Reviewerly/exHarmony huggingface dataset:
Kegiatan Donor Darah Sebagai Gerakan Sosial Di Jurusan Teknik Kimia ITATS

Blood donation is an activity where a person voluntarily gives blood which will later be stored in a blood bank for transfusion purposes for those in need. Generally, blood requirements are not proportional to the number of donors. Information on the importance of the danar donors is still unknown. This activity aims to help the Surabaya City Indonesian Red Cross meet and increase the blood stock needed in the city of Surabaya and to socialize all students of the Surabaya Adhi Tama Institute of Technology about the importance of blood donation. This blood donation activity is a collaboration between the Chemical Engineering Student Association and the Indonesian Red Cross Donor City of Surabaya. The technical implementation of this activity is divided into 4 stages, namely distribution of donor forms, donor registration, health checks, blood donors. This blood donation activity is supported not only by the chemical engineering community, but also by the faculty community, namely students and lecturers. The blood donation activity lasted for 1 day with 60 blood donor participants and it went well. The participants involved consisted of 52 students and 8 lecturers. Based on the health check, it was found that 40 participants did not meet the requirements to become blood donors and 20 people did, so they received 20 bags of blood.
Online Diagnosis and Classification of CT Images Collected by Internet of Things Using Deep Learning

Deep learning technology has recently played an important role in image, language processing, and feature extraction. In the past disease diagnosis, most medical staff fixed the images together for observation and then combined with their own work experience to judge. The diagnosis results are subjective, time-consuming, and inefficient. In order to improve the efficiency of diagnosis, this paper applies the deep learning algorithm to the online diagnosis and classification of CT images. Based on this, in this paper, the deep learning algorithm is applied to CT image online diagnosis and classification. Based on a brief analysis of the current situation of CT image classification, this paper proposes to use the Internet of things technology to collect CT image information and establishes the Internet of things to collect the CT image model. In view of image classification and diagnosis, the convolution neural network algorithm in the deep learning algorithm is proposed to diagnose and classify CT images, and several factors affecting the accuracy of classification are proposed, including the convolution number and network layer number. Using the CT image of the hospital brain for simulation analysis, the simulation results confirm the effectiveness of the deep learning algorithm. With the increase of convolution and network layer and the decrease of compensation, the accuracy of image classification will decline. Using the maximum pool method, reducing the step size can improve the classification effect. Using relu function as the activation function can improve the classification accuracy. In the process of large data set processing, appropriately adding a network layer can improve classification accuracy. In the diagnosis and analysis of brain CT images, the overall classification accuracy is close to 70%, and in the diagnosis of tumor diseases, the accuracy is higher, up to 80%.
Autoethnography based on the educational experience of deaf dancers who speak languages

This study was conducted through an auto-netograhy methodology based on the educational experience of deaf dancer who speak languages. It was explained through mutual interviews between research participant and researcher, and in auto-netograhy research, participants were divided into childhood education experiences and adult education experiences. In the childhood education experiences there was a habit of denying teachers’ educational methods, moverment acquisition methods, career experiences, and memory without understanding the hearing impaired. In terms of educational experiences as an adult, it dealt with and interpreted in-depth education methods, music learning, breathing training, educational experience for the deaf, essential aspects of the deaf, and dance philosophy.
A Unified Visual Saliency Model for Automatic Image Description Generation for General and Medical Images

An enduring vision of Artificial Intelligence is to build robots that can recognize and learn the visual world and who can speak about it in natural language. Automatic image description generation is a demanding problem in Computer Vision and Natural LanguageProcessing.The applications of image description generation systems are in biomedicine, military, commerce, digital libraries, education, and web searching.A description is needed to understand the semantics of the image.The main motive of the work is to generate description of the image using visually salient features.The encoder-decoder architecture with a visual attention mechanism for image description generation is implemented.The system uses a Densely connected convolutional neural network as an encoder and Bidirectional LSTM as a decoder.The visual attention mechanism is also incorporated in this work.The optimization of the caption is also done using a Cooperative game-theoretic search.Finally, an integrated framework for an automatic image description generation system is implemented.The performance of the system is measured using accuracy, loss, BLEU score and ROUGE.The grammatical correctness of the description is checked using a new evaluation measure called GCorrect.The system gives a the-state-of-art performance on the Flickr8k and ImageCLEF2019 challenge dataset.
Modification of posterior probability variable with frequency factor according to Bayes Theorem

