ELASTIC: Edge Workload Forecasting based on Collaborative Cloud-Edge Deep Learning

With the rapid development of edge computing in the post-COVID19 pandemic period, precise workload forecasting is considered the basis for making full use of the edge limited resources, and both edge service providers (ESPs) and edge service consumers (ESCs) can benefit significantly from it. Existing paradigms of workload forecasting (i.e., edge-only or cloud-only) are improper, due to failing to consider the inter-site correlations and might suffer from significant data transmission delays. With the increasing adoption of edge platforms by web services, it is critical to balance both accuracy and efficiency in workload forecasting. In this paper, we propose ELASTIC, which is the first study that leverages a cloud-edge collaborative paradigm for edge workload forecasting with multi-view graphs. Specifically, at the global stage, we design a learnable aggregation layer on each edge site to reduce the time consumption while capturing the inter-site correlation. Additionally, at the local stage, we design a disaggregation layer combining both the intra-site correlation and inter-site correlation to improve the prediction accuracy. Extensive experiments on realistic edge workload datasets collected from China's largest edge service provider show that ELASTIC outperforms state-of-the-art methods, decreases time consumption, and reduces communication cost. © 2023 ACM.

The Moderating Effects of Corporate Social Responsibility on Corporate Financial Performance: Evidence from OECD Countries

This study aims to investigate the nature and intensity of the changes in corporate financial performance due to the corporate social responsibility (CSR) disclosures as a result of certain relationships between corporate governance and company performance in the non-financial sector. This study selected 625 non-financial companies across six organizations for economic cooperations (OECD) countries' stock markets for the period of 10 years (2012–2021). For this qualitative study, corporate governance, financial performance, and corporate social responsibility score data were collected from the DataStream, a reliable database for examining the research on OECD countries' listed companies. For the data analysis we applied various statistical tools such as regression analysis and moderation analysis. The findings of the study show that all attributes of the corporate governance mechanism, except for audit board attendance, have significant positive impacts on financial performance indicators for all the selected OECD economies except the country France. France's code of corporate governance has a significant negative impact on return on asset (ROA) and return on equity (ROE) due to differences in cultural and operational norms of the country. The audit board attendance has no significant impact on ROA. Moreover, all the attributes except board size (BSIZ) have significant positive impacts on the earnings per share (EPS) in Spain, The United Kingdom (UK) and Belgium. The values obtained from the moderation effect show that Corporate social responsibility is the key factor in motivating corporate governance practices which eventually improves corporate financial performance. However, this study advocated the implications, Investors and stakeholders should consider both corporate governance and CSR disclosures when making investment decisions. Companies that prioritize both governance and CSR tend to have better financial performance and are more likely to mitigate risks. Moreover, the policy makers can improve the code of corporate governance in order to attain sustainable development in the stock market.

AlphaFold Spreads through Protein Science

Two years ago, as the COVID-19 pandemic swept across the world, researchers at DeepMind, the artificial intelligence (AI) and research laboratory subsidiary of Alphabet Inc., demonstrated how it could use machine learning to achieve a breakthrough in the ability to predict how proteins, the workhorses of the living cell, fold into the intricate shapes they take on. The work gave hope to biologists that they could use this kind of tool to tackle diseases such as the SARS-CoV-2 coronavirus much more quickly in the future. Researchers were able to assess the abilities of DeepMind's AlphaFold2 thanks to its inclusion in the 14th Critical Assessment of Structure Prediction (CASP14), a benchmarking competition that ran through 2020 and which added a parallel program to uncover the structures of key proteins from the SARS-CoV2 virus to try to accelerate vaccine and drug development.

