Reviewerly/exHarmony huggingface dataset:
ONLINE EDUCATION: CHALLENGES AND OPPORTUNITIES DURING THE COVID-19 PANDEMIC. STUDY ON PUBLIC ADMINISTRATION STUDENTS` AND TEACHERS` PERCEPTION

COVID-19 had a tremendous impact on education and the well-being of both students and teachers. The paper analyses the undergraduate students’ and academics’ perception of the online education services provided by the Faculty of Administration and Public Management (FAPM) of the Bucharest University of Economic Studies (BUES). Individual interviews and an online survey were carried out in order to reflect both the pros and cons of online learning and the determinants that significantly impact the satisfaction of the main stakeholders of the education services. Research findings reveal that despite the great challenges related to online learning, many opportunities have emerged to advance the quality of education. Thus, students and teachers agree on the fact that traditional education and online education provide both advantages and disadvantages, suggesting that, in the future, a hybrid teaching-learning system might help bring together the strengths of both approaches.
Improving the Relevance of Product Search for Queries with Negations

Product search engines (PSEs) play an essential role in retail websites as they make it easier for users to retrieve relevant products within large catalogs. Despite the continuous progress that has led to increasingly accurate search engines, a limited focus has been given to their performance on queries with negations. Indeed, while we would expect to retrieve different products for the queries “iPhone 13 cover with ring” and “iPhone 13 cover without ring”, this does not happen in popular PSEs with the latter query containing results with the unwanted ring component. The limitation of modern PSEs in understanding negations motivates the need for further investigation.
Prevention and reduction of school deviance through programs of psychosocial rehabilitation of pupils

In the school environment, interventions focused on positive things are effective in managing deviant behavior problems among students. The ideal objective is for the school to begin to adopt systemic changes that involve managing problem behaviors in a positive and preventive manner and with the help of the other factors involved, with the aim of fostering a collaborative partnership between adults and children, which encourages the latter to solve the problems that affect their lives. The best solutions must be found to lead to the improvement of communication relationships, to help children, adults learn, and model positive skills related to human nature: empathy, honesty, to help them understand that inappropriate behavior that can affect others and that the solution is to solve problems without conflict.
Approach to collaborative fuzzy clustering in large data analysis

Abstract— When data sets have one or more similar characteristics, the clustering in each of these data sets will have an effect on the other data sets. However, for various reasons such as data security issues, these data cannot be stored centrally but in different places. Collaborative clustering is a clustering technique that allows to performance of local clustering on each sub-data set and to exchange of information with other data sets. A collaborative process will be performed to adjust the clustering results on each subset to achieve better clustering results on the subsets of data. This paper presents a collaborative fuzzy clustering approach in big data analysis based on a high-performance computational model to improve the computation speed. Experiments on the Kitsune network attack dataset show that the proposed algorithm significantly improves the calculation speed compared to the previous method.
An Improved VGG16 and CNN-LSTM Deep Learning Model for Image Forgery Detection

As the field of image processing and computer vision continues to develop, we are able to create edited images that seem more natural than ever before. Identifying real photos from fakes has become a formidable obstacle. Image forgery has become more common as the multimedia capabilities of personal computers have developed over the previous several years. This is due to the fact that it is simpler to produce fake images. Since image object fabrication might obscure critical evidence, techniques for detecting it have been intensively investigated for quite some time. The publicly available datasets are insufficient to deal with these problems adequately. Our work recommends using a deep learning based image inpainting technique to create a model to detect fabricated images. To further detect copy-move forgeries in images, we use an CNN-LSTM and Improved VGG adaptation network. Our approach could be useful in cases when classifying the data is impossible. In contrast, researchers seldom use deep learning theory, preferring instead to depend on tried-and-true techniques like image processing and classifiers. In this article, we recommend the CNN-LSTM and improved VGG-16 convolutional neural network for intra-frame forensic analysis of altered images.
Smart Edge Detection Technique in X-ray Images for Improving PSNR using Robert Edge Detection Algorithm with Gaussian Filter in Comparison with Laplacian Algorithm

Aim: This study aims to propose smart edge detection techniques in x-ray images for improving PSNR using the Robert edge detection algorithm and compared it with the laplacian algorithm. Materials and Methods: For the design of edge detection technique to improve PSNR Robert edge detection algorithm is used along with the gaussian filter and it is compared with the laplacian algorithm. Robert edge detection algorithm and laplacian algorithm are the two groups considered in this study. For each group, the sample size is 20 and the total sample size is 40. Sample size calculation was done using clinicalc.com by keeping g-power at 80%, confidence interval at 95%, and the threshold at 0.05%. Result: When comparing the two algorithms, it is clear that the Robert edge detection algorithm has a higher mean PSNR value of 43.83 db than the laplacian algorithm 43.33 db. It is observed that the Robert edge detection algorithm has statistically insignificant difference from the laplacian algorithm by performing an independent sample t-test with p value greater than 0.05. Conclusion: Robert edge detection has significantly greater PSNR when compared to the Laplacian algorithm
Analysis and Comparison of Image Enhancement Techniques for Improving PSNR of Liver Images by Linear Contrast Algorithm over Median Filtering

