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Dynamic Quantum Federated Learning for UAV-based Autonomous Surveillance

IEEE Transactions on Vehicular Technology 2025年第5期74卷 8158-8170页

作者： Park, Soohyun Son, Seok Bin Jung, Soyi Kim, Joongheon Sookmyung Women's University Division of Computer Science Seoul Korea Republic of Korea University School of Electrical Engineering Seoul Korea Republic of Ajou University Department of Electrical and Computer Engineering Suwon16499 Korea Republic of

In recent years, unmanned aerial vehicles (UAVs) have proven their effectiveness in surveillance due to their superior mobility. By utilizing multiple UAVs with collaborated learning, surveillance of a huge area while consuming minimum resources is possible. However, the performance of UAV collaborative learning systems is still severely limited because the number of parameters in a classical NN is generally very large, which is unsuitable for unstable wireless communication. To address this issue, this paper uses a quantum neural network (QNN), which has large computational capabilities and uses fewer parameters to overcome the problems caused by many parameters. However, it is still difficult to send all the parameters of the QNN to the server under poor channel conditions. Therefore, this paper proposes dynamic quantum federated learning (DQFL), a novel framework designed for UAVs employing quantum computing (QC) and federated learning (FL). The proposed DQFL uses a dynamic quantum neural network (DQNN) with a multi-depth circuit and employs dynamic control of the circuit layer to improve the efficiency of local parameter transmission between UAVs in unstable wireless communication environments. Extensive simulations conducted under real-world autonomous surveillance conditions demonstrate the robustness of DQFL to non-iidness, varying signal-to-noise ratios (SNRs), and poor communication channel conditions in the UAV environment. © 1967-2012 IEEE.

关键词： Federated learning

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Zero-dynamics attack detection based on data association in feedback pathway

Cognitive Robotics

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Cognitive Robotics 2025年 5卷 126-139页

作者： Zhang, Zeyu Li, Hongran Todo, Yuki Division of Electrical Engineering and Computer Science Kanazawa University Ishikawa Kanazawa9201192 Japan School of Computer Engineering Jiangsu Ocean University Jiangsu Lianyungang222005 China Faculty of Electrical Information and Communication Engineering Kanazawa University Ishikawa Kanazawa9201192 Japan

This paper considers the security of non-minimum phase systems, a typical kind of cyber-physical systems. Non-minimum phase systems are characterized by unstable zeros in their transfer functions, making them particularly susceptible to disturbances and attacks. The non-minimum phase systems are more vulnerable to zero-dynamics attack (ZDA) than minimum phase systems. ZDA is a stealthy attack strategy that exploits the internal dynamics of a system, remaining undetectable while causing gradual system destabilization. Recent cyber incidents have demonstrated the increasing risk of such hidden attacks in critical infrastructures, such as power grids and transportation systems. This paper first demonstrates that the existing ZDA has the limitation of falling into local convergence, and then proposes an enhanced zero-dynamics attack (EZDA), which overcomes local convergence by diverging system data. Furthermore, this paper presents an autoregressive model which can build the data association between the original data and the forged data. By observing the fluctuations in state values, the presented model can detect not only ZDA, but also EZDA. Finally, numerical simulations and an application example are provided to verify the theoretical results. © 2025

关键词： Cyber attacks

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Terahertz Integrated Polarization Filter Based on Slotted Bragg Grating

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IEEE Transactions on Terahertz science and Technology 2025年第3期15卷 473-480页

作者： Chen, Linxi Gao, Weijie Fumeaux, Christophe Withayachumnankul, Withawat The University of Adelaide Terahertz Engineering Laboratory AdelaideSA5005 Australia Osaka University Graduate School of Engineering Science Toyonaka Osaka560-8531 Japan The University of Queensland School of Electrical Engineering and Computer Science St LuciaQLD4072 Australia

All-silicon substrateless integrated systems are suitable to wide-ranging terahertz applications due to their low-loss characteristics. A broadband, high-extinction level polarization filter is in demand for these on-chip systems to select the desired polarization when monomode operation of the dielectric host waveguide is required. Existing photonic designs for polarization filtering cannot cope with a large fractional bandwidth in the terahertz range. To increase the bandwidth and rejection level of the filter, a longitudinal slot is introduced into the host waveguide. Experimental validation shows a high average polarization extinction ratio of 23.5 dB between the TM and TE polarizations and an insertion loss of around 1.5 dB for the TM polarization over a 3-dB bandwidth of 87 GHz covering the whole IEEE communications band between 252 and 325 GHz. The proposed filter will be an essential component for terahertz communications to maintain the polarization purity in substrateless dielectric waveguides. © 2011-2012 IEEE.

