Reviewerly/exHarmony huggingface dataset:
Pelatihan Pembuatan Sasirangan Media Baju Kaos Peluang Usaha Anak Panti Asuhan Yatim Putra Muhammadiyah Banjarmasin

Lack of knowledge and skills in developing sasirangan t-shirt media which can be used as profitable business opportunities, there is no initiator of the sasirangan t-shirt business among young people at the Banjarmasin Muhammadiyah Orphanage. Community Service Providing training and education related to sasirangan cloth, innovation in the form of t-shirts, providing motivation and inspiration for creative entrepreneurship as business opportunities that can be developed with the hope that there will be interest in developing into independent creative businesses. The method used is lectures providing material and direct practice. As a result of this training, the young people at the Yatim Putra Muhammadiyah Orphanage were able to understand and practice it directly in the process of making sasirangan using t-shirt media with the given working mechanism. Of the fifteen participants who took part in this training, all of them succeeded in making sasirangan t-shirts which were shown in the final process where the participants had gone through a series of manufacturing stages starting from drawing patterns, basting, threading, dyeing colors, releasing threads to drying. From this training, it can be concluded that all participants, the majority of whom are male, have talent in making this sasirangan and have an interest in being able to create according to trendy and contemporary motifs as a business opportunity independently or in groups for young people as a source of income for the children of the orphanage in supporting economy.
Analysis of Risk Assessment on Attendance Information Systems using COBIT 5 Framework

TVRI Yogyakarta has implemented digitalized information technology in achieving its organizational goals.To support its business processes, TVRI Yogyakarta uses an E-Presence Work Service Application which is used to manage the recording of employee attendance data so that employee attendance data can be identified automatically through the system and stored in the system database, but several risks occur such as data storage that is not recorded.properly so that it causes data loss and disrupted internet connections that affect the running of ongoing system processes.This research was conducted according to the COBIT 5 risk assessment guideline which focuses on two domains APO12 (Managing Risk) consisting of APO12.01 to APO12.06 and EDM03 (Ensuring Risk Optimization) consisting of EDM03.01 to EDM03.02.The assessment of the capability level used in the process assessment is done by collecting observation data, interviews, and questionnaires and then analyzing it with the Process Assessment Model so that it can find out the current capability value, expected capability value, gap and determine recommendations for each risk.Based on the results of testing the Capability Level in the APO12 (Managing Risk) domain, it is obtained a value of 3.67 which indicates the implementation of the process is at level 4 (Predictable Process) and EDM03 (Ensuring Risk Optimization) obtained a value of 3 which indicates the implementation of the process is at level 3 (Established).Process).The results of the gap based on calculations carried out by the APO12 process (Managing Risk) found a gap of 0.33 and EDM03 (Ensuring Risk Optimization) it was found that a gap of 1 ha was used as a recommendation.This research has been carried out well following the expected research objectives.
Writer-Independent Offline Signature Verification Using Deep Learning

Abstract: The signature of humans is an important feature in the field of biometrics. It is used as an authentication tool especially in the banking sector because all humans have a distinct signature and each signature has its features. So human signature is used to recognize a person. There has been a fair amount of work done in the field of handwritten signature verification but still, the problem is unsolved. The main intent of signature verification is to distinguish whether the signature is genuine or forged. The signature verification can be offline or online. This is a tedious task, principally in the case of offline because the dynamic information of the signature is not available. A brief survey of various offline signature authentication methods and recent advancements in the field has been represented in this paper. Keywords: Feature extraction, Neural Network, Offline Signature verification, Preprocessing, Writer-independent.
Face Recognition with Mask

The COVID-19 is an uncommon emergency that has brought about an enormous number of setbacks and security issues. Individuals habitually wear covers to safeguard themselves from the spread of Covid. Since specific pieces of the face are covered up, face acknowledgment turns out to be incredibly troublesome. During the progressing Covid pandemic, one of the specialists' essential objectives is to concocted answers for the issue that are both fast and effective. The reason for this paper is to give a survey of different strategies and calculations for human acknowledgment with a facial covering. This paper depicts different methodologies, for example, Haar course, Adaboost, VGG-16 CNN Model, etc. An examination of these techniques is acted to figure out which approach is achievable. With innovative progressions. Keywords — Viola Jones, Adaboost, Computer Vision, Convolutional Neural Network, MobileNetV2, VGG – 16 Model.
Insulator String Detection Method Based on the InST-Net Network

