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RGBT tracking via reliable feature configuration

science China(Information sciences) 2022年第4期65卷 128-140页

作者： Zhengzheng TU Wenli PAN Yunsheng DUAN Jin TANG Chenglong LI Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Network Information Center Anhui University

There is an increasing interest in RGBT tracking recently because of the complementary benefits of RGB and thermal infrared data. However, the reliability of each modality will change over time quite possibly, and the modality with bad reliability will disturb tracking performance. Thus, we propose a novel reliability-based feature configuration approach in the correlation filter framework for robust RGBT tracking. Specifically, we configure a feature set based on RGB, thermal, and RGBT data. To measure the reliabilities of different feature configurations, we equip each feature configuration with a tracker and design a guideline judging whether the tracker is reliable. We use the tracker with the best reliability for *** results show that the proposed tracker achieves promising performance against other RGBT tracking methods.

关键词： RGBT tracking multi-modal reliability guideline feature configuration correlation filter

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Short-term Power Load Forecasting based on Feature Fusion of Parallel LSTM-CNN 4

Short-term Power Load Forecasting based on Feature Fusion of...

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4th IEEE International Conference on Power, Intelligent Computing and Systems, ICPICS 2022

作者： Li, Cheng Hu, Rong Hsu, Chih-Yu Han, Yu Fujian University of Technology School of Computer Science and Mathematics Fuzhou China Fujian University of Technology Fujian Provincial Key Laboratory of Automotive Electronics and Electric Drive Fuzhou China Fujian University of Technology School of Transportation Fuzhou China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

Accurate power load forecasting is beneficial to grid scheduling and operation. In order to fully exploit the potential relationship between load and feature. This research suggests a short-term power load forecasting strategy based on feature fusion to increase prediction accuracy, which combines parallel LSTM and CNN. The model first determines the optimal features of the model input through the random forest method and mutual information method, then classifies the features, and inputs the classified categories into the parallel LSTM modules respectively, and then uses the CNN network to output the neural network of different LSTM modules at the same time. The elements are convolved to extract the features of each moment, finally get the prediction result through the fully connected layer. The results of two short-term power load forecasting cases show that the proposed method's prediction effect outclasses existing methods. with model evaluation parameter MAPE of 1.367% and 0.974% respectively. © 2022 IEEE.

关键词： Forecasting

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Research on Real-Time Linux Task Scheduling Algorithm for Multiprocessors Based on Particle Swarm Optimisation 9

Research on Real-Time Linux Task Scheduling Algorithm for Mu...

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9th International Conference on computer and Communications, ICCC 2023

作者： Qiu, Tong Yin, Zhenyu Wang, Jun Wang, Zisong School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic HardwareSoftware Shenyang China Liaoning Provincial Key Laboratory of Industrial Intelligent Technology for Chemical Processes Shenyang China Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798350317251

Schedulability analysis in real-time operating systems is one of the hot research topics in the field. While Linux has become the preferred choice for industrial systems due to its excellent portability, it cannot meet the hard real-time requirements of specific industrial environments as a time-sharing operating system. To address this issue, we introduce the Real-Time Application Interface (RTAI) 5.2 real-time operating system kernel patch and combine it with Linux 4.4.176 to form the RTAI-Linux dual-core real-time system. Building upon this foundation, we proposes a Particle Swarm Optimization algorithm based on Earliest Deadline First (PSO-EDF) to improve task scheduling in real-time Linux multiprocessor environments. This method introduces a metric for evaluating processor utilization, when a new task arrives, it considers the allocation of free processor resources. Compared with the original method EDF, PSO-EDF provides a broader search space with better global characteristics. The algorithm finds the globally optimal scheduling scheme through iteration, which effectively improves the processor resource utilisation and alleviates the task deadline missing problem encountered in the original method to a certain extent, thus improving the real-time task completion rate. Through extensive simulation experiments, the PSO-EDF scheduling algorithm proves that the scheduling model can further improve the real-time performance of the RTAI-Linux system compared with the classical EDF algorithm. © 2023 IEEE.

