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Cluster Assumption-Guided Timestamp-Supervised Temporal Action Segmentation

IEEE Transactions on Multimedia 2025年

作者： Ren, Ziyou Li, Guozhang Cheng, Nan Wu, Anqi Wang, Nannan Gao, Xinbo Xidian University State Key Laboratory of Integrated Services Networks School of Telecommunications Engineering Shaanxi Xi'an710071 China Carnegie Mellon University Robotics Institute School of Computer Science PittsburghPA15213 United States Beijing Normal University School of Artificial Intelligence Haidian District Beijing100875 China Georgia Institute of Technology School of Computational Science and Engineering College of Computing AtlantaGA30308 United States Xidian University School of Electronic Engineering Xi'an710071 China Chongqing University of Posts and Telecommunications Chongqing Key Laboratory of Image Cognition Chongqing400065 China

Current timestamp-supervised temporal action segmentation (TS-TAS) methods typically follow a two-phase pipeline: initializing the model with timestamp labels and refining it with pseudo-labels. However, limited by the sparsity of timestamp annotations, current methods' performance is sub-optimal. Specifically, initializing the model with only timestamp annotations may cause overfitting to labeled frames. Additionally, sparse timestamp annotations cannot capture the diverse action representations throughout the whole instance, especially those near the ambiguous action boundaries, leading to pseudo-label noise. Inspired by the cluster assumption of semi-supervised learning (SSL) that points within the same manifold likely share the same label, we here model TS-TAS as an SSL problem. Specifically, we propose a Temporal Embedding Consistency (TEC) strategy to mitigate the excessive focus on annotated frames. The TEC strategy encourages frames with similar representations within the video to have similar classification probability distributions, thereby propagating labeled frames' information to implicit ones. Besides, we design a TS-Mix strategy to further leverage unlabeled data to mitigate the influence of pseudo-label noise in a consistency regularization manner. The TS-Mix strategy includes intra-mix, which adds linear interpolation of two adjacent timestamps to every frame between them, and inter-mix, which mixes frames from two different untrimmed videos frame-by-frame. Then the mixed video is trained with the correspondingly mixed pseudo-labels. Comprehensive experimental results on different benchmarks show that we achieve new state-of-the-art performances. Furthermore, the proposed method can seamlessly enhance existing methods, significantly improving their performances. © 1999-2012 IEEE.

关键词： Self-supervised learning

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Error correction for short-range optical interconnect using COTS transceivers

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Science China(Information Sciences) 2019年第6期62卷 72-78页

作者： Ziyuan ZHENG Chuanchuan YANG Dan ZHAO Ziyu WANG State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University

Nowadays, quantities of commercial off-the-shelf(COTS) optical transceivers are widely equipped in both exiting and abuilding data centers. As data centers are becoming larger and containing more servers, the method to ensure longer and steady transmission with COTS optical transceivers becomes one of the key issues. Meanwhile, variations in the manufacturing of COTS optical transceivers bring in fluctuation of packet error rate(PER) in communication. In this paper, we present an application layer adaptive forward error correction(AL-AFEC) coding scheme for COTS optical transceivers in optical interconnect to solve the problems mentioned above and test its performance in a 10 Gbps Ethernet(10 GbE)link using 10 GBASE-SR SFP+ modules. Experimental results show that the proposed scheme enables longer transmission distance and the instability introduced by manufactural variations is well settled. Since our scheme requires no extra hardware on optical transceivers, it is a considerable low-cost alternative to improve system reliability and achieve longer transmission distance for COTS optical transceivers.

关键词： short-range optical interconnect vertical cavity surface emitting laser(VCSEL) ethernet application layer adaptive forward error correction commercial off-the-shelf optical transceiver

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A Fast Light Baking System for Mobile VR Game Based on Edge Computing Framework 21

A Fast Light Baking System for Mobile VR Game Based on Edge ...

