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Mode Competition of Low Voltage Backward Wave Oscillator near 500 GHz with Parallel Multi-Beam

Chinese Journal of Electronics 2024年第2期33卷 488-495页

作者： Xiaoyan ZHAO Jincheng HU Haoran ZHANG Sidou GUO Yuming FENG Lin TANG Kaichun ZHANG Diwei LIU THz Center School of Electronic Science and Engineering University of Electronic Science and Technology of China School of Computer Science Southwest Petroleum University

A backward wave oscillator with parallel multiple beams and multi-pin slow-wave structure(SWS)operating at the frequency above 500 GHz is studied. Both the cold-cavity dispersion characteristics and CST Particle Studio simulation results reveal that there are obvious mode competition problems in this kind of terahertz *** that the structure of the multi-pin SWS is similar to that of two-dimensional photonic crystals, we introduce the defects of photonic crystal with the property of filtering into the SWS to suppress high-order ***, a detailed study of the effect of suppressing higher-order modes is carried out in the process of changing location and arrangement pattern of the point defects. The stable, single-mode operation of the terahertz source is realized. The simulation results show that the ratio of the output peak power of the higher-order modes to that of the fundamental mode is less than 1.9%. Also, the source can provide the output peak power of 44.8 m W at the frequency of 502.2 GHz in the case of low beam voltage of 4.7 kV.

关键词： Mode competition Backward wave oscillator Multi-pin slow-wave structure Parallel multiple beams Defects

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Robust video question answering via contrastive cross-modality representation learning

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science China(Information sciences) 2024年第10期67卷 211-226页

作者： Xun YANG Jianming ZENG Dan GUO Shanshan WANG Jianfeng DONG Meng WANG School of Information Science and Technology University of Science and Technology of China Institute of Artificial Intelligence Hefei Comprehensive National Science Center School of Computer Science and Information Engineering Hefei University of Technology Institutes of Physical Science and Information Technology Anhui University School of Computer Science and Technology Zhejiang Gongshang University

Video question answering(VideoQA) is a challenging yet important task that requires a joint understanding of low-level video content and high-level textual semantics. Despite the promising progress of existing efforts, recent studies revealed that current VideoQA models mostly tend to over-rely on the superficial correlations rooted in the dataset bias while overlooking the key video content, thus leading to unreliable results. Effectively understanding and modeling the temporal and semantic characteristics of a given video for robust VideoQA is crucial but, to our knowledge, has not been well investigated. To fill the research gap, we propose a robust VideoQA framework that can effectively model the cross-modality fusion and enforce the model to focus on the temporal and global content of videos when making a QA decision instead of exploiting the shortcuts in datasets. Specifically, we design a self-supervised contrastive learning objective to contrast the positive and negative pairs of multimodal input, where the fused representation of the original multimodal input is enforced to be closer to that of the intervened input based on video perturbation. We expect the fused representation to focus more on the global context of videos rather than some static keyframes. Moreover, we introduce an effective temporal order regularization to enforce the inherent sequential structure of videos for video representation. We also design a Kullback-Leibler divergence-based perturbation invariance regularization of the predicted answer distribution to improve the robustness of the model against temporal content perturbation of videos. Our method is model-agnostic and can be easily compatible with various VideoQA backbones. Extensive experimental results and analyses on several public datasets show the advantage of our method over the state-of-the-art methods in terms of both accuracy and robustness.

关键词： video question answering cross-modality fusion contrastive learning cross-media reasoning

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CLIP-Flow:Decoding images encoded in CLIP space

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Computational Visual Media 2024年第6期10卷 1157-1168页

作者： Hao Ma Ming Li Jingyuan Yang Or Patashnik Dani Lischinski Daniel Cohen-Or Hui Huang Visual Computing Research Center College of Computer Science and Software EngineeringShenzhen UniversityShenzhen 518060China Department of Computer Science Tel Aviv UniversityTel Aviv 6997801Israel School of Computer Science and Engineering the Hebrew University of JerusalemJerusalem 91904Israel

This study introduces CLIP-Flow,a novel network for generating images from a given image or *** effectively utilize the rich semantics contained in both modalities,we designed a semantics-guided methodology for image-and text-to-image *** particular,we adopted Contrastive Language-Image Pretraining(CLIP)as an encoder to extract semantics and StyleGAN as a decoder to generate images from such ***,to bridge the embedding space of CLIP and latent space of StyleGAN,real NVP is employed and modified with activation normalization and invertible *** the images and text in CLIP share the same representation space,text prompts can be fed directly into CLIP-Flow to achieve text-to-image *** conducted extensive experiments on several datasets to validate the effectiveness of the proposed image-to-image synthesis *** addition,we tested on the public dataset Multi-Modal CelebA-HQ,for text-to-image *** validated that our approach can generate high-quality text-matching images,and is comparable with state-of-the-art methods,both qualitatively and quantitatively.

