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ALSTNet:Autoencoder fused long-and short-term time-series network for the prediction of tunnel structure

Deep Underground Science and Engineering 2025年第1期4卷 72-82页

作者： Bowen Du Haohan Liang Yuhang Wang Junchen Ye Xuyan Tan Weizhong Chen State Key Laboratory of Software Development Environment Beihang UniversityBeijingChina State Key Laboratory of Geomechanics and Geotechnical Engineering Institute of Rock and Soil MechanicsChinese Academy of SciencesWuhanChina

It is crucial to predict future mechanical behaviors for the prevention of structural *** for underground construction,the structural mechanical behaviors are affected by multiple internal and external factors due to the complex *** that the existing models fail to take into account all the factors and accurate prediction of the multiple time series simultaneously is difficult using these models,this study proposed an improved prediction model through the autoencoder fused long-and short-term time-series network driven by the mass number of monitoring ***,the proposed model was formalized on multiple time series of strain monitoring ***,the discussion analysis with a classical baseline and an ablation experiment was conducted to verify the effectiveness of the prediction *** the results indicate,the proposed model shows obvious superiority in predicting the future mechanical behaviors of *** a case study,the presented model was applied to the Nanjing Dinghuaimen tunnel to predict the stain variation on a different time scale in the future.

关键词： autoencoder deep learning structural health monitoring time-series prediction

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ICCG:low-cost and efficient consistency with adaptive synchronization for metadata replication

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Frontiers of Computer Science 2025年第1期19卷 53-70页

作者： Chenhao ZHANG Liang WANG Jing SHANG Zhiwen XIAO Limin XIAO Meng HAN Bing WEI Runnan SHEN Jinquan WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China School of Cyberspace Security Hainan UniversityHaikou 570228China China Mobile Information Technology Center Beijing 100033China

The rapid growth in the storage scale of wide-area distributed file systems (DFS) calls for fast and scalable metadata management. Metadata replication is the widely used technique for improving the performance and scalability of metadata management. Because of the POSIX requirement of file systems, many existing metadata management techniques utilize a costly design for the sake of metadata consistency, leading to unacceptable performance overhead. We propose a new metadata consistency maintenance method (ICCG), which includes an incremental consistency guaranteed directory tree synchronization (ICGDT) and a causal consistency guaranteed replica index synchronization (CCGRI), to ensure system performance without sacrificing metadata consistency. ICGDT uses a flexible consistency scheme based on the state of files and directories maintained through the conflict state tree to provide an incremental consistency for metadata, which satisfies both metadata consistency and performance requirements. CCGRI ensures low latency and consistent access to data by establishing a causal consistency for replica indexes through multi-version extent trees and logical time. Experimental results demonstrate the effectiveness of our methods. Compared with the strong consistency policies widely used in modern DFSes, our methods significantly improve the system performance. For example, in file creation, ICCG can improve the performance of directory tree operations by at least 36.4 times.

关键词： metadata management metadata replication consistency directory tree replica index

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IDEAL: A malicious traffic detection framework with explanation-guided learning

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Knowledge-Based Systems 2025年 317卷

作者： Jia, Huiting Lang, Bo Li, Xiangyu Yan, Yuhao State Key Laboratory of Software Development Environment Beihang University Beijing100191 China Zhongguancun Laboratory Beijing100086 China

The credibility of deep learning models is crucial for network security tasks. Ensuring that these models make reasonable decisions based on truly relevant features is key to enhancing their credibility. Explanation supervision, which guides models to reason rationally by adding supervision to model explanations, has shown promising results in terms of improving overall model performance. However, the complexity of cyberattack characteristics and packet structures increases the difficulty of integrating explanation supervision and makes it challenging to obtain human annotations that encode domain knowledge concerning cyberattacks. In this paper, we propose a general framework called malIcious traffic Detection with ExplAnation-guided Learning (IDEAL). This framework integrates explanation supervision techniques into malicious traffic detection for the first time, enables researchers to supervise model explanations based on domain knowledge, and jointly optimizes the task and explanation losses. As a result, both the detection performance and explainability of the model improve. In addition, to address the problem of obtaining human annotations, we propose an automatic explanation annotation generation method based on the expert-defined detection rules of the famous Snort firewall. We conduct extensive experiments on the CIC-IDS-2017 and TON_IoT datasets. The experimental results show that, with only approximately 1% of the training data used for explanation supervision, our proposed IDEAL method significantly improves upon the detection performance and explainability of the state-of-the-art models. In addition, IDEAL is applicable across different neural network architectures and exhibits considerable robustness to incomplete low-quality manual annotations. © 2025 Elsevier B.V.

