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International Conference on Network and Service Management

作者： Quang-Trung Luu Minh-Thanh Nguyen Tai-Hung Nguyen Michel Kieffer Van-Dinh Nguyen Quang-Lap Luu Trung-Toan Nguyen School of Electrical and Electronic Engineering Hanoi University of Science and Technology Hanoi Vietnam Université Paris-Saclay - CNRS - CentraleSupélec - L2S France College of Engineering and Computer Science & Center for Environmental Intelligence VinUniversity Hanoi Vietnam

ISBN: (数字)9783903176669

ISBN: (纸本)9798331505158

Network slicing has appeared a key feature in 5G and beyond communication networks that enables the creation of multiple virtual networks (i.e., slices) over a shared physical network infrastructure. This process involves efficiently embedding (or mapping) each slice element, including virtual network functions (VNFs) and their interconnections, onto the physical network. This paper explores a scenario where the order of VNFs can be adjusted during slice embedding, offering greater flexibility to increase the number of services deployed on the infrastructure. We formulate a novel optimization framework to tackle the challenges of slice admission control and embedding with this flexibility. A heuristic is also introduced to derive embedding solutions in a timely manner. Simulation results demonstrate that allowing flexible VNF ordering significantly increases the number of slices that can be deployed in the network infrastructure.

关键词： Network topology Simulation Network slicing Admission control Linear programming Dynamic scheduling Deep reinforcement learning Resource management Communication networks Optimization

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Hybrid Deep Relation Matrix Bidirectional Approach for Relational Triple Extraction

Hybrid Deep Relation Matrix Bidirectional Approach for Relat...

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International Conference on computer Supported Cooperative Work in Design

作者： Yushuai Hu Jing Zhao Ming Li Lin Yuan Qingzhi Zou Ronghuan 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Relation extraction is a crucial task within information extraction, and numerous models have demonstrated impressive results. However, most of the tagging-based relation triple extraction methods employ unidirectional approaches to extract subjects, objects, and relations, which may overlook crucial information. In this paper, we introduce a novel deep matrix-based bidirectional relation extraction model. Firstly, we extract forward and backward entity pairs. During the bidirectional extraction process,there may be some redundant relationships,so we use a shared encoder to connect and enhance the extraction process. Secondly, we design a low-complexity relation extraction matrix to allocate all possible relations. We assess our model using diverse benchmark datasets, and comprehensive experiments show that our approach effectively addresses subsequent triple extraction issues stemming from entity extraction failures.

关键词： Learning systems Federated learning Computational modeling Benchmark testing Information retrieval Data mining Task analysis

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Authentication through residual attention-based processing of tampered optical responses

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Advanced Photonics 2024年第5期 45-54页

作者： Blake A.Wilson Yuheng Chen Daksh Kumar Singh Rohan Ojha Jaxon Pottle Michael Bezick Alexandra Boltasseva Vladimir M.Shalaev Alexander V.Kildishev Purdue University Elmore Family School of Electrical and Computer Engineering Quantum Science Center Oak Ridge National Laboratory Purdue University School of Aeronautics and Astronautics Purdue University Department of Computer Science

The global chip industry is grappling with dual challenges: a profound shortage of new chips and a surge of counterfeit chips valued at $75 billion, introducing substantial risks of malfunction and unwanted surveillance. To counteract this, we propose an optical anti-counterfeiting detection method for semiconductor devices that is robust under adversarial tampering features, such as malicious package abrasions,compromised thermal treatment, and adversarial tearing. Our new deep-learning approach uses a RAPTOR(residual, attention-based processing of tampered optical response) discriminator, showing the capability of identifying adversarial tampering to an optical, physical unclonable function based on randomly patterned arrays of gold nanoparticles. Using semantic segmentation and labeled clustering, we efficiently extract the positions and radii of the gold nanoparticles in the random patterns from 1000 dark-field images in just 27 ms and verify the authenticity of each pattern using RAPTOR in 80 ms with 97.6% accuracy under difficult adversarial tampering conditions. We demonstrate that RAPTOR outperforms the state-ofthe-art Hausdorff, Procrustes, and average Hausdorff distance metrics, achieving a 40.6%, 37.3%, and6.4% total accuracy increase, respectively.

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Dynamic Scene Adjustment for Player Engagement in VR Game

Dynamic Scene Adjustment for Player Engagement in VR Game

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Zhitao Liu Yi Li Ning Xie Youteng Fan Haolan Tang Wei Zhang AVIC Chengdu Aircraft Design & Research Institute Chengdu China Center for Future Media School of Computer Science and Engineering UESTC Chengdu China

Virtual reality (VR) produces a highly realistic simulated environment with controllable environment variables. This paper proposes a Dynamic Scene Adjustment (DSA) mechanism based on the user interaction status and performance, which aims to adjust the VR experiment variables to improve the user's game engagement. We combined the DSA mechanism with a musical rhythm VR game. The experimental results show that the DSA mechanism can improve the user's game engagement (task performance).

