Reviewerly/exHarmony huggingface dataset:
COVID-19 Symptom Analysis and Prediction Using Machine Learning Techniques

The globe is still in a state of panic as the epidemic of COVID-19 continues to spread. Vaccines have been introduced all across the world, but many people continue to be affected. As a result, knowing former patients’ medical information may benefit medics in their battle against the disease. Artificial Intelligence (AI) has developed as a groundbreaking tool with capabilities such as meteorology, forecasts in the medical sector, predictive analytics, and so on. One of the most prominent areas of AI is Machine Learning (ML) that has recently shown promising results in a variety of fields, including medicine and, most recently, COVID analysis. In this paper, we have performed two works with regard to COVID crisis. Firstly, we have conducted a study on the most impacting symptoms noticed in a person with COVID by applying a correlation analysis with Pearson and Spearman correlation coefficients. Second, the COVID dataset was analyzed using six machine learning methods for classification tasks: Random Forest (RF), Gradient Boosting, Decision Trees (DTs), Naïve Bayes (NB), Bernoulli Naïve Bayes, and Support Vector Machine (SVM), with the most influential symptoms as inputs. The performance of these algorithms are measured using the metrics, namely accuracy, F1-score, precision score, and Area under the Receiver Operating Characteristic Curve (ROC-AUC) score. On evaluating the applied machine learning algorithms, it can be concluded that all the six algorithms were found to be efficient in distinguishing the positive and negative cases of COVID with promising values of the performance metrics.
Blockchain for CCTV Surveillance

Smart city application uses various IoT devices to provide better service and infrastructure. Despite a number of potential benefits, it is also facing many problems related to information security and privacy. Surveillance applications play a crucial role in jurisdiction, crime prevention, and examinations. These applications are over and over again vulnerable against different video altering attacks. To deal with this issue, a secure and tamper proof information sharing mechanism is proposed. This system integrates the blockchain technology and InterPlanetary File System (IPFS) with smart IoT devices involved in smart city applications. Edge computing is a process of handling data in the place where it has been generated without sending it to cloud platforms in order to reduce the delay in data transmission and decision-making. The videos generated from CCTV are block chained and processed in its source itself. It would help in improving security, integrity, latency, and provide maximum bandwidth for data transfer in the smart city applications such as city surveillance, smart parking, cross-border surveillance, and traffic monitoring systems.
Twitter Bot Detection Using One-Class Classifier and Topic Analysis

Social media has emerged as the primary means of information sharing over the last few years. Twitter is a popular social media platform. However, increasingly twitter bots which are autonomous software agents are used to spread misinformation, false propaganda and influence public opinion. This paper outlines the design and development of a machine learning-based approach for classifying a twitter account as a bot or genuine user account. A one-class classifier coupled with account-based and tweet-based features has yielded an F-measure of 93% when tested on a subset of the Memory in Base (MIB) dataset.
Shot Boundary Detection and Video Captioning Using Neural Networks

The proliferation of multimedia data in cyberspaces necessitates the exploration of competent techniques to process and analyze data content. Videos are the most common data type on the internet and are often voluminous—this makes it relevant to index the information for storing and arranging content. Shot boundary detection (SBD) is a video segmentation technique commonly applied as an initial step in content-based video indexing and video retrieving systems. Thereafter, details about the shots can be used for advanced search, video summarizing, and filtering. In this paper, we propose a system that segments a video by its primary units, i.e., shots, and provides descriptions, summarizing each segment of the video. To do so, we have employed an architecture based on dilated convolutional neural network for SBD and a combination of CNN and LSTM for captioning. Additionally, a sentence transformers model is used to compute the semantic textual similarity of the resulting captions, aiding in eliminating redundancy. We achieved an F1-score of 0.924 for SBD and a CIDEr score of 0.692 for the captioning model.
Common cause failure analysis of lights power supply system

In order to improve the accuracy of the common cause failure (CCF) analysis results of Aviation Ground Lighting(AGL) power supply system, an improved integrated part method (UPM) is proposed. Taking a medium-sized airport light station black-out accident (SBO) as the top event, a system fault tree is constructed. Calculate and analyze the Fussell-Vesely importance of minimal cut sets in fault trees. Identify CCF groups and build a system fault tree that includes common cause failures. UPM was applied to determine the beta factor value for each CCF group. The comparative analysis considers the occurrence probability of important system events before and after CCF. The results show that the application of the UPM-CCF analysis method more truly reflects the reliability level of the lights power supply system.
THE CONSTRUCTION PATH OF FIRST-CLASS UNDERGRADUATE MAJORS IN LOCAL APPLICATION-ORIENTED UNIVERSITIES IN CHINA-- AN EMPIRICAL STUDY BASED ON STUDENT SATISFACTION EVALUATION

