Reviewerly/exHarmony huggingface dataset:
A Game-Theoretic Approach to Multi-agent Trust Region Optimization

Trust region methods are widely applied in single-agent reinforcement learning problems due to their monotonic performance-improvement guarantee at every iteration. Nonetheless, when applied in multi-agent settings, the guarantee of trust region methods no longer holds because an agent’s payoff is also affected by other agents’ adaptive behaviors. To tackle this problem, we conduct a game-theoretical analysis in the policy space, and propose a multi-agent trust region learning method (MATRL), which enables trust region optimization for multi-agent learning. Specifically, MATRL finds a stable improvement direction that is guided by the solution concept of Nash equilibrium at the meta-game level. We derive the monotonic improvement guarantee in multi-agent settings and show the local convergence of MATRL to stable fixed points in differential games. To test our method, we evaluate MATRL in both discrete and continuous multiplayer general-sum games including checker and switch grid worlds, multi-agent MuJoCo, and Atari games. Results suggest that MATRL significantly outperforms strong multi-agent reinforcement learning baselines.
A Real-Time Social Distancing and Face Mask Detection System Using Deep Learning

It has been more than two years since the transmission of COVID-19 virus has affected the public health globally. Due to its natural characteristic, the virus is very likely to undergo mutation over time and consistently changes to a new variant with higher severity and transmission rate. The pandemic is expected to prolong with the increment in number of daily cases which leads to why preventive measures like practising distance apart rule and wearing facemask are still mandatory in the long run. This paper is prepared to develop a social distancing model using deep learning for COVID-19 pandemic. The tracking accuracy of the proposed model is discussed in the paper and compared with other deep learning methods as well. The efficiency of the detection model is observed and evaluated by performing quantitative metrics. The monitoring model is trained by implementing YOLOv4 algorithm and has achieved an accuracy of 93.79% with F1-score of 0.87 in detecting person and facemask. The model is applicable for real-time and video detection to monitor social distance violation as an effort to flatten the curve and slow down the transmission rate in the community.
The Effects of Bitcoin Futures on Bitcoin Market

In the five years after the launch of bitcoin futures, academics and investors' perceptions have shifted from the early view that they raise the risk of bitcoin to the present acceptance of their ability to serve as derivatives. This change indicates that bitcoin futures have the potential to improve. What influences the bitcoin market has received from bitcoin futures is investigated in this paper. The introduction of bitcoin futures has offered a feasible hedging strategy for bitcoin investors, enhanced the stability and information effectiveness of the bitcoin market, also eased the investment barrier for bitcoin, based on study and comparison of previous research on bitcoin futures. However, because bitcoin futures are the novel type of futures contract, the market is complicated, and investors prefer to trade unregulated futures, whereas regulated futures are traded in considerably lesser quantities due to position limitations. Exchanges that regulate bitcoin futures should consider changing their contract positions to allow more investors to engage in trading while obtaining regulatory protections in to enable the bitcoin futures market to develop more maturely in the future.
Social Media Bot Detection Using Machine Learning Approach

Nowadays, social media platforms are thronged with social bots spreading misinformation. Twitter has become the hotspot for social bots. These bots are either automated or semi-automated, spreading misinformation purposefully or not purposefully is influencing society’s perspective on different aspects of life. This tremendous increase in social bots has aroused huge interest in researchers. In this paper, we have proposed a social bot detection model using Random Forest Classifier, we also used Extreme Gradient Boost Classifier, Artificial Neural Network, and Decision Tree Classifier on the top 8 attributes, which are staunch. The attribute is selected after analyzing the preprocessed data set taken from Kaggle which contains 37446 Twitter accounts having both human and bots. The overall accuracy of the proposed model is above 83%. The result demonstrated that the model is feasible for high-accuracy social bot detection.
Computer-based Software Model for Examination Monitoring and Assessment System in Academic Environment

This work describes the design and implementation of a computer-based examination system in cases when exam malpractice has become rampant.This report gives an application with a centralized database that can quickly locate students' details for an online assessment.Furthermore, it helps provide a workspace for mathematical calculations to be carried out.The application is a web-based application developed using several programming languages like HTML, CSS, JavaScript and PHP.The application assists in the immediate grading of students and the real-time processing of results for students.
The effect of middle school students’ commitment to physical education classes using blended learning on grit and class satisfaction