关键词： Linux

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Few-shot Open Relation Extraction with Gaussian Prototype and Adaptive Margin

arXiv

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arXiv 2024年

作者： Guo, Tianlin Zhang, Lingling Wang, Jiaxin Lei, Yunkuo Li, Yifei Wang, Haofen Liu, Jun School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China School of Computer Science and Technology Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi’an710049 China College of Design and Innovation Tongji University Shanghai200092 China

Few-shot relation extraction with none-of-the-above (FsRE with NOTA) aims at predicting labels in few-shot scenarios with unknown classes. FsRE with NOTA is more challenging than the conventional few-shot relation extraction task, since the boundaries of unknown classes are complex and difficult to learn. Meta-learning based methods, especially prototype-based methods, are the mainstream solutions to this task. They obtain the classification boundary by learning the sample distribution of each class. However, their performance is limited because few-shot overfitting and NOTA boundary confusion lead to misclassification between known and unknown classes. To this end, we propose a novel framework based on Gaussian prototype and adaptive margin named GPAM for FsRE with NOTA, which includes three modules, semi-factual representation, GMM-prototype metric learning and decision boundary learning. The first two modules obtain better representations to solve the few-shot problem through debiased information enhancement and Gaussian space distance measurement. The third module learns more accurate classification boundaries and prototypes through adaptive margin and negative sampling. In the training procedure of GPAM, we use contrastive learning loss to comprehensively consider the effects of range and margin on the classification of known and unknown classes to ensure the model's stability and robustness. Sufficient experiments and ablations on the FewRel dataset show that GPAM surpasses previous prototype methods and achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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From Entanglement Purification Scheduling to Fidelity-Constrained Entanglement Routing

From Entanglement Purification Scheduling to Fidelity-Constr...

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International Conference on Network Protocols

作者： Ziyue Jia Lin Chen School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Provincial Key Laboratory of Information Security Technology Guangzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

Recently emerged as a disruptive networking paradigm, quantum networks rely on the mysterious quantum entanglement to teleport qubits without physically transferring quantum particles. However, the state of quantum systems is extremely fragile due to environment noise. A promising technique to combat against quantum decoherence is entanglement purification. To fully exploit its benefit, two fundamental research questions need to be answered: (1) given an entanglement path, what is the optimal entanglement purification schedule? (2) how to compute min-cost end-to-end entanglement paths subject to fidelity constraint? In this paper, we give algorithmic solutions to both questions. For the first question, we develop an optimal entanglement purification scheduling algorithm for the singlehop case and analyze the PURIFY-AND-SWAP strategy in the multi-hop case by establishing the closed-form condition for its optimality. For the second question, we design a polynomialtime algorithm constructing an $\epsilon$ -optimal fidelity-constrained path. The effectiveness of our algorithms are also numerically demonstrated by extensive simulations.

关键词： Schedules Quantum system Protocols Purification Quantum entanglement Scheduling algorithms Qubit Routing Quantum decoherence Quantum networks

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An Ensemble-based Joint Training Framework for Robust Anomaly Detection

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IEEE Access 2025年

作者： Zhang, Jia Xie, Junde Chen, Shaomiao School of Computer and Mathematics Central South University of Forestry and Technology Changsha410014 China Hunan Provincial Key Laboratory of Geochemical Processes and Resource Environmental Effects Geophysical and Geochemical Survey Institute of Hunan Changsha410014 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