关键词： image-to-image text-to-image contrastive language-image pretraining(CLIP) flow StyleGAN

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A dynamic incentive and reputation mechanism for energy-efficient federated learning in 6G

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Digital Communications and Networks 2023年第4期9卷 817-826页

作者： Ye Zhu Zhiqiang Liu Peng Wang Chenglie Du School of Computer Science Northwestern Polytechnical UniversityXi'an710072ShaanxiChina School of Cybersecurity Northwestern Polytechnical UniversityXi'an710072ShaanxiChina School of Cyber Engineering Xidian UniversityXi'an710071ShaanxiChina

As 5G becomes commercial,researchers have turned attention toward the Sixth-Generation(6G)network with the vision of connecting intelligence in a green energy-efficient *** learning triggers an upsurge of green intelligent services such as resources orchestration of communication infrastructures while preserving privacy and increasing communication ***,designing effective incentives in federated learning is challenging due to the dynamic available clients and the correlation between clients'contributions during the learning *** this paper,we propose a dynamic incentive and reputation mechanism to improve energy efficiency and training performance of federated *** proposed incentive based on the Stackelberg game can timely adjust optimal energy consumption with changes in available clients during federated ***,clients’contributions in reputation management are formulated based on the cooperative game to capture the correlation between tasks,which satisfies availability,fairness,and *** simulation results show that the proposed scheme can significantly motivate high-performance clients to participate in federated learning and improve the accuracy and energy efficiency of the federated learning model.

关键词： Federated learning Incentive mechanism Reputation management Cooperative game Stackelberg game Green communication

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Leveraging power consumption for anomaly detection on IoT devices in smart homes

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 14045-14056页

作者： Nimmy, K. Dilraj, M. Sankaran, Sriram Achuthan, Krishnashree Department of Computer Science and Engineering Amrita Vishwa Vidyapeetham Amritapuri India Center for Cybersecurity Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri India

Anomaly detection in smart homes is paramount in the prevailing information age as smart devices remain susceptible to sophisticated cyber-attacks. Hackers exploit vulnerabilities such as weak passwords and insecure, unencrypted data transfer to launch Distributed Denial of Service (DDoS) attacks. Sensible deployment of conventional security measures is jeopardized by the heterogeneity and resource constraints of smart devices. This article presents a novel approach that leverages the power consumption of Internet of Things (IoT) devices to detect anomalous behavior in smart home environments. We prototype a smart camera using Raspberry Pi and gather power traces for normal activity. Furthermore, we model DDoS attacks on the experimental setup and generate attack traces of power consumption. Besides, we compare the performances of several machine learning models for accurate prediction of the presence of anomalies. A deep feed-forward neural network model achieves an accuracy of 99.2% compared to other models. Empirical evaluations of the proposed concept affirm that power consumption is a promising parameter in detecting anomalies in smart homes. The proposed method is suitable for smart homes as it does not impose additional overhead on resource-constrained IoT devices. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Internet of things

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Cross-Dimension Attentive Feature Fusion Network for Unsupervised Time-Series Anomaly Detection

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computer Modeling in engineering & sciences 2024年第6期139卷 3011-3027页

作者： Rui Wang Yao Zhou Guangchun Luo Peng Chen Dezhong Peng School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu611731China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer Science Sichuan UniversityChengdu610065China National Innovation Center for UHD Video Technology Chengdu610095China

Time series anomaly detection is crucial in various industrial applications to identify unusual behaviors within the time series *** to the challenges associated with annotating anomaly events,time series reconstruction has become a prevalent approach for unsupervised anomaly ***,effectively learning representations and achieving accurate detection results remain challenging due to the intricate temporal patterns and dependencies in real-world time *** this paper,we propose a cross-dimension attentive feature fusion network for time series anomaly detection,referred to as ***,a series and feature mixing block is introduced to learn representations in 1D ***,a fast Fourier transform is employed to convert the time series into 2D space,providing the capability for 2D feature ***,a cross-dimension attentive feature fusion mechanism is designed that adaptively integrates features across different dimensions for anomaly *** results on real-world time series datasets demonstrate that CAFFN performs better than other competing methods in time series anomaly detection.

