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7th IEEE International Conference on Transportation information and Safety, ICTIS 2023

作者： Li, Jiamin Liu, Jin Zhang, Xiliang Li, Xingye Wang, Junxiang Wu, Zhongdai Shanghai Maritime University College of Information Engineering Shanghai China Cosco Shipping Technology Co. Ltd National Engineering Research Center of Ship Transportation Control Systems Shanghai China Shanghai Ship and Shipping Research Institute State Key Laboratory of Maritime Technology and Safety Shanghai China

ISBN: (纸本)9798350308532

The Abnormal Behavior Detection of Vessels (ABVD) is crucial for enhancing Vessels' situational awareness as well as achieving effective management of maritime traffic. Clustering algorithms are extensively employed for ABVD tasks, however, they are not only time-consuming but also frequently inaccurate when dealing with complex and dynamic vessel trajectory detection tasks. To address the above problems, we propose a novel hybrid approach, CSDLSTM(Course/Speed-DBSCAN and LSTM), which combines Density-based Clustering(DBSCAN) and long short-term memory networks to improve the efficiency of ABVD. Specifically, a three-dimensional CS-DBSCAN (Course/Speed-DBSCAN) algorithm is proposed, which incorporates speed and course features along with geospatial information in the clustering process to accurately describe vessel behavior. In addition, the clustering results are used as traffic patterns to train the LSTM predictor for real-time maritime anomaly detection. Experiments conducted on real AIS datasets demonstrate that the CSD-LSTM method has greater precision and efficiency in ABVD. © 2023 IEEE.

关键词： Efficiency

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A Low Complexity ML-Based Methods for Malware Classification

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Computers, Materials & Continua 2024年第9期80卷 4833-4857页

作者： Mahmoud E.Farfoura Ahmad Alkhatib Deema Mohammed Alsekait Mohammad Alshinwan Sahar A.El-Rahman Didi Rosiyadi Diaa Salama Abd Elminaam Cybersecurity Department Al-Zaytoonah University of JordanAmman11733Jordan Department of Computer Science and Information Technology Applied CollegePrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Computer Systems Program-Electrical Engineering Department Faculty of Engineering-ShoubraBenha UniversityCairo11629Egypt Research Center for Artificial Intelligence and Cybersecurity Electronics and Informatics OrganizationNational Research and Innovation Agency(BRIN)KST Samaun SamadikunBandung40135Republic of Indonesia Jadara Research Center Jadara UniversityIrbid21110Jordan MEU Research Unit Middle East UniversityAmman11831Jordan

The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.

关键词： Malware detection ML-based models dimensionality reduction feature engineering

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A Large-Scale Multi-objective Evolutionary Algorithm Based on Two-layer Encoding Scheme for Neural Network Training

A Large-Scale Multi-objective Evolutionary Algorithm Based o...

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Automation, Robotics and Computer engineering (ICARCE), International Conference on

作者： Wanting Yang Jianchang Liu Tianzi Zheng College of Information Science and Engineering Northeastern University National Frontiers Science Center for Intelligence and Systems Optimization Northeastern University Shenyang China

ISBN: (数字)9798331529505

ISBN: (纸本)9798331529512

Evolutionary algorithms (EAs) have achieved great performance in solving multi-objective optimization problems (MOPs). However, there are more complex features in real-world MOPs. For example, the neural network training problem is characterized by large-scale decision variables and sparse Pareto-optimal solutions. This makes it difficult for most EAs to solve the neural network training problem. Therefore, this paper proposes a large-scale multi-objective evolutionary algorithm based on two-layer encoding scheme (MOEA- TES). In the proposed algorithm, the two-layer encoding scheme is employed. Moreover, a two-stage evolutionary strategy is designed for guiding the genetic operator. MOEA- TES is assessed by four real-world neural network training problems. Compared with five recent state-of-the-art algorithms, the proposed MOEA- TES achieves competitive results.

关键词： Training Automation Neural networks Evolutionary computation Encoding Vectors Optimization Robots Genetic operators

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Learning-Based Predictive Cruise Control for Fuel Consumption Model in Heavy-Duty Trucks 27

Learning-Based Predictive Cruise Control for Fuel Consumptio...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Gao, Yuan Sun, Kai Hong, Jinlong Na, Xiaoxiang Gao, Bingzhao Chen, Hong School of Automotive Studies Tongji University Shanghai201804 China University of Cambridge Centre for Sustainable Road Freight Cambridge United Kingdom College of Electronics and Information Engineering Tongji University Shanghai201804 China Frontiers Science Center for Intelligent Autonomous Systems Shanghai201210 China

ISBN: (纸本)9798331505929

The Predictive Cruise Control (PCC) algorithm is currently being utilized to address the high energy consumption issue in heavy-duty trucks. The premise for achieving significant energy-saving effectiveness with the PCC algorithm lies in accurately obtaining engine fuel consumption model. In this paper, we develop a data-driven fuel consumption model, which uses Gaussian processes regression to learn the prediction error between static fuel consumption map and actual fuel consumption rate. This model possesses the following characteristics: (i) Improved accuracy in predicting fuel consumption rates;(ii) Online parameter updating;(iii) Assisting PCC algorithm in higher energy-saving rates without increasing computational complexity. Through simulation validation, the learning-based model improves fuel consumption rate prediction accuracy by 26.9%. Moreover, the PCC algorithm, which uses this advanced model, has been compared with Adaptive Cruise Control (ACC) and the original PCC, achieving energy-saving improvements of up to 9.6% and 3.1%, respectively. © 2024 IEEE.

