ABSTRACT
Smart cities are concepts much loved by politicians and technologists but are very difficult to bring about in practice. There are many isolated applications in cities such as operating streetlamps, but very few, if any, examples of integrated applications sharing data and managing the city as a holistic entity rather than a set of disparate and unconnected applications. This is despite hundreds of trials and indicates how difficult bringing about a smart city will be. The key challenge is the wide range of interested parties in a city including the elected city authority, subcontractors and suppliers to the authority, emergency services, transport providers, businesses, residents, workers, tourists, and other visitors. Some of these entities will be primarily driven by finance, such as businesses and transport providers. Some will be driven by political considerations. Some will be concerned with the quality of life as well as financial costs. In some cases, there will be conflicting interests-the city may want as much information as possible on people in the city, whereas individuals may want privacy and the minimum data stored concerning their movements and attributes. COVID-19 does not change any of these issues, but it does increase the importance of some applications such as smart health, logistics, people surveillance, data security, and crisis management, while reducing the importance of others such as traffic management. It may result in more willingness for monitoring and data sharing if this can be shown to result in better control of the virus. © 2023 the authors.
ABSTRACT
The internet is often thought of as a democratizer, enabling equality in aspects such as pay, as well as a tool introducing novel communication and monetization opportunities. In this study we examine athletes on Cameo, a website that enables bi-directional fan-celebrity interactions, questioning whether the well-documented gender pay gaps in sports persist in this digital setting. Traditional studies into gender pay gaps in sports are mostly in a centralized setting where an organization decides the pay for the players, while Cameo facilitates grass-roots fan engagement where fans pay for video messages from their preferred athletes. The results showed that even on such a platform gender pay gaps persist, both in terms of cost-per-message, and in the number of requests, proxied by number of ratings. For instance, we find that female athletes have a median pay of 30$ per-video, while the same statistic is 40$ for men. The results also contribute to the study of parasocial relationships and personalized fan engagements over a distance. Something that has become more relevant during the ongoing COVID-19 pandemic, where in-person fan engagement has often been limited. © 2023 Owner/Author.
ABSTRACT
During the COVID-19 pandemic, many countries have developed contact tracing technologies to curb the spread of the disease by locating and isolating people who have been in contact with coronavirus carriers. Subsequently, understanding why people install and use contact tracing applications is becoming central to their effectiveness and impact. However, involuntary systems can crowd out the use of voluntary applications when several contact tracing initiatives are employed simultaneously. To investigate this hypothesis, we analyze the concurrent deployment of two contact tracing technologies in Israel: centralized mass surveillance technologies and a voluntary contact tracing mobile app. Based on a representative survey of Israelis (n=519), our findings show that positive attitudes toward mass surveillance were related to a reduced likelihood of installing contact tracing apps and an increased likelihood of uninstalling them. These results also hold when controlling for privacy concerns, attitudes toward the app, trust in authorities, and demographic properties. We conclude the paper by suggesting a broader framework for analyzing crowding out effects in ecosystems that combine involuntary surveillance and voluntary participation. © 2023 ACM.
ABSTRACT
Although remote working has been adopted in some firms for many years, it has been largely marginalized in the sense that the mainstream of software engineering practice has involved groups of software professionals congregating on a daily basis in centralized offices. However, with the onset of the COVID-19 pandemic, remote working has rapidly become widespread. Companies and their employees have grappled with difficult adjustments during this transition, as might be expected in face of an international emergency. One suspects however that remote working will endure in some form in the post-emergency phase. Rather than merely coping as we do today, to be effective and sustainable, post-pandemic remote working will require a deep rethink of fundamental practice. This article provides some signposts to aid the journey from coping with remote work to thriving in remote working contexts. Author
ABSTRACT
In view of the fact that Covid-19 is highly contagious, which poses great threat and inconvenience to people's production and life, a multifunctional robot control system with single-chip microcomputer as the control core is designed, aiming at the problems of centralized isolation points in communities, complicated situation and difficult management. Firstly, Gmapping algorithm is used to realize the robot's autonomous positioning and avoidance. Secondly, a three-degree-of-freedom robot arm is designed to disinfect any indoor space. Finally, Gmapping algorithm is used to recognize and measure the temperature of human face. Through the simulation experiment, this method can improve the efficiency of searching the shortest path and carry out disinfection work while reducing human contact, improving public safety and has practical value. © 2023 IEEE.
