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Robust Semi-supervised Federated Learning for Images Automatic Recognition in Internet of Drones

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arXiv 2022年

作者： Zhang, Zhe Ma, Shiyao Yang, Zhaohui Xiong, Zehui Kang, Jiawen Wu, Yi Zhang, Kejia Niyato, Dusit The School of Data Science and Technology Heilongjiang University China The College of Information and Communication Engineering Dalian Minzu University China The Department of Electronic and Electrical Engineering University College London LondonWC1E 6BT United Kingdom The Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore The Automation School Guangdong University of Technology China The School of Mathematical Science Heilongjiang University China School of Computer Science and Engineering Nanyang Technological University Singapore

Air access networks have been recognized as a significant driver of various Internet of Things (IoT) services and applications. In particular, the aerial computing network infrastructure centered on the Internet of Drones has set off a new revolution in automatic image recognition. This emerging technology relies on sharing ground truth labeled data between Unmanned Aerial Vehicle (UAV) swarms to train a high-quality automatic image recognition model. However, such an approach will bring data privacy and data availability challenges. To address these issues, we first present a Semi-supervised Federated Learning (SSFL) framework for privacy-preserving UAV image recognition. Specifically, we propose model parameters mixing strategy to improve the naive combination of FL and semi-supervised learning methods under two realistic scenarios (labels-at-client and labels-at-server), which is referred to as Federated Mixing (FedMix). Furthermore, there are significant differences in the number, features, and distribution of local data collected by UAVs using different camera modules in different environments, i.e., statistical heterogeneity. To alleviate the statistical heterogeneity problem, we propose an aggregation rule based on the frequency of the client’s participation in training, namely the FedFreq aggregation rule, which can adjust the weight of the corresponding local model according to its frequency. Numerical results demonstrate that the performance of our proposed method is significantly better than those of the current baseline and is robust to different non-IID levels of client data. Copyright © 2022, The Authors. All rights reserved.

关键词： Drones

Vision-Language Models in Remote Sensing: Current Progress and Future Trends

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arXiv 2023年

作者： Li, Xiang Wen, Congcong Hu, Yuan Yuan, Zhenghang Zhu, Xiao Xiang The King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Electrical and Computer Engineering New York University Abu Dhabi Abu Dhabi129188 United Arab Emirates The Institute of Remote Sensing and Geographic Information Systems Peking University Beijing100871 China Data Science in Earth Observation Technical University of Munich Munich80333 Germany The Munich Center for Machine Learning Munich80333 Germany

The remarkable achievements of ChatGPT and GPT-4 have sparked a wave of interest and research in the field of large language models for Artificial General Intelligence (AGI). These models provide intelligent solutions close to human thinking, enabling us to use general artificial intelligence to solve problems in various applications. However, in remote sensing (RS), the scientific literature on the implementation of AGI remains relatively scant. Existing AI-related research in remote sensing primarily focuses on visual understanding tasks while neglecting the semantic understanding of the objects and their relationships. This is where vision-language models excel, as they enable reasoning about images and their associated textual descriptions, allowing for a deeper understanding of the underlying semantics. Vision-language models can go beyond visual recognition of RS images, model semantic relationships, and generate natural language descriptions of the image. This makes them better suited for tasks requiring visual and textual understanding, such as image captioning, and visual question answering. This paper provides a comprehensive review of the research on vision-language models in remote sensing, summarizing the latest progress, highlighting challenges, and identifying potential research opportunities. Specifically, we review the application of vision-language models in mainstream remote sensing tasks, including image captioning, text-based image generation, text-based image retrieval, visual question answering, scene classification, semantic segmentation, and object detection. For each task, we analyze representative works and discuss research progress. We also summarize the commonly used remote sensing vision-language datasets, codebases, and online accessible resources. Finally, we delineate the limitations of current studies and suggest potential avenues for future advancements. This review aims to provide a comprehensive review of the current research pro

关键词： Semantics

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

IEEE International Conference on Industrial Informatics (INDIN)

作者： Weipeng Cao Xuyang Yao Zhiwu Xu Yinghui Pan Yixuan Sun Dachuan Li Bohua Qiu Muheng Wei Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen) Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (数字)9798331527471

ISBN: (纸本)9798331527488

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model's capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods.

