Masked Face Recognition Model with Explainable AI

Due to the recent COVID-19 pandemic, people tend to wear masks indoors and outdoors. Therefore, systems with face recognition, such as FaceID, showed a tendency of decline in accuracy. Consequently, many studies and research were held to improve the accuracy of the recognition system between masked faces. Most of them targeted to enhance dataset and restrained the models to get reasonable accuracies. However, not much research was held to explain the reasons for the enhancement of the accuracy. Therefore, we focused on finding an explainable reason for the improvement of the model's accuracy. First, we could see that the accuracy has actually increased after training with a masked dataset by 12.86%. Then we applied Explainable AI (XAI) to see whether the model has really focused on the regions of interest. Our approach showed through the generated heatmaps that difference in the data of the training models make difference in range of focus. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Impact of Social Media Like WhatsApp for Education Domain

The pandemic situation is affected in various ways in the education domain. The sudden transformation from offline to online teaching-learning process made students and teachers use different tools like WhatsApp for communication. The reason for this consideration is to investigate the impacts of WhatsApp utilized for instruction and decide the suppositions of understudies towards the method. The study is designed, keeping in mind the current COVID-19 situation and how it affected the education system turning it into online mode. On different questionnaires, regression and heatmap analysis is performed. The investigation showed that both learning situations have diverse impacts on the victory of understudies while supporting the conventional environment by utilizing WhatsApp is more successful for the increment of victory. The assessment moreover showed that students had superior pleasant reviews closer to the usage of WhatsApp in their courses. They requested the same workout in their one-of-a-kind courses as well. They expressed that picking up information can moreover take out unwittingly and the messages with pics were more prominent and viable for their picking up information. Be that as it may, some college understudies have communicated harming audits approximately the timing of a few posts and the repetitive posts within the bunch. At long last, it is supported that the utilization of WhatsApp within the preparing framework is to be energized as a steady innovation. . © 2023 IEEE.

Risk heatmaps as visual displays: Opening movie studios after the COVID-19 shutdown.

Upon shutting down operations in early 2020 due to the COVID-19 pandemic, the movie industry assembled teams of experts to help develop guidelines for returning to operation. It resulted in a joint report, The Safe Way Forward, which was created in consultation with union members and provided the basis for negotiations with the studios. A centerpiece of the report was a set of heatmaps displaying SARS-CoV-2 risks for a shoot, as a function of testing rate, community infection prevalence, community transmission rate (R0), and risk measure (either expected number of cases or probability of at least one case). We develop and demonstrate a methodology for evaluating such complex displays, in terms of how well they inform potential users, in this case, workers deciding whether the risks of a shoot are acceptable. We ask whether individuals making hypothetical return-to-work decisions can (a) read display entries, (b) compare display entries, and (c) make inferences based on display entries. Generally speaking, respondents recruited through the Amazon MTurk platform could interpret the display information accurately and make coherent decisions, suggesting that heatmaps can communicate complex risks to lay audiences. Although these heatmaps were created for practical, rather than theoretical, purposes, these results provide partial support for theoretical accounts of visual information processing and identify challenges in applying them to complex settings.

Why COVID-19 disruptions to shopping behaviours underscore the need for a coordinated, cross-functional conversion rate optimisation strategy

As consumers shifted consumption patterns to online avenues in the wake of the COVID-19 pandemic, businesses increasingly looked for ways to grab their attention and convert traffic into sales and leads. This paper analyses how studying website visitor behaviour through the practice of conversion rate optimisation (CRO) and its associated tools allows teams to find opportunities to optimise user experience to grow conversions. Although traditionally an initiative led by marketing teams, the practice of CRO can help teams from user experience, sales, customer service, product and development improve their understanding of target audiences and how to connect with them online. © Henry Publications 2050-0076 (2022).

