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18th International Conference on Wireless Artificial Intelligent computing Systems and Applications, WASA 2024

作者： Ou, Pingjie Chen, Ningjiang Huang, Zizhan School of Computer and Electronic Information Guangxi University Guangxi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi University Guangxi Nanning530004 China

ISBN: (纸本)9783031714696

Due to the limited resources of edge networks, the heterogeneity of user content requests, high-cost caching from direct resource hits, and redundancy in resource retention time hinder system performance. Traditional caching methods based on learning and popularity tend to encounter data skew when handling vehicle requests, leading to untimely cache data updates. To address these issues, this paper proposes an efficient edge cooperative caching strategy with recommendation awareness. Firstly, a time series prediction model based on a multi-dense multilayer perceptron is designed. This model segments historical request information into sub-sequence level slivers to enhance positional correlations between information, predicting user requests through historical time series and covariates. Secondly, to optimize cache utilization, comprehensively considers user mobility and contribution to enable recommendation awareness. Additionally, an optimized lifecycle is introduced to design a cache replacement strategy. Simulation experiments demonstrate that, compared to caching schemes based on content popularity and deep reinforcement learning, the proposed method achieves lower caching costs and higher cache hit rates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep reinforcement learning

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A Counterfactual Ultrasound Anti-Interference Self-Supervised Network for B-mode Ultrasound Tongue Extraction

A Counterfactual Ultrasound Anti-Interference Self-Supervise...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jia, Yan Cheng, Yuqing Xu, Kele Dou, Yong Qiao, Peng He, Zhouyu National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Systems Engineering National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

B-mode ultrasound tongue imaging is a non-invasive and real-time method for visualizing vocal tract deformation. However, accurately extracting the tongue's surface contour remains a significant challenge due to the low signal-to-noise ratio (SNR) and prevalent speckle noise in ultrasound images. Traditional supervised learning models often require large labeled datasets, which are labor-intensive to produce and susceptible to noise interference. To address these limitations, we present a novel Counterfactual Ultrasound Anti-Interference Self-Supervised Network (CUAI-SSN), which integrates self-supervised learning (SSL) with counterfactual data augmentation, progressively disentangles confounding factors, ensuring that the model generalizes well across varied ultrasound conditions. Our approach leverages causal reasoning to decouple noise from relevant features, enabling the model to learn robust representations that focus on essential tongue structures. By generating counterfactual image-label pairs, our method introduces alternative, noise-independent scenarios that enhance model training. Furthermore, we introduce attention mechanisms to enhance the network's ability to capture fine-grained details even in noisy conditions. Extensive experiments on real ultrasound tongue images demonstrate that CUAI-SSN outperforms existing methods, setting a new benchmark for automated contour extraction in ultrasound tongue imaging. Our code is publicly available at https://***/inexhaustible419/CounterfactualultrasoundAI. © 2025 IEEE.

关键词： contour extraction counterfactual inference self-supervised learning speech production ultrasound image

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Memory Replay with Unlabeled Data for Semi-Supervised Class-Incremental Learning via Temporal Consistency

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Frontiers of Computer Science 2025年第12期19卷 0-0页

作者： Wang, Qiang Xu, Kele Feng, Dawei Ding, Bo Wang, Huaimin National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China

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Graph Structure Learning via Transfer Entropy for Multivariate Time Series Anomaly Detection

Graph Structure Learning via Transfer Entropy for Multivaria...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Mingyu Wang, Yijie Zhou, Xiaohui Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (纸本)9798350368741

Multivariate time series anomaly detection (MTAD) poses a challenge due to temporal and feature dependencies. The critical aspects of enhancing the detection performance lie in accurately capturing the dependencies between variables within the sliding window and effectively leveraging them. Existing studies rely on domain knowledge to pre-set the window size, and overlook the strength of dependencies while calculating direction based on variable similarity. This paper proposes GSLTE, a graph structure learning method for MTAD. GSLTE employs Fast Fourier Transform to conduct iterative segmentation of the whole series, selecting the dominant Fourier frequency as the window size for each subsequence within the minimum interval. GSLTE quantifies the direction and strength of the dependencies based on variable-lag transfer entropy which is achieved through Dynamic Time Warping method to learn asymmetric links between variables. Extensive experiments show that GNN-based MTAD methods applying GSLTE can further improve anomaly detection performance while outperforming state-of-the-art competitors. © 2025 IEEE.