关键词： Three-dimensional displays Pipelines Medical treatment Games Virtual reality User interfaces Rhythm

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FEST: Feature Enhancement Swin Transformer for Remote Sensing Image Semantic Segmentation

FEST: Feature Enhancement Swin Transformer for Remote Sensin...

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International Conference on computer Supported Cooperative Work in Design

作者： Ronghuan Zhang Jing Zhao Ming Li Qingzhi Zou 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 Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology 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 School of Intelligence and Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

The global context is crucial for the precise segmentation of remote sensing images. However, the large volumes and high spatial resolutions of remote sensing images make efficient analysis of the entire scene challenging for most convolutional neural network (CNN)-based methods. To address this issue, we propose to design an innovative framework for semantic segmentation of remote sensing images called Feature Enhancement Swin Transformer (FEST). Firstly, we utilize the Swin Transformer as the encoder and incorporates a Global Information Enhancement Model (GIEM) within each Swin Transformer block to reduce information loss and enable encoding of more accurate spatial information. Secondly, we introduce an enhanced decoding structure called Enhanced Feature Fusion Module (EFFM) with added enhanced channel and spatial attention modules to retain localized information while obtaining extensive contextual information. Finally, for loss calculation, we utilize the dice and cross-entropy loss to jointly supervise the model, aiming to achieve a competitive performance. We comprehensively evaluated FEST on the ISPRS-Vaihingen and Potsdam datasets. The results indicate that our approach has achieved significant improvements in semantic segmentation tasks compared to existing methods.

关键词： Deep learning Federated learning Semantic segmentation Semantics Transformers Information retrieval Encoding

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Securing Consumer IoT Swarms Using Graph Transformers and SRAM for Firmware Attestation

Securing Consumer IoT Swarms Using Graph Transformers and SR...

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Consumer Communications and Networking Conference, CCNC IEEE

作者： Varun Kohli Bhavya Kohli Muhammad Naveed Aman Biplab Sikdar Department of Electrical and Computer Engineering National University of Singapore Singapore Center for Machine Intelligence and Data Science Indian Institute of Technology Bombay India School of Computing University of Nebraska-Lincoln USA

ISBN: (数字)9798331508050

ISBN: (纸本)9798331508067

Consumer Internet of Things (IoT) networks have gained widespread popularity due to their convenience, automation, and security provisions in personal and home environments. Ubiquitous resource-constrained devices, however, are plagued with security issues that often arise from firmware-related issues and their propagated effects. While various studies on firmware attestation are available, they require firmware copies, specific hardware, and complex computation on the IoT device. This paper presents a study on the application of Graph Transformer Networks (GTN) in verifying the firmware integrity of consumer IoT swarms using SRAM as an attestation feature. The proposed method achieves an overall 0.99 accuracy on authentic samples from development and physical twin networks, 0.99 on malware, and 0.97 on propagated misbehavior at a $\sim 10^{-4}$ second inference latency on a laptop CPU.

关键词： Portable computers Accuracy Random access memory Transformers Malware Graph neural networks Hardware Internet of Things Security Microprogramming

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A Hybrid Model of Deep Learning Features and Clinical Features for Severe Cases Predication of COVID-19

A Hybrid Model of Deep Learning Features and Clinical Featur...

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Medical Imaging 2023: computer-Aided Diagnosis

作者： Kitrungrotsakul, Titinunt Chen, Qingqing Wu, Huitao Srichola, Preeyanuch Hu, Hongjie Zhu, Wenchao Chen, Chao Xu, Fangyi Zhou, Yong Lin, Lanfen Tong, Ruofeng Li, Jingsong Chen, Yen-Wei Research Center for Healthcare Data Science Zhejiang Lab Hangzhou China Sir Run Run Shaw Hospital Zhejiang University School of Medicine Hangzhou China Intelligent Healthcare Team Baidu Inc. Beijing China Kasetsart Agricultural and Agro-Industrial Product Improvement Institute Kasetsart University Bangkok Thailand College of Computer Science and Technology Zhejiang University Hangzhou China Graduate School of Information Science and Engineering Ritsumeikan University Shiga Japan

ISBN: (纸本)9781510660359

COVID-19 has spread around the world since 2019. Approximately 6.5% of COVID-19 a risk of developing severe disease with high mortality rate. To reduce the mortality rate and provide appropriate treatment, this research established an integrated models with to predict the clinical outcome of COVID-19 patients with clinical, deep learning and radiomics features. To obtain the optimal feature combination for prediction, 9 clinical features combination was selected from all available clinical factors after using LASSO, 18 deep learning features from U-Net architecture, and 9 radiomics features from segmentation result. A total of 213 COVID-19 patients and 335 non-COVID-19 patients from 5 hospitals were enrolled and used as training and test sample in this research. The proposed model obtained an accuracy, precision, recall, specificity, F1-score and ROC curve of 0.971, 0.943, 0.937, 0.974, 0.941 and 0.979, respectively, which exceeds the related work using only clinical, deep learning or radiomics factors. © 2023 SPIE.

