Query：
学者姓名：董晨
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Digital microfluidic biochips (DMFBs), by precisely controlling and manipulating minute fluids, have realized the integration, automation, and cost-effectiveness of biochemical experiments and are applied in various fields such as medical diagnostics, drug development, and environmental monitoring. However, due to the composition of microelectronic component arrays, DMFBs are prone to electrode faults, leading to erroneous biochemical operations and, consequently, inaccurate experimental results. In this paper, a test path optimization algorithm combining an improved grey wolf algorithm and priority strategy is proposed to solve the problem of an extended test droplet path when DMFB tests faulty electrodes. By encoding the priority of the electrodes of the DMFB and the paths between the electrodes, the test droplet routing is performed according to the priority order. The priority coefficients are dynamically adjusted using the improved grey wolf algorithm to shorten the testing path length. Experimental results demonstrate that the proposed path optimization algorithm reduces the path length by 0.45% to 2.08% compared to the Eulerian circuit method in offline testing, achieving the theoretical optimum value, and by 4.90% to 10.53% compared to the ant colony algorithm in online testing. This provides an essential foundation for accelerating the safe and reliable development of DMFBs in healthcare. © 2024 IEEE.
Keyword ：
Ant colony optimization Ant colony optimization Diagnosis Diagnosis Digital microfluidics Digital microfluidics Microfluidic chips Microfluidic chips Swarm intelligence Swarm intelligence
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Yang, Zhongliao , Xie, Zhengye , Dong, Chen et al. Digital Microfluidic Biochips Test Path Planning Based on Swarm Intelligence Optimization and Internet of Things Technology [C] . 2024 : 82-89 . |
| MLA | Yang, Zhongliao et al. "Digital Microfluidic Biochips Test Path Planning Based on Swarm Intelligence Optimization and Internet of Things Technology" . (2024) : 82-89 . |
| APA | Yang, Zhongliao , Xie, Zhengye , Dong, Chen , Fan, Xinmin , Chen, Zhenyi . Digital Microfluidic Biochips Test Path Planning Based on Swarm Intelligence Optimization and Internet of Things Technology . (2024) : 82-89 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
With each advancement in internet technology, new security challenges arise. The prevalence of malicious programs continues to increase, which makes it crucial to detect and address them effectively. Many researchers focus on solving different datasets by using deep learning methods and make significant progress. However, these strategies must be continuously improved to adapt to the latest data. In this paper, an improved model based on CNN-LSTM is proposed to detect and classify malware programs, named malDetect I. At the same time, the Transformer Encoder module is also modified based on model Bert to adapt to the classification task. Lastly, two models are compared with prediction results on evaluation indicators. The data used in this paper is the Windows API sequence extracted after dynamic operation. The text processing methods are also suitable for processing sequence data. The experiment uses Word2Vec and two different learning rate strategies, and the improved model accuracy is 9.83% higher than the original CNN-LSTM model. The model integrated the BiLSTM model with the Self-Attention mechanism, named malDetect II, is 11.46% higher than the basic model CNN-LSTM and 2.82% higher than the Transformer Encoder classification model. © 2024 IEEE.
Keyword ：
Data handling Data handling Deep learning Deep learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Jingjing , Lin, Jingsong , Lyu, Chenxi et al. MalDetect: Malware Classification Using API Sequence and Comparison with Transformer Encoder [C] . 2024 : 133-140 . |
| MLA | Lin, Jingjing et al. "MalDetect: Malware Classification Using API Sequence and Comparison with Transformer Encoder" . (2024) : 133-140 . |
| APA | Lin, Jingjing , Lin, Jingsong , Lyu, Chenxi , Fan, Xinmin , Dong, Chen . MalDetect: Malware Classification Using API Sequence and Comparison with Transformer Encoder . (2024) : 133-140 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Addressing privacy concerns and the evolving nature of user preferences, it is crucial to explore collaborative training methods for federated recommendation models that match the performance of centralized models while preserving user privacy. Existing federated recommendation models primarily rely on static relational data, overlooking the temporal patterns that dynamically evolve over time. In domains like travel recommendations, factors such as the availability of attractions, introduction of new activities, and media coverage constantly change, influencing user preferences. To tackle these challenges, we propose a novel approach called FedNTF. It leverages an LSTM encoder to capture multidimensional temporal interactions within relational data. By incorporating tensor factorization and multilayer perceptrons, we project users and items into a latent space with time encoding, enabling the learning of nonlinear relationships among diverse latent factors. This approach not only addresses the privacy concerns by preserving the confidentiality of user data but also enables the modeling of temporal dynamics to enhance the accuracy and relevance of recommendations over time. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.
Keyword ：
Factorization Factorization Learning systems Learning systems Long short-term memory Long short-term memory Recommender systems Recommender systems Signal encoding Signal encoding Tensors Tensors User profile User profile
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Ye, Jingzhou , Lin, Hui , Wang, Xiaoding et al. Efficient and Reliable Federated Recommendation System in Temporal Scenarios [C] . 2024 : 97-107 . |
| MLA | Ye, Jingzhou et al. "Efficient and Reliable Federated Recommendation System in Temporal Scenarios" . (2024) : 97-107 . |
| APA | Ye, Jingzhou , Lin, Hui , Wang, Xiaoding , Dong, Chen , Liu, Jianmin . Efficient and Reliable Federated Recommendation System in Temporal Scenarios . (2024) : 97-107 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
