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Audio-Visual Collaborative Learning for Weakly Supervised Video Anomaly Detection

IEEE Transactions on Multimedia 2025年

作者： Meng, Jingke Tian, Huilin Lin, Ge Hu, Jian-Fang Zheng, Wei-Shi Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Peng Cheng Laboratory Shenzhen518005 China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-sen University Ministry of Education China

Weakly supervised anomaly detection is to identify the time window when an anomaly event happened based on the video-level label indicating whether the video contains anomaly event or not. Recent efforts have focused on leveraging multi-modal data, specifically combining visual and audio information, to enhance detection accuracy. While some studies have explored intra-video separation techniques, the primary emphasis remains on distinguishing potentially anomalous events scoring highest from those scoring lowest as normal events. Nevertheless, challenges persist in delineating boundaries between normal and abnormal events, particularly when visual differences are subtle. Our proposed framework, called Audio-Visual Collaborative Learning (AVCL), addresses the challenge of ambiguity in weakly supervised anomaly detection. Our core idea centers around utilizing both audio track variations and the perceptual robustness of visual information to detect and differentiate challenging cases, which composed of two essential modules: the Audio-Visual collaborative Hard cases Separation (AVHS) module and the Multi-modal Mutual Learning (MML) module. The AVHS module aims to address the challenge of discerning visually ambiguous clips in anomaly videos, differentiating between normal and abnormal events. To further improve detection accuracy, we introduce the Multi-modal Mutual Learning (MML) module, and this module enables a process of mutual learning to facilitate the exchange of knowledge and expertise between the single-modal branch and the multi-modal branch. We demonstrate that the proposed approach achieves state-of-the-art detection performance on benchmarks of XD-Violence and CCTV-Fightssub datasets. © 1999-2012 IEEE.

关键词： Self-supervised learning

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MS²-GNN: Exploring GNN-Based Multimodal Fusion Network for Depression Detection

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IEEE transactions on cybernetics 2023年第12期53卷 7749-7759页

作者： Tao Chen Richang Hong Yanrong Guo Shijie Hao Bin Hu Key Laboratory of Knowledge Engineering with Big Data Ministry of Education and the School of Computer Science and Information Engineering Hefei University of Technology Hefei China Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou University Lanzhou China

Major depressive disorder (MDD) is one of the most common and severe mental illnesses, posing a huge burden on society and families. Recently, some multimodal methods have been proposed to learn a multimodal embedding for MDD detection and achieved promising performance. However, these methods ignore the heterogeneity/homogeneity among various modalities. Besides, earlier attempts ignore interclass separability and intraclass compactness. Inspired by the above observations, we propose a graph neural network (GNN)-based multimodal fusion strategy named modal-shared modal-specific GNN, which investigates the heterogeneity/homogeneity among various psychophysiological modalities as well as explores the potential relationship between subjects. Specifically, we develop a modal-shared and modal-specific GNN architecture to extract the inter/intramodal characteristics. Furthermore, a reconstruction network is employed to ensure fidelity within the individual modality. Moreover, we impose an attention mechanism on various embeddings to obtain a multimodal compact representation for the subsequent MDD detection task. We conduct extensive experiments on two public depression datasets and the favorable results demonstrate the effectiveness of the proposed algorithm.

关键词： Task analysis Feature extraction Semantics Depression Graph neural networks Electroencephalography Data mining Mental disorders Multisensory integration

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Optimal Dispatch Strategy of Electric Vehicles under Coupled Transportation Network and Power Grid

Optimal Dispatch Strategy of Electric Vehicles under Coupled...

