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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Wang, Huaijun Li, Junhuai Tian, Jubo Wang, Kan Shaanxi Key Laboratory for Network Computing and Security Technology School of Computer Science and Engineering Xian University of Technology Xian710048 China

ISBN: (纸本)9781728129778

WebIDE is leveraged for IOT applications development, which could adapt to the rapid growth of IOT applications and meanwhile facilitate the rapid development. Resource allocation is of vital significance in the WebIDE cloud service system. Existing resource allocation approaches may encounter issues such as unbalanced resource assignments, which could lead to the reduced system resource utilization or extended system response time. The fundamental reason behind it lies in the fact that existing ones are typically on the basis of predetermined resource demands for each task, and not applicable to the case with dynamic and unknown resource demands. Thus, in this paper, we propose a novel approach based on task pre-scheduling, which schedules beforehand each task into a virtual machine local task queue by means of demand prediction. Firstly, all tasks are classified, based on the execution state, execution operations and cloud server resource requirements. Secondly, the grouped tasks are mapped to different system states, with the Markov state transition probability matrix leveraged to model the transition probability between tasks, followed by the prediction model constructed. Finally, integrating both task pre-scheduling and ant colony algorithm, cloud server resource allocation is carried out. Simulation results show that the task prediction model could significantly not only reduce the task response time, but also improve the cloud server resource utilization. © 2019 IEEE.

关键词： Resource allocation

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Segment Anything Model is a Good Teacher for Local Feature Learning

arXiv

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arXiv 2023年

作者： Wu, Jingqian Xu, Rongtao Wood-Doughty, Zach Wang, Changwei Xu, Shibiao Lam, Edmund Y. The University of Hong Kong Pokfulam Hong Kong The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100190 China Northwestern University EvanstonIL60201 United States The Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Qilu University of Technology Shandong Academy of Sciences Jinan250013 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China The School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China

Local feature detection and description play an important role in many computer vision tasks, which are designed to detect and describe keypoints in any scene and any downstream task. Data-driven local feature learning methods need to rely on pixel-level correspondence for training. However, a vast number of existing approaches ignored the semantic information on which humans rely to describe image pixels. In addition, it is not feasible to enhance generic scene keypoints detection and description simply by using traditional common semantic segmentation models because they can only recognize a limited number of coarse-grained object classes. In this paper, we propose SAMFeat to introduce SAM (segment anything model), a foundation model trained on 11 million images, as a teacher to guide local feature learning. SAMFeat learns additional semantic information brought by SAM and thus is inspired by higher performance even with limited training samples. To do so, first, we construct an auxiliary task of Attention-weighted Semantic Relation Distillation (ASRD), which adaptively distillates feature relations with category-agnostic semantic information learned by the SAM encoder into a local feature learning network, to improve local feature description using semantic discrimination. Second, we develop a technique called Weakly Supervised Contrastive Learning Based on Semantic Grouping (WSC), which utilizes semantic groupings derived from SAM as weakly supervised signals, to optimize the metric space of local descriptors. Third, we design an Edge Attention Guidance (EAG) to further improve the accuracy of local feature detection and description by prompting the network to pay more attention to the edge region guided by SAM. SAMFeat’s performance on various tasks such as image matching on HPatches, and long-term visual localization on Aachen Day-Night showcases its superiority over previous local features. The release code is available at https://***/vignywang/SAMFeat

关键词： Computer vision

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MedSteGAN: Medical Embedding-Optimized Steganography with Improved GAN

SSRN

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SSRN 2023年

作者： Peng, Yuxiang Fu, Chong Zheng, Yu Tian, Yunjia Cao, Guixing Chen, Junxin School of Computer Science and Engineering Northeastern University Liaoning Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Liaoning Shenyang110819 China Department of Information Engineering The Chinese University of Hong Kong Ma Liu Shui Shatin New Territories Hong Kong Institute of Telecommunication Satellite China Academy of Space Technology Beijing100094 China School of Software Dalian University of Technology Liaoning Dalian116621 China

