RESUMEN
This paper designs a texture-hidden QR code to prevent the illegal copying of a QR code due to its lack of anti-counterfeiting ability. Combining random texture patterns and a refined QR code, the code is not only capable of regular coding but also has a strong anti-copying capability. Based on the proposed code, a quality assessment algorithm (MAF) and a dual feature detection algorithm (DFDA) are also proposed. The MAF is compared with several current algorithms without reference and achieves a 95% and 96% accuracy for blur type and blur degree, respectively. The DFDA is compared with various texture and corner methods and achieves an accuracy, precision, and recall of up to 100%, and also performs well on attacked datasets with reduction and cut. Experiments on self-built datasets show that the code designed in this paper has excellent feasibility and anti-counterfeiting performance.
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AlgoritmosRESUMEN
The current power distribution Internet of Things (PDIoT) lacks security protection terminals and techniques. Network security has a large exposure surface that can be attacked from multiple paths. In addition, there are many network security vulnerabilities and weak security protection capabilities of power distribution Internet of Things terminals. Therefore, it is crucial to conduct a scientific assessment of the security of PDIoT. However, traditional security assessment methods are relatively subjective and ambiguous. To address the problems, we propose to use the entropy-weight method and cloud model theory to assess the security risk of the PDIoT. We first analyze the factors of security risks in PDIoT systems and establish a three-layer PDIoT security evaluation index system, including a perception layer, network layer, and application layer. The index system has three first-level indicators and sixteen second-level indicators. Then, the entropy-weight method is used to optimize the weight of each index. Additionally, the cloud model theory is employed to calculate the affiliation degree and eigenvalue of each evaluation index. Based on a comprehensive analysis of all evaluation indexes, we can achieve the security level of PDIoT. Taking the PDIoT of Meizhou Power Supply Bureau of Guangdong Power Grid as an example for empirical testing, the experimental results show that the evaluation results are consistent with the actual situation, which proves that the proposed method is effective and feasible.
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With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people's work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.
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OBJECTIVE: To establish strategies for preventing graft versus host disease (GVHD) and reducing treatment associated morbidity while preserving graft versus leukemia (GVL) effect in nonmyeloablative allogeneic bone marrow transplantation (allo-BMT), with or without donor lymphocyte infusion (DLI) after BMT. METHODS: 3 x 10(7) bone marrow cells mixed with 1 x 10(7) spleen cells from the same BALB/c mouse were transplanted into the nonablative irradiated inbred 615 mouse which received a single subcutaneous injection of 1 x 10(6) L615 leukemia cells three days before. The experiments were designed as follows (ten mice in each group): myeloablative BMT control group (group A), nonmyeloablative conditioning without BMT group (group B), nonmyeloablative BMT group (group C), and nonmyeloablative BMT + DLI group (group D). GVL effects were assessed by survival time, white blood cell count and L615 cells in peripheral blood and histologic changes. GVHD was assessed by signs of weight loss, ruffled fur, diarrhea and histologic changes of skin, liver and small intestines. Chimerism was detected by cytogenetic analysis and PCR technique. RESULTS: The survival time of group A, B, C and D was (20.3 +/- 13.4), (15.9 +/- 1.1), (21.6 +/- 1.7) and (37.8 +/- 2.0) days, respectively, being no significant difference between group A and group C (P > 0.05). The survival time of group C was longer than that of group B (P < 0.01). And among group B, C and D, group D had the longest survival time (P < 0.01). GVHD signs and histologic changes were observed in 60% of control group mice at + 14 day, but none of group C and group D. 40% of mice in group A died of treatment associated morbidity within two weeks, but none in group C and group D. Allogeneic chimerism was kept in group A, but excluded gradually in group C. CONCLUSION: GVL effect seems preserved in nonmyeloablative BMT mice, but weaker than that in myeloablative BMT mice. GVL effect seems to be enhanced by DLI after nonmyeloablative BMT. GVHD and transplantation associated morbidity seems to be reduced in nonmyeloablative BMT.