Design of upper limb rehabilitation evaluation system based on deep learning

The topic of non-contact diagnosis became a hot topic during COVID-19 and online consultation gained popularity. In this research, a deep learning-based autonomous limb evaluation system is developed for online consultation and remote rehabilitation training for people with physical limitations. Its main goal is to collect and analyze information about limb states. The patient can evaluate the limb state at home using the mobile app, and the doctor can view the data and connect with the patient via the web's chat module to offer diagnostic opinions. Deep learning is used for the Start/End Attitude Determination Model and OpenCV for the limb and hand evaluation model, with the results being uploaded to the server. © The Authors. Published under a Creative Commons Attribution CC-BY 3.0 License.

Research on blended teaching reform based on computational thinking

The outbreak of COVID-19 has impacted traditional teaching methods in schools, and blended teaching in the post-pandemic has gradually become a hot topic of research in higher education. Computational thinking, as one of the core literacies to be acquired in the 21st century, can help students realize the importance of computers as well as enable them to solve specific problems more effectively when facing real-life situations. The article takes the C language programming course as an example, analyzes the problems faced in teaching in the post-pandemic, introduces the concept of computational thinking and integrates it into all aspects of blended teaching design, pays attention to students' individual differences, and proposes a blended teaching model based on computational thinking and puts it into practice. The results show that this teaching model can improve students' learning performance, exercise students' computational thinking skills, and promote blended teaching reform and students' personalized development. © 2022 IEEE.

Analysis of environmental performance and interactivity of ports and regions

The port is the basic support for regional economic development and the global allocation of resources. With the rapid development of China's economy and growing ecological awareness, the assessment of port and regional efficiency has received unprecedented attention. In the current context of the COVID-19 pandemic, how the port and its region will be coordinated under the common goal of development has become a hot topic. In this study, the port subsystem (P-subsystem) and the regional subsystem (R-subsystem) are unified into the port–region system (PR system), and a new meta-frontier two-stage data envelopment analysis model is constructed to evaluate the P-subsystem efficiency and the environmental efficiency of the PR system. This research also measures the port–regional coordination level using the coordination index and explores the inefficiency of the PR system with the help of management improvement and technology improvement indices. Main results show that the overall efficiency of the Chinese PR system is increasing. The technological level of the PR system in coastal areas is close to the optimal level. The inefficiency of the Chinese PR system is mainly affected by management inefficiency. The coordination of regional and port development in China is also poor. Finally, on the basis of the research findings, this study provides targeted countermeasure suggestions to promote the efficiency enhancement and coordinated development of the PR system. © 2023 Elsevier Ltd

Disinformation: A Bibliometric Review.

OBJECTIVES: This paper aimed to provide a systematic review of relevant articles from the perspectives of literature distribution, research hotspots, and existing results to obtain the frontier directions in the field of disinformation. METHODS: We analyzed disinformation publications published between 2002 and 2021 using bibliometric methods based on the Web of Science. There were 5666 papers analyzed using Derwent Data Analyzer (DDA). RESULTS: The result shows that the USA was the most influential country in this area, while Ecker and Lewandowsky from the University of Western Australia published the largest volumes of papers. Keywords such as "social media", "COVID-19", and "vaccination" have gained immense popularity recently. CONCLUSIONS: We summarized four themes that are of the biggest concern to scholars: group heterogeneity of misinformation in memory, disinformation mechanism in social media, public health related to COVID-19, and application of big data technology in the infodemic. The future agenda of disinformation is summarized from three aspects: the mechanism of disinformation, social media users, and the application of algorithms. This work can be a meaningful resource for researchers' study in the area of disinformation.

Mask Detection for Covid-19 under Extreme Conditions

Mask detection has become a hot topic since the COVID-19 pandemic began in recent years. However, most scholars only focus on the speed and accuracy of detection, and fail to pay attention to the fact that mask detection is not suitable for people living under extreme conditions due to the degraded image quality. In this work, a denoising convolutional auto-encoder, a multitask cascaded convolutional networks (MTCNN) and a MobileNet were used to solve the problem of mask detection for COVID-19 under extreme environments. First of all, a network based on AlexNet is designed for the auto-encoder. This study found that the two-layer max pooling layers in AlexNet could not accurately extract image features but damage the quality of restored image. Therefore, they were deleted, and other parameters such as channel number were also modified to fit the new net, and finally trained using cosine distance. In addition, for MTCNN, this study changed the output condition of ONet from thresholding to maximum return, and lowered the thresholds of PNet and RNet to solve the problem that faces might not be found in low-quality images with mask and other covers. Furthermore, MobileNet was trained using categorical cross entropy loss function with adam optimizer. In the end, the accuracy of system for the photos captured under extreme conditions enhance from 50 % to 85% in test images. © 2022 IEEE.

