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Event cameras are bio-inspired sensors that perform well in challenging illumination conditions and have high temporal resolution. However, their concept is fundamentally different from traditional frame-based cameras. The pixels of an event camera operate independently and asynchronously. They measure changes of the logarithmic brightness and return them in the highly discretised form of time-stamped events indicating a relative change of a certain quantity since the last event. New models and algorithms are needed to process this kind of measurements. The present work looks at several motion estimation problems with event cameras. The flow of the events is modelled by a general homographic warping in a space-time volume, and the objective is formulated as a maximisation of contrast within the image of warped events. Our core contribution consists of deriving globally optimal solutions to these generally non-convex problems, which removes the dependency on a good initial guess plaguing existing methods. Our methods rely on branch-and-bound optimisation and employ novel and efficient, recursive upper and lower bounds derived for six different contrast estimation functions. The practical validity of our approach is demonstrated by a successful application to three different event camera motion estimation problems.

This paper highlights the necessity to use modern blockchain technology in traditional banking sector to reduce frauds and enable high-security transactions on a permanent blockchain ledger. Reviewing different channels through which the traditional banking servers could integrate blockchain use, it is signified how a huge anti-fraud stand can be taken against bank servers allowing fraudulent transactions daily. Usage of a blockchain-based ledger is highly impactful in terms of security of a banking organization. Blockchain-based currency tokens, also referred to as Cryptocurrencies are not regulated by the government, highly volatile, and anonymous to use. Furthermore, there is no security for any funds invested in a cryptocurrency market. However, the integration of a blockchain ledger in a traditional banking organization would strengthen the security to provide more stability and confidence to its customers and at the same time, make blockchain a more reliable method to consider due to being trusted by large financial organizations.

The Internet of Things (IoT) is increasingly present in many family homes, yet it is unclear precisely how well families understand the cyber security threats and risks of using such devices, and how possible it is for them to educate themselves on these topics. Using a survey of 553 parents and interviews with 25 families in the UK, we find that families do not consider home IoT devices to be significantly different in terms of threats than more traditional home computers, and believe the major risks to be largely mitigated through consumer protection regulation. As a result, parents focus on teaching being careful with devices to prolong device life use, exposing their families to additional security risks and modeling incorrect security behaviors to their children. This is a risk for the present and also one for the future, as children are not taught about the IoT, and appropriate cyber security management of such devices, at school. We go on to suggest that steps must be taken by manufacturers and governments or appropriate trusted institutions to improve the cyber security knowledge and behaviors of both adults and children in relation to the use of home IoT devices.

Modern web services routinely provide REST APIs for clients to access their functionality. These APIs present unique challenges and opportunities for automated testing, driving the recent development of many techniques and tools that generate test cases for API endpoints using various strategies. Understanding how these techniques compare to one another is difficult, as they have been evaluated on different benchmarks and using different metrics. To fill this gap, we performed an empirical study aimed to understand the landscape in automated testing of REST APIs and guide future research in this area. We first identified, through a systematic selection process, a set of 10 state-of-the-art REST API testing tools that included tools developed by both researchers and practitioners. We then applied these tools to a benchmark of 20 real-world open-source RESTful services and analyzed their performance in terms of code coverage achieved and unique failures triggered. This analysis allowed us to identify strengths, weaknesses, and limitations of the tools considered and of their underlying strategies, as well as implications of our findings for future research in this area.

Timed automata are a common formalism for the verification of concurrent systems subject to timing constraints. They extend finite-state automata with clocks, that constrain the system behavior in locations, and to take transitions. While timed automata were originally designed for safety (in the wide sense of correctness w.r.t. a formal property), they were progressively used in a number of works to guarantee security properties. In this work, we review works studying security properties for timed automata in the last two decades. We notably review theoretical works, with a particular focus on opacity, as well as more practical works, with a particular focus on attack trees and their extensions. We derive main conclusions concerning open perspectives, as well as tool support.

A decision tree looks like a simple computational graph without cycles, where only the leaf nodes specify the output values and the non-terminals specify their tests or split conditions. From the numerical perspective, we express decision trees in the language of computational graph. We explicitly parameterize the test phase, traversal phase and prediction phase of decision trees based on the bitvectors of non-terminal nodes. As shown later, the decision tree is a shallow binary network in some sense. Especially, we introduce the bitvector matrix to implement the tree traversal in numerical approach, where the core is to convert the logical `AND' operation to arithmetic operations. And we apply this numerical representation to extend and unify diverse decision trees in concept.

