Reviewerly/exHarmony huggingface dataset:
PENGEMBANGAN BAHAN AJAR ELEKTRONIK MENGGUNAKAN APLIKASI FLIP PDF CORPORATE EDITION PADA PEMBELAJARAN TEMATIK TERPADU DI SEKOLAH DASAR

The research objective is to develop and produce electronic teaching material products using applications Flip PDF Corporate Edition in learning Theme 6(Heat and Its Transfer) Sub-theme 1(Temperature and Heat) in learning 3 class V (Five) SDN 12 Kampung Durian, Padang City. Research development also aims to see the validity and feasibility of the electronic teaching materials that have been developed. Development of electronic teaching materials using applications Flip PDF Corporate Editions tudied using the ADDIE model which consists of five stages namely Analysis, Design, Development, Implementation, Evaluation. Research on the development of electronic teaching materials begins with analyzing class needs, designing or designing electronic teaching materials, then carrying out the development of electronic teaching materials, then conducting validation by experts, revising electronic teaching materials. After carrying out revisions from experts, implementation of electronic teaching materials with predetermined subjects was carried out. The average of the validation values by the material, language, and teaching material validators shows that electronic teaching materials can be used in the learning process with an overall score of 94.17% validation percentage with 95% material validation results, 90% language validation results and material validation results teaching 97.5%. In the implementation and practicality stages, the response from the teacher and students was very good for the electronic teaching materials that had been used. The result of the teacher's response is 97.14%. And students 96.4%. From the research results it can be concluded that electronic teaching materials use applications Flip PDF Corporate Edition has been valid, very interesting, and appropriate for students to use in the learning process
А New Personality for Digital Technologies: New Challenges in Higher Education

Introduction. The article highlights the problems arising in the field of higher education in connection with the introduction of digital technologies in the practice of teaching. Currently, such concepts as information space, information society, digitalization and the like are constant and ubiquitous in cultural and business environment. These terms describe the current stage of development of society, its technological state, the ways of its further evolution, as well as the consciousness and attitude of the modern individual. Digitalization of the educational process as an advanced effective tool provides the teacher with new means of improving the quality of learning through visualization and updating of the studied material, a huge selection of additional resources, as well as encourages the student’s desire for self-education.Materials and methods. In the course of writing this article, the following methods of scientific research were used: analysis, synthesis, abstraction and philosophical generalization, as well as induction and testing. When analyzing the main directions of the use of digital methods in education, a characteristic of the new reality of higher school work is given, the advantages and disadvantages of the formation of a digital educational environment are highlighted.Results of the study. The author comes to the conclusion that the widespread introduction of digital methods leads to a general transformation not only of the industrial and business environment, but also to significant changes in the worldview of new generations, as well as their approaches to obtaining information, its analysis and evaluation, as well as to communication challenges.Discussion and conclusions. The paper concludes, that digital learning, with all its undeniable advantages and wide opportunities, cannot be considered an equivalent replacement for traditional forms of education due to largely limited communication between teachers and students. Other serious challenges of the modern digital society in terms of the educational process are cardinal changes in the thinking of young people due to the difference in their everyday life experience from the routines of previous “predigital” generations. Thus, the optimal solution to the problem of adapting higher education to new realities is an effective combination of the latest resources and techniques with a traditional approach to the process of forming a new personality of the 21st century.
Modify PRESENT Algorithm by New technique and key Generator by External unit

Recently, the widespread use of devices in the field of electronics has caused people to worry about safety. Using traditional cryptographic algorithms to build a complete cryptographic environment for embedded applications or the Internet of Things (IoT) will not be possible due to space, power, and speed limitations. The focus is on lightweight encryption to overcome these problems. In this paper, we have developed the (PRESENT) algorithm, which is among the lightweight algorithms that are used in the IOT and that keeps pace with the requirements of communication speed at the present time. We also used a physical part with the developed algorithm (an external unit that is a USB and using it as a fingerprint for the system) to generate the key while ensuring that the system does not work until after the external unit is connected to the calculator to provide more security for the system from breaches. The developed algorithm achieved a high speed of execution faster than the original algorithm and passed standard tests for various files (text, image, audio, video). In addition to increasing the degree of complexity than the original algorithm.
A Novel Family of Online Censoring Based Complex-Valued Least Mean Kurtosis Algorithms

