Reviewerly/exHarmony huggingface dataset:
PENGARUH PENGGUNAAN MEDIA KOMIK TANPA TEKS TERHADAP KETERAMPILAN MENULIS TEKS DIALOG SISWA KELAS IV SD

This study examines the effect of using comics without text on the writing skills of fourth grade students. The formulation of the problem in this study are: (1) What is the description of the use of comics without text on the skills of writing dialogue texts for fourth grade students of SD Inpres BTN IKIP 1 Kota Makassar?; (2) What is the description of the results of the students' dialogue text writing skills in SD Inpres BTN IKIP 1 Kota Makassar?; (3) What is the effect of using comics without text on the dialogue text writing skills of fourth graders of SD Inpres BTN IKIP I Kota Makassar?. The objectives of this research are; This study aims to determine the description and influence of the use of comics without text on the skills of writing dialogue texts for fourth grade students of SD Inpres BTN IKIP 1 Kota Makassar. This study uses a quantitative research with the type of research is True Experiment with a research design of Pretest-Posttest Control Group Design. The population in this study were all fourth grade students using the simple random sampling method, where grade IV A consisted of 34 students and class IV B consisted of 32 students, so that the total sample was 66 students. Data were collected using a research instrument in the form of a dialogue text writing skill test. Technical analysis of the data used the Independent Sample t-test technique with the help of the IBM SPSS Statistic Version 16 application. The results of the descriptive analysis of the description of dialogue text writing skills showed the writing skills of fourth graders of SD Inpres BTN IKIP I Kota Makassar using comics media without text categorized as high and the learning process runs very effectively. The results and conclusions show that there is an effect of comics without text on students' dialogue text writing skills in the experimental class when compared to the control class that does not use comics without text.
DESIGN OF WHEEZING SOUND DETECTION WEARABLE DEVICE BASED ON INTERNET OF THINGS

&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Wheezing is one of the most common manifestations of airway obstruction. The use of a stethoscope in the wheezing examination has several disadvantages such as subjective results and depends on the auditor's hearing sensitivity. So an easy device is needed that helps determine the wheezing sound precisely. This study assembled a single tool to detect wheezing sounds based on the internet of things.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Method&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;This tool is designed with a microprocessor hardware connected to an electric stethoscope so that it can be attached to the patient's chest wall. Collection of chest breathing voice data accessed on kaggle.com. The creation of algorithms with Convolutional Neural Networks (CNN) was later changed to Mel Frequency Cepstral Coefficients (MFCC). This model will be implanted in a microprocessor and use python language to be able to record the sound of chest wall vibrations. The recorded sound is converted into MFCC to make it easier to perform wheezing sound detection. MFCC image results and detection results are sent to the database via the firebase database feature which stores MFCC photos in real-time as they are detected. Designing android application software using Flutter builds communication between android applications and firebase databases that allows applications to retrieve MFCC images as the final result. &lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Result&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;The results of the tool trial on five volunteers, three exacerbation asthma patients and two healthy people showed the device can detect wheezing sounds at a frequency of 400Hz with 80% accuracy through CNN and MFCC algorithms Internet of things based.&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;This tool can help health workers to accurately determine wheezing sounds, enforce the diagnosis faster, the prognosis of the disease to be better, so as to reduce the number morbidity and mortality of diseases with airway abnormalities in Indonesia&lt;strong&gt;&lt;/strong&gt;&lt;/p&gt;&lt;div&gt; &lt;/div&gt;
Diagramming Ancient Text

We discuss several diagramming methods that could be used to find more structure and meaning in ancient texts, in particular in the text of the Art of War.
Phase measurement with dual-frequency grating in a nonlinear system

The predecessor of China Optics was China Optics and Applied Optics Digest, which was founded in 1985. At that time, it was the only retrieval journal in the field of optics in China. At the end of 2008, China Optics and Applied Optics Digest was renamed
Rancang Bangun Game Getuk Shooter Menggunakan Algoritma Collision Detection Berbasis Android

Android Based Game has grown rapidly, with the growing user of smartphones and tables in this world. The game maker industry also has many opportunities to produce games. Many advantages of producing games because of many enthusiasts for the game. Making game is an interesting field to learn because of the great potential both in terms of science and in terms of commercial. In this study the author designs and builds an application of the Android based Getuk Shooter game using Collision Detection algorithm for collision attack detection. The method used in this research is RUP method, which is method of software development done repeatedly. The result of this research is to produce Android based mobile game in the form of single player getuk vertical shot that can train the dexterity of player with 2D display and it consists of 3 levels, which can run on android smartphone.Key words : Android, game, collision, shooting
Visual Guidance of an On-Orbit Free-Floating Manipulator Using a Mobile Camera