Medical diagnosing systems contain sensitive information, especially the resulting certificate of diagnosis that involves the patient’s privacy, required to be protected carefully. An effective way is to secretly embed privacy-sensitive information into medical-generated images, eventually being viewed as arbitrary images of nobody/nothing in public. The current research line of medical steganography targets simple data-hiding methods, yet remains barren in optimal embedding and stronger robustness. To fill this gap, we propose an end-to-end steganographic framework for medical images. Our idea is to integrate medical-image adaptive steganography with a generative adversarial network (GAN), named MedSteGAN. To improve both robustness, we design an optimized U-shape gen- erator that extracts features more efficiently utilizing larger kernel convolutions and the ReLU6 function. Also, deconvolutions are replaced by upsampling and convolution layers to avoid the checkerboard effect. To enhance security and image quality, we employ visual quality loss and mean squared error (MSE) loss based on the multi-scale structural similarity index metric (MS- SSIM) and the high-pass filters, respectively. Extensive experiments demonstrate that MedSteGAN outperforms state-of-the-art steganographic schemes in terms of robustness and image quality. © 2023, The Authors. All rights reserved.

关键词： Image quality

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Crsanet: Class Representations Self-Attention network for the Segmentation of Thyroid Nodules

SSRN

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SSRN 2023年

作者： Sun, Shiyao Fu, Chong Xu, Sen Wen, Yingyou Ma, Tao School of Computer Science and Engineering Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China General Hospital of Northern Theatre Command Shenyang110016 China Medical Imaging Research Department Neusoft Research of Intelligent Healthcare Technology Co. Ltd. Shenyang China Dopamine Group Ltd. Auckland1542 New Zealand

Segmenting accurate thyroid nodules in medical ultrasound images is always non-trivial due to the large variation in size, shape and texture of nodules, and also the presence of surrounding tissues and organs with similar intensity values. Existing algorithms solve this by designing multi-scale spatial context modeling modules and introducing the emerging self-attention based paradigm, while failing to learn the class-wise relational context around nodules. To this end, we propose a novel coarse-to-fine framework, namely, CRSANet, which leverages both class-wise relational context and spatial multi-scale context for comprehensive context perception. Specifically, we first introduce the concept of Class Representations, which describe the overall representation of thyroid nodules in ultrasound images from a categorical perspective. Secondly, we propose a backbone-free dual-branch Class Representation Self-Attention Module (CRSA) to refine the coarse segmentation results from segmentation backbones. Concretely, Fine Class Region (FCR) branch is proposed to impose consistency between pixels and their corresponding Class Representations to correct the wrongly classified pixels. Meanwhile, Class Contextual Representation (CCR) branch is introduced to enhance the ability of pixel representations with the corresponding Class Representations. We evaluate the effectiveness and generality of CRSANet using three types of backbone networks. In the experiments, our proposed U-CRSANet achieves a mIoU of 78.86% and 81.35%, an accuracy of 97.11% and 98.63%, a Dice value of 74.26% and 87.57%, respectively, which outperforms most state-of-the-art methods on the public DDTI and TN-SCUI datasets. © 2023, The Authors. All rights reserved.

关键词： Pixels

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Risk Modeling and Traceability of Boundary Attacks on Internet of Power Systems Based on Complex networks

Risk Modeling and Traceability of Boundary Attacks on Intern...

引用

IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Xin Li Zhaoyang Qu Tong Yu Ming Xie Fu Yu Wei Ding School of Electrical Engineering Northeast Electric Power University Jilin China People’s Bank of China Qinghai Provincial Branch Xining China China Southern Power Grid Guangxi Power Grid Co. Ltd. Research Institute Nanning China Bijie Power Supply Bureau China Guizhou Power Grid Co. Ltd. Bijie China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) 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)9798350391367

ISBN: (纸本)9798350391374

With the increasing intelligence of power IoT terminal devices, the massive volume of data transmission, and the widespread adoption of shared services, traditional terminal boundary security mechanisms are unable to keep up with the rapid development of network technologies and applications. Additionally, they fail to trace and source network attacks, leading to frequent organized and purposeful malicious attack incidents. To address these issues, this paper proposes a research method for risk modeling and traceability of boundary attacks on power IoT terminals based on complex networks. First, based on the node information of power IoT terminal devices and complex network theory, a terminal boundary attack network (TBAN) is established. Next, using indicators related to complex network theory, such as degree, in-degree, and out-degree, the terminal boundary attack network is analyzed. Furthermore, a staged TBAN is proposed to analyze the attack risks at different times. Then, combining causal theory, a method for tracing and sourcing terminal boundary attacks based on a causal Bayesian network is proposed to identify and trace malicious attacks. Finally, validation and testing analysis are conducted using the actual deployment of power IoT terminal devices. The proposed analysis method transforms the spatial characteristics of the power IoT terminal boundary into a temporally sequential TBAN, which not only visually reflects attack risks but also reveals the temporal relationships of terminal boundary attacks between lines.

