Reviewerly/exHarmony huggingface dataset:
The Effects of Lifestyle on Physical Activity Promotion System and Physical Satisfaction Since the Onset of COVID-19: Data from Middle School Students

This study aimed to find out the relationship between health-related physical fitness and physical satisfaction according to the lifestyle of middle school students in the COVID-19 situation. To this end, PAPS was measured for students attending S Middle School at G City in Gyeongsangnam-do, and lifestyle and physical satisfaction surveys were conducted. It was conducted on a total of 292 people, and data of 265 people were analyzed after excluding unwritten or unfaithful responses. For the collected data, exploratory factor analysis and reliability analysis were conducted using the SPSS(ver. 23.0) statistical program, and correlation analysis and regression analysis were conducted to verify the research hypothesis. Main results were as follows: 1) Exercise habits were associated with higher power, muscular strength, and cardiopulmonary endurance (p.05), while daily habits and dietary habits had no significant effect on power, muscular strength, and cardiopulmonary endurance. 2) Exercise habits were associated with lower body image dissatisfaction (p.05), and dietary habits were associated with lower obesity and body image dissatisfaction (p.05). However, daily habits had no significant effect on obesity, hyperactivity, or body image dissatisfaction. Taken together, these findings suggest that students with positive lifestyles generally have higher levels of health-related fitness and body satisfaction.
Evaluation of Advertising Images with Laptop Camera Eye Tracking

Object: The goal of this article is to evaluate the quality of the eye tracking method for studying the perception of advertising images using a laptop camera.&#x0D; Methods: The method of scientific experiment was chosen as the main method of research, with hypotheses, their verification, confirmation or refutation. For processing the results, the calculation of the mean value of the percentage of fixation and t-test were used to determine the statistical significance of differences in the results.&#x0D; Findings: Options were developed to improve the eye tracking method: recommendations to improve the reliability of the results, and factors influencing the respondent's actions were identified. The described methods for improving the quality of images are of a qualitative nature. The main criteria for obtaining the most reliable result when working with a focus group are determined. The necessity of using artificial intelligence to combine eye-catching areas of drawings with a heat map is revealed. Recommendations for teaching the use of the research method under consideration are given.&#x0D; Conclusions: An eye tracking tool using a laptop camera was evaluated. Possible reasons for the distortion of the results were identified. The ways of further improvement of the tool, methods of its use and processing of the obtained results were proposed. Due to the limited sample size, the reasoning is qualitative. Possible directions for further work are identified.
Modification of Semi-supervised Algorithm Based on Gaussian Random Fields and Harmonic Functions

In this paper we propose an improvement for a semi-supervised learning algorithm based on Gaussian random fields and harmonic functions. Semi-supervised learning based on Gaussian random fields and harmonic functions is a graph-based semi-supervised learning method that uses data point similarity to connect unlabeled data points with labeled data points, thus achieving label propagation. The proposed improvement concerns the way of determining similarity between two points by using a hybrid RBF-kNN kernel. This improvement makes the algorithm more resilient to noise and makes label propagation more locality-aware. The proposed improvement was tested on five synthetic datasets. Results indicate that there is no improvement for datasets with big margin between classes, however in datasets with low margin proposed approach with hybrid kernel outperforms existing algorithms with a simple kernel.
Research on Interactive Interface Optimization of Public Library Mini-programs Based on Eye-tracking Experiment

[Purpose/Significance] The wide application of the WeChat mini-program in the library field has promoted the construction of the library mobile service ecosystem. This research aims to provide new ideas and methods for the research and practice of library mini-programs interface optimization design. [Method/Process] Focusing on the key problems in the design of the library's mini-programs interface, this paper follows the "people-oriented" design concept, and combines the relevant theories of visual design, interaction design and cognitive psychology to put forward the optimization design ideas and plans. To verify the effectiveness of optimization plans, we conducted an eye-tracking experiment comparing user eye movement indicator data, thermal map, and eye movement trajectory map before and after optimizing the mini-program interface. [Results/Conclusion] The eye-tracking experiment method can effectively measure the user experience of library mini-programs, and the research can provide experimental data support and useful reference for the design and optimization of the mini-program interface. It also provides process reference and data analysis reference for related studies using physiological testing methods such as eye movement testing, and helps to apply eye movement testing methods to a wider range of scenarios.
Development of a Deep Image Retrieval Network Using Hierarchical and Multi-scale Spatial Features

