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43rd Chinese Control Conference, CCC 2024

作者： Zhang, Mei Jin, Ye Zhu, Jinhui South China University of Technology School of Automation Science and Engineering Guangzhou510641 China South China University of Technology School of Software Engineering Guangzhou510006 China Key Laboratory of Big Data and Intelligent Robot Ministry of Education Guangzhou510006 China

ISBN: (纸本)9789887581581

Multi-exposure image fusion is a cost-effective method to improve the dynamic range of images. For the problems of inadequate detail extraction in bright and dark areas and the over simplicity of existing fusion rules by current deep learning multi-exposure image fusion algorithms, we propose a DenseNet-based framework using adaptive Gabor convolution. Building on the Dense network architecture, we design an adaptive Gabor convolution module, which the parameters of the Gabor filter can be updated along with the network, enhancing the ability of Gabor to extract image features. Additionally, a cross-domain aggregation attention module is proposed, which effectively aggregates and enhances information. Comparing our method with seven advanced methods on the multi-exposure image fusion benchmark dataset, the subjective and objective evaluations outperform the other methods, proving the effectiveness and superiority of our method. The experiments of different exposure images fusion demonstrate the good generalization performance of our method. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Convolution

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data Aggregation Point Placement and Subnetwork Optimization for Smart Grids

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Computers, Materials & Continua 2025年第4期83卷 407-434页

作者： Tien-Wen Sung Wei Li Chao-Yang Lee Yuzhen Chen Qingjun Fang Fujian Provincial Key Laboratory of Big Data Mining and Applications College of Computer Science and MathematicsFujian University of TechnologyFuzhou350118China Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin640301TaiwanChina

To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the *** number of DAPs installed and the installation location greatly impact the whole *** the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the ***,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the *** address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different *** algorithm adds an allocation mechanism to optimize the subnetwork in the *** is evaluated under three different areas and compared with other DAP placement *** experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.

关键词： Smart grid data aggregation point placement network cost average transmission distance load gap

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Anatomical Prior Guided Spatial Contrastive Learning for Few-Shot Medical Image Segmentation 24

Anatomical Prior Guided Spatial Contrastive Learning for Few...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Huang, Wendong Hu, Jinwu Bi, Xiuli Xiao, Bin Chongqing Key Laboratory of Image Cognition Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education Chongqing University of Post and Telecommunications Chongqing China Pazhou Lab School of Software Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400706868

Few-shot semantic segmentation has considerable potential for low-data scenarios, especially for medical images that require expert-level dense annotations. Existing few-shot medical image segmentation methods strive to deal with the task by means of prototype learning. However, this scheme relies on support prototypes to guide the segmentation of query images, ignoring the rich anatomical prior knowledge in medical images, which hinders effective feature enhancement for medical images. In this paper, we propose an anatomical prior guided spatial contrastive learning, called APSCL, which exploits anatomical prior knowledge derived from medical images to construct contrastive learning from a spatial perspective for few-shot medical image segmentation. The new framework forces the model to learn the features in line with the embedded anatomical representations. Besides, to fully exploit the guidance information of the support samples, we design a mutual guidance decoder to predict the label of each pixel in the query image. Furthermore, our APSCL can be trained end-to-end in the form of episodic training. Comprehensive experiments on three challenging medical image datasets, i.e., CHAOS-T2, MS-CMRSeg, and Synapse, prove that our method significantly surpasses state-of-the-art few-shot medical segmentation methods, with a mean improvement of 3.61%, 2.30%, and 6.38% on the Dice score, respectively. © 2024 ACM.

关键词： Contrastive Learning

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Multi-modal Fine-grained Retrieval with Local and Global Cross-Attention 14

Multi-modal Fine-grained Retrieval with Local and Global Cro...

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14th International Conference on Ubiquitous and Future Networks, ICUFN 2023

作者： Chen, Qiaosong Zhang, Ye Liu, Junzhuo Wang, Zhixiang Deng, Xin Wang, Jin Chongqing University of Posts and Telecommunications Key Laboratory of Data Engineering and Visual Computing Chongqing400065 China Maastricht Netherlands

ISBN: (纸本)9798350335385

The goal of cross-modal retrieval is that the user gives any sample as a query sample, and the system retrieves and feeds back various modal samples related to the query sample. At present, the cross-modal retrieval method mainly focuses on coarse-grained, which is far from being satisfied in practical application. However, there are many difficulties in fine-grained retrieval, such as the heterogeneous gap and semantic gap between multi-modal data, the difficulty of similarity measurement, and the small difference in fine-grained sample features. To overcome these limitations, we propose a novel multi-modal fine-grained retrieval method with the LAGC-Attention module, which can fully extract and fuse feature information from different modalities and represent them in a common space. Specifically, we use local and global cross self-attention to extract the neighboring and global context information for each single modal data, which greatly enhances the feature representation capability of each modality (image, text, audio, video), and especially reduce the gap between different feature distributions. Finally, Extensive experiments and ablation studies demonstrate that our method achieves state-of-the-art on the public dataset PKU FG-XMedia. © 2023 IEEE.

