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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Sun, Yi Wang, Wenna Zhang, Qianyu Ni, Han Zhang, Xiuwei Big Data Application Technology National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Xi'an710072 China Northwestern Polytechnical University School of Cybersecurity Xi'an710072 China Northwestern Polytechnical University School of Computer Science Xi'an710072 China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

With the development of SAR technology, large scene object detection on SAR images has attracted more and more attention. Exiting large scene object detection is mainly based on the CNN network, which limits the obtaining of global context information. On the other hand, due to the high acquisition cost of SAR images, there are no existing public datasets in military vehicle detection. To solve these problems, we adopt the Transformer module to construct the neck block based on YOLOv5. This design can gain global context information, and also has better performance for small objects detection. Furthermore, to achieve the detection of large-scale military ground vehicles, we construct a dataset based on the MSTAR dataset, named LSGVOD. Extensive experiments have been conducted on LSGVOD, and experimental results show that the proposed method greatly improves detection accuracy. Compared to other methods, it achieves the best accuracy with 93.3% mAP. © 2022 IEEE.

关键词： Synthetic aperture radar

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Adaptive offloading scheme of fine-grained tasks based on reinforcement learning in multi-access edge computing

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Simulation Modelling Practice and Theory 2025年 143卷

作者： Li, Jie Chen, Ge Chen, Kansong School of Computer Science Hubei University Wuhan430062 China Wuhan430062 China Engineering Research Center of Hubei Province in Intelligent Government Affairs and Application of Artificial Intelligence Wuhan430062 China Ministry of Education Wuhan430062 China

In the context of multi-access edge computing, the constraints on device resources and energy pose a challenge in meeting the requirements of delay-sensitive and high-resource demand tasks. In order to address this issue, this paper puts forth an adaptive offloading decision based on fine-grained task sequencing (AOD-FTS). To enhance task execution efficiency and resource utilization, we design scheduling optimization for subtasks algorithm within the fine-grained task model to optimize the execution order of subtasks and improve subtask parallelism. Additionally, to reduce task delay and energy consumption, we propose an adaptive multi-objective reinforcement learning offloading algorithm based on ordered subtasks, formulated through multi-objective Markov decision processes. The scheme dynamically adjusts optimization preferences according to environmental changes and task completion states to make offloading decisions. Simulation results demonstrate that compared with two benchmark algorithms, the proposed AOD-FTS scheme achieves: (1) higher task completion rates, (2) improved resource utilization efficiency, (3) 81.66 % and 51.53 % reductions in delay respectively, and (4) 39.72 % and 71.36 % reductions in energy consumption respectively. © 2025

关键词： Reinforcement learning

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AdvMap: Crafting Adversarial Maps to Counter AI Aimbot in First-Person Shooter Games

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IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

AI-based automatic aiming cheats (a.k.a., AI aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since AI aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter AI aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various AI aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

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Empirical Evaluation of Stock Market Prediction System using Intelligent Learning Scheme with Data Processing Logic

Empirical Evaluation of Stock Market Prediction System using...

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Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), International Conference on

作者： S. Prabakar Ankit Kumar R Jayakarthik D. Venkatesh Pratheeba R S Bhola Khan Finance and Accounting ISBR Business School Bangalore Department of Computer Application Faculty of Science Swami Vivekanand Subharti University Meerut Dept of Computer science Saveetha College of Liberal Arts and Sciences SIMATS Assistant Professor of Commerce School of Commerce and Management Mohan Babu University Tirupati Department of Computer Science and Engineering Velammal Institute of Technology Panchetti Department of Regional Economics MJP Rohilkhand University Bareilly India

The Stock Market Prediction System using Intelligent Learning Scheme (SMS-ILS) presented in this study integrates advanced methodologies to enhance the accuracy and reliability of stock market predictions. The system encompasses a preprocessing phase, addressing missing data and outliers, followed by feature extraction utilizing Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) to capture intricate temporal dependencies. A Deep Belief Network (DBN) is employed for classification, categorizing stocks based on learned representations from historical and technical indicators. Grey Wolf Optimization (GWO) optimizes the system's parameters, ensuring adaptability to dynamic market conditions. Notably, the system achieves an impressive accuracy of 97%, emphasizing its efficacy in making correct predictions. This multi-model approach, integrating LSTM, RNN, DBN, and GWO, provides a comprehensive and adaptive framework for navigating the complexities of stock market prediction. The study emphasizes the importance of choosing metrics aligned with specific goals and conducting thorough analysis to ensure the robustness of the evaluation results in the challenging and dynamic context of financial markets.

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Development of an Electric Automation Control Model Using Artificial Intelligence

Development of an Electric Automation Control Model Using Ar...

