Reviewerly/exHarmony huggingface dataset:
Boosting Simple Learners

Boosting is a celebrated machine learning approach which is based on the idea of combining weak and moderately inaccurate hypotheses to a strong and accurate one. We study boosting under the assumption that the weak hypotheses belong to a class of bounded capacity. This assumption is inspired by the common convention that weak hypotheses are "rules-of-thumbs" from an "easy-to-learn class". (Schapire and Freund~'12, Shalev-Shwartz and Ben-David '14.) Formally, we assume the class of weak hypotheses has a bounded VC dimension. We focus on two main questions: (i) Oracle Complexity: How many weak hypotheses are needed to produce an accurate hypothesis? We design a novel boosting algorithm and demonstrate that it circumvents a classical lower bound by Freund and Schapire ('95, '12). Whereas the lower bound shows that $\Omega({1}/{\gamma^2})$ weak hypotheses with $\gamma$-margin are sometimes necessary, our new method requires only $\tilde{O}({1}/{\gamma})$ weak hypothesis, provided that they belong to a class of bounded VC dimension. Unlike previous boosting algorithms which aggregate the weak hypotheses by majority votes, the new boosting algorithm uses more complex ("deeper") aggregation rules. We complement this result by showing that complex aggregation rules are in fact necessary to circumvent the aforementioned lower bound. (ii) Expressivity: Which tasks can be learned by boosting weak hypotheses from a bounded VC class? Can complex concepts that are "far away" from the class be learned? Towards answering the first question we {introduce combinatorial-geometric parameters which capture expressivity in boosting.} As a corollary we provide an affirmative answer to the second question for well-studied classes, including half-spaces and decision stumps. Along the way, we establish and exploit connections with Discrepancy Theory.
Practice of Caste Hierarchy in Nepali Muslim Society

A nation like Nepal, where Hindus make up the bulk of the population, places a greater emphasis on the study of religious minorities. Caste is a distinctive type of stratification. It is purely a Hindu phenomenon that is comprehensible and explicable in terms of Hindu principles and justifications. In addition to the Indian subcontinent, caste is widespread in the Arab world, Polynesia, North and East Africa, Japan, and North America. Like in the Hindu community, there is caste purity and practice of hierarchy in Muslim community. Saiyedism in Muslims society (the Arab domination) like Brahminism in Hindu society is a primary cause of caste prejudice in the Muslim community.&#x0D; This paper outlines the crucial steps that must be completed at each stage of the caste hierarchy and is based on qualitative research. The subject is being generalized through an observational technique, in-depth interviews, and library research. Muslim scholars and elderly persons who had experienced such practice were picked as the respondents for the purpose of learning more. The respondents are identified as Res.1, Res.2, Res.3, and so forth because they requested anonymity. The Muslim caste hierarchical system in Nepal is the area of this study. It tries to combat the issue of caste hierarchy among Muslim people in Nepal.
The Impact of Cryptocurrencies on Developing Market Economies

Cryptocurrencies are electronic forms of money that are seen as credible investments; these currencies ranging from Bitcoin, Litecoin, and Etherium are increasingly viewed as a separate asset class with unique characteristics, driven by the distributed ledger technology (DLT) commonly referred to as Blockchain has increased trust in Bitcoin as a store of value and medium of exchange.This paper looks at the evolution of bitcoin and the challenges that have come to characterize the Nobel currency.From sound governance, tax compliance, data privacy and portability, cybersecurity, and fair competition; the currency in its current form exposes economies to grave economic and financial stability risks.Policymakers should regulate the functioning of bitcoin as they would a speculative asset class, by constraining risk-taking from banks by increasing requirements for deposits, whilst designing a mechanism that monitors and improve the functioning of markets that allow the proliferation of the currency.
Improving Learning Management Systems to Better Assist Computer Science Teaching in Qatar Higher Education Institutions

Hybrid learning first appeared in the late 1990s as a new way of teaching for distance learning that used computers and the internet to improve students' learning and encourage teachers to change their teaching techniques, resulting in a shift in learning from a teacher-centered model to a more student-centered model. This study will contribute to theory, practice, empirical and policy. Theoretically, the study considers the investigate the mediating effect of human resource information system on effect of perceived usefulness, perceived enjoyments, hybrid attitude, hedonic motivation, learning experience, and engagement, on satisfaction in Community College of Qatar (CCQ). Furthermore, as a result of the COVID-19 epidemic, many postsecondary schools have moved to online delivery and learning, forcing many instructors to experiment with online and HL approaches, such as online multimedia and video presentations. In comparison to traditional face-to-face techniques or pure online settings, current research has largely verified that HL is seen to be beneficial (Hill et al., 2017) and has the potential to boost student happiness (Owston et al., 2013).
Intra-class data augmentation with deep generative models of threat objects in baggage radiographs

