Reviewerly/exHarmony huggingface dataset:
the The Influence of E-Learning as Learning Media Usage and Peers on the Learning Interest Of SMK Students Majoring In BPD

Interest is a sense of preference or interest in an activity indicated by a desire, a tendency to pay attention to the activity without being told by anyone, performed with self-awareness and followed by a sense of pleasure. Unfortunately, students' learning enthusiasm remains low at SMKN 1 Ponorogo, particularly in class X of the BDP major. It was shown by the observation result that there were 35% of students accessed applications other than e-learning during the learning process, such as opening the Tik Tok, Instagram, and Snack Video applications. Also, 20% of students were chatting with their friends about topics other than the subject matter during the learning process, like discussing rising KPOP members and trending outfits. Therefore, it is necessary to study more deeply about the factors influencing the students' low interest in learning. Internal and external factors influence interest in learning. Internal factors consist of physical factors and psychological factors. On the other hand, external factors consist of family, school, and community factors. This study was designed with a quantitative approach, the analytical technique used is the classical assumption test of simple linear regression and multiple linear regression. from the results of this study, it was found that e-learning and peer learning media had an effect on student interest in learning at SMKN 1 Ponorogo in the 2021/2022 academic year.
Kerja Sama Kerjasama guru bimbingan konseling dengan guru mat pelajaran dalam menangani kecanduan game online di sekolah menegah atas negeri 10 Pekanbaru

Sahriza, (2022): The Cooperation between Guidance and Counseling Teachers and Subject Teachers in Dealing with Online Game Addiction at State Senior High School 10 PekanbaruThis research aimed at knowing 1) the form of cooperation between guidance and counseling teachers and subject teachers in dealing with online game addiction (2) supporting and obstructing factors of the cooperation between guidance and counseling teachers and subject teachers in dealing with online game addiction, (3) the factors causing addiction to online games at State Senior High School 10 Pekanbaru. It was a descriptive qualitative research. The main informants of this research were 4 guidance and counseling teachers, and the additional informants were 3 subject teachers and 3 students. The object of this research was the cooperation between guidance and counseling teachers and subject teachers in dealing with online game addiction. Interview and documentation techniques were used for collecting the data. The data analysis was presented with narrative technique. The findings of this research showed that 1) the form of cooperation between guidance and counseling teachers and subject teachers in dealing with online game addiction were a) sharing information about students' attitudes and behavior, especially students who were addiction of playing online games, b) moving the problems experienced by students from subject teachers to guidance and counseling teachers, c) holding a meeting between the parents, teachers, and students concerned. 2) The supporting and obstructing factors of the cooperation between guidance and counseling teachers and subject teachers in dealing with online game addiction: a) the supporting factors: school personnel supported the cooperation between guidance and counseling teachers with subject teachers in dealing with online game addiction, supporting facilities and infrastructure, b) the obstructing factors: the background of the guidance and counseling teachers who did not come from undergraduate of guidance and counseling, limited time to provide guidance and counseling services, busyness and lack of attention from parents in controlling children playing games. 3) Factors causing addiction to online games: a) internal factors: the strong students’ desire to get high scores in playing online games and the boredom felt by students that made them more interested in playing games, b) external factors: the environment that was very influential, so it caused addiction in playing games to forget time, and the many types of online games that triggered addiction students wanted to keep trying new things.Keywords: Cooperation, Online Game Addiction
Pengembangan media pembelajaran vidsi (video animasi) dengan vlognow untuk pembelajaran PAI mengenai kepemimpinan abu bakar ash-shidiq di SD kelas VI

Been doing research on PAI's animated media development on the material of leadership material ash-shidiq with an application Vlognow. The goal of research is to describe the development of the media of learning and media tests are based on the response of teachers, the teachers' response, impact, as well as obstacles. Kind of quality research with a footage of research covering the stage of preliminary studies, the libraka studies, the field survey, and the draft product drafts, as well the development phase with limited trials. This research target is a student of VI Manager's class. The result of this research of the pro-educated interest responders of the media is well reviewed from the components formatic, quality, clarity, as well as the interest of the educated. The media has impact on the competitors the education of studying PAI, understanding of concept, and the arousal of the study and the pleasure of learning, and the interest of learning not only by method of speech as well as they're looking more attractive and not boring. Where the response of SDN Manangga students held, as many as 1 student stated the animated video was not understood, 17 students understood the video and 15 students expressed very understanding the animated video about the leadership of Abu Bakar Ash-Shidiq can be concluded that the animated video based. Vlognow application on Abu Bakar Ash-sidiq leadership material can improve motivation and learning achievements and is considered more understandable and fun and worthy used in the learning process.
Research on Key Technologies of target location based on Intelligent Robot

