Reviewerly/exHarmony huggingface dataset:
Image Segmentation Using Structural SVM and Core Vector Machines

In this paper, we study the performance of two variants of support vector machines, namely structural support vector machines and core vector machines, on large-scale data. We have used image segmentation as a mechanism to test the classification abilities of these variants on large-scale data. The images are converted to numeric data using various filters, and the labels are generated using the available ground truth image segmentation mask.
Fraud Detection Model Using Semi-supervised Learning

Everything is moving to online platforms in this digital age. The frauds connected to this are likewise rising quickly. After COVID, the amount of fraudulent transactions increased, making this a very essential area of research. This study intends to develop a fraud detection model using machine learning’s semi-supervised approach. It combines supervised and unsupervised learning methods and is far more practical than the other two. A bank fraud detection model utilizing the Laplacian model of semi-supervised learning is created. To determine the optimal model, the parameters were adjusted over a wide range of values. This model’s strength is that it can handle a big volume of unlabeled data with ease.
Contrastive Learning for Topic-Dependent Image Ranking

In e-commerce, users’ feedback may vary depending on how the information they encounter is structured. Recently, ranking approaches based on deep learning successfully provided good content to users. In this line of work, we propose a novel method for selecting the best from multiple images considering a topic. For a given product, we can commonly imagine selecting the representative from several images describing the product to sell it with intuitive visual information. In this case, we should consider two factors: (1) how attractive each image is to users and (2) how well each image fits the given product concept (i.e., topic). Even though it seems that existing ranking approaches can solve the problem, we experimentally observed that they do not consider the factor (2) correctly. In this paper, we propose CLIK (Contrastive Learning for topic-dependent Image ranKing) that effectively solves the problem by considering both factors simultaneously. Our model performs two novel training tasks. At first, in topic matching, our model learns the semantic relationship between various images and topics based on contrastive learning. Secondly, in image ranking, our model ranks given images considering a given topic leveraging knowledge learned from topic matching using contrastive loss. Both training tasks are done simultaneously by integrated modules with shared weights. Our method showed significant offline evaluation results and had more positive feedback from users in online A/B testing compared to existing methods.
Variability of Diffusion Properties Over Fib Images by Fem

This study investigated the variability of diffusion properties in cement materials over a porous network FIB image volume. A 3D FIB volume of a CEMI hydrated cement paste considered saturated is analyzed and processed in order to create finite-element meshes by using Avizo software. In order to characterize the transport of ions in cementitious materials, the homogenized diffusive properties associated to the nonlinear Nernst-Planck-Poisson system characterizing the ionic transfer in saturated porous media are calculated on crops of different sizes. By using finite element software the numerical calculations are performed on real pore volume obtained from a stack of FIB images, neglecting the electrical double layer (EDL) effects.
Monetary Policy, Digital Assets, and DeFi Activity

This paper studies the effects of unexpected changes in US monetary policy on digital asset returns. We use event study regressions and find that monetary policy surprises negatively affect BTC and ETH, the two largest digital assets, but do not significantly affect the rest of the market. Second, we use high-frequency price data to examine the effect of the FOMC statements release and Minutes release on the prices of the assets with the higher collateral usage on the Ethereum Blockchain Decentralized Finance (DeFi) ecosystem. The FOMC statement release strongly affects the volatility of digital asset returns, while the effect of the Minutes release is weaker. The volatility effect strengthened after December 2021, when the Federal Reserve changed its policy to fight inflation. We also show that some borrowing interest rates in the Ethereum DeFi ecosystem are affected positively by unexpected changes in monetary policy. In contrast, the debt outstanding and the total value locked are negatively affected. Finally, we utilize a local Ethereum Blockchain node to record the activity history of primary DeFi functions, such as depositing, borrowing, and liquidating, and study how these are influenced by the FOMC announcements over time.
Label-Guided Graph Contrastive Learning for Semi-Supervised Node Classification

