Reviewerly/exHarmony huggingface dataset:
Rain Streaks Removal in Single Low-light Image

Unmanned platforms have been used more and more in our life. Most unmanned platforms obtain environmental information through cameras for subsequent operations. But when working outdoors, unmanned platforms may encounter rainy and low-light environments. Rain streaks and low-light environment will introduce undesired interference and influence the visibility of the images, which affect the safe work of unmanned platforms. To solve this problem, there are several methods proposed to remove rain streaks. However, the existing methods only consider the daytime images, which do not achieve the rain streaks removal in low-light environment. In this paper, we propose a novel approach to achieve rain streaks removal in low-light environment. We firstly decompose the low-light rainy day image into the background layer and the rain layer by using layer priors. And we enhance the background layer by using improved low-light enhancement algorithm, regarding the enhanced background layer as the final result to achieve the enhancement of low-light rainy day images. The image enhanced by our method can recover detailed information well, and has a high degree of naturalness, which provides strong support for the subsequent operations of unmanned platforms. In the end, we proved the superiority of our algorithm through some experiments.
The Fault Diagnosis System for Constant Wind Speed Control Device based on Expert System

The constant wind speed control device requires higher diagnostic ability for the characteristics of high integration, complex control structure and difficult maintenance. Therefore, an online fault diagnosis system based on expert system was proposed in this paper. Firstly, the knowledge base was established by using the knowledge and experience of domain experts, in order to build the fault tree model. Then through the working state monitoring of the constant wind speed control device, the inference engine was formed through the working status and fault tree model for forward and reverse reasoning. The testing results demonstrated that the proposed system could use sensor information and expert knowledge to infer the correct cause of the fault and improve the efficiency of fault diagnosis dramatically.
THE PERCEPTION ABOUT DIFFICULTIES OF DIALECT BUGINESE TOWARD STUDENT’S SPEAKING IN MUHAMMADIYAH UNIVERSITY OF MAKASSAR

The difficulty of speaking English, especially for first graders It will be a sustainable issue. Seeing that phenomenon, this study aimed to see difficulty in speaking English among first graders of English education Muhammadiyah University of Makassar. There are two in this study. This study takes a qualitative approach of choosing a location and choosing Students will batch 2020 as a participant in this study. Participants were selected through recommendations from all instructors who taught the course. About difficulty in speaking English, 4 difficulties in this study. The result was Interference from students' dialect according to the research results, the dominant the interference to students‟ spoken language is interfere with pronunciation stress any part of the word. Student dialect factors difficulties speaking English (internal factor = self-confidence) main distractions to students performance was an interference with spoken language performance lasses that make students more confident, those students who felt wrong in the lecture English and their friends can’t understand because they spoke English was like they speak the local dialect, it interferes the motivation of the student to speak.
Pengaruh Metode Mengajar Guru, Fasilitas Belajar, Lingkungan Belajar di Sekolah dan Motivasi Terhadap Hasil Belajar IPS Siswa Kelas VII SMP Negeri 5 Kodi Balagahar

In the classroom, the level of intelligence and student activity varies, therefore the teacher must be able to treat students according to their level of intelligence and be able to make all students active in learning. The research carried out is a type of qualitative research. The sample used in this study was class VII SMP Negeri 5 Kodi Balaghar, totaling 67 students. Researchers used several questionnaire techniques, observation techniques, and documentation techniques. The data analysis technique used is descriptive statistics and statistical analysis. The results showed (1) the teaching method did not have a significant effect on the learning outcomes of class VII students of SMP Negeri 5 Kodi Balagahar. It is proven by the result of Sig.count of 0.930 &gt; 0.05. (2) Learning facilities have a significant influence on the learning outcomes of class VII students. It is proven by the result of Sig.count of 0.001 &lt; 0.05. (3) The learning environment does not have a significant effect on the learning outcomes of class VII students. It is proven by the result of Sig.count of 0.677 &gt; 0.05. (4) Motivation has a significant influence on the learning outcomes of class VII students. It is proven by the result of Sig.count of 0.023 &lt;0.05. (5) Teachers' teaching methods, learning facilities, learning environment in schools and motivation simultaneously affect the Social Studies learning outcomes of Class VII students of SMP Negeri 5 Kodi Balagahar. It is proved with the sig.count result of 0.008 &lt; 0.05. This means that simultaneously the teacher's teaching methods, learning facilities, learning environment in schools and motivation affect the Social Studies learning outcomes of Class VII students of SMP Negeri 5 Kodi Balagahar.
Scalar Minimax Filter-based Phase Tracking for Continuous-Variable Quantum Key Distribution