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IEEE International Conference on Power and Power and Renewable Energy (ICPRE)

作者： Keming Luo Xiaohan Fang Yuan Fan Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Electrical Engineering and Automation Anhui University Hefei China

This paper addresses the complex nature of electric vehicles (EVs) as both transportation means and mobile loads, and proposes an optimal dispatching scheduling strategy for EVs in the coupled transportation network and power grid. Firstly, a dynamic traffic network model is established by utilizing a vast amount of real-world traffic data, including Origin-Destination (OD) information, battery states, and departure times. A graph theory-based analysis is then employed to determine the optimal paths for EVs in the traffic network. Secondly, the charging behavior of EVs is integrated with the power grid, and charge-discharge scheduling is conducted for charging stations to optimize the power grid load curve while meeting the charging demands of EVs. Additionally, adaptive electricity prices are formulated based on the power grid’s load generation through a dynamic pricing mechanism, further optimizing the charging strategy for EVs. Lastly, the impact of EV charging on the power grid load is analyzed by contrasting ordered and unordered charging and discharging modes. The research results demonstrate that the proposed optimal charging scheduling strategy for EVs in traffic-aware power grids can significantly reduce peak-valley differences in power grid load, enhance the economic and operational stability of the power grid, and effectively meet the charging demands of EV users. These findings provide valuable insights and promising potential for practical applications and widespread adoption.

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BandwidthBreach: Unleashing Covert and Side Channels through Cache Bandwidth Exploitation

arXiv

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arXiv 2023年

作者： Wang, Han Tang, Ming Xu, Ke Wang, Quancheng Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China

In the modern CPU architecture, enhancements such as the Line Fill Buffer (LFB) and Super Queue (SQ), which are designed to track pending cache requests, have significantly boosted performance. To exploit this structure, we deliberately engineered blockages in the L2 to L1d route by controlling LFB conflict and triggering prefetch prediction failures, while consciously dismissing other plausible influencing factors. This approach was subsequently extended to the L3 to L2 and L2 to L1i pathways, resulting in three potent covert channels, termed L2CC, L3CC, and LiCC, with capacities of 10.02 Mbps, 10.37 Mbps, and 1.83 Mbps, respectively. Strikingly, the capacities of L2CC and L3CC surpass those of earlier non-shared-memory-based covert channels, reaching a level comparable to their shared memory-dependent equivalents. Leveraging this congestion further facilitated the extraction of key bits from RSA and EdDSA designs. Coupled with SpectreV1 and V2, our covert channels effectively evade the majority of traditional Spectre defenses. Their confluence with Branch Prediction (BP) Timing assaults additionally undercuts balanced branch protections, hence broadening their capability to infiltrate a wide range of cryptography libraries. Copyright © 2023, The Authors. All rights reserved.

关键词： Memory architecture

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Co-Ru alloy nanoparticles decorated onto two-dimensional nitrogen doped carbon nanosheets towards hydrogen/oxygen evolution reaction and oxygen reduction reaction

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Journal of Energy Chemistry 2023年第12期87卷 286-294,I0008页

作者： Huizhen Wang Pengfei Yang Xiaoyuan Sun Weiping Xiao Xinping Wang Minge Tian Guangrui Xu Zhenjiang Li Yubing Zhang Fusheng Liu Lei Wang Zexing Wu Key Laboratory of Eco-chemical Engineering Ministry of EducationInternational Science and Technology Cooperation Base of Eco-chemical Engineering and Green ManufacturingCollege of Chemistry and Molecular EngineeringQingdao University of Science and TechnologyQingdao 266042ShandongChina College of Science Nanjing Forestry UniversityNanjing 210037JiangsuChina Scientific Green(Shandong)Environmental Technology Co.Ltd Jining Economic Development ZoneJining 272499ShandongChina School of Materials Science and Engineering Qingdao University of Science and TechnologyQingdao 266042ShandongChina College of Chemical Engineering Qingdao University of Science and TechnologyQingdao 266042ShandongChina

