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Artificial intelligence in physiological characteristics recognition for internet of things authentication

Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Edge-guided nonlinear dynamic convolution network for lightweight semantic segmentation 2

Edge-guided nonlinear dynamic convolution network for lightw...

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2nd International Conference on Physics, Photonics, and Optical engineering, ICPPOE 2023

作者： Zhang, Chunyu Xu, Fang Wu, Chengdong Zhang, Chenghao Faculty of Robot Science and Engineering Northeastern University Shenyang China Shenyang Siasun Robot and Automation Company Ltd. Shenyang110168 China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin China

ISBN: (纸本)9781510674684

As the demand for autonomous driving and robot vision arises, semantic segmentation has developed rapidly. Semantic segmentation provides the means for machines to understand the environment. However, current methods face a balance issue between segmentation quality and computational resources. Our proposed edge-guided non-linear dynamic convolutional network (ENNet) achieves real-time and accurate semantic segmentation. The core of our network is a novel non-linear, dynamic combination (Nd-conv) that allows for a non-linear, dynamic combination (Nd-conv) of multiple convolution weights, improving the convolution's encoding ability while maintaining a low computational burden. The Non-linear Dynamic Convolutional Modules (NDCM) is introduced to enhance the segmentation accuracy of the network while maintaining efficiency. The Multi-stage Feature Fusion Module (MFFM) is also introduced to fuse low-level details and high-level semantic information to improve the segmentation accuracy of the network. Experiments conducted on a 1080Ti GPU show that our model achieves 74.6 mIOU on the Cityscapes dataset while being able to perform inference at 202 FPS. Our network design has achieved an optimal balance between speed and accuracy. © COPYRIGHT 2024 SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Convolution

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An adaptive multi-Bernoulli filter for coexisting point target and extended target tracking with unknown detection probability

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IEEE Access 2025年 13卷 84593-84606页

作者： Li, Yuansheng Cui, Ling Ma, Junhu Luo, Zhaohui Chongqing Vocational Institute of Engineering College of Big Data and Internet of Things Chongqing402260 China Kunming University of Science and Technology Faculty of Information Engineering and Automation Kunming650500 China Southwest China Institute of Electronic Technology Chengdu610036 China Shunde Polytechnic College of Intelligent Manufacturing Shunde528300 China

This paper proposes an adaptive multi-Bernoulli (MB) filter for coexisting point target and extended target tracking, where the detection probability of each target is unknown and time-varying. The detection probability of the target directly affects the tracking accuracy in multi-sensor multi-target tracking. In realistic tracking circumstances, the detection probability of the target is unknown. Therefore, it is crucial to adaptively estimate the detection probability of the target in real-time. In addition, as the resolution of sensor increases, the point target and extended target often coexist in the field of view. Therefore, it is necessary to construct a generalized measurement model for tracking coexisting point target and extended target. For the coexistence of point and extended target, we model the spatial probability density function which can accommodate both point target and extended target based on a MB filter. For the unknown detection probability, we integrate the detection probability into the target state and the detection probability is characterized as a Beta distribution. To obtain a closed-form solution, the spatial probability density function of each MB component is modeled as a mixture of the Beta-Gaussian distribution and the Beta Gamma Gaussian Inverse Wishart (BGGIW) distribution. Based on the estimated probability of the point target, the target is determined as either a point target or an extended target. Finally, the detection probability, kinematic state, Poisson measurement rate (MR) and extended state of each target are estimated synchronously. Simulation results verify the effectiveness and robustness of the proposed algorithm. © 2013 IEEE.

关键词： Gaussian distribution

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GACP:graph neural networks with ARMA flters and a parallel CNN for hyperspectral image classification

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International Journal of Digital Earth 2023年第1期16卷 1770-1800页

作者： Jing Yang Jie Sun Yaping Ren Shaobo Li Shujie Ding Jianjun Hu School of Mechanical Engineering Guizhou UniversityGuiyangPeople’s Republic of China State Key Laboratory of Public Big Data Guizhou UniversityGuiyangPeople’s Republic of China School of Intelligent Systems Science and Engineering Jinan UniversityZhuhaiPeople’s Republic of China Department of Computer Science and Engineering University of South CarolinaColumbiaSCUSA

