Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Teknologi Informasi Sebagai Media Pembelajaran Guru dalam Pendidikan Jarak Jauh (PJJ) Selama Covid-19

Distance Learning (PJJ) is a form of social distancing that is carried out so that every student has the right to continue to study during the Covid-19 pandemic. Distance Learning (PJJ) provides higher education services to community groups who cannot attend face-to-face education, and expands access and facilitates higher education services in learning. The learning system carried out by students and teachers through learning media by utilizing information technology. The purpose of this study was to describe the use of information technology as a medium of learning by teachers and to find out the state of technology available in schools as a means of supporting distance learning. This study uses a qualitative descriptive method with a case study approach. Data collection was carried out by studying literature and interviewing four resource persons from state elementary school teachers, junior high schools, and high schools located in the city of Tulungagung, East Java. The collaboration of students, teachers, schools, parents, and the government determines the success of PJJ, especially for the availability of learning support tools and internet access. Teachers adapt and are required to innovate in utilizing technology. There is a change in the use of technology for the sustainability of education in Indonesia.
Analysis of Various Deep learning Algorithms for Tamil Character Recognition

Process of converting Handwritten Tamil letters into editable form for extracting the information present in documents, Invoices that in handwritten letters that are difficult to find out the word present in the document because of bad handwriting and poor quality of the picture, Social media comments for analyzing and extracting the sentiment behind the text.Nowadays people started to deliver their thoughts through their own language Native Tamil.People find it easy to post thighs in Tamil which also leads to false information, violated messages, and cyberbullying.To address this, the notion of deep learning algorithms is implemented to classify the letters in digital form and also to suggest that the combination of letters obtained from deep learning results in better performance and good accuracy as well.The method involves the identification of optical characters to obtain feedback for the model and the creation of a model using neural networks which include CNN, Yolo and ResNet.Existing works involves using a simple OCR system but it results in a low-level recognition rate it finds difficult blurred images, shaken images, slightly tilted image's, poor quality images to recognize the text and give it back as an editable form to overcome this above situation selecting these models for testing with the Tamil character's to choose upon the best fit model to proceed for the future work of increasing the dataset and to recognize all the combination of words given in the input source of Tamil letters.The dataset used here is from HP-Labs India, which provides a large dataset of around 60,000 samples of isolated Tamil letters collected from both male and female candidates with different age groups.Tamil text classification is generally used to classify particular letters from handwritten letters in the digital form of images.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Double-blind • Conference submission management system: All the papers were submitted papers via the conference email: amms2021@163.com • Number of submissions received: 132 • Number of submissions sent for review: 105 • Number of submissions accepted: 56 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100):42.4% • Average number of reviews per paper: 2 • Total number of reviewers involved:81 • Any additional info on review process: AMMS2021 uses a plagiarism software to detect instances of overlapping and similar text in submitted manuscripts. • Contact person for queries : Ms. Yolanda Xu, Advanced Science and Industry Research Center, Hong Kong, amms2021@163.com
A Fast General Image Encryption Method Based on Deep Learning Compressed Sensing and Compound Chaotic System

Compressed sensing aims to reduce image storage space and transmission costs. It is widely used in image reconstruction and encryption algorithm. Nowadays, an increasing number of researchers are focusing on the combination of these two tasks, but most of the current algorithms have low reconstruction quality, long running time, small key space and poor security. For this reason, this paper proposes a general image compression encryption algorithm based on deep learning compressed sensing and compound chaotic systems. This algorithm has a larger key space and great advantages in terms of time-consuming. By using bilinear interpolation, fully connected layer and convolutional network compress the image. Then the two-dimensional cloud model and Logistic composite chaotic system encrypt the compressed image, and complete the scrambling and XOR in one step. In the reconstruction network, after the decrypted image is amplified by bilinear interpolation, the convolutional neural and the fully connected layer reconstruct the image contour and color information respectively. The experimental results show that the compression encryption algorithm can be applied to both grayscale images and RGB format color images, and the reconstruction quality is greatly improved. The composite chaotic encryption algorithm closely associates the plaintext with the key to achieve the encryption effect of one image and one encryption. Scrambling and XOR are performed at the same time so that the compression encryption algorithm has higher security.
Digital Image Segmentation Resulting from X-Rays of Covid Patients using K-Means and Extraction Features Method

