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Cyber security initiatives provide immense opportunities for governments to educate, train, create awareness, and promote cyber hygiene among businesses and the general public. Creating and promoting these initiatives are necessary steps governments take to ensure the cyber health of a nation. To ensure users are safe and confident, especially online, the UK government has created initiatives designed to meet the needs of various users such as small charity guide for charity organisations, small business guide for small businesses, get safe online for the general public, and cyber essentials for organisations, among many others. However, ensuring that these initiatives deliver on their objectives can be daunting, especially when reaching out to the whole population. It is, therefore, vital for the government to intensify practical ways of reaching out to users to make sure that they are aware of their obligation to cyber security. This study evaluates sixteen of the UK government's cyber security initiatives and discovers four notable reasons why these initiatives are failing. These reasons are insufficient awareness and training, non-evaluation of initiatives to measure impact, insufficient behavioural change, and limited coverage to reach intended targets. The recommendation based on these findings is to promote these initiatives both nationally and at community levels.

Smart contracts are self-executing programs on a blockchain to ensure immutable and transparent agreements without the involvement of intermediaries. Despite the growing popularity of smart contracts for many blockchain platforms like Ethereum, smart contract developers cannot prevent copying their smart contracts from competitors due to the absence of technical means available. However, applying existing software watermarking techniques is challenging because of the unique properties of smart contracts, such as a code size constraint, non-free execution cost, and no support for dynamic allocation under a virtual machine environment. This paper introduces a novel software watermarking scheme, dubbed SmartMark, aiming to protect the piracy of smart contracts. SmartMark builds the control flow graph of a target contract runtime bytecode and locates a series of bytes randomly selected from a collection of opcodes to represent a watermark. We implement a full-fledged prototype for Ethereum, applying SmartMark to 27,824 unique smart contract bytecodes. Our empirical results demonstrate that SmartMark can effectively embed a watermark into smart contracts and verify its presence, meeting the requirements of credibility and imperceptibility while incurring a slight performance degradation. Furthermore, our security analysis shows that SmartMark is resilient against foreseeable watermarking corruption attacks; e.g., a large number of dummy opcodes are needed to disable a watermark effectively, resulting in producing illegitimate smart contract clones that are not economical.

When factorized approximations are used for variational inference (VI), they tend to underestimate the uncertainty -- as measured in various ways -- of the distributions they are meant to approximate. We consider two popular ways to measure the uncertainty deficit of VI: (i) the degree to which it underestimates the componentwise variance, and (ii) the degree to which it underestimates the entropy. To better understand these effects, and the relationship between them, we examine an informative setting where they can be explicitly (and elegantly) analyzed: the approximation of a Gaussian,~$p$, with a dense covariance matrix, by a Gaussian,~$q$, with a diagonal covariance matrix. We prove that $q$ always underestimates both the componentwise variance and the entropy of $p$, \textit{though not necessarily to the same degree}. Moreover we demonstrate that the entropy of $q$ is determined by the trade-off of two competing forces: it is decreased by the shrinkage of its componentwise variances (our first measure of uncertainty) but it is increased by the factorized approximation which delinks the nodes in the graphical model of $p$. We study various manifestations of this trade-off, notably one where, as the dimension of the problem grows, the per-component entropy gap between $p$ and $q$ becomes vanishingly small even though $q$ underestimates every componentwise variance by a constant multiplicative factor. We also use the shrinkage-delinkage trade-off to bound the entropy gap in terms of the problem dimension and the condition number of the correlation matrix of $p$. Finally we present empirical results on both Gaussian and non-Gaussian targets, the former to validate our analysis and the latter to explore its limitations.

In this paper, we consider distributed optimization problems where $n$ agents, each possessing a local cost function, collaboratively minimize the average of the local cost functions over a connected network. To solve the problem, we propose a distributed random reshuffling (D-RR) algorithm that invokes the random reshuffling (RR) update in each agent. We show that D-RR inherits favorable characteristics of RR for both smooth strongly convex and smooth nonconvex objective functions. In particular, for smooth strongly convex objective functions, D-RR achieves $\mathcal{O}(1/T^2)$ rate of convergence (where $T$ counts epoch number) in terms of the squared distance between the iterate and the global minimizer. When the objective function is assumed to be smooth nonconvex, we show that D-RR drives the squared norm of gradient to $0$ at a rate of $\mathcal{O}(1/T^{2/3})$. These convergence results match those of centralized RR (up to constant factors) and outperform the distributed stochastic gradient descent (DSGD) algorithm if we run a relatively large number of epochs. Finally, we conduct a set of numerical experiments to illustrate the efficiency of the proposed D-RR method on both strongly convex and nonconvex distributed optimization problems.

