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学者姓名：董晨
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Aspect-level multimodal sentiment analysis aims to ascertain the sentiment polarity of a given aspect from a text review and its accompanying image. Despite substantial progress made by existing research, aspect-level multimodal sentiment analysis still faces several challenges: (1) Inconsistency in feature granularity between the text and image modalities poses difficulties in capturing corresponding visual representations of aspect words. This inconsistency may introduce irrelevant or redundant information, thereby causing noise and interference in sentiment analysis. (2) Traditional aspect-level sentiment analysis predominantly relies on the fusion of semantic and syntactic information to determine the sentiment polarity of a given aspect. However, introducing image modality necessitates addressing the semantic gap in jointly understanding sentiment features in different modalities. To address these challenges, a multi-granularity visual-textual feature fusion model (MG-VTFM) is proposed to enable deep sentiment interactions among semantic, syntactic, and image information. First, the model introduces a multi-granularity hierarchical graph attention network that controls the granularity of semantic units interacting with images through constituent tree. This network extracts image sentiment information relevant to the specific granularity, reduces noise from images and ensures sentiment relevance in single-granularity cross-modal interactions. Building upon this, a multilayered graph attention module is employed to accomplish multi-granularity sentiment fusion, ranging from fine to coarse. Furthermore, a progressive multimodal attention fusion mechanism is introduced to maximize the extraction of abstract sentiment information from images. Lastly, a mapping mechanism is proposed to align cross-modal information based on aspect words, unifying semantic spaces across different modalities. Our model demonstrates excellent overall performance on two datasets.
Keyword ：
Aspect-level sentiment analysis Aspect-level sentiment analysis Constituent tree Constituent tree Multi-granularity Multi-granularity Multimodal data Multimodal data Visual-textual feature fusion Visual-textual feature fusion
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| GB/T 7714 | Chen, Yuzhong , Shi, Liyuan , Lin, Jiali et al. Multi-granularity visual-textual jointly modeling for aspect-level multimodal sentiment analysis [J]. | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (1) . |
| MLA | Chen, Yuzhong et al. "Multi-granularity visual-textual jointly modeling for aspect-level multimodal sentiment analysis" . | JOURNAL OF SUPERCOMPUTING 81 . 1 (2025) . |
| APA | Chen, Yuzhong , Shi, Liyuan , Lin, Jiali , Chen, Jingtian , Zhong, Jiayuan , Dong, Chen . Multi-granularity visual-textual jointly modeling for aspect-level multimodal sentiment analysis . | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (1) . |
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The objective of dialogue state tracking (DST) is to dynamically track information within dialogue states by populating predefined state slots, which enhances the comprehension capabilities of task-oriented dialogue systems in processing user requests. Recently, there has been a growing popularity in using graph neural networks to model the relationships between slots and slots as well as between dialogue and slots. However, these models overlook the relationships between words and phrases in the current turn dialogue and dialogue history. Specific syntactic dependencies (e.g., the object of a preposition) and constituents (e.g., noun phrases) have a higher probability of being the slot values that need to be retrieved at current moment. Neglecting these syntactic dependency and constituent information may cause the loss of potential candidate slot values, thereby limiting the overall performance of DST models. To address this issue, we propose a Hierarchical Fine-grained State Aware Graph Attention Network for Dialogue State Tracking (HFSG-DST). HFSG-DST exploits the syntactic dependency and constituent tree information, such as phrase segmentation and hierarchical structure in dialogue utterances, to construct a relational graph between entities. It then employs a hierarchical graph attention network to facilitate the extraction of fine-grained candidate dialogue state information. Additionally, HFSG-DST designs a Schema-enhanced Dialogue History Selector to select the most relevant turn of dialogue history for current turn and incorporates schema description information for dialogue state tracking. Consequently, HFSG-DST is capable of constructing the dependency tree and constituent tree on noise-free utterances. Experimental results on two public benchmark datasets demonstrate that HFSG-DST outperforms other state-of-the-art models.
