The distributed optimisation problem with privacy-preserving properties is considered in this paper. To solve this problem, a zero-gradient-sum algorithm based on output mask is proposed. An event-triggered condition is designed by using the output mask, which reduces the communication burden of the system effectively. The theoretical results show that the proposed algorithm can solve the optimisation problem, while the privacy information of nodes is preserved. In addition, the event-triggered condition designed based on the output mask can effectively avoid Zeno behaviour. Two simulation cases are performed to validate the effectiveness of the algorithm.
Files with the observed and model displacements, along with predicted model time series, which derive from the paper of 'Postseismic Deformation Due To the 2012 MW 7.8 Haida Gwaii and 2013 MW 7.5 Craig Earthquakes and Its Implications for regional rheological structure'. SITE.obs files: observed postseismic time series due to the 2012 Mw 7.8 Haida Gwaii and 2013 Mw 7.5 Craig earthquakes SITE.mod files: Model postseismic displacements, along with predicted time series. Detailed explanations please see readme.txt. GPS site names are the same with the study of Tian et al. (2021).
The Ethereum ecosystem has introduced a pervasive blockchain platform with programmable transactions. Everyone is allowed to develop and deploy smart contracts. Such flexibility can lead to a large collection of similar contracts, i.e., clones, especially when Ethereum applications are highly domain-specific and may share similar functionalities within the same domain, e.g., token contracts often provide interfaces for money transfer and balance inquiry. While smart contract clones have a wide range of impact across different applications, e.g., security, they are relatively little studied. Although clone detection has been a long-standing research topic, blockchain smart contracts introduce new challenges, e.g., syntactic diversity due to trade-off between storage and execution, understanding high-level business logic etc.. In this paper, we highlighted the very first attempt to clone detection of Ethereum smart contracts. To overcome the new challenges, we introduce the concept of smart contract birthmark, i.e., a semantic-preserving and computable representation for smart contract bytecode. The birthmark captures high-level semantics by effectively sketching symbolic execution traces (e.g., data access dependencies, path conditions) and maintain syntactic regularities (e.g., type and number of instructions) as well. Then, the clone detection problem is reduced to a computation of statistical similarity between two contract birthmarks. We have implemented a clone detector called EClone and evaluated it on Ethereum. The empirical results demonstrated the potential of EClone in accurately identifying clones. We have also extended EClone for vulnerability search and managed to detect CVE-2018-10376 instances.
In order to avoid data redundancy, many methods of compressing Bayer images before interpolation were proposed. Structure conversion presented by Koh has been an effective method for Bayer patterned images to improve the compression quality. On this basis, Xie proposed an improved structure conversion algorithm to further improve the compression performance. Combining the improved structure conversion using 9/7 wavelet transform with lifting scheme and set partitioning in hierarchical trees (SPIHT) algorithm, this paper proposes an efficient method in digital cameras with color filter array (CFA). Experimental results show that the proposed algorithm outperforms the improved structure conversion algorithm both in objective and subjective aspects.
Abstract The use of constant weights reduces the accuracy of cognitive evaluation results, and the current design decision-making methods ignore the relationships between Kansei images. To solve these problems, an improved cobweb grey target decision-making method for multiple Kansei images based on variable weight theory is proposed. We take a hand-held electric drill as an example for exploration. First, according to the initial weight relationships of Kansei images, variable weight theory is used to identify the Kansei image variable weights of samples, and the variable weight comprehensive evaluation results for each sample are obtained. Then, based on the correlation and angle of the Kansei images, a cobweb diagram is drawn to represent the Kansei image relationship of each sample. Combined with the cobweb grey target decision-making model for multiple Kansei images, an improved cobweb grey target decision-making method for multiple Kansei images is constructed. The decision coefficients of 10 samples are obtained as 0.0567, 0, 0.0205, 0.0478, 0.0155, 0.0272, 0.0292, 0.0402, 0.0155 and 0.0470. Through the comparison and ranking of the decision coefficients, sample 2 is determined to be the relatively optimal design reference sample. Finally, the constructed model is compared with the cobweb grey target decision-making model for multiple Kansei images and the technique for order preference by similarity to ideal solution (TOPSIS). The difference coefficients of the three methods are obtained, namely, 0.5627, 0.4957 and 0.3613. The results show that the difference coefficient of the proposed method is the largest, and it can reflect the decision-making thinking of designers and improve the discrimination among the decision-making results to a certain extent.
This paper transforms sequential power flow problem to a parallel problem and solves it on GPU. In particular, we implement parallel Gauss-Seidel solver, Newton-Raphson solver, and P-Q decoupled solver using CUDA (Compute Unified Device Architecture) on GPU. The aim is to investigate the performance of the three different parallel power flow solvers. We use four IEEE standard power systems and one actual running power system from Shang dong Province as the test cases when comparing the speedups that a GPU system can provide. The results show that Newton-Raphson solver has the best speedup when it is operated on GPU, Gauss-Seidel solver performs the worst, and P-Q decoupled solver is in the middle. The test results also indicate that when the size of the system is small, GPU does not seem to have advantages over CPU from computation time perspective. However, as the size of the system increases, the advantages of GPU becomes more clear. For instance, when the system has close to one thousand bus counts, the GPU can provide as high as over fifty-three times speedup.
We develop the concept of Trusted and Confidential Program Analysis (TCPA) which enables program certification to be used where previously there was insufficient trust. Imagine a scenario where a producer may not be trusted to certify its own software (perhaps by a foreign regulator), and the producer is unwilling to release its sources and detailed design to any external body. We present a protocol that can, using trusted computing based on encrypted sources, create certification via which all can trust the delivered object code without revealing the unencrypted sources to any party. Furthermore, we describe a realization of TCPA with trusted execution environments (TEE) that enables general and efficient computation. We have implemented the TCPA protocol in a system called TCWasm for web assembly architectures. In our evaluation with 33 benchmark cases, TCWasm managed to finish the analysis with relatively slight overheads.
Image retrieval holds significant importance within the realm of computer vision. This paper introduces SCDNet, a novel network model, leveraging selected feature aggregation for enhanced image retrieval. Using Thangka images as an illustrative example, the proposed model aims to address issues such as inadequate semantic feature extraction and image feature distortions due to background or noise. SCDNet firstly uses VGG16 network to extract semantic features from different convolutional layers of Thangka images, then passes through feature screening module, feature aggregation module, and rearrangement module, thereby completing the retrieval. Our experimental validation on homemade Thangka image dataset and publicly available fine-grained image dataset, demonstrating the superiority of SCDNet compared with other retrieval algorithms.
The current conventional data acquisition system has low processing performance due to the lack of optimization of the hardware processing chip, for which an ARM-based secure data acquisition and storage system for communication is proposed. Based on ARM chip, the hardware parts such as data serial port and chip are designed, and USART data are acquired, file reading and writing modules are designed, and recursive average filtering algorithm is used to reduce data noise. The experimental results show that the proposed data acquisition system has a high accuracy rate and a more desirable data processing performance.
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