Reviewerly/exHarmony huggingface dataset:
Development of a Website for Mathematical Concept Formation Through Analysis of the Concept Formation Process

Through the analysis of artificial neural networks and t-tests, we identified linear independence and linear dependence as the points at which students stumble in linear algebra. Furthermore, we developed mathematics websites in the fields of calculus, linear algebra and probability statistics that allow high school and university students to form concepts interactively, regardless of time and place. The websites were designed so that visitors can complement their knowledge with hyperlinks, regardless of their learning history. Teachers can also use the website to easily implement Peer Instruction in their classes. The website provides a useful structure for both students and teachers and is in line with the aims of the Digital Transformation in Education.
A Trust-Based Blockchain System for Secured Migration of BLE Devices in IoT Networks

The Internet of Things (IoT) has gained popularity recently, leading in the rapid invention and enormous advancement of ubiquitous applications that are seamlessly interwoven into our daily life. Because privacy is becoming more important, questions about secure management and effective access control of IoT devices have gotten a lot of attention. Hacking BLE modules in devices that have previously validated a connection is nearly impossible. However, devices must first pair in order to connect, and this is where the fundamental risk of BLE-enabled systems lies. In this study, we focused on blockchain-based pairing of BLE devices in Bluetooth enabled IoT devices and proposed the architecture of a blockchain- connected gateway that adaptive and safely maintains user privacy preferences for IoT devices in the blockchain network. Individual privacy leaks can be prevented because the gateway successfully protects users’ sensitive data from being accessed without their permission. In this paper, the seamless secured communication and migration of BLE enabled IoT devices is achieved by utilizing the full potential of blockchain technology.
Data Sub-sampling Method for Developing Personalized Human Activity Model Based on Incremental Learning

This paper proposes a method to conduct data sub-sampling for developing personalized human activity recognition (HAR) model based on incremental learning. The quality of training dataset required to execute incremental learning is a critical factor affecting its performance. To generate a high-quality training dataset, this paper performs two-phase sub-sampling. The first phase discriminates the activity data into the borderline data with a high risk of mislabeling and the non-borderline data with a low risk of mislabeling. To do so, entropy-based distances between the decision boundaries and the activity data are calculated. The second phase separates the correctly labeled borderline data from the entire borderline data. This paper generates the KDE-based probability density function to discover the correctly labeled borderline data and performs binary clustering. Finally, incremental learning is performed to obtain the personalized HAR model using the non-borderline and correctly labeled borderline data. To show the superiority of the proposed method, we conduct experiments on two benchmark datasets named the HAPT and WISDM datasets.
A Practical Detection and Defense Scheme Against Smart Contract Attacks Based on Transaction Features

As a main component of blockchain technology, smart contracts support multiple functions and hold large amounts of assets, which makes them a target for attackers. Statistics show that attacks on smart contracts account for three-quarters of all attacks on the blockchain applications, causing huge economic losses to users of smart contracts. The existing research mainly focuses on vulnerability detection of contracts that cannot achieve real-time protection of deployed contracts. In this paper, we propose a practical detection and defense scheme against smart contract attacks. Specifically, We use an attack detection method based on transaction features to detect attacks using miner nodes and use the attack detection results as a consensus to block the executions of attacks and achieve real-time defense against attacks. Theoretical analysis and simulation results show that our scheme only requires a small increase in storage and computational overhead to achieve effective defense against smart contract attacks.
Analysis of Weather Data for Rainfall Prediction using C5.0 Decision Tree Algorithm

Rainfall has an essential role in human life, including the agricultural aspect. By knowing the estimated intensity of rainfall that will fall in an area at a particular time, we can determine a good planting period for commodities that require rainfall prediction. This study aims to produce a rainfall prediction model using the C5.0 Algorithm on the Bogor Regency daily weather dataset in the previous five years (2017 - 2021). The dataset is divided into two categories, namely nine explanatory factors (date, month, minimum temperature, maximum temperature, average temperature, average humidity, sun exposure, maximum wind speed, and average wind speed) and one target class rainfall category (low, medium, and high). The best model variation was generated using a 5-fold CV, which resulted in five model partitions with a total accuracy of 86.33% in the training data and 84.22% in the test data. The resultant rules include 72 attributes, two partitions pick the average humidity as root node, and the remaining three choose the average temperature. The model rules produce rainfall prediction information that can assist in determining the best cultivation time for an agricultural commodity to increase yield productivity.
ECO RECOVERY OF EVAPORATION LOSS AT COOLING TOWER OF THERMAL POWER PLANT

