Reviewerly/exHarmony huggingface dataset:
Prediction Model for Soybean Land Suitability Using C5.0 Algorithm

Soybean is one of the protein main sources that can be used for consumption in tempeh, tofu, milk, etc. Based on projection results, soybean production and consumption balance in Indonesia, in 2018-2022, it is estimated that deficit will increase by 6.18% per year. So, it's necessary to guide soybean land suitability, which can be carried out by evaluating existing land suitability to support soybean farming expansion and production. This study conducted an analytical study to evaluate soybean land suitability using C5.0 algorithm based on land and weather characteristics. The C5.0 algorithm is an extension of spatial decision tree, an ID3 decision tree extension. Dataset is divided into two categories: explanatory factors representing seven land characteristics (drainage, land slope, base saturation, cation exchange capacity, soil texture, soil pH, and soil mineral depth) and two weather data (rainfall and temperature), and a target class represent soybean land suitability in two study areas, namely Bogor and Grobogan Regency. The result generated two land suitability models with the best model obtained accuracy for training data 98.58%, while testing data was 97.17%. The best model rules are 69 rules that do not involve three attributes: cation exchange capacity, soil mineral depth, and rainfall.
Chemical Safety Sign Detection: A Survey and Benchmark

There is a high danger of accidents in chemical manufacturing plants. A devise that could automatically detect safety signs in the vicinity of a person could issue verbal warnings in order to reduce this risk. The most important task here is to correctly identify such signs from images. While there have been many achievements in the field of traffic sign detection, there is only very little research on detecting safety signs. In this work, we first provide an open and comprehensive benchmark dataset with 4650 images (expanded to 27900 images) of 30 chemical safety signs, each with a class label and bounding box and annotated with other image features. We then conduct a comprehensive analysis comparing the performance of the state-of-the-art deep learning models Faster R-CNN, SSD, YOLOv3-spp, and YOLOv5 on this dataset. In our study, YOLOv5 performs the best. It has the best mean average precision (98.9%), the best average recall (96.9%), and can process the highest number of images per second (71) on our hardware. It is already close to be sufficient for real-world application, but, like all investigated methods, suffers when detecting many signs at once, small signs, or signs in front of complex backgrounds. Our study closes an important gap in research and lays the foundation for solid future work in the domain of sign detection for improving worker safety.
Learning Multi-Party Adversarial Encryption and Its Application to Secret Sharing

Neural networks based cryptography has seen a significant growth since the introduction of adversarial cryptography which makes use of Generative Adversarial Networks (GANs) to build neural networks that can learn encryption. The encryption has been proven weak at first but many follow up works have shown that the neural networks can be made to learn the One Time Pad (OTP) and produce perfectly secure ciphertexts. To the best of our knowledge, existing works only considered communications between two or three parties. In this paper, we show how multiple neural networks in an adversarial setup can remotely synchronize and establish a perfectly secure communication in the presence of different attackers eavesdropping their communication. As an application, we show how to build Secret Sharing Scheme based on this perfectly secure multi-party communication. The results show that it takes around 45,000 training steps for 4 neural networks to synchronize and reach equilibria. When reaching equilibria, all the neural networks are able to communicate between each other and the attackers are not able to break the ciphertexts exchanged between them.
Violence Detection using Deep Learning Techniques

In the past years, human action recognition has been improved. Violence recognition is one of the best challenging research topics in the field of computer vision. Its one of the specific application is to find violence from surveillance cameras in public places, private places etc. We want an immediate control on these violent incidents. Human operator needed for monitoring the screen of surveillance video, which often leads to mistake and neglect to pinpoint the occurrence of unusual events, its require to them in a powerful search for automated violence detection systems. This paper discusses this research problem and explores LSTM and BiLSTM based solution to solve it. In addition, a layer of attention is present and used a new content database that collected from surveillance camera and normal recorded videos available on YouTube, Facebook etc. This database is publicly available. From the comprehensive tests conducted on Hockey Fight and dataset containing crowd and normal public scenes. It seems that BiLSTM model has more accuracy in the combat situation than LSTM model.
Scene Text detection and Recognition by Using Multi-Level Features Extractions Based on You Only Once Version Five (YOLOv5) and Maximally Stable Extremal Regions (MSERs) with Optical Character Recognition (OCR)

