Reviewerly/exHarmony huggingface dataset:
Training Need of Rural Teachers in Primary Schools: Basis for Professional Development

Rural education is an important component of China's education industry, and the professional development of weak rural schools and their teachers directly affects the quality of basic education in China. This article adopts a combination of qualitative and quantitative research methods to randomly select 95 rural primary school teachers in Guizhou Province. The focus is on the training needs of rural primary school teachers, meeting the needs of teaching development, scientific development, personal development, technological development, and organizational development. The findings and conclusions of the post service training needs of rural primary school teachers are summarized and analyzed. In the comprehensive development stage of teachers, under the guidance of the theory of teacher reflection , with the support of constructivist theory and other related theories, and referring to typical experiences of in-service teacher training at home and abroad, specific training needs and suggestions were proposed, which promoted the professional development of rural primary school teachers in Guizhou Province and provided strong reference for the post service training of rural primary school teachers in western China.
Kyle Banerjee’s Getting Ready for the Big Move

With a potential migration on the horizon, a colleague discovered the Fall 2022 Northern Ohio Technical Services Librarians (NOTSL) meeting "Migration Station: Tips &amp; Tricks to Manage Your Data" and a number of us viewed it. I was particularly impressed by the first of three presentations made during the meeting and chose to evaluate Getting ready for the big move, now available on YouTube. Additionally, you can access the presentation slides from this meeting on their website. The presenter, Kyle Banerjee, who works as Senior Implementation Consultant for EBSCO on the Open Library Foundation (OLF) project FOLIO, gave a two-hour presentation on integrated library system (ILS) migration.
Implementing E-Book Demand Driven Acquisition (DDA) At Asbury Theological Seminary

As we explored the potential for DDA, we quickly realized everything that had been promised was not as it appeared.
Deep Learning Neural Networks in the Cloud

Deep Neural Networks (DNNs) are currently used in a wide range of critical real-world applications as machine learning technology. Due to the high number of parameters that make up DNNs, learning and prediction tasks require millions of floating-point operations (FLOPs). Implementing DNNs into a cloud computing system with centralized servers and data storage sub-systems equipped with high-speed and high-performance computing capabilities is a more effective strategy. This research presents an updated analysis of the most recent DNNs used in cloud computing. It highlights the necessity of cloud computing while presenting and debating numerous DNN complexity issues related to various architectures. Additionally, it goes into their intricacies and offers a thorough analysis of several cloud computing platforms for DNN deployment. Additionally, it examines the DNN applications that are already running on cloud computing platforms to highlight the advantages of using cloud computing for DNNs. The study highlights the difficulties associated with implementing DNNs in cloud computing systems and provides suggestions for improving both current and future deployments.
Virtual Currency - Current Affairs Through the Prism of Legal Regulations

Treating the “virtual good” in the manner of a sci-fi story with a smile on our face, but without realizing that we are already part of a technological era, meets the point where the will to meaning must be correlated with mechanisms that involve readjusting the thinking on the processes of legal, social and economic relations. For example, Law no. 30 of January 10th 2019 (The normative act published in Official Gazette with number 914 dated November 13th 2019; “Art. 114: Defining revenues from other sources: (1) The incomes from other sources are any incomes identified as taxable, which do not fall into the categories provided in art. 61 lit. a)–h), other than non-taxable income in accordance with this title; (2) This category includes, but is not limited to, the following revenues: m) revenues from the transfer of virtual currency. (N.R. applies from income tax due for income earned in 2019, The art. 116: Declaration, establishment and payment of tax for some income from other sources. 2. The income tax due shall be calculated by the taxpayer, on the basis of the single declaration on income tax and social contributions due by individuals by applying the rate of 10% to: c) the gain from the transfer of virtual currency in the case of the incomes provided in art. 114 para. (2) lit. m), determined as a positive difference between the sale price and the purchase price, including the direct costs related to the transaction. Income below the level of 200 lei/transaction is not taxed provided that the total income from a fiscal year does not exceed the level of 600 lei (N.R. applies starting with the income tax due for the income realized in 2019);”) is not only a new normative act amending the Tax Code but also a signal on the quintessence of the near future, a signal that in turn spreads to all segments related to our daily lives, not only on how as taxpayers we declare and pay the tax for the gain from the virtual currency transfer or we formulate the customs declaration regarding the amounts actually held on us, therefore including virtual. This article is an analysis of the reality in correlation with the existing legal framework at national and European level and is intended to represent a ‘movement’ that alternates through proposals of lex ferenda the correct and effective placement in the proposed text of social relations, economic interest, policies legislative but also the results of academic-scientific research in the realm of virtual currency.
MFG-HUI: An Efficient Algorithm for Mining Frequent Generators of High Utility Itemsets

