Reviewerly/exHarmony huggingface dataset:
Application of PSO-based LSTM Neural Network for Outpatient Volume Prediction

In order to study the construction method of long- and short-term memory neural network model, which is based on particle swarm optimization algorithm and its application in hospital outpatient management, we have selected historical data of outpatient volume of relevant departments in our hospital. Furthermore, we have designed and developed the outpatient volume prediction model, which is based on long- and short-term memory neural network. Additionally, we have used particle swarm optimization algorithm (PSO) to optimize various parameters of long- and short-term memory network and then utilized this optimized model to accurately predict the outpatient volume. Experimental observations, which are collected through the results of monthly outpatient volume prediction, show that Root Mean Square Error (RMSE) of the particle swarm optimized LTMN model on the test set is reduced by 48.5% compared with the unoptimized model. The particle swarm optimization algorithm has efficiently optimized the prediction model, which makes the model better predict the trend of outpatient volume and thus provide decision support for medical staff's outpatient management.
Privacy Protection of Blockchain Security Development Status

Blockchain is a decentralized, non tamperable and verifiable ledger, which is used to record the transactions of digital assets and ensure the non tamperability of transaction records. It is widely used in education, government, medical and other fields.However, the security and potential privacy of blockchain have also become the focus of current research. Based on the current security situation of the blockchain industry, this paper discusses the problems and threats of the blockchain system; Focus on analyzing the cutting-edge technologies of blockchain security and privacy protection; Finally, the future research direction of blockchain privacy protection technology is discussed.
FEEDBACK OF ENGINEERING STUDENTS ABOUT REMOTE CLASSES DURING COVID-19 PANDEMIC

Appears in: ICERI2021 Proceedings Publication year: 2021Pages: 754-758ISBN: 978-84-09-34549-6ISSN: 2340-1095doi: 10.21125/iceri.2021.0233Conference name: 14th annual International Conference of Education, Research and InnovationDates: 8-9 November, 2021Location: Online Conference
Organic Connection between Poverty Alleviation and Rural Revitalization from the Perspective of Government Public Information——a Big Data Analysis based on 53 Poor Counties in Henan Province

Not only the task of poverty alleviation in poverty-stricken counties is arduous, but also the foundation of Rural Revitalization is weak. In the period of convergence of the two strategies, how to effectively use the achievements of poverty alleviation and orderly promote the pace of rural revitalization is critical to realize the organic connection between them two. Based on 1124 news related to rural revitalization published by 53 poverty-stricken counties in Henan Province in recent two years, through natural language processing method, this paper deeply excavated the key information of organic connection between poverty alleviation and rural revitalization, and judged the development stages of rural revitalization in different counties. It was found that the time of getting rid of poverty was the key factor affecting the number and frequency of news. And to strengthen the grass-roots organizations and carry out extensive research was a common practice. However, there were only 5 counties whose system mechanism of rural revitalization had basically taken shape. Most counties were still in the stage of planning and design or key area breakthrough. According to the conclusions of this paper, it is useful to grasp the progress of rural revitalization of poverty-stricken counties in Henan Province, so as to adjust the work pace of the whole province.
Precision Instruction of College English Teaching Assisted by Information Technology

With the omni-directional popularization of Internet science and technology, with the help of a variety of mobile intelligent terminals, the era of big data has quietly come. In order to solve the problems of large college English teaching class, uneven foundation of students and lack of learning motivation, the author implements accurate college English teaching based on intelligent classroom in the class. Under the background of big data, through the learning data resource database and personalized training provided by the wisdom classroom to assist teaching, it is convenient for teachers to accurately grasp the real situation of each student. This paper discusses the ways, advantages and problems of accurate college English teaching with the assistance of information technology, in order to contribute to the reform of college English teaching.
Insights on embodiment induced by visuo-tactile stimulation during robotic telepresence

Using a simple neuroscience-inspired procedure to beam human subjects into robots, we previously demonstrated by visuo-motor manipulations that embodiment into a robot can enhance the acceptability and closeness felt towards the robot. In that study, the feelings of likeability and closeness toward the robot were significantly related to the sense of agency, independently of the sensations of enfacement and location. Here, using the same paradigm we investigated the effect of a purely sensory manipulation on the sense of robotic embodiment associated to social cognition. Wearing a head-mounted display, participants saw the visual scene captured from the robot eyes. By positioning a mirror in front of the robot, subjects saw themselves as a robot. Tactile stimulation was provided by stroking synchronously or not with a paintbrush the same location of the subject and robot faces. In contrast to the previous motor induction of embodiment which particularly affected agency, tactile induction yields more generalized effects on the perception of ownership, location and agency. Interestingly, the links between positive social feelings towards the robot and the strength of the embodiment sensations were not observed. We conclude that the embodiment into a robot is not sufficient in itself to induce changes in social cognition.
Multilabel classification of disease prediction in patients presenting with dyspnea

