SPCBIG-EC: A Robust Serial Hybrid Model for Smart Contract Vulnerability Detection.

With countless devices connected to the Internet of Things, trust mechanisms are especially important. IoT devices are more deeply embedded in the privacy of people's lives, and their security issues cannot be ignored. Smart contracts backed by blockchain technology have the potential to solve these problems. Therefore, the security of smart contracts cannot be ignored. We propose a flexible and systematic hybrid model, which we call the Serial-Parallel Convolutional Bidirectional Gated Recurrent Network Model incorporating Ensemble Classifiers (SPCBIG-EC). The model showed excellent performance benefits in smart contract vulnerability detection. In addition, we propose a serial-parallel convolution (SPCNN) suitable for our hybrid model. It can extract features from the input sequence for multivariate combinations while retaining temporal structure and location information. The Ensemble Classifier is used in the classification phase of the model to enhance its robustness. In addition, we focused on six typical smart contract vulnerabilities and constructed two datasets, CESC and UCESC, for multi-task vulnerability detection in our experiments. Numerous experiments showed that SPCBIG-EC is better than most existing methods. It is worth mentioning that SPCBIG-EC can achieve F1-scores of 96.74%, 91.62%, and 95.00% for reentrancy, timestamp dependency, and infinite loop vulnerability detection.

Reconstructing Floorplans from Point Clouds Using GAN.

This paper proposed a method for reconstructing floorplans from indoor point clouds. Unlike existing corner and line primitive detection algorithms, this method uses a generative adversarial network to learn the complex distribution of indoor layout graphics, and repairs incomplete room masks into more regular segmentation areas. Automatic learning of the structure information of layout graphics can reduce the dependence on geometric priors, and replacing complex optimization algorithms with Deep Neural Networks (DNN) can improve the efficiency of data processing. The proposed method can retain more shape information from the original data and improve the accuracy of the overall structure details. On this basis, the method further used an edge optimization algorithm to eliminate pixel-level edge artifacts that neural networks cannot perceive. Finally, combined with the constraint information of the overall layout, the method can generate compact floorplans with rich semantic information. Experimental results indicated that the algorithm has robustness and accuracy in complex 3D indoor datasets; its performance is competitive with those of existing methods.

Symbiotic microbial studies in diverse populations of Aphis gossypii, existing on altered host plants in different localities during different times.

Complex interactions between symbiotic bacteria and insects ultimately result in equilibrium in all aspects of life in natural insect populations. In this study, abundance of principal symbiotic bacteria was estimated using qPCR in 1553 individuals of aphids, Aphis gossypii. Aphids were sampled from primary and secondary host plants-hibiscus and cotton. Hibiscus aphids were collected from 24 different locations in April, September, and November, whereas cotton aphids were collected between 2015 and 2017 from areas with wide variations in climatic conditions. About 30%-45% aphids were recorded with the most dominant symbiont, Arsenophonus. The other symbionts were in low frequency, and about 7% of aphids were noted with Hamiltonella, Acinetobacter, and Microbacterium, and 3% of aphids were verified with Serratia and Pseudomonas. Aphids infected with Hamiltonella, Arsenophonus, and Serratia can influence Buchnera densities. Hamiltonella has positive interaction with densities of Arsenophonus and Serratia. Almost 100% coinfection of Hamiltonella and Arsenophonus was detected in Xinxiang aphids and 50% coinfection was reported in aphids from North China, while no coinfection was detected in Hainan aphids. These findings describe the prevalence pattern and richness of core community of symbiotic bacteria in naturally occurring populations of A. gossypii and provide new insights for the study of symbiotic bacteria.