关键词： Self-supervised learning

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Spatial-Frequency Cross-Domain Mutual Guidance for Visible-Infrared Image Fusion

Spatial-Frequency Cross-Domain Mutual Guidance for Visible-I...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhili Lin Biao Leng School of Computer Science and Engineering State Key Laboratory of Software Development Environment Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The primary objective of Visible-Infrared Image Fusion (VIF) is to combine the rich texture and color information from visible light images with the comprehensive thermal radiation data provided by infrared images. However, most current fusion algorithms focus solely on spatial domain feature transformations, which results in fused images lacking sufficient detail and failing to effectively preserve crucial details from the source images. In this paper, we propose Spatial-Frequency Mutual Guidance for VIF. The framework comprises two branches. Each branch reconstructs input features in the frequency and spatial domains. We introduce a novel cross-domain mutual guidance mechanism. It fully integrates information between the frequency and spatial domains to enhance the detail quality of fused images. Furthermore, a weight allocation network is utilized to adaptively assign importance to visible and infrared images based on scene characteristics. Experiments on three VIF datasets show that our method outperforms recent advanced algorithms.

关键词： Image color analysis Frequency-domain analysis Heuristic algorithms Signal processing algorithms Signal processing Resource management Speech processing Image fusion Image reconstruction Visual perception

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Video Motion Blur Attack via Grad-Weighted and Discrete-Fusion based Perturbation Generation

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IEEE Transactions on Multimedia 2025年

作者： Wu, Guoming Li, Jun Xu, Yangfan Shi, Zhiping Liu, Xianglong Capital Normal University Information Engineering College Beijing100048 China Beihang University State Key Laboratory of Software Development Environment Beijing100191 China

Recent research has shown that deep learning networks are vulnerable to adversarial samples. Although there has been great progress in the study of adversarial attacks on images, there is relatively little research on adversarial attacks in the video domain, especially on intrinsic factors of videos, such as motion blur. In this paper, we devise a novel Grad-Weighted based One-step Motion Blur Attack (GWO-MBA) and a Discrete-Fusion based Progressive Motion Blur Attack (DFP-MBA) for video recognition, starting from the idea of integrating global adversarial attacks and adversarial patch attacks. Concretely, we use gradient maps to filter and weighted fusion motion blur (termed GWO-MBA) to achieve the attack that matches the motion information in the context of the video. In order to make the generated motion blur attack perturbations more natural and improve the attack success rate, we further introduce a progressive decomposition motion blur strategy (termed DFP-MBA) to progressively fuse more realistic discrete motion blurs. Besides, we propose an Aggressive Motion Blur Generation (AMBG), which generates natural motion blur based on the video context and has a better attack effect. The extensive experiments, on the HMDB-51 and UCF-101 datasets, demonstrate the effectiveness and superiority of our proposed attack method. In addition, the attack effectiveness of the mainstream denoising defense model and the deblur model further validates the robustness of our attack method. © 1999-2012 IEEE.

