Passenger Perceptions, Information Preferences, and Usability of Crowding Visualizations on Public Displays in Transit Stations and Vehicles

Large crowds in public transit stations and vehicles introduce obstacles for wayfinding, hygiene, and physical distancing. Public displays that currently provide on-site transit information could also provide critical crowdedness information. Therefore, we examined people's crowd perceptions and information preferences before and during the pandemic, and designs for visualizing crowdedness to passengers. We first report survey results with public transit users (n = 303), including the usability results of three crowdedness visualization concepts. Then, we present two animated crowd simulations on public displays that we evaluated in a field study (n = 44). We found that passengers react very positively to crowding information, especially before boarding a vehicle. Visualizing the exact physical spaces occupied on transit vehicles was most useful for avoiding crowded areas. However, visualizing the overall fullness of vehicles was the easiest to understand. We discuss design implications for communicating crowding information to support decision-making and promote a sense of safety. © 2023 ACM.

Model Construction and Empirical Study on Unsustainable Usage Behavior of Paid Knowledge Users

Knowledge payment is a new method of electronic learning that has developed in the era of social media. With the impact of the COVID-19 pandemic, the market for knowledge payment is rapidly expanding. Exploring the factors that influence users' sustained willingness is beneficial for better communication between knowledge payment platforms and users, and for achieving a healthier and more sustainable development of the knowledge payment industry. The model of unsustainable usage behavior of knowledge payment users was constructed on the basis of expectation inconsistency theory, price equilibrium theory, and perceived value theory, using the 'cognitive-emotional-behavioral' model framework of cognitive emotion theory. The data were collected from 348 users through a web-based questionnaire and analyzed using structural equation modeling. Findings show that expectation inconsistency, price equilibrium, and quality value, emotional value, and social value have significant effects on discontinuous use intentions. Discontinuous use intentions also significantly affect discontinuous use behavior. © 2013 IEEE.

Projection of COVID-19 Jakarta using CHIME Technology (Covid-19 Hospital Impact Model for Epidemics) Application

After the coronavirus outbreak, the disease known as COVID-19 has been infecting millions of people, and the number of deaths is pilling up to hundreds of thousands. In Indonesia, especially Jakarta, some of the deaths are caused by pandemic-related surges that strain hospital capacity. Besides, people had many obstacles in this pandemic condition because of the lack of knowledge about COVID-19. On that matter, several models emerged worldwide to help inform public decision making in this pandemic situation. With today's technological advances the CHIME (COVID-19 Hospital Impact Model for Epidemics) application is designed to assist hospitals and public health officials with understanding hospital capacity needs as they relate to the COVID pandemic. This paper aims to help inform public health decision making regarding the transmission of COVID-19 in Jakarta using CHIME. This work uses Jakarta COVID-19 data from November 24th, 2021 and its accumulation from 14 days before (November 10th, 2021) to predict the course of COVID-19 in 30 days. With ArcGIS Pro and ArcGIS Experience, this work successfully made a map that uses CHIME to inform about peak demand of each city in DKI Jakarta and the daily new admissions and hospitalization graph. In addition, a Jakarta COVID-19 dashboard is also made to inform more about the transmission of COVID-19. © 2022 IEEE.

Evolutionary game analysis of crowding risk control strategies during the COVID-19 Pandemic

To discuss the decision-making scheme of crowding risk management during the COVID-19 pandemic, this paper constructs an evolutionary game model based on the changes of pedestrian and government strategies, and simulates the strategy selection under different states. The results show that under the condition of pedestrian rationality, when the difference between the benefits and costs of the government's active response strategy is less than the benefits of inaction, the government will choose the strategy of inaction. If the benefit of rational action is less than the additional benefit of irrational action, pedestrians will choose irrational action. By establishing the replication dynamic equations of governments and pedestrians, the stability strategy of the system is analyzed. It is found that the values of R1, R2, R3, R4, R5, C1, C2, C3, C4, C5, C6, C7 will affect the strategy choices of the players, and how to measure the benefits and costs under different circumstances becomes the key to the problem. These findings provide a theoretical basis for the risk control decision of human crowding during the COVID-19 epidemic. © 2023 SPIE.