&lt;p&gt;In this paper, we propose a novel family of on?line censoring (OC) based complex-valued least mean kurtosis (CLMK) algorithms by inspiring the advantages of the online censoring strategy and kurtosis based cost function. We first develop important members of this family of algorithms such as OC based CLMK (OC-CLMK) and augmented CLMK (OC-ACLMK). These algorithms censor less informative complex-valued data streams in an online manner and keep only the most informative ones for performing their weight vector updates. Thus, they reduce the costs of data processing without markedly affecting performance accuracy. However, they do not take into account the possible outliers that disturb their performances considerably. Therefore, we also develop robust members such as robust OC-CLMK (ROC-CLMK) and OC-ACLMK (ROC?CLMK) algorithms, which censor the possible outliers as well as less informative complex-valued data streams. The convergence analyses of the proposed algorithms in the sense of Lyapunov are also presented to derive the bounds of their step sizes in a compact form. The simulation results on large-scale system identification and regression scenarios affirm the mentioned attractive features of the proposed algorithms. This study also shows that the noteworthy properties of the proposed algorithms will provide important contributions to the processing of complex-valued big data streams. &lt;/p&gt;
Securing Financial Transactions: Exploring the Role of Federated Learning and Blockchain in Credit Card Fraud Detection

&lt;p&gt;Credit card fraud detection is a significant challenge for the financial industry, and the privacy of sensitive financial data is of utmost importance. Federated learning is a decentralized machine learning technique that can enable collaborative model training while preserving privacy. Blockchain, with its decentralized and secure nature, can further enhance the privacy and security of federated learning. This paper explores the opportunities, challenges, and future directions of blockchain-enabled federated learning for credit card fraud detection. The combination of federated learning and blockchain can provide a secure and private platform for credit card fraud detection. Blockchain-enabled federated learning offers several opportunities, including improved privacy, security, and collaboration among different financial institutions. The successful implementation of blockchain-enabled federated learning can revolutionize credit card fraud detection by providing a secure and private platform for collaborative model training. This paper emphasizes the potential of blockchain-enabled federated learning for credit card fraud detection and highlights the need to address the challenges associated with this technology. It is essential to continue exploring and developing blockchain-enabled federated learning to ensure the security and privacy of sensitive financial data while promoting collaboration and innovation in the financial industry.&lt;/p&gt;
Awesome-META+: Meta-Learning Research and Learning Platform

&lt;p&gt; Artificial intelligence technology has already had a profound impact in various fields such as economy, industry, and education, but still limited. Meta-learning, also known as "learning to learn", provides an opportunity for general artificial intelligence, which can break through the current AI bottleneck. However, meta learning started late and there are fewer projects compare with CV, NLP etc. Each deployment requires a lot of experience to configure the environment, debug code or even rewrite, and the frameworks are isolated. Moreover, there are currently few platforms that focus exclusively on meta-learning, or provide learning materials for novices, for which the threshold is relatively high. Based on this, Awesome-META+, a meta-learning framework integration and learning platform is proposed to solve the above problems and provide a complete and reliable meta-learning framework application and learning platform. The project aims to promote the development of meta-learning and the expansion of the community, including but not limited to the following functions: 1) Complete and reliable meta-learning framework, which can adapt to multi-field tasks such as target detection, image classification, and reinforcement learning. 2) Convenient and simple model deployment scheme which provide convenient meta-learning transfer methods and usage methods to lower the threshold of meta-learning and improve efficiency. 3) Comprehensive researches for learning. 4) Objective and credible performance analysis and thinking. &lt;/p&gt;
Adaptive Exponential Weight Discretization for Compact and energy-efficient CNN Inference

&lt;p&gt;Convolutional Neural Networks (CNN) compression and optimization techniques for the inference process attract attention due to CNN’s broad range of applications in many fields. Implementing CNNs for embedded deceives, however, faces great challenges due to the devices’ limited hardware resources. Recently, real-time pplications based on complex CNNs require optimization and compression techniques to make CNNs run faster in compact and energyefficient embedded devices. CNN odel optimization and compression include pruning and weight quantization. Conventional pruning techniques often suffer from poor compression ratio, so they still have a large number of float point weights leading to embedded devices with excessive hardware cost or slow inference speed. Conventional quantization techniques often result in an unacceptable loss in inference accuracy. To overcome the above problems, the proposed method introduces an adaptive exponential weight discretization process, which selects discretization parameters based on the sensitivity and the number of the weights in each layer. Unlike many previous compression methods, the proposed method does not require any retraining process nor any fine-tuning methods. The proposed weight discretization has been evaluated using the VGG16 CNN model with the ImageNet dataset for classification. The evaluation demonstrated that it saves 48 % of computation for fully connected layers and it reduces the overall weight memory size from 528 MB to 44.24 MB – a reduction of 11.9 times. We also demonstrate that the optimized CNN loses negligible loss – a loss of only .43% in the top-5 accuracy and a loss of only 1.44% in the top-1 accuracy.&lt;/p&gt;
Safety Risk Evaluation of Tourism Management System Based on PSO-BP Neural Network

