How the COVID-19 Pandemic Influences Human Mobility? Similarity Analysis Leveraging Social Media Data

This paper aims to understand the impact of the COVID-19 on human mobility. We explore individual traces through spatial-temporal check-ins on social media. In particular, we leverage geo-tagged tweets, to extrapolate people's geo-locations in New York City (NYC) when they check in Twitter. Building on these data, we perform gyration and travel similarity analysis to study the change of travel pattern during the pandemic. We make a comparison of users' gyration and the number of COVID-19 deaths across time. We find that (1) Users' gyration decreased by 35% after the stay-at-home order. (2) Check-in activities on social media is related to the fear of coronavirus: User's gyration has a negative correlation (-0.7) with the number of deaths across time. (3) Travel similarity decreased by 15% from March 2020 to June 2020 because many people did not travel outside after the stay-at-home order. (4) Inter-personal travel similarity among users was lower than 0.2 and individual traces of a majority of people had no overlap during the pandemic. © 2022 IEEE.

Trait Empathy Modulates Patterns of Personal and Social Emotions During the COVID-19 Pandemic.

The COVID-19 pandemic has caused profound consequences on people's personal and social feelings worldwide. However, little is known about whether individual differences in empathy, a prosocial trait, may affect the emotional feelings under such threat. To address this, we measured 345 Chinese participants' personal emotions (e.g., active, nervous), social emotions (i.e., fearful and empathetic feelings about various social groups), and their empathy traits during the COVID-19 pandemic. Using the representational similarity analysis (RSA), we calculated the pattern similarity of personal emotions and found the similarity between the positive and negative emotions was less in the high vs. low empathy groups. In addition, people with high (vs. low) empathy traits were more likely to have fearful and sympathetic feelings about the disease-related people (i.e., depression patients, suspected COVID-19 patients, COVID-19 patients, flu patients, SARS patients, AIDS patients, schizophrenic patients) and showed more pattern dissimilarity in the two social feelings toward the disease-related people. These findings suggest a prominent role of trait empathy in modulating emotions across different domains, strengthening the polarization of personal emotions as well as enlarging social feelings toward a set of stigmatized groups when facing a pandemic threat.

Sequence similarity analysis between SARS-COV-2 and other viruses based on variant logic framework and topological entropy

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) popular in just a few months the world, the present study found that the virus belongs to the β-coronavirus family. We study the sequence similarity, which can be coronavirus vaccine development and analysis provides a scientific help, including SARS-CoV-2 high similarity with Bat-CoV, SARS, etc. But the low accuracy of long time-consuming problems sequences analysis method. In this paper, the topological entropy of different combination dimensions of each sequence was calculated based on the Variant Logic Framework. the sequences of SARS CoV-2 and other categories of viruses were taken as input data. The sequence similarity matrix of mutual information among different sequences was obtained by calculating Euclidean distance. Finally, using a visualization diagram, generate the phylogenetic tree. The experimental results show that topology entropy is fast and effective for virus sequence processing and similarity analysis, which also provides a new idea for virus sequence research and traceability. © 2021 ACM.

Savitzky-Golay filter energy features-based approach to face recognition using symbolic modeling.

Face recognition is a well-researched domain however many issues for instance expression changes, illumination variations, and presence of occlusion in the face images seriously affect the performance of such systems. A recent survey shows that COVID-19 will also have a considerable and long-term impact on biometric face recognition systems. The work has presented two novel Savitzky-Golay differentiator (SGD) and gradient-based Savitzky-Golay differentiator (GSGD) feature extraction techniques to elevate issues related to face recognition systems. The SGD and GSGD feature descriptors are able to extract discriminative information present in different parts of the face image. In this paper, an efficient and robust person identification using symbolic data modeling approach and similarity analysis measure is devised and employed for feature representation and classification tasks to address the aforementioned issues of face recognition. Extensive experiments and comparisons of the proposed descriptors experimental results indicated that the proposed approaches can achieve optimal performance of 96-97, 92-96, 100, 84-93, and 87-96% on LFW, ORL, AR, IJB-A datasets, and newly devised VISA database, respectively.