Semi-supervised node classification is a task of predicting the labels of unlabeled nodes using limited labeled nodes and numerous unlabeled nodes. Recently, Graph Neural Networks (GNNs) have achieved remarkable success in this task. However, existing GNNs models typically have shallow architectures and only consider labeled nodes and their low-order neighbors during training. As a result, the supervision signals from the massive unlabeled nodes remain underutilized. To address this limitation, graph contrastive learning has been applied in semi-supervised node classification, which pulls together positive nodes and pushes away negative nodes in the embedding space. Nevertheless, existing node-level contrastive learning methods usually sample the same node in different augmented views as positive nodes and all other nodes as negative nodes. Consequently, many semantically similar nodes are not sampled as positive nodes but are mistakenly sampled as negative nodes. To tackle this issue, we propose a novel Label-guided Graph Contrastive Learning~(LGGCL) training algorithm for semi-supervised node classification. Specifically, we first propose a Label-guided Graph Contrastive Learning framework as the basis of LGGCL training algorithm. Then we incorporate a self-checking mechanism based on deep clustering to ensure the authenticity of the sampled positive nodes. Moreover, we design a reweighting strategy based on the probability distribution of the anchor node to enhance the effect of hard negative nodes. Finally, experimental results on various graph benchmarks demonstrate the superiority of LGGCL compared to other state-of-the-art approaches.
Interdependencies in Crypto Ecosystems: Drivers, Implications and Policy Responses

A paradigm shift is manifesting in the global crypto ecosystem. Akin to traditional financial systems, crypto markets are fast-developing into networks of complex interrelationships between systems and significant market participants. The events of the crypto winter, which began in early 2022 with the crash of sister tokens USDTerra and Luna and resulted in a series of cascading crashes, underscore the significant potential that interconnection, interdependencies, systemic risks and contagion can have in the evolving ecosystem. Compared to traditional finance, which is underpinned by a wide range of regulatory and supervisory interventions of central banks and other international and domestic regulatory bodies, the crypto space remains largely unregulated. In this paper, we explore the role of interdependencies in the effectiveness of internal risk management practices and crisis control mechanisms in the crypto ecosystem. In particular, we highlight the role that known factors contributing to interdependencies in traditional financial systems have had in crypto ecosystems. Following this, we use case studies of several significant events of 2022-2023 in evaluating the role of these emerging interdependencies in recurring crisis cycles by overwhelming internal risks management mechanisms and propelling known and unknown systemic risks. These interdependencies create a clear web of “systemically important crypto institutions” in the crypto ecosystem. These institutions take a variety of forms, spanning the range of crypto infrastructure, crypto intermediaries and crypto conglomerates. These interdependencies have been worsened by a lack of transparency, poor risk management and market misconduct. There is a need to build regulations for crypto based on where the risks actually are in the crypto ecosystem, focusing on identifying actual vulnerabilities and applying focused approaches to address market failures and negative externalities in order to support market functioning and development. If we are to design regulations, we must first properly understand the dynamics that underlie market failures and negative externalities in the crypto ecosystem which have been put into stark relief by both the crypto contagion of 2022-2023 as well as a range of other crises which have emerged over the evolution of crypto.From this basis, we suggest three fundamental adjustments as potential approaches to risk management and crisis mitigation going forward: (1) substantial remodelling of internal risk frameworks to consider broader risks perspectives arising from direct and indirect interdependencies; (2) restructuring of risk control and crisis mitigation practices through regulation and supervision to address interconnections and interdependencies arising in the crypto ecosystem, similar to those which have evolved in traditional finance; and (3) supporting the evolution of decentralised frameworks through appropriate guidance to take into account lessons and experiences in system design and operation.
Exploring Recent Trends in Blockchain Technology: A Systematic Literature Review

