Reviewerly/exHarmony huggingface dataset:
Introducing HTTP and REST

Before we dive into ASP.NET Core 8 and API development, let's first go back to the basics of any web application. Whether a website is run from a browser or a web service (web API), it's always the same principle: a client and a server will communicate together; a client will send a request to a server, which will then respond to the client. This is all possible with the magic of the HTTP communication protocol. Under this protocol, data can be transported using different formats and constraints. Here is REST! REST is an architectural concept of data representation. Of course, these two should not be confused. In this chapter, we will cover the following content:
Semantic Communication Method Based on Compression Ratio Optimization for Vision Tasks in the Artificial Intelligence of Things

With the increasing amount of image data required for vision tasks, traditional data transmission methods face the problems of high latency and low reliability, which greatly limit the performance and application potential of the Artificial Intelligence of Things (AIoT). In order to solve this problem, this paper proposes a semantic communication method based on compression ratio optimization, which aims to reduce the communication delay and improve the reliability of data transmission by optimizing the compression and transmission process of image data. First, in the feature extraction phase the method extracts key visual features from the original image data. Subsequently, in the semantic relation extraction phase, we further analyze the association relations between these features to understand the semantic content of the image data. Finally, during the semantic compression stage, we implemented an adaptive optimization method that automatically selects the optimal compression ratio. Compared to traditional methods of wireless data transmission, this approach can significantly minimize transmission delay while ensuring that no critical data is lost. The experimental results show that this semantic communication method not only significantly reduces the completion time of the intelligent task, but also improves the antijamming ability of the image semantic communication.
Light-YOLOv5: A Lightweight Drone Detector for Resource Constrained Cameras

Critical Infrastructures (CIs) such as military bases and airports are putting a lot of attention into defending against attacks delivered via drones by deploying drone detection systems. However, the CI area might be very large, with no-fly zones extending to regions where it might not be possible to deploy a power line for resourceful cameras. To this aim, the CI might deploy an Internet of Things (IoT)-based surveillance camera system to capture drone images. However, these IoT cameras are resource-constrained devices that cannot support the currently available detectors. In this paper, we propose Light-YOLOv5, a lightweight image-based drone detector for resource-constrained cameras. We make targeted improvements to YOLOv5, including the replacement of the backbone network, the introduction of the transformer module, and the design of a Parallel mixed Efficient Attention Module (PEAM). We show that our modifications allow for reduced network size while achieving better classification than other state-of-the-art solutions. To prove these claims, we expanded an already available dataset of blurred drone images by adding clear images of aircraft and birds. Since airplanes and birds are easily confused as drones by image classifiers, our addition proves the effectiveness of our solution. Experiments show that Light-YOLOv5 can achieve a very good trade-off between performance (74.8% mAP) and efficiency (170 FPS). Compared to YOLOv5, Light-YOLOv5 improves mAP by 4.1%, reduces the number of network parameters by 15.7%, can perform detection at 170 Frames per Second (FPS), and achieves an average accuracy rate of 93.8%.
Development of Canva Application Learning Media Increase Learning Outcomes Class X Economic Studies at SMAN 3 Bangkalan

This research is research into the development of learning media products using the Canva application for economics subjects for class X students at SMAN 3 Bangkalan. This research aims to (1) develop interactive learning media in economics subjects for students of SMAN 3 Bangkalan. (2) determine the feasibility of interactive learning media for economics subjects for students at SMAN 3 Bangkalan which is being developed. (3) Knowing the use of learning media using the Canva application can improve the learning outcomes of class X students at SMAN 3 Bangkalan. This development research is carried out using R&amp;D (research and development) which consists of potential and problems, information gathering, product design, design validation, design improvement, product testing, product revision, use testing, product revision, and product manufacturing. Based on this research, for the assessment, material experts obtained a percentage score of 78% in the (worthy) category, media experts obtained a percentage of 83.3% in the (very worthy) category, field practitioners obtained a percentage of 98% in the (very worthy) category. category (very worthy). category (very worthy), and improve learning outcomes with a score of 0.82 or 82% in the category (high). Based on the pretest posttest student learning outcomes were initially 60-70, after using learning media using the Canva application it became 90-95, increasing from 15-30.
A Novel Attention-DeblurGAN-Based Defogging Algorithm