ABSTRACT
Rapid improvements in healthcare services and affordable IoT in the past decade have been a big help in dealing with the issue of fewer medical facilities. Unfortunately, some people still choose not to get immunized, thus fear and reluctance remain a part of human existence despite widespread vaccination initiatives. Therefore, it is important to screen this group of potential spreaders as soon as possible since they may become infected and transfer viruses to others. It is in this context that the pharmaceutical sector might benefit from highly developed health monitoring systems. This work has created and tested a multi-node architecture based on Fog computing to perform real-time initial screening and recording of such individuals, therefore addressing the demand and reducing the unpredictability of the scenario. In addition to capturing photographs of the subject's face, the suggested device also recorded the subject's current body temperature and GPS locations. As an added bonus, the suggested system could upload information to a remote server over the internet. To test the viability of the proposed system, a thorough examination of the existing work environment was carried out, including implementation and evaluations. From the results of the statistical analysis, it was seen that the suggested IoT Fog-enabled ecosystem may be put to good use. © 2023 IEEE.
ABSTRACT
The government addresses that one of the biggest problems in the country is lacking an effective contact tracing solution. The Philippines' current contact tracing systems have encountered a lot of challenges because of the lack of features that would ensure safety and awareness to users around. The study aims to propose a system framework that can be used as Contact Tracing Solution using data warehousing and edge computing would improve the tracing in small and concentrated areas such as universities and offices. The researchers gather reviews and studies on how to develop a system that would address the current problem in the contact tracing scenario in the Philippines, particularly in the education field. The researcher will be going to apply the descriptive and development design for the conduct of the study and the waterfall methodology will be the software model for the development of the proposed system. Therefore, it is better to develop a contact tracing application that will be used by universities whose main objective is to spread awareness to potentially close contacts of a COVID-19 positive case and further implement the system framework to provide a proactive solution for contact tracing in the academe. © 2022 IEEE.
ABSTRACT
In 2019, the corona virus was found in Wuhan, China. The corona virus has traveled several countries in the world from the beginning of 2020. The early estimation of COVID-19 cases is one of the efficient approaches to control the pandemic. Many researchers had proposed the deep learning model for the efficient estimation of COVID-19 cases for different provinces in the world. The research work had not focused on the discussion of robustness in the model. In this study, centralized federated-convolutional neural network–gated recurrent unit (Fed-CNN–GRU) model is proposed for the estimation of active cases per day in different provinces of India. In India, the uneven transmission of COVID-19 virus was seen in 36 provinces due to the different geographical areas and population densities. So, the methodology of this study had focused on the development of single deep learning algorithm, which is robust and reliable to estimate the active cases of COVID-19 in different provinces of India. The concept of transfer and federated learning is involved to enhance the estimation of active cases of COVID-19 by the CNN–GRU model. The study had considered the active cases per day dataset for 36 provinces in India from 12 March, 2020 to 17 January, 2022. Based on the study, it is proven that the centralized CNN–GRU model by federated learning had captured the transmission dynamics of COVID-19 in different provinces with an enhanced result. IEEE
ABSTRACT
The COVID-19 pandemic has radically transformed the work-from-home (WFH) paradigm, and expanded an organization's cyber-vulnerability space. We propose a novel strategic method to quantify the degree of sub-optimal cybersecurity in an organization of employees, all of whom work in heterogeneous WFH 'siloes'. Specifically, we model the per-unit cost of asymmetric WFH employees to invest in security-improving effort units as time-discounted exponential martingales over time, and derive as benchmark - the centrally-planned socially optimal aggregate employee effort at any given time instant. We then derive the time-varying strategic Nash equilibrium amount of aggregate employee effort in cybersecurity in a distributed setting. The time-varying ratio of these centralized and distributed estimates quantifies the free riding dynamics, i.e., security sub-optimality, within an organization. Rigorous estimates of the degree of sub-optimal cybersecurity will drive organizational policy makers to design appropriate (customized) solutions that voluntarily incentivize WFH employees to invest in required cybersecurity best practices. © 2022 IEEE.
