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arXiv 2024年

作者： Xiang, Wenzhao Liu, Chang Su, Hang Yu, Hongyang Beijing100190 China Peng Cheng Laboratory Shenzhen10587 China University of the Chinese Academy of Sciences Beijing100049 China Institute of Image Communication and Networks Engineering Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Computer Science and Technology Tsinghua University Beijing100084 China

Masked image modeling (MIM) has gained significant traction for its remarkable prowess in representation learning. As an alternative to the traditional approach, the reconstruction from corrupted images has recently emerged as a promising pretext task. However, the regular corrupted images are generated using generic generators, often lacking relevance to the specific reconstruction task involved in pre-training. Hence, reconstruction from regular corrupted images cannot ensure the difficulty of the pretext task, potentially leading to a performance decline. Moreover, generating corrupted images might introduce an extra generator, resulting in a notable computational burden. To address these issues, we propose to incorporate adversarial examples into masked image modeling, as the new reconstruction targets. Adversarial examples, generated online using only the trained models, can directly aim to disrupt tasks associated with pre-training. Therefore, the incorporation not only elevates the level of challenge in reconstruction but also enhances efficiency, contributing to the acquisition of superior representations by the model. In particular, we introduce a novel auxiliary pretext task that reconstructs the adversarial examples corresponding to the original images. We also devise an innovative adversarial attack to craft more suitable adversarial examples for MIM pre-training. It is noted that our method is not restricted to specific model architectures and MIM strategies, rendering it an adaptable plug-in capable of enhancing all MIM methods. Experimental findings substantiate the remarkable capability of our approach in amplifying the generalization and robustness of existing MIM methods. Notably, our method surpasses the performance of baselines on various tasks, including ImageNet, its variants, and other downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Image reconstruction

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SJA: Server-driven Joint Adaptation of Loss and Bitrate for Multi-Party Realtime Video Streaming

SJA: Server-driven Joint Adaptation of Loss and Bitrate for ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Kai Shen Dayou Zhang Zi Zhu Lei Zhang Fangxin Wang Dan Wang The Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen School of Science and Engineering The Chinese University of Hong Kong Shenzhen College of Computer Science and Software Engineering Shenzhen University Peng Cheng Laboratory The Guangdong Provincial Key Laboratory of Future Networks of Intelligence Department of Computing The Hong Kong Polytechnic University

The outbreak of COVID-19 has dramatically promoted the explosive proliferation of multi-party realtime video streaming (MRVS) services, represented by Zoom and Microsoft Teams. Different from Video-on-Demand (VoD) or live streaming, MRVS enables all-to-all realtime video communication, bringing significant challenges to service providing. First, unreliable network transmission can cause network loss, resulting in delay increase and visual quality degradation. Second, the transformation from two-party to multi-party communication makes resource scheduling much more difficult. Moreover, optimizing the overall QoE requires a global coordination, which is quite challenging given the various impact factors such as bitrate and *** this paper, we propose the SJA framework, which is, to our best knowledge, the first server-driven joint loss and bitrate adaptation framework in multi-party realtime video streaming services towards maximized QoE. We comprehensively design an appropriate QoE model for MRVS services to capture the interplay among perceptual quality, variations, bitrate mismatch, loss damage, and streaming delay. We mathematically formulate the QoE maximization problem in MRVS services. A Lyapunov-based relaxation and the SJA algorithm are further designed to address the optimization problem with close-to-optimal performance. Evaluations show that our framework can outperform the SOTA solutions by 18.4% ∼ 46.5%.

