Reviewerly/exHarmony huggingface dataset:
The Application Status and Thinking of VR in Architecture

The development of information technology has helped the application of VR technology in the field of construction to become popular, and assisted construction practitioners to achieve a lot of achievements in scientific research, design, teaching, etc. A timely understanding of the application status of VR technology in the field of construction is of great help for the cooperation and development of VR technology and architectural disciplines. In this paper, by combing the application of VR technology in scientific research, design and education in the field of architecture in recent years, combined with the achievements of Beijing University of Civil Engineering and Architecture - the spatial perception experiment based on panorama roaming, the VR full-cycle participation and the topic of urban design detail teaching with the theme of “Red Core Area, Re-walking the May Fourth Road” pointed out the application requirements of architecture majors for VR technology, summarized the current limitations of VR technology in the field of architecture, and the discipline's impact on VR expectations for technological development.
Changes in the Lifelong Learning Participation Patterns in the Early Stage of Covid-19 Spread and Their Implications

코로나19 팬데믹으로 인한 비대면 원격수업은 학교교육과 평생학습 분야에 학력격차라는 심각한 영향을 미쳤다. 그러나 학교교육에서는 이와 관련된 연구가 활발하게 진행된 반면 평생학습 분야에서는 실증적인 연구가 이루어지지 못했다. 이에 따라 본 연구에서는 코로나19 확산 초기 우리나라 평생학습 분야에서 발생한 전반적인 변화의 특징을 파악하기 위해 평생학습 전체 참여율, 원격형태를 통한 평생학습 참여 비율, 계층 간 평생학습 참여율의 격차를 「평생학습 개인실태조사」를 바탕으로 비교·분석하였다. 자료를 분석한 결과, 전반적으로는 이 세 가지 영역에서의 변화가 두드러지지 않았으나 세부적으로는 상당히 다른 수준의 변화가 나타났다. 따라서 본문에서는 이들 세 가지 영역에서 얻어진 분석 결과를 종합하여 지금까지 평생학습 분야에서 감지된 변화와 그 변화가 앞으로의 우리나라 평생학습 영역에 주는 함의가 무엇인지 논의하려 한다. With the forced adoption of online classes due to the spread of COVID-19, an educational gap has become severe in school education. While empirical research on this matter in the context of school education has burgeoned, little has been done in the field of lifelong education. To address this loophole, this paper empirically analyzed the changes in Korea’s lifelong education in the aftermath of COVID-19 regarding the overall participation rate of lifelong education, the participation rate of online lifelong education, and the gap in participation rate between classes. From the analysis, we identified a few significant changes and drew implications for the future of lifelong education.
Application of biotechnology for recycling and remanufacturing based onspeech recognition technology

In order to better respond to the requirements of energy saving and emission reduction and achieve the goal of product recycling and remanufacturing, a design method of recycling and remanufacturing sharing information platform based on speech recognition technology is proposed. Based on speech recognition technology, the service cycle of remanufactured products is investigated, and the structure and model of sharing information platform are constructed, so as to better improve the efficiency of the platform. Finally, the experiment proves that the recycling and remanufacturing sharing information platform based on speech recognition technology has higher effectiveness and practicability in the practical application process, and fully meets the research requirements.
Application of Gesture Estimation Method Based on Computer

This paper will study the computer vision pose estimation algorithm and apply it to fall detection. At present, the problem of aging cannot be ignored. There are more and more elderly people living alone. Due to their poor physical fitness, they are very easy to fall in daily life and bring great harm to the body and mind of the elderly. Therefore, they are in great need of means to monitor the daily life of the elderly in real time and make judgments on fall events. Driven by the rapid development of computer vision technology, human pose estimation has been widely used in fields such as motion recognition, motion posture scoring, and assisted rehabilitation therapy. The core of pose estimation is to detect the joint information and bone information of the human body from the image, and connect them to form a bone image to help the computer understand the human body information. The use of pose estimation for fall detection requires the extraction of human joint point information and bone information. In order to extract human joint point coordinates and bone images from video sequences, this paper has done a lot of research on computer vision pose estimation. First, the background subtraction method is studied. After that, the openpose pose estimation algorithm is studied. then studied the attitude estimation of Azure Kinect DK depth camera, focusing on its TOF principle and body tracking principle. After comparison, it is found that traditional pose estimation such as openpose is based on optical RGB images. In this paper, we mainly use Azure Kinect DK depth camera for fall detection. In the fall detection method, this paper uses the threshold method combined with pattern recognition method to detect falls. The thresholding method uses the head fall speed and the body tilt angle. The tilt angle of the upper body is used to solve the bending and squatting errors. The pattern recognition method transforms fall detection into two classification problems, fall and no-fall, and uses a support vector machine binary classification method combined with a directed gradient histogram to extract features from skeletal images for classification. The final experimental data show that this paper implements fall detection based on human posture estimation using Azure Kinect DK depth camera with high accuracy and practicality.
Double exponential smoothing and its tuning parameters: A re-exploration