Aim: The goal of this research is to reduce noise present in the liver images in order to enhance it using linear contrast and median filters. And also to analyze output of both the filters based on its Peak Signal to Noise Ratio (PSNR). Materials and Methods: The research includes two groups; each group has a sample size of 20. Grayscale medical images collected from the kaggle website were used in this research. Samples were considered as (N=20) for guided filter and (N=20) for fast bilateral filter with total sample size 40 calculated using clinicalc.com. For this study, the affected and normal liver images were collected from the Kaggle website. Then the linear filtering, median filtering algorithms were executed using Matlab software. Sample size was calculated using clinicalc.com, and the comparison analysis has been carried out through SPSS software. This research contains two groups, with a Gpower of 80 percent. The performance of the novel median filter is evaluated and the performance measure PSNR is compared with the linear contrast filter. Result: Based on Matlab simulation results, the PSNR of novel median filters is 76.0355 and linear contrast filters have PSNR of 57.1785. From the statistical analysis, it is observed that the significant value of PSNR (Peak Signal to Noise Ratio) (0.409) and p&gt;0.05. Conclusion: This study reveals that for the medical image enhancement purpose the novel median filter provides better PSNR than the linear contrast algorithm on ultrasound liver images.
Rain detection and removal from slowly varying background videos

Rain in bad weather becomes very challenging issue for automated driver assistance systems, security applications and many other computer vision algorithms. Number of rain removal algorithms have been proposed in these years but none of them has been able to increase the accuracy to large extent. In this paper a novel algorithm has been proposed where the slowly varying back ground scenes are taken for analysis. One part of the algorithm is based on the detection of rain pixels and the other part is removing them such that the original image must not lose its originality. Initially one background is modelled by taking the intensities of neighbourhoods of pixels. Then rain is detected by comparing each pixel with the modelled back ground followed by updating the background. The detected rain pixels are repaired with the help of some temporal neighbours. The results are promising to prove the work.
Q-based Equilibria

In dynamic environments, Q-learning is an adaptative rule that provides an estimate (a Q-value) of the continuation value associated with each alternative. A naive policy consists in always choosing the alternative with highest Q-value. We consider a family of Q-based policy rules that may systematically favor some alternatives over others, for example rules that incorporate a leniency bias that favors cooperation. In the spirit of Compte and Postlewaite (2018), we look for equilibrium biases (or Qb-equilibria) within this family of Q-based rules. We examine classic games under various monitoring technologies.
PENGEMBANGAN APLIKASI NASKAH DINAS ELEKTRONIK PADA BAG TIK KORLANTAS POLRI

This study aims to create a system that can recap the state of correspondence in the form of a desktop-based application that can manage correspondence management quickly, accurately, relevantly, and effectively. This correspondence management administration system is designed using the SDLC (Software Development Life Cycle) method, as a software development method and built using Netbeans IDE 8.2 software which uses the Java programming language and MySQL as its database. This research produces an administrative system that can help the admin personnel of the ICT Korlantas Polri in managing mail data in a fast and efficient time. And with this application, it is hoped that it will help work activities that require accurate information and be able to minimize human errors that often occur in the data collection process for correspondence management when using a manual system.
ANALISIS SENTIMEN BERBASIS ASPEK PADA ULASAN APLIKASI KAI ACCESS MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