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Proceedings of the 2021 ACM International Conference on Intelligent Computing and its Emerging Applications

作者： Ma Cheng Wang Lu School of Information Engineering Shandong Youth University of Political Science China Qilu University of Technology (Shandong Academy of Sciences) Shandong Computer Science Center (National Supercomputer Center in Jinan) Shandong Provincial Key Laboratory of Computer Networks

ISBN: (纸本)9781450391603

Metaverse development has often focused on bettering virtual reality technologies due to benefits in establishing immersion in virtual environment1. Its dependency on VR technology brings limitations including the lack of high-quality graphics and a lack of mobility. The mobile platform is the most prevalent interface for users to gain VR experience. There is a big challenge that mobile VR must face: it has limited computing power, but VR requires high rendering speed and image quality. The traditional solution is to complete lighting and shading computation offline and bake the result to light map. Thus, only texture lookups are needed at runtime which allows very high rendering speed. The problem of the traditional baking is that they usually take seconds to minutes to finish. In this paper, we introduce a novel edge computing framework to reduce the baking time to less than a second. Our method uses reservoir-based spatiotemporal importance resampling (ReSTIR) to bake low resolution light maps at edge server. At mobile side, the light maps are enlarged using super resolution technique. We also use denoiser to make sure the baked image is noise-free. The experimental results show that our method can have near real-time performance for scene with average complexity. The bottleneck is the bandwidth of mobile device when performing super resolution. Once it gets improved, we can expect our entire edge computing framework becomes real-time.

关键词： ray tracing virtual reality Edge computing

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CRS-Diff: Controllable Remote Sensing Image Generation with Diffusion Model

arXiv

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arXiv 2024年

作者： Tang, Datao Cao, Xiangyong Hou, Xingsong Jiang, Zhongyuan Liu, Junmin Meng, Deyu The School of Computer Science and Technology the Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Information and Communications Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China The School of Cyber Engineering Xidian University Shaanxi Xi’an710049 China The School of Mathematics and Statistics the Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an710049 China Guangdong Guangzhou510555 China

The emergence of generative models has revolutionized the field of remote sensing (RS) image generation. Despite generating high-quality images, existing methods are limited in relying mainly on text control conditions, and thus do not always generate images accurately and stably. In this paper, we propose CRS-Diff, a new RS generative framework specifically tailored for RS image generation, leveraging the inherent advantages of diffusion models while integrating more advanced control mechanisms. Specifically, CRS-Diff can simultaneously support text-condition, metadata-condition, and image-condition control inputs, thus enabling more precise control to refine the generation process. To effectively integrate multiple condition control information, we introduce a new conditional control mechanism to achieve multi-scale feature fusion, thus enhancing the guiding effect of control conditions. To our knowledge, CRS-Diff is the first multiple-condition controllable RS generative model. Experimental results in single-condition and multiple-condition cases have demonstrated the superior ability of our CRS-Diff to generate RS images both quantitatively and qualitatively compared with previous methods. Additionally, our CRS-Diff can serve as a data engine that generates high-quality training data for downstream tasks, e.g., road extraction. The code is available at https://***/Sonettoo/CRS-Diff. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep learning

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Coverage Probability Analysis of RIS-Assisted High-Speed Train Communications

Coverage Probability Analysis of RIS-Assisted High-Speed Tra...

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IEEE Conference on Wireless Communications and Networking

作者： Changzhu Liu Ruisi He Yong Niu Bo Ai Zhu Han Meilin Gao Zhangdui Zhong State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Department of Electrical and Computer Engineering University of Houston Houston USA Beijing National Research Center for Information Science and Technology Tsinghua University China

Reconfigurable intelligent surface (RIS) has received increasing attention due to its capability of extending cell coverage by reflecting signals toward receivers. This paper considers a RIS-assisted high-speed train (HST) communication system to improve coverage probability. We derive the closed-form expression of coverage probability. Moreover, we analyze impacts of some key system parameters, including transmission power, signal-to-noise ratio threshold, and horizontal distance between base station and RIS. Simulation results verify the efficiency of RIS-assisted HST communications in terms of coverage probability.

关键词： Wireless communication Base stations Closed-form solutions Simulation Receivers Downlink Signal to noise ratio

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Time-Efficient EV Energy Management Through In-Motion V2V Charging

Time-Efficient EV Energy Management Through In-Motion V2V Ch...

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IEEE Conference on Vehicular Technology (VTC)

作者： Shuohan Liu Wei Zhang Yue Cao Qiang Ni Carsten Maple Hai Lin Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China School of Cyber Science and Engineering Wuhan University Wuhan China School of Computing and Communications Lancaster University Lancaster U.K. Warwick Manufacturing Group University of Warwick Coventry U.K.

