COVID-19 pandemic undoubtedly lingers on and has brought unprecedented changes globally including travel arrangements. Blockchain-based solutions have been proposed to aid travel amid the pandemic hap. Presently, extant solutions are country or regional-based, downplay privacy, non-responsive, often impractical, and come with blockchain-related complexities presenting technological hurdle for travelers. We therefore propose a solution namely, to foster global travel allowing travelers and countries collaboratively engage in a secure adaptive proof protocol dubbed Proof-of-COVID-19 status a number of arbitrary statements to ascertain the fact that the traveler poses no danger irrespective of the country located. As far as we know, this is first of its kind. is implemented as a decentralized application leveraging blockchain as a trust anchor and decentralized storage technology. Security analysis and evaluation are performed proving security, privacy-preservation, and cost-effectiveness along with implementation envisioning it as a blueprint to facilitate cross-border travel during the present and future pandemics. Our experimental results show it takes less than 60 and 3 s to onboard users and perform proof verification respectively attesting to real usability scenarios along with the traits of arbitrary proofs to aid responsiveness to the dynamics of pandemics and blockchain abstraction from travelers.

The COVID-19 pandemic has emerged as a highly transmissible disease which has caused a disastrous impact worldwide by adversely affecting the global economy, health, and human lives. This sudden explosion and uncontrolled worldwide spread of COVID-19 has revealed the limitations of existing healthcare systems regarding handling public health emergencies. As governments seek to effectively re-establish their economies, open workplaces, ensure safe travels and progressively return to normal life, there is an urgent need for technologies that may alleviate the severity of the losses. This article explores a promising solution for secure Digital Health Certificate, called NovidChain, a Blockchain-based privacy-preserving platform for COVID-19 test/vaccine certificates issuing and verifying. More precisely, NovidChain incorporates several emergent concepts: (i) Blockchain technology to ensure data integrity and immutability, (ii) self-sovereign identity to allow users to have complete control over their data, (iii) encryption of Personally Identifiable Information to enhance privacy, (iv) W3C verifiable credentials standard to facilitate instant verification of COVID-19 proof, and (v) selective disclosure concept to permit user to share selected pieces of information with trusted parties. Therefore, NovidChain is designed to meet a high level of protection of personal data, in compliant with the GDPR and KYC requirements, and guarantees the user's self-sovereignty, while ensuring both the safety of populations and the user's right to privacy. To prove the security and efficiency of the proposed NovidChain platform, this article also provides a detailed technical description, a proof-of-concept implementation, different experiments, and a comparative evaluation. The evaluation shows that NovidChain provides better financial cost and scalability results compared to other solutions. More precisely, we note a high difference in time between operations (i.e., between 46% and 56%). Furthermore, the evaluation confirms that NovidChain ensures security properties, particularly data integrity, forge, binding, uniqueness, peer-indistinguishability, and revocation.

The Covid-19 pandemic has emerged as one of the most disquieting worldwide public health emergencies of the 21st century and has thrown into sharp relief, among other factors, the dire need for robust forecasting techniques for disease detection, alleviation as well as prevention. Forecasting has been one of the most powerful statistical methods employed the world over in various disciplines for detecting and analyzing trends and predicting future outcomes based on which timely and mitigating actions can be undertaken. To that end, several statistical methods and machine learning techniques have been harnessed depending upon the analysis desired and the availability of data. Historically speaking, most predictions thus arrived at have been short term and country-specific in nature. In this work, multimodel machine learning technique is called EAMA for forecasting Covid-19 related parameters in the long-term both within India and on a global scale have been proposed. This proposed EAMA hybrid model is well-suited to predictions based on past and present data. For this study, two datasets from the Ministry of Health & Family Welfare of India and Worldometers, respectively, have been exploited. Using these two datasets, long-term data predictions for both India and the world have been outlined, and observed that predicted data being very similar to real-time values. The experiment also conducted for statewise predictions of India and the countrywise predictions across the world and it has been included in the Appendix.

Since the end of 2019, computed tomography (CT) images have been used as an important substitute for the time-consuming Reverse Transcriptase polymerase chain reaction (RT-PCR) test; a new coronavirus 2019 (COVID-19) disease has been detected and has quickly spread through many countries across the world. Medical imaging such as computed tomography provides great potential due to growing skepticism toward the sensitivity of RT-PCR as a screening tool. For this purpose, automated image segmentation is highly desired for a clinical decision aid and disease monitoring. However, there is limited publicly accessible COVID-19 image knowledge, leading to the overfitting of conventional approaches. To address this issue, the present paper focuses on data augmentation techniques to create synthetic data. Further, a framework has been proposed using WoT and traditional U-Net with EfficientNet B0 to segment the COVID Radiopedia and Medseg datasets automatically. The framework achieves an -score of 0.96, which is best among state-of-the-art methods. The performance of the proposed framework also computed using Sensitivity, Specificity, and Dice-coefficient, achieves 84.5%, 93.9%, and 65.0%, respectively. Finally, the proposed work is validated using three quality of service (QoS) parameters such as server latency, response time, and network latency which improves the performance by 8%, 7%, and 10%, respectively.

