ABSTRACT
In 2020, as the novel coronavirus spread globally, face masks were recommended in public settings to protect against and slow down viral transmission. People complied to varying extents, and their reactions may have been driven by a variety of psychological fac-tors. Based on the literature on social influence and on mask-wearing, we define three cus-tomer segments: Fully-Compliant customers wear masks, and they seem motivated primarily by concerns about their own health risk. Partially-Compliant customers also wear masks, but with improper and ineffective coverage;our empirical analysis suggests that they are moti-vated primarily by a desire to comply with social norms. Finally, Unmasked customers do not wear masks. We examine changes in shopping behaviors with the onset of the pandemic to corroborate the conjectured mask-wearing motives. We find that the three groups made significantly different behavior changes: Fully-Compliant customers shopped significantly faster and practiced stricter social distancing with the onset of the pandemic, whereas the other two groups did not adjust their shopping duration or social distancing.
ABSTRACT
Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirus-based pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. © 2022 IEEE.
ABSTRACT
Purpose: Zakat (Islamic almsgiving) plays a considerable role in dealing with the socioeconomic issues in times of COVID-19 pandemic, and such roles have been widely discussed in virtual events. This paper aims to discover knowledge of the current global zakat administration from virtual events of zakat (e.g. webinars) on YouTube and Zoom via text mining approach. Design/methodology/approach: The authors purposefully sampled 12 experts from four different virtual zakat events on YouTube and Zoom. The automated text transcription software is used to pull the information from the sampled videos into text documents. A qualitative analysis is operated using text mining approach via machine learning tool (i.e. Orange Data Mining). Four research questions are developed under the Word Cloud visualisation, hierarchal clustering, topic modelling and graph and network theory. Findings: The machine learning identifies the most important words, the relationship between the experts and their top words and discovers hidden themes from the sample. This finding is practically substantial for zakat stakeholders to understand the current issues of global zakat administration and to learn the applicable lessons from the current issues of zakat management worldwide. Research limitations/implications: This study does not establish a positivist generalisation from the findings because of the nature and objective of the study. Practical implications: A policy implication is drawn pertaining to the legislation of zakat as an Islamic financial policy instrument for combating poverty in Muslim society. Social implications: This work supports the notion of "socioeconomic zakat”, implying that zakat as a religious obligation is important in shaping the social and economic processes of a Muslim community. Originality/values: This work marks the novelty in making sense of the unstructured data from virtual events on YouTube and Zoom in the Islamic social finance research. © 2022, Emerald Publishing Limited.
ABSTRACT
—The current COVID-19 pandemic has elevated the importance of cleanliness and social distancing. These needs will continue to be important as the world moves to a new normal whilst navigating through a post-covid environment. This paper presents a use case application that focuses on enforcing safe distance measures inside a campus building where there is limited manpower resources. Amidst the social setting within the university, staff or students may at times accidentally congregate, which may lead to spread of diseases inconveniencing all affected parties. Our proposed integrated solution consists of a network of video cameras and sensors which allows one to monitor behavior within the building. The integrated smart devices communicate with (1) an analytics server that processes the data from the various sensors and (2) a platform that integrates the analytic results and optimizes the action items to be reflected to the environment. A pilot prototype has been deployed and evaluated within a living lab setting on campus. Results show that the system is useful in streamlining the operational process resulting in more efficient processes and procedures to help enforce safe management measures needed to maintain proper social distancing among occupants in campus. © 2022 by the authors.
ABSTRACT
Purpose: Zakat (Islamic almsgiving) plays a considerable role in dealing with the socioeconomic issues in times of COVID-19 pandemic, and such roles have been widely discussed in virtual events. This paper aims to discover knowledge of the current global zakat administration from virtual events of zakat (e.g. webinars) on YouTube and Zoom via text mining approach. Design/methodology/approach: The authors purposefully sampled 12 experts from four different virtual zakat events on YouTube and Zoom. The automated text transcription software is used to pull the information from the sampled videos into text documents. A qualitative analysis is operated using text mining approach via machine learning tool (i.e. Orange Data Mining). Four research questions are developed under the Word Cloud visualisation, hierarchal clustering, topic modelling and graph and network theory. Findings: The machine learning identifies the most important words, the relationship between the experts and their top words and discovers hidden themes from the sample. This finding is practically substantial for zakat stakeholders to understand the current issues of global zakat administration and to learn the applicable lessons from the current issues of zakat management worldwide. Research limitations/implications: This study does not establish a positivist generalisation from the findings because of the nature and objective of the study. Practical implications: A policy implication is drawn pertaining to the legislation of zakat as an Islamic financial policy instrument for combating poverty in Muslim society. Social implications: This work supports the notion of “socioeconomic zakat”, implying that zakat as a religious obligation is important in shaping the social and economic processes of a Muslim community. Originality/values: This work marks the novelty in making sense of the unstructured data from virtual events on YouTube and Zoom in the Islamic social finance research. © 2022, Emerald Publishing Limited.
