Efficient and Extensible Policy Mining for Relationship-Based Access Control

Relationship-based access control (ReBAC) is a flexible and expressive framework that allows policies to be expressed in terms of chains of relationship between entities as well as attributes of entities. ReBAC policy mining algorithms have a potential to significantly reduce the cost of migration from legacy access control systems to ReBAC, by partially automating the development of a ReBAC policy. Existing ReBAC policy mining algorithms support a policy language with a limited set of operators; this limits their applicability. This paper presents a ReBAC policy mining algorithm designed to be both (1) easily extensible (to support additional policy language features) and (2) scalable. The algorithm is based on Bui et al.'s evolutionary algorithm for ReBAC policy mining algorithm. First, we simplify their algorithm, in order to make it easier to extend and provide a methodology that extends it to handle new policy language features. However, extending the policy language increases the search space of candidate policies explored by the evolutionary algorithm, thus causes longer running time and/or worse results. To address the problem, we enhance the algorithm with a feature selection phase. The enhancement utilizes a neural network to identify useful features. We use the result of feature selection to reduce the evolutionary algorithm's search space. The new algorithm is easy to extend and, as shown by our experiments, is more efficient and produces better policies

Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation

Deep Gaussian processes (DGPs) are multi-layer hierarchical generalisations of Gaussian processes (GPs) and are formally equivalent to neural networks with multiple, infinitely wide hidden layers. DGPs are probabilistic and non-parametric and as such are arguably more flexible, have a greater capacity to generalise, and provide better calibrated uncertainty estimates than alternative deep models. The focus of this paper is scalable approximate Bayesian learning of these networks. The paper develops a novel and efficient extension of probabilistic backpropagation, a state-of-the-art method for training Bayesian neural networks, that can be used to train DGPs. The new method leverages a recently proposed method for scaling Expectation Propagation, called stochastic Expectation Propagation. The method is able to automatically discover useful input warping, expansion or compression, and it is therefore is a flexible form of Bayesian kernel design. We demonstrate the success of the new method for supervised learning on several real-world datasets, showing that it typically outperforms GP regression and is never much worse

Class based Influence Functions for Error Detection

Influence functions (IFs) are a powerful tool for detecting anomalous examples in large scale datasets. However, they are unstable when applied to deep networks. In this paper, we provide an explanation for the instability of IFs and develop a solution to this problem. We show that IFs are unreliable when the two data points belong to two different classes. Our solution leverages class information to improve the stability of IFs. Extensive experiments show that our modification significantly improves the performance and stability of IFs while incurring no additional computational cost.Comment: Thang Nguyen-Duc, Hoang Thanh-Tung, and Quan Hung Tran are co-first authors of this paper. 12 pages, 12 figures. Accepted to ACL 202

Appropriate Antibiotic Use and Associated Factors in Vietnamese Outpatients

Background: Inappropriate antibiotic use among outpatients is recognized as the primary driver of antibiotic resistance. A proper understanding of appropriate antibiotic usage and associated factors helps to determine and limit inappropriateness. We aimed to identify the rate of appropriate use of antibiotics and identify factors associated with the inappropriate prescriptions. Methods: We conducted a cross-sectional descriptive study in outpatient antibiotic use at a hospital in Can Tho City, Vietnam, from August 1, 2019, to January 31, 2020. Data were extracted from all outpatient prescriptions at the Medical Examination Department and analyzed by SPSS 18 and Chi-squared tests, with 95% confidence intervals. The rationale for antibiotic use was evaluated through antibiotic selection, dose, dosing frequency, dosing time, interactions between antibiotics and other drugs, and general appropriate usage. Results: A total of 420 prescriptions were 51.7% for females, 61.7% with health insurance, and 44.0% for patients with one comorbid condition. The general appropriate antibiotic usage rate was 86.7%. Prescriptions showed that 11.0% and 9.5% had a higher dosing frequency and dose than recommended, respectively; 10.2% had an inappropriate dosing time; 3.1% had drug interactions; and only 1.7% had been prescribed inappropriate antibiotics. The risk of inappropriate antibiotic use increased in patients with comorbidities and antibiotic treatment lasting >7 days (p < 0.05). Conclusions: The study indicated a need for more consideration when prescribing antibiotics to patients with comorbidities or using more than 7 days of treatment