Reviewerly/exHarmony huggingface dataset:
TRANSLATION STRATEGIES AND METHODS USED IN THE DEPARTMENT OF ENVIRONMENT AND FORESTRY

This research was based on translation problems due to the absence of equivalent words found during the translation process. Translators are required to translate the source language with the appropriate context into the target language. Therefore, researchers use translation strategies and methods to overcome these problems to produce the right equivalent. This study uses a qualitative method that focuses on applying translation strategies and methods. The data for this study was sourced from the official website and statistical books from the Department of Environment and Forestry, Central Java Province. The object of research is a collection of words or phrases that do not have a direct equivalent in the target language because they are non-equivalent and have limited expression in the target language. The results of this study indicate that the use of translation strategies and methods can support the translation process to be more effective and overcome translation problems. In the future, it is hoped that this finding will be able to contribute by increasing understanding for readers as well as providing references for translators on statistics and forestry topics.&#x0D;
Modeling Digital Image Segmentation with Canny Method

In general, the segmentation process is divided into three parts. classification, by edge, and by area. The digital image segmentation process divides an object whose surface or background maintains the RGB value of all pixels of a digital image so that the object can be processed for other purposes. This system aims to execute drawing objects using intelligent methods. In the Canny method, the process begins with taking digital images and continues with the grayscale process. In addition, technique selection starts with performing a complex operator or Laplacian edge detection and finally unlocks it. Successful segmentation results using edge detection with intelligent operators to separate objects. This system uses the Matlab 2017 application.
A FRAMEWORK DESIGN FOR SIGN LANGUAGE GESTURE RECOGNITION WITH EFFICIENT HAND GESTURE REPRESENTATION

Nowadays keyboards and mouse are used for humancomputer interaction (HCI) but for fast computing and rapid machine control new types of HCI methods (vision-based technology) have been introduced.The gestures are the symbols of physical behavior or emotional expression.It includes body gestures and hand gestures.Hand gesture recognition is an important part of HCI.Hand gesture recognition is an emerging research area with a wide range of applications such as robotics, machine control, video games, telesurgery, etc. Hand gesture recognition is also used for the translation of sign language, which is a complicated structured form of hand gestures.The importance of hand gesture recognition has increased due to the prevalence of touchless applications and the rapid growth of the hearingimpaired population.However, developing an efficient recognition system needs to overcome the challenges of hand segmentation, local hand shape representation, global body configuration representation, and gesture sequence modeling.This paper presents an extensive review of hand gesture recognition used in sign language recognition which is the heart of sign language recognition.In this paper, we have performed an analysis of various techniques used in recent hand gesture and sign language recognition research.The techniques reviewed are suitably categorized into different stages: data acquisition, preprocessing, segmentation, feature extraction, and classification, where the various algorithms at each stage are elaborated and their merits compared.Further, we have also discussed the challenges and limitations faced by gesture recognition research in general, as well as those exclusive to sign language recognition.Overall, it is hoped that the study will provide readers with a rigid system for automated gesture and sign language recognition, and can expand future research efforts in this area.
Data-driven optical method for full-field stress measurements

We demonstrate an optical method to directly determine full-field stress by combining a data-driven method with digital image correlation (DIC) strain data. In order to obtain the full-field stress distribution, the full-field strain measured by 2D-DIC and an isotropic ideal elastoplastic material stress-strain dataset are used. The proposed data-driven full-field stress measurement method determines the full-field stress components by performing iterative calculations using the dataset and a distance-functional-based data-driven algorithm without assuming any constitutive equations. The proposed method is implemented in an experiment-in-loop simulation and a practical application. The stress fields of uniaxial tensile and compact tensile processes are determined by the developed algorithm. The results show that the proposed data-driven method can obtain accurate full-field stress using an already established material stress-strain dataset. Furthermore, the proposed method satisfies the equilibrium and compatibility constraints and therefore allows the correction of erroneous strain results from DIC calculations, resulting in more accurately calculated full-field strain and stress values.
A study on the acquisition of steel mill rolling production data using filters and preventive maintenance methods by improving the efficiency of machine learning