关键词： Trucks

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Explainable Application Intent for Zero-Touch Networking: An Incorporation of Hypergraph and Transformer

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IEEE Transactions on Communications 2025年

作者： Wu, Bing Zou, Sai Liwang, Minghui Ni, Wei Wang, Xianbin Guizhou University College of Big Data and Information Engineering China National Key Laboratory of Autonomous Intelligent Unmanned Systems Department of Control Science and Engineering China Tongji University Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Shanghai China Data61 CSIRO Australia Department of Electrical and Computer Engineering Western University Canada

The autonomous interpretation of application intent (APPI) represents the primary step towards achieving closed-loop autonomy in zero-touch networking (ZTN) and also a prerequisite for intent-based networking (IBN). However, understanding APPIs and invoking the corresponding network resources require network professionals with extensive technical expertise to customize network service requests (NSRs), which presents significant challenges for the large-scale deployment of ZTN. This paper investigates an interesting problem of autonomous interpretation of APPIs for ZTN, where a novel mechanism integrating hypergraph and transformer with completeness assurance (HyperTrans-CA) is proposed. In particular, we first involve the Bayesian theory to model APPIs interpretability as maximizing the correct transition probability, where hypergraph is used to describe the complex relationship between application characteristics (e.g., scenario function, and performance) and NSRs, including network devices, virtual network functions (VNFs), and resources. Then, the hypergraph is integrated into the encoder, decoder, and attention mechanisms of Transformer, and a completeness assurance mechanism is designed to improve the prediction accuracy. The convergence of HyperTrans-CA and the corresponding convergence speed of the hypergraph-boosted Transformer in the graph search process are also analyzed. Comprehensive simulations and empirical measurements regarding industrial internet demonstrate that HyperTrans-CA can effectively explain/understand APPIs. Compared to the state-of-the-art Transformer and ChatGPT3.5 models, HyperTrans-CA improves the prediction accuracy of APPIs mapped to VNFs by 23% and 46%, respectively, while raising the prediction accuracy of VNF locations by 8.6 and 17.3 times. © 2025 IEEE.

关键词： Bayesian networks

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Online Game with Time-Varying Coupled Inequality Constraints

arXiv

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arXiv 2023年

作者： Meng, Min Li, Xiuxian Hong, Yiguang Chen, Jie Wang, Long Department of Control Science and Engineering College of Electronics and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai China Center for Systems and Control College of Engineering Peking University Beijing China

In this paper, online game is studied, where at each time, a group of players aim at selfishly minimizing their own time-varying cost function simultaneously subject to time-varying coupled constraints and local feasible set constraints. Only local cost functions and local constraints are available to individual players, who can share limited information with their neighbors through a fixed and connected graph. In addition, players have no prior knowledge of future cost functions and future local constraint functions. In this setting, a novel decentralized online learning algorithm is devised based on mirror descent and a primal-dual strategy. The proposed algorithm can achieve sublinearly bounded regrets and constraint violation by appropriately choosing decaying stepsizes. Furthermore, it is shown that the generated sequence of play by the designed algorithm can converge to the variational GNE of a strongly monotone game, to which the online game converges. Additionally, a payoff-based case, i.e., in a bandit feedback setting, is also considered and a new payoff-based learning policy is devised to generate sublinear regrets and constraint violation. Finally, the obtained theoretical results are corroborated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Intraradicular Diameter, Remnant Dentin Thickness, and Endodontic Post to Dentin Distance: CBCT and Microscopic Analyses

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Biomedical Materials and Devices 2024年第1期2卷 384-396页

作者： Souza, Júlio C. M. Soares, Catarina Fernandes, Valter Carvalho, Oscar Silva, Filipe Özcan, Mutlu Henriques, Bruno University Institute of Health Sciences (IUCS) CESPU Center for MicroElectroMechanical Systems (CMEMS-UMINHO) University of Minho LABBELS - Associate Laboratory University of Minho Division of Dental Biomaterials Center of Dental Medicine Clinic of Reconstructive Dentistry University of Zurich Ceramic and Composite Materials Research Group (CERMAT) Dept. of Mechanical Engineering (EMC) Federal University of Santa Catarina (UFSC)

