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Boosting Robustness in Automatic Modulation Recognition for Wireless Communications

IEEE Transactions on Cognitive Communications and Networking 2024年第3期11卷 1635-1648页

作者： Zhao, Yuhang Wang, Yajie Zhang, Chuan Li, Chunhai Xiong, Zehui Zhu, Liehuang Niyato, Dusit Beijing Institute of Technology School of Cyberspace Science and Technology Beijing100081 China Guilin University of Electronic Technology Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin541004 China Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University College of Computing and Data Science 639798 Singapore

In the radio frequency field, deep neural networks have been widely used for automatic modulation recognition tasks due to their superior accuracy. However, it has been shown that these models are susceptible to adversarial examples, which are the kinds of carefully crafted perturbations that can lead to model misclassification and raise security issues in applications. To solve this problem, we propose an Ultra-Fusion Adversarial Training method, which combines adversarial training and ensemble learning to enable the model robustness to withstand different attack strengths. We explore the number and distribution of ensembled attacks and introduce a Fermi-function-like distribution to optimally balance the performance of different attack strengths. Additionally, we investigate the effect of the signal-to-noise ratio (SNR) interval on the model accuracy and robustness, suggesting the effective SNR interval for training. Considering the demand for practical application scenarios of modulation recognition, we propose a comprehensive robustness metric based on weighted integral to evaluate the robustness of the trained models. Numerical experiments demonstrate that our method improves the model's robustness by 31.89% against white-box attacks, and achieves up to an 80.54% improvement in black-box scenarios. These results show that our method has the ability to resiliently resist potential attacks of various strengths and can be applied to spectrum application scenarios with high-security requirements. © 2015 IEEE.

关键词： Signal modulation

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Improved Density Peaking Algorithm for Community Detection Based on Graph Representation Learning

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computer Systems science & engineering 2022年第12期43卷 997-1008页

作者： Jiaming Wang Xiaolan Xie Xiaochun Cheng Yuhan Wang Guangxi Key Laboratory of Embedded Technology and Intelligent System Guilin541006GuangxiChina School of Information Science and Engineering Guilin University of TechnologyGuilin541006GuangxiChina Department of Computer Science Middlesex UniversityLondon

There is a large amount of information in the network data that we canexploit. It is difficult for classical community detection algorithms to handle network data with sparse topology. Representation learning of network data is usually paired with clustering algorithms to solve the community detection ***, there is always an unpredictable distribution of class clusters outputby graph representation learning. Therefore, we propose an improved densitypeak clustering algorithm (ILDPC) for the community detection problem, whichimproves the local density mechanism in the original algorithm and can betteraccommodate class clusters of different shapes. And we study the communitydetection in network data. The algorithm is paired with the benchmark modelGraph sample and aggregate (GraphSAGE) to show the adaptability of ILDPCfor community detection. The plotted decision diagram shows that the ILDPCalgorithm is more discriminative in selecting density peak points compared tothe original algorithm. Finally, the performance of K-means and other clusteringalgorithms on this benchmark model is compared, and the algorithm is proved tobe more suitable for community detection in sparse networks with the benchmarkmodel on the evaluation criterion F1-score. The sensitivity of the parameters ofthe ILDPC algorithm to the low-dimensional vector set output by the benchmarkmodel GraphSAGE is also analyzed.

关键词： Representation learning data mining low-dimensional embedding community detection density peaking algorithm

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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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Deep Learning for Cross-Domain Few-Shot Visual Recognition: A Survey

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ACM Computing Surveys 2025年第8期57卷 1-37页

作者： Xu, Huali Zhi, Shuaifeng Sun, Shuzhou Patel, Vishal Liu, Li College of Computer Science Nankai University Tianjin China College of Electronic Science and Technology National University of Defense Technology Changsha China Faculty of Information Technology and Electrical Engineering University of Oulu Oulu Finland Johns Hopkins University Baltimore United States

While deep learning excels in computer vision tasks with abundant labeled data, its performance diminishes significantly in scenarios with limited labeled samples. To address this, Few-shot learning (FSL) enables models to perform the target tasks with very few labeled examples by leveraging prior knowledge from related tasks. However, traditional FSL assumes that both the related and target tasks come from the same domain, which is a restrictive assumption in many real-world scenarios where domain differences are common. To overcome this limitation, Cross-domain few-shot learning (CDFSL) has gained attention, as it allows source and target data to come from different domains and label spaces. This article presents the first comprehensive review of Cross-domain Few-shot Learning (CDFSL), a field that has received less attention compared to traditional FSL due to its unique challenges. We aim at providing both a position article and a tutorial for researchers, covering key problems, existing methods, and future research directions. The review begins with a formal definition of CDFSL, outlining its core challenges, followed by a systematic analysis of current approaches, organized under a clear taxonomy. Finally, we discuss promising future directions in terms of problem setups, applications, and theoretical advancements. © 2025 Copyright held by the owner/author(s).