Textual information within scene images is very important in computer vision applications such asimage retrieval based on its content, Tourist translator, and Navigation systems assistant. This paper presented ascene text recognition system based on YOLO. The main steps of the suggested methodology are: text detectionand localization (using YOLOv5), text segmentation (using Morphological processing as a new method), featuresextraction (using MSERs), character segmentation and word segmentation (using bounding boxes with graph), andfinally character recognition (using OCR). In this work we create a new dataset model that includes most of thetext challenges such as Font type, Font size, Font color, and Font. The proposed system gives higher performancefor detection, localization, and recognition when using dataset containing many challenges, the results were 80%,96%, and 87.6 for precision, recall, and F-score respectively. Comparing with other similar works it was better.The accuracy of (OCR) is more than 99%.
The Existential Approach to Students’ Educational Success

The relevance of an existential approach to the issue of students’ educational success analyzed. The problem related to the contradiction between the existential needs of a young person and the need to achieve social success, including in educational activities, formulated. The socio­economic context of the requirements imposed on a young person (the ability to generate income, demand in the labor market, human and educational capital) discussed. At the same time, educational success is not limited to academic achievements, At the same time, it is noted that educational success is not limited to academic achievements, but implies a high level of existential well­being of the student. It means that students’ educational activities are filled with meaning. Students creatively and responsibly approach the learning process. Students develop reflection and access to their internal processes, emotions, feelings and experiences. This thesis is confirmed by the existing ideas of the authors about educational success, which are analyzed in the context of the main existential data according to I. Yalom. Among them are the search for meaning, the problem of freedom and responsibility, being with others, life and its finiteness. The possibilities and limitations of the existential approach to the problem of educational success discussed. A model of educational success proposed, in which an important component is the students’ “existential well­being”. The conclusion made about the prospects of the existential approach in the research of educational success.
Logarithmic transformation for enhancing keypoint matching of SIFT in augmented reality

Augmented Reality (AR) is the technology to combine virtual information and the real world. Scale-Invariant Feature Transform (SIFT) is a method that could be implemented in AR. SIFT has good stability and invariance, give high performance, and implemented by many applications. However SIFT itself has a disadvantage, it is not the flawless matching of the SIFT descriptor. In this research, we propose one of the image processing techniques, which is called logarithmic transformation. Our research aims to produce output value of combination Logarithmic Transformation and SIFT algorithm. The steps of our proposed method start from extracting video to sequential image, increase the quality of each frame using logarithmic transformation, and implement SIFT and evaluate this method with total keypoint matching. Combination LIP and SIFT will be compared with SIFT standard. We examined our research with rotation and distance. The result of LIP SIFT can increase the performance of SIFT standard as much as 16% for rotation and 1,5 % for distance-based SIFT standard.
Leveraging Past Experience for Path planning of Marine Vessel: A Docking Example

Path planning before maneuvering is crucial for the safe and efficient operations of marine vessels.The past successful human maneuvering experience can be leveraged to enable the safe and efficient path planning of vessels.In this paper, the previous successful maneuvering operations from ship operators are leveraged to find the optimal path.A deep conditional generative model is used to learn the distribution from those experiences.The model is then combined with the sampling-based RRT* planning algorithm to guide the search process.In this way, the theoretical guarantee of RRT* is preserved while the sampling process is more efficient.The docking operation is used as an example to validate the method.Experimental results show that the presented method not only improves the success rate and convergence speed to the optimal cost but also generalizes well to starting points beyond maneuvering experience.
Pengembangan E-Comic sebagai Media Pembelajaran Matematika Kelas IV SD pada Materi Pecahan

