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As a worldwide pandemic, the coronavirus disease-19 (COVID-19) has caused serious restrictions in people's social life, along with the loss of lives, the collapse of economies and the disruption of humanitarian aids. Despite the advance of technological developments, we, as researchers, have witnessed that several issues need further investigation for a better response to a pandemic outbreak. With this motivation, researchers recently started developing ideas to stop or at least reduce the spread of the pandemic. While there have been some prior works on wireless networks for combating a pandemic scenario, vehicular networks and their potential bottlenecks have not yet been fully examined. This article provides an extensive discussion on vehicular networking for combating a pandemic. We provide the major applications of vehicular networking for combating COVID-19 in public transportation, in-vehicle diagnosis, border patrol and social distance monitoring. Next, we identify the unique characteristics of the collected data in terms of privacy, flexibility and coverage, then highlight corresponding future directions in privacy preservation, resource allocation, data caching and data routing. We believe that this work paves the way for the development of new products and algorithms that can facilitate the social life and help controlling the spread of the pandemic.

Permissionless blockchains such as Bitcoin have excelled at financial services. Yet, opportunistic traders extract monetary value from the mesh of decentralized finance (DeFi) smart contracts through so-called blockchain extractable value (BEV). The recent emergence of centralized BEV relayer portrays BEV as a positive additional revenue source. Because BEV was quantitatively shown to deteriorate the blockchain's consensus security, BEV relayers endanger the ledger security by incentivizing rational miners to fork the chain. For example, a rational miner with a 10% hashrate will fork Ethereum if a BEV opportunity exceeds 4x the block reward. However, related work is currently missing quantitative insights on past BEV extraction to assess the practical risks of BEV objectively. In this work, we allow to quantify the BEV danger by deriving the USD extracted from sandwich attacks, liquidations, and decentralized exchange arbitrage. We estimate that over 32 months, BEV yielded 540.54M USD in profit, divided among 11,289 addresses when capturing 49,691 cryptocurrencies and 60,830 on-chain markets. The highest BEV instance we find amounts to 4.1M USD, 616.6x the Ethereum block reward. Moreover, while the practitioner's community has discussed the existence of generalized trading bots, we are, to our knowledge, the first to provide a concrete algorithm. Our algorithm can replace unconfirmed transactions without the need to understand the victim transactions' underlying logic, which we estimate to have yielded a profit of 57,037.32 ETH (35.37M USD) over 32 months of past blockchain data. Finally, we formalize and analyze emerging BEV relay systems, where miners accept BEV transactions from a centralized relay server instead of the peer-to-peer (P2P) network. We find that such relay systems aggravate the consensus layer attacks and therefore further endanger blockchain security.

In the industry, blockchains are increasingly used as the backbone of product and process traceability. Blockchain-based traceability participates in the demonstration of product and/or process compliance with existing safety standards or quality criteria. In this perspective, services and applications built on top of blockchains are business-critical applications, because an intended failure or corruption of the system can lead to an important reputation loss regarding the products or the processes involved. The development of a blockchain-based business-critical application must be then conducted carefully, requiring a thorough justification of its dependability and security. To this end, this paper encourages an engineering perspective rooted in well-understood tools and concepts borrowed from the engineering of safety-critical systems. Concretely, we use a justification framework, called CAE (Claim, Argument, Evidence), by following an approach based on assurance cases, in order to provide convincing arguments that a business-critical blockchain-based application is dependable and secure. The application of this approach is sketched with a case study based on the blockchain HYPERLEDGER FABRIC.

In this work, we design a novel game-theoretical framework capable of capturing the defining aspects of quantum theory. We introduce an original model and an algorithmic procedure that enables to express measurement scenarios encountered in quantum mechanics as multiplayer games and to translate physical notions of causality, correlation, and contextuality to particular aspects of game theory. Furthermore, inspired by the established correspondence, we investigate the causal consistency of games in extensive form with imperfect information from the quantum perspective and we conclude that counterfactual dependencies should be distinguished from causation and correlation as a separate phenomenon of its own. Most significantly, we deduce that Nashian free choice game theory is non-contextual and hence is in contradiction with the Kochen-Specker theorem. Hence, we propose that quantum physics should be analysed with toolkits from non-Nashian game theory applied to our suggested model.

Language is not only used to inform. We often seek to persuade by arguing in favor of a particular view. Persuasion raises a number of challenges for classical accounts of belief updating, as information cannot be taken at face value. How should listeners account for a speaker's "hidden agenda" when incorporating new information? Here, we extend recent probabilistic models of recursive social reasoning to allow for persuasive goals and show that our model provides a new pragmatic explanation for why weakly favorable arguments may backfire, a phenomenon known as the weak evidence effect. Critically, our model predicts a relationship between belief updating and speaker expectations: weak evidence should only backfire when speakers are expected to act under persuasive goals, implying the absence of stronger evidence. We introduce a simple experimental paradigm called the Stick Contest to measure the extent to which the weak evidence effect depends on speaker expectations, and show that a pragmatic listener model accounts for the empirical data better than alternative models. Our findings suggest potential avenues for rational models of social reasoning to further illuminate decision-making phenomena.