A Data Mining Approach to Finding Face Mask from Bangladeshi News Channel

Face Mask Detection is currently a hot topic that has piqued the interest of researchers all over the world. Today, the entire world is dealing with the COVID-19 pandemic. To control the spread of the Coronavirus the most important task people need to do is use a mask. There is still a lot of research and study being done on COVID-19. Several studies have also shown that wearing a face mask significantly reduces the problem of viral transmission. In addition, a person wearing a face mask perceives a sense of protection. When we are at home, we take care of everything, but when we are in public places such as offices, malls, and colleges, it becomes more difficult to keep people safe. Machine Learning and Data Mining are a collection of technologies that provide effective solutions to complex problems in a variety of fields. We attempted to develop a face mask recognition system using machine learning in order to prevent the spread of the Coronavirus. This is a good system for detecting a face mask in news channel images and videos. It can recognize both Mask and No Mask faces. With the advancement of this system, it will be possible to detect whether or not a person is wearing a face mask. If the person is not wearing a face mask, it will display a message such as "No Mask,"otherwise it will display "Mask Detected." © 2022 IEEE.

Cross-Language Vocabulary Teaching Based on Information Network Technology: The Example of Loanwords

Loanwords have always been a hot topic in the study and teaching of Chinese as a foreign language, but there are also many different views and debates. This article summarises and outlines the hotspots of research into and teaching of loanwords in linguistics in recent decades and discusses the current issues of language teaching and cross-linguistic education in the context of the new COVID-19 pandemic in relation to information network technology. First, the concept and scope of loanwords are defined, and the rationale of loanwords is discussed from the perspective of word formation. On this basis, the characteristics of loanwords are summarised, the online teaching of loanwords in the context of the new pandemic is discussed from the perspective of information technology and the Sinicization of loanwords is discussed from the perspective of language development. © 2022 ACM.

Evolution of Public Opinion on COVID-19 Based on Microblog Visualization

In January 2020, the outbreak of COVID in China attracted widespread attention and discussion on social media. Evolution of public opinion can help us understand users' hot topics and the evolution rule among these topics. Therefore, a public opinion evolution model based on microblog data is proposed in this paper. Firstly, a web crawler is used to obtain microblog data. Then the idea of sentiment analysis and topic extraction in order is used to analyze, and divide the stages through the emotional conflict evolution diagram. Finally, fine-grained emotion visualization is carried out for the hot topics in each stage, the evolution rule of public opinion on COVID-19 is summarized, and the effectiveness and scientificity of the method are also verified. © 2022 ACM.

Face Mask Wearing Recognition System for Big Data Video Streaming

With the outbreak of the covid-19 pandemic in recent years, Video Stream Analytics technology quickly became a hot topic of discussion across technology forums. As it has appeared, in the pandemic situation in recent years, the use of masks when interacting with the community is a must, that's why the research works on mask identification today and more. receiving more and more attention. Understanding the situation, the team conducted facial recognition analysis inside the video to determine if the people appearing in the video were wearing masks. to then apply the trained model into practice. After a period of research, the team has also successfully built a mask recognition system that can generate images and can display the results as real-time video. Especially, the model is trained successful using systemml machine learning system. This is considered a positive result with real-time masked face recognition analysis. © 2022 IEEE.

Journey towards competency-based grading for mechanical engineering computer applications

Over the past several years, increasing effort has been invested interrogating the very structure of assessment in higher education. In addition to a rising awareness of the mental health impacts of high-stakes assessment, questions have arisen around accessibility and equity in our assessment practices. From this conversation, the practice of competency-based or mastery-based education has become a hot topic in pedagogically minded circles. To summarize, competency-based assessment is the practice of developing targeted assessments and standards of performance for each individual skill or outcome present in the course and building an assessment scheme based on how many of those outcomes are sufficiently mastered in the allotted time. A defining trait of these schemes is the ability to repeat individual outcome assessments as needed to demonstrate mastery, significantly lowering the stakes of any individual attempt. Efforts have manifested at every level, up to and including entire mastery-based programs. In this work, the five-year-long reinvention of a mechanical engineering computer applications course is examined as it was transformed from traditional to flipped to competency-based, navigating the onset of COVID along the way. In the most recent iteration, the course involves a framework of repeatable assessments across an array of outcomes, including both traditional exam format assessment as well as more involved project-based assessments, a set of video modules, and a group project. The rationales for and lessons learned from this journey are explored, along with student comments and evaluations, and an examination of overall course grades and achievement of course outcomes. © American Society for Engineering Education, 2022.