关键词： Visualization Adaptation models Semantics Zero shot learning Neural networks Termination of employment Transformers Market research Sun Optimization

Feature Selection for Robust Spoofing Detection: A Chi-Square-based Machine Learning Approach

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Feature Selection for Robust Spoofing Detection: A Chi-Squar...

Electrical, Energy, and Artificial Intelligence (EICEEAI), International engineering Conference on

作者： Qais Al-Na'amneh Mohammad Aljaidi Hasan Gharaibeh Ahmad Nasayreh Rabia Emhamed Al Mamlook Sattam Almatarneh Dalia Alzu'bi Abla Suliman Husien Dep. of Cyber Security Applied Science Private University Amman Jordan Dep. of Computer Science Zarqa University Zarqa Jordan Dep. Computer Science Yarmouk University Irbid Jordan Dep. of Business Administration Trine University Indiana USA Department of Industrial Engineering University of Zawia Tripoli Libia Dep. of Data Science and Artificial Intelligence Zarqa University Zarqa Jordan Dep. Information and Systems Engineering Concordia University Montreal Canada

ISBN: (数字)9798350373363

ISBN: (纸本)9798350373370

Nowadays, the Internet of Things (IoT) system is vulnerable to spoofing attacks that can easily where attackers can easily pose as a legal entity of the network. A “spoofing attack” refers to a type of cyber-attack when an attacker purposefully impersonates or masquerades as someone or something else to deceive the target or obtain unauthorized access to systems, information, or resources. In such attacks, the attacker alters their name, IP address, or other attributes to fool the victim into thinking they are engaging with a legitimate entity. Spoofing attacks can take place via a variety of channels, including, ARP and DNS spoofing. Therefore. Spoofing attacks can have serious consequences. We proposed a new approach based on three machine learning models LightGBM, Gradient Boost, and XGBoost to classify attacks on spoofing, we used Chi-square to select the best features to get the highest performance, and we demonstrated that the results using Chi-square achieved higher results than without Chi-square and improved the result with a rate three percent of accuracy. In terms of results, LightGBM outperformed other models by achieving 89%, 91 %, 87 %, and 89 % for accuracy, precision, recall, and f1-score, respectively. This shows the potential and efficiency of Chi-square to achieve the best performance by selecting the best features, thus providing a secure system and identifying cyber-attacks on systems.

关键词： Electric potential Accuracy Law Machine learning Learning (artificial intelligence) Feature extraction Internet of Things

TEVA: Training-Efficient and Verifiable Aggregation for Federated Learning for Consumer Electronics in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Xia, Yuanjun Liu, Yining Chen, Jingxue Liang, Yangfan Khan, Fazlullah Alturki, Ryan Wang, Xiaopei Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software School of Computer and Information Security Guilin541004 China Wenzhou University of Technology School of Data Science and Artificial Intelligence Wenzhou325027 China University of Technology School of Mathematical and Physical Sciences SydneyNSW2007 Australia Jiaxing University Provincial Key Laboratory of Multimodal Perceiving and Intelligent Systems The Key Laboratory of Medical Electronics and Digital Health of Zhejiang Province The Engineering Research Center of Intelligent Human Health Situation Awareness of Zhejiang Province Jiaxing314001 China University of Nottingham Ningbo China School of Computer Science Faculty of Science and Engineering Zhejiang Ningbo315104 China Umm AI-Qura University Makkah Department of Software Engineering College of Computing Saudi Arabia Riverside Department of Computer Science and Engineering University of California CA92521 United States

Federated learning (FL) has been widely used for privacy-preserving model updates in Industry 5.0, facilitated by 6G networks. Despite FL's privacy-preserving advantages, it remains vulnerable to attacks where adversaries can infer private data from local models or manipulate the central server (CS) to deliver falsified global models. Current privacy-preserving approaches, primarily based on the FedAvg algorithm, fail to optimize training efficiency for non-independent and identically distributed (non-IID) data. This article proposes training-efficient and verifiable aggregation (TEVA) for FL to resolve these issues. This scheme combines threshold Paillier homomorphic encryption (TPHE), verifiable aggregation, and an optimized double momentum update mechanism (OdMum). TEVA not only leverages TPHE to protect the privacy of local models but also ensures the integrity of the global model through a verifiable aggregation mechanism. Additionally, TEVA integrates the OdMum algorithm to effectively address the challenges posed by non-IID data, promoting rapid model convergence and significantly enhancing overall training efficiency. Security analysis indicates that TEVA meets the requirements for privacy protection. Extensive experimental results demonstrate that TEVA can accelerate model convergence while incurring lower computational and communication overheads. © 1975-2011 IEEE.

