (VMC) framework. There are two phases in VMC: the virtual machine (VM) selection and the virtual machine allocation. There are many researchers who proposed the solution with VM allocation and VM selection separately. Related on these two policies, we proposed the fast up and slow down (FUSD) load prediction-based energy-efficiency VMC for data-intensive jobs in multimedia cloud infrastructure. According to the simulation results in CloudSim with real trace data of cloud platform illustrated that the proposed load prediction policy shows up to better performance for efficiency of energy consumption, service level agreement violation (SLAV), and a number of VM migrations (VMM), respectively, compared to existing traditional VMC. The proposed VMC policy can use for large-scale multimedia cloud platform where minimal QoS assurance, SLAV, and energy consumption is inevitable.
RETRACTED: Research on the Teaching Mode of College English Listening Based on Computer Multimedia Platform

Abstract Single teaching environment, limited teaching conditions and backward teaching equipment hinder the development of listening teaching. In recent years, with the development of science and technology, the application of computer multimedia in the classroom has greatly improved the teaching equipment and conditions of listening class, and made the classroom teaching of English listening have a qualitative leap.
“This is Houston. Say again, please”. The Behavox System for the Apollo-11 Fearless Steps Challenge (Phase II)

We describe the speech activity detection (SAD), speaker diarization (SD), and automatic speech recognition (ASR) experiments conducted by the Behavox team for the Interspeech 2020 Fearless Steps Challenge (FSC-2). A relatively small amount of labeled data, a large variety of speakers and channel distortions, specific lexicon and speaking style resulted in high error rates on the systems which involved this data. In addition to approximately 36 hours of annotated NASA mission recordings, the organizers provided a much larger but unlabeled 19k hour Apollo-11 corpus that we also explore for semi-supervised training of ASR acoustic and language models, observing more than 17% relative word error rate improvement compared to training on the FSC-2 data only. We also compare several SAD and SD systems to approach the most difficult tracks of the challenge (track 1 for diarization and ASR), where long 30-minute audio recordings are provided for evaluation without segmentation or speaker information. For all systems, we report substantial performance improvements compared to the FSC-2 baseline systems, and achieved a first-place ranking for SD and ASR and fourth-place for SAD in the challenge.
Telecommunications health and safety: The Covid-19 pandemic and 5G cellular telecommunication systems

Recently, there were several odd or unusual reports coming out of the UK about linking the coronavirus disease (COVID-19) pandemic to the rollout of 5G communication systems [1]. It sounded rather bizarre; even as a conspiracy theory, it did not make sense! While both 5G and COVID-19 are global phenomena happening at around the same time, it boggles the mind how the two got entangled. On second thought, it is not as shocking as it may seem upon first encounter!
MDR Cluster-Debias: A Nonlinear Word Embedding Debiasing Pipeline

Existing methods for debiasing word embeddings often do so only superficially, in that words that are stereotypically associated with, e.g., a particular gender in the original embedding space can still be clustered together in the debiased space. However, there has yet to be a study that explores why this residual clustering exists, and how it might be addressed. The present work fills this gap. We identify two potential reasons for which residual bias exists and develop a new pipeline, MDR Cluster-Debias, to mitigate this bias. We explore the strengths and weaknesses of our method, finding that it significantly outperforms other existing debiasing approaches on a variety of upstream bias tests but achieves limited improvement on decreasing gender bias in a downstream task. This indicates that word embeddings encode gender bias in still other ways, not necessarily captured by upstream tests.
Joyful Learning in Elementary School

Teaching in elementary school is another challenge for most teachers. While kids as students considered as hard in creating good learning process, some researchers try to propose joyful learning for students in elementary school. However, many barriers come in that effort for real enjoyable learning environment. This chapter tries to propose mobile learning as option to create joyful learning. Since that mobile learning can includes many types of joyful learning components and its familiriaty to students, it means that should be easier for teachers to implement that. This chapter also tries to explain how can teachers create mobile learning easily, thus teachers themselves can feel enjoy in learning process. So, whenever both teachers and students already feel their happiness, they will also express their enthusiasm as one of the most important thing in creating joyful learning
Execution of Long-Duration Multi-Cloud Serverless Functions Using Selective Migration-Based Approach