A review on tele-manipulators for remote diagnostic procedures and surgery

With modern medicine and healthcare services improving in leaps and bounds, the integration of telemedicine has helped in expanding these specialised healthcare services to remote locations. Healthcare telerobotic systems form a component of telemedicine, which allows medical intervention from a distance. It has been nearly 40 years since a robotic technology, PUMA 560, was introduced to perform a stereotaxic biopsy in the brain. The use of telemanipulators for remote surgical procedures began around 1995, with the Aesop, the Zeus, and the da Vinci robotic surgery systems. Since then, the utilisation of robots has steadily increased in diverse healthcare disciplines, from clinical diagnosis to telesurgery. The telemanipulator system functions in a master–slave protocol mode, with the doctor operating the master system, aided by audio-visual and haptic feedback. Based on the control commands from the master, the slave system, a remote manipulator, interacts directly with the patient. It eliminates the requirement for the doctor to be physically present in the spatial vicinity of the patient by virtually bringing expert-guided medical services to them. Post the Covid-19 pandemic, an exponential surge in the utilisation of telerobotic systems has been observed. This study aims to present an organised review of the state-of-the-art telemanipulators used for remote diagnostic procedures and surgeries, highlighting their challenges and scope for future research and development.

Telemedicine: A Survey of Telecommunication Technologies, Developments, and Challenges

The emergence of the COVID-19 pandemic has increased research outputs in telemedicine over the last couple of years. One solution to the COVID-19 pandemic as revealed in literature is to leverage telemedicine for accessing health care remotely. In this survey paper, we review several articles on eHealth and Telemedicine with emphasis on the articles' focus area, including wireless technologies and architectures in eHealth, communications protocols, Quality of Service, and Experience Standards, among other considerations. In addition, we provide an overview of telemedicine for new readers. This survey reviews several telecommunications technologies currently being proposed along with their standards and challenges. In general, an encompassing survey on the developments in telemedicine technology, standards, and protocols is presented while acquainting researchers with several open issues. Special mention of the state-of-the-art specialist application areas are presented. We conclude the survey paper by presenting important research challenges and potential future directions as they pertain to telemedicine technology.

Design and Implementation of a Real-Time Crowd Monitoring System Based on Public Wi-Fi Infrastructure: A Case Study on the Sri Chiang Mai Smart City

The COVID-19 pandemic has caused significant changes in many aspects of daily life, including learning, working, and communicating. As countries aim to recover their economies, there is an increasing need for smart city solutions, such as crowd monitoring systems, to ensure public safety both during and after the pandemic. This paper presents the design and implementation of a real-time crowd monitoring system using existing public Wi-Fi infrastructure. The proposed system employs a three-tiered architecture, including the sensing domain for data acquisition, the communication domain for data transfer, and the computing domain for data processing, visualization, and analysis. Wi-Fi access points were used as sensors that continuously monitored the crowd and uploaded data to the server. To protect the privacy of the data, encryption algorithms were employed during data transmission. The system was implemented in the Sri Chiang Mai Smart City, where nine Wi-Fi access points were installed in nine different locations along the Mekong River. The system provides real-time crowd density visualizations. Historical data were also collected for the analysis and understanding of urban behaviors. A quantitative evaluation was not feasible due to the uncontrolled environment in public open spaces, but the system was visually evaluated in real-world conditions to assess crowd density, rather than represent the entire population. Overall, the study demonstrates the potential of leveraging existing public Wi-Fi infrastructure for crowd monitoring in uncontrolled, real-world environments. The monitoring system is readily accessible and does not require additional hardware investment or maintenance. The collected dataset is also available for download. In addition to COVID-19 pandemic management, this technology can also assist government policymakers in optimizing the use of public space and urban planning. Real-time crowd density data provided by the system can assist route planners or recommend points of interest, while information on the popularity of tourist destinations enables targeted marketing.

The Ayushman Bharat Digital Mission (ABDM): making of India's Digital Health Story

Technology is being leveraged worldwide to deliver services to citizens in all domains, including healthcare. The COVID-19 pandemic has pushed everyone to embrace digital transformation and reconsider current healthcare trends. In response to the emerging need for digitization of healthcare in India, the Ayushman Bharat Digital Mission was launched in September 2021. It creates and uses Digital Public Goods in increasing the availability, accessibility, affordability and acceptability of health care through different building blocks. The purpose of this mission is to establish a national digital health ecosystem that is integrated, effective and inclusive. The interoperable frameworks, open protocols and consent artefact enable citizens, public and private healthcare providers, digital innovations and other stakeholders to come together and drive equitable digitization of healthcare across the country.

Information and Communication Technology in Brazilian Public Schools: A Sustainable Legacy of the Pandemic?