Despite state-of-the-art deep learning-based computer vision models achieving high accuracy on object recognition tasks, x-ray screening of baggage at checkpoints is largely performed by hand. Part of the challenge in automation of this task is the relatively small amount of available labeled training data. Furthermore, realistic threat objects may have forms or orientations that do not appear in any training data, and radiographs suffer from high amounts of occlusion. Using deep generative models, we explore data augmentation techniques to expand the intra-class variation of threat objects synthetically injected into baggage radiographs using openly available baggage x-ray datasets. We also benchmark the performance of object detection algorithms on raw and augmented data.
Research and Practice on the Engineering Skills Training Pattern in College-Enterprise Cooperative Studios

The development of vocational education is of great significance to the problem of difficult employment in enterprises. The cornerstone of sustainable development for skill studios are solving technical challenges and imparting technology. This will be beneficial for promoting and inheriting the IoT and artificial intelligence hardware technologies that enterprises need. The "Three-Mentor-Mechanism" proposed by the College-Enterprise Cooperative Studios help students personalized and rapid growth. General basic projects, professional skills projects, ability expansion projects, and on-the-job internship projects will be set up and run through the "four stages" of professional talent cultivation. College enterprise collaboration, college park co-education, project penetration finally construct a professional talent training model.
Study on the Relationship between Learning Motivation and Learning Delay: Mediating Role of Occupational Self-efficacy

Learning motivation; Professional self-efficacy; Learning delay
Attitudes of Secondary School Teachers Towards Professional Development and Innovation

Being a teacher in vocational education is a challenging profession. The demands on teachers' personal and professional qualities are increasing. Maintaining constant positive motivation to work and further professional development is not easy. In secondary vocational schools in the Czech Republic, teachers of practical teaching and theoretical vocational subjects often work side by side. This paper aims to report the results of a survey conducted at a secondary vocational school focused on gastronomic technology that focused on teachers' attitudes to professional development, preferences in further education and attitudes to innovation. The survey was completed through a questionnaire in the spring of 2022, and 117 teachers responded. This paper will present the results of the survey aimed explicitly at comparing the attitudes and opinions of above defined two different groups of teachers, as well as comparing the results according to professional-demographic characteristics.
Survey Response Analysis on the Use of Environmental Data for Sports Information Systems Development

Sport is primarily a mental and physical activity focused on competition with oneself, others, or the elements of nature—the contribution of sport to the modern world's social development, the nation, and the state. As history has shown, technical advancements have never diminished sports and linked to civilization's advancement in human life. Walking and bicycling are increasingly promoted as active modes of transportation and boosting physical activity levels. The elevated breathing rates in traffic microenvironments have generated worries about increased exposure to ultrafine particles. Amateur half marathoners did detrimental significantly when the temperature, heat index, and ozone levels rise. The athlete or recreational sportsperson can earn environmental information through several channels and devices. There are applications and widgets now embedded or separated from the device operating systems. This research aimed to find appropriate environmental information from recreational sportspeople's understanding and awareness of the environment's effect on sports performance. Walking (77) and running (48) were the most sports activities picked by the respondents. Then 99 (73%) respondents used environmental data before exercising. Weather (118) was the respondents' choice, followed by the temperature (47) and the air quality (30) picked by the respondents. Most respondents (72%) get environmental information from their smartphones, and 27 (18%) get it from the website. Environmental factors may impact sports activity performance, and the respondents used environmental information before the sports activities began. This research will later support the sports information system web application.
Editorial Modern Compression

Modern computation and networking environments are struggling to store, communicate and process data in unprecedented volumes. These data, which come in new and evolving structures and formats, necessitate compression, lossless and lossy. Recent years have witnessed the emergence of new techniques, approaches, architectures and modes for data compression. This special issue is dedicated to cutting-edge research geared toward providing information theoretic insight into this space.
DIGITAL LEARNING RESOURCES: PROBLEMS OF DEFINITION, CLASSIFICATION AND DESIGN