Contact Tracking Development Trend Using Bibliometric Analysis

The new crown pneumonia (COVID-19) has become a global epidemic. The disease has spread to most countries and poses a challenge to the healthcare system. Contact tracing technology is an effective way for public health to deal with diseases. Many experts have studied traditional contact tracing and developed digital contact tracking. In order to better understand the field of contact tracking, it is necessary to analyze the development of contact tracking in the field of computer science by bibliometrics. The purpose of this research is to use literature statistics and topic analysis to characterize the research literature of contact tracking in the field of computer science, to gain an in-depth understanding of the literature development status of contact tracking and the trend of hot topics over the past decade. In order to achieve the aforementioned goals, we conducted a bibliometric study in this paper. The study uses data collected from the Scopus database. Which contains more than 10,000 articles, including more than 2,000 in the field of computer science. For popular trends, we use VOSviewer for visual analysis. The number of contact tracking documents published annually in the computer field is increasing. At present, there are 200 to 300 papers published in the field of computer science each year, and the number of uncited papers is relatively small. Through the visual analysis of the paper, we found that the hot topic of contact tracking has changed from the past “mathematical model,” “biological model,” and “algorithm” to the current “digital contact tracking,” “privacy,” and “mobile application” and other topics. Contact tracking is currently a hot research topic. By selecting the most cited papers, we can display high-quality literature in contact tracking and characterize the development trend of the entire field through topic analysis. This is useful for students and researchers new to field of contact tracking ai well as for presenting our results to other subjects. Especially when comprehensive research cannot be conducted due to time constraints or lack of precise research questions, our research analysis can provide value for it. © 2022. KIPS

Fifteen years of crowdfunding – a bibliometric analysis

Crowdfunding emerged as a flexible method of financing various projects during the financial crisis but has since developed into a fully fledged financing instrument that requires special attention from the academia and legislators. The purpose of this study is to perform a quantitative analysis of the up-to-date crowdfunding research employing bibliometric analysis. We have used data from the Scopus database and have extracted a number of 1951 research papers. Using VOSviewer software, we have looked into the yearly research production, the categories in which the studies fall, citation records, highly cited authors and papers, most prolific authors and country of origin in crowdfunding. We grouped keywords into clusters and identified emerging trends. We then revealed the thematic progression of the occurring keywords into four time ranges. We find that crowdfunding research has been growing steadily from 2006 to date, with a particular focus on business and finance. The most productive countries are highly developed economies, but other countries have started exploring crowdfunding, too. The most cited references are published between 2013 and 2016, which is when the bases of this stream of research have been established. We finally discuss the theoretical and practical implications.

Extracting Feelings of People Regarding COVID-19 by Social Network Mining

In 2020, COVID-19 became one of the most critical concerns in the world. This topic is even still widely discussed on all social networks. Each day, many users publish millions of tweets and comments around this subject, implicitly showing the public's ideas and points of view regarding this subject. In this regard, to extract the public's point of view in various countries at the early stages of this outbreak, a dataset of Coronavirus-related tweets in the English language has been collected, which consists of more than two million tweets starting from 23 March until 23 June 2020. To this end, we first use a lexicon-based approach with the GeoNames geographic database to label each tweet with its location. Next, a method based on the recently introduced and widely cited Roberta model is proposed to analyse each tweet's sentiment. Afterwards, some analysis showing the frequency of the tweets and their sentiments is reported for each country and the world as a whole. We mainly focus on the countries with Coronavirus as a hot topic. Graph analysis shows that the frequency of the tweets for most countries is significantly correlated with the official daily statistics of COVID-19. We also discuss some other extracted knowledge that was implicit in the tweets. © 2022 World Scientific Publishing Co.

An Analysis on the Weibo Topic Detection Based on K-means Algorithm

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data. © 2022 IEEE.

Sentiment analysis of Covid 19 Vaccines using Twitter Data

Sentiment Analysis is an approach for identifying the polarity of the text. In recent times Social Media is a popular platform for people to socialize and express their opinions on various topics. Given the Covid situation around the globe Social media is facilitating most of the social interactions among paople. In the last two years the most popular topic of discussion has been covid related topics. People have been discussing on various aspects of covid like infections, lockdowns etc. When the vaccines were rolled out by many nations, vaccinations became the hot topic. All the social media platforms like face book and Twitter were flooded with messages related to corona vaccines. The data sets used for analysis in this work specifically belong to the vaccines rolled out by two countries India and USA. TextBlob was used for analysing the sentiments. The results are presented in form of polarity and subjectivity scores and also wordclouds. © 2022 IEEE.

