Reviewerly/exHarmony huggingface dataset:
Multi-Stage Spatial Resolution Enhancement Framework of FY-3D MWRI

The data acquired by the Microwave Radiation Imager (MWRI) on the FengYun-3D(FY3D) satellite has been affected by numerous degradation factors, leading to a decrease in spatial resolution. Due to the irreversible high-frequency information degradation that occurs during the degradation process, existing spatial resolution enhancement methods are unable to perfectly restore the high-frequency information of the raw measurements.In this paper, instead of single-stage approaches, a multi-stage approach is proposed to handle MWRI’s image restoration problem, which enables the gradual recovery of high-frequency information. Experiments performed using simulated and actual MWRI data demonstrate the method’s convenience and effectiveness.
Innovative Exploration of Higher Vocational English Education and Teaching in the New Media Era

Higher vocational English education and teaching are undergoing a profound change in the era of new media. With the continuous development and application of information technology, new media technologies such as digitization, intelligence and networking have profoundly changed the form and content of English education and teaching. Teachers and educators should explore innovative education and teaching models and teaching methods from a new perspective. They should combine the advantages of new media technology, give full play to the role of digital and intelligent tools, improve students’ comprehensive English listening, speaking, reading and writing skills, and cultivate English talents in the new era of the 21st century with opportunities and challenges, which is also an inevitable requirement to adapt to the times and social development.
Computational Intelligence Based Point of Interest Detection by Video Surveillance Implementations

Latest advancement of the computer vision literature and Convolutional Neural Networks (CNN) reveal many opportunities that are being actively used in various research areas. One of the most important examples for these areas is autonomous vehicles and mapping systems. Point of interest detection is a rising field within autonomous video tracking and autonomous mapping systems. Within the last few years, the number of implementations and research papers started rising due to the advancements in the new deep learning systems. In this paper, our aim is to survey the existing studies implemented on point of interest detection systems that focus on objects on the road (like lanes, road marks), or objects on the roadside (like road signs, restaurants or temporary establishments) so that they can be used for autonomous vehicles and automatic mapping systems. Meanwhile, the roadside point of interest detection problem has been addressed from a transportation industry perspective. At the same time, a deep learning based point of interest detection model based on roadside gas station identification will be introduced as proof of the anticipated concept. Instead of using an internet connection for point of interest retrieval, the proposed model has the capability to work offline for more robustness. A variety of models have been analysed and their detection speed and accuracy performances are compared. Our preliminary results show that it is possible to develop a model achieving a satisfactory real-time performance that can be embedded into autonomous cars such that streaming video analysis and point of interest detection might be achievable in actual utilisation for future implementations.
Automatic Threshold RanSaC Algorithms for Pose Estimation Tasks

When faced with data corrupted by both noise and outliers robust estimation algorithms like RanSaC are used, especially in the field of Multi-View Stereo (MVS) or Structure-from-Motion (SfM). To find the best model fitting the data, from numerous minimal samples it evaluates models and rates them according to their number of inliers. The classification as inlier depends on a user-set threshold that should be tailored to the noise level of the data. This requires the knowledge of this level, which is rarely available. The few existing adaptive threshold algorithms solve this problem by estimating the value of the threshold while computing the best model. However, it is hard to obtain ground-truth for MVS and SfM tasks and usually test datasets are based on the output on some state of the art algorithm, which prevents the objective evaluation of new algorithms. We propose a new method to generate artificial datasets based on true data to get realistic and measurable results. We use this method to benchmark different automatic RanSaC algorithms and find out how they compare to each other and identify each algorithm’s strengths and weaknesses. This study reveals uses cases for each method and the possible tradeoffs between performance and execution time.
Asynchronous Gaussian Filtering Fusion for Human Motion Estimation Based on RGB-D Cameras

This article studies the asynchronous Gaussian filtering fusion (AGFF) problem of 3-D human motion estimation (HME) for human-machine cooperation based on RGB-D cameras, and a flexible filtering structure is proposed for integrating asynchronous measurements with a prior motion model and compensating for measurement uncertainties implicitly. Considering the complexity for capturing human pose from the color images, the measurements from the depth camera are introduced to correct prior estimates periodically, while the measurements from the color camera are generated by an event-triggered strategy. To improve the robustness against misrecognition, the measurements are fused progressively and sequentially in the AGFF method. Moreover, under the filtering structure, a new detection and compensation mechanism for misrecognition is designed to improve the filtering performance by using color-based measurements. Finally, the effectiveness of the proposed AGFF method is verified through simulations and experiments.
DIaC: Re-Imagining Decentralized Infrastructure As Code using Blockchain