&#x0D; &#x0D; &#x0D; The Globe as well as national stage also understands shockingly crucial sustainability threats, i.e., H2O shortage and pandemic situation (COVID-19). The thoughtful H2O utilization strength in a variety of brand dominion technology. According to countrywide disorder is a prerequisite for understanding the prospective shock of influence on H2O reserves. Evaporation loss is 968t/h at Target Sector, this paper review on operational losses to Eco Design 475.5t/h. At the Current Situation power is not sustainable energy with reverence to H2O preservation, so revision at power should be enhanced to propose and recaptures to reduce losses from 968t/h, to 475.5t/h for Environment sustainability.&#x0D; &#x0D; &#x0D;
Solving inference problems of Bayesian networks by probabilistic computing

Recently, probabilistic computing approach has shown its broad application in problems ranging from combinatorial optimizations and machine learning to quantum simulation where a randomly fluctuating bit called p-bit constitutes a basic building block. This new type of computing scheme tackles domain-specific and computationally hard problems that can be efficiently solved using probabilistic algorithms compared to classical deterministic counterparts. Here, we apply the probabilistic computing scheme to various inference problems of Bayesian networks with non-linear synaptic connections without auxiliary p-bits. The results are supported by nanomagnet-based SPICE (Simulation Program with Integrated Circuit Emphasis) results, behavioral model, and hardware implementations using a field-programmable gate array. Two types of Monte Carlo sampling methods are tested, namely rejection and importance samplings, where clamping of p-bits is applicable as in Boltzmann networks in the latter. Partial parallelism that can be used for the sequential update of each p-bit in Bayesian networks is presented. Finally, the model is directly applied to temporal Bayesian networks with relevant inference problems. We believe that the proposed approaches provide valuable tools and practical methods for various inference problems in Bayesian networks.
Functionality and Merits of a Faceted Thesaurus: The Case of the <i>Nuovo soggettario</i>

The Nuovo soggettario, the official Italian subject indexing system edited by the National Central Library of Florence, is made up of interactive components, the core of which is a general thesaurus and some rules of a conventional syntax for subject string construction. The Nuovo soggettario Thesaurus is in compliance with ISO 25964: 2011-2013, IFLA LRM, and FAIR principle (findability, accessibility, interoperability, and reusability). Its open data are available in the Zthes, MARC21, and in SKOS formats and allow for interoperability with l library, archive, and museum databases. The Thesaurus’s macrostructure is organized into four fundamental macro-categories, thirteen categories, and facets. The facets allow for the orderly development of hierarchies, thereby limiting polyhierarchies and promoting the grouping of homogenous concepts. This paper addresses the main features and peculiarities which have characterized the consistent development of this categorical structure and its effects on the syntactic sphere in a predominantly pre-coordinated usage context.
Support attack detection algorithm for recommendation system based on deep learning

Abstract In order to enhance the performance of recommendation systems that support attack detection algorithms, we have designed a novel approach based on deep learning. Specifically, our algorithm focuses on improving convergence, user scoring accuracy, algorithm efficiency, and detection accuracy. To achieve this, we first construct a preliminary user rating matrix, which is optimised by incorporating the user preference word matrix and the weight of the preference word. Additionally, we adjust the size of the matrix using principal component analysis. Next, we construct a deep, bidirectional RNN model based on the deep learning network. This model is then combined with the user scoring matrix to identify the type of user. In the case of abnormal or false users, our algorithm is able to identify the recommendation system’s support attack through the detection results. The experimental results demonstrate the effectiveness of our algorithm. Specifically, our approach achieves fast convergence speeds, with the loss value remaining low throughout the process. Moreover, we achieve high average accuracy in user scoring, with a score of 97.14%. The detection time of the recommendation system support attack is also consistently lower than 0.7 s. Furthermore, our approach achieves an average accuracy of 98.09% in the detection of recommendation system support attacks. Overall, our algorithm shows promising results for practical applications in the field of recommendation systems.
Decolonizing Data Visualization: A History and Future of Indigenous Data Visualization

On the whole, the field of data visualization is white. Contemporary views of historical data visualization tend to trace back to a few iconic visuals tied to European wars and conquests. The modern explosion of the field has been centered around the ideas of white men, as if they invented data visualization. Yet, Indigenous populations world-wide have been incorporating data visualization into their record keeping for centuries before anyone had heard of Edward Tufte. In this article, three Indigenous evaluators (Mohican/Munsee, Cherokee, and Tlingit) along with a non-Indigenous co-conspirator, will discuss their journeys creating space to weave together Western notions of data visualization best practices and Indigenous ways of knowing and storytelling. The authors focus their evaluative work on the support of Indigenous communities and will reflect on what has worked in communicating data, what hasn't, and how far data visualization has to go in all four directions.
Elementary online learning: strategies and designs for building virtual education, Grades K-5

