Reviewerly/exHarmony huggingface dataset:
Effectiveness of Automatic Detection of Osteoarthritis using Convolutional Neural Network (CNN) Method with DenseNet201 on Digital Images of Knee Joint Radiography

The manual detection of osteoarthritis using Kellgren Lawrence system depends on experience and agreement between doctors. The study was conducted to develop DenseNet201 to assist doctors in making a diagnosis of osteoarthritis grading. This study analyzes the accuracy; sensitivity; specificity; positive predictive value (PPV) and negative predictive value (NPV) of DenseNet201 in grading osteoarthritis and compares the classification results between DenseNet201 and radiologists in detecting osteoarthritis on knee joint images. This study is an applied experiment that compares the classification results of DenseNet201 and radiology specialists. Firstly, DenseNet201 is built with the MATLAB R2021a. Tests are carried out by measuring accuracy, sensitivity, specificity, PPV and NPV of 75 images of knee joint. Lastly, the data is analyzed using the Wilcoxon statistical test. The study has shown that the performance of DenseNet201 was good in detecting osteoarthritis, with accuracy value 91.84%; sensitivity value 76.61%; specificity value 94.32%; PPV 82.60% and NPV 94.32%. There was no significant difference between classification results using DenseNet201 and radiologist with a value (p&gt;0.05) of 0.119. DenseNet201 can be considered as an alternative diagnostic tool for osteoarthritis with the condition that verification of the diagnostic decision still refers to the confirmation and justification of the radiologist.
Design and Implementation of Omnidirectional Mobile Robot for Materials Handling among Multiple Workstations in Manufacturing Factories

This paper introduces the mechanical design and control system of a mobile robot for logistics transportation in manufacturing workshops. The robot is divided into a moving part and a grasping part. The moving part adopts the mecanum wheel four-wheel-drive chassis, which has omnidirectional moving ability. The mechanical system is based on four mechanical wheels, and a modular suspension mechanism is designed. The grasping part is composed of a depth camera, a cooperative manipulator, and an electric claw. Finally, the two are coordinated and controlled by computer. The controller hardware of the mobile platform is designed, and the functional modules of the mobile platform are designed based on the RT thread embedded system. For the navigation part of the mobile robot, a fuzzy PID deviation correction algorithm is designed and simulated. Using the Hough circular transform algorithm, the visual grasping of the manipulator is realized. Finally, the control mode of the computer-controlled manipulator and the manipulator-controlling mobile platform is adopted to realize the feeding function of the mobile robot, and the experimental verification is carried out.
The role of STEAM projects in the interactive and captivating involvement of students in the learning process

The aim of the paper is to present the importance of using the STEAM approach in education, from the perspective of developing students' creativity, increasing their motivation and involvement in the learning process. In a broad sense, the term STEAM covers science, technology, engineering, arts and mathematics. But to be 'STEAM expert' does not mean that you must excel in every subject. Instead, it requires transferable skills, independent of any one specific domain of subject knowledge. The benefits it offers are many: developing problem-solving skills, facilitating cooperation, teamwork and communication, stimulating innovation, reducing student anxiety.
Lifting Factor Graphs with Some Unknown Factors

Lifting exploits symmetries in probabilistic graphical models by using a representative for indistinguishable objects, allowing to carry out query answering more efficiently while maintaining exact answers. In this paper, we investigate how lifting enables us to perform probabilistic inference for factor graphs containing factors whose potentials are unknown. We introduce the Lifting Factor Graphs with Some Unknown Factors (LIFAGU)algorithm to identify symmetric subgraphs in a factor graph containing unknown factors, thereby enabling the transfer of known potentials to unknown potentials to ensure a well-defined semantics and allow for (lifted) probabilistic inference.
Use of Emojis and Stickers for Online Interaction Facilitation: A Gender-Based Semiotic Discourse Analysis

Modern technology has revolutionized our methods of communication. Emojis and stickers are the newest advancements that transcend linguistic barriers and act as a universal language. This research paper applies a social semiotic framework to investigate the functions of emojis and stickers in digital discourse. Utilizing a mixed method approach, data has been collected through a web-based questionnaire from 137 participants and semi-structured interviews taken from 10 selected participants. The study aims to explore the central role of emojis and stickers in promoting positive online interaction and identify how their usage facilitates both genders while potentially differing interpretations. The findings suggest that emojis and stickers aid in expressing emotions, clarifying messages, and improving mood during conversations. Furthermore, the study reveals that users utilize emoticons strategically and functionally for various purposes beyond emotional expression. The study concludes that emojis and stickers serve as a global language and are vital in managing interrelations and message comprehension in online communication.
THE ROLE OF DIGITAL PUBLIC RELATIONS IN IMPROVING THE QUALITY OF AIRLINES ORGANIZATIONS SERVICES