Keyword ：
Dialogue state tracking Dialogue state tracking Hierarchical graph attention network Hierarchical graph attention network Schema enhancement Schema enhancement Syntactic information Syntactic information
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| GB/T 7714 | Liao, Hongmiao , Chen, Yuzhong , Chen, Deming et al. Hierarchical fine-grained state-aware graph attention network for dialogue state tracking [J]. | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (5) . |
| MLA | Liao, Hongmiao et al. "Hierarchical fine-grained state-aware graph attention network for dialogue state tracking" . | JOURNAL OF SUPERCOMPUTING 81 . 5 (2025) . |
| APA | Liao, Hongmiao , Chen, Yuzhong , Chen, Deming , Xu, Junjie , Zhong, Jiayuan , Dong, Chen . Hierarchical fine-grained state-aware graph attention network for dialogue state tracking . | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (5) . |
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Due to the complexity of integrated circuit design and manufacturing process, an increasing number of third parties are outsourcing their untrusted Intellectual Property (IP) cores to pursue greater economic benefits, which may embed numerous security issues. The covert nature of hardware Trojans (HTs) poses a significant threat to cyberspace, and they may lead to catastrophic consequences for the national economy and personal privacy. To deal with HTs well, it is not enough to just detect whether they are included, like the existing studies. Same as malware, identifying the attack intentions of HTs, that is, analyzing the functions they implement, is of great scientific significance for the prevention and control of HTs. Based on the fined detection, for the first time, this paper proposes a two-stage Graph Neural Network model for HTs’ multifunctional classification, GNN4HT. In the first stage, GNN4HT localizes HTs, achieving a notable True Positive Rate (TPR) of 94.28 the Trust-Hub dataset and maintaining high performance on the TRTC-IC dataset. GNN4HT further transforms the localization results into HT Information Graphs (HTIGs), representing the functional interaction graphs of HTs. In the second stage, the dataset is augmented through logical equivalence for training and HT functionalities are classified based on the extracted HTIG from the first stage. For the multifunctional classification of HTs, the correct classification rate reached as high as 80.95% at gate-level and 62.96% at RTL. This paper marks a breakthrough in HT detection, and it is the first to address the multifunctional classification issue, holding significant practical importance and application prospects. Authors
Keyword ：
Gate-level Gate-level Golden-free Golden-free Hardware Hardware Hardware Trojan Hardware Trojan HTIG HTIG HT location HT location HT multifunctional classification HT multifunctional classification Integrated circuit modeling Integrated circuit modeling Location awareness Location awareness Logic gates Logic gates RTL RTL Security Security Training Training Trojan horses Trojan horses
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| GB/T 7714 | Chen, L. , Dong, C. , Wu, Q. et al. GNN4HT: A Two-stage GNN Based Approach for Hardware Trojan Multifunctional Classification [J]. | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2024 : 1-1 . |
| MLA | Chen, L. et al. "GNN4HT: A Two-stage GNN Based Approach for Hardware Trojan Multifunctional Classification" . | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (2024) : 1-1 . |
| APA | Chen, L. , Dong, C. , Wu, Q. , Liu, X. , Guo, X. , Chen, Z. et al. GNN4HT: A Two-stage GNN Based Approach for Hardware Trojan Multifunctional Classification . | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2024 , 1-1 . |
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The recommendation system aims to recommend items to users by capturing their personalized interests. Traditional recommendation systems typically focus on modeling target behaviors between users and items. However, in practical application scenarios, various types of behaviors (e.g., click, favorite, purchase, etc.) occur between users and items. Despite recent efforts in modeling various behavior types, multi-behavior recommendation still faces two significant challenges. The first challenge is how to comprehensively capture the complex relationships between various types of behaviors, including their interest differences and interest commonalities. The second challenge is how to solve the sparsity of target behaviors while ensuring the authenticity of information from various types of behaviors. To address these issues, a multi-behavior recommendation framework based on Multi-View Multi-Behavior Interest Learning Network and Contrastive Learning (MMNCL) is proposed. This framework includes a multi-view multi-behavior interest learning module that consists of two submodules: the behavior difference aware submodule, which captures intra-behavior interests for each behavior type and the correlations between various types of behaviors, and the behavior commonality aware submodule, which captures the information of interest commonalities between various types of behaviors. Additionally, a multi-view contrastive learning module is proposed to conduct node self- discrimination, ensuring the authenticity of information integration among various types of behaviors, and facilitating an effective fusion of interest differences and interest commonalities. Experimental results on three real-world benchmark datasets demonstrate the effectiveness of MMNCL and its advantages over other state-of-the-art recommendation models. Our code is available at https://github.com/sujieyang/MMNCL.