"Elementary online learning: strategies and designs for building virtual education, Grades K-5." Education 3-13, ahead-of-print(ahead-of-print), pp. 1–2 AcknowledgmentsThe authors would like to express their heartfelt appreciation and gratitude to the Lembaga Pengelola Dana Pendidikan- LPDP (Indonesia Endowment Fund for Education) under the Ministry of Finance of the Republic of Indonesia for sponsoring the author master’s degree as well as for supporting this publication and collaboration.
Fast Incremental Image Reconstruction with CNN-enhanced Poisson Interpolation

We present a novel image reconstruction method from scattered data based on multigrid relaxation of the Poisson equation and convolutional neural networks (CNN). We first formulate the image reconstruction problem as a Poisson equation with irregular boundary conditions, then propose a fast multigrid method for solving such an equation, and finally enhance the reconstructed image with a CNN to recover the details. The method works incrementally so that additional points can be added, and the amount of points does not affect the reconstruction speed. Furthermore, the multigrid and CNN techniques ensure that the output image resolution has only minor impact on the reconstruction speed. We evaluated the method on the CompCars dataset, where it achieves up to 40% error reduction compared to a reconstruction-only approach and 9% compared to a CNN-only approach.
Résumés en Français

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn Email Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Résumés en Français. Am J Intellect Dev Disabil 1 July 2023; 128 (4): 344–345. doi: https://doi.org/10.1352/1944-7558-128.4.344 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest Search
A study on the direction of Korean learners' spoken corpus: focusing on the problems in transcription and distribution process

Language and Information 27.1, 1-18. This paper explores the direction of Korean learners’ spoken corpus, focusing on the problems within the transcription and distribution process of spoken corpus. This paper points out that inter-transcriber reliability poses a problem in the process of transcribing the learners’ spoken interlanguage. To solve this problem, this paper proposes a method of displaying the locations of pronunciation errors while maintaining transcription at an orthographic level under the assumption that the audio files are disclosed. For the discussion of the distribution process, this paper refers to the learners’ corpus abroad. This study confirms that the end-user should be considered in the distribution process as spoken corpus is used by human. In relation to this, the provision of a search service, analysis tools, and user manuals are proposed as a specific plan. In addition, this paper suggests that speaker classification and search by genre are necessary improvements in the distribution of the Korean learners’ spoken corpus currently provided by the National Institute of the Korean Language. This paper is meaningful in that it suggests a direction for constructing a Korean learners’ spoken corpus, which has been lacking in the past.
Artificial intelligence in cardiology

In this „avalanche of data” Artificial intelligence (AI), is programmed to imitate human behavior, so AI works with human-made programs and algorithm.&nbsp; Within AI has been developed so called automatic learning, machine learning, when computers learn without programs, through examples from the hundreds and thousands of received information (e.g., electrocardiograms, computed tomography or other data).
The Use of Cloud Computing to Process Big Data: An Applied Study of the Virtual Library at the University of Mosul

Several issues have arisen for investigators and informational establishments as a result of the ever-increasing amount of creative work and the wide variety of its themes, resources, and dialects. In addition to difficulties with knowledge transfer and ways to get value for all of this, there are other issues associated with the supply of storing place for knowledge and the variety of processed technology. The current study set out to gain insight on virtualization, elucidating its core concepts and the offerings it offers and the concepts it has inspired in academic libraries as well as data commons for the benefit of scholars and users alike. The researcher will employ the qualitative approach by drawing from a wide range of relevant sources, such as textbooks, papers, dissertations, and academic papers, to provide the necessary findings. The study found that informational facilities and librarians may benefit greatly from cloud applications because of the expense savings and expansion of activities they can also provide. Virtualization has several advantages, but only a small percentage of institutions have adopted them. It's challenging to adopt cutting-edge tech when there aren't enough educated personnel to manage it, however, in the not-too-distant era, many institutions will provide their services using drop box.
Contract Cheating – Dead or Reborn?