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In recent years, Electric Vehicles (EVs) have emerged as a sustainable alternative to internal combustion vehicles, noted for better efficiency, lower operational costs, and reduced carbon emissions. However, with the growing adoption of EVs and limited charging infrastructure, challenges such as charging congestion arise. Traditional plug-in and in-Parking Vehicle-to-Vehicle (V2V) charging modes, constrained by fixed charging locations, lack flexibility and necessitate long charging times. Therefore, this paper introduces a novel in-Motion V2V charging mode, termed V2V (M) mode, allowing an EV as an energy Provider (EV-P) and an EV as an energy Consumer (EV-C) to form a V2V charging Pair (V2V-Pair). Then, the V2V-Pair can transfer energy via wireless V2V charging service while on-the-move. In this paper, the proposed V2V (M) management framework employs a Path Proximity-based V2V Pair matching algorithm and spatio-temporal cooperative path planning, to enhance charging efficiency and reduce charging trip duration. The urban environment simulation results demonstrate marked improvements of the proposed V2V (M) mode. It shorters the charging trip duration and enhances charging service efficiency, offering a viable solution to current EV charging constraints.

关键词： Wireless communication Vehicular and wireless technologies Renewable energy sources Simulation Urban areas Vehicular ad hoc networks Path planning

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Zero-Added-Loss Entangled-Photon Multiplexing for Ground- and Space-Based Quantum networks

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Physical Review Applied 2023年第5期19卷 054029-054029页

作者： Kevin C. Chen Prajit Dhara Mikkel Heuck Yuan Lee Wenhan Dai Saikat Guha Dirk Englund Research Laboratory of Electronics Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge Massachusetts 02139 USA Wyant College of Optical Sciences The University of Arizona Tucson Arizona 85721 USA NSF-ERC Center for Quantum Networks The University of Arizona Tucson Arizona 85721 USA Department of Electrical and Photonics Engineering Technical University of Denmark Kgs. Lyngby 2800 Denmark College of Information and Computer Science University of Massachusetts Amherst Massachusetts 01003 USA Brookhaven National Laboratory Upton New York 11973 USA

We propose a scheme for optical entanglement distribution in quantum networks based on a quasideterministic entangled photon-pair source. By combining heralded photonic Bell-pair generation with spectral mode conversion to interface with quantum memories, the scheme eliminates switching losses due to multiplexing in the source. We analyze this “zero-added-loss multiplexing” (ZALM) Bell-pair source for the particularly challenging problem of long-baseline entanglement distribution via satellites and ground-based memories, where it unlocks additional advantages: (i) the substantially higher channel efficiency η of downlinks versus uplinks with realistic adaptive optics, and (ii) photon loss occurring before interaction with the quantum memory—i.e., Alice and Bob receiving rather than transmitting—improve entanglement generation rate scaling by O(η). Based on numerical analyses, we estimate our protocol to achieve >10ebit/s at memory multiplexing of 102 spin qubits for ground distance >102km, with the spin-spin Bell-state fidelity exceeding 99%. Our architecture presents a blueprint for realizing global-scale quantum networks in the near term.

关键词： Light-matter interaction Nonlinear optics Optical quantum information processing Photon pairs & parametric down-conversion Quantum communication Quantum networks Quantum optics Multiplexers Optical sources & detectors

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A residual attention-based privacy-preserving biometrics model of transcriptome prediction from genome

A residual attention-based privacy-preserving biometrics mod...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Cheng Tian Song Liu Jinbao Li Guangchen Wang Luyue Kong Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Engineering Research Center of Big Data Applied Technology Faculty of Computer Science and Technology Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Department of Medicine Shandong Medical College Jinan China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Transcriptome prediction from genetic variation data is an important task in the privacy-preserving and biometrics field, which can better protect genomic data and achieve biometric recognition through transcriptome. Many transcriptome prediction methods have achieved good accuracy from genetic variation data. However, these traditional transcriptome prediction methods have the problems of linear assumption, overfitting, expose personal privacy, and extensive manual optimization. To solve these shortcomings, we propose an attention-based transcriptome prediction model from genetic variation named RATPM that improves the accuracy of transcriptome prediction and protects participant genomic data. In RATPM, we introduce and improve the deep learning model with multi-head self-attention into the transcriptome prediction stage of Predixcan, which uncovers the non-linear relationship between genetic variation and transcriptome. Moreover, we introduce a residual attention module to generate attention-aware features and extract more accurate features at different levels from genetic variation. Furthermore, we introduce the BERT pre-training module to encode genetic variation fully utilizing their contextual information. Our research enables scientific institutions to publish only predicted transcriptomic data for biometric purposes, thus protecting the genomic information of the subjects. Finally, we evaluated our model on the 1000 Genomes and Geuvadis projects datasets to compare with other baseline models.

