Reviewerly/exHarmony huggingface dataset:
Digital skills of regular basic education teachers in the context of COVID-19

Background: Objective of the study: The purpose of this study has been to identify the level of digital skills of regular basic education (EBR) teachers in their pedagogical practice in the context of covid 19 through the review of the available literature disseminated among teachers. years 2016 and 2021. Method: The PRISMA methodology has been used; Searches were made in the Scopus database using keywords related to the research topic. Of the 299 investigations identified through the search, 16 were selected that coincided with the inclusion criteria, all of them were research articles, carrying out the compilation and systematic review of the studies referring to digital competences in EBR teachers. Results: The results showed that EBR teachers show a low level of digital competence due to several factors, including a low inclusion in this field since their professional training.&#x0D;
Optical Remote Sensing Image Object Detection Model Based On Improved YOLOv5s

Abstract Aiming at the problem of error and omission caused by the complex image background and the high similarity between the remote sensing objectt and the background in remote sensing image detection, an improved model of remote sensing image detection based on YOLOv5s is proposed. The dual attention mechanism of channel and space is added to the convolutional layer of the trunk feature extraction network and the feature fusion network, which enhances the features with high correlation of the objectt to be detected and suppresses the features with low correlation, and improves the ability of the model to extract the features of remote sensing objectts in a complex background, so as to improve the detection accuracy of the algorithm. In this study, a comparative experiment was conducted on the NWPU VHR-10 dataset, and the results showed that the average accuracy of the proposed model was 94% when the dataset was turned over and the ratio was 0.5, which was 2.3% higher than that of the original YOLov5s, which verified the effectiveness of the proposed method.
Pelatihan Pengembangan Potensi dan Menciptakan Ketahanan Pangan Masyarakat Desa Ngargosari

The purpose of this Village Thematic Community Service Program is to help and provide solutions to the problems faced by the community as well as to provide real work experience in the field to apply the knowledge gained during college to the community. The thematic KKN-Thematic to build this village has been carried out in Ngargosari Village, Sumber Lawang District, Sragen Regency. Several program activities that have been implemented are models for developing local potential to improve the standard of living of the local community. Activities carried out include Socialization and Training on Maggot Cultivation, Vegetable Cultivation Training with the Floating Raft Hydroponic Method, Vegetable Crop Cultivation Training with Polybag Media, Socialization on Ecosystem Conservation and Fish Seed Release in Kedungombo Reservoir. The method used is empowering the target group including learning techniques in the form of providing theory and simulation to the target community and then direct practice with students and the community so that it provides enormous benefits in food security for villagers.
The Impact of Information Technology Methods on Accounting Information Quality

Information Technology is a key factor in eliminating the limitations posed by time and location and gives a much better and a faster access to informaition. In other words, technology has revolutionized work methods as a result of which, the paper which was used as a substrate for writing the work upon it has now been replaced by an electronic substrate. The changes created by technology include the use of Blockchain, IoT (Internet of Things), Cloud Accounting and Big Data to do accounting in automated form.The statistical sample of this study is given on the basis of the 171 person Cochran formulae consisting of financial managers, accountants and auditors of the Tehran city. Empirical results show that the use of Information Technology has a very significant role on the quality of Accounting Information and all these factors are influential in the quality of accounting system.
GACT: Activation Compressed Training for Generic Network Architectures

Training large neural network (NN) models requires extensive memory resources, and Activation Compressed Training (ACT) is a promising approach to reduce training memory footprint. This paper presents GACT, an ACT framework to support a broad range of machine learning tasks for generic NN architectures with limited domain knowledge. By analyzing a linearized version of ACT's approximate gradient, we prove the convergence of GACT without prior knowledge on operator type or model architecture. To make training stable, we propose an algorithm that decides the compression ratio for each tensor by estimating its impact on the gradient at run time. We implement GACT as a PyTorch library that readily applies to any NN architecture. GACT reduces the activation memory for convolutional NNs, transformers, and graph NNs by up to 8.1x, enabling training with a 4.2x to 24.7x larger batch size, with negligible accuracy loss. We implement GACT as a PyTorch library at https://github.com/LiuXiaoxuanPKU/GACT-ICML.
IMPLEMENTASI MOODLE PENGELOLAAN PEMBELAJARAN KELAS X SMA NEGERI 12 SIJUNJUNG

