Reviewerly/exHarmony huggingface dataset:
System-Activity Approach in Physics Course at Technological University on the Example of the Section “Magnetismˮ

Modern trends in the development of Russian society define new tasks for the education system, one of which is the active involvement of students in the practical activities of the discipline being studied. We propose the introduction of a system-activity approach to teaching physics at technological university. The advantages of the approach presented include its consistency and productivity, more individual and flexible character in comparison with traditional training. The most important component of the system-activity approach of teaching physics is a properly structured sequence of educational activities that organizes the development of the studied material; the acquisition of skills to perform computational tasks; the implementation of experimental activities and receiving research products. The implementation of the system-activity approach is considered on the example of the section “Magnetismˮ of the physics course. The purpose of the study. Determination of the specifics of using a system-activity approach in the process of teaching students at a technological university. Methodology (materials and methods). In the course of the research, methods of studying and analyzing psychological, pedagogical and methodological literature, generalizing pedagogical experience of using a system-activity approach in the process of teaching physics were used. Results. The article presents a meaningful model of a system-activity approach in teaching physics and describes the possibilities of its usage as an effective approach in the process of teaching students at technological university. An example of the usage of a system-activity approach in teaching physics from the section “Magnetismˮ in the form of an algorithm for the work of students to obtain a research product is presented. The effective usage of the approach in educational activities by the method of statistical verification of the student T-criterion at technological university is reasoned. The conclusion is made that the system-activity approach allows to combine cognitive, developmental and motivational functions; to identify professional knowledge, skills and methods of activity, personal qualities, the system of professional values of students at technological university.
Management Competencies in the Activities of the Classroom Teacher

The article provides a rationale for the importance of managerial competencies of a modern class teacher. Along with pedagogical activity, the class teacher also carries out managerial activities, providing a solution to the problems of education. The scale and depth of the tasks that the class teacher solves imply productive cooperation with society: parents of students, teachers, social partners. The purpose of the article is to reveal the importance of managerial competencies in the activities of a class teacher, to show possible ways of their formation. The methodological basis of the article was the provisions of the theory of educational systems by L. I. Novikova (class teacher as a subject of the educational system), the theory of pedagogical systems N. I. Kuzmina (functional components of the pedagogical system), the theory of pedagogical management by V. P. Simonov, ideas of the thought-activity approach by P. G. Shchedrovitsky. The levels of functions that the class teacher implements, examples of managerial competencies, ways of their formation in the educational practice of the Novosibirsk State Pedagogical University are given.
Empirical correlation with dimensionless parameters for following the kinetic separation performance via reverse osmosis desalination process of groundwater

&lt;p&gt;The present paper carries for novel modeling that are often used for evaluate the kinetic performance of groundwater reverse osmosis (GWRO) desalination system by empirical correlation with dimensionless parameters. About three hundred dimensionless empirical correlationshave been proposed and validated for the calculation of three cumulative dimensionless volumes: alimentation, permeate and rejection as a function of the dimensionless filtration time and vice versa. This correlation has obtained by inspiration of the models already proposed in the literature from four unitary operation processes: filtration, adsorption, drying and extraction. The experimental data consists of 2561 points taken during the lifetime of 66 organics RO membranes. The interpreting of regression and residual results is validated by four statistical criteria: reduced chi-Sqr, residual sum of residual, r-square (COD) and adj. R-square (R²) and with two criteria in ANOVA analysis: sum of squares, mean square. Results show that 95 dimensionless empirical correlations (DEC) among 192 are highly capable to describe the separation kinetics on reverse osmosis desalination system curve with a least R2 was around 0.970 in comparison to the best correlation (DEC 1) with R2 of 0,998, 0,995 and 0,993 for alimentation, permeate and rejection, respectively, with negligible errors and perfect alignment of correlation with RO kinetic separation according to statistical criteria.&lt;/p&gt;&#x0D;
Learning-Based Video Coding with Joint Deep Compression and Enhancement

The end-to-end learning-based video compression has attracted substantial attentions by paving another way to compress video signals as stacked visual features. This paper proposes an efficient end-to-end deep video codec with jointly optimized compression and enhancement modules (JCEVC). First, we propose a dual-path generative adversarial network (DPEG) to reconstruct video details after compression. An $\alpha$-path facilitates the structure information reconstruction with a large receptive field and multi-frame references, while a $\beta$-path facilitates the reconstruction of local textures. Both paths are fused and co-trained within a generative-adversarial process. Second, we reuse the DPEG network in both motion compensation and quality enhancement modules, which are further combined with other necessary modules to formulate our JCEVC framework. Third, we employ a joint training of deep video compression and enhancement that further improves the rate-distortion (RD) performance of compression. Compared with x265 LDP very fast mode, our JCEVC reduces the average bit-per-pixel (bpp) by 39.39\%/54.92\% at the same PSNR/MS-SSIM, which outperforms the state-of-the-art deep video codecs by a considerable margin.
A Two-Stream AMR-enhanced Model for Document-level Event Argument Extraction