A direct visual-servoing algorithm for control a space-based manipulator is proposed. A two-arm manipulator is assumed as a baseline scenario for this investigation, with one of the arms performing the manipulation and the second arm dedicated to the observation of the target zone of manipulation. The algorithm relies on images taken independently from de-localized cameras, e.g. at the end-effector of a second manipulator. Through the implementation of a Kalman filter, the algorithm can estimate the movements of the features in the image plane due to the relative movements between the camera and the target and then calculate the torques to be provided to the joints of the manipulator by adopting a visual servoing control strategy. Simulations results in two different scenarios have been presented to show an adequate behaviour of the presented approach in on-orbit-servicing operations.
Comparative Study of First-Order Moving Asymptotes Optimizers for the Moving Morphable Components Topology Optimization Framework

Abstract Machine learning-accelerated topology optimization faces the challenge of generating large amounts of optimal topologies for supervised learning on a training dataset. This data generation step is challenging for the method of Moving Morphable Components (MMC) which presents an oscillatory behavior near local optimum that negatively affects its convergence speed and therefore present a high data generation cost. This paper presents a comparative study of the most-used first-order optimizers applied to minimum compliance problems under the MMC framework for topology optimization. The Method of Moving Asymptotes (MMA), its Globally Convergent version (GCMMA) and the hybrid MMA-GCMMA optimizer are compared using their final compliance and the total number of iterations until convergence as performance metrics. An extensive set of diversified boundary conditions for a rectangular beam minimum compliance problem is used as case study. The method of performance profiles is utilized to provide for each solver a probability distribution of outperforming its counterparts. Numerical results show that using a hybrid optimizer can accelerate the convergence speed of the MMC framework while still producing equally compliant topologies when compared to the MMA optimizer.
Implementation of a Consistent White Blood Cell Segmentation Based on Handcraft Approach

White blood cell segmentation is one of the most important and complex tasks in medical computer image analysis. In this article, we propose a simple, efficient, straightforward, and consistent algorithm that does not require high-performance processing hardware such as GPUs or TPUs for white blood cell segmentation in blood and bone marrow smear images. The performance of the novel method is tested with 2371 images, which no other state-of-the-art method has done. Experimental results have shown that the proposed method outperforms several state-of-the-art methods.
DirectTracker: 3D Multi-Object Tracking Using Direct Image Alignment and Photometric Bundle Adjustment

Direct methods have shown excellent performance in the applications of visual odometry and SLAM. In this work we propose to leverage their effectiveness for the task of 3D multi-object tracking. To this end, we propose DirectTracker, a framework that effectively combines direct image alignment for the short-term tracking and sliding-window photometric bundle adjustment for 3D object detection. Object proposals are estimated based on the sparse sliding-window pointcloud and further refined using an optimization-based cost function that carefully combines 3D and 2D cues to ensure consistency in image and world space. We propose to evaluate 3D tracking using the recently introduced higher-order tracking accuracy (HOTA) metric and the generalized intersection over union sim-ilarity measure to mitigate the limitations of the conventional use of intersection over union for the evaluation of vision-based trackers. We perform evaluation on the KITTI Tracking benchmark for the Car class and show competitive performance in tracking objects both in 2D and 3D.
Characteristics, behaviors, and teaching tools that promoted online learning among university students during COVID-19

The purpose of this research was to identify the characteristics, behaviors, and teaching tools that, according to the students of a degree in human resources from western Mexico, promoted learning during online classes due to COVID-19. The research was qualitative, non-experimental, exploratory, and cross-sectional. An open-ended questionnaire was applied to 109 participants (78% women and 22% men; average age 22.6 years). As a result, the teaching staff that promotes learning is characterized by being explanatory, understanding, patient, dynamic and empathetic; In behavioral terms, he explains the topics, schedules practical activities, gives constant feedback, sets flexible tasks, and is available to the student when required. In addition, the most effective teaching tools were videos, slides, video calls, virtual classrooms, and digital repositories.
Validitas Video Animasi Powtoon Kelas X SMA Berdasarkan Hasil Inventarisasi Jamur Makroskopis

Powtoon is an software animation video that can be accessed online, contains simple and interesting animations, and provides music facilities and can record narration. This study aims to determine the feasibility of an animated video powtoon based on the results of an inventory of macroscopic fungi found in the Sekadau Mount Protection Forest, Sambas Regency. The research instrument used is a validation sheet, which includes aspects of format, content, language, simplicity, illustrations, media quality, and attractiveness. This type of research is research and development with the Plomp model. The results showed that the validation of the animated video powtoon was carried out by five validators with a CVI (Content Validity Index) value of 0.99 which was declared feasible. Based on the validation results, it can be concluded that the animated powtoon video in the class X high school mushroom classification submaterial is declared suitable for use as a learning medium.
DB-GAN: A Low Contrast Image Enhancer Based on NIR-RGB Fusion