Image retrieval aims to find similar images to a given query by matching features extracted directly from the images of a database. Deep convolutional neural networks provide an excellent framework for obtaining highly representative feature vectors from images to improve an image retrieval method. Deep residual networks perform better than existing deep networks, as they can incorporate useful information into the feature vectors through residual learning by designing appropriate operations in the employed residual block. One type of such information is spatial information obtained at different scales and levels of abstraction. In this paper, a novel residual block is proposed to generate a rich set of features for the task of image retrieval. The development of the residual block consists of three modules: a hierarchical spatial feature extraction module focusing on spatial information at different abstraction levels, a multi-scale feature extraction module that generates features at three different scales, and a feature fusion module. The results of experiments on various datasets and an ablation study show that the proposed residual block noticeably improves the representational capacity of the network, which, in turn, significantly enhances the retrieval performance of the deep image retrieval network.
Live Demonstration: A Low-Power Dual-Core Motion Estimation Chip Design and Validation for a Wireless Panoramic Endoscopy

A low-power motion estimation chip is designed for a wireless panoramic endoscope system. This chip consists of two motion estimation cores and is implemented by a dual-Vdd low-power technique. The proposed technique is efficient in decreasing power consumption without reducing the operation frequency of the chip. From the full-function chip measurements, this dual-Vdd chip can reduce power consumption by 20%~40% than the operation in single-Vdd for different clock frequencies.
Classification of Online Sentiment Data tweets using Various Approaches in Machine Learning

Online data is the data that is present on an online platform such as social media. Social media contains various information through which it can analyze users' attitudes & behavior. Sentimental analysis enables the automatic analysis of expression whether it is positive or negative. It can be about places, people, events, organizations, brands, products, etc. By using tools provided by machine learning & natural language processing with different approaches sentiment analysis can be made possible. The ubiquitous use of the Online World has given people a new method for expressing their emotions. It also functions as a platform with a shedload of knowledge where viewers may examine other users' thoughts, which are categorized into so many sentiment classifications and have become more and more essential in judgment. The representation of the number of comments that contain complex multidimensional sentiments that are either good or bad or anything in between the two can really be analyzed by means of this paper's approach to sentiment classification for consumer review classification. In this research, sentiment contribution in the field of online data tweets are collected, and using various machine learning algorithms results are identified as collective actions using different accuracy.
Mask detection system at the entry of a room

This study focuses on automatic mask detection tools that can open doors in a room to minimize violations of health protocols, one of which is the use of masks during the pandemic. The method used in this study is the CNN classification method. Where the CNN calcification method has several stages in it, including pre-processing, training, and testing. In the pre-processing, all image data used will be labeled using Labeling.axe. The training process at CNN uses TensorFlow framework version 1.15. In the testing process, the test and data testing will be carried out in real-time by entering new images and models that are made and then a classification process is carried out on objects caught by the camera, classified images are marked with boxes and names of data classes. This data class is divided into two, namely data on wearing masks and without masks. The results of the test were carried out by entering 200 facial image data. The system can correctly detect as much as 190 times from 200 data tested with an Accuracy rate of 95%. Based on the test results, it shows that the resulting model is good and suitable for the classification process of recognizing mask detection images. However, to produce a better model requires data with more variety and a larger amount of data.
Psychological Readiness of Pianist for Concert and Stage Activity as a Basis of His Performing Stability

The author pays attention to the problem how to prepare a pianist for concert activity in front of a large audience from the point of view of interpreting musical works in order to form and demonstrate his performing stability. The article suggested the foundations for a comprehensive analysis of the conceptual content regarding the "psychological readiness of a pianist for concert and stage activity" from the standpoint of various branches of science. It was found that the specified phenomenon is a certain basis of the pianist's performance stability. The interdependence of the pianist's psychological readiness for concert and stage activity and the quality preparation of the concert program by him is the basic foundation for the successful concert activity of a musician. The author specified in detail the role of the synergy of these two factors in mobilizing the pianist's creative potential to clearly construct in his imagination a program of technical-tactical interpretation of musical works. They are also important for the successful implementation of the program with the help of faultless reproductions of executive skills under the influence of internal or external stressors on the course of the specified process. The creative and interpretative potential of the pianist is characterized from the standpoint of consolidation of his psychological and physiological resources aimed at achieving performance stability in concert and stage activities. It has been established that even a fragmentary awareness by a pianist of the conditions of a future concert and stage performance, taking into account the perspective of realizing personal creative and interpretive potential, plays an important role in strengthening personal motivation for the purposeful formation of psychological stability as a component of his performance stability. The article presents the specifics of the growth of the pianist's creative and interpretive potential, which is determined by the activation of his intellectual and figurative sphere, aimed at the imaginary "prediction" of stressors in the future concert and stage performance and the formation of psychological resistance to their negative effects. The author defines the innate and acquired properties of the pianist's psychological stability, emphasizing that they are manifested in the ability to avoid or neutralize the negative impact of stressors on the success of the performance process, as well as in the ability to mobilize the creative and interpretive potential for sensory-motor endurance or resistance to existing stressors. It has been proven that the functioning of the pianist's psychological stability under the conditions of stressors of concert and stage activity is made possible by the interaction of his cognitive processes with emotional and volitional states, the synergy of which makes it possible to achieve the desired result without involving the body's reserve forces
MarIA and BETO are sexist: evaluating gender bias in large language models for Spanish

