Reviewerly/exHarmony huggingface dataset:
Synthesis Method of Self-Checking Devices with Calculation Control by Two Diagnostic Parameters with Preliminary Compression of Signals from the Object of Diagnosis

The structure of self-checking digital devices with calculation control by two diagnostic parameters has been proposed. The belonging of being formed in in-built control circuit codeword to constant-weight code “2-out-of-4” is used as a first parameter. A second parameter is the belonging of each being calculated function to the class of self-dual Boolean functions. The specificity of being described in the article the organization structure of self-checking digital devices is the presence of the circuit of preliminary compression of signals from diagnosis object. Its usage allows to cut structural redundancy of end device down essentially. Besides, nevertheless, errors can be masked at the inputs of compression elements. The article underlines the features of the choice of being compressed outputs of diagnosis object and the algorithm is proposed allowing to minimize the risk of undetectable error occurrence at compression scheme output. The algorithm of built-in control scheme synthesis by two diagnosis parameters has been presented which takes into account conditions for the formation of full set of test combinations for testers and elements of transformation in signal correction block. The example of realization of synthesis algorithm for fully self-checking device Logisim is considered. Key features of built-in control scheme are denoted; the scheme is embodied according to the proposed structure. The way of organizing calculation control by two diagnostic parameters is of interest at the synthesis of fully self-checking digital calculation devices and systems.
The Role of Video Games in Learning English

The aim of this study was to elucidate the role of video games as medium to be applied in a technique of learning English in Indonesia. Playing games has a vital role as a means of enhancing learners' reading, writing, listening, and speaking capabilities. In addition, video games offer unique opportunities for learners to master English grammar and expand their vocabularies. Furthermore, playing video games has a pivotal role in motivating EFL learners to communicate using EFL through their challenges, active player interaction, audio and visual elements, as well as contexts. The qualitative descriptive analysis was employed to explore the roles of video games as an EFL acquisition technique through the distribution of a Google Form questionnaire containing 39 questions, to which 57 respondents gave their opinions. Through the analysis, the researcher was able to confirm the validity of the aforementioned roles; the average values of the responses indicated that, in overall, the respondents were either in agreement or strong agreement with the provided statements. However, the results of the questionnaire were only able to illuminate the roles of video games as a medium to be applied in technique of learning English specifically in Indonesia. Thus, the researcher suggests that there is an opportunity to utilize the presented data to support future studies of larger scopes.
A Modified Dijkstra Algorithm for ROS Based Autonomous Mobile Robots

Autonomous Mobile Robots (AMRs) are frequently used in many fields of technology. In this study, an AMR was designed to execute different path planning algorithms. Firstly, working principle, system architecture and motion planning of AMR are presented. Then, a map for the current environment is produced by a Robot Operating System (ROS) powered AMR which was designed for this study. The AMR locates itself on the produced map with the aid of an integrated Light Detection and Ranging sensor (LIDAR). The locomotion of AMR to a user-defined target on the produced map is performed by an optimal path based on AMR's own navigation plan. Two different path planning algorithms, which are Dijkstra’s algorithm and a modified version of Dijkstra’s algorithm, are executed on a cost-effective AMR platform, which has the capability of Simultaneous Localization and Mapping (SLAM). The reason why Dijkstra algorithm is handled in this study rather than A*, D* and RRT algorithms is that this algorithm is a basic and widely used algorithm. Dijkstra’s algorithm is modified, and pros and cons of the modified algorithm are analysed compared to Dijkstra algorithm. The proposed algorithm and navigation of AMR are tested both in real time in real world and as a simulation in Gazebo. Two algorithms were compared according to the results obtained from the robot locomotion both in real application and simulation environment. It is observed that the modified version of the Dijkstra’s algorithm comparatively yielded a bit more satisfactory results in the aspect of path planning.
Perancangan Media Pembelajaran Interaktif Bahasa Isyarat Mengenal Huruf Dan Angka Bagi Siswa Penyandang Disabilitas Tunarungu Berbasis Andorid

In the current era, learning adheres to the 2013 curriculum which includes HOTS (Higher Order Thinking Skills) consisting of critical thinking competencies, creativity and innovation, communication skills, collaboration skills and selfconfidence. These five things need to be trained or improved for deaf children so that they can take part in learning well. So learning media products are made that can be used to recognize letters and numbers. This Learning Media is equipped with sign language to support the understanding of deaf children in addition, it is packaged with an attractive appearance so that deaf children are interested in learning it. Often in daily teaching and learning activities students still often experience problems in remembering the material being taught both in the&#x0D; introduction of letters and also the introduction of numbers and what becomes an obstacle for students learning must be done repeatedly so that they can&#x0D; remember it and can really understand what it means. being taught and also they are sometimes bored and lazy in learning. From the problems that occur above,&#x0D; it is necessary to have a learning media that can help children with disabilities, especially the deaf, especially for the introduction of numbers and letters&#x0D; so that they can easily understand and understand letters and numbers. not bored, bored and lazy in learning. Learning media in the form of an android&#x0D; application so that it can be used on smartphones which are currently very widely used by both teachers and parents of students with disabilities.&#x0D;
Classification methods in the diagnosis of breast cancer