关键词： Zero-shot learning

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MediServe:An IoT-Enhanced Deep Learning Framework for Personalized Medication Management for Elderly Care

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computers, Materials & Continua 2025年第4期83卷 935-976页

作者： Smita Kapse Ganesh Yenurkar Vincent Omollo Nyangaresi Gunjan Balpande Shravani Kale Manthan Jadhav Sahil Lawankar Vikrant Jaunjale Department of Computer Technology Yeshwantrao Chavan College of EngineeringWanadongariNagpur441110MaharashtraIndia Computer Science&Engineering Jaramogi Oginga Odinga University of Science&TechnologyBondo40601Kenya Department of Applied Electronics Saveetha School of EngineeringSIMATSChennai602105TamilnaduIndia

In today’s fast-paced world,many elderly individuals struggle to adhere to their medication schedules,especially those with memory-related conditions like Alzheimer’s disease,leading to serious health risks,hospital-izations,and increased healthcare *** reminder systems often fail due to a lack of personalization and real-time *** address this critical challenge,we introduce MediServe,an advanced IoT-enabled medication management system that seamlessly integrates deep learning techniques to provide a personalized,secure,and adaptive *** features a smart medication box equipped with biometric authentication,such as fingerprint recognition,ensuring authorized access to prescribed medication while preventing misuse.A user-friendly mobile application complements the system,offering real-time notifications,adherence tracking,and emergency alerts for caregivers and healthcare *** system employs predictive deep learning models,achieving an impressive classification accuracy of 98%,to analyze user behavior,detect anomalies in medication adherence,and optimize scheduling based on an individual’s habits and health ***,MediServe enhances accessibility by employing natural language processing(NLP)models for voice-activated interactions and text-to-speech capabilities,making it especially beneficial for visually impaired users and those with cognitive ***-based data analytics and wireless connectivity facilitate remote monitoring,ensuring that caregivers receive instant alerts in case of missed doses or medication ***,machine learning-based clustering and anomaly detection refine medication reminders by adapting to users’changing health *** combining IoT,deep learning,and advanced security protocols,MediServe delivers a comprehensive,intelligent,and inclusive solution for medication *** innovative approach not only improves the quality of life for elderly

关键词： MediServe medication health risks smart medication box

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Leveraging user's preference and social circle for personalized recommendation via matrix factorization with sub-linear convergence rate

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Journal of Intelligent and Fuzzy Systems 2024年第1-2期47卷 1-13页

作者： Qiu, Laixiang Zhou, Wang Tian, Ying Ul Haq, Amin Ahmad, Sultan School of Computer and Software Engineering Xihua University Chengdu China Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College LuZhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj Saudi Arabia

Recommender systems aim to filter information effectively and recommend useful sources to match users' requirements. However, the exponential growth of information in recent social networks may cause low prediction accuracy for recommendation systems. This article proposes a unified personalized recommendation architecture referred to as PSRec, which incorporates user preference and social relationship into matrix factorization framework. Specifically, PSRec generates two collections for textual reviews and contextual information respectively, and performs preference learning for each user via the Latent Dirichlet Allocation topic model. Moreover, PSRec exploits the inner relations within the social circle for recommendation, including direct trust relationship and indirect trust relationship. Additionally, it's certificated that PSRec can converge at a sub-linear rate via theoretical analysis. Experimental results over DoubanMovie, CiaoDVDs and Yelp demonstrate the superiority of the proposed PSRec, which can achieve significant improvements and provide much better user experience while compared with other benchmark models. © 2024 - IOS Press. All rights reserved.

关键词： Matrix factorization

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Pruning method for dendritic neuron model based on dendrite layer significance constraints

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CAAI Transactions on Intelligence technology 2023年第2期8卷 308-318页

作者： Xudong Luo Xiaohao Wen Yan Li Quanfu Li Key Lab of Education Blockchain and Intelligent Technology Ministry of EducationGuangxi Normal UniversityGuilinChina Guangxi Key Lab of Multi‐Source Information Mining and Security Guangxi Normal UniversityGuilinChina Teachers College for Vocational and Technical Education Guangxi Normal UniversityGuilinChina Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNew JerseryUSA College of Electronic Engineering Guangxi Normal UniversityGuilinChina

The dendritic neural model(DNM)mimics the non-linearity of synapses in the human brain to simulate the information processing mechanisms and procedures of *** enhances the understanding of biological nervous systems and the applicability of the model in various ***,the existing DNM suffers from high complexity and limited generalisation *** address these issues,a DNM pruning method with dendrite layer significance constraints is *** method not only evaluates the significance of dendrite layers but also allocates the significance of a few dendrite layers in the trained model to a few dendrite layers,allowing the removal of low-significance dendrite *** simulation experiments on six UCI datasets demonstrate that our method surpasses existing pruning methods in terms of network size and generalisation performance.

