Customer Cybersecurity and Supplier Cost Management Strategy

In this paper, we explore the spillover effect of customer firms’ data breaches on their upstream supplier firms’ cost management strategies, proxied by cost stickiness. Our primary analyses suggest that data breaches suffered by customer firms are associated with a decrease in cost stickiness among supplier firms. Furthermore, the reductions in supplier cost stickiness are stronger if suppliers are managed by CEOs from national cultural groups with high uncertainty avoidance, low long-term orientations, and/or low individualism. In sum, the findings contribute to both Information Systems (IS) and Operations Management (OM) disciplines in terms of data breach, cost management strategy, and the role of national culture in OM. In particular, the findings can facilitate the management and regulation of data breaches for managers and regulators

Robust MIMO Detection With Imperfect CSI: A Neural Network Solution

In this paper, we investigate the design of statistically robust detectors for multi-input multi-output (MIMO) systems subject to imperfect channel state information (CSI). A robust maximum likelihood (ML) detection problem is formulated by taking into consideration the CSI uncertainties caused by both the channel estimation error and the channel variation. To address the challenging discrete optimization problem, we propose an efficient alternating direction method of multipliers (ADMM)-based algorithm, which only requires calculating closed-form solutions in each iteration. Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies. Moreover, in order to relieve the computational burden, a low-complexity ADMM-based robust detector is developed using the Gaussian approximation, and the corresponding deep unfolding network LCRADMMNet is further established. On the other hand, we also provide a novel robust data-aided Kalman filter (RDAKF)-based channel tracking method, which can effectively refine the CSI accuracy and improve the performance of the proposed robust detectors. Simulation results validate the significant performance advantages of the proposed robust detection networks over the non-robust detectors with different CSI acquisition methods.Comment: 15 pages, 8 figures, 2 tables; Accepted by IEEE TCO

Distributed Temporal Link Prediction Algorithm Based on Label Propagation

Link prediction has steadily become an important research topic in the area of complex networks. However, the current link prediction algorithms typically neglect the evolution process and they tend to exhibit low accuracy and scalability when applied to large-scale networks. In this article, we propose a novel distributed temporal link prediction algorithm based on label propagation (DTLPLP), governed by the dynamical properties of the interactions between nodes. In particular, nodes are associated with labels, which include details of their sources, and the corresponding similarity value. When such labels are propagated across neighbouring nodes, they are updated based on the weights of the incident links, and the values from same source nodes are aggregated to evaluate the scores of links in the predicted network. Furthermore, DTLPLP has been designed to be distributed and parallelised, and thus suitable for large-scale network analysis. As part of the validation process, we have designed a prototype system developed in Pregel, which is a distributed network analysis framework. Experiments are conducted on the Enron e-mails and the General Relativity and Quantum Cosmology Scientific Collaboration networks. The experimental results show that compared to the most of link prediction algorithms, DTLPLP offers enhanced accuracy, stability and scalability