Aiming at the problem of detecting insulator strings in aerial images, a detection method of insulator strings based on the InST-Net network is proposed in this paper. First, the ResNet50 network pretrained on the ImageNet dataset is used as the backbone network for insulator string feature extraction. Subsequently, for insulator strings of different imaging sizes in the image, three detection branches are designed based on the design ideas of the existing YOLO model. Finally, an SPP module is adopted to improve the feature extraction capability of each detection branch of the proposed InST-Net network. The experimental results show that the InST-Net network detection accuracy rate reaches 90.63%, which is higher than that of the four classic one-stage target detection networks and the existing insulator string detection network.
Infusing Biomedical Knowledge into BERT for Chinese Biomedical NLP Tasks with Adversarial Training

Biomedical text mining is becoming increasingly important. Recently, biomedical pre-trained language models such as BioBERT and SciBERT, which can capture biomedical knowledge from text, have achieved promising results in biomedical NLP tasks. However, most biomedical pre-trained language models rely on the traditional masked language model (MLM) pre-training strategy, which cannot fully capture the semantic relations of context. It is challenging to learn biomedical knowledge via language models in the Chinese biomedical fields due to the lack of training resources and the extreme complexity and diversity of Chinese medical terminologies. To this end, we propose MedBERT-adv, which utilizes a biomedical knowledge infusion method that can effectively complement BERT-like models. Instead of using time-consuming medical expert annotation and inaccurate automatic annotation, we use the article structure in Baidu Encyclopedia as a weakly supervised signal, utilizing each medical term and its category as labels to pre-train the model. We also leverage adversarial training strategies like FGM for fine-tuning downstream tasks to further improve the performance of MedBERT-adv. We experimented with MedBERT-adv on the Chinese biomedical dataset CBLUE using eight NLP tasks. Among all of them, our proposed model obtained an average 1.8% improvement in average score than four baseline models, demonstrating the effectiveness of MedBERT-adv on Chinese biomedical text mining.
Implementation of E-Learning, Motivation and Learning Independence to Improve Learning Outcomes

his study aims to determine the effect of using e-learning, learning motivation and learning independence on student learning outcomes in class XI IPS 1 MAN 1 Nganjuk. The population in this study were 99 students of class XI IPS MAN 1 Nganjuk. The sample was determined as many as 30 students based on Roscoe's that for research, the appropriate sample size in the study was between 30 to 500. The sampling technique used purposive sampling. Data were collected using questionnaires and documentation, which were then analyzed using multiple linear regression analysis with the help of the SPSS Version 26 program. The results of data analysis showed that either partially or simultaneously the use of e-learning, learning motivation and learning independence had a significant effect on learning outcomes. Keywords: e-learning, learning motivation, independent learning, learning outcomes.
A highway investment risk assessment method based on extreme learning machine

Investment risk assessment refers to the scientific prediction and assessment of the risk factors that may exist in the project, and then provide the necessary auxiliary decision-making opinions for the implementation of the project. Due to the importance of risk prediction in the process of project implementation, how to carry out investment risk assessment more accurately has become a hot research topic in the scientific community. The main research object of this paper is the highway. At present, the risk assessment methods for highways mainly include questionnaire survey method, expert evaluation method, etc. However, these methods have large subjective errors and cannot deal with a large number of data. In view of the above problems, this paper uses the extreme learning machine to establish the investment risk assessment model, and uses the kernel function and PSO algorithm to optimize the model. Compared with the traditional risk assessment, as well as the SVM, BP neural network and other existing risk assessment methods, the model proposed in this paper has certain advantages in training time and other aspects. At the same time, it also compares the basic ELM, the ELM combined with the kernel function and the ELM combined with the PSO algorithm to predict the risk. The experimental results show that the addition of the kernel function and the PSO algorithm can alleviate the overfitting problem of the ELM model and improve the accuracy of risk prediction. Therefore, the investment risk assessment method based on extreme learning machine proposed in this paper can provide some auxiliary decision-making for highway investment projects in China.
Effects of Heart Rate Feedback on an Asymmetric Platform using Augmented Reality and Laptop

Asymmetric systems enable remote collaboration using multiple different devices. In this paper, we have designed an asymmetric system that enables collaboration between Augmented Reality (AR) and Laptop users. Remote collaboration systems often lack mutual emotional understanding between the collaborators. Researchers in virtual reality have used physiological data sharing as a way to increase social awareness between collaborators. No such effort was made in AR and in asymmetric domains. A key contribution of this work is that we have enabled physiological (heart rate) feedback sharing between the collaborators to generate more awareness about one another in an asymmetric system. We executed a user study with 20 participants where they performed a collaborative and a competitive task. Our results indicated that providing heart rate feedback is beneficial for AR devices. We noticed the experimental tasks had an effect on galvanic skin response although no such effect was found on heart rate signal.
A Time Reversal Symmetry Based Real-time Optical Motion Capture Missing Marker Recovery Method