Double Exponential Smoothing (DES) has broad application in various fields primarily as a forecasting tool. The values of the two smoothing parameters α and, involved in DES, are traditionally chosen by the users which yield minimum MSE. In this work the authors endeavor to assess the performance of the DES as a filter and tried to suggest the suitable values of these tuning parameters for which DES perform best as a filter. In this regard along with the conventional MSE method, the dependency of the stability of the filter on these tuning parameters and other aspects associated with the frequency response of the filter like transfer function, cutoff frequency, bandwidth and center frequency on these smoothing parameters are also studied. The values of the parameters close to 0.5 are found to be most appropriate when DES acts as a filter.
Empirical evaluation of classifiers for breast cancer diagnosis

Breast cancer is the second most type of cancer diagnosed in women; it is also the leading cause of cancer caused deaths in women after lung cancer. Breast lumps can be classified as cancerous and non-cancerous. Non-cancerous breast lump development is very common in women. It is important to correctly diagnose the type of breast lump to administer the correct treatment and give the needed care and attention. Intensive research is being done to improve the diagnosis of the type of breast lumps. In this paper we will study different machine learning algorithms for the diagnosis of breast tumors and to predict whether its cancerous or non-cancerous. In this paper we will be building four different classification methods SVM, KNN, RF and CART. We will be using the breast cancer Wisconsin (diagnostic) dataset to train the models. We will base the performance of our models based on the accuracy and other classification evaluation parameters. For the final model we were able to achieve a prediction model with an f1 score of 0.9927.
Supplemental Material: Answering Geosciences Research Questions at a Global Scale via a Hybrid Machine-Human Learning Approach: A Case Study of the Link between Climate and Volcanism

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NiPA: Nicknames Pool Approach for Protecting Privacy of User Data in Intelligent Transportation Systems

The intelligent transportation system has made a huge leap in the level of human services, which has had a positive impact on the quality of life of users. On the other hand, these services are becoming a new source of risk due to the use of data collected from vehicles, on which intelligent systems rely to create automatic contextual adaptation. Most of the popular privacy protection methods, such as Dummy and obfuscation, cannot be used with many services because of their impact on the accuracy of the service provided itself, they depend on changing the number of vehicles or their physical locations. This research presents a new approach based on the shuffling Nicknames of vehicles. It fully maintains the quality of the service and prevents tracking users permanently, penetrating their privacy, revealing their whereabouts, or discovering additional details about the nature of their behavior and movements. Our approach is based on creating a central Nicknames Pool in the cloud as well as distributed sub-pools in fog nodes to avoid intelligent delays and overloading of the central architecture. Finally, we will prove by simulation and discussion by examples the superiority of the proposed approach and its ability to adapt to new services and provide an effective level of protection. In the comparison, we will rely on the well-known privacy criteria: Entropy, Ubiquity, and Performance.
A Cloud Secure Storage Mechanism Based on Data Dispersion and Encryption

This describes cloud storage system, it support for rapid development of cloud computing data leakage prevention algorithm and it implemented secure storage mechanism. CSSM adopted a hierarchical management approach and combined user password with secret sharing to prevent cryptographic materials leakage. Advanced encryption standard algorithm(AES) is implemented for encryption of data. The proposed scheme ensuring the reduced overhead and latency in system. The objective is to secure the cloud data with reduced leakage system. The main objective of the proposed mechanism is to secure cloud storage against data breach, which may be the result of targeted attack or management negligence (e.g. misconfiguration), in case hackers even some malicious administrator is able to steal user data.
A Novel Approach for Analyze and Prediction of Bitcoin Price using Machine Learning

Bitcoin is a type of Internet currency that is both a digital asset and a payment method. It enables for anonymous payment from one person to another, making it a popular payment mechanism for online illegal activity. Due to its recent price increase, Bitcoin has gotten a lot of attention from the media and the general public. The goal of this research is to discover the Bitcoin price's predictable price direction. Machine learning models are likely to provide us with the information we require to understand the future of cryptocurrency. It won't tell us what will happen in the future, but it might show us the overall trend and direction in which prices are likely to move. The proposed methodology aims to create a machine learning model that uses data to learn about the patterns in the dataset and then uses a machine learning algorithm to forecast the bitcoin price.
Geospatial Analysis of Safe Delivery App Events Based on Geographically Weighted Regression Tool

