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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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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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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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Grating pitch comparator traceable to the Cr atom transition frequency

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Measurement: Journal of the International Measurement Confederation 2025年 242卷

作者： Lin, Zichao Deng, Xiao Wu, Yize Feng, Jingtong Zhu, Hongyu Yang, Yaao Yu, Jing Xue, Dongbai Wang, Jianbo Shi, Yushu Jin, Tao Zhang, Wentao Dun, Xiong Cheng, Xinbin Li, Tongbao MOE Key Laboratory of Advanced Micro-Structured Materials Shanghai Frontiers Science Center of Digital Optics Institute of Precision Optical Engineering School of Physics Science and Engineering Tongji University Shanghai200092 China National Institute of Metrology China Beijing100029 China College of metrology and measurement engineering China Jiliang University Hangzhou310018 China Guangxi Key Laboratory of Optoelectronic Information Processing School of Optoelectronic Engineering Guilin University of Electronic Technology Guilin541004 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China

Calibration of the grating pitch typically requires metrological instruments or diffraction techniques. The traceability and stability demands for laser wavelength in these techniques present substantial challenges for on-site grating calibration. This paper introduces a novel traceability approach to grating pitch calibration using a grating pitch comparator that utilizes the transition frequency of the chromium. The chromium transition frequency is converted into a chromium grating through atom lithography, establishing a self-traceable length standard dCr = 212.7787 ± 0.0049 nm. Calibration of the test grating's pitch is achieved by comparing the phase shift caused by the simultaneous movement of the chromium grating and the test grating. By using the dCr, this approach combines the high-accuracy of fundamental physical constants with the environmental stability of grating interferometers, eliminating the need for measuring laser wavelength, air refraction, diffraction angles. It significantly enhances the precision and makes more straightforward and portable, crucial for facilitating traceable on-site grating measurements. © 2024

关键词： Comparators (optical)

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Efficiency and Precision: Control Systems Empowered by Smart Image Processing

Efficiency and Precision: Control Systems Empowered by Smart...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Gunji Sreenivasulu T Padmavathi Anagandula Nirisha Dudi Bhanu Prakash Mohammed Yousif Oudah K Saikumar Department of Computer Science & Engineering Madanapalle Institute of Technology & Science Madanapalle Department of ECE CVR College of engineering Department of Computer Science and Information Technology MLR Institute of Technology Hyderabad Telangana India Department of ECE CVR College of Engineering Hyderabad Scientific Research Center Al-Ayen University Thi-Qar Iraq Computer Science School of Engineering Malla Reddy University Hyderabad Telangana

The rapid increase in the population has resulted in traffic jams on the roads. There was a corresponding increase in the number of calls for emergency services, such as ambulances, police cars, and fire trucks. Maintaining rapid response times for emergency vehicles is critical right now. In this study, we present a smart traffic control system that integrates Internet of Things (IoT) chips into traffic signals and modifies their timing parameters. These chips will function in response to a road's estimated automobile count and the subsequent need for emergency vehicles along that route. The number of people in each automobile is going to be calculated using digital image analysis of the live camera feed. To make sure emergency vehicles get the help they require, GPS systems are going to be utilized to track the vehicles and update the chips with their current locations. It is envisioned that traffic lights on a road network will function by dynamically assigning weights for a particular node or the time among two consecutive lights. In order to maintain track of each node in the network, the weights assigned to each one must be updated in real-time.

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GDSG: Graph Diffusion-based Solution Generator for Optimization Problems in MEC Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Liang, Ruihuai Yang, Bo Chen, Pengyu Cao, Xuelin Yu, Zhiwen Debbah, Merouane Niyato, Dusit Poor, H. Vincent Yuen, Chau Northwestern Polytechnical University School of Computer Science Shaanxi Xi'an710129 China Xidian University School of Cyber Engineering Shaanxi Xi'an710071 China Harbin Engineering University Heilongjiang Harbin150001 China Khalifa University of Science and Technology Center for 6G Technology Abu Dhabi United Arab Emirates Nanyang Technological University College of Computing and Data Science Singapore Princeton University Department of Electrical and Computer Engineering PrincetonNJ08544 United States Nanyang Technological University School of Electrical and Electronics Engineering Singapore

