Reviewerly/exHarmony huggingface dataset:
Research on Introducing Sports Education Model into College Physical Education

college physical education has always been an indispensable part of college education, the teaching model of physical education has attracted much attention from the society.  Therefore, it has become imperative to conduct innovative research on college physical education.  Reforming the methods and strategies of physical education are all issues that people should think about.  This article studies the reform of college physical education under the new sports model, and hopes that the majority of students will pay attention to the new attempt and transformation of the physical education model.
2D-Based Saliency Prediction Framework for Omnidirectional–360° Video

This paper investigates the performance of 2D-Based visual saliency models applied to omnidirectional videos. Indeed, most existing 2D-Based saliency prediction techniques for dynamic and static scene analysis consider essentially the planar projection format. Moreover, some of the well-known prediction saliency models in the literature were implemented and widely tested with specific equirectangular (ERP) and cubemap (CMP) projections in order to track visual attention in 360° videos, using head-mounted displays (HMDs). Furthermore, the geometric difference between the two spaces raises a critical bound, on the performance of 2D models adapted to 360° data using some ERP and CMP projection methods. The main issue is the merging of 2D saliency probability distributions of some ERP and CMP projections to deal with the saliency prediction in 360° videos. Our framework can integrate new 2D saliency models without requiring any adaptation of the existing prediction algorithm. Our experiments on Salient-360 data sets show that the proposed framework integrating the 2D-based models in Omnidirectional saliency prediction is competitive with the state-of-the-art, at the head and eye movement saliency prediction.
Analyzing DevOps Teaching Strategies: An Initial Study

DevOps refers to a set of practices that integrate software development and operations with the primary aim to enable the continuous delivery of high-quality software. DevOps has also promoted several challenges to software engineering teaching. In this paper, we present a preliminary study that analyzes existing teaching strategies reported in the literature. Our findings indicate a set of approaches highlighting the use of environments to support teaching. Our work also investigates how these environments can contribute to address existing challenges and recommendations of DevOps teaching.
Structural and geochemical ore-forming processes in deformed gold deposits: towards a multiscale and multimethod approach

Abstract Integrating structural control on mineralization and geochemical ore-forming processes is crucial when studying deformed ore deposits. Yet structural and geochemical data are rarely acquired at the same scale: structural control on mineralization is typically investigated from the district to the deposit and macroscopic scales whereas geochemical ore processes are described at the microscopic scale. The deciphering of a deformation–mineralization history valid at every scale thus remains challenging. This study proposes a multiscale approach that enables the reconciliation of structural and geochemical information collected at every scale, applied to the example of the Galat Sufar South gold deposit, Nubian shield, NE Sudan. It gathers field and laboratory information by coupling a classical petrological–structural study with high-resolution X-ray computed tomography, electron back-scattered diffraction and laser ablation inductively coupled plasma mass spectrometry on mineralized sulfide mineral assemblages. This approach demonstrates that there is a linear control on mineralization expressed from the district to microscopic scales at the Galat Sufar South gold deposit. We highlight the relationships between Atmur–Delgo suturing tectonics, microdeformation of sulfide minerals, syn-pyrite recrystallization metal remobilization, gold liberation and ore upgrading. Our contribution therefore represents another step forward in a holistic field-to-laboratory approach for the study of any other sulfide-bearing, structurally controlled ore deposit type.
Traffic Sign Recognition Using an Attentive Context Region‐Based Detection Framework

Accurate small traffic sign recognition is more important for the safety of intelligent transportation systems. A recognition framework named attentive context region-based detection framework (AC-RDF) is proposed in this paper. We construct the attentive context feature for the recognition of small traffic signs, which combines the target information and the contextual information by the concatenation operation following a pointwise convolutional layer. The proposed attentive context feature exploits the surrounding information for a given object proposal. Next, we propose a novel attentive loss function to replace the original cross-entropy function. It distinguishes hard negative samples from easy positive ones in the total loss, allows the proposed framework to obtain enough training, and further improve the recognition accuracy. The proposed method is evaluated on the challenging Tsinghua-Tencent 100K dataset. The experimental results indicate that the attentive context region-based detection framework is superior at detecting small traffic signs and achieves state-of-the-art performance compared with other methods.
Vietnamese Complaint Detection on E-Commerce Websites

