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This study proposes a novel solution that provides secure interoperability for blockchains, which improves the overall scalability of the whole blockchain network. In our solution, a cross-chain task will build a one-time cross-blockchain contract. Each blockchain system can follow the contract to complete or this task. The result of tasks is bound with the system, hence can be anchored to all other blockchain systems through the gossip network. This work shows our result can provide linear scalability for the whole system and achieve consistency among honest systems.

This paper revisits the ubiquitous problem of achieving state machine replication in blockchains based on repeated consensus, like Tendermint. To achieve state machine replication in blockchains built on top of consensus, one needs to guarantee fairness of user transactions. A huge body of work has been carried out on the relation between state machine replication and consensus in the past years, in a variety of system models and with respect to varied problem specifications. We systematize this work by proposing novel and rigorous abstractions for state machine replication and repeated consensus in a system model that accounts for realistic blockchains in which blocks may contain several transactions issued by one or more users, and where validity and order of transactions within a block is determined by an external application-dependent function that can capture various approaches for order-fairness in the literature. Based on these abstractions, we propose a reduction from state machine replication to repeated consensus, such that user fairness is achieved using the consensus module as a black box. This approach allows to achieve fairness as an add-on on top of preexisting consensus modules in blockchains based on repeated consensus.

Mobile banking applications have gained popularity and have significantly revolutionised the banking industry. Despite the convenience offered by M-Banking Apps, users are often distrustful of the security of the applications due to an increasing trend of cyber security compromises, cyber-attacks, and data breaches. Considering the upsurge in cyber security vulnerabilities of M-Banking Apps and the paucity of research in this domain, this study was conducted to empirically measure user-perceived security of M-Banking Apps. A total of 315 responses from study participants were analysed using covariance-based structural equation modelling (CB-SEM). The results indicated that most constructs of the baseline Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) structure were validated. Perceived security, institutional trust and technology trust were confirmed as factors that affect user's intention to adopt and use M-Banking Apps. However, perceived risk was not confirmed as a significant predictor. The current study further revealed that in the context of M-Banking Apps, the effects of security and trust are complex. The impact of perceived security and institutional trust on behavioural intention was moderated by age, gender, experience, income, and education, while perceived security on use behaviour was moderated by age, gender, and experience. The effect of technology trust on behavioural intention was moderated by age, education, and experience. Overall, the proposed conceptual model achieved acceptable fit and explained 79% of the variance in behavioural intention and 54.7% in use behaviour of M-Banking Apps, higher than that obtained in the original UTAUT2. The guarantee of enhanced security, advanced privacy mechanisms and trust should be considered paramount in future strategies aimed at promoting M-Banking Apps adoption and use.

Engaging with natural environments and representations of nature has been shown to improve mood states and reduce cognitive decline in older adults. The current study evaluated the use of virtual reality (VR) for presenting immersive 360 degree nature videos and a digitally designed interactive garden for this purpose. Fifty participants (age 60 plus), with varied cognitive and physical abilities, were recruited. Data were collected through pre/post-intervention surveys, standardized observations during the interventions, and post-intervention semi structured interviews. The results indicated significant improvements in attitudes toward VR and in some aspects of mood and engagement. The responses to the environment did not significantly differ among participants with different cognitive abilities; however, those with physical disabilities expressed stronger positive reactions on some metrics compared to participants without disabilities. Almost no negative impacts (cybersickness, task frustration) were found. In the interviews some participants expressed resistance to the technology, in particular the digital garden, indicating that it felt cartoonish or unappealing and that it could not substitute for real nature. However, the majority felt that the VR experiences could be a beneficial activity in situations when real-world contact with nature was not immediately feasible.

We study time-series classification (TSC), a fundamental task of time-series data mining. Prior work has approached TSC from two major directions: (1) similarity-based methods that classify time-series based on the nearest neighbors, and (2) deep learning models that directly learn the representations for classification in a data-driven manner. Motivated by the different working mechanisms within these two research lines, we aim to connect them in such a way as to jointly model time-series similarities and learn the representations. This is a challenging task because it is unclear how we should efficiently leverage similarity information. To tackle the challenge, we propose Similarity-Aware Time-Series Classification (SimTSC), a conceptually simple and general framework that models similarity information with graph neural networks (GNNs). Specifically, we formulate TSC as a node classification problem in graphs, where the nodes correspond to time-series, and the links correspond to pair-wise similarities. We further design a graph construction strategy and a batch training algorithm with negative sampling to improve training efficiency. We instantiate SimTSC with ResNet as the backbone and Dynamic Time Warping (DTW) as the similarity measure. Extensive experiments on the full UCR datasets and several multivariate datasets demonstrate the effectiveness of incorporating similarity information into deep learning models in both supervised and semi-supervised settings. Our code is available at //github.com/daochenzha/SimTSC