Abstract. This study explores the spatial relationship between the number of recorded events of the Safe Delivery App, which is a mobile learning tool to train midwives in developing countries, during three months, and several independent variables, including the number of health facilities, pregnancies, number of women of childbearing age, number of infants aged 0–1 years, mobile network coverage data and total population density. The study aims to identify and analyse the reach of the Safe Delivery App at the district level in Ghana country, considering the correlation between dependent and independent variables. The geospatial analysis of App usage events layered with several related explanatory variables is based on Geographically Weighted Regression. The explanatory variables were able to predict and explain the number of events with of accuracy score of 90 % at the district level. The results have provided valuable insights into the further roll-out of the App and helped to highlight the districts that need further support to roll out the Safe Delivery App considering the analysed independent variables.
Three-Periods Optimization Algorithm: A New Method for Solving Various Optimization Problems

Using appropriate tools to solve optimization problems is a major challenge in various sciences. In this paper, a new optimization algorithm called Three-Period Optimization Algorithm (TPOA) is designed to provide suitable quasi-optimal solutions for various optimization problems. The main idea in designing the proposed TPOA is to manage the updating of population members during replication of the algorithm. In this way, the total execution time of the algorithm is divided into three periods in which each period follows a specific update process for members of the population. In the first period, a composite member, in the second period, the worst member of the population, and in the third period, the best member of the population is used to update the members of the population. The proposed TPOA is mathematically modeled and implemented on a standard set of objective functions of unimodal and multimodal types. The optimization results indicate the optimal ability of the TPOA to solve various optimization problems and provide appropriate quasi-optimal solutions. To analyze the quality of the proposed TPOA, the results are compared with the performance of eight optimization algorithms, including Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Grey Wolf Optimization (GWO), Whale Optimization Algorithm (WOA), Marine Predators Algorithm (MPA), Tunicate Swarm Algorithm (TSA), Gravitational Search Algorithm (GSA), and Genetic Algorithm (GA). Examining the simulation results and comparing the performance of optimization algorithms shows that the proposed TPOA has high power in solving optimization problems and is superior and much more competitive than the eight compared algorithms.
Index/Impressum: Vakuum in Forschung und Praxis 4/2022

Vakuum in Forschung und PraxisVolume 34, Issue 4 p. 59-59 Magazin Index/Impressum: Vakuum in Forschung und Praxis 4/2022 First published: 25 August 2022 https://doi.org/10.1002/vipr.202270408AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume34, Issue4August/September 2022Pages 59-59 RelatedInformation
Internet of Things (IoTs)-enabled security using artificial intelligence and blockchain technologies

Blockchain is a private key cryptography protocol of network-to-network chain. Blockchain technology is used in many sectors since it improves the value-exchange business to an advanced level. Starting from bitcoin to healthcare, it plays a substantial role in the application. In this real-world, blockchain applications transform society to a higher level by protecting business deals, quick processes, error free, safety, and efficiency. The following are some of the protruding applications of blockchain. Blockchain healthcare is for the secure sharing of medical data. Blockchain music is for royalties tracking, transparency, and ownership rights. Blockchain asset management is a real estate processing platform used for crossborder payments. The blockchain ledger simplifies the whole process, reduces error by encrypting the records along with no intermediaries. The Internet of Things (IoT) is a peer-to-peer computing device. It transfers data without human-computer interaction. IoT with blockchain brings an improvement in the lifestyle through smart cities project. It helps to combat security breaches, and it can control devices when away from home, alert when cookies are ready, etc.Blockchain plays an important role in supply chain and logistics monitoring. It uses sensors through that it provides location and condition transparency around the world. The supply chain is one of the application areas among blockchain. It has tremendous scope since it is an integral part of the Indian economy. Also, it is the best use-case for blockchain. Other use-cases are a charity, trading, certificate verification, digital identity, and copyright protection. Many mobile-based and online-based applications are available for specific processing benefits, such as Burstiq, Mediachain, Propy, Opskins, Chainalysis, Filament, Hypr, Xage Security, Civic, Evernym, Ocular, Dhl, Maersk, Madhive, Voatz, and Delaware.Blockchain application demand is fascinating and advancing since it is a new technology. Every year, it increases the market growth. Blockchain technology and crypto economics are the heart of many growing industries. Blockchain applications are the solution to improve data integrity to the highest standards. Their technology has the potential to revolutionize big data with better security and data quality.
Physics Case Study: Platformer Game

