Reviewerly/exHarmony huggingface dataset:
Scaling Blockchains: Can Committee-Based Consensus Help?

In the high-stakes race for scalability, some blockchains have turned to committee-based consensus (CBC), whereby the chain’s recordkeeping rights are entrusted to a committee of block producers elected via approval voting. Smaller committees boost speed and scalability but can compromise security when voters have limited information. In this environment, voting strategies are naturally nonlinear, and equilibria can become intractable. Despite this, we show that elections converge to optimality asymptotically (in voter numbers), exponentially quickly, and under relatively weak informational requirements. Compared to popular stake-weighted lottery and single-vote protocols used in practice, we find that CBC, when paired with approval voting, can offer meaningful efficiency and robustness gains if enough voters are engaged. This paper was accepted by Will Cong, Special Section of Management Science: Blockchains and Crypto Economics. Funding: This work was funded by The Hogeg Blockchain Research Institute. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.03177 .
An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings

Identifying and managing osteosarcoma pose significant challenges, especially in resource-constrained developing nations. Advanced diagnostic methods involve isolating the nucleus from cancer cells for comprehensive analysis. However, two main challenges persist: mitigating image noise during the capture and transmission of cellular sections, and providing an efficient, accurate, and cost-effective solution for cell nucleus segmentation. To tackle these issues, we introduce the Twin-Self and Cross-Attention Vision Transformer (TSCA-ViT). This pioneering AI-based system employs a directed filtering algorithm for noise reduction and features an innovative transformer architecture with a twin attention mechanism for effective segmentation. The model also incorporates cross-attention-enabled skip connections to augment spatial information. We evaluated our method on a dataset of 1000 osteosarcoma pathology slide images from the Second People’s Hospital of Huaihua, achieving a remarkable average precision of 97.7%. This performance surpasses traditional methodologies. Furthermore, TSCA-ViT offers enhanced computational efficiency owing to its fewer parameters, which results in reduced time and equipment costs. These findings underscore the superior efficacy and efficiency of TSCA-ViT, offering a promising approach for addressing the ongoing challenges in osteosarcoma diagnosis and treatment, particularly in settings with limited resources.
The Effects of Flipped Learning in Social Welfare Administration

본 연구의 목적은 사회복지교육에 플립드러닝을 적용하고 효과를 분석함으로써 사회복지교육 효과 증진방안을 탐색하는 것이다. 이를 위해 I대학교 사회복지학과 사회복지행정론 교과목에 적용하고 그 효과를 분석하였다. 사전사후 단일집단설계를 기반으로 수집한 자료를 기술통계분석, paired t-test, Wilcoxon test 등 양적분석과 개방형 수업 평가 응답 결과를 질적분석 하였다. 연구 결과, 학생들은 플립드러닝 수업에 만족하며 ‘교과목에 흥미’를 느끼는 등 학습 동기가 향상되었고 ‘내 것을 만들어가는’ 자기주도학습 태도가 형성되고 자기효능감이 유의미하게 증가하였다. 또한 지속적인 소통 과정을 통해 ‘나의 생각을 정리하고 말하는’ 의사소통능력이 유의미하게 향상되었다. 이러한 연구 결과를 바탕으로 사회복지교육 효과 증진방안을 제안하였다.The purpose of this study is to explore on how to improve the effectiveness of social welfare education by analyzing flipped learning effectiveness. For this, Flipped learning was applied to 30 students who took ‘Social Welfare Administration’ subject at I University. Quantitative analysis such as paired sample t-test, Wilcoxon test and descriptive analysis and qualitative analysis of class-evaluation were conducted. The results of this study showed that students were satisfied with Flipped learning and interested in the contents of subject. Self-directed learning attitude, ‘organizing my knowledge’ by class activities was established and self-efficacy was improved. Also communications competency, ‘organize thought then speak’ was improved through team works. From these results, several implications were suggested.
Effects of Korean Secondary English Teachers’ Perceived Criterion Importance on Scoring Behavior in L2 Writing Assessment