Constructing highly-efficient electrocatalysts toward hydrogen evolution reaction(HER)/oxygen evolution reaction(OER)/oxygen reduction reaction(ORR)with excellent stability is quite important for the development of renewable energy-related ***,Co-Ru based compounds supported on nitrogen doped two-dimensional(2D)carbon nanosheets(NCN)are developed via one step pyrolysis procedure(Co-Ru/NCN)for HER/ORR and following low-temperature oxidation process(Co-Ru@RuO_(x)/NCN)for *** specific 2D morphology guarantees abundant active sites ***,the synergistic effects arising from the interaction between Co and Ru are crucial in enhancing the catalytic ***,the resulting Co-Ru/NCN shows remarkable electrocatalytic performance for HER(70 mV at 10 mA cm^(-2))in 1 M KOH and ORR(half-wave potential E_(1/2)=0.81 V)in 0.1 M ***,the Co-Ru@RuO_(x)/NCN obtained by oxidation exhibits splendid OER performance in both acid(230 mV at 10 mA cm^(-2))and alkaline media(270 mV at 10 mA cm^(-2))coupled with excellent ***,the fabricated two-electrode water-splitting device exhibits excellent performance in both acidic and alkaline *** research provides a promising avenue for the advancement of multifunctional nanomaterials.

关键词： Electrocatalyst 2D Carbon nanosheet Hydrogen/oxygen evolution reaction Oxygen reduction reaction Water-splitting

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DEEPNOISE:Learning Sensor and Process Noise to Detect Data Integrity Attacks in CPS

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China Communications 2021年第9期18卷 192-209页

作者： Yuan Luo Long Cheng Yu Liang Jianming Fu Guojun Peng School of Cyber Science and Engineering Wuhan UniversityWuhan 430072China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of EducationWuhan 430072China School of Computing Clemson UniversityUSA Tencent Technology Shenzhen Company ShenzhenChina

Cyber-physical systems(CPS)have been widely deployed in critical infrastructures and are vulnerable to various *** integrity attacks manipulate sensor measurements and cause control systems to fail,which are one of the prominent threats to *** detection methods are proposed to secure ***,existing anomaly detection studies usually require expert knowledge(e.g.,system model-based)or are lack of interpretability(e.g.,deep learning-based).In this paper,we present DEEPNOISE,a deep learning-based anomaly detection method for CPS with ***,we utilize the sensor and process noise to detect data integrity *** noise represents the intrinsic characteristics of physical devices and the production process in *** key enabler is that we use a robust deep autoencoder to automatically extract the noise from measurement ***,an LSTM-based detector is designed to inspect the obtained noise and detect *** integrity attacks change noise patterns and thus are identified as the root cause of anomalies by *** on the SWaT testbed,DEEPNOISE achieves higher accuracy and recall compared with state-of-the-art model-based and deep learningbased *** average,when detecting direct attacks,the precision is 95.47%,the recall is 96.58%,and F_(1) is 95.98%.When detecting stealthy attacks,precision,recall,and F_(1) scores are between 96% and 99.5%.

关键词： cyber-physical systems anomaly detection data integrity attacks

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A New Adversarial Perspective for LiDAR-Based 3D Object Detection 39

A New Adversarial Perspective for LiDAR-Based 3D Object Dete...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Zheng, Shijun Liu, Weiquan Guo, Yu Zang, Yu Shen, Siqi Wang, Cheng Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University China Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China College of Computer Engineering Jimei University China

ISBN: (纸本)157735897X

Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Vehicle detection

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SLSNet: Weakly-Supervised Skin Lesion Segmentation Network with Self-attentions 20th

SLSNet: Weakly-Supervised Skin Lesion Segmentation Network w...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pei, Songwen Huang, Junjie School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Engineering Research Center of Software/Hardware Co-design Technology and Application Ministry of Education East China Normal University Shanghai200062 China