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： V. Srikanth P. Aswini V. Asha Khobragade Pithamber Rajeev Sobti Z. Salman School of Sciences and Computer Studies CMR University (SATELLITE CAMPUS-OMBR LAYOUT) Bengaluru India Computer Science and Engineering(Data Science) Institute of Aeronautical Engineering Hyderabad India Department of Master of Computer Application New Horizon College of Engineering Bangalore India Department of Electronics and Communication Engineering MLR Institute of Technology Hyderabad Telangana India Lovely Professional University Phagwara India Hilla University College Babylon Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

The design of an AI-based electrical computerization control system is implemented to efficiently address the challenges encountered by contemporary electrical engineering. A model of an AI-based control system for electrical automation is presented. To achieve optimal control settings, the control strategy makes use of an artificial intelligence system. Even in the presence of a twenty per cent load intervention and 2.1 Hz wavelength interference, the research demonstrates that the organisation has significant anti-interference capabilities, as shown by an acceptable fail rate of 0.02 for each at the system management level. Consequently, using an AI algorithm in automated electrification control may lead to a significant improvement in control reaction time, cost savings, and efficient construction.

关键词： Industries Automation Costs Interference Electrification Control systems Time factors Artificial intelligence Turbines Load modeling

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Space-Air-Ground Integrated Network (SAGIN) for 6G: Requirements, Architecture and Challenges

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China Communications 2022年第2期19卷 90-108页

作者： Huanxi Cui Jun Zhang Yuhui Geng Zhenyu Xiao Tao Sun Ning Zhang Jiajia Liu Qihui Wu Xianbin Cao School of Electronic and Information Engineering Beijing Key Laboratory for Network-Based Cooperative Air application ManagementBeihang UniversityBeijing 100191China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing 100081China China Mobile Research Institute Beijing 100053China Department of Electrical and Computer Engineering University of WindsorONCanadaN9B 3P4 National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology the School of CybersecurityNorthwestern Polytechnical UniversityXi’anShaanxi710072China College of Electronics and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210007China

As the fifth-generation(5G)mobile communication network may not meet the requirements of emerging technologies and applications,including ubiquitous coverage,industrial internet of things(IIoT),ubiquitous artificial intelligence(AI),digital twins(DT),etc.,this paper aims to explore a novel space-air-ground integrated network(SAGIN)architecture to support these new requirements for the sixth-generation(6G)mobile communication network in a flexible,low-latency and efficient ***,we first review the evolution of the mobile communication network,followed by the application and technology requirements of *** the current 5G non-terrestrial network(NTN)architecture in supporting the new requirements is deeply *** that,we proposes a new flexible,low-latency and flat SAGIN architecture,and presents corresponding use ***,the future research directions are discussed.

关键词： 6G AI DT SAGIN NTN Network architecture

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An Unsupervised Domain Adaption Framework for Aerial Image Semantic Segmentation Based on Curriculum Learning 7

An Unsupervised Domain Adaption Framework for Aerial Image S...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Ran, Lingyan Ji, Cheng Zhang, Shizhou Zhang, Xiaoqiang Zhang, Yanning Ningbo Institute of Northwestern Polytechnical University Ningbo315103 China Natl. Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi'an China Northwestern Polytechnical University School of Computer Science Xi'an710129 China Southwest University of Science and Technology School of Information Engineering Mianyang China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

With the development of deep learning, semantic segmentation has made breakthrough progress, but supervised learning requires a large amount of data with pixel-level annotation. However, for remote sensing data, it is difficult to obtain large-scale pixel-level datasets. There is visual differences between the data of different geospatial regions inevitably. In particular, this difference is often referred to as a "domain gap"and can lead to significant performance degradation. The unsupervised domain adaptive method can effectively solve the above problems, by making the most of existing source domain annotated data, without re-annotating the target dataset, better semantic segmentation results can be obtained on the target dataset. In this paper, we propose a novel unsupervised domain adaptive framework based on curriculum learning (UDA-CL), and a class-aware pseudo-label filtering strategy to dynamically learn the class information during training. Comprehensive experiments show that this method achieves the encouraging semantic segmentation performance on aerial image datasets. © 2022 IEEE.

