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学者姓名：杜永萍
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Abstract ：
In-Context Learning refers to the ability of large language models to understand and generate appropriate responses based on the provided demonstration without requiring explicit retraining, which is closely related to the quality of the demonstration. Existing methods typically use traditional selection algorithm for demonstration construction, which fails to fully exploit the In-Context Learning ability of large language models. This paper adopts Dynamic Clustering and Label Mapping Distribution (DCLMD) to enhance the In-Context Learning ability of large language models. Our approach first dynamically selects relevant samples based on the distribution of the target sample in the sample space and introduces a hierarchical clustering filtering method to achieve diversified sample selection within different semantic clusters. Next, we design a ranking selection method based on loss functions to evaluate the demonstration's ability to learn correct mapping relationships in label spaces, and the final demonstration is selected through a ranking strategy. Experimental results on seven different datasets across various tasks, such as natural language inference, show that our method improves the accuracy of two large language models by up to 3.2% and 8.9%, respectively.
Keyword ：
In-context learning In-context learning Dynamic clustering Dynamic clustering Demonstration construction Demonstration construction Large language models Large language models
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| GB/T 7714 | Du, Yongping , Zhang, Qi , Fu, Shuyi et al. DCLMD: dynamic clustering and label mapping distribution for constructing in-context learning demonstrations [J]. | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (5) . |
| MLA | Du, Yongping et al. "DCLMD: dynamic clustering and label mapping distribution for constructing in-context learning demonstrations" . | JOURNAL OF SUPERCOMPUTING 81 . 5 (2025) . |
| APA | Du, Yongping , Zhang, Qi , Fu, Shuyi , Hou, Ying , Han, Honggui . DCLMD: dynamic clustering and label mapping distribution for constructing in-context learning demonstrations . | JOURNAL OF SUPERCOMPUTING , 2025 , 81 (5) . |
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Abstract ：
Multi-hop reasoning is critical for natural language understanding but poses challenges for current models, requiring models capable of aggregating and reasoning across multiple sources of information. We propose an Adversarial Entity Graph Convolutional Network (AEGCN) to improve multi-hop inference performance. Unlike previous GNNs-based models, AEGCN places a greater emphasis on the construction of rich entity graph which focuses on identifying the related entities from support document and connecting these entities with innovative edge relationships. The entity graph built by AEGCN preserves both the semantic information and structure of the original text. Further, adversarial training is adopted to generate challenging embeddings for the entity graph, increasing the model's robustness against the interference. The experiments evaluated on WIKIHOP and MEDHOP dataset indicate that AEGCN achieves 6.8 % and 7.7 % accuracy improvement over baseline model respectively, confirming the model's advanced capability in multi-hop reasoning tasks.
Keyword ：
Multi-hop reasoning Multi-hop reasoning Graph neural networks Graph neural networks Question answering Question answering Adversarial training Adversarial training
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| GB/T 7714 | Du, Yongping , Yan, Rui , Hou, Ying et al. Adversarial Entity Graph Convolutional Networks for multi-hop inference question answering [J]. | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 258 . |
| MLA | Du, Yongping et al. "Adversarial Entity Graph Convolutional Networks for multi-hop inference question answering" . | EXPERT SYSTEMS WITH APPLICATIONS 258 (2024) . |
| APA | Du, Yongping , Yan, Rui , Hou, Ying , Pei, Yu , Han, Honggui . Adversarial Entity Graph Convolutional Networks for multi-hop inference question answering . | EXPERT SYSTEMS WITH APPLICATIONS , 2024 , 258 . |
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Abstract ：
Crowdsourced on-demand delivery is being required by many industries with the increasing demand of online shopping and on-demand food. However, it is difficult to ensure the delivery efficiency due to the uncertainty of delivery time for crowdsourced couriers in total delivery process. An ant colony algorithm based on total logistics delivery efficiency metrics is proposed in order to ensure the efficiency of crowdsourced on-demand delivery. Firstly, two metrics are designed to describe the total logistics delivery efficiency quantitatively. The average delivery time of each order and the number of orders completed on time are calculated in total logistics delivery efficiency metrics. Secondly, a total logistics crowdsourced on-demand delivery model (TLCODM) is built considering the transport efficiency and order punctuality. This model serves as the fundamental support for crowdsourced on-demand delivery. Finally, an ant colony algorithm based on total logistics delivery efficiency metrics (ACO-TLDE) is proposed to obtain the optimal scheduling solutions. The total logistics delivery efficiency metrics are used to guide the update of ant colony algorithm. The effectiveness of TLCODM and ACO-TLDE are verified by experiments.