The present study aimed to analyze the sustainability of the post-COVID-19 pandemic Information and Communication Technology (ICT) legacy. The survey was conducted using raw secondary data from three census studies, one carried out before the pandemic and two after the return to in-person classes. The descriptive survey focused on Brazilian public schools and used a comparative intersectional design. Descriptive statistics were used to analyze the raw data. The poorest conditions in terms of the availability of technological resources were found in municipal public school systems. The amount of equipment available, bandwidth, and Internet data transmission rate in most public schools were far below desirable, despite advances in 2021 compared to 2019. Although there have been important improvements in ICT in Brazilian public schools, there was no evidence of inherited ICT resources as a legacy of the Government's COVID-19 policies related to education. The study highlights the need for government to implement enduring public policies that guarantee the use of sustainable ICT resources to improve education, irrespective of global or national health challenges.

Wearable Temperature Sensor and Artificial Intelligence to Reduce Hospital Workload

Patient sensing and data analytics provide information that plays an important role in the patient care process. Patterns identified from data and Machine Learning (ML) algorithms can identify risk/abnormal patients' data. Due to automatization this process can reduce workload of medical staff, as the algorithms alert for possible problems. We developed an integrated approach to monitor patients' temperature applied to COVID-19 elderly patients and an ML process to identify abnormal behavior with alerts to physicians. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Digital pathology: an overview

This article is an overview of digital pathology (DP) and its myriad applications. In addition, the trajectory of DP in India, with its successes, limitations and barriers is described. Covid pandemic provided new impetus to development and adoption of DP worldwide and India was no exception. With growing interest in Artificial Intelligence based algorithms for diagnosis and abilities to prognosticate and predict, the field of DP continues to evolve relentlessly to provide a new dimension to precision oncology.

Detecting Parallel Covert Data Transmission Channels in Video Conferencing Using Machine Learning

Covert communication channels are a concept in which a policy-breaking method is used in order to covertly transmit data from inside an organization to an external or accessible point. VoIP and Video systems are exposed to such attacks on different layers, such as the underlying real-time transport protocol (RTP) which uses Transmission Control Protocol (TCP) or User Datagram Protocol (UDP) packet streams to punch a hole through Network address translation (NAT). This paper presents different innovative attack methods utilizing covert communication and RTP channels to spread malware or to create a data leak channel between different organizations. The demonstrated attacks are based on a UDP punch hole created using Skype peer-to-peer video conferencing communication. The different attack methods were successfully able to transmit a small text file in an undetectable manner by observing the communication channel, and without causing interruption to the audio/video channels or creating a noticeable disturbance to the quality. While these attacks are hard to detect by the eye, we show that applying classical Machine Learning algorithms to detect these covert channels on statistical features sampled from the communication channel is effective for one type of attack.

Who's Next: Rising Star Prediction via Diffusion of User Interest in Social Networks

Finding items with potential to increase sales is of great importance in online market. In this paper, we propose to study this novel and practical problem: rising star prediction. We call these potential items Rising Star , which implies their ability to rise from low-turnover items to best-sellers in the future. Rising stars can be used to help with unfair recommendation in e-commerce platform, balance supply and demand to benefit the retailers and allocate marketing resources rationally. Although the study of rising star can bring great benefits, it also poses challenges to us. The sales trend of rising star fluctuates sharply in the short-term and exhibits more contingency caused by some external events (e.g., COVID-19 caused increasing purchase of the face mask) than other items, which cannot be solved by existing sales prediction methods. To address above challenges, in this paper, we observe that the presence of rising stars is closely correlated with the early diffusion of user interest in social networks, which is validated in the case of Taocode (an intermediary that diffuses user interest in Taobao). Thus, we propose a novel framework, RiseNet, to incorporate the user interest diffusion process with the item dynamic features to effectively predict rising stars. Specifically, we adopt a coupled mechanism to capture the dynamic interplay between items and user interest, and a special designed GNN based framework to quantify user interest. Our experimental results on large-scale real-world datasets provided by Taobao demonstrate the effectiveness of our proposed framework.

