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Against Mobile Collusive Eavesdroppers: Cooperative Secure Transmission and Computation in UAV-Assisted MEC Networks

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IEEE Transactions on Mobile Computing 2025年第6期24卷 5280-5297页

作者： Zhao, Mingxiong Wang, Zirui Guo, Kun Zhang, Rongqian Quek, Tony Q. S. Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China Yunnan University Yunnan Key Laboratory of Software Engineering Kunming650091 China East China Normal University Shanghai Key Laboratory of Multidimensional Information Processing School of Communications and Electronics Engineering Shanghai200241 China Yunnan University School of Information Science and Engineering Kunming650500 China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore National Pilot School of Software Yunnan University Kunming China

In Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) networks, the security of transmission faces significant challenges due to the vulnerabilities of line-of-sight links and potential eavesdropping on two-hop links. This paper addresses these challenges with an innovative Cooperative Secure Transmission and Computation strategy (CSTC), specifically engineered for time-slotted UAV-assisted MEC networks plagued by mobile collusive eavesdroppers. These eavesdroppers significantly bolster their interception capabilities through coordinated and optimized movements, escalating the security threats. To neutralize these risks, the proposed CSTC employs the UAV and remote devices as helper nodes to emit jamming signals, thereby thwarting eavesdropping activities, while simultaneously facilitating the efficient relay of users' tasks to the base station for advanced processing. The CSTC aims to maximize the sum Secrecy Transmission Rate (STR) satisfying task latency constraints. It involves a joint optimization of UAV trajectory, jamming beamformers, transmit power, and data offloading strategy to expedite task transmission. Additionally, a real-time computation scheduling approach is developed based on a newly defined metric, the Urgency Degree of Users (UDoU), to enhance task processing efficiency. Our extensive simulations validate that the CSTC not only elevates the sum STR but also consistently meets latency constraints, demonstrating its robustness against advanced mobile eavesdropping techniques. © 2002-2012 IEEE.

关键词： Jamming

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Segment Together: A Versatile Paradigm for Semi-Supervised Medical Image Segmentation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Zeng, Qingjie Xie, Yutong Lu, Zilin Lu, Mengkang Wu, Yicheng Xia, Yong Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Xi’an710072 China The University of Adelaide Australian Institute for Machine Learning AdelaideSA5000 Australia Monash University Faculty of Information Technology Department of Data Science and AI Australia

The scarcity of annotations has become a significant obstacle in training powerful deep-learning models for medical image segmentation, limiting their clinical application. To overcome this, semi-supervised learning that leverages abundant unlabeled data is highly desirable to enhance model training. However, most existing works still focus on specific medical tasks and underestimate the potential of learning across diverse tasks and datasets. In this paper, we propose a Versatile Semi-supervised framework (VerSemi) to present a new perspective that integrates various SSL tasks into a unified model with an extensive label space, exploiting more unlabeled data for semi-supervised medical image segmentation. Specifically, we introduce a dynamic task-prompted design to segment various targets from different datasets. Next, this unified model is used to identify the foreground regions from all labeled data, capturing cross-dataset semantics. Particularly, we create a synthetic task with a CutMix strategy to augment foreground targets within the expanded label space. To effectively utilize unlabeled data, we introduce a consistency constraint that aligns aggregated predictions from various tasks with those from the synthetic task, further guiding the model to accurately segment foreground regions during training. We evaluated our VerSemi framework against seven established SSL methods on four public benchmarking datasets. Our results suggest that VerSemi consistently outperforms all competing methods, beating the second-best method with a 2.69% average Dice gain on four datasets and setting a new state of the art for semi-supervised medical image segmentation. Code is available at https://***/maxwell0027/VerSemi. © 1982-2012 IEEE.

关键词： Self-supervised learning

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Few-Shot learning with Complex-valued Neural Networks 31

Few-Shot Learning with Complex-valued Neural Networks

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31st British Machine Vision Conference, BMVC 2020

作者： Liu, Zhen Zhang, Baochang Guo, Guodong Beihang University Beijing China Institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application China

Feature representation is fundamental and attracts much attention in few-shot learning. Convolutional neural networks (CNNs) are among the best feature extractors so far in this field, which are successfully combined with metric learning, leading to the state-of-the-art performance. However, the subtle difference among inter-class samples challenges existing CNN based methods, which only use real-valued CNNs that fail to extract more detailed information. In this paper, we introduce complex metric module (CMM) into metric learning, aiming to better measure the inter- and intra-class relations based on both amplitude and phase information. Specifically, building upon the recent episodic training mechanism, our CMM can enhance the representation capacity by extracting robust complex-valued features to facilitate modeling subtle relationships among samples, which can enhance the performance of the few-shot classification task when only few samples are available. Moreover, we introduce a new transductive method into CMM, by considering not only query and support but also query and query relationships to predict classes of unlabeled samples. Experiments on two benchmark datasets show that the proposed CMM significantly improves the performance over other approaches and achieves the state-of-the-art results. © 2020. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： Benchmarking

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POEM: 1-bit Point-wise Operations based on Expectation-Maximization for Efficient Point Cloud Processing 32

POEM: 1-bit Point-wise Operations based on Expectation-Maxim...

