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学者姓名：董晨
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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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The rapid advancement of Industry 4.0 has revolutionized manufacturing, shifting production from centralized control to decentralized, intelligent systems. Smart factories are now expected to achieve high adaptability and resource efficiency, particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands. To address the challenges of dynamic task allocation, uncertainty, and realtime decision-making, this paper proposes Pathfinder, a deep reinforcement learning-based scheduling framework. Pathfinder models scheduling data through three key matrices: execution time (the time required for a job to complete), completion time (the actual time at which a job is finished), and efficiency (the performance of executing a single job). By leveraging neural networks, Pathfinder extracts essential features from these matrices, enabling intelligent decision-making in dynamic production environments. Unlike traditional approaches with fixed scheduling rules, Pathfinder dynamically selects from ten diverse scheduling rules, optimizing decisions based on real-time environmental conditions. To further enhance scheduling efficiency, a specialized reward function is designed to support dynamic task allocation and real-time adjustments. This function helps Pathfinder continuously refine its scheduling strategy, improving machine utilization and minimizing job completion times. Through reinforcement learning, Pathfinder adapts to evolving production demands, ensuring robust performance in real-world applications. Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches, offering improved coordination and efficiency in smart factories. By integrating deep reinforcement learning, adaptable scheduling strategies, and an innovative reward function, Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
Keyword ：
customization customization deep reinforcement learning deep reinforcement learning multi-robot system multi-robot system production scheduling production scheduling Smart factory Smart factory task allocation task allocation
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| GB/T 7714 | Lyu, Chenxi , Dong, Chen , Xiong, Qiancheng et al. Pathfinder: Deep Reinforcement Learning-Based Scheduling for Multi-Robot Systems in Smart Factories with Mass Customization [J]. | CMC-COMPUTERS MATERIALS & CONTINUA , 2025 , 84 (2) : 3371-3391 . |
| MLA | Lyu, Chenxi et al. "Pathfinder: Deep Reinforcement Learning-Based Scheduling for Multi-Robot Systems in Smart Factories with Mass Customization" . | CMC-COMPUTERS MATERIALS & CONTINUA 84 . 2 (2025) : 3371-3391 . |
| APA | Lyu, Chenxi , Dong, Chen , Xiong, Qiancheng , Chen, Yuzhong , Weng, Qian , Chen, Zhenyi . Pathfinder: Deep Reinforcement Learning-Based Scheduling for Multi-Robot Systems in Smart Factories with Mass Customization . | CMC-COMPUTERS MATERIALS & CONTINUA , 2025 , 84 (2) , 3371-3391 . |
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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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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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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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Network traffic anomaly detection involves the rapid identification of intrusions within a network through the detection, analysis, and classification of network traffic data. The variety of cyberattacks encompasses diverse attack principles. Employing an indiscriminate feature selection strategy may lead to the neglect of key features highly correlated with specific attack types. This oversight could diminish the recognition rate for that category, thereby impacting the overall performance of the detection model. To address this issue, this paper proposes a network traffic anomaly detection model based on the fusion of attack-dimensional features. Firstly, construct binary classification datasets independently for each attack class and perform individual feature selection to extract positively correlated features for each class. The features are then fused by employing a combination methods. Subsequently, based on the fused sub-datasets, base classifiers are trained. Finally, an ensemble learning approach is introduced to integrate the predictions of individual classifiers, enhancing the robustness of the model. The proposed approach, validated on NSL-KDD and UNSW-NB15 benchmark datasets, outperforms the latest methods in the field by achieving a 2%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$2\%$$\end{document} and 7%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$7\%$$\end{document} increase in precision on weighted averages.
Keyword ：
Attack dimension Attack dimension Ensemble learning Ensemble learning Feature fusion Feature fusion Network intrusion detection Network intrusion detection
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| GB/T 7714 | Sun, Xiaolong , Gu, Zhengyao , Zhang, Hao et al. Network intrusion detection based on feature fusion of attack dimension [J]. | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (6) . |
| MLA | Sun, Xiaolong et al. "Network intrusion detection based on feature fusion of attack dimension" . | JOURNAL OF SUPERCOMPUTING 81 . 6 (2025) . |
| APA | Sun, Xiaolong , Gu, Zhengyao , Zhang, Hao , Gu, Jason , Liu, Yanhua , Dong, Chen et al. Network intrusion detection based on feature fusion of attack dimension . | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (6) . |
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Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and obtaining a golden reference chip; however, artificial intelligence can make everything possible. For the first time, this paper presents a systematic review of artificial-intelligence-based integrated circuit security approaches, focusing on the latest attack and defense strategies. First, the security threats of integrated circuits are analyzed. For one of several key threats to integrated circuits, hardware Trojans, existing attack models are divided into several categories and discussed in detail. Then, for summarizing and comparing the numerous existing artificial-intelligence-based defense strategies, traditional and advanced artificial-intelligence-based approaches are listed. Finally, open issues on artificial-intelligence-based integrated circuit security are discussed from three perspectives: in-depth exploration of hardware Trojans, combination of artificial intelligence, and security strategies involving the entire life cycle. Based on the rapid development of artificial intelligence and the initial successful combination with integrated circuit security, this paper offers a glimpse into their intriguing intersection, aiming to draw greater attention to these issues. © 2025 by the authors.