The Role of Animal Health Components in a Biosurveillance System: Concept and Demonstration

Biosurveillance defines the process of gathering, integrating, interpreting, and communicating essential information related to all-hazards threats or disease activity affecting human, animal, or plant health to achieve early detection and warning, contribute to overall situational awareness of the health aspects of an incident, and to enable better decision making for action at all levels. Animal health surveillance is an important component within biosurveillance systems comprising a continuum of activities from detecting biological threats, to analyzing relevant data, to managing identified threats, and embracing a One Health concept. The animal health community can strengthen biosurveillance by adopting various developments such as increasing the alignment, engagement, and participation of stakeholders in surveillance systems, exploring new data streams, improving integration and analysis of data streams for decision-making, enhancing research and application of social sciences and behavioral methods in animal health surveillance, and performing timely evaluation of surveillance systems. The aim of this paper is to explore components of a biosurveillance system from an animal health perspective and identify opportunities for the animal health surveillance community to enhance biosurveillance. Structural and operational diagrams are presented to demonstrate the required components and relevant data of animal health surveillance as an effective part within a biosurveillance system.

Development of Pandemic Monitoring System Based on Constellation of Nanosatellites

Covid-19 is a global crisis and the greatest challenge we have faced. It affects people in different ways. Most infected people develop a mild to moderate form of the disease and recover without hospitalization. This presents a problem in spreading the pandemic with unintentionally manner. Thus, this paper provides a new technique for COVID-19 monitoring remotely and in wide range. The system is based on satellite technology that provides a pivotal solution for wireless monitoring. This mission requires a data collection technique which can be based on drones' technology. Therefore, the main objective of our proposal is to develop a mission architecture around satellite technology in order to collect information in wide range, mostly, in areas suffer network coverage. A communication method was developed around a constellation of nanosatellites to cover Saudi Arabia region which is the area of interest in this paper. The new proposed architecture provided an efficient monitoring application discussing the gaps related to thermal imaging data. It reached 15.8 min as mean duration of visibility for the desired area. In total, the system can reach a coverage of 5.8 h/day, allowing to send about 21870 thermal images. © 2023 CRL Publishing. All rights reserved.

EDALoCo: Enhancing the accessibility of blockchains through a low-code approach to the development of event-driven applications for smart contract management

Blockchain is a cutting-edge technology based on a distributed, secure and immutable ledger that facilitates the registration of transactions and the traceability of tangible and intangible assets without requiring central governance. The agreements between the nodes participating in a blockchain network are defined through smart contracts. However, the compilation, deployment, interaction and monitoring of these smart contracts is a barrier compromising the accessibility of blockchains by non-expert developers. To address this challenge, in this paper, we propose a low-code approach, called EDALoCo, that facilitates the development of event-driven applications for smart contract management. These applications make blockchain more accessible for software developers who are non-experts in this technology as these can be modeled through graphical flows, which specify the communications between data producers, data processors and data consumers. Specifically, we have enhanced the open-source Node-RED low-code platform with blockchain technology, giving support for the creation of user-friendly and lightweight event-driven applications that can compile and deploy smart contracts in a particular blockchain. Additionally, this platform extension allows users to interact with and monitor the smart contracts already deployed in a blockchain network, hiding the implementation details from non-experts in blockchain. This approach was successfully applied to a case study of COVID-19 vaccines to monitor and obtain the temperatures to which these vaccines are continuously exposed, to process them and then to store them in a blockchain network with the aim of making them immutable and traceable to any user. As a conclusion, our approach enables the integration of blockchain with the low-code paradigm, simplifying the development of lightweight event-driven applications for smart contract management. The approach comprises a novel open-source solution that makes data security, immutability and traceability more accessible to software developers who are non-blockchain experts. • EDALoCo, an approach for integrating blockchain and low-code paradigms. • Developing event-driven applications for smart contract management. • Deploying the event-driven applications in lightweight devices. • Providing an open-source solution. [ FROM AUTHOR]

SWeeT: Security Protocol for Wearables Embedded Devices' Data Transmission

Motivated by the quest for decreased healthcare costs and further fueled by the COVID pandemic, wearable devices have gained major attention in recent years. Yet, their secure usage and patients' privacy continue to be concerning. To address these issues, the paper presents SWeeT, a novel lightweight protocol for allowing flexible and secure access to the collected data by multiple caregivers while sustaining the patient's privacy. Particularly, SWeeT deploys Physically Unclonabale Functions (PUFs) to generate encryption keys to safeguard the patients' data during transmission. The computation overhead is significantly reduced by applying very simple encryption operations while enabling frequent change of the keys to sustain robustness. SWeeT is shown to counter impersonation, Sybil, man-in-the-middle, and forgery attacks. SweeT is validated through experiments using implementation on an Artix7 FPGA and through formal security analysis. © 2022 IEEE.

