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

作者： Yunrui Dong Guiduo Duan Tianxi Huang Yunhao Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of China Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu China Chengdu Textile College College of Humanities and General Education Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the domain of Multimodal Relation Extraction (MRE), we present the $\color{Red}{\text{W}}$atcher-$\color{Red}{\text{M}}$ediated $\color{Red}{\text{A}}$ttention $\color{Red}{\text{J}}$oint $\color{Red}{\text{L}}$earning Model ($\color{Red}{\text{WMAJL}}$), a novel approach addressing the challenges of modality alignment noise, cross-modal fusion disparity, preservation of textual relative position information, and the distinctiveness of classification labels. WMAJL employs an integrative framework leveraging contrastive learning and variational autoencoder constraints to mitigate modality alignment noise by prioritizing relevant semantic data and effectively reducing extraneous noise that does not contribute to the task. The model’s innovative architecture includes a mediator watcher, which facilitates enhanced cross-modal fusion by enabling nuanced information exchange between textual and visual modalities while preserving the unique characteristics of each modality. Additionally, the design of auxiliary tasks, such as Named Entity Recognition (NER), and output supervision constructs loss functions that preserve relative position information, ensuring a precise depiction of entity relationships throughout the multilayer encoding processes. A key differentiator of WMAJL is its label-centric self-information loss technique, inspired by InfoNCE, which trains the model to cluster similar relation labels in semantically coherent areas, thereby optimizing classification label uniqueness by discerning subtle differences among relation types. The synergistic application of these strategies has led to a significant enhancement of WMAJL’s performance, as evidenced by its state-of-the-art F1 score of $\color{Red}{84.93\%}$ on the MNRE dataset. This achievement surpasses existing benchmarks and sets a new standard for multimodal knowledge extraction, underscoring WMAJL’s potential to revolutionize the MRE landscape.

关键词： Resistance Visualization Technological innovation Noise Autoencoders Semantics Contrastive learning Nonhomogeneous media Speech processing Standards

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SPA: A Graph Spectral Alignment Perspective for Domain Adaptation

arXiv

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arXiv 2023年

作者： Xiao, Zhiqing Wang, Haobo Jin, Ying Feng, Lei Chen, Gang Huang, Fei Zhao, Junbo College of Computer Science and Technology Zhejiang University China School of Software Technology Zhejiang University China Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University China CUHK-SenseTime Joint Lab The Chinese University of Hong Kong Hong Kong School of Computer Science and Engineering Nanyang Technological University Singapore Alibaba Group China

Unsupervised domain adaptation (UDA) is a pivotal form in machine learning to extend the in-domain model to the distinctive target domains where the data distributions differ. Most prior works focus on capturing the inter-domain transferability but largely overlook rich intra-domain structures, which empirically results in even worse discriminability. In this work, we introduce a novel graph SPectral Alignment (SPA) framework to tackle the tradeoff. The core of our method is briefly condensed as follows: (i)-by casting the DA problem to graph primitives, SPA composes a coarse graph alignment mechanism with a novel spectral regularizer towards aligning the domain graphs in eigenspaces;(ii)-we further develop a fine-grained message propagation module — upon a novel neighbor-aware self-training mechanism — in order for enhanced discriminability in the target domain. On standardized benchmarks, the extensive experiments of SPA demonstrate that its performance has surpassed the existing cutting-edge DA methods. Coupled with dense model analysis, we conclude that our approach indeed possesses superior efficacy, robustness, discriminability, and transferability. Code and data are available at: https://***/CrownX/SPA. Copyright © 2023, The Authors. All rights reserved.

关键词： Benchmarking

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Hybrid Convolutional-Transformer Neural Network for Driver Distraction Detection

Hybrid Convolutional-Transformer Neural Network for Driver D...