Keyword ：
Artificial life Artificial life Computer hardware Computer hardware Embedded systems Embedded systems Hardware security Hardware security Integrated circuit design Integrated circuit design Integrated circuits Integrated circuits Life cycle Life cycle Malware Malware Network security Network security Security systems Security systems Timing circuits Timing circuits
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| GB/T 7714 | Dong, Chen , Qiu, Decheng , Li, Bolun et al. A Review: The Beauty of Serendipity Between Integrated Circuit Security and Artificial Intelligence [J]. | Sensors , 2025 , 25 (15) . |
| MLA | Dong, Chen et al. "A Review: The Beauty of Serendipity Between Integrated Circuit Security and Artificial Intelligence" . | Sensors 25 . 15 (2025) . |
| APA | Dong, Chen , Qiu, Decheng , Li, Bolun , Yang, Yang , Lyu, Chenxi , Cheng, Dong et al. A Review: The Beauty of Serendipity Between Integrated Circuit Security and Artificial Intelligence . | Sensors , 2025 , 25 (15) . |
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Biological assays around "lab-on-a-chip (LoC)" are required in multiple concentration (or dilution) factors, satisfying specific sample concentrations. Unfortunately, most of them suffer from non-locality and are non-protectable, requiring a large footprint and high purchase cost. A digital geometric technique can generate arbitrary gradient profiles for digital microfluidic biochips (DMFBs). A next- generation DMFB has been proposed based on the microelectrode-dot-array (MEDA) architectures are shown to produce and disperse droplets by channel dispensing and lamination mixing. Prior work in this area must address the problem of reactant and waste minimization and concurrent sample preparation for multiple target concentrations. This paper proposes the first splitting-droplet sharing algorithm for reactant and waste minimization of multiple target concentrations on MEDAs. The proposed algorithm not only minimizes the consumption of reagents but also reduces the number of waste droplets by preparing the target concentrations concurrently. Experimental results on a sequence of exponential gradients are presented in support of the proposed method and demonstrate its effectiveness and efficiency. Compared to prior work, the proposed algorithm can achieve up to a 24.8% reduction in sample usage and reach an average of 50% reduction in waste droplets.
Keyword ：
Biochip Biochip Dilution Dilution Microelectrode-dot-array (MEDA) Microelectrode-dot-array (MEDA) Mixing tree Mixing tree Reactant minimization Reactant minimization Sample preparation Sample preparation
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| GB/T 7714 | Dong, Chen , Chen, Xiao , Chen, Zhenyi . Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting Trees [J]. | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS , 2024 , 40 (1) : 87-99 . |
| MLA | Dong, Chen et al. "Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting Trees" . | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS 40 . 1 (2024) : 87-99 . |
| APA | Dong, Chen , Chen, Xiao , Chen, Zhenyi . Reactant and Waste Minimization during Sample Preparation on Micro-Electrode-Dot-Array Digital Microfluidic Biochips using Splitting Trees . | JOURNAL OF ELECTRONIC TESTING-THEORY AND APPLICATIONS , 2024 , 40 (1) , 87-99 . |
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The development of the software and hardware has brought about the abundance and overflow of computing resources. Many Internet companies can lease idle computing resources based on the peak and valley cycles of usage load to provide IaaS service. After the surging development, more and more companies are gradually paying attention to the specific user experience in cloud computing. But there are few works about that aspect in load balance problem. Therefore, we consider the load balancing resource allocation problem, from the perspective of user experience, mainly based on geographical distance and regional Equilibrium, combined with user usage habits, in multiple service periods. A detailed mathematical definition has been established for this problem. This article proposes a mathematical model for the problem, along with an modified CMA-ES (Covariance Matrix Adaptation Evolution Strategy) algorithm, called WS-ESS-CMA-ES algorithm, to allocate computing resources and solve the above problem. The process of using the proposed algorithm to solve the problem of cloud computing resource allocation in real scenarios is simulated, and compared with some other algorithms. The experiment results show that our algorithm performs well. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Keyword ：
Cloud computing Cloud computing Covariance matrix Covariance matrix Evolutionary algorithms Evolutionary algorithms Resource allocation Resource allocation
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| GB/T 7714 | Luo, Jihai , Dong, Chen , Chen, Zhenyi et al. A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm [C] . 2024 : 47-62 . |
| MLA | Luo, Jihai et al. "A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm" . (2024) : 47-62 . |
| APA | Luo, Jihai , Dong, Chen , Chen, Zhenyi , Xu, Li , Chen, Tianci . A Cloud Computing User Experience Focused Load Balancing Method Based on Modified CMA-ES Algorithm . (2024) : 47-62 . |
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With the development of technology, robots are gradually being used more and more widely in various fields. Industrial robots need to perform path planning in the course of their tasks, but there is still a lack of a simple and effective method to implement path planning in complex industrial scenarios. In this paper, an improved whale optimization algorithm is proposed to solve the robot path planning problem. The algorithm initially uses a logistic chaotic mapping approach for population initialization to enhance the initial population diversity, and proposes a jumping mechanism to help the population jump out of the local optimum and enhance the global search capability of the population. The proposed algorithm is tested on 12 complex test functions and the experimental results show that the improved algorithm achieves the best results in several test functions. The algorithm is then applied to a path planning problem and the results show that the algorithm can help the robot to perform correct and efficient path planning. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
Keyword ：
Industrial robots Industrial robots Mapping Mapping Motion planning Motion planning Optimization Optimization Robot programming Robot programming
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| GB/T 7714 | Huang, Peixin , Dong, Chen , Chen, Zhenyi et al. An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm [C] . 2024 : 209-222 . |
| MLA | Huang, Peixin et al. "An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm" . (2024) : 209-222 . |
| APA | Huang, Peixin , Dong, Chen , Chen, Zhenyi , Zhen, Zihang , Jiang, Lei . An Industrial Robot Path Planning Method Based on Improved Whale Optimization Algorithm . (2024) : 209-222 . |
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