The discovery of frequent generators of high utility itemsets (FGHUIs) holds great importance as they provide concise representations of frequent high utility itemsets (FHUIs). FGHUIs are crucial for generating nonredundant high utility association rules, which are highly valuable for decision-makers. However, mining FGHUIs poses challenges in terms of scalability, memory usage, and runtime, especially when dealing with dense and large datasets. To overcome these challenges, this paper proposes an efficient approach for mining FGHUIs using a novel lower bound called $$lbu$$ on the utility. The approach includes effective pruning strategies that eliminate non-generator high utility branches early in the prefix search tree based on $$lbu$$ , resulting in faster execution and reduced memory usage. Furthermore, the paper introduces a novel algorithm, MFG-HUI, which efficiently discovers FGHUIs. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches in terms of efficiency and effectiveness.
Unearthing Undiscovered Interests: Knowledge Enhanced Representation Aggregation for Long-Tail Recommendation

Graph neural networks have achieved remarkable performance in the field of recommender systems. However, existing graph-based recommendation approaches predominantly focus on suggesting popular items, disregarding the significance of long-tail recommendation and consequently falling short of meeting users’ personalized needs. To this end, we propose a novel approach called Knowledge-enhanced Representation Aggregation for Long-tail Recommendation (KRALR). Firstly, KRALR employs a user long-tail interests representation aggregation procedure to merge historical interaction information with rich semantic data extracted from knowledge graph (KG). By utilizing random walks on the KG and incorporating item popularity constraints, KRALR effectively captures the long-tail interests specific to the target user. Furthermore, KRALR introduces a long-tail item representation aggregation procedure by constructing a co-occurrence graph and integrating it with the KG. This integration enhances the quality of the representation for long-tail items, thereby enabling KRALR to provide more accurate recommendations. Finally, KRALR predicts rating scores for items that users have not interacted with and recommends the top N un-interacted items with the highest rating scores. Experimental results on the real-world dataset demonstrate that KRALR can improve recommendation accuracy and diversity simultaneously, and provide a wider array of satisfactory long-tail items for target users. Code is available at https://github.com/ZZP-RS/KRALR .
Assessing Multi-Objective Search Engines for GE: A Case Study in CNN Generation

In recent years, the number of available Convolutional Neural Networks (CNNs) has increased significantly, making it difficult to select an appropriate CNN for a specific problem. To address this challenge, researchers have proposed automated techniques for optimizing CNN architectures, with Grammatical Evolution (GE) being one of the most promising approaches. GE uses context-free grammar to generate programs (e.g., CNNs) and a search engine to find the best solutions. Although several grammars have been proposed for CNN generation, there has been no research evaluating the impact of different search engines in the GE optimization process. This study treats the CNN generation as a multi-objective problem by optimizing accuracy and F1-score, and evaluates seven different multi-objective optimizers listed in the literature as potential search engines. The goal is to investigate the strengths and weaknesses of each optimizer in CNN generation. The experiments were performed on the widely-used CIFAR-10 image classification dataset, and the results showed that selecting the right optimizer for the task is crucial and can have a significant impact on the final result, especially when the number of generations is limited.
Enhanced Functionality of Footing Machine through Deep Learning

In this paper, we report an enhancement in the functionality of footing machine using deep neural networks. In our earlier work, we have replaced a large array of sensors, traditionally used on footing machines, with a roof-mounted camera to ensure the robust operation of the machine and designed an image processing-based algorithm to measure the area of irregular leather pieces. However, it is also desired that footing machine be able to separate the defective and non-defective leather pieces. Subsequently, we have added this feature to the footing machine using deep neural networks and report the results of proposed enhancement in this paper. Owing to the availability of small dataset, pre-trained deep network models are considered. Results obtained through MATALB environment show the validity of the proposed enhancement.
PENGEMBANGAN E-MODUL BERBASIS WEB APLIKASI PADA MATERI GELOMBANG KELAS XI

Abstract: This study aims to determine the design of web-based e-module applications in physics learning, the feasibility level of web-based e-module applications in physics learning, and the effectiveness of web-based e-module applications in increasing students' learning motivation. This development uses the ADDIE development model. This research was conducted at SMA NEGERI 5 METRO in April 2022. The data collection instrument used in the research on the development of this web-based e-module was in the form of an expert validation questionnaire and a student response questionnaire as the effectiveness of the web-based e-module media application Data analysis techniques used in this study is an analytical technique related to the purpose of this research, so that data analysis is carried out on media design, media feasibility and the level of media effectiveness on students' learning motivation, so that researchers can easily understand the data and draw conclusions. Based on the results of the e-module feasibility test of material and media experts, through expert validation and student response trials, an average percentage value of 82.45% was obtained in the very feasible category. Based on data from learning motivation, an average value of 82.16% was obtained so that in measuring student motivation using web-based e-modules this application was successful.Keywords: E-Module, Google Sites, Learning Motivation
ANALYSIS OF THE URGENCY OF USING DIGITAL LITERACY IN THE IMPLEMENTATION OF EDUCATION DURING A PANDEMIC IN SDN 106790 SEI MENCIRIM KAB.DELI SERDANG