Abstract The study of bias in language models is a growing area of work, however, both research and resources are focused on English. In this paper, we make a first approach focusing on gender bias in some freely available Spanish language models trained using popular deep neural networks, like BERT or RoBERTa. Some of these models are known for achieving state-of-the-art results on downstream tasks. These promising results have promoted such models’ integration in many real-world applications and production environments, which could be detrimental to people affected for those systems. This work proposes an evaluation framework to identify gender bias in masked language models, with explainability in mind to ease the interpretation of the evaluation results. We have evaluated 20 different models for Spanish, including some of the most popular pretrained ones in the research community. Our findings state that varying levels of gender bias are present across these models.This approach compares the adjectives proposed by the model for a set of templates. We classify the given adjectives into understandable categories and compute two new metrics from model predictions, one based on the internal state (probability) and the other one on the external state (rank). Those metrics are used to reveal biased models according to the given categories and quantify the degree of bias of the models under study.
Monocular human depth estimation with 3D motion flow and surface normals

We propose a novel monocular human depth estimation method using video sequences as training data. We jointly train the depth and 3D motion flow networks with photometric and 3D geometric consistency constraints. Instead of depth ground truth, we take the surface normal as the pseudo-label to supervise the depth network learning. The estimated depth may exist texture copy artifact when the clothes on the human body have patterns and text marks (non-dominant color). Thus, we also propose an approach to alleviate the texture copy problem by estimating and adjusting the color of non-dominant color areas. Extensive experiments on public datasets and the Internet have been conducted. The comparison results prove that our method can produce competitive human depth estimation and has better generalization ability than state-of-the-art methods.
IMPROVING STUDENTS’ ENGLISH COMMUNICATION CAPABILITY THROUGH HABITUAL SPOKEN AND WRITTEN

The objectives of this research are to find out whether the use of communication in habitual activities improves the students' speaking capability to do interaction in English spoken and written, the This research consisted of two cycles in which there were four steps in each cycle, namely planning, implementing, observation, and reflection. The researcher collected quantitative data of speaking and writing tests and qualitative data of classroom situation, field note, photograph, interview, and questionnaires. The results are (1) the students' speaking skill improved, the improvement of speaking score from 3. 86 up to 5.95 (2) Direct interview and written type established good communication activity between the teacher and the student and improved classroom management. On the other hand the disadvantage of direct interview and written type was difficulty in time management to conduct one to one interview with students in a big speaking and writing class. Therefore, the teacher should arrange good time allocation in large class to achieve the best outcome. ABSTRAKTujuan dari penelitian ini adalah untuk mengetahui apakah penggunaan komunikasi dalam kegiatan pembiasaan meningkatkan kemampuan berbicara siswa untuk melakukan interaksi dalam bahasa Inggris lisan dan tulisan, Penelitian ini terdiri dari dua siklus dimana terdapat empat langkah dalam setiap siklusnya, yaitu perencanaan, pelaksanaan, observasi, dan refleksi. Peneliti mengumpulkan data kuantitatif tes berbicara dan menulis dan data kualitatif situasi kelas, catatan lapangan, foto, wawancara, dan angket. Hasilnya adalah (1) keterampilan berbicara siswa meningkat, peningkatan nilai berbicara dari 3,86 menjadi 5,95 (2) Wawancara langsung dan tulisan membentuk aktivitas komunikasi yang baik antara guru dan siswa serta pengelolaan kelas yang lebih baik. Di sisi lain kelemahan dari wawancara langsung dan tertulis adalah kesulitan dalam manajemen waktu untuk melakukan wawancara satu lawan satu dengan siswa di kelas berbicara dan menulis yang besar. Oleh karena itu, guru harus mengatur alokasi waktu yang baik di kelas besar untuk mencapai hasil yang terbaik.
Large Language Models for Epidemiological Research via Automated Machine Learning: Case Study Using Data From the British National Child Development Study