Online question-answering sites such as Chegg and Bartleby made contract cheating affordable to a wider audience by outsourcing answer services to developing countries such as India and often providing answers within 30 minutes. GPT (Generative Pre-trained Transformer) language models can now give such answers in real-time and for free (or nearly free).This paper assesses the quality and promptness of answers provided by Chegg to a range of questions from CS1 and CS2 over a two-year period, comparing them to GPT-generated answers. Results demonstrate that GPT answers are of equal or better quality compared to those provided by human experts, and thus provide an equitable platform for students seeking answers. The paper considers some of the implications from a pedagogical standpoint.
Sentiment Analysis Using Machine Learning Model for Qatar World Cup 2022 among Different Arabic Countries Using Twitter API

The World Cup is a global event that attracts much attention on social media, with millions of people around the world sharing their opinions and sentiments about the tournament on platforms like Twitter. In recent years, there has been a growing interest in using sentiment analysis to understand and analyze the attitudes and opinions expressed in social media data related to the World Cup. The objective of this research paper is to explore the opinion of Qatar World Cup 2022 among Twitter users in Arabic countries (Egypt - Oman - Syria - Palestine - Algeria - Kuwait - Iraq - Sudan - Iraq - Sudan - Saudi - Jordon - Bahrain - Qatar - Yemen -Emirates) using a machine learning algorithm. To achieve this objective, we created a new dataset. Four algorithms were employed in this study: logistic regression, random forest classifier, Naive Bayes classifier, and support vector machine classifier. The best average accuracy of logistic regression was 93%, the average accuracy of the Random Forest Classifier was 92%, the average accuracy of the Naive Bayes Classifier was 88%, and the average accuracy of the Support Vector Machine-Classifier was 93%.
GOVERNMENT SUPPORT TO ENCOURAGE THE USAGE OF ICT IN AGRICULTURE: PERSPECTIVE FROM GOVERNMENT OFFICERS IN THE NORTHERN REGION

The agricultural sector in Malaysia is a crucial contributor to the country's economy. However, Malaysia has had one of the lowest agricultural productivity growth rates in recent years, trailing Indonesia, the Philippines, Vietnam, and Thailand. The industry is dominated by smallholders with an ageing farmer population, low levels of mechanisation, and a high dependence on labour, resulting in numerous inefficiencies and low productivity. This study explores government efforts to boost agricultural ICT adoption. assesses the effectiveness government's initiative and the obstacles it faces in promoting ICT in this industry. An in-person interview was conducted to collect perspectives and insights from eleven government officials from grades 27 to 52. The data was analysed using manual thematic analysis. The findings indicate that the ICT initiatives encompass a range of technological solutions and financial support. The efficiency, frequency, exposure, and variety of facilities are key performance indicators for evaluating the success of government initiatives. However, age, knowledge, cost, expertise, and inadequate network accessibility hinder ICT integration in agriculture. To address these challenges, it is imperative that all relevant stakeholders, including both governmental and private entities, collaborate to enhance the various tiers of information and communication technology
Professional Competence of Teaching Staff As a Condition for Achieving the Quality of Teacher Education

The article discusses the problem of improving the training of personnel for schools, which is due to economic and socio-cultural changes in Russian society. The need for a qualitatively different training of a teacher is reflected, which allows combining the fundamentality of professional basic knowledge with innovative thinking and a practice-oriented, research approach.
Ecosystem of School Education Quality

The article analyzes the concepts of «ecosystem» and «ecosystem approach», their evolution in the field of business, economics, education, the possibility of using the potential of the ecosystem approach in relation to the design and organization of the educational process at school as a cross-multidimensional phenomenon. A detailed interpretation of the methodology of the cross-multidimensional educational process in a secondary school as a result of interaction and intersection of various spaces and environments is given. The article also presents and discusses the results of a survey of teaching staff of secondary schools in St. Petersburg and the Leningrad region on the formation and development of the ecosystem of a cross-multidimensional educational process at school. On the basis of theoretical and experimental studies, generalizing conclusions are formulated, prospects for further study of the phenomenon of «ecosystem of the cross-multidimensional educational process in school» are determined.
Methods and Techniques for Increasing the Motivation of Schoolchildren for Project and Research Activities

Teaching and research activity is an activity, the main purpose of which is the educational result, it is aimed at teaching students, developing their research type of thinking [1]. The main function of educational and research activities is the activation of the cognitive activity of students, the development of their desire for knowledge of the world and themselves in this world. The concepts of "research" and "training and research" activities of students are interrelated, but not identical. The relationship of these concepts is due to the fact that they represent different types of research activities and are aimed primarily at the development of the personality of younger students. Their difference lies in the nature of the result: the research activity of students involves the acquisition of new scientific knowledge of general cultural significance. In educational and research activities, students acquire subjectively new knowledge that is new and personally significant for a particular child.
Approach With Caution: An Innovative Educator Reviews the Impact AI Could Have on Education

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsMindy BinghamAuthor of the Career Choices series, Mindy Bingham has been awarded a Certificate of Special Congressional Recognition for Innovative Approaches to Curricula, by the United States Congress, as well as The Breaking Traditions Award of the Equity Council of the National Association for Career and Technical Education.