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arXiv 2024年

作者： Mao, Haobin Pi, Xiangyu Zhu, Lipeng Xiao, Zhenyu Xia, Xiang-Gen Zhang, Rui The School of Electronic and Information Engineering The State Key Laboratory of CNS/ATM Beihang University Beijing100191 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore The Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

In this letter, we propose to employ movable antenna (MA) to enhance covert communications with noise uncertainty, where the confidential data is transmitted from an MA-aided access point (AP) to multiple users with a warden attempting to detect the existence of the legal transmission. To maximize the sum rate of users under covertness constraint, we formulate an optimization problem to jointly design the transmit beamforming and the positions of MAs at the AP. To solve the formulated non-convex optimization problem, we develop a block successive upper-bound minimization (BSUM) based algorithm, where the proximal distance algorithm (PDA) and the successive convex approximation (SCA) are employed to optimize the transmit beamforming and the MAs' positions, respectively. Simulation results show that the proposed MAs-aided system can significantly increase the covert sum rate via antenna position optimization as compared to conventional systems with fixed-position antennas (FPAs). Copyright © 2024, The Authors. All rights reserved.

关键词： Convex optimization

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Federated Learning-Based Intelligent Indoor Smoke and Fire Detection System for Smart Buildings

Federated Learning-Based Intelligent Indoor Smoke and Fire D...

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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Aymene Berkani, Mohamed Rafik Chouchane, Ammar Himeur, Yassine Abdennebi, Anes Sagiroglu, Seref Amira, Abbes Medea Algeria University Biskra University Center of Barika Laboratory of LI3C Amdoukal Road Barika05001 Algeria University of Dubai College of Engineering and Information Technology Dubai United Arab Emirates École de Technologie Supérieure Software and Information Technology Engineering Montréal Canada Analytics Security R&D Center Gazi University Artificial Intelligence and Big Data Ankara Turkey University of Sharjah Department of Computer Science Sharjah United Arab Emirates

ISBN: (纸本)9798331527396

Ensuring safety in smart buildings is crucial due to the increasing prevalence of smoke and fire hazards in modern environments. This paper introduces a novel privacy-preserving FL approach based on a CNN1D for smoke and fire detection in smart buildings. Our system integrates data from wearable environmental sensors to train a lightweight, edge-deployable DL-CNN1D model, ensuring data privacy while enabling collaborative learning across distributed clients using Federated Averaging (FedAvg) aggregation. Experiments conducted on a comprehensive air measurement dataset for smoke and fire detection demonstrate exceptional performance, with the global model achieving 99.97% accuracy, 99.96% precision, in smoke and fire recognition. Our model demonstrates a low communication cost of 0.4 MB, underscoring its efficiency for real-time applications. Our FL-based approach represents a significant step towards balancing the need for robust safety systems with growing privacy concerns in smart building environments. © 2024 IEEE.

关键词： Federated learning

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Collaborative Perception datasets for Autonomous Driving: A Review

arXiv

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arXiv 2025年

作者： Wang, Naibang Shang, Deyong Gong, Yan Hu, Xiaoxi Song, Ziying Yang, Lei Huang, Yuhan Wang, Xiaoyu Lu, Jianli Beijing100083 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China State Key Laboratory of Intelligent Green Vehicle and Mobility Tsinghua University Beijing100084 China Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence School of Computer Science and Technology Beijing Jiaotong University China School of Mechanical and Aerospace Engineering Nanyang Technological University Singapore School of Mechatronics Engineering Harbin Institute of Technology 92 West Dazhi St. Harbin China Department of Electronic & Electrical Engineering University of Bath United Kingdom

Collaborative perception has attracted growing interest from academia and industry due to its potential to enhance perception accuracy, safety, and robustness in autonomous driving through multi-agent information fusion. With the advancement of Vehicle-to-Everything (V2X) communication, numerous collaborative perception datasets have emerged, varying in cooperation paradigms, sensor configurations, data sources, and application scenarios. However, the absence of systematic summarization and comparative analysis hinders effective resource utilization and standardization of model evaluation. As the first comprehensive review focused on collaborative perception datasets, this work reviews and compares existing resources from a multi-dimensional perspective. We categorize datasets based on cooperation paradigms, examine their data sources and scenarios, and analyze sensor modalities and supported tasks. A detailed comparative analysis is conducted across multiple dimensions. We also outline key challenges and future directions, including dataset scalability, diversity, domain adaptation, standardization, privacy, and the integration of large language models. To support ongoing research, we provide a continuously updated online repository of collaborative perception datasets and related literature: https://***/frankwnb/Collaborative-Perceptio n-datasets-for-Autonomous-Driving. © 2025, CC BY-SA.