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Comments on "BSIF: Blockchain-Based Secure, Interactive, and Fair Mobile Crowdsensing"

TechRxiv

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TechRxiv 2023年

作者： Wang, Haiyan Luo, Fucai Yan, Xingfu The School of Mathematics and Statistics Minnan Normal University Zhangzhou China The Department of New Networks Peng Cheng Laboratory Shenzhen China The Guangdong Provincial Key School of Computer Science and Engineering South China University of Technology Guangzhou China

Mobile crowdsensing (MCS) is a promising sensing paradigm which allows users to outsource a range of sensing tasks to a crowd of mobile workers with mobile devices. Location-dependent MCS, as the name implies, is a geographically-dependent sensing paradigm in which service requestors outsource location-specific tasks to many workers with mobile devices, and the workers accepting the tasks collect data at a particular location by physically arriving at the desired locations. Many efforts have been devoted to protecting location privacy of the workers accepting the tasks while ensuring task allocation accuracy and efficiency. In 2022, Wang et al. [5] proposed BSIF, a blockchain-based MCS system (published in IEEE Journal on Selected Areas in Communications (JSAC)) that can prevent not only illegitimate participants but also location privacy leakage. For the purpose of protecting location privacy, they proposed a location-based symmetric key generation algorithm, which coordinates session keys for the target ranges between the service requestor and the workers accepting the tasks without revealing the location information. However, in this paper, we will point out a flaw in their location-based symmetric key generation algorithm, which indicates that BSIF does not work. Furthermore, we propose a novel approach to achieve the location-based symmetric key generation using a witness encryption scheme. © 2023, CC BY.

关键词： Cryptography

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100 Gb/s NRZ OOK signal regeneration on a silicon chip

100 Gb/s NRZ OOK signal regeneration on a silicon chip

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CLEO: Science and Innovations, S and I 2022

作者： Wen, Hua Shun Cui, Jia Bin Zhou, Heng Chen, Yin Fang Jin, Ya Xu, Bo Rui Zhai, Kun Peng Sun, Jia Zheng Guo, Yuan Yuan Wu, Yan Ran Chen, Wen Chen, Wei Wang, Xin Zhu, Ning Hua Lu, Guo-Wei Ji, Gui Jun Zhou, Dennis Chi Cheng, Yiu-Kwok Yang, Da-Quan Li, Ming State Key Laboratory on Integrated Optoelectronics Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China School of Electronic Electrical and Communication Engineering University of Chinese Academy of Sciences Beijing100049 China State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Key Laboratory of Optical Fiber Sensing and Communication Networks School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China Division of Computer Engineering The University of Aizu Fukushima965-8580 Japan Ltd. Zhuhai519000 China

All-optical regeneration of 100 Gb/s NRZ OOK signal with 3.6 dB extinction ratio and 0.5 dB quality factor improvements is experimentally demonstrated using a silicon waveguide with reverse-biased p-i-n junction for t... 详细信息

ISBN: (纸本)9781557528209

关键词： Amplitude shift keying

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Using Depth-Enhanced Spatial Transformation for Student Gaze Target Estimation in Dual-View Classroom Images

Using Depth-Enhanced Spatial Transformation for Student Gaze...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Haonan Miao Peizheng Zhao Yuqi Sun Fang Nan Xiaolong Zhang Yaqiang Wu Feng Tian School of Computer Science and Technology Xi’an Jiaotong University Xi’an China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an China School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Dual-view gaze target estimation in classroom environments has not been thoroughly explored. Existing methods lack consideration of depth information, primarily focusing on 2D image information and neglecting the latent 3D spatial context, which could lead to suboptimal transformation and cause the gaze cone to intersect with an incorrect object. This paper introduces a novel dual-view gaze target estimation method tailored for classroom settings, leveraging depth-enhanced spatial transformations. By formulating a depth-enhanced 2D space, our method uses depth-enhanced spatial transformation to accurately project students’ gaze cones to the teacher-oriented image. Additionally, we collected a dataset named DVSGE, specifically for student gaze target estimation in dual-view classroom images. Experimental results demonstrate significant performance improvements of 9.8% in AUC and 19.9% in L2-Distance for our method, surpassing existing methods.

