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6th International Congress on Blockchain and Applications, BLOCKCHAIN 2024

作者： Rodríguez Santamaría, Pablo González-Briones, Alfonso Faculty of Law University of Salamanca C. Miguel de Unamuno. P. Francisco Tomás y Valiente Salamancas/n.37007 Spain BISITE Research Group Department of Computer Science and Automation Faculty of Science University of Salamanca Instituto de Investigación Biomédica de Salamanca Calle Espejo 2 24.2 Salamanca37007 Spain

ISBN: (纸本)9783031819278

his article delves into the intricacies of hidden services on the Tor darknet, with a focus on their indexing and deanonymization. It details a sophisticated system that compiles hidden service addresses through darknet scrapping and analyses service characteristics using tools like Shodan browser. The system employs various strategies, including using Onion Proxies, Crawler.c, and the *** script for data extraction. It also explores the challenges in collecting hidden service addresses and suggests solutions, such as the creation of an Onion Router within the Tor network. The article highlights the use of metadata for indexing, deanonymization and analysis in categorizing hidden services according to their content. Technologies like BeautifulSoup, NLTK, and lang detect are utilized for language and content processing. The study also covers the deanonymization of hidden services, the categorization of these services, and the anonymity features of cryptocurrencies like Monero and Zcash, comparing them with the limitations of the Bitcoin cryptocurrency. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Anonymity

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Investigations and Time Estimation on Federated Learning for Future Internet of Vehicles

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IEEE Internet of Things Journal 2025年第11期12卷 17387-17398页

作者： Fukumoto, Shun Li, Ruidong Zeng, Kai Nan, Haihan Su, Zhou Kanazawa University Intelligent Computation and Network Laboratory Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan Kunming University of Science and Technology Faculty of Information Engineering Automation Kunming066000 China Kanazawa University Division of Electrical Information and Communication Engineering Kanazawa920-1192 Japan George Mason University Electrical and Computer Engineering Department FairfaxVA22030 United States Xi’an Jiaotong University School of Cyber Science and Engineering Xi’an710049 China

For future Internet of Vehicles (IoV), communications and computing will converge to provide services. Federated learning (FL), as one of the typical distributed computing technologies, needs to be integrated with IoV. For such integration, FL suffers from the straggler effect that the entire learning speed is lowered down, because of the existence of the devices, such as low-powered road side units and vehicles, taking more time to complete their tasks. Although the existing mechanisms reduce straggler effects by adopting asynchronous mechanisms and clustering mechanisms, they lack the detailed analysis of the reasons and the impacts of each cause, leading to inefficiencies in the design of algorithm. Additionally, most of the existing work only considered the impact of a single factor in computation, communication, or data distribution, which lacks comprehensive on research for causes of stragglers effects. The bottleneck is that it is laborious to observe the time delay precisely with the existing high-calculating evaluations. In this article, we elaborately explore the effects of computing power, communication capability, and data distributions on the straggler effects with carefully designing and conducting the extensive experiments. After investigations, we propose a novel learning completion time estimation formula for low computing capability devices with mini-batch stochastic gradient decent (SGD). We compare our proposed estimation formula with the one based on floating operation per second (FLOPs). Through the evaluations, our formula can demonstrate the improvement up to 72.4% at docker and 32.4% at Raspberry Pi device compared to the existing work. © 2014 IEEE.

关键词： Stochastic systems

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Formation Control of Heterogeneous Multi-Agent Systems Under Fixed and Switching Hierarchies

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IEEE Access 2024年 12卷 97868-97882页

作者： Shamrooz Aslam, Muhammad Bilal, Hazrat Chang, Wen-Jer Yahya, Abid Anjum Badruddin, Irfan Kamangar, Sarfaraz Hussien, Mohamed China University of Mining and Technology Artificial Intelligence Research Institute Xuzhou221116 China University of Science and Technology of China Department of Automation Hefei230026 China Department of Marine Engineering Keelung202 Taiwan Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana King Khalid University College of Engineering Department of Mechanical Engineering Abha Saudi Arabia King Khalid University Faculty of Science Department of Chemistry Abha61413 Saudi Arabia

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature's cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi-agent systems with time-delays. First, the formation control of heterogeneous multi-agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi-agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first- and second-order multi-agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies. © 2013 IEEE.

关键词： Topology

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A Short-Term Approach for Enhancing Efficiency in Agglomerated BEV Charging Areas 2nd

A Short-Term Approach for Enhancing Efficiency in Agglomerat...