Can Universities Combat the 'Wrong Kind of AI'?

Dubhashi examines whether universities can combat the wrong kind of artificial intelligence (AI). Excessive focus on automation is a central defining characteristic of "the wrong kind of AL" Several factors seem to indicate that automation will accelerate following the COVID-19 pandemic. While people argue about how far AI and automation will completely eliminate jobs, in talking about the "wrong kind of AI," we also must consider what is happening to existing jobs. The central leadership role in combating the "wrong kind of AI" should thus be the responsibility of the university.

COVID-19 Modeling for India and a Roadmap for the Future

A number of models have been developed in India to forecast the spread of the coronavirus disease or COVID-19 in the country. While these have largely been variants of the classical susceptible-exposed-infectious-recovered (SEIR) compartmental model, other approaches using time-series analysis, machine-learning, network models, and agent-based simulations have also helped to provide specific insights into questions of policy. Model building has had to incorporate our evolving knowledge of the disease, including the appearance of new variants, immune escape leading to reinfections, time-varying non-pharmaceutical interventions, the pace of the vaccination program, and breakthrough infections. The predictive power of these models has been hampered by the lack of availability of quality data on infection and deaths as a function of age, the nature of social contacts, demography, and the clinical consequence of infection. An early emphasis on "ensemble models," a thrust toward increased data availability, a greater engagement of modelers with the epidemiological and public health communities, and a more nuanced approach to communicating the limitations of modeling could have substantially increased the usefulness of models during the COVID-19 pandemic in India.

Walk a Mile in Their Shoes

Butler and Yeh share the stories of four fictional people made up from the amalgamated diaries of 20 individuals who submitted more than 150 diary entries over the first year of the pandemic. They follow these four composite characters as they navigate a year of COVID while shipping one of the largest software products in the world. While the past 20 months have been a challenge, evidence suggests the next year and beyond will continue to be filled with changes in how people work as they settle into a new normal. These four stories are meant to help readers better understand experiences in this new world and to operate in a more empathetic and productive way as they move into the uncertain future of hybrid work.

Intelligence-based optimized cognitive radio routing for medical data transmission using IoT

The Internet of Things (IoT) is considered an effective wireless communication, where the main challenge is to manage energy efficiency, especially in cognitive networks. The data communication protocol is a broadly used approach in a wireless network based IoT. Cognitive Radio (CR) networks are mainly concentrated on battery-powered devices for highly utilizing the data regarding the spectrum and routing allocation, dynamic spectrum access, and spectrum sharing. Data aggregation and clustering are the best solutions for enhancing the energy efficiency of the network. Most researchers have focused on solving the problems related to Cognitive Radio Sensor Networks (CRSNs) in terms of Spectrum allocation, Quality of Service (QoS) optimization, delay reduction, and so on. However, a very small amount of research work has focused on energy restriction problems by using the switching and channel sensing mechanism. As this energy validation is highly challenging due to dependencies on various factors like scheduling priority to the registered users, the data loss rate of unlicensed channels, and the possibilities of accessing licensed channels. Many IoT-based models involve energy-constrained devices and data aggregation along with certain optimization approaches for improving utilization. In this paper, the cognitive radio framework is developed for medical data transmission over the Internet of Medical Things (IoMT) network. The energy-efficient cluster-based data transmission is done through cluster head selection using the hybrid optimization algorithm named Spreading Rate-based Coronavirus Herding-Grey Wolf Optimization (SR-CHGWO). The network lifetime is improved with a cognitive- routing based on IoT framework to enhance the efficiency of the data transmission through the multi-objective function. This multi-objective function is derived using constraints like energy, throughput, data rate, node power, and outage probability delay of the proposed framework. The simulation experiments show that the developed framework enhances the energy efficiency using the proposed algorithm when compared to the conventional techniques. © 2022 the Author(s)