关键词： Terahertz waves

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Legal Query RAG

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IEEE Access 2025年 13卷 36978-36994页

作者： Wahidur, Rahman S. M. Kim, Sumin Choi, Haeung Bhatti, David S. Lee, Heung-No Gwangju Institute of Science and Technology School of Electrical Engineering and Computer Science Gwangju61005 Korea Republic of Gwangju Institute of Science and Technology Artificial Intelligence Graduate School Gwangju61005 Korea Republic of

Recently, legal practice has seen a significant rise in the adoption of Artificial Intelligence (AI) for various core tasks. However, these technologies remain in their early stages and face challenges such as understanding complex legal reasoning, managing biased data, ensuring transparency, and avoiding misleading responses, commonly referred to as hallucinations. To address these limitations, this paper introduces Legal Query RAG (LQ-RAG), a novel Retrieval-Augmented Generation framework with a recursive feedback mechanism specifically designed to overcome the critical shortcomings of standard RAG implementations in legal applications. The proposed framework incorporates four key components: a custom evaluation agent, a specialized response generation model, a prompt engineering agent, and a fine-tuned legal embedding LLM. Together, these components effectively minimize hallucinations, improve domain-specific accuracy, and deliver precise, high-quality responses for complex queries. Experimental results demonstrate that the fine-tuned embedding LLM achieves a 13% improvement in Hit Rate and a 15% improvement in Mean Reciprocal Rank (MRR). Comparisons with general LLMs reveal a 24% performance gain when using the Hybrid Fine-Tuned Generative LLM (HFM), the specialized response generation model integrated into the LQ-RAG framework. Furthermore, LQ-RAG achieves a 23% improvement in relevance score over naive configurations and a 14% improvement over RAG with Fine-Tuned LLMs (FTM). These findings underscore the potential of domain-specific fine-tuned LLMs, combined with advanced RAG modules and feedback mechanisms, to significantly enhance the reliability and performance of AI in legal practice. The reliance of this study on a proprietary model as the evaluation agent, combined with the lack of feedback from human experts, highlights the need for improvement. Future efforts should focus on developing a specialized legal evaluation agent and enhancing its performance

关键词： Information management

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On Traffic Prediction With Knowledge-Driven Spatial–Temporal Graph Convolutional Network Aided by Selected Attention Mechanism

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2025年 3卷 369-380页

作者： Qian, Yuwen Qiu, Tianyang Ma, Chuan Ni, Yiyang Yuan, Long Zhou, Xiangwei Li, Jun Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China Chongqing University College of Computer Science Chongqing400044 China Jiangsu Second Normal University School of Computer Engineering Nanjing210037 China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States

Intelligent transportation systems grapple with the formidable task of precisely forecasting real-time traffic conditions, where the traffic dynamics exhibit intricacies arising from spatial and temporal dependencies. The urban road network presents a complex web of interconnected roads, where the state of traffic on one road can influence the conditions of others. Moreover, the prediction of traffic conditions necessitates the consideration of diverse temporal factors. Notably, the proximity of a time point to the present moment wields a more substantial impact on subsequent states. In this paper, we propose the knowledge-driven graph convolutional network (KGCN) aided by the gated recurrent unit with a selected attention mechanism (GSAM) to predict traffic flow. In particular, KGCN is employed to capture the correlation of the external knowledge factors for the road and the spatial dependencies, and the gated recurrent unit (GRU) is used to cope with temporal dependence. Furthermore, to improve traffic prediction accuracy, we propose the GRU combined with a selected attention mechanism with Gumble-Max to predict traffic at the temporal dimension, where a selector is chosen to dynamically assign the feature in various time intervals with different weights. Experimental results with real-life data show the proposed KGCN with GSAM can achieve high accuracy in traffic prediction. Compared to the traditional traffic prediction method, the proposed KGCN with GSAM can achieve higher efficacy and robustness when capturing global dynamic temporal dependencies, external knowledge factor correlations, and spatial correlations. © 2023 IEEE.