With the rapid development of machine learning, deep learning-based image defogging algorithms are receiving more and more attention from scholars compared to traditional image defogging method. A novel method for synthesizing high-quality haze images in fixed surveillance scenes is proposed, based on a dark channel-based approach for image fusion. The method also contributes a synthetic haze image dataset, which accurately corresponds to real-world scenarios, for use in deep learning network training and evaluation of defogging effects. To address the issue of chromatic aberration in the sky and local areas when using a deblurring network for defogging, the SKNet module in the Attention mechanism is integrated into DeblurGAN. The defogging network is trained using the synthetic haze image dataset, and is tested on various sets of synthetic and actual haze images, yielding improved results in metrics such as SSIM, PSNR, and MSE.
The Effect of WhatsApp Messaging on Learning Paediatrics During Clinical Rotation of Final Year MBBS Medical Undergraduates

Objective: To determine the impact of WhatsApp Messaging on learning paediatrics during clinical rotation of final year MBBS medical undergraduates. Study Design: Quasi-Experimental study Place and Duration of Study: Bahria University Medical and Dental College, Karachi Pakistan, from Apr to Oct 2018. Methodology: Final year MBBS students attending the Department of Paediatrics for three months of clinical rotation were randomly allocated to two Groups. Group-A was the control Group without WhatsApp, and Group-B was the intervention Group with WhatsApp messaging. Both Groups had a pre-test and post-test before and after their clinical rotation. The evaluation was performed before and after clinical rotation using Multiple choice questions (MCQs) for the final evaluation. Results: Of a total of 80 students, 19(23.8%) were males, and 61(76.3%) were females, with a median age of 23(22-23) years. Of 40 students in the WhatsApp Group, active participation was observed in 37(92.5%) students, whereas the quality of the shared content by students was found to be adequate/relevant in 35(87.5%) cases. The median score was significantly higher in both Groups' clinical rotation test than in the pre-clinical rotation assessment (p-value &lt;0.001). However, the median score of the post-clinical rotation test was significantly higher in the WhatsApp Group than in the non-WhatsApp Group (p-value &lt;0.001). Conclusion: WhatsApp was found to be a highly effective tool for learning paediatrics among medical students during their clinical attachment.
PENGARUH PENGGUNAAN BAHASA IBU TERHADAP CAPAIAN NILAI BAHASA INDONESIA MAHASISWA POLITEKNIK

This study is aimed to identify the phenomenon of the use of mother tongue among students of Polban and their effects on the students’ score achievement of Indonesian Language subject. The response variable is the Indonesian language scores achieved by students and the independent variables consists of the mother tongue (the language used in the family), sex, place of origin, high school backgroundand study program. The analysis used is descriptive, correlation analysis, and CHAID analysis. The data used are primary data and secondary data. The results shows that the use of mother tongue among Polban’sstudents does not affect much into the score of Indonesian Language subject. Other variables that significantly influence the score of language achievement are gender and study program.&#x0D; &#x0D; Keywords: score achievements of Indonesian language, mother tongue, CHAID analysis
Research on The Integrated Education Adaptability of Visually Impaired Students—A Study on Binzhou Medical University from China

The education of visually impaired students in China has also gone through a developmental stage from "segregated education" to "special education" and then to "integrated education". In this paper, we take the visually impaired students at Binzhou Medical College, the first university in China to provide higher education for the disabled, as the research object. The study was carried out to investigate the adaptability of visually impaired students, using various research methods such as literature review method, questionnaire method and case study method, we investigated and researched the adaptation of visually impaired students to integrated education from several aspects such as quality of life, social support, mental health level, self-esteem and self-efficacy study. Finally, the study proposed several countermeasures and suggestions, such as attaching importance to the physical and psychological rehabilitation of visually impaired students, emphasising the integration of education and rehabilitation, strengthening the training of professional teachers for visually impaired students, emphasising the complementarity between schools and professions, and improving the employment support service system for visually impaired students, which are of great significance in promoting the development of the cause of integrated education for visually impaired students and improving the adaptability of integrated education for visually impaired students.
Komparasi CNN dengan ResNet Untuk Klasifikasi Paling Akurat Tingkat Keganasan Diabetes Berdasarkan Citra Retinopathy

