Reviewerly/exHarmony huggingface dataset:
A Secure and Efficient Analytical Encryption Method for Industrial Internet Identification based on SHA-256 and RSA

With the development of Industrial Internet identification analysis, various encryption methods have been widely used in identification analysis to ensure the security of identification encoding and data. However, the past encryption methods failed to consider the problem of encryption efficiency in the case of high concurrency, so it will reduce the identification resolution efficiency and increase the computational pressure of secondary nodes when applying these methods to the identification analysis. In this paper, in order to improve the efficiency of identification analysis under the premise of ensuring information security, a safe and efficient analytical encryption method for industrial Internet identification based on Secure Hash Algorithm 256 (SHA-256), and Rivest-Shamir-Adleman (RSA) is presented. Firstly, by replacing the secret key in the identification encoding encryption with the SHA-256 function, the number of secret keys is reduced, which is beneficial to improve the efficiency of identification analysis. Secondly, by replacing the large prime number of the RSA encryption algorithm with multiple small prime numbers, the generation speed of RSA key pair is improved, which is conducive to reduce the computation of secondary nodes. Finally, by assigning a unique RSA private key to the identification code during the identification registration phase, SHA-256 and RSA are associated, the number of key exchanges is reduced during the encryption process, which is conducive to improve the security of encryption. The experiment verifies that the proposed method can improve security of encryption and efficiency of identification analysis, by comparing the complexity of ciphertext cracking and the identification security analysis time between the traditional encryption method and this method.
Keynote: Engineering the New Fabric of the Distributed Compute Continuum

As humans, things, software and AI continue to become the entangled fabric of distributed systems, systems engineers and researchers are facing novel challenges. In this talk, we analyze the role of IoT, Edge, and Cloud, as well as AI in the co-evolution of distributed systems for the new decade. We identify challenges and discuss a roadmap that these new distributed systems have to address. We take a closer look at how a cyber-physical fabric will be complemented by AI operationalization to enable seamless end-to-end distributed systems.
Interoperability, Anonymity and Privacy Issues in Blockchain for Healthcare Systems

The healthcare system of today is considered complex and expensive. This burden of complexity can be minimized by improving healthcare record management, utilizing insurance agencies, and leveraging blockchain technologies. Initially, blockchain implementations were developed to eliminate the dependency on centralized companies and or financial institutions for account-related money exchanges. Healthcare records, insurance billing, and smart contracts could be enhanced by using blockchain technology, which enables stable access, security of data, and the centralization of transactional information. Blockchain technology is most attractive due to its features of immutability, privacy, transparency, decentralization, and distribute ledger. The main concern is that healthcare records containing confidential patient information are very complicated because they pose a privacy risk. The medical system would be better prepared to deal with public health threats if it had direct access to patient data during deadly disease outbreaks. A concise study is provided in this paper on blockchain technology in healthcare. The paper deeply works on the security and traceability issues in healthcare and pharmaceutical industries. This paper also presents blockchain as one of the complete solutions for security and traceability in healthcare and pharmaceutical industries.
Shaping the Future of Healthcare Through Blockchain-Powered Technology

Learn the latest developments, solutions and forward-looking approaches with blockchain technology for healthcare. How do panelists view healthcare delivery systems changing through blockchain-powered technology? What solutions are they spearheading along with new models of collaborations with which they are involved? The panel will highlight how blockchain is leveraged to transform health systems.&nbsp;&nbsp;&nbsp;
Different Techniques of Pedestrian Detection

This paper describes the different techniques or methods for the pedestrian crossing. This paper defines the techniques like shape-based pedestrian detection, Pedestrian detection by means of far-infrared stereo vision, Pedestrian Detection using Infrared images and Histograms of Oriented Gradients
Investigation of mutual aerodynamic infl uence of high-speed passenger and freight trains moving on adjacent tracks

The article considers the issues of impact of high-speed passenger traffic on the sections of JSC “Uzbekiston Temir Yillari” on removal of freight trains from the traffic schedule, the mutual aerodynamic influence of trains moving on adjacent tracks is investigated. In order to increase the tourist attractiveness of the republic and provide high-quality transport services to the population, it is planned to significantly increase the volume of passenger transportation by high-speed electric trains Afrosiyob. In the conditions of the projected increase in the size of freight traffic, this may lead to difficulties in passing trains through sections. In order to effectively use the capacity of the sections, in compliance with the conditions for ensuring the safety of train traffic, the possibility of simultaneous passage of freight trains of a passing or oncoming direction along the second main track when moving along the section of the Afrosiyob high-speed electric train is being considered.
Analysis on Influencing Factors of Pure Electric Vehicle Sales in Big Data Environment