Network Analysis of German COVID-19 Related Discussions on Telegram

We present an effective way to create a dataset from relevant channels and groups of the messenger service Telegram, to detect clusters in this network, and to find influential actors. Our focus lies on the network of German COVID-19 sceptics that formed on Telegram along with growing restrictions meant to prevent the spreading of COVID-19. We create the dataset by using a scraper based on exponential discriminative snowball sampling, combining two different approaches. We show the best way to define a starting point for the sampling and to detect relevant neighbouring channels for the given data. Community clusters in the network are detected by using the Louvain method. Furthermore, we show influential channels and actors by defining a PageRank based ranking scheme. A heatmap illustrates the correlation between the number of channel members and the ranking. We also examine the growth of the network in relation to the governmental COVID-19 measures. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Measuring Speaking Time from Privacy-Preserving Videos

The ongoing pandemic caused by the COVID-19 virus is challenging many aspects of daily life such as restricting the conversation time. A vision-based face analyzing system is considerable for measuring and managing the person-wise speaking time, however, pointing a camera to people directly would be offensive and intrusive. In addition, privacy contents such as the identifiable face of the speakers should not be recorded during measuring. In this paper, we adopt a deep multimodal clustering method, called DMC, to perform unsupervised audiovisual learning for matching preprocessed audio with corresponding locations at videos. We set the camera above the speakers, and by feeding a pair of captured audio and visual data to a pre-trained DMC, a series of heatmaps that identify the location of the speaking people can be generated. Eventually, the speaking time measurement of each speaker can be achieved by accumulating the lasting speaking time of the corresponding heatmap. © 2022 SPIE.

PoseMapGait: A model-based Gait Recognition Method with Pose Estimation Maps and Graph Convolutional Networks

Gait recognition is a particularly effective way to avoid the spread of COVID-19 while people are under surveillance. Because of its advantages of non-contact and long-distance identification. One category of gait recognition methods is appearance-based, which usually extracts human silhouettes as the initial input feature and achieves high recognition rates. However, the silhouette-based feature is easily affected by the view, clothing, bag, and other external variations. Another category is based on model-based, one popular model-based feature is extracted from human skeletons. The skeleton-based feature is robust to many variations because it is less sensitive to human shape. However, the performance of skeleton-based methods suffers from recognition accuracy loss due to limited input information. In this paper, instead of relying on coordinates from skeletons, we exploit that pose estimation maps, the byproduct of pose estimation. It not only preserves richer cues of the human body compared with the skeleton-based feature, but also keeps the advantage of being less sensitive to human shape compared with the silhouette-based feature. Specifically, the evolution of pose estimation maps is decomposed as one heatmaps evolution feature (extracted by gaitMap-CNN) and one pose evolution feature (extracted by gaitPose-GCN), which denote the invariant features of whole body structure and body pose joints for gait recognition, respectively. Our method is evaluated on two large datasets, CASIA-B and the CMU Motion of Body (MoBo) dataset. The proposed method achieves the new state-of-the-art performance compared with recent advanced model-based methods.

Student Learning Analysis with Impact of Self-learning and Effectiveness During COVID-19

Coronavirus has a great impact in some other ways of everybody's life. It greatly affected the education sector in India. All of the sudden changes from traditional teaching–learning to online teaching–learning become difficult to manage students as well as faculties. This research focuses on examining students’ learning habits during a pandemic when all school colleges closed because of the spread of COVID-19. A data was collected to examine Indian (Maharashtra) student’s learning time spent throughout epidemic, academic, were inactive due to the novel coronavirus—SARS-CoV-2 (COVID-19). In response to understanding the potential effects of the coronavirus epidemic, the questionnaires were spread over Facebook, WhatsApps a network of educational group from September 30 to October 20, 2020. This research focuses on the impact of different factors on students learning during COVID-19. To represent correlations between independent and dependent variables, heat maps are used. It helps to analyze the impact in terms of their values that exist between variables. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Social Distancing Crowd Segmentation, Estimation and Visualisation

This research shows a modern crowd counting solution which alters typical prediction solutions into a segmentation of individuals based on a distance threshold, allowing for better visualisation and results. The study proposes using YOLOv4-normal and YOLOv4-tiny models, which have shown great results throughout calibration with an MAE of 14 and 36 respectively. However it did present some issues of accuracy degradation when trained on head annotations at any level of crowd density. As for visualisation, perspective transformation was used which directly helped in providing the distance calculation that was absent from standard transformation. If any variants of YOLOv4 are to be used, the main argument is the choice between speed over accuracy while relying on native implementations. In the case of distance regulation, any transformation that maps itself onto the region of interest, such as perspective transformation should be used to precisely determine distances from a camera to the region of interest itself. © 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.