This paper proposes a deep learning model based on time reversal symmetry for real-time recovery of continuous missing marker sequences in optical motion capture. This paper firstly uses time reversal symmetry of human motion as a constraint of the model. BiLSTM is used to describe the constraint and extract the bidirectional spatiotemporal features. This paper proposes a weight position loss function for model training, which describes the effect of different joints on the pose. Compared with the existing methods, the experimental results show that the proposed method has higher accuracy and good real-time performance.
Deskripsi Belajar Anatomi Tingkat Kesulitan pada Sistem Online dan Offline Mahasiswa Fakultas Kedokteran, Wijaya Kusuma Surabaya

Anatomy is a basic science that is learned in medical education as a basis related to clinical skills in the skills to become a doctor. The Covid-19 outbreak has changed the entire education system. The Covid-19 pandemic has changed the shape of the education model at universities until the Faculty of Medicine is closed. The COVID-19 pandemic has changed the pattern of the learning system, from which the offline model was changed to online. This learning pattern forces students to usually participate in online activities. Bring up the pattern of levels that appear in the two models of this learning system. The cross sectional method and observational research were then analyzed and the descriptions were observed. simple random sampling method was used in this study. The purpose of this research is to understand descriptive anatomy learning, seen from the level that appears in students' difficulties, obtained from 20 questionnaires consisting of 7 categories, namely: understanding, effectiveness, good communication, learning hours, clear material, easily accessible lectures. There were no students who took part in the remedial course (RC). From the results of filling out the questionnaire and processing the data, it can be concluded that there is no picture of the difficulties between the two learning systems, namely online and face-to-face.
Analysis and research on the economic model of supply-side reform in Anshan

Anshan, the third largest city in Liaoning Province, plays a pivotal role in the revitalization of Liaoning in 2021—the beginning of the "14th Five-Year Plan” and the transition of two centennial goals. Analyzing economic, population, and innovation data in Anshan over the past ten years; the paper based on the perspective of economic statistics establishes the Cobb-Douglas supply-side economic model of Anshan, and draws the following conclusions: Anshan’s economic returns to scale are increasing. Local government should increase investment promotion, and strengthen scientific research and talent reserves, to speed up the transformation and upgrading of local economic structure and industrial structure innovation, which aim to promote the rapid and high-quality development of Anshan’s economy.
Robust End-to-end Speaker Diarization with Generic Neural Clustering

End-to-end speaker diarization approaches have shown exceptional performance over the traditional modular approaches. To further improve the performance of the end-to-end speaker diarization for real speech recordings, recently works have been proposed which integrate unsupervised clustering algorithms with the end-to-end neural diarization models. However, these methods have a number of drawbacks: 1) The unsupervised clustering algorithms cannot leverage the supervision from the available datasets; 2) The K-means-based unsupervised algorithms that are explored often suffer from the constraint violation problem; 3) There is unavoidable mismatch between the supervised training and the unsupervised inference. In this paper, a robust generic neural clustering approach is proposed that can be integrated with any chunk-level predictor to accomplish a fully supervised end-to-end speaker diarization model. Also, by leveraging the sequence modelling ability of a recurrent neural network, the proposed neural clustering approach can dynamically estimate the number of speakers during inference. Experimental show that when integrating an attractor-based chunk-level predictor, the proposed neural clustering approach can yield better Diarization Error Rate (DER) than the constrained K-means-based clustering approaches under the mismatched conditions.
Correlation-Based Template Tracking Of Moving Object

This paper presents the correlation-based motion estimation technique for the 3D displacement of objects. Two high-speed cameras are configured as a stereovision system and synchronized in real-time. Finger and hand motions are captured in form of digital images at 1500 fps and 2000 fps respectively. A complete motion acquisition system is calibrated to determine the intrinsic and extrinsic parameters which were later used in the correlation algorithm. The grayscale image frames acquired from the cameras are correlated using square templates of 10x10 pixels created from the reference image. The finger and hand motion are discussed with varying camera speed as a measure of brightness inconsistency. The observations in the correlation coefficient indicate that the proposed algorithm is efficient up to 20 and 50 templates for the finger and hand motion cases respectively. The correlation coefficient for finger motion was increased to 0.987 and 0.972 for the left and right cameras, respectively, while the correlation coefficient for hand motion was 0.924 and 0.898. The proposed algorithm is developed in MATLAB and validated by tracing the sinusoidal motion of a solid rectangular element from the image correlation technique and an accelerometer sensor mounted over the block.
EFFECTIVENESS OF ONLINE FILM POSTERS ON STUDENTS’ ACHIEVEMENT IN WRITING DESCRIPTIVE TEXT

