Reviewerly/exHarmony huggingface dataset:
A Study on China"s Digital Protetionism and Data Sovereignty

4차 산업혁명으로 인한 디지털 경제 시대를 맞이하여 데이터는 국가와 산업 및 개인의 일상생활 등 각 분야에서 가장 중요한 핵심 요소로 자리 잡게 되었다. 중국 정부는 데이터를 중요한 경제적 생산요소로 취급하고 글로벌 데이터 경제 선점을 위해 ｢국가안전법(国家安全法)｣, ｢네트워크안전법(网络安全法)｣, ｢데이터안전법(数据安全法)｣을 차례로 제정하는 등 국가 차원에서 정보 및 데이터 보안 분야의 법적인 틀을 마련하고 있다. 중국의 이러한 입법 작업들은 모두 ‘데이터’와 ‘국가안보’라는 핵심어로 서로 연결되어 있으며 이는 미·중 기술패권 경쟁과도 관련이 있다. 즉, 데이터는 미·중 첨단산업 및 군사안보, 패권 확보를 위한 핵심 요소이기 때문이다. 미·중 데이터 전쟁은 국가안보라는 이름으로 더욱 확대될 것이며 영향이 주변국으로 확산될 가능성이 더욱 커졌다. 결국 우리 입장에서 보면 데이터 주권 확보를 위한 생존전략 수립이 시급해진 것이다. 따라서 정부는 디지털 보호주의를 기반으로한 중국의 데이터 주권에 관한 법제도 정비 상황을 파악하여 우리나라에 필요한 데이터 전략과 향후 데이터법의 비전을 분명하게 설정해야 한다. 특히, 데이터의 경제적 가치 보호와 활용촉진 방안 등에 관한 국가전략 수립 및 데이터 산업의 육성·지원 방안을 마련해야 한다. 이에 본고에서는 중국의 데이터 관련법과 각각의 내용에 대해 분석하여 우리에게 적절한 시사점을 제공하고자 한다. Considering the era of the digital economy fueled by the 4th industrial revolution, data has become the most important core element in all fields of the country, industry and individual. The Chinese government considers data as an important economic production factor and, for the preoccupation of the data economy, has established a legal framework in the field of information and data security at the national level, including consecutive enactment of ｢National Security Act｣(国家安全法), ｢Network Security Act｣(网络安全法), and ｢Data Security Act｣(数据安全法). All of these legislative works in China, which are linked with the key words of ‘data’ and ‘national security’, are also related to the competition for technological supremacy between the US and China. In other words, data is a key factor in the US-China confrontation in high-tech industries, military security, and securing hegemony. The US-China data war is expected to escalate further in the name of national security and will affect neighboring countries. This means that the Korean government urgently needs to secure data sovereignty as a survival strategy. Therefore, the government needs to identify the status of legislative reform related to data sovereignty in China based on digital protectionism, and establish the data strategy and future data law vision clearly needed in Korea. In particular, a national strategy should be established to protect the economic value of and promote the use of data and nurture and support the data industry. The purpose of this study was, accordingly, to provide timely and appropriate implications to the Korean government through analysis of the contents of each data-related Chinese Acts.
Image Recognition of Skeletal Action for Online Physical Education Class based on Convolutional Neural Network

In recent years, due to the sudden outbreak of public health events, online teaching has become a mainstream teaching approach, and the number of teaching videos has increased rapidly. Therefore, extracting active image information from videos is of great importance in understanding video. This research proposes extracting image features from the spatiotemporal dimension based on deep learning, usinga spatiotemporal network for action recognition of skeletal action, and building a CSTGAT model based on a convolutional neural network. The experimental results show that the CSTGAT model has an accuracy of 98.47%, a precision rate of 97.43%, and a recall rate of 71.65% after being trained by the convolutional neural network. Furthermore, it only needs 217 iterations to achieve stable target convergence. After 100 tests, the F1 value of the CSTGAT model was 96.83%. In summary, the proposed model has high accuracy, a comprehensive query rate, and good model expressiveness. This model could provide a solution for intelligent longdistance interaction between a human and a machine and could be used in online teaching.
Entanglement properties of superconducting qubits coupled to a semi-infinite transmission line