A Survey of Human-Computer Interaction Technology in Intelligent Home for Children's Learning

The development of the COVID-19 epidemic has increased the home learning time of children. More researchers began to pay attention to children's learning in home. This survey reviewed the frontier and classic cases in the field of interactive design of children's home learning in the past five years, analyzed tangible user interface, augmented reality, and multimodal interaction in human-computer interaction of children's home learning. This paper reviewed the application of interactive system in children's learning and points out its positive side in development of ability, process of learning, habits of learning, and environment of learning of children. Through analysis, we advise that it is necessary to create home learning applications, link smart home systems, and build an interactive learning environment for smart home learning environment design. Finally, we point out the technical and ethical problems existing in the current research, proposes that intelligent perception, emotion recognition, and expression technologies should be introduced in the future, and looks forward to the development of this field. © 2023 Institute of Computing Technology. All rights reserved.

Impact of COVID-19 Pandemic on Cultural Products Interests

The COVID-19 pandemic has had a significant impact on human behaviors and how it influenced peoples' interests in cultural products is an unsolved problem. While prior studies mostly adopt subjective surveys to find an answer, these methods are always suffering from high cost, limited size, and subjective bias. Inspired by the rich user-oriented data over the Internet, this work explores the possibility to leverage users' search logs to reflect humans' underlying cultural product interests. To further examine how the COVID-19 mobility policy might influence cultural interest changes, we propose a new regression discontinuity design that has the additional potential to predict the recovery phase of peoples' cultural product interests. By analyzing the 1592 search interest time series in 6 countries, we found different patterns of change in interest in movies, music, and art during the COVID-19 pandemic, but a clear overall incremental increase. Across the six countries we studied, we found that changes in interest in cultural products were found to be strongly correlated with mobility and that as mobility declined, interest in movies, music, and art increased by an average of 35, 27 and 20, respectively, with these changes lasting at least eight weeks. © 2023 ACM.

The Impact of COVID-19 Routine Life - A Survey

The COVID-19 pandemic, caused by the novel coronavirus, has had a significant impact on daily life, education, business, and trade. The virus spreads quickly through direct contact with droplets, fecal-oral transmission, and water contamination. The consequences of the pandemic can be classified into three categories: health, economic, and social. The physical, mental, and psychological behaviors of individuals have also changed due to the pandemic. This study aimed to assess the impact of COVID-19 on the general population. A survey questionnaire with ten questions was distributed through an online portal, and the responses were analyzed using SPSS software. The results showed that healthcare workers were among the most affected, with the primary impact on their social and psychological well-being. Although previous research suggested that all fields were equally affected, this study found that healthcare workers were the most impacted group. The study concluded that the COVID-19 pandemic had a significant impact on the social and psychological well-being of the general population, with healthcare workers being the most affected. © 2023 IEEE.

Less About Privacy: Revisiting a Survey about the German COVID-19 Contact Tracing App

The release of COVID-19 contact tracing apps was accompanied by a heated public debate with much focus on privacy concerns, e.g., possible government surveillance. Many papers studied people's intended behavior to research potential features and uptake of the apps. Studies in Germany conducted before the app's release, such as that by Häring et al., showed that privacy was an important factor in the intention to install the app. We conducted a follow-up study two months post-release to investigate the intention-behavior-gap, see how attitudes changed after the release, and capture reported behavior. Analyzing a quota sample (n=837) for Germany, we found that fewer participants mentioned privacy concerns post-release, whereas utility now plays a greater role. We provide further evidence that the results of intention-based studies should be handled with care when used for prediction purposes. © 2023 ACM.

A Low-Cost Wireless Body Area Network for Human Activity Recognition in Healthy Life and Medical Applications

Moved by the necessity, also related to the ongoing COVID-19 pandemic, of the design of innovative solutions in the context of digital health, and digital medicine, Wireless Body Area Networks (WBANs) are more and more emerging as a central system for the implementation of solutions for well-being and healthcare. In fact, by elaborating the data collected by a WBAN, advanced classification models can accurately extract health-related parameters, thus allowing, as examples, the implementations of applications for fitness tracking, monitoring of vital signs, diagnosis, and analysis of the evolution of diseases, and, in general, monitoring of human activities and behaviours. Unfortunately, commercially available WBANs present some technological and economic drawbacks from the point of view, respectively, of data fusion and labelling, and cost of the adopted devices. To overcome existing issues, in this paper, we present the architecture of a low-cost WBAN, which is built upon accessible off-the-shelf wearable devices and an Android application. Then, we report its technical evaluation concerning resource consumption. Finally, we demonstrate its versatility and accuracy in both medical and well-being application scenarios. Author