关键词： Anomaly detection Graph structure learning Multivariate time series Window size selection

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PEbfs: Implement High-Performance Breadth-First Search on PEZY-SC3s 24th

PEbfs: Implement High-Performance Breadth-First Search on P...

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24th International Conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Guo, Weihao Wang, Qinglin Liu, Xiaodong Peng, Muchun Yang, Shun Liang, Yaling Shi, Yongzhen Cao, Ligang Liu, Jie Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China Engineering Research Center for National Fundamental Software National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615506

The breadth-first search (BFS) algorithm is a fundamental algorithm in graph theory, and it’s parallelization can significantly improve performance. Therefore, there have been numerous efforts to leverage the powerful parallel computing capabilities of hardware like GPGPU to implement high-performance BFS algorithms. However, the energy efficiency is relatively low due to the high power consumption of the platforms on which the algorithm is adapted to. To deal with these challenges, this paper introduces PEbfs that is a high-performance BFS algorithm based on the PEZY-SC3s efficient processor. We integrated three search algorithms, two algorithm optimization strategies, and a directional optimization scheme into PEbfs. Through multiple evaluations of the performance of PEbfs on the public SNAP dataset, the results demonstrate that the average energy efficiency ratio of PEbfs is higher than that of Enterprise and Tigr, the two most advanced implementations on Nvidia’s GPGPU: It achieves 3.08× the average energy efficiency ratio of Enterprise and 4.53× that of Tigr. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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YOLO-RSOD: Improved YOLO Remote Sensing Object Detection 27th

YOLO-RSOD: Improved YOLO Remote Sensing Object Detection

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Xu, Yang Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031784460

Remote sensing object detection has important application value in fields such as environmental monitoring and resource detection and analysis. However, the current universal object detectors are not very effective in detecting remote sensing objects. To this end, this paper proposes an efficient, low-complexity and anchor-free remote sensing object detection framework YOLO-RSOD based on YOLOv7. First, an additional Tiny Object Head is proposed for better detection of micro-remote sensing objects. The original Head is then replaced with Decoupled Head (DH) to explore the detection potential of the decoupled detection head structure. Then the Explicit Vision Center (EVC) in the Centralized Feature Pyramid Network (CFP) is added to further improve the detection ability of remote sensing objects. Finally, this article also integrates a global attention module (GAM) to find attention areas in dense object scenes. Ablation experiments on the general remote sensing target detection dataset VisDrone2021 demonstrate the effectiveness of several modules introduced in this paper in remote sensing target detection. On the VisDrone2021 data set, YOLO-RSOD can achieve accuracy rates of 30.7% AP50:95 and 51.7% AP50, which are 3.1% and 3.2% higher than the baseline model respectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Object tracking

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False Positive Detection for Text-Based Person Retrieval 21st

False Positive Detection for Text-Based Person Retrieval

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Cao, Yan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9789819601189

Text-based person retrieval (TBPR) is a challenging topic in cross-modal retrieval tasks, aiming to query corresponding person images based on textual descriptions. This task is complicated by noisy correspondences between images and text due to incorrect text annotations and low-quality images. Although many robust noise learning methods have been proposed with satisfactory performance, they tend to directly filter out a portion of the noise in the dataset, which decreases the ability to distinguish the noise as the number of training epochs increases. This paper proposes a noise-robust learning strategy based on false positive detection (FPD). Precisely, FPD consists of two main components: 1) The key-value library (KVL) module utilizes a set of clean samples, assigns confidence weights to each training sample during the training process, and adaptively adjusts the contribution of each sample to reduce the accumulation of noise. 2) To improve the performance of TBPR, this paper designs a local token-based selection method, which utilizes the contribution of each local token to the weights in the self-attention matrix and selects the local tokens with vital information as global representations. Extensive experiments are conducted on three datasets (CUHK-PEDES, ICFG-PEDES, and RSTPReID) to evaluate the performance and robustness of FPD under noisy conditions. The experimental results show that the FPD has better results at high noise ratios, and all the metrics outperform the state-of-the-art method under the RSTPReID dataset with a noise ratio of 80%, where Rank-1 outperforms the best method by 2.28%, 1.64% for mAP, and 1.92% for mINP. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image annotation

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DCI-Net: Remote Sensing Image-Based Object Detector 27th

DCI-Net: Remote Sensing Image-Based Object Detector

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Cui, Quanyue Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Shandong Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Heilongjiang University Harbin150080 China