关键词： Semantic Segmentation

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Enhanced Consumer Healthcare Data Protection Through AI-Driven TinyML and Privacy-Preserving Techniques

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IEEE Access 2025年 13卷 97428-97440页

作者： S. Aanjankumar Monoj Kumar Muchahari Shabana Urooj Ishmeet Kaur Rajesh Kumar Dhanaraj Hanan Abdullah Mengash S. Poonkuntran Parag Ravikant Kaveri School of Computing Science and Engineering VIT Bhopal University Sehore Madhya Pradesh India Department of Computer Application and Science Institute of Engineering and Management University of Engineering and Management Kolkata West Bengal India Department of Electrical Engineering College of Engineering Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia School of Computing Science and Artificial Intelligence VIT Bhopal University Sehore Madhya Pradesh India Symbiosis Institute of Computer Studies and Research (SICSR) Symbiosis International (Deemed University) Pune India Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh Saudi Arabia

In the recent digital landscape, securing healthcare data stored on personal devices has become imperative due to increasing cyberattacks. Healthcare data inside an organization is often vulnerable to security breaches and requires privacy-preserving mechanisms to ensure secure storage and sharing across various platforms. This research proposes a novel method by integrating TinyML (machine learning) with federated learning (FL) and differential privacy (DP) to ensure the security and privacy of health-related data on resource-constrained edge devices. The proposed method with TinyML looks at patient data, like ECG readings and reports of unusual heartbeats, right on local edge devices that have limited resources, enabled immediate privacy checks while using minimal computing power. Federated learning trains the model on local edge devices by ensuring sensitive data remains on user devices, which are accessed centrally. Finally, differential privacy techniques strengthen security by additionally adding noise to data for safeguarding against malicious attacks without compromising the data’s accessibility. The proposed model achieves a high accuracy of 99%, significantly outperforming the traditional models, like decision trees at 76%, random forests at 98.1%, and federated learning with deep Q at 95%. The proposed methodology provides a scalable and efficient solution for real-time healthcare data sharing by ensuring data privacy and security across healthcare data source environments. The analysis of experimental results validates the model’s efficiency in securing healthcare data, with implications for broader applications in secure, privacy-preserving medical data security analysis.

关键词： Medical services Data models Security Data privacy Electrocardiography Differential privacy Training Privacy Tiny machine learning Federated learning

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Dual Attention Guided R2 U-Net Architecture for Right Ventricle Segmentation in MRI Images 11th

Dual Attention Guided R2 U-Net Architecture for Right Ventri...

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11th International Conference on Image and Graphics, ICIG 2021

作者： Jiang, Lei Cui, Hengfei Yuwen, Chang Zhang, Yanning National Engineering Laboratory for Integrated Aero-Space-Ground -Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University Xi’an710072 China School of Computer Science Northwestern Polytechnical University Xi’an710072 China

ISBN: (纸本)9783030873578

Right ventricle segmentation plays an important role in the computer-aided diagnosis of heart diseases. However, due to the small area of right ventricle and limited dataset, the performances of the existing deep learning segmentation methods are not good enough. For some small areas of right ventricle that are difficult to segment, we apply a novel dual attention module on the decoding path of Dilated R2 U-net to extract better feature representations in this work. The dual attention module in this work is divided into position attention module and channel attention module. The positional attention module suppresses the irrelevant feature representations in the feature map and enhances the useful feature representations to improve the sensitivity and prediction accuracy of the model. The channel attention module enhances the interdependence of the feature representation of channels by gathering the information of the associated channels in the feature map. We use dilated convolutions to expand the receptive field of the model. By adding dual attention modules, our model shows higher precision than Dilated U-net on the Right Ventricle Segmentation Challenge (RVSC) test dataset. © 2021, Springer Nature Switzerland AG.

关键词： computer aided diagnosis

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Learning Concept Prerequisite Relation via Global Knowledge Relation Optimization 39

Learning Concept Prerequisite Relation via Global Knowledge ...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zhang, Miao Wang, Jiawei Xiao, Kui Wang, Shihui Zhang, Yan Chen, Hao Li, Zhifei School of Computer Science and Information Engineering Hubei University China Hubei Key Laboratory of Big Data Intelligent Analysis and Application China Key Laboratory of Intelligent Sensing System and Security Ministry of Education China

ISBN: (纸本)157735897X

Learning concept prerequisite relations helps better master and build a logically coherent knowledge structure. Many studies use graph neural networks to create heterogeneous knowledge networks that enhance concept representations. However, different types of relations in these networks can influence each other. Existing research often focuses solely on concept relations, neglecting other types of knowledge connections. To address this issue, this paper proposes a novel concept prerequisite relation learning model, named the Global Knowledge Relation Optimization Model(GKROM). Specifically, we capture the impact of different knowledge relation types on document and concept semantic representations separately, integrating the document and concept semantic representations. Then, we introduce multi-objective learning to optimize the knowledge relation network from a global perspective. Through the above optimization, GKROM learns richer semantic representations for concepts and documents, improving the accuracy of concept prerequisite relation learning. Extensive experiments on public datasets demonstrate the effectiveness of our GKROM, achieving state-of-the-art performance in concept prerequisite relation learning. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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