The role of raters in performance-based assessment is crucial, but due to the interactions between raters and rating criteria, rater-induced errors often interfere with the accurate evaluations of examinees’ performances. Based on the hypothesis (Eckes, 2012) that severity bias is combined with higher criterion importance and leniency bias is aligned with lower criterion importance, this study investigates the relationship between perceived criterion importance and scoring behavior by employing hierarchical clustering and a Many-facet Rasch analysis. In a survey, 30 in-service secondary Korean English teachers indicated their perceived criterion importance and rated thirty English essays from Yonsei English Learner’s Corpus (YELC). The results of the survey responses and the measures from the MFRM interaction analysis between participants and the rating criteria were applied respectively to form two rater types: cognitive raters types (CRTs), based on criterion perception and operational rater types (ORTs), based on criterion-related bias. The comparison of these two rater types and the analysis of an individual rater’s criterion importance rating together with their criterion-related bias measure showed that the hypothesized link between rater criterion perception and rating behavior only existed in content. The findings of the study will provide valuable information for rater training and Korean English teachers evaluating writing performances.
FLoRSA: Fuzzy Logic-Oriented Resource Scheduling Algorithm in IaaS Cloud

Cloud computing enables rapid access to elastic and economical computing resources by distributing various services, such as servers, storage, databases, networking, applications, insights, and intelligence, over the Internet. Resource scheduling is the allocating resources among different cloud users per governing policies for resource usage in a given cloud environment. Cloud resource scheduling has significant challenges, including heterogeneity, uncertainty, and resource dispersion. These challenges raise the issues of allocation, de-allocation, and re-allocation of resources efficiently and optimally. Various scheduling criteria, such as the number of tasks executed, the pool of resources, length, and deadline, can be considered for the optimum allocation of resources. This paper proposes Fuzzy Logic-Oriented Resource Scheduling Algorithm (FLoRSA) and Extended Weighted Sum Method (EWSM) for resource scheduling in IaaS clouds. Both algorithms prioritize the cloudlets based on length, deadline, and quantity of resources as input parameters. A fuzzy inference system is developed in FLoRSA, while the EWSM algorithm extends the widely used Weighted Sum Method, Multi-Criteria Decision-Making (MCDM) technique. Experiments have been carried out in an open-source CloudSim 3.0.3 simulation tool. Comparison of FLoRSA and EWSM algorithms on different performance evaluation factors is calculated for 2200 cloudlets. FLoRSA brings down turnaround time by 18% when compared to EWSM. FLoRSA throughput is 20% improved than EWSM. FLoRSA significantly deals with imprecise user requirements to improve the QoS in resource scheduling and optimize performance parameters.
Models of Learning Bahasa applied in High School Level: A Literature Study

Education is the main thing in improving human resources. Education is something that is consciously done to combine the abilities of various aspects of human beings. Education is the most important thing in human life from ancient times to the present. Education is important as an assessment of human destiny in building a better nation in the future. Nation building is not carried out by the previous people, but is carried out by the next generation. Thus, to form a better nation, proper education is needed. Good education is carried out with appropriate and effective learning models so that learning materials can be understood by students. In addition, students must also be equipped with language skills. Because these language skills can be used in social life. Language skills must be obtained optimally so that students can optimize their abilities. This research uses descriptive qualitative research literature study. This research collects data from books or journals that have relevance to the topic of discussion in this study. Keywords: language education, learning models, high school. DOI: http://dx.doi.org/10.61436/rietm/v1i1.pp29-33
Clinicopathological profile of Non-Hodgkin and Hodgkin Lymphoma at the Anatomical Pathology Laboratory of Saiful Anwar General Hospital Malang from January 2018 until May 2023