The COVID-19 pandemic has significant impact on people's lives such as economic, social, psychological and health conditions. The health sector, which is spearheading the handling of the outbreak, has conducted a lot of research and trials related to COVID-19. Coughing is a common symptoms among humans affected by COVID-19 in earlier stage. The first step when a patient shows symptoms of COVID-19 was to conduct a chest x-ray imaging. The chest x-rayss can be used as a digital image dataset for analysing the spread of the virus that enters the lungs or respiratory tract. In this study, 864 x-rays were used as datasets. The images were still raw, taken directly from Covid-19 patients, so there were still a lot of noise. The process to remove unnecessary images would be carried out in the pre-processing stage. The images used as datasets were not mixed with the background which can reduce the value at the next stage. All datasets were made to have a uniform size and pixels to obtain a standard quality and size in order to support the next stage, namely segmentation. The segmentation stage of the x-ray datasets of Covid-19 patients was carried out using the k-means method and feature extraction. The Confusion Matrix method used as testing process. The accuracy value was 78.5%. The results of this testing process were 78.5% of precision value, 78% of recall and 79% for f-measure
A report on Privacy Preservation Methods on Big Data, Social Network Data, Medical Data.

Now a days Privacy-preserving of data is an essential philosophy in the digital computer era. As the leakage of preserved data has been increasing in the daily life. In this survey paper we have collected the different types of privateness maintenance in the view of big records of data , clinical and social networking data. In this paper we had explained deeply about the privacy preserving methods on these three types of data. We also gathered the various implementations which are implemented by various researchers until the present trend. In this survey paper we have given a clean view on the kind of arts of the secure and privateness maintaining techniques of big analytic data, in medical field data, and in social networking data. We also mentioned about the future works to be done for preserving the data. In now a days the attackers who stole the preserved data has been increased so we should increase the security levels of privacy gradually. In this survey paper we had mentioned what are the infrastructures that are preserving based on the type of data.
Does Website Usability Impact Webometrics Ranking of University?

Academic Website is one of the internet-based information media used by educational institutions such as universities, institutes, schools, and other non-formal institutions. The general information contained in the academic website includes study programs, scholarly activities, academic achievements, study plans, academic calendars, and academic information systems. Webometrics is a system that provides an assessment of all the best universities in the world through the university&#x0027;s website. Webometric ranks more than 22,000 Universities around the world. There are three components that become the leading indicators of this Webometrics assessment, namely: visibility/impact, transparency/openness, and excellence. In addition, usability can be exploited to provide an evaluation of the website&#x0027;s quality. Usability refers to how easy it is to use the interface of a website. Usability can be measured by using five criteria, which are navigation, personalization, download speed, accessibility, and ease of use. This work investigates the impact of website usability on webometrics ranking. The sample of this research is 75 websites of regular universities in Indonesia. For data collection, several research instruments were used in the form of an online checker. The data was then analyzed by exploiting Structural Equation Modeling. The result shows the ranking of university websites based on usability criteria gives significant results similar to the ranking results with three standards released by Webometrics.
Tsunami evacution building model (TEBM) in Teluk Palu (Palu Bay) area

Abstract The research was conducted in the coastal area of Palu Bay, Lere Village. Data were collected using observation, interviews, and literature study techniques. The data were analyzed using qualitative descriptive methods and focused on the concept of the design of the tsunami evacuation building on the coast of Palu Bay. Data analysis was carried out to obtain an overview of the problems that occurred at the planning location which was analyzed with the concept of architectural design by considering the existing conditions of the site and the surrounding environment, as well as utilizing the potential of the site so that it has the potential for building functions. The conclusion obtained is based on the results of data analysis, it is necessary to have a building that can function as an evacuation container for the community when a disaster occurs in the coastal area of Palu Bay, thereby reducing the number of casualties. The benefit of this research is that it is a solution for local people to save themselves when the tsunami disaster.
Hajdú Péter és Eugen Helimszkij szölkup nyelvleírása: az igei toldalékok szembesítése

