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arXiv 2024年

作者： Xu, Junhong Yin, Kai Gregory, Jason M. Hauser, Kris Liu, Lantao Luddy School of Informatics Computing and Engineering Indiana University-Bloomington United States Expedia Group U.S. Army Research Laboratory United States Department of Computer Science University of Illinois at Urbana-Champaign United States

Navigation safety is critical for many autonomous systems such as self-driving vehicles in an urban environment. It requires an explicit consideration of boundary constraints that describe the borders of any infeasible, non-navigable, or unsafe regions. We propose a principled boundary-aware safe stochastic planning framework with promising results. Our method generates a value function that can strictly distinguish the state values between free (safe) and non-navigable (boundary) spaces in the continuous state, naturally leading to a safe boundary-aware policy. At the core of our solution lies a seamless integration of finite elements and kernel-based functions, where the finite elements allow us to characterize safety-critical states’ borders accurately, and the kernel-based function speeds up computation for the non-safety-critical states. The proposed method was evaluated through extensive simulations and demonstrated safe navigation behaviors in mobile navigation tasks. Additionally, we demonstrate that our approach can maneuver safely and efficiently in cluttered real-world environments using a ground vehicle with strong external disturbances, such as navigating on a slippery floor and against external human intervention. © 2024, CC BY.

关键词： Markov processes

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On the Effectiveness of Function-Level Vulnerability Detectors for Inter-Procedural Vulnerabilities

On the Effectiveness of Function-Level Vulnerability Detecto...

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International Conference on Software engineering (ICSE)

作者： Zhen Li Ning Wang Deqing Zou Yating Li Ruqian Zhang Shouhuai Xu Chao Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security Cluster and Grid Computing Lab School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jin YinHu Laboratory Wuhan China Department of Computer Science University of Colorado Colorado Springs Colorado Springs Colorado USA Institute for Network Sciences and Cyberspace Tsinghua University Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798400702174

ISBN: (纸本)9798350382143

Software vulnerabilities are a major cyber threat and it is important to detect them. One important approach to detecting vulnerabilities is to use deep learning while treating a program function as a whole, known as function-level vulnerability detectors. However, the limitation of this approach is not understood. In this paper, we investigate its limitation in detecting one class of vulnerabilities known as inter-procedural vulnerabilities, where the to-be-patched statements and the vulnerability-triggering statements belong to different functions. For this purpose, we create the first Inter -Procedural Vulnerability Dataset (InterPVD) based on C/C++ open-source software, and we propose a tool dubbed VulTrigger for identifying vulnerability-triggering statements across functions. Experimental results show that VulTrigger can effectively identify vulnerability-triggering statements and inter-procedural vulnerabilities. Our findings include: (i) inter-procedural vulnerabilities are prevalent with an average of 2.8 inter-procedural layers; and (ii) function-level vulner-ability detectors are much less effective in detecting to-be-patched functions of inter-procedural vulnerabilities than detecting their counterparts of intra-procedural vulnerabilities.

关键词： Deep learning Detectors Open source software Software engineering

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Computationally Efficient Constraint-Optimized Radar Mismatched Filtering for Waveform-Agile Systems

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IEEE Transactions on Aerospace and Electronic Systems 2025年第3期61卷 6711-6732页

作者： Jones, Christian C. McCormick, Patrick M. Heintzelman, Matthew B. U.S. Naval Research Laboratory WashingtonDC20375 United States University of Kansas Department of Electrical Engineering and Computer Science LawrenceKS66045 United States University of Kansas Institute for Information Sciences LawrenceKS66045 United States

Mismatched filtering is a well-known approach of range sidelobe suppression in radar pulse compression;however, advances in radar waveform agility and spectrum sharing require the generation of filters to be efficiently computed (for possible real-time operation) with consideration of possible interference via collisions with other spectral users. Here, a computationally efficient radar mismatched filter (MMF) design framework is proposed which provides the optimal integrated sidelobe level (ISL) or spectral template match for a constrained signal-to-interference-plus-noise (SINR) loss via the Lagrange dual problem with known interference. This approach builds off of previous works which examine ISL MMFs with constrained signal-to-noise (SNR) loss and provides further extensions, analysis, and bounds for alternative quadratic constraints and design objectives. Furthermore, by leveraging the Toeplitz structure that arises in time-series problems and the bounds of the Lagrange dual function, efficient solvers are developed that leverage infinite-impulse-response based circulant approximations and/or preconditioned conjugate gradient (PCG). The proposed filter design is compared against current linear solvers and is found to have a lower order computational complexity with less required sequential calculations. Filter performance is assessed via hardware-in-the-loop and open-air experimental measurements. © 1965-2011 IEEE.