Blockchain technology is used to maintain the ever-growing list of data records. Blockchain can be an authentic ledger which includes applications such as fund transfers, settling trades voting, and several other useful components. However, there are still many underlying challenges associated with blockchain technology. Blockchain (database) is older but a new approach in information technology whose first implementation is Bitcoin (crypto currency). Bitcoin has also used Blockchain technology at its back end to keep a permanent record (chains of block) of all the data. Bitcoin is the primary appliance that formed decentralized surroundingsdesigned for Crypto money, wherever the participant is capable of acquiring and substituting production with Digital cash. Therefore, a confirmed contract could involve crypto cash, contracts, report, or other information. The current study aims to describe a comprehensive synopsis of the blockchain concept which could also be used in many other applications. The most essential material goods of blockchain are made with the aimthat there is no essential overseer or any centralized information storage space routine. Hence, Blockchain transactions occur only in peer to peer network which is distributed among several nodes. Therefore, Blockchain expertise could be an innovative implementation of possible applications, in favor of an organization that enables a safe and sound connection exclusive of the necessitated essential power. The current study proposed and analyzed the possible function of blockchains within a structure throughout a case examination. In addition to do a systematic literature analysis Blockchain technology is expected to bring developments in diverse fields, such as politics, economy, culture, industry, and business models. Therefore, the scholarly research discovered the variety of drivers designed for blockchain accomplishment while highlighting the barriers and risk inborn within the chunk string (Blockchain).
Batching of Tasks by Users of Pseudonymous Forums: Anonymity Compromise and Protection

There are a number of forums where people participate under pseudonyms. One example is peer review, where the identity of reviewers for any paper is confidential. When participating in these forums, people frequently engage in "batching": executing multiple related tasks (e.g., commenting on multiple papers) at nearly the same time. Our empirical analysis shows that batching is common in two applications we consider -- peer review and Wikipedia edits. In this paper, we identify and address the risk of deanonymization arising from linking batched tasks. To protect against linkage attacks, we take the approach of adding delay to the posting time of batched tasks. We first show that under some natural assumptions, no delay mechanism can provide a meaningful differential privacy guarantee. We therefore propose a "one-sided" formulation of differential privacy for protecting against linkage attacks. We design a mechanism that adds zero-inflated uniform delay to events and show it can preserve privacy. We prove that this noise distribution is in fact optimal in minimizing expected delay among mechanisms adding independent noise to each event, thereby establishing the Pareto frontier of the trade-off between the expected delay for batched and unbatched events. Finally, we conduct a series of experiments on Wikipedia and Bitcoin data that corroborate the practical utility of our algorithm in obfuscating batching without introducing onerous delay to a system.
Paced-curriculum distillation with prediction and label uncertainty for image segmentation

In curriculum learning, the idea is to train on easier samples first and gradually increase the difficulty, while in self-paced learning, a pacing function defines the speed to adapt the training progress. While both methods heavily rely on the ability to score the difficulty of data samples, an optimal scoring function is still under exploration. Distillation is a knowledge transfer approach where a teacher network guides a student network by feeding a sequence of random samples. We argue that guiding student networks with an efficient curriculum strategy can improve model generalization and robustness. For this purpose, we design an uncertainty-based paced curriculum learning in self-distillation for medical image segmentation. We fuse the prediction uncertainty and annotation boundary uncertainty to develop a novel paced-curriculum distillation (P-CD). We utilize the teacher model to obtain prediction uncertainty and spatially varying label smoothing with Gaussian kernel to generate segmentation boundary uncertainty from the annotation. We also investigate the robustness of our method by applying various types and severity of image perturbation and corruption. The proposed technique is validated on two medical datasets of breast ultrasound image segmentation and robot-assisted surgical scene segmentation and achieved significantly better performance in terms of segmentation and robustness. P-CD improves the performance and obtains better generalization and robustness over the dataset shift. While curriculum learning requires extensive tuning of hyper-parameters for pacing function, the level of performance improvement suppresses this limitation.
Exploring Digital Citizenship Competency in ‘Youth and Media’ Textbooks for Middle School Students