Large language models have had a huge impact on natural language processing (NLP) in recent years. However, their application in epidemiological research is still limited to the analysis of electronic health records and social media data.To demonstrate the potential of NLP beyond these domains, we aimed to develop prediction models based on texts collected from an epidemiological cohort and compare their performance to classical regression methods.We used data from the British National Child Development Study, where 10,567 children aged 11 years wrote essays about how they imagined themselves as 25-year-olds. Overall, 15% of the data set was set aside as a test set for performance evaluation. Pretrained language models were fine-tuned using AutoTrain (Hugging Face) to predict current reading comprehension score (range: 0-35) and future BMI and physical activity (active vs inactive) at the age of 33 years. We then compared their predictive performance (accuracy or discrimination) with linear and logistic regression models, including demographic and lifestyle factors of the parents and children from birth to the age of 11 years as predictors.NLP clearly outperformed linear regression when predicting reading comprehension scores (root mean square error: 3.89, 95% CI 3.74-4.05 for NLP vs 4.14, 95% CI 3.98-4.30 and 5.41, 95% CI 5.23-5.58 for regression models with and without general ability score as a predictor, respectively). Predictive performance for physical activity was similarly poor for the 2 methods (area under the receiver operating characteristic curve: 0.55, 95% CI 0.52-0.60 for both) but was slightly better than random assignment, whereas linear regression clearly outperformed the NLP approach when predicting BMI (root mean square error: 4.38, 95% CI 4.02-4.74 for NLP vs 3.85, 95% CI 3.54-4.16 for regression). The NLP approach did not perform better than simply assigning the mean BMI from the training set as a predictor.Our study demonstrated the potential of using large language models on text collected from epidemiological studies. The performance of the approach appeared to depend on how directly the topic of the text was related to the outcome. Open-ended questions specifically designed to capture certain health concepts and lived experiences in combination with NLP methods should receive more attention in future epidemiological studies.
Design of Solar Outdoor Air Purifier with Air Quality Control

Air pollution is an addition of pollutants to the atmosphere that causes environmental damage. Industrial development and deforestation, which are two of the main causes of air pollution, have increased the mortality rates of people dying from diseases such as respiratory diseases and lung cancer. To fight against this serious threat to humanity, we decided to support this society and do something to detect the amount of pollution in the air and greatly reduce the amount of pollution in the air by using MQ135 and dust sensor.
Penerapan Algoritma K-Means Untuk Menentukan Jumlah Produksi Kayu Bulat Berdasarkan Jenis Kayu Di Provinsi Jawa Barat

Introduction: According to data from the Central Bureau of Statistics (BPS), log production fluctuated every quarter of 2020. Log production experienced a decline in the second quarter from a total production of 14.58 million m3 in the first quarter to 13.87 million m3. Purpose: to apply the K-Means data mining technique which is classified as a potential log production based on wood species with high and low criteria. Method: The type of research to be used is quantitative research. Discussion result: based on data on production and types of logs from 2016 to 2020, the West Java Forestry Service, log production in each district/city area in West Java is not evenly distributed for products and types of logs processed, therefore with the application of the K-Means algorithm is expected to help the production potential and types of logs in the West Java region. Therefore, the West Java Forestry Service determines the grouping of logs based on wood species into 2 clusters, namely high and low. Conclusion: The data is calculated based on 2 clusters, namely clusters with low potential (C1) and clusters with high potential (C2). The Forest Management Unit (KPH) area with the highest log production potential (C2) is the North Bandung KPH.
Text Data Augmentation Using Generative Adversarial Networks, Back Translation and EDA

Data or information augmentation techniques have been explored in NLP to create greater textual information for training. But, the overall performance advantage of present strategies is regularly marginal. Text augmentation can generate additional variations of the original text and improve the generalization ability of a machine learning model that processes natural language text data. The paper represents the performance of Generative Adversarial Networks for the overall performance in text classification. The results show that the Generative models give the best overall performance advantage over the EDA or back translation accuracy. The proposed models process text augmentation using GANs compared to methods like Easy Data Augmentation and back translation. The goal of EDA is to generate new, semantically similar sentences from an existing sentence, to increase the size and diversity of a dataset for training natural language processing models. Back Translation is an improved method with increased accuracy. Generative Adversarial Networks (GANs) generate new, synthetic data that is similar to a given training dataset using neural network architecture. In the context of text augmentation, GANs can be used to generate new, realistic text similar to a given text dataset.