Reviewerly/exHarmony huggingface dataset:
sEMG-based hand posture recognition and visual-feedback training

Purpose sEMG-based recognition system provides the intuitive and accurate classification of various gestures and useful for human-computer interaction.Especially, recognition of hand postures is often suggested as the interaction protocol of rehabilitation systems or robotic orthosis for the elderly and disabled [1] .However, the variability of the sEMG signal was caused by the individual differences and the change of the force or proficiency.In this study, the hand posture recognition algorithm was developed using sEMG sensors, and the visual-feedback training was performed to improve the classification performance.Method Four healthy adults and a forearm amputee participated in this study to perform twelve hand postures (Rest, Hook, Spread, Scissor-sign, Finger pointing, V-sign, O.K.-sign, Cylindrical grasp, Spherical grasp, Lateral pinch, Palmar pinch, and Tip pinch) [2] .Commercial sEMG sensors (Delsys Trigno; 1926 Hz) were placed on the nine forearm muscles of the healthy adults (FDS, ED, EDM, EP, FCR, FCU, ECR, ECU, BRD) and the eight forearm muscles of an amputee (based on 3D reconstruction data from MRI; BRD, FCR, ECR, ED, ECU, FDP, FDS, FCU) (Figure 1).A LabVIEW-based radar plot was used for hand posture training (1 day for the healthy adults and 5 days for the amputee) to minimize the variability of the sEMG signal.In the session for recording the sEMG signal, all participants repeated the hand postures 10 times with 20% MVC [3] .The 10-fold cross validation was performed on the ANN classifiers with the feature vector of MAV or the Hudgins' set (MAV, WL, ZC, and SSC) [4] on MATLAB.Results and Discussion The classification accuracy was improved by increasing the number of training data in both trained and untrained sessions (Table 1).ANN classifiers of the healthy adults with MAV showed the classification accuracies of 84.3% and 85.8% in untrained session and trained session respectively.The classification performance was improved as 88.4%(untrained) and 91.6%(trained) by the Hudgins' set.In untrained session of the forearm amputee, poor classification accuracies appeared in both MAV(31.7%)and the Hudgins' set(33.0%).After the 5 days training, MAV and the Hudgins' set showed the classification accuracy of 76.3% and 84.7% respectively.In this study, the Hudgins' set and the posture training were useful to improve the classification accuracy.Furthermore, the great improvement of the classification performance appeared in the forearm amputee with an appropriate posture training (day 5).These results supported that the posture training with visual feedback had an effect to minimize the variability of sEMG signals.
Penerapan System Application and Product (SAP) pada Administrasi Kepegawaian (Penerima Santunan Hari Tua (SHT)) di PT. Perkebunan Nusantara II Kebun Bandar Klippa

The rapid development of information technology can now be used as a facility to facilitate all processes of managing a company's information data. With the information technology can facilitate the company in doing all its daily activities. Since January 2019 PT. Perkebunan Nusantara II has utilized this information technology, known as the SAP (System Application and Product) software, this module or application can integrate all functions of the company's sub divisions end to end, especially in the personnel administration section. The majority of phenomena directly observed at the PTPN II Kebun Bandar Klippa Office is the provision of SHT (Old Age Benefits) for employees who have entered retirement age. This research method uses descriptive qualitative analysis method, namely by using observation instruments or direct observations in the field and also documentation instruments. The research subjects in this study were 2 employees of the HR department, and the object of research in this study was the application of the SAP application itself, especially in the field of employee administration for SHT recipients. With the SAP application, it can make it easier for HR employees to input data properly and regularly and can access data quickly.&#x0D; Keywoard: SAP (System Application and Product), officialdom administration, SHT
Validation of post-COVID-19 pandemic teaching in higher education through the SEEQ questionnaire