Probability theory is a branch of science that statistically analyzes random events. Thanks to this branch of science, machine learning techniques are used inferences for the prediction or recommendation system. One of the statistical methods at the forefront of these techniques is Bayesian theory. Bayes is a simple mathematical formula used to calculate conditional probabilities and obtain the best estimates. The two most important parts of the formula are the concepts of a priori probability and posterior/conditional probability. In a priori probability, the most rational assessment of the probability of an outcome is made based on the available data, while in posterior probability, the probability of the event occurring is calculated after considering all evidence or data. In this study, a new mathematical model is presented to calculate the posterior probability variable of Bayesian theory more precisely. According to this new mathematical model, equal priority probabilities of some variables should be recalculated according to frequency. Calculations are applied to two nodes. The first of these two nodes is the node consisting of the existing data, and the second is the queried node. The positive frequency value will be applied when the variables consisting of existing data and having the same a priori probabilities are found at the questioned node, and negative frequency value will be applied for the other variables. Thus, while calculating a standard probability value according to Bayesian Theory, frequency-based values are taken into account with the help of the newly created mathematical model. With the help of these frequencies, the modification of the system reveals more precise results according to these two basic principles. The results obtained were tested with the cross validation method and high accuracy rates were determined.
A METHOD FOR IMPROVING REASONING AND REALIZATION PROBLEM SOLVING IN DESCRIPTIVE LOGIC- BASED AND ONTOLOGY-BASED REASONERS

Recently, many methods have been developed for representing knowledge, reasoning, and result extraction extracting results based on the respective domain knowledge in question. Despite the ontological success in knowledge representation, the reasoning method has faces some challenges. The main challenge in ontology reasoning methods is the failure in solving realization problems in the reasoning process. Apart from the complexity of solving realization problems, this already daunting challenge is compounded by computational complexity the time complexity of the solving realization problem solving process problems is equal to that of NEXP TIME. This important issue problem is achieved solved by solving the subsumption and satisfiability problems. Thus, to solve the realization problem, we first partition the ontology or extract partitions related to the query. Then, the satisfiability problem is solved by extracting partitions, and all concepts related to the query are extracted. This study proposes a method to overcome this problem, where a new solution is proposed with an appropriate time position. Finally, the efficiency of the proposed method, is evaluated against other reasoning engines, and the results show optimized performance vis-a-vis previous studies.
ONLINE CHEATING METHODS VERSUS CHEATING IN THE CLASSROOM IN HIGHER EDUCATION: HOW DO PREVENTION MECHANISMS IN THESE ENVIRONMENTS DIFFER?

In the past year, remote learning has become commonplace in colleges across the globe. However, even before the Covid-19 pandemic, academic dishonesty had been starting to gain more attention as universities looked to online learning as a new method for increasing enrollment and providing additional opportunities for non-traditional students. Given the nature of online learning, it is important to understand not only the ways in which students may engage in dishonest behavior but also what mechanisms are available to ensure that students are complying with the rules. The goal of this paper is to review the literature on academic dishonesty in both the traditional classroom setting and the online environment and to provide an analysis of what areas need additional focus. The paper first reviews the most common reasons for why students cheat in the two environments and then provides a list of cheating methods that have been highly cited in the literature. Next, a list of methods that have been employed to curtail cheating will be defined and analyzed. Finally, the paper will seek to review how cheating prevention mechanisms are working and what new areas of prevention are being considered.
Bogazici university smartphone accelerometer sensor dataset

Mobile devices especially smartphones have gained high popularity and become a part of daily life in recent years. Therefore, there are many studies that investigate users' interactions with smartphones and try to extract meaningful information from various inputs. Actually, the main motivation behind these studies is the behavioral differences of users in their interactions with smartphones. In these studies, motion sensors in devices such as accelerometer and gyroscope are widely used. Data obtained from motion sensors allows to detect information such as age-group, gender, activity type, identity of users. In this context, we develop an Android application that gathers accelerometer sensor data while users perform different activities. This application records all accelerometer data and touch event information generated while users are using their devices. Then, we perform two experiments and collect two different data using this application. In the first experiment, we collect data from 107 child users and 100 adult users to analyze the impact of different age-groups' behavior on sensor data. This dataset includes more than 11.000 taps data for child and adult users, in total. In the second experiment, data is collected from 60 female and 60 male users for different activities like sitting and walking. There are more than 6.000 taps data for sitting and walking scenarios separately in the second dataset. This dataset makes it possible to analyze the changes created by different gender and activity types in the sensor data. These data can be used for behavioral biometric analyses on smartphones such as user age-group and gender detection, user identification and authentication.
JPR volume 59 issue 1 Cover and Front matter

An abstract is not available for this content so a preview has been provided. As you have access to this content, a full PDF is available via the ‘Save PDF’ action button.
The impact of e-marketing services on the satisfaction of the clients of emirates Islamic banks

The study aims to analyze the extent of customer satisfaction with e-marketing services in Islamic banks in the United Arab Emirates through studying the reality and quality of e-marketing services provided by Islamic banks in the United Arab Emirates. The study reviews the most important customer demands from e-marketing banking services provided by Islamic banks in the United Arab Emirates. The study discusses the most important problems and obstacles facing the work of Islamic banks in the United Arab Emirates. Achieve the objectives of the study, the researcher uses the descriptive and analytical research method. The study concludes with a set of results and recommendations, the most important of which is that there is a correlation between the quality of the electronic service in the bank and between the satisfaction and loyalty of customers in addition to the presence of a statistical effect of the impact of the quality of e-marketing services on customer attitudes. The researcher recommends enhancing the use of e-marketing to benefit from its advantages, strengthening attention to customers, enhancing the utilization of the latest technological means to practice banking work, and to enable bank workers to use electronic marketing applications for banking services provided to customers.
Covid 19 Data Analysis in India Using Linear and Polynomial Regression Algorithms

Abstract: In past two years there is a pandemic called covid-19, which has shook the world. The world has suffered a lot and suffering till now by the disastrous effect of corona virus globally. It has affect the world in all parameter i.e. economically, mentally and so on. The world don't when will this pandemic end yet can make forecast by utilizing AI calculations to make moves in the event that this occurs in later days ,how might human and government make counteractions from Covid. This project “Analysis on covid-19 in India using Linear and Polynomial algorithms” analyze the covid-19 datasets from 01-03-2021 to 08-05-2021 for India and also for its top 4 states ,having more number of confirmed cases and predicted the results by using machine learning algorithms (linear regression and polynomial regression with degree of 5).The predicted results will be helpful for government to take actions against this pandemic. Keywords: Data Analysis, Linear Regression, Polynomial Regression, Preprocessing of data, Flask
A Multiresolution Adaptive Wavelet Method for Nonlinear Partial Differential Equations

Modern computational science and engineering applications are inherently multiphysics and multiscale. Therefore, novel algorithms are required to solve partial differential equations (PDEs) with features evolving on a wide range of spatial and temporal scales. In this work, we propose a wavelet-based method which is ideally suited for problems developing localized structures, which might occur intermittently anywhere in the computational domain, or change their locations and scales in space and time. Wavelet-based numerical methods have been shown to be efficient for modeling multiscale and multiphysics problems because they provide spatial adaptivity through the use of multiresolution basis functions. In our work, we develop the Multiresolution Wavelet Toolkit (MRWT) that requires far fewer unknowns than other algorithms when applied to problems with a great range of spatial and temporal scales. In addition, the computational grid can be refined locally and our a priori error estimates safeguard the accuracy of the numerical solution. MRWT is designed to execute in a hybrid CPU/GPU environment and scale to 10^3-10^4 cores. Consequently, MRWT provides high fidelity simulations with significant data compression. We explain how this technique uses differentiable wavelet basis functions and second-generation wavelets, to solve nonlinear PDEs on finite domains. Moreover, we provide a priori error estimates for the wavelet representation of fields, their derivatives, and the aliasing errors associated with the nonlinear terms in PDEs. Then, by projecting fields and spatial derivative operators onto the wavelet basis, our estimates are used to construct a sparse multiresolution spatial discretization which guarantees the prescribed accuracy for each field. Additionally, MRWT utilizes a predictor-corrector procedure within the time advancement loop to dynamically adapt the computational grid, maintaining the prescribed accuracy of the solutions of the PDEs as they evolve. We show verification of the MRWT algorithm, demonstrating mathematical correctness and physics simulation capabilities. Furthermore, spatial convergence is achieved at a rate which agrees with a priori estimates. Finally, we apply MRWT to problems with shocks in gasses and to high-strain rate damage nucleation and propagation in nonlinear solids.
CONSUMER SOCIETY AND PERSUASIVE DISCOURSE

The purpose of this paper is to analyze language choice in advertising discourse and how individuals are affected by persuasion. In order to examine a society supported by consumption, Aristotle’s (1979) concepts of rhetoric will be central to this work. Observing how lexical resources are used in order to compose the persuasive discourse, we will use the writings of Roman Jakobson (1991). As an analytical procedure, we will turn our attention to five contemporary advertisements in the media, such as newspapers, television and Internet, highlighting how the advertising language becomes essential to induce the receiver to consumption. At the same time that consumerist practices are inserted in a cycle, individuals are led to make certain decisions and thus constitute themselves as agents in the process of social construction, going beyond the acquisition of only one good or service.
VIOLATION OF COOPERATIVE PRINCIPLES OF MAURICE IN THE BEAUTY AND THE BEAST MOVIE

This study was created based on concerning the importance of efficient language use. The language use can be called efficient if the message is unambiguous, clear, and can be understood by both speaker and listener. This study purposes to investigate the violation of quality maxim, quantity maxim, relevance maxim and manner maxim of Grice Maurice maxim’s Cooperative Principle in the conversation of characters in Beauty and the Beast movie. In fact, there are several maxim violations that happened in human life especially figured in the movie. The maxim violation may cause misunderstanding while the conversation is going on. Descriptive qualitative method was used in this research, the data are collected from the conversation in the movie which contains coversational implicature in the character’s utterance. The result of the research shows that there is four type of maxims that are violated by Maurice’s coversation in Beauty and the Beast movie. The four type of it such as maxim of quality, maxim of quantity, maxim of relevance and maxim of manner. The result of the research shows that the maxim of quality is dominant, because the character give lacks evident, maxim quantity, maxim of relation, and maxim of manner are also violated in this movie. Keywords: Cooperative Principles, Movie, Violation
A new approach for image inpainting by modification on total variation model

In this paper, we present a new method for inpainting an image part of which is damaged or lost. Our approach contains a modification on the total variation model. It is based on a new insight into the neighborhood pixels of the damaged areas. Some numerical experiments are given to confirm our model.
Data Fusion Algorithm in Subtitle Translation of English VR Video Course

In recent years, VR technology has developed rapidly, and its application range has also developed from entertainment to education. The English video teaching made by VR technology can effectively improve students’ learning enthusiasm. VR technology will surely lead to the arrival of an era, and it will also be a tool that must be carried out in the future development of many industries. Since the first year of VR, subtitle translation has also tried to use VR technology, but it has not been able to give full play to the advantages of VR technology. However, the existing video subtitle translation technology is still in the exploratory stage. Due to the frequent problems in subtitle translation, the actual teaching effect is greatly reduced. And this has just become one of the prerequisites for the application of data fusion algorithms. The characteristics of video course subtitle translation are that the course video is the source text, which is restricted by the form of audio-visual text, the actual operation of subtitle translation, and the course content. in principle. Combined with the case, the first translation and the proofreading are compared and analyzed. With the use of data fusion algorithms, it can solve the problem of letter translation in English VR video teaching. In view of this situation, this article studies the characteristics and strategies of subtitle translation in English VR video course. This study is mainly divided into three parts. The first part is the research into the characteristics of existing video subtitle translation, which analyzes the advantages and disadvantages of existing video subtitle translation one by one. Through the research, this article believes that the main shortcomings of the existing technology are cultural differences and human work deficiencies, but they can be overcome and improved. In view of these problems, the second part of this article is to improve the problem of English letter translation in the actual application process through the use of data fusion algorithms. The third part of this article is the investigation and research, using the way of investigation to verify the specific effect of the improvement method in this article. Through the analysis of the survey data, we can see that the improved English VR video course has been widely praised by the students, which not only increases the learning enthusiasm, but also improves the learning effect.
Ekaterina Yuryevna Genieva’s mental portrait

The author expresses his positive attitude to the three-volume book by Yakov Shrayberg: “My friend Katia Genieva” (Moscow, 2021). The reviewer argues that by addressing to E. Yu. Genieva’s personality and activities, the author, in fact, portrays the historical experience of the recent forty years rich in social, political, economic and ideological developments. Ekaterina Yuryevna Genieva – late Director General of the All-Russia State Library for Foreign Literature named after M. I. Rudomino, a global personality, famous for her public, cultural, philanthropic, philological, library and library science initiatives. Her efforts coincided with Russia’s hinge period, and she found herself “in the right place at the right time”. The book conveys Genieva’s exceptionalism; her leadership, compassion and contribution to many aspects of life, are well-documented in the book. The book focuses on her many good points; while humanly downsides may make the theme of another book, if anyone volunteers – then we will get the objective profile of our outstanding contemporary.