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32nd British Machine Vision Conference, BMVC 2021

作者： Xu, Sheng Li, Yanjing Zhao, Junhe Zhang, Baochang Guo, Guodong Beihang University Beijing China National Engineering Laboratory for Deep Learning Technology and Application Institute of Deep Learning Baidu Research Beijing China

Real-time point cloud processing is fundamental for lots of computer vision tasks, while still challenged by the computational problem on resource-limited edge devices. To address this issue, we implement XNOR-Net-based binary neural networks (BNNs) for an efficient point cloud processing, but its performance is severely suffered due to two main drawbacks, Gaussian-distributed weights and non-learnable scale factor. In this paper, we introduce point-wise operations based on Expectation-Maximization (POEM) into BNNs for efficient point cloud processing. The EM algorithm can efficiently constrain weights for a robust bi-modal distribution. We lead a well-designed reconstruction loss to calculate learnable scale factors to enhance the representation capacity of 1-bit fully-connected (Bi-FC) layers. Extensive experiments demonstrate that our POEM surpasses existing the state-of-the-art binary point cloud networks by a significant margin, up to 6.7%. © 2021. The copyright of this document resides with its authors.

关键词： Computer vision

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A new method of region embedding for text classification 6

A new method of region embedding for text classification

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6th International Conference on learning Representations, ICLR 2018

作者： Qiao, Chao Huang, Bo Niu, Guocheng Li, Daren Dong, Daxiang He, Wei Yu, Dianhai Wu, Hua Baidu Inc. Beijing China National Engineering Laboratory of Deep Learning Technology and Application China

To represent a text as a bag of properly identified "phrases" and use the representation for processing the text is proved to be useful. The key question here is how to identify the phrases and represent them. The traditional method of utilizing n-grams can be regarded as an approximation of the approach. Such a method can suffer from data sparsity, however, particularly when the length of n-gram is large. In this paper, we propose a new method of learning and utilizing task-specific distributed representations of n-grams, referred to as "region embeddings". Without loss of generality we address text classification. We specifically propose two models for region embeddings. In our models, the representation of a word has two parts, the embedding of the word itself, and a weighting matrix to interact with the local context, referred to as local context unit. The region embeddings are learned and used in the classification task, as parameters of the neural network classifier. Experimental results show that our proposed method outperforms existing methods in text classification on several benchmark datasets. The results also indicate that our method can indeed capture the salient phrasal expressions in the texts. © learning Representations, ICLR 2018 - Conference Track *** right reserved.

关键词： Embeddings

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SFedXL: Semi-Synchronous Federated learning With Cross-Sharpness and Layer-Freezing

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IEEE Internet of Things Journal 2025年

作者： Zhao, Mingxiong Zhao, Shihao Feng, Chenyuan Yang, Howard H. Niyato, Dusit Quek, Tony Q. S. Yunnan University National Pilot School of Software Kunming650500 China Ministry of Education Engineering Research Center of Integration and Application of Digital Learning Technology Beijing100039 China Ministry of Education Engineering Research Center of Cyberspace Kunming650504 China Yunnan University of Finance and Economics Yunnan Key Laboratory of Service Computing Kunming650221 China EURECOM Sophia Antipolis06410 France Zhejiang University University of Illinois Urbana-Champaign Institute Zhejiang University Haining314400 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Singapore University of Technology and Design Information Systems Technology and Design Pillar 487372 Singapore

Federated learning (FL) emerges as a potential solution for enabling multiple terminal devices to collaboratively accomplish computational tasks within an Unmanned Aerial Vehicle (UAV) swarm. However, traditional FL approaches, predicated on synchronous data aggregation, are not feasible for a UAV swarm owing to the inherently variable and dynamic nature of their communication networks compared with terrestrial systems. Furthermore, the data procured by UAVs is often highly heterogeneous, attributable to disparities in deployment environments and device attributes. Considering the distinct flight paths and unique operational conditions encountered by different UAVs, a considerable amount of data remains unlabeled. To tackle the challenges associated with asynchronous operations and the prevalence of unlabeled data, we introduce a novel framework termed Semi-synchronous FL with Cross-Sharpness and Layer-Freezing (SFedXL), tailored for a UAV swarm. In particular, we devise a cross-sharpness model training strategy aimed at optimizing the utilization of both labeled and unlabeled datasets. Additionally, we propose an innovative semi-synchronous model aggregation protocol, complemented by client-specific layer-freezing and client cluster scheduling, designed to expedite the training process. Our simulation results indicate that the proposed algorithm surpasses current FL methods in terms of object recognition accuracy and communication efficiency, albeit with a trade-off of increased local computation latency. © 2014 IEEE.