Digital learning resources: problems of definition, classification and designCurrently, in pedagogical research, the concept of "digital learning resource" is in the area of heterogeneous multi-vector interpretations.The problem of classification of such resources is inextricably linked with the issue of their nomenclature and is one of the most difficult and ambiguous to solve [6].The concept itself has several synonymous designations: "electronic educational resource", "e-learning resource", "computer-based training tools", "educational software", "computer learning tool", "digital education tools", etc., the simultaneous rotation of which leads to terminological inconsistency and confusion.Redundancy and disorder of terms actualize the need for systematization of concepts.Today, scientists do not have a single point of view on the classification of elearning resources and their nomenclature system.Despite some differences, these concepts have a lot in common.Many scholars (I.Krevskiy, T. Glotova, M. Deev, S. Matyukin et al.) define the concept "electronic educational resource" or "e-learning resource" as an educational resource represented in digital form and included structure, subject content and metadata about them.ELR may include data, information, and software necessary for its use in the learning process.The term "digital learning resource" is more recent and "refers to digital resources such as applications (apps), software, programs, or websites that engage students in learning activities and support students' learning goals" [7].
The Reality of Algerian Banking Sector Commitment to Social Responsibility Principles &amp; its Contribution to Encouraging SMES to Adopt a Culture of Sustainable Social &amp; Environmental Practices

The study aims to show that the social responsibility of banks is an effective mechanism to support the sustainability of social and environmental practices of small and medium enterprises - a field study of banks operating in tebessa, To test the hypotheses of the study, a set of statistical methods, such as arithmetic mean, standard deviation and T test for a single sample
Language-guided Residual Graph Attention Network and Data Augmentation for Visual Grounding

Visual grounding is an essential task in understanding the semantic relationship between the given text description and the target object in an image. Due to the innate complexity of language and the rich semantic context of the image, it is still a challenging problem to infer the underlying relationship and to perform reasoning between the objects in an image and the given expression. Although existing visual grounding methods have achieved promising progress, cross-modal mapping across different domains for the task is still not well handled, especially when the expressions are complex and long. To address the issue, we propose a language-guided residual graph attention network for visual grounding (LRGAT-VG), which enables us to apply deeper graph convolution layers with the assistance of residual connections between them. This allows us to better handle long and complex expressions than other graph-based methods. Furthermore, we perform a Language-guided Data Augmentation (LGDA), which is based on copy-paste operations on pairs of source and target images to increase the diversity of training data while maintaining the relationship between the objects in the image and the expression. With extensive experiments on three visual grounding benchmarks, including RefCOCO, RefCOCO+, and RefCOCOg, LRGAT-VG with LGDA achieves competitive performance with other state-of-the-art graph network-based referring expression approaches and demonstrates its effectiveness.
Factors Influencing English Language Learning in the Rural Malagasy Context: A Case Study of the Ambohidratrimo and Atsimondrano Districts

Qualified as an international language of communication and development, English is used worldwide and is essential for a country's development. This subject is taught in Malagasy public institutions from the sixth grade and is more advanced in private schools. Rural contexts, which are sometimes complicated and relatively weaker, compared to urban areas, in terms of technology and didactic materials, have an impact on the adoption of this language. The scarcity of linguistic immersion is among the challenges that rural education must face. A number of written questionnaires and oral interviews have been addressed to the target population, such as rural students, English teachers in public and private high schools, parents of students, and educational leaders or advisors. The XLSTAT statistical software was used to derive results that affirm the need to consider the contexts of English teaching and learning in rural areas, as well as the impacts of the linguistic environment. This requires the adoption of modeling, new approaches, and methods.&#x0D;
Blockchain Applications in Cybersecurity Solutions

Applications of Blockchain in Cybersecurity Solutions is a comprehensive guide to blockchain applications in computer security. it presents the concepts and practical techniques that are useful in creating and designing decentralized cybersecurity software through 9 carefully edited chapters. Topics covered in the book include - An introduction to the use of blockchain technology in cybersecurity - Attack surfaces in blockchains - Anti-counterfeit solutions in blockchains - blockchain based access control systems - Multi-chain security deployment over smart contracts - Cybersecurity as a decentralized service The book is an essential primer for computer science students and researchers, and a quick reference for IT professionals on blockchain based cybersecurity.
Introduction to Blockchain Technology

A blockchain is a linked set of records maintained in a decentralized environment. The records in blockchain are publicly available but cryptographically secured. The interesting property exhibited by blockchain is that once some information is recorded, it is infeasible to modify the information. Blockchain is generated as a chain of blocks that contains time-stamped digital documents so that it is infeasible to back date them or tamper the documents. These time-stamped digital documents are stored as a collection of records and grouped as a set of blocks, chronologically linked in order of time. A new block is created and appended to the existing blockchain, whenever there is a need for updating the blockchain. Every block in the blockchain comprises of a hash of the preceding block, collections of records of its own, and the hashed value records known as merkle tree. The information inside the blocks varies depending on the nature of blockchain. For example, when the nature of blockchain is bitcoin, they are supposed to store the details about a transaction viz., sender, receiver and amount of coins where as blockchain used for medical records stores the complete health history of a patient over time. Since blockchain is stored in a distributed way, efficient hashing techniques are used to ensure the integrity and robustness of blockchain. This chapter describes the nuances of blockchain technology along with the protocol stack and the most common consensus mechanisms. Furthermore, recent advances, challenges and future trends of blockchain are discussed.
#3815 A DEEP LEARNING APPROACH TO PERSONALISED ANTI-HYPERTENSIVE MEDICATION TITRATION