Optimization is crucial for the efficiency and reliability of multi-access edge computing (MEC) networks. Many optimization problems in this field are NP-hard and do not have effective approximation algorithms. Consequently, there is often a lack of optimal (ground-truth) data, which limits the effectiveness of traditional deep learning approaches. Most existing learning-based methods require a large amount of optimal data and do not leverage the potential advantages of using suboptimal data, which can be obtained more efficiently. To illustrate this point, we focus on the multi-server multi-user computation offloading (MSCO) problem, a common issue in MEC networks that lacks efficient optimal solution methods. In this paper, we introduce the graph diffusion-based solution generator (GDSG), designed to work with suboptimal datasets while still achieving convergence to the optimal solution with high probability. We reformulate the network optimization challenge as a distribution-learning problem and provide a clear explanation of how to learn from suboptimal training datasets. We develop GDSG, a multi-task diffusion generative model that employs a graph neural network (GNN) to capture the distribution of high-quality solutions. Our approach includes a straightforward and efficient heuristic method to generate a sufficient amount of training data composed entirely of suboptimal solutions. In our implementation, we enhance the GNN architecture to achieve better generalization. Moreover, the proposed GDSG can achieve nearly 100% task orthogonality, which helps prevent negative interference between the discrete and continuous solution generation training objectives. We demonstrate that this orthogonality arises from the diffusion-related training loss in GDSG, rather than from the GNN architecture itself. Finally, our experiments show that the proposed GDSG outperforms other benchmark methods on both optimal and suboptimal training datasets. Regarding the minimization of

关键词： Graph neural networks

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Explicit Spatial Hint and Implicit Logits Relation: Distilling Heterogeneous Knowledge From Vision Transformer to CNN

Explicit Spatial Hint and Implicit Logits Relation: Distilli...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wenkuan Li Dongyuan Li Shuo Gao Xubin Wu Ming Li Haifang Li School of Computer Science and Technology Taiyuan University of Technology Taiyuan China Engineering Training Center Shandong University Jinan China Faculty of Applied Sciences Simon Fraser University Burnaby Canada

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

A lightweight Convolutional Neural Network (CNN) typically requires knowledge transfer from a large powerful network before it is employed in resource-limited edge devices. Vision Transformer (ViT) possesses an unparalleled capability for global modeling but remains largely unexplored in Knowledge Distillation (KD). The main reason is that the gap in receptive fields between ViT and CNN causes representation discrepancy and logits confidence-bias. In this paper, we propose a novel heterogeneous distillation method based on explicit spatial hint and implicit logits relation for transferring knowledge from ViT to CNN. By exploiting class discriminative regions of an input image, class attention transfer is developed to adaptively identify common regions of interest as a spatial hint, bridging the representation gap between the heterogeneous architectures. Meanwhile, a learnable projector equipped with our soft maximum function is introduced to refine the logits relation to evaluate all classification results more evenly with smoother gradient flows, which helps CNN to effectively learn with larger capacity gaps. Extensive experiments demonstrate that our proposed method can achieves state-of-the-art performance on multiple benchmark datasets, which is simple yet efficient without relying on any auxiliary network of homogeneous architectures.

关键词： Knowledge engineering computer vision Benchmark testing Signal processing Transformers Acoustics Convolutional neural networks Speech processing Knowledge transfer

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Deep Stacked Ensemble Learning Model for COVID-19 Classification

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computers, Materials & Continua 2022年第3期70卷 5467-5486页

作者： G.Madhu B.Lalith Bharadwaj Rohit Boddeda Sai Vardhan K.Sandeep Kautish Khalid Alnowibet Adel F.Alrasheedi Ali Wagdy Mohamed Department of Information Technology VNRVJIETHyderabad500090India Department of Computer Science and Engineering VNRVJIETHyderabad500090India LBEF Campus Kathmandu44600Nepal Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12588Egypt