Customer product reviews play a role in improving the quality of products and services for business organizations or their brands. Complaining is an attitude that expresses dissatisfaction with an event or a product not meeting customer expectations. In this paper, we build a Vietnamese Open-domain Complaint Detection dataset (UIT-ViOCD), including 5,485 human-annotated reviews on four categories about product reviews on e-commerce sites. After the data collection phase, we proceed to the annotation task and achieve the inter-annotator agreement (Am) of 87%. Then, we present an extensive methodology for the research purposes and achieve 92.16% by F1-score for identifying complaints. With the results, in future, we aim to build a system for open-domain complaint detection on E-commerce websites.
Foreign Language Teaching of Ukrainian University Students in a Distance Learning Environment

The Covid-19 pandemic has forced Ukraine, like other countries, to move to distance education. The study aims to analyze the distance learning experience of Ukrainian philology students, to find out the pros and cons of distance education, those things that motivate students, and the challenges they face while studying online. The survey consisted of a questionnaire and embraced 105 respondents. The survey goal was to collect data that would allow concluding about the effectiveness of distance education for Ukrainian philology students at the National University of Life and Environmental Sciences of Ukraine, particularly for the study of foreign languages. The significant results of the survey include: obtaining reliable data on the effectiveness of distance learning of Ukrainian philology students through their feedback in questionnaires, online testing, a variety of writing assignments, and assessment in the online classroom, finding out the most effective distance learning tools, the main features of teacher – students interactions in a distance-learning environment. The research proves that Ukrainian philology students overall positively evaluate their distance learning experience. It does not negatively impact students’ foreign language learning motivation. Despite a positive attitude to distance learning, Ukrainian philology students face particular challenges connected with access to devices, lack of information and communications technology skills, Internet connection, and lack of communication with their peers. The finding reveals, whatever are the benefits of distance education, it cannot entirely replace face-to-face learning.
Reading Habits and their Correlation with Reading Achievement among ESL Learners in Selected Malaysian Secondary Schools

Reading is a complex process that involves cognitive, physical, intellectual, cultural, and linguistic processes. Research shows that learners who read extensively tend to accomplish a high level of academic achievement. In Malaysia, the educational goal has shifted from simply forming literate citizens to nurturing a well-educated and intellectual society. Promoting efficient reading habits can be an effective approach to realizing this goal. This study, therefore, aims to examine reading habits and ascertain their correlation with reading achievement among final-year English as a Second Language (ESL) learners in selected Malaysian secondary schools. The study utilized a cross-sectional survey in which data were gathered from a sample of 351 ESL learners using questionnaires and written tests as data-gathering instruments. The analysis was conducted by means of descriptive statistics, Pearson correlation, and simple linear regression. The findings revealed a significant relationship between ESL learners’ reading habits and their reading achievement. Additionally, ESL learners’ reading habits contribute significantly to their reading achievement. These findings highlight the significance of reading habits in determining ESL learners’ reading achievement itself. However, this study is limited to reading habits and reading achievement among students in selected secondary schools. Thus, further research may replicate this study in the context of computer-assisted or blended learning
Development of Automatic Gear Shifting for Bicycle Riding Based on Physiological Information and Environment Sensing

This paper presents the development of an intelligent bicycle system with automatic gear shifting based on the user’s physiological information and environment sensing. The sensor data are collected during the ride, uploaded to the cloud database, and analyzed using random forest to derive the appropriate riding requirement. Our proposed technique performs the automatic gear shifting operation using inexpensive sensor components and embedded system development boards. The gear shifting module is designed and installed as an external device for the off-the-shelf bicycle, without the need to dismantle or modify the body structure. A bicycle riding specific online management interface is developed for web access and administration. It allows the cloud data being displayed and let users acquire and understand their riding behaviors. The experiments and evaluation in the outdoor environment have demonstrated the feasibility of the proposed intelligent gear shifting system.
Solar Irradiation Evaluation through GIS Analysis Based on Grid Resolution and a Mathematical Model: A Case Study in Northeast Mexico

The estimation of the solar resource on certain surfaces of the planet is a key factor in deciding where to establish solar energy collection systems. This research uses a mathematical model based on easy-access geographic and meteorological information to calculate total solar radiation at ground surface. This information is used to create a GIS analysis of the State of Nuevo León in Mexico and identify solar energy opportunities in the territory. The analyzed area was divided into a grid and the coordinates of each corner are used to feed the mathematical model. The obtained results were validated with statistical analyses and satellite-based estimations from the National Aeronautics and Space Administration (NASA). The applied approach and the results may be replicated to estimate solar radiation in other regions of the planet without requiring readings from on-site meteorological stations and therefore reducing the cost of decision-making regarding where to place the solar energy collection equipment.
Modification of Learning Feedforward Neural Networks with the BP Method