목적: 본 연구의 목적은 코로나19 상황이 지속되는 시점에 중학교 학생들의 블랜디드 러닝에 따른 체육수업이 몰입과 그릿 및 수업만족도에 어떠한 영향을 미치는지를 알아보고자 하는데 있다.방법: 이를 위해 J도 11개 중학교, 715명을 대상으로 설문지를 통해 조사하여 최종분석 하였다. 수집된 자료는 요인분석, 신뢰도분석, T검정, 회귀분석을 실시하였으며, SPSS 23.0 프로그램을 사용하여 결과를 도출하였다.결과: 본 연구 결과 첫째, 행동적몰입이 흥미유지에 정적(+) 영향을 미치는 것으로 나타났다. 둘째, 인지적 몰입, 정의적 몰입, 행동적 몰입이 노력지속에 정적(+) 영향을 미치는 것으로 나타났다. 셋째, 인지적 몰입, 정의적 몰입, 행동적 몰입이 심리적 만족도와 신체적 만족도에 정적(+) 영향을 미치는 것으로 나타났다. 넷째, 흥미유지와 노력지속이 심리적 만족도와 신체적 만족도에 정적(+) 영향을 미치는 것으로 나타났다. 다섯째, 노력지속이 교육적 만족도에 정적(+) 영향을 미치는 것으로 나타났다.결론: 이러한 결과를 종합해 볼 때 코로나19가 지속되는 상황에 블랜디드 러닝을 했음에도 불구하고 학생들의 수준을 고려한 체계적이고 전략적인 수업 설계를 통해 학생들의 체육수업 몰입을 높여줌으로서 흥미유지와 노력지속, 수업만족도를 높여주었다고 판단된다. Purpose: The purpose of this study is to develop customized warm-up exercises for middle school physical education classes and explore the application effects.Methods: This study used a qualitative and quantitative mixed research method, and the research participants conducted a quantitative study of 80 middle school students through a total of 3 semesters from March 2021 to August 2022 at B Middle School in N Innovation City, Jeonnam. Qualitative study Six students were selected. To achieve the purpose of this study, a t-test was conducted after descriptive statistics, exploratory factor analysis, and reliability analysis using the SPSS Ver.23 statistical program quantitatively. The in-depth interview method was conducted secondarily.Results: First, the developed warm-up exercise was developed for the purpose of external use appropriateness, convenience, motion recognition accessibility, and activity stabilization by body part. Second, in comparing the effects of movable force by body part and perceived usefulness according to the warm-up exercise tailored to middle school physical education, the warm-up exercise tailored to middle school physical education was found to be more effective than national gymnastics and new millenium health workout. Third, the effect factors shown in national gymnastics and new millenium health workout were 7 factors. Fourth, 8 factors appeared in the preparation exercise customized for physical education in middle school.Conclusion: It is expected that the warm-up exercise customized for middle school physical education, which is composed of warm-up exercises based on dynamic stretching, can be used easily and conveniently and effectively, so that it will be practically applied and further activated in school physical education.
Performance Improvement of Conditional Replenishment Algorithm Using Temporal Correlation

지상파 방송 환경에서 지상파 UHD 사용자 선택형 입체미디어 서비스를 제공할 때, 비대칭적인 양안 영상의 화질 문제는 항상 대두됐다. 이를 해결하기 위해, 양안 영상의 상관도를 활용한 조건부 대체 알고리즘이 연구된 바 있다. 기존의 알고리즘은 고화질 기준 영상을 시차 보상하여 저화질 부가 영상에 적용하는 방식으로 화질을 개선 시켜왔다. 본 논문은 조건부 대체 알고리즘에 시간적 상관도를 이용하여 더욱 효율적으로 부호화 하는 확장된 알고리즘을 제안한다. 이전 시각에 이미 보상되어 복원된 고화질의 부가 영상을 이용해, 현재 시각의 저화질 부가영상을 움직임 보상한다. 제안하는 알고리즘은 이전 알고리즘에 비해 실사 및 애니메이션 540P, 1080P 클립에 대해 각각 평균 30.5%, 19.8% 의 개선된 비트 절감율을 보였다. When providing fixed/mobile broadcast convergence 3DTV services in a terrestrial broadcasting environment, the problem of image quality of asymmetric binocular images has always emerged. To solve this problem, a conditional replenishment algorithm using the correlation of binocular images has been studied. Existing algorithms have improved image quality by compensating the high-definition reference image in a parallax manner and applying it to the low-definition additional image. This paper proposes an extended algorithm that uses temporal correlation to encode conditional replenishment algorithms more efficiently. By using a high-definition additional image that has already been compensated and restored at the previous time, motion compensation is performed on the low-definition additional image of the present time. The proposed algorithm showed Bit saving performance of 19.8% and 30.5% on average for real-film and animation clips 540P and 1080P clips, respectively, compared to previous algorithms.
Design and Development of a Deep Learning-Based Pain Behavior Monitoring System through Individual Recognition of Hand Positions and Hand Gestures