Keyword ：
delivery efficiency delivery efficiency ant colony algorithm ant colony algorithm total logistics total logistics crowdsourced on-demand delivery crowdsourced on-demand delivery
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| GB/T 7714 | Hou, Ying , Qin, Xuemin , Han, Honggui et al. Ant Colony Algorithm Based on Total Logistics Delivery Efficiency Metrics for Crowdsourced On-Demand Delivery [J]. | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 : 2383-2388 . |
| MLA | Hou, Ying et al. "Ant Colony Algorithm Based on Total Logistics Delivery Efficiency Metrics for Crowdsourced On-Demand Delivery" . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 (2024) : 2383-2388 . |
| APA | Hou, Ying , Qin, Xuemin , Han, Honggui , Huang, Yanting , Du, Yongping . Ant Colony Algorithm Based on Total Logistics Delivery Efficiency Metrics for Crowdsourced On-Demand Delivery . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 , 2383-2388 . |
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Abstract ：
Differentiable architecture search (DARTS) is an important branch of neural architecture search (NAS), aiming at searching the optimal network structure efficiently. However, the backbone and FPN of the detection model tend to suffer from over-parameterization effects during the search process, resulting in DARTS failing to efficiently search the detection network structure. In this paper, we propose an efficient differentiable architecture search (EDARTS-DET) with backbone and FPN for object detection. Firstly, the joint search space of backbone and FPN networks is designed to facilitate DARTS to search the detection network as a whole at one shot. Secondly, an architecture operation mask weight sharing mechanism is proposed to effectively reduce the search memory occupation and computational cost. Finally, an attention-based partial channel selection strategy is introduced to select important channels to be fed into the search space. The experimental results show that the proposed EDARTS-DET achieves lower search consumption time and memory utilization in the COCO dataset and the Haier appliance disassembly dataset compared with other SOT A methods. Meanwhile, the searched detection networks are also improved in terms of detection performance, verifying that the proposed EDARTS-DET method achieves a well-balanced performance and efficiency.
Keyword ：
object detection object detection efficient efficient DARTS DARTS Neural architecture search Neural architecture search
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| GB/T 7714 | Zhang, Qiyu , Han, Hongui , Li, Fangyu et al. Efficient Differentiable Architecture Search with Backbone and FPN for Object Detection [J]. | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 : 1908-1913 . |
| MLA | Zhang, Qiyu et al. "Efficient Differentiable Architecture Search with Backbone and FPN for Object Detection" . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 (2024) : 1908-1913 . |
| APA | Zhang, Qiyu , Han, Hongui , Li, Fangyu , Du, Yongping . Efficient Differentiable Architecture Search with Backbone and FPN for Object Detection . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 , 1908-1913 . |
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Abstract ：
Deep neural networks are important models in the task of recognition of waste electrical appliances. However, deep neural networks are nonlinear models, leading to the lack of self-interpretation of appliance recognition models as a critical issue. We propose an adaptive sparse semantic representation (ASSR) model to improve the self-interpretation capability of appliance recognition models. First, an adaptive sparse representation component is constructed to enhance the ability of the appliance recognition model for sparse feature extraction. Second, residual sparse blocks are designed to ensure reliable network architecture and enhance the semantic representation ability of the appliance recognition model. Finally, the appliance recognition model obtains an interpretable network architecture by computing the optimal sparse solution, ensuring the strong self-interpretability of the recognition model. The experimental results show that the recognition accuracy of ASSR is improved compared with the traditional deep neural network, and ASSR has linear self-interpretation capability.
Keyword ：
Interpretability Interpretability Sparse representation Sparse representation Deep interpretable model Deep interpretable model Waste electrical appliance recognition Waste electrical appliance recognition
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| GB/T 7714 | Liu, Yiming , Han, Honggui , Li, Fangyu et al. Adaptive Sparse Semantic Representation for Waste Electrical Appliance Recognition [J]. | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 : 135-140 . |
| MLA | Liu, Yiming et al. "Adaptive Sparse Semantic Representation for Waste Electrical Appliance Recognition" . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 (2024) : 135-140 . |
| APA | Liu, Yiming , Han, Honggui , Li, Fangyu , Du, Yongping . Adaptive Sparse Semantic Representation for Waste Electrical Appliance Recognition . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 , 135-140 . |
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Abstract ：
Multi-Agent Path Finding (MAPF) aims to plan conflict-free joint optimal paths effectively for a group of agents from their respective starting points to destinations. This task is challenging due to its computational complexity. Current works focus on reducing the number of potential conflicts to further decrease the time required for path planning. In this study, we integrate deep learning-based architecture with the sub-dimension expansion method, aiming to transform the multi-agent pathfinding task into an image classification task. We employ visual models as single-agent path planner to predict optimal path based on the observation space of each agent, which reduces the number of conflicts and improves the performance of the planner significantly. The experimental results show that, in comparison to Learning-Assisted M*, our method achieves a reduction in potential conflicts and implements MAPF task effectively only with a minor increase in time consumption.