<b>Introduction:</b> Dyspnea is related to a wide range of health conditions. The underlying causes of dyspnea are not mutually exclusive; patients could have symptoms of multiple different diseases at the same time. The aim of this study is to identify patterns in patient data that indicate a probability associated with COPD, pneumonia, asthma, pulmonary embolism or heart insufficiency. <b>Methods:</b> The study was based on retrospective data from the Cantonal Hospital of Baselland in Switzerland from 2014. All adult patients with chief complaints of dyspnea, shortness of breath and air hunger were screened and the final diagnosis was evaluated independently by two General Internal Medicine residents. Predictors encompassed demographics, diagnostic tests and laboratory findings (using CREATE system, a tool for big data research). We applied a multilabel classification algorithm (which transforms the multilabel problem into binary classification problems, one for each label). The dataset was split into training (80%) and testing (20%) datasets and 10-fold cross validation was used. <b>Results:</b> Out of 4891 patients hospitalized at the internal medicine ward, 602 patients were included with chief complaints of dyspnea. Patients without any diagnosis of COPD, pneumonia, asthma, pulmonary embolism or heart failure (n=84) were excluded from further analyses. Preliminary results showed that classifier chains achieved good performance that is 78% precision, 65% recall, 67% F1 score. <b>Conclusion:</b> Multilabel classification might be useful to predict the risks of these five main diagnosis of dyspnea. However, we need further dedication to feature selection from the hospital patient records to improve predictions.
Overview of Mobile Autonomous Learning in Chinese Higher Education

Modern technology information makes students to acquire knowledge straightforwardly and quickly, assisting learners to achieve autonomous learning efficiently. In the context of Chinese higher education, autonomous learning increasingly involves Internet-based learning. Thus, by reviewing related literature, this study focuses on students’ autonomous learning in mobile learning environment.
Computer Training Programs And Testing As Tools For Distance Mathematical Disciplines Delivery

Mathematical disciplines play an important role in training quality specialists in all areas of economics and finance.Programs in mathematical disciplines at the Financial University under the Government of the Russian Federation provide a basis for the mastery of a wide range of competencies by students, which will make future specialists competitive in the labor market.A full-fledged study of mathematical disciplines and mathematical competencies development require a large number of academic hours, and in case of extramural students, most of the educational material is mastered independently.In this regard, an important role is played by interactive forms of organizing the educational process and independent work of students using computer learning technologies, such as computer testing, electronic textbooks, computer programs performing the functions of both teaching and the acquired knowledge verification.In connection with the 2020 pandemic, all higher education partially or completely adopted distance learning.Therefore, it is relevant to use and develop those methods and technologies that have been successfully used in extramural education being video lectures, computer training programs, computer final testing and testing for self-preparation, online seminars (webinars).All these modern methods do not exclude, but only complement the teacher's work with students and taken in totality create an effective learning environment contributing to quick adaptation to external constraints while maintaining and developing the main learning outcomes.It is stipulated by the fact that computer testing benefits self-training and verification of final knowledge as well as serves as an objective assessment in using a grade-rating system.
Crypto Dispute Resolution: An Empirical Study

Cryptocurrencies, such as Bitcoin and Dogecoin, are taking the financial world by storm. Like all financial instruments, these cryptocurrencies and the crypto exchanges they are traded on give rise to complex legal issues. This article concerns an understudied yet crucial aspect of crypto trading, namely crypto disputes and their resolution. The sheer number and growing popularity of cryptocurrencies means that disputes arising from their trading are likely to increase, yet it remains unclear how they are being, or will be, resolved. Novel issues surrounding the identity of traders, the jurisdictional limits of domestic courts, the applicable governing law(s), and complex technical evidence may mean that traditional non-binding mechanisms—such as negotiation and mediation, as well as binding legal mechanisms—such as litigation and arbitration, may be ill-equipped in the crypto context. At the same time, tailored crypto-specific dispute resolution mechanisms are only at nascent stages of development. In this article, we take a first empirical look at the mechanisms by which cryptocurrencies and crypto exchanges choose to resolve disputes with their users. We situate our results in the larger commercial dispute resolution literature and provide a preliminary account of how and why cryptocurrencies and exchanges are making these dispute resolution choices. We find that crypto platforms predominantly resort to domestic litigation and international arbitration to resolve future disputes. Moreover, we find that the ability to prohibit class proceedings seems to be the strongest explanatory factor in crypto platforms’ choice of international arbitration. In contrast, providing for the venue seems to be most strongly linked with choosing domestic litigation.
TWO-DIMENSIONAL GMM-BASED CLUSTERING IN THE PRESENCE OF QUANTIZATION NOISE