The process of learning in SMA Negeri 12 Sijunjung still use the conventional way through face-to-face between teacher and students. Learning methods such as these have limitations that may hamper the delivery of science that developed quickly due to the limitations of time and place. From this condition then the e-Learning System is expected to become an alternative solution to overcoming existing limitations on SMA Negeri 12 Sijunjung especially in Class X. The goal of the research is to make it easy for teachers and students in teaching and learning activities, facilitate the access of all forms in the process of teaching and learning activities and make it easier to accept the results or information from the teaching and learning activities. As for the benefits that can be gained from the analysis and design of web-based E-learning it is can make it easier for students to obtain material and lessons, allowing teachers to distribute subject matter, open communication between teachers and students or between students outside of lessons through discussion forums. Development of e-learning uses a Content Management System (CMS) Moodle, while analysis using Flow Information System, for designing system using Hyrarcy Plus Input-Process-Output (HIPO), Data Flow diagrams (DFD), and Entity Relationship Diagram (ERD).
PENERAPAN PEMBELAJARAN KOOPERATIF MODEL THINK PAIRS SHARE (TPS) TERHADAP HASIL BELAJAR PADA MATA PELAJARAN SISTEM KOMPUTER SISWA KELAS XI JURUSAN RPL SMK NEGERI 2 PADANG

The purpose of the research is to explain the hrgher percentage of increasing students’ score in using Think Pairs Share (TPS) strategy in learning computer system at SMK N 2 Padang in the academic year 2017/2018. This in an experiment research, that hold on March 27th until April 11th 2018 at SMK N 2 Padang. The population of the research are XI students of RPL majon’ty at SMK N 2 Padang. The XI students of majon’ty RPL1 and RPL 2 as control classes that choosen by the using Simple Random Sampling. Bosed on the experiment, the average students’ score in the experiment class is 84,33 than the control classes 77,93. From the hypothesis (H0) was rejected an hypothesis (H1) received the result of the research is Think Pairs Share (TPS) strategy give increasing and improvement towards students’ score in learning computer system at SMK N 2 Padang.
PERANCANGAN SISTEM INFORMASI PENGELOLAAN MATA PELAJARAN MENGGABUNGKAN AUDIO KE DALAM SAJIAN MULTIMEDIA DI SMK NEGERI 5 SIJUNJUNG

Management interpreted by as an work network or effort done by a group of people to with refer to activity in reaching specific purpose. Conducted management in study cover preparation of study, execution of study and also assessment of efficacy of study. Target of this research that is to yield Information System Management Of Subject Join Audio Into Form of Multimedia in SMK Negeri 5 Sijunjung. Research model in scheme of information system management of this subject use concept of System Development Life Cycle (SDLC) have 4 step in forming of information system that is planning, analyse, implementation and scheme. Scheme of analysis appliance and system use information system stream (ASI), for the scheme of system use HIPO, Context Diagram, Data of Flow Diagram, Entity Relationship Diagram. Result of information systems analysis management of subject join audio into multimedia expected to be sexpected by this system can walk and assist in course of learning and teaching, like appliance assist in submitting student and items can study items self-supportingly. Available and also of facility of interaktif to facilitate student in course of study. Result and solution in the form of existence of input process, process process and processing of output to woke up system. Input process by 7 entri form that is student data entri, teacher data entri, data entri of matpel, schedule entri and attendance data, items data entri and of entri value. Data processing which in input of processed and kept in a database. Process output present data which have been processed into a website. So that can yield an information system management of subject join audio Into Form of multimedia in SMK Negeri 5 sijunjung.
Pengaruh Metode Pembelajaran, Motivasi Belajar dan Lingkungan Sekolah terhadap Hasil Belajar Akuntansi Kelas X di SMK N 1 Sutera Kabupaten Pesisir Selatan Tahun Ajaran 2021/2022

This study aims to analyze: 1) To determine the effect of learning methods on the learning outcomes of class X accounting students at SMK N 1 Sutera. 2) To determine the effect of learning motivation on the learning outcomes of class X accounting students at SMK Negeri 1 Sutera. 3) To determine the effect of the school environment on the learning outcomes of class X accounting students at SMK Negeri 1 Sutera. 4) To determine the effect of learning methods, learning motivation and school environment simultaneously affect the learning outcomes of class X accounting students at SMK Negeri 1 Sutera. This type of research is associative and descriptive. The population of this research is accounting students of class X at SMK Negeri 1 Sutera totaling 101 people. The sampling technique used is proportional random sampling with a sample size of 81 people. Data analysis was assisted by SPSS version 15 and eviews version 8. The results of this study indicate that: 1) Learning methods have a positive and significant effect on student learning outcomes. Learning motivation has a positive and significant effect on student learning outcomes. The school environment has a positive and significant effect on learning outcomes. There is a simultaneous influence of learning methods, learning motivation and school environment on learning outcomes, so it can be concluded that Hypothesis 4 is accepted which means there is an influence of learning methods (X1), learning motivation (X2) and school environment (X3). Based on the results of this study, the authors suggest that teachers can provide varied learning methods and make students not bored, suggest students to further increase student learning motivation, it is hoped that in learning, especially in completing the tasks given by the teacher, it should be done faster and not negligently as well as the environment. a clean and good school to take part in learning because the school environment is very important for the learning process.
Kelayakan dan Praktikalitas Media Pembelajaran Thunkable Berbasis Android pada Materi Ketahanan Pangan Industri dan Energi Kelas XI IIS SMAN 1 Painan

