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Design Automation Conference

作者： Guoqi Xie Chenglai Xiong Wei Wu Renfa Li Wanli Chang Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University China

Many embedded applications demand both resource efficiency and timing guarantee. However, resource sharing naturally complicates the analysis that extracts the worst-case scenario out of contention. Global Fixed-Priority (GFP) preemptive multiprocessor scheduling is one of the mainstream strategies to resolve contention on computational resources. It allows jobs of the same task to be executed on different processors, hence potentially enabling better parallelism and more efficient resource utilization. Unfortunately, its worst-case response time (WCRT) analysis is challenging. Existing approaches divide a high-priority task into three workloads, namely, carry-in workload, body workload, and carry-out workload, trying to optimize them individually. In this work, we propose a holistic WCRT analysis for GFP preemptive multiprocessor scheduling, where a task is no longer divided. Specifically, (i) we establish the tight interference scenario for the task being analyzed to find the most interfering high-priority jobs in any time interval; (ii) we obtain the starting released instant of each high-priority task’s first job to determine the maximum interference from high-priority tasks’ first jobs to the task being analyzed; (iii) we build the worst-case tight interference scenario for the task being analyzed by combining the tight interference scenario and the starting released instants; (iv) we prove that the WCRT of the task being analyzed can be decided by the worst-case tight interference scenario. Evaluation on schedulability shows that our proposed analysis achieves 4.2%-8.6% higher acceptance ratio in randomly generated data sets than the state-of-the-art workload division approaches.

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Stacking-Based Attention Temporal Convolutional network for Action Segmentation 48

Stacking-Based Attention Temporal Convolutional Network for ...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Yang, Liu Jiang, Yu Hong, Junkun Wu, Zhenjie Yang, Zhan Long, Jun Central South University School of Computer Science and Engineering China Central South University Big Data Institute Central South University China Central South University Network Resources Management and Trust Evaluation Key Laboratory of Hunan Province Changsha China

ISBN: (纸本)9781728163277

Action segmentation plays an important role in video understanding, which is implemented by frame-wise action classification. Recent works on action segmentation capture long-term dependencies by increasing temporal convolution layers in Temporal Convolution networks (TCNs). However, high layers in TCNs are more coarse access to video features, resulting in the loss of fine-grained information for frame-wise action classification. To address the above issues, we propose a novel Attention-based Temporal Convolution (ATC) block to capture fine-grained information of temporal dependencies for frame-wise action classification by self-attention mechanism. Via stacking ATC blocks, we design a Stacking-based Attention Temporal Convolutional network (SATC) to adaptively capture long-term and short-term dependencies, according to the semantic similarity of features on different temporal receptive fields simultaneously. The experimental results demonstrate that our SATC outperforms other baselines on all three challenging datasets: GTEA, 50Salads and Breakfast. © 2023 IEEE.

关键词： Convolution

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Matrix Factorization Informed Interpretable Deep network for Unregistered Hyperspectral and Multispectral Images Fusion

Matrix Factorization Informed Interpretable Deep Network for...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Tongzhen Zhang Jiahui Qu Yunsong Li Qian Du Wenqian Dong Xidian University State Key Laboratory of Integrated Service Network Xi′an China Mississippi State University Electronic and Computer Engineering Starkwille MS USA

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Considering the existing issues in unregistered hyper-spectral images (HSI) and multispectral images (MSI) fusion methods: i) the designed registration modules introduce a significant computational burden, and registration errors accumulate in fusion errors; ii) the methods lack model guidance, resulting in poor interpretability of the network. In this paper, we propose a matrix factorization informed interpretable deep network to address the challenges of unregistered HSI and MSI fusion (IUFNet). In particular, we derive an extended matrix factorization model for unregistered fusion (EUMF), which substitutes the abundance matrix of HSI containing low-resolution and distorted spatial information by the high-resolution abundance matrix of MSI. This substitution ingeniously eliminates the dependence of fusion performance on registration accuracy. Subsequently, IUFNet is designed to unfold the iterative results obtained by proximal gradient descent into the deep learning network, where each operation has a clear physical meaning. Overall, this network achieves the fusion of unregistered HSI and MSI and exhibits inter-pretability. Experimental results on the widely used Paiva Center dataset demonstrate the effectiveness and superiority of the proposed method.

关键词： Deep learning Accuracy Computational modeling Geoscience and remote sensing Matrices Iterative methods Image fusion

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Transmission Energy Allocation for Over-the-Air Computation with Energy Harvesting

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Journal of Communications and Information networks 2024年第2期9卷 126-136页

作者： Siyao Zhang Zixiang Ren Xinmin Li Yin Long Jie Xu Shuguang Cui School of Computer Science and Technology Southwest University of Science and TechnologyMianyang 621010China Shenzhen Future Network of Intelligence Institute(FNii-Shenzhen) The Chinese University of Hong KongShenzhenGuangdong 518172China School of Information Science and Technology University of Science and Technology of ChinaHefei 230027China FNii-Shenzhen The Chinese University of Hong KongShenzhenGuangdong 518172China College of Computer Science Chengdu UniversityChengdu 610100China School of Science and Engineering FNii-Shenzhenand Guangdong Provincial Key Laboratory of Future Networks of IntelligenceThe Chinese University of Hong KongShenzhenGuangdong 518172China