关键词： Semantics

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PS3: Precise Patch Presence Test Based on Semantic Symbolic Signature 24

PS3: Precise Patch Presence Test Based on Semantic Symbolic ...

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44th ACM/IEEE International Conference on Software engineering, ICSE 2024

作者： Zhan, Qi Hu, Xing Li, Zhiyang Xia, Xin Lo, David Li, Shanping Zhejiang University The State Key Laboratory of Blockchain and Data Security Hangzhou China Zhejiang University Hangzhou China Software Engineering Application Technology Lab Huawei China Singapore Management University Singapore

ISBN: (纸本)9798400702174

During software development, vulnerabilities have posed a significant threat to users. Patches are the most effective way to combat vulnerabilities. In a large-scale software system, testing the presence of a security patch in every affected binary is crucial to ensure system security. Identifying whether a binary has been patched for a known vulnerability is challenging, as there may only be small differences between patched and vulnerable versions. Existing approaches mainly focus on detecting patches that are compiled in the same compiler options. However, it is common for developers to compile programs with very different compiler options in different situations, which causes inaccuracy for existing methods. In this paper, we propose a new approach named PS3, referring to precise patch presence test based on semantic-level symbolic signature. PS3 exploits symbolic emulation to extract signatures that are stable under different compiler options. Then PS3 can precisely test the presence of the patch by comparing the signatures between the reference and the target at semantic level. To evaluate the effectiveness of our approach, we constructed a dataset consisting of 3,631 (CVE, binary) pairs of 62 recent CVEs in four C/C++ projects. The experimental results show that PS3 achieves scores of 0.82, 0.97, and 0.89 in terms of precision, recall, and F1 score, respectively. PS3 outperforms the state-of-the-art baselines by improving 33% in terms of F1 score and remains stable in different compiler options. © 2024 ACM.

关键词： Software design

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BKA optimization algorithm based on sine-cosine guidelines 4

BKA optimization algorithm based on sine-cosine guidelines

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： Zhou, Yanqiu Wu, Xiao Liu, Yuxiang Jiang, Xinghe Jiangnan Institute of Mechanical and Electrical Design Guiyang China Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China

ISBN: (纸本)9798350354560

In order to address the limitations of traditional BKA optimization algorithms in terms of stability, data processing capability, and computational speed, this study proposes a new and improved BKA algorithm that combines the sine-cosine criterion, i.e., SCBKA. This new algorithm significantly enhances its solution accuracy and convergence speed by integrating the sine-cosine oriented mechanism. We conducted systematic comparison experiments of the SCBKA algorithm with six recognized classical algorithms on the highest challenge dimension of the CEC 2020 benchmark test function, the 20th dimension. The experimental results clearly show that SCBKA not only obtains significant improvement in convergence and accuracy, but also demonstrates significant advantages in the stability of the algorithm. In addition, by applying the Friedman rank sum test, we further confirm the efficiency and reliability of the SCBKA algorithm. © 2024 IEEE.

关键词： Optimization algorithms

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On the Robustness of Editing Large Language Models

On the Robustness of Editing Large Language Models

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Xinbei Ju, Tianjie Qiu, Jiyang Zhang, Zhuosheng Zhao, Hai Liu, Lifeng Wang, Yulong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Baichuan Intelligent Technology China

ISBN: (纸本)9798891761643

Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and the behavior of language generation without retraining. However, the robustness of model editing remains an open question. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI. We focus on three key research questions. RQ1: Can edited LLMs behave consistently resembling communicative AI in realistic situations? RQ2: To what extent does the rephrasing of prompts lead LLMs to deviate from the edited knowledge memory? RQ3: Which knowledge features are correlated with the performance and robustness of editing? Our empirical studies uncover a substantial disparity between existing editing methods and the practical application of LLMs. On rephrased prompts that are flexible but common in realistic applications, the performance of editing experiences a significant decline. Further analysis shows that more popular knowledge is memorized better, easier to recall, and more challenging to edit effectively. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A multi-target UAV fault diagnosis method based on feature discriminant dimension reduction under small samples 4

A multi-target UAV fault diagnosis method based on feature d...