关键词： Complex networks Transforms Data science Data models Bayes methods Power systems Internet security Data communication Testing

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Chinese named entity recognition method for the field of network security based on RoBERTa

Chinese named entity recognition method for the field of net...

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International Conference on networking and network Applications (NaNA)

作者： Xiaoyan Zhu Yu Zhang Lei Zhu Xinhong Hei Yichuan Wang Feixiong Hu Yanni Yao School of Computer Science and Engineering Xi’an Jiaotong University Xi’an China Shaanxi Key Laboratory for Network Computing and Security Technology Xi’an China School of Computer Science and Technology Xi’an University of technology Xi’an China Shenzhen Tencent Computer System Co. Ltd Shenzhen China

As the mobile Internet is developing rapidly, people who use cell phones to access the Internet dominate, and the mobile Internet has changed the development environment of online public opinion and made online public opinion events spread more widely. In the online environment, any kind of public issues may become a trigger for the generation of public opinion and thus need to be controlled for network supervision. The method in this paper can identify entities from the event texts obtained from mobile Today's Headlines, People's Daily, etc., and informatize security of public opinion in event instances, thus strengthening network supervision and control in mobile, and providing sufficient support for national security event management. In this paper, we present a SW-BiLSTM-CRF model, as well as a model combining the RoBERTa pre-trained model with the classical neural network BiLSTM model. Our experiments show that this approach provided achieves quite good results on Chinese emergency corpus, with accuracy and F1 values of 87.21% and 78.78%, respectively.

关键词： Text recognition Cellular phones Semantics Neural networks network security Feature extraction Internet

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Scene text image super-resolution with semantic-aware interaction

引用

Engineering Applications of Artificial Intelligence 2025年 158卷

作者： Mingle Zhou Wenlong Liu Jin Wan Delong Han Min Li Gang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. China

Scene text image super-resolution aims to enhance the resolution of images containing text in various scenes, which amplifies the prominence of the text and improves its recognizability. Existing methods struggle to accurately localize text regions in high-noise environments, which hampers their ability to effectively implement targeted super-resolution. To address these issues, we propose a Text image Super-resolution Semantic-aware Interaction network (TSSIN) by embedding a text region segmentation network. First, we propose to use a pre-trained text region segmentation network (TRSN) to extract text region information. This approach semantically guides our model to address the challenge of the model not performing targeted super-resolution processing of text in high-noise environments. Secondly, we propose a multi-modal semantic information interaction module (MSIIM) to mitigate the issue of insufficient global information exchange. Comprehensive experiments conducted on the TextZoom dataset demonstrate that our TSSIN significantly enhances image quality. Furthermore, it shows a clear superiority over state-of-the-art methods on TextZoom, achieving an average text recognition accuracy improvement of +1.0% over Transformer-Based Super-Resolution network (TBSRN) (49.6%, 56.2%, 60.1% vs. 48.1%, 55.9%, 58.9%), +0.83% over Parallelly Contextual Attention network (PCAN), and +1.46% over Text Prior Guided Super-Resolution (TPGSR), as evaluated by three pre-trained text recognition models. Code is available at https://***/ads2d/TSSIN .

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MemMambaAD: Memory-augmented state space model for multivariate time series anomaly detection

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Engineering Applications of Artificial Intelligence 2025年 158卷

作者： Gang Li Mingchao Ge Jin Wan Delong Han Min Li Mingle Zhou Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China SHANDONG SCICOM Information and Economy Research Institute Co. Ltd. Jinan China

Multivariate time series anomaly detection focuses on recognizing abnormal patterns to reduce system failures and improve production efficiency and product quality. Accurately detecting anomalies in data remains challenging because existing reconstruction-based methods are prone to overfitting. Recently, reconstruction methods guided by memory modules have been used to address this issue. However, these methods still suffer from insufficient feature extraction of time series prototypes and inadequate storage of normal sample prototype patterns in memory modules. To address these issues, we propose a memory-augmented state space model for multivariate time series anomaly detection. Specifically, we introduce a sequence decomposition state space model-temporal convolutional encoder, which independently extracts trend and seasonal features of multivariate time series in global and local manner, capturing the intrinsic patterns of time series more comprehensively. In addition, we propose a dynamic memory update mechanism, which flexibly updates the memory item through the memory selection mechanism, to more accurately record the prototype pattern of normal samples to improve anomaly detection performance. Extensive experiments on five benchmark datasets demonstrate that our method achieves state-of-the-art performance and reduces memory usage compared with other methods.