Keyword ：
Contrastive learning Contrastive learning Interest learning network Interest learning network Meta learning Meta learning Multi-behavior recommendation Multi-behavior recommendation
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| GB/T 7714 | Su, Jieyang , Chen, Yuzhong , Lin, Xiuqiang et al. Multi-view multi-behavior interest learning network and contrastive learning for multi-behavior recommendation [J]. | KNOWLEDGE-BASED SYSTEMS , 2024 , 305 . |
| MLA | Su, Jieyang et al. "Multi-view multi-behavior interest learning network and contrastive learning for multi-behavior recommendation" . | KNOWLEDGE-BASED SYSTEMS 305 (2024) . |
| APA | Su, Jieyang , Chen, Yuzhong , Lin, Xiuqiang , Zhong, Jiayuan , Dong, Chen . Multi-view multi-behavior interest learning network and contrastive learning for multi-behavior recommendation . | KNOWLEDGE-BASED SYSTEMS , 2024 , 305 . |
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Math word problem (MWP) represents a critical research area within reading comprehension, where accurate comprehension of math problem text is crucial for generating math expressions. However, current approaches still grapple with unresolved challenges in grasping the sensitivity of math problem text and delineating distinct roles across various clause types, and enhancing numerical representation. To address these challenges, this paper proposes a Numerical Magnitude Aware Multi-Channel Hierarchical Encoding Network (NMA-MHEA) for math expression generation. Firstly, NMA-MHEA implements a multi-channel hierarchical context encoding module to learn context representations at three different channels: intra-clause channel, inter-clause channel, and context-question interaction channel. NMA-MHEA constructs hierarchical constituent-dependency graphs for different levels of sentences and employs a Hierarchical Graph Attention Neural Network (HGAT) to learn syntactic and semantic information within these graphs at the intra-clause and inter-clause channels. NMA-MHEA then refines context clauses using question information at the context-question interaction channel. Secondly, NMA-MHEA designs a number encoding module to enhance the relative magnitude information among numerical values and type information of numerical values. Experimental results on two public benchmark datasets demonstrate that NMA-MHEA outperforms other state-of-the-art models. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
Keyword ：
Graph2Tree model Graph2Tree model Hierarchical constituent-dependency graph Hierarchical constituent-dependency graph Math word problem Math word problem Number encoding Number encoding
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| GB/T 7714 | Xu, J. , Chen, Y. , Xiao, L. et al. A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving [J]. | Neural Computing and Applications , 2024 , 37 (3) : 1651-1672 . |
| MLA | Xu, J. et al. "A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving" . | Neural Computing and Applications 37 . 3 (2024) : 1651-1672 . |
| APA | Xu, J. , Chen, Y. , Xiao, L. , Liao, H. , Zhong, J. , Dong, C. . A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving . | Neural Computing and Applications , 2024 , 37 (3) , 1651-1672 . |
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为了提高数字微流控生物芯片(Digital Microfluidic Biochip,DMFB)的便携性,促进其发展及应用,提出可移动DMFB模型.该模型将微控制器通过无线模块与云服务器传输生化协议,并实时获得响应结果.考虑芯片与云服务器访问的安全隐患,采用对称加密算法SM4 和SM2 构造的数字签名算法保证数据传输及云服务器访问安全.设计数据在传输中的处理细节和用户使用操作过程对其身份验证的各个流程.实验结果表明,该模型在时间和空间上具有可行性.