This study aims to produce E-Comic Math learning media for the fourth grade of SD Negeri Dadapan 02 Wajak on Fractions. The model used in this E-Comic development research is the 4-D model, namely Define, Design, Develop, and Disseminate. Data collection techniques used in this development research were validation questionnaires from material experts, media experts, and mathematics teachers, as well as student response questionnaires. The average percentage result of validation by material experts reached 89,21% which was categorized as “very feasible”. E-Comic learning media is worth trying out. The average percentage of validation results from media experts reached 86,17% which was categorized as "very feasible". The results of the math teacher validation show that the percentage of the feasibility of E-Comic reaches 87,95% which is categorized as "very feasible". The data on the test results of students' responses to the attractiveness of the E-Comic media, the average result reached 85,83% which was categorized as "very interesting". Thus the results of the development of E-Comic learning media have met the eligibility and attractiveness criteria so that the media is very suitable for use.
Readjusting Design Education for Japanese High School Students During the Covid-19 Pandemic: A Case Study to Investigate Students’ Attitude in Using Online Whiteboard for Group Discussions During Design Activities

The purpose of this study is to understand and describe Japanese high school students’ perspectives in using online whiteboards during face-to-face group discussions for design activities in the Covid-19 pandemic. Using the SDGs Challenge Project in Fukusho High School in Fukuoka City, a qualitative research approach where the case study which included quantitative and qualitative inputs was adopted. Based on the findings, the following main points can be concluded. Firstly, even though students may feel that they are not competent users of digital devices, they may still think that effective group discussions can be achieved using an online whiteboard during face-to-face group discussions. This may be because real-time social interactions can still be achieved as lessons are conducted face-to-face. Secondly, students’ perceptions of achieving effective group discussions and the ease of use of the online whiteboard may influence their motivations in using the online whiteboard in future lessons. This may be due to the perception that as long as the online whiteboard is easy to use and it enhances performance, students may generally feel motivated in using the online whiteboard in future discussions. The findings from this study will provide insights for academics and high school teachers when conducting face-to-face lessons and attempting to use of online tools to mitigate challenges offered by the pandemic.
Arabic speech recognition using deep learning methods: Literature review

Automatic speech recognition is a research field for enabling computers to receive voice feedback from humans and to translate it with the greatest probability of correctness. Speech is the easiest way to communicate the message in basic context. Speech recognition is referring the allowing of machines to perceive and accept speech information from humans for the greatest probability of accuracy. Speech recognition will play an ever more important role in the future of human with machine interfaces. Arabic speech recognition (ASR) is a most complex job and has received little attention than other languages. In this paper we reviewing the most important deep learning methods used in speech recognition in Arabic language. Through the search the main finding in this review that the Arabic is one of the most spoken languages and least highlighted in terms of speech recognition. One of the emerging techniques is using neural networks with deep learning for speech recognition. Deep Neural Networks (DNNs) have reached to suitable performance, Recurrent Neural Networks (RNN) is one of the best models applied for sequential data and convolution neural network (CNN) has obtained a best accuracy rate in speech recognition.
DEVELOPING STUDENTS SPEAKING SKILL THROUGH SHORT EXPRESSION

This research was classroom action research (CAR) design that through short expressions to develop speaking skill of Seventh grade of SMPIT AL HIKMAH Jakarta, which amount of 20 students. It focused on developing the real condition of English teaching and learning. To recognize the scholars’ development fulfillment, the researcher used oral test in cycle I, and cycle II. Earlier than implementing exam cycle I and cycle II, the researcher carried out movement to the students through cycles. The result of cycle I had confirmed the development of students speaking ability, but that development did not complete the standard score which is 7, because of that the researcher persevered to use cycle II. And in the test cycle II finally the scholars showed the development drastically. The result the students speaking score at pre-cycle to cycle II are increasing. In cycle I by 62% and improved in cycle II to 85%. The results indicate there is a development in students speaking ability through short expression. It could be concluded that developing speaking ability through short expression are powerful and successful.
Neural Path Planning With Multi-Scale Feature Fusion Networks

Path planning is critical for planetary rovers that perform observation and exploration missions in unknown and dangerous environment. And due to the communication delay, it is difficult for the planet rover to receive instructions from Earth in time to guide its own movement. In this work, we present a novel neural network-based algorithm to solve the global path planning problem for planetary rovers. Inspired by feature pyramid networks used for object detection, we construct a deep neural network model, termed the Pyramid Path Planning Network (P3N), which has a well-designed backbone that efficiently learns a global feature representation of the environment, and a feature pyramid branch that adaptively fuses multi-scale features from different levels to generate the local feature representation with rich semantic information. The P3N learns environmental dynamics from terrain images of planetary surface taken by satellites, without using additional elevation information to construct an explicit environmental model in advance, and can perform path planning policy after end-to-end training. We evaluate the effectiveness of the proposed method on synthetic grid maps and a realistic data set constructed from the lunar terrain images. Experimental results demonstrate that our P3N has higher prediction accuracy and faster computation speed compared to the baseline methods, and generalize better in large-scale environments.
Generalization of Fibonacci Codes to the Non-Binary Case