In recent years,the use of convolutional neural networks(CNNs)and graph neural networks(GNNs)to identify hyperspectral images(HSIs)has achieved excellent results,and such methods are widely used in agricultural remote sensing,geological exploration,and marine remote *** many generalization classification algorithms are designed for the purpose of learning a small number of samples,there is often a problem of a low utilization rate of position information in the empty spectral *** on this,a GNN with an autoregressive moving average(ARMA)-based smoothingfilter samples the node information in the null spectral domain and then captures the spatial information at the pixel level via spatial feature convolution;then,the null spectral domain position information lost by the CNN is located by a coordinate attention(CA)***,autoregressive,spatial convolution,and CA mechanisms are combined into multiscale features to enhance the learning capacity of the network for tiny *** conducted on the widely used Indian Pines(IP)dataset,the Botswana(BS)dataset,Houton 2013(H2013),and the WHU-Hi-HongHu(WHU)benchmark HSI dataset demonstrate that the proposed GACP technique can perform classification work with good accuracy even with a small number of training examples.

关键词： Graph neural networks word convolutional neural networks hyperspectral image classification attention mechanisms

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Random Walk Operator-Based Fourier Transform in Connected Directed Acyclic Graphs

Random Walk Operator-Based Fourier Transform in Connected Di...

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International Scientific-Professional Conference on Information Technology (IT)

作者： Miloš Brajović Isidora Stanković Miloš Daković Ali Bagheri Bardi Ljubiša Stanković Faculty of Electrical Engineering University of Montenegro Podgorica Montenegro Faculty of Intelligent Systems Engineering and Data Science Persian Gulf University Busher Iran

Spectral analysis of signals defined on Directed Acyclic Graphs (DAGs) poses significant challenges due to the presence of zero eigenvalues in the adjacency matrix and equivalent shift operators, such as the random walk matrix. This characteristic hinders the differentiation between spectral components of signals on such graphs, rendering conventional spectral analysis impossible. To mitigate this issue, a zero-padding technique for signals defined on DAGs was recently proposed. Given the similarity between the properties of the random walk matrix and the adjacency matrix, this paper explores the feasibility of Fourier analysis using the eigen-decomposition basis of such matrices. The extension of the zero-padding concept to signals on DAGs described by the random walk matrix involves introducing additional nodes connected to the existing structure, with the signal values on these added nodes set to zero. The primary objective of this approach is to facilitate the computation of vertex-domain convolution, thereby enabling the output of graph filters without encountering aliasing issues.

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AFuNet: an attention-based fusion network to classify texts in a resource-constrained language

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Neural Computing and Applications 2025年第9期37卷 6725-6748页

作者： Hossain, Md. Rajib Hoque, Mohammed Moshiul Dewan, M. Ali Akber Hoque, Enamul Siddique, Nazmul Department of Computer Science & Engineering Chittagong University of Engineering & Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada School of Information Technology York University TorontoONM3J 1P3 Canada School of Computing Engineering and Intelligent Systems Ulster University Coleraine United Kingdom

In the era of widespread Internet use and extensive social media interaction, the digital realm is accumulating vast amounts of unstructured text data. This unstructured data often contain undesirable information, necessitating time-consuming manual classification efforts. An intelligent text classification system capable of automatically categorizing digitized texts based on semantic meaning is crucial. However, this task is particularly challenging for low-resource languages like Bengali due to a shortage of annotated corpora, issues with out-of-vocabulary words, lack of domain-specific hyperparameter tuning, limited ability to extract generalized text features, and class imbalances within the corpus. AFuNet: an attention-based fusion network to classify texts in a resource-constrained language. AFuNet undergoes a comprehensive four-phase experimental process, including baseline model evaluation and hyperparameter tuning, late fusion and model selection, attention-based early fusion and model identification, and an ablation study with impact analysis. Fine-tuned based on five Bengali text classification corpora, AFuNet achieves impressive accuracies: 96.60 ± 0.2 (BTCC11), 85.37 ± 0.2 (OSBC), 97.35 ± 0.2 (BARD), 93.74 ± 0.2 (IndicNLP), and 96.51 ± 0.2 (ProthomAlo). In comparison with previous state-of-the-art models on these corpora, AFuNet demonstrates significant accuracy improvements ranging from 0.54% to 4.49%, showcasing its effectiveness in advancing text classification capabilities for the Bengali language. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Semantics

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Signal Filtering on Directed Acyclic Graphs

Signal Filtering on Directed Acyclic Graphs

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Telecommunications Forum (TELFOR)

ISBN: (数字)9798350391060

ISBN: (纸本)9798350391077

A directed acyclic graph (DAG) is a specialized form of directed graph used extensively for analyzing relationships and dependencies across various domains. However, spectral analysis—a core technique in signal processing on graphs—has been challenging to apply directly to DAGs due to their inherent acyclic structure. In this paper, we introduce a spectral filtering approach for DAGs using graph zero-padding. By adding extra nodes to the DAG, we create a modified graph structure that enables spectral analysis. This new structure supports the application of the graph Fourier transform (GFT) and spectral filtering techniques, enabling spectral filtering while maintaining key aspects of the DAG’s topology and dynamics.