In local oscillator (LLO)-based continuous-variable quantum key distribution (CV-QKD), a difference between the linewidth values of two free-running lasers at Alice and Bob induces a phase drift noise. This work proposes a novel minimax filter-based phase tracking that aims to minimize the phase drift considering maximum residual phase error to achieve optimal phase estimation. Simulation results show that the minimax filter offers a lower phase estimation mean square error (MSE) value compared to the Kalman filter when worst-case phase drift error due to high linewidth difference or high measurement noise values are considered.
Exploration on the Construction of Scientific Research Talents under the Background of "Breaking the Five Only" in Higher Vocational Colleges

The development of scientific research talents determines the contribution of scientific research results to the social economy and the advanced level of educational development.It is necessary to implement the fundamental task of building morality and cultivating people, and to break through the stubborn diseases and high levels of evaluation of scientific research talents in the "five only" papers.The motivation for promotion of scientific research by a single professional title in vocational colleges is to return to the spiritual essence of scientific research, so that scientific research talents can concentrate on research to serve talent training, economic and social construction, improve the quality of talent team construction and the conversion rate of scientific research achievements.In terms of evaluation mechanism, achievement transformation-oriented scientific research evaluation mechanism, and scientific research literacy building of scientific research talents and management teams, the "doublequalified" front-line practice drives the expansion of scientific research projects, and explores multiinstitutional cooperation in the form of problem discovery and recognition.Research collaboration mode, and establish a systematic scientific research talent training system and whole-process management environment.
Human Activity Recognition using CNN and Pretrained Machine Learning Models

People's lives are enriched by human activity recognition (HAR), which extracts action-level details about human behaviour from raw input data. There are a variety of uses for Human Activity Recognition, including elderly surveillance systems, abnormal behaviour, and so forth. However, deep learning models such as Convolutional Neural Networks have outperformed traditional machine learning methods. As a result of CNN, it is possible to extract features and reduce computational costs. Transfer Learning, on the other hand, refers to the use of pre-trained machine learning models that can be used to detect human activity using a special type of Artificial Neural Network known as Leveraging CNN. Resnet-34 Use of a CNN model can provide detection accuracy up to 96.95 percent for human activity recognition Numerous studies and research have been conducted on HAR. There are only a few models in the majority of the paper, however What we know is that the more data we have, the better the model and the more accurate the model will be.
Desain Alat Smart Farming Penyiram Bawang Merah Menggunakan Arduino Uno Berbasis Android

One of the technological developments in agriculture is smart farming. Shallot farmers still carry out the process of watering shallot plants manually. This takes a long time because farmers have to go around the agricultural land during the watering process. To solve these problems, an automatic shallot sprinkler machine was designed that can be applied by farmers. The Arduino Uno ATMega328 microcontroller is used on this machine as the control system, while Android is used for the interface. The android interface on this machine can provide a graphical display, therefore farmers can give direct orders to drive a DC motor (Direct Current) and a DC pump on/off switch (Direct Current). The results of the smart farming research on this automatic onion sprinkler help farmers get 72% efficient time. The comparison can be seen in the manual watering process on 10,000m2 agricultural land that takes 7 days, on the other hand, it only takes 2 days when it is done with smart farming technology.
Classification of Actors in an Animated Video using a Novel Yolo Framework in Comparison with SVM Algorithm

Aim:The major goal of this study is Classification of actors in an animated video using the novel YOLO (You Only Look Once) framework in comparison with the SVM (Support Vector Machine) algorithm.Materials and Methods: Sample groups that are considered in the project can be classified into two, one for YOLO and other for SVM, which are tested using 0.80 for G-power to determine the sample size and for t-test analysis.Results and Discussion:-The analysis of results show that the You Only Look Once has a high accuracy (87.45%) in comparison with the Support Vector Machine (84.74%).The statistical significance difference (two-tailed) is 0.001 (p<0.05).Conclusion: Novel YOLO framework seems to be better in classification of actors in an animated video over the SVM algorithm.
Performance Improvement Validation of Decision Tree Algorithms with Non-normalized Information Distance in Experiments

