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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Chen, Lu Bi, Suzhi Lin, Xiaohui Yang, Zheyuan Wu, Yuan Yet, Qiang Shenzhen University State Key Laboratory of Radio Frequency Heterogeneous Integration Shenzhen518060 China The Chinese Unviersity of Hong Kong Department of Information Engineering N. T. Shatin Hong Kong The University of Macau State Key Lab of Internet of Things for Smart City Macao China Memorial University of Newfoundland Department of Computer Science Canada

ISBN: (纸本)9781665494274

In this paper, we consider a multi-UAV enabled wireless sensor network (WSN) where multiple unmanned aerial vehicles (UAVs) gather data from multiple randomly moving sensor nodes (SNs). We aim to minimize the long-term average energy consumption of all SNs while satisfying their average data rate requirements and energy constraints of the UAVs. We solve the problem by jointly optimizing the multi-UAV's trajectories, communication scheduling and SN's association decisions. In particular, we formulate it as a multi-stage stochastic mixed integer non-linear programming (MINLP) problem and design an online algorithm that integrates Lyapunov optimization and deep reinforcement learning (DRL) methods. Specifically, we first decouple the original multi-stage stochastic MINLP problem into a series of per-slot deterministic MINLP subproblems by applying Lyapunov optimization. For each per-slot problem, we use model-free DRL to obtain the optimal integer UAV-SN associations and model-based method to optimize the UAVs' trajectories and resource allocation. Simulation results reveal that although the communication environments change stochastically and rapidly, our proposed online algorithm can produce real-time solution that achieves high system performance and satisfies all the constraints. © 2023 IEEE.

关键词： Sensor nodes

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Complementary to Multiple Labels: A Correlation-Aware Correction Approach

arXiv

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arXiv 2023年

作者： Gao, Yi Xu, Miao Zhang, Min-Ling School of Cyber Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China University of Queensland Australia School of Computer Science and Engineering Southeast University Nanjing210096 China

Complementary label learning (CLL) requires annotators to give irrelevant labels instead of relevant labels for instances. Currently, CLL has shown its promising performance on multi-class data by estimating a transition matrix. However, current multi-class CLL techniques cannot work well on multi-labeled data since they assume each instance is associated with one label while each multi-labeled instance is relevant to multiple labels. Here, we show theoretically how the estimated transition matrix in multi-class CLL could be distorted in multi-labeled cases as they ignore co-existing relevant labels. Moreover, theoretical findings reveal that calculating a transition matrix from label correlations in multi-labeled CLL (ML-CLL) needs multi-labeled data, while this is unavailable for ML-CLL. To solve this issue, we propose a two-step method to estimate the transition matrix from candidate labels. Specifically, we first estimate an initial transition matrix by decomposing the multi-label problem into a series of binary classification problems, then the initial transition matrix is corrected by label correlations to enforce the addition of relationships among labels. We further show that the proposal is classifier-consistent, and additionally introduce an MSE-based regularizer to alleviate the tendency of BCE loss overfitting to noises. Experimental results have demonstrated the effectiveness of the proposed method. Copyright © 2023, The Authors. All rights reserved.

关键词： Matrix algebra

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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Improving Cross-modal Alignment with Synthetic Pairs for Text-only Image Captioning

arXiv

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arXiv 2023年

作者： Liu, Zhiyue Liu, Jinyuan Ma, Fanrong School of Computer Electronics and Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology China

Although image captioning models have made significant advancements in recent years, the majority of them heavily depend on high-quality datasets containing paired images and texts which are costly to acquire. Previous works leverage the CLIP’s cross-modal association ability for image captioning, relying solely on textual information under unsupervised settings. However, not only does a modality gap exist between CLIP text and image features, but a discrepancy also arises between training and inference due to the unavailability of real-world images, which hinders the cross-modal alignment in text-only captioning. This paper proposes a novel method to address these issues by incorporating synthetic image-text pairs. A pre-trained text-to-image model is deployed to obtain images that correspond to textual data, and the pseudo features of generated images are optimized toward the real ones in the CLIP embedding space. Furthermore, textual information is gathered to represent image features, resulting in the image features with various semantics and the bridged modality gap. To unify training and inference, synthetic image features would serve as the training prefix for the language decoder, while real images are used for inference. Additionally, salient objects in images are detected as assistance to enhance the learning of modality alignment. Experimental results demonstrate that our method obtains the state-of-the-art performance on benchmark datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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DM-YOLOv5:An Improved Safety Helmet Detection Algorithm at Complex Construction Scenes

DM-YOLOv5:An Improved Safety Helmet Detection Algorithm at C...