This study aims to determine and analyze the feasibility and practicality of Android-based thunkable learning media on industrial food security and energy materials for class XI IIS SMAN 1 Painan. The type of research used in this research is research and development (Research and Development). With a 4D model that includes 4 stages, namely: Define, Design, Development, Desiminate. The subjects in this study were students of class XI IIS as many as 64 students. The results of the study found that: 1.)Thunkable learning media based on Android is declared very feasible after analyzing data processing using Microsoft Excel so that the total score is 277 with a mean level of 4.85 and a percentage rate of 97% with “Very Eligible” category. 2.) Thunkable learning media based on Android is declared practical after processing the research data obtained from the questionnaire, and a practical test is carried out so that a total score of 2.204 is obtained with a mean level of 4.45 and a percentage of 89% in the "Very Practical" category. Keywords: Appropriateness, Practicality, Android.
Preprocessing Breast Cancer Dataset from Ultrasound Screening to Improve Data Quality for Diagnosis Modelling

The quality of data assists to develop good prediction models for diagnosis and treatment; thus, improving will the outcome of the patients. This study aimed to use the preprocessing approach in the home medical dataset to improve the quality of data to create a better model for the prediction. The home dataset was obtained from ultrasound results for breast cancer screening from patients’ records at Benghazi Medical Centre. Regression imputation and SMOTE techniques were used to resolve missing values and imbalance of data. Also, they used to generate three datasets in addition to the original dataset. Cfs and ReliefF techniques were selected for feature selection. This study found that (SMOTE) dataset and (Regression imputation +SMOTE) dataset had the best results of performance and ROC area. The Cfs feature selecting technique also gained better results compared to ReliefF feature selecting technique. The "BIRADs4" is a difficult class to recognize the important patterns for correct classification. The study concluded that (Regression imputation +SMOTE) dataset with Cfs feature selection was the most suitable preprocessing for this dataset.
Ethical Recommenders in the Public Library Sector

Recommender Systems as an algorithmic class hide lurking risks despite their prevalence in academic and commercial circles. My specific research revolves around tracking and mitigating potential risks specifically in the Public Library domain. In collaboration with the National Library of The Netherlands, I am working on investigating whether the incorporation of Recommenders in a library's loaning system serves their social responsibility and purpose, with securing inclusivity being the main point of interest.
Privacy Preserving Machine Learning Systems

Machine learning(ML) tools are among the promising data-driven techniques that can help solve many real-life problems. However these tools rely on the collection of large volumes of data, which raises many privacy concerns and more broadly trustworthiness concerns. Privacy Preserving technologies aim at solving the issue by integrating privacy enhancing technologies (PETs) within the machine learning pipelines.
Semantic analysis of offensive language categories from existing annotated corpora

There exists a vast amount of different offensive language corpora for English language, annotation criteria and category naming. In this paper, we explore 21 different categories of offensive language. We use natural language processing techniques to find correlations between the categories based on seven different data sets. We employ several traditional (TF–IDF) and advanced (fastText, GloVe, Word2Vec, BERT, and other deep NLP methods) techniques to uncover similarities among different offensive language categories. The findings reveal that most of the categories are densely interconnected, while a two-level hierarchical representation of them can be provided. We also transfer the analysis to the Slovenian language and compare the findings between both researched languages.
Hybrid Deep Learning Technique with One Class Svm for Anomaly Detection in Crowded Environment