The electric vehicle industry in the field of new energy is developing rapidly, but the proportion of pure electric vehicles in the whole automobile industry is relatively small. In order to promote the rapid development of pure electric vehicle industry, Firstly, combined with the big data of consumers' car purchase, this paper analyzes the influencing factors of electric vehicle sales, and then conduct a questionnaire on customers and car owners who have purchased vehicles in different Direct stores of Wei lai automobile, The reliability and validity of the questionnaire were verified by SPSS data processing software, and the hypotheses were effectively verified by regression analysis. The analysis results show that the assumption that the product performance image, brand image, consumers' perception of Wei lai pure electric vehicles and government policies have a significant impact on the sales of Wei lai vehicles is correct. Accordingly, the corresponding specific measures are put forward.
<i>COVID Hasn’t Finished with Us Yet; Also, University of Toronto’s “Physics in the Time of COVID-19”</i>

First Page
CNN-based path planning on a map

An approach for fast 2D path planning within a map is presented in this paper. The proposed solution relies on a Convolutional Neural Network to estimate the path generated with a traditional Theta* algorithm. The network receives as input a top-down image of the environment with the obstacles distribution and the location of the start and goal points. It provides as output the coordinates of the path waypoints. This approach is validated on three environment datasets representing realistic indoor scenarios. It is demonstrated that, according to the environment complexity, with the help of a refinement algorithm safe and reliable paths can be created within very limited computation time and with high success rate.
Object optimal grasping recognition method based on local point cloud model

With the explosive growth of intelligent recognition and manipulator grasping in modern logistics, the ability of autonomous operation of unknown objects has become an essential skill for intelligent robots. In this paper, we propose a new method to determine the optimal grasp way based on the local point cloud model of objects. Firstly, the contour points of the point cloud model are extracted, and then we calculate the center point of the object based on that. Secondly, the grasp cuboids are generated based on the center point, and the point sets of the contour points captured by the grasp cuboids are calculated. Finally, according to the grasp evaluation rules, the scores of the grasp cuboids are calculated quantitatively to determine the optimal grasp way. The experimental results show that our method is feasible and effective, and can be applied to the field of industrial logistics.
Empirical Evaluation of Linked Data Visualization Tools

In this chapter, we evaluate the tools presented in Chapter 3. The evaluation of each tool is performed based on the use cases defined in Chapter 4. An overview and a comparison of the main features of the tools are presented in Section 5.1. The evaluation of the tools on the 15 use cases is presented in Section 5.2.
Modelling of a GSA-based framework of an agent-based system within parameter boundaries: applications to robotics and automatic voltage regulator systems

The enormous potential of the agent-based nature-inspired algorithm named the gravitational search algorithm (GSA) remains untapped, so it is the subject of this work. Existing GSA has provided ways to select the super-agents or prune the poor-performing agents. Here, a holistic framework is proposed by introducing a survival boost which re-establishes self-significance of a poor-performing agent and liberates all agents from an infinity trap for acceleration. It restricts the agents within the search space whose violation produces infeasible solutions. The analysis is extended to solve a number of benchmark test functions, an inverse kinematic problem of a 7 DOF robot manipulator, and a voltage regulation problem of an automatic voltage regulator system. The results and comparative studies show better performances in than some of the popular methods from existing literature (e.g., existing GSA, chaotic GSA, different variants of particle swarm optimisation, artificial bee colony, differential evolution, Jaya algorithm).
Use of Mobile Apps to Enhance Learning Among the Research Scholars of Periyar University - A Study

At present, a day's Smartphone has been spread around the world. A Smartphone is a cell phone running a total working framework in a way like a conventional PC, which offers propelled processing capacities and availability alternatives. These highlights empower new sorts of versatile administrations that thus shape the use propensities for Smartphone clients. This paper overlooked understudies’ Smartphone use and how it influences them. It analyzes the job which incessant utilization of Smartphone's plays in their examinations. Surveys were structured and conveyed to 103 respondents of the research scholars of different branches of Periyar University, Salem. Out of the 103 polls conveyed, 102 were totally filled and returned back, giving an arrival pace of 99%. The information gathered was broken down utilizing frequencies; rates and diagram portrayals. It was prescribed that understudies ought to figure out how to deal with their time appropriately and to advance the learning procedure
IPSE DIXIT. BOOK REVIEW: KAVUNENKO L.F., VELENTEICHIK T.N. PREDETERMINATION AND UNEXPECTEDNESS. SCIENCE-OF-SCIENCE ESSAYS ON THE CITATION LEADERS IN THE FIELD OF THE HISTORY OF SCIENCE AND TECHNOLOGY

