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学者姓名：宋志刚
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针对当前工业系统缺乏统一的产品数据共享服务,限制了用户获取可信的产品追溯信息的问题,基于区块链设计可信分布式工业数据治理方案,实现高效、安全的产品数据共享与治理.产品数据生成者将数据提交到区块链系统之前,在离链状态下对产品数据进行压缩和加密.为了在离链过程中使产品数据可用,系统通过 2 种类型的区块链交易(生成者交易和数据交易)支持离链/链上数据访问.提供混合访问控制机制用于加密产品数据,将秘密密钥仅提供给经过授权的数据用户.该方案能够有效地保护产品数据的隐私性,提供细粒度的访问控制,能够对产品数据生成的全流程进行溯源.系统性能的测试结果表明,在secp256k1 椭圆曲线上(提供 128 bit安全性),密钥生成阶段的计算和通信开销不高于 81.592 ms和 2.83 kB,数据提交阶段不高于 50.251 ms和 3.59 kB,数据更新时间不超过251.596 ms,数据读取时间不高于311.104 ms.与同类方案的性能比较结果证实了该方案的高效性.
Keyword ：
全流程溯源 全流程溯源 区块链 区块链 数据可信治理 数据可信治理 细粒度访问控制 细粒度访问控制 隐私保护 隐私保护
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| GB/T 7714 | 黄荣 , 杨文琴 , 宋志刚 . 基于区块链的可信分布式工业数据治理方案 [J]. | 浙江大学学报（工学版） , 2025 , 59 (2) : 269-277 . |
| MLA | 黄荣 等. "基于区块链的可信分布式工业数据治理方案" . | 浙江大学学报（工学版） 59 . 2 (2025) : 269-277 . |
| APA | 黄荣 , 杨文琴 , 宋志刚 . 基于区块链的可信分布式工业数据治理方案 . | 浙江大学学报（工学版） , 2025 , 59 (2) , 269-277 . |
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It has been widely known that long non-coding RNA (lncRNA) plays an important role in gene expression and regulation. However, due to a few characteristics of lncRNA (e.g., huge amounts of data, high dimension, lack of noted samples, etc.), identifying key lncRNA closely related to specific disease is nearly impossible. In this paper, the authors propose a computational method to predict key lncRNA closely related to its corresponding disease. The proposed solution implements a BPSO based intelligent algorithm to select possible optimal lncRNA subset, and then uses ML-ELM based deep learning model to evaluate each lncRNA subset. After that, wrapper feature extraction method is used to select lncRNAs, which are closely related to the pathophysiology of disease from massive data. Experimentation on three typical open datasets proves the feasibility and efficiency of our proposed solution. This proposed solution achieves above 93% accuracy, the best ever.
Keyword ：
Binary Particle Swarm Optimization Binary Particle Swarm Optimization Expression Profile Expression Profile Extreme Learning Machine Extreme Learning Machine Long Non-Coding RNA Long Non-Coding RNA
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| GB/T 7714 | Yang, Wenqing , Zheng, Xianghan , Huang, QiongXia et al. Combining BPSO and ELM Models for Inferring Novel lncRNA- Disease Associations [J]. | INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING , 2023 , 19 (2) . |
| MLA | Yang, Wenqing et al. "Combining BPSO and ELM Models for Inferring Novel lncRNA- Disease Associations" . | INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING 19 . 2 (2023) . |
| APA | Yang, Wenqing , Zheng, Xianghan , Huang, QiongXia , Liu, Yu , Chen, Yimi , Song, ZhiGang . Combining BPSO and ELM Models for Inferring Novel lncRNA- Disease Associations . | INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING , 2023 , 19 (2) . |
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The unsupervised domain-adaptive vehicle re-identification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain; however, there are knowledge differences between the target domain and the source domain. To mitigate domain discrepancies, existing unsupervised domain-adaptive re-identification methods typically require access to source domain data to assist in retraining the target domain model. However, for security reasons, such as data privacy, data exchange between different domains is often infeasible in many scenarios. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a source-free domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model's generalization performance. In the experiment, two mainstream public datasets in this field, VeRi776 and VehicleID, are tested experimentally, and the obtained rank-k (the cumulative matching features) and mAP (the mean Average Precision) indicators are both improved, which are suitable for object re-identification tasks when data between domains cannot be interoperated.