Using the qualitative analysis software NVivo to code and analyze 16 authoritative and representative documents related to the construction standards of first-class undergraduate majors, and drawing lessons from the CIPP evaluation model, this paper constructs four dimensions, including professional background, professional investment, professional construction process and professional construction effect, covering the source of students The theoretical framework of the first-class undergraduate specialty construction index system with 8 first-class indicators such as specialty positioning and 17 second-class indicators and 55 third-class indicators. Based on this evaluation framework, the questionnaire on student satisfaction with first-class undergraduate specialty construction is compiled. The multivariate linear analysis model shows that the overall interpretability of the main factors affecting student satisfaction reaches 99.9%. The research shows that students are generally satisfied with the construction of first-class undergraduate majors in a Local Application-oriented University in China, have the highest satisfaction with the teaching staff and professional quality assurance, and have the lowest satisfaction with the professional school running conditions. There is a long way to go to improve students' satisfaction and promote the construction of first-class undergraduate majors in China's Local Application-oriented Universities. We should build a new three-dimensional development pattern of “one body, two wings and three integration” of discipline, curriculum, specialty and education; Build a new model of characteristic development in the “12345” period; Build a new first-class talent training system guided by “quality improvement''.
Cloud Platform Credibility Assessment System Based on D-S Theory and Blockchain Technology

Even well-known cloud platforms will have sudden credibility problems in the long-term application process. Effectively evaluating the credibility of the cloud platform and providing users with scientific evaluation results can help users reasonably choose a trusted cloud platform. However, there are often conflicting opinions or malicious assessments in the process of assessment. In addition, the personal privacy information of the users participating in the assessment is at risk of being leaked, and the data that the users have evaluated is also easy to be modified. In order to solve the above problems, this paper defines the credibility category and confidence interval of cloud platform, puts forward a quantitative assessment method combined with fuzzy theory, and realizes the fusion of different users’ assessment results based on D-S theory. On this basis, this paper further proposes an effective cloud platform credibility assessment system combined with blockchain technology. Finally, through experimental analysis, this paper shows that the credibility assessment system proposed in this paper is feasible and illustrates the characteristics of the system through method comparison. The system solves the problem of conflicting information in the assessment process, can effectively assess the credibility of the cloud platform, and effectively protects user privacy and the security of assessment data with blockchain technology.
President's Page: Urban geophysics: Where geophysics meets society head-on

Today, 55% of the world's population lives in urban areas, consumes between 67% and 76% of global energy, and generates about 70% of global carbon emissions. In less than 300 years since the beginning of the British Industrial Revolution, many urban centers in the developed world have evolved from simple congregations of human labor with overcrowded tenements, frequent epidemics, inequitable resources, and insufferable sanitary or safety conditions to become “smart cities” that utilize cutting-edge technologies to enhance the advantages and minimize the inconveniences of urbanization for residents. Cities such as Tokyo, Japan; Zurich, Switzerland; and Auckland, New Zealand; have been consistently ranked among the smartest cities worldwide based on their technological smartness in the areas of health and safety, mobility, activities, opportunities, and governance. Residents in these cities enjoy advanced health care, affordable housing, superior mobility, excellent education, and abundant employment opportunities. On the other hand, urban slums such as Neza-Chalco-Itza in Mexico, Dharavi in Mumbai, India, and the favelas in San Paulo, Brazil, do not look much different from the early 19 th century London streets.
Research on emotion cognitive classification based on eye movement in virtual reality

With the rise of virtual reality technology and eye movement tracking, the cognitive classification of eye movement emotion combined with virtual reality technology was a research hotspot recently. In this research, 96 pictures were selected from the Chinese Affective Picture System (CAPS), and 35 subjects were stimulated with pictures in virtual scene. The corresponding eye movement data were tracked synchronously. Eye movement data were progressed with blink data reconstruction and baseline correction, after which time and frequency domain features were extracted. Finally, for three emotion model dimensions including valence, arousal and dominance expressed as high, medium and low, models are trained with Support Vector Machine (SVM), with the highest classification accuracy of single person reaches 78.1%. The results of this paper are instructive for the processing of eye movement data, and the eye tracking data combined with virtual reality technology can be used for emotion recognition research through the proposed preprocessing methods and feature extraction.
Filters