Future workVe′rdd has relations other than translation relations to deal with.These include: "Etymological relation" for marking associations between cognates; "Compound relation" for associating compound words with the elements; "Derivation relation" for associating derived words with their source stems and derivational affixes.These could, in the future, be predicted automatically by incorporating methods such as the ones by Alnajjar et al. ( 2017), Hämäläinen -Rueter (2019) and Alnajjar (2021), test materials could be found in Keresztes (2013).Etymological relations between words of the Mordvin idioms may be readily augmented in Ve′rdd at the present by entering documentation metadata from sources such as (Keresztes 2011: 107-118), and even more extensive alignments can be found in the Mordwinisches Wörterbuch (1990Wörterbuch ( , 1992Wörterbuch ( , 1994Wörterbuch ( , 1996)), both at the lemma level and the etymology level (cf.Rueter 2020).Future work will involve the enhancement of parallel corpora development for Erzya and Moksha, both literary and vernacular in collaboration with other centers of Mordvin studies.The lexical resources we have created and described here could be used in future in research on morphological derivations, dialect areas and lexical distributions.Furthermore, this approach can be useful for other endangered languages as well.
Membangun Website Menggunakan Framework CodeIgniter di STMIK YMI Tegal

Background: The development of websites in various fields is increasing. One of the reasons is that it can facilitate access to the information needed without any limitations of space and time, and there is the ease in its management. This training is expected to increase the mastery and ability of students in building a website by using a framework, especially CodeIgniter. STMIK YMI Tegal is one of the partners who work together on this training with 62 (sixty-two) participants. This training is carried out by providing materials and demonstrations to participants in using the website using the CodeIgniter framework with the support of online media in the form of zoom, youtube, and google drive links. The training results carried out can be seen from the level of student acceptance of the material provided. This training received feedback, including the material presented by the resource persons, received a very good response with a score of 62.9%, and the suitability of the material to the needs of students received a very good response with a value of 53.2%. The sustainability of this activity is expected to have advanced training. Based on the results of this training, this training can provide benefits and mastery of participants in building a website, especially using the codeigniter framework currently needed by the industrial world.
Application of Artificial Intelligence Technology in Distance Guiding of Cooking Practice

Cooking teaching is an important part of vocational education, and it is of great significance to cultivate excellent cooking talents in China. At present, the cooking teaching in vocational colleges in China is too theoretical, and many teaching contents cannot adapt to the development of cooking technology in China. Therefore, the cooking knowledge that students learn is often limited to the cooking textbooks due to the restriction of the teaching syllabus. A general joint learning model for optical camera and depth camera is proposed and applied to distance teaching mode. This model tries to overcome the shortcomings of the traditional threshold color and random forest model and avoid the complex artificial design and the limitation of the threshold and random forest features in the application scene.
Scene Graph Prediction with Concept Knowledge Base

Image understanding is an emerging research direction in computer vision, and scene graphs are the most mainstream form of understanding. A scene graph is a topological graph with objects in the scene as nodes and relationships as edges, used to describe the composition and semantic association of objects in an image scene. Scene graph prediction requires not only object detection, but also relationship prediction. In this work, we propose a scene graph prediction method based on a conceptual knowledge base, which uses the condensed human understanding stored in the knowledge base to assist the generation of the scene graph. We designed a simple model to fuse image features, label features and knowledge features. Then the data filtered by the model is used as the input of the classic scene graph generation model, and better prediction results are obtained. Finally, we analyzed the reasons for the slight increase in the results, and summarized and prospected.
Audio Events Detection to Help TIAGo to Act as a Medical Robot

Nowadays service robots are used extensively in and out hospitals to improve the level of patient care. In this paper we present how Moving Picture Experts Group-7 features together with the Hidden Markov Models can aid the TIAGo service robot to understand the context through audio signals, in such a way that can be used as a medical robot. Due to the help of medical robots, the person-to-person contact can be reduced, thus the physicians and medical staff workload can be narrow down, also improving the healthcare facilities. Through experimental results we prove that, in the test phase, the overall classification accuracy is 96.86%, at least for the given framework.
On Pooled OLS and Panel Regression Models for Assessing the Contributions of Electronic Payment System on Commercial Banks Profitability

