Blockchain’s immutable and traceable records and independence from third-party involvement make it an irreplaceable tool in applications involving multiple stakeholders. However, securely sharing off-chain files among stakeholders while ensuring non-repudiation is a significant challenge. This is because blockchain cannot monitor off-chain behavior, and stakeholders may refuse to acknowledge records on the chain. In this study, we propose an efficient solution for secure file sharing among stakeholders through on-chain and off-chain collaboration for blockchain applications with additional off-chain storage modules. Specifically, we design an adapted blockchain structure and propose a consensus process integrated with the sharing process to manage off-chain behavior and prevent delivery repudiation. We also incorporate a ciphertext policy into the sharing process to ensure the integrity and confidentiality of the shared file. Additionally, we propose a watermarking protocol in conjunction with blockchain records to hold unauthorized disclosure behavior accountable. Our scheme extends the management scope of blockchain to off-chain and achieves 32x, 19x, and 1.48x higher throughput than Bitcoin, Ethereum, and Fabric, respectively.

For structural performance functions with characteristics of multivariable, non-normality, small failure probability and nonlinearity, it is usually difficult to pursue satisfactory accuracy by means of traditional probabilistic methods such as first-order reliability method and second-order reliability method. According to the idea of adaptive subdivision, a backward recursive method based on the universal generating function is suggested for structural reliability calculation. In the sensitive region, the variable space is subdivided partially, the discrete interval length is reduced, and the random variables are discretized nonuniformly and adaptively by the backward recursion operation. Compared with the current method of forward recursion operator and the corresponding similar items merging operation, the backward recursion operator can not only avoid low accuracy owing to subdividing the state combinations in the sensitive region, but avert the high cost of calculation owing to merging state combinations in the nonsensitive region of the variable space as well. Three examples are presented to demonstrate the rationality of the backward recursive algorithm. The results indicate that the accuracy of the proposed method is significantly higher than that of traditional methods, and the computational efficiency of the proposed method meets the engineering requirement. The research work provides a new path for reliability calculation with consideration of complex performance functions.

This paper develops an improved dynamic reliability model for compressor rotor system, defined as a “unconventional active system” here. First, regarding to the variety of system-component connections, system specific reliability modeling technique is presented, in which the stress and strength are both nonnegative stochastic processes. The new model fully considers the load characteristics of rotor blades, and inherently embodied the effect of the statistical dependence between the failures of the rotor blades. Then, calculated by the new model, the relationship between reliability and time, and that between the hazard rate and time, for a compressor rotor system are discussed, respectively. Comparing to the traditional series system reliability model, the new system reliability model developed in this paper has higher accuracy for compressor rotor system, since the traditional models neglect the difference between compressor rotor system and traditional active system. Moreover, in order to master the cruise characteristics of the aero-plane during reliability analysis of its compressor rotor blade set, a flight cruise is separated into several stages based on the value of Mach number and its change rate, and a subsection solution method is innovatively proposed accordingly. At the end of the paper, a certain cruise of fighter plane is analyzed to verify the accuracy of the new method. The results show that, as to a flight cruise with different stages, the new method is basically reasonable to most of the load histories, especially for the situation with impact loads existing.

Traffic state estimation is among the most critical issues in intelligent transport systems, and it has been widely explored for years considering its significance for diverse industrial applications such as vehicle dispatch and route planning. The availability of vehicle trajectories has provided a promising data source for traffic estimation, as it shed light on traffic status and characterizes human transition activates that are otherwise extremely hard to track. However, there are major challenges in incorporating trajectories in traffic estimation largely because of its inherent uncertainty from data sparsity and semantic ambiguity. Entangled in complicated road topology and spatiotemporal traffic dynamics, properly embedding trajectories in modeling is especially difficult. To address the aforementioned challenges, in this paper, we propose a Boosted Trajectory Calibration (BTRAC) framework to model traffic states of complex road networks that effectively integrates trajectory information. In the framework, we first use a deep neural network to predict the traffic states of each road individually from historical traffic information, along with the prediction uncertainty. Then we refine the predictions by an iterative boosting calibration procedure with embedded trajectories. We conduct extensive large-scale empirical studies on two cities and demonstrate the effectiveness of the proposed approach.