The COVID-19 pandemic has changed the teaching-learning process at all educational levels, just as it has changed the lives of people around the world.Introduction: This research has been carried out with the students of the Information Security subject that is taught in two University Degrees of the University of Extremadura, analyzing the data collected from the 2012-13 academic year to the current 2021-22 academic year.Objectives: To analyze if there has been a significant change in the teaching-learning process from the COVID-19 pandemic in higher education in the field of computer and telematics engineering using the validated SEEQ questionnaire.Methods: The statistical analysis has been carried out with the SPSS program of the results of the application of the SEEQ questionnaire.To do this, all the data has been divided into two large groups, one called pre-pandemic and the other postpandemic, which correspond to the 2019-20 academic year as the last year of the prepandemic group and the two subsequent ones as the post-pandemic group.pandemic.Results: the statistical analysis of the data leads us to have to use non-parametric tests since the normality requirements of the Kolmogorov-Smirnov and Shapiro-Wilks tests are not met.Consequently, we have applied the Mann-Whitney U, where in all cases the result is to preserve the null hypothesis, which indicates that the changes are not significant. Conclusions:The general conclusion of this study determines that although the mean of all the categories of the SEEEQ questionnaire continues to increase, this change is not statistically significant.Likewise, it is very important to highlight that the average of all the categories is above half on the Likert scale, moreover, the vast majority is around 4 out of 5 points, a result that is very positive.
AN ACTOR’S SPEECH TOOLS IN THE 21ST CENTURY

In the 21st century, it is hard to imagine actors’ speech tools without technical aids. Society’s rapid adoption of life on the Internet furnished by modern technolo- gies is creating a fast progressing explosion of knowledge. These new standards un- doubtedly influence and determine the development of theatrical language, and thus actors’ speech.
Digital interference signal filtering on laser interface for optical fiber communication

INTRODUCTION: Fiber laser communication is a communication method that uses laser and fiber medium to realize data transmission and information output OBJECTIVES: In order to reduce the signal interference of optical fiber communication laser interface and ensure the communication quality of optical fiber network. A filtering method of optical fiber communication laser interface interference signal based on digital filtering technology is designed. METHODS: In this paper, the interface model of optical fiber communication network is firstly constructed, and the interface noise signal is input into the digital filter bank. The digital quadrature filtering method and the least square algorithm are used to separate the denoised signals to reduce the crosstalk between the signals in the channel. In this way, the crosstalk component in the signal can be filtered out, and a better filtering processing effect of the laser interface interference signal can be achieved. RESULTS: The results of peak signal-to-noise ratio are above 25, which effectively filters the interference signal in the signal, and retains the effective signal completely. The intelligibility of optical fiber communication network in signal communication is above 0.94, and the highest value is 0.986. The distortion degree are all below 0.025, and the minimum value is 0.004. The communication bit error rate are all below 0.001, which ensures the communication quality of the network. CONCLUSION: The experimental results show that the signal noise reduction effect of the proposed method is good, which provides a reliable basis for filtering and separating interference signals of optical fiber communication laser interface.
Social constructions of science in students of higher education

What is science for students of a professional career?This simple phrase entails a great deal of study in details, similarities, differences between the various scientific disciplines, gender, semester, particular context, etc.We start from this questioning to delve into the social representations of science in 297 students belonging to 6 centers of the Network of the University of Guadalajara, Mexico.For this, a free association exercise was used, whose analysis was done through the qualitative thematic content analysis technique.Among the findings, it stands out that the vast majority of students, regardless of their affiliation center, see science as that set of knowledge systematized and verified through experimentation; that is, there is a homogeneous social representation of science among young students, which is not surprising, since previous studies have reached the same conclusion.This gives rise to reaffirm that an education in science is necessary, there is not a single way of understanding and doing science, because the contributions of the different scientific disciplines are lost, leading to inaccurate conceptions.
Recommendations of Tourism Destinations Using Genetic Algorithm: A Case Study of Cirebon City

A genetic Algorithm is a search algorithm inspired by natural selection. Genetic algorithms are used in solving optimization problems. Determination of the shortest route in the recommendation of tourism objects has problems in optimization. This study aims to obtain the shortest route from several tourist attractions in Cirebon City, West Java, Indonesia, using a Genetic Algorithm to get the best and optimal route. The search for the shortest route can be completed using a genetic algorithm. The first step is to present the location of the tourist attraction in a sequence code, then form the initial population and then continue in the selection, crossover, and mutation stages until the best route is formed. From the results obtained, the route has the best fitness value of 268 and the best route from 10 attractions, starting with Sunyaragi Cave Park - Kasepuhan Palace - Sang Cipta Rasa Great Mosque - Kanoman Palace - Kacirebonan Palace - Panjunan Red Mosque - At-Taqwa Mosque - Gunung Jati Tomb - Trusmi Batik – Gronggong Hill.
Solving Subset Sum Problems using Quantum Inspired Optimization Algorithms with Applications in Auditing and Financial Data Analysis