关键词： Federated learning

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GINet: Graph Interaction Network for Scene Parsing 1

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16th European Conference on Computer Vision, ECCV 2020

作者： Wu, Tianyi Lu, Yu Zhu, Yu Zhang, Chuang Wu, Ming Ma, Zhanyu Guo, Guodong Institute of Deep Learning Baidu Research Beijing China National Engineering Laboratory for Deep Learning Technology and Application Beijing China Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9783030585204

ISBN: (纸本)9783030585198

Recently, context reasoning using image regions beyond local convolution has shown great potential for scene parsing. In this work, we explore how to incorperate the linguistic knowledge to promote context reasoning over image regions by proposing a Graph Interaction unit (GI unit) and a Semantic Context Loss (SC-loss). The GI unit is capable of enhancing feature representations of convolution networks over high-level semantics and learning the semantic coherency adaptively to each sample. Specifically, the dataset-based linguistic knowledge is first incorporated in the GI unit to promote context reasoning over the visual graph, then the evolved representations of the visual graph are mapped to each local representation to enhance the discriminated capability for scene parsing. GI unit is further improved by the SC-loss to enhance the semantic representations over the exemplar-based semantic graph. We perform full ablation studies to demonstrate the effectiveness of each component in our approach. Particularly, the proposed GINet outperforms the state-of-the-art approaches on the popular benchmarks, including Pascal-Context and COCO Stuff. © 2020, Springer Nature Switzerland AG.

关键词： Convolution

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Adaptive cross-fusion learning for multi-modal gesture recognition

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Virtual Reality & Intelligent Hardware 2021年第3期3卷 235-247页

作者： Benjia ZHOU Jun WAN Yanyan LIANG Guodong GUO Macao University of Science and Technology Macao 999078China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China Baidu Research Beijing 100193Chinaand National Engineering Laboratory for Deep Learning Technology and ApplicationBeijing 100193China

Background Gesture recognition has attracted significant attention because of its wide range of potential *** multi-modal gesture recognition has made significant progress in recent years,a popular method still is simply fusing prediction scores at the end of each branch,which often ignores complementary features among different modalities in the early stage and does not fuse the complementary features into a more discriminative *** This paper proposes an Adaptive Cross-modal Weighting(ACmW)scheme to exploit complementarity features from RGB-D data in this *** scheme learns relations among different modalities by combining the features of different data *** proposed ACmW module contains two key functions:(1)fusing complementary features from multiple streams through an adaptive one-dimensional convolution;and(2)modeling the correlation of multi-stream complementary features in the time *** the effective combination of these two functional modules,the proposed ACmW can automatically analyze the relationship between the complementary features from different streams,and can fuse them in the spatial and temporal *** Extensive experiments validate the effectiveness of the proposed method,and show that our method outperforms state-of-the-art methods on IsoGD and NVGesture.

关键词： Gesture recognition Multi-modal fusion RGB-D

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IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image 15th

IAFA: Instance-Aware Feature Aggregation for 3D Object Detec...

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Zhou, Dingfu Song, Xibin Dai, Yuchao Yin, Junbo Lu, Feixiang Liao, Miao Fang, Jin Zhang, Liangjun Baidu Research Beijing China National Engineering Laboratory of Deep Learning Technology and Application Beijing China Northwestern Polytechnical University Xi’an China Beijing Institute of Technology Beijing China

ISBN: (纸本)9783030695248

3D object detection from a single image is an important task in Autonomous Driving (AD), where various approaches have been proposed. However, the task is intrinsically ambiguous and challenging as single image depth estimation is already an ill-posed problem. In this paper, we propose an instance-aware approach to aggregate useful information for improving the accuracy of 3D object detection with the following contributions. First, an instance-aware feature aggregation (IAFA) module is proposed to collect local and global features for 3D bounding boxes regression. Second, we empirically find that the spatial attention module can be well learned by taking coarse-level instance annotations as a supervision signal. The proposed module has significantly boosted the performance of the baseline method on both 3D detection and 2D bird-eye’s view of vehicle detection among all three categories. Third, our proposed method outperforms all single image-based approaches (even these methods trained with depth as auxiliary inputs) and achieves state-of-the-art 3D detection performance on the KITTI benchmark. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Interactive grounded language acquisition and generalization in a 2D world 6

Interactive grounded language acquisition and generalization...

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6th International Conference on learning Representations, ICLR 2018

作者： Yu, Haonan Zhang, Haichao Xu, Wei Baidu Research Sunnyvale United States National Engineering Laboratory for Deep Learning Technology and Applications Beijing China

We build a virtual agent for learning language in a 2D maze-like world. The agent sees images of the surrounding environment, listens to a virtual teacher, and takes actions to receive rewards. It interactively learns the teacher’s language from scratch based on two language use cases: sentence-directed navigation and question answering. It learns simultaneously the visual representations of the world, the language, and the action control. By disentangling language grounding from other computational routines and sharing a concept detection function between language grounding and prediction, the agent reliably interpolates and extrapolates to interpret sentences that contain new word combinations or new words missing from training sentences. The new words are transferred from the answers of language prediction. Such a language ability is trained and evaluated on a population of over 1.6 million distinct sentences consisting of 119 object words, 8 color words, 9 spatial-relation words, and 50 grammatical words. The proposed model significantly outperforms five comparison methods for interpreting zero-shot sentences. In addition, we demonstrate human-interpretable intermediate outputs of the model in the appendix. © learning Representations, ICLR 2018 - Conference Track *** right reserved.

关键词： Visual languages

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