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EK-Net++: Real-time scene text detection with expand kernel distance and Epoch Adaptive Weight

Expert Systems with Applications 2025年 267卷

作者： Zhu, Boyuan Chen, Xi Tang, Quan Chen, C.L. Philip Liu, Fagui Computer Science and Engineering South China University of Technology Guangzhou510006 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518066 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100080 China

Recently, scene text detection has received significant attention due to its wide applications. Accurate detection in complex scenes of multiple scales, orientations, and curvature remains a challenge. Component-based methods have emerged as the dominant approach for offering a more flexible way to achieve superior performance. However, Component-based methods employ the Vatti clipping algorithm for multi-instance training to handle the difficulty of arbitrary-shaped text. Yet we identify several bias results from these approaches called the "shrinked kernel" leading to decreased accuracy. Additionally, almost all text detection methods fail to consider the characteristics of different datasets and directly assign fixed loss weights to multi-instances. To address these limitations, we propose a new approach named Expand Kernel Network (EK-Net++), which presents the expand kernel distance map to compensate for the previous deficiency and incorporates three-stage regression to complete integral instance detection. Furthermore, we design the Epoch Adaptive Weight algorithm which dynamically distinguishes the characteristics of different datasets. EK-Net++ not only achieves precise positioning of arbitrary-shaped text but also strikes a balance between performance and speed. Evaluation results show that EK-Net++ attains state-of-the-art or competitive performance compared to other advanced methods, e.g., F-measure of 86.72% at 35.42 FPS on ICDAR 2015, F-measure of 85.83% at 40.13 FPS on CTW1500. © 2024 Elsevier Ltd

关键词： Epoch Adaptive Weight Expand kernel distance Real-time Scene text detection

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SHIELD: Assessing Security-by-Design in Federated Data Spaces Using Attack Graphs 25

SHIELD: Assessing Security-by-Design in Federated Data Space...

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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Palma, Alessandro Papadakis, Nikolaos Bouloukakis, Georgios Garcia-Alfaro, Joaquin Sospetti, Mattia Magoutis, Kostas Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy Samovar Télécom SudParis Institut Polytechnique de Paris Palaiseau91120 France GrottiniLab S.r.L. Porto Recanati Macerata Italy Computer Science Department University of Crete Greece Crete Greece

ISBN: (纸本)9798400706295

Federated data spaces allow organizations to share and control their own data across various domains, but their exposure to cyber attacks has increased due to a surge in newly discovered vulnerabilities. Existing solutions to secure them focus on messaging protocol protection (e.g., using cryptographic means), but this is not sufficient. Attackers may exploit additional vulnerabilities to cause significant issues (e.g., disrupting the availability of services). To this end, we propose SHIELD, a security-by-design approach for federated data spaces, which leverages attack graphs and trust computation to mitigate the risks of cyber attacks. Mitigation is accomplished by proactively assessing the data spaces' weaknesses and implementing security messaging measures to prevent detrimental attacks. A prototype implementation of SHIELD using publish/subscribe as a messaging mechanism is experimentally evaluated over a real architecture in a V2X (Vehicle-to-Everything) scenario. Copyright © 2025 held by the owner/author(s).

关键词： Trusted computing

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An Efficient and Secured Energy Management System for Automated Guided Vehicles

An Efficient and Secured Energy Management System for Automa...

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IEEE International Conference on Big Data

作者： Jia-Hao Syu Jerry Chun-Wei Lin Dariusz Mrozek Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland

ISBN: (纸本)9781665480468

In this paper, we propose a Secure Energy Management System (SEMS) with anomaly detection and Q-Learning decision modules for Automated Guided Vehicles (AGV). The anomaly detection module is a multi-task learning network to simultaneously classify suppliers and predict the real supply quantities. The Q-learning decision module can then determine operating reserve and subsidies to manage the energy grid. Experimental results illustrate that the proposed anomaly detection module has an excellent performance in classifying malicious suppliers, excels at shaping supply distribution, and outperforms the existing benchmark systems.

关键词： Q-learning Remotely guided vehicles Big Data Benchmark testing Multitasking Energy management systems Anomaly detection

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Battery State of Energy Estimation Method Based on Conditional Selection Neural Network Model 8th

Battery State of Energy Estimation Method Based on Condition...