Keyword ：
DMFB DMFB SM2 SM2 SM4 SM4 无线通信 无线通信
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| GB/T 7714 | 柳煌达 , 董晨 , 刘灵清 et al. 便携式隐私安全数字微流控生物芯片 [J]. | 计算机应用与软件 , 2024 , 41 (11) : 268-272,340 . |
| MLA | 柳煌达 et al. "便携式隐私安全数字微流控生物芯片" . | 计算机应用与软件 41 . 11 (2024) : 268-272,340 . |
| APA | 柳煌达 , 董晨 , 刘灵清 , 连思璜 . 便携式隐私安全数字微流控生物芯片 . | 计算机应用与软件 , 2024 , 41 (11) , 268-272,340 . |
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Digital Microfluidic Biochip (DMFB) -based molecular diagnostic techniques have recently become hot topics. Compared with traditional molecular diagnostic techniques, digital microfluidic biochips have advantages in precise control of discrete droplets and execution of biochemical protocols. However, as components of networked cyber-physical systems, potential privacy and security issues of biochips are increasingly prominent, for instance, eavesdropping attacks on communication channels, tampering attacks on biochemical protocols, and copyright attacks on physical structure protection. Differential Privacy (DP), a de facto standard for achieving privacy, is trying to incorporate DMFB applications to protect user privacy. However, as the intersection of privacy-preserving technology, DMFB applications, and DMFB security, comprehensive research on this area is relatively rare. Investigating and analyzing the implementation mechanisms and threat models of biochip applications, including biochemical protocols, cyber-physical systems, and Enhanced privacy protection DMFB’s user data security platform, this paper proposes the application scenarios and protection schemes of differential privacy techniques on DMFB user data platform. Firstly, the application scenarios and protection schemes of Laplace mechanism, Gaussian mechanism, and random response mechanism were described on the DMFB user data platform. Secondly, parameter security publishing algorithms were proposed based on three strategies: user level sensitivity, routing weight set, and routing intersection parameter set. Finally, tamper proof probability was created as a security indicator, while confidence scores, calibration, and cumulative error rate were established to measure data availability. The simulation experiment results show that the overall privacy security of the scheme can reach 100%, and the average data availability can reach 93.3%. The algorithm performance test shows that the optimal privacy budget range of the scheme is 0.4. In addition, compared with similar algorithms, the proposed scheme improves privacy security by 12.09% on average, and data availability by 7.02%. Therefore, this scheme can be a secure and effective user data platform for DMFB to execute biochemical protocols. © 2024 Chinese Academy of Sciences. All rights reserved.
Keyword ：
biochemical protocol biochemical protocol data security data security differential privacy differential privacy digital microfluidic biochip digital microfluidic biochip
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| GB/T 7714 | Chen, X. , Dong, C. . Differential Privacy Scheme for Digital Microfluidic Biochips; [面向数字微流控生物芯片的差分隐私方案] [J]. | Journal of Cyber Security , 2024 , 9 (6) : 43-59 . |
| MLA | Chen, X. et al. "Differential Privacy Scheme for Digital Microfluidic Biochips; [面向数字微流控生物芯片的差分隐私方案]" . | Journal of Cyber Security 9 . 6 (2024) : 43-59 . |
| APA | Chen, X. , Dong, C. . Differential Privacy Scheme for Digital Microfluidic Biochips; [面向数字微流控生物芯片的差分隐私方案] . | Journal of Cyber Security , 2024 , 9 (6) , 43-59 . |
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Electronic tickets (e-tickets) are gradually being adopted as a substitute for paper-based tickets to bring convenience to customers, corporations, and governments. However, their adoption faces a number of practical challenges, such as flexibility, privacy, secure storage, and inability to deploy on IoT devices such as smartphones. These concerns motivate the current research on e-ticket systems, which seeks to ensure the unforgeability and authenticity of e-tickets while simultaneously protecting user privacy. Many existing schemes cannot fully satisfy all these requirements. To improve on the current state-of-the-art solutions, this paper constructs a blockchain-enhanced privacy-preserving e-ticket system for IoT devices, dubbed PriTKT, which is based on blockchain, structure-preserving signatures (SPS), unlinkable redactable signatures (URS), and zero-knowledge proofs (ZKP). It supports flexible policy-based ticket purchasing and ensures user unlinkability. According to the data minimization and revealing principle of GDPR, PriTKT empowers users to selectively disclose subsets of (necessary) attributes to sellers as long as the disclosed attributes satisfy ticket purchasing policies. In addition, benefiting from the decentralization and immutability of blockchain, effective detection and efficient tracing of double spending of e-tickets are supported in PriTKT. Considering the impracticality of existing e-tickets schemes with burdensome ZKPs, we replace them with URS/SPS or efficient ZKP to significantly improve the efficiency of ticket issuing and make it suitable for use on smartphones.