关键词： Signal to noise ratio

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A 3D Implementation of Convolutional Neural Network for Fast Inference

A 3D Implementation of Convolutional Neural Network for Fast...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Narasinga Rao Miniskar Pruek Vanna-Iampikul Aaron Young Sung Kyu Lim Frank Liu Jieun Yoo Corrinne Mills Nhan Tran Farah Fahim Jeffrey S Vetter Computer Science and Mathematic Division Oak Ridge National Laboratory Oak Ridge USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA Department of Physics University of Illinois Chicago Chicago USA Scientific Computing Division Fermi National Accelerator Laboratory Chicago USA

Low latency inference has many applications in edge machine learning. In this paper, we present a run-time configurable convolutional neural network (CNN) inference ASIC design for low-latency edge machine learning. By implementing a 5-stage pipelined CNN inference model in a 3D ASIC technology, we demonstrate that the model distributed on two dies utilizing face-to-face (F2F) 3D integration achieves superior performance. Our experimental results show that the design based on 3D integration achieves 43% better energy-delay product when compared to the traditional 2D technology.

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Intracardiac Electrogram Signals Key Feature Points Recognition Using Dense Convolutional Network

Intracardiac Electrogram Signals Key Feature Points Recognit...

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Pattern Recognition and Machine Learning (PRML), IEEE International Conference on

作者： Jiang Yihang Gu Kaihao Wu Xiaomei Department of Biomedical Engineering School of information Science and Technology Fudan University Shanghai China Department of Biomedical Engineering Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention (MICCAI) of Shanghai School of information Science and Technology Fudan University Academy for Engineering and Technology Shanghai Engineering Research Center of Assistive Devices Shanghai China Yiwu Research Institute of Fudan University Zhejiang China

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

During radiofrequency ablation procedures for atrial fibrillation patients, physicians can assist in locating ablation targets by analyzing the conduction pathways and low-voltage areas of the intracardiac electrogram signals. The feature points of intracardiac electrogram signals serve as criteria for assessing excitation patterns and low-voltage areas. Therefore, extracting feature points from intracardiac signals is of significant value. To identify key points in intracardiac electrogram signals through deep learning, this experiment collected intracardiac electrogram signals from 21 clinical patients, which were then filtered using wavelet transformation. Subsequently, each excitation was treated as a signal group, indicating the key feature points within. Finally, a total of 36,183 groups of intracardiac electrogram signals annotated with key points were obtained. The dataset consisted of data from 17 patients allocated for training, data from 2 patients reserved for testing, and data from the remaining 2 patients designated for validation purposes. Training was conducted using a DenseNet network incorporating both convolutional attention and feature pyramid modules. The final network successfully identifies the feature points of intracardiac electrogram signals. It outputs six feature points, including the starting point, first rising point, first falling point, peak point, valley point, and termination point, with average absolute errors of only 0.34, 0.27, 0.31, 0.01, 0.27, and 0.42 ms, respectively. The final detection results are highly satisfactory, indicating the potential utility of the findings in assisting the analysis of intracardiac electrogram signals. The research outcomes presented in this paper lay a foundation for the analysis of intracardiac electrogram signals.

关键词： Training Wavelet transforms Radio frequency Low voltage Training data Medical services Myocardium Feature extraction Pattern recognition Testing

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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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Analytical-Chemistry-Informed Transformer for Infrared Spectra Modeling 39

Analytical-Chemistry-Informed Transformer for Infrared Spect...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Huang, Shiluo Jin, Yining Jin, Wei Mu, Ying School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Research Centre for Analytical Instrumentation State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Infrared (IR) spectroscopy is a fundamental technique in analytical chemistry. Recently, deep learning (DL) has drawn great interest as the modeling method of infrared spectral data. However, unlike vision or language tasks, IR spectral data modeling is faced with the problem of calibration transfer and has distinctive characteristics. Introducing the prior knowledge of IR spectroscopy could guide the DL methods to learn representations aligned with the domain-invariant characteristics of spectra, and thus improve the performance. Despite such potential, there is a notable absence of DL methods that incorporate such inductive bias. To this end, we propose Analytical-Chemistry-Informed Transformer (ACT) with two modules informed by the field knowledge in analytical chemistry. First, ACT includes learnable spectral processing inspired by chemometrics, which comprises spectral pre-processing, tokenization, and post-processing. Second, a straightforward yet effective representation learning mechanism, namely spectral-attention, is incorporated into ACT. Spectral-attention utilizes the intra-spectral and inter-spectral correlations to extract spectral representations. Empirical results show that ACT has achieved competitive results in 9 analytical tasks covering applications across pharmacy, chemistry, and agriculture. Compared with existing networks, ACT reduces the root mean square error of prediction (RMSEP) by more than 20% in calibration transfer tasks. These results indicate that DL methods in IR spectroscopy could benefit from the integration of prior knowledge in analytical chemistry. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep learning