This study examined the impact of electronic payment system on the profitability of commercial banks in Nigeria. Pooled OLS and Panel regression models were fitted on the data extracted from the banks’ annual reports, Nigerian interbank settlement scheme, and central bank of Nigeria website. The assessment of the contribution of the various electronic payment systems considered were measured using Breusch and Pagan Lagrangian Multiplier (LM) Test, the Hausman Test, Stationarity Test, The Schwarz Criterion, and the Akaike Information Criterion. Results obtained showed that the random effect model was more appropriate than the fixed effect model for all the electronic payment systems considered in this study. Furthermore, it was discovered that there exists a positive relationship between the electronic payment systems and profitability of the commercial banks in Nigeria.
Relationship between the assessment of the formation of conflict resolution compo-nent in the teacher's image and the desire to enter the teaching profession

Introduction. The article attempts to study the correlation between the desire to enter the teaching profession and the formation of the conflict resolution component in the teacher’s image. The purpose of the article is to identify and describe the relationship between the assessment of the formation of the conflict resolution component in the teacher’s image and the desire to pursue a career in education. Materials and Methods. The study is based on the systematic approach and uses theoretical and empirical research methods. To collect primary empirical data, a survey of undergraduates doing their degrees in education was conducted. The study involved 152 undergraduate students (Years 2-5). For the mathematical processing of the research data, the H - Kruskal Wallace test and factor analysis were used Results. The factor analysis revealed two factors within the structure of the conflict resolution component of the teacher’s image. Its content differs when students assess school teachers, university teachers and themselves. According to students’ perceptions, the structure of conflict resolution component of the school teacher’s image and of the image of the university teacher coincide to a great extent. On the other hand, the structure of conflict resolution component of future teachers’ ‘self-image’ differs. ‘Refusing violence’ in the second factor is associated with ‘following rules’ for students, and with ‘personal choice’ for university teachers and school teachers. The research findings indicate that students with different degrees of professional motivation have different perceptions of the formation of conflict resolution component within professional images of university teachers, school teachers and themselves. When assessing school teachers and university teachers, significant differences were found according to the ‘internal stability’ criterion. ‘Doubting students’ rated university teachers lower according to this criterion, on the other hand, ‘students who do not want to enter the teaching profession’ rated this criterion low both among school teachers and university teachers. Conclusions. The study reveals the correlation between the assessment of conflict resolution component of the teacher's image and motivation for entering the teaching career. The article concludes that the desire to pursue a career in education is associated with such components of the teacher's image as ‘positive goals’, ‘equal communication’ and ‘personal choice’. Low self-evaluation of students’ conflict resolution component of the teacher's image negatively affects the motivation for teaching career in the future.
Parallel Local Tridirectional Feature Extraction Using GPU

Texture is a characteristic of image, and it plays a very important role in image analysis. Many image retrieval applications make use of texture features in describing images. In the proposed work, the texture feature used is the local tridirectional pattern. It uses the intensity of neighboring pixels in three directions. The method is tested for DICOM images using sequential and parallel programming, and also the time required to compute the pattern for both is calculated.
Explainable Deep Learning

Recent advances in deep learning have made tremendous progress in the adoption of neural network models for tasks from resource utilization to autonomous driving. Most deep learning models are opaque black-box models that are not easily explainable. Unlike linear models, the weights of a neural network are not inherently interpretable to humans. The need for explainable deep learning has led to the development of a variety of methods that can help us better understand the decisions and decision-making process of neural network models. We note that many of the general post-hoc model-agnostic methods presented in Chap. 5 are applicable to deep learning models. This chapter presents a collection of explanation approaches that are specifically developed for neural networks by leveraging architecture or learning method.
ESTÁGIO SUPERVISIONADO EM ENFERMAGEM NA ATENÇÃO BÁSICA NO CONTEXTO DA COVID-19: RELATO DE EXPERIÊNCIA

Objective: to report the experiences of nursing students during the supervised curricular internship in primary care, in the context of the Coronavirus pandemic.Method:
Using Probabilistic Functional Dependencies for Determining Correlations in Skewed Data

In this paper we present a method of analysis of skew data. Skew data exists in many areas including medical data. Our contributions consist in the use of probabilistic functional dependencies together with statistical evaluators and elements of information theory to find correlations in skewed data. The experimental part uses real medical data that shows the skew property. The results highlighted the most representative classes in the practical field analyzed which was determining HLA-CMV correlation in renal transplantation.