ISBN: (纸本)9783031781124

Recently the analysis of remotely sensed images has played a vital role in various aspects of research. The current researches ignore the unique prior knowledge in remote sensing images and do not consider exploring the contextual information of the object, while the existence of multi-scale and high image resolution of objects in remote sensing images also affects the accuracy of the object detection task. Based on the above problems, this paper proposes a object detector DCI-Net (Dynamic Context-Aware IoU Network) based on remote sensing images, in which the proposed CASK (Context-Aware Selective Kernel) module can explicitly model the interdependence between the convolutional feature channels. A loss function Pi_IoU is proposed, which adaptively adjusts the penalty factor in combination with the size of the detected object. A DySample module is introduced, which is able to effectively extract and utilize the spatial structure features. The model in this paper improves the detection accuracy of complex objects in remote sensing images. On the DIOR dataset, compared with the baseline model YOLOV9, the accuracy is improved by 0.6%, the number of parameters is decreased by 4%, and the floating point operation speed is improved by 27.8%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical remote sensing

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A Weakly Supervised Semantic Segmentation Model with Enhanced CLIP Feature Extraction

A Weakly Supervised Semantic Segmentation Model with Enhance...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Kong, Fanxuan Lu, Jun College of Computer Science and Technology Heilongjiang University Harbin150080 China Jiaxiang Industrial Technology Research Institute of Heilongjiang University Jining272400 China Key Laboratory of Database and Parallel Computing of Heilongjiang Province Harbin150080 China

ISBN: (纸本)9798350368741

This paper addresses the limitations of the Contrastive Language-Image Pre-training (CLIP) model's image encoder and proposes a segmentation model WSSS-ECFE with enhanced CLIP feature extraction, aiming to improve the performance of the Weakly Supervised Semantic Segmentation (WSSS) task. WSSS-ECFE employs the Enhanced Bottleneck module proposed in this paper and adds dynamic residual connection to improve the model's processing effect on complex scenes. In terms of implementation, the Enhanced Bottleneck module employs the Swish activation function and the Depthwise Separable Convolution to enhance the feature extraction and segmentation capability of the model, and uses multiple attention mechanisms to further optimize the feature representation and segmentation accuracy. The WSSS task on the public datasets PASCAL VOC 2012 and MS COCO 2014 achieves 82.6% and 56.3% mean intersection over union (mIoU), achieving state-of-the-art performance in models with low resource requirements. © 2025 IEEE.

关键词： Attention Mechanism Bottleneck Feature Extraction Residual Connection Weakly Supervised Semantic Segmentation (WSSS)

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Deep anomaly detection with partition contrastive learning for tabular data

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Data Mining and Knowledge Discovery 2025年第4期39卷 1-38页

作者： Li, Yizhou Wang, Yijie Xu, Hongzuo Li, Bin Zhou, Xiaohui National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology Changsha China Intelligent Game and Decision Lab (IGDL) Beijing China

Self-supervised anomaly detection (AD) methods define transformations and surrogate tasks to deeply learn data “normality”, presenting superior performance. Different from most existing work designed for images, this paper considers self-supervised AD for tabular data, which has two desiderata: (i) transformation operations need to generate diverse transformed samples for downstream contrastive learning; (ii) the learning of surrogate tasks is required to perceive the semantics of tabular data, i.e., local–global patterns including intra-instance regularities and inter-instance structures. Related studies devise applicable transformations, but their surrogate tasks often neglect the inter-instance structures, failing to describe comprehensive data “normality" accurately. To fill these gaps, this paper proposes a novel partition contrastive learning-based anomaly detection method. We first devise a new transformation. Transformed samples are created by representing sub-vectors generated from different partitions of the whole feature space, during which different feature couplings are embedded in the transformed samples to make them sufficiently diverse. To capture intra-instance regularities, our approach learns a representation space where the transformed samples repel each other while concurrently resembling their corresponding spatial centers. A constraint is posed on the frames of these spatial centers to preserve their similarities between the corresponding original instances, facilitating the learning of inter-instance structures. The synergy of these two learning objectives encourages the modeling of tabular data semantics, thereby comprehensively modeling data “normality”. Abnormal degrees of the testing data are obtained by evaluating whether they conform to these learned patterns shared in the majority of data. Extensive experiments show that our approach achieves significant improvements (13% AUC-ROC and 30% AUC-PR) over state-of-the-art AD methods.

关键词： Anomaly detection Contrastive learning Data mining Deep learning Local–global contrastive loss Self-supervised learning Subspace partition Tabular data Transformation Unsupervised learning

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