Many applications in automated auditing and the analysis and consistency check of financial documents can be formulated in part as the subset sum problem: Given a set of numbers and a target sum, find the subset of numbers that sums up to the target. The problem is NP-hard and classical solving algorithms are therefore not practical to use in many real applications. We tackle the problem as a QUBO (quadratic unconstrained binary optimization) problem and show how gradient descent on Hopfield Networks reliably finds solutions for both artificial and real data. We outline how this algorithm can be applied by adiabatic quantum computers (quantum annealers) and specialized hardware (field programmable gate arrays) for digital annealing and run experiments on quantum annealing hardware.
A Descriptive Research on Learning Difficulties Encountered by PPAPK Students of The English Language Program

This study aims to find out the difficulties in learning PPAPK 2017/2018 of English students. The analysis focused on the analysis of difficulties based on interviews and questionnaires. The researcher used the descriptive quantitative technique to evaluate ordinal data to determine the percentage of students who had learning difficulties. There are 30 participants in this research. The results showed that learning difficulties received a high percentage. The questionnaire fields included reading, spelling and writing, math and logic, emotion and self-control, listening, and attention. The researcher got the data from the questionnaire that had been distributed. One of the most prominent suggestions made in this study is to focus on the need to diversify teaching methods, reduce the teaching burden for teachers from lessons, and open channels of communication between family and university to follow up on students'' affairs
A Vision-Based Bio-Inspired Reinforcement Learning Algorithms for Manipulator Obstacle Avoidance

Path planning for robotic manipulators has proven to be a challenging issue in industrial applications. Despite providing precise waypoints, the traditional path planning algorithm requires a predefined map and is ineffective in complex, unknown environments. Reinforcement learning techniques can be used in cases where there is a no environmental map. For vision-based path planning and obstacle avoidance in assembly line operations, this study introduces various Reinforcement Learning (RL) algorithms based on discrete state-action space, such as Q-Learning, Deep Q Network (DQN), State-Action-Reward- State-Action (SARSA), and Double Deep Q Network (DDQN). By positioning the camera in an eye-to-hand position, this work used color-based segmentation to identify the locations of obstacles, start, and goal points. The homogeneous transformation technique was used to further convert the pixel values into robot coordinates. Furthermore, by adjusting the number of episodes, steps per episode, learning rate, and discount factor, a performance study of several RL algorithms was carried out. To further tune the training hyperparameters, genetic algorithms (GA) and particle swarm optimization (PSO) were employed. The length of the path travelled, the average reward, the average number of steps, and the time required to reach the objective point were all measured and compared for each of the test cases. Finally, the suggested methodology was evaluated using a live camera that recorded the robot workspace in real-time. The ideal path was then drawn using a TAL BRABO 5 DOF manipulator. It was concluded that waypoints obtained via Double DQN showed an improved performance and were able to avoid the obstacles and reach the goal point smoothly and efficiently.
Production Industry Supply Chain Management Based On the Ethereum Blockchain

Currently, the composition and structure of the production industry's supply chain is becoming increasingly complex. The loss and untimely transmission of supply chain information exacerbated the bullwhip effect. At the same time, due to the lack of a reliable repository of information, difficulties in traceability and accountability have also made supply chain management difficult. Blockchain has the characteristics of supporting distributed networks, synchronization of information between nodes, digital encryption, traceable information and unforgeable block content, which is suitable for use in supply chain and can provide a solution for it. In this paper, a design scheme of an integrated platform for information services provided by supply chain participants and based on the Ethereum blockchain is proposed. Using Ethereum smart contracts, the regular trade involved in the supply chain is realized using blockchain technology, and key information about the production and circulation of the supply chain is stored on the blockchain to ensure that the information cannot be falsified. At the same time, a reputation evaluation method based on smart contracts is used to evaluate the reputation of enterprises in the supply chain, which can provide references for supplier selection among enterprises.
Model of Change in People’s Movement Due to Closed Environment During the Covid 19 Pandemic