In this chapter, we will consider how important game physics is in the development of games. We will also see how physics is implemented in the FXGL game engine and how it can be used in FXGL games. More specifically, game physics typically serves two purposes: collision detection and simulations, both of which will be covered in the context of FXGL. By completing this chapter, you will learn the following: commonly used collision detection techniques, how to add collision handling code and how to implement physics in games.
CDEF: Cross-Domain Explanatory Framework for Non-overlapping Data

A cross-domain recommender system utilizes the concept of transfer learning for deciphering the data sparsity problem by fetching knowledge from a dense source domain to help with recommendations in a relatively sparse target domain. The performance of a cross-domain recommender system can be influenced by a variety of factors such as various data characteristics of the source domain. The purpose of this research is to evaluate the effect of various source domain data features on the performance of the target domain. Towards this goal, this work presents a regression-based model using analytical modeling approach to test the hypothesis of whether source domain data properties can impact the performance of the target domain system. We ran various experiments and showed that the target recommendation accuracy is dependent on the identified source data characteristics. The proposed Cross-Domain Explanatory Framework can lead to an appropriate data collection process to maximize the performance of the target domain and the expected performance of the system can be estimated in advance.
Adaptive Backpropagation Algorithm for Clustered Indoor Motion Planning

Grid based navigation is the simplest navigation methodology for unmanned ground vehicles (UGV) particularly for indoors. The grid map formation, grid cell occupancy, self-localization and avoiding obstacles in derived path are major considerations in navigation. This research work elaborate a new strategy of mobile robot navigation to goal point using an Adaptive Backpropagation tree based algorithm. For a confined stopping point like a charging station for an autonomous vehicles, this work provide minimal solution to reach that point. The path exploration begin from the stop point rather than the start point where robot is located. This backpropagation strategy implemented in real time scenario for its effectiveness. The simulation and experimental results gives the scope for robotic applications such as advance to stationary parking point or charging point.
An Efficient Salt and Pepper Noise Reduction Approach for Video(s) Using Optimized Filter Approach

In today's digital world, most information is shared through images or videos. Images and videos are more infor-mative than textual data. During the image or video capturing process, addition of noise is one of the major problems. The presence of noise in captured data causes results to be misleading, particularly in computer vision and image processing tasks. As a result, preprocessing is required before tackling tasks like edge detection, object detection, object recognition, salient object detection, video summarization, and so on. Gaussian, Salt & Pepper, Poison, and Speckle noise are the most common types of noise which can affect the video. In this article, the author(s) presented an efficient noise reduction approach for reducing the salt and pepper noise present in the image or video. The performance of the proposed approach is compared with traditional and widely used filters for noise reduction, such as mean, mode, and median. The experimental analysis of the proposed approach is done on the dataset where the videos are captured using the camera as well as from the other resources such as Internet. Noisy videos are prepared by mixing the Salt & Pepper noise with different noise densities (levels) in the video dataset. The experimental results show that the proposed approach outperforms the traditional approaches not only in terms of noise reduction but also in preserving the details in the video.
Personal Aide Application Using A.I Techniques … !

Now a days Machine Learning Methods are doing an excellent job at analysing and preventing damage before it occurs., however the models developed are only intended to be used in the applications for which they were designed. We are attempting to build an application in order to tackle this issue by combining several models to work together. This is an application that seems to have forecasts about a disease based on symptoms., weather., and admission analysis by utilising a chatbot. To analyse and process user instructions NLP Techniques are used so that the given commands may provide the necessary result. The main goal is to combine multiple machine Learning models and develop a Chatbot that can access the properties of the combined model. We conducted a thorough review of recent literature in this work. We looked at a lot of papers over the previous years that were about chatbots. Then we discussed many relevant efforts as well as the AI ideas required to create an intelligent conversational agent based on Machine Learning and deep learning models. Finally., we provided a functional architecture for developing an intelligent chatbot for Personal Assistance.
Tackling Threatening behavior through a Semantic Approach

We introduce a new approach to characterize and detect threatening behaviors in surveillance systems, without relying on history or expertise. This approach consists in simulating the worst-case attack plans, fusing their semantic descriptions and using the produced patterns to raise alerts in operational conditions. We demonstrate our set of tools on a simple scenario involving geolocated sensors looking for moving vehicles targeting a protected objective. We find that the system is able to recover well-grounded graph patterns defining detection rules which make sense in the operational context. We believe that our approach achieves a relevant compromise between data-based and expertise-based systems, and allows for a good balance between efficiency and understandability.