The backpropagation (BP) algorithm is a worldwide used method for learning neural networks. The BP has a low computational load. Unfortunately, this method converges relatively slowly. In this paper a new approach to the backpropagation algorithm is presented. The proposed solution speeds up the BP method by using vector calculations. This modification of the BP algorithm was tested on a few standard examples. The obtained performance of both methods was compared.
Leveraging Pre-Trained Contextualized Word Embeddings to Enhance Sentiment Classification of Drug Reviews

Traditionally, pharmacovigilance data are collected during clinical trials on a small sample of patients and are therefore insufficient to adequately assess drugs. Nowadays, consumers use online drug forums to share their opinions and experiences about medication. These feedbacks, which are widely available on the web, are automatically analyzed to extract relevant information for decision-making. Currently, sentiment analysis methods are being put forward to leverage consumers' opinions and produce useful drug monitoring indicators. However, these methods' effectiveness depends on the quality of word representation, which presents a real challenge because the information contained in user reviews is noisy and very subjective. Over time, several sentiment classification problems use machine learning methods based on the traditional bag of words model, sometimes enhanced with lexical resources. In recent years, word embedding models have significantly improved classification performance due to their ability to capture words' syntactic and semantic properties. Unfortunately, these latter models are weak in sentiment classification tasks because they are unable to encode sentiment information in the word representation. Indeed, two words with opposite polarities can have close word embeddings as they appear together in the same context. To overcome this drawback, some studies have proposed refining pre-trained word embeddings with lexical resources or learning word embeddings using training data. However, these models depend on external resources and are complex to implement. This work proposes a deep contextual word embeddings model called ELMo that inherently captures the sentiment information by providing separate vectors for words with opposite polarities. Different variants of our proposed model are compared with a benchmark of pre-trained word embeddings models using SVM classifier trained on Drug Review Dataset. Experimental results show that ELMo embeddings improve classification performance in sentiment analysis tasks on the pharmaceutical domain.
Automatic Evaluation System for Piano Performance Based on the Internet of Things Technology under the Background of Artificial Intelligence

Ubiquitous sensors cover many areas of modern society. As the sensor network matures, various applications based on the Internet of Things are setting off a new revolution in all aspects of social life. In order to in-depth study whether the Internet of Things technology can be used in the automatic evaluation of piano performance, this article uses artificial system comparison method, database establishment method, and model construction method to collect samples, analyze the automatic evaluation model, and streamline the algorithm, and based on these foundations, a practical automatic evaluation system for piano performance was created. However, the role of this article does not stop there. There are also a variety of algorithm-like models and the construction of technical models. First, the practicality of the created model is studied, and it is found that the traditional manual recognition rate is about 52%, while the recognition rate of the system in this paper is more than 90%, and the average recognition time of the system is 1.1 s. The start-up process and recognition process time of other systems are all no more than 6 s, indicating the superior performance of the system. On this basis, select the classic piano textbook: Thompson’s Simple Piano Tutorial. From it, select representative pieces as test samples. We can find that the overall F-measure value is above 90%, and the average F-measure value is 96.8%; the system performance test is good and can provide accurate evaluation results for piano learners. The results show that the number of identifications and missing numbers of the system are not much different from those of manual identification, which further proves its superiority. It is basically realized that starting from the Internet of Things technology, a system model that can automatically evaluate most piano repertoires has been designed.
DiscoDVT: Generating Long Text with Discourse-Aware Discrete Variational Transformer

Despite the recent advances in applying pretrained language models to generate highquality texts, generating long passages that maintain long-range coherence is yet challenging for these models.In this paper, we propose DISCODVT, a discourse-aware discrete variational Transformer to tackle the incoherence issue.DISCODVT learns a discrete variable sequence that summarizes the global structure of the text and then applies it to guide the generation process at each decoding step.To further embed discourse-aware information into the discrete latent representations, we introduce an auxiliary objective to model the discourse relations within the text.We conduct extensive experiments on two open story generation datasets and demonstrate that the latent codes learn meaningful correspondence to the discourse structures that guide the model to generate long texts with better long-range coherence.
Students’ Personality, Self-Efficacy, and Its Impact on the Academic Performance of the Senior High School Students