关键词： Time series anomaly detection unsupervised feature learning feature fusion

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Secure Speculation via Speculative Secret Flow Tracking

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Journal of computer science & Technology 2023年第2期38卷 422-438页

作者：崔宏伟杨春程旭 School of Computer Science Engineering Research Center of Microprocessor and System Peking UniversityBeijing 100871China

Speculative execution attacks can leak arbitrary program data under malicious speculation,presenting a severe security *** on two key observations,this paper presents a software-transparent defense mechanism called speculative secret flow tracking(SSFT),which is capable of defending against all cache-based speculative execution attacks with a low performance ***,we observe that the attacker must use array or pointer variables in the victim code to access arbitrary memory ***,we propose a strict definition of secret data to reduce the amount of data to be ***,if the load is not data-dependent and control-dependent on secrets,its speculative execution will not leak any ***,this paper introduces the concept of speculative secret flow to analyze how secret data are obtained and propagated during speculative *** tracking speculative secret flow in hardware,SSFT can identify all unsafe speculative loads(USLs)that are dependent on ***,SSFT exploits three different methods to constrain USLs’speculative execution and prevent them from leaking secrets into the cache and translation lookaside buffer(TLB)*** paper evaluates the performance of SSFT on the SPEC CPU 2006 workloads,and the results show that SSFT is effective and its performance overhead is very *** defend against all speculative execution attack variants,SSFT only incurs an average slowdown of 4.5%(Delay USL-L1Miss)or 3.8%(Invisible USLs)compared to a non-secure *** analysis also shows that SSFT maintains a low hardware overhead.

关键词： cache side channel attack hardware speculative execution attack security

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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FGM-SPCL:Open-Set Recognition Network for Medical Images Based on Fine-Grained Data Mixture and Spatial Position Constraint Loss

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Chinese Journal of Electronics 2024年第4期33卷 1023-1033页

作者： Ruru ZHANG Haihong E Lifei YUAN Yanhui WANG Lifei WANG Meina SONG School of Computer Science Beijing University of Posts and Telecommunications Engineering Research Center of Information Networks Ministry of Education Hebei Eye Hospital

The current intelligent auxiliary diagnosis models all follow the closed-set recognition setting. After the model is deployed online, the input data is often not completely controlled. Diagnosing an untrained disease as a known category would lead to serious medical malpractice. Therefore, realizing the open-set recognition is significant to the safe operation of the intelligent auxiliary diagnosis model. Currently, most open-set recognition models are studied for natural images, and it is very challenging to obtain clear and concise decision boundaries between known and unknown classes when applied to fine-grained medical images. We propose an open-set recognition network for medical images based on fine-grained data mixture and spatial position constraint loss(FGM-SPCL) in this *** the fine graininess of medical images and the diversity of unknown samples, we propose a fine-grained data mixture(FGM) method to simulate unknown data by performing a mixing operation on known data to expand the coverage of unknown data difficulty levels. In order to obtain a concise and clear decision boundary, we propose a spatial position constraint loss(SPCL) to control the position distribution of prototypes and samples in the feature space and maximize the distance between known classes and unknown classes. We validate on a private ophthalmic OCT dataset, and extensive experiments and analyses demonstrate that FGM-SPCL outperforms state-of-the-art models.

关键词： Analytical models Fine-grained image recognition Prototypes Aerospace electronics Spatial databases Robustness Data models

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Tensor Factorization for Accurate Anomaly Detection in Dynamic Networks

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2024年第3期10卷 439-450页

作者： Li, Xiaocan Wen, Jigang Xie, Kun Xie, Gaogang Liang, Wei Hunan University College of Computer Science and Electronics Engineering Changsha China Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Chinese Academy of Sciences Computer Network Information Center China

Accurately detecting traffic anomalies becomes increasingly crucial in network management. Algorithms that model the traffic data as a matrix suffers from low detection accuracy, while the work using the tensor model often assumes the tensor is regular without considering that network nodes may dynamically join in or leave, which will fail in a practical network with the change of node set as a result of mobility and churn behaviors. We propose a novel Tensor Recovery scheme in a Dynamic Network (TRDN) with traffic data modeled as a practical irregular tensor for accurate anomaly detection. To take advantage of correlations among small tensors, each formed with a short time duration to capture more hidden information in the data for higher detection accuracy, we propose several novel techniques: 1) a new joint tensor factorization model to capture the characteristic shared by the common nodes of small tensors, 2) a tensor partition algorithm to identify the data that can be applied to train the shared parameters efficiently, and 3) a bar-based algorithm that partitions nodes into the minimum number of no-overlapping subsets to form the shared tensor model. Extensive experiments on two Internet traffic data sets, Abilene and GEANT, demonstrate the effectiveness of the proposed TRDN. © 2016 IEEE.

关键词： Data accuracy

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