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2023 World Wide Web Conference, WWW 2023

作者： Wang, Kaibin He, Qiang Chen, Feifei Chen, Chunyang Huang, Faliang Jin, Hai Yang, Yun School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia School of Information Technology Deakin University Australia Faculty of Information Technology Monash University Australia Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781450394161

Mobile and Web-of-Things (WoT) devices at the network edge account for more than half of the world's web traffic, making a great data source for various machine learning (ML) applications, particularly federated learning (FL) which offers a promising solution to privacy-preserving ML feeding on these data. FL allows edge mobile and WoT devices to train a shared global ML model under the orchestration of a central parameter server. In the real world, due to resource heterogeneity, these edge devices often train different versions of models (e.g., VGG-16 and VGG-19) or different ML models (e.g., VGG and ResNet) for the same ML task (e.g., computer vision and speech recognition). Existing FL schemes have assumed that participating edge devices share a common model architecture, and thus cannot facilitate FL across edge devices with heterogeneous ML model architectures. We explored this architecture heterogeneity challenge and found that FL can and should accommodate these edge devices to improve model accuracy and accelerate model training. This paper presents our findings and FlexiFed, a novel scheme for FL across edge devices with heterogeneous model architectures, and three model aggregation strategies for accommodating architecture heterogeneity under FlexiFed. Experiments with four widely-used ML models on four public datasets demonstrate 1) the usefulness of FlexiFed;and 2) that compared with the state-of-the-art FL scheme, FlexiFed improves model accuracy by 2.6%-9.7% and accelerates model convergence by 1.24 × -4.04 ×. © 2023 ACM.

关键词： Speech recognition

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IoT data analytic algorithms on edge-cloud infrastructure:A review

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Digital Communications and Networks 2023年第6期9卷 1486-1515页

作者： Abel E.Edje M.S.Abd Latiff Weng Howe Chan School of Computing Faculty of EngineeringUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia Department of Computer Science Delta State UniversityAbrakaP.M.B 01 AbrakaDelta StateNigeria UTM Big Data Centre Ibnu Sina Institute of Scientific and Industrial ResearchUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime ***,it is constrained by limited data storage and processing *** offloads its massive data stream to edge devices and the cloud for adequate storage and *** further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data *** paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load *** review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load *** addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been *** classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.

关键词： Internet of things Cloud platform Edge Analytic algorithms Processes Network communication protocols

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Gradient Weighting for Speaker Verification in Extremely Low Signal-to-Noise Ratio

Gradient Weighting for Speaker Verification in Extremely Low...

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

作者： Yi Ma Kong Aik Lee Ville Hautamäki Meng Ge Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong School of Computing University of Eastern Finland Finland Shenzhen Research Institute of Big Data Shenzhen China School of Data Science The Chinese University of Hong Kong Shenzhen China

Speaker verification is hampered by background noise, particularly at extremely low Signal-to-Noise Ratio (SNR) under 0 dB. It is difficult to suppress noise without introducing unwanted artifacts, which adversely affects speaker verification. We proposed the mechanism called Gradient Weighting (Grad-W), which dynamically identifies and reduces artifact noise during prediction. The mechanism is based on the property that the gradient indicates which parts of the input the model is paying attention to. Specifically, when the speaker network focuses on a region in the denoised utterance but not on the clean counterpart, we consider it artifact noise and assign higher weights for this region during optimization of enhancement. We validate it by training an enhancement model and testing the enhanced utterance on speaker verification. The experimental results show that our approach effectively reduces artifact noise, improving speaker verification across various SNR levels.

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Codebook Design and Performance Analysis for Wideband Beamforming in Terahertz Communications

arXiv

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arXiv 2024年

作者： Ning, Boyu Mei, Weidong Zhu, Lipeng Chen, Zhi Zhang, Rui National Key Laboratory of Science and Technology on Communications University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China

The codebook-based analog beamforming is appealing for future terahertz (THz) communications since it can generate high-gain directional beams with low-cost phase shifters via low-complexity beam training. However, conventional beamforming codebook design based on array response vectors for narrowband communications may suffer from severe performance loss in wideband systems due to the "beam squint" effect over frequency. To tackle this issue, we propose in this paper a new codebook design method for analog beamforming in wideband THz systems. In particular, to characterize the analog beamforming performance in wideband systems, we propose a new metric termed wideband beam gain, which is given by the minimum beamforming gain over the entire frequency band given a target angle. Based on this metric, a wideband analog beamforming codebook design problem is formulated for optimally balancing the beamforming gains in both the spatial and frequency domains, and the performance loss of conventional narrowband beamforming in wideband systems is analyzed. To solve the new wideband beamforming codebook design problem, we divide the spatial domain into orthogonal angular zones each associated with one beam, thereby decoupling the codebook design into a zone division sub-problem and a set of beamforming optimization sub-problems each for one zone. For the zone division sub-problem, we propose a bisection method to obtain the optimal boundaries for separating adjacent zones. While for each of the per-zone-based beamforming optimization sub-problems, we further propose an efficient augmented Lagrange method (ALM) to solve it. Numerical results demonstrate the performance superiority of our proposed codebook design for wideband analog beamforming to the narrowband beamforming codebook and also validate our performance analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： Frequency domain analysis