The Effects of Information Layout, Display Mode, and Gender Difference on the User Interface Design of Mobile Shopping Applications

Online shopping has revolutionized our daily lives in the modern era. We can purchase needed goods on mobile shopping applications (apps) anytime and anywhere without leaving home. Especially during the COVID-19 pandemic, we have become increasingly dependent on various mobile shopping activities. However, the visual design of the shopping app interface often affects the user's interactive experience and the efficiency of browsing product information. In addition, gender differences are also worth being considered in the shopping interface design process. To achieve the goal, the research conducted a user study (N=40) of a 2× 2 x 2 mixed factorial design (i.e., information layout x display mode x gender difference). Each participant performed four tasks during the experiment. The authors measured the task completion time, collected the subjective responses from the SUS and the 7-point Likert scale questionnaire, and interviewed participants. The results revealed that: (1) females perform faster in lighter mode when searching for information location, while males perform faster in darker mode. (2) The information layout affects the user's visual search performance and subjective evaluation;females prefer the list style, but men prefer the matrix style. (3) Participants (both males and females) perceived matrix style as more popular than list style in dark mode;however, the result was reversed in light mode. The findings generated from the research can serve as a good reference for the development of user experience in the user interface design of mobile shopping apps. © 2013 IEEE.

A Machine Learning Approach for COVID-19 Tweet Classification

Vaccine-related information is awash on social media platforms like Twitter and Facebook. One party supports vaccination, while the other opposes vaccination and promotes misconceptions and misleading information about the risks of vaccination. The analysis of social media posts can give significant information into public opinion on vaccines, which can help government authorities in decision-making.This paper describes the dataset used in the shared task, and compares the performance of different classification that are provax, antivax and last neutral for identifying effective tweets related to Covid vaccines.We experimented with a classification-based approach. Our experiment shows that SVM classification performs well in order to effiective post.We're going to do this because vaccination is an important step for Covid19 so people can easily fix the news about the vaccine and grab their own slot and symptom detection is also playing a important part to arrest the spread of disease. © 2022 Copyright for this paper by its authors.

Understanding occupancy pattern of university libraries in the post-pandemic era

Understanding indoor occupancy patterns is crucial for energy model calibration, efficient operations of fresh air systems, and COVID-19 exposure risk assessment. University libraries, as one of centers of campus life, due to the high mobility and "foot-voting” nature of them, i.e., occupants pick seats in the micro-environments they prefer, provide a non-intrusive opportunity to carry out post-occupancy evaluations. We conducted a long-term online monitoring of occupancy in libraries of a university in China by web-crawling the online seat reservation system, based on which, we constructed two sets of databases consisting of around 70 million records of nearly 3, 000 seats in 4 library sections, with seat-level resolution and sampling frequency up to every 10 seconds. The informative data set depicts not only the overall spatio-temporal occupancy patterns, but also nuances hidden within seats and visits. The daily flow of the main libraries exceeded two visits per seat. Half of the visitors stayed at the libraries for 3-6 hours during a single occupancy. Semester schedules and campus accessibility together influence students' decisions on when and which library to go, while even within the same zone, some seats were always more popular than their neighbours. "Semi-isolation” is one of the candidate attractive features proposed to understand the underlying patterns. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Americans' COVID-19 risk perceptions and risk perception predictors changed over time

Identifying and understanding risk perceptions—"how bad are the harms” to humans or to what they value that people see as potentially or actually arising from entities or events—has been critical for risk analysis, both for its own sake, and for expected associations between risk perceptions and subsequent outcomes, such as risky or protective behavior, or support for hazard management policies. Cross-sectional surveys have been the dominant method for identifying and understanding risk perceptions, yielding valuable data. However, cross-sectional surveys are unable to probe the dynamics of risk perceptions over time, which is critical to do while living in a dynamically hazardous world and to build causal understandings. Building upon earlier longitudinal panel studies of Americans' Ebola and Zika risk perceptions using multi-level modeling to assess temporal changes in these views and inter-individual factors affecting them, we examined patterns in Americans' COVID-19 risk perceptions in six waves across 14 months. The findings suggest that, in general, risk perceptions increased from February 2020 to April 2021, but with varying trends across different risk perception measures (personal, collective, affective, affect, severity, and duration). Factors in baseline risk perceptions (Wave 1) and inter-individual differences across waves differed even more: baseline ratings were associated with how immediate the threat is (temporal distance) and how likely the threat would affect people like oneself (social distance), and following the United States news about the pandemic. Inter-individual trend differences were shaped most by temporal distance, whether local coronavirus infections were accelerating their upward trend, and subjective knowledge about viral transmission. Associations of subjective knowledge and risk trend with risk perceptions could change signs (e.g. from positive to negative) over time. These findings hold theoretical implications for risk perception dynamics and taxonomies, and research design implications for studying risk perception dynamics and their comparison across hazards. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