A relatively new paradigm for the Cloud-based software deployment is serverless computing. By adopting stateless loosely-coupled functions, the system can obtain many compositions for several purposes. Contrarily to monolithic approach, serverless computing facilitates the evolution of the applications, since the functions may be independently scheduled for reconstitution. Nevertheless, serverless computing dictates that function execution should be within a short duration (five minutes max in most Cloud platforms), after which the function is abruptly ended even if it has not completed its task. This leads to prevent functions requiring longer time from being adopted as Serverless functions. This paper deals with this drawback. It proposes a migration-based approach that promotes the execution of long-duration serverless functions: each running function that reaches the maximum time limit is repeatedly transferred to another cloud platform where it is carried on. At each migration step, the destination cloud is selected regarding the most relevant criteria.
Analysis of Ranging Error of Parallel Binocular Vision System

The problem of ranging error has posed great challenges for 3D reconstruction and obstacle avoidance in parallel binocular vision system. For robot platform, parallel binocular vision system calibrated in fixed distance leads to low accuracy in depth estimation when distance changes. The error transfer function model in the system is applied to solve the problem. However, the theoretical ranging error along central axis of binocular cameras decreases with increasing depth and eventually converges to a limit value, and the ranging error near the central axis is sensitive to a minor change of field of view (FOV). These results are inconsistent with real scene. In this paper, an innovative error analysis method is proposed by introducing spatial uncertainty due to discretization of pixels to geometric model. The proposed method indicates theoretically that the ranging error of object within common FOV along the central axis grows with increasing depth and the magnitude of ranging error is modified. Also, experiments are implemented to further validate the reasonability of the proposed method.
A Vulnerability Risk Assessment Method for Industrial Control System

Aiming at how to accurately assess the vulnerability risk of industrial control system, this paper proposes an algorithm for vulnerability risk assessment of industrial control system. This method firstly establishes the attack-defense game model of industrial control system and gives the expression of the attacker's maximum return expectation. The loss degree of the industrial control system after being attacked is calculated according to the three security attributes of the industrial control system. The vulnerability value is calculated by using the attacker's maximum return expectation and system loss degree. Then the expression of comprehensive connectivity between vulnerabilities is given according to vulnerability correlation graph and risk matrix. The own risk and associated risk of vulnerability are calculated by using vulnerability value and comprehensive connectivity between vulnerabilities, the vulnerability comprehensive risk is assessed finally. The example analysis shows that the method combined with the security attributes of industrial control system, not only considers the influence of mutual restriction factors of information security attack and defense sides, but also reflects the correlation between vulnerabilities. The method is feasible and effective, the results are objective and accurate. The method is used to evaluate the key vulnerability of industrial control system, it has the advantages of safety assessment and defense.
What to Do When the Users of an Ontology Merging System Want the Impossible? Towards Determining Compatibility of Generic Merge Requirements

Ontology merging systems enable the reusability and interoperability of existing knowledge. Ideally, they allow their users to specify which characteristics the merged ontology should have. In prior work, we have identified Generic Merge Requirements (GMRs) reflecting such characteristics. However, not all of them can be met simultaneously. Thus, if a system allows users to select which GMRs should be met, it needs a way to deal with incompatible GMRs. In this paper, we analyze in detail which GMRs are (in-)compatible, and propose a graph based approach to determining and ranking maximum compatible supersets of user-specified GMRs. Our analysis shows that this is indeed feasible to detect the compatible supersets of the given GMRs that can be fulfilled simultaneously. This approach is implemented in the open source $$\mathcal {C}$$ o $$\mathcal {M}$$ erger tool.
A History of Censorship in the United States

Censorship is a centuries-old issue for the United States. The importance of intellectual freedom and the freedom of speech is particularly evident in libraries, organizations dedicated to the access and spread of information. Issues regarding censorship and intellectual freedom have even reached the US Supreme Court. The following essay serves as a history of censorship in the United States, particularly in its libraries, and how the same issues of censorship have now transitioned into the digital age.
A Study on the Correlation between Engaging Fun and Converged Academic Enjoyment in After-school Programs through Magic Class