关键词： COVID-19

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From Address Blocks to Authorized Prefixes: Redesigning RPKI ROV with a Hierarchical Hashing Scheme for Fast and Memory-Efficient Validation 22

From Address Blocks to Authorized Prefixes: Redesigning RPKI...

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22nd USENIX Symposium on Networked Systems Design and Implementation, NSDI 2025

作者： Ni, Zedong Xu, Yinbo Zou, Hui Li, Yanbiao Cheng, Guang Xie, Gaogang Computer Network Information Center Chinese Academy of Sciences China School of Cyber Science & Engineering Southeast University China University of Chinese Academy of Sciences China Purple Mountain Laboratories China

ISBN: (纸本)9781939133465

Route Origin Validation (ROV) with Route Origin Authorizations (ROAs), built on top of the Resource Public Key Infrastructure (RPKI), serves as the only formally standardized and production-grade defense mechanism against route hijackings in global interdomain routing infrastructures. However, the widespread adoption of RPKI has introduced escalating scalability challenges in validating high volumes of route messages against massive ROA entries. In this paper, we attribute the performance bottleneck of existing ROV schemes to their underlying validation model, where the route is matched against rules in the form of address blocks. To overcome this bottleneck, we propose the Authorized Prefix (AP) model that enables validation at the prefix granularity, and redesign RPKI ROV based on this new model with a hierarchical hashing scheme named h2ROV. Extensive evaluations verify h2ROV's superiority over state-of-the-art approaches in IPv4, with a speedup of 1.7× ∼ 9.8× in validation and a reduction of 49.3% ∼ 86.6% in memory consumption. System emulations using real-world network topologies further demonstrate h2ROV confines its impact to routing convergence to below 8.5% during update burst events, while reducing ROV-induced delays by 30.4% ∼ 64.7% compared to existing solutions. © 2025 by The USENIX Association All Rights Reserved.

关键词： Topology

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DATDM: Dynamic attention transformer diffusion model for underwater image enhancement

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Alexandria engineering Journal 2025年 126卷 591-604页

作者： Hu, Wang Chen, Shitu Luo, Tuyan Zhang, Lijun Zhang, Hongjin Liu, Zhixiang Zhang, Shiwen Xu, Jingxiang College of Engineering Science and Technology Shanghai Ocean University Shanghai201306 China Institute of Agricultural Quality Standards and Testing Technology Fujian Academy of Agricultural Sciences Fuzhou350003 China Shanghai Engineering Research Center of Marine Renewable Energy Shanghai201306 China College of Information Technology Shanghai Ocean University Shanghai201306 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

Underwater image degradation presents complex challenges, significantly impairing the efficiency of underwater tasks. The mainstream underwater image enhancement (UIE) methods are roughly divided into traditional physics-based and data-driven deep-learning methods. However, physical model-based methods have poor generalization capabilities. Data-driven methods require high-quality training data and suffer from limited model stability. We propose a novel UIE framework, DATDM, to address the above limitations and improve degraded underwater images. We first introduce a color correction module (CCM) to strengthen the model's image restoration capabilities. On the other hand, we propose a novel dynamic attention transformer (DAT) denoising network with excellent performance. The proposed DAT denoising network utilizes a dynamic attention transformer mechanism that adaptively extracts feature information based on the complexity of the feature map, effectively capturing rich features while optimizing computational efficiency. The proposed DATDM method significantly outperforms the existing state-of-the-art methods, with peak signal-to-noise ratios of 24.05 and 27.39 and structural similarity index measures of 0.9233 and 0.9504 on the UIEB and LSUI datasets, respectively. The final experiments demonstrate that our DATDM achieves better performance and visual effects on UIE tasks. © 2025 The Authors

关键词： Image enhancement

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Basis for the quotient space of matrices under equivalence

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science China(Information sciences) 2020年第8期63卷 248-250页

作者： Kuize ZHANG ACCESS Linnaeus Center School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology College of Automation Harbin Engineering University

Dear editor,The semi-tensor product (STP) of matrices, proposed by Cheng in 2001 [1], is a generalization of the conventional matrix product and well defined at every two finite-dimensional matrices. In2016, Cheng [2] proposed a new concept of semitensor addition (STA), which is a generalization of the conventional matrix addition and well defined at every two finite-dimensional matrices with the same ratio between the numbers of rows and

关键词： matrix equivalence basis Boolean network semitensor product semitensor addition

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