关键词： Privacy Biometrics (access control) Biological system modeling Genomics Predictive models Feature extraction Gene expression

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Lightweight Salient Object Detection in Optical Remote-Sensing Images via Semantic Matching and Edge Alignment

arXiv

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arXiv 2023年

作者： Li, Gongyang Liu, Zhi Zhang, Xinpeng Lin, Weisi Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University Shanghai200444 China School of Communication and Information Engineering Shanghai University Shanghai200444 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore

Recently, relying on convolutional neural networks (CNNs), many methods for salient object detection in optical remote sensing images (ORSI-SOD) are proposed. However, most methods ignore the huge parameters and computational cost brought by CNNs, and only a few pay attention to the portability and mobility. To facilitate practical applications, in this paper, we propose a novel lightweight network for ORSI-SOD based on semantic matching and edge alignment, termed SeaNet. Specifically, SeaNet includes a lightweight MobileNet-V2 for feature extraction, a dynamic semantic matching module (DSMM) for high-level features, an edge self-alignment module (ESAM) for low-level features, and a portable decoder for inference. First, the high-level features are compressed into semantic kernels. Then, semantic kernels are used to activate salient object locations in two groups of high-level features through dynamic convolution operations in DSMM. Meanwhile, in ESAM, cross-scale edge information extracted from two groups of low-level features is self-aligned through L2 loss and used for detail enhancement. Finally, starting from the highest-level features, the decoder infers salient objects based on the accurate locations and fine details contained in the outputs of the two modules. Extensive experiments on two public datasets demonstrate that our lightweight SeaNet not only outperforms most state-of-the-art lightweight methods but also yields comparable accuracy with state-of-the-art conventional methods, while having only 2.76M parameters and running with 1.7G FLOPs for 288×288 inputs. Our code and results are available at https://***/MathLee/SeaNet. © 2023, CC BY-NC-SA.

关键词： Optical remote sensing

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Towards Evidence-Based Cybersecurity Assessment of Programmable Systems to Ensure the Protection of Critical IT Infrastructure

Towards Evidence-Based Cybersecurity Assessment of Programma...

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IEEE International Workshop on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications

作者： Oleg Illiashenko Vyacheslav Kharchenko Oleg Odarushchenko Department of computer systems and networks National aerospace university “KhAI” Kharkiv Ukraine Software Engineering & Dependable Computing Laboratory Institute of Information Science and Technologies “Alessandro Faedo”—ISTI CNR Pisa Italy Research and Production Enterprise RPC Radics LLC Kropyvnytskyi Ukraine

The paper is devoted to developing scientific principles, methods, means, and information technology of model-oriented verification and evidence-based assessment using functional safety and cybersecurity cases for programmable systems of critical applications (PSCA). In particular, information and control systems important for the safety of nuclear power plants (NPPs), aerospace systems, railway domains, etc., developed using Field Programmable Gate Arrays (FPGAs) and hardware platforms are reviewed. The goal is to ensure the guaranteed completeness and reliability of its functional safety and cybersecurity assessment by developing and implementing a set of formal and semi-formal methods and tools that consider defects of different nature – physical, design, trojans, and vulnerabilities that can be attacked and lead to a fatal system failure, which results in damaging the critical IT infrastructure. The methods shown are based on integrating algebraic, tabular, graph models, and case assessment methodology. The methods are implemented as appropriate technologies for evidence-based verification and evaluation of PSCA. For formal methods, a prototype of a translator of Very High-Speed Integrated Circuits Hardware Description Language (VHDL) code is developed into an algebra of behaviors. It provides evidence-based verification and a framework and tools to design reports on assessing the cybersecurity and functional safety of programmable systems.

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