Summary This paper concerns the problem of diagnosing the type of cancer with the use of machine learning and statistical methods. Nowadays, the problem of neoplasms, in particular breast cancer, is one of humanity's greatest challenges. The identification of cancer and its type is extremely important. In solving this problem, classification methods can be used as objective tools that may be helpful for doctors making a diagnosis. For this reason, we discuss many efficient classifiers in the context of cancer detection. In addition, we consider the topic of data set transformations to deal with the problem of data unbalance, as well as measures of classification quality. In the experimental part, an attempt will be made to find the best classifier and to improve the quality of the original data set to obtain the highest values of classification quality measures for a particular data set.
A PALEOLIMNOLOGICAL APPROACH TO UNDERSTAND REGIME-SHIFT DYNAMICS IN LADIK LAKE (NORTH TURKEY)

Paleolimnological assessment of Ladik Lake (N Turkey) using diatoms was carried out along a 62 cm-long sediment core recovered from the southeastern part of the lake.The chronology of the sediments was established by 137 Cs -210 Pb dating.Accordingly, three major chronostratigraphical diatom assemblage zones (DAZ) were defined by the CONISS (constrained cluster analysis) program.The oldest assemblage, DAZ C (AD 1925(AD -1958)), was dominated by Ulnaria ulna (Nitzsch) Compère, Cocconeis placentula Ehrenb.and Cocconeis pediculus Ehrenb.taxon.In DAZ B (AD 1958(AD -1986)), in addition to Ulnaria ulna species that dominated through the whole core, Epithemia spp.occasionally dominated the assemblage in this zone.Ulnaria ulna and Pantocsekiella ocellata (Pant.)K.T. Kiss & Ács predominated since the beginning of DAZ A (AD 1986(AD -2006)).After the first regime of the lake with dystrophic character, the diatom community underwent a significant shift, with decreased abundance of periphytic diatom samples.The improvement of the regulator in 1986 resulted in a larger water level increase that affected the lake ecology, causing a shift from a diverse periphytic diatom to planktonic diatom assemblage.Based on our monitoring it can be inferred that the limnological regime shifts in Ladik Lake during the 20th century are of anthropogenic origin, rather than being due to climatic changes.
Determine the Emotional States of People by Taking a Picture of Their Faces Using Artificial Intelligence

There are some limitations to consider when using AI to identify emotional states. One limitation is that people can sometimes be difficult to read, and their emotional states may not always be evident from their facial expressions or language. Additionally, people can sometimes be intentionally misleading or difficult to read, which can make it challenging for AI algorithms to accurately identify their emotional states. Finally, AI algorithms may not always be able to accurately identify subtle or complex emotions, as these can be difficult for even humans to accurately interpret. In this article, we will address an important topic, which is determining the emotional state of people using artificial intelligence, the different applications in which artificial intelligence was used to determine the emotional state, we will also learn about the different ways to determine the face and some algorithms that help in that.
Hierarchical Federated Learning with Gaussian Differential Privacy

Federated learning is a privacy preserving machine learning technology. Each participant can build the model without disclosing the underlying data, and only shares the weight update and gradient information of the model with the server. However, a lot of work shows that the attackers can easily obtain the client’s contributions and the relevant privacy training data from the public shared gradient, so the gradient exchange is no longer safe. In order to ensure the security of Federated learning, in the differential privacy method, noise is added to the model update to obscure the contribution of the client, thereby resisting member reasoning attacks, preventing malicious clients from knowing other client information, and ensuring private output. This paper proposes a new differential privacy aggregation scheme, which adopts a more fine-grained hierarchy update strategy. For the first time, the f-differential privacy (f-DP) method is used for the privacy analysis of federated aggregation. Adding Gaussian noise disturbance model update in order to protect the privacy of the client level. We prove that the f-DP differential privacy method improves the previous privacy analysis by experiments. It accurately captures the loss of privacy at every communication round in federal training, and overcome the problem of ensuring privacy at the cost of reducing model utility in most previous work. At the same time, it provides a federal model updating scheme with wider applicability and better utility. When enough users participate in federated learning, the client-level privacy guarantee is achieved while minimizing model loss.
Automatic identification system of aircraft cockpit indicators based on machine vision