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Time-series surface water reconstruction method(TSWR)based on spatial distance relationship of multi-stage water boundaries

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International Journal of Digital Earth 2022年第1期15卷 2335-2354页

作者： Mingyang Li Shanlong Lu Cong Du Yong Wang Chun Fang Xinru Li Hailong Tang Muhammad Hasan Ali Baig Harrison Odion Ikhumhen International Research Center of Big Data for Sustainable Development Goals Key Laboratory of Digital Earth ScienceAerospace Information Research InstituteChinese Academy of SciencesBeijingPeople’s Republic of China College of Resources and Environment University of Chinese Academy of SciencesBeijingPeople’s Republic of China School of Earth Sciences and Spatial Information Engineering Hunan University of Science and TechnologyXiangtanPeople’s Republic of China School of Geospatial Engineering and Science Sun Yat-Sen UniversityZhuhaiPeople’s Republic of China Institute of Geo-Information & Earth-Observation (IGEO) PMAS Arid Agriculture University RawalpindiRawalpindiPakistan Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems College of the Environment and EcologyXiamen UniversityXiamenPeople’s Republic of China

Spatiotemporal continuity of surface water datasets widely known for its significance in the surface water dynamic monitoring and assessments,are faced with drawbacks like cloud influence,which hinders the direct extraction of data from time-series remote sensing *** study proposes a Time-series Surface Water Reconstruction method(TSWR).The initial stage of this method involves the effective use of remote sensing images to automatically construct multi-stage surface water boundaries based on Google Earth Engine(GEE).Then,we reconstructed regions the reconstruction of regions with missing water pixels using the distance relationship between the multi-stage water boundaries in previous and later *** applied to 10 large rivers around the world,this method yielded an overall accuracy of 98%for water extraction,an RMSE of 0.41 ***,time-series reconstruction tests conducted in 2020 on the Lancang and Danube rivers revealed a significant improvement in the image *** findings demonstrated that this method could not only be used to accurately reconstruct the surface water distribution missing water images,but also to depict a more pronounced time variation *** successful application of this method on GEE demonstrates its importance for use on large scales or in global studies.

关键词： Google earth engine sentinel-2 surface water reconstruction time-series surface water data

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Golden Gemini is All You Need: Finding the Sweet Spots for Speaker Verification

arXiv

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arXiv 2023年

作者： Liu, Tianchi Lee, Kong Aik Wang, Qiongqiong Li, Haizhou Singapore The Department of Electrical and Computer Engineering National University of Singapore Singapore The Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen China

The residual neural networks (ResNet) demonstrate the impressive performance in automatic speaker verification (ASV). They treat the time and frequency dimensions equally, following the default stride configuration designed for image recognition, where the horizontal and vertical axes exhibit similarities. This approach ignores the fact that time and frequency are asymmetric in speech representation. We address this issue and postulate Golden-Gemini Hypothesis, which posits the prioritization of temporal resolution over frequency resolution for ASV. The hypothesis is verified by conducting a systematic study on the impact of temporal and frequency resolutions on the performance, using a trellis diagram to represent the stride space. We further identify two optimal points, namely Golden Gemini, which serves as a guiding principle for designing 2D ResNet-based ASV models. By following the principle, a state-of-the-art ResNet baseline model gains a significant performance improvement on VoxCeleb, SITW, and CNCeleb datasets with 7.70%/11.76% average EER/minDCF reductions, respectively, across different network depths (ResNet18, 34, 50, and 101), while reducing the number of parameters by 16.5% and FLOPs by 4.1%. We refer to it as Gemini ResNet. Further investigation reveals the efficacy of the proposed Golden Gemini operating points across various training conditions and architectures. Furthermore, we present a new benchmark, namely the Gemini DF-ResNet, using a cutting-edge model. Codes and pre-trained models are available at https://***/Tianchi-Liu9/GoldenGemini-for-Speaker-Verification. © 2023, CC BY-NC-ND.

