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arXiv 2025年

作者： Zhang, Ruoyu Wang, Lulu He, Yi Pan, Tongling Yu, Zhengtao Li, Yingna Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming650504 China Key Laboratory of Computer Technologies Application Kunming650504 China Hongyun Honghe Group Honghe Cigarette Factory Kunming650202 China

Recent advancements in large language models (LLMs) have significantly enhanced the fluency and logical coherence of image captioning. Retrieval-Augmented Generation (RAG) is widely adopted to incorporate external knowledge into LLMs;however, existing RAG-based methods rely on separate retrieval banks, introducing computational overhead and limiting the utilization of LLMs’ inherent zero-shot capabilities. To address these limitations, we propose TPCap, a novel trigger-augmented and multi-modal purification framework for zero-shot image captioning without external retrieval libraries. TPCap consists of two key components: trigger-augmented (TA) generation and multi-modal purification (MP). The TA module employs a trigger projector with frozen and learnable projections to activate LLMs’ contextual reasoning, enhance visual-textual alignment, and mitigate data bias. The MP module further refines the generated entity-related information by filtering noise and enhancing feature quality, ensuring more precise and factually consistent captions. We evaluate TPCap on COCO, NoCaps, Flickr30k, and WHOOPS datasets. With only 0.82M trainable parameters and training on a single NVIDIA RTX 4090 GPU, TPCap achieves competitive performance comparable to state-of-the-art models. Copyright © 2025, The Authors. All rights reserved.

关键词： Information filtering

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SFDE-net: A Spatial-Frequency Domain Feature Enhancement Network for Cloud Detection

SFDE-net: A Spatial-Frequency Domain Feature Enhancement Net...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Baotong Su Siyan Li Wenguang Zheng Yao Chen The School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Technology in Geo-Spatial Information Processing and Application System Chinese Academy of Sciences and Aerospace Information Research Institute Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Cloud detection is a crucial step in the preprocessing of satellite remote sensing images. Existing methods tend to have misjudgments when dealing with specific scenarios, such as challenges in distinguishing thin clouds from the background and addressing missing cloud boundaries. To solve this problem, we designed a novel Spatial–Frequency Domain Feature Enhancement Block (SFDE) embedded in a U-shaped network called SFDE-net. SFDE consists of three units: the Dual Frequency Feature Unit (DFF), the Spatial Domain Feature Unit (SDF), and the Cross-Domain Feature Fusion Unit (CDF). DFF globally learns the boundaries and overall structure of clouds in the frequency domain, SDF captures fine-grained information in the spatial domain, and CDF adaptively fuses features from both DFF and SDF. Our method’s effectiveness was evaluated on two public datasets, GF-1 WFV and LandSat8. Extensive experiments demonstrated that the proposed SFDE-net achieved accurate detection accuracy and outperformed several state-of-the-art methods.

关键词： Earth Accuracy Satellites Artificial satellites Fuses Frequency-domain analysis Merging

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A Hierarchical and Ensemble Surrogate-Assisted Evolutionary Algorithm With Model Reduction for Expensive Many-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Yang, Qi-Te Li, Jian-Yu Zhan, Zhi-Hui Jiang, Yunliang Jin, Yaochu Zhang, Jun School of Computer Science and Engineering South China University of Technology Guangzhou China College of Artificial Intelligence Nankai University Tianjin China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua China School of Engineering Westlake University Hangzhou China

The Kriging model has been widely used in regression-based surrogate-assisted evolutionary algorithms (SAEAs) for expensive multiobjective optimization by using one model to approximate one objective, and the fusion of all the models forms the fitness surrogate. However, when tackling expensive many-objective optimization problems, too many models are required to construct such a fitness surrogate, which incurs cumulative prediction uncertainty and higher computational cost. Considering that the fitness surrogate works to predict different objective values to help select promising solutions with good convergence and diversity, this article proposes a novel model reduction idea to change the many-models-based fitness surrogate to a two-models-based indicator surrogate (TIS) that directly approximates convergence and diversity indicators. Based on TIS, a hierarchical and ensemble surrogate-assisted evolutionary algorithm (HES-EA) is proposed with three stages. Firstly, the HES-EA transforms the many objectives of the real-evaluated solutions into two indicators (i.e., the convergence and diversity indicators) and divides these solutions into different clusters. Secondly, a HES consisting of a cluster surrogate and different TISs is trained through these clustered solutions and their indicators. Thirdly, during the optimization process, the HES can predict the candidate solutions’ cluster information via the cluster surrogate and indicator information via the TISs. Promising solutions can thus be selected based on the predicted information via a clustering-based sequential selection strategy without real fitness evaluation consumption. Compared with state-of-the-art SAEAs on three widely used benchmark suites up to 184 instances and one real-world application, HES-EA shows its superiority in both optimization performance and computational cost. IEEE

关键词： Kriging

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Short-term Traffic Flow Prediction by Graph Deep Learning with Spatial Temporal Modeling 2

Short-term Traffic Flow Prediction by Graph Deep Learning wi...