The performance of ID3 algorithm in decision tree depends on the information gain but it has a drawback because of tending to select attributes with many values as the branching attributes. The gain ratio (especially in C4.5) is proposed to improve the information gain, but it does not always improve the performance, nor is it always defined. Some scientists use normalized information distance to improve the gain ratio, however, it is ineffective. In this paper, we investigate two non-normalized information distance selection criteria to replace the information gain and the gain ratio and conduct detailed experiments on 13 datasets classified into four types with theoretical analysis. Surprisingly, on the datasets where the number of values of each attribute differ greatly i.e. in Type1 and Type2, non-normalized information distance-based algorithms can increase the accuracy of about 15–25% of ID3 algorithm. The first reason is that more values for an attribute does not reduce the distances, which is suggested by Mántaras. The second reason is that the conditional entropy which is the opposite one used in the information gain can bring balance to the multi-valued biased values. Furthermore, our methods can maintain results comparable to those of existing algorithms on other cases. Compared to the gain ratio, the algorithms with non-normalized information distances conquer the drawback much better on Type1 datasets, which is strongly confirmed by experiments and corresponding analysis. It can be presumed that “normalization” improvement methods such as normalized information distance and the gain ratio are not always effective.
Hangar-exporting optimization schedule of shipborne helicopter based on genetic algorithm

In order to shorten the hangar-exporting time of shipboard helicopter, the optimization of shipborne helicopter hangarexporting scheduling was studied. Firstly, the mathematic model of the shipboard helicopter hangar-exporting optimization is established. Secondly, based on the genetic algorithm, the algorithm for solving the schedule problem of shipborne helicopter out of the hangar is completed. Finally, the algorithm is verified by a case study. The case shows that the model and algorithm are feasible to get an optimal solution of the shipboard helicopter hangar-exporting scheduling.
CASA: Cricket Action Similarity Assessment in Video Footage Using Deep Metric Learning

Cricket batters will often measure their performance through comparisons against successful batters or feedback provided by experts. Action Similarity Assessment is the task of comparing the similarity or dissimilarity of an action between two actors to determine how similar the actions they perform are to one another. This research paper proposes the use of a Siamese Convolution Neural Network to compute the similarity distances between different batters using video footage. Due to the limited research surrounding action similarity, a new dataset is proposed to help foster future works pertaining to action similarity. Three architectures are proposed to determine which architecture is best suited for the domain: a custom CNN, Inception Resnet V2, and Xception. From the results obtained, it can be concluded that the best solution for the action similarity assessment task within cricket video footage is a Siamese Xception architecture, achieving a model accuracy of 98%.
Peningkatan Keterampilan Menulis Karangan dengan Metode Quantum Teaching pada Siswa Kelas XI SMA Muhammadiyah Kupang Tahun Ajaran 2019/2020

Indonesia language learning is integrated into four language skills, namely listening skill, speaking skill, reading skill, and writing skill. Writing skill is more difficult to master than the other three language skills even for native speaker of the language concerned. Writing skill is not automatically mastered but it should be practice regularly. This research was conducted on 30 students in eleventh grade science students of SMA Muhammadiyah Kupang 2019/2020 academic year. The study aimed at describing the improvement of the process and the improvement of learning outcomes on writing essay skill using Quantum Teaching method. This Classroom Action Research was conducted for two cycles. The data were obtained through interview, observation, test, questionnaire and documentation. Then, the data were analyzed quantitatively and qualitatively. The results showed that the Quantum Teaching method could improve the process and the score of students' writing essay skill.
METHODICAL TRANSFORMATIONS OF PREPARING STUDENTS FOR TEACHING FOREIGN LANGUAGES AT SCHOOL

The paper features the problem of methodical transformations preparing students for teaching foreign languages at school, in accordance with today’s challenges. Knowledge of English or another foreign language, awareness of cultural traditions and life of the world in crisis provide less traumatic adaptation of children and youth in terms of emigration. The article emphasizes the need for methodological transformations in preparing students for teaching foreign languages in the New Ukrainian School, which is considered as complex changes in the implementation of basic regulation of the discipline "Methods of teaching foreign languages" as a theoretical and practical component of knowledge and skills in philology. The relevance of two components is described: trends in teaching foreign languages and the specifics of teaching foreign languages in school for living in the modern world; their interdependence, dialectical unity and integrity are pointed out. Two directions of purposeful methodical transformations are outlined: first, providing multicultural potential of education and enhancement of practical speech skills for personal educational and vital functional students’ needs; secondly, modernization of forms and methods of teaching foreign language lessons at school as a brand-new primary school in general system of NUS and in accordance with education trends in Ukraine. The author illustrates methodological aspect of preparing students to use digital technologies during the lessons at school; soft skills; storytelling and visualization; virtual and augmented reality; gamification; asynchronous learning, etc. The author reveals that an innovative phenomenon-trend as virtual and augmented reality are the least investigated in the methodological aspect, but we recognize its prospects for cooperation between teacher and student for educational purposes. As this educational trend changes the course of the educational process, it cannot be implemented without students’ help. Virtual and augmented reality add elements of play and interactivity to learning, make it visual and interesting, help to master educational topics. It is noted that each of the transformations of specific methods of teaching a foreign language both in higher education and at school leads to new prospects for research in pedagogy.
A BSO-based Feature Selection Algorithm Under Privacy Protection