Lymphomas are a lymphocyte-driven malignancy that comprise up to 10% of cancers diagnosed in individuals &lt; 20 years of age globally. Malignant lymphomas are divided into Hodgkin Lymphoma (HL) and Non Hodgkin Lymphoma (NHL). In 2019 at United States, more than 82.000 new patients diagnosed with lymphoma. Since the epidemiology of Lymphoma is different across regions and may have change over time, its global distribution pattern, risk factors, and temporal trends need to be assessed for developing preventive measures. The purpose of this study was to determine the frequency of NHL and HL based on histopathological type, age, and gender. This research is a descriptive analytic study. Data were obtained from patients who underwent histopathological and immunohistochemistry examinations at the Anatomical Pathology laboratory of Saiful Anwar General Hospital Malang from January 2018 until May 2023. There are 408 cases of Lymphoma consisting of 56 HL and 352 NHL. In HL, the percentage higher in men, and occurred mostly during 21 – 30 years old age group. The most common histopathological type of HL is Classical Hodgkin Lymphoma, with mostly Nodular Sclerosis subtype. In NHL, the percentage higher in men, and occurred mostly during 51 – 60 years old age group. The most common histopathological type of Non Hodgkin Lymphoma is B cell type.
Retracted: Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans

[This retracts the article DOI: 10.1155/2022/2872461.].
Retracted: Deep Learning-Based Emergency Care Process Reengineering of Interventional Data for Patients with Emergency Time-Series Events of Myocardial Infarction

[This retracts the article DOI: 10.1155/2022/7339930.].
Retracted: Evaluation of the Effectiveness of Artificial Intelligence Chest CT Lung Nodule Detection Based on Deep Learning

[This retracts the article DOI: 10.1155/2021/9971325.].
Retracted: Study on Maximal Oxygen Uptake of Respiration and Heart Rate in Exercise Training Based on Regression Equation

[This retracts the article DOI: 10.1155/2022/5961197.].
Retracted: Prediction of Hearing Prognosis of Large Vestibular Aqueduct Syndrome Based on the PyTorch Deep Learning Model

[This retracts the article DOI: 10.1155/2022/4814577.].
Retracted: The Evaluation Model of College Students’ Mental Health in the Environment of Independent Entrepreneurship Using Neural Network Technology

[This retracts the article DOI: 10.1155/2021/4379623.].
Retracted: Microscopic Tumour Classification by Digital Mammography

[This retracts the article DOI: 10.1155/2021/6635947.].
Retracted: Optimization of Surface Electromyography-Based Neurofeedback Rehabilitation Intervention System

[This retracts the article DOI: 10.1155/2021/5546716.].
Retracted: MeQryEP: A Texture Based Descriptor for Biomedical Image Retrieval

[This retracts the article DOI: 10.1155/2022/9505229.].
Retracted: CT Image Examination Based on Virtual Reality Analysis in Clinical Diagnosis of Gastrointestinal Stromal Tumors

[This retracts the article DOI: 10.1155/2021/9996565.].
Retracted: Effects of Natural Convalescent Factors Combined with Motor Intelligence Management on Patients’ Blood Pressure

[This retracts the article DOI: 10.1155/2021/2917226.].
Retracted: Research on the Improved CNN Deep Learning Method for Motion Intention Recognition of Dynamic Lower Limb Prosthesis

[This retracts the article DOI: 10.1155/2021/7331692.].
Retracted: Contrast Agent and Molecular Imaging Meta-Analysis of the Clinical Effect of Intelligent Image Sensor Combined with Visual Training in the Treatment of Children with Intermittent Exotropia in China

[This retracts the article DOI: 10.1155/2022/5387928.].
Retracted: Prediction and Analysis of Length of Stay Based on Nonlinear Weighted XGBoost Algorithm in Hospital

[This retracts the article DOI: 10.1155/2021/4714898.].
Retracted: Self-Attention-Guided Recurrent Neural Network and Motion Perception for Intelligent Prediction of Chronic Diseases

[This retracts the article DOI: 10.1155/2021/6382619.].
Retracted: Effect of Carbon Dioxide on Bispectral Index of EEG under Intravenous Target-Controlled Anesthesia Based on Intelligent Medical Treatment

[This retracts the article DOI: 10.1155/2022/4696128.].
Retracted: Artificial Intelligence in Repairing Meniscus Injury in Football Sports with Perovskite Nanobiomaterials

