Based on the influence of dark obstacles caused by insufficient light in an underground mine on the driving safety of an electric locomotive. This paper proposes an improved YOLOX target detection algorithm to effectively identify and classify the track obstacles of the unmanned electric mine locomotive. On the basis of the YOLOX target detection network, the CBAM attention module is added to the CSPDarket and the FPN part of the feature pyramid, and the loss function of YOLO head part is replaced by SIOU. The collected image data of track obstacles of electric locomotive under different lighting conditions are used as the training set. The Pytorch deep learning framework is used to construct an object detection model for training and verification. Experiments show that the average accuracy and recall rate of the improved YOLOX underground electric locomotive track obstacle detection model can reach 93.05% and 88.29%, and the speed is improved to 45.3 fps. Compared with other target detection models, this model can better realize the accuracy and real-time detection of underground electric locomotive track obstacles. It provides the basis for the intelligence of underground mine transportation equipment.
Over the past several years, due to the progression toward data-driven scientific disciplines, the field of Big Data has gained significant importance. These developments pose certain challenges in the area of efficient, effective, and secure management and transmission of digital information. This paper presents and evaluates a novel Distributed Ledger Technology (DLT) system, Fibereum, in a variety of use-cases, including a DLT-based system for Big Data exchange, as well as the fungible and non-fungible exchange of artwork, goods, commodities, and digital currency. Fibereum’s innovations include the application of non-linear data structures and a new concept of Lazy Verification. We demonstrate the benefits of these novel features for DLT system applications’ cost performance and their added resilience towards cyber-attacks via the consideration of several use cases.
Machine learning-based models are one of the main methods for detecting reentrant vulnerabilities. However, these models extract smart contract features only from a single form, resulting in incompleteness and inaccuracy of features. To address this problem, we propose a novel machine learning-based model for reentrant vulnerabilities detection. We extract and fuse features from abstract syntax trees, opcodes, control flow graph basic blocks, and combine machine learning algorithms for reentrant vulnerabilities detection. Additionally, to address the time-consuming problem of manual labeling, we also propose an approach for automatically adding dataset labels. We perform experiments on Smartbugs and SolidiFi-benchmark datasets and results show that our model outperforms existing models.
DNA is one of the most important information in every living thing. The DNA matching experiment is helpful for the study of paternity testing, species identification, gene mutation, suspect determination, and so on. How to study the DNA matching in the case of privacy protection has become the inevitable problems in the research of information security. The Hamming distance can reflect the similarity degree of two DNA sequences. The smaller the Hamming distance is, the more similar the two DNA sequences are. In this paper, the DNA sequence with l length is encoded with a 0–1 string with 3l length, and the protocol of confidentially computing Hamming distance is designed, which calculated the matching degree of two DNA under the premise of protecting DNA privacy. In addition, in view of the criminal suspect DNA matching problem, we design a secure computation protocol against malicious adversaries using the zero-knowledge proof and the cut-choose method to prevent or find malicious behaviors, which can resist malicious attacks.
Mediation analysis was concerned with the decomposition of the total effect of exposure on the outcome into the indirect effects and the remaining indirect effects, through a given mediation. However, when longitudinal data including time varying exposure and mediator variables, the estimated causal effects are affected by time varying confounders. Standard generalized linear equations did not give unbiased estimates. In this paper, we introduced inverse probability weighting technique to adjust such time varying confounders. Considering that the amount of data may be small and the distribution is not uniform, we decide to use Bayesian Inference to estimate the Structural Equation Model (SEM) parameters, and finally estimates the causal effect through counterfactual thought. This paper summarized the relevant theoretical knowledge of this method, verified the feasibility of this method by using the simulated data, and compared the performance of different methods.