급격한 딥러닝 기술의 발전으로 헬스케어 분야에 딥러닝을 접목시켜 환자의 생체정보나 복합적인 상태를 인식하는 연구가 활발히 수행되고 있다. 특히 통증의 효과적인 관리는 환자의 건강상태 개선 및 의료 만족도의 상승으로 이어진다. 본 논문은 손목 밴드 디바이스를 통해 수집된 IMU 센서 데이터를 입력으로 하는 딥러닝 기반 통증행동 모니터링 시스템을 제안한다. 통증행동의 정밀한 인식을 위해 손 위치와 손동작을 개별 인식하는 2개의 CNN 모델과 통증행동을 일상행동으로 인식하는 오인식을 줄이는 HMM 모델을 결합한 모델을 설계했다. IMU센서 데이터를 수집하는 손목 밴드 디바이스는 저전력 설계를 위해 nRF5240 기반의 MDBT50Q 모듈을 기반으로 설계되었다. 제안하는 시스템은 부산대학교병원의 자문을 받아 선정한 28가지 통증행동을 평균 87.06%의 정확도로 인식한다. With the rapid development of deep learning technology, research on recognizing patients’ biometric information or complex conditions by incorporating deep learning into the healthcare field is being actively conducted. In particular, effective management of pain leads to an improvement in the patient’s health condition and an increase in medical satisfaction. This paper proposes a deep learning-based pain behavior monitoring system with IMU sensor data collected through wristband devices as input. For precise recognition of pain behavior, we designed a combination of two CNN models that individually recognize hand positions and hand movements and an HMM model that reduces misrecognition of pain behavior as daily behavior. The wristband device, which collects IMU sensor data, is designed based on an nRF5240, based MDBT50Q module for low-power design. The proposed system recognizes 28 pain behaviors selected with advice from Pusan National University Hospital with an average accuracy of 87.06%.
Special issue on online motion planning and model predictive control

"Special issue on online motion planning and model predictive control." Advanced Robotics, 37(5), p. 297
Attitude Detection Classification based on Feature Selection and Integrated Machine Learning Model

This paper classifies 19 human actions based on a dataset of 1.2 million human actions acquired from sensors. The Xgboost classification model is established, and the MIV algorithm is used as an index to evaluate the importance of each variable to the dependent variable. The MIV values of all features are sorted according to the absolute value of each variable, and finally the top 10 groups of features are selected as the features of the reduced data set, and fine-tuned by grid search. Select the simplified data set, get the maximum roc_auc through continuous testing, and get the optimal model. The model has a recall of 1 and a precision, F1-score, and AUC of 0.99. Then, in order to make the model have a good generalization ability under the limited data set, a feasible method is designed to evaluate the generalization ability of the model. using the SMOTE-Tomek integrated sampling method to calculate k-nearest neighbor samples for each minority class sample, select the class samples whose neighbor similarity coefficients meet the requirements. Randomly generate new samples according to the adjacency relationship between the linearly interpolated sample and its neighbor samples. According to the original data training set, generate similar data samples and put them into the model trained with the original data for prediction evaluation. Finally, the classification accuracy rate of the generated samples is obtained, the precision rate is 0.98, and the recall rate is 0.99. The F1 value is 0.98, and the Roc_AUC value is 0.98, which proves that the evaluation model has good generalization ability.
Comparison of the Quantum and Conventional Algorithms: Evidence from Genetic Algorithm and Ant Colony Algorithm