Most previous studies aim at extracting events from a single sentence, while document-level event extraction still remains under-explored. In this paper, we focus on extracting event arguments from an entire document, which mainly faces two critical problems: a) the long-distance dependency between trigger and arguments over sentences; b) the distracting context towards an event in the document. To address these issues, we propose a Two-Stream Abstract meaning Representation enhanced extraction model (TSAR). TSAR encodes the document from different perspectives by a two-stream encoding module, to utilize local and global information and lower the impact of distracting context. Besides, TSAR introduces an AMR-guided interaction module to capture both intra-sentential and inter-sentential features, based on the locally and globally constructed AMR semantic graphs. An auxiliary boundary loss is introduced to enhance the boundary information for text spans explicitly. Extensive experiments illustrate that TSAR outperforms previous state-of-the-art by a large margin, with 2.54 F1 and 5.13 F1 performance gain on the public RAMS and WikiEvents datasets respectively, showing the superiority in the cross-sentence arguments extraction. We release our code in https://github.com/ PKUnlp-icler/TSAR.
MUTR3D: A Multi-camera Tracking Framework via 3D-to-2D Queries

Accurate and consistent 3D tracking from multiple cameras is a key component in a vision-based autonomous driving system. It involves modeling 3D dynamic objects in complex scenes across multiple cameras. This problem is inherently challenging due to depth estimation, visual occlusions, appearance ambiguity, etc. Moreover, objects are not consistently associated across time and cameras. To address that, we propose an end-to-end \textbf{MU}lti-camera \textbf{TR}acking framework called MUTR3D. In contrast to prior works, MUTR3D does not explicitly rely on the spatial and appearance similarity of objects. Instead, our method introduces \textit{3D track query} to model spatial and appearance coherent track for each object that appears in multiple cameras and multiple frames. We use camera transformations to link 3D trackers with their observations in 2D images. Each tracker is further refined according to the features that are obtained from camera images. MUTR3D uses a set-to-set loss to measure the difference between the predicted tracking results and the ground truths. Therefore, it does not require any post-processing such as non-maximum suppression and/or bounding box association. MUTR3D outperforms state-of-the-art methods by 5.3 AMOTA on the nuScenes dataset. Code is available at: \url{https://github.com/a1600012888/MUTR3D}.
Deep Bingham Networks: Dealing with Uncertainty and Ambiguity in Pose Estimation

In this work, we introduce Deep Bingham Networks (DBN), a generic framework that can naturally handle pose-related uncertainties and ambiguities arising in almost all real life applications concerning 3D data. While existing works strive to find a single solution to the pose estimation problem, we make peace with the ambiguities causing high uncertainty around which solutions to identify as the best. Instead, we report a family of poses which capture the nature of the solution space. DBN extends the state of the art direct pose regression networks by (i) a multi-hypotheses prediction head which can yield different distribution modes; and (ii) novel loss functions that benefit from Bingham distributions on rotations. This way, DBN can work both in unambiguous cases providing uncertainty information, and in ambiguous scenes where an uncertainty per mode is desired. On a technical front, our network regresses continuous Bingham mixture models and is applicable to both 2D data such as images and to 3D data such as point clouds. We proposed new training strategies so as to avoid mode or posterior collapse during training and to improve numerical stability. Our methods are thoroughly tested on two different applications exploiting two different modalities: (i) 6D camera relocalization from images; and (ii) object pose estimation from 3D point clouds, demonstrating decent advantages over the state of the art. For the former we contributed our own dataset composed of five indoor scenes where it is unavoidable to capture images corresponding to views that are hard to uniquely identify. For the latter we achieve the top results especially for symmetric objects of ModelNet dataset.
A Study on the Stability of Graph Edit Distance Heuristics

