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12th International Conference on CYBER Technology in Automation, Control, and intelligent Systems, CYBER 2022

作者： Zuo, Guoyu Liu, Yan Huang, Gao Gong, Daoxiong Faculty of Information Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

The challenge in image-based visual servoing is to deal with the visibility constraints, which require the image features to always remain in the field of view (FOV) of the camera. In this paper, a novel constraint function is proposed to address this problem. The FOV constraint function represented by the coordinates of the features and the region boundaries defined in the image is added to the classical proportional controller, which leads the image features from the margin to the center of the image. Simulation results verify the efficiency of the modified controller by controlling a robot manipulator with eye-in-hand configuration. © 2022 IEEE.

关键词： Robot applications

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A Binary Function Name Prediction Method Based on Variable Alignment and Translation Model 23

A Binary Function Name Prediction Method Based on Variable A...

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Proceedings of the 2023 4th International Conference on Machine Learning and Computer Application

作者： Bing Xia Jiabin Yin Yunxiang Ge Ruinan Yang Key Laboratory of Mathematical Engineering and Advanced Computing ZhongYuan University of Technology China Henan Key Laboratory on Public Opinion Intelligent Analysis ZhongYuan University of Technology China ZhongYuan University of Technology China

ISBN: (纸本)9798400709449

Binary function naming is a code analysis task that generates functional descriptions of functions, and its results can be applied in the fields of malicious code analysis, vulnerability causation analysis, and algorithm governance. Aiming at the shortcomings of the pseudocode abstract syntax tree being difficult to extract and the binary function naming scheme having low accuracy rate, a binary function naming prediction model A2N based on variable alignment and sequence translation model is proposed. First, A2N extracts the function variable features of binary files from debugging information and performs variable alignment with the pseudocode obtained from decompiling; then, it obtains the hierarchical structure of the binary functions and designs the node extraction rules to generate an abstract syntax tree AST for each function; then, extract the paths between the leaf nodes of the AST and serialize the tree structure to represent it; finally, with the help of the neural network translation model, establish a mapping between the AST and the binary function names to realize the prediction function. The experimental results show that compared with Dire, Nero and XFL models, the F1 value of A2N is improved by 84%, 44% and 14% on file-level isolation experiments respectively, and the F1 value reaches 80.94% on function-level isolation experiments.

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WiViPose: A Video-aided Wi-Fi Framework for Environment-Independent 3D Human Pose Estimation

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IEEE Transactions on Multimedia 2025年

作者： Zhang, Lei Ning, Haoran Tang, Jiaxin Chen, Zhenxiang Zhong, Yaping Han, Yahong Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Advanced Network Technology and Application Tianjin300050 China Key Laboratory of Computing Power Network and Information Security Ministry of Education China University of Jinan Shandong Provincial Key Laboratory of Ubiquitous Intelligent Computing the School of Information Science and Engineering Jinan250022 China Wuhan Sports University Sports Big-data Research Center Wuhan430079 China Tianjin University College of Intelligence and Computing the Tianjin Key Laboratory of Machine Learning Tianjin300350 China

The inherent complexity of Wi-Fi signals makes video-aided Wi-Fi 3D pose estimation difficult. The challenges include the limited generalizability of the task across diverse environments, its significant signal heterogeneity, and its inadequate ability to analyze local and geometric information. To overcome these challenges, we introduce WiViPose, a video-aided Wi-Fi framework for 3D pose estimation, which attains enhanced cross-environment generalization through cross-layer optimization. Bilinear temporal-spectral fusion (BTSF) is initially used to fuse the time-domain and frequency-domain features derived from Wi-Fi. Video features are derived from a multiresolution convolutional pose machine and enhanced by local self-attention. Cross-modality data fusion is facilitated through an attention-based transformer, with the process further refined under a supervisory mechanism. WiViPose demonstrates effectiveness by achieving an average percentage of correct keypoints (PCK)@50 of 91.01% across three typical indoor environments. © 2025 IEEE.

关键词： Frequency domain analysis

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DreamView: Injecting View-specific Text Guidance into Text-to-3D Generation

arXiv

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arXiv 2024年

作者： Yan, Junkai Gao, Yipeng Yang, Qize Wei, Xihan Xie, Xuansong Wu, Ancong Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Institute for Intelligent Computing Alibaba Group China

Text-to-3D generation, which synthesizes 3D assets according to an overall text description, has significantly progressed. However, a challenge arises when the specific appearances need customizing at designated viewpoints but referring solely to the overall description for generating 3D objects. For instance, ambiguity easily occurs when producing a T-shirt with distinct patterns on its front and back using a single overall text guidance. In this work, we propose DreamView, a text-to-image approach enabling multi-view customization while maintaining overall consistency by adaptively injecting the view-specific and overall text guidance through a collaborative text guidance injection module, which can also be lifted to 3D generation via score distillation sampling. DreamView is trained with large-scale rendered multi-view images and their corresponding view-specific texts to learn to balance the separate content manipulation in each view and the global consistