Reviewerly/exHarmony huggingface dataset:
Key-Value Data Collection with Distribution Estimation under Local Differential Privacy

Local differential privacy (LDP) is a promising privacy-preserving technology from users’ perspective, as users perturb their private information locally before reporting to the aggregator. We study the problem of collecting heterogeneous data, that is, key-value pairs under LDP, which is widely involved in real-world applications. Although previous LDP work on key-value data collection achieves a good utility on frequency estimation of key and distribution estimation of value, they have three downfalls: (1) existing work perturbs numerical value in a discrete manner that does not exploit the ordinal nature of the numerical domain and lead to poor accuracy, (2) they do not lead to improved privacy budget composition and consume more privacy budget than necessary to achieve the given privacy level, and (3) the frequency estimation of the key is not the most accurate due to the lack of consistency requirement. In this paper, we propose a novel mechanism to collect key-value data under LDP leveraging the numerical nature of the domain and result in better utility. Due to our correlated perturbation, the mechanism consumes less privacy budget than previous work while keeping the privacy level. We also adopt consistency as the postprocessing, which is applied to the estimated key frequency to further improve the accuracy. Comprehensive experiments demonstrate that our approach consistently outperforms the state-of-the-art mechanisms under the same LDP guarantee.
Design Of Monitoring Information System Stock Items At Partner Stores In Web-Based Semarang

Toko Mitra is a grocery or retail store that is engaged to meet daily needs. Monitoring stock and transaction financial reports at Partner Stores in Semarang often experience problems. This is because the shop owner still uses a manual system, so that the recording of incoming and outgoing goods still does not match the last stock. Therefore, I want to provide a solution to Partner Shop owners by creating a web-based stock monitoring program. In building a stock monitoring system, I use programming languages PHP and JavaScript, MySQL as a database, and the CodeIgniter framework. The existence of a web-based stock monitoring system at Partner Stores is expected to help monitor activities that occur in the store to make it faster and more efficient.
An IGWOCNN Deep Method for Medical Education Quality Estimating

The deep learning and mining ability of big data are used to analyze the shortcomings in the teaching scheme, and the teaching scheme is optimized to improve the teaching ability. The convolution neural network optimized by improved grey wolf optimization is used to train the data so as to improve the efficiency of searching the optimal value of the algorithm and prevent the algorithm from tending to the local optimal value. In order to solve the shortcoming of grey wolf optimization, an improved grey wolf optimization, that is, grey wolf optimization with variable convergence factor, is used to optimize the convolution neural network. The grey wolf optimization with variable convergence factor is to balance the global search ability and local search ability of the algorithm. The testing results show that the quality estimating accuracy of convolutional neural networks optimized by improved grey wolf optimization is 100%, the quality estimating accuracy of convolutional neural networks optimized by grey wolf optimization is 93.33%, and the quality estimating accuracy of classical convolutional neural networks is 86.67%. We can conclude that the medical education quality estimating ability of convolutional neural network optimized by improved grey wolf optimization is the best among convolutional neural networks optimized by improved grey wolf optimization and classical convolutional neural networks.
Application Smart Internship System Unika Soegijapranata (SISTA) berbasis Web (Progressive Web Apps)

Apprenticeship is a student activity carried out on the basis of cooperation between the University and the institution where the work apprenticeship program is implemented which aims to be a place of learning as well as training for students to assess students' knowledge and understanding of the learning that has been given on campus and applied directly to the world of work at once Where there are difficulties in getting information on job apprenticeship vacancies, students usually search by contacting related agencies or seeing job internship vacancies on the online platform of each agency, which if accommodated will be very time consuming.Therefore a media is needed to accommodate information on vacancies as well as a place to register for an internship program that is easy, fast and easy to use by students and companies to create job apprenticeship vacancies.Purpose of this research is to make an application that can accommodate all of these activities from opening and looking for job apprenticeship program vacancies, approving and collaborating on apprenticeship programs as well as providing value or results of an integrated internship program with transcript information and Certificate of Companion Diploma (SKPI) Students. This media will be designed using a web application as well as the use of a progressive web app to be used on all types of platforms.
Application of the Deep Pretrained Language Model Processing Method in Social Network Sentiment Analysis

In social network, users can manage their social network and social identity, publish information on various topics, and obtain information published by other users through friend relationship. The resulting large amount of text data attract more and more scholars to study it. Text sentiment analysis has become a hot spot in social network data analysis and has important application value in academic field, social field, and business field. Based on the idea of pre-training, this paper improves the random word masking algorithm of deep pretraining task in the BERT (Bidirectional Encoder Representation) model to improve the efficiency and stability of model pretraining. Second, a new pretraining task of original sentence judgment is designed to enable the model to measure the degree of sentence smoothness, so that the BERT model can better understand the semantics of context. By referring to the idea of attention mechanism, a deep learning framework with attention weight added into gated convolution is constructed and the special attention weight method is adopted to enhance semantic information. Second, gating convolution and attention mechanism are combined to model aspect-related semantic information and text complete semantic information. Finally, classify the emotion classifier layer of social network, use Softmax function to complete negative, positive, and neutral multiple classifications and calculate the result of emotion classification. By applying the optimized convolutional neural network cyclic optimization network to single task and multitask in practice, the feasibility of applying the optimized convolutional neural network and cyclic neural network to social network sentiment analysis is verified.
IMPLEMENTASI KAMPUS MENGAJAR UNTUK MENANAMKAN LITERASI BACA TULIS SISWA SEKOLAH DASAR

This study aims to explain the implementation of campus teaching in instilling reading and writing literacy. This research uses a qualitative approach with a case study design. The subjects in this study were fifth grade students, fifth grade homeroom teachers and university students teaching. Data collection techniques used in this study are observation, interviews and documentation. Data analysis techniques used in this study are data reduction, data presentation, and verification/conclusion. The results showed that the implementation of campus teaching in instilling literacy in reading and writing, namely reading fifteen minutes before learning began, daily reading journals and graphic organizers were effective to help students practice literacy skills.&#x0D;
Gab as Disseminator of Antisemitic Conspiracy Myths and Enabler of Offline Violence

Abstract While there are increasing moves by mainstream social media platforms to control far-right activity as well as the prevalence of hate speech such as antisemitism, such regulation has in turn fueled the rise of platforms dedicated to being “bastions” of free speech where regardless of its content, discourse seems completely uncontrolled-one such platform is Gab. This paper seeks to chart the rise of Gab as a location for unrestrained antisemitic conspiracy myths and to demonstrate that there is a spillover of such online discourse into real-world criminal action. In order to do so, the paper’s aims are fourfold: to overview current research on online antisemitism and the role of Gab in it; it will look at cases where online hate was taken offline; it will examine what makes the role of Gab unique in this regard and how conspiracy theories find an open house there; and it will examine the Gab Dissenter, a browser extension that facilitates the circumvention of normal moderation. Ultimately, the paper strives to answer whether Gab serves as an enabler for offline antisemitic violence by allowing and even inviting conspiracy theories on its platform.
The Decoding Antisemitism Project—Reflections, Methods, and Goals

Abstract This article introduces the pilot project “Decoding Antisemitism: An AI-driven Study on Hate Speech and Imagery Online.” The aim of the project is to analyse the frequency, content and linguistic structure of online antisemitism, with the eventual aim of developing AI machine learning that is capable of recognizing explicit and implicit forms of antisemitic hate speech. The initial focus is on comments found on the websites and social media platforms of major media outlets in the United Kingdom, Germany, and France. The article outlines the project’s multi-step methodological design, which seeks to capture the complexity, diversity and continual development of antisemitism online. The first step is qualitative content analysis. Rather than relying on surveys, here a pre-existing “real-world” data set-namely, threads of online comments responding to media stories judged to be potential triggers for antisemitic speech-is collected and analysed for antisemitic content and linguistic structure by expert coders. The second step is supervised machine learning. Here, models are trained to mimic the decisions of human coders and learn how antisemitic stereotypes are currently reproduced in different web milieus-including implicit forms. The third step is large-scale quantitative analyses in which frequencies and combinations of words and phrases are measured, allowing the exploration of trends from millions of pieces of data.
Research on the application of incentive Mechanism in the management of College PE teachers

PE teachers are the first resources of college physical education, a set of effective incentive mechanism can fully mobilize the enthusiasm of the majority of PE teachers, improve the quality and efficiency of school physical education. How to establish effective incentive mechanism has become an urgent problem for colleges and universities to deepen the reform of internal management. Therefore, establishing a good teacher incentive mechanism, strengthening and improving the overall quality of teachers is a key problem that every university is facing. This paper makes a systematic analysis of the current situation of the research on the incentive mechanism of college PE teachers, so as to optimize and perfect the incentive mechanism of college PE teachers.
Pre-Questioning Technique Towards Students Reading Comprehension

The aim of this research is to know the influence of the Pre-Questioning technique towards students’ reading comprehension. In this research, the writer used the experimental method or known as descriptive quantitative research. The population was 103 students in three classes. The sample was taken by using Cluster Random Sampling Technique. The writer took two classes as the sample of research namely experimental and control class, it consisted of 73 students. The main technique in measuring students’ reading comprehension was multiple choices which consisted of 40 items. Each item has five options; A, B, C, D, and E. The score for each item was 2,5. In calculating the data analysis, the writer used the t_test formula. Based on the data analysis by using the t_test formula, the writer got the result that H_a is accepted. It was obtained that t_test =4.07, t_table for α = 0.05 is 1.67 and for α = 0.01 is 2.39. Therefore, the conclusion is there was an influence of Pre-Questioning towards students’ reading comprehension of the tenth grade at SMK Budi Utomo Way Jepara in 2020/2021. Based on the result of the research, it is clear that the Pre-Questioning technique can improve students’ reading comprehension.
Review on Fuzzy Classifications Techniques and Applications

The concept of fuzzy classification has been significantly used in various Purposes.The fuzzy classification area has been increased rapidly in the past few years and it has been successfully adopted .In this work , we propose to develop a means to understand Fuzzy Classification .Particularly , this article tends to present a deep review of the most important topics of Fuzzy classification including new improvements in the field.This article explains the significance of Fuzzy classification, displays the various methods of Fuzzy classification and different applications.The paper ends with a summary and conclusion.
Representative Service Providers and their Selection in Cloud Computing Domain

The popularity of cloud computing is growing owing to its large data storage capacity and high computation power.It provides online, on-demand, scalable application solution, removes hardware and software barriers for non-specialist, rapidly integrates and deploys desired and necessary facilities, supports quick upgrading and addition of features.Users get benefitted with the selection of the appropriate cloud computing platform for their projects.Here, our paper provides a comprehensive overview of the services provided to the users by the most common cloud computing service providers.This paper could be used as a reference while selecting the best service provider based on the requirements of the projects.
Implementing Steganalysis using Machine Learning

Nowadays hackers are using many novel techniques to hide malicious files inside an image and later sent these images to target users.These malicious image files allow the hackers to get access to the system and control the victim system from their remote location.In order to prevent this, a Django interface is proposed in this research work to verify whether the file is malicious or not?The interface allows the user to choose an image after downloading from an email or any other sources.The image is then sent to the back end server to verify if there is any malicious file hidden inside the image or not?If there is any hidden file then it is extracted from the image using steganalysis, which is a technique used to extract a hidden file.This classification is done by using machine learning technique where the machine is trained using the dataset containing features of malicious and non-malicious files.Random Forest algorithm is used for classification purpose to classify the file is malicious or not.So when an executable file is hidden inside an image, the file is extracted and the features of that executable file are then sent to the already trained machine learning model.If it is malicious then a message will appear displaying that the file is malicious and if not, a message will appear, displaying that the file is safe to use.This research work is implemented and is then deployed in IIS web server in windows server 2019, so that any user can access the website and can upload single image or multiple images.The website can be accessed through mobile phones or laptops.
Utilizing Transformer for Sentence Similarity Modeling and Answer Generation in the Machine Reading Comprehension Task

In the Machine Reading Comprehension (MRC) task, the objective is to understand the given text in order to answer questions about it.For this task, selecting relevant passages or sentences is an important step.In this paper, we utilize the pretrained transformer encoder for sentence similarity modeling to select rel-evant passage(s) or sentence(s) for the MRC problem and then we fed the relevant passage(s) or sentence(s) to a question answering model for answer generation.Experimental results in the Microsoft MAchine Reading COmpre-hension (MS-MARCO) dataset for the QA task show that our proposed approach is effective to generate abstractive answers in the MRC task.
A Survey on IOMT based Food Recommendation System

Many individuals are affected with numerous ailments and diseases in today's globe.As a general rule, it is quite difficult to recommend a diet rapidly.AI-based medical information systems have potential to extend human life expectancy, prevent additional sickness, as well as enhance general health.Recommendation system for patients monitors a person health in a systematic approach.Considering patient health, monitoring their health conditions and providing the solution instantly are the biggest challenges.Some of the researchers have carried out their work on monitoring a user health by considering users body nutrition values, some of the researchers build a system as per the user preferences.Some of the researchers have considered IOMT data to build a food recommendation system.Using these recommended systems encourage people to maintain good diet and health.
A Review Study of Big Data and Cloud Computing

Big data and Cloud Computing are the latest technologies that are high in demand.Big data refers to the massive amount of data generated by various internet sources such as retail applications, banking services, social media, stock exchanges, airlines, etc.A huge amount of data is produced which cannot be handled by traditional database software like RDBMS.Cloud computing is a game changer in the way we store, process, analyze data and access it.It provisions infrastructure, hardware, and software on the internet as per user demand.This paper has reviewed various research papers.What is big data?Its meaning with respect to different authors, characteristics, deployment on cloud, its various models, scope, and challenges are discussed.
Anti-Counterfeit Method for Computer Hardware using Blockchain

Counterfeited computer hardware are products designed looks exactly the same as their genuine products.Most of the people are tricked by the counterfeiters using online markets.This influences the need for a secure and efficient mechanism to identify fake/counterfeited products.The proposed method is implemented using the Blockchain technology.Each Block represents a product and the hash key of that product, calculated using the specified Block attributes.The buyer details were updated by a verified retailer.Thereafter any user can check the validity of the product using the hash key and retailer name.Tampered Block is notified to the customer and then the product is invalid.This system can be upgraded by hosting the application on a web server for distribution and separating the application functions according to the user levels (Manufacturer, retailer, and buyer).Therefore, the proposed method provides a more secure and reliable way to handle computer hardware counterfeits.
A case study on conventional security practices v/s AI-powered approaches in Internet of Things

The internet of things (IoT) is an advancement that can change the way that we live, in regions going from transport to prosperity, from redirection to our associations with government. The usage of the IoT has extended decisively, and network insurance concerns have extended close by it. IoT systems might perhaps increase effectiveness, obligation, detectability, and efficiency. The extreme front line of organization assurance is Artificial Intelligence (AI), which is used to improve bewildering computations to shield associations and structures, including IoT systems. As to security, the IoT will be gone up against extra outrageous troubles. In this paper, we discuss the conventional security methods and the role of artificial intelligence in governing security in the Internet of things.
Comparative Study of Blood Pressure, Body Composition and Physical Fitness According to Types of Taekwondo Class Participation of Military Science Majors in Pandemic Situation

The purpose of this experiment is to compare blood pressure, body composition, and physical strength of military science students who participated in Taekwondo class in a pandemic situation. 50 male and female military science major students in the Taekwondo class were grouped in two: contact class and non-contact class and they were compared and analyzed about blood pressure, body composition and physical fitness in this study. ANCOVA method was used to analyze each dependent variables. The blood pressure, body composition and physical fitness in the male group who performed the face-to-face group were improved compared to the non-face-to-face group. Also, the female group showed the same study results as the male group.
Between traditions, reputation and best practices: Development and promotion ways of the academic journal

The article shows how, when developing strategies for promoting a scientific journal, it is important not only to follow the trends of publishing activities and take into account the current level of development of the scientific discipline, to which most of the published materials are devoted, but also to preserve the traditions that have contributed to successful communications between scientists from different countries for many years, translation of new discoveries and activation of cognitive activity of both individual scientists and entire research institutes. The main problems arising in the organization of the work of the editorial board and members of the editorial office are briefly listed. The problems are covered by the example of the journal “Studies in the History of Science and Technology”. Ways are proposed for solving a number of problems and promoting the journal in the direction of openness to authors from different countries and strengthening the interdisciplinary research topics.
The Impact of Creative Leadership on Enhancing the Level of Organizational Culture (A Field Study on Non-profit Sector Organizations in the Kingdom of Saudi Arabia)

The study aimed to identify the impact of creative leadership in enhancing the level of organizational culture within non-profit organizations in the Kingdom of Saudi Arabia, to identify the impact of the dimensions of creative leadership (sensitivity to problems, perseverance, initiative, creativity) on organizational culture in the organization, to reveal the extent of the difference in opinions and perceptions Participants reported creative leadership due to demographic variables such as educational qualification, years of experience, and job title. The importance of the study stems from the direct discovery of creative leadership within the institutions of the non-profit sector in the Kingdom of Saudi Arabia to harness their creative capabilities to enhance the organizational culture of the organization. The results obtained by applying the study tool to the sample members showed: There is a statistically significant relationship with the dimensions of creative leadership, on organizational culture in non-profit organizations in the Kingdom of Saudi Arabia; there are no statistically significant differences between the members of the study sample about creative leadership due to personal data. The study reached a number of recommendations, the most important of which are: The importance of including the concept of the non-profit sector in the administrative education curricula, The importance of establishing independent centers specialized in publishing scientific research and studies on the Saudi non-profit sector in all fields, establishing a center concerned with maximizing and measuring the social impact.