关键词： Sensor data fusion

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A Region Enhanced Discrete Multi-Objective Fireworks Algorithm for Low-Carbon Vehicle Routing Problem

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Complex System Modeling and Simulation 2022年第2期2卷 142-155页

作者： Xiaoning Shen Jiaqi Lu Xuan You Liyan Song Zhongpei Ge Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Jiangsu Key Laboratory of Big Data Analysis TechnologySchool of AutomationNanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Institute of Trustworthy Autonomous Systems and also with the Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent ComputationDepartment of Computer Science and EngineeringSouthern University of Science and TechnologyShenzhen 518055China

A constrained multi-objective optimization model for the low-carbon vehicle routing problem(VRP)is established.A carbon emission measurement method considering various practical factors is *** minimizes both the total carbon emissions and the longest time consumed by the sub-tours,subject to the limited number of available *** to the characteristics of the model,a region enhanced discrete multi-objective fireworks algorithm is proposed.A partial mapping explosion operator,a hybrid mutation for adjusting the sub-tours,and an objective-driven extending search are designed,which aim to improve the convergence,diversity,and spread of the non-dominated solutions produced by the algorithm,*** low-carbon VRP instances with different scales are used to verify the effectiveness of the new ***,comparison results with four state-of-the-art algorithms indicate that the proposed algorithm has better performance of convergence and distribution on the low-carbon *** provides a promising scalability to the problem size.

关键词： vehicle routing problem carbon emission multi-objective optimization fireworks algorithm region enhanced

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Conceptual Method of Temperature Sensation in Bionic Hand by Extraordinary Perceptual Phenomenon

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Journal of Bionic engineering 2021年第6期18卷 1344-1357页

作者： Saeed Bahrami Moqadam Ahamd Saleh Asheghabadi Farzaneh Norouzi Hamed Jafarzadeh Ali Khosroabadi AfshinAlagheband Ghazal Bangash Negar Morovatdar Jing Xu State Key Laboratory of Tribology Department of Mechanical EngineeringTsinghua UniversityBeijing100084China Medical University of Mashhad 13131–99137MashhadIran Center for Computational and Data Intensive Science and Engineering(CDISE) Skolkovo Institute of Science and Technology(Skoltech)Moscow121205Russia Department of Mechanical Engineering Ferdowsi UniversityMashhad***Iran Department of Electrical Engineering Ferdowsi UniversityMashhad***Iran Department of Computer Engineering Ferdowsi UniversityMashhad***Iran Clinical Research Unit Imam Reza HospitalMashhad University of Medical Sciences13131–99137MashhadIran

Lack of temperature sensation of myoelectric prosthetic hand limits the daily activities of *** this end,a noninvasive temperature sensation method is proposed to train amputees to sense temperature with psychophysical sensory *** this study,22 healthy participants took part besides 5 amputee *** duration time of the study was 31 days with five test steps according to the Leitner *** adjustable temperature mug and a Peltier were used to change the temperature of the water/phantom digits to induce temperature to ***,to isolate the surroundings and show colors,a Virtual Reality(VR)glass was *** statistical results conducted are based on the response of participants with questionnaire *** Chi-square tests,it is concluded that participants answer the experiment significantly correctly using the Leitner technique(P value<0.05).Also,by applying the“Repeated Measures ANOVA”,it is noticed that the time of numbness felt by participants had significant(P value<0.001)*** could remember lowest and highest temperatures significantly better than other temperatures(P value<0.001);furthermore,the well-trained amputee participant practically using the prosthesis with 72.58%could identify object’s temperature with only once time experimenting the color temperature.