ABSTRACT
The rapid growth in technology and several IoT devices make cyberspace unsecure and eventually lead to Significant Cyber Incidents (SCI). Cyber Security is a technique that protects systems over the internet from SCI. Data Mining and Machine Learning (DM-ML) play an important role in Cyber Security in the prediction, prevention, and detection of SCI. This study sheds light on the importance of Cyber Security as well as the impact of COVID-19 on cyber security. The dataset (SCI as per the report of the Center for Strategic and International Studies (CSIS)) is divided into two subsets (pre-pandemic SCI and post-pandemic SCI). Data Mining (DM) techniques are used for feature extraction and well know ML classifiers such as Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF) for classification. A centralized classifier approach is used to maintain a single centralized dataset by taking inputs from six continents of the world. The results of the pre-pandemic and post-pandemic datasets are compared and finally conclude this paper with better accuracy and the prediction of which type of SCI can occur in which part of the world. It is concluded that SVM and RF are much better classifiers than others and Asia is predicted to be the most affected continent by SCI. Author
ABSTRACT
The objective of the paper is to demonstrate digitalization of Floating Structures Integrity Management Program (FSIMP) and its application for the structural integrity of floating structure assets. The framework of FSIMP is being developed by adopting Risk Based Inspection (RBI) methodology and complemented with technical know-how and industry best-practices. Implementing the methodology provides strategic planning for maintenance by reducing the anticipated risk. Hence, ensuring uninterrupted service of the floating structure assets throughout the service life. This paper presents a systematic approach for digitalization of the integrity management program for a nominated floating structure asset. The methodology offers a procedure to acquire necessary data management gathering, risk assessment, and RBI survey plan to maintain the structural integrity in the centralized web-based platform of FSIMP. RBI process is adopted into the FSIMP to investigate all deterioration and failure mechanisms. These structures will be identified by qualitative and quantitative risk assessment methods. The implementation of FSIMP offers a wide range of capabilities in structural integrity management such as integrating all floating structure fleet assets in a single dashboard of web-based platform, clear line of sight for reliable structural integrity, and an holistic overview across all levels of management. FSIMP with RBI methodology evaluates all data gathering to optimize inspection resources based on the risk assessment through an optimum combination of inspection methods and frequencies. The whole process is aligned to the requirements from Classification to ensure reliability for continuous operations. It also observes the essential need of digitalization for FSIMP during the time of post-COVID19 pandemic and the ever-expanding offshore oil, gas and energy frontiers that demand the adoption of new and advanced technologies, especially in the field of digitalization. It is shown that FSIMP has great potential as a digitalization tool and system to integrate with the RBI risk assessment that aligns to the requirements from Classification. It is strategically to maximize the effectiveness and improved efficiency for inspection and monitoring plan. The paper provides information on the solution of digitalization to the Floating Structures Integrity Management Program (FSIMP) in ensuring that the integrity of floating structure asset during the service life is intact for continuous operation and a holistic overview for all the assigned fleet assets in a centralized dashboard web-based platform. In addition to that, RBI is as added benefit to the FSIMP with its structure methodology of data evaluation and risk assessment in order to objectively optimizing inspection and maintenance resources. Copyright © 2023, International Petroleum Technology Conference.