Anomaly detection has been widely studied in many domains where abnormal samples with more valuable information than normal samples are of interest to researchers. Numerous methods have been developed for detecting anomalies, with one-class classification techniques being especially popular due to their ability to train using only normal samples. However, the presence of anomalies in the unlabeled data may negatively impact the accuracy of the model. Although some studies have attempted to improve robustness by removing potential anomalies or assigning lower weights to them during data preprocessing, these approaches are limited in effectiveness. This limitation arises because sample preprocessing is conducted independently of anomaly scoring, leading to the potential misclassification of anomalies as normal samples or their assignment of high weights, ultimately resulting in suboptimal detection performance. In this paper, an ensemble-based joint training framework is proposed for anomaly detection, called E-ORAD, to enhance robustness. E-ORAD jointly optimizes sample weights and anomaly scores within a unified ensemble model. Specifically, to compute the weight of each sample, a prior knowledge-based regularization term is incorporated into the objective function. Additionally, an anomaly score-based hinge loss function is developed to ensure that abnormal samples are assigned higher scores than normal samples. An alternating iteration method is then introduced to solve the proposed model. Experimental results on 10 real-world datasets demonstrate that E-ORAD outperforms other state-of-the-art methods in terms of detection performance. © 2024 IEEE.

关键词： Anomaly detection Joint training One-class classification Robustness

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Medical Image Segmentation Based on 3d Pdc with Swin Transformer

SSRN

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SSRN 2025年

作者： Fan, Lin Ding, Xiaojia Wang, Zhongmin Wang, Hai Zhang, Rong School of Computer Science and Technology Xi’an University of Posts and Telecommunications Shaanxi Xi’an710121 China Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Shaanxi Xi’an710121 China Xi’an Key Laboratory of Big Data and Intelligent Computing Shaanxi Xi’an710121 China The School of Information Science and Technology Northwest University Shaanxi Xi’an710121 China

3D medical image segmentation is vital for disease diagnosis and effective treatment strategies. Despite the advancements in Convolutional Neural networks (CNN), their fixed receptive fields constrain global context modeling, leading to suboptimal performance, particularly with complex shapes and multi-scale variations. Transformer enhances the global modeling capability through self-attention mechanism. However, challenges persist in capturing fine-grained local features and extracting edge features. To overcome these issues, In this study, a dual-path encoder is proposed, which consists of a 3D PDC and a Swin Transformer and is combined with a CNN for the decoder. The 3D PDC module extracts local features by calculating the differences between neighboring pixels. To improve the ability of capturing edge structures, three 3D PDC variants (3D CPDC, 3D APDC, and 3D RPDC) are proposed to optimize the processing ability of complex edges, multi-directional edges, and multi-scale structures, respectively. After performance evaluation, the selected CARV combinations (3D CPDC, 3D APDC, 3D RPDC, and 3D ordinary convolution) achieve a compromise between high segmentation accuracy and low computational cost. The Swin Transformer encoder captures global context using a hierarchical shift-window mechanism, while the CNN decoder fuses features progressively to produce precise pixel-level segmentation. The proposed method exceeds the performance of existing state-of-the-art techniques, as shown by experiments conducted on the BTCV, FLARE21, and AMOS22 datasets. © 2025, The Authors. All rights reserved.

关键词： Image segmentation

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FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide Image Classification 38

FAST: A Dual-tier Few-Shot Learning Paradigm for Whole Slide...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Fu, Kexue Luo, Xiaoyuan Qu, Linhao Wang, Shuo Xiong, Ying Maglogiannis, Ilias Gao, Longxiang Wang, Manning Qilu University of Technology Shandong Academy of Sciences Jinan China Digital Medical Research Center School of Basic Medical Sciences Fudan University China Shanghai Key Lab of Medical Image Computing and Computer Assisted Intervention China Fudan University China University of Piraeus Greece Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