关键词： Knowledge graph

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ML-Driven Compact Models for RRAMs: Addressing Variability and Simulation Efficiency

IEEE Electron Device Letters

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IEEE Electron Device Letters 2025年第5期46卷 876-879页

作者： Hong, Giyong Huh, In Hyung You, Joo Myung Choe, Jae Kim, Younggu Jeong, Changwook Graduate School of Semiconductor Materials and Devices Engineering Ulsan44919 Korea Republic of Purdue University Elmore Family School of Electrical and Computer Engineering West LafayetteIN47907 United States Innovation Center Samsung Electronics Computational Science and Engineering Team Hwaseong-si18448 Korea Republic of Graduate School of Semiconductor Materials and Devices Engineering The Artificial Intelligence Graduate School Ulsan44919 Korea Republic of

Machine Learning (ML)-based compact modeling provides a promising alternative to traditional physics-based methods, enabling faster development of compact models for novel devices while offering improved predictive performance. For Resistive Random Access Memory (RRAM) devices, several ML-based compact models have been developed. However, these models often face two key challenges: they fail to capture stochastic cycle-to-cycle variations effectively, and they are difficult to accurately convert into Verilog-A models for SPICE simulations. To address these challenges, we propose a novel variation-aware ML-based compact model for RRAM, using modified deep ensemble techniques to account for cycle-to-cycle variations and model uncertainty, along with a newly designed state determination function to accurately capture resistive switching characteristics. Furthermore, by introducing knowledge distillation combined with a pruning-retraining process, the proposed model achieves a 67% reduction in simulation turnaround time while maintaining predictive accuracy, ensuring strong compatibility with SPICE simulations. © 1980-2012 IEEE.

关键词： SPICE

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STViT+: improving self-supervised multi-camera depth estimation with spatial-temporal context and adversarial geometry regularization

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Applied Intelligence 2025年第5期55卷 1-20页

作者： Chen, Zhuo Zhao, Haimei Hao, Xiaoshuai Yuan, Bo Li, Xiu Shenzhen International Graduate School Tsinghua University Guangdong Shenzhen518055 China School of Computer Science The University of Sydney SydneyNSW2008 Australia Beijing Academy of Artificial Intelligence Beijing China School of Electrical Engineering and Computer Science The University of Queensland BrisbaneQLD4072 Australia

Multi-camera depth estimation has gained significant attention in autonomous driving due to its importance in perceiving complex environments. However, extending monocular self-supervised methods to multi-camera setups introduces unique challenges that existing techniques often fail to address. In this paper, we propose STViT+, a novel Transformer-based framework for self-supervised multi-camera depth estimation. Our key contributions include: 1) the Spatial-Temporal Transformer (STTrans), which integrates local spatial connectivity and global context to capture enriched spatial-temporal cross-view correlations, resulting in more accurate 3D geometry reconstruction;2) the Spatial-Temporal Photometric Consistency Correction (STPCC) strategy that mitigates the impact of varying illumination, ensuring brightness consistency across frames during photometric loss calculation;3) the Adversarial Geometry Regularization (AGR) module, which employs Generative Adversarial Networks to impose spatial constraints by using unpaired depth maps, enhancing performance under adverse conditions such as rain and nighttime driving. Extensive evaluations on large-scale autonomous driving datasets, including Nuscenes and DDAD, confirm that STViT+ sets a new benchmark for multi-camera depth estimation. © The Author(s) 2025.