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8th International Conference on Life System Modeling and Simulation, LSMS 2024 and 8th International Conference on Intelligent Computing for Sustainable Energy and Environment, ICSEE 2024

作者： Liu, Kaituo Li, Yihuan Peng, Qiao School of Control and Computer Engineering North China Electric Power University Beijing102206 China Group of Information Technology Analytics and Operations Queen’s University Belfast BelfastBT9 5EE United Kingdom

ISBN: (纸本)9789819602315

There are differences in the discharge data of batteries at different life stages. Considering these differences, this paper proposes a conditional selection neural network model for estimating the SOE. It can flexibly select the appropriate network for prediction based on a set selection mechanism under different conditions, thereby improving the model’s applicability and generalization ability. Firstly, the concept of Squeeze-and-Excitation is incorporated into the CNN and the resulting SE-CNN model is pretrained. Next, a selection mechanism is designed according to the degree of battery aging, thus the training data is divided into four parts based on the number of discharge cycles to further train the pre-trained network to form a "four-in-one" CS-SE-CNN model. During SOE estimation, the CS-SE-CNN model selects the prediction network based on the number of discharge cycles in the input data. The results demonstrate that the proposed method exhibits high accuracy and robustness in estimating the SOE of lithium-ion batteries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： State of charge

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General Decay Synchronization of State and Spatial Diffusion Coupled Reaction-diffusion Memristive Neural Networks

General Decay Synchronization of State and Spatial Diffusion...

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Chinese Automation Congress (CAC)

作者： Yanli Huang Xin Zhao Jianmou Lu Jiang Li School of Computer Science and Technology Tiangong University Tianjin China School of Control and Computer Engineering North China Electric Power University Beijing China Center for Engineering Internship and Training Tiangong University Tianjin China

The purpose of this paper is to investigate the general decay synchronization (GDS) of coupled reaction-diffusion memristive neural networks (CRDMNNs) with state coupling and spatial diffusion coupling. Firstly, by devising a suitable controller and choosing an appropriate Lyapunov functional, we analyze the GDS of CRDMNNs with state coupling and derive a sufficient condition for achieving GDS of this kind of network. Secondly, a criterion for GDS of CRDMNNs with spatial diffusion coupling is also proposed by using several inequality techniques. Finally, one numerical example with simulation results is shown to demonstrate the validity of the obtained GDS result.

关键词： Couplings Sufficient conditions Automation Simulation Neural networks Stability criteria Synchronization

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Fair Division of Indivisible Goods: A Survey

arXiv

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arXiv 2022年

作者： Amanatidis, Georgios Birmpas, Georgios Filos-Ratsikas, Aris Voudouris, Alexandros A. Department of Mathematical Sciences University of Essex United Kingdom Department of Computer Control and Management Engineering Sapienza University of Rome Italy Department of Computer Science University of Liverpool United Kingdom School of Computer Science and Electronic Engineering University of Essex United Kingdom

Allocating resources to individuals in a fair manner has been a topic of interest since the ancient times, with most of the early rigorous mathematical work on the problem focusing on infinitely divisible resources. Recently, there has been a surge of papers studying computational questions regarding various different notions of fairness for the indivisible case, like maximin share fairness (MMS) and envy-freeness up to any good (EFX). We survey the most important results in the discrete fair division literature, focusing on the case of additive valuation functions and paying particular attention to the progress made in the last 10 years. Copyright © 2022, The Authors. All rights reserved.

关键词： Surveys

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A Real-time Cutting Model Based on Finite Element and Order Reduction

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computer Systems Science & engineering 2022年第10期43卷 1-15页

作者： Xiaorui Zhang Wenzheng Zhang Wei Sun Hailun Wu Aiguo Song Sunil Kumar Jha Wuxi Research Institute Nanjing University of Information Science&TechnologyWuxi214100China Engineering Research Center of Digital Forensics Ministry of EducationJiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China School of Automation Nanjing University of Information Science&TechnologyNanjing210044China State Key Laboratory of Bioelectronics Jiangsu Key Lab of Remote Measurement and ControlSchool of Instrument Science and EngineeringSoutheast UniversityNanjing210096China IT Fundamentals and Education Technologies Applications University of Information Technology and Management in RzeszowRzeszow Voivodeship100031Poland

Telemedicine plays an important role in Corona Virus Disease 2019(COVID-19).The virtual surgery simulation system,as a key component in telemedicine,requires to compute in ***,this paper proposes a realtime cutting model based on finite element and order reduction method,which improves the computational speed and ensure the real-time *** proposed model uses the finite element model to construct a deformation model of the virtual ***,a model order reduction method combining proper orthogonal decomposition and Galerkin projection is employed to reduce the amount of deformation *** addition,the cutting path is formed according to the collision intersection position of the surgical instrument and the lesion area of the virtual ***,the Bezier curve is adopted to draw the incision outline after the virtual lung has been ***,the simulation system is set up on the PHANTOM OMNI force haptic feedback device to realize the cutting simulation of the virtual *** results show that the proposed model can enhance the real-time performance of telemedicine,reduce the complexity of the cutting simulation and make the incision smoother and more natural.