개발된 칼만필터와 베이지안 기계학습을 응용한 예방적 유지보수 모델의 필요성은 정의된 수준의 가용성과 안정성을 유지하면서 총 유지 관리 비용을 최소화하는 것이다. 본 연구는 복잡한 수리 가능한 시스템에서 비용을 최적화하고 기계의 유효 수명을 향상시키기 위한 예방적 유지보수 스케줄링 모델을 제안한다. 유지보수 비용에는 임의 고장으로 인한 손실 비용, 수리 비용, 교체 비용 및 계획된 총 다운타임 비용이 포함된다. 검사, 수리 및 교체와 같은 다단계 예방유지 관리 조치는 전체 계획 기간에 포함된다. 제철공장의 스마트팩토리 생산관리시스템에서 추출한 압연공정의 데이터를 기반으로 최적화 모델을 이용하였으며 최적의 솔루션을 제공하기 위해 시뮬레이션을 수행하였다. 데이터분석 프로그래밍 언어 R은 오픈 소스 플랫폼이며 RStudio는 R프로그램 작성 및 실행을 위한 통합 개발환경 프로그램(IDE)이다. 16GB RAM 및 Windows 10 Pro가 탑재된 Intel Core i5에서 실행되는 작업공간에서 R의 프로그램을 사용하여 칼만 필터와 베이지안 기계학습 알고리즘을 응용했다. 제안된 알고리즘과 수학적 모델을 제 4차 산업혁명의 스마트팩토리에서 활용할 경우 유지보수 비용을 8~10%의 절감을 목표로 원가절감을 통한 기업경쟁력의 향상이 기대된다. This study proposes a predictive maintenance scheduling model to optimize the cost and improve useful life of machines in complex repairable systems. A preventive maintenance model was applied using a developed Kalman filter and Bayesian machine learning to minimize the total maintenance cost while maintaining a defined level of availability and stability. Maintenance costs include loss costs due to random failures, repair costs, replacement costs, and total planned downtime costs. Multi-level preventive maintenance measures such as inspection, repair, and replacement are included in the overall planning period. An optimization model was used based on data from a rolling process extracted from a smart-factory production-management system for a steel mill. A simulation was performed to provide an optimal solution. A program in R was used to apply the Kalman filter and Bayesian machine learning algorithm. To provide an optimal solution using an optimization model, a Kalman filter and a Bayesian machine learning algorithm were applied using R. If the proposed algorithm and mathematical model are used in a smart factory, it could improve competitiveness through cost reduction by reducing maintenance costs by 8-10%.
Software Engineering Mathematics

Mathematics plays a key role in engineering, and it may potentially assist software engineers in delivering high-quality software products that are safe to use. However, it is important to recognize that while the use of mathematics is suitable for some areas of software engineering (especially in the safety and security critical fields), less rigorous techniques (such as software inspections and testing) are sufficient in many other areas of software engineering. The extent to which mathematics should be employed remains a topic of active debate between those who advocate a solid engineering approach with mathematical rigor and those who argue for a lighter approach with minimal mathematics. There is not a one size that fits all in the use of mathematics—the mathematics that the software engineer needs to employ depends on the particular domain that the software engineer is working as specialized mathematical knowledge required in some domains.
Pengembangan Bahan Ajar Membaca Permulaan Berbasis Kartu Huruf Untuk Siswa Kelas 1 Sekolah Dasar

This development research aims to produce teaching materials for reading the beginning of the letters of the alphabet, vowels, and. The method used is research and development. The subjects were grade I elementary school students, totaling 20 people. Teaching materials developed using the ADDIE model, namely: (1) Analysis, (2) Design, (3) Development, (4) Implementation, (5) Evaluation. Data collection using questionnaires and tests. The data analysis technique was carried out with validity analysis, practicality analysis and effectiveness analysis. The results of data analysis are categorized as valid with an average value of 89.64%, teaching materials are categorized as practical in a one to one trial with an average value of 91.3%, small group trials with an average value of 90.7% and teaching materials are categorized as effective with an average score of 84%. Based on the validation results, practicality results, and effectiveness results, it is concluded that this textbook is suitable for use in learning Indonesian..
Deep Spatio-Temporal Multiplex Graph Learning for Cardiac Imaging Classification

Cardiovascular diseases are the leading cause of mortality worldwide. Cardiac imaging is critical for precise characterization of cardiac structure and function, and is key in diagnosis, therapeutic management, and prognosis. In this work, we propose a novel representation of spatio-temporal cardiac data as a multiplex graph and develop a multi-level message passing neural network to classify clinical groups corresponding to different cardiovascular diseases. Using open data from the Automated Cardiac Diagnosis Challenge (N=150), our results show that the multiplex representation extracts discriminative features from the data (up to 94% accuracy compared to up to 80% for well-tuned baselines such as XGboost and MLP). Ablation studies show the bias induced by the graph’s spatio-temporal structure improves generalization.
Deep Learning Methods on High- Resolution Image and Video – A Profound Assessment

Conversion of law resolution images into high resolution is known as super resolution job.This is a very challenging job which is now years are performed by multiple deep learning approaches.In this article we discuss these deep learning approaches and compare their results.Apart from it we also discuss the different characteristics of super resolution approaches and compare these with the peak signal noise ratio and SSIM measurements.Several deep learning approaches or algorithms are discussed.
An Alternate Approach for Single Image Haze Removal Using Path Prediction