关键词： Adversarial Attack Discrete-Fusion Gradient-Weighted Motion Blur Video Action Recognition

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CODESWIFT: Accelerating LLM Inference for Efficient Code Generation

arXiv

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arXiv 2025年

作者： Zhao, Qianhui Zhang, Li Liu, Fang Lian, Xiaoli Meng, Qiaoyuanhe Jiao, Ziqian Zhou, Zetong Zhang, Borui Guo, Runlin Li, Jia School of Computer Science and Engineering State Key Laboratory of Complex & Critical Software Environment Beihang University China Key Lab of High Confidence Software Technology Peking University China

Code generation is a latency-sensitive task that demands high timeliness, but the autoregressive decoding mechanism of Large Language Models (LLMs) leads to poor inference efficiency. Existing LLM inference acceleration methods mainly focus on standalone functions using only built-in components. Moreover, they treat code like natural language sequences, ignoring its unique syntax and semantic characteristics. As a result, the effectiveness of these approaches in code generation tasks remains limited and fails to align with real-world programming scenarios. To alleviate this issue, we propose CODESWIFT, a simple yet highly efficient inference acceleration approach specifically designed for code generation, without comprising the quality of the output. CODESWIFT constructs a multi-source datastore, providing access to both general and project-specific knowledge, facilitating the retrieval of high-quality draft sequences. Moreover, CODESWIFT reduces retrieval cost by controlling retrieval timing, and enhances efficiency through parallel retrieval and a context- and LLM preference-aware cache. Experimental results show that CODESWIFT can reach up to 2.53× and 2.54× speedup compared to autoregressive decoding in repository-level and standalone code generation tasks, respectively, outperforming state-of-the-art inference acceleration approaches by up to 88%. Our code and data are available at https://***/r/CodeSwift. Copyright © 2025, The Authors. All rights reserved.

关键词： Decoding

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Identifying Performance Inefficiencies of Parallel Program with Spatial and Temporal Trace Analysis

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IEEE Transactions on Parallel and Distributed Systems 2025年

作者： Xuan, Zhibo Sun, Xin You, Xin Yang, Hailong Luan, Zhongzhi Liu, Yi Qian, Depei Beihang University State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beijing100191 China

Performance inefficiencies can lead to performance anomalies in parallel programs. Existing performance analysis tools either have a limited detection scope or require significant domain knowledge to use, which constrains their practical adoption to identify performance inefficiencies. In this paper, we propose STAD, a performance analysis tool for parallel programs that considers both spatial and temporal patterns within trace data. STAD captures the spatial communication patterns between processes using a spatial communication pattern graph. It then adopts a dynamic graph neural network-based unsupervised model to learn the evolving temporal patterns along the timeline. Additionally, STAD diagnoses the root causes of performance anomalies by exploiting the aggregated feature of anomalies along the call tree. Our evaluation results demonstrate that STAD can effectively detect performance anomalies with acceptable overhead and diagnose the root causes attributed to both the program itself and the running environment. © 1990-2012 IEEE.

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Multi-View Incongruity Learning for Multimodal Sarcasm Detection 31

Multi-View Incongruity Learning for Multimodal Sarcasm Detec...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Guo, Diandian Cao, Cong Yuan, Fangfang Liu, Yanbing Zeng, Guangjie Yu, Xiaoyan Peng, Hao Yu, Philip S. Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences China State Key Laboratory of Software Development Environment Beihang University China School of Computer Science and Technology Beijing Institute of Technology China Department of Computer Science University of Illinois Chicago United States

ISBN: (纸本)9798891761964

Multimodal sarcasm detection (MSD) is essential for various downstream tasks. Existing MSD methods tend to rely on spurious correlations. These methods often mistakenly prioritize non-essential features yet still make correct predictions, demonstrating poor generalizability beyond training environments. Regarding this phenomenon, this paper undertakes several initiatives. Firstly, we identify two primary causes that lead to the reliance of spurious correlations. Secondly, we address these challenges by proposing a novel method that integrate Multimodal Incongruities via Contrastive Learning (MICL) for multimodal sarcasm detection. Specifically, we first leverage incongruity to drive multi-view learning from three views: token-patch, entity-object, and sentiment. Then, we introduce extensive data augmentation to mitigate the biased learning of the textual modality. Additionally, we construct a test set, SPMSD, which consists potential spurious correlations to evaluate the the model's generalizability. Experimental results demonstrate the superiority of MICL on benchmark datasets, along with the analyses showcasing MICL's advancement in mitigating the effect of spurious correlation. © 2025 Association for Computational Linguistics.