An excursion is given through the pages of the book dedicated to the key employees of S.I. Vavilov Institute for the History of Science and Technology of the Russian Academy of Sciences (IHST RAS). In their analysis, the authors of the book rely on a strict scientometric approach. However, the analysis is not limited to this approach. It is shown that the authors of the book appear to be pioneers in the combined genre of scientometrics «with a human face». The strict statistics is supplemented with the biographies of scientists and the memoirs of their colleagues as well. The history of the uneasy relations between the government and IHST, based on the example of the fate of leading scientists, can be traced from the moment of the establishment of the institute to the present day. In the Soviet times, the institute experienced ideological pressure, while today IHST shares the fate of other institutes of the Russian Academy of Sciences. In accordance with the title of the book, the authors are apparently trying to reveal the hidden predetermination of events that determined precisely this or that creative «trajectory» in the biographies of the heroes of the book.
Integrating Metaliteracy into the Design of a Collaborative Online International Learning (COIL) Course in Digital Storytelling

This paper explores the redesign of a course in <em>Digital Storytelling</em> that integrates metaliteracy to advance Collaborative Online International Learning (COIL). Students from the United States and Prague, Czech Republic are active participants in this fully online global course as they learn to produce digital stories. The metaliteracy framework encourages metacognitive reflection and the active participation in social information environments. A collaborative teaching team from the United States and Europe redesigned this course to integrate metaliteracy into the learning activities and self-assessments. As students learn to become digital storytellers, they self-identify with specific metaliterate learner roles (producer, collaborator, researcher, etc.), assess their learning through four domains of metaliterate learning (affective, behavioural, cognitive, and metacognitive), and lastly reflect on their work in the course through the metaliteracy characteristics (collaborative, open, reflective, civic-minded, etc.). The internationalization of education in this COIL course is enhanced by the novel combination of metaliteracy and digital storytelling.
An Empirical Evaluation of Blockchain Security Models from a Fuzzy Statistical Perspective

Data must be conveyed in a way that only the intended entities can decode and comprehend it. Security is critical in the design of any data and process-intensive system. Researchers have developed a variety of protocols and security models to provide security – encryption models, hashing models, blockchain systems, secret sharing systems, etc. All these systems have their own nuances, advantages and limitations, making it challenging for system designers to choose the best security model. Delay-sensitive security systems, for example, should be able to secure data at high speeds with minimal complexity, however, this trait may jeopardise the system's security. In blockchain technology, secret sharing approaches improve the security performance of the systems. This review compares different blockchain and secret sharing techniques in terms of security, QoS performance, and computational complexity. These techniques pave the way for future security systems and can be combined for better QoS and security performance.
Reinforcement Learning-Based HTTP Adaptive Streaming Scheme in Edge Computing Environments

Dynamic Adaptive Streaming over HTTP (DASH) is a representative scheme to improve Quality of Experience (QoE) of video streaming users. However, the QoE fairness is degraded when multiple users stream videos over the same network. The reasons of this problem are a lack of coordination among clients and bitrate adaptation with the fixed heuristics. In this paper, we propose an HTTP adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme generates a policy based on reinforcement learning to optimize QoE fairness and utilizes edge computing to assist policy generation. We evaluated the proposed scheme through simulations under various network conditions. The experimental results show that the proposed scheme improves QoE fairness of multiple clients compared with the existing scheme.
Real-World Superresolution by Using Deep Degradation Learning

Most current deep convolutional neural networks can achieve excellent results on a single image superresolution and are trained using corresponding high-resolution (HR) images and low-resolution (LR) images. Conversely, their superresolution performance in real-world superresolution tests is reduced because these methods create paired LR images by simply interpolating and downsampling HR images, which is very different from natural degradation. In this article, we design a new unsupervised framework conditioned by degradation representations of real-world hyperresolution problems. The approach presented in this paper consists of three stages: we first learn the implicit degradation representation from real-world LR images and then acquire LR images by shrinking the network, which will share similar degradation with real-world images. Finally, we make paired data of the generated real LR images and HR images for training the SR network. Our approach can obtain better results than the recent SR approach on the NTIRE2020 real-world SR challenge Track1 dataset.
Long Subject Questions in French: An Insight into the Left Periphery of Selected CPs