This research aimed to examine the relationship between digital public relation (DPR) towards improving the quality in the Aviation sector in Jordan. The researcher examined quality based on the variable of the study. A Descriptive and statistics analyses have been used for this research. The sample of the research included managers from digital public relation departments. The research hypotheses were tested using linear regression. The study concluded that there is a relationship exists between the (DPR) and quality in the airline organizations. As Reliability variable was found the most factor related towards DPR rather than the other response and accessibility. A number of recommendations were mentioned. Airlines organizations should pay attention to the importance if using DPR and to train their employees in order to achieve progress and prosperity.
Performance de Sistemas Fixos e Rastreadores Solares Correlacionando o Índice de Irradiância Difusa em Diferentes Áreas no Brasil

Solar energy is available abundantly in nature, and a substantial drop in the costs related to photovoltaic systems was observed in the last years. Therefore, solar-based systems have assumed a leading position in the search for alternative sources of energy. Although the use of solar tracker in those systems is not essential, it is known that its use can boost the collected energy. To meet this increasing demand, it is necessary to consider the effect of several parameters that can harm the performance of solar trackers, such as different periods of time, geographical and weather conditions. For that reason, this paper aims to compare the performance of fixed photovoltaic systems and tracking systems in twenty cities in different regions in Brazil as well as their efficiency under different levels of diffuse irradiance and spacing among arrays of photovoltaic modules. Simulations were performed and compared for each location. The results show that the correlation between the tracker gain and diffuse index is -0.9063 for a 6 m pitch, -0.9101 for a 7 m pitch and -0.9184 for an 8 m pitch and the change of the pitch from 6 m to 8 m led to an average increase of 4% in the gain obtained. Therefore, it is envisaged the existence of an inverse correlation between the diffuse indexes and tracker gain as well as the influence of the chosen pitch for each location.
Data Augmentation Strategies for Music Composition using Generative Adversarial Networks

The field of Algorithmic Art has been following technological advances in Artificial Intelligence and, as Generative Adversarial Networks (GANs) have become popular, applications on art generation began to emerge. For most deep neural networks, large amounts of training data are essential to achieve satisfactory model quality. But there are cases, such as in MIDI musical melodies, where it is not trivial to acquire data in such a high volume. Data augmentation strategies play an important role on these cases. This paper presents a data augmentation pipeline, composed of three strategies, with the objective of improving the quality of a GAN- based musical melody generator. The proposed data augmentation pipeline was compared with a non-augmented dataset and a replicated dataset, which had the same size of the augmented dataset, but composed only of replicas. From the statistical tests performed it can be stated that the augmented dataset outperformed the non-augmented dataset and the replicated dataset, when evaluating the Fréchet Inception distance (FID) score.
Historical analysis of the cost of acquiring a coronary stent and feasibility of the new ministry of health ordinance: a retrospective observational longitudinal study

Introduction: According to the World Health Organization (WHO), cardiovascular diseases (CVD) are the leading cause of death in the world. It is estimated that 17.7 million people died from CVD in 2015. According to data from DATASUS, in 2008, 44,138 coronary angioplasties were performed with or without stents. In 2016, 79,997 angioplasties were performed. The cost of orthoses, stents, and special materials for hospitals impacts the financial balance of the single health system (SUS). Objective: It was to carry out a cost analysis of stents for angioplasty from the perspective of the Brazilian Unified Health System. Methods: The present study followed a retrospective longitudinal observational model (STROBE). Data on stents about the years 2020 and 2021 and the cost assessment were carried out by searching the intra-hospital financial database of Santa Casa in the city of Jaú, São Paulo, Brazil, and the SUS table. The research was in the field of incomplete economic evaluation (cost analysis and brands of stents) through macro-costing of stent supply. The variables were presented as percentages, mean, and standard deviation. Comparisons of variables were performed using the Kruskal-Wallis test, with p&lt;0.05, and Tukey analysis (ANOVA-One-Way) with p&gt;0.05 with a statistical difference. Predictive logistic regression analysis was performed to better understand the trend in stent costs each year, with a significant p&lt;0.05. Results: After analyzing the intra-hospital financial data and the SUS table, they showed a significant increase in the transfer value of stents from 2020 to 2021, with average values in reais of R$1,399 to R$2,478, respectively. This difference of R$1,079.00 can be justified by the period of the COVID-19 pandemic, as well as the increase in demand for stents. Conclusion: Lower average values for stent costs were observed in 2020 and 2021 compared to other stent values that were published in studies covering the same periods. However, the average cost of stents in this study in 2021 showed significantly higher values compared to 2020, which further burdened the budget of the Unified Health System. The future perspective is that a greater transfer of funds from the Federal Government for the Unified Health System, as well as a reduction in the cost prices of stents by manufacturers and suppliers in the post-COVID-19 period, to obtain a balance in public spending. Furthermore, it is expected that drug-eluting stents will be more widely used in the Unified Health System. Furthermore, it is necessary to establish public policies to standardize stent prices.
The Impact of COVID-19 Pandemic on Cardiovascular Diseases in Brazil