Abstract Background and Aims Hypertension is the number one risk factor for premature death worldwide. Artificial Intelligence (AI) Clinical Decision Support Systems are an important next step for hypertension management but require rigorous evaluation before assimilation into routine clinical practice. Our aim is to develop and evaluate an AI clinical decision support tool for hypertension management trained on randomised clinical trial data. Method The Systolic Blood Pressure Intervention Trial (SPRINT) trial showed that intensive BP control to SBP &amp;lt;120 mm Hg results in significant cardiovascular benefit in high-risk patients with hypertension compared with routine BP control to &amp;lt;140 mm Hg. We trained a feed-forward neural network using Keras and Tensorflow for R and data from 9361 persons in the SPRINT randomized clinical trial to predict the probability of increase, reduce or no-change in total number of anti-hypertensive medications at each visit. The network is designed to model SPRINT investigator deviations from the protocol. Six baseline patient variables (age, sex, race, aspirin use, eGFR, group assignment (intensive or standard)), two visit patient variables (Systolic and Diastolic Blood Pressure), and two previous visit variables (Systolic and Diastolic Blood Pressure) were inputted into the model after data normalisation and centering. Hyperparameters were tuned using a grid search method. We conducted internal validation using a 20% validation set. Clinical validation was performed using an unseen test set of typical hypertension scenarios (n = 50). An R Shiny app was developed to enter new patient information and display a sensitivity analysis of probabilities calculated after changing each baseline variable by plus and minus 10 (Figure 1). To avoid poor performance on new out of distribution cases we tested five methods for Out of Distribution (OOD) detector, 1. Autoencoders, 2. Normalizing flow, 3. Local Outlier Factor (LOF), 4. Probabilistic Principal Component Analysis (PPCA) and Kernel Density Estimation (KDE). Results The accuracy was 76.7% on the validation dataset after 1000 Epochs. Clinical validation revealed suboptimal performance for unseen Out of Distribution data (e.g. recommending reduction of medication when SBP &amp;gt;160 and number of medications was zero). This poor performance on OOD samples prompted the creation of an OOD detector to use in series with the AI Decision Support System for Hypertension. The AUROCs were 0.77 for AE, 0.96 for Normalizing Flow, 0.82 for LOF, 0.88 for PPCA and 0.96 for the Kernel Density Estimation. Conclusion Artificial Intelligence (AI) Clinical Decision Support Systems for Hypertension are feasible and an important next step for hypertension management but generalisability and safety require rigorous evaluation before assimilation into routine clinical practice.
Imprint: Geomechanics and Tunneling 3/2023

Geomechanics and TunnellingVolume 16, Issue 3 p. 335-335 ImprintFree Access Imprint: Geomechanics and Tunneling 3/2023 First published: 13 June 2023 https://doi.org/10.1002/geot.202370307AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume16, Issue3June 2023Pages 335-335 RelatedInformation
Editorial to the Special Issue “The Impact of Digitalization on International Law” – CORRIGENDUM

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Tele Contextual Study Development: Unfolding Case Study Using E-KOM to Improve Analytical Thinking to Content UJIKOM Ners

Introduction: Case-based learning is often used to maximize critical thinking skills by matching clinical cases with the body of knowledge. The use of applications that can be accessed anywhere can be an innovative learning media solution. Therefore an application called e-KOM was designed to support learning for nurse students in preparing for competency tests. Objective: This study at developing e-KOM as a learning media in clinical learning with the Tele Contextual Study method: Unfolding Case Study. Methods: The research design method used the Research and Development method. Based on Borg and Gall's stages, the researchers modified it according to the application's needs as a learning medium. They adapted the ADDIE development model, namely Analysis, Design, Development or Production or Implementation or Delivery and Evaluations. Results: This study showed that the application's feasibility level was based on validation by material and media experts. The average score of 4.4 is in the feasible category, while the user's assessment is at an average score of 85.76%, which means it is easy to use. Conclusions: e-KOM, as a nursing student learning application, is expected to improve student's learning outcomes and improve the statistical graduates in Nursing Competencies Test.