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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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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 ：
Benchmarking Benchmarking Encoding (symbols) Encoding (symbols) Graph algorithms Graph algorithms Graphic methods Graphic methods Graph neural networks Graph neural networks Network coding Network coding Network theory (graphs) Network theory (graphs) Semantics Semantics Syntactics Syntactics Word processing Word processing
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| GB/T 7714 | Xu, Junjie , Chen, Yuzhong , Xiao, Lingsheng et al. A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving [J]. | Neural Computing and Applications , 2025 , 37 (3) : 1651-1672 . |
| MLA | Xu, Junjie et al. "A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving" . | Neural Computing and Applications 37 . 3 (2025) : 1651-1672 . |
| APA | Xu, Junjie , Chen, Yuzhong , Xiao, Lingsheng , Liao, Hongmiao , Zhong, Jiayuan , Dong, Chen . A numerical magnitude aware multi-channel hierarchical encoding network for math word problem solving . | Neural Computing and Applications , 2025 , 37 (3) , 1651-1672 . |
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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 article 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% on 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 register transfer level. This article marks a breakthrough in HT detection, and it is the first to address the multifunctional classification issue, holding significant practical importance and application prospects.
Keyword ：
Gate level Gate level golden free golden free Hardware Hardware hardware Trojan (HT) hardware Trojan (HT) HT information graph (HTIG) HT information graph (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 register transfer level (RTL) register transfer level (RTL) Security Security Training Training Trojan horses Trojan horses
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| GB/T 7714 | Chen, Lihan , Dong, Chen , Wu, Qiaowen 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 , 2025 , 44 (1) : 172-185 . |
| MLA | Chen, Lihan 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 44 . 1 (2025) : 172-185 . |
| APA | Chen, Lihan , Dong, Chen , Wu, Qiaowen , Liu, Ximeng , Guo, Xiaodong , Chen, Zhenyi 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 , 2025 , 44 (1) , 172-185 . |
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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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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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In the field of intelligent digital healthcare, Continuous-flow microfluidic biochip (CFMB) has become a research direction of widespread concern. CFMB integrates a large number of microvalves and large-scale microchannel networks into a single chip, enabling efficient execution of various biochemical protocols. However, as the scale of the chip increases, the routing task for CFMB becomes increasingly complex, and traditional manual routing is no longer sufficient to meet the requirements. Therefore, this paper proposes an automatic routing framework for CFMB based on Genetic algorithm (GA) and A* algorithms. Specifically, we adopt a two-stage A* algorithm to design the routing between modules, using the routing results obtained from the A* algorithm as the basis for evaluating the quality of solutions in the GA algorithm. Then, the GA algorithm is used to search for the optimal approximate solution in the solution space. Experimental results show that this method can reduce routing length and minimize routing crossings, thereby improving the parallel transmission speed of reagents on CFMB. This approach provides a feasible solution for large-scale automated routing of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Keyword ：
Biochips Biochips Digital microfluidics Digital microfluidics Genetic algorithms Genetic algorithms Health care Health care Routing algorithms Routing algorithms
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| GB/T 7714 | Huang, Huichang , Yang, Zhongliao , Zhong, Jiayuan et al. Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare [C] . 2024 : 209-223 . |
| MLA | Huang, Huichang et al. "Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare" . (2024) : 209-223 . |
| APA | Huang, Huichang , Yang, Zhongliao , Zhong, Jiayuan , Xu, Li , Dong, Chen , Bao, Ruishen . Genetic-A* Algorithm-Based Routing for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare . (2024) : 209-223 . |
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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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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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Continuous-Flow Microfluidic Biochip (CFMB), with their integrated features, bring traditional biochemical experiments on a single chip to accomplish complex operations and reactions through precise control, efficient reactions and emerging ways of saving reagents. In the field of intelligent digital healthcare, CFMB have attracted a lot of attention. However, traditional manual design schemes can no longer meet the needs of increasingly complex chip architecture design. Therefore, this paper proposes an automated design method for resource binding and module placement of CFMB based on a list scheduling algorithm and an improved Simulated Annealing algorithm. Through the resource binding and scheduling design based on the list scheduling algorithm, an effective scheduling strategy is generated, which effectively improves the biochip execution efficiency. In addition, the improved Simulated Annealing algorithm solves the module placement problem in the biochip in a limited physical space. Compared with some benchmark algorithms, the experimental results demonstrate the effectiveness of the method in the biochip design process and provide a practical framework for further development of CFMB in the field of intelligent digital healthcare. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Keyword ：
Biochips Biochips Chemical reactions Chemical reactions Digital microfluidics Digital microfluidics Health care Health care Scheduling algorithms Scheduling algorithms Simulated annealing Simulated annealing
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| GB/T 7714 | Yang, Zhongliao , Huang, Huichang , Liu, Zeyi et al. Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare [C] . 2024 : 266-281 . |
| MLA | Yang, Zhongliao et al. "Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare" . (2024) : 266-281 . |
| APA | Yang, Zhongliao , Huang, Huichang , Liu, Zeyi , Dong, Chen , Xu, Li . Resource Binding and Module Placement Algorithms for Continuous-Flow Microfluidic Biochip in Intelligent Digital Healthcare . (2024) : 266-281 . |
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