The main purpose of this study was to determine the personality and self-efficacy on the academic performance of the students. Further, this examined the impact of personality, and on their academic performance. The descriptive-correlation research method was utilized to describe the respondents’ profile in terms of their personality, self-efficacy and academic performance. A total of one hundred thirty-seven (137) grade 11 senior high school students of St. Paul College of Bocaue participated in this study. The null hypothesis was tested at .05 alpha level of significance to determine the impact of personality, self-efficacy on the academic performance of the students. The computed multiple regression analysis results revealed that personality and self-efficacy do not significantly affect the academic performance of senior high school students.
Weakly Supervised Solar Panel Mapping Using Residual Aggregated Network for Aerial Images

With the rapid development of solar distribution, mapping the locations and sizes of solar panels is becoming increasingly valuable. Weakly supervised methods have been proposed to reduce the reliance on expensive pixel-wise annotations by adopting weak labels, but inevitably suffer from low mapping accuracy and poor boundary maintenance. To solve these problems, a novel weakly supervised residual aggregated network (WS-RAN) is presented in this paper. In the WS-RAN, pixel-wise labels are automatically generated from image-level labels by a classification network. Then, the produced pixel-wise labels are used to train the residual aggregated network (RAN) for target mapping, which is designed to cope the variations in size and shapes of individual solar panel layout. Particularly, for better boundary maintenance, the residual aggregated block is developed as the basic module in the bottom-up path of the RAN. Experiment results reveal that the proposed WS-RAN significantly outperforms four state-of-the-art weakly supervised methods with 76.0% F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score and 61.3% IoU score.
Applying Bayesian hyperparameter optimization towards accurate and efficient topic modeling in clinical notes

The increased adoption of electronic health record systems has led to boons for downstream analyses within the clinical domain. The identification of relevant patient data for focal analyses remains a key challenge. In this study, we applied sequential model-based global optimization towards tuning the hyperparameters of latent Dirichlet allocation, a standard topic modeling technique. We showcase the identification of physician notes specific to chronic lymphocytic leukemia treatment as a generalizable use-case. Using each identified topic component as a pseudo binary classifier, our best predictive model achieved an area under the receiver operating characteristic curve of 0.9. Additionally, metrics associated with hyperparameter tuning are in line with higher level domain understanding. This study demonstrated the efficacy of the hyperparameter tuning process towards topic modeling and we make the generalized tool available at https://github.com/sema4hai/ehr-topic-model.
Multi-View Optical Image Fusion and Reconstruction for Defogging without a Prior In-Plane

Image fusion and reconstruction from muldti-images taken by distributed or mobile cameras need accurate calibration to avoid image mismatching. This calibration process becomes difficult in fog when no clear nearby reference is available. In this work, the fusion of multi-view images taken in fog by two cameras fixed on a moving platform is realized. The positions and aiming directions of the cameras are determined by taking a close visible object as a reference. One camera with a large field of view (FOV) is applied to acquire images of a short-distance object which is still visible in fog. This reference is then adopted to the calibration of the camera system to determine the positions and pointing directions at each viewpoint. The extrinsic parameter matrices are obtained with these data, which are applied for the image fusion of distant images captured by another camera beyond visibility. The experimental verification was carried out in a fog chamber and the technique is shown to be valid for imaging reconstruction in fog without a prior in-plane. The synthetic image, accumulated and averaged by ten-view images, is shown to perform potential applicability for fog removal. The enhanced structure similarity is discussed and compared in detail with conventional single-view defogging techniques.
TSR-DSAW: Table Structure Recognition via Deep Spatial Association of Words

Existing methods for Table Structure Recognition (TSR) from camera-captured or scanned documents perform poorly on complextables consisting of nested rows / columns, multi-line texts and missing cell data.This is because current data-driven methods work by simply training deep models on large volumes of data and fail to generalize when an unseen table structure is encountered.In this paper, we propose to train a deep network to capture the spatial associations between different word pairs present in the table image for unravelling the table structure.We present an end-to-end pipeline, named TSR-DSAW: TSR via Deep Spatial Association of Words, which outputs a digital representation of a table image in a structured format such as HTML.Given a table image as input, the proposed method begins with the detection of all the words present in the image using a text-detection network like CRAFT which is followed by the generation of word-pairs using dynamic programming.These word-pairs are highlighted in individual images and subsequently, fed into a DenseNet-121 classifier trained to capture spatial associations such as same-row, same-column, same-cell or none.Finally, we perform post-processing on the classifier output to generate the table structure in HTML format.We evaluate our TSR-DSAW pipeline on two public table-image datasets -PubTabNet and ICDAR 2013, and demonstrate improvement over previous methods such as TableNet and DeepDeSRT.