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2nd International Conference on Electronic Information technology and Smart Agriculture, ICEITSA 2022

作者： Yuan, Lilian Fang, Weilin Xiao, Hongbo Xiao, Jianhua Shi, Yuanquan Yang, Yujun School of Computer Science and Engineering Huaihua University Hunan Huaihua418000 China Intelligent Processing and Application in Hunan Province Universities Key Laboratory of Wuling-Mountain Health Big Data Hunan Huaihua418000 China Wuling-Mountain Ecological Agriculture in Hunan Province Key Laboratory of Intelligent Control Technology for Huaihua Hunan418000 China

ISBN: (纸本)9781665464017

An extensive amount of research applications on traffic flow prediction models taking into account spatio-temporal modeling have been undertaken in recent years. Relative to traditional spatio-temporal modeling approaches, good application results have been achieved in predicting traffic flow. However, in real life, traffic flow proves to be time- and space-dependent, which poses difficulties for accurate modeling of traffic flow prediction. To address these problems, we propose the ST-GNN-GRU model, which mainly contains S-GNN and T-GRU modules. The model uses a combination of spatio-temporal modeling and deep learning, which can not only cope with the spatio-temporal modeling problem, but also improve the traffic flow prediction accuracy. The experiments are conducted using the METR-LA dataset, and the outcomes show that the proposed method performs more effectively than other baseline approaches. © 2022 IEEE.

关键词： Graph neural networks

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Enhancing source code summarization from structure and semantics

Enhancing source code summarization from structure and seman...

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International Joint Conference on Neural Networks (IJCNN)

作者： Xurong Lu Jun Niu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China Zhejiang Engineering Research Center of Advcanced Mass Spectrometry and Clinical Application Ningbo China

Source code summarization aims to generate concise and high-quality natural language descriptions for code snippets. High-quality code summaries can help developers better understand source codes. Researchers have made great efforts to generate more accurate summaries; however, due to the lack of preservation of source code structure and semantics, previous approaches have struggled to generate summaries that accurately describe the functionality or other major characteristics of codes. In this paper, we propose a novel approach called Code Structure and Semantic Fusion (CSSF) for automatically generating summaries for source code. CSSF can utilize both the structural and semantic information of source codes. To achieve this, we extract the overall structure of the Abstract Syntax Tree (AST) by expanding the AST and using a heterogeneous graph attention network. Furthermore, we use an additional sequence model to obtain the semantic information of the code fragment. Finally, we fuse the two kinds of information through a novel modality fusion method. We evaluate our approach on a widely used Java dataset; experimental results confirm that our approach outperforms existing methods.

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Classifying SPECT Bone Scan Images Using Improved Convolutional Neural Networks 2

Classifying SPECT Bone Scan Images Using Improved Convolutio...

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2nd International Conference on Signal Image Processing and Communication, ICSIPC 2022

作者： Wang, Yubo Lin, Qiang Cao, Xu Cao, Yongchun Man, Zhengxing Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education Northwest Minzu University Gansu Lanzhou730030 China School of Mathematics and Computer Science Northwest Minzu University Gansu Lanzhou730124 China Key Laboratory of Streaming Data Computing Technologies and Application Northwest Minzu University Gansu Lanzhou730030 China

ISBN: (纸本)9781510657694

SPECT imaging is an effective medical method for diagnosis, treatment, evaluation, and prevention of a range of serious diseases and medical conditions. SPECT bone scans have the potential to provide more accurate assessment of disease staging and severity. To classify medical images more effectively, in this work, we propose an AlexNet-based image classification model. Specifically, the network structure of AlexNet is fine-tuned and attention mechanism is added to construct an improved model. Lastly, experimental results show that the improved AlexNet classification model is feasible for classifying SEPCT bone scan images, with obtaining scores of 0.7377, 0.7677, 0.7723, and 0.7674 for Acc, Pre, Rec and F-1, respectively. © 2022 SPIE.

关键词： Image classification

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An algorithm for multi-armed bandit based on variance change sensitivity

Engineering Research Express

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Engineering Research Express 2024年第2期6卷 025209-025209页

作者： Zhu, Canxin Yang, Jingmin Zhang, Wenjie Zheng, Yifeng School of Computer Science Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Fujian Province University Zhangzhou363000 China Department of Electronic Engineering National Taipei University of Technology Taipei106344 Taiwan

The Multi-Arm Bandit problem is becoming increasingly popular as it enables real-world sequential decision making across application domains, including clinical trials, recommender systems, and online decision making. The Multi-Arm Bandit problem is a classical problem of exploration and exploitation dilemma in reinforcement learning and it needs to decide on the optimal strategy based on the reward situation of each rocker arm. However, the existing Multi-Arm Bandit algorithms have many shortcomings, such as blind exploration, weak generalization ability and endless exploration. Aiming at the shortcomings of the existing Multi-Armed Bandit algorithms, this paper proposes a Multi-Armed Bandit algorithm based on variance change sensitivity. The algorithm takes the variance change of reward as a clue, and adjusts the exploration probability by the average variance change of all actions, and selects the action with the largest variance change when exploring. At the same time, in order to reduce the waste of action selection times and maximize the cumulative reward, A parameter N c o n was introduced to record the number of consecutive selections of the same action. The exploration stops when N c o n reaches a certain value. Experiments show that the exploration algorithm can obtain higher reward value and lower regret value in the end. © 2024 IOP Publishing Ltd.