Keyword ：
joint training joint training pseudo label pseudo label source-free knowledge transfer source-free knowledge transfer unsupervised domain adaptation unsupervised domain adaptation vehicle re-identification vehicle re-identification
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| GB/T 7714 | Song, Zhigang , Li, Daisong , Chen, Zhongyou et al. Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer [J]. | APPLIED SCIENCES-BASEL , 2023 , 13 (19) . |
| MLA | Song, Zhigang et al. "Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer" . | APPLIED SCIENCES-BASEL 13 . 19 (2023) . |
| APA | Song, Zhigang , Li, Daisong , Chen, Zhongyou , Yang, Wenqin . Unsupervised Vehicle Re-Identification Method Based on Source-Free Knowledge Transfer . | APPLIED SCIENCES-BASEL , 2023 , 13 (19) . |
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Person re-identification (re-ID) is the task of matching the same individuals across multiple cameras, and its performance is greatly influenced by background clutter. Most re-ID methods remove background clutter using hard manners, such as the use of segmentation algorithms. However, the hard manner may damage the structure information and smoothness of original images. In this work, we propose a unidirectional information-interaction network ((UIN)-N-2) that consists of a global stream (G-Stream) and a background-graying stream (BGgStream). The G-Stream and BGg-Stream carry out unidirectional information interaction such that their features are complementary. We further propose a soft manner with the (UIN)-N-2 to weaken background clutter by background-graying. The soft manner can help the (UIN)-N-2 filter out background interference and retain some informative background cues. Extensive evaluations demonstrate that our method significantly outperforms many state-of-the-art approaches in the challenging Market-1501, DukeMTMC-reID, and CUHK03-NP datasets. (C) 2021 SPIE and IS&T
Keyword ：
background-grey background-grey person re-identification person re-identification unidirectional information interaction unidirectional information interaction
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| GB/T 7714 | Yang, Qingqing , Wu, Junyi , Song, Qishan et al. Unidirectional information-interaction network for person re-identification [J]. | JOURNAL OF ELECTRONIC IMAGING , 2021 , 30 (4) . |
| MLA | Yang, Qingqing et al. "Unidirectional information-interaction network for person re-identification" . | JOURNAL OF ELECTRONIC IMAGING 30 . 4 (2021) . |
| APA | Yang, Qingqing , Wu, Junyi , Song, Qishan , Gao, Zhipeng , Huang, Liqin , Song, Zhigang . Unidirectional information-interaction network for person re-identification . | JOURNAL OF ELECTRONIC IMAGING , 2021 , 30 (4) . |
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This paper mainly studies the dynamic identification of hot topics and their trend prediction: the identification methods of hot topics are studied from the two dimensions of content and form; the trend prediction method is completed from the two dimensions of media attention and emotional tendency. This paper develops a hot topic recognition method based on formal feature ranking. This paper compares the advantages and disadvantages of traditional methods, and proposes a high-frequency co-occurrence clustering strategy based on minimum similarity, which effectively solves the timeliness requirements of real-time dynamic hot spot recognition. Based on the recognition of hot topics, this article will jointly complete the trend prediction of hot topics from the changes in media attention and emotional orientation. We have encapsulated the hot topic recognition method based on high-frequency co-occurrence into a module and applied it in the national language and writing public opinion monitoring system. © 2021 IEEE.
Keyword ：
Forecasting Forecasting Social aspects Social aspects
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| GB/T 7714 | Song, Zhigang , Song, Kang , Cheng, Nanchang et al. Research on Internet Public Opinion Recognition Method Based on High Frequency Co-occurrence [C] . 2021 : 237-242 . |
| MLA | Song, Zhigang et al. "Research on Internet Public Opinion Recognition Method Based on High Frequency Co-occurrence" . (2021) : 237-242 . |
| APA | Song, Zhigang , Song, Kang , Cheng, Nanchang , Li, Jiao , Shang, Wenqian , Zou, Yu . Research on Internet Public Opinion Recognition Method Based on High Frequency Co-occurrence . (2021) : 237-242 . |
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Traditional centralized digital copyright protection is weak in security and difficult to manage. In order to protect digital assets from unauthorized use and to solve the centralized regulatory challenges, blockchain technology is used to enhance the protection of digital works copyrights. This paper proposed a digital works copyright protection architecture based on the Consortium Blockchain, and elaborate on the security problems and solutions faced by the architecture. Moreover, the Diffie-Hellman algorithm is improved based on the Shamir algorithm to make it more suitable for the characteristics of blockchain. Finally, a smart contract is written according to the characteristics and process of digital copyright protection, and a Fabric-based digital copyright protection solution is implemented. The feasibility and effectiveness of using blockchain for digital copyright protection proposed in this paper are proved through experiments, Furthermore, the extensibility of the solution is discussed. © 2021, Springer Nature Singapore Pte Ltd.