Quantum entanglement, a key resource in quantum information processing, is reduced by interaction between the quantum system concerned and its unavoidable noisy environment. Therefore it is of particular importance to study the dynamical properties of entanglement in open quantum systems. In this work, we mainly focus on two qubits coupled to an adjustable environment, namely a semi-infinite transmission line. The two qubits’ relaxations, through individual channels or collective channel or both, can be adjusted by the qubits’ transition frequencies. We examine entanglement dynamics in this model system with initial Werner state, and show that the phenomena of entanglement sudden death and revival can be observed. Due to the hardness of preparing the Werner state experimentally, we introduce a new type of entangled state called pseudo-Werner state, which preserves as much entangling property as the Werner state, and more importantly, it is experiment friendly. Furthermore, we provide detailed procedures for generating pseudo-Werner state and studying entanglement dynamics with it, which can be straightforwardly implemented in a superconducting waveguide quantum electrodynamics system.
Development and Evaluation of Operational Lightning Forecasting System based on&amp;#160;Lighting Detection Network

&amp;lt;p&amp;gt;&amp;amp;#160;Recently, severe weather such as heavy rainfall, typhoon, hail, and lightning has become more frequent due to climate change. Especially heavy rains and localized precipitation systems accompanied by lightning strikes has not only human casualties but also social and economic impacts. To minimize&amp;amp;#160;the damage caused by lighting, research into the causes of lighting and their predictions are necessary.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;amp;#160;Korea Meteorological Administration(KMA) has been operating the Lightning Forecasting model to predict lightning forecast information on the Korean Peninsula. This model uses the energy density of lightning strikes detected by a total of 21 sensors of the LINET(LIghtning NETwork) systems(Nowcast, Germany). The energy density is calculated as the energy per unit area per unit time(kAkm&amp;lt;sup&amp;gt;-2&amp;lt;/sup&amp;gt;hr&amp;lt;sup&amp;gt;-1&amp;lt;/sup&amp;gt;) using CC(cloud-to-cloud) and CG(cloud-to-ground) lightning observation. This provides quantitative information on where strong lightning strikes are concentrated. In addition, the vector fields derived by applying the variational Echo Tracking(VET) algorithm are performed on the energy density and these are advected to the entire density field calculated by the semi-Lagrangian backward advection method. Finally, This very short-term quantitative lightning forecast data is produced every 10 minutes up to 6 hours ahead.&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;amp;#160;The evaluation of lightning forecasting is performed from 2019 to 2021 based on each month and season which is a total of 192 cases, and regions for the Korean Peninsula. The verification methods range from simple traditional skill scores to methods for quantitative comparison between the prediction model and lightning frequency observation. Based on the results of the evaluation, the record score of the total critical success index(CSI) is 0.48 within 1 hour, autumn is the highest season(0.51), and winter season(January, CSI=0.17) is the lowest(0.17).&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;amp;#160;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;&amp;lt;strong&amp;gt;Acknowledgements: &amp;lt;/strong&amp;gt;&amp;lt;/p&amp;gt; &amp;lt;p&amp;gt;This research was supported by the &amp;amp;#8221;Development of Integrated radar analysis and customized radar technology (KMA2021-03021)&amp;amp;#8221; of &amp;amp;#8220;Development of integrated application technology for Korea weather radar&amp;amp;#8221; project funded by the Weather Radar Center, Korea Meteorological Administration.&amp;lt;/p&amp;gt;
Survey On Parameters of Data Compression

The rapid development in the hardware and the software gives rise to data growth. This data growth has numerous impacts, including the need for a larger storage capacity for storing and transmitting. Data compression is needed in today’s world because it helps to minimize the amount of storage space required to store and transmit data. Performance measures in data compression are used to evaluate the efficiency and effectiveness of data compression algorithms. In recent times, numerous data compression algorithms are developed to reduce data storage and increase transmission speed in this internet era. In order to analyses how data compression performance is measured in terms of text, image, audio, and video compressions. This survey presents discussion made for important data compression parameters according to their data types.
OAOF - Obstacle Avoidance using Optical Flow Algorithm for Unmanned Aerial Vehicles