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4th International Symposium on Computer Technology and Information Science, ISCTIS 2024

作者： An, Xue Zhang, Yizong Li, Shaobo Xiong, Xuan Guizhou University School of Mechanical Engineering Guiyang China Guizhou University State Key Laboratory of Public Big Data Guiyang China Guizhou University College of Computer Science and Technology Guiyang China

ISBN: (纸本)9798350354560

In recent years, deep learning has provided many efficient and accurate solutions to UAV fault diagnosis, but most of the current deep learning can only deal with a single task, whereas the UAV system is a complex coupled system, which needs to perform several different fault position diagnostics (FPD) and fault degree diagnostics (FDD) at the same time. Therefore, this paper proposed a multi-target UAV fault diagnosis method based on feature discriminant dimensionality reduction (MFD-FDDR) under small samples. Firstly, We connect specific features of different tasks with the same spatial definition through the channel dimension. Then, we perform feature discriminant dimensionality reduction (FDDR) for each task feature, and the output characteristics satisfy the input size of the following layer of LSTM. Finally, we validate the effectiveness of our proposed method with extensive experiments on real UAV flight data. © 2024 IEEE.

关键词： discriminant dimensionality reduction fault diagnosis multi-task learning small samples

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A Method for Extracting Electronic Medical Record Entities by Fusing Multichannel Self-Attention Mechanism with Location Relationship Features 9th

A Method for Extracting Electronic Medical Record Entities b...

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9th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Xu, Hongyan Wang, Hong Feng, Yong Wang, Rongbing Zhang, Yonggang College of Information Liaoning University Shenyang110036 China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun130012 China

ISBN: (纸本)9789819959709

With the implementation of the "Internet+" strategy, electronic medical records are generally applied in the medical field. Deep mining of electronic medical record content data is an effective means to obtain medical knowledge and analyse patients’ states, but the existing methods for extracting entities from electronic medical records have problems of redundant information, overlapping entities, and low accuracy rates. Therefore, this paper proposes an entity extraction method for electronic medical records based on the network framework of BERT-BiLSTM, which incorporates a multichannel self-attention mechanism and location relationship features. First, the text input sequence was encoded using the BERT-BiLSTM network framework, and the global semantic information of the sentence was mined more deeply using the multichannel self-attention mechanism. Then, the position relation characteristic was used to extract the local semantic message of the text, and the position relation characteristic of the word and the position embedding matrix of the whole sentence were obtained. Next, the extracted global semantic information was stitched with the positional embedding matrix of the sentence to obtain the current entity classification matrix. Finally, the proposed method was validated on the dataset of Chinese medical text entity relationship extraction and the 2010i2b2/VA relationship corpus, and the experimental results indicate that the proposed method surpasses existing methods in terms of precision, recall, F1 value and training time. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Location

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PSRDP:A Parallel Processing Method for Pulsar Baseband data

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Research in Astronomy and Astrophysics 2024年第1期24卷 300-310页

作者： Ya-Zhou Zhang Hai-Long Zhang Jie Wang Xin-Chen Ye Shuang-Qiang Wang Xu Du Han Wu Ting Zhang Shao-Cong Guo Meng Zhang Xinjiang Astronomical Observatory Chinese Academy of SciencesUrumqi 830011China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Radio Astronomy Chinese Academy of SciencesNanjing 210008China National Astronomical Data Center Beijing 100101China Southeast University Nanjing 211189China School of Cyber Science and Engineering Qufu Normal UniversityQufu 273165China

To address the problem of real-time processing of ultra-wide bandwidth pulsar baseband data,we designed and implemented a pulsar baseband data processing algorithm(PSRDP)based on GPU parallel computing *** can perform operations such as baseband data unpacking,channel separation,coherent dedispersion,Stokes detection,phase and folding period prediction,and folding integration in GPU *** tested the algorithm using the J0437-4715 pulsar baseband data generated by the CASPSR and Medusa backends of the Parkes,and the J0332+5434 pulsar baseband data generated by the self-developed backend of the Nan Shan Radio *** obtained the pulse profiles of each baseband *** experimental analysis,we have found that the pulse profiles generated by the PSRDP algorithm in this paper are essentially consistent with the processing results of Digital Signal Processing Software for Pulsar Astronomy(DSPSR),which verified the effectiveness of the PSRDP ***,using the same baseband data,we compared the processing speed of PSRDP with DSPSR,and the results showed that PSRDP was not slower than DSPSR in terms of *** theoretical and technical experience gained from the PSRDP algorithm research in this article lays a technical foundation for the real-time processing of QTT(Qi Tai radio Telescope)ultra-wide bandwidth pulsar baseband data.

关键词： (stars:)pulsars:general methods:data analysis techniques:miscellaneous

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