Graph edit distance (GED) is a powerful tool to model the dissimilarity between graphs. However, evaluating the exact GED is NP-hard. To tackle this problem, estimation methods of GED were introduced, e.g., bipartite and IPFP, during which heuristics were employed. The stochastic nature of these methods induces the stability issue. In this paper, we propose the first formal study of stability of GED heuristics, starting with defining a measure of these (in)stabilities, namely the relative error. Then, the effects of two critical factors on stability are examined, namely, the number of solutions and the ratio between edit costs. The ratios are computed on five datasets of various properties. General suggestions are provided to properly choose these factors, which can reduce the relative error by more than an order of magnitude. Finally, we verify the relevance of stability to predict performance of GED heuristics, by taking advantage of an edit cost learning algorithm to optimize the performance and the k-nearest neighbor regression for prediction. Experiments show that the optimized costs correspond to much higher ratios and an order of magnitude lower relative errors than the expert cost.
Global sentiment analysis over third wave COVID19 tweets

Internet users are increasing rapidly during the last decade, and after the Covid-19 outbreak, social media platforms became the favorite source to express public responses. They are using Twitter, a free microblogging site, to express their thoughts, joys, and sorrows spontaneously. Researchers take great interest in analyzing public sentiments with the help of Data science techniques like natural language processing and machine learning methods, to predict public suggestions on topics of social concerns. In the proposed research article, we have collected public tweets during the third wave of Covid19 from 21st to 31st January 2022, and public sentiments are observed with 12 popular Machine Learning algorithms and commonly used words are represented as n-grams and here three n-grams (Unigram, Bigram, and Trigram) are collected and prediction is also observed on these data. It is observed that in all the cases LinearSVC presents the highest classification accuracy of approx. 96% for covid datasets. It has also worked well on all the three n-gram datasets with accuracies of 96.39% for unigram, 95.24 for bigram, and 87.70% for trigram. data science
Design of Real-Time Face Recognition and Emotion Recognition on Humanoid Robot Using Deep Learning

A robot is capable of mimicking human beings, including recognizing their faces and emotions. However, current studies of the humanoid robot have not been implemented in the real-time system. In addition, face recognition and emotion recognition have been treated as separate problems. Thus, for real-time application on a humanoid robot, this study proposed a combination of face recognition and emotion recognition. Face and emotion recognition systems were developed concurrently in this study using convolutional neural network architectures. The proposed architecture was compared to the well-known architecture, AlexNet, to determine which architecture would be better suited for implementation on a humanoid robot. Primary data from 30 respondents was used for face recognition. Meanwhile, emotional data were collected from the same respondents and combined with secondary data from a 2500-person dataset. Surprise, anger, neutral, smile, and sadness were among the emotions. The experiment was carried out in real-time on a humanoid robot using the two architectures. Using the AlexNet model, the accuracy of face and emotion recognition was 87 % and 70 %, respectively. Meanwhile, the proposed architecture achieved accuracy rates of 95 % for face recognition and 75 % for emotion recognition, respectively. Thus, the proposed method performs better in terms of recognizing faces and emotions, and it can be implemented on a humanoid robot.
Feasibility Study of Raw Material for Hybrid Power Plant in Coastal Cilacap Selatan

Cilacap Regency is a coastal area south of the island of Java. The purpose of this study is to determine the potential of raw materials available in the South Cilacap Coastal area and to determine the potential power that can be generated for the PLTH system through the calculation of the raw material data that has been obtained. This research method begins with direct measurements of wind speed and light intensity. Both devices read data in real time and connect online. The data obtained is processed to determine the wind speed and the average intensity of sunlight. The results of these calculations are entered into the equations to calculate the potential power that can be generated. Based on the calculation, the average wind speed is 3-4 m/s and the light intensity is 54612 watt/m2. The results of the calculation of the potential power that can be generated from the PLTB system in the coastal area of ​​South Cilacap is equivalent to 508 watts/day. The potential power that can be generated from PLTS is 10.8 kW/day. The total potential power that can be generated from the PLTH system on the coast of South Cilacap is equivalent to 273.22 kWh/day.
Stock administration framework utilizing AWS

Unsuccessful stock results in discouraged customers and a significant investment in more delayed deals and distribution facilities. Proposed work has eliminated paperwork, human errors, manual delays, and slow processes. This stock management system can track sales and available inventory, letting a shop know how much to replenish and when to do so. With distributed computing, tasks can be delegated to a combination of software and administrations across an enterprise. The cloud is this network of servers. Distributed computing can help businesses transform their static server foundations into dynamic ones that can expand and contract in response to client demands. A powerful distributed computing stage individually configures, designs, reconfigures, and deprovisions servers. cloud-based servers can be actual machines or virtual machines.
Web-based Knowledge Sharing System in Faculty of Engineering Bangka Belitung University