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2nd International Conference on Cognitive Mobility, COGMOB 2023

作者： Wengritzky, Zsuzsánna Benedek, Balázs Faculty of Economic and Human Sciences Sapientia University Miercurea Ciuc Romania Faculty of Automation and Computer Science Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (纸本)9783031876196

Battery Electric Vehicles (BEVs) are becoming increasingly popular on a global level and have a promising perspective towards a sustainable and environmental-friendly future in the transportation sector. This paper makes use of cognitive mobility elements and suggests a theoretical approach in that intercommunication of these elements creates a better distribution of BEVs in need of charge. The paper is organized as follows: Sect. 1 presents the existing literature and Sect. 2 briefly introduces the evolution of the charging infrastructure pointing out the existence of highly targeted charging areas and the infrastructure they lack. Section 3 suggests an innovative short-term approach for reducing charging waiting time done by identification of the most frequented charging stations and development of a payment-based booking interface system. Eventually, an AI-based approach is presented, which has the potential to optimize route algorithms by considering traffic circumstances, availability of charging stations and user preferences. Section 4 presents the results and concludes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Fast charging (Batteries)

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Quantifying Controllability for Nonlinear State-Dependent Riccati Equation Control

Quantifying Controllability for Nonlinear State-Dependent Ri...

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2022 International Russian automation Conference, RusAutoCon 2022

作者： Hu, Yuhui Neusypin, Konstantin Avenirovich Shen, Kai Bauman Moscow State Technical University Faculty of Computer Science and Control Systems Moscow Russia Beijing Institute of Technology School of Automation Beijing China

ISBN: (数字)9781665466554

ISBN: (纸本)9781665466554

Degree of controllability (DOC) characterizes how controllable a given system is and thus quantifying controllability can facilitate the control system synthesis and optimization. In this paper, the controllability of nonlinear input-affine systems and the state-dependent-coefficient (SDC) factorization are first reviewed. A computational procedure based on the scalarization of the controllability Gramian is proposed to quantify the controllability of both system and state variables of the SDC factored system for state-dependent Riccati equation (SDRE) control. The simulation of coordinate satellite control is carried out to validate the effectiveness of the proposed DOC criterion. It is shown that SDC-parameterized models with higher DOC can promote the performance of the SDRE control algorithm. © 2022 IEEE.

关键词： Controllability

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Efficient Time-Delay System Optimization with Auto-Configured Metaheuristics

Efficient Time-Delay System Optimization with Auto-Configure...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, MacIej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 IEEE.

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Stereo Matching for Face Images Based on Feature Points

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Chinese Journal of Electronics 2023年第2期19卷 241-244页

作者： Yinghui Wang Bing Xiao Qiongfang Wang Faculty of Computer Science and Engineering Xi'an University of Technology Xi'an China Department of Computer Science Shaanxi Normal University Xi'an China Faculty of of Automation and Information Engineering Xi'an University of Technology Xi'an China

Stereo matching is the key of reconstructing 3D face model from 2D face images and Barnard's algorithm is one of the classical stereo matching algorithms based on feature points. In order to enhance the stability of extracting feature points, reduce computational complexity and minimize the effect of subjective factors, in this paper, Barnard's algorithm of edge point matching was improved from two aspects: edge point sets of left and right face images were extracted with dyadic wavelet transform, rather than comparing gray values of adjacent pixels; on the other hand, edge points in binocular stereo face images were matched under epipolar constraint in parallel binocular stereo matching. Experimental results demonstrate that stability and efficiency of stereo matching algorithm are both improved.

关键词： Wavelet transforms Technological innovation Solid modeling Three-dimensional displays Image edge detection Education Medical services

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Detection of Detonators in Baggage Using Deep CNN Architecture: A Comparative Study of Contrast Enhancement and Feature Extraction Methods 8

Detection of Detonators in Baggage Using Deep CNN Architectu...