In the previous chapter, we learned the functionality found in the scipy.ndimage module of the SciPy library. We learned how to apply transformations such as shift and zoom on an image. We also learned how to obtain statistical information about an image. We saw how to compute and plot the histogram for an image.
Transformations and Measurements

In the previous chapter, we were introduced to the Scientific Python stack. We learned the basics of NumPy and matplotlib. We explored the useful modules ndarray and pyplot from NumPy and matplotlib, respectively. We also learned about the scipy.misc module and how to perform basic image processing with it. In this chapter, we will further explore the SciPy library. We will learn to use the scipy.ndimage library for processing images. We will also explore methods to use for image transformation and image measurement.
Color Transfer Based on Frequency Tuning

Color transfer has always been an important topic in the field of image processing. The existing color transfer methods often have problems such as loss of detail, lack of hierarchy and color mistransmission. Therefore, a color transfer algorithm based on frequency tuning is proposed in this paper. Firstly, the significance detection method of frequency tuning is introduced to separate the significant region and non significant region of the image. Secondly, the color transfer algorithm adopts the principle of probability density histogram transfer, which can achieve accurate color transfer, and greatly reduce the complexity of the algorithm and the kernel load required by the algorithm. Finally, in order to reduce the graininess of the resulting image, a gradient filter is introduced to smooth the image, which greatly improves the visual effect of the image. Experimental analysis shows that, compared with the traditional color transfer algorithm, the color transfer algorithm used in this paper makes the resulting image visual effect better, the sense of hierarchy richer, the details can be retained more, there will be no color mistransmission, the image as a whole is more natural and has a wide range of application.
Advanced Operations on Images

The previous chapter explored the arithmetic and logical operations to use on images with Pillow. There is more to the world of image processing than that, however. Pillow has a lot more functionality to offer. You can enhance and filter images. You can also calculate the histogram of an image as well as its channels. We will cover the following topics in this chapter:
Morphology, Thresholding, and Segmentation

In the previous chapter, we studied the theory behind image filters, along with the types and practical applications of filters used to enhance images.
Video Processing

In the previous chapter, we learned about using morphological operations in the domain of image processing. We have also demonstrated all those operations with libraries such as NumPy, SciPy, and matplotlib.
Introducing a mobile learning model in medical education during COVID-19; a critical review.

Mobile learning is one of the innovative teaching techniques that help medical students gain knowledge and skills. One of the factors that expanded the use of this strategy was the COVID-19 pandemic. However, the educational pedagogy of such technology has been neglected. This article aimed to critically review available mobile learning models in medical education to suggest a comprehensive model in the field of mobile learning.We conducted this critical review based on the five steps of the Carnwell and Daly method. For a comprehensive systematic search from 2000 to April 2021, the following keywords were used: Personal Digital Assistant, m learning, Mobile learning, Ubiquitous learning, U learning, medical students, and medical education. 3176 studies in PubMed, Scopus, ERIC, Magiran, and Web of Science were identified. In total, 8 articles entered the study.Eight models of mobile learning in medical education were identified. The key features of each model were extracted and integrated into the new model for the successful design and implementation of mobile learning. This model includes three main elements of mobile learning: 1-stakeholders, 2-interaction, and 3-technology, which are influenced by external factors including Mobiquette, legitimacy, and awareness.The results of this study are an important contribution to the knowledge collection in mobile learning in medical education. We introduced a comprehensive model of mobile learning including specific characteristics of strategies in the context of medical education.
Closed-Domain Multiple-Choice Question Answering System for Science Questions

There is a huge amount of textual information in digital form growing exponentially over the years. With this data explosion, retrieving relevant information through information retrieval (IR) systems has become crucial. Due to the growing popularity of various tools such as voice assistants and chatbots, that rely on human–machine interaction, it is important that such systems are able to answer any query directly, rather than leading the user to a set of references. Recently developed question answering (QA) systems aim at providing direct answers to the user’s queries, hence, becoming of great interest to a large community. In this work, we propose an end-to-end pipeline that performs this task by creating a knowledge graph from the corpus. It uses embeddings to predict the missing links in the knowledge graph and a unique answer selection module in order to reach the correct answer. We test the proposed methodology on the SciQ dataset and obtain an accuracy of 62%. We also test this methodology on a curated knowledge base, Aristo, and present a comparative study highlighting the aspects on which information extraction models can improve, thus opening possibilities for future work in this field.
Perceived Readiness of Information and Communication Technology Policy in Supporting Mobile Learning in Times of COVID-19 at South African Schools