PT Kereta Api Indonesia melakukan inovasi dengan meluncurkan aplikasi yang diberi nama KAI Access. Aplikasi KAI Access memiliki fitur pemesanan tiket, ticket rescheduling, pembatalan tiket hingga e-boarding pass. Tujuan dari penelitian ini adalah untuk mengetahui sentimen dari sebuah produk mobile. Opini terkait Aplikasi KAI Access dapat digunakan PT Kereta Api Indonesia sebagai parameter kunci untuk mengetahui tingkat kepuasan publik sekaligus bahan evaluasi bagi PT Kereta Api Indonesia. Berdasarkan hasil pengujian yang telah dilakukan pada ulasan pengguna aplikasi KAI Access dengan total 8.078 ulasan, lebih banyak pengguna memberikan opini positif dalam aspek satisfaction dan opini negatif pada aspek learnability, efficiency, dan errors. Digunakan model CRISP-DM (Cross Industry Standard Process for Data Mining) dan algoritma Support Vector Machine untuk melakukan klasifikasi. Hasil klasifikasi terbaik diperoleh nilai accuracy, precision, recall, dan F-measure yang dihasilkan dari tiap aspek yaitu untuk Learnability 94.73%, 100.00%, 89.50%, dan 94.64%, Efficiency 94.38%, 72.00%, 100.00%, dan 94.46%, Errors 85.13%, 97.11%, 72.41%, dan 82.96%, Satisfaction 87.26%, 98.46%, 73.78%, dan 84.20%.&#x0D; PT Kereta Api Indonesia innovates by launching an application called KAI Access. The KAI Access application has features for ticket ordering, ticket rescheduling, ticket cancellation and e-boarding pass. The purpose of this study is to determine the sentiment of a mobile. Opinion regarding the KAI Access Application can be used by PT Kereta Api Indonesia as a key parameter to determine the level of public satisfaction as well as evaluation material for PT Kereta Api Indonesia. Based on the results of tests conducted on user reviews of the KAI Access application with a total of 8,078 reviews, more users give positive opinions on the satisfaction and negative opinions on the learnability, efficiency and errors. Model CRISP-DM (Cross Industry Standard Process for Data Mining) and Support Vector Machine algorithm are used to perform classification. The best classification results obtained accuracy, precision, recall, and F-measure resulting from each aspect, namely for Learnability 94.73%, 100.00%, 89.50%, and 94.64%, Efficiency 94.38%, 72.00%, 100.00%, and 94.46%, Errors 85.13%, 97.11%, 72.41%, and 82.96%, Satisfaction 87.26%, 98.46%, 73.78%, and 84.20%.
Electronic Health Records Using CP-ABE Access Policy in Blockchain Technology

Abstract: In this paper, we have proposed ciphertext policy attribute based encryption (CP-ABE), which is secured against the attack under the chosen encrypted message attack and indistinguishable under the chosen cipher text attack . On comparing with other schemes, it has the least energy consumption in terms of communication and computation. Based on the proposed CP-ABE, we have implemented a cloud-based internet of medical things enabled smart healthcare system. The healthcare system has achieved secure patients details, and public integrity of patient details stored on the cloud without revealing information to any third entity. Further, we have scrutinized the performance of the proposed health care system in terms of computation energy and communication energy consumption.
Education in the Digital World: From the Lens of Millennial Learners

Purpose -The objective of this study is to determine Education in the Digital World: from the lens of millennial learners.This also identifies the cybergogical implications of the issue with digital education as seen through the lens of the outlier.Method -This study uses a mixed-methods sequential explanatory design.A quantitative method was employed during the first phase and the instruments of the study were distributed using google forms.The survey received a total of 85 responses and the results were analyzed using descriptive methods.Following up with a qualitative method, during Recommendations -The utilization of e-learning as a medium of instruction for millennial learners.Research Implications -The findings can be used by the institution to create guidelines, procedures, and policies for successfully implementing digital education or E-Learning.
Hubungan Perbedaan Beban Trauma Dengan Gambaran Histopatologi Hematoma Otak Tikus Pasca Cedera Otak Traumatik

Traumatic brain injury is defined as trauma to the head that can cause physical, intellectual, emotional, social or behavioral changes. Traumatic brain injury is one of the leading causes of morbidity and mortality worldwide. Brain injury Traumatic disorders are among the most common disorders in neurology. in the brain, it accounts for 2 to 15% of all head injuries. This study aims to determine the histopathological picture of brain hemorrhage in rats after experiencing traumatic brain injury and to analyze the relationship between differences in trauma load and the histopathological appearance of brain hemorrhage in rats after experiencing traumatic brain injury. This research is an experimental study conducted by giving treatment to the object under study and then observing it. Sampling in this research will use purposive sampling. Where the researcher has determined the criteria of the sample to be used in the study so that it can represent the population. : The results of this study there was no significant relationship between the percentage of the degree of brain hematoma in rats after experiencing traumatic brain injury. These results explain that bleeding in the brain usually progresses about 4-8 hours after brain injury occurs. In addition, the percentage of bleeding areas with trauma burden was p = 2.890.
The History of Furniture Objects: An Intelligent Augmented Reality Application

This chapter investigates how AR processes and user experience can be improved with Machine Learning (ML) techniques and proposes a solution for an intelligent AR mobile application named "FURNITURE". The fusion of AI with AR is not a new initiative but at the same time, its potential is yet to be maximized. Currently, Artificial Intelligence (AI) is applied to almost every domain. As is the case with other technologies, Augmented Reality (AR) can take advantage of the current advancements in AI to allow the creation of a new class of AR applications. The FURNITURE application was designed with two complementary functionalities: (a) to use an ML trained model to decide if an image of a piece of furniture corresponds to a pre-defined style, i.e. is authentic; (b) to augment the piece of the furniture shown in a real context with a history of that furniture style and a 3D model representative for that furniture style, the augmentation being conditioned by a previous validation of authenticity. The "FURNITURE" application uses Google AutoML framework to train an ML model for image classification, and Wikitude AR framework for the development of the mobile AR application. The designed purpose of the AR application is to support students in the fields of art, interior design, architecture and art history, professionals, and the public. The application demonstrates the concept and is open for future extensions, by expanding the object list that can be classified as corresponding to a certain style.
Pembuatan Alat Inspeksi Visual Jalur PCB Menggunakan Pengolahan Citra Untuk Kegiatan Praktikum Pengawatan Dan Teknologi PCB