In the Internet of Things (IoT) era, vehicles and other intelligent components in an intelligent transportation system (ITS) are connected, forming Vehicular Networks (VNs) that provide efficient and secure traffic and ubiquitous access to various applications. However, as the number of nodes in ITS increases, it is challenging to satisfy a varied and large number of service requests with different Quality of Service and security requirements in highly dynamic VNs. Intelligent nodes in VNs can compete or cooperate for limited network resources to achieve either an individual or a group's objectives. Game Theory (GT), a theoretical framework designed for strategic interactions among rational decision-makers sharing scarce resources, can be used to model and analyze individual or group behaviors of communicating entities in VNs. This paper primarily surveys the recent developments of GT in solving various challenges of VNs. This survey starts with an introduction to the background of VNs. A review of GT models studied in the VNs is then introduced, including its basic concepts, classifications, and applicable vehicular issues. After discussing the requirements of VNs and the motivation of using GT, a comprehensive literature review on GT applications in dealing with the challenges of current VNs is provided. Furthermore, recent contributions of GT to VNs integrating with diverse emerging 5G technologies are surveyed. Finally, the lessons learned are given, and several key research challenges and possible solutions for applying GT in VNs are outlined.

Machine learning (ML) has been pervasively researched nowadays and it has been applied in many aspects of real life. Nevertheless, issues of model and data still accompany the development of ML. For instance, training of traditional ML models is limited to the access of data sets, which are generally proprietary; published ML models may soon be out of date without update of new data and continuous training; malicious data contributors may upload wrongly labeled data that leads to undesirable training results; and the abuse of private data and data leakage also exit. With the utilization of blockchain, an emerging and swiftly developing technology, these problems can be efficiently solved. In this paper, we conduct a survey of the convergence of collaborative ML and blockchain. We investigate different ways of combination of these two technologies, and their fields of application. We also discuss the limitations of current research and their future directions.

We study Nash-dynamics in the context of blockchain protocols. Specifically, we introduce a formal model, within which one can assess whether the Nash dynamics can lead utility maximizing participants to defect from "honest" protocol operation, towards variations that exhibit one or more undesirable infractions, such as abstaining from participation and extending conflicting protocol histories. Blockchain protocols that do not lead to such infraction states are said to be compliant. Armed with this model, we study the compliance of various Proof-of-Work (PoW) and Proof-of-Stake (PoS) protocols, with respect to different utility functions and reward schemes, leading to the following results: i) PoS ledgers under resource-proportional rewards can be compliant if costs are negligible, but non-compliant if costs are significant, ii) PoW and PoS under block-proportional rewards exhibit different compliance behavior, depending on the lossiness of the network, iii) considering externalities, such as exchange rate fluctuations, we quantify the benefit of economic penalties in the context of PoS protocols with respect to compliance.

The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called energy Internet, is also emerging as an innovative approach to ensure the energy from anywhere at any time. The ultimate goal of these developments is to build a sustainable society. However, integrating and coordinating a large number of growing connections can be a challenging issue for the traditional centralized grid system. Consequently, the smart grid is undergoing a transformation to the decentralized topology from its centralized form. On the other hand, blockchain has some excellent features which make it a promising application for smart grid paradigm. In this paper, we have an aim to provide a comprehensive survey on application of blockchain in smart grid. As such, we identify the significant security challenges of smart grid scenarios that can be addressed by blockchain. Then, we present a number of blockchain-based recent research works presented in different literatures addressing security issues in the area of smart grid. We also summarize several related practical projects, trials, and products that have been emerged recently. Finally, we discuss essential research challenges and future directions of applying blockchain to smart grid security issues.

To make deliberate progress towards more intelligent and more human-like artificial systems, we need to be following an appropriate feedback signal: we need to be able to define and evaluate intelligence in a way that enables comparisons between two systems, as well as comparisons with humans. Over the past hundred years, there has been an abundance of attempts to define and measure intelligence, across both the fields of psychology and AI. We summarize and critically assess these definitions and evaluation approaches, while making apparent the two historical conceptions of intelligence that have implicitly guided them. We note that in practice, the contemporary AI community still gravitates towards benchmarking intelligence by comparing the skill exhibited by AIs and humans at specific tasks such as board games and video games. We argue that solely measuring skill at any given task falls short of measuring intelligence, because skill is heavily modulated by prior knowledge and experience: unlimited priors or unlimited training data allow experimenters to "buy" arbitrary levels of skills for a system, in a way that masks the system's own generalization power. We then articulate a new formal definition of intelligence based on Algorithmic Information Theory, describing intelligence as skill-acquisition efficiency and highlighting the concepts of scope, generalization difficulty, priors, and experience. Using this definition, we propose a set of guidelines for what a general AI benchmark should look like. Finally, we present a benchmark closely following these guidelines, the Abstraction and Reasoning Corpus (ARC), built upon an explicit set of priors designed to be as close as possible to innate human priors. We argue that ARC can be used to measure a human-like form of general fluid intelligence and that it enables fair general intelligence comparisons between AI systems and humans.