关键词： Wavelet transforms Directed acyclic graph Fourier transforms Systematics Low-pass filters Directed graphs Topology Telecommunications Noise measurement Spectral analysis

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Water Surface Autonomus Navigation Simulation via Multi-agent Proximal Policy Optimization

Water Surface Autonomus Navigation Simulation via Multi-agen...

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intelligent Robotics and Automatic Control (IRAC), International Conference on

作者： Ziang Yuan Yinghui Li Junwei Duan Faculty of Data Science City University of Macau Macau China School of Intelligent Systems Science and Engineering Jinan University Guangzhou China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

To address the challenges of autonomous navigation and intelligent obstacle avoidance for clusters of homogeneous unmanned surface vehicles (USVs) in intricate environments, a self-driving algorithm utilizing multi-agent reinforcement learning (MARL) is introduced. A multi-obstacle scenario is created, where the interactions among unmanned underwater vehicles are used to assess the game situation. The USV fleet undergoes training through the multi-agent proximal policy optimization algorithm (MAPPO) to facilitate autonomous driving. This approach allows for efficient route planning and effective obstacle evasion, ensuring that the vehicles reach their destination with the desired orientation and relative positioning. The simulation environment is constructed with the Unity game engine, providing visualization during training to enhance algorithm robustness. Experimental results demonstrate that both the proposed algorithm and simulation setup exhibit strong feasibility and good performance.

关键词： Training Visualization Clustering algorithms Process control Games Reinforcement learning Robustness Collision avoidance Optimization Testing

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Semantic association rules for data interestingness using domain ontology

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International Journal of Metadata, Semantics and Ontologies 2022年第1期16卷 47-67页

作者： Abhilash, C.B. Mahesh, Kavi Department of Computer Science and Engineering Indian Institute of Information Technology Dharwad Karnataka Dharwad India Department of Data Science and Intelligent Systems Indian Institute of Information Technology Dharwad Karnataka Dharwad India

The COVID-19 pandemic is a major public health crisis threatening people’s health, well-being, freedom to travel and the global economy. Understanding COVID-19 symptoms for determining the severity of cases is critical. This study aimed to discover interesting facts from the COVID-19 data set considering symptoms, medicines and comorbidity. For data mining research, the semantic web raises new possibilities. Resource Description Framework (RDF) triple format is commonly used to express semantic web data. Association Rule Mining (ARM) is one of the most effective methods of detecting frequent patterns. However, finding potential rules is a difficult task. We propose an improved method that uses ontology with ARM for finding semantic-rich rules from COVID-19 data sets. The outcomes are semantic association rules that are potentially beneficial for decision-makers. We compare our results with one of the most recent approaches in this field to demonstrate the importance of ontology-based methods. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： COVID-19

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Analyzing and Mitigating Common Vulnerabilities in Smart Contracts in Web3 Ecosystem: A Comprehensive Study 2

Analyzing and Mitigating Common Vulnerabilities in Smart Con...

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2nd IEEE International Conference on data, Decision and systems, ICDDS 2023

作者： Hegadi, Rajendra Akella, Syam Sankar Karthik Reddy, Koushik Kumar Pavan, C. Indian Institute of Information Technology Dharwad Department of Data Science and Intelligent Systems Karnataka India Indian Institute of Information Technology Dharwad Department of Computer Science and Engineering Karnataka India

ISBN: (纸本)9798350314168

As we enter a new era of decentralized applications and blockchain technology, the issue of Web3 security becomes crucial and requires immediate attention. Given the growing popularity of using smart contracts in web3, it is vital to ensure the protection of digital assets and user data online. This paper aims to examine the different security risks associated with smart contracts in web3 and propose measures to mitigate them. This research article delves into the identification, causes, and potential mitigation steps for common vulnerabilities found in smart contracts in web3. Specifically, we focus on six major vulnerabilities: overflow and underflow, access control vulnerabilities, re-entrancy attacks, price manipulation attacks, signature reply attacks and self-destruct attacks. By comprehensively discussing these vulnerabilities and offering mitigation strategies, this research aims to disseminate the security vulnerabilities in smart contracts in Web3 ecosystem and provides a path way for adoption of blockchain technology. © 2023 IEEE.

关键词： Smart contract

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