Reviewerly/exHarmony huggingface dataset:
Factors Related to Physical Activity in Adolescents Attending Schools

Introduction. Currently, there has been a significant decrease in physical activity in adolescents, which increases the risk of diseases with high morbidity and mortality. Therefore, this study seeks to understand the risk and protective factors related to physical activity. Objective. To determine the factors related to physical activity in adolescents between 13 and 17 years old attending educational centers in Santiago, Dominican Republic Methods. A descriptive, cross-sectional, correlational study of primary source was used to examine adolescents between 13 and 17 years old. A sample of 640 students was obtained by means of a sampling by conglomerate, where the schools were chosen first and after that, 9th to 12th grades were selected. Lastly, 10 students were picked in each classroom, in a three-month period between September and November 2017. Results. This research found that 24.6% of adolescents do not perform any physical activity on any day of the week and 16.9% do so according to World Health Organization recommendations. A statistically significant relationship was observed between sex (p = 0.000), grade (p = 0.002), school type (p = 0.000) and place of residence (p = 0.000). It was determined that males perform more physical activity (27.1%) than females. Students of 11th grade exercised more than the rest. Students from private schools have a higher percentage of physical activity than students from public schools. On the other hand, 28.2% did not perform more than 1 hour of sedentary activities and only 7.3% performed more than 8 hours a day. The sedentary behavior had a statistical significance for school level (p = 0.000), type of school (p = 0.000), and area of residence (p = 0.038). It was found that students in their second year of high school had a higher percentage for more than 8 hours of sedentary activities. Public schools and urban areas have less sedentary behavior. This study found that 50% of the adolescents who performed 7 days of physical activity have a best friend who performs physical activity. Conclusions. Physical activity among adolescents in Santiago is low. It was seen that physical activity and sedentary behavior have a significant relationship but are not opposite variables. It is recommended to perform more in-depth investigations and to do more observational studies to accurately measure physical activity of adolescents in schools. Key Words. Adolescents, Sedentary Lifestyle, Exercise, Physical Education.
Bridge the gap between full-reference and no-reference: A totally full-reference induced blind image quality assessment via deep neural networks

Blind image quality assessment (BIQA) is of fundamental importance in low-level computer vision community. Increasing interest has been drawn in exploiting deep neural networks for BIQA. Despite of the notable success achieved, there is a broad consensus that training deep convolutional neural networks (DCNN) heavily relies on massive annotated data. Unfortunately, BIQA is typically a small sample problem, resulting the generalization ability of BIQA severely restricted. In order to improve the accuracy and generalization ability of BIQA metrics, this work proposed a totally opinion-unaware BIQA in which no subjective annotations are involved in the training stage. Multiple full-reference image quality assessment (FR-IQA) metrics are employed to label the distorted image as a substitution of subjective quality annotation. A deep neural network (DNN) is trained to blindly predict the multiple FR-IQA score in absence of corresponding pristine image. In the end, a self-supervised FR-IQA score aggregator implemented by adversarial auto-encoder pools the predictions of multiple FR-IQA scores into the final quality predicting score. Even though none of subjective scores are involved in the training stage, experimental results indicate that our proposed full reference induced BIQA framework is as competitive as state-of-the-art BIQA metrics.
Border Effects and Its Principle Analysis

The resolution of measurement, the stability of resolution, the fuzzy area of measurement, the border characteristics of the fuzzy area, the concentrated and discrete fuzzy areas, the stepped multifuzzy area in digital measurement and its border characteristics, and the factors affecting the border effect of the stepped multifuzzy area are introduced. It is pointed out that the stability of the border resolution of the fuzzy region is a decisive factor affecting the actual measurement precision, called the border effect. The phase change law is analyzed in detail, the measurement precision can be improved by using the principle of the border effect, and the application of the border effect is outlined. These analyses are not limited to a specific physical quantity, but are of general applicability to the measurement of different physical quantities, and thus contribute to the overall technological progress in the field of measurement technology.
Peningkatan Hasil Belajar Menggunakan Model Talking Stick pada Pembelajaran Tematik Terpadu di Sekolah Dasar