Background: The novel coronavirus pandemic is an international public health emergency due to the high contagious potential of the virus on a global scale and, similarly to what happened globally, Brazilian authorities took measures to reduce its impact on the healthcare system, including social distancing. In this context of exhaustion of the system, it is worth questioning the situation of patients who need medical care for other diseases, such as the cardiovascular ones. Objectives: Describe the changes in the epidemiological profile of hospitalizations cardiovascular diseases during the COVID-19 pandemic in Brazil. Methods: Assessment of relative variations on the number of hospitalizations, associated average time, mortality rate and financial investments. Results: After the beginning of the pandemic, there was reduction of 21.46% in hospitalizations, more accentuated in females and in the Northern region, and increase of 14.14% in mortality, of greater significance in the Midwestern region. In younger ages, mortality and permanence time presented more accentuated positive variations. In elective admissions, growth in mortality and in hospital expenses were more significant. Conclusion: The pandemic impacted on cardiovascular diseases in a multifactorial way. The number of hospitalizations decreased, and the mortality rate increased, as efforts concentrated on moderate and severe cases. Therefore, there is a need for policies that minimize mid- and long-term impacts, as well as studies that investigate possible late complications of infections by the novel coronavirus.
Object Detection of UAV Aerial Image based on YOLOv8

With the development of technology, unmanned aerial vehicles (UAVs) have shed their military uses and gradually expanded to civilian and commercial fields. With the development of drone technology, object detection technology based on deep learning has become an important research topic in the field of drone applications. Apply object detection technology to unmanned aerial vehicles to achieve object detection and recognition of ground scenes from an aerial perspective. However, in aerial images taken by drones, the detection objects are mostly small targets, and the target scale changes greatly due to the influence of aerial perspective; The image background is complex, and the target object is easily occluded. It has brought many challenges to the target detection of unmanned aerial vehicles. Conventional object detection algorithms cannot guarantee detection accuracy when applied to drones, and optimizing the target detection performance of drones has become an important research topic in the field of drone applications. We improve the WIoUv3 loss function on the basis of YOLOv8s to reduce regression localization loss during training and improve the regression accuracy of the model. The experimental results indicate that the improved model mAP@0.5 It increased by 0.6 percentage points to 40.7%.
Collaborative Prediction in Anti-Fraud System Over Multiple Credit Loan Platforms

Anti-fraud engineering for online credit loan (OCL) platforms is getting more challenging due to the developing specialization of gang fraud. Associations are critical features referring to assessing the credibility of loan applications for OCL fraud prediction. State-of-the-art solutions employ graph-based methods to mine hidden associations among loan applications effectively. They perform well based on the information asymmetry which is guaranteed by the huge advantage of platforms over fraudsters in terms of data quantity and quality at their disposal. The inherent difficulty that can be foreseen is the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">data isolation</i> caused by mistrust between multiple platforms and data control legislations for privacy preservation. To maintain the advantage owned by the platforms, we design a privacy-preserving distributed graph learning framework that ensures critical association repairs by merging parameter sharing and data sharing. Specially, we propose the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">association reconstruction mechanism</i> (ARM) that consists of the devised exploration, processing, transmission and utilization schemes to realize data sharing. For parameter sharing, we design a hybrid encryption technique to protect privacy during collaboratively learning graph neural network (GNN) models among different financial client platforms. We conduct the experiments over real-life data from large financial platforms. The results demonstrate the effectiveness and efficiency of our proposed methods.
Toward Designing High-Speed Cost-Efficient Quantum Reversible Carry Select Adders

Compared to classical computing implementations, reversible arithmetic adders offer a valuable platform for implementing quantum computation models in digital systems and specific applications, such as cryptography and natural language processing. Reversible logic efficiently prevents energy wastage through thermal dissipation. This study presents a comprehensive exploration introducing new carry-select adders (CSLA) based on quantum and reversible logic. Five reversible CSLA designs are proposed and compared, evaluating various criteria, including speed, quantum cost, and area, compared to previously published schemes. These comparative metrics are formulated for arbitrary n-bit size blocks. Each design type is described generically, capable of implementing carry-select adders of any size. As the best outcome, this study proposes an optimized reversible adder circuit that addresses quantum propagation delay, achieving an acceptable trade-off with quantum cost compared to its counterparts. This article reduces calculation delay by 66%, 73%, 82%, and 87% for 16, 32, 64, and 128 bits, respectively, while maintaining a lower quantum cost in all cases.
Deep Learning-Based Robust Automatic Modulation Classification Using Higher Order Cumulant Features