RGB images captured under haze or over-/under-exposure conditions frequently have low contrast and lack of detail. Due to the limited information in the original image, the majority of enhancement techniques that rely solely on visible information fail to restore the original image satisfactorily. This emphasizes the need for information beyond the visible spectrum. In this paper, we formulate the low contrast image enhancement problem based on near-infrared (NIR)-RGB fusion. A Dual-Branch Generative Adversarial Network (DB-GAN) is designed based on the specific characteristics of NIR-RGB fusion problem. To be specific, with the guidance of the two discriminators that respectively extract information from RGB and NIR images, a U-net based generator generates informative, high-quality fused images. In addition, we create an NIR-RGB dataset with over 1300 aligned image pairs for training the network. Quantitative and qualitative experimental results show the superior performance of our proposed framework.
A Comprehensive Real-World Photometric Stereo Dataset for Unsupervised Anomaly Detection

Unsupervised anomaly detection is a technology that trains a model to detect anomalies using only normal data. One of the main challenges of anomaly detection is detecting defects that are not visible or obscured by lighting conditions. However, most existing methods use a single image as an input to detect anomalies in the target object, thus it is difficult to properly cope with such a challenge. In this paper, we explore an anomaly detection model using multiple images taken under different lighting conditions. For this purpose, we propose a new anomaly detection dataset based on the photometric stereo (PS) setup, and demonstrate its characteristics and advantages. In addition, we apply existing anomaly detection methods to our new dataset to verify their performance and present benchmark results. Furthermore, we present a technique for fusing multi-image and 3D surface information. Anomaly detection based on our new setup using multiple images achieves dramatic improvement of +10.15%, +11.68% and +4.38% over baseline setup using a single image for Skip-GANomaly, MKDAD and DifferNet models, respectively. Our PS-based anomaly detection dataset and analysis reports will be publicly available for future researches.
Localized Feature Ranking approach for Multi-Target Regression

Multi-target regression (MTR) aims at designing models able to predict multiple continuous variables simultaneously. The key for designing an effective feature selection model for MTR is to develop a framework under which the feature importances are measured using the correlation between features and targets in a natural way. So far, feature importances in MTR problems were evaluated in a global sense where proposed approaches generate a single ordered list of features common for all the targets. In this work, we adapt the Ensemble of Regressor Chains algorithm in tandem with the random forest paradigm to appropriately model both dependencies among features and targets in a target-specific (localized) feature ranking process. We provide empirical results on several benchmark MTR data sets indicating the effectiveness of our strategy to perform better than selecting one global ranking for all targets with existing state-of-the-art algorithms.
Rancang Bangun Aplikasi Game Pembelajaran Augmented Reality Berbasis Android Dengan Metode Co-CreaARGBL

In a time of pandemic like this, all sector activities, in urban and rural areas, become very difficult and activities are limited. The public is asked to maintain health protocols by using masks, washing hands, maintaining distance, and staying away from crowds. Many activities such as school, working, and worship and many other activities that can gather many people do online with the help of video conferencing support software. Institution for teaching and learning activities including those affected by the Coronavirus Disease 2019 (COVID-19) caused by Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). The impact of activities carried out online and carried out almost every time in the world of learning is a decrease in participants’ enthusiasm due to boredom and monotonous impression without further interaction between participants and interactive media. So from these problems it is felt that there is a need for new media that can make online video conferencing learning activities not bored, which can make participants more active and not get bored easily during video conference. By using the Co-CreaARGBL method, making interactive augmented reality game-based learning media into a fun learning game with 3 main stages, that a Training, Interactive Design, and Classroom Evaluation, so that online learning becomes easy and fun by combining interactive multimedia elements that make participants interact more with fun learning media so they are not easily bored and seem monotonous.
Upaya Meningkatkan Keaktifan Dan Hasil Belajar Materi Jenis-Jenis Tanggung Jawab Sebagai Warga Masyarakat Melalui Model Pembelajaran Contextual Teaching And Learning