关键词： Contrastive Learning

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A PSO-CNN-LSTM Model for Seismic Facies Analysis: Methodology and Applications

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IEEE Access 2025年 13卷 84094-84111页

作者： Liao, Luyao Zhou, Huailai Liu, Junping Zhou, Jie Zhang, Donghang Wang, Jian Chengdu University of Technology Key Lab of Earth Exploration and Information Techniques Ministry of Education College of Geophysics Sichuan Chengdu610059 China Geophysical Technology Research and Development Center of BGP Inc China Chengdu University of Technology State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Sichuan Chengdu610059 China Chengdu University of Technology College of Environment and Civil Engineering Sichuan Chengdu610059 China Sinopec Southwest Oil & Gas Company Sichuan Chengdu610095 China

Seismic facies analysis, as a crucial step in the study of depositional facies, effectively delineates the distribution patterns of depositional facies between wells. To address the limitations of conventional manual interpretation methods, particularly their low efficiency and strong subjectivity, this study proposes a hybrid CNN-LSTM model integrated with Particle Swarm Optimization (PSO-CNN-LSTM). The model systematically extracts spatial features of seismic reflections through CNN architecture while capturing temporal waveform dependencies via LSTM networks, with PSO automatically optimizing critical parameters including initial learning rate and LSTM neuron count. Experimental results demonstrate that PSO-CNN-LSTM achieves a classification accuracy of 89.74%, surpassing CNN (81.48%), LSTM (81.63%), and basic CNN-LSTM (84.61%) models by 8.26%, 8.11%, and 5.13% respectively. The model exhibits superior performance on the SEG 2020 benchmark dataset, confirming that automated parameter optimization effectively reduces manual intervention while enhancing convergence stability. Practical applications reveal consistent interpretation outcomes between the model's predictions (using limited training samples) and expert analyses, providing reliable evidence for identifying favorable zones in heterogeneous carbonate reservoirs. The established intelligent waveform classification workflow validates PSO-CNN-LSTM model's robustness and offers an efficient solution for seismic facies analysis, particularly in complex geological settings. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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RobNODDI: Robust NODDI Parameter Estimation with Adaptive Sampling Under Continuous Representation

RobNODDI: Robust NODDI Parameter Estimation with Adaptive S...

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15th International Workshop on Computational Diffusion MRI, CDMRI 2024, held in conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Yang, Jing Li, Cheng Dong, Enqing Wang, Shanshan School of Mechanical Electrical and Information Engineering Shandong University Weihai264209 China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Zhejiang Hangzhou China Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Shenzhen China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9783031869198

Neurite Orientation Dispersion and Density Imaging (NODDI) is an important imaging technology used to evaluate the microstructure of brain tissue, which is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods perform parameter estimation through diffusion magnetic resonance imaging (dMRI) with a small number of diffusion gradients. These methods speed up parameter estimation and improve accuracy. However, the diffusion directions used by most existing deep learning models during testing needs to be strictly consistent with the diffusion directions during training. This results in poor generalization and robustness of deep learning models in dMRI parameter estimation. In this work, we first verify that the parameter estimation performance of current mainstream methods will significantly decrease when the testing diffusion directions and the training diffusion directions are inconsistent. A robust NODDI parameter estimation method with adaptive sampling under continuous representation (RobNODDI) is proposed. Furthermore, long short-term memory (LSTM) units and fully connected layers are selected to learn continuous representation signals. To this end, we use a total of 100 subjects to conduct experiments based on the Human Connectome Project (HCP) dataset, of which 60 are used for training, 20 are used for validation, and 20 are used for testing. The test results indicate that RobNODDI improves the generalization performance and robustness of the deep learning model, enhancing the stability and flexibility of deep learning NODDI parameter estimatimation applications. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

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