This research aimed to determine whether the online film posters as a teaching medium significantly affected students’ achievement in writing descriptive text. This research was conducted using a quasi experimental research with pre-test and post-test design. The population was 60 students of the tenth grade senior high school of MAS Muhammadiyah 13 Sei Rampah, Medan. There were two parallel classes as the population, namely Class X IPA and X IPS. Each class consisted of 30 students. The sample in this research was taken using the cluster sampling technique to decide the experimental and control groups. X IPA was the experimental group and X IPS was the control group. The experimental group consisted of 30 students was taught using online film posters and the control group consisted of 30 students was taught without using any media. There were five meetings during the research. The first and the fifth meetings were administered as pre-test and post-test while the other three meetings were administered as the treatment sessions.The instrument used to collect the data was a writing test. The students of both groups were assigned to write a descriptive text to be administered as the research data. It was obtained that the mean score of experimental group in pre-test was 74.86 and 79.93 in post-test while the mean score of control group in pre-test was 73.66 and 69.9 in post-test respectively. These statistical data were then analyzed using t-test formula. The result of t-test calculation showed the value of t-observed was 3.20 and the value of t-table was 2.00 in level of significance of 0.05 and df=58. This meant that t-observed was higher than t-table (3.20 &gt; 2.00). Based on the finding, then alternative hypothesis (Ha) was accepted and null alternative (Ho) was rejected. Thus, it was concluded that online film posters significantly affected students’ achievement in writing descriptive text.
Deep neural network approaches in hate speech detection

Over the past decade, the number of automated communication and posts on social media platforms has made it much easier to generate and spread hate speech in tandem with the related social implications. Social media companies have experienced intense pressure to address the issue and help minimize incidences of hate speech on their platforms. In this regard, machine language processing techniques such as natural language processing can help detect online hate speech. Natural language processing is a branch of machine language that enables one to understand human speech, analyze, manipulate it, and potentially understand language generated by humans. Other deep learning techniques that could help explore this subject and improve hate speech detection, such as convolutional neural network, recurrent neural network, and graph neural network, will be explored in this paper.
Analysis of Chinese Teaching Strategies in Primary Schools in the New Era

In the Chinese teaching activities of primary schools in the new era, primary school students not only need to learn basic Chinese knowledge and skills, but also need to have Chinese thinking ability under the perception of Chinese excellent traditional culture and basic literacy, etc. Therefore, in the new era Chinese teaching, teachers should pay attention to the cultivation of students' comprehensive quality, the cultivation of students' subjectivity and the accumulation of students' basic Chinese knowledge. In addition, the current era is an era of information technology and globalization. It is also a new era of China's development. Thus, in primary school Chinese teaching, teachers should also need to combine the teaching content with the development direction of the times, comprehensively use all kinds of information-based teaching means, build a smart classroom for students, and constantly enhance the enthusiasm and initiative of students in Chinese learning, so as to improve the quality of Chinese teaching.
RELEVANCE OF APPLIED SOCIOLOGICAL RESEARCH

. In the article, the authors, through a structural and functional approach, considered the relevance and validation of the practices of the program of applied sociological research. The apodictic contribution of the stages of institutionalization of applied sociology as a special branch of science is actualized. The problem field of ontogenesis of heuristic functions of empirical science during the presented time continuum is determined. The methodological analysis of the substantiation of each section of applied sociological research, as well as the concepts of its error forms: subjectivism and research relativity, induced by the program of methods for studying social phenomena, is carried out.
DISTANCE AND CORRESPONDENCE LEARNING OF THE SUBJECT "DESCRIPTIVE GEOMETRY. ENGINEERING AND COMPUTER GRAPHICS ”FOR STUDENTS OF THE SPECIALTY "AUTOMOBILE TRANSPORT"

10.2 Distance and correspondence learning of the subject "Descriptive geometry.Engineering and computer graphics" for students of the specialty "Automobile transport" Research of distance learning as one of the options of electronic full-time or parttime learning, which adapts traditional forms of classes and paper learning tools in telecommunications.Distance learning is designed to solve specific problems related to the development of the creative component of education and its problems.The use of Internet technologies and distance learning of descriptive geometry opens new opportunities for continuous training of specialists and retraining of specialists, obtaining second education, makes training more accessible.