The expensive fine-grained annotation and data scarcity have become the primary obstacles for the widespread adoption of deep learning-based Whole Slide Images (WSI) classification algorithms in clinical practice. Unlike few-shot learning methods in natural images that can leverage the labels of each image, existing few-shot WSI classification methods only utilize a small number of fine-grained labels or weakly supervised slide labels for training in order to avoid expensive fine-grained annotation. They lack sufficient mining of available WSIs, severely limiting WSI classification performance. To address the above issues, we propose a novel and efficient dual-tier few-shot learning paradigm for WSI classification, named FAST. FAST consists of a dual-level annotation strategy and a dual-branch classification framework. Firstly, to avoid expensive fine-grained annotation, we collect a very small number of WSIs at the slide level, and annotate an extremely small number of patches. Then, to fully mining the available WSIs, we use all the patches and available patch labels to build a cache branch, which utilizes the labeled patches to learn the labels of unlabeled patches and through knowledge retrieval for patch classification. In addition to the cache branch, we also construct a prior branch that includes learnable prompt vectors, using the text encoder of visual-language models for patch classification. Finally, we integrate the results from both branches to achieve WSI classification. Extensive experiments on binary and multi-class datasets demonstrate that our proposed method significantly surpasses existing few-shot classification methods and approaches the accuracy of fully supervised methods with only 0.22% annotation costs. All codes and models will be publicly available on https://***/fukexue/FAST. © 2024 Neural information processing systems foundation. All rights reserved.

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DPCA: Dynamic Probability Calibration Algorithm in LMaaS

DPCA: Dynamic Probability Calibration Algorithm in LMaaS

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Deng, Zhongyi Chen, C.L. Philip Zhang, Tong the Guangdong Provincial Key Laboratory of AI Large Model and Intelligent Cognition the School of Computer Science and Engineering South China University of Technology Guangzhou 510006 China and is with the Pazhou Lab Guangzhou 510335 China and is with Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

ISBN: (纸本)9781665410205

Probability calibration is a method to improve the reliability of models by linking the predicted probability to accuracy. Most research follow a static strategy of full fine-tuning. These studies do not consider dynamic data and sparse parameters in Language Models as a Service(LMaaS), leading to limited effectiveness of probability calibration. To address above issues, we propose a dynamic probability calibration algorithm (DPCA) to consider both data flow and parameter freezing. DPCA consists of streaming annotation (SA) task and dynamic calibration (DC) task. The SA task takes a specified number of samples from the training data stream. The sampled data is automatically annotated according to the deviation between predicted probability and true label. The DC task injects the probability deviation of the SA task into next training epoch through adapter-tuning. DPCA achieves data augmentation in LMaaS through joint learning of sample labels and their predicted probability deviation. This work validates DPCA through the implementation on BERT architecutre. The proposed model achieves overall performance improvement on both Chinese and English NLP tasks. Experimental results demonstrate a 2.17% decrease in average ECE without de-creasing in accuracy. Experimental analysis demonstrate the effectiveness and generalizability of DPCA in LMaaS. © 2024 IEEE.

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DHFusion: Deep Hypergraph Convolutional Network for Multi-Modal Physiological Signals Fusion

DHFusion: Deep Hypergraph Convolutional Network for Multi-Mo...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Shen, Yuanhang Chen, C.L. Philip Zhang, Tong School of Computer Science and Engineering South China University of Technology Guangdong Provincial Key Laboratory of Ai Large Model and Intelligent Cognition Guangzhou510006 China Pazhou Lab Guangzhou510335 China Engineering Research Center of the Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Guangzhou China

ISBN: (纸本)9781665410205

Multi-modal physiological signals fusion integrates multiple heterogeneous physiological signals to characterize human physiological activities, which is basic research on biomedical signal processing. Most researches on multi-modal physiological signal fusion are based on heterogeneous graph fusion networks (HGFNs) while they ignore the high-order correlations between multi-modal physiological signals. Moreover, most HGFNs suffer from the over-smoothing problem, causing failures in deeper networks. To address these issues, this paper proposes a deep hypergraph convolutional network called DHFusion for multi-modal physiological signal fusion. Specially, DHFusion designs deep hypergraph convolution (DHGCN) layer to effectively integrate multiple physiological signals and obtain multi-modal features. DHFusion then introduces JK readout layer to enable multi-layer DHGCN to capture deep multi-modal features based on high-order correlations. DHFusion effectively solves the over-smoothing of HGFNs and performs well in representing deep multi-modal features. Experimental results over the state-of-the-art methods on two benchmark datasets demonstrate the effectiveness of the proposed method. © 2024 IEEE.

关键词： Biomedical signal processing

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