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International Conference on computer Supported Cooperative Work in Design

作者： Dexu Yao Aimin Li Deqi Liu Mengfan Cheng Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

At complex construction sites, detecting whether workers are wearing safety helmets is often affected by occlusion and sunlight change. In addition, the helmets are relatively small, which can easily lead to missed detection and false detection. This paper proposes an improved YOLOv5 model. Firstly, we added a 152×152 detection layer for scale of small objects, enhancing its feature extraction capabilities. Secondly, we added the Multi-Spectral Channel Attention (MSCA) mechanism to enhance feature learning for small objects, addressing the information loss problem caused by Global Average Pooling (GAP). Finally, different activation functions, bounding box regression loss functions, and post-processing methods were adopted for YOLOv5. The Mish function serves as the activation function to enhance the model’s generalization ability and mitigate the gradient vanishing problem associated with the Leaky Relu function. The CIoU loss was employed as the loss function to improve bounding box regression accuracy and accelerate prediction convergence. DIoU-NMS, instead of NMS, was used as the post-processing method to enhance the filtering ability for prediction boxes of occluded small objects. We tested our algorithm on a dataset with 7000 images and achieved an mAP of 95.1%, surpassing the YOLOv5 model by 2.9%. Our method significantly improves safety helmet detection performance at complex construction scenes.

关键词： YOLO Representation learning Head Filtering Federated learning Prediction algorithms Feature extraction

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Parallel Pattern Matching over Brotli Compressed network Traffic

Parallel Pattern Matching over Brotli Compressed Network Tra...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Xiuwen Sun Guangzheng Zhang Di Wu Qingying Yu Jie Cui Hong Zhong School of Computer Science and Technology Anhui University Anhui Provincial Key Laboratory of Network and Information Security Anhui Normal Unviersity

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Pattern matching is a crucial technique for network traffic detection applications. As a fundamental computation model used by pattern matching, the finite state automata execute sequential matching due to the state dependence among transitions. Meanwhile, most services tend to compress their data to improve transmission or storage efficiency. The increased compressed data challenges the straightforward method of matching the whole decompressed data and incurs data dependence among the compression encodings. The related approaches either leverage techniques to break the state dependence of matching uncompressed data or accelerate matching compressed data in a single-threaded manner without considering the state and data dependence. None of them can perform parallel matching over compressed data. This paper provides PETALS, a parallel pattern matching method over Brotli compressed network traffic. PETALS partitions the original compressed traffic into fixed- length blocks for parallel matching and patches the broken compression encodings crossing blocks to break the data dependence. Then, it merges the compressed traffic matching method into path fusion, an enumerative parallelization of finite state automata, to present parallel matching over compressed traffic. Evaluation using real-world network traffic and regular expressions shows that PETALS can raise the speedup from 1.53x to 3.53x of the state-of-the-art parallelization schemes on a 56-cores machine.

关键词： Privacy Instruction sets Computational modeling Automata Telecommunication traffic Programming Parallel processing

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A Novel Early Warning Model for Hand, Foot and Mouth Disease Prediction Based on a Graph Convolutional network

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Biomedical and Environmental Sciences 2022年第6期35卷 494-503页

作者： JI Tian Jiao CHENG Qiang ZHANG Yong ZENG Han Ri WANG Jian Xing YANG Guan Yu XU Wen Bo LIU Hong Tu NHC Key Laboratory of Medical Virology and Viral Diseases National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijing 100026China Academy of Cyber Science and Engineering Southeast UniversityNanjing 211189JiangsuChina Center for Biosafety Mega Science Chinese Academy of SciencesWuhan 430071HubeiChina Guangdong Center for Disease Control and Prevention Guangzhou 511430GuangdongChina Shandong Center for Disease Control and Prevention Jinan 250014ShandongChina LIST Key Laboratory of Computer Network and Information Integration(Southeast University)Ministry of EducationSoutheast UniversityNanjing 211189JiangsuChina

