Digital Transformation and Divide of Cities through the COVID-19 Pandemic

The COVID-19 pandemic has affected almost all sectors of society in a short period. In this paper, we study the impact of the COVID-19 pandemic on smart cities through analyses of 311 data of cities and the residents in the United States. We have compared various aspects of municipal governments' service platforms and citizens' requests during pre-COVID, the lockdown, and the rest of the pandemic duration. Among multiple observations from the data, we discover the noticeable changes in the digital transformation of citizens' voices during the COVID-19 pandemic. We observe disparities in service adaptation across many cities, where only a few cities have quickly added pandemic responsive service types and favorable 311 mobile apps in addition to phone and online web services. Besides the digital transformation of residents and municipal governments, we also find that various aspects of divides of residents, such as economic, COVID-related health, and demands are closely related to each other. We have built a comprehensive website that dynamically collects 311 data from municipal open data of cities in the United States that other researchers or urban planners can use to understand citizens' voices better and draw insights. © 2022 IEEE.

Crowd Safety Sensing (CroSS) for the Post Pandemic Era

Due to its long incubation period, aggressive asymptomatic transmission, and new mutations of the virus, COVID-19 is causing multiple pandemic waves worldwide. Despite recent vaccination, social distancing, and social restriction efforts, false negatives, and dormant positives can make pandemics challenging to restrain. In addition to rapid vaccination, effective contact tracing, mask-wearing, and social distancing are critical for out-break containment and for achieving herd immunity. However, the existing technology solutions, such as contact tracing apps and social-distance sensing, have been met with suspicion due to privacy and accuracy concerns and have not been widely adopted. Without achieving a critical mass of individual users, these personal technologies have been rendered useless. On the other hand, large-scale policy efforts have been complicated, requiring the coordination of federal, state, and local governments and regulation enforcement logistics. However, local communities balance these approaches and are an unrealized, powerful resource to prevent future outbreaks.This paper proposes a novel Crowd Safety Sensing (CroSS) for building a sustainable safe community cluster against COVID-19 and beyond using affordable Internet of Things (IoT) technologies. CroSS monitors social distancing policies to small, focused communities for accommodating efficient technology penetration, greater accuracy, effective practices, and privacy policy assistance. We implemented a social distancing method and integrated it into an edge-based IoT system. The experimental results show that CroSS detects false-positive social distancing cases. © 2021 IEEE.

Mosaic: Modeling Safety Index in Crowd by Detecting Face Masks against COVID-19 and beyond

In addition to rapid vaccination, predicting possible trajectories of the COVID-19 pandemic is critical to health-care-related policy decisions and infrastructure planning. Growing evidence shows that face masks and social distancing can considerably reduce the spread of respiratory viruses like COVID-19. However, the current pandemic trajectory predictions take overly simplified policy input rather than actual observations of face masks and social distancing practices in a crowd. Thus, it is crucial to monitor and understand the extent of masking practices and assess the safety level in a scalable manner. This paper proposes a novel face masking detection system for Modeling Safety Index in Crowd (Mosaic), a Machine Learning (ML)-based approach for detecting masking in a crowd by building new dense mode crowd mask datasets. Mosaic detects, counts, and classifies the crowd's masking condition and calculates spatiotemporal Safety Index (SI) values for each community instead of detecting individual masking cases. SI data can be shared or published to calculate the area-based SI maps (as opt-in data) for assisting effective policy decisions and relief plans against COVID-19. The experimental results show that Mosaic detects various conditions and types of masking states and calculates SI values of a crowd effectively. This paper proposes a novel face masking detection system for Modeling Safety Index in Crowd (Mosaic), a Machine Learning (ML)-based approach for detecting masking in a crowd by building new dense mode crowd mask datasets. Mosaic detects, counts, and classifies the crowd's masking condition and calculates spatiotemporal Safety Index (SI) values for each community instead of detecting individual masking cases. SI data can be shared or published to calculate the area-based SI maps (as opt-in data) for assisting effective policy decisions and relief plans against COVID-19. The experimental results show that Mosaic detects various conditions and types of masking states and calculates SI values of a crowd effectively. © 2021 IEEE.

CATS: Crowd-based Alert and Tracing Services for building a Safe Community Cluster against COVID-19

COVID-19 has been causing several pandemic waves worldwide due to its long incubation period and hostile asymptomatic transmission. Society should continue to practice social distancing and masking in public despite aggressive vaccinations until achieving population immunity. However, the existing technology solutions, such as contact tracing apps and social-distancing devices, have been faced with suspicion due to privacy and accuracy concerns and have not been widely adopted. This paper proposes a novel infection management system named Crowd-based Alert and Tracing Services (CATS) to build a safe community cluster. CATS applies social distancing and masking principles to small, focused communities to provide higher privacy protection, efficient penetration of technology, and greater accuracy. We have designed a smart tag for managing social distancing. We also implemented a Machine Learning (ML)-based face mask tracking system to build non-binary Safety Impact Values (SIV). © 2021 IFIP.