With the recent advances in concepts like decentralized “cloud” and blockchain-enabled decentralized computing environments, the legacy modeling and orchestration tools developed to support centrally managed cloud-based ICT infrastructures are challenged by such a new paradigm built on top of decentralization. On the other hand, decentralized “cloud” and computing infrastructures need to support many Dapp use cases. As the complexity of these targeted application scenarios increases, there is an urgent need for developing automation and modeling tools for deploying and managing decentralized infrastructures. Instead of creating such tools from scratch, a natural approach is extending mature infrastructure modeling tools for Dapps and decentralized computing environments. To this end, in this work, we have developed extensions to the TOSCA domain-specific language to support smart contract specification of decentralized computing infrastructures for supporting Dapps, where smart contracts or chain codes manage a decentralized computing environment. The result is blockchainbased orchestration and automation for decentralized “cloud” and computing environments that use existing infrastructure as code tools to deploy and manage decentralized applications.
S206 Predicting Recurrent C. difficile Infection in IBD Patients: An Application of AutoML Tabular and Text Classification Models on Electronic Health Records and Clinical Notes

Introduction: The utilization of traditional machine learning models on big data demands substantial expertise. To overcome this challenge, Automated Machine Learning (AutoML) has emerged as a promising tool to automate parts of the machine learning pipeline. We evaluated AutoML to predict recurrent C difficile infection (rCDI) in Inflammatory Bowel Disease (IBD) patients. Methods: We included IBD patients with primary CDI and developed 2 models: one leveraging structured Electronic Health Record data for a supervised machine learning model, and another using clinical notes to predict recurrent CDI using natural language processing (NLP) for patient with > 5 clinical notes. Data were processed and uploaded onto a HIPPA compliant Google platform via the Mayo Clinic AI Factory. Data files were formatted to be compatible with the AutoML platform. The dataset was divided into 80/10/10 split for training, validation, and testing sets respectively. For tabular model, performance was evaluated based on the Area under the Receiver Operating Characteristic curve (AuROC), accuracy, precision, and recall and for text classification, the metrics included average precision which is Area under the Precision Recall curve (PR AUC). Results: Of 2,573 patients, 655 (25.4%) had recurrent CDI, an ML model using structured data was trained, validated, and tested on a dataset consisting of 2058, 257, and 257 patients respectively. The model demonstrated promising results to predict rCDI, achieving an AuROC 0.853 and precision and recall, both standing at 78%. (Figure 1A). For the NLP model, we utilized clinical notes from 2,100 patients, of which 508 (24.1%) had rCDI. The model was trained, validated, and tested on a dataset divided into 1680, 210, and 210 items, respectively. The NLP model performed well with PR AUC of 0.827, with both precision and recall at 77.6%. (Figure 1B). We observed similar accuracy and performance with a supervised learning model for tabular data using XGBoost with manual coding (abstract submitted separately to ACG 2023). Conclusion: Leveraging minimal coding and diverse data, we developed high-performing algorithms to predict recurrent CDI using autoML. These models demonstrate comparable performance with traditional machine learning approaches. This emphasizes a promising potential of AutoML in delivering accurate predictions while streamlining the development process.Figure 1.: Performance of Automated Machine Learning models using AutoML Tabular and Text Classification.
Quantum steering in two-forked tree-shaped networks

Abstract Network quantum steering (NQS) arises from network models required with classification of trusted and untrusted parties. The network local hidden state (NLHS) models have first been proposed to define the NQS in a linear network with end points being trusted. In the paper, we devote to establishing the NLHS model to define the NQS in a kind of more complex and applied-extensively networks, namely, the two-forked tree-shaped network. Here we assume that the parties at the last layer are trusted while the remaining parties are untrusted in this network. According to the NLHS model, we observe that network nonlocality implies network steerability. Furthermore, we pay more attentions to discovering the relationship between the network quantum unsteerability and separability/unsteerability of bipartite sources in this two-forked tree-shaped network.&amp;#xD;Moreover, we generalize two kinds of bipartite steering inequality criteria as the NQS criteria. They are built based on statistical quantities, which can be directly evaluated in experiments.
THE ROLE OF NURSING IN HYPERBARIC THERAPY ADMINISTRATION

Hyperbaric oxygen therapy has evolved over time, as well as the role of nurses and is currently recommended in several treatments. Although the concept of hyperbaric oxygen therapy has received some attention, as a structural theoretical construction among different professional sectors, it was possible to verify that there are few studies carried out on the subject. This justifies the need to map and organize the studied knowledge. Thus, with this study, we intend to contribute to the knowledge of the nurse’s role in the administration of hyperbaric therapy and to reinforce their role in this context. This study has, therefore, as a starting point the following research question: “What are the nursing interventions in the use of hyperbaric therapy?”. Hyperbaric oxygen therapy, despite still having limited availability in our country, has a recognized importance in the health sector. With this review, it is hoped that a detailed analysis and clarification of the role of nurses will be possible. By mapping the respective impact in practice, it is intended to encourage future research on the subject, with a focus on improving our health services.
Development of Sound Wave E-learning Material by Integrating Contextual Teaching with Smartphone to Improve Students’ Critical and Creative Thinking Skills