With the recovery of the tourism industry and the development of the field of artificial intelligence, the application of intelligent neural network technology to management systems for safety risk assessment is the choice of the times and an urgent real need for relevant practitioners. In this study, the BP neural network algorithm is used as a tool for safety evaluation of the tourism management system. The three-layer structure of the BP neural network and the role of nodes in it are introduced, the weight values and thresholds of nodes in each of the three layers are calculated, and the particle swarm algorithm is added to optimize the model. In the practical stage, the tourism data of a place in Switzerland was selected as the training data. After 12,859 iterations, the model achieved the best calibration error of 0.126. After 300 iterations of learning, the BP algorithm optimized with the PSO algorithm has a faster convergence rate, which indicates that the performance of the optimized algorithm has improved significantly and has the global search capability that was not available before, which significantly outperforms the FastText and LSTM models. With the increase in the number of samples of tourism data, macroaccuracy always remains above 80%, so it proves that the optimization algorithm used in this study is an effective and reliable model.
Doubly robust joint learning for factorization machine on data missing not at random

When a recommendation system is preferred to use for predicting the ratings, it is common that many users-to-items ratings cannot be found. And what makes the prediction even trickier is that these missing ratings are missing not at random (MNAR), meaning that there are other factors contributing to why some ratings cannot be found. In order to make better predictions, common approaches to reducing the prediction errors for those missing ratings are using imputed errors and weighting the ratings that are observed with the observed propensities, but each of them is not perfect enough (since they still show bad performance with bias or heavily influenced by the variance of propensities in estimation). To generate a better recommendation system algorithm that could produce a satisfactory outcome, a new estimator combining the imputed errors and propensities in a doubly-robust way is designed to perform an unbiased estimation and meanwhile reduce the influence caused by the propensity variance. In addition, joint learning with error imputation and rating prediction based on the estimator is adopted to acquire a more reliable performance, which shows a better result than the dataset without doubly-robust joint learning.
Nursing professionals working remotely in the context of the Covid-19 pandemic: an experience report

The objective of this study was to report the experience of performing remote work by the nursing team of an Oncology Unit, during the pandemic of COVID-19. This is a descriptive, exploratory study, of the experience report type, carried out at a Federal University Hospital in the city of Maceió, Alagoas. The report occurred through data collected from April 2020 to July 2022, based on the experience of nursing professionals facing the remote work in the context of the COVID-19 pandemic. The technological innovation, through remote work, provided a beneficial experience for the most vulnerable health professionals, i.e., employees who were part of the risk group, because it allowed social isolation and compliance with the preventive measures of the pandemic, ensuring greater safety for all professionals in the risk group.
The Capable Web

In this paper, I discuss arguments in favor and in disfavor of building for the Web. I look at three extraordinary examples of apps built for the Web, and analyze reasons their creators provided for doing so. In continuation, I look at the decline of interest in cross-platform app frameworks with the exception of Flutter, which leads me to the two research questions RQ1 "Why do people not fully bet on PWA" and RQ2 "Why is Flutter so popular". My hypothesis for why developers don’t more frequently set on the Web is that in many cases they (or their non-technical reporting lines) don’t realize how powerful it has become. To counter that, I introduce a Web app and a browser extension that demonstrate the Web’s capabilities.
Understanding the Impact of Label Skewness and Optimization on Federated Learning for Text Classification

Federated Learning (FL), also known as collaborative learning, is a distributed machine learning approach that collaboratively learns a shared prediction model without explicitly sharing private data. When dealing with sensitive data, privacy measures need to be carefully considered. Optimizers have a massive role in accelerating the learning process given the high dimensionality and non-convexity of the search space. The data partitioning in FL can be assumed to be either IID (independent and identically distributed) or non-IID. In this paper, we experiment with the impact of applying different adaptive optimization methods for FL frameworks in both IID and non-IID setups. We analyze the effects of label and quantity skewness, learning rate, and local client training on the learning process of optimizers as well as the overall performance of the global model. We evaluate the FL hyperparameter settings on biomedical text classification tasks on two datasets ADE V2 (Adverse Drug Effect: 2 classes) and Clinical-Trials (Reasons to stop trials: 17 classes).
Contextual Response Interpretation for Automated Structured Interviews: A Case Study in Market Research