In recent years, the country has proposed the strategic development goal of “Made in China 2025”, and the intelligent manufacturing industry has gradually received national attention. Intelligent robots rely on the advantages of high efficiency and strong adaptability to the working environment to promote the development of intelligent manufacturing industry. Machine vision technology makes robots more intelligent, and the robot system integrating vision has also developed rapidly. Compared with monocular vision, binocular vision can obtain target stereo information, and it has become a key research topic in related fields. Under this background, this paper designs an intelligent robot target recognition and positioning system based on binocular vision according to the actual industrial demand. This paper mainly studies the recognition and localization algorithm of circular object workpiece by intelligent robot based on binocular vision system, and designs and realizes the whole experimental system according to the research content. Based on the system experiment, this paper introduces the calibration of the binocular camera system and the robot vision system, the target image pretreatment algorithm, the recognition and positioning algorithm of the target workpiece and so on. Finally, the visual interface is designed to control the robot to complete the simulated grasping action and realize the whole system. The object image is acquired by binocular camera, the object identification and location are completed according to the image processing algorithm, and the robot is controlled to complete the simulation grasping experiment, which has certain reference significance in practical application.
On the building of self-adaptable systems to efficiently manage medical data

The systems that meet non-functional requirements (NFRs) are key for e-health services to face up events such as service outages and violations of confidentiality. How-ever, traditional NFR systems produce overhead in execution time, which could affect critical decision-making processes. This paper presents a dynamic parallel pattern construction model to design and create efficient NFR self-adaptable sys-tems. The construction of patterns is performed in two design phases: in the first one, the designers build NFR systems by creating pipelines including as many applications as required to meet the NFRs established by healthcare organizations. In the second phase, a pipeline is converted into a worker that auto-matically is added to a dynamic pattern. In a dynamic pattern, the workers can be cloned to be executed by different parallel patterns (e.g., manager/worker, divide&#x0026;conquer, etc.) to face changes in the incoming workload during execution time, which converts a worker into a self-adaptable NFR system. A proto-type was implemented to create self-adaptable NFR systems, which were used in a case study to manage spirometry studies, tomography images, and electrocardiograms. The evaluation showed the effectiveness of this dynamic pattern model to create self-adaptable systems when processing multiple types of medical data/contents. The evaluation also revealed that the self-adaptable NFR systems built by dynamic patterns yielded significant performance gain in a direct comparison with the implementation of NFR application pipelines built by a traditional framework called Jenkins.
Role of Machine Learning in Managing Cloud Computing Security

Cloud computing is one of the most trending technology through which digitalized data management and storing becomes easier and more effective. However, besides the advancement of technology, data protection is another top priority concerning factor as millions of sensitive data are gets stored and transferred through the cloud computing system. As per the current days of cyber-activities, data security threat in cloud computing have increased, which posses threat in the development of the business. In that field, machine learning based advanced algorithm develop the virtual encrypted environment to protect the data from unauthorized access and hacking.
Examining the Practices and Challenges of Distance Education of PhD Candidates in the Context of COVID-19

The distance education system is actively developing in the world due to the COVID-19 pandemic. The sharp transition of PhD candidates to distance education caused difficulties in organising the educational process. The aim of this study was to analyse the methods of distance education for postgraduate students in the context of a COVID-19 pandemic. A survey of graduate students was conducted through specially- designed and semi-standardised interviews of focus groups of producers and consumers of educational services. The study showed that the process of adaptation of postgraduate students majoring in Physical Culture and Sports and Biology was much more difficult than in the major Educational, Pedagogical Sciences and Philology. The reasons for the problems of distance education of PhD candidates included the complexity of creating educational and methodological materials for distance learning; lack of a centralised system of certification and accreditation of electronic courses; insufficient motivation of teachers; shortage of teachers who could competently develop distance learning courses in higher education. The study identified opportunities to implement promising areas of online learning in the system of training of academic and teaching staff: retraining of a large proportion of the teaching staff, implementation of a system approach to the development of the online environment of educational institutions, development of skills and abilities to use educational content. Prospects for further research include the study of problems of violation of academic integrity by postgraduate students in the course of distance learning.
Research on E-Commerce User Interest Recommendation Method Based on TF-IDF Algorithm