The purpose of this researchais to describe theaplanning, implementation and improvementclearning outcomes of students using a Talking Stick model in integrated thematic learning in grade IV Elementary School. This research uses PTK research with qualitative and quantitative approaches. This research was conducted at SDN 12 Bukit Cangang, consists of 29 students. This study consisted of 2 cycles, namely cycle 1 consisted of 2 meetings, and cycle 2 consisted of 1 meeting. The results of this study indicate an increase in student learning outcomes, namely in the first cycle with an average value of 75 (B) and the second cycle with an average of 84. Based on this it can be said that the Talking Stick model can improve student learning outcomes.
Binary Logistics Regression To Predict The Opportunity Of SNMPTN Graduation In Statistics Study Program Of Tanjungpura University

The National Selection of State University Entrance or Seleksi Nasional Masuk Perguruan Tinggi Negeri (SNMPTN) is one of the selections for high school students seeking higher education. The Statistics Study Program as one of the study programs at Tanjungpura University has a capacity of 20 seats in the SNMPTN. This limited capacity causes prospective students to prepare the right strategy in order to be accepted through the SNMPTN. In this study, logistics regression was used to predict the probability of graduation status on the SNMPTN path in the Statistics Study Program of Untan. Binary logistic regression is a statistical analysis technique for representing the relationship between a response variable with two (binary) categories and one or more predictor variables on a continuous or categorical scale. Data for this study were primary data from a questionnaire that received 93 samples. The response variable used is graduation status (Y) through the SNMPTN in Statistics Study Program, Tanjungpura University classified as 1 (passed) and 0 (not passed). Based on the results of the study, it is known that the variables that have a significant effect on graduation status are the status of choice in Statistics Study Program (X1), national level achievement ownership (X3), the average value of Mathematics (X4), the average value of Chemistry (X6), Biology average score (X7), Indonesian average score (X8), and English average score (X9). Meanwhile, provincial level achievement (X2) and Physics average (X5) did not have a significant effect on graduation status. The binary logistic regression model obtained has an accuracy error of 15,05% with an accuracy rate of 84,95%, meaning that this model has a good criteria.
Evaluation of Implantation Strategies for Integrating Drama Elements into Music Teaching from the Perspective of Educational Ecology

With the continuous expansion of music education, the theory about education ecology has a close relationship with the development of music education because of its novel and scientific viewpoints on nature, environment, ecology, etc.Therefore, many scholars regard educational ecological theory as an important research method.Judging from the current development of music teaching, there are many problems in the current music teaching, such as lack of culture, the disconnect between music education and real life, and the imbalance of music teaching methods.Based on this, from the perspective of education ecology, this paper proposed the rational application of dramatic elements to the music teaching model.The teaching quality was evaluated by data mining method to analyze the effectiveness of the method.At the same time, it can promote the promotion of drama culture, which is conducive to strengthening the inheritance of school drama culture and improving the school's sense of responsibility for the inheritance of drama culture.The experimental results showed that after the music teaching integrates the drama element, the music scores of the students were all above 85 points; the average score of the grade with the least improvement in music increased by 4.1 points, and the average score of the grade with the largest improvement increased by 10.4 points.It can be seen that the music teaching method with dramatic elements can significantly improve the students' music performance.
Structure-enhanced pairwise feature learning for face clustering

Abstract Face clustering groups massive unlabeled face images according to their underlying identities and has proven to be a valuable tool for data analysis. Most recent studies have utilized graph convolutional networks (GCNs) to explore the structural properties of faces, thereby effectively achieving improved clustering performance. However, these methods usually suffer from computational intractability for large-scale graphs and tend to be sensitive to some postprocessing thresholds that serve to purify the clustering results. To address these issues, in this paper, we consider each pairwise relationship between two samples as a learning unit and infer clustering assignments by evaluating a group of pairwise connections. Specifically, we propose a novel clustering framework, named structure-enhanced pairwise feature learning (SEPFL), which mixes neighborhood information to adaptively produce pairwise representations for cluster identification. In addition, we design a combined density strategy to select representative pairs, thus ensuring training effectiveness and inference efficiency. The extensive experimental results show that SEPFL achieves better performance than other advanced face clustering techniques.
PBCStereo: A Compressed Stereo Network with Pure Binary Convolutional Operations

Although end-to-end stereo matching networks achieve great performance for disparity estimation, most of them require far too many floating-point operations to deploying on resource-constrained devices. To solve this problem, we propose PBCStereo, the first lightweight stereo network using pure binarized convolutional operations. The degradation of feature diversity, which is aggravated by binary deconvolution, is alleviated via our novel upsampling module (IBC). Furthermore, we propose an effective coding method, named BIL, for the insufficient binarization of the input layer. Based on IBC modules and BIL coding, all convolutional operations become binary in our stereo matching pipeline. PBCStereo gets $$39\times $$ saving in OPs while achieving comparable accuracy on SceneFlow and KITTI datasets.
Analisis Faktor Penyebab Rendahnya Hasil Belajar Mata Pelajaran PAI-BP Kelas Tinggi di SD Negeri 166/III Cut Mutia