Your Eyes Show What Your Eyes See (Y-EYES): Challenge-Response Anti-Spoofing Method for Mobile Security Using Corneal Specular Reflections

As the need for contactless biometric authentication becomes more significant during COVID-19, and beyond, the popular biometric authentication method for mobile devices, iris detection, and facial recognition confronts various usability, security, and privacy concerns, including mask-wearing and various Presentation Attacks (PA). Specifically, liveness detection against spoofed artifacts is one of the most challenging tasks as many existing methods cannot conclusively assess the user's physical presence in unsupervised environments. Even though several methods have been proposed for tackling PA with motion challenges and 3D mapping, most of them require expensive depth sensors and fail to detect sophisticated 3D reconstruction attacks. We present a software-based face PA Detection (PAD) method named "Your Eyes Show What Your Eyes See (Y-EYES),"which creates challenges and detects meaningful corneal specular reflection responses from human eyes. To detect human liveness, Y-EYES creates multiple screen image patterns as a challenge, then captures the response of corneal specular reflections using the front camera and analyzes the images using lightweight Machine Learning (ML) techniques. Y-EYES system components include challenge pattern generation, reflection image augmentation (e.g., super-resolution), and ML-based analyses. We have implemented Y-EYES as Android, iOS, and web apps. Our extensive experimental results show that Y-EYES achieves liveness detection with high accuracy at around 200 ms against various types of sophisticated PA. Y-EYES liveness detection can be applied for multiple contactless biometric authentications accurately and efficiently without any costly extra sensors. © 2021 ACM.

Informal Technology Education for Women Transitioning from Incarceration

As society increasingly relies on digital technologies in many different aspects, those who lack relevant access and skills are lagging increasingly behind. Among the underserved groups disproportionately affected by the digital divide are women who are transitioning from incarceration and seeking to reenter the workforce outside the carceral system (women-in-transition). Women-in-transition rarely have been exposed to sound technology education, as they have generally been isolated from the digital environment while in incarceration. Furthermore, while women have become the fastest-growing segment of the incarcerated population in the United States in recent decades, prison education and reentry programs are still not well adjusted for them. Most programs are mainly designed for the dominant male population. Consequently, women-in-transition face significant post-incarceration challenges in accessing and using relevant digital technologies and thus have added difficulties in entering or reentering the workforce. Against this backdrop, our multi-disciplinary research team has conducted empirical research as part of technology education offered to women-in-transition in the Midwest. In this article, we report results from our interviews with 75 women-in-transition in the Midwest that were conducted to develop a tailored technology education program for the women. More than half of the participants in our study are women of color and face precarious housing and financial situations. Then, we discuss principles that we adopted in developing our education program for the marginalized women and participants' feedback on the program. Our team launched in-person sessions with women-in-reentry at public libraries in February 2020 and had to move the sessions online in March due to COVID-19. Our research-informed educational program is designed primarily to support the women in enhancing their knowledge and comfort with technology and nurturing computational thinking. Our study shows that low self-efficacy and mental health challenges, as well as lack of resources for technology access and use, are some of the major issues that need to be addressed in supporting technology learning among women-in-transition. This research offers scholarly and practical implications for computing education for women-in-transition and other marginalized populations. © 2021 Owner/Author.

Erratum: Development of a laboratory-safe and low-cost detection protocol for SARS-CoV-2 of the coronavirus disease 2019 (COVID-19) (Experimental Neurobiology (2020) 29:2 (107-119) DOI: 10.5607/en.20009)

We would like to correct author’s affiliations, add an author and an edit one sentence as shown below. 1) The corrected affiliations (switch affiliation 1 and 2) and added author are marked by bold and underlines. Joungha Won1,2, Solji Lee2, Myungsun Park2, Tai Young Kim2, Mingu Gordon Park2,3, Byung Yoon Choi4, Dongwan Kim5,6, Hyeshik Chang5,6, Won Do Heo1, V. Narry Kim5,6 and C. Justin Lee2* 1Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, 2Center for Cognition and Sociality, Cognitive Glioscience Group, Institute for Basic Science, Daejeon 34126, 3KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, 4Department of Otorhinolaryngology, Seoul National University Bundang Hospital, Seongnam 13620, 5Center for RNA Research, Institute for Basic Science, Seoul 08826, 6School of Biological Sciences, Seoul National University, Seoul 08826, Korea 2) In Quantitative rtPCR section (Page 110, in material and methods) we would like to correct the following sentence from;“In brief, each reaction buffer consisted of a total volume of 20 µl containing 8 µl of 100 µM forward and reverse primers (4 µl for each primer), 2 µl of cDNA, and 10 µl power SYBR Green PCR Master Mix.” to;“In brief, each reaction buffer consisted of a total volume of 20 µl containing 2 µl of 10 µM forward and reverse primers (1 µl for each primer), 2 µl of cDNA, and 10 µl power SYBR Green PCR Master Mix”. © 2020 Korean Society for Neurodegenerative Disease. All rights reserved.

Smart Education: Opportunities and Challenges Induced by COVID-19 Pandemic : d Study]

The viability of online education and comparisons of modes of education have long been a topic in educational study. Due to the COVID-19 pandemic declared in Spring 2020, a stay-at-home order was made in many cities in the United States and other countries, which caused the conversion of university education entirely online right in the middle of a semester. Students have experienced both face-to-face and online instruction in a single semester with almost the same duration. This paper discusses our survey-based study of over 300 students taking a course from the departments in computing and engineering college of a higher education institution in the US, in order to understand the effectiveness of face-to-face and online education through quantitative and qualitative research methods. The familiarity of online resources is relatively high to students and instructors in the computing and engineering disciplines than other disciplines. That technology use itself might be less likely a barrier in the instruction allows us to focus on the effectiveness of teaching and learning. We also offer discussions on the challenges and opportunities of online education that are likely to be a persistent future education option. © 2020 IEEE.