In the development process of the e-commerce industry, with the continuous increase of the number of consumer user groups, the base of online shopping user groups is getting larger and larger. In this process, the research on recommendation algorithms for different user groups is very important. The current research has made some progress, but there are still some problems, such as the data classification for user portraits is not specific enough, the feature extraction algorithm has related deficiencies, and the data recommendation accuracy is not enough. On this basis, this paper adjusts the word frequency and inverse document frequency of the recommended keywords. It introduces the TF-IDF algorithm to recommend targeted product keywords for different user portraits, effectively solving inaccurate recommendation results.
Research on a New Model of Teaching Chinese as a Foreign Language Based on Network and Multimedia

The most obvious change in the promotion of modern Chinese is the change of teaching methods from paper-based to multimedia teaching.As the number of foreign Chinese learners continues to rise, there is a need to introduce multimedia technology to build a new education and teaching model.In this regard, based on the analysis of the current situation of teaching Chinese as a foreign language, we introduce the concept of multimedia education and propose new ideas of teaching Chinese as a foreign language, including the setting of teaching objectives, the division of teaching contents and the subsequent educational evaluation.The emergence of multimedia networks can not only provide more educational teaching resources for teaching Chinese as a foreign language，but also can effectively improve students' learning efficiency and has an important impact on the classroom atmosphere and learning results.
Dense-Pose2SMPL: 3D Human Body Shape Estimation From a Single and Multiple Images and Its Performance Study

The shape and pose estimation of a human body are essential for human behavior analysis, sports and medical analysis, and virtual reality. Although 2D image data are much easier to acquire than 3D scan data, the estimation accuracy using 2D images is still far below the 3D scanning methods&#x2019; ones. In this paper, we propose a 2D image-based human body estimation method appropriate for body shape and size measurement. The proposed method uses SMPL (Skinned Multi-Person Linear model), a human body model, and rich correspondences of Dense-Pose network so named Dense-Pose2SMPL, which estimates the SMPL parameters estimation by minimizing the re-projection error of the correspondences between the pixels in the human image and 3D surface points in SMPL. The previous SMPL parameter estimation methods use the sparse joint correspondence so that they show very limited performance in shape estimation. We compare the body measurement accuracy of Dense-Pose2SMPL with a single human image with SMPLify, a joint-based estimation method, and DecoMR, a Dense-Pose-based neural network regression method. The experiments results show a dramatic improvement of Dense-Pose2SMP over SMPLify and DecoMR. In the circumference estimation error, over 30 percent decrease for the overweight and underweight and 10 to 20 percent decrease on average. We also provide an analysis of the effects of the input conditions: the subject&#x2019;s BMI level, pose, clothing style, and camera viewpoint. A-pose, side (profile) camera views and minimal and tight clothing style show higher accuracy than other conditions. Finally, the extension with multiple (two) images can give around 15 percent more improvement in body size estimation accuracy over the single image-based method.
Robotic Vehicle with Hand Motion Control Using a RF Module

This paper focuses on the materials, working principle of a robotic vehicle which will be controlled with hand motion. The aim of this research is to enhance industrialization by creating a hand motion controlled robotic vehicle, since it uses hand motion it will be easier to use in automation and various industries. It is also very beneficial for people with disabilities since only hand motion is required. There were various materials that were used in the research. 2 microcontrollers, an accelerometer, RF modules, encoder, decoder, diode, motor driver IC, DC motor and batteries. The microcontrollers are small computers which can be programmed to be utilized in various different ways. The Accelerometer is a PCB or a sensor which detects speed. The RF modules are of two types which are transmitters and receivers and they are components which are used to send data and information wirelessly. The encoder and decoder are used to convert the binary to any n number of output terminals. The diode is used to send the electricity in one direction. The motor driver IC controls the DC motor from the information given by the microcontroller ICs and lastly a 9v Battery will be used to power the system. The arduino software will be used to program the IC so it can perform the required task. The gadget features a receiver circuit that is intended to be worn on top of the user's glove. The vehicle's circuit incorporates an RF receiver, an 8051 CPU, and a Driver IC to power the motors. This method is extremely useful for persons with impairments since it allows a robotic vehicle to drive itself using hand gestures. The person only needs to move his hand to move the car forward, backward, left, or right. As a result, the user is not required to push any buttons
Modeling Empathy Episodes with ARD and DMN