Contemporarily, with the slow-down development speed of classical computing, quantum computing is becoming the focus of research as a replacing technique. It is a well-known approach that can offer exponential speed-up for a certain type of calculation issues based on the state-of-art optical facilities and techniques. In this paper, the development of quantum computing will be briefly introduced firstly. Subsequently, this paper will demonstrate the principle of different types of quantum algorithms as well as the realization scenarios. Afterward, the similarities, as well as differences between the conventional algorithms and the quantum algorithms in the genetic algorithm and ant colony algorithm, will be compared and analyzed. Based on the analysis, it is obvious that quantum algorithms are more powerful in solving specific problems compared with conventional algorithms, where the speed is much quicker than the traditional approaches. According to the results, it’s necessary to study the practical application of quantum algorithms. These results shed light on guiding further exploration of quantum algorithms.
CARSINOS DETECTION USING DEEP LEARNING IN FEMININE GONADS

A women's reproductive system would not be complete without her ovaries.These tiny glands, essential for reproduction, produce female intercourse hormones in addition to female gametes.They are almond-shaped organs outside ducts situated on either side of the womb.There are different aspects that can lead to ovarian malignancy, which is discovered by a distinct method.Consequently, we have convolutional neural networks.This is one of the most universal ways in which ovarian cancer is identified.In this project, convolutional neural networks will be used to classify ovarian malignancy tumors.Furthermore, we intend to equate the submitted approach with the two machine learning algorithms.
A Survey of Drones Using IOT Technology

A Drone is a new invention or era in last recent years which is very effective; Drones are commonly known as Unmanned Aircraft System (UAV) or Flying Robots. Previously Unmanned Aircraft System (UAV) was used by militaries all around the world for different kind of technologies like Intelligence, Surveillance and Reconnaissance Operations. The use of Drones has been using continuously in different and wide areas, like Cargo, Parcel Delivery, Internet provision in remote areas, Emergency Aid, Industrial Asset monitoring pipelines, surveillance in crowded areas, providing food in flooded area and many more. Drones are a microcosm of the Internet of Things(IOT), also it has been realized that Drones have potential to increase the business by using the Technology for their Organizations.
Fashion Designer Application Based on Generative Adversarial Network

Embodying the design from scratches has been a common problem for all designers. In some situation, clothes designers have trouble creating works that match their idea the most. Creating a fine work from a simple sketch is a tedious process of endless try-and-error. Designers spend days, weeks, or even months drawing drafts and discard them, because they need to draw the picture that best embodies the idea in their head. This study proposes a deep learning method that can generate clothes images from human sketch input, explores the effect of different image to sketch translators, different Generative Adversarial Network (GAN) architecture, loss functions and regularization methods. Many previous works have demonstrated strong results on conditioned image generation, and this study shows that similar settings can be transferred to help designers to embody their designs. The result shows that the model can generate clothes images with high fidelity while faithful to the input sketch.
On a Moving Target Selection Model in Virtual Reality Based on Decision Trees

Virtual reality (VR) systems have been used in various industries.Highly effective humancomputer interaction (HCI) and natural HCI experience have become the key indicators for evaluating a VR system, where target selection is the key for interaction efficiency and experience.In this paper, we propose a moving target selection prediction model, based on the probabilistic Fitts's Law and in combination with decision trees, for moving target selection in VR systems.Firstly, we verified the feasibility of predicting the user intention based on the size and distance of moving targets in VR scenarios through two selection task experiments with a sphere as the target.Then, also through two experiments, we proposed an improved moving target prediction model by factoring in head posture with target size and distance and taking into account the influence of head orientation.Based on the decision tree algorithm, we calculated its prediction accuracy and compared it with the distance scoring function.The results show that the improved prediction model has significantly better accuracy and can accurately predict the user's moving target selection intention in a VR system.
Workplace Ostracism and Contextual Performance: The Moderating Effects of Self-Efficacy

