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[Author] Zhe CHEN(5hit)

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  • Multi-Task Learning in Deep Neural Networks for Mandarin-English Code-Mixing Speech Recognition

    Mengzhe CHEN  Jielin PAN  Qingwei ZHAO  Yonghong YAN  

     
    LETTER-Acoustic modeling

      Pubricized:
    2016/07/19
      Vol:
    E99-D No:10
      Page(s):
    2554-2557

    Multi-task learning in deep neural networks has been proven to be effective for acoustic modeling in speech recognition. In the paper, this technique is applied to Mandarin-English code-mixing recognition. For the primary task of the senone classification, three schemes of the auxiliary tasks are proposed to introduce the language information to networks and improve the prediction of language switching. On the real-world Mandarin-English test corpus in mobile voice search, the proposed schemes enhanced the recognition on both languages and reduced the relative overall error rates by 3.5%, 3.8% and 5.8% respectively.

  • New Impossible Differential Attack on SAFER Block Cipher Family

    Jingyuan ZHAO  Meiqin WANG  Jiazhe CHEN  Yuliang ZHENG  

     
    PAPER-Cryptography and Information Security

      Vol:
    E98-A No:3
      Page(s):
    843-852

    SAFER block cipher family consists of SAFER K, SAFER SK, SAFER+ and SAFER++. As the first proposed block cipher of them, SAFER K is strengthened by SAFER SK with improved key schedule. SAFER+ is designed as an AES candidate and Bluetooth uses a customized version of it for security. SAFER++, a variant of SAFER+, is among the cryptographic primitives selected for the second phase of the NESSIE project. In this paper, we take advantage of properties of the linear transformation and S-boxes to identify new impossible differentials for SAFER SK, SAFER+, and SAFER++. Moreover, we give the impossible differential attacks on 4-round SAFER SK/128 and 4-round SAFER+/128(256), 5-round SAFER++/128 and 5.5-round SAFER++/256. Our attacks significantly improve previously known impossible differential attacks on them. Specifically, our attacks on SAFER+ are the best attack in terms of number of rounds.

  • Strength-Strength and Strength-Degree Correlation Measures for Directed Weighted Complex Network Analysis

    Shi-Ze GUO  Zhe-Ming LU  Zhe CHEN  Hao LUO  

     
    LETTER-Artificial Intelligence, Data Mining

      Vol:
    E94-D No:11
      Page(s):
    2284-2287

    This Letter defines thirteen useful correlation measures for directed weighted complex network analysis. First, in-strength and out-strength are defined for each node in the directed weighted network. Then, one node-based strength-strength correlation measure and four arc-based strength-strength correlation measures are defined. In addition, considering that each node is associated with in-degree, out-degree, in-strength and out-strength, four node-based strength-degree correlation measures and four arc-based strength-degree correlation measures are defined. Finally, we use these measures to analyze the world trade network and the food web. The results demonstrate the effectiveness of the proposed measures for directed weighted networks.

  • A Tree-Structured Deterministic Small-World Network

    Shi-Ze GUO  Zhe-Ming LU  Guang-Yu KANG  Zhe CHEN  Hao LUO  

     
    LETTER-Artificial Intelligence, Data Mining

      Vol:
    E95-D No:5
      Page(s):
    1536-1538

    Small-world is a common property existing in many real-life social, technological and biological networks. Small-world networks distinguish themselves from others by their high clustering coefficient and short average path length. In the past dozen years, many probabilistic small-world networks and some deterministic small-world networks have been proposed utilizing various mechanisms. In this Letter, we propose a new deterministic small-world network model by first constructing a binary-tree structure and then adding links between each pair of brother nodes and links between each grandfather node and its four grandson nodes. Furthermore, we give the analytic solutions to several topological characteristics, which shows that the proposed model is a small-world network.

  • Digital Rights Management System of Media Convergence Center Based on Ethereum and IPFS

    Runde YU  Zhuowen LI  Zhe CHEN  Gangyi DING  

     
    PAPER-Multimedia Pattern Processing

      Pubricized:
    2023/05/02
      Vol:
    E106-D No:8
      Page(s):
    1275-1282

    In order to solve the problems of copyrights infringement, high cost and complex process of rights protection in current media convergence center, a digital rights management system based on blockchain technology and IPFS (Inter Planetary File System) technology is proposed. Considering that large files such as video and audio cannot be stored on the blockchain directly, IPFS technology is adopted as the data expansion scheme for the data storage layer of the Ethereum platform, IPFS protocol is further used for distributed data storage and transmission of media content. In addition, smart contract is also used to uniquely identify digital rights through NFT (Non-fungible Tokens), which provides the characteristics of digital rights transferability and traceability, and realizes an open, transparent, tamper-proof and traceable digital rights management system for media convergence center. Several experimental results show that it has higher transaction success rate, lower storage consumption and transaction confirmation delay than existing scheme.