This study contributes to our understanding of how the emergence of the COVID-19 pandemic changes the global Banking Financial Services and Insurance (BFSI) landscape. Before the COVID-19 pandemic, BFSIs corporate strategy was solely aligned to the quest for operational efficiency. However, during the ongoing COVID-19 pandemic, global BFSIs are forced to adopt digital transformation in their operations due to a rise in transaction volumes. The ongoing COVID-19 pandemic already triggers holistic innovations concerning the global BFSI's product, process, concept, trend, or idea. Thus, the BFSI cannot survive without efficient and innovative system software for global operations. The study plots the hype cycle to identify relevant technologies to deal with real-world business problems. The hype cycle indicates that the need for advanced data integration is growing and COVID-19 pandemic has already triggered it. The study argues that the incorporation of data integration might be challenging initially for BFSIs but eventually it may result in an efficient model to handle these types of pandemic or unexpected circumstances.

Internet of Things (IoT), fog computing, cloud computing, and data-driven techniques together offer a great opportunity for verticals such as dairy industry to increase productivity by getting actionable insights to improve farming practices, thereby increasing efficiency and yield. In this paper, we present SmartHerd, a fog computing-assisted end-to-end IoT platform for animal behavior analysis and health monitoring in a dairy farming scenario. The platform follows a microservices-oriented design to assist the distributed computing paradigm and addresses the major issue of constrained Internet connectivity in remote farm locations. We present the implementation of the designed software system in a 6-month mature real-world deployment, wherein the data from wearables on cows is sent to a fog-based platform for data classification and analysis, which includes decision-making capabilities and provides actionable insights to farmer towards the welfare of animals. With fog-based computational assistance in the SmartHerd setup, we see an 84% reduction in amount of data transferred to the cloud as compared with the conventional cloud-based approach.

In recent years, persistent homology has become an attractive method for data analysis. It captures topological features, such as connected components, holes, and voids from point cloud data and summarizes the way in which these features appear and disappear in a filtration sequence. In this project, we focus on improving the performance of Eirene, a computational package for persistent homology. Eirene is a 5000-line open-source software library implemented in the dynamic programming language Julia. We use the Julia profiling tools to identify performance bottlenecks and develop novel methods to manage them, including the parallelization of some time-consuming functions on multicore/manycore hardware. Empirical results show that performance can be greatly improved.

Android apps share resources, such as sensors, cameras, and Global Positioning System, that are subject to specific usage policies whose correct implementation is left to programmers. Failing to satisfy these policies may cause resource leaks, that is, apps may acquire but never release resources. This might have different kinds of consequences, such as apps that are unable to use resources or resources that are unnecessarily active wasting battery. Researchers have proposed several techniques to detect and fix resource leaks. However, the unavailability of public benchmarks of faulty apps makes comparison between techniques difficult, if not impossible, and forces researchers to build their own data set to verify the effectiveness of their techniques (thus, making their work burdensome). The aim of our work is to define a public benchmark of Android apps affected by resource leaks. The resulting benchmark, called AppLeak, is publicly available on GitLab and includes faulty apps, versions with bug fixes (when available), test cases to automatically reproduce the leaks, and additional information that may help researchers in their tasks. Overall, the benchmark includes a body of 40 faults that can be exploited to evaluate and compare both static and dynamic analysis techniques for resource leak detection.

The introduction of software technology in a life-dependent environment requires the development team to execute a process that ensures a high level of software reliability and correctness. Despite their popularity, agile methods are generally assumed to be inappropriate as a process family in these environments due to their lack of emphasis on documentation, traceability, and other formal techniques. Agile methods, notably Scrum, favor empirical process control, or small constant adjustments in a tight feedback loop. This paper challenges the assumption that agile methods are inappropriate for safety-critical software development. Agile methods are flexible enough to encourage the rightamount of ceremony; therefore if safety-critical systems require greater emphasis on activities like formal specification and requirements management, then an agile process will include these as necessary activities. Furthermore, agile methods focus more on continuous process management and code-level quality than classic software engineering process models. We present our experiences on the image-guided surgical toolkit (IGSTK) project as a backdrop. IGSTK is an open source software project employing agile practices since 2004. We started with the assumption that a lighter process is better, focused on evolving code, and only adding process elements as the need arose. IGSTK has been adopted by teaching hospitals and research labs, and used for clinical trials. Agile methods have matured since the academic community suggested they are not suitable for safety-critical systems almost a decade ago, we present our experiences as a case study for renewing the discussion.