The type of research is Classroom Action Research (CAR) which includes four components, namely planning, implementation, observation, and reflection. The research subjects were 17 students. Data collection techniques using observation techniques, tests and documentation. Validation of data using triangulation technique. The data analysis method used qualitative data analysis. The results showed an increase in student activity from 6 students or 35.29% in the initial condition, in the first cycle there were 11 students or 64.71%, and in the second cycle there were 17 students or 100%, and student learning completeness from the condition only 4 students or 23.53%, in the first cycle there were 10 students or 58.82%, and in the second cycle there were 15 students or 88.24%. The average learning outcomes increased from 58.82 to 67.06 and 77.06 in the second cycle. Based on the explanation above, it can be concluded that the CTL method with the Media Voice Note WhatsApp can increase the activity and learning outcomes of class V students at SDN Bonang Semester 2 for the 2020/2021 academic year.
Analyzing on My Turf Game Over Some Finite Non-Abelian Groups

The aim of this paper is to solve the “ON MY TURF" game over some finite nonabelian groups. Also, it presents the following results: 1-) If G has odd order, and the set F contains the identity element, then the first player A has a winning strategy. If F does not contain the identity, then B has a winning strategy. 2-) If G has an even order with only one element of order two, there is a winning strategy related to set F. 3-) If G has an even order with only three elements of order two which generate a subgroup isomorphic to Z2 × Z2, there is a winning strategy related to the set F.
OpenCEMS: An Open Solution for Easy Data Management in Connected Environments

Automating the life cycle of data management projects is a challenging issue that has attracted the interest of both academic researchers and industrial companies. Therefore, several commercial and academic tools have been proposed to be used in a broad range of contexts. However, when dealing with data generated from connected environments (e.g., smart homes, cities), the data acquisition and management becomes more complex and heavily dependant on the environmental context thus rendering traditional tools less efficient and appropriate. In this respect, we introduce here OpenCEMS, an open platform for data management and analytics that can be used in various application domains and contexts, and more specifically in designing connected environments and analysing their generated/simulated data. Indeed, OpenCEMS provides a wide array of functionalities ranging from data pre-processing to post-processing allowing to represent and manage data from the different components of a connected environment (e.g., hardware, software) and to define the interactions between them. This allows to both simulate data with respect to different parameters as well as to contextualise collected data from the connected devices (i.e., consider environmental/sensing contexts). In this paper, we compare OpenCEMS with existing solutions and show how data is represented and processed.
Optimal investment allocation problem

In several aspects of our lives, the company witnesses' development and progress as the needs of people and groups grow. These changes are the major drivers of the company's development and growth, as well as its enhanced profitability. In such cases, the company needs support in dealing with the changes. The aim of this article is thus to highlight an algorithm to explain the process of solving the problem of investment in various sectors and increase knowledge of the investment idea. Moreover, we talked about a compromise set as a solution. Which is the responsibility of establishing the best option to solve the problem of the allocation of labor resources for optimal investment is linked to certain economic theory problems.
Aplikasi Pengenalan Nama Benda dalam Bahasa Jepang dengan Metode GDLC Berbasis Android

The development of information and communication technology in the field of education can turn abstract learning into concrete through android-based learning media. The development of learning media requires software development methods, one of which is the Game Development Life Cycle (GDLC) method. Game Development Life Cycle (GDLC) is a method that handles game development from start to finish. This educational game contains elements of text, images, audio and 3D animation that are designed as attractive as possible to increase the attraction for beginners in learning Japanese. In making this educational game using Adobe Photoshop and Unity 3D software. The purpose of this study is to help beginners learn to know more about recognizing object names in Japanese with an attractive appearance and provide a different atmosphere and learning method.
Yakuza: History and Double Stroke in the Pandemic Crisis

The article presents a brief excursus into the history of the formation and development of yakuza, the Japanese mafia, and analyzes the features of its development from the formation of urban culture in Japan until 2022. Starting from the formation of modern forms of yakuza, the Japanese mafia has been in the focus of the authorities’ attention not only as a criminal phenomenon, but also as a possible tool in the struggle with the opposition movements. The activities of the yakuza have always been associated with the political vector of the ruling groups. However, in the pre-war and war periods, under the conditions of strict totalitarian rule, the Japanese mafia withdrew into the shadows. In the post-war period, the yakuza groups divided the territory and business geographically and by spheres of interest. In the 1960s, the number of yakuza reached its historical maximum of about 200 000 people, almost equaling the size of the country’s police corps or The Japan Ground Self-Defense Force. Yakuza groups were growing rapidly but, from the early 1970s, the authorities began to lose interest in them, and this process continues up to now. A significant role in reducing the number and influence of the mafia was played by political and economic stability, as well as numerous scandals involving large companies in the activities of the yakuza, which caused serious damage to the image of Japan in the world market. By 2019, the number of yakuza has sharply decreased as well as their income due to the increased pressure on the Japanese mafia and the police preventional talks with ordinary people. However, the pandemic and the restrictive measures related to it have changed not only the number, but also the structure of criminal communities, as well as the attitude towards them within the Japanese society.