关键词： compression computational intelligence deep learning neural network machine learning

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Resource Allocation and Task Offloading for Slicing-Based Communication and Computing in Space-Air-Ground Integrated Networks 21

Resource Allocation and Task Offloading for Slicing-Based Co...

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21st Annual IEEE International Conference on Sensing, Communication, and Networking, SECON 2024

作者： Song, Yueqian Yuan, Guiyuan Zeng, Qingtian Chen, Geng College of Electronic and Information Engineering Shandong University of Science and Technology China College of Computer Science and Engineering Shandong University of Science and Technology China

ISBN: (纸本)9798331519186

In the upcoming 6G era, the demand for network communication and computing is expected to surge and diversify. A Space-Air-Ground Integrated Network (SAGIN) is introduced as a solution to provide seamless global connectivity. Meanwhile, network slicing technology can further enhance network capabilities by supporting customized services. However, the joint optimization of multi-dimensional resource allocation and task offloading decisions presents a significant challenge in dynamic and complex environments with diverse task types. In this work, we establish an SAGIN paradigm that integrates network slicing. A multi-level optimization scheme for resource allocation and task offloading is proposed to improve Key Performance Indicators (KPIs) and Quality of Service (QoS). Specifically, we propose a State Encoding-based Multi-Agent Soft Actor-Critic algorithm (SE-MASAC) within a Centralized Training with Decentralized Execution (CTDE) architecture. This algorithm processes current sensing states and historical information, leveraging reinforcement learning to make inter-slice resource and offloading scheduling decisions. Intra-slice decisions for intelligent User Equipments (iUEs) are determined based on their sensing data-driven priority. Simulation results demonstrate that our approach outperforms baselines in terms of system utility, QoS satisfaction, and various KPIs. © 2024 IEEE.

关键词： Quality of service

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Prediction of banana leaf disease by intelligent algorithm through digital image using neural network technique

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Multimedia Tools and Applications 2025年 1-17页

作者： Nagu, Bharathiraja Kaur, Gaganpreet Shanmugavelu, Murugesan M.R.M, Veeramanickam Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Information Technology Tagore Engineering College Tamil Nadu Chennai India Apex Institute of Technology-Computer Science and Engineering Chandigarh University Punjab India

A lot of research shows that there could be several reasons why the duality of agricultural products has been reduced. Plant diseases make up one of the most important components of this quality. Therefore, the reduction of plant diseases allows for much higher product quality. The automated diagnosis of plant diseases using CNN techniques is presented in this article. The simple approach alters the 25 color channels and 24H histograms. The Banana Leaf Disease Detection Technique (BLDDT), which uses two attention mechanisms to assess, identify and categorize traits, was also suggested in this research. In the analysis phase, the testing carried out using the reference information reveals that the disease, detection system may be reasonably more appropriate than the current techniques because it both identifies and diagnoses the diseased/infected areas. The researchers were able to identify Esca, black decay, and isariopsis ––––with a 99.93% accuracy level using the suggested disease detector. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Advanced Techniques for Cyber Threat Intelligence-Based APT Detection and Mitigation in Cloud Environments

Advanced Techniques for Cyber Threat Intelligence-Based APT ...

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2023 International Conference on Cyber Security, Artificial Intelligence, and Digital Economy, CSAIDE 2023

作者： Alshaikh, Adel Alanesi, Mohammed Yang, Daoguo Alshaikh, Abdulrahman Guilin University of Electronic Technology Computer Science and Information Security Guilin541000 China Guilin University of Electronic Technology Electrical and Mechanical Engineering Guilin541000 China

ISBN: (纸本)9781510666597

In this study, we investigate the effectiveness of machine learning models for the detection and mitigation of Advanced Persistent Threats (APTs) in cloud environments, which pose a significant risk to cybersecurity. Using a publicly available APT malware dataset, we evaluate the performance of Random Forest and Support Vector Machines (SVMs) models. Our results demonstrate that both models achieve high accuracy scores, with the Random Forest model achieving a low mean squared error. We present the results of ROC analysis and cross validation scores for the Random Forest model, which further demonstrate its potential for APT detection and mitigation. Our study highlights the significant potential of machine learning-based approaches for improving cybersecurity in cloud environments. However, further research is necessary to evaluate the performance of both models on larger datasets and in different scenarios. To enhance the accuracy and effectiveness of APT detection and mitigation, future work will focus on investigating other machine learning algorithms and techniques, such as deep learning and natural language processing. Overall, our findings provide a promising starting point for further research in this area, emphasizing the potential for machine learning-based approaches to enhance cybersecurity in the cloud. By leveraging these advanced techniques, we can mitigate the risks associated with APT attacks and better protect sensitive data and information. © 2023 SPIE.

关键词： Support vector machines

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