Along with increasingly rapid technological developments, the use of digital literacy in the implementation of education is very much needed, especially in the era of the Covid 19 pandemic which requires all parties involved in the learning process to be skilled in the use of facilities and various platforms that can facilitate all learning activities. Therefore, the urgency in using digital literacy during the educational period during this pandemic is a top priority and a necessity. The purpose of carrying out this literacy study is to determine the urgency of using digital literacy in the implementation of education during the pandemic at SDN 106780 Sei Mencirem. The method used is literature study. The steps taken are identifying, reviewing, evaluating, and interpreting all available research that is relevant with a study of the urgency of digital literacy in the implementation of online education. The results obtained are that digital literacy is needed and a priority that must be developed in education, especially during the pandemic of online-based education, digital literacy in education is no longer just important but has become a necessity.
INTEGRATIVE EDUCATIONAL TECHNOLOGIES OF INTELLECTUAL CULTURE DEVELOPMENT IN FUTURE TEACHERS

Mazkur maqolada boʻlajak oʻqituvchilarning intellektual madaniyatini rivojlantirishning integrativ taʼlim texnologiyalari, oʻstirish, globallashuv sharoitida erkin, ozod va fikran mustaqil, har qanday axborotni mantiqiy tahlil qila olish koʻnikmasini rivojlantirishda falsafa fanining oʻqitilishi muhim ahamiyat kasb etishi toʻgʻrisidagi fikr-mulohazalar bayon qilingan.
An Elliptic Curve-Based Privacy-Preserving Recommender System

In this research, we present a way to improve a secure multi-party computation-based recommender system solution. This solution is both efficient and protective of users’ personal information. Both theoretical and empirical research demonstrate that the proposed technique protects the participants’ personal information while maintaining the recommender system’s accuracy. The proposed solution is also more cost-effective in terms of both communication and computing than the original ones.
Machine Learned KPI Goal Preferences for Explainable AI based Production Sequencing

In this paper we show how machine learned KPI goal preference relations based on interactions between KPI goals are used to explain results of an AI algorithm for optimization of real-world production sequences. It is also shown how such algorithms can be both parameterized and reparametrized in an explainable ad-hoc and post-hoc manner. The explanations are also used to manage contradictory and counterfactual optimization effects so that uncertainty in the decision situations before releasing the sequences to production is handled better than if only the pure sequences were presented.
Autonomous Artificial Intelligence Systems for Fraud Detection and Forensics in Dark Web Environments

Artificial Intelligence (AI) influenced technical aspects of research for generating automated intelligent behaviors covering divergent domains but has shown appreciable results when used in cyber forensic technology for crime analysis and detection. AI experts warned about possible security risks associated with algorithms and training data, as AI inherits computing features dealing with IoT-based Smart applications and autonomous transportation, and may be found to be susceptible to vulnerability and threats. The present work discusses models for analyzing terrorists related information and categorizing malicious events by covering the literature review on security risks and AI-related criminality by presenting a taxonomy of criminal behavior and signatures covering tools and criminal targets used in fraudulent activities using AI features by digital forensic techniques. We’ve also shown how AI may make existing crimes more potent, and that new sorts of crimes could emerge that haven't been identified previously. This study has presented a systematic structure for AI crime and dealing strategies. Furthermore, we have proposed AI forensics, a unique strategy for combating AI crime. We discovered that several concepts of DF are still not preferred in AI-based forensics after conducting a comparative examination of forensics.
ADDRESSING NEW SOCIETAL EXPECTATIONS FOR HIGHER EDUCATIONAND ONLINE LEARNING IN THE POST-COVID-19 ERA

This article examines how people's lives and work environments have been altered as a result of technology, how these advancements have altered societal expectations for higher education, and three solutions for higher education institutions to address these new expectations. Firstly, to survive the new normal, higher education institutions must shift their student solutions and orientations to accommodate online learners. Secondly, faculty development must concentrate on training faculty to utilize online tools to teach. Thirdly,institutions must better prepare students to be savvy virtual collaborators through online learning and to translate these new skills to the workforce. Finally, this paper uses the lens of the COVID-19 pandemic to present three strategies for achieving these solutions.
PROBLEMS OF IMPROVING THE SYSTEM OF DEVELOPING THE CREATIVE ACTIVITY OF FUTURE EDUCATORS THROUGH PERSONALLY-ORIENTED LEARNING