Empathy, the ability to emotionally understand other people’s feelings, might be modeled in various ways. In this paper, we try to look at the empathy episodes from the knowledge engineering perspective. We present the possibility of modeling the empathy episodes using the Attribute Relationship Diagrams (ARD) and Decision Model and Notation (DMN) modeling notations and methods.
Kernel-Based Matrix Factorization With Weighted Regularization for Context-Aware Recommender Systems

As an essential task for recommender systems, the rating prediction problem over several contexts has attracted more attention over the recent years. The traditional approaches ignore the contexts and thus fail to predict the ratings for the unseen data in the rating tensor for varying contextual scenarios. Matrix factorization is preferred over decomposing the rating tensor for avoiding the burden of very high computational complexity while learning the interaction of users&#x2019; and items&#x2019; latent features. In this work, we propose a novel kernel loss function for optimizing the objective function of matrix factorization in a non-linearly projected rating space under multiple contexts in an optimum manner and also incorporate the implicit feedback of items in the learning process. Further, the optimization is regularized by applying different weights for each regularization term depending on the users&#x2019; and items&#x2019; participation. Extensive experimental evaluation on five benchmark context-aware datasets indicates the superiority of the proposed work for capturing the non-linearity and predicting the ratings of unseen items for users under varying contexts over the existing and baseline methods. The proposed kernel loss function is also shown to be resistant against shilling attacks in the recommender system. A detailed ablation study demonstrates the validity of the proposed work and the results are shown to be statistically significantly better with an improvement in the range of 3% to 11% over the baseline methods.
To the Question of International Experience in the Professional Training of Future Specialists in the Socionomic Sphere to Work With Families Raising Children With Special Needs

The article is devoted to the analysis of scientists’ views on international experience in the professional training of future specialists in the socionomic sphere to work with families raising children with special needs (master's level). Peculiarities of theoretical and practical training of future specialists in the social sphere (master's level) in colleges and universities of the USA, Canada, Sweden, Great Britain, France, Germany, Poland are discussed. According to the world standards, the selected countries demonstrate breakthrough achievements, a high level of professional training of the target group of specialists. The main approaches to the organization of professional training of future specialists in the socionomic sphere are specified, namely the integration of theory and practice in the learning process, interdisciplinary of programs and disciplines, interactivity of educational process organization, participation of stakeholders, practical orientation of the training (activity of students in social agencies under the guidance of mentors). The emphasis is put on certain teaching methods, specifically on discussion, situational, problematic methods, the use of «reflexive breaks», «open communication», practical activities in the laboratory, group forms of work.
Intelligent Auxiliary System of Shared Study Room

With the rapid development of mobile Internet, Sharing Resource Information Service has entered people's life.Sharing study rooms became a requirement.This paper introduces an intelligent assistant system of shared study room, which is constructed by means of sensor, wireless communication, Internet of things and so on.The system is composed of main control chip, wireless communication module, Light Sensor, pressure sensor, ultrasonic sensor, RFID and so on.The utility model can realize auxiliary functions such as lighting, monitoring of learning time, correcting of sitting posture and charging by time in the shared study room.The equipment of self-study room realizes the linkage of software and hardware.Compared with the management mode of traditional study room, the intelligent assistant system of shared study room has the characteristic function of satisfying people's needs, provides a good learning atmosphere, and can effectively solve the problem of insufficient learning resources at present.It provides an idea for the future development of the shared study room industry in the field of business models.
Punctuation Restoration in Spoken Italian Transcripts with Transformers

In this paper, we propose an evaluation of a Transformer-based punctuation restoration model for the Italian language. Experimenting with a BERT-base model, we perform several fine-tuning with different training data and sizes and tested them in an in- and cross-domain scenario. Moreover, we conducted an error analysis of the main weaknesses of the model related to specific punctuation marks. Finally, we test our system either quantitatively and qualitatively, by offering a typical task-oriented and a perception-based acceptability evaluation.