A camera sensor may be a useful tool for a number of robotic and autonomous systems when used with MATLAB Simulink for obstacle avoidance. Using a camera sensor and MATLAB Simulink's optical flow algorithm, the obstacle avoidance system is shown in this work. For the purpose of identifying and following the motion of objects in the picture, the system analyses visual data from the camera. The motion vectors of the objects are computed using the optical flow technique, and the robot and obstacles' relative motion is calculated using those motion vectors. In order to provide safe and easy navigation, the system can recognise and avoid obstacles in real-time. The system is easily adjustable and adaptable to diverse applications thanks to the usage of MATLAB Simulink, which enables the integration of several sensor and control systems. The suggested system's findings demonstrate that using a camera sensor with the optical flow algorithm may be a useful tool for obstacle avoidance in a range of robotic and autonomous systems.
Assessment of the variability and accuracy of readability scores across online calculators

Readability assessment is the most common approach for evaluating the health literacy demands of written health information. However, there are no uniform guidelines for conducting and reporting readability analyses. Readability scores can vary by up to five reading grades across formulas. They may also vary depending on the online calculator used and subjective decisions about how to prepare the text for analyses. This study aimed to quantify the variability of readability scores, assess the effect of text preparation, and to assess the agreement of online calculated scores with the reference standard (hand calculated scores). Two readability scores for ten samples of text were obtained by each calculator: one using unedited text and one in which text was prepared (e.g., by removing incomplete sentences). We reported the Simple Measure of Gobbledygook (SMOG), Flesch Kincaid Grade Level (FKGL) and the Automated Readability Index (ARI). We used Bland-Altman plots to assess the agreement of online readability scores with the reference standard. We identified eight online calculators. There were 16 combinations of calculator and formula (4 for SMOG, 6 for FKGL and 6 for ARI). Across the online calculators, the same text produced scores that varied by up to 12.9 grade reading levels even when the same formula was used. Text preparation markedly decreased this variability between online calculators (range: 2.1 grade levels). SMOG index scores from two calculators, and FKGL scores from one calculator showed good agreement with the reference standard. The remaining calculators showed poor agreement with the reference standard. Readability scores are inconsistent and often inaccurate. There is a need for more comprehensive and accessible guidelines for conducting and reporting on readability studies. Those evaluating written health information may consider complementing readability assessment with other tools, especially when the text is largely fragmented.
Cybersecurity in medical devices

As medical devices become more advanced and the Software as a Medical Device (SaMD) industry boom, it is crucial to make sure your medical devices are cyber-secure. Like all technologies, anytime a medical device has software, vigilance is required as it can become vulnerable to cybersecurity threats and attacks. The healthcare industry has long been target of cyber-attacks because of their vast amounts of health information and data such as patient health, product performance, or data from other devices connected to the same network. Cyberattacks used Microsoftâ€s Windows system to target computers globally by encrypting peopleâ€s data and demanding payments in the cryptocurrency Bitcoin before allowing access to it. Because of events like this, regulatory bodies like the FDA are finally taking the problem of cybersecurity more seriously. In 2019, the U.S. Food and Drug Administration (FDA) issued a warning about two security flaws affecting dozens of implantable cardioverter defibrillators.
Adaptive Range Guided Multi-view Depth Estimation with Normal Ranking Loss

Deep learning algorithms for Multi-view Stereo (MVS) have surpassed traditional MVS methods in recent years, due to enhanced reconstruction quality and runtime. Deep-learning based methods, on the other side, continue to generate overly smoothed depths, resulting in poor reconstruction. In this paper, we aim to Boost Depth Estimation (BDE) for MVS and present an approach, termed as BDE-MVSNet, for reconstructing high-quality point clouds with precise depth prediction. We present a non-linear strategy that derives an adaptive depth range (ADR) from the estimated probability, motivated by distinctive differences in estimated probability between foreground and background pixels. ADR also tends to decrease fuzzy boundaries via upsampling low-resolution depth maps between stages. Additionally, we provide a novel structure-guided normal ranking (SGNR) loss that imposes geometrical consistency in boundary areas by using the surface normal vector. Extensive experiments on DTU dataset, Tanks and Temples benchmark, and BlendedMVS dataset demonstrate that our method outperforms known methods and achieves state-of-the-art performance.
Cross-View Self-fusion for Self-supervised 3D Human Pose Estimation in the Wild