본 연구는 중학생의 미디어 리터러시 증진을 위해 개발된 검정 교과서인 ‘청소년과 미디어’의 활동이 디지털 시민 역량과 어떤 관련이 있는지 그 내용을 탐색하고 향후 효과적인 교육을 위한 제언을 도출하는 것을 목적으로 한다. 탐색을 위해 중학생용 청소년과 미디어 교과서에 포함된 31개 활동이 디지털 시민 역량 요소와 어떠한 관련이 있는지를 파악하였다. 디지털 시민 역량 요소는 UNESCO의 2019년 DKAP(Digital Kids Asia Pacific) 프로젝트에서 개발된 총 5개 영역 16개 역량 프레임을 사용하였다. 탐색 결과 디지털 활용(Digital Literacy), 디지털 보안 및 탄력성(Digital Safety & Resilience), 디지털 참여(Digital Participation & Agency), 디지털 정서지능(Digital Emotional Intelligence), 창의･혁신 (Creativity & Innovation) 영역의 16개 역량이 빠짐없이 활동에 포함되어 있음을 알 수 있다. 향후 이 교과서가 효과적인 디지털 시민성 교육의 도구로 기능하기 위해서는 모두가 공평하게 참여할 수 있는 교실 만들기, 교수학습 지원 및 네트워크 구성 지원, 교육과정 필수과목 편성 등의 제도 마련, 디지털 시민 역량 기반 교육과정 개발, 효과 검증 연구 등이 필요하다. 앞으로 더 많은 학생이 교육을 통해 현명한 디지털 시민으로 거듭나기를 바란다. This study aimed to examine the relationship between the activity contents of the “Youth and Media” textbook for middle school students and digital citizenship competency, and to provide suggestions for effective education in the future. This study analyzed 27 sub-unit activities and 4 project activities in the textbook by, utilizing the digital citizenship competency framework developed by the UNESCO"s 2019 Digital Kids Asia Pacific(DKAP) project. According to the results, all the 16 competencies, including “Digital Literacy,” “Digital Safety and Resilience,” “Digital Participation and Agency,” “Digital Emotional Intelligence,” and “Creativity and Innovation” were covered in the textbook. The effectiveness of the textbook as a tool for digital citizenship education can be enhanced by establishing a supportive system such as an organizational structure, developing a curriculum based on digital citizenship competency, and conducting empirical research to determine its impact. This study can contribute in fostering better digital citizens through education.
Design and Simulation of Fuzzy PID-based Control System

This paper proposes a fuzzy PID control method for the servo control system, the control method overcomes the control precision of fuzzy control is not high, and the traditional PID control for high order, parameter time varying, nonlinear and other controlled objects can not provide better control performance shortcomings, discusses the design method of fuzzy PID servo control system, MATLAB/Simulink and fuzzy control toolbox are used to design and simulate the control system, the results show that the performance of the servo control system is improved obviously.
Multi-element association and regional geochemistry of regolith in tashan jatau area, northwestern nigeria: implications for gold exploration

Soil geochemical surveys are widely used in the early stages of gold exploration, especially in areas with poor outcrops and thick overburden. Fifty-one (51) soil samples were collected in duplicate with the aim of analyzing them geochemically and determining the gold grade through panning and weighing methods. The geochemical results were subjected to multivariate statistical treatment through Factor analysis and Pearson correlation matrix. Four factors were generated from the PCA. These are Factor 1: Cu-Sr-Nb-Ba-La-W-Pb-Zr, suggesting sulfide mineralization that is related to granitic rock while Factor 2 has Au with a low to negative correlation with Mo and Nb suggesting a second phase of intrusion-related activity which must have emplaced gold in this area. Factor 3 is made up of W-Pb suggesting a second sulfide mineralization distinct from the first. While Factor 4 gives a single element factor, Hg. Single-elementt maps were constructed to show the element dispersion in the catchment. In general gold concentrations in this study is erratic and attain a high of 0.67ppm and 0.90g/t. The study delineates the northwestern part of the catchment to be the most prolific in terms of gold potential and shows that the granitic batholiths are the most primary gold-hosting lithology.&#x0D;
Optimization of Task Scheduling in Cloud Computing Using the RAO-3 Algorithm