关键词： Geometry

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Two Generic Constructions of MDS Array Codes With Optimal Repair Bandwidth From Two Special Sets

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IEEE Transactions on Information Theory 2025年第5期71卷 3582-3601页

作者： Zhu, Hongwei Lv, Jingjie Xia, Shu-Tao Hou, Hanxu Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518055 China Dongguan University of Technology School of Electrical Engineering and Intelligentization Dongguan523808 China City University of Hong Kong Department of Computer Science Hong Kong

The maximum distance separable (MDS) codes are the optimal codes to achieve Singleton bound, providing maximum error tolerance under a given number of parity nodes. Ye and Barg leveraged permutation matrices and Reed-Solomon type codes to devise 7 explicit constructions for constructing MDS array codes with optimal repair property (as known as MSR codes) or even optimal access property. Drawing inspiration from these explicit constructions, we provide two generic constructions for constructing MSR codes from high-rate MDS codes or MDS array codes. In this paper, we introduce the concepts of s-pairwise MDS codes sets and s-pairwise MDS array codes sets. Two generic constructions (Generic Constructions I and II) for constructing the MSR code using the s-pairwise MDS codes sets or the s-pairwise MDS array codes sets are given. Constructions 1 to 3 proposed by Ye and Barg can be regarded as some special cases of Generic Construction I, and Constructions 1 to 3 proposed by Li et al., can be regarded as some special cases of Generic Construction II. It is worth mentioning that Generic Construction II can be applied to any finite field, including the binary field. We also demonstrate how to obtain the s-pairwise MDS code sets and the s-pairwise MDS array code sets from a high-rate MDS code or MDS array code over Fq . We obtain a novel class of MSR codes by utilizing the MDS array codes provided by Lv et al., as component codes according to Generic Construction II. As a byproduct of Constructions 4 to 7, we obtain a new class of MSR codes with optimal access property. Using two types of sets Γ1 and Γ2 with the property that matrices commute, we present several new constructions for MSR codes with the optimal access property over any finite field. In this paper, compared with the constructions over binary field proposed by Li et al., the sub-packetization of our constructions applicable to the binary field is significantly reduced. © 1963-2012 IEEE.

关键词： Reed-Solomon codes

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Enhancing Radiology Report Interpretation with Large-Scale Language Models: A Two-Stage Fine-Tuning Approach 27

Enhancing Radiology Report Interpretation with Large-Scale L...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Pi, Sun-Woo Park, Myeong-Soo Lee, Byoung-Dai Department of Computer Science Graduate School Kyonggi University Suwon Korea Republic of Division of AI and Computer Engineering Kyonggi University Suwon Korea Republic of

ISBN: (纸本)9791188428137

Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. This study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural language processing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. The resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. These findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Radiology

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A Survey on Hypergraph Mining: Patterns, Tools, and Generators

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ACM Computing Surveys 2025年第8期57卷 1-36页

作者： Lee, Geon Bu, Fanchen Eliassi-Rad, Tina Shin, Kijung Kim Jaechul Graduate School of Ai Korea Advanced Institute of Science and Technology Seoul Korea Republic of School of Electrical Engineering Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Khoury College of Computer Sciences Northeastern University Boston United States

Hypergraphs, which belong to the family of higher-order networks, are a natural and powerful choice for modeling group interactions in the real world. For example, when modeling collaboration networks, which may involve not just two but three or more people, the use of hypergraphs allows us to explore beyond pairwise (dyadic) patterns and capture groupwise (polyadic) patterns. The mathematical complexity of hypergraphs offers both opportunities and challenges for hypergraph mining. The goal of hypergraph mining is to find structural properties recurring in real-world hypergraphs across different domains, which we call patterns. To find patterns, we need tools. We divide hypergraph mining tools into three categories: (1) null models (which help test the significance of observed patterns), (2) structural elements (i.e., substructures in a hypergraph such as open and closed triangles), and (3) structural quantities (i.e., numerical tools for computing hypergraph patterns such as transitivity). There are also hypergraph generators, whose objective is to produce synthetic hypergraphs that are a faithful representation of real-world hypergraphs. In this survey, we provide a comprehensive overview of the current landscape of hypergraph mining, covering patterns, tools, and generators. We provide comprehensive taxonomies for each and offer in-depth discussions for future research on hypergraph mining. © 2025 Copyright held by the owner/author(s).

关键词： Taxonomies

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