Keyword ：
Blockchain Blockchain Copyrights Copyrights
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| GB/T 7714 | Song, Zhigang , Yu, Zaifu , Shang, Wenqian et al. A Digital Copyright Protection Method Based on Blockchain [C] . 2021 : 486-498 . |
| MLA | Song, Zhigang et al. "A Digital Copyright Protection Method Based on Blockchain" . (2021) : 486-498 . |
| APA | Song, Zhigang , Yu, Zaifu , Shang, Wenqian , Li, YaXuan . A Digital Copyright Protection Method Based on Blockchain . (2021) : 486-498 . |
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With the development of information technology, machine translation technologies play a crucial role in cross-language communication. However, there is a problem of information loss in machine translation. In view of the common problem, this paper proposes three Transform-Information Combination (Transformer-IC) models based on information combination method. The models are based on the Transformer and select different middle-layer information to compensate the output through arithmetic mean combination method, linear transformation method and multi-layer information combination method respectively. Experimental results based on Linguistic Data Consortium (LDC) Chinese-to-English corpus and International Workshop on Spoken Language Translation (IWSLT) English-to-German corpus show that the BLEU values of all kinds of Transformer-IC model are higher than that of the reference model, in particular the arithmetic mean combination method improves the BLEU value by 1.9. Compared with the Bert model, the results show that even though the Bert model has a good performance, the Transformer-IC models are better than the Bert model. Transformer-IC models can make full use of the middle-layer information and effectively avoid the problem of information loss. © 2021 IEEE
Keyword ：
Computational linguistics Computational linguistics Computer aided language translation Computer aided language translation Integrated circuits Integrated circuits Linear transformations Linear transformations Mathematical transformations Mathematical transformations
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| GB/T 7714 | Song, Zhigang , Chai, Jiazhao , Shang, Wenqian et al. Transformer-ic: The solution to information loss [C] . 2021 : 97-101 . |
| MLA | Song, Zhigang et al. "Transformer-ic: The solution to information loss" . (2021) : 97-101 . |
| APA | Song, Zhigang , Chai, Jiazhao , Shang, Wenqian , Yuning, Guo . Transformer-ic: The solution to information loss . (2021) : 97-101 . |
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To tackle the problems of the underutilization of context, the sparseness of data and the dependence on human-designed features in existing Chinese microblog sentiment classification methods, a Chinese microblog sentiment classification method based on convolutional neural network is proposed. Firstly, microblog messages are extended using the interaction context, and then they are initialized with dense vectors in the low-dimension space. Secondly, a convolutional neural network model is constructed for extracting and combining features. Finally, the sentiment of each microblog message is estimated by softmax function. Experimental results show that compared with baselines, the proposed method obtains higher accuracies and F1 values. © 2016, Science Press. All right reserved.
Keyword ：
Chinese Microblog; Convolutional Neural Network; Sentiment Classification Chinese Microblog; Convolutional Neural Network; Sentiment Classification
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| GB/T 7714 | Liao, X. , Zhang, L. , Song, Z. et al. Chinese microblog sentiment classification based on convolutional neural network [J]. | Pattern Recognition and Artificial Intelligence , 2016 , 29 (12) : 1075-1082 . |
| MLA | Liao, X. et al. "Chinese microblog sentiment classification based on convolutional neural network" . | Pattern Recognition and Artificial Intelligence 29 . 12 (2016) : 1075-1082 . |
| APA | Liao, X. , Zhang, L. , Song, Z. , Cheng, X. , Chen, G. . Chinese microblog sentiment classification based on convolutional neural network . | Pattern Recognition and Artificial Intelligence , 2016 , 29 (12) , 1075-1082 . |
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针对现有中文微博观点分类方法对上下文利用不足、数据表示稀疏和特征依赖于人工设计等问题,提出基于卷积神经网络的中文微博观点分类方法.首先利用交互上下文扩充不同主题下的微博内容,使用低维密集向量初始化微博文本.然后构造卷积神经网络模型,实现特征抽取和组合.最后基于softmax分类函数估计中文微博观点类别.实验表明,相比基准方法,文中方法在精确度和F1值上的效果更好.
Keyword ：
中文微博 中文微博 卷积神经网络 卷积神经网络 观点分类 观点分类
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| GB/T 7714 | 廖祥文 , 张丽瑶 , 宋志刚 et al. 基于卷积神经网络的中文微博观点分类 [J]. | 模式识别与人工智能 , 2016 , 29 (12) : 1075-1082 . |
| MLA | 廖祥文 et al. "基于卷积神经网络的中文微博观点分类" . | 模式识别与人工智能 29 . 12 (2016) : 1075-1082 . |
| APA | 廖祥文 , 张丽瑶 , 宋志刚 , 程学旗 , 陈国龙 . 基于卷积神经网络的中文微博观点分类 . | 模式识别与人工智能 , 2016 , 29 (12) , 1075-1082 . |
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提出了一种基于C-Chord算法的节点Cache共享模型,实现对象的搜索、存储和分发,有效地利用了客户节点的缓存内容,提高了客户间的合作,减小了客户的等待时间,降低了服务器的压力.仿真实验证明,采用G-Chord算法处理节点的路由表长度有了显著的缩减,能够保持较好的平均路径长度.此外,对分组数量的不同取值、节点负载的研究也为G-Chord的分组方案提供了一定的参考依据.
Keyword ：
Cache共享 Cache共享 G-Chord G-Chord 性能 性能
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| GB/T 7714 | 宋志刚 . 基于G-Chord的Cache共享模型 [J]. | 南京师范大学学报（工程技术版） , 2009 , 9 (4) : 77-81 . |
| MLA | 宋志刚 . "基于G-Chord的Cache共享模型" . | 南京师范大学学报（工程技术版） 9 . 4 (2009) : 77-81 . |
| APA | 宋志刚 . 基于G-Chord的Cache共享模型 . | 南京师范大学学报（工程技术版） , 2009 , 9 (4) , 77-81 . |
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