Aiming at the problem that the test of civil aircraft is greatly affected by human factors in the batch manufacturing process, the automatic identification system of aircraft cockpit indicators based on machine vision is researched. Firstly, the slant screen images are automatically corrected and segmented by image processing technology. Secondly,different methods are used to identify different regions according to the specificity of different regions in the images. YOLOv5 algorithm is used for target detection of unconventional indicators; EasyOCR algorithm is used for character recognition, especially in decimal recognition, this research improved EasyOCR, in which using the projection of the legal decimal point position and cover at the data input end, modify the initial characteristics, to avoid the wrong identification interference of the decimal point. At the output end, the correct decimal point is reset between digits. The accuracy is improved by 27.65% and the average accuracy is 96.60%; Other general indicators recognition, using common image processing techniques, such as Hough transform, HSV color matching, etc. Experimental results show that the average error rate of identification results of various indicators is only 1.96%, the speed of machine recognition is 4.8 times that of manual test. Compared with manual test, it can effectively solve the problems of misjudgment, heavy workload and inefficient, and improve the degree of automation in the batch production process of civil aircraft.
An Improved Weighted Median Algorithm for Spatial Outliers Detection

A spatial outlier is an object that significantly deviates from its surrounding neighbors. The median algorithm is one of the spatial outlier methods, which is robust. However, it assumes that all spatial objects have the same characteristics. Meanwhile, the Average Difference Algorithm (AvgDiff) has accommodated the differences in spatial characteristics, but it does not use statistical tests to determine the status of an object, whether it is an outlier or not. The research developed an improved version of the median algorithm and AvgDiff, called the Weighted Median Algorithm (WMA) which combined the advantages of the two methods. From the median algorithm, WMA adopted median and statistical test concepts. Meanwhile, from AvgDiff, WMA adopted the concept of using differences in objects’ spatial characteristics as weights. A combination of the two advantages was innovated by calculating WMA’s neighborhood score using a weighted median. Then, a simulation was conducted to analyze the accuracy of the method. The result confirms that when objects have heterogeneous spatial characteristics, WMA performs better than the median algorithm. The accuracy of WMA is not much higher than AvgDiff, but the use of WMA can prevent a serious false detection problem. The methods can be applied to an incidence rate of Covid-19 data in East Java.
Path Planning and Tracking Control Method Based on Bessel Curve

In the process of path planning, in order to reduce the number of path points, will be a smooth line into line, driverless vehicles in the path of turning is not smooth, thus put forward a kind of based on Bessel path planning and tracking control method, through the establishment of unmanned vehicle kinematics model, mapping the coordinates of the current point of the vehicle into Bessel curve points, The line segment to be tracked by path tracking at the moment is calculated, so as to realize the smooth processing of turning path. The experimental results show that the actual route trajectory of the driverless vehicle is in good agreement with the theoretical route trajectory, so as to realize the smooth processing of the turning path of the driverless vehicle.
Vision-driven High Precision Positioning Method for Bracket Assembly with Industrial Robot

The quality of the bracket assembly is an important guarantee for the successful launch of the rocket. The assembly accuracy of the bracket installed inside rockets is affected by bracket pose estimation, robot grasping, and positioning. We have proposed a recognition and pose estimation algorithm that integrates 2D and 3D vision to improve the bracket assembly accuracy. A position-based visual servo bracket grasping method is used to avoid the poor absolute positioning accuracy of the industrial robot. Then a small field-of-view vision system was established to achieve precise positioning of the bracket. We also developed a quick and precise hole detection algorithm based on YOLOv5 and caliper vision operator to compensate for the absolute positioning error of industrial robots. With the help of a vision servo grasping strategy and high precision re-positioning algorithm, the bracket's assembly hole positioning accuracy can finally reach 0.05mm. It is worth mentioning that the methods proposed in this paper have been successfully applied in the flexible automatic assembly line of the brackets used in rockets.
DEVELOPING A PICTURE BOOK OF LOCAL FOLKTALES TO FACILITATE READING LITERACY FOR THE 8TH GRADE STUDENTS OF SMP ANAK NEGERI SANGGAU

This study aimed to develop local folktales content for junior high school students based on local folktales from the Sanggau regency. To achieve the aim, this research was done by focusing on knowing what kind of a picture book was developed, how a picture book was done, and how appropriate the product was. The book was to facilitate class VIII students in reading literacy programs. This research used Design and Development by Branch. There were 20 students in the eighth grade and an English teacher of SMP Anak Negeri Sanggau as the objects. Design and development based on ADDIE consist of analysis, design, and development. The data collection of this research was done through interviews, questionnaires, and documentation. The findings of this study revealed the needs analysis at the student level, product needs, product topics, and product design. Thus, it was found that the product was appropriate for the students through the validations.
Sistem Pakar Mendiagnosa Penyakit Osteochondroma Dengan Metode Certainty Factor