Massive random access plays a central role in supporting the Internet of Things (IoT), where a subset of a large population of users simultaneously transmit small packets to a central base station. While there has been much research on the design of protocols for massive access in the uplink, the problem of providing message acknowledgments back to the users has been somewhat neglected. Reliable communication needs to rely on two-way communication for acknowledgement and retransmission. Nevertheless, because of the many possible subsets of active users, providing acknowledgments requires a significant amount of bits. Motivated by this, we define the problem of massive ARQ (Automatic Retransmission reQuest) protocol and introduce efficient methods for joint encoding of multiple acknowledgements in the downlink. The key idea towards reducing the number of bits used for massive acknowledgements is to allow for a small fraction of false positive acknowledgments. We analyze the implications of this approach and the impact of acknowledgment errors in scenarios with massive random access. Finally, we show that these savings can lead to a significant increase in the reliability when retransmissions are allowed since it allows the acknowledgment message to be transmitted more reliably using a much lower rate.

5G New Radio (NR) technology operating in millimeter wave (mmWave) band is expected to be utilized in areas with high and fluctuating traffic demands such as city squares, shopping malls, etc. The latter may result in quality of service (QoS) violations. To deal with this challenge, 3GPP has recently proposed NR unlicensed (NR-U) technology that may utilize 60 GHz frequency band. In this paper, we investigate the deployment of NR-U base stations (BS) simultaneously operating in licensed and unlicensed mmWave bands in presence of competing WiGig traffic, where NR-U users may use unlicensed band as long as session rate requirements are met. To this aim, we utilize the tools of stochastic geometry, Markov chains, and queuing systems with random resource requirements to simultaneously capture NR-U/WiGig coexistence mechanism and session service dynamics in the presence of mmWave-specific channel impairments. We then proceed comparing performance of different offloading strategies by utilizing the eventual session loss probability as the main metric of interest. Our results show non-trivial behaviour of the collision probability in the unlicensed band as compared to lower frequency systems. The baseline strategy, where a session is offloaded onto unlicensed band only when there are no resources available in the licensed one, leads to the best performance. The offloading strategy, where sessions with heavier-than-average requirements are immediately directed onto unlicensed band results in just $2-5\%$ performance loss. The worst performance is observed when sessions with smaller-than-average requirements are offloaded onto unlicensed band.

Recent advances in Federated Learning (FL) have brought large-scale collaborative machine learning opportunities for massively distributed clients with performance and data privacy guarantees. However, most current works focus on the interest of the central controller in FL,and overlook the interests of the FL clients. This may result in unfair treatment of clients which discourages them from actively participating in the learning process and damages the sustainability of the FL ecosystem. Therefore, the topic of ensuring fairness in FL is attracting a great deal of research interest. In recent years, diverse Fairness-Aware FL (FAFL) approaches have been proposed in an effort to achieve fairness in FL from different perspectives. However, there is no comprehensive survey which helps readers gain insight into this interdisciplinary field. This paper aims to provide such a survey. By examining the fundamental and simplifying assumptions, as well as the notions of fairness adopted by existing literature in this field, we propose a taxonomy of FAFL approaches covering major steps in FL, including client selection, optimization, contribution evaluation and incentive distribution. In addition, we discuss the main metrics for experimentally evaluating the performance of FAFL approaches, and suggest promising future research directions towards fairness-aware federated learning.

The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. Socio-economic and well being fallout will affect the food security. Vulnerable groups of people will suffer malnutrition. To cater to the needs of the increasing population, the agricultural industry needs to be modernized, become smart, and automated. Traditional agriculture can be remade to efficient, sustainable, eco-friendly smart agriculture by adopting existing technologies. In this survey paper the authors present the applications, technological trends, available datasets, networking options, and challenges in smart agriculture. How Agro Cyber Physical Systems are built upon the Internet-of-Agro-Things is discussed through various application fields. Agriculture 4.0 is also discussed as a whole. We focus on the technologies, such as Artificial Intelligence (AI) and Machine Learning (ML) which support the automation, along with the Distributed Ledger Technology (DLT) which provides data integrity and security. After an in-depth study of different architectures, we also present a smart agriculture framework which relies on the location of data processing. We have divided open research problems of smart agriculture as future research work in two groups - from a technological perspective and from a networking perspective. AI, ML, the blockchain as a DLT, and Physical Unclonable Functions (PUF) based hardware security fall under the technology group, whereas any network related attacks, fake data injection and similar threats fall under the network research problem group.