Cloud computing provides computer resources such as hardware and software as a service to consumers over a network. The primary idea of cloud computing is to distribute massive amounts of storage, processing, and information to scientific applications. In cloud computing, user tasks are organized and executed with appropriate resources to supply services successfully. In order to complete task scheduling, a variety of task allocation approaches are employed. This work proposes an efficient task scheduling method to improve the task scheduling approach. Optimization techniques are often used to solve NP-hard situations. This work presents an effective scheduling task method for cloud computing systems based on the RAO-3 algorithm. We evaluate the performance of our approach by applying it to three instances. According to the data, the proposed technique provided the optimal solution in terms of schedule length, speedup, efficiency, and throughput.
Digitization of Museum Curation Through Incorporating Virtual Reality Technology

Virtual reality technology has grown increasingly valuable as a result of the wave of digitization and the arrival of the information era, and it is highly regarded by people from all walks of life.Along with the social economy of the nation expanding quickly, socialist spiritual culture is also progressing at a rapid rate, and China's cultural endeavors are thriving.The state and government departments are paying an increasing amount of attention to museums, art museums, and exhibition halls as monuments and city business cards as China gradually becomes more urbanized.The museum's primary duties as a multifaceted organization are to gather and present exhibits, protect, and conduct research.The museum serves the general public by providing social, cultural, and educational services as an educational institution.The design of museum exhibits is constantly being infused with high-tech technology as a result of the development and emergence of virtual reality technology [1].This has transformed the formerly dull physical museum exhibition, increased the popularity of the museum, and greatly improved the experience and enjoyment of a museum visit.
Population Monitoring

This chapter describes the importance of population monitoring, a complex task with many interrelated parts. The theoretical foundation for monitoring lies in the random sample and the field of statistics. Effective survey design provides the building blocks for monitoring all relevant quantities in a general and reusable fashion. Population models help to formalize hypotheses, design monitoring programs, find efficient uses of resources, and make proactive assessments of management actions. By combining the guidance received from population models with strict sampling procedures, the state of the population can efficiently be established, which facilitates adoption of the scientific method and rigorous decision making.
Image Quality Improvement of SENSE Parallel Imaging MRI Post-Acquisition Using Denoising Non-Local Mean Filter Technique

Abstract. Magnetic Resonance Imaging (MRI) is a medical tool that is used to form images of organs, soft tissues, bones and almost all internal body structures. The MRI image acquisition process takes a long time. One of the efforts to shorten the examination acquisition time is using the parallel imaging technique, namely SENSE. However, SENSE has a weakness, namely reducing the Signal Noise to Ratio (SNR). One of the denoising methods that can increase SNR is the Nonlocal mean filter (NLM). Post-image acquisition denoising becomes a cheaper and more effective alternative to use. The aim of this research is to measure the increase of SNR value in MRI SENSE images between before the denoising technique and after the denoising technique. This research is expected to produce a faster scanning time and maintain the quality of the MRI image. This experimental research was carried out by applying the SENSE parallel imaging technique to R-factors 2 and 4. The sequence used is T2WI TSE on axial slice phantom. The T2WI TSE SENSE phantom MRI image was corrected with the NLM denoising technique to produce a better quality image. Each variation is measured image information before and after the denoising technique. Image information is assessed quantitatively by measuring SNR. The results of the parametric test showed that there was an increase in the SNR value after the application of the denoising technique on the Phantom T2WI TSE SENSE MRI image at r-factor 2 and r-factor 4. The different test on the SNR assessment resulted in a p value &lt; 0.001.