The gear transmission system is a typical time-domain series system, and its fatigue reliability evaluation under variable amplitude loading has long attracted the interests from scholars. The widely used Monte Carlo (MC) method to evaluate the reliability of a system with material and load uncertainties is time consuming, especially when used in combination with a fatigue analysis software. Concerning this problem, this paper proposes a “deterministic damage-random threshold (DDRT)” reliability model. The model converts the randomness from damage to the threshold, thus saving time because only the median damage and the damage for key reliability of each fatigue part are required in reliability evaluation. Based on this model, a reliability calculation flow of a gear transmission system is proposed considering the time-domain characteristic of gears. Finally, the model is verified by numerical examples and the fatigue test data of gears.

Cataract is defined as a lenticular opacity presenting usually with poor visual acuity. It is considered the most common cause of blindness. Early diagnosis and treatment can reduce the suffering of patients and prevent visual impairment from turning into blindness. Recently, cataract diagnosis applying pattern recognition is in a rising period. For retinal fundus images, the task is usually cataract classification. However, it needs complex manual processing, which demands dexterous people and time taking exertion. Besides, it faces the challenge of effective interpretability and dependability. In this paper, we develop a deep-learning algorithm to intuitively identify cataract attributes to solve these limitations. Our model, is a 18(50)-layer convolutional neural network that inputs retinal image in G channel and outputs the prediction with heatmap. The heatmap localizes the areas where most indicative of different levels of cataract. Furthermore, we extend the training strategy for the corresponding task, which aims at improving the performance of the network. Comparing with other methods in cataract classification, we succeeded to achieve state of the art accuracy of proposed method on detection and grading task. Most importantly, our model provides a compelling reason via localizing the areas revealing cataract in the image.

Many data mining applications involve a set of related learning tasks. Multi-task learning (MTL) is a learning paradigm that improves generalization performance by transferring knowledge among those tasks. MTL has attracted so much attention in the community, and various algorithms have been successfully developed. Recently, distributed MTL has also been studied for related tasks whose data is distributed across different geographical regions. One prominent challenge of the distributed MTL frameworks is to maintain the privacy of the data. The distributed data may contain sensitive and private information such as patients' records and registers of a company. In such cases, distributed MTL frameworks are required to preserve the privacy of the data. In this paper, we propose a novel privacy-preserving distributed MTL framework to address this challenge. A privacy-preserving proximal gradient algorithm, which asynchronously updates models of the learning tasks, is introduced to solve a general class of MTL formulations. The proposed asynchronous approach is robust against network delays and provides a guaranteed differential privacy through carefully designed perturbation. Theoretical guarantees of the proposed algorithm are derived and supported by the extensive experimental results.

In collaborative medical research settings, a moderate number of groups (sites) may wish to merge local analyses of private subject data. Differential privacy offers one way to guarantee privacy for these local analyses. We describe a novel ensemble learning method that we call the "feature method" for aggregating binary classifiers or regressors trained on local data. Our method leverages a public data set available at the aggregator to optimize a linear combination of local predictors. We provide some analysis of the method and show how it is effective when the local sites are required to learn classifiers that are differentially private. We prove that this method has near-optimal performance when local data sets are large enough under certain requirements on the parameters. Experimentally, we give a comparison of the feature method and the standard approach of averaging the local classifiers.

General failure event data from various sources are often used to estimate the failure probability for the system of interest, especially when s-dependence exists among component failures, where common cause failure plays an important role. Failure event data from different sources must be reasonably explained, and correctly applied, so that the information about load environment, and component/system property can be used correctly. In estimating the probability for s-dependent system failure, both the load distribution, and component strength distribution are much more important than component failure probability index. Based on the relationship among different multiple failures, this paper presents a data mapping approach to estimating dependent system failure probability through multiple failure event data of other systems with different sizes. The underlying assumption on data mapping is that failures of different multiples (including single) are correlated with each other for a group of components if they are subjected to the same or correlated random load (loads). Taking the situation of a group of s-independent components operating under the same random load as an example, the likelihood of a component failure at a trial depends not only on the strength of the individual component but also on the realization of the random load. The likelihood of a specific multiple failure at a trial is also determined by both the component strengths, and the realization of the random load. Furthermore, if a larger load sample appears, the likelihoods for failure are higher. Conversely, if a smaller load sample appears, the likelihoods of failure are lower. We emphasized in this paper that system failure event data should be interpreted & applied under the principle that various multiple failures are distinguished by their respective failure multiplicity and/or system size, and are inherently interrelated through correlated load environments. The approach starts with determining the load parameter, and component strength parameter according to multiple (including single) failure event data available. Then, these parameters are used to calculate the probability of multiple failures for systems of different sizes. This approach is applicable to predict high multiple failure probability based on low multiple failure event data. Examples of estimating multiple failure probabilities of EDG (emergency diesel generators) with mapped data illustrate that the proposed approach is desirable.