Keyword ：
blockchain blockchain double-spending detection double-spending detection electronic tickets electronic tickets IoT IoT privacy-preserving privacy-preserving
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| GB/T 7714 | Zhan, Yonghua , Yuan, Feng , Shi, Rui et al. PriTKT: A Blockchain-Enhanced Privacy-Preserving Electronic Ticket System for IoT Devices [J]. | SENSORS , 2024 , 24 (2) . |
| MLA | Zhan, Yonghua et al. "PriTKT: A Blockchain-Enhanced Privacy-Preserving Electronic Ticket System for IoT Devices" . | SENSORS 24 . 2 (2024) . |
| APA | Zhan, Yonghua , Yuan, Feng , Shi, Rui , Shi, Guozhen , Dong, Chen . PriTKT: A Blockchain-Enhanced Privacy-Preserving Electronic Ticket System for IoT Devices . | SENSORS , 2024 , 24 (2) . |
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Digital microfluidic biochips (DMFBs) have a significant stride in the applications of medicine and the biochemistry in recent years. DMFBs based on micro -electrode -dot -array (MEDA) architecture, as the nextgeneration DMFBs, aim to overcome drawbacks of conventional DMFBs, such as droplet size restriction, low accuracy, and poor sensing ability. Since the potential market value of MEDA biochips is vast, it is of paramount importance to explore approaches to protect the intellectual property (IP) of MEDA biochips during the development process. In this paper, an IP authentication strategy based on the multi-PUF applied to MEDA biochips is presented, called bioMPUF, consisting of Delay PUF, Split PUF and Countermeasure. The bioMPUF strategy is designed to enhance the non -linearity between challenges and responses of PUFs, making the challenge-response pairs (CRPs) on the MEDA biochips are difficult to be anticipated, thus thwarting IP piracy attacks. Moreover, based on the easy degradation of MEDA biochip electrodes, a countermeasure is proposed to destroy the availability of piracy chips. Experimental results demonstrate the feasibility of the proposed bioMPUF strategy against the brute force attack and modeling attack.
Keyword ：
Hardware security Hardware security IP protection IP protection MEDA biochips MEDA biochips Modeling attack Modeling attack Multi-PUF Multi-PUF
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| GB/T 7714 | Dong, Chen , Guo, Xiaodong , Lian, Sihuang et al. Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips [J]. | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (3) . |
| MLA | Dong, Chen et al. "Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips" . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES 36 . 3 (2024) . |
| APA | Dong, Chen , Guo, Xiaodong , Lian, Sihuang , Yao, Yinan , Chen, Zhenyi , Yang, Yang et al. Harnessing the advances of MEDA to optimize multi-PUF for enhancing IP security of biochips . | JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES , 2024 , 36 (3) . |
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Active learning (AL) tries to maximize the model's performance when the labeled data set is limited, and the annotation cost is high. Although it can be efficiently implemented in deep neural networks (DNNs), it is questionable whether the model can maintain the ability to generalize well when there are significant distributional deviations between the labeled and unlabeled data sets. In this article, we consider introducing adversarial training and adversarial samples into AL to mitigate the problem of degraded generalization performance due to different data distributions. In particular, our proposed adversarial training AL (ATAL) has two advantages, one is that adversarial training by different networks enables the network to have better prediction performance and robustness with limited labeled samples. The other is that the adversarial samples generated by the adversarial training can effectively expand the labeled data set so that the designed query function can efficiently select the most informative unlabeled samples based on the expanded labeled data set. Extensive experiments have been performed to verify the feasibility and efficiency of our proposed method, i.e., CIFAR-10 demonstrates the effectiveness of our method-new state-of-the-art robustness and accuracy are achieved.
Keyword ：
Active learning (AL) Active learning (AL) adversarial learning adversarial learning adversarial samples adversarial samples Bayes methods Bayes methods data distribution data distribution Data models Data models Generative adversarial networks Generative adversarial networks Labeling Labeling robustness robustness Robustness Robustness Training Training Uncertainty Uncertainty
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| GB/T 7714 | Lin, Xuanwei , Liu, Ximeng , Chen, Bijia et al. ATAL: Active Learning Using Adversarial Training for Data Augmentation [J]. | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) : 4787-4800 . |
| MLA | Lin, Xuanwei et al. "ATAL: Active Learning Using Adversarial Training for Data Augmentation" . | IEEE INTERNET OF THINGS JOURNAL 11 . 3 (2024) : 4787-4800 . |
| APA | Lin, Xuanwei , Liu, Ximeng , Chen, Bijia , Wang, Yuyang , Dong, Chen , Hu, Pengzhen . ATAL: Active Learning Using Adversarial Training for Data Augmentation . | IEEE INTERNET OF THINGS JOURNAL , 2024 , 11 (3) , 4787-4800 . |
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