This study has the aim of specifically describing what factors influence the low learning outcomes of high-grade PAI-BP at SD Negeri 166/III Cut Mutia Kerinci Jambi. This research is a quantitative research the usage of descriptive approach, with a population of 50 people. The number of samples is 50 students with a population sampling technique. Analysis of the data used, descriptive analysis, prerequisite test analysis, factor analysis and hypothesis testing. The results of the factor analysis showed that there were 3 factors that influenced the low learning outcomes, namely factor 1 (variables of motivation and learning methods), factor 2 (variables of physical health conditions, building conditions and parental upbringing). And factor 3 (interest variable, method and media). The results of the F test were 0.002 &lt;0.05, and F count 5.570 &gt; F table 2.81. So, together the independent variables (X1, X2, X3) have a effect on the dependent variable (Y). The t test on XI gets the result t count 3.969 &gt; t table which is 2.012, a significance of 0.000 &lt;0.05, So variable X1 has a significant effect on variable Y. The t test on X2 gets the result t count 2.129 &gt; t table 2.012 significance 0.039 &lt;0.05 , Then the X2 variable has a significant effect on the Y variable. The t test on X3 gets the result t count 1.566 &lt; t table, namely 2.012 and a significance of 0.124 &gt; 0.05, So the X3 variable has no effect on the Y variable. 26.6% low learning outcomes PAI-BP High grade at SD Negeri 166/III Cut Mutia is influenced by variables X1, X2, X3. And the remaining 73.4% is encouraged by using other variables.
Practical Research on Large-Scale Examinations for Undergraduate Online Teaching

In early 2020, the sudden outbreak of COVID-19 completely changed the teaching form of higher education in China. Under such circumstances, online teaching and examinations have become new teaching methods. By studying the existing literature on online tests and analyzing the characteristics of the online assessment, this paper introduced the large-scale online final examination of the Central University of Finance and Economics from the aspects of regulations, organization, and quality control. Finally, relevant suggestions for improving the quality of online examinations were put forward.
HSDVS-DPoS: A Secure and Heterogeneous DPoS Consensus Protocol Using Heterogeneous Strong Designated Verifier Signature

With the rapid development of blockchain, in which the nodes may use different cryptographic mechanism to achieve the secure consensus, research on a secure and heterogeneous consensus protocol is becoming a hot issue. Based on the idea of electronic voting, coercion-resistance and receipt-freeness are introduced for consensus protocol to prevent vote buying and candidate cheating. And then, a secure and heterogeneous DPoS consensus protocol using heterogeneous designated verifier signature is presented, which has authentication, secrecy, anonymity, fairness, coercion-resistance, verifiability and receipt-freeness. After that, the proposed DPoS consensus protocol is implemented on eclipse platform using the Java language. The experimental results show that the throughput and the time of producing blocks stay stable over 15–5000 of the number of nodes.
Hadoop Dataset for Job Estimation in the Cloud with Limited Bandwidth

Hadoop MapReduce is a well-known open source framework for processing a large amount of data in a cluster of machines; it has been adopted by many organizations and deployed on-premise and on the cloud. MapReduce job execution time estimation and prediction are crucial for efficient scheduling, resource management, better energy consumption, and cost saving. In this paper, we present our new dataset of MapReduce job traces in a cloud environment with limited network bandwidth; we describe the process of generating and collecting the dataset in this paper. We believe that this dataset will help researchers develop new scheduling approaches and improve Hadoop MapReduce job performance.
Assessing Imbalanced Datasets in Binary Classifiers

Despite continuous improvements in learning from imbalanced datasets, it remains a challenging research problem in machine learning. Classification models exhibit biases toward reducing the error rate on the majority samples by neglecting the minority samples. This paper aims to determine the impact of varying degrees of imbalances on a few selected classification models. Using comparative analysis of classification models on six binary imbalanced datasets with varying degrees of imbalances, we empirically analyze the effect of the degree of imbalancing on four classification models, namely decision tree (DT), random forest (RF), multilayer perceptron (MLP), and support vector machine (SVM). We show that imbalance distribution affects the performance of classification models, and the relation between the imbalance ratio and accuracy rate is convex.
Effects of Library Workshop Attendance and Library Website Visit Frequency on Health Professions Students’ LibGuides Awareness