Twenty first century skills are necessary for students to solve problems and be successful in learning. One of the core subjects of 21st century skills is learning and innovation skills. Learning and innovation skills include critical thinking and creative thinking skills. Problems found in the field show that students' critical and creative thinking skills are still low. The solution is to develop sound wave E-learning material by integrating contextual teaching with smartphones to improve students' critical and creative thinking skills. The purpose of this study was to determine the results of the needs analysis, the validity test, and the practicality test of the use of e-learning material. The research method used is the Hannafin and Peck development model which consists of needs analysis, design, and development and implementation. The research data were in the form of questionnaires, performance questions, validation and practicality instruments. Data analysis used a Likert scale, using Aiken's V formula to analyze the validity level and statistical analysis formulas to analyze the practicality of e-learning material. The results of the analysis showed that students' critical and creative thinking skills were still low. Then the developed e-learning material are valid with a value of 0.89 and the level of practicality is very good with a value of 86.52. Based on the research conducted, it can be concluded that the sound wave e-learning material by integrating contextual teaching with smartphone are valid and practical so that they can be used in the learning process
Supplemental Material: Ordovician–Early Devonian granitic magmatism as the consequence of intracontinental orogenic activity along the Qinhang belt in South China

&lt;p&gt;Table S1: Trace element(x10-6) of zircon from the Miaoershan-Yuechengling batholith. Table S2: Ages and types of early Paleozoic igneous rocks in the SCB.&lt;/p&gt;
Local sampling of the SU(1,1) Wigner function

The Wigner phase-space formulation for systems possessing SU(1,1) symmetry has been defined by Seyfarth et al. [Quantum 4 , 317 (2020)] tackling the difficulty in defining a suitable operational definition of the Wigner function. To further investigate this formulation, we propose a non-linear optical setup that incorporates photon-number-resolving detectors, which would enable a direct and comprehensive point-by-point sampling of the SU(1,1) Wigner function. We discuss the visualization of various two-mode quantum states and the effect of the losses in such a detection scheme.
A Novel Approach of Satellite Image Enhancement Using DWT Based on 2-Type Fuzzy Logic

This study proposes a new approach to enhance the dark and low-contrast satellite images captured under poor illumination. Images are converted from RGB to HSV color space in order to separate the Hue (H), Saturation (S), and luminance (V) components. Enhancement is achieved in saturation and luminance components using Discrete Wavelet Transform (DWT) and 2-type fuzzy logic. DWT breaks the images into high and low-frequency sub-bands and results in approximation and detailed coefficients. The approximation coefficient consists of low-frequency components, which are further modified using the 2-type Fuzzy Logic technique. Fuzzy logic is basically a point operation-based technique based on the fact that human perceived brightness characteristic is not linear. Inverse Wavelet Transform is used to achieve the enhanced saturation components. The luminance component (V) is passed through class-limited adaptive histogram equalization. All the enhanced components are then combined to get the enhanced image and finally reconverted to RGB color space. The proposed scheme significantly works well for enhancing and sharpening dark and low-contrast satellite images. The experimental results obtained by the proposed method show improved performance in terms of PSNR, MSE, mean, and variance.
Methods of studying the symmetry of elementary particles in higher education

The article addresses the current issue of teaching symmetry in elementary particles within technicalhigher education institutions. The study of symmetry in elementary particles plays a vital role in helpingstudents understand the fundamental laws of nature and the properties of fundamental particles. However,symmetry is a complex concept that requires deep understanding and analysis. To successfully master thisintricate topic in an educational environment, effective teaching methods and tools need to be developed.Modern students demand an active and engaging approach to learning, making the use of innovative methodscritically important.The article presents an analysis of contemporary approaches to teaching symmetry in elementaryparticles within higher education institutions. It places significant emphasis on innovative methodologiesdesigned to help students grasp complex symmetry concepts in physics. Specifically, it discusses the utilizationof modern pedagogical technologies, interactive tools, virtual laboratories, and computer simulations. Thepractical aspects of conducting laboratory work and experimental research focused on studying symmetry arealso explored.The article explores possible ways to enhance the effectiveness of teaching in this field. An essentialpart of the article involves analyzing the impact of symmetry education on the professional training of futureexperts in the field of physics and related sciences. Achievements in the study of symmetry also necessitate an33effective assessment system. It is crucial to consider the knowledge and skills acquired by students in this fieldso that they can independently analyze and apply symmetrical principles in their further research.The prospects for further scientific research involve the investigation of modern approaches to physicseducation, including the use of computer simulations, virtual laboratories, and interactive learning tools. Theseinnovations contribute to a better understanding of complex symmetry concepts. Research in this direction canhelp develop effective pedagogical approaches and tools that support students in the process of studyingsymmetry in elementary particles and enhance the quality of physics education.
The Implementation of Data Mining for Predicting XAU/USD Price Trends in the Forex Market on MetaTrader 5 using Naïve Bayes Method