[This retracts the article DOI: 10.1155/2021/4324138.].
Retracted: Design of Optimal Scheduling Model for Emergency Medical Supplies by Blockchain Technology

[This retracts the article DOI: 10.1155/2022/4608761.].
Retracted: Health Recognition Algorithm for Sports Training Based on Bi-GRU Neural Networks

[This retracts the article DOI: 10.1155/2021/1579746.].
Retracted: Artificial Intelligence Auxiliary Algorithm for Wushu Routine Competition Decision Based on Feature Fusion

[This retracts the article DOI: 10.1155/2021/1632393.].
Retracted: Comparative Anesthesia Effect of Brachial Plexus Block Based on Smart Electronic Medical Ultrasound-Guided Positioning and Traditional Anatomical Positioning

[This retracts the article DOI: 10.1155/2021/6676610.].
Retracted: Recognition of Volleyball Player’s Arm Motion Trajectory and Muscle Injury Mechanism Analysis Based upon Neural Network Model

[This retracts the article DOI: 10.1155/2022/8114740.].
Retracted: Application of MRI Image Based on Computer Semiautomatic Segmentation Algorithm in the Classification Prediction of Breast Cancer Histology

[This retracts the article DOI: 10.1155/2021/6088322.].
Retracted: Action Recognition, Tracking, and Optimization Analysis of Training Process Based on the Support Vector Regression Model

[This retracts the article DOI: 10.1155/2022/2174240.].
Retracted: Applied Research on the Combination of Weighted Network and Supervised Learning in Acupoints Compatibility

[This retracts the article DOI: 10.1155/2021/4699420.].
Retracted: Smart Bracelet System for Temperature Monitoring and Movement Tracking Analysis

[This retracts the article DOI: 10.1155/2021/8347261.].
Retracted: Speckle Tracking Algorithm-Based Ultrasonic Cardiogram in Evaluation of the Efficacy of Dexmedetomidine Combined with Bundle Strategy on Patients with Severe Sepsis

[This retracts the article DOI: 10.1155/2021/7179632.].
Retracted: IoT-Based Wearable Sensors and Bidirectional LSTM Network for Action Recognition of Aerobics Athletes

[This retracts the article DOI: 10.1155/2021/9601420.].
Perceived spatial presence and body orientation affect the recall of out-of-sight places in an immersive sketching experiment

Abstract The orientation of sketch maps of remote but familiar city squares produced from memory has been shown to depend on the distance and airline direction from the production site to the remembered square (position-dependent recall, Röhrich et al. in PLoS One 9(11): e112793, 2014). Here, we present a virtual reality version of the original experiment and additionally study the role of body orientation. Three main points can be made: First, “immersive sketching” is a novel and useful paradigm in which subjects sketch maps live on paper while being immersed in virtual reality. Second, the original effect of position-dependent recall was confirmed, indicating that the sense of being present at a particular location, even if generated in a virtual environment, suffices to bias the imagery of distant places. Finally, the orientation of the produced sketch maps depended also on the body orientation of the subjects. At each production site, body orientation was controlled by varying the position of the live feed in the virtual environment, such that subjects had to turn towards the prescribed direction. Position-dependent recall is strongest if subjects are aligned with the airline direction to the target and virtually goes away if they turn in the opposite direction. We conclude that the representation of out-of-sight target places depends on both the current airline direction to the target and the body orientation.
Design and implementation of design platform for automatic generated micro-application system based on domain model

Domain model is a visual representation of conceptual classes in the domain or objects in the real world, also known as conceptual model, domain object model, and analysis object model. Domain model driven development creates in a visual way or introduces domain model from mature model repository, and automatically generates micro-service application project based on domain model through transcoding. Based on the Eclipse SDK and its plug-in development environment (PDE), the domain model design plug-in is implemented. IDE development environment installation domain model design plug-in realizes the design platform with domain model driven modeling and automatic code generation function, which greatly improves the development efficiency of micro-application system.
Optimal design about line loss calculation system of electric power system based on the Internet of Things