Human pose estimation methods have recently shown remarkable results with supervised learning that requires large amounts of labeled training data. However, such training data for various human activities does not exist since 3D annotations are acquired with traditional motion capture systems that usually require a controlled indoor environment. To address this issue, we propose a self-supervised approach that learns a monocular 3D human pose estimator from unlabeled multi-view images by using multi-view consistency constraints. Furthermore, we refine inaccurate 2D poses, which adversely affect 3D pose predictions, using the property of canonical space without relying on camera calibration. Since we do not require camera calibrations to leverage the multi-view information, we can train a network from in-the-wild environments. The key idea is to fuse the 2D observations across views and combine predictions from the observations to satisfy the multi-view consistency during training. We outperform state-of-the-art methods in self-supervised learning on the two benchmark datasets Human3.6M and MPI-INF-3DHP as well as on the in-the-wild dataset SkiPose. Code and models are available at https://github.com/anonyAcc/CVSF_for_3DHPE .
Application of Augmented Reality for the Training in the Field of Refrigeration and Air-Conditioning

Augmented Reality (AR) technology is one of the key technologies in the digital transformation area. AR is the technology that enables the overlaying of physical objects with computer-generated virtual perceptible data in real time to provide an interactive user experience in the real environment. This technology is currently used in many business fields such as engineering, education, medicine, logistics and transport either for experimentation or productivity. Due to the rapid development of display hardware and mobile devices in terms of performance, as well as the rapid development of the range of functions, the implementation of powerful augmented reality applications for industrial applications has become easier. This paper presents a use case of an AR training application for an ammonia screw compressor asset using the Video-See-Through AR method. The intention of this use case is to show the benefits and potential of AR technology for teaching and training complex technical systems in terms of increasing learning efficiency. The paper discusses the gathered experiences in the course of the use case and during the use of the AR training application in the university and in the industry.
Re-parameterization Making GC-Net-Style 3DConvNets More Efficient

For depth estimation using a stereo pair, deep learning methods using 3D convolution have been proposed. While the estimation accuracy is high, 3D convolutions on cost volumes are computationally expensive. Hence, we propose a method to reduce the computational cost of 3D convolution-based disparity networks. We apply kernel re-parameterization, which is used for constructing efficient backbones, to disparity estimation. We convert learned parameters, and these values are used for inference to reduce the computational cost of filtering cost volumes. Experimental results on the KITTI 2015 dataset show that our method can reduce the computational cost by 31–61% from those of trained models without any performance loss. Our method can be used for any disparity network that uses 3D convolution for cost volume filtering.
Augmented Reality Combined with Machine Learning to Increase Productivity in Fruit Packing

This paper studies the benefits of using Augmented Reality and Machine Learning in the agricultural industry for the purpose of fruit classification. During fruit classification colour plays a vital role in determining fruit quality and attractiveness. It is for this reason that technology in agriculture is being adopted that can visually grade produce. Currently, the study of Augmented Reality and Machine learning technologies in the Agricultural sector is limited, specifically cornering productivity improvement resulting from the implementation of these technologies. Therefore, technology which offers collaboration between employees and visual technology, in the form of Augmented Reality using the HoloLens 1, was studied. Augmented Reality requires strong data analytical support because the effectiveness of Augmented Reality is directly proportional to the quality of the information utilised. To ensure accurate data analytics Machine Learning was used. To analyse the use of Augmented Reality and Machine Learning in agriculture these two technologies were used to classify avocados in terms of both fruit grade and size. Machine Learning was implemented using Microsoft Azure which was used to grade the fruit. This was done by providing 1053 photos of avocados to Microsoft Azure from which the Machine Learning algorithm could learn how the fruit was to be graded. To determine the size of the avocado the number of pixels and the distance of the avocado from the HoloLens was used. An Augmented Reality and Machine Learning prototype was implemented, and the time taken to pack an avocado box was taken. It was found that there was a packing speed increase of 29.87% and a decrease in the variation of this speed by 96.2% when the prototype was implemented. Doing a t-test it was quantified that the increase in packing speed was statistically significant. Therefore, it can be concluded that the use of Augmented Reality and Machine Learning can be used to aid employees to improve tasks in the agricultural industry.
Pengaruh Media Pembelajaran Jeopardy Game terhadap Aktivitas dan Hasil Belajar Peserta Didik