In this paper, unlike to the commonly considered clustering, wherein data attributes are accurately presented, it is researched how successful clustering can be performed when data attributes are represented with smaller accuracy, i.e. by using the small number of bits. In particular, the effect of data attributes quantization on the two-dimensional two-component Gaussian mixture model (GMM)-based clustering by using expectation–maximization (EM) algorithm is analyzed. An independent quantization of data attributes by using uniform quantizers with the support limits adjusted to the minimal and maximal attribute values is assumed. The analysis makes it possible to determine the number of bits for data presentation that provides the accurate clustering. These findings can be useful in clustering wherein before being grouped the data have to be represented with a finite small number of bits due to their transmission through the bandwidth-limited channel.
Human Activities Detection for Patient Convalescence

A simple activity recognition method can allow a solitary human being to monitor all the surroundings with the purpose to guarantee safety and confidentiality while protective maintenance cost and efficiency with the rising level of accuracy. This monitoring system with real-time video surveillance can be deployed for patients and the elderly in a hospital or old age home and airport along with numerous human activities. For speedy analysis of action and accurate result while working with complex human behavior, we decided to use YOLOv4 (You Only Look Once) algorithm which is the latest and the fastest among them all. This technique uses bounding boxes to highlight the action. In this case, we have collected 4,674 number of dissimilar data from the hospital with different condition of ourselves. During this study, we divided the human action into three different patterns such as standing, sitting and walking. This model is able to detect and recognize numerous patients and other various human activities. This research accomplishes an average accuracy of 94.6667% while recognizing images and about 63.00% while recognizing activity from video clips. This study works with YOLOv4 while it performs better than TensorFlow and OpenPose platforms. The article proposed the outcome for patients in early recovery based on human activities investigation and analysis.
Flexural properties of electrospun polymethyl methacrylate microfiber-reinforced BisGMA for dental post prefabrication

Introduction: A dental post is a restoration to preserve the remaining tooth structure thus can be functioned normally. Many researchers suggested a fiber dental post due to its biomechanical properties that are similar to dentin structure. This study aims to analyse the flexural properties of electrospun polymethyl methacrylate microfiber-reinforced BisGMA for dental post prefabrication. Methods: The sample used was following the ADA guideline, as well as for the number of samples. The sample size was 25×2×2mm, which is close to the average dental post size. PMMA microfibers were prepared by dissolving heat cure PMMA powder with 99% acetone, then electrospinning with a rotary collector. Acquired PMMA microfibers were immersed into the resin matrix containing BisGMA, camphorquinone, and 2-dimethylaminoethyl methacrylate (DMAEMA) as a monomer, initiator, and co-initiator, respectively, to prepare the dental posts. Results: PMMA microfibers structure and surface fracture of dental posts were confirmed by Scanning Electron Microscopy (SEM). PMMA microfibers show unaligned fiber morphology with an approximate diameter size of 1-5 µm. A universal testing machine was used to measure the dental post's flexural properties (flexural strength and flexural modulus). Dental posts with PMMA fibers showed higher flexural strength (83.5 ± 10.7 MPa) compared to the dental post without PMMA fibers (61.7 ± 3.03 MPa) with a p-value &lt;0.05. On the other hand, PMMA fibers' addition did not significantly increase the dental post's flexural modulus. Conclusion: The PMMA microfibers can intimately adhere to the BisGMA mixture as the resin matrix. Therefore, the PMMA microfiber significantly improves the flexural strength of the BisGMA for dental post prefabrication.
API Usage Pattern Search Based on Model Checking