This study answers the research question, “how do teachers perceive the readiness of information and communication technology (ICT) school policies in supporting the provision of remote m-learning as a strategy for mitigating learning disruptions caused by COVID-19?” To investigate this question, the study adopted and adapted the UNESCO policy guideline for m-learning as an evaluation framework. Data were collected through semi-structured interviews from five schools in South Africa, and ten teachers participated in the study. The sampled teachers perceived the ICT policies at the selected schools as outdated, not recognizing mobile devices as tools for learning, and silent on providing teachers with resources that facilitate m-learning. The results indicate that the ICT policies at the sampled schools were perceived as not supporting the UNESCO policy guideline for m-learning. Perceptions of teachers were investigated in this study because teachers are the custodians of m-learning at schools.
Usability Evaluation Using Unmoderated Remote Usability Testing on Angkasa LMS Website Case Study

The pandemic has made digital transformation faster, and Indonesia is no exception, especially in education. Learning management system (LMS) is a learning media that is widely used in educational institutions. However, not all educational institutions have sufficient resources to build an LMS from scratch. “Angkasa LMS” Web is a Web that allows educational institutions to order a ready-to-use LMS easily. The target users of this service are pretty varied because they consist of education management and educational management foundations, especially in areas of Indonesia that have Internet access. To be used easily by these varied target users, this Website must have good usability in its user interface. For this reason, usability testing needs to be carried out in order to get feedback for improving the user interface design before the application is delivered to the public. However, the developer has obstacles related to the COVID-19 pandemic policy to carry out usability testing, which limits direct interaction with people. Moreover, the project time is quite narrow, and the schedule is quite tight for the developer team. Based on these limitations, this study conducted usability testing of the “Angkasa LMS” Web case study using the unmoderated remote usability testing method. The experimental results show that this method can be used to obtain insightful feedback from the participants, with additional treatment such as the use of convenience sampling, periodic reminders for participants, and the increasing number of participants exceeding the target.
Design and Implementation of Verifiable Blockchain-Based e-voting System

Voting is the most representative way to express individual decision making in a democratic society. The classic voting method requires a lot of confirmation procedures, and requires a lot of time and money. In order to solve these kinds of problems, there have been efforts to introduce e-voting with IT services advantages, such as reduced election costs and shorter tally hours, compared to the existing voting methods. Despite many advantages, e-voting is not widely used for various technical concerns, including the risk of data manipulation. There has been much effort recently to apply the blockchain for voting data integrity to e-voting as a way to reduce the risk of voting data security and integrity. Blockchain could guarantee the integrity of voting data, but it has problems to be directly applied to e-voting system. In this paper, by applying cryptographic algorithms to the blockchain, we propose a verifiable blockchain-based e-voting system that allows voters to verify their votes while separating voters and voting results. Proposed the verifiable blockchain-based e-voting system satisfies the requirements of voting, including completeness, soundness, privacy, un-reusability, eligibility, fairness, verifiability.
The use of CG in Japanese animation

course Share on The use of CG in Japanese animation Authors: Andreas Bauer View Profile , Marc Salvati View Profile Authors Info & Claims SIGGRAPH '22: ACM SIGGRAPH 2022 CoursesAugust 2022 Article No.: 16Pages 1–207https://doi.org/10.1145/3532720.3535626Published:02 August 2022Publication History 0citation264DownloadsMetricsTotal Citations0Total Downloads264Last 12 Months264Last 6 weeks41 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Design and Evaluation of Self-Tuning Fuzzy PID Controller with Reference Model for Ball and Beam Mechanism

—Fuzzy Logic controller is one of the popular techniques in control system. However, the accuracy depends on numbers and the design of membership functions. Further, the output does not always fulfill specific design requirements. In this study, Fuzzy PID controller with first-order reference model had been designed and evaluated. The proposed new structure is able to guide fuzzy mechanism to produce desired output response as specified by the first-order model such as the time constant, rise time and settling time. The study was implemented on a ball and beam system to control the position of the ball while rolling on a beam. The control of a ball and beam system is challenging because it is an open-loop unstable system as the ball will continuously moves on the beam without control. The study also compares the results acquired from Fuzzy PID and Fuzzy PI plus PD against the proposed Fuzzy PID with first-order reference model controller. The evaluation covers for a pole starting from 0 until 1.0 for the first-order model. As a results, the proposed controller is able to produce an output response as specified where the fastest response was acquired when pole is set to 0.9. Whereas the Fuzzy PID and Fuzzy PI plus PD always produce the same transient response during step input test.