Mazkur maqolada shaxsga yo‘naltirilgan ta’lim orqali bo‘lajak tarbiyaсhilar ijodiy faoliyatini shaklantirish va rivojlantirish tizimini takomillashtirish yo‘nalishidagi muammolarga bag‘ishlangan bo‘lib, MTT bo‘lajak tarbiyachilarining ijodiy faoliyatini rivojlantirish va takomillashtirish yo‘nalishida samaradorlikka erishishi uchun mazkur faoliyatining mazmuni va mohiyati hamda pedagogik jarayonlar subyektlarining ijodiy, aqliy va intellektual salohiyatini namoyon etishiga zaruriy shart-sharoitlar yaratishi zarurligini anglatishga yo‘naltirilgan.
THE INFLUENCE OF USING CODI ON STUDENT’S MOTIVATION IN READING

Students' reading motivation is likely low due to internal and external factors. The internal factors, for example, are intelligence, age, gender, reading ability, attitudes, and psychological needs. The external factors include the theme, level of reading difficulty, and the type of reading. This research was conducted to know whether the use of Content Differentiated Instructions (CoDI) in teaching could affect students' motivation in reading, especially descriptive text. This kind of text was chosen because it is the simplest text among five other kinds of text that are given to beginner-level students, in this case, students of junior high school. The researcher tried to find out whether the implementation of CoDI could affect the students’ motivation in reading. In this study, the researcher used a qualitative method. The data used were in the form of answers or student responses in the form of questionnaires. The result was that using CoDI affects the students’ motivation in reading descriptive text.
Quantifying the impact of image degradation on Deep Learning models in face recognition systems

Significant advancements in computer vision, particularly in facial recognition systems, have been witnessed in recent years. However, it is imperative to comprehend how these systems perform under real-world conditions, specifically when confronted with degraded images. This paper presents a comprehensive analysis of the impact of image degradation on facial recognition systems that rely on deep neural networks. The study evaluates three facial detection algorithms and eight facial recognition algorithms, with experiments conducted on four diverse datasets. A total of 14 types of image degradations, encompassing pure and mixed variations, were employed at six different intensity levels. Three distinct types of image pairs were generated to encompass various scenarios. The primary objective of this research is to enhance the understanding and assessment of facial recognition system outcomes, thereby strengthening the overall analysis of these systems. On average, the models had a minimum impact of 17% and a maximum of 43% for the datasets used in the experiment.
Natural Language Processing and Social Media: a systematic mapping on Brazilian leading events

The number of social media platforms has increased significantly, as well as the number of active users. More than 18.2 million text messages are transmitted every minute on these platforms. Given the amount of data available, Natural Language Processing (NLP) techniques have been used by several researchers to analyze this large amount of unstructured data. Thus, it is essential to understand social media analysis’s main trends and challenges. From this perspective, this study presents a systematic mapping of NLP for social media analysis considering papers published in five well-established academic Brazilian events: BRACIS, BraSNAM, ENIAC, STIL, and PROPOR. The study aims to identify the main tools and techniques used, tasks performed, data sources, and evaluation measures. For this purpose, 186 studies were analyzed and carefully selected among the 654 papers published in these events in the three years (2020 to 2022). The results show a glimpse of the current scenario on the subject and point out areas that can be improved in future research with techniques for tasks such as text classification, sentiment analysis, and named-entity recognition. Therefore, this work can be helpful for academics interested in exploring the potential NLP for social media analysis and having a clear view of gaps, challenges, and research opportunities in this area. Nevertheless, it should guide the productive sector in this knowledge transfer, reducing the gap between the state of the art and practice, consequently increasing the competitiveness and innovation of social media analysis tools.
Positive Unlabeled Learning: Adapting NMF for text classification

Due to the overwhelming data generation that surpasses human evaluation capacity, manually labeling data for training machine learning models is becoming increasingly impractical. This article focuses on analyzing techniques to address the challenges of Positive Unlabeled Learning (PUL). To this end, we propose structural adaptations to the Non-Negative Matrix Factorization (NMF) algorithm, specifically tailored for PU data (NMFPUL). We compare NMFPUL with state-of-the-art techniques to identify improvements in the performance of textual data classification. Our study reveals that NMFPUL consistently outperforms most baseline algorithms across diverse document collections even with a limited number of labeled documents, and mainly on these situations.