Over-the-air computation(AirComp)has recently emerged as a promising multiple-access technique for fast wireless data aggregation(WDA)from distributed wireless devices(WDs). This paper investigates an energy harvesting (EH) AirComp system, in which multiple EH-powered single-antenna WDs simultaneously send wireless signals to a single-antenna access point (AP) with conventional energy supply for WDA via AirComp. Under this setup, we minimize the average computation mean square error(MSE)over a particular time period, by jointly optimizing the transmit energy allocation at the WDs and the AirComp denoising factors at the AP over time, subject to the energy causality constraints at individual WDs. First, we consider the offline scenario by assuming that the energy state information(ESI)and channel state information (CSI) are non-causally known at the beginning of the period, in which the formulated average MSE minimization corresponds to a non-convex optimization problem. We present a high-quality converged solution by using the techniques of alternating optimization and convex optimization. It is shown that for each WD,if the EH rate is sufficiently high,then the channel inversion power allocation is adopted;while if the EH rate is low, then all the harvested energy should be used up for transmission with proper energy allocation over time. Next, we consider the online scenario with causal ESI and CSI,in which the MSE minimization becomes a stochastic optimization *** this scenario, we present an offline-inspired online algorithm to obtain efficient online energy allocation designs by utilizing the obtained offline solutions. Finally,numerical results show that the proposed designs significantly outperform two benchmark schemes with power-halving and full-power transmission,respectively.

关键词： over-the-air computation(AirComp) power allocation energy harvesting(EH) optimization

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Tensor Based Sparse Estimation for RIS-Aided Massive MIMO 23

Tensor Based Sparse Estimation for RIS-Aided Massive MIMO

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Li, Weilai Wei, Li Huang, Chongwen Yang, Jun Yuen, Chau Zhang, Zhaoyang School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China School of Electrical and Electronics Engineering Nanyang Technological University Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zte Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen China

ISBN: (纸本)9798350325959

Reconfigurable intelligent surface (RIS) is intended as a potential technique for future 6-th Generation wireless communication system. However, acquiring accurate channel state information is challenging since the channel matrices are high-dimensional due to large number of reflective elements (REs) in RIS. In this paper, we investigate the problem of sparse channel estimation (CE) in massive multiple-input multiple-output (MIMO) system equipped with large RIS (IRIS), where there are only few REs that are active during CE phase. We propose a two-stage tensor based CE algorithm that exploits the merits of IRIS. Specifically, IRIS act as reflector in the first stage of CE. Then, we identify the condition for the IRIS to activate few specified REs in the second stage. Simulation results demonstrate that the proposed algorithm outperforms existing CE schemes. © 2023 IEEE.

关键词： massive MIMO RIS Sparse channel estimation tensor decomposition

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3D visualization and detection of glomeruli in whole mouse kidney 16

3D visualization and detection of glomeruli in whole mouse k...

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16th International Conference on Photonics and Imaging in Biology and Medicine, PIBM 2023

作者： Li, Yuxin Cao, Jia Li, Anan Li, Xiangning Feng, Zhao Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xi an University of Technology Xi an710048 China Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics MoE Key Laboratory for Biomedical Photonics Huazhong University of Science and Technology Wuhan430074 China HUST-Suzhou Institute for Brainsmatics Suzhou215123 China

ISBN: (纸本)9781510667341

The kidney is an important organ in the body to excrete metabolic waste, and the glomerulus is an essential structure for the kidney to play a role in blood filtration. Abnormal glomerular numbers are associated with nephropathy or circulatory disease. With the development of imaging technology, mesoscopic optical imaging can obtain whole kidney images at single cell resolution. The detection of glomeruli from images is very crucial for understanding the renal function and studying nephropathy. Existing detection methods cannot balance both accuracy and efficiency, so we proposed a deep learning-based glomeruli detection method. First, we imaged an entire mouse kidney with High-Definition fluorescent Micro-Optical Sectioning Tomography (HD-fMOST) and obtained a three-dimensional (3D) kidney image at cellular resolution. Then, we designed an end-To-end 3D convolutional neural network based on the morphological features of glomeruli in the kidney image, which can directly read 3D images and predict the precise coordinates of glomeruli. We used the acquired kidney dataset to train the network and validated the effect of glomeruli detection. Finally, we applied our approach to detect glomeruli in large-scale mouse kidney. The results showed that the proposed method reached the state-of-The-Art level, which is more efficient and accurate compared to similar methods. The proposed method will provide a powerful tool for kidney-related research. © 2023 SPIE.