Osteochondroma is a benign bone tumor that mostly develops in children and adolescents. Osteochondromas are more likely to develop during the growth spurt period between the ages of 13 and 15 years, but tumor development stops when the patient stops growing. Based on research conducted to diagnose Osteochondroma disease is still carried out directly by doctors, there is no system that helps diagnose patients who have symptoms of Osteochondroma disease. As a result, steps to diagnose Osteochondroma cannot be done if a doctor is not available. With these problems, an expert system was created to diagnose Osteochondroma disease with the aim of assisting the polyclinic in diagnosing Osteochondroma disease. An expert system is an application system that has the ability to think like an expert in solving problems related to Osteochondroma so that it can produce a conclusion or solution. The results of this study get an output in the form of diseases experienced by patients and solutions for handling diseases experienced by these patients using the Certainty Factor method, so that they can help the polyclinic in dealing with Osteochondroma disease
Development of Canva-Based Digital Teaching Materials to Train Students' Understanding of Dynamic Fluid Concepts

The research aims to analyze students' needs for Canva-based digital teaching materials on dynamic fluid material. Research is a type of research and development. The design of the development model uses the ADDIE steps (Analysis, Design, Develop, Implementation, Evaluation). However, this article will only discuss the results at the analysis stage. What was done at the analysis stage was distributing questionnaires to find out students' needs for Canva-based digital teaching materials. The sample in this study were students of class XI IPA at SMAN 4, SMAN 5, and SMAN 7 Bengkulu City. Data collection techniques used include non-test techniques. Data analysis techniques using qualitative and quantitative data analysis techniques. The results of the study show that students need electronic teaching materials (E-Modules) that can train their ability to understand concepts in dynamic fluid materials.
Implementation of Spatial-Level Augmentation on Pneumonia Classification with Convolutional Neural Network

Pneumonia is a disease with symptoms of difficulty breathing, fever, dry cough, and chest pain. As an indication that a person has COVID-19, it is necessary to immediately identify pneumonia by a doctor whether the lung X-ray image of the patient is classified as pneumonia or not. The importance of early diagnosis can reduce the risk of death in patients. Convolutional Neural Network is one of the fields of study in the realm of Computer Science that can perform image-based object classification. CNN can be used to identify pneumonia based on thorax images based on the features or features displayed on the image. One of the important elements in CNN is the amount of data that can be used for training, validation, and testing. Generally, the more data entered, the more learning material for the CNN system so that the system can classify more accurately. This study aims to measure the accuracy of the CNN model on thoracic-pneumonia images with spatial level augmentation changes. Image augmentation is implemented to increase image variance with initial data of 5856 images. The applied augmentations are Affine, Flip, Pixel Dropout, Random Size Crop, and Shift Scale Rotate. The stages of this research are manually grouping images, implementing augmentation on images, applying training-validation-testing on CNN, and analyzing the output results of the developed system. By using 5 types of augmentation, the dataset used as learning material can be increased up to 5x the original amount. From the research carried out, it was found that the Random-sized Crop type augmentation gave the highest accuracy value of 94.719% or an increase of 3.808% from the non-augmentation testing data. From this research, it is hoped that studies related to augmentation can be a reference regarding the type of augmentation process and its results in finding the CNN accuracy value, especially in the case of pneumonia classification
Implementasi Algoritma Resilient untuk Prediksi Potensi Produksi Bawang Merah di Indonesia

Shallots are seasonal horticultural crops with high economic value. They are one of the horticultural commodities prioritized by the Director General of Horticulture and the Ministry of Agriculture in their development and handling. Therefore, it is necessary to predict the potential of shallot production in Indonesia so that the government has benchmarks and information in determining the right economic policy so that shallot production can continue to be increased or at least be unstable every year. In this study, the prediction algorithm used is the Resilient algorithm. The research data used are shallot production data obtained from the Indonesian Central Statistics Agency. This research will be analyzed using four network architecture models: 6-5-1, 6-10-1, 6-17-1 and 6-29-1. Based on the analysis of the four models used, the results show that the 6-17-1 model is the best because it has a lower Mean Square Error (MSE) value than the other three models, which is 0.0337792, and the accuracy level is quite good. Of 79% with an error rate of 0.04 used. This architectural model will be used to predict the potential for shallot production in Indonesia. Based on the overall prediction results from each province, the potential for Indonesian shallot production at the end of 2022 tends to decrease compared to 2021. The conclusion can be drawn that the application of the Resilient algorithm to the problem of red onion production data in Indonesia is quite good, but the accuracy is not too high, so a more profound study is needed