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Penghua Li Qiyun Mou Jie Hou Yushan Tu Key Laboratory of Intelligent Computing for Big Data College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Automation Chongqing University of Posts and Telecommunications Chongqing China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China

Distracted driving causes a significant number of traffic accidents each year, making it a crucial area of research to monitor the driver’s state and prevent accidents. Although convolutional networks are widely used for detecting driver distraction, they face several issues, such as poor generalization ability, high model capacity, and response latency. To tackle these challenges, this paper introduces a nimble and low-latency algorithm named MViTCNet, which utilizes the MobileViT module for the identification of driver distraction while operating within constrained computational resources. The MViTCNet algorithm extracts local features and constructs an original feature map, which is fused with a global feature representation processed by a Transformer to capture finer details and obtain a larger receptive field. Additionally, the classic convolutional module MBConv is employed to reduce spatial redundancy, and downsampling is used to obtain low-resolution feature maps. By stacking multiple modules to learn more complex feature representations, the network structure increases in depth and width, resulting in a better fit for complex data distributions. Experiments on an embedded hardware platform show that MViTCNet achieves an acceptable trade-off, with an accuracy of 91.04% on the StateFarm dataset and 97.80% on the LDDB dataset at a latency of 28.9±5.3 milliseconds, outperforming existing methods.

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Enhancing EEG-Based Cross-Subject Emotion Recognition via Adaptive Source Joint Domain Adaptation

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IEEE Transactions on Affective computing 2024年

作者： Liu, Ke Luo, Xin Zhu, Wenrui Yu, Zhuliang Yu, Hong Xiao, Bin Wu, Wei Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Intelligent Computing China South China University of Technology College of Automation Science and Engineering Guangzhou510641 China Shanghai Jiao Tong University School of Medicine Department of Neurology Songjjiang Hospital and Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China

EEG emotion recognition is crucial in both human-machine interaction and healthcare. However, recognizing emotions across different subjects remains challenging due to individual variability. While existing multi-source domain adaptation methods have been utilized for cross-subject EEG emotion decoding, they often struggle with irrelevant or weakly relevant source domains, leading to negative transfer. Additionally, variations within subdomains are often neglected in these studies. We propose a joint domain adaptation method, Adaptive Source Joint Domain Adaptation (ASJDA) to address these issues. ASJDA utilizes an unsupervised adaptive source selection strategy to select a subset of source domains by evaluating the Jensen-Shannon divergence between the source and target domains, choosing those most relevant to the target. Subsequently, it implements joint domain adaptation with these chosen sources at both the domain and category subdomain levels. Our proposed method outperforms existing state-of-the-art methods, achieving cross-subject accuracies of 96.81% in SEED, 89.69% in SEED-IV, and 69.31% in DEAP. This work significantly advances the state of the art in EEG emotion recognition by effectively addressing the challenges of cross-subject variability. The source code for ASJDA is available at https://***/Pam098/ASJDA. © 2010-2012 IEEE.

关键词： adaptive source selection cross-subject domain adaptation EEG emotion recognition

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SoLar: sinkhorn label refinery for imbalanced partial-label learning 22

SoLar: sinkhorn label refinery for imbalanced partial-label ...

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Haobo Wang Mingxuan Xia Yixuan Li Yuren Mao Lei Feng Gang Chen Junbo Zhao Key Lab of Intelligent Computing based Big Data of Zhejiang Province Zhejiang University School of Software Technology Zhejiang University Department of Computer Sciences University of Wisconsin-Madison College of Computer Science Chongqing University and Center for Advanced Intelligence Project RIKEN

ISBN: (纸本)9781713871088

Partial-label learning (PLL) is a peculiar weakly-supervised learning task where the training samples are generally associated with a set of candidate labels instead of single ground truth. While a variety of label disambiguation methods have been proposed in this domain, they normally assume a class-balanced scenario that may not hold in many real-world applications. Empirically, we observe degenerated performance of the prior methods when facing the combinatorial challenge from the long-tailed distribution and partial-labeling. In this work, we first identify the major reasons that the prior work failed. We subsequently propose SoLar, a novel Optimal Transport-based framework that allows to refine the disambiguated labels towards matching the marginal class prior distribution. SoLar additionally incorporates a new and systematic mechanism for estimating the long-tailed class prior distribution under the PLL setup. Through extensive experiments, SoLar exhibits substantially superior results on standardized benchmarks compared to the previous state-of-the-art PLL methods.