关键词： Convolutional neural networks

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Evolutionary Classifier Chain for Multi-Dimensional Classification 39

Evolutionary Classifier Chain for Multi-Dimensional Classifi...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Yu-Yang Jia, Bin-Bin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China College of Electrical and Information Engineering Lanzhou University of Technology Lanzhou730050 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

ISBN: (纸本)157735897X

In multi-dimensional classification (MDC), the classifier chain approach is based on a chain structure to model dependencies between class spaces. However, current research on constructing a chain order is usually based on a greedy criterion or random generation, which is highly likely to lead to an incorrect chain order and fit incorrect class dependencies. Moreover, existing classifier chain-based approaches do not consider the misleading effects of irrelevant input features on the classifiers. To fill the above gap, a classifier chain-based approach incorporating evolutionary chain order optimization and feature selection (ECCO) is proposed. Specifically, this approach designs a meta-heuristic algorithm to optimize the chain order of multiple classifiers. Simultaneously, the approach selects dimension-specific feature combinations that are more conducive to class prediction of each dimension. These strategies enhance the class prediction capability of the constructed MDC model. Comparative experiments on 14 real datasets validate that ECCO outperforms 7 state-of-the-art MDC approaches. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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UW-Adapter: Adapting Monocular Depth Estimation Model in Underwater Scenes

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IEEE Transactions on Multimedia 2025年

作者： Ye, Xinchen Chang, Yue Xu, Rui Li, Haojie DUT-RU International School of Information Science & Engineering Dalian University of Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Liaoning China Shandong University of Science and Technology College of Computer Science and Engineering China

Estimating depth maps from monocular underwater images poses one of the most challenging problems in underwater applications. Due to the lack of large-scale paired underwater color-depth datasets for effective training, existing style transfer-based and self-supervision-based approaches can improve the performance of depth estimation to some extent, but they remain unsatisfactory. Leveraging the power of massive training datasets, foundation models designed for terrestrial monocular depth estimation have demonstrated superior performance across various scenes. These models provide rich prior knowledge of 3D perception, which can be valuable for underwater depth estimation. Upon this, we introduce tunable adapters (UW-Adapter) that tailor a pre-trained foundation model specifically for underwater depth estimation, customizing it to the unique characteristics of underwater imagery. Our approach involves freezing the parameters of the pre-trained model and updating only the adapters through self-supervision. To address the complex degradation of underwater images, we propose two adapters: the transmission adapter and the high-frequency adapter. These adapters incorporate depth clues and high-frequency information as prior knowledge, thereby enhancing the performance of pre-trained model in underwater depth estimation. Experimental results demonstrate that by integrating lightweight adapters into off-the-shelf depth estimation foundation models, our method achieves superior performance across multiple datasets. © 2025 IEEE.

关键词： Underwater photography

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Partial multi-label learning with probabilistic graphical disambiguation 23

Partial multi-label learning with probabilistic graphical di...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Jun-Yi Hang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China

In partial multi-label learning (PML), each training example is associated with a set of candidate labels, among which only some labels are valid. As a common strategy to tackle PML problem, disambiguation aims to recover the ground-truth labeling information from such inaccurate annotations. However, existing approaches mainly rely on heuristics or ad-hoc rules to disambiguate candidate labels, which may not be universal enough in complicated real-world scenarios. To provide a principled way for disambiguation, we make a first attempt to explore the probabilistic graphical model for PML problem, where a directed graph is tailored to infer latent ground-truth labeling information from the generative process of partial multi-label data. Under the framework of stochastic gradient variational Bayes, a unified variational lower bound is derived for this graphical model, which is further relaxed probabilistically so that the desired prediction model can be induced with simultaneously identified ground-truth labeling information. Comprehensive experiments on multiple synthetic and real-world data sets show that our approach outperforms the state-of-the-art counterparts.

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UAD-DPL: An Unknown Encrypted Attack Detection Method Based on Deep Prototype Learning 27th

UAD-DPL: An Unknown Encrypted Attack Detection Method Based ...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Chen, Liang-Chen Gao, Shu Liu, Bao-Xu Zhang, Xu-Yao School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan430063 China Institute of Automation NLPR Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Computer China University of Labor Relations Beijing100048 China Key Laboratory of Network Assessment Technology Chinese Academy of Sciences Beijing100093 China

ISBN: (纸本)9783031781681

In the field of cybersecurity, a significant concern is the potential for unknown attacks to occur at any moment. The primary obstacle faced by intrusion detection systems lies in the identification of unknown attacks and the differentiation of such threats from benign network traffic and known attack patterns. Many current deep learning approaches struggle to discern the underlying features of these new attack types, especially in the case of encrypted attacks, resulting in inherent inaccuracies. To tackle these challenges, a novel unknown attack detection method based on deep prototype learning (UAD-DPL) is proposed to improve the accuracy of unknown encrypted attack detection. Firstly, a spatiotemporal fusion feature extraction network for encrypted traffic is employed to enhance the feature representation ability. Subsequently, an innovative prototype-based deep feature space learning model is proposed, which utilizes discriminative loss and open loss training to ensure that the learned encrypted traffic features meet the predetermined prior distribution. Finally, an unknown attack recognition approach is designed to effectively identify both known and unknown attacks, and a three-stage training strategy is proposed to train the model. Experimental results indicate that the UAD-DPL method is highly proficient in detecting both known and unknown attacks in encrypted traffic. Moreover, UAD-DPL exhibits promising application potential in open-set network intrusion detection systems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： network intrusion

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