Diabetes becomes an infectious disease that has a very significant increase, it's increasing not only in old age or even suffered by a young age. Based on the Data and Information Center of the Ministry of Health of the Republic of Indonesia, the prevalence of diabetes is 9% in female genitals and 9.65% in male genitals and is estimated to increase to 19.9% with the addition of population age. Currently, the Machine Learning (Depp learning) approach is widely used in carrying out the calcification of medical image data. Retinopathy Diabetes image data can be used as material to build a classification model by utilizing the Convolutional Neural Network (CNN) algorithm and Residual Network (ResNet). The test results in the model developed in this study were the comparison showing the accuracy level of CNN 68.49% while the ResNet was 81.23%, in the CNN loss test 32.57% while the ResNet was 12.59%. In the Scenario of the application of Learning Rate 0.0005 also the ResNet is better than CNN, in the CNN Learning Rate Application Scenario producing an accuracy value of 68.49% while the ResNet adopts an accuracy value of 81.84% and on the CNN loss value of 20.14 while the 10.2% Resnet has a higher level of accuracy has a higher accuracy rate Compared to CNN and the application of learning rates it also affects in building a more accurate model in the case study of Retinopathy Diabetes.
An Approach for Automatic Generating RESTFul API Code based on SQL DDL Code

RESTFul API is an emerging technology that is used for communication between various applications or systems. RESTFul API is commonly used in the development of systems and applications. Various application, such as mobile, web, or desktop, are use RESTFul API to handle and compute business processes. Currently, Development of the RESTFul API is easier with the code generator tools and some approaches that will generate RESTful API application automatically. With that tools, developers more easier dan efficient when developing their applications. In this research, we try to create a new approach to create a RESTFul API code generator. This approach is based on the SQL DDL code in the SQL dump file as input. We use this approach because, in the initial phase of building a system, developers must provide their database structure before building their applications. With proposed approach, RESTFul API code generator is more effective and suitable with the developer's workflow. Based on testing result, The proposed approach can perform well for automatic generating code for RESTful API based on SQL DDL given. And provide basic functionality in RESTFul API such as create, read, update, and delete data.
Expert System to Diagnose Diseases in Corn Plant Using Mobile-Based Forward Chaining And Certainty Factor Methods

Technological developments are so rapid and have an impact on all fields. For example in agriculture. The expert system is part of artificial intelligence (AI) which can be used in diagnosing diseases in corn plants. Corn is a food crop that can be found in Indonesia. Erratic season changes can trigger diseases in corn plants so that it becomes a problem for farmers. The problem that is often encountered by farmers is the difficulty in diagnosing the disease from the corn plant. Therefore an expert system will be built that aims to diagnose diseases in corn plants using the forward chaining method and mobile-based certainty factor. By using the forward chaining method and the certainty factor, the accuracy value obtained by the system for 25 times the testing process is 84% and makes this system good enough to use. The system is applied to a mobile-based application so that usage can be more efficient.
Determining Student Achievements using Multicriteria Approach with Electre Method

. In the field of education, assessing student achievement is critical for determining students' levels of success and academic progress. However, the high cost of desire is frequently complicated since it incorporates a variety of student characteristics and aspects that must be taken into account. In this study, a multi-criteria approach using the ELECTRE method was suggested to measure student achievement holistically. The ELECTRE technique is a multi-criteria analysis method that employs a comparison matrix to evaluate alternatives based on a variety of criteria. This way to evaluate student progress allows for more accurate and objective decision-making than a single strategy that focuses on a single factor. An application was created to make its use easier. Applications that can be utilized to assist determine excellent students via black box testing are stated to be successful because 33 features provided in the application work as intended. This study is expected to help improve student accomplishment assessment policies and methods at various levels of education.
An Analysis Of Students’ Attitude In Online English Learning At Fifth Semester Of English Education Study Program In UIN Sjech M. Djamil Djambek Bukittinggi

The purpose of this study was to find out the students’ attitude in Online English learning using zoom at fifth semester of English education study program in UIN Sjech M. Djamil Djambek Bukittinggi. This study used a quantitative approach with a survey design. The population of this study were the students of English education study program at fifth semester academic year 2021/2022, consisting of 127 students from 4 classes. The researcher used simple random sampling technique because the population in this study was more than one hundred so sample in this study was 25% or 32 students. In collecting the data, the researcher used an instrument in the form of a questionnaire.The results of this research related to students' attitude in online English learning using zoom at the fifth semester of English education study program in UIN Bukitinggi academic year 2021/2022 show that some of students had negative and positive attitudes. Based on the findings, the negative attitude that students generally did was turned off the zoom camera when did other activities during English class using zoom with a percentage of 100%. Sometimes most of them felt sleepy while learning English using zoom with a percentage of 90.6%. Besides, they were also had positive attitude such as they liked to speak English in English class on zoom with percentage of 62.5%. So, in general indicated that the students of English education study program at fifth semester academic year 2021/2022 in UIN Sjech M. Djamil Djambek Bukittinggi had negative attitude in learning English online using zoom.
Sistem Pendukung Keputusan Rekomendasi Aplikasi Pembuat Kuis Edukasi Untuk Pembelajaran Menerapkan Metode OCRA dan Pembobotan ROC