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Robust Beamforming for Localization-Aided Millimeter Wave Communication Systems

arXiv

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arXiv 2022年

作者： Sun, Junchang Ma, Shuai Li, Shiyin Yang, Ruixin Min, Minghui Seco-Granados, Gonzalo The School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China The Department of Telecommunications and Systems Engineering Universitat Autònoma de Barcelona Barcelona08193 Spain The Engineering Research Center of Intelligent Control for Underground Space Ministry of Education China University of Mining and Technology Xuzhou221116 China The National Mobile Communications Research Laboratory Southeast University Nanjing210096 China The Shanxi Key Laboratory of Information Communication Network and Security Xian University of Posts and Telecommunications Xian710121 China

In this letter, we investigate a robust beamforming problem for localization-aided millimeter wave (mmWave) communication systems. To handle this problem, we propose a novel restriction and relaxation (R&R) method. The proposed R&R method aims at minimizing the total transmit power while the positioning error follows a Gaussian distribution. Specifically, in the restriction phase of R&R, the probabilistic constraint is transformed into the deterministic form by using the Bernstein-type inequality. In the relaxation phase of R&R, the non-convex optimization problem is reformulated into a convex semidefinite program (SDP) by using semidefinite relaxation (SDR) and first-order Taylor expansion methods. To the best of our knowledge, we first consider the impact of the distribution of the positioning error on the channel state information (CSI), which further influences the data rate. Numerical results present the trade-off of the beamforming between the communication and positioning. © 2022, CC BY.

关键词： Beamforming

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FedBT: Effective and Robust Federated Unlearning via Bad Teacher Distillation for Secure Internet of Things

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IEEE Internet of Things Journal 2025年

作者： Wang, Fangwei Huo, Jiashuai Wang, Wei Zhang, Xi Liu, Yan Tan, Zhiyuan Wang, Changguang Hebei Normal University Key Laboratory of Network and Information Security of Hebei Province College of Computer and Cyber Security Shijiazhuang China Xidian University Hangzhou Institute of Technology Hangzhou China Edinburgh Napier University School of Computing Engineering and the Built Environment EdinburghEH10 5DT United Kingdom

Smart Internet of Things (IoT) devices generate vast, distributed data, and their limited computational and storage capacities complicate data protection. Federated Learning (FL) enables collaborative model training across clients, enhancing performance and protecting data privacy. The Right to be Forgotten (RTBF) raises the demand for precise data removal. Federated Unlearning (FU) offers a solution for accurate data deletion in FL systems. Existing FU methods often struggle to simultaneously ensure effective data forgetting and preserve model generalization. To mitigate these challenges, an effective and robust FU framework has been proposed, which is based on the "Bad Teacher" knowledge distillation (KD), termed FedBT. First, the "Bad Teacher" KD guides the trained model to eliminate specific client contributions from the global model. Next, the frequency domain extracts the global model’s generalization components. Finally, orthogonal constraints are applied to the KD-generated gradients within the orthogonal subspace of these components, ensuring the gradients preserve the trained model’s generalization ability. FedBT eliminates the need to store historical records of parameter updates. Using orthogonal space constraints, the generalization ability of the trained model is safeguarded during unlearning. Extensive experiments on three datasets with various metrics show our method reduces accuracy by only 0.53% on MNIST, 0.26% on Fashion-MNIST, and 4.67% on CIFAR10, surpassing the best approach. Furthermore, FedBT obtains an unlearning performance that most closely approximates the results obtained from retraining from scratch. FedBT boosts IoT security by enabling the "forgetting" of certain client data, crucial for protecting user privacy and ensuring secure device interactions. © 2014 IEEE.

关键词： Sensitive data

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