Haze removal is an important problem that existing studies have addressed from slight to extreme levels. It finds wide application in landscape photography where the haze causes low contrast and saturation, but it can also be used to improve images taken during rainy and foggy conditions. In this paper, considering the importance of haze removal and possible limitations of a well-known existing method, DehazeNet, we propose an alternate end-to-end method for single image haze removal using simple yet efficient image processing techniques. DehazeNet is among well performing haze removal schemes in the literature, however the problem of coloration and artifacts being produced in the output images has been observed for a certain set of images. Addressing this problem, the proposed solution is devised by taking advantage of the color features of the three color channels of an image to establish a decision criteria. Based on that, a suitable dehazing method for an input hazy image is selected. Removing the problem of poor coloration in output images, we have come up with an alternate method to remove the haze while retaining the visual and perceived quality of the image. The experimental results show that the proposed method yields better structural restoration, reduces haze content significantly, and does not cause any artifacts in the output image.
Simulation Data-driven Enhanced Unsupervised Domain Adaptation for Bearing Fault Diagnosis

Abstract：The existing unsupervised cross-domain fault diagnosis studies of bearing usually utilize sufficient experimental data collected from test rigs as the source domains, the marginal distribution and conditional distribution alignments between domains are difficult to be considered simultaneously, and all source-domain samples are endowed with the same importance in the process of domain adaptation.Aiming at the above challenges, a new method of simulation data-driven enhanced unsupervised domain adaptation for bearing fault diagnosis is proposed.The bearing fault data with rich fault information and sufficient label data obtained by numerical simulation is used to construct the source domain, thus reducing the dependence on the resources of test rigs.An enhanced loss function embedded with the joint max mean discrepancy is designed to achieve simultaneous alignments of marginal and conditional distributions between different domains in unsupervised scenarios.A weight allocation mechanism for source domain samples is developed to measure the similarity between each individual source domain sample and target domain samples through domain prediction error and to adaptively allocate their weights to suppress negative transfer.Two sets of experimental data collected from test rigs are used as the target domains to validate the effectiveness of the proposed method.The results show that the proposed method can fully adapt the deep feature distributions of simulation domain and experimental domain to improve cross-domain fault diagnosis accuracy in unsupervised scenarios.
Combined CRC and Bit Framing for Enhanced Error Detection

The error detection performance of cyclic redundancy check (CRC) codes combined with bit framing in digital serial communication systems is evaluated. Advantages and disadvantages of the combined method are treated in light of the probability of undetected errors. It is shown that bit framing can increase the burst error detection of the CRC but it can also adversely affect CRC random error detection performance. To quantify the effect of bit framing on CRC error detection the concept of error "exposure" is introduced. Our investigations lead us to propose resilient generator polynomials that, when combined with bit framing, can result in improved CRC error detection performance at no additional implementation cost. Example results are generated for short codewords showing that proper choice of CRC generator polynomial can improve error detection performance when combined with bit framing. The implication is that CRC combined with bit framing can reduce the probability of undetected errors even under high error rate conditions.
Fast Domain Adaptation in Face Recognition by Decomposed Meta Batch Normalization

We suggested a face recognition technique based on Decomposed Meta Batch Normalization (DMBN) for this procedure.The strategy consists of two steps: batch normalization and deep facial recognition.Face recognition therefore involves feature matching and feature extraction.The first step is to collect the face photos from the dataset.The representation is an image with a large number of channels for the Gaussian receptive map.By using supervised learning, we turn on a handful of the most distinct channels.The characteristics of the facial picture are taken second.The person's face and emotion are then recognised using feature classification.The look on the face is recognisable.Recognizing a person's face characteristics and expression is the main goal, along with minimising feature mismatching to increase process performance.
HIGH PERFORMANCE FACIAL RECOGNITION MATCHER

The utilization of biometric products is an expanding landscape; from general consumers employing it for authenticating into their devices to governments deploying it at the forefront of crime and border control. One sizeable organization required an expansion in their offering within the industryThis study aims to develop a facial matching solution that offers high performance and meets the requirements of the organization’s biometric Subject Matter Experts in order to meet the current gap in the offering. A facial recognition approach known as FaceNet was utilized along with the GO language and MongoDB to produce an application capable of performing enrolments and matches against a persistent set of candidates. This solution was validated against the labeled Faces in the Wild dataset, a challenging set of facial biometric data in function, performance, and accuracy testing. For a subset of 6000 images from the dataset, a 100 % accuracy was recorded from multiple test runs demonstrating no false matches. The application's performance against this subset was averaged over multiple executions using two concurrent connections, which concluded an average enroll response time of 70ms and 236ms for match requests giving transactions per second values of 29 and 8 respectively.
Improving Accented Speech Recognition with Multi-Domain Training