COVID-19 is a growing problem worldwide with a high mortality *** a result,the World Health Organization(WHO)declared it a *** order to limit the spread of the disease,a fast and accurate diagnosis is required.A reverse transcript polymerase chain reaction(RT-PCR)test is often used to detect the ***,since this test is time-consuming,a chest computed tomography(CT)or plain chest X-ray(CXR)is sometimes *** value of automated diagnosis is that it saves time and money by minimizing human *** significant contributions are made by our *** initial purpose is to use the essential finetuning methodology to test the action and efficiency of a variety of vision models,ranging from Inception to Neural Architecture Search(NAS)***,by plotting class activationmaps(CAMs)for individual networks and assessing classification efficiency with AUC-ROC curves,the behavior of these models is visually ***,stacked ensembles techniques were used to provide greater generalization by combining finetuned models with six ensemble neural *** stacked ensembles,the generalization of the models ***,the ensemble model created by combining all of the finetuned networks obtained a state-of-the-art COVID-19 accuracy detection score of 99.17%.The precision and recall rates were 99.99%and 89.79%,respectively,highlighting the robustness of stacked *** proposed ensemble approach performed well in the classification of the COVID-19 lesions on CXR according to the experimental results.

关键词： COVID-19 classification class activation maps(CAMs)visualization finetuning stacked ensembles automated diagnosis deep learning

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Security Governance in IOT Environment: A State of Art

Security Governance in IOT Environment: A State of Art

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International Conference on Cloud Technologies and Applications (CloudTech)

作者： Ilyass Mokhtari Ayyoub El Outmani El Miloud Jaara Computer Science Research Laboratory Faculty of Sciences Mohammed First University Oujda Morocco Research Center High Studies of Engineering School EHEI High Studies of Engineering School EHEI Oujda Morocco

With the increase in utilization of the internet of things (IOT) in information systems (IS), providing appropriate lever and type of governance in environment and security control in the same environment has become the new challenge limiting the adoption of this new paradigm. The aim of this paper is to compare the latest models of IOT's governance frameworks applicable for this type of environment such as: CobiT, ITIL and ISO 2700X. This paper aims at identifying the strengths and weaknesses of each model and proposing a list of evaluation criteria to be used while using IOT and to solve the problem of security by adopting a better governance.

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Study on the Impact of Situational Explanations and Prior Information Given to Users on Trust and Perceived Intelligence in Autonomous Driving in a Video-based 2x2 Design

Study on the Impact of Situational Explanations and Prior In...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Kolja Kühnlenz Barbara Kühnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Arts Coburg Germany Academic Center for Sciences and Humanities Coburg University of Applied Sciences and Arts Coburg Germany

In this paper, results from a video-based study on the influence of prior information given to users and explanations situationally given by the vehicle itself on trust and perceived intelligence are presented using a simulated autonomous vehicle in an ambiguous driving situation. A 2x2 between-subjects design is chosen with two independent variables ‘prior information’ (extended/short) and ‘explanations’ (yes/no) with users pseudo-randomly assigned to one of the four conditions. Significant results from 189 test persons reveal, that trust depends on how the capabilities of the intelligent vehicle are explained a priori and not on situational explanations, while perceived intelligence is influenced by both variables. Additional interactions of prior information and user gender is noted with respect to perceived intelligence. As one side effect, it is found, that male users felt significantly more safe than female users with also higher ratings of intention to use the vehicle independently of given information and explanations. Another side effect is that situational explanations lead to better ratings of subjective performance, while also here a significant interaction of gender and prior information is noted. Thus, contrary to expectations, a dominant role of continuous situational explanations (Explainable AI) of the intelligent vehicle for increasing trust is not confirmed and the extent of given prior information seems the deciding factor for initial trust building, which is an important aspect for the introduction of new intelligent technology into society. This is remarkable as at the same time perceived intelligence seems to be dependent on both variables. So it appears, that a vehicle being able to explain its actions may well appear more intelligent, but not necessarily appear more trustworthy.

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