Education quizzes are a form of interactive test or game designed to support the learning process in a fun and interactive way. Despite the availability of many applications for creating educational quizzes, users often face difficulties in selecting the ones that suit their learning needs and objectives. Therefore, this research suggests the use of a Decision Support System by implementing the OCRA and Weighted Rank Order Centroid (ROC) methods to recommend educational quiz maker applications. The ROC method is utilized to determine the weights or relative values of predefined criteria, thus facilitating the ranking process based on the importance of each criterion. Furthermore, the OCRA method is employed to analyze the operational competitiveness level of various alternative educational quiz maker applications. The research findings indicate that out of the 5 evaluated alternatives, ProProfs Quiz Maker scored the highest with a value of 1.515, making it the top choice for online learning media. Thus, the Decision Support System based on OCRA and ROC provides accurate recommendations for selecting educational quiz maker applications that align with learning needs and objectives.
Prediksi Penyebaran Virus COVID-19 Dari Hasil PCR Menggunakan Metode Naïve Bayes

The Covid-19 pandemic in 2020 that occurred in Indonesia is a complex health problem and requires fast handling and collaborative solutions from various disciplines. Covid-19 patients who receive treatment in hospitals have different conditions and severity. This affects the treatment actions that will be carried out by medical officers. The large number of patients and the lack of medical personnel have resulted in the need for technological support to help classify patient status based on their condition so that treatment is concentrated on patients who are very critical and require rapid treatment. The number of employees at the Jambi District Attorney's Office were exposed to the Covid-19 virus in particular, and it is necessary to predict PCR results. This study applies a prediction technique from the data mining discipline section to classify the status of employees at the Jambi District Attorney's Office who are confirmed to be Covid-19. Classifiers using the Naive Bayes method were applied to build a model based on a dataset of patients infected with Covid-19. The Covid-19 PCR result dataset was obtained from the Jambi District Attorney and was applied using RapidMiner. The model built can predict the confirmed status of Covid-19 based on age, gender, health condition, and vaccinations. The results of this study indicate that the classification of the Naive Bayes method has a high level of accuracy in classifying the confirmed status of Covid-19, namely 97%.
Image Segmentation Using Haar Wavelet Transform and Artificial Network for Identification of Ant Nest Pattern

Ant nest is a powerful medicinal plant to treat various diseases such as heart disease, tumors, cancer, hemorrhoids, tuberculosis, rheumatism, ulcers and prostate disorders. The many benefits generated cause the ant nest to have a selling value in the community, so it is not uncommon to trigger ant nest forgery. This study aims to identify the image of an ant nest by testing the accuracy of the backpropagation network and haar wavelet. Before the identification process with the backpropagation method is carried out, first the process of taking the characteristics of the ant nest is carried out which will later be used as test data input in the identification process. The output of the network will be compared with the target to get the error output. Then these errors are propagated again to increase the network weight to minimize errors. This research resulted in an ant nest identification application. The backpropagation ANN architecture for the training data used is 0.95 momentum, 0.01 target error with MSE criteria, the maximum number of training iterations (epochs) is set at 5000, the learning rate is 0.1 and the number of hidden layers is 20 by using wavelet haar feature extraction level 3 and the backpropagation momentum method can be used. used to identify ant nest images with 100% accuracy.
Penerapan Algoritma K-Means Pada Penyebaran Covid-19 Di Provinsi Jambi

The purpose of this study using the K-Means Cluster method is to determine the level of distribution of Covid-19 cases in the high, medium, and low categories in each region in Jambi Province. There are several aspects that can be measured such as population, population density, positive cases infected with Covid-19, recovered patients, and patients who died. The data collection method used is the documentation method in the form of secondary data obtained from the Jambi Provincial Government website. The data used were positive, recovered, and died and were analyzed using the WEKA application. From the results of research with the K-Means method using 3 clusters. Cluster 0 is a cluster with a high level of distribution category, which is in the city of Jambi. Cluster 1 is a cluster with a medium level distribution category consisting of Batanghari, Merangin, Muaro Jambi, Tanjab Timur. Cluster 2 is a cluster with a low-level distribution category consisting of Bungo, Kerinci, Sarolangun, Sungai Penuh, Tanjab Barat, Tebo.