Organizational isolation is different from social isolation. The social isolation could be due to society or the indi vidual isolating Williams, (1997) from social activities. Similar to that the organizational isolation could be due to the factors which prevail in the work environment or it could be due to the individual working in that situation. The reasons and the influencing factors are really hard to determine as we could find individuals feeling that they are secluded and isolated and ignored in work and work situations and their challenges. It also indicates that there is a need for support and helps to be provided to these employees and if left unattended would lead to depression Fowler, Wareham-Fowler &amp; Barnes, (2013) and counterproductive behavior. The consequence of this WPO is that employee does not feel associated or related to the organization anymore as the Ferris, Brown, Berry &amp;Lian, (2008) sense of isolation and psychological depression seem to prevail more. This could also become a mass phenomenon as this sense of isolation and depression could spread to other employees as well which indicates the seriousness of the issue. The factors of culture, religion, and ethnic diversity, Nezlek et al (2012) cross-cultural workplace and dynamics could lead to ostracism in workplace situations. As these dimensions and issues have implications for organizational development, human psychology &amp; workplace performance it is an important dimension to be studied, evaluated, and assessed. The factors which lead to organizational ostracism have been evaluated and pro vided as abusive supervision, Wu, Wei &amp; Hui, (2011) voice behavior, and supervisory support. Organizational sup port is helpful in mitigating organizational stress and workplace ostracism Anjum, Ming, Siddiqi &amp; Rasool, (2018).
An evaluation environment for high-performance computing combining supercomputing and cloud

In recent computer environments that use a wide variety of data, users need a system that can efficiently process data according to the scale and type of data. There are two types of typical computing environments: supercomputers and cloud systems. Cloud services can provide computer resources based on the scale of the computer environment desired by users. On the other hand, in conventional large-scale computer environment that only consists of CPUs or GPUs, the processing time greatly increases according to the scale of the calculation processing. Based on the characteristics of the supercomputer and the cloud system installed in the Hokkaido University Information Initiative Center, Japan, we aim to construct a high-performance computing environment by linking the two types of systems. In this paper, we propose an evaluation environment for high-performance computing combining supercomputing and cloud systems. Our proposed system can construct a high-performance computing environment based on the scale of the computing process by the cooperation of the supercomputing and cloud systems with short physical distance and short network distance. In our evaluation of deep reinforcement learning using our proposed system, we confirmed that computer resources can be effectively used by allocating suitable processing for the supercomputer and the cloud according to the usage situations of the CPU, the GPU, and the memory.
Kemampuan Mahasiswa Menganalisis Struktur Retorika Debat pada Mata Kuliah Menyimak Komprehensif dan Kritis

This study aims to determine the ability of students to analyze the rhetorical structure of debate in Comprehensive and Critical Listening courses. The method used in this research is descriptive qualitative. The data source in this study is the result of student analysis of the 2019 presidential and vice-presidential debates. The results show that students' ability to analyze the rhetorical structure of the 2019 Presidential and Vice Presidential debates is categorized as good, with an average score of 71.18. In conclusion, students can explore the structure of the preliminary rhetoric, the content of arguments and findings from the 2019 Presidential and Vice Presidential Candidates debate. &#x0D; Keywords: Debate, Listening, Rhetoric Structure
Session 34 Overview: Cryo-CMOS for Quantum Computing

This session focuses on cryo-CMOS ICs for quantum computing.The first paper of the session describes a low-power 260GHz cryo-CMOS backscatter transceiver for communication between 300K to 4K.The following three papers describe cryo-CMOS quantum control ICs for superconducting quantum processors.Next, a cryo-CMOS VCO achieving a state-of-the-art FoM is described.
Terrain Referenced Navigation Technique Using Correlation Algorithms for Cruise Vehicle

The Cruise vehicle is guided by an Inertial Navigation Systems (INS) and Satellite Navigation (SatNav).Where INS provide very accurate and reliable solution for navigation, but gyroscope and accelerometers errors accumulate with time, and accordingly navigation errors also grow w.r.to time leading to very high Position drifts for long duration missions.While in SatNav performance is poor due to the signal interference and jamming.The solution to these problems leads to an introduction of another device to perform precise measurements of the vehicle's geographical position along its flight path.TERCOM is a well approved navigation technique which deals with terrain elevation.TERCOM compares the measured elevation value and on-board stored elevation value using correlation algorithm.A TERCOM system is a self-contained and provides positional fixes to cruise vehicle, aircraft, and drones.TERCOM is the only fixtaking system that can operate autonomously in a RF/ SatNav denied scenarios.TERCOM can be used in both tactical and strategic systems and operates under ECM (electronic countermeasures) conditions and provides day/night, all weather capability in low altitude terrain following scenarios.This paper presents correlation algorithm and its performance evaluation in the Western Ghats of India.