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arXiv 2023年

作者： Gui, Shangtong Shao, Chenze Ma, Zhengrui Zhang, Xishan Chen, Yunji Feng, Yang State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China Cambricon Technologies China

Non-autoregressive Transformer(NAT) significantly accelerates the inference of neural machine translation. However, conventional NAT models suffer from limited expression power and performance degradation compared to autoregressive (AT) models due to the assumption of conditional independence among target tokens. To address these limitations, we propose a novel approach called PCFG-NAT, which leverages a specially designed Probabilistic Context-Free Grammar (PCFG) to enhance the ability of NAT models to capture complex dependencies among output tokens. Experimental results on major machine translation benchmarks demonstrate that PCFG-NAT further narrows the gap in translation quality between NAT and AT models. Moreover, PCFG-NAT facilitates a deeper understanding of the generated sentences, addressing the lack of satisfactory explainability in neural machine translation.2 Copyright © 2023, The Authors. All rights reserved.

关键词： Computer aided language translation

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Accurate Interpolation of Scattered Data Via Learning Relation Graph

Accurate Interpolation of Scattered Data Via Learning Relati...

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

作者： Shizhe Ding Boyang Xia Jingyan Sui Dongbo Bu Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Central China Institute for Artificial Intelligence Technologies Zhengzhou China

Interpolation of scattered data is crucial across various domains, and neural networks have proved effective in developing accurate interpolators. While these neural network-based approaches excel in capturing data distributions, their failure to leverage inherent locality in computations can lead to overly dense correlation modeling. This might result in capturing spurious correlations and thereby affecting accuracy. To address these shortcomings, we propose a relation-aware interpolation framework named REIN. REIN uses a relational inference module to efficiently identify neighboring observed data points for each interpolation location, and integrate the relation graph as constraints into a neural interpolator. Experimental results on both synthetic and real-world datasets show that REIN outperforms the existing interpolation methods, even those employing heuristic local constraints. The analysis also suggests that compared with the widely-used heuristic local constraints, the learned local relation graphs exhibit improved adaptability and interpretability.

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KF-TMIAF: An Efficient Key-Frames-Based Temporal Modeling of Images and AU Features structure for Masked Facial Expression Recognition

KF-TMIAF: An Efficient Key-Frames-Based Temporal Modeling of...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Huijie Gu Hanpu Wang Shiwei He Mengyan Li Jianmeng Zhou Tong Chen College of Electronic and Information Engineering Southwest University Chongqing China Chongqing Key Laboratory of Generic Technology and System of Service Robots SWU China Institute of Legal Psychology and Intelligent Computing Southwest University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Masked Facial Expression refers to facial expression that people deliberately make which do not correspond to their true emotions. The purpose of masked facial expression is to conceal one’s genuine feelings. Precisely identifying masked facial expression aids in revealing the genuine emotions, and finds applications in diagnosis of mental illness. However, the masked facial expression sequences have redundant information, temporal modeling is difficult, and the expressive ability of AU features needs to be improved. To solve the existing problems, we first propose a Key frame Selection and Data Augmentation (KS-DA) method to obtain the key frame in the sequence. In addition, we propose a Global Feature Relation Block (GFR) to aggregate global information and combine it with Temporal Convolutional Networks (TCN) to better realize temporal modeling. Finally, we propose an Adaptive Weight Generation Module (Ada-WGM) to form different weights from adaptation, allowing for better extraction of the temporal changes in AU features. Finally, the experiments have shown that the proposed method has improved the mixed expression classification task (36 task) by 15.23% and the experienced emotions classification task (6E task) by 17.77%, respectively, and obtain sort results.

关键词： Training Gold Image recognition Face recognition Computational modeling Video sequences Feature extraction Data augmentation Computational efficiency Convolutional neural networks

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Family of Mutually Uncorrelated Codes for DNA Storage Address Design

IEEE Transactions on Nanobioscience

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IEEE Transactions on Nanobioscience 2025年 PP卷 PP页

作者： Liu, Zhenlu Cao, Ben Shao, Qi Zheng, Yanfen Wang, Bin Zhou, Shihua Zheng, Pan Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China Dalian University of Technology School of Computer Science and Technology Dalian116024 China University of Canterbury Upper Riccarton Department of Accounting and Information Systems Christchurch8140 New Zealand

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system. © 2002-2011 IEEE.