Modern applications, such as social networking systems and e-commerce platforms are centered around using large-scale databases for storing and retrieving data. Accesses to the database are typically enclosed in transactions that allow computations on shared data to be isolated from other concurrent computations and resilient to failures. Modern databases trade isolation for performance. The weaker the isolation level is, the more behaviors a database is allowed to exhibit and it is up to the developer to ensure that their application can tolerate those behaviors. In this work, we propose stateless model checking algorithms for studying correctness of such applications that rely on dynamic partial order reduction. These algorithms work for a number of widely-used weak isolation levels, including Read Committed, Causal Consistency, Snapshot Isolation, and Serializability. We show that they are complete, sound and optimal, and run with polynomial memory consumption in all cases. We report on an implementation of these algorithms in the context of Java Pathfinder applied to a number of challenging applications drawn from the literature of distributed systems and databases.

While human mobility plays a crucial role in determining air pollution exposures and health risks, research to-date has assessed risks based solely on residential location. Here we leveraged a database of ~ 130 million workers in the US and published PM2.5 data between 2011-2018 to explore how incorporating information on both workplace and residential location changes our understanding of disparities in air pollution exposure. In general, we observed higher workplace exposures (W) relative to home exposures (H), as well as increasing exposures for non-white and less educated workers relative to the national average. Workplace exposure disparities were higher among racial and ethnic groups and job-types than by income, education, age, and sex. Not considering workplace exposures can lead to systematic underestimations in disparities to exposure among these subpopulations. We also quantified the error in assigning workers H, instead of a weighted home-and-work (HW) exposure. We observed that biases in associations between PM2.5 and health impacts by using H instead of HW were highest among urban, younger populations.

With the proliferation of devices that display augmented reality (AR), now is the time for scholars and practitioners to evaluate and engage critically with emerging applications of the medium. AR mediates the way users see their bodies, hear their environment and engage with places. Applied in various forms, including social media, e-commerce, gaming, enterprise and art, the medium facilitates a hybrid experience of physical and digital spaces. This article employs a model of real-and-imagined space from geographer Edward Soja to examine how the user of an AR app navigates the two intertwined spaces of physical and digital, experiencing what Soja calls a 'Third-space'. The article illustrates the potential for headset-based AR to engender such a Thirdspace through the author's practice-led research project, the installation Through the Wardrobe. This installation demonstrates how AR has the potential to shift the way that users view and interact with their world with artistic applications providing an opportunity to question assumptions of social norms, identity and uses of physical space.

Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment. Fulfilling this goal requires both that the explanations generated by these methods are correct and that people can easily and reliably understand them. While the former has been addressed in prior work, the latter is often overlooked, resulting in informal model understanding derived from a handful of local explanations. In this paper, we introduce explanation summary (ExSum), a mathematical framework for quantifying model understanding, and propose metrics for its quality assessment. On two domains, ExSum highlights various limitations in the current practice, helps develop accurate model understanding, and reveals easily overlooked properties of the model. We also connect understandability to other properties of explanations such as human alignment, robustness, and counterfactual minimality and plausibility.

Graph convolutional neural networks have recently shown great potential for the task of zero-shot learning. These models are highly sample efficient as related concepts in the graph structure share statistical strength allowing generalization to new classes when faced with a lack of data. However, multi-layer architectures, which are required to propagate knowledge to distant nodes in the graph, dilute the knowledge by performing extensive Laplacian smoothing at each layer and thereby consequently decrease performance. In order to still enjoy the benefit brought by the graph structure while preventing dilution of knowledge from distant nodes, we propose a Dense Graph Propagation (DGP) module with carefully designed direct links among distant nodes. DGP allows us to exploit the hierarchical graph structure of the knowledge graph through additional connections. These connections are added based on a node's relationship to its ancestors and descendants. A weighting scheme is further used to weigh their contribution depending on the distance to the node to improve information propagation in the graph. Combined with finetuning of the representations in a two-stage training approach our method outperforms state-of-the-art zero-shot learning approaches.

Incorporating knowledge graph into recommender systems has attracted increasing attention in recent years. By exploring the interlinks within a knowledge graph, the connectivity between users and items can be discovered as paths, which provide rich and complementary information to user-item interactions. Such connectivity not only reveals the semantics of entities and relations, but also helps to comprehend a user's interest. However, existing efforts have not fully explored this connectivity to infer user preferences, especially in terms of modeling the sequential dependencies within and holistic semantics of a path. In this paper, we contribute a new model named Knowledge-aware Path Recurrent Network (KPRN) to exploit knowledge graph for recommendation. KPRN can generate path representations by composing the semantics of both entities and relations. By leveraging the sequential dependencies within a path, we allow effective reasoning on paths to infer the underlying rationale of a user-item interaction. Furthermore, we design a new weighted pooling operation to discriminate the strengths of different paths in connecting a user with an item, endowing our model with a certain level of explainability. We conduct extensive experiments on two datasets about movie and music, demonstrating significant improvements over state-of-the-art solutions Collaborative Knowledge Base Embedding and Neural Factorization Machine.