关键词： Three-dimensional displays Estimation Focusing Signal processing Acoustics Speech processing

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Sal²RN: A Spatial-Spectral Salient Reinforcement Network for Hyperspectral and LiDAR Data Fusion Classification

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IEEE Transactions on Geoscience and Remote Sensing 2023年 61卷 1页

作者： Li, Jiaojiao Liu, Yuzhe Song, Rui Li, Yunsong Han, Kailiang Du, Qian Xidian University State Key Laboratory of Integrated Service Networks Xi'an710071 China Cas Key Laboratory of Spectral Imaging Technology Xi'an710119 China Shanghai Institute of Technical Physics Chinese Academy of Sciences Key Laboratory of Intelligent Infrared Perception Shanghai200083 China Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data fusion have been widely employed in HSI classification to promote interpreting performance. In the existing deep learning methods based on spatial-spectral features, the features extracted from different layers are treated fairly in the learning process. In reality, features extracted from the continuous layers contribute differentially to the final classification, such as large tracts of woodland and agriculture typically count on shallow contour features, whereas deep semantic spectral features have meaningful constraints for small entities like vehicles. Furthermore, the majority of existing classification algorithms employ a patch input scheme, which has a high probability to introduce pixels of different categories at the boundary. To acquire more accurate classification results, we propose a spatial-spectral saliency reinforcement network (Sal2RN) in this article. In spatial dimension, a novel cross-layer interaction module (CIM) is presented to adaptively alter the significance of features between various layers and integrate these diversified features. Moreover, a customized center spectrum correction module (CSCM) integrates neighborhood information and adaptively modifies the center spectrum to reduce intraclass variance and further improve the classification accuracy of the network. Finally, a statistically based feature weighted combination module is constructed to effectively fuse spatial, spectral, and LiDAR features. Compared with traditional and advanced classification methods, the Sal2RN achieves the state-of-the-art classification performance on three open benchmark datasets. © 1980-2012 IEEE.

关键词： Optical radar

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ISGW Bandpass Filtering Power Divider for Ku Band Applications 13

ISGW Bandpass Filtering Power Divider for Ku Band Applicatio...

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13th International Conference on Microwave and Millimeter Wave Technology, ICMMT 2021

作者： Chen, Jipeng Shen, Dongya Yuan, Hong Zhang, Xiupu Yunnan University Yunnan Provincial Engineering Laboratory Of Cloud Wireless Access Heterogeneous Networks Kunming650500 China Concordia University Photonics Labs Department Of Electrical And Computer Engineering MontrealQCH3G1M8 Canada

ISBN: (纸本)9781665434379

This paper presents a new power divider with two transmission zeros. The structure is implemented by encapsulating the substrate integrated waveguide (SIW) with the integrated substrate gap waveguide (ISGW). The bottom substrate is SIW structure, and the rest of the two layers constitute ISGW structure, which improves structure performance. The designed power divider has 30.1% fractional bandwidth (FBW), two transmission zeros at 10.4 and 20.2 GHz introduced by ISGW, isolation of up to 11 dB, and insertion loss of 0.7 dB. © 2021 IEEE.

关键词： Substrate integrated waveguides

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Embedded propagation graph model for reflection and scattering and its millimeter-wave measurement-based evaluation

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 191-202页

作者： Liu, Yuan Yin, Xuefeng Ye, Xiaokang He, Yongyu Lee, Juyul College of Electronics and Information Engineering Tongji University Shanghai China Guangdong Communications and Networks Institute Guangzhou China National Computer and Information Technology Practical Education Demonstration Center Tongji University Shanghai200092 China Telecommunication and Media Research Laboratory Electronics and Telecommunications Research Institute Daejeon34129 Korea Republic of