关键词： Image recognition

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Embedding VLAD in Transformer for Video Question Answering

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Jisuanji Xuebao/Chinese Journal of Computers 2023年第4期46卷 671-689页

作者： Guo, Dan Yao, Shen-Tao Wang, Hui Wang, Meng School of Computer and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230094 China Key Laboratory of Knowledge Engineering with Big Data（Hefei University of Technology） Ministry of Education Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province（Hefei University of Technology） Hefei230601 China

Video question answering（VideoQA）is a typical cross-modal understanding task. Its challenge lies in how to learn appropriate multimodal representation and cross-modal correlation for answer inference. Most existing video question answering methods focus on the latter，e. g.，relationship learning between each video frame or clip and word. In this work， we devote to advanced feature embedding of both video and query. We develop a clustering-based VLAD technique for VideoQA. The novelty of our work is the joint exploitation of temporal aggregation and correlation in multimodality. We propose an end-to-end trainable Transformed VLAD embedding network， named TVLAD-Net. TVLAD-Net constructs a differentiable aggregation network module（i. e.， convolutional Residual-less VLAD Block）to generate compact VLAD descriptors（transforming N frames，clips or words to compact K descriptors while K © 2023 science Press. All rights reserved.

关键词： Semantics

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An improved LED junction temperature test method based on FCM 5

An improved LED junction temperature test method based on FC...

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2022 5th International Conference on Power Electronics and Control engineering, ICPECE 2022

作者： Zhang, Jie Zhang, Junchao Xu, Bingshe Fu, Qiang Wang, Chen Zhao, Haodi College of Electrical and Power Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China Key Laboratory of Interface Science and Engineering of New Materials. Ministry of Education Taiyuan University of Technology Shanxi Taiyuan030024 China Shanxi Electric Drive and Internet of Things Engineering Research Center Shanxi Taiyuan030024 China Intelligent City Lighting Data Sharing and Pub. Serv. Platform Eng. Res. Ctr. of 1331 Proj. in Shanxi Prov. Based on Big Data Shanxi Taiyuan030024 China Shanxi Intelligent Digital Lighting Union Laboratory Shanxi Taiyuan030024 China Shanxi Intelligent Digital Cultural Tourism Industry Research Institute Shanxi Taiyuan030024 China

High junction temperature (Tj) of LED will affect the performance and life of LED, so this paper proposes a high-power LED junction temperature test method. Mainly aiming at the problem that the accuracy of the forward current method (FCM) will be affected by the calibration procedure, equipment aging, and technical differences when batch testing LED Tj, this paper uses the relationship between relative current and Tj to calibrate the procedure based on this method, to limit the negative affection of sample heterogeneity. Ten LED samples were tested in the experiment, and the Tj errors under two test methods were obtained. The results showed that the improved method was more accurate than the traditional method. Then, the Tj error of the improved method was compared with that of the forward voltage method, and the error of the new method was smaller. © Published under licence by IOP Publishing Ltd.

关键词： Light emitting diodes

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Robust Decoding of the Auditory Attention from EEG Recordings Through Graph Convolutional Networks

Robust Decoding of the Auditory Attention from EEG Recording...

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

作者： Siqi Cai Ran Zhang Haizhou Li Department of Electrical and Computer Engineering National University of Singapore Singapore Machine Listening Lab University of Bremen Germany South China University of Technology Guangzhou China Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Shenzhen (CUHK-Shenzhen) China

Auditory attention decoding (AAD) with electroencephalography (EEG) holds great promise in brain-computer interface (BCI). Despite much progress, it remains a research topic on how to effectively evaluate the performance of EEG-based AAD algorithms under an appropriate setting that reflects the use scenarios. It is desired that systems are evaluated under cross-subject and cross-trial settings. However, systems are often reported under same-subject, same-trial settings, where test data are not truly separated from the training data, thus potentially leading to model overfitting due to data leakage. In this paper, we study the robustness of graph convolutional network (GCN), a novel approach to learning the intricate spatial patterns in multi-channel EEG signals, by comparing GCN across cross-subject, cross-trial, and same-subject, same-trial settings. On two publicly available AAD datasets, it is found that GCN exhibits remarkable robustness, outperforming previous conventional convolutional neural network (CNN) solutions. We confirm the superiority of our GCN-based AAD model in terms of generalization and robustness.

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Robust Cross-Domain Speaker Verification with Multi-Level Domain Adapters

Robust Cross-Domain Speaker Verification with Multi-Level Do...

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

作者： Wen Huang Bing Han Shuai Wang Zhengyang Chen Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen China

Speaker verification encounters significant challenges when confronted with diverse domain data, often resulting in performance degradation due to domain mismatch. To enhance performance in cross-domain scenarios, we introduce the Domain Adapter, an adaptable module designed for specific domains. This module learns and integrates domain-specific information with speaker-related data, mitigating domain-related variations and promoting convergence of utterance embeddings from the same speaker across diverse domains. It offers configurability across multiple levels and is adaptable to various backbone architectures. Our proposed module substantially enhances cross-domain performance with minimal parameter increments while effectively generalizing to previously unseen domains. In our experiments, we present results on the 3D-Speaker dataset, which provides acoustically-relevant attributes crucial for domain categorization and the subsequent learning of domain information. The top-performing system integrated with domain adapters achieved 10.8%, 14.8%, and 21.1% EER improvements over the baseline across three 3D-Speaker dataset trials.

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