Abstract This article presents experimental data assessing the acceptability status of long subject questions (LSQs) in French. It shows that their suboptimal score does not reflect dialect variation and it fails to detect a change in progress. An analysis, developing further the rescuing strategy proposed in Rizzi and Shlonsky's freezing account, is presented. The crux of the proposal is that a Case mismatch obtains within the embedded complementizer, which is shown to be necessarily syncretic in LSQs. The full acceptability of embedded subject questions is then discussed and accounted for within this framework.
Machine Learning Using Driver Monitoring Cameras to Detect Drunk Driving

Drunk driving is a major cause of road traffic injuries and fatalities. To address this, we support recent efforts for driver monitoring systems, which are deemed a promising means to detect impaired driver states. In this paper, we develop an in-vehicle machine learning system to determine the blood alcohol concentration (BAC) levels of drivers. Specifically, our system leverages driver monitoring cameras, which are nowadays mandated in numerous countries worldwide, to predict critical BAC thresholds. We evaluate our system with n=30 participants in an interventional clinical study with a research-grade driving simulator. Our system detects driving under moderate levels of alcohol influence (i.e., 0.00 g/dL < BAC ≤ 0.03 g/dL) with an area under the receiver operating characteristic curve (AUROC) of 0.88 and driving above the WHO recommended legal BAC threshold of 0.05 g/dL with an AUROC of 0.79. By inspecting the trained machine learning model, we find that our system relies on known pathophysiological effects associated with alcohol consumption. Our results highlight the potential of driver monitoring cameras to determine BAC levels and then to intervene to prevent accidents caused by drunk driving.
A Delphi Study on Performance Indicators of Capstone Design Class

This study aims to clarify the performance indicators of capstone design class. Delphi survey on a panel of experts was chosen to be the main methodology for this study, drawing the main factors of constructs for the performance indicators of capstone design class. From March. 12 to April. 20, 2022, a three-round delphi survey was implemented to collect data. A panel of twenty-five experts were involved in this survey. For statistical processing, descriptive statistics including frequency, percentage, mean and standard deviation were carried out along with internal reliability test on the survey instrument. The main findings were as follows : First, the delphi research on creativity and innovation suggested coming up with a unique idea that other people cannot easily think of, suggesting ways to solve a problem in a creative way, finding connections between seemingly unrelated things etc. Second, the delphi analysis on critical thinking and problem-solving suggested categorizing a problem based on its characteristics, and identifying and organizing relevant information, understanding clearly the intent inherent in and perspective of a situation, understanding a problem and the process of solving the problem when developing an argument etc. Third, the delphi analysis on collaboration suggested free exchange and sharing of various information from the perspective of inclusion and equality, performing a task in collaboration enables one to be immersed, having hard fun. etc.
The Comparative Analysis of the Illustrations and Questions of Elementary School Science Textbooks and Authorized Science Textbooks for 3rd and 4th Grade According to the 2015 Revised Curriculum

This study compared and analyzed the types of illustrations and the functions of basic science process skills in the questionnaire of elementary school science textbooks for 3rd and 4th grade according to the 2015 revised curriculum and science textbooks authorized by ice cream media. First, in the type of illustration and the type of illustration by subject area, both elementary school science textbooks and authorized science textbooks had the highest proportion of photography. Second, in the function of the basic science process skills shown in the questionnaire, observation accounted for the highest proportion of both elementary school science textbooks and authorized science textbooks. Third, in the material domain, the proportion of observation and life area was high, and the proportion of communication increased in the motion and energy domain, and in the earth and space domain. Through this study, we will present changes and flows in textbooks according to the conversion to authorized textbooks, provide basic data to effectively use accredited science textbooks reflecting the 2015 revised curriculum, and suggest implications for effective science teaching and learning.
The Effect of Real-time Online Learning Interactivity and the Risk Perception of COVID-19 on Real-Time Online Learning Persistence: Moderating Role of Grade Using Multi-Group Analysis

COVID-19 is having a huge impact on education field all over the world. Even if COVID-19 crisis ends, educators’ need for online learning will never decline. Especially in real-time online learning interactivity is a key factor that can have a positive influence on learning effectiveness. However, there are few researches on the interactivity of real-time online learning and COVID-19 risk perception affecting the learning persistence. Therefore, this study aimed to identify the influence of interactivity of real-time online learning on behavior intention and the influence of COVID-19 risk perception on behavior intention and learning persistence. Additionally, it aimed to identify the moderating role of grade in the influencing relation of COVID-19 risk perception on learning behavior intention and learning persistence. As a result of analysis, it appeared that interactivity of real-time online learning had a positive influence on real-time online learning behavior intention and learning persistence. It was also found that grade played significant moderating roles in this relationships between COVID-19 risk perception, learning behavior intention and learning persistence. The findings from this study provided academic implications and simultaneously practical implications for sustainable growth of real-time online learning.