ABSTRACT
The Internet of Things (IoT) has revamped service-oriented architectures by enabling edge-based devices to collect and share information that is vital for the service provisioning process. IoT devices have evolved from simple data acquirers and have become part of the service provisioning process. These devices are now able to sense, acquire, communicate, and process data in an intelligent manner. With the support of Artificial Intelligence (AI), IoT devices can now support users with minimal reliance on centralized entities, such as the Cloud. IoT devices are now able to share raw and processed information securely, without or with minimal reliance on centralized devices. This paper proposes a general framework for Health 4.0 to provide edge-based health services with the support of AI. IoT devices collect and share patient information in a secure manner to enable user-side disease diagnosis. The solution enables both federated and centralized learning to coexist under one framework. As a proof-of-concept, the solution considers a COVID-19 diagnosis use-case. A Machine Learning (ML) web-based user application is developed to analyze frontal chest X-ray (CXR) images and make predictions on whether patients' lungs are damaged. The solution provides an experimental study on mechanisms and approaches needed to increase learning accuracy with reduced dataset sizes and image quality through Federated Learning (FL). © 2022 IEEE.
ABSTRACT
Over the years, the number of orphans in India have been increasing and the adoptions taking place have not seen significant growth in them. The current system provided by the Central Adoption Resource Authority (also known as CARA) is inefficient and backward. Due to this, the parents get frustrated and give up on the idea of adopting a child. With the recent introduction of COVID-19 pandemic, the problem has become more relevant. There are also various other issues such as child trafficking, illegal adoption, child labor, etc. that have come into the picture due the lack of security in the existing system. To solve this problem, there is a need to develop a system that will replace the current parent-centric process to a child-centric process. The system will be at a centralized location so that it is accessible to all the stakeholder. The system will enable the adoption procedure which will be faster and more responsive. © 2022 IEEE.
ABSTRACT
The evolution of a disease in a large population is a function of the top-down policy measures from a centralized planner and the self-interested decisions (to be socially active) of individual agents in a large heterogeneous population. This paper is concerned with understanding the latter based on a mean-field type optimal control model. Specifically, the model is used to investigate the role of partial information on an agent's decision-making and study the impact of such decisions by a large number of agents on the spread of the virus in the population. The motivation comes from the presymptomatic and asymptomatic spread of the COVID-19 virus, where an agent unwittingly spreads the virus. We show that even in a setting with fully rational agents, limited information on the viral state can result in epidemic growth. © 2022 IEEE.
ABSTRACT
We conducted semi-structured interviews with 20 users of Canada's exposure-notification app, COVID Alert. We identified several types of users' mental models for the app. Participants' concerns were found to correlate with their level of understanding of the app. Compared to a centralized contact-tracing app, COVID Alert was favored for its more efficient notification delivery method, its higher privacy protection, and its optional level of cooperation. Based on our findings, we suggest decision-makers rethink the app's privacy-utility trade-off and improve its utility by giving users more control over their data. We also suggest technology companies build and maintain trust with the public. Further, we recommend increasing diagnosed users' motivation to notify the app and encouraging exposed users to follow the guidelines. Last, we provide design suggestions to help users with Unsound and Innocent mental models to better understand the app. © 2022 ACM.
ABSTRACT
This study was motivated by the challenges experienced by parents and guardians in ensuring the safety of children in cyberspace. In the last two or three years, online education has become very popular all over the world due to the Covid 19 pandemic. Therefore parents, guardians and teachers must ensure the safety of children in cyber space. Children are more likely to go astray and there are plenty of online programs waiting to get them on wrong track and also children who are engaging in the online education can be distracted at any moment. Therefore, parents should keep a close check on their children's online activity. Apart from that due to the unawareness of children, they tempt to share their sensitive information, chance of being a victim of phishing attacks from outsiders. These problems can be overcome through the proposed web-based system. We use feature extraction, web tracking and analysis mechanisms, image processing and name entity recognition to implement this web-based system. © 2022 IEEE.