Statistics on the operational status of after-school programs for 2018 posted on the Education Ministry's website shows that about 2.93 million students are attending after-school classes at 11, 746 schools ranging from elementary to high school, both public and private, across the country.Education magic classes are categorized into other programs among specialties.Most after-school programs are recognized as short-term education focused on supplementing insufficient subjects or focusing on interests rather than being selected considering individual students' aptitude and special ability.Education magic classes are also included in this category.This approach to magic, previously thought to be the domain of a person with expertise, has led to a positive response to its effect on education.Minimal research were undertaken on the effect on education of this type of class.Therefore, the Correlation between engaging fun and converged academic enjoyment in after-school classes was analyzed in this study in order to determine the applicability of magic classes to actual education environment.The analysis of the pre-and post-examination showed that the interest and interest alone in the humanities of Korean Language and Chinese characters as well as the natural sequence of mathematics and science has a significant effect on the converged academic enjoyment of the after-school magic.
Continual Learning of Control Primitives : Skill Discovery via Reset-Games

Reinforcement learning has the potential to automate the acquisition of behavior in complex settings, but in order for it to be successfully deployed, a number of practical challenges must be addressed. First, in real world settings, when an agent attempts a task and fails, the environment must somehow reset so that the agent can attempt the task again. While easy in simulation, this could require considerable human effort in the real world, especially if the number of trials is very large. Second, real world learning often involves complex, temporally extended behavior that is often difficult to acquire with random exploration. While these two problems may at first appear unrelated, in this work, we show how a single method can allow an agent to acquire skills with minimal supervision while removing the need for resets. We do this by exploiting the insight that the need to reset an agent to a broad set of initial states for a learning task provides a natural setting to learn a diverse set of reset-skills. We propose a general-sum game formulation that balances the objectives of resetting and learning skills, and demonstrate that this approach improves performance on reset-free tasks, and additionally show that the skills we obtain can be used to significantly accelerate downstream learning.
Improving Neural Network Training in Low Dimensional Random Bases

Stochastic Gradient Descent (SGD) has proven to be remarkably effective in optimizing deep neural networks that employ ever-larger numbers of parameters. Yet, improving the efficiency of large-scale optimization remains a vital and highly active area of research. Recent work has shown that deep neural networks can be optimized in randomly-projected subspaces of much smaller dimensionality than their native parameter space. While such training is promising for more efficient and scalable optimization schemes, its practical application is limited by inferior optimization performance. Here, we improve on recent random subspace approaches as follows: Firstly, we show that keeping the random projection fixed throughout training is detrimental to optimization. We propose re-drawing the random subspace at each step, which yields significantly better performance. We realize further improvements by applying independent projections to different parts of the network, making the approximation more efficient as network dimensionality grows. To implement these experiments, we leverage hardware-accelerated pseudo-random number generation to construct the random projections on-demand at every optimization step, allowing us to distribute the computation of independent random directions across multiple workers with shared random seeds. This yields significant reductions in memory and is up to 10 times faster for the workloads in question.
A False Negative Study of the Steganalysis Tool Stegdetect

In this study, we evaluated one of the modern automated steganalysis tools, Stegdetect, to study its false negative rates when analysing a bulk of images. In so doing, we used JPHide method to embed a randomly generated messages into 2000 JPEG images. The aim of this study is to help digital forensics analysts during their investigations by means of providing an idea of the false negative rates of Stegdetect. This study found that (1) the false negative rates depended largely on the tool’s sensitivity values, (2) the tool had a high false negative rate between the sensitivity values from 0.1 to 3.4 and (3) the best sensitivity value for detection of JPHide method was 6.2. It is therefore recommended that when analysing a huge bulk of images forensic analysts need to take into consideration sensitivity values to reduce the false negative rates of Stegdetect.
High Throughput Morphology-Based Cell Screening by Reservoir Computing

A new low-cost and data-efficient method for high-throughput microfluidic single-cell screening is proposed and demonstrated. Only one low-speed single-pixel photodetector is needed. Morphology-based object classification is achieved using reservoir computing. Our experiment shows that the frames involving the target cell can be recognized distinctly.
JBNU at MRP 2020: AMR Parsing Using a Joint State Model for Graph-Sequence Iterative Inference

This paper describes the Jeonbuk National University (JBNU) system for the 2020 shared task on Cross-Framework Meaning Representation Parsing at the Conference on Computational Natural Language Learning. Among the five frameworks, we address only the abstract meaning representation framework and propose a joint state model for the graph-sequence iterative inference of (Cai and Lam, 2020) for a simplified graph-sequence inference. In our joint state model, we update only a single joint state vector during the graph-sequence inference process instead of keeping the dual state vectors, and all other components are exactly the same as in (Cai and Lam, 2020).