The objective of this work was to measure the tooth root canals’ diameter, remnant dentin thickness, endodontic post to dentin distance, and resin-matrix cement layer after three types of root canal shaping. Thirty extracted human premolars were endodontically treated and groups of specimens were divided according to the cementation with two different endodontic posts as follow: A) Fibio Fiberglass Post™, Anthogyr, France;B) multi-filament GFRC (Rebilda GT™, VOCO, Germany). Cone beam computed tomography (CBCT) and conventional X-ray analyses were performed before and after the endodontic post cementation. After cementation, specimens were cross-sectioned and inspected by optical microscopy and scanning electron microscopy at magnification ranging from × 30 up to × 2000. Changes in shaping of the root canals caused a decrease in the thickness of the remnant tooth tissues. CBCT and microscopic analyses also revealed a clear variation of resin-matrix cement around the glass fiber-reinforced composite (GFRC) posts. A multi-filament GFRC post provided an adequate distribution of filaments although the resin-matrix cement revealed a high volume among the filaments. An increase in thickness and volume of resin-matrix cement was noticed at the coronal third since the fitting was compromised due to tooth anatomic variations and root canal preparation. Defects such macro-scale pores, cracks, and voids were also detected by microscopic analyses. The root canal shaping can promote a decrease in the thickness of the remnant tooth tissues that can increase the risks of clinical failures by fracture. The thickness and volume of resin-matrix cement varied around both GFRC posts and increased from the apex up to the coronal third due to the lack of fitting. Graphical Abstract: (Figure presented.) © The Author(s) 2023.

关键词： Endodontic posts Fitting Glass fiber-reinforced composite posts Intraradicular Root canal

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Domain-Adaptive Power Profiling Analysis Strategy for the Metaverse

Domain-Adaptive Power Profiling Analysis Strategy for the Me...

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作者： Li, Xiang Yang, Ning Liu, Weifeng Chen, Aidong Zhang, Yanlong Wang, Shuo Zhou, Jing Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Institute of Semiconductors Chinese Academy of Sciences Beijing China College of Robotics Beijing Union University Beijing China Research Center for Multi-Intelligent Systems Beijing Union University Beijing China Information System Laboratory Beijing Microelectronics Technology Institution Beijing China

In the surge of the digital era, the metaverse, as a groundbreaking concept, has become a focal point in the technology sector. It is reshaping human work and life patterns, carving out a new realm of virtual and real interaction. However, the rapid development of the metaverse brings along novel challenges in security and privacy. In this multifaceted and complex technological environment, data protection is of paramount importance. The innovative capabilities of high-end devices and functions in the metaverse, owing to advanced integrated circuit technology, face unique threats from side-channel analysis (SCA), potentially leading to breaches in user privacy. Addressing the issue of domain differences caused by different hardware devices, which impact the generalizability of the analysis model and the accuracy of analysis, this paper proposes a strategy of portability power profiling analysis (PPPA). Combining domain adaptation and deep learning techniques, it models and calibrates the domain differences between the profiling and target devices, enhancing the model's adaptability in different device environments. Experiments show that our method can recover the correct key with as few as 389 power traces, effectively recovering keys across different devices. This paper underscores the effectiveness of cross-device SCA, focusing on the adaptability and robustness of analysis models in different hardware environments, thereby enhancing the security of user data privacy in the metaverse environment. © 2024 John Wiley & Sons Ltd.

关键词： Data privacy

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A Novel Support Recovery Algorithms and Its Applications to Multiple-Access Channels

A Novel Support Recovery Algorithms and Its Applications to ...

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2022 IEEE International Multi-Conference on engineering, Computer and information Sciences, SIBIRCON 2022

作者： Fernandez, Marcel Kabatiansky, Grigory Miao, Ying Universitat Politecnica de Catalunya Telecomm. Engineering School Barcelona Spain Skolkovo Institute of Science and Technology Center for Wireless Communication & IoT Moscow Russia University of Tsukuba Engineering Information & Systems Tsukuba Japan

ISBN: (纸本)9781665464802

A novel algorithm of a sparse vector support recovery in presence of noise is proposed. In contrary to known algorithms based on matrices with RIP-property our algorithm exploits properties of so-called separating codes. Moreover the new algorithm has less complexity that previously known and possess some useful properties, not valid for other known algorithms. © 2022 IEEE.

关键词： Computational complexity

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Quantum Photonics Enhances Continuous Variable Quantum Key Distribution

Quantum Photonics Enhances Continuous Variable Quantum Key D...

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2023 Photonics and Electromagnetics Research Symposium, PIERS 2023

作者： Li, Lang Wang, Tao Huang, Peng Zeng, Guihua Institute of Quantum Sensing and Information Processing School of Sensing Science and Engineering Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and Networks Department of Electronic Engineering 200240 China Shanghai Research Center for Quantum Sciences Hefei National Laboratory Shanghai201315 China

ISBN: (纸本)9798350312843

Continuous-variable quantum key distribution (CV-QKD) technology has the characteristics of high channel capacity and well compatibility with existing optical communication infrastructures, and has become an important implementation method of quantum secure communication. Quantum photonics technology provides a promising solution for the construction of low-cost, miniaturized, portable, scalable and highly stable CV-QKD systems that can be deployed at large scale [1]. Therefore, it is of great significance to research and build a chip-based CV-QKD system with higher integration, higher system performance and higher practical security for the development of high-performance and practical quantum secure communication technology. This paper mainly reports the latest progress of the research of on-chip CV-QKD system in terms of system practical security [2] and integration of high-performance light sources [3]. © 2023 IEEE.

关键词： Quantum cryptography

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