ABSTRACT
eHealth or Digital health is the pioneer project funded by Government of India and Department of Health and Family Welfare, Government of Kerala, designed to provide residents of Kerala with convenient centralized healthcare system. It describes the integration of information technology and electronic communications used for different healthcare processes for people's health and their wellbeing. It has introduced since 1920s as Telemedicine and later it expanded in 2009 as e-Health due to advancement of technology. When the countries healthcare industry was facing tough times, we observed that the e-Health came to the rescue of many times during multiple waves of the pandemic. This study mainly focusses on analysing various digital health initiatives by MHFW and an attempt has been made to know what are telemedicine schemes, web portals and mobile applications and global digital health agenda used for implementation of digital health and how far it benefited in the pandemic days. Copyright © 2022, Anka Publishers. All rights reserved.
ABSTRACT
Due to the COVID-19 pandemic, the global supply chain is disrupted at an unprecedented scale under uncertain and unknown trends of labor shortage, high material prices, and changing travel or trade regulations. To stay competitive, enterprises desire agile and dynamic response strategies to quickly react to disruptions and recover supply-chain functions. Although both centralized and multi-agent approaches have been studied, their implementation requires prior knowledge of disruptions and agent-rule-based reasoning. In this paper, we introduce a model-based multi-agent framework that enables agent coordination and dynamic agent decision-making to respond to supply chain disruptions in an agile and effective manner. Through a small-scale simulated case study, we showcase the feasibility of the proposed approach under several disruption scenarios that affect a supply chain network differently, and analyze performance trade-offs between the proposed distributed and centralized methods. © 2022 IEEE.
ABSTRACT
The COVID-19 pandemic has led to a worldwide surge in demand for masks, protective clothing, and other epidemic prevention materials. The lack of epidemic prevention materials has put the lives of frontline health care workers at serious risk. However, epidemic prevention materials are not being distributed fairly and efficiently. This, coupled with the occasional scramble for scarce materials, makes epidemic prevention materials scarcer. The traditional centralized donation model makes it difficult to obtain the demand for materials in a timely manner, and the existing blockchain-based donation systems have not improved the efficiency of material donation. Moreover, most of the donation systems do not consider privacy and security issues. In this paper, we propose a blockchain-based material donation platform designed and implemented through the Ethereum platform. We solve the difficulty of demand acquisition and improve the transparency of the donation process through blockchain;reduce the possibility of a second disaster and improve the efficiency of material distribution through smart contracts;and protect the privacy and security of the donation process through zero-knowledge proof. We validate the security and efficiency of the proposed epidemic donation platform. © 2022 IEEE.
ABSTRACT
Both 5G Internet and COVID-19 pandemic have increasingly prompted thousands of companies and organizations to shift from a centralized office model to a distributed home model, which poses a new requirement: how to securely and rapidly share private data for coworkers on Internet. The covert communication systems are widely used to deliver private information because of the possibility of extending the system to Internet-scale size. However, most existing systems are inadequate to solve the requirement, since either the servers in centralized systems face the risk of being monitored and infiltrated, or the multi-hop routing schemes in decentralized systems lead to diverse attacks and high delivery latency. To this end, we proposed a scalable covert communication service for coworkers, called SC2. For security and hiddenness, we adopt the content slicing and multichannel routing to prevent adversary from monitoring and analyzing data. For scalability, we design a two-hop logic overlay to support low latency routing, and an adaptive channel selction technique to exploit the available bandwidth of the system. To evaluate the performance of SC2, we deploy the system in various IoT clouds and storage clouds. The experimental results demonstrate that SC2 is able to transmit both short messages and bulk content. Under various parameter settings, the delivery latency of SC2 linearly decreases with the number of channels, and SC2 takes full advantage of the available bandwidth with the growing number of users. © 2022 IEEE.