关键词： Differential privacy

The Last JITAI? Exploring Large Language Models for Issuing Just-in-Time Adaptive Interventions: Fostering Physical Activity in a Conceptual Cardiac Rehabilitation Setting

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arXiv 2024年

作者： Haag, David Kumar, Devender Gruber, Sebastian Hofer, Dominik Sareban, Mahdi Treff, Gunnar Niebauer, Josef Bull, Christopher Schmidt, Albrecht Smeddinck, Jan David Ludwig Boltzmann Institute for Digital Health and Prevention Salzburg Austria Department of Psychology Paris-Lodron-University of Salzburg Salzburg Austria Digital Health Information Systems Center for Health & Bioresources AIT Austrian Institute of Technology GmbH Graz Austria University of Southern Denmark Odense Denmark Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria Human Motion Analytics Salzburg Research Forschungsgesellschaft Salzburg Austria LMU Munich Munich Germany University Institute for Sports Medicine Prevention and Rehabilitation Salzburg Austria Institute of Molecular Sports and Rehabilitation Medicine Paracelsus Medical University Salzburg Austria Open Lab School of Computing Newcastle University Newcastle upon Tyne United Kingdom

We evaluated the viability of using Large Language Models (LLMs) to trigger and personalize content in Just-in-Time Adaptive Interventions (JITAIs) in digital health. As an interaction pattern representative of context-aware computing, JITAIs are being explored for their potential to support sustainable behavior change, adapting interventions to an individual’s current context and needs. Challenging traditional JITAI implementation models, which face severe scalability and flexibility limitations, we tested GPT-4 for suggesting JITAIs in the use case of heart-healthy activity in cardiac rehabilitation. Using three personas representing patients affected by CVD with varying severeness and five context sets per persona, we generated 450 JITAI decisions and messages. These were systematically evaluated against those created by 10 laypersons (LayPs) and 10 healthcare professionals (HCPs). GPT-4-generated JITAIs surpassed human-generated intervention suggestions, outperforming both LayPs and HCPs across all metrics (i.e., appropriateness, engagement, effectiveness, and professionalism). These results highlight the potential of LLMs to enhance JITAI implementations in personalized health interventions, demonstrating how generative AI could revolutionize context-aware computing. © 2024, CC BY.

关键词： Electronic health record

Scientometric analysis on the use of ChatGPT, artificial intelligence, or intelligent conversational agent in the role of medical training

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Educacion Medica 2024年第2期25卷

作者： Mayta-Tovalino, Frank Espinoza-Carhuancho, Fran Alvitez-Temoche, Daniel Mauricio-Vilchez, Cesar Munive-Degregori, Arnaldo Barja-Ore, John Faculty of Systems and Computer Engineering Department of Master's in information and Knowledge Management Universidad Nacional Mayor de San Marcos Lima Peru Grupo de Bibliometria Evaluación de evidencia y Revisiones Sistemáticas (BEERS) Human Medicine Career Universidad Científica del Sur Lima Peru Postgraduate Department Universidad Nacional Federico Villarreal Lima Peru Academic Department Faculty of Dentistry Universidad Nacional Mayor de San Marcos Lima Peru Academic Department Dirección de Investigación Universidad Privada del Norte Lima Peru