This paper presents a prototype of a low-cost Unmanned Surface Vehicle (USV) that is operated by wave and solar energy which can be used to minimize the cost of ocean data collection. The current prototype is a compact USV, with a length of 1.2m that can be deployed and recovered by two persons. The design includes an electrically operated winch that can be used to retract and lower the underwater unit. Several elements of the design make use of additive manufacturing and inexpensive materials. The vehicle can be controlled using radio frequency (RF) and a satellite communication, through a custom developed web application. Both the surface and underwater units were optimized with regard to drag, lift, weight, and price by using recommendation of previous research work and advanced materials. The USV could be used in water condition monitoring by measuring several parameters, such as dissolved oxygen, salinity, temperature, and pH.

Vaccination passports are being issued by governments around the world in order to open up their travel and hospitality sectors. Civil liberty campaigners on the other hand argue that such mandatory instruments encroach upon our fundamental right to anonymity, freedom of movement, and are a backdoor to issuing "identity documents" to citizens by their governments. We present a privacy-preserving framework that uses two-factor authentication to create a unique identifier that can be used to locate a person's vaccination record on a blockchain, but does not store any personal information about them. Our main contribution is the employment of a locality sensitive hashing algorithm over an iris extraction technique, that can be used to authenticate users and anonymously locate vaccination records on the blockchain, without leaking any personally identifiable information to the blockchain. Our proposed system allows for the safe reopening of society, while maintaining the privacy of citizens.

Digital connectivity gap in the global south hampered the education of millions of school children during the COVID-19 pandemic. If not actions are taken to remedy this problem, future prospects of millions of children around will be bleak. This paper explores the feasibility of using the SpaceX Starlink satellite constellation as a means to alleviate the problem of the digital connectivity divide in the global south. First, the paper discusses the issues of digital connectivity in education in rural Sri Lanka and other countries in the global south. Then, the paper gives an introduction to Starlink broadband internet technology and discusses its advantages over traditional technologies. After that, the paper discusses a possible mechanism of adopting Starlink technology as a solution to the rural digital connectivity problem in the global south. Technological, as well as economical aspects of such scheme, are discussed. Finally, challenges that may arise in deploying a system such as Starlink to improve rural digital connectivity in Sri Lanka or any another country in the global south will be discussed with possible remedies.

It is becoming increasingly easy to automatically replace a face of one person in a video with the face of another person by using a pre-trained generative adversarial network (GAN). Recent public scandals, e.g., the faces of celebrities being swapped onto pornographic videos, call for automated ways to detect these Deepfake videos. To help developing such methods, in this paper, we present the first publicly available set of Deepfake videos generated from videos of VidTIMIT database. We used open source software based on GANs to create the Deepfakes, and we emphasize that training and blending parameters can significantly impact the quality of the resulted videos. To demonstrate this impact, we generated videos with low and high visual quality (320 videos each) using differently tuned parameter sets. We showed that the state of the art face recognition systems based on VGG and Facenet neural networks are vulnerable to Deepfake videos, with 85.62% and 95.00% false acceptance rates respectively, which means methods for detecting Deepfake videos are necessary. By considering several baseline approaches, we found that audio-visual approach based on lip-sync inconsistency detection was not able to distinguish Deepfake videos. The best performing method, which is based on visual quality metrics and is often used in presentation attack detection domain, resulted in 8.97% equal error rate on high quality Deepfakes. Our experiments demonstrate that GAN-generated Deepfake videos are challenging for both face recognition systems and existing detection methods, and the further development of face swapping technology will make it even more so.

Privacy is a major good for users of personalized services such as recommender systems. When applied to the field of health informatics, privacy concerns of users may be amplified, but the possible utility of such services is also high. Despite availability of technologies such as k-anonymity, differential privacy, privacy-aware recommendation, and personalized privacy trade-offs, little research has been conducted on the users' willingness to share health data for usage in such systems. In two conjoint-decision studies (sample size n=521), we investigate importance and utility of privacy-preserving techniques related to sharing of personal health data for k-anonymity and differential privacy. Users were asked to pick a preferred sharing scenario depending on the recipient of the data, the benefit of sharing data, the type of data, and the parameterized privacy. Users disagreed with sharing data for commercial purposes regarding mental illnesses and with high de-anonymization risks but showed little concern when data is used for scientific purposes and is related to physical illnesses. Suggestions for health recommender system development are derived from the findings.