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8th IEEE International Conference on Image and Signal Processing and their Applications, ISPA 2024

作者： Lynda, Oulhissane Mostefa, Merah Moldovanu, Simona Moraru, Luminita LSS Faculty of Science and Technology Abdelhamid Ibn Badis University Laboratory of Signals and Systems Mostaganem Algeria Faculty of Automation Computers Electrical Engineering and Electronics Dunarea de Jos University of Galati Modelling & Simulation Laboratory MSlab Department of Computer Science and Information Technology Galati Romania Faculty of Sciences and Environment Modelling & Simulation Laboratory Dunarea de Jos University of Galati MSlab Department of Chemistry Physics & Environment Galati Romania

ISBN: (纸本)9798350309249

Visual inspection of dual-energy X-ray radiographic images of cabin baggage requires high performance, but is hindered by various challenges such as low target prevalence, variability in target visibility, possible presence of multiple targets, and security personnel fatigue and inattention. Artificial intelligence (AI) techniques, particularly deep convolutional neural networks (CNNs), have shown promise in improving the automatic detection of explosives, even with low-resolution radiographic images, especially in high baggage throughput scenarios. In this paper, we focus on the detection of detonators as components of improvised explosive devices. The proposed approach involves comparing two experiments implemented in a deep CNN architecture using TensorFlow and Keras libraries. In the first experiment, raw dual-energy radiographic images without any enhancement were used. The second experiment includes three methods for contrast enhancement and feature extraction: the Contrast Limited Adaptive Histogram Equalization (CLAHE) method, the wavelet transform-based method, and the mixed CLAHE RGB-Wavelet method. In the latter two methods, Haar, Db2, Coif2, and Sym2 mother wavelet functions at two levels (HH and HL) were employed. The analysis of results focuses on a comparative study of performance measures such as accuracy, precision, recall, and F1 score. It was found that the preprocessing methods used in experiment 2, for the two evaluated classes (detonator and no detonator), achieved higher accuracy compared to the raw radiographic images used in experiment 1 (98.08%). The highest accuracies in experiment 2, with a value of 100%, were obtained with the CLAHE method (green channel in grayscale, blue channel in grayscale, and RGB channels) and the wavelet transform method with Haar mother wavelet at two levels HL © 2024 IEEE.

关键词： Feature extraction

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Research on Citrus Leaf Disease Recognition Using Class-Agnostic Contrastive Learning and Supervised Organizational Mapping

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IEEE Access 2025年 13卷 101592-101608页

作者： Xiao, Wen Shang, Jinxia Li, Fan Ao, Ou Wang, Xin Tian, Shiyi Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Kunming University of Science and Technology Yunnan Key Lab of Computer Technology Applications Kunming650500 China

Early detection of plant diseases is crucial for enhancing agricultural productivity and ensuring crop protection. While computer vision offers scalable alternatives to manual inspection, existing methods face two understudied challenges: (1) performance degradation in cluttered real-world conditions, and (2) ineffective feature separation for randomly distributed lesions with diverse symptoms. Notably, current research predominantly relies on controlled-environment datasets, lacking publicly accessible benchmarks with authentic field complexities. To address these, we present the first large-scale citrus leaf disease dataset (5,763 images, 6 categories) capturing complex field conditions including varying illumination, occlusion, multi-scale lesions, and deceptive backgrounds. Our key methodological innovations include a three-stage weakly supervised framework that systematically resolves background interference and feature redundancy: (i) Class-agnostic contrastive localization enabling precise lesion mapping without region annotations, (ii) Stable feature initialization through region-guided classification training, (iii) Sparse orthogonal mapping with experimentally-determined nodes introducing orthogonality constraints to enhance feature separation. Through feature alignment and feature fusion, an accuracy of 97.1% is achieved (+2.4% vs ResNet-18). Evaluations on citrus leaf and public datasets demonstrate superior accuracy and interpretable lesion localization, advancing plant disease diagnosis. © 2013 IEEE.

关键词： Citrus fruits

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Integrated Extended SIR-type Epidemiological Modeling Platform for Covid-19 10

Integrated Extended SIR-type Epidemiological Modeling Platfo...

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10th E-Health and Bioengineering Conference, EHB 2022

作者： Brezulianu, Adrian Popa, Iolanda Geman, Oana Chiuchisan, Iuliana Nastase, Carmen GREENSOFT S.R.L. Iasi Romania Computer Science Electronics and Automation Department Faculty of Electrical Engineering and Computer Science "Stefan cel Mare" University of Suceava Suceava Romania Faculty of Economics Administration and Business "Stefan cel Mare" University of Suceava Romania

ISBN: (纸本)9781665485579

In these conditions of Covid-19 pandemic, the design and implementation of an epidemiological modeling platform with an API-type integration with beneficiary entities and data providers is a priority for the healthcare system and other organizations. An epidemiological platform (PLIS), replicable and adaptable for managing an epidemic, based on an integrated extended SIR-type model, is presented in this paper. This solution has an open architecture, respecting the scalability and interoperability, requirements, and is based on international communication standards and protocols, and SOA technology. © 2022 IEEE.

关键词： COVID-19

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