关键词： Virtual surgery cutting model finite element model model order reduction Bezier curve

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Bi-layered coded metasurface for multi-functional hologram with broadband transmission and efficient reflection

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Chinese Optics Letters 2024年第5期22卷 8-16页

作者：朱磊李绪胜董亮谢文静尚关玉 Shah Nawaz Burokur 丁旭旻 School of Computer and Control Engineering Qiqihar UniversityQiqihar 161006China Communication and Electronics Engineering Institute Qiqihar UniversityQiqihar 161006China Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis Qiqihar UniversityQiqihar 161000China Department of Microwave Engineering Harbin Institute of TechnologyHarbin 150001China LEME UPLUniversitéParis NanterreF92410 Ville d’AvrayFrance Advanced Microscopy and Instrumentation Research Center Harbin Institute of TechnologyHarbin 150080China Key Laboratory of Millimeter Waves Southeast UniversityNanjing 210096China

A multi-functional full-space metasurface based on frequency and polarization multiplexing is *** metasurface unit consists of metallic patterns printed on the two faces of a single-layered dielectric *** unit cell can control electromagnetic wavefronts to achieve a broadband transmission with amplitudes greater than 0.4 from 4.4 to 10.4 ***,at 11.7 GHz and 15.4 GHz,four high-efficiency reflection channels with a reflection amplitude greater than 0.8 are also *** illuminated by linearly polarized waves,five different functions can be realized at five different frequencies,which are demonstrated by theoretical calculations,full-wave simulations,and experimental measurements.

关键词： multi-functional metasurface broadband transmission efficient reflection full space

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Reliability Analysis of Concurrent Data based on Botnet Modeling 4

Reliability Analysis of Concurrent Data based on Botnet Mode...

引用

4th International Conference on Inventive Systems and control, ICISC 2020

作者： Cui, Lianhe Huang, Dehai Zheng, Xiaodong Qiqihar University College of Computer and Control Engineering Heilongjiang Qiqihar161006 China

ISBN: (数字)9781728128139

ISBN: (纸本)9781728128139

Reliability analysis of concurrent data based on Botnet modeling is conducted in this paper. At present, the detection methods for botnets are mainly focused on two aspects. The first type requires the monitoring of high-privilege systems, which will bring certain security risks to the terminal. The second type is to identify botnets by identifying spam or spam, which is not targeted. By introducing multi-dimensional permutation entropy, the impact of permutation entropy on the permutation entropy is calculated based on the data communicated between zombies, describing the complexity of the network traffic time series, and the clustering variance method can effectively solve the difficulty of the detection. This paper is organized based on the data complex structure analysis. The experimental results show acceptable performance. © 2020 IEEE.

关键词： Botnet

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HaloFL: Efficient Heterogeneity-Aware Federated Learning Through Optimal Submodel Extraction and Dynamic Sparse Adjustment

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年

作者： Lian, Zirui Cao, Qianyue Liang, Chao Cao, Jing Zhu, Zongwei Yang, Zhi Ji, Cheng Li, Changlong Zhou, Xuehai School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China School of Software Engineering University of Science and Technology of China Hefei230026 China Xi'an Modern Control Technology Research Institute China Dfh satellite co. Ltd Cast China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science and Technology East China Normal University Shanghai200063 China

Federated Learning (FL) is an advanced framework that enables collaborative training of machine learning models across edge devices. An effective strategy to enhance training efficiency is to allocate the optimal submodel based on each device's resource capabilities. However, system heterogeneity significantly increases the difficulty of allocating submodel parameter budgets appropriately for each device, leading to the straggler problem. Meanwhile, data heterogeneity complicates the selection of the optimal submodel structure for specific devices, thereby impacting training performance. Furthermore, the dynamic nature of edge environments, such as fluctuations in network communication and computational resources, exacerbates these challenges, making it even more difficult to precisely extract appropriately sized and structured submodels from the global model. To address the challenges in heterogeneous training environments, we propose an efficient FL framework, namely HaloFL. The framework dynamically adjusts the structure and parameter budget of submodels during training by evaluating three dimensions: model-wise performance, layer-wise performance, and unit-wise performance. First, we design a data-aware model unit importance evaluation method to determine the optimal submodel structure for different data distributions. Next, using this evaluation method, we analyze the importance of model layers and reallocate parameters from non-critical layers to critical layers within a fixed parameter budget, further optimizing the submodel structure. Finally, we introduce a resource-aware dual-UCB multi-armed bandit agent, which dynamically adjusts the total parameter budget of submodels according to changes in the training environment, allowing the framework to better adapt to the performance differences of heterogeneous devices. Experimental results demonstrate that HaloFL exhibits outstanding efficiency in various dynamic and heterogeneous scenarios, achieving up to a 14

关键词： Federated learning

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