With the development of technologies such as the Internet of IOT, data mining is becoming mature, and enterprise management will adopt these new technologies, and the safety management information platform can reach the bottom distributed hardware directly from the top decision-making layer, relying on the timely collection of data and real-time data analysis. Making use of intelligent analysis is of great value in monitoring the safety production of enterprises. In this thesis, the key points of the is the safety monitoring of Electric Power System. Using the characteristics of industrial Internet of Things (IoT), such as "connecting things", data collection and intelligent analysis, will improve the efficiency of Electric Power System. The enterprises will generate huge IoT monitoring data every day, which has high reference value for production process analysis and production decision making. storing and analyzing these IoT data in real time is a thorny problem. About the above problems, this paper proposes to use industrial IoT and big data mining technology to achieve monitoring and monitoring resource integration, real-time analysis and early warning.
A mapping and principal component dimensionality reduction analysis neural network for structured multi-classification

Structured multi-classification is a typical supervised learning problem, where each training instance is equipped with a set of candidate labels among which only one is the true label. This problem is often plagued by the slow training speed and the low classification accuracy. The goal of this paper is to propose a novel framework called normalizing quadratic Mapping and principal component Dimensionality Reduction analysis (MDR) Neural Network. Thorough experiments demonstrate it sets the new state of the art, accelerating the convergence of neural network and improving the accuracy of classification recognition with a few number of training rounds.
Increasing Student Learning Enthusiasm Through Multimedia-Based Learning

A teaching device is a medium that can be used to convey material from a teacher to students. The delivery of material designed in the form of multimedia aims to make it easier for students to understand the material both in the form of desktop and web-based applications. A common problem faced by students is that there are still many who have not achieved satisfactory learning achievement. With the existing limitations, learning tools that support learning achievement are absolutely necessary. This research focuses on increasing student enthusiasm for learning in the Ulumul Quran course. Learning activities with multimedia-based applications aim to increase students' enthusiasm for learning, so that the material is absorbed more quickly and students are not bored. Initial observations included a literature review and interviews to identify class issues for lesson analysis and possible remedies. In developing the solution, the problem is identified based on course material, class schedule, student characteristics, class atmosphere, learning methods/approaches, process assessment, and learning outcomes. Then analyse data and demands and design a multimedia-based e-learning system. The conclusion of this study states that students are able to increase their enthusiasm for learning the subject 'Ulumul Qur'an. There is an increase in student enthusiasm for learning with multimedia-based learning. This can be seen in the average N-Gain value obtained 0.466. The significant value obtained from the calculation results is smaller than the significant level α = 0.05 (0.000 0.05) than H0, which states that there is no difference in the enthusiasm for learning of students who study.
Emulating Quantum Circuits With Generalized Ising Machines

The primary objective of this paper is to present an exact and general procedure for mapping any sequence of quantum gates onto a network of probabilistic p-bits which can take on one of two values 0 and 1. The first $n$ p-bits represent the input qubits, while the other p-bits represent the qubits after the application of successive gating operations. We can view this structure as a Boltzmann machine whose states each represent a Feynman path leading from an initial configuration of qubits to a final configuration. Each such path has a complex amplitude $\psi$ which can be associated with a complex energy. The real part of this energy can be used to generate samples of Feynman paths in the usual way, while the imaginary part is accounted for by treating the samples as complex entities, unlike ordinary Boltzmann machines where samples are positive. Quantum gates often have purely imaginary energy functions for which all configurations have the same probability and one cannot take advantage of sampling techniques. However, if we can use suitable transformations to introduce a real part in the energy function then powerful sampling algorithms like Gibbs sampling can be harnessed to get acceptable results with far fewer samples and perhaps even escape the exponential scaling with $nd$. This algorithmic acceleration can then be supplemented with special-purpose hardware accelerators like Ising Machines which can obtain a very large number of samples per second through a combination of massive parallelism, pipelining, and clockless mixed-signal operation made possible by codesigning circuits and architectures to match the algorithm. Our results for mapping an arbitrary quantum circuit to a Boltzmann machine with a complex energy function should help push the boundaries of the simulability of quantum circuits with probabilistic resources and compare them with NISQ-era quantum computers.