Reusing existing class libraries can improve the productivity of software development. API usage patterns are useful resources for programmers in reusing class libraries. Existing approaches often exploit API graphs to model semantic relations between API elements of class libraries, and traversal graphs to capture API usage patterns. However, those approaches cannot describe the number of parameters and the number of overload methods, and may not search for various API usage patterns. In this paper, we propose an automatic and complete approach to searching for API usage patterns by model checking technique. We introduce a novel kind of API transition system model to completely describe the relations between all API elements of existing class libraries. We obtain API usage constraints from queries, namely the numbers and types of input and output objects and class types transformed based on APIs replacement model, and give the logical characterizations of the constraints by suitable CTL* (computation tree logic) formulas. Then, we can obtain suitable API usage patterns by model checking the logical formulas in related API transition system models based on model checker NuSMV. The experiments indicate that, in contrast to existing approaches, our approach can automatically and effectively search for various API usage patterns.
Developing Resistance To The Challenges Of Higher Education Sustainability In Covid-19 Conditions

The prerequisites of the study underlying this paper were the growing threats and challenges to the security of society, including the security of education in general, and universities in particular.The purpose of the study is to identify the leading directions of the transformation of the educational process in higher education, which may significantly increase its flexibility, which simultaneously meets the needs of introducing flexible management in the economy and social sphere, as well as the trends in the development of creative potential and the resulting need to improve talent management approaches.These areas include the transition to a non-linear model of education, the introduction of transdisciplinary methods, and the expansion of openness and creativity of the educational process.The main methods of study were the analysis of the situation formed in higher professional education, which was fundamentally changed in the conditions of the coronavirus pandemic, the study of trends in the development of higher education by Russian and foreign authors, observation and a pedagogical experiment conducted by the authors in 2020 as part of the educational process at Moscow Automobile and Road Construction State Technical University (MADI).As a result of the study, the most promising directions for the development of non-linear and transdisciplinary forms of higher professional education were noted, new forms of higher educational process and the teaching methods corresponding to them were identified, which were tested at the MADI Department of Sociology and Management and could be introduced into the practice of other higher educational institutions.
Hybrid Approach for Detection of Objects from Images Using Fisher Vector and PSO Based CNN

Owing to the near connection between object recognition and video processing and picture perception, a lot of research interest has been received in recent years. Standard methods of object detection are focused on manufactured technologies and slow-moving architectures. Fisher Vectors (FV) and Convolutional Neural Networks (CNN) are two picture arrangement pipelines with various qualities. While CNNs have indicated predominant exactness on various order assignments, FV classifiers are normally less exorbitant to prepare and assess. In this paper we propose a mechanism for detection of objects in image based on Fisher kernel and CNN with a PSO optimization technique. Here fisher kernel draws the global or statically features from the image object and CNN is used for local and more complex feature extraction from an image and here we use CNN with PSO to reduce the training complexity. Performance results shows that the proposed model is detect the object better than the existing models.
Exploring and Transforming Spaces Through High-Dimensional Gestural Interactions

Abstract. Almost every map or globe we come into contact with is distorted in some way, be it through cartographic projection, vertical exaggeration or data-driven morphing of distances in cartograms. And yet, once we utilize Virtual Reality technologies to position ourselves in a virtual reconstruction of a real or planned space, we usually default to a strict adherence to its real-world proportions and spatial relations. In search of an alternative conception of how such environments can be explored, this paper investigates a novel way of using the embodiment and high interactivity afforded by current VR technology to let users apply a wide range of transformations to their surroundings. Instead of utilizing a large number of predefined gestures that need to be learned before use, the full state of a user’s hand (including rotation, position, and joint angles) is tracked, directly mapped to a transformation matrix, and then selectively applied to the 3D environment. This is a complex and high-dimensional form of interaction, but through its embodied nature users can develop familiarity with it by unguided trial and error. Once accustomed, they can bend, shear, and manipulate the space around them with a variety of self-discovered gestural interactions. In the course of this paper, we discuss technical considerations, physiological limitations, possible use cases, as well as a number of recognizable gestures that emerged from the space of possible interactions after prolonged use.
Unsupervised Named Entity Recognition for Hi-Tech Domain

This paper presents named entity recognition as a multi-answer QA task combined with contextual natural-language-inference based noise reduction. This method allows us to use pre-trained models that have been trained for certain downstream tasks to generate unsupervised data, reducing the need for manual annotation to create named entity tags with tokens. For each entity, we provide a unique context, such as entity types, definitions, questions and a few empirical rules along with the target text to train a named entity model for the domain of our interest. This formulation (a) allows the system to jointly learn NER-specific features from the datasets provided, and (b) can extract multiple NER-specific features, thereby boosting the performance of existing NER models (c) provides business-contextualized definitions to reduce ambiguity among similar entities. We conducted numerous tests to determine the quality of the created data, and we find that this method of data generation allows us to obtain clean, noise-free data with minimal effort and time. This approach has been demonstrated to be successful in extracting named entities, which are then used in subsequent components.