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IDA: Informed Domain Adaptive Semantic Segmentation

arXiv

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arXiv 2023年

作者： Chen, Zheng Ding, Zhengming Gregory, Jason M. Liu, Lantao Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States The Department of Computer Science Tulane University United States DEVCOM Army Research Laboratory United States

Mixup-based data augmentation has been validated to be a critical stage in the self-training framework for unsupervised domain adaptive semantic segmentation (UDA-SS), which aims to transfer knowledge from a well-annotated (source) domain to an unlabeled (target) domain. Existing self-training methods usually adopt the popular region-based mixup techniques with a random sampling strategy, which unfortunately ignores the dynamic evolution of different semantics across various domains as training proceeds. To improve the UDA-SS performance, we propose an Informed Domain Adaptation (IDA) model, a self-training framework that mixes the data based on class-level segmentation performance, which aims to emphasize small-region semantics during mixup. In our IDA model, the class-level performance is tracked by an expected confidence score (ECS). We then use a dynamic schedule to determine the mixing ratio for data in different domains. Extensive experimental results reveal that our proposed method is able to outperform the state-of-the-art UDA-SS method by a margin of 1.1 mIoU in the adaptation of GTA-V to Cityscapes and of 0.9 mIoU in the adaptation of SYNTHIA to Cityscapes. Code link: https://***/ArlenCHEN/*** Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Hyperspectral and Multispectral Image Fusion Target Detection based on Cloud-Edge Collaboration

Hyperspectral and Multispectral Image Fusion Target Detectio...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jun Hu Shanshan Wu Zebin Wu Yi Zhang Javier Plaza Antonio Plaza School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Nanjing Research Institute of Electronics Engineering (NRIEE) Nanjing China Department of Technology of Computers and Communications Hyperspectral Computing Laboratory University of Extremadura Cáceres Spain

Hyperspectral target detection (HTD) aims to detect fine targets in hyperspectral images (HSIs). The traditional HTD method in low-resolution hyperspectral image (LR-HSI) is incapable of detecting small targets, clearly and precisely. Accordingly, in this paper, we propose a hyperspectral and multispectral image fusion target detection method based on cloud-edge collaboration. In this method, LR-HSI is first employed for coarse detection with the output of some suspicious target areas. Afterwards, the hyperspectral images and multispectral images (HSI-MSI) fusion is performed on these areas for precise target detection. In order to ensure the efficiency of HTD, we intend to accelerate our method in parallel based on the cloud-edge collaborative architecture. Furthermore, we establish an optimization model and design a greedy strategy to achieve the optimal deployment for minimizing the shortest runtime on the cloud-edge collaborative architecture. The experimental results demonstrate that our proposed method can significantly improve the computational efficiency while ensuring the accuracy.

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Semi-Asynchronous Federated Learning with Trajectory Prediction for Vehicular Edge computing

Semi-Asynchronous Federated Learning with Trajectory Predict...

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International Workshop on Quality of Service

作者： Yuxuan Deng Xiuhua Li Chuan Sun Qilin Fan Xiaofei Wang Victor C. M. Leung School of Big Data & Software Engineering Chongqing University Chongqing China School of Computer Science and Engineering Nanyang Technological University Singapore TKLAN College of Intelligence & Computing Tianjin University Tianjin China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen China College of Computer Science & Software Engineering Shenzhen University Shenzhen China Department of Electrical & Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350350128

ISBN: (纸本)9798350350135

Federated learning, as a distributed machine learning paradigm, offers promising solutions for vehicular edge computing (VEC) networks. However, federated learning in VEC with classification tasks still faces two key challenges: i) Delayed data labeling hampers supervised training; ii) Dynamic vehicle behavior complicates training scheduling and model uploads to edge servers. In this paper, we propose a semi-asynchronous federated learning algorithm for VEC. Specifically, it utilizes knowledge distillation to generate soft labels from raw data for supervised training, and estimates model training and uploading time through trajectory prediction. We further logically group vehicles based on the characteristics of their dynamic behavior. We then employ synchronous aggregation within groups and asynchronous aggregation between groups to optimize model performance while reducing latency. Finally, we conduct separate comparative experiments for all components, demonstrating that each component possesses unique advantages. Experiment results show that the proposed algorithm outperforms existing schemes in terms of accuracy and latency. The code is available at: https://***/dyxcode/Semi-Asynchronous-Federated-Learning.

关键词： Training Federated learning Heuristic algorithms Predictive models Prediction algorithms Data models Trajectory

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