Abstract Since the occurrence of the Corona-19 virus in 2020, the Ministry of Transportation has issued various policies, including PM No. 18 of 2020, PM No. 25 of 2020, and SE No. 4 of 2020. These policies have certainly had an impact on human movement. The purpose of this study was to see the magnitude of changes in community movement due to a closed environment during the Covid-19 pandemic and the model of community movement during the COVID-19 pandemic. To get a model of community movement that is appropriate by looking at the aspect of the relationship between community movements in DKI Jakarta and the spread of covid as the independent variable in building the model. Data sources for the decline in movement come from related agencies such as the Covid-19 Task Force (https://covid19.go.id/), and Google Mobility Report (GMR). The decrease in community movement due to the COVID-19 pandemic for retail and recreational activities decreased by 37%, grocery and pharmacy decreased by 13%, parking decreased by 48%, transit stations decreased by 61%, workplace decreased by 73%, and residential increased by 22 %. The results of the cubic polynomial modeling between the spread of Corona-19 Virus cases are in line with the independent variable time increment with the model results, namely Y = 5749.095 - 217.504 T + 2,403 T2 + 0.001 T3, with the dependent variable - Y, which is a cumulative case of Corona-19 Virus while the independent variable - X, is the time of the spread of Corona-19 Virus. This means that the number of people who are confirmed positive for the corona-19 virus will increase along with the cubic polynomial from the time after being infected following the Y = 5749.095 - 217.504·T + 2,403 T·2 + 0.001T·3 model.
Spatial Relationship Extraction of Geographic Entities Based on BERT Model

Geographic entity relationship extraction from text is an important way to acquire geographic knowledge. Entity relations in Chinese text are difficult to discover because of implicit representations of relations between entities in Chinese text. Therefore, using existing pattern matching and machine learning methods to extract entity relations from Chinese text often has problems such as insufficient artificial features, poor generality, inability to resolve word polysemy, and difficulty in making full use of contextual information. However, deep learning methods can better solve the above problems. This paper takes the spatial relation between geographic entities as the main research object to build a geographic entity spatial relation corpus, and propose a geographic entity relationship extraction method that combines BERT and attention mechanism. The method is trained and tested on the corpus. The results show that the F1 value of the BERT-BiGRU-Attention model proposed in this paper reaches 85.2% on the test set, which is a great improvement in relation extraction ability compared with other baseline models. The geographic entity relationship extraction method based on the BERT model can effectively learn the context information in Chinese text sentences, improve the accuracy of relationship extraction, and provide support for geographic knowledge graph construction and geographic entity information retrieval.
CMSFL-Net: Consecutive Multi-scale Feature Learning-based Image Classification Model

Abstract Extracting useful features at multiple scales is one of the most crucial tasks in computer vision. Emergence of deep learning(DL) techniques and progress of convolutional neural networks (CNNs) allow powerful multi-scale feature extraction that result in stable performance increase in numerous real-life applications. However, currently available state-of-the-art powerful methods primarily rely on parallel multi-scale feature extraction approach and despite providing competitive accuracy, the models lead to poor results in efficient computation and lack at generalizing on small-scale images. On the other hand, efficient and lightweight networks cannot learn useful features properly and result in underfitting problem in case of training with small-scale images or datasets with limited number of samples. Considering these problems, we proposed a novel image classification system based on elaborate data pre-processing step and carefully designed CNN model architecture. Specifically, consecutive multiscale feature learning network (CMSFL-Net) employs consecutive feature learning approach based on usage of various feature maps with different receptive fields to obtain faster training/inference and higher accuracy. In the conducted experiments using 6 real-life image classification datasets, including small-scale, large-scale, and limited data, the proposed system obtained comparable accuracy when compared with the existing state-of-the-art powerful and efficient networks by outperforming them in terms of efficiency and speed. Moreover, the proposed system achieved the best results in accuracy-efficiency tradeoff in comparison with the currently available models.
How Blockchain Network Factors and Market Forces Determine Bitcoin Returns

The creation of distributed ledgers technologies for information storage spiral secured peer-to-peer interactions which allow for the invention of Bitcoin. Since its invention the price of bitcoin has exhibited excessive volatility, which have attracted undulated attentions. The study considers the isolated influence of network activities, mining (technology) and market information as fundamentals drivers of bitcoin prices. A long-term equilibrium and short-term dynamic is confirmed amongst endogenous system variables in the VECM. This supposes that any deviation from the equilibrium dynamics due to perturbations of market forces (total bitcoin supply and trade volume on exchanges), mining Information (network difficulty, Hashrate, and total transaction fees and network activity (confirmed payments and users adoptions) would be minimized. The model indicates that the cointegration relationship has a reverse adjustment effect on bitcoin return. This explains the why the price, and by implication the return experience different massive run-up, spiky protrusions, resistance, reversals, strong supports and consolidations in the short