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PurposePhishing attacks are the most common cyber threats targeted at users. Digital nudging in the form of framing and priming may reduce user susceptibility to phishing. This research focuses on two types of digital nudging, framing and priming, and examines the impact of framing and priming on users' behavior (i.e. action) in a cybersecurity setting. It draws on prospect theory, instance-based learning theory and dual-process theory to generate the research hypotheses.Design/methodology/approachA 3 × 2 experimental study was carried out to test the hypotheses. The experiment consisted of three levels for framing (i.e. no framing, negative framing and positive framing) and two levels for priming (i.e. with and without priming).FindingsThe findings suggest that priming users to information security risks reduces their risk-taking behavior, whereas positive and negative framing of information security messages regarding potential consequences of the available choices do not change users' behavior. The results also indicate that risk-averse cybersecurity behavior is associated with greater confidence with the action, greater perceived severity of cybersecurity risks, lower perceived susceptibility to cybersecurity risks resulting from the action and lower trust in the download link.Originality/valueThis research shows that digital nudging in the form of priming is an effective way to reduce users' exposure to cybersecurity risks.

Young breast cancer survivors (YBCS) face unique challenges in coping with disease, distress, and relationship concerns. The purposes of this study were to understand the acceptability and feasibility of an online Mindfulness-Based Intervention (MBI) for YBCS and their partners (i.e., Couples Mindfulness-Based Intervention: C-MBI) and to compare the effectiveness of the C-MBI to a closely-matched control, an online MBI for individuals (I-MBI).YBCS and their partners were recruited. Couples were randomly assigned to an 8-week C-MBI (couples = 41) or to I-MBI (couples = 36), which included one-hour video modules, a manual, and guided-meditation audios. Both couple members participated in the C-MBI; only the YBCS participated in the control I-MBI. Participants answered surveys about individual- and couple-level functioning at baseline and post-intervention.Online delivery was shown to be feasible and acceptable. For YBCS and their partners, levels of perceived stress, anxiety, depression, and fatigue were lower after the intervention, in both conditions. Unexpectedly, however, participating in the C-MBI appeared to have detrimental effects on dyadic adjustment and relationship quality.Although YBCS and their partners reported online delivery was acceptable and benefited well-being, for couple-based MBIs to have benefits for relationship functioning, it may be necessary for couples to have the support of other couples and an instructor. Online delivery may be particularly acceptable and effective for clinical populations, including YBCS. Medical professionals may be more likely to recommend online-MBI programs to cancer survivors, because the programs are of little or no cost.

Blockchain technology has gained growing popularity in recent years. While the technology works well for most applications, the vulnerability of the blockchain-based system is not well understood. The core part of a blockchain-based system is the consensus algorithm. It can be regarded as a protocol used by all network users to make decisions on the growth of the chain. Malicious users can exploit the vulnerabilities of the consensus algorithm to launch block withholding attacks to earn more profit by increasing their winning possibilities, which are significant threats to the security of blockchain-based applications. In this paper, we describe some scenarios of block withholding attacks and suggest effective mitigation methods accordingly. We propose two hybrid consensus algorithms against the block withholding attacks and evaluate the effectiveness of these algorithms, both analytically and through simulation. Simulation results from the SimBlock simulator verified the effectiveness of the proposed algorithms.

This paper considers the problem of sparse signal recovery where there is a structure in the signal. Efficient recovery schemes can be designed to leverage the signal structure. Following the model-based compressive sensing framework, we have developed an efficient algorithm for both head and tail approximations for the model-projection problem. The problem is modeled as a constrained graph optimization problem, which is an NP-hard optimization problem. Solving the NP-hard optimization program is then transformed to solving a linear program and finding a randomized algorithm to find an integral solution. The integral solution is optimal-in-expectation. The algorithm is proved to have the same geometric convergence as previous work. The algorithm has been tested on various compressing matrices. The proposed algorithm demonstrated improved recoverability and used fewer number of iterations to recover the signal.

In this paper, we consider the power line outage identification problem as a graph signal classification problem, where the signal at each vertex is given as a time series. We propose graph convolutional networks (GCNs) for the task of classifying signals supported on graphs. An important element of the GCN design is filter design. We consider filtering signals in either the vertex (spatial) domain, or the frequency (spectral) domain. Two basic architectures are proposed. In the spatial GCN architecture, the GCN uses a graph shift operator as the basic building block to incorporate the underlying graph structure into the convolution layer. The spatial filter directly utilizes the graph connectivity information. It defines the filter to be a polynomial in the graph shift operator to obtain the convolved features that aggregate neighborhood information of each node. In the spectral GCN architecture, a frequency filter is used instead. A graph Fourier transform operator first transforms the raw graph signal from the vertex domain to the frequency domain, and then a filter is defined using the graph's spectral parameters. The spectral GCN then uses the output from the graph Fourier transform to compute the convolved features. There are additional challenges to classify the time-evolving graph signal as the signal value at each vertex changes over time. The GCNs are designed to recognize different spatiotemporal patterns from high-dimensional data defined on a graph. The application of the proposed methods to power line outage identification shows that these GCN architectures can successfully classify abnormal signal patterns and identify the outage location.

Many power systems applications such as power flow and short-circuit analysis require very large sparse matrix computations. With the increase in reliance on our electric infrastructure, power systems are continually growing in size, creating greater computational complexity in solving these large linear systems within reasonable time. For sparse matrix applications, it is desirable to have an algorithm with low runtime complexity in terms of the number of nonzeros in the matrix. There have been several recent advances in computational methods in other fields that, if applied to power system, could make real-time dynamic simulation a reality. Much work has been done for specific types of these problems where the system is symmetric and diagonally dominant, similar to the form of power system matrices. This paper details an expansion on the current work in fast linear solvers to develop power system specific methods that show potential for accurate solutions in $O(m \log ^2 n)$ run times, where $n$ represents the number of nodes and $m$ represents the number of nonzeros in the power system matrix. This paper presents the simulation validation of a recently developed recursively solved iterative chain method for sparse matrices using a low stretch spanning tree preconditioner.

In wireless ad hoc networks, maintaining network connectivity is very important as high level network functions all depend on it. However, how to measure network connectivity remains a fundamental challenge. For example, a network can have good overall k-connectivity and yet still have a communication bottleneck. In this paper, we address how to locate bottlenecks and relieve them. A new connectivity measure based on the Cheeger's Constant is used for bottleneck discovery, and a partition algorithm that divides the network at the bottleneck is developed. After the network is partitioned, we consider deploying a relay node to increase the conductance of the network across the partition. The relay node deployment problem is formulated as an integer linear program to maximize the number of connections between the two sides of the cut, and then a convex optimization algorithm is used to find the precise location of the relay node, which is within the convex hull defined by the radio transmission ranges of all the nodes that can connect to the relay node. We show that the relay node significantly relieves the bottleneck and improves network connectivity, which is manifested by several network connectivity metrics. The partition and relay node deployment algorithms are then extended to the case where multiple relay nodes are available. Multiway partition and multiple relay node deployment algorithms are presented. Simulation results show this approach effectively enhances network connectivity with a small number of relay nodes.