关键词： DNA sequences

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Uncertainty-boosted Robust Video Activity Anticipation

arXiv

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arXiv 2024年

作者： Qi, Zhaobo Wang, Shuhui Zhang, Weigang Huang, Qingming The School of Computer Science and Technology Harbin Institute of Technology Weihai264209 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518066 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Video activity anticipation aims to predict what will happen in the future, embracing a broad application prospect ranging from robot vision and autonomous driving. Despite the recent progress, the data uncertainty issue, reflected as the content evolution process and dynamic correlation in event labels, has been somehow ignored. This reduces the model generalization ability and deep understanding on video content, leading to serious error accumulation and degraded performance. In this paper, we address the uncertainty learning problem and propose an uncertainty-boosted robust video activity anticipation framework, which generates uncertainty values to indicate the credibility of the anticipation results. The uncertainty value is used to derive a temperature parameter in the softmax function to modulate the predicted target activity distribution. To guarantee the distribution adjustment, we construct a reasonable target activity label representation by incorporating the activity evolution from the temporal class correlation and the semantic relationship. Moreover, we quantify the uncertainty into relative values by comparing the uncertainty among sample pairs and their temporal-lengths. This relative strategy provides a more accessible way in uncertainty modeling than quantifying the absolute uncertainty values on the whole dataset. Experiments on multiple backbones and benchmarks show our framework achieves promising performance and better robustness/interpretability. Source codes are available at https://***/qzhb/UbRV2A. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Evolutionary Multi-Objective Neural Architecture Search for Generalized Cognitive Diagnosis Models

Evolutionary Multi-Objective Neural Architecture Search for ...

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Data-driven Optimization of Complex Systems (DOCS), International Conference on

作者： Shangshang Yang Cheng Zhen Ye Tian Haiping Ma Yuanchao Liu Panpan Zhang Xingyi Zhang Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Artificial Intelligence Anhui University Hefei China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education Institutes of Physical Science and Information Technology Anhui University Hefei China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

Cognitive diagnosis models (CDMs) with high generalization are essential for intelligent education systems to reveal students' knowledge states in multiple datasets. However, existing CDMs' architectures are designed dependent on researcher expertise and experience from observing and summarizing partial students' learning behaviors, which makes handcrafted models not cover all learning behaviors well and thus limits their generalization. To develop generalized CDMs, this paper proposes an evolutionary neural architecture search to design CDMs' architectures effective on multiple datasets automatically. Specifically, we first formulate the search task as a multi-objective optimization problem (MOP), which maximizes model performance on multiple datasets containing learning behaviors as many as possible to ensure model generalization. Then, an expressive search space is devised to contain as many potential architectures as possible, where each architecture is denoted by a unified form, taking three given inputs and integrating them in a linear or no-linear manner for prediction. Finally, an evolutionary algorithm with a tailored deduplication strategy is employed to solve the MOP, where each architecture is further represented by a single-root computation tree for easy optimization. Experiments on multiple datasets demonstrate the generalization and effectiveness of the best architecture searched by the proposed approach, and the searched architecture also holds as good interpretability as handcrafted architectures.

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Adaptive ELM-Based Time-Constrained Control of Uncertain Robotic Manipulators With Minimum Learning Computation

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IEEE Transactions on Emerging Topics in computing 2025年

作者： Jin, Xiaozheng Hou, Yuhan Wu, Xiaoming Chi, Jing Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center National Supercomputer Center in Jinan Shandong Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Jinan250013 China Qilu Institute of Technology School of Intelligent Manufacturing and Control Engineering Shandong Jinan250200 China Shandong University of Finance and Economics School of Computing and Artificial Intelligence Shandong Jinan250014 China

This paper studies the extreme learning machine (ELM)-based time-constrained trajectory tracking control of a class of robotic manipulator systems with model/input uncertainties, matched and mismatched disturbances. A uniform robust exact differentiator (URED) is developed to construct a multi-layer virtual control framework of the robotic manipulator system. Under the control framework, adaptive ELM-based control strategies are proposed to suppress the influence of the uncertainties and disturbances layer-by-layer, combining with the minimum learning parameter (MLP) technique which is employed to reduce the calculation complexity of ELM parameters. The significance of this study is that general model/input uncertainties, matched and mismatched disturbances can be effectively compensated within a limited time, so that bounded trajectory tracking of the robotic manipulator can be achieved avoiding the complexity explosion issue. The time-constrained stability of the closed-loop robotic error systems is proved through Lyapunov stability theory. Finally, comparative simulations are employed to display the feasibility and superiority of the designed robust adaptive ELM-based control schemes of a robotic manipulator system. © 2013 IEEE.

关键词： Robust control

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A Novel Home Safety IoT Monitoring Method Based on ZigBee Networking

A Novel Home Safety IoT Monitoring Method Based on ZigBee Ne...