PCBs are very influential on the manufacture of electronic devices, for example when there is even a small number of PCB paths that are cut off or damaged, the electronic device cannot be operated properly. Therefore, in this study, the author tried to create and analyze a defect checking tool on PCBs to replace human vision to make it easier and can save costs. This tool is equipped with the help of a Logitech c920 Webcam and a Raspberry Pi 3b+ microprocessor which is used to store and run programs that have been created on Python programming software, so this tool can be used portablely. With these two technologies, Image Processing can be used to detect objects with the OpenCv library and Google Colab. PCB defect detection tool with the help of Image Processing uses yolo convolutional neural network method to help determine path damage on the PCB. You Only Look Once (YOLO) algorithm with five detection classifications, namely short, open circuit, missing hole, mouse bite, and spur. From the results of the study, the results were obtained that the YOLO algorithm was able to detect these five classifications with a value of mAP@0.5 short 90.67%, open circuit 97.86%, Mouse Bite 94.43%, Missing Hole 96.09%, and spur 97.56%.
Marine Life Detection Algorithm Based on Deep Convolutional Generative Adversarial Network and ResNeXt50 with FPN Faster R-CNN Network

The development and utilization of marine resources has always been a focus of attention, and a prerequisite for the rational development of marine resources is the understanding of the distribution of biological resources in the ocean, so the detection of marine life has become an inevitable challenge in the development of marine resources. The acquisition of marine life images by underwater cameras is particularly difficult due to the complexity of the underwater terrain and the difficulty of underwater imaging. Therefore, the detection of marine life often faces problems of difficulties in dataset acquisition, scarcity of data in the dataset and low quality of images. To address the above problems, a marine life detection algorithm based on the combination of deep convolutional generative adversarial network and ResNeXt50 with FPN Faster R-CNN network is proposed in this paper. The algorithm uses Faster R-CNN as the basic framework for marine life detection and uses deep convolutional generative adversarial network to generate data for small dataset to effectively expand the dataset without the difficulty of underwater image acquisition. For the feature extraction network, we use ResNeXt50, which employs the idea of group convolution, as the basic network for feature extraction. Then, the network is combined with feature pyramid network to enhance the feature extraction capability. The expanded dataset fed into the ResNeXt50 with FPN Faster R-CNN target detection network for training. It can be demonstrated that the detection results of expanded dataset generated by DCGAN in ResNeXt50 with FPN Faster R-CNN network is better compared to the original dataset and the Resnet50 with FPN Faster R-CNN network.
Gangguan Perkembangan Bahasa pada Anak Usia 5 Tahun (Observasi pada Kasus Ahmad Djiat) Desa Moutong, Kecamatan Tilongkabila

This study aims to describe the condition of speech disorders experienced by children aged 5 years. Technical data collection is done through observation and interviews with children who are the object of research. The results of observations and analyzes carried out refer to psycholinguistic studies related to language disorders. It was found that the speech disorder experienced by Djiat was caused by a disturbance in his nervous system which was triggered by an injury or trauma during the prenatal period. Speech disorders are also caused by the lack of a good stimulus from the environment. Thus, children who experience difficulties in transformation really need communication activities. One of the symptoms in children with language and speech disorders is that there are pronunciation errors, both in the mechanism of the movement of the points of articulation and in the pronunciation. Handling that can be done for children who experience language and speech disorders is to do speech therapy, oral motor, and melodic intonation
PASS POINT SELECTION OF AUTOMATIC GRAPHICAL PASSWORD AUTHENTICATION TECHNIQUE BASED ON HISTOGRAM METHOD

Graphical passwords, as opposed to textual passwords, require the user to pick pictures or draw symbols rather than input written letters. They are an option that may be explored in order to get over the issues that are caused by the system of passwords that are based on text. It has been hypothesized that graphical passwords are more difficult to crack using a brute force technique or to figure out through guessing. This paper proposes an authentication system based on a graphical password method. The proposed system computes the password points using histogram arithmetic and encrypts the chosen password points using SHA512. The envisioned system has been realized as an android application and evaluated with existing research considering multiple measurements such as required login time, password space, and entropy. The findings reveal that the new suggested system outperforms the reference work by more than 85% in terms of login latency and more than 72% in terms of entropy results.