ABSTRACT
The recent pandemic emergency raised many challenges regarding the countermeasures aimed at containing the virus spread, and constraining the minimum distance between people resulted in one of the most effective strategies. Thus, the implementation of autonomous systems capable of monitoring the so-called social distance gained much interest. In this paper, we aim to address this task leveraging a single RGB frame without additional depth sensors. In contrast to existing single-image alternatives failing when ground localization is not available, we rely on single image depth estimation to perceive the 3D structure of the observed scene and estimate the distance between people. During the setup phase, a straightforward calibration procedure, leveraging a scale-aware SLAM algorithm available even on consumer smartphones, allows us to address the scale ambiguity affecting single image depth estimation. We validate our approach through indoor and outdoor images employing a calibrated LiDAR + RGB camera asset. Experimental results highlight that our proposal enables sufficiently reliable estimation of the inter-personal distance to monitor social distancing effectively. This fact confirms that despite its intrinsic ambiguity, if appropriately driven single image depth estimation can be a viable alternative to other depth perception techniques, more expensive and not always feasible in practical applications. Our evaluation also highlights that our framework can run reasonably fast and comparably to competitors, even on pure CPU systems. Moreover, its practical deployment on low-power systems is around the corner.
ABSTRACT
Managing the ongoing COVID-19 (aka Coronavirus) pandemic has presented both challenges and new opportunities for urban local body administrators. With the Indian government's Smart City mission taking firm roots in some of the Indian cities, the authors share their learnings and experiences of how a Smart City Integrated Command and Control Centre (ICCC) can be extended to become the nerve centre of pandemic-related operations and management, leveraging the Smart City IoT infrastructure such as surveillance cameras for monitoring and enforcement. The authors are of the opinion that the lessons learned and experiences gained from these cities are extremely valuable and can easily be replicated in other cities in a relatively short time period, thus providing a standard and uniform method across the nation for handling epidemics in the future. © 2020 ACM.
ABSTRACT
Recently, Coronavirus Disease (COVID-19) has spread rapidly across the world and thus social distancing and face mask has become one of the mandatory preventive measures.The Artificial Intelligence community has been focusing on developments for monitoring social distancing and identifying face masks, which have become the hotspot and made the headlines.A powerful approach for fast and more accurate detection and monitoring would greatly enhance the sole purpose, save the lives of many and also alleviate the burden of doing it manually to some extent.You Only Look Once(Yolov3) and improved Single Shot multi-box Detector (SSD) algorithm has been deployed for an improved object detection model to lend a helping hand in the fight against this deadly disease.The proposed framework utilizes Single Shot multi-box Detector (SSD) with MobileNetV2, to enhance the feature extraction ability of the object detection model.After taking into account the significance of developing an accurate model and also the limitations of existing models,herein we have proposed a framework based on deep learning,which would automate and simplify the task of monitoring social distancing and face mask through intelligent video analytics. © 2021 IEEE.
ABSTRACT
With the ever-increasing internet penetration across the world, there has been a huge surge in the content on the worldwide web. Video has proven to be one of the most popular media. The COVID-19 pandemic has further pushed the envelope, forcing learners to turn to E-Learning platforms. In the absence of relevant descriptions of these videos, it becomes imperative to generate metadata based on the content of the video. In the current paper, an attempt has been made to index videos based on the visual and audio content of the video. The visual content is extracted using an Optical Character Recognition (OCR) on the stack of frames obtained from a video while the audio content is generated using an Automatic Speech Recognition (ASR). The OCR and ASR generated texts are combined to obtain the final description of the respective video. The dataset contains 400 videos spread across 4 genres. To quantify the accuracy of our descriptions, clustering is performed using the video description to discern between the genres of video. © 2022, Springer Nature Switzerland AG.
ABSTRACT
A difficulty for teachers in COVID-era online teaching settings is assessing engagement and student attention. This has made adapting teaching to the responses of the class a challenge. We developed a system called Engage AI for assessing engagement during live lectures. Engage AI uses video-based machine learning models to detect drowsiness and emotions like happiness and neutrality, and aggregates them in a dashboard that instructors can view as they speak. This provides real-time feedback to instructors, allowing them to adjust their teaching to keep students engaged. There is no video data transmitted outside of students' web browsers, and individual students are anonymous to the instructor. Testing in undergraduate engineering lectures resulted in 78.2% reporting feeling at least potentially more engaged during the lecture and at least 34.4% of students reporting feeling more engaged during the lecture. These approaches could be applicable to many forms of remote and in-person education. © American Society for Engineering Education, 2021