关键词： Temperature perceptions Leitner learning technique Classical conditioning Psychophysical sensory substitution Extraordinary perceptual phenomenon Bionic hand prosthesis

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Practitioners’ Expectations on Log Anomaly Detection

arXiv

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arXiv 2024年

作者： Ma, Xiaoxue Li, Yishu Keung, Jacky Yu, Xiao Zou, Huiqi Yang, Zhen Sarro, Federica Barr, Earl T. Department of Electronic Engineering and Computer Science Hong Kong Metropolitan University Hong Kong Department of Computer Science City University of Hong Kong Hong Kong State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Department of Computer Science Johns Hopkins University Baltimore United States School of Computer Science and Technology Shandong University Shandong China Department of Computer Science University College London London United Kingdom

Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over the past decade, it remains unclear what are practitioners’ expectations on log anomaly detection and whether current research meets their needs. To fill this gap, we conduct an empirical study, surveying 312 practitioners from 36 countries about their expectations on log anomaly detection. In particular, we investigate various factors influencing practitioners’ willingness to adopt log anomaly detection tools. We then perform a literature review on log anomaly detection, focusing on publications in premier venues from 2014 to 2024, to compare practitioners’ needs with the current state of research. Based on this comparison, we highlight the directions for researchers to focus on to develop log anomaly detection techniques that better meet practitioners’ expectations. © 2024, CC BY-NC-SA.

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MetaGCD: Learning to Continually Learn in Generalized Category Discovery

MetaGCD: Learning to Continually Learn in Generalized Catego...

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International Conference on computer Vision (ICCV)

作者： Yanan Wu Zhixiang Chi Yang Wang Songhe Feng Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing Jiaotong University Beijing China School of Computer and Information Technology Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Toronto Toronto Canada Department of Computer Science and Software Engineering Concordia University Montreal Canada

In this paper, we consider a real-world scenario where a model that is trained on pre-defined classes continually encounters unlabeled data that contains both known and novel classes. The goal is to continually discover novel classes while maintaining the performance in known classes. We name the setting Continual Generalized Category Discovery (C-GCD). Existing methods for novel class discovery cannot directly handle the C-GCD setting due to some unrealistic assumptions, such as the unlabeled data only containing novel classes. Furthermore, they fail to discover novel classes in a continual fashion. In this work, we lift all these assumptions and propose an approach, called MetaGCD, to learn how to incrementally discover with less forgetting. Our proposed method uses a meta-learning framework and leverages the offline labeled data to simulate the testing incremental learning process. A meta-objective is defined to revolve around two conflicting learning objectives to achieve novel class discovery without forgetting. Furthermore, a soft neighborhood-based contrastive network is proposed to discriminate uncorrelated images while attracting correlated images. We build strong baselines and conduct extensive experiments on three widely used benchmarks to demonstrate the superiority of our method.

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On the noise robustness of in-context learning for text generation 24

On the noise robustness of in-context learning for text gene...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Hongfu Gao Feipeng Zhang Wenyu Jiang Jun Shu Feng Zheng Hongxin Wei Department of Statistics and Data Science Southern University of Science and Technology and School of Economics and Finance Xi'an Jiaotong University School of Economics and Finance Xi'an Jiaotong University Department of Statistics and Data Science Southern University of Science and Technology and National Key Laboratory for Novel Software Technology Nanjing University School of Mathematics and Statistics Xi'an Jiaotong University Department of Computer Science and Engineering Southern University of Science and Technology Department of Statistics and Data Science Southern University of Science and Technology

ISBN: (纸本)9798331314385

Large language models (LLMs) have shown impressive performance on downstream tasks by in-context learning (ICL), which heavily relies on the quality of demonstrations selected from a large set of annotated examples. Recent works claim that in-context learning is robust to noisy demonstrations in text classification. In this work, we show that, on text generation tasks, noisy annotations significantly hurt the performance of in-context learning. To circumvent the issue, we propose a simple and effective approach called Local Perplexity Ranking (LPR), which replaces the "noisy" candidates with their nearest neighbors that are more likely to be clean. Our method is motivated by analyzing the perplexity deviation caused by noisy labels and decomposing perplexity into inherent perplexity and matching perplexity. Our key idea behind LPR is thus to decouple the matching perplexity by performing the ranking among the neighbors in semantic space. Our approach can prevent the selected demonstrations from including mismatched input-label pairs while preserving the effectiveness of the original selection methods. Extensive experiments demonstrate the effectiveness of LPR, improving the EM score by up to 18.75 on common benchmarks with noisy annotations. Our code is available at https://***/ml-stat-Sustech/Local-Perplexity-Ranking

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Discrimination between leucine-rich glioma-inactivated 1 antibody encephalitis and gamma-aminobutyric acid B receptor antibody encephalitis based on ResNet18

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 245-256页

作者： Jian Pan Ruijuan Lv Qun Wang Xiaobin Zhao Jiangang Liu Lin Ai School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Department of Neurology Beijing Tiantan HospitalCapital Medical UniversityChina National Clinical Research Center for Neurological DiseasesBeijing 100070China Department of Nuclear Medicine Beijing Tiantan HospitalCapital Medical UniversityBeijing 100070China School of Engineering Medicine Beihang UniversityBeijing 100191China Key Laboratory of Big Data-Based Precision Medicine(Beihang University) Ministry of Industry and Information Technology of the People’s Republic of ChinaBeijing 100191China.