In many practical problems, data describing the same task may be stored among different participants in decentralized form. Considering the case that different features are stored in different participants and data held by each participant can not be shared with each other, a vertical federated feature selection algorithm with brain storm optimization (BSOFFS) under privacy protection is proposed. BSOFFS combines the feature selection process into the SecureBoost framework, whose purpose is to reduce the dimension of distributed data jointly without sharing the local original data between participants, so that the obtained feature subset has the same or better performance with the original feature set. Finally, experimental results on several test problems show that BSOFFS can improve the classification performance of the selected feature subset remarkably while protecting data privacy.
THE SIMPLE VERB FORMS IN THE HEADLINES OF THE ELECTRONIC MEDIUMS IN THE RNM

This paper examines the usage and distribution of simple finite and non-finite verb forms in the headlines of articles in the electronic media in the RNM, in order to identify their specific morphological features in the Macedonian language, as well as their similarities and differences with other world languages as well. The sample consists of 50 randomly chosen headlines on the website time.mk, while data analysis and the findings are conducted with the use of the general qualitative interpretative/inductive method. The research results confirm the rare usage of imperative forms and the contribution of verbal nouns in the condensation of information, which is the main feature of headlines. With reference to the finite verb forms, the results suggest the more frequent use of the second meanings of the present simple tense, unlike the most frequent use of the first meanings of the past definite tenses (imperfect and aorist). Correspondingly, besides their own specific feature, the headlines in the Macedonian language show similarities with headlines in the English language.
Blockchain-Based Federated Learning: A Systematic Survey

Federated Learning (FL) is a distributed machine learning paradigm that trains models across multiple devices without exchanging users' data, thereby providing stronger data privacy guarantees. However, some research reveals that FL may face security and privacy issues, such as single point of failure, model poisoning, and parameter privacy disclosure. Recently, the field of combining blockchain and FL has trend to become a hot research topic. More and more researchers attempt to use blockchain to decentralize and secure FL frameworks. To further understand recent advances in blockchain-based FL (BFL) systems, this article aims to provide a systematic survey to deconstruct BFL systems. We propose a taxonomy of BFL systems following the lifecycle of FL tasks and divide them into three layers, i.e., the blockchain layer, the training layer, and the aggregation layer. We review and summarize representative work in each layer. We also discuss several open challenges for designing more secure and efficient BFL systems.
A Comprehensive Survey on Visual Question Answering Debias

With the rise of multi-modal computing, visual question answering (VQA) have attracted wide attention. It takes an image and a question as input, the VQA system can answer the question according to the given image. However, most of models suffer from language prior problem, they excessively rely on superficial linguistic correlations between the questions and answers without considering the image, which is coursed by inherent data bias. For example, for a certain type of question (e.g., How many apples are there on the table?), the system will tend to return the result (e.g., Tow) that appear frequently in the answer space rather than answering question based on facts (e.g., Five) in the image. Therefore, VQA debias becomes especially important for answering the question correctly. There are many methods have been proposed to deal with such a problem. We summarize the existing methods into the following three categories: 1) Data augmentation 2) Weaken language information 3) Enhance image information. They solve existing problems from data perspective and information perspective respectively, aiming to get a higher accuracy and making VQA system more robust.
Learning media course development of chemistry learning program

The objectives of this study are to: (1) develop animation media in P3Kim courses, (2) validate and revise the animation media that has been developed, and (3) conduct individual trials, small group trials, and large group trials. The product developed in this research is the animation media for the P3Kim course. The method used is descriptive-explorative method and will identify various symptoms and root problems that occur in P3Kim course The data analysis technique is calculated using the percentage of feasibility. The results showed that the animation media has a feasibility value of 89.38%. Furthermore, student’s responses obtained a score of 82 with a very good criteria. The individual trials were carried out with 3 different category of student and the outcome is 83% in the very good category. Next, The findings of small group trial with 12 students were 87.5% in the very good category. Finally, the outcomes of the large group trial with 25 students is 83% in the very good category. At last, there are as many as 82% of students responded positively to the developed learning media. This research produces animation media that can be used in P3Kim courses to improve student’s learning proces.