Keyword ：
multi-agent path finding multi-agent path finding visual model visual model subdimension expansion subdimension expansion multi model aggregation multi model aggregation deep learning deep learning
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| GB/T 7714 | Zhang, Bochao , Wang, Binrui , Du, Yongping et al. Improving Multi-Agent Path Finding via Multi Visual Model and Channel [J]. | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 : 1697-1702 . |
| MLA | Zhang, Bochao et al. "Improving Multi-Agent Path Finding via Multi Visual Model and Channel" . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 (2024) : 1697-1702 . |
| APA | Zhang, Bochao , Wang, Binrui , Du, Yongping , Li, Fangyu , Han, Honggui . Improving Multi-Agent Path Finding via Multi Visual Model and Channel . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 , 1697-1702 . |
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Abstract ：
Ant colony optimization (ACO) algorithm is widely used in the instant delivery order scheduling because of its distributed computing capability. However, the order delivery efficiency decreases when different logistics statuses are faced. In order to improve the performance of ACO, an adaptive ACO algorithm based on real-time logistics features (AACO-RTLFs) is proposed. First, features are extracted from the event dimension, spatial dimension, and time dimension of the instant delivery to describe the real-time logistics status. Five key factors are further selected from the above three features to assist in problem modeling and ACO designing. Second, an adaptive instant delivery model is built considering the customer's acceptable delivery time. The acceptable time is calculated by emergency order mark and weather conditions in the event dimension feature. Third, an adaptive ACO algorithm is proposed to obtain the instant delivery order schedules. The parameters of the probability equation in ACO are adjusted according to the extracted key factors. Finally, the Gurobi solver in Python is used to perform numerical experiments on the classical datasets to verify the effectiveness of the instant delivery model. The proposed AACO-RTLF algorithm shows its advantages in instant delivery order scheduling when compared to the other state-of-the-art algorithms.
Keyword ：
Vehicle dynamics Vehicle dynamics Feature extraction Feature extraction instant delivery instant delivery Information entropy Information entropy Heuristic algorithms Heuristic algorithms logistics status logistics status real-time logistics feature real-time logistics feature Adaptation models Adaptation models Real-time systems Real-time systems Logistics Logistics feature extraction feature extraction Ant colony optimization (ACO) algorithm Ant colony optimization (ACO) algorithm
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| GB/T 7714 | Hou, Ying , Guo, Xinyu , Han, Honggui et al. Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery [J]. | IEEE TRANSACTIONS ON CYBERNETICS , 2024 , 54 (11) : 6358-6370 . |
| MLA | Hou, Ying et al. "Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery" . | IEEE TRANSACTIONS ON CYBERNETICS 54 . 11 (2024) : 6358-6370 . |
| APA | Hou, Ying , Guo, Xinyu , Han, Honggui , Wang, Jingjing , Du, Yongping . Adaptive Ant Colony Optimization Algorithm Based on Real-Time Logistics Features for Instant Delivery . | IEEE TRANSACTIONS ON CYBERNETICS , 2024 , 54 (11) , 6358-6370 . |
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Abstract ：
A large number of unanswered products-related questions appear in the E-commerce platforms, necessitating the deployment of question-answering models to automatically provide precise responses for the user. However, the substantial absence of ground truth presents challenges for implementing supervised learning, and existing unsupervised contrastive learning methods have not addressed this issue fundamentally, with limitations in integrating multi-level features. This paper presents a dual-tower cross-biased contrastive learning model for answer selection, named CCLM-AS. By taking questions and augmented product reviews as positive pairs for unsupervised contrastive learning and employing a dual-tower structure encoder, CCLM-AS learns sentence representations from unlabeled data effectively. Furthermore, the designed training objective realizes multi-level feature integration, including character, sentence and dialogue levels, which enhances the model's inference ability. The experimental results on AmazonQA dataset indicate that our proposed model outperforms existing contrastive learning-based sentence representation models and also achieves comparable performance to supervised answer selection models. This demonstrates that CCLM-AS can not only alleviate the data sparsity problem effectively but also retain the excellent performance.