Motivated by expected technological developments in which the basic unit of storage might possibly be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -ary elements, with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> > 2, and not just bits, we extend the traditional Fibonacci code to non-binary codes of higher order, and prove their theoretical properties, as follows: (1) these codes are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fixed in advance</i> , and therefore do not need to be generated for each new probability distribution, yielding very simple and fast encoding and decoding procedures; (2) the codes are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prefix-free</i> : no codeword in the code is the prefix of any other codeword; (3) the codes are <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complete</i> : if one adjoins any other <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -ary string as an additional codeword, the obtained extended set of codewords is not uniquely decipherable anymore, and is therefore not useful as it might lead to ambiguities; and (4) the codes provide <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">robustness</i> against decoding errors: the number of lost codewords in case of an error will be limited. An error is defined as a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -ary digit changing its value, or an insertion of an extraneous <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -ary digit, or an erroneous deletion of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> -ary digit. We provide experimental results on the compression performance, illustrating that the compression efficiency of non-binary Fibonacci codes is very close to the savings achieved by the corresponding non-binary Huffman coding of the same order, while providing simplicity and robustness.
Deep learning-based production and test bug report classification using source files

Classifying production and test bug reports can significantly improve not only the accuracy of performance evaluation but also the performance of information retrieval-based bug localization (IRBL). However, it is time-consuming for developers to classify these bug reports manually. This study proposes a production and test bug report classification method based on deep learning. Our method uses a set of source files and model tuning to solve the problem of insufficient and sparse bug reports when applying deep learning. Our experimental results reveal that the macro f1-score of our method is 0.84 and can improve the IRBL performance by 20%.
Comparison of Map Visualization Techniques Used for Spatial and Spatio-Temporal Data: An Analytical Survey Applied to COVID-19 Data

Currently, there are numerous techniques that are either in use or proposed for use for spatial and spatio-temporal visualization of data. Visualization techniques are commonly used in various areas including errands of daily life, weather, medical care, economics, social media, politics, and science, among many others. We have previously demonstrated the application of several tools and spatial data visualization techniques to visualize and analyze COVID-19 data. In this study, we aim to investigate the frequency of encounter and the extent and type of use of six data visualization techniques, namely: Choropleth maps, Heat maps, Hexagonal binning, Dot maps, Bubble maps, and Cartogram maps, using a survey of popular techniques.
Relationship Between Medication Adherence Level and Recovery of Pulmonary Tuberculosis Patients at Gapura Public Health Center Sumenep

Pulmonary tuberculosis is an infectious disease caused by Mycobacterium tuberculosis (MTB). Regarding this, Indonesia is ranked second globally in terms of tuberculosis prevalence. TB cases in Sumenep Regency are increasing every year. The high incidence of TB in Sumenep mostly is caused by the patients' lack of adherence to taking medication. The level of compliance in taking drugs can be influenced by several factors including age, educational level, and gender. This study aimed to ascertain the association between medication adherence and recovery in patients with pulmonary tuberculosis at the Gapura Public Health Center Sumenep. This study employed an analytical observational design with a cross-sectional approach, utilizing secondary data from medical records and TB.01 treatment cards. The population in this study consisted of 40 individuals. Total sampling is used to choose samples. The study's findings revealed that most patients 62.5% were male, 37.5% were between the ages of 26-45, 15% were from Gapura Barat, 35% had an elementary school education, and 55% were farmers. As many as 90% of patients adhere to treatment and 87.5% recover. Additionally, statistical tests yielded a p-value of &lt;0.001. The study concludes a correlation between medication adherence and recovery of patients with pulmonary tuberculosis at the Gapura Public Health Center Sumenep. It is advised that health personnel maintain as many programs as possible, including counseling, home visits, and remote monitoring via telecommunications.