The one of the trading instruments is forex or foreign exchange. The forex market provides various commodities, one of which is XAU/USD. XAU/USD dataset with daily, h4, h1 time frames obtained from the MetaTrader 5 application via the FBS broker. Predicting forex prices is difficult because there are various factors that influence it, so data mining methods are needed to predict increases or decreases. Naïve Bayes is a method and logic that can be applied in making predictions. So the research objective of this final project is to apply naïve Bayes methods and logic in predicting the price of XAU/USD on the daily, h4, h1 time frames. The application of the Naïve Bayes method uses several libraries to support research, namely pandas, jcopml, and sklearn. In naïve Bayesian logic research, this is called from the sklearn library using gaussianNB. In this study, the performance reference uses an f1 score matrix because the number of false positives and false negatives is not tight (symmetrical). This study produces values for each time frame obtained from the confusion matrix formula with f-scores of 49.99% (daily), 53.52% (h4), 55.44% (h1).
Design and Implementation of a Web-Based Delivery Service for President University: A Case Study of President's Pick-Up

&lt;p&gt;&lt;strong&gt;The advent of web-based delivery services has brought about a revolutionary change in the transportation of packages and documents, offering unprecedented convenience and efficiency to users. In line with this trend, President's Pick-Up, a dedicated platform implemented with PHP and MySQL, emerged to cater to the package delivery needs of President University students. This thesis investigates the impact of President's Pick-Up as a web-based delivery service on user satisfaction, efficiency, and speed of package delivery within the President University community. Leveraging the power of PHP and MySQL, President's Pick-Up provides a user-friendly website platform for students to conveniently send packages to different dormitories on campus. The research evaluates the effectiveness of President's Pick-Up in meeting user needs, tracking package deliveries in real-time, and enhancing the overall package delivery experience for students. The study contributes valuable insights into PHP and MySQL-driven web-based delivery services and aims to guide future improvements in similar platforms catering to academic institutions.&lt;/strong&gt;&lt;/p&gt;
Chord Generation Based on Transformer and Color Harmony Theory

Artificial intelligence has been widely used in the field of music and achieved good results. As an important concept in music, chord generation is one of the basic tasks in composition. The application of machine learning in music is of great significance to human’s understanding of artificial intelligence and music, and artificial intelligence technology can also enrich people’s research in music. In this paper, a chord generation model is constructed based on Transformer to further dig into the internal structure of chords and consider chords from the aspect of chord notes, hoping to generate more delicate and flexible chords. At the same time, combined with the color harmony theory, the evaluation method and mapping method meeting the rules of music theory are constructed, hoping to get better output effect through a simple model, and the template mapping method is used to improve the actual effect and extensibility of the model.
Blockchain Technology Adoption in the State of Qatar: Qualitative Risk Analysis

Businesses may gain from implementing Blockchain-based technology in terms of profitability, productivity, and efficiency, which may cause them to re-evaluate their current business strategies. However, because the technology is still evolving and there needs to be more study on the effects of the various Blockchain networks (such as public, private, and consortium), their significance in developing new business models needs more consideration. In this article, we identified the potential risks/opportunities associated with Blockchain adoption in the state of Qatar through the standard technique of Qualitative Risk Analysis suggested by the Project Management Institution (PMI) using a standard (5 x 5) probability impact diagram (PID) with seven areas of impact namely Cost/Budget, Schedule, Quality, Customer Satisfaction, Business Continuity or Service Disruption, Governance &amp; Compliance, and Environment. We found that circa 84% (i.e. 10 risks) of the most significant risks are related to the organisational and environmental risks, while only 16% (2 risks) are related to the technology itself. Our research highlights the benefits of Blockchain advancements to decision-makers for strategic planning.
The Effect of Using Monopoli Kata Media On Beginning Reading Skills for 1st-Grade Students

Students must learn the beginning reading since it is a crucial ability. This is due to the fact that reading abilities acquired during early reading activities have a significant impact on subsequent abilities. This study aims to investigate the effects of the Monopoli Kata Media used with first-gr