ISBN: (纸本)9789819970247

Computer-aided skin lesion segmentation with high precision is crucial to diagnose skin cancers in the early stage. However, the lack of pixel-level labels makes the skin lesion segmentation tasks challenging. To tackle this problem, a new weakly-supervised skin lesion segmentation network with self-attentions named SLSNet is proposed. SLSNet contains two modules and uses image-level labels as supervision information. One module named Intra-image Self-attention Seed Expansion (ISE) digs intra-image self-attentions with an expansion loss and a confidence loss to expand seed areas. The other module named Inter-image Affinity-based Noise Suppression (IAS) suppresses the noise pixels in attention maps via inter-image correlations. Extensive experiments conducted on ISIC-2017 dataset show that SLSNet achieves relatively high performance while reducing human labeling efforts. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Expansion

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An Enhanced Data-Driven Model for Lithium-Ion Battery State-of-Health Estimation with Optimized Features and Prior Knowledge

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Automotive Innovation 2022年第2期5卷 134-145页

作者： Huanyang Huang Jinhao Meng Yuhong Wang Lei Cai Jichang Peng Ji Wu Qian Xiao Tianqi Liu Remus Teodorescu College of Electrical Engineering Sichuan UniversityChengduChina Faculty of Computer Science and Engineering Shanxi Key Laboratory for Network Computing and Security TechnologyXi’an University of TechnologyXi’anChina Smart Grid Research Institute Nanjing Institute of TechnologyNanjingChina Department of Automation University of Science and Technology of ChinaHefeiChina Key Laboratory of Smart Grid of Ministry of Education Tianjin UniversityTianjinChina Department of Energy Technology Aalborg University9220 AalborgDenmark

In the long-term prediction of battery degradation,the data-driven method has great potential with historical data recorded by the battery management *** paper proposes an enhanced data-driven model for Lithium-ion(Li-ion)battery state of health(SOH)estimation with a superior modeling procedure and optimized *** Gaussian process regression(GPR)method is adopted to establish the data-driven estimator,which enables Li-ion battery SOH estimation with the uncertainty level.A novel kernel function,with the prior knowledge of Li-ion battery degradation,is then introduced to improve the mod-eling capability of the *** for the features,a two-stage processing structure is proposed to find a suitable partial charging voltage profile with high *** the first stage,an optimal partial charging voltage is selected by the grid search;while in the second stage,the principal component analysis is conducted to increase both estimation accuracy and computing *** of the proposed method are validated on two datasets from different Li-ion batteries:Compared with other methods,the proposed method can achieve the same accuracy level in the Oxford dataset;while in Maryland dataset,the mean absolute error,the root-mean-squared error,and the maximum error are at least improved by 16.36%,32.43%,and 45.46%,respectively.

关键词： Li-ion battery State of health Gaussian process regression Kernel function Feature optimization

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Novelty Calculation in Imbalanced Dynamic Interconnected Specialized IoT Network Traffic

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IEEE Internet of Things Journal 2025年第12期12卷 20174-20190页

作者： Fan, Mingrui Zuo, Jinxin Gao, Jiaqi He, Yaru Shi, Weidong Lu, Yueming Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Beijing University of Posts and Telecommunications School of Information and Communication Engineering Beijing100876 China

Quantifying the deviation of testing samples from known benign traffic in the form of novelty scores is essential for identifying new malicious traffic and detecting concept drifts of benign traffic. Most existing solutions generate novelty scores based on the outputs from the supervised neural network's last layers. However, the performance of these supervised techniques is significantly compromised in the unbalanced dynamic interconnected specialized IoT network traffic such as Electronic Power Grid. To solve this challenge, we investigate an improved novelty score calculation approach. It employs the targeted distance loss to ensure that different known classes form class-specific, dense clusters within the embedding space outputted by the neural network. The minimum distance between the testing sample and the centers of each known class is utilized as the novelty score. Extensive comparison experiments on public datasets and real Electronic Power Grid traffic demonstrate that our approach outperforms existing supervised techniques. As the imbalance in known class distributions increases, our proposed method consistently achieves higher AUROC and AUPR than baselines, with a reduced FPR95. This study analyzes imbalanced known class distributions' negative impact on novelty detection and provides practical improvement. © 2014 IEEE.

关键词： Internet of things

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