Thanks to the rise of self-supervised learning, automatic speech recognition (ASR) systems now achieve near human performance on a wide variety of datasets. However, they still lack generalization capability and are not robust to domain shifts like accent variations. In this work, we use speech audio representing four different French accents to create fine-tuning datasets that improve the robustness of pre-trained ASR models. By incorporating various accents in the training set, we obtain both in-domain and out-of-domain improvements. Our numerical experiments show that we can reduce error rates by up to 25% (relative) on African and Belgian accents compared to single-domain training while keeping a good performance on standard French.
University Students’ Readiness for Teaching Media Competence

The article presents some actual experience of introducing media education in modern higher education.The authors present a review of current Russian and foreign publications that consider media education as an important factor in the professional training of present-day university graduates.The article also outlines the main directions for integrating media education in extracurricular activities of contemporary university students, including a wide range of creative and educational activities.All of them are aimed at expanding university students' knowledge about the world of media culture, actualizing their interest in media creation and media education projects.Curricular and extracurricular forms of media education are focused on solving the major task of promoting media competence among young people, which is one of the key vectors for successful exploration of the today's information field, the ability to think analytically, master the argument, strengthen the evidence base of one's own reasoning, include creative amateur media production.
Designing English materials promoting HOTS for senior high school students of the science study program

This study aimed to develop English supplementary reading materials promoting HOTS for senior high school students majoring in the science study program. The procedures of conducting this study were the identification of needs, exploration of needs, contextual realization and pedagogical realization of materials, materials production, evaluation by the experts, student use of materials, and evaluation by the students. It was concluded that the appropriate supplementary textbook for the eleventh grade students majoring in the science study program was a textbook which promoted HOTS, followed the 2013 curriculum, and considered the students’ needs. EXCELLENT, the textbook prototype developed in this study was considered appropriate for teaching and learning English since it met those criteria. It was proved by the results of the evaluation by the expert and students. The mean value of the expert judgement on the textbook quality was 4.71, which was classified into “very good” category. Meanwhile, the mean value of the students’ evaluation on the textbook quality was 3.92 categorized as “good”.
Cancer Prognosis &amp; Stratification with Sentimental Analysis using Deep Learning and Machine Learning Techniques

For therapy and monitoring, it is crucial to provide prognostic information at the time of cancer diagnosis.Even while factors including cancer staging, histopathological evaluation, genetic characteristics, and clinical variables might offer helpful prognostic clues, risk stratification still has to be improved.All these data generate defined patterns and those patterns can be examined with the help of Machine Learning and Deep Learning.The most promising algorithm for this use case is artificial neural networks.Decision trees might be used to the best extent as they provide an adequate balance of speed and accuracy.An ideal approach would be through the effective combination of ANN and Random Forests.Ensembling models would also be able to boost the performance of the system.The metrics and scores for the project must be in-scope of the development and at the same time extendable.
Application-driven quantum and statistical physics: a short course for future scientists and engineers, Volume 3: Transitions <b>Application-driven quantum and statistical physics: a short course for future scientists and engineers, Volume 3: Transitions</b> , by Jean-Michel Gillet, London, World Scientific, 2020, £ 80 (E-Book), ISBN: 9781786347886. Scope: textbook. Level: undergraduate, advanced undergraduate, early career researcher, researcher, Bachelor and Master's level engineering and …

"Application-driven quantum and statistical physics: a short course for future scientists and engineers, Volume 3: Transitions." Contemporary Physics, ahead-of-print(ahead-of-print), pp. 1–2
AUTOMATIC MONITORING AND CONTROL SYSTEM IN HYDROPONICS PLANT AGRICULTURE

Hydroponic plant system is becoming increasingly popular due to their efficiency, sustainability, and productivity.However, maintaining optimal growing conditions can be challenging, requiring constant monitoring and adjustments of environmental factors such as temperature, humidity, light, pH, and nutrient levels.An automatic control system using a LattePanda as the main processing unit and an adjustable interface can be implemented in a hydroponic plant system to address these challenges.This system consists of sensors to monitor environmental factors, a microcontroller to process data, control of actuators, and communication with the user interface.The adjustable interface can be tailored to the plant's specific needs, allowing growers to monitor and adjust environmental factors as needed.
A Voting classification approach for Sentiment Extraction from Bengali text

Sentiment extraction is one of the most challenging tasks in Natural Language Processing (NLP). It is essential for analysing consumer and user feedback on social media sites and in the commercial world. Finding sentiments or emotions in raw text data and identifying their polarity, or whether they are positive or negative, is the main objective of sentiment extraction. This area has been the focus of various research projects for English and other significant natural languages. In this article, we offer a voting classification method that uses a variety of machine learning classifiers to extract sentiment from Bengali language text. We explored Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier, Multinomial Nave Base and Ridge Classifier, and lastly, we used a voting classification strategy to extract sentiments from social media comments.