The Jeopardy Game is a learning media designed to assess students' ability to identify and remember factual information. This study aimed to describe students' learning activities and outcomes, whether or not they used Jeopardy Game learning media. Another aim was to define the impact of the media on the activities and students' learning outcomes. This was a quasi-experimental study with a non-equivalent control group design. The data analysis techniques were descriptive statistical analysis and inferential statistics. The sample in this study was 20 students from class VIII A, selected as the experimental class, and class VIII C, as the control class. The instrument used is a learning outcome test on respiratory system material. The results showed that students taught using the Jeopardy Game learning media had an average learning outcome of 72.82 and learning activities of 70.85. Meanwhile, students taught using conventional learning models have an average learning outcome of 58.35 and a mean value of learning activities 45.6. The results of inferential statistical analysis obtained a significant value for activity and learning outcomes of 0.000. This value indicates that the sign &lt; = 0.00 &lt; 0.05 so that H0 is rejected and H1 is accepted. It appears that the Jeopardy Game learning media influenced students' activities and learning outcomes in PondokPesantren Al-Wahid PancaLautang's respiratory system material for class VIII Sidenreng Rappang District.
A Blockchain Based Secure Framework for User-centric Multi-party Skyline Queries

For commercial purposes, business organizations with mutual interests while competing with one another usually intend to keep their datasets to be confidential. Secure multiparty skyline queries enable these organizations to retrieve dominated data objects from their sensitive datasets. Here, a distinction among datasets according to priority, retaining data privacy along with integrity, targeted enquires, etc., are crucial. This paper proposes a secure framework for multi-party skyline queries that incorporates these requirements together. To prioritize datasets it assigns distinct weights to parties’ datasets. To retain adequate privacy it exploits a cryptosystem that engages a single key for encryption but multiple separate keys for decryption. To attain data anonymity it re-encrypts and shuffles the encrypted data. To assure data integrity it deploys a unique block comprising encrypted data designated for every party within the blockchain storage. To enable enlisted users to ask intended queries it accepts their inquiries. This paper is a preliminary report and evaluation of the proposed framework.
Design and Development of Insurance System Based on PoW Consensus Algorithm

Since the outbreak of the epidemic, people not only need to pay attention to their own safety and health, but also need insurance services to protect themselves. However, there are some problems in the traditional insurance system. First, the information of clients is incomplete or untrue. Second, privacy is compromised. Third, transaction information is tampered with. These problems lead to various insurance disputes. Therefore, this paper develops an insurance system based on blockchain by using the characteristics of decentralization, transparency, and immutability. The construction of the system is divided into three steps. First, insurance contracts are published on the Merkel tree. Next, RSA encryption technology is used to protect clients' information security. Last, SHA256 secure hash algorithm and consensus mechanism are used to resist the external attack of the blockchain. On this basis, this paper designs and develops a safe insurance system. Also, it studies the performance of insurance systems and the consensus mechanism of blockchain. The former compares the differences between the traditional insurance system and the blockchain insurance system in each performance index during operation. The result shows that some performance indices of blockchain insurance system are lower than those of traditional insurance system. However, because of its higher security than traditional insurance system, blockchain technology is selected as the core technology of this system. Also, two consensus algorithms, PoW and PBFT, compares with each other in blockchain technology. The experimental results show that the PoW consensus algorithm is more suitable for blockchain insurance system.