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Flow Characteristic of Scroll Compressor with Combined Profile Scroll 7th

Flow Characteristic of Scroll Compressor with Combined Profi...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Liu, Zhenghui Chen, Zhongli Qian, Lijuan Lin, Jian Liu, Yongqing College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Zhejiang Santian Automotive Air Conditioning Compressor Co. Ltd. Zhejiang Lishui323000 China

ISBN: (纸本)9789811672125

Scroll compressor is widely used in machines for gases compression, and scroll profile is the basic of a scroll compressor. The combined profile of scroll has advantages of several single scroll profiles. For improving the performance of a scroll compressor, it is necessary to investigate characteristics of the internal flow of combined profile of scroll compressors. In this paper, the combined profile is composed of the base circle involute-variable radius and base circle involute. A two-dimensional transient flow model of the combined profile of scroll refrigeration compressor operating with ammonia is established. The pressure, temperature, and velocity distribution of the working chamber are collected using the computational fluid dynamic method. Based on comparison between the combined profile and the circular involute profile, it is found that the combined profile is more favorable to bear the pressure, decrease the leakage, and decrease the fluctuation of mass flow rate than the circular involute profile. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Computational fluid dynamics

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Comparative Analysis Between New Combined Profile Scroll and Circular Involute Profile Scroll 7th

Comparative Analysis Between New Combined Profile Scroll and...

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7th International Conference on Life System Modeling and Simulation, LSMS 2021, and the 7th International Conference on intelligent computing for Sustainable Energy and Environment, ICSEE 2021

作者： Liu, Zhenghui Qian, Lijuan Shao, Xianyue College of Mechanical and Electrical Engineering China Jiliang University Zhejiang Hangzhou310018 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Zhejiang Hangzhou310018 China Zhejiang Ruifeng Wufu Pneumatic Tools Co. Ltd. Zhejiang Taizhou317500 China

ISBN: (纸本)9789811672125

Scroll compressor is widely used in machines for gases compression, and scroll profile is the basic of a scroll compressor. To further study the combined profile of scroll compressor, a new type of combined profile scroll composed of base circle involutes, variable radius and base circle involutes is introduced. Firstly, it is modeled according to the parametric equation of the vortex disk generatrix and the isometric method. Then, compression ratio is analyzed, deformation of the scroll and pressure distribution in internal scroll are simulated using ANSYS software. The results are compared with circular involute profile scroll. The results show that, under the condition of a certain number of scrolls, the new profile scroll compressor has larger compression ratio, less deformation, and better pressure condition than the circular involute profile scroll. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Deformation

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StyleGene: Crossover and Mutation of Region-level Facial Genes for Kinship Face Synthesis

StyleGene: Crossover and Mutation of Region-level Facial Gen...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Hao Li Xianxu Hou Zepeng Huang Linlin Shen Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University School of AI and Advanced Computing Xi'an Jiaotong-Liverpool University Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University

High-fidelity kinship face synthesis has many potential applications, such as kinship verification, missing child identification, and social media analysis. However, it is challenging to synthesize high-quality descendant faces with genetic relations due to the lack of large-scale, high-quality annotated kinship data. This paper proposes RFG (Region-level Facial Gene) extraction framework to address this issue. We propose to use IGE (Image-based Gene Encoder), LGE (Latent-based Gene Encoder) and Gene Decoder to learn the RFGs of a given face image, and the relationships between RFGs and the latent space of Style-GAN2. As cycle-like losses are designed to measure the $\mathcal{L}_data$ distances between the output of Gene Decoder and image encoder, and that between the output of LGE and IGE, only face images are required to train our framework, i.e. no paired kinship face data is required. Based upon the proposed RFGs, a crossover and mutation module is further designed to inherit the facial parts of parents. A Gene Pool has also been used to introduce the variations into the mutation of RFGs. The diversity of the faces of descendants can thus be significantly increased. Qualitative, quantitative, and subjective experiments on FIW, TSKinFace, and FF-databases clearly show that the quality and diversity of kinship faces generated by our approach are much better than the existing state-of-the-art methods.