Objectives Hand,foot and mouth disease(HFMD)is a widespread infectious disease that causes a significant disease burden on *** achieve early intervention and to prevent outbreaks of disease,we propose a novel warning model that can accurately predict the incidence of *** We propose a spatial-temporal graph convolutional network(STGCN)that combines spatial factors for surrounding cities with historical incidence over a certain time period to predict the future occurrence of HFMD in Guangdong and Shandong between 2011 and *** 2011-2018 data served as the training and verification set,while data from 2019 served as the prediction *** important parameters were selected and verified in this model and the deviation was displayed by the root mean square error and the mean absolute *** As the first application using a STGCN for disease forecasting,we succeeded in accurately predicting the incidence of HFMD over a 12-week period at the prefecture level,especially for cities of significant *** This model provides a novel approach for infectious disease prediction and may help health administrative departments implement effective control measures up to 3 months in advance,which may significantly reduce the morbidity associated with HFMD in the future.

关键词： HFMD Early warning model STGCN Disease prediction

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MF-YOLO: Multimodal Fusion for Remote Sensing Object Detection Based on YOLOv5s

MF-YOLO: Multimodal Fusion for Remote Sensing Object Detecti...

引用

International Conference on computer Supported Cooperative Work in Design

作者： Wenqiang Li Aimin Li Xiaotong Kong Yuechen Zhang Zhiyao Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Remote sensing object detection has flourished at a rapid rate of development. In remote sensing images (RSI), most algorithms perform well in detecting medium and large objects but perform poorly on small objects. This is because small objects occupy fewer pixels in the entire image, and the detector is more likely to be biased towards detecting large objects. In addition, the huge background in the image also introduces noise, causing many detection failures. Most approaches solve the problem of small object detection by increasing the depth of the neural network, but the results are not satisfactory. To solve the above problems, we propose a new method, MF-YOLO, that utilizes fused infrared (IR) and red-green-blue (RGB) images for remote sensing object detection. Through multimodal fusion (MF), we can obtain more positive information, thereby improving detection accuracy. In addition, we introduced the Bi-Level Routing Attention (BRA) module to improve the YOLOv5s model structure, and proposed a new loss function to enhance the model’s learning discrimination ability. Experimental results show that the accuracy of MF-YOLO on the VEDAI and NWPU VHR-10 datasets is 76.62% and 91.63% respectively (in terms of mAP @0.5 ). Compared with the existing state-of-the-art, the detection accuracy is significantly improved. In addition, our model has fewer weight parameters than YOLOv5s, allowing it to achieve relatively high near-real-time detection speed.

关键词： YOLO Accuracy Federated learning Noise Neural networks Prediction algorithms Routing

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RTS-DETR: Efficient Real-Time DETR for Small Object Detection

RTS-DETR: Efficient Real-Time DETR for Small Object Detectio...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Wenqiang Li Aimin Li Zhiyao Li Xiaotong Kong Yuechen Zhang Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

In recent years, object detection models DEtection TRansformer (DETR) series based on Transformer architecture have played a huge role in various fields. However, the DETR series models are not satisfactory in small object detection. Mainly due to the huge amount of calculation of DETR, a lot of feature information will be lost in the feature fusion stage and the low tolerance of small objects to Intersection over Union (IoU). In order to solve the above problems, we propose a near real-time detection model RTS-DETR. In this paper, we revisit Real-Time DEtection TRansformer (RT-DETR), which effectively handles multi-scale features by decoupling intra-scale interaction and cross-scale fusion, but this will lose a lot of positive local information. To this end, we have improved the efficient hybrid encoder. We propose a new positional encoding method that enables the hybrid encoder to more accurately convert the input feature sequence into a high-dimensional representation, and propose a new feature fusion module to enhance the model's ability to capture local features. Furthermore, in order to improve the tolerance of small objects to IoU, we combine Normalized Wasserstein Distance (NWD) with Shape-IoU for the optimization model. This method more accurately takes into account the shape and size of objects, thereby improving detection accuracy. Our model achieves an accuracy of 38.8% (in terms of mAP @0.5 ) on the widely used VisDrone dataset, which improves the accuracy by 2.5% compared to RT-DETR with ResNet-18 as the backbone network.

关键词： Accuracy Shape Fuses Optimization models Semantics Object detection Feature extraction Transformers Real-time systems Encoding

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