This research article examines data from an in-person 2017 survey on LibGuides usage, perceptions, and awareness of health professions students seeking bachelor and graduate-level degrees. Almost 45% (n = 20, N = 45) of participants who visited the library's website at least once per week indicated awareness of library-created LibGuides. Nearly 90% (n = 8, N = 9) of health professions students who had not visited the library's website were unaware of the guides. The statistical analysis shows significant associations between various variables (academic level, library workshop attendance, research guide type usage, research guide page usage) and library guide awareness. The data did not reveal any significant relationships between other variables (undergraduate class level, field of study, and library website visit frequency) and guide awareness. The authors discuss implications for health sciences libraries and suggestions for future research.
A Privacy-preserving Image Retrieval Scheme in Edge Computing Environment

Traditional cloud computing faces some challenges such as huge energy consumption, network delay and single point of failure.Edge computing is a typical distributed processing platform which includes multiple edge servers closer to the users, thus is more robust and can provide real-time computing services.Although outsourcing data to edge servers can bring great convenience, it also brings serious security threats.In order to provide image retrieval while ensuring users' data privacy, a privacy preserving image retrieval scheme in edge environment is proposed.Considering the distributed characteristics of edge computing environment and the requirement for lightweight computing, we present a privacy-preserving image retrieval scheme in edge computing environment, which two or more "honest but curious" servers retrieve the image quickly and accurately without divulging the image content.Compared with other traditional schemes, the scheme consumes less computing resources and has higher computing efficiency, which is more suitable for resource-constrained edge computing environment.Experimental results show the algorithm has high security, retrieval accuracy and efficiency.
PEACH: Proactive and Environment-Aware Channel State Information Prediction with Depth Images

Up-to-date and accurate prediction of Channel State Information (CSI) is of paramount importance in Ultra-Reliable Low-Latency Communications (URLLC), specifically in dynamic environments where unpredictable mobility is inherent. CSI can be meticulously tracked by means of frequent pilot transmissions, which on the downside lead to an increase in metadata (overhead signaling) and latency, which are both detrimental for URLLC. To overcome these issues, in this paper, we take a fundamentally different approach and propose PEACH, a machine learning system which utilizes environmental information with depth images to predict CSI amplitude in beyond 5G systems, without requiring metadata radio resources, such as pilot overheads or any feedback mechanism. PEACH exploits depth images by employing a convolutional neural network to predict the current and the next 100 ms CSI amplitudes. The proposed system is experimentally validated with extensive measurements conducted in an indoor environment, involving two static receivers and two transmitters, one of which is placed on top of a mobile robot. We prove that environmental information can be instrumental towards proactive CSI amplitude acquisition of both static and mobile users on base stations, while providing an almost similar performance as pilot-based methods, and completely avoiding the dependency on feedback and pilot transmission for both downlink and uplink CSI information. Furthermore, compared to demodulation reference signal based traditional pilot estimation in ideal conditions without interference, our experimental results show that PEACH yields the same performance in terms of average bit error rate when channel conditions are poor (using low order modulation), while not being much worse when using higher modulation orders, like 16-QAM or 64-QAM. More importantly, in the realistic cases with interference taken into account, our experiments demonstrate considerable improvements introduced by PEACH in terms of normalized mean square error of CSI amplitude estimation, up to 6 dB, when compared to traditional approaches.
Demand Survey Method for Commercialization of Police Science Technology and Equipment

This study is a demand research for the selection of public safety science and technology equipment and suggests an empirical research method.The technology demand survey is the beginning of the selection of innovative technology.And it is the basis of collecting information required for the technology required in the market and helping to apply it to the field.The demand survey for police science and technology can reduce the uncertainty of crime prevention and help the smooth implementation of security policies.However, in Korea, adoption of security science and technology equipment was centered on social issues or researchers' opinions rather than the demands of field users.Until, there was no research has been conducted on the demands of field police officers for selection of security science and technology equipment in Korea.Also, there was no preferential study for the demand for security science and technology equipment.Therefore, this study proposes a methodology that can systematically identify the needs for the technology and equipment of field experts suitable for the public security situation for the selection of security science and technology equipment.Specifically, we propose a sample design for a technology classification system and a survey tool for technology awareness and satisfaction.It is expected that this tool will provide a classification system for security science and technology equipment selected for the Korean police and will help determine the priority of equipment suitable for the field.