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Turbidity currents propagating down an inclined slope: particle auto-suspension

arXiv

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arXiv 2023年

作者： Xie, Jiafeng Hu, Peng Zhu, Chenlin Yu, Zhaosheng Pähtz, Thomas Ocean College Zhejiang University Zhoushan316021 China Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province China Jiliang University Hangzhou310018 China State Key Laboratory of Fluid Power and Mechatronic Systems Department of Mechanics Zhejiang University Hangzhou310027 China Ocean Research Center of Zhoushan Zhejiang University Zhoushan316021 China Donghai Lab Zhoushan316021 China

The Turbidity current (TC), a ubiquitous fluid-particle coupled phenomenon in the natural environment and engineering, can transport over long distances on an inclined terrain due to the suspension mechanism. A large-eddy simulation and discrete element method coupled model is employed to simulate the particle-laden gravity currents over the inclined slope in order to investigate the auto-suspension mechanism from a Lagrangian perspective. The particle Reynolds number in our TC simulation is 0.01 ∼ 0.1 and the slope angle is 1/20 ∼ 1/5. The influences of initial particle concentration and terrain slope on the particle flow regimes, particle movement patterns, fluid-particle interactions, energy budget and auto-suspension index are explored. The results indicate that the auto-suspension particles predominantly appear near the current head and their number increases and then decreases during the current evolution, which is positively correlated with the coherent structures around the head. When the turbidity current propagates downstream, the average particle Reynolds number of the auto-suspension particles remains basically unchanged, and is higher than that of other transported particles. The average particle Reynolds number of the transported particles exhibits a negative correlation with the Reynolds number of the current. Furthermore, the increase in particle concentration will enhance the particle velocity, which allows the turbidity current to advance faster and improves the perpendicular support, thereby increasing the turbidity current auto-suspension capacity. Increasing slope angle will result in a slightly larger front velocity, while the effect of that on the total force is insignificant. Copyright © 2023, The Authors. All rights reserved.

关键词： Large eddy simulation

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Exploring Patent Transformation Event: Forecasting Patent Transfer Time

Exploring Patent Transformation Event: Forecasting Patent Tr...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jinchen Huo Weidong Liu Yang Li Yan Cao College of Computer Science Inner Mongolia University China Institute of Scientific and Technical Information of China China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Social Computing and Data Processing China Inner Mongolia Engineering Laboratory for Big Data Analysis Technology China

As the largest source of technical information around the world, patents are regarded as an essential crystallization and carrier of knowledge and technological innovation. Patent transformation is conducive not only to enhancing economic efficiency, but also to improving productivity and the rational utilization of resources. There is an imbalance between high patent ownership and low transformation rates. We try to predict the occurrence of transformation events from the patent assignment. However, there are some challenges in predicting patent transformation: (1) how to capture transformation features of patents, especially combined with the transfer time factor. (2) how to predict patent transfer time effectively. To address these challenges, a Patent Transfer Time Forecasting Model (PTTFM) is proposed. The model includes: (1) extraction of time-varying features of patents. (2) the patent transfer time is forecast using a Neural Temporal Point Process. By testing the model on patents under different classifications, the experimental results are obtained to show that the proposed model is applicable to predict the timing of patent assignment within a certain time frame, especially one month. Our work may facilitate patent transformation while interpretability is ensured for transformation events.

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