Introduction: In recent times, there has been a noticeable surge in the usage of artificial intelligence, including ChatGPT and other types, in the field of health sciences education. In this regard, an exploratory bibliometric study was carried out to examine the utilization of smart conversational agents, ChatGPT, and artificial intelligence bots in medical education. Methods: A retrospective, observational, cross-sectional bibliometric analysis was employed to assess the scientific publications listed in Scopus. This study was conducted on March 11, 2023 in search for information in Scopus. A total of 220 relevant documents were identified that were available in the Scopus database during the period between 2017 and 2022. Elsevier's SciVal software was used. Subsequently, statistical tables and graphs were prepared for presentation in Bibliometrix software. Results: Among the authors, Timothy W. Bickmore, from the United States, has the highest number of publications (10) and citations received (172), and an h-index of 45, suggesting a significant influence in the field of study. The subcategory with the highest academic output is Health Informatics with 133 publications, while Geriatrics and Gerontology has the least with only 3. Most of the analyzed publications (44.2%) originated from collaborations within the same country. Notably, the Swiss Federal Institute of Technology Zurich and Imperial College London stood out with 12 publications each that received over 200 citations indicating their significant impact on their respective fields. Despite having the highest number of academic publications (15), Brazil had a relatively low field-weighted citation impact (0.64) and received the lowest number of citations (81). A clustering analysis was performed on a sample of 10 concepts using 2 dimensions. The results indicated that all terms were part of the same cluster. Notably, the terms 'conversational agents', 'chatbots', 'conversational agent', and 'chatbot' wer

关键词： Artificial intelligence Bibliometrics Bibliometrix Biblioshiny ChatGPT

Scalable teacher forcing network for semi-supervised large scale data streams

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arXiv 2021年

作者： Pratama, Mahardhika Za’in, Choiru Lughofer, Edwin Pardede, Eric Rahayu, Dwi A.P. School of Computer Science and Engineering Nanyang Technological University Singapore Singapore Faculty of Information Technology Monash University Australia Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Austria Department of Computer Science and IT La Trobe University Australia Indonesia

The large-scale data stream problem refers to high-speed information flow which cannot be processed in scalable manner under a traditional computing platform. This problem also imposes expensive labelling cost making the deployment of fully supervised algorithms unfeasible. On the other hand, the problem of semi-supervised large-scale data streams is little explored in the literature because most works are designed in the traditional single-node computing environments while also being fully supervised approaches. This paper offers Weakly Supervised Scalable Teacher Forcing Network (WeScatterNet) to cope with the scarcity of labelled samples and the large-scale data streams simultaneously. WeScatterNet is crafted under distributed computing platform of Apache Spark with a data-free model fusion strategy for model compression after parallel computing stage. It features an open network structure to address the global and local drift problems while integrating a data augmentation, annotation and auto-correction (DA3) method for handling partially labelled data streams. The performance of WeScatterNet is numerically evaluated in the six large-scale data stream problems with only 25% label proportions. It shows highly competitive performance even if compared with fully supervised learners with 100% label proportions. © 2021, CC BY-NC-SA.

关键词： Fuzzy sets

Investigating the significance of adversarial attacks and their relation to interpretability for radar-based human activity recognition systems

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arXiv 2021年

作者： Ozbulak, Utku Vandersmissen, Baptist Jalalvand, Azarakhsh Couckuyt, Ivo van Messem, Arnout de Neve, Wesley Department of Electronics and Information Systems Ghent University Belgium Department of Information Technology Ghent University–imec Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium Department of Mechanical and Aerospace Engineering Princeton University United States Center for Biotech Data Science Ghent University Global Campus Korea Republic of

Given their substantial success in addressing a wide range of computer vision challenges, Convolutional Neural Networks (CNNs) are increasingly being used in smart home applications, with many of these applications relying on the automatic recognition of human activities. In this context, low-power radar devices have recently gained in popularity as recording sensors, given that the usage of these devices allows mitigating a number of privacy concerns, a key issue when making use of conventional video cameras. Another concern that is often cited when designing smart home applications is the resilience of these applications against cyberattacks. It is, for instance, well-known that the combination of images and CNNs is vulnerable against adversarial examples, mischievous data points that force machine learning models to generate wrong classifications during testing time. In this paper, we investigate the vulnerability of radar-based CNNs to adversarial attacks, and where these radar-based CNNs have been designed to recognize human gestures. Through experiments with four unique threat models, we show that radar-based CNNs are susceptible to both white- and black-box adversarial attacks. We also expose the existence of an extreme adversarial attack case, where it is possible to change the prediction made by the radar-based CNNs by only perturbing the padding of the inputs, without touching the frames where the action itself occurs. Moreover, we observe that gradient-based attacks exercise perturbation not randomly, but on important features of the input data. We highlight these important features by making use of Grad-CAM, a popular neural network interpretability method, hereby showing the connection between adversarial perturbation and prediction interpretability. Copyright © 2021, The Authors. All rights reserved.

关键词： Automation