关键词： Reinforcement learning

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Classroom Teaching Effect Monitoring and Evaluation System with Deep Integration of Artificial Intelligence

Classroom Teaching Effect Monitoring and Evaluation System w...

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作者： Aili Wang Yingluo Song Haibin Wu Shanshan Ding Linlin Yang Yunhong Yang Yuji Iwahori Heilongjiang Province Key Laboratory of Laser Spectroscopy Technology and Application Harbin University of Science and Technology Department of Computer Science Chubu University

With the progress and development of the times, education informatization has become the primary content to promote the progress of ***, with the rapid advancement of technology, many problems have arisen in the integration of online education and information technology. From a practical point of view, advanced information technology should be fully integrated with school education activities to promote education reform and development, and students will definitely obtain a better learning experience. This study aimed to introduce a classroom teaching effect monitoring and evaluation system that incorporates artificial intelligence. In view of the background conditions for the development of this system, reforming measures and specific implementation methods for the integration of information technology and online classroom education are proposed.

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Smart Contract Service Optimization in Blockchain-Cloud Collaborative Computing 24

Smart Contract Service Optimization in Blockchain-Cloud Coll...

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24th IEEE International Conference on Mobile Data Management, MDM 2023

作者： Wan, Ji Hu, Kai Li, Jie Su, Hao Wu, Qingshun Li, Mingyuan Feng, Libo Pan, Yan Beihang University State Key Laboratory of Software Development Environment Beijing China Beihang Yunnan Innovation Institute Yunnan Key Laboratory of Blockchain Application Technology Kunming China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou China The First Affiliated Hospital of Zhengzhou University Zhengzhou China Yunnan University National Pilot School of Software Kunming China China Industrial Control Systems Cyber Emergency Response Team Institute of Software Beijing China

ISBN: (纸本)9798350341010

Smart contract is a trusted service provided on the blockchain, while cloud service is a traditional service mode with a large number of resources. The combination of the blockchain and cloud service is of great significance to the trusted expansion of services and the access to services inside and outside the blockchain. In this paper, we study the smart contract extension service in blockchain-cloud collaborative computing. The service module decoupling method of smart contract is proposed, and the parallel execution algorithm of smart contract service is designed, which improves the execution efficiency of smart contract service. Finally, this paper designs a secure data interaction method of smart contract and cloud service, which helps to maintain the data consistency between cloud computing and blockchain. The experimental results show that the proposed method can save at most 42.13% of the running time, and it can promote the data consistency between the cloud service and the blockchain. © 2023 IEEE.

关键词： Smart contract

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Untargeted Attack against Federated Recommendation Systems via Poisonous Item Embeddings and the Defense

arXiv

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arXiv 2022年

作者： Yu, Yang Liu, Qi Wu, Likang Yu, Runlong Yu, Sanshi Lei Zhang, Zaixi Anhui Province Key Laboratory of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China State Key Laboratory of Cognitive Intelligence China

Federated recommendation (FedRec) can train personalized recommenders without collecting user data, but the decentralized nature makes it susceptible to poisoning attacks. Most previous studies focus on the targeted attack to promote certain items, while the untargeted attack that aims to degrade the overall performance of the FedRec system remains less explored. In fact, untargeted attacks can disrupt the user experience and bring severe financial loss to the service provider. However, existing untargeted attack methods are either inapplicable or ineffective against FedRec systems. In this paper, we delve into the untargeted attack and its defense for FedRec systems. (i) We propose ClusterAttack, a novel untargeted attack method. It uploads poisonous gradients that converge the item embeddings into several dense clusters, which make the recommender generate similar scores for these items in the same cluster and perturb the ranking order. (ii) We propose a uniformity-based defense mechanism (UNION) to protect FedRec systems from such attacks. We design a contrastive learning task that regularizes the item embeddings toward a uniform distribution. Then the server filters out these malicious gradients by estimating the uniformity of updated item embeddings. Experiments on two public datasets show that ClusterAttack can effectively degrade the performance of FedRec systems while circumventing many defense methods, and UNION can improve the resistance of the system against various untargeted attacks, including our ClusterAttack. Copyright © 2022, The Authors. All rights reserved.

关键词： Embeddings

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