Propagation graph (PG) is a stochastic channel simulation method for scattering propagation. In this article, an embedded-PG (EPG) approach, an extension of conventional PG, is proposed to simulate reflection and scattering multi-path behaviors in wireless channels. In this method, multiple propagation paths are categorized into scattering-path, reflecting-path, and scattering-reflecting mixed paths among reflectors and scatterers. The matrix recursive formula of conventional PG modeling is used to calculate scattering-path, a recursive mathematical transformation is applied to adapt reflecting-path into the recursive formula, and an embedded graph method is used to decompose mixed-path into scattering effects and reflection effects. The proposed simulation approach is validated by comparison with conventional PG and measurement in 39 GHz millimeter-wave (mm-wave) time-variant corridor scenario. Power delay profiles (PDPs) and spatial consistency of multiple paths observed in concatenated-PDPs (CPDPs) obtained by EPG are more consistent with measurement than conventional PG, differences of mean delay and delay spread between simulations and measurement in typical snapshots are within 3 ns and 1.5 ns, respectively. © 2020 IEEE.

关键词： Millimeter waves

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SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Similarity Computation Model Based on Optimised SBERT

SMGC-SBERT: A Multi-Feature Fusion Chinese Short Text Simila...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuo Zhao Qiliang Gu Jianqiang Zhang Gongpeng Song Qin Lu 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 Shandong Branch of China Mobile Communication Group Design Institute Co. Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Chinese short text similarity computation stands as a pivotal task within natural language processing, garnering significant attention. However, existing models grapple with limitations in handling intricate semantic relationships, such as the challenge of discerning subtle semantic nuances in text, inadequacies in effectively integrating diverse levels of semantic information, and the struggle to capture polysemous meanings accurately. In addressing these issues, this paper introduces an innovative Chinese short text similarity computation model, SMGC-SBERT. This model addresses shortcomings of the existing models by employing a multi-module fusion strategy, thereby enabling a more precise measurement of semantic similarity between texts. Primarily, the model incorporates SAT embedding to acquire phrase-level semantic information and leverages the MS-BERT model to encode text, improving the model's comprehension of textual polysemy and obtaining richer semantic representation. Then, the fusion of module features, including multi-branch convolutional networks and mix pooling, enables the extraction of textual features from varied levels, bolstering the model's representational capacity. Additionally, to further reduce overfitting risks while improving accuracy and other performance, a multi-layer feature adjustment network is utilized for short text similarity calculation. The final resultant experimental findings showcase the superiority of the SMGC-SBERT model over other neural network models, demonstrating significant advancements across both Chinese-SNLI and CCKS2018_Task3 Chinese short text datasets.

关键词： Deep learning Computational modeling Semantics Neural networks Feature extraction Natural language processing Multilingual Convolutional neural networks Cybernetics Overfitting

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A Novel Text Matching Model Based on Multilayer Coding and Feature Enhancement

A Novel Text Matching Model Based on Multilayer Coding and F...

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International Conference on computer Supported Cooperative Work in Design

作者： Yu Zhang Jianqiang Zhang Gongpeng Song Qin Lu 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 Shandong Branch of China Mobile Communication Group Design Institute Co Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

With the rapid development of Natural Language Processing (NLP), text matching has become the basis of many downstream tasks in NLP, and the study of text matching is of great research significance for solving tasks such as question and answer and information retrieval in NLP. Most of the current text matching methods tend to have problems such as mismatch of grammatical structures and insufficient interaction information in sentences. In order to solve the problems of insufficient interaction information and lack of features and ability to capture keyword and sequence information in text matching, this paper proposes a text matching method based on multi-layer coding and soft attention mechanism. The method first embeds the text and sends it to a gating module for processing, then sends the processed result to a module containing a combination of multilayer coding and soft attention mechanism for further operations such as multiple alignment, and finally sends it to a classifier containing a three-layer fully-connected network for predicting whether the input text pairs match or not. The two modules proposed in this paper are practically feasible, and comparison and ablation experiments have been conducted on the publicly available datasets LCQMC dataset and BQ dataset, and the experimental results show that the two modules improve the accuracy of text matching by 2.44% and 1.02%, respectively.

关键词： Attention mechanisms Accuracy Computational modeling Impedance matching Nonhomogeneous media Information retrieval Encoding

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