Extracting Major Topics of COVID-19 Related Tweets

With the outbreak of the Covid-19 virus, the activity of users on Twitter has significantly increased. Some studies have investigated the hot topics of tweets in this period;however, little attention has been paid to presenting and analyzing the spatial and temporal trends of Covid-19 topics. In this study, we use the topic modeling method to extract global topics during the nationwide quarantine periods (March 23 to June 23, 2020) on Covid-19 tweets. We implement the Latent Dirichlet Allocation (LDA) algorithm to extract the topics and then name them with the "reopening", "death cases", "telecommuting", "protests", "anger expression", "masking", "medication", "social distance", "second wave", and "peak of the disease"titles. We additionally analyze temporal trends of the topics for the whole world and four countries. By analyzing the graphs, fascinating results are obtained from altering users' focus on topics over time. © 2021 IEEE.

Design of Campus Health Information System using Face Recognition and Body Temperature Detection

Currently under the epidemic crisis of COVID-19, campus epidemic prevention has become a hot topic, and temperature detecting equipment has become a necessity in public spaces. However, temperature detection systems that are widely sold on the market are relatively simple and cannot recognize personal identity. Besides, they cannot record individual temperature changes, and they are still inadequate in terms of managing personal health information. In this study, we proposed a system that can meet the needs of campus epidemic prevention called CHIS (Campus Health Information System). In CHIS, an infrared sensor is used for temperature detection and combined with face recognition. The body temperature is recorded while face recognition is performed, and the face ID and the collected real-time body temperature are transmitted to the cloud for viewing and managing by the school. The data will be managed centrally in the cloud and will be cleaned during daily processing. In the end, the student's health data history will be stored only in their personal Pod (Personal online datastore), a decentralized personal cloud data model that prevents the risk of large-scale data leakage due to centralized management. The combination of body temperature detection and face recognition avoids substituting the presence of a real person with photos or pictures, which further enhances security. It also reduces the risk of infection prompted by human detection, which increases safety. © 2021 IEEE.

Sentiment Analysis of COVID-19 Vaccines from Indonesian Tweets and News Headlines using Various Machine Learning Techniques

COVID-19 vaccine is a hot topic in online platforms due to the ongoing pandemic. Most studies on sentiment analysis of COVID-19 vaccines on Indonesian social media posts only used one or two classifiers with few modifications. This research investigated sentiment analysis using seven machine learning techniques on Twitter dataset in which the one with the highest evaluation value will be used to predict on other unlabeled Twitter datasets as well as news headlines dataset. The same classifier is also used to build a visualization dashboard that reflect the result of the sentiments. The result from the sentiment classification is then used to identify the topics, by using word cloud. The experiment revealed that SVM classifier has the highest accuracy and micro average F1-measure, which is 84% and 0.76. This classifier managed to capture similar patterns of sentiments in Twitter and news headlines datasets, which is dominated by neutral sentiment. Some of the topics from each sentiment, managed to reflect the real condition when the datasets were collected. © 2021 IEEE.

Classification network of COVID-19 based on multi-modality fusion network

Since 2019, COVID-19 has been a major problem for the world's population. COVID-19 is known for its fast transmission and strong infection. Therefore, how to reduce the burden of medical system is becoming a hot topic in current research. Previous researchers have used deep learning techniques to effectively classify COVID-19. Although the results are remarkable, the input method (X-ray images) is simple. Therefore, a new multi-modality fusion network is proposed in this paper. In this network, the spatial and structural feature information in the image were highlighted by means of thermal map. Experiments show the effectiveness of the proposed network. © 2021 IEEE.

Capturing Uncertainty in Opinions on Distance Learning Using Fuzzy Sets

Due to the COVID-19 pandemic, distance learning has become a hot topic in education. Universities worldwide were forced to replace traditional learning with distance learning without deeper preparation. The focus was mostly put on technical solutions. However, the opinions and feelings of the groups involved - mainly teachers, students, and IT staff, should be also considered. Given the novelty of the situation, it is unreasonable to expect that the collected answers are without vagueness and hesitance. To solve this problem, this work proposes a method of capturing and aggregating such answers. At the first level, the answers of a single respondent are represented as fuzzy numbers whose mean is then calculated to represent their overall opinion. At the second level, the aggregation by the relative quantifier most of reveals whether the majority of members of a group inclines towards a positive or negative opinion. In this way, the uncertainties in answers and non-equal sizes of groups are covered. This model is illustrated by a numerical example followed by discussion and concluding remarks. © Proceedings of the 14th International Conference on Strategic Management and its Support by Information Systems 2021, SMSIS 2021.