This study aims to discriminate between leucine-rich glioma-inactivated 1(LGI1)antibody encephalitis and gammaaminobutyric acid B(GABAB)receptor antibody encephalitis using a convolutional neural network(CNN)model.A total of 81 patients were recruited for this ***18,VGG16,and ResNet50 were trained and tested separately using 3828 positron emission tomography image slices that contained the medial temporal lobe(MTL)or basal ganglia(BG).Leave-one-out cross-validation at the patient level was used to evaluate the CNN *** receiver operating characteristic(ROC)curve and the area under the ROC curve(AUC)were generated to evaluate the CNN *** on the prediction results at slice level,a decision strategy was employed to evaluate the CNN models’performance at patient *** ResNet18 model achieved the best performance at the slice(AUC=0.86,accuracy=80.28%)and patient levels(AUC=0.98,accuracy=96.30%).Specifically,at the slice level,73.28%(1445/1972)of image slices with GABAB receptor antibody encephalitis and 87.72%(1628/1856)of image slices with LGI1 antibody encephalitis were accurately *** the patient level,94.12%(16/17)of patients with GABAB receptor antibody encephalitis and 96.88%(62/64)of patients with LGI1 antibody encephalitis were accurately *** of the image slices extracted using gradient-weighted class activation mapping indicated that the model focused on the MTL and BG for *** general,the ResNet18 model is a potential approach for discriminating between LGI1 and GABAB receptor antibody *** in the MTL and BG is important for discriminating between these two encephalitis subtypes.

关键词： ResNet18 Fluorodeoxyglucose-positron emission tomography GABAB receptor antibody encephalitis Deep learning LGI1 antibody encephalitis

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A Dual-Level Cancelable Framework for Palmprint Verification and Hack-Proof data Storage

arXiv

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arXiv 2024年

作者： Yang, Ziyuan Kang, Ming Teoh, Andrew Beng Jin Gao, Chengrui Chen, Wen Zhang, Bob Zhang, Yi The College of Computer Science Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China The School of Cyber Science and Engineering Sichuan University Chengdu610065 China The School of Electrical and Electronic Engineering College of Engineering Yonsei University Seoul Korea Republic of The College of Computer Science Sichuan University Chengdu610065 China The Pattern Analysis and Machine Intelligence Group Department of Computer and Information Science University of Macau Taipa China The School of Cyber Science and Engineering Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China

In recent years, palmprints have been widely used for individual verification. The rich privacy information in palmprint data necessitates its protection to ensure security and privacy without sacrificing system performance. Existing systems often use cancelable technologies to protect templates, but these technologies ignore the potential risk of data leakage. Upon breaching the system and gaining access to the stored database, a hacker could easily manipulate the stored templates, compromising the security of the verification system. To address this issue, we propose a dual-level cancelable palmprint verification framework in this paper. Specifically, the raw template is initially encrypted using a competition hashing network with a first-level token, facilitating the end-to-end generation of cancelable templates. Different from previous works, the protected template undergoes further encryption to differentiate the second-level protected template from the first-level one. The system specifically creates a negative database (NDB) with the second-level token for dual-level protection during the enrollment stage. Reversing the NDB is NP-hard and a fine-grained algorithm for NDB generation is introduced to manage the noise and specified bits. During the verification stage, we propose an NDB matching algorithm based on matrix operation to accelerate the matching process of previous NDB methods caused by dictionary-based matching rules. This approach circumvents the need to store templates identical to those utilized for verification, reducing the risk of potential data leakage. Extensive experiments conducted on public palmprint datasets have confirmed the effectiveness and generality of the proposed framework. Upon acceptance of the paper, the code will be accessible at https://***/Deep-Imaging-Group/NPR. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital storage

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