The Internet of Things (IoT) represents one of the pivotal technologies in our daily lives. Within these systems, wireless communication plays an indispensable role in connecting IoT devices. Consequently, signal modulation emerges as a technique within wireless communication systems, enabling the transmission of baseband signals at higher frequencies. Given the array of modulation types, modulation classification comes into play to differentiate the modulation type of the received signal. Due to the presence of noise and attenuation, automatic modulation classification (AMC) stands as the primary method for such classification. AMC methods can operate without requiring any prior knowledge regarding the received signal. Multiple research groups have extended the depth of deep learning (DL) models or augmented the neuron count in each layer to enhance performance, albeit at the expense of increased model complexity. To tackle this challenge, we present model that merges the gated recurrent unit (GRU) and long short-term memory (LSTM) architectures. Furthermore, data pre-processing techniques, specifically Min-Max normalization and fourth-order cumulant (FOC), have been employed to enhance classification accuracy. The outcomes underscore that our proposed model surpasses both LSTM and GRU models. Moreover, the classification accuracy of normalized data outperforms that of data without normalization.
Algorithmic moderation: contexts, perceptions, and misconceptions

Algorithmic moderation is often presented as the only solution to the scale of harmful and hateful content disseminated online. This chapter questions this assertion by contextualizing the need and scope of algorithmic moderation based on normative considerations, followed by a set of critical issues to be approached in the study of algorithmic moderation, such as the systemic biases that underlie technical approaches to algorithmic performance and asymmetries in access to AI data and knowledge. We then illustrate these issues with the specific case of hate speech and finally consider some of the differences in how human and algorithmic moderators are perceived. This chapter is structured as a starting point for scholars who wish to venture into the study of algorithmic moderation, its antecedents, and consequences.
Barriers to regulating AI: critical observations from a fractured field

This chapter reflects upon the current state of artificial intelligence (AI) regulation, observing how both AI and its regulation are fragmented and fractured. We observe this fracturing across three axes. The first axis concerns definitional fractures, drawing attention to the fragmented state of AI as a concept and as an object of regulation. The second axis considers regulatory interventions, particularly how regulatory responses have not been unified or consistent in responding to the development and spread of AI. The final axis points to the unequal dispersal of benefits and burdens, and how AI regulation unevenly treats different actors. Drawing on Jackson’s concept of repair work, we argue that these fractures, while being evidence of suboptimal regulation, also provide new opportunities to reflect on the underlying dynamics of AI and its regulation in context.
Application of trapped ions in quantum-computing: Fidelity, scalability and integrated technology

Abstract Quantum computing, which is based on the rules of quantum mechanics, has great potential in many fields for its theoretical ability to simulate any natural process based on quantum mechanics. Many types of physical implementations of quantum computers have been developed, and the method based on trapped ions is a promising one. However, there is still a lot of work that need to be done, like how to improve the fidelity and scalability of those quantum computers. Besides laser light, microwave and waveguides have also been used in some experiments.
Current Situation and Optimization Strategy of Classroom Management of English Teachers in Junior High School

The classroom is not a single role shown by teachers, but a teaching interaction field constructed between teachers and students under the guidance of teachers, so that every student can actively integrate into learning in the classroom. In Junior High school, students' own self-awareness is relatively strong, the sense of classroom cooperation with teachers is relatively weak, which will produce a strong rebellious psychology and requires teachers to scientifically allocate their own teaching time in the classroom. Fully understand the shortcomings and deficiencies of students, develop teaching activities to help students get better improvement and growth. In order to maximize their enthusiasm for learning, maximize their development, and achieve effective teaching based on the development of subjects. Therefore, this paper studies the current situation and optimization strategies of classroom management of English teachers in Junior High school.
Research and Analysis of Teaching Functions Based on Virtual Reality Technology

This paper aims to investigate the application scenarios of virtual reality technology in teaching and its impact on student learning outcomes. The article first introduces the application scenarios of virtual reality technology in teaching, including practical simulation, gamified learning, and situation creation. It then discusses the impact of virtual reality technology on student learning outcomes, such as enhancing learning motivation, improving practical experience, and increasing learning effectiveness. The paper also analyzes the challenges of virtual reality technology in teaching, including device and cost limitations, teacher training and technology application, data privacy and security issues, and the design of blended learning models. Finally, recommendations are provided for constructing effective teaching programs based on virtual reality technology, including goal setting and instructional design, selection of learning content and scenarios, student engagement and feedback mechanisms, as well as evaluation and improvement.