ABSTARCTNowadays, the analysis of abnormal events becomes more and more exhausting due to the divine use of surveillance cameras.Reliability of normal and abnormal samples is generally different in practice.In the existing work, developed machine learning and swarm intelligence based approaches for anomaly detection by extracting spatiotemporal features from video sequences.This proposed abnormal event detection mechanism is obvious, but the tracking result is less precise.Some of the reasons are low quality video, system noise, small object, and other factors.In order to improve the precisions of the tracked object, this research work proposed a new hybrid deep learning and robust segmentation method for better and faster tracking result.In the proposed approach, a video frame is subjected to a series of operations to extract most salient information from it.Initially, a 2D variance plane is constructed to encode local spatio-temporal variations around each pixel in a video frame.The Improved Particle Swarm Optimization algorithm is then applied to isolate the most salient regions based on motion information in the 2D variance plane.Grey Level Co-occurrence Matrix is applied to the extracted salient pixels in the video.And then the segmentation process is done with the help of fuzzy c-means clustering of successive frames are exploited for pattern matching in a simple feature space.Finally developed a hybrid deep learning based on a pre-trained Convolution Neural Network and One-class SVM is trained with spatial features for robust classification of abnormal shapes.Thus the experimental result suggests that the proposed anomaly detection techniques outperform the existing techniques in context of accuracy and time complexity.
Anti-Adversarially Manipulated Attributions for Weakly Supervised Semantic Segmentation and Object Localization

Obtaining accurate pixel-level localization from class labels is a crucial process in weakly supervised semantic segmentation and object localization. Attribution maps from a trained classifier are widely used to provide pixel-level localization, but their focus tends to be restricted to a small discriminative region of the target object. An AdvCAM is an attribution map of an image that is manipulated to increase the classification score produced by a classifier before the final softmax or sigmoid layer. This manipulation is realized in an anti-adversarial manner, so that the original image is perturbed along pixel gradients in directions opposite to those used in an adversarial attack. This process enhances non-discriminative yet class-relevant features, which make an insufficient contribution to previous attribution maps, so that the resulting AdvCAM identifies more regions of the target object. In addition, we introduce a new regularization procedure that inhibits the incorrect attribution of regions unrelated to the target object and the excessive concentration of attributions on a small region of the target object. Our method achieves a new state-of-the-art performance in weakly and semi-supervised semantic segmentation, on both the PASCAL VOC 2012 and MS COCO 2014 datasets. In weakly supervised object localization, it achieves a new state-of-the-art performance on the CUB-200-2011 and ImageNet-1K datasets.
The response time analysis of queuing model in cloud computing environment

In this paper, the queuing theory is used to model cloud computing environment. The aim of this job is to analyze the response time as a measure of the Quality of Service (QoS) of computer services. In the cloud computing, multi resources need to be allocated simultaneously to multiple customers. When a customer requests for a service, if servers are busy, the requested job enters into the waiting line until a server completes its service. So, this may lead to a bottleneck in the network. By modeling cloud platforms by a queuing network, it can be easy to determine and to measure the QoS. The arrival rate and the service rate of processing servers are two main parameters that can affect the performance of the model; so, they are used to analyze the performance of the model. This paper proposes a queuing model which is applied at multiple servers in order to analyze the response time and also to improve the network performance and QoS effectively in a cloud computing environment.
Text sampling strategies for predicting missing bibliographic links

The paper proposes various strategies for sampling text data when performing automatic sentence classification for the purpose of detecting missing bibliographic links. We construct samples based on sentences as semantic units of the text and add their immediate context which consists of several neighbouring sentences. We examine a number of sampling strategies that differ in context size and position. The experiment is carried out on the collection of STEM scientific papers. Including the context of sentences into samples improves the result of their classification. We automatically determine the optimal sampling strategy for a given text collection by implementing an ensemble voting when classifying the same data sampled in different ways. Sampling strategy taking into account the sentence context with hard voting procedure leads to the classification accuracy of 98% (F1-score). This method of detecting missing bibliographic links can be used in recommendation engines of applied intelligent information systems. Keywords: text sampling, sampling strategy, citation analysis, bibliographic link prediction, sentence classification.
Design and Validation of Quantum Key Management System for Construction of KREONET Quantum Cryptography Communication

As it has been recently proven that the public key-based RSA algorithms that are currently used in encryption can be unlocked by Shor’s algorithm of quantum computers in a short time, conventional security systems are facing new threats, and accordingly, studies have been actively conducted on new security systems. They are classified into two typical methods: Post Quantum Cryptography (PQC) and Quantum Key Distribution (QKD). PQC aims to design conventional cryptography systems in a more robust way so that they will not be decrypted by a quantum computer in a short time whereas QKD aims to make data tapping and interception physically impossible by using quantum mechanical characteristics. In this paper, we design a quantum key management system, which is most crucial for constructing a QKD network and analyze the design requirements to apply them to Korea Research Environment Open NETwork (KREONET). The quantum key management system not only manages the lifecycle, such as storage, management, derivation, allocation, and deletion of the symmetric key generated in QKD but also enables many-to-many communication in QKD communication based on the key relay function and P2P communication to overcome the limitation of distance, which is a disadvantage of QKD. We have validated the designed quantum key management system through simulations to supplement the parts that were not considered during the initial design.