Structured interviews are used in many settings, importantly in market research on topics such as brand perception, customer habits, or preferences, which are critical to product development, marketing, and e-commerce at large. Such interviews generally consist of a series of questions that are asked to a participant. These interviews are typically conducted by skilled interviewers, who interpret the responses from the participants and can adapt the interview accordingly. Using automated conversational agents to conduct such interviews would enable reaching a much larger and potentially more diverse group of participants than currently possible. However, the technical challenges involved in building such a conversational system are relatively unexplored. To learn more about these challenges, we convert a market research multiple-choice questionnaire to a conversational format and conduct a user study. We address the key task of conducting structured interviews, namely interpreting the participant’s response, for example, by matching it to one or more predefined options. Our findings can be applied to improve response interpretation for the information elicitation phase of conversational recommender systems.
Towards a Critical Open-Source Software Database

Open-source software (OSS) plays a vital role in the modern software ecosystem. However, the maintenance and sustainability of OSS projects can be challenging. In this paper, we present the CrOSSD project, which aims to build a database of OSS projects and measure their current project “health” status. In the project, we will use both quantitative and qualitative metrics to evaluate the health of OSS projects. The quantitative metrics will be gathered through automated crawling of meta information such as the number of contributors, commits and lines of code. Qualitative metrics will be gathered for selected “critical” projects through manual analysis and automated tools, including aspects such as sustainability, funding, community engagement and adherence to security policies. The results of the analysis will be presented on a user-friendly web platform, which will allow users to view the health of individual OSS projects as well as the overall health of the OSS ecosystem. With this approach, the CrOSSD project provides a comprehensive and up-to-date view of the health of OSS projects, making it easier for developers, maintainers and other stakeholders to understand the health of OSS projects and make informed decisions about their use and maintenance.
Ways to develop students' research activities in algebra class of grade 9

This article discusses the results of the study, which determine the theoretical foundations and pedagogical features of the development of research activities of students in the lesson «Algebra» of the 9th grade. One of the most pressing issues is the identification and development of research activities of students in the course of teaching mathematics in general secondary schools. In the course of conducting research activities, methods of special pedagogical analysis, control, survey of the subject of Algebra, the method of pedagogical experiment of research activities were used. Based on the results of the research, a set of special tasks for the development of research activities of students was compiled. Through specially compiled tasks, the research activity of students increased from 8% to 25%. First of all, in the course of research activities, students were surveyed. 72 students took part in the survey, as a result of which it was found that 36 (50%) of the students were motivated to self-study and engage in research work, but only 18 (25%) of them completed research tasks. Based on the results of the survey, it was found that students who can work with additional sources of literature, actively participate in self-search, research work, were able to perform these exercises. The definition of «research activity» in teaching mathematics has been clarified. The concept of «research task» was considered and examples of some types of research problems were given. A pedagogical analysis of the textbook «Algebra» of the 9th grade was carried out and the advantages in it were shown. As a result of research activities, teachers who teach algebra in schools use a set of tasks designed specifically for the development of students' research activities in the form of a methodological manual.
Evaluation Algorithm for Hosting Capacity of PV System using LDC Method of SVR in Distribution Systems

This paper deals with an evaluation algorithm of the hosting capacity for PV system based on the LDC (line drop compensation) method of SVR (step voltage regulator) in order to improve the hosting capacity when large-scaled PV systems are installed in distribution system. Specifically, optimal LDC setting values are obtained by calculation algorithm of LDC setting values for SVR using the Least-squares method that minimizes the error for the entire time interval based on the distribution characteristics of upper and lower limits for the sending voltage of SVR considering the reverse power flow by PV systems. And also, this paper performs a modeling of complex distribution system which is composed of large-scaled PV system and SVR with LDC method by using PSCAD/EMTDC S/W. From the simulation results, it is confirmed that the proposed algorithm and modeling is a useful and practical tool to improve the hosting capacity of PV system because customer voltages are maintained within the allowable voltage limits.