Real human close contact-based analysis on COVID-19 combined interventions

COVID-19 has threatened human lives. However, the efficiency of combined interventions on COVID-19 has not been accurately analyzed. In this study, an improved SEIR model considering both real human indoor close contact behaviors and personal susceptibility to COVID-19 was established. Taking Hong Kong as an example, a quantitative efficiency assessment of combined interventions (i.e. close contact reduction, vaccination, mask-wearing, school closures, workplace closures, and body temperature screening in public places) was carried out. The results showed that the infection risk of COVID-19 of students, workers, and non-workers/students were 3.1%, 8.7%, and 13.6%, respectively. The basic reproduction number R0 was equal to 1 when the close contact reduction rate was 59.9% or the vaccination rate reached 89.5%. The results could provide scientific support for interventions on COVID-19 prevention and control. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Visual analysis of the trend of mental health changes among Chinese college students in the context of the normalisation of the new crown epidemic

Public health events are sudden, public in nature and have serious social hazards. The COVID-19 outbreak coincided with the Lunar New Year, which had a direct or indirect impact on all areas of society. Previous studies related to emergencies have found that a considerable number of college students lacked experience in dealing with emergencies, were not emotionally stable enough, lacked analysis and decision-making ability, were easily suggestible and acted more impulsively. Therefore, in this paper, based on the existing actual information, combined with the awareness and understanding of college students' mental health, and based on the existing research results, the Hopfield-mental health model is used as a theoretical basis to study the trend of changes in college students' mental health. The results of the study show that 83.21% of the people are more concerned about the situation of this new crown pneumonia epidemic and they think that the new crown epidemic has seriously affected their living habits;65.45% thought that this new crown pneumonia epidemic did not have any major impact on their school life. The five sources of psychological stress, including academic, employment, economic, interpersonal relationship and love, were calculated and analysed in the model, which showed that employment stress, academic stress and economic stress were the largest sources of psychological stress among college students in this new pneumonia epidemic, accounting for 89%, 81% and 93%, respectively. They were followed by interpersonal and romantic stress, with 31% and 52%, respectively. © 2022 Liping Zhang.

Exploring cloud computing adoption: COVID era in academic institutions

Cloud computing (CC) is a revolution that can provide information technology (IT) as a service. CC offers infrastructure, platform, and software services, as demand peaks and surges. This paper aims to investigate how prospective adopters behave when external factors such as "Coronavirus Pandemic- COVID-19” impact their technology adoption decision-making. The study also explores how a prospective adopter behaves i.e., if his/her intention to adopt any new innovation increases in presence of stronger disruptive factors (COVID-19). This research empirically examines if the intent to adopt secured (online) services impacts actual CC adoption (CCA) in pre-COVID-19 and during COVID-19 eras. It also provides an idea of how prospective adopters behave when they face disruptions caused by the pandemic situation, and how the holistic relation is reflected in terms of its influence on academic performance. This study has used Technology Acceptance Model (TAM) with sequential mediation effect of intent to adopt secured online services and CCA on Academic Performance (AP) using a sample of 867 students from 25 different Indian universities in Tier 1 and Tier 2 cities. Using AMOS, a structural equation modelling was conducted to test the research model. The results highlight that there is a significant difference between the influence of perceived usefulness (PU) as well as perceived ease of use (PEOU) on CCA due to COVID-19. The results also provide empirical evidence of gender moderating the relationship of PU as well as PEOU with CCA. This is the first study that provides comparative results from pre-COVID and post-COVID era, this work provides a reference point to practitioners and academicians, especially when evaluating factors before making a final decision regarding any emerging technology's adoption. © 2023 Elsevier Inc.

