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Despite Kerckhoff's principle, there are secret ciphers with unknown components for diplomatic or military usages. The side-channel analysis of reverse engineering (SCARE) is developed for analyzing secret ciphers. Considering the side-channel leakage, SCARE attacks enable the recovery of some secret parts of a cryptosystem, e.g., the substitution box table. However, based on idealized leakage assumption, most of these attacks have a few limitations on prior knowledge or implementations. In this paper, we focus on AES-like block ciphers with a secret S-box and demonstrate an attack which recovers both the secret key and the secret S-box. On the one hand, the key is recovered under profiled circumstance by leakage analysis and collision attack. On the other hand, the SCARE attack is based on mathematical analysis. It relies on Hamming weight of MixColumns intermediate results in the first round, which can restore the secret S-box. Experiments are performed on real power traces from a software implementation of AES-like block cipher. Moreover, we evaluate the soundness and efficiency of our method by simulations and compare with previous approaches. Our method has more advantages in intermediate results location and the required number of traces. For simulated traces with gaussian noise, our method requires 100000 traces to fully restore the secret S-box, while the previous method requires nearly 300000 traces to restore S-box. [ABSTRACT FROM AUTHOR]

The performance of XPath query is the key factor to the capacity of XML processing. It is an important way to improve the performance of XPath by making full use of multi-threaded computing resources for parallel processing. However, in the process of XPath parallelization, load imbalance and thread inefficiency often lead to the decline of parallel performance. In this paper, we propose a cost optimization-based parallel XPath query method named coPXQ. This method improves the parallel processing effect of navigational XPath query through a series of optimization measures. The main measures include as follows: first, by optimizing the storage of XML node relation index, both storage and access efficiency of the index are improved. Secondly, load balancing is realized by a new cost estimation method according to the number of XML node relations to optimize parallel relation index creation and parallel primitive execution. Thirdly, the strategy of determining the number of worker threads based on parallel effectiveness estimation is utilized to ensure the effective use of threads in query. Compared with the existing typical methods, the experimental results show that our method can obtain better parallel performance. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) have achieved great success in many computer vision tasks. However, it is difficult to deploy CNN models on low-cost devices with limited power budgets, because most existing CNN models are computationally expensive. Therefore, CNN model compression and acceleration have become a hot research topic in the deep learning area. Typical schemes for speeding up the feed-forward process with a slight accuracy loss include parameter pruning and sharing, low-rank factorization, compact convolutional filters and knowledge distillation. In this study, we propose a general acceleration scheme that replaces the floating-point multiplication with integer addition. To this end, we propose a general accelerate scheme, where the floating point multiplication is replaced by integer addition. The motivation is based on the fact that every floating point can be replaced by the summation of an exponential series. Therefore, the multiplication between two floating points can be converted to the addition among exponentials. In the experiment section, we directly apply the proposed scheme to AlexNet, VGG, ResNet for image classification, and Faster-RCNN for object detection. The results acquired from ImageNet and PASCAL VOC show that the proposed quantized scheme has a promising performance, even with only one item of exponential. Moreover, we analyzed the eciency of our method on mainstream FPGAs. The experimental results show that the proposed quantized scheme can achieve acceleration on FPGA with a slight accuracy loss. [ABSTRACT FROM AUTHOR]

The popularity of multi-core systems makes software parallelization become an important way to improve performance. As a mainstream XML query language, XQuery is the core of XML processing. It is critical to take full advantage of multi-core computing to improve XML processing performance through parallelization of XQuery. However, usually it is difficult to parallelize XQuery programs because of the nested style of XQuery expressions. Moreover, implicit parallelism is necessary to simplify the development of parallel XML application. In this paper, we propose an automatic parallelization approach, which can automatically select proper types of parallelism for a specific XQuery query. Specifically, we propose a functional intermediate language called pFXQL (parallel Functional XML Query Language) to describe parallel query plans. pFXQL has parallel semantics and is well complied with XQuery. We propose a cost model to effectively support plan generation and the selection of a preferred plan. The model estimates both computational cost and parallel cost. We implement our approach in XQuery engine and conduct experiments on various multi-core systems. Experimental results verify the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

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