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International Conference on Wireless Communications, Networking and Applications, WCNA 2021

作者： An, Ning Li, Peng Wang, Xiaoming Wu, Xiaojun Dang, Yuntong School of Computer Science Shaanxi Normal University Xi’an710119 China Key Laboratory of Intelligent Computing and Service Technology for Folk Song Ministry of Culture and Tourism Xi’an710119 China Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an710062 China Engineering Laboratory of Teaching Information Technology of Shaanxi Province Xi’an710119 China School of Music Shaanxi Normal University Xi’an710119 China

ISBN: (纸本)9789811924552

This paper realizes the design of home safety early warning system by studying the wireless communication networking technology of ZigBee and WiFi, as well as sensor communication technology, which is based on taking home safety monitoring as the application background. In this study, CC2530 chip was used as ZigBee wireless communication module. A novel home security IoT monitoring method was proposed through sensor triggering, human activity trajectory perception algorithm design, and wireless networking and communication optimization. Meanwhile, the safety early warning and remote monitoring of home staff can be realized, and home safety can be guaranteed. The system can achieve the purpose of home monitoring and early warning with low software and hardware cost through the experimental design and result analysis. It can not only provide reference for the design of sensor communication system, but also provide technical reference for aging society and response. © 2022, The Author(s).

关键词： Zigbee

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HSI-MFormer: Integrating Mamba and Transformer Experts for Hyperspectral Image Classification

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： He, Yan Tu, Bing Liu, Bo Li, Jun Plaza, Antonio Institute of Optics and Electronics State Key Laboratory Cultivation Base of Atmospheric Optoelectronic Detection and Information Fusion Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection Jiangsu Engineering Research Center for Intelligent Optoelectronic Sensing Technology of Atmosphere Nanjing University of Information Science and Technology Nanjing210044 China China University of Geosciences Faculty of Computer Science Wuhan430074 China University of Extremadura Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politecnica 10003 Spain

Hyperspectral image (HSI) classification is fundamental to numerous remote sensing applications, enabling detailed analysis of material properties and environmental conditions. Recent Mamba built upon selective state space models (SSMs) (S6) have demonstrated exceptional advantages in long-range sequence modeling with linear computational efficiency, while Transformer based on self-attention mechanisms is particularly adept at capturing short-range dependencies. To leverage the complementary strengths of these models, this article introduces a novel hybrid Mamba-Transformer framework (HSI-MFormer), effectively exploring the multiscale properties of hyperspectral data for HSI classification. Initially, a multiscale token generation (MTG) module is developed, which converts the HSI cube into multiple spatial-spectral token groups across different scales. To adequately capture fine-grained multiscale spatial-spectral patterns, an inner-scale transformer expert (ITE) is designed, which incorporates grouped self-attention operations to perform short-range sequence modeling within token groups at each scale. Meanwhile, a cross-scale Mamba expert (CME) is introduced, which integrates a cross-scale serialization mechanism and bidirectional Mamba block for long-range sequence modeling, further exploring the interactions and complementarity between token groups across different scales. Several hybrid strategies for integrating the ITE and CME are investigated to maximize their complementarity, including parallel, interval, and serial structures. Extensive experiments demonstrate that the proposed HSI-MFormer significantly outperforms the state-of-the-art Transformer-based and Mamba-based HSI classification methods. © 1980-2012 IEEE.

关键词： Image classification

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MixSA: Training-free Reference-based Sketch Extraction via Mixture-of-Self-Attention

arXiv

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arXiv 2025年

作者： Yang, Rui Wu, Xiaojun He, Shengfeng The School of Computer Sciences Shaanxi Normal University Xi'An China The School of Computer Science The Key Laboratory of Intelligent Computing and Service Technology for Folk Song of the Ministry of Culture and Tourism Shaanxi Normal University Xi'An710119 China The School of Computing and Information Systems Singapore Management University Singapore

Current sketch extraction methods either require extensive training or fail to capture a wide range of artistic styles, limiting their practical applicability and versatility. We introduce Mixture-of-Self-Attention (MixSA), a training-free sketch extraction method that leverages strong diffusion priors for enhanced sketch perception. At its core, MixSA employs a mixture-of-self-attention technique, which manipulates self-attention layers by substituting the keys and values with those from reference sketches. This allows for the seamless integration of brushstroke elements into initial outline images, offering precise control over texture density and enabling interpolation between styles to create novel, unseen styles. By aligning brushstroke styles with the texture and contours of colored images, particularly in late decoder layers handling local textures, MixSA addresses the common issue of color averaging by adjusting initial outlines. Evaluated with various perceptual metrics, MixSA demonstrates superior performance in sketch quality, flexibility, and applicability. This approach not only overcomes the limitations of existing methods but also empowers users to generate diverse, high-fidelity sketches that more accurately reflect a wide range of artistic expressions. Copyright © 2025, The Authors. All rights reserved.

关键词： Image texture

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