Keyword ：
Multi-level feature fusion Multi-level feature fusion Data augmentation Data augmentation Answer selection Answer selection Product-related question answering Product-related question answering Contrastive learning Contrastive learning
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| GB/T 7714 | Jin, Xingnan , Du, Yongping , Wang, Binrui et al. Cross-biased contrastive learning for answer selection with dual-tower structure [J]. | NEUROCOMPUTING , 2024 , 610 . |
| MLA | Jin, Xingnan et al. "Cross-biased contrastive learning for answer selection with dual-tower structure" . | NEUROCOMPUTING 610 (2024) . |
| APA | Jin, Xingnan , Du, Yongping , Wang, Binrui , Zhang, Qi . Cross-biased contrastive learning for answer selection with dual-tower structure . | NEUROCOMPUTING , 2024 , 610 . |
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Abstract ：
Multimodal aspect-based sentiment analysis (MABSA) aims to predict the sentiment of aspect by the fusion of different modalities such as image, text and so on. However, the availability of high-quality multimodal data remains limited. Therefore, few-shot MABSA is a new challenge. Previous works are rarely able to cope with low-resource and few-shot scenarios. In order to address the above problems, we design a Few-shot Multimodal aspect-based sentiment analysis framework based on Contrastive Finetuning (FMCF). Initially, the image modality is transformed to the corresponding textual caption to achieve the entailed semantic information and a contrastive dataset is constructed based on similarity retrieval for finetuning in the following stage. Further, a sentence encoder is trained based on SBERT, which combines supervised contrastive learning and sentence-level multi-feature fusion to complete MABSA. The experiments demonstrate that our framework achieves excellent performance in the few-shot scenarios. Importantly, with only 256 training samples and limited computational resources, the proposed method outperforms fine-tuned models that use all available data on the Twitter dataset.
Keyword ：
Aspect-based sentiment analysis Aspect-based sentiment analysis Few-shot learning Few-shot learning Contrastive learning Contrastive learning Multimodal fusion Multimodal fusion
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| GB/T 7714 | Du, Yongping , Xie, Runfeng , Zhang, Bochao et al. FMCF: Few-shot Multimodal aspect-based sentiment analysis framework based on Contrastive Finetuning [J]. | APPLIED INTELLIGENCE , 2024 , 54 (24) : 12629-12643 . |
| MLA | Du, Yongping et al. "FMCF: Few-shot Multimodal aspect-based sentiment analysis framework based on Contrastive Finetuning" . | APPLIED INTELLIGENCE 54 . 24 (2024) : 12629-12643 . |
| APA | Du, Yongping , Xie, Runfeng , Zhang, Bochao , Yin, Zihao . FMCF: Few-shot Multimodal aspect-based sentiment analysis framework based on Contrastive Finetuning . | APPLIED INTELLIGENCE , 2024 , 54 (24) , 12629-12643 . |
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Abstract ：
本发明提出了一种基于形变卷积神经网络的废旧手机表面缺陷精准分级方法，针对废旧手机回收过程中表面缺陷难以精准分级的问题。本发明设计混合注意力机制模型，能够优化模型的性能，建立基于形变卷积神经网络的识别模型，实现对废旧手机表面缺陷的准确分级。本发明对不同场景下的手机表面缺陷分级均保持较好的快速性和准确性，能够提高废旧手机回收的效率和回收企业经济效益。
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| GB/T 7714 | 韩红桂 , 张奇宇 , 甄晓玲 et al. 一种基于混合注意力形变卷积神经网络的手机表面缺陷精准分级方法 : CN202210590772.X[P]. | 2022-05-27 . |
| MLA | 韩红桂 et al. "一种基于混合注意力形变卷积神经网络的手机表面缺陷精准分级方法" : CN202210590772.X. | 2022-05-27 . |
| APA | 韩红桂 , 张奇宇 , 甄晓玲 , 李方昱 , 杜永萍 , 吴玉锋 . 一种基于混合注意力形变卷积神经网络的手机表面缺陷精准分级方法 : CN202210590772.X. | 2022-05-27 . |
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