New scenarios for experiments in Psychology: The online modality

An increasing number of behavioral studies are conducted online with the aim of including a wider and more heterogeneous sample of participants. This practice was encouraged by the outbreak of the Covid-19 pandemic, which implied social distancing, hence preventing access to laboratories as a measure to contain the infection. In the present work some online platforms will be discussed, considering their characteristics, advantages and limitations, in order to facilitate researchers in the possible selection of the most suitable tool for their needs. (PsycInfo Database Record (c) 2023 APA, all rights reserved) (Italian) Sempre piu ricerche comportamentali vengono condotte online con lo scopo di raggiungere un piu ampio e diversificato campione di partecipanti. Con l'avvento della pandemia da Covid-19 questa pratica si e diffusa sempre di piu a causa delle misure precauzionali di distanziamento sociale che hanno impedito l'accesso ai laboratori. Nel presente lavoro verranno descritte alcune piattaforme online, le loro caratteristiche e potenzialita, nonche i limiti ad esse connessi allo scopo di agevolare i ricercatori nella possibile scelta dello strumento piu adatto alle proprie esigenze. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Impacts of COVID-19-Related Non-Pharmaceutical Interventions on Mobility and Accidents in Bangladesh

Transport plays a major role in spreading contagious diseases such as COVID-19 by facilitating social contacts. The standard response to fighting COVID-19 in most countries has been imposing a lockdown—including on the transport sector—to slow down the spread. Though the Government of Bangladesh also imposed a lockdown quite early, it was forced to relax the lockdown for economic reasons. This motivates this study to assess the interaction between various non-pharmaceutical intervention (NPI) policies and transport sector outcomes, such as mobility and accidents, in Bangladesh. The study explores the effect of NPIs on both intra-and inter-regional mobility. Intra-regional mobility is captured using Google mobility reports which provide information about the number of visitors at different activity locations. Inter-regional, or long-distance, mobility is captured using vehicle count information from toll booths on a major bridge. Modeling shows that, in most cases, the policy interventions had the desired impact on people's mobility patterns. Closure of education institutes, offices, public transport, and shopping malls reduced mobility at most locations. The closure of garment factories reduced mobility for work and at transit stations only. Mobility was increased at all places except at residential locations, after the wearing of masks was made mandatory. Reduced traffic because of policy interventions resulted in a lower number of accidents (crashes) and related fatalities. However, mobility-normalized crashes and fatalities increased nationally. The outcomes of the study are especially useful in understanding the differential impacts of various policy measures on transport, and thus would help future evidence-based decision-making. © National Academy of Sciences: Transportation Research Board 2021.

Ensemble Learning using Transformers and Convolutional Networks for Masked Face Recognition

Wearing a face mask is one of the adjustments we had to follow to reduce the spread of the coronavirus. Having our faces covered by masks constantly has driven the need to understand and investigate how this behavior affects the recognition capability of face recognition systems. Current face recognition systems have extremely high accuracy when dealing with unconstrained general face recognition cases but do not generalize well with occluded masked faces. In this work, we propose a system for masked face recognition. The proposed system comprises two Convolutional Neural Network (CNN) models and two Transformer models. The CNN models have been fine-tuned on FaceNet pre-trained model. We ensemble the predictions of the four models using the majority voting technique to identify the person with the mask. The proposed system has been evaluated on a synthetically masked LFW dataset created in this work. The best accuracy is obtained using the ensembled models with an accuracy of 92%. This recognition rate outperformed the accuracy of other models and it shows the correctness and robustness of the proposed model for recognizing masked faces. The code and data are available at https://github.com/Hamzah-Luqman/MFR. © 2022 IEEE.

Teleworking and sustainable behaviour in the context of COVID-19: the case of Lithuania

COVID-19 played a significant role in the spread of telework worldwide, changing people's lives and behaviour. The paper aims to identify how teleworking affected the sustainable behaviour of employees during the COVID-19 pandemic. The research design applies a multi-method approach, combining systematic and comparative scientific literature analysis and a semi-structured interview. The authors of the paper present the theoretical conceptual model, which illustrates links between teleworking during the COVID-19 pandemic and the sustainable behaviour of employees. The results of empirical research revealed that teleworking during the COVID-19 pandemic changed employee behaviour in economic, environmental and social dimensions. Positive changes were identified due to reduced commuting and shopping;decreased costs for transport, food, clothing, and beauty services;better access to healthy and nutritious food;better opportunities for professional development. On the contrary, costs for home energy and household waste increased. Adverse effects on employees' physical and mental health have been identified due to teleworking and COVID-19. Despite the identified negative effects, employees would like to continue teleworking even after the pandemic. © 2023 Ramunė Čiarnienė et al., published by Sciendo.