There are two main contradictions in the existing electronic voting schemes, one is to ensure the legality and compliance of election behavior while ensuring the anonymity of election process, and the other is to ensure the privacy security of ballot information while ensuring the public verifiability of election results. Focusing on these contradictions, a decentralized electronic voting scheme based on Ethereum blockchain and zero-knowledge proof was proposed. In the proposed scheme, the non-interactive zero-knowledge proof algorithm and decentralized blockchain architecture were fused to build zero knowledge proof of voter identity and zero knowledge proof of ballot legality. And smart contract and Paillier algorithm were used to realize self-counting without trusted third-party counting mechanism. The theoretical analysis and simulation results show that the scheme can achieve security requirements of electronic voting and can be applied to small-scale community election.

With the development of the application of computer vision in the field of marine fisheries, fish image retrieval has played a huge role in fishery resource survey and fish behavior analysis. It is found that the background information of fish images can greatly interfere with fish image retrieval, and the fish image retrieval results only using color, texture, shape and other characteristics of fish images are not accurate due to the lack of spatial position information. To solve the above problems, a novel fish image retrieval algorithm based on HSVG (Hue, Saturation, Value, Gray) four-channel and spatial pyramid was proposed. Firstly, a visual saliency map was extracted to separate the foreground and the background, thereby reducing the interference of the image background on the retrieval. Then, in order to contain certain spatial position information, the fish image was converted into an HSVG four-channel map, and on this basis, the theory of spatial pyramid was used to segment the image and extract the SURF (Speed Up Robust Feature). Finally, the search results were obtained. In order to verify the effectiveness of the proposed algorithm, the recall and precision of the algorithm were compared with classic HSVG algorithm and saliency block algorithm on QUT_fish_data dataset and DLOU_fish_data dataset. Compared with traditional HSVG algorithm, the precision on two datasets is increased at most by 12% and 5%, and the recall is increased at most by 7% and 22%, respectively. Compared with saliency block algorithm, the precision on two datasets is increased at most by 15% and 5%, and the recall is increased at most by 36% and 22%, respectively. So, the proposed algorithm is effective and can improve the retrieval results significantly.

In order to improve the storage efficiency of Hadoop Distributed File System (HDFS) and its load balance ability, this paper presented an improved solution named Noah to replace the original multiple-replication strategy. Noah introduced a coding module to HDFS. Instead of adopting the multiple-replication strategy by the original system, the module encoded every data block of HDFS into a greater number of data sections (pieces), and saved them dispersedly into the clusters of the storage system in distributed fashion. In the case of cluster failure, the original data would be recovered via decoding by collecting any 70% of the sections, while the dynamic replication strategy also worked synchronously, in which the amount of copies would dynamically change with the demand. The experimental results in analogous clusters of storage system show the feasibility and advantages of new measures in proposed solution.