New lecturers must be quickly adapted to their work environment. Adaptation was difficult when colleagues did not provide space for discussion to the new lecturers. This condition occurred at the Faculty of Engineering Bangka Belitung University, with the planned to open a new study program, new lecturers had to be in a temporary homebase that was not their field. The expectation from the Dean was the existence of a knowledge sharing system that can be accessed anytime and anywhere by lecturers. But in reality, the knowledge sharing system still did not exist and knowledge sharing carried out manually by face-to-face meetings between lecturers and through Whatsapp groups. This research was conducted at the Faculty of Engineering, Bangka Belitung University. The knowledge sharing system methodology uses a combination of two methods, which are Socialization, Externalization, Combination, Internalization (SECI), and Knowledge Management Life Cycle. The result of this research was a development of web-based knowledge sharing system with an object-oriented approach which has been tested with black box testing and resulted as the system functions worked well.
Implementation of Data Mining Clustering Using the K-Medoids Method in Grouping Library Books Politeknik Negeri Balikpapan

Politeknik Negeri Balikpapan’s Library, annually adds to its collection of reading books. In order to procure the book according to the needs of users, namely students, information on the collection of books needed or in demand is needed. To answer this problem, it is necessary to have a clustering system for existing books in the library by looking at the aspect of borrowing frequency. The Clustering system is made using the K-Medoids method with the selection of 3 Clusters, namely, very attractive, desirable and less desirable. From the results of data processing through the Rapid Miner Application with K = 3 , the results obtained are cluster_0 (low) consisting of 97 book titles with the frequency of borrowing books in the rare category in other words less desirable to borrow, cluster_1 (high) consists of 2 book titles which are the most in-demand group of books namely Teknologi Bahan dan Teori dan Praktik Hotel Front Office. Cluster_2 (medium) consisting of 8 titles which are books with moderate borrowing frequenc. The results of the performance obtained Davies Bouldin index value of 0.287. The results of grouping the data of these books can be used as input for library managers in procuring book collections based on the frequency of borrowing books.
FACTORS ASSOCIATED WITH MENTAL SUFFERING IN PEOPLE WITH DIABETES MELLITUS DURING THE COVID-19 PANDEMIC

Objective: to analyze the factors associated with mental distress in people with diabetes mellitus during the COVID-19 pandemic. Method: cross-sectional study, conducted in two Brazilian virtual communities of the Facebook platform, in the period from August 2020 to January 2021. The sample totaled 111 people with diabetes. The evaluation of health conditions was performed using an electronic form, and the Self Report Questionnaire-20 was used to identify mental suffering. Descriptive and inferential analyses were expressed by frequencies and simple and multiple logistic regression. Results: mental suffering is associated with being female, having a previous history of mental disorders (depression and anxiety), having been diagnosed with diabetes for more than six years, and the presence of ophthalmologic complications, which increase the chances of emotional instability. Conclusion: the study offers subsidies to the direction of support strategies that minimize the psychosocial impacts of the pandemic on people with diabetes.
(Review) Xu, J. J. (eds.) (2020). Jiudian Yingyu Cihui Shouce (The Wordbook of Hotel English). Beijing: Foreign Language Teaching and Research Press, ix+290

In this article, the merits and demerits of Jiudian Yingyu Shouce (The Wordbook of Hotel English), a newly published corpus-based wordbook on hotel English, is reviewed. It has three distinctive features, including relying on a specially constructed corpus, HotelCOPE, to select headwords and provide some unique lexicographical information, offering some useful appendixes to enhance its usefulness, and seamlessly integrating itself with modern technology for better learning efficiency in the age of mobile learning. Meanwhile, it has also left room for further improvement in the future, including the translation of some headwords. Overall, it makes a valuable addition to the repertoire of references to those learning hotel English in China.
Mastery of English vocabulary trough pop up method

Mastering of vocabulary is part of mastering a language, especially English, because if someone mastered the language, it means that person mastering of vocabulary. In other words, the more vocabulary students have the easier for students to compose words and communicate well. Such as learning English they say that the main reason was found difficulties to understand the lesson and the solution to all of these problems was the teachers should make learning media as attractive as possible the can, so that they can attract students to make small notes for the vocabulary they get from media learning. Besides, the teacher can motivate students to be more active in adding new vocabulary trough pop up method. In this research using a qualitative method in which the process of research was observed the students vocabulary. In the results of this study was the weakness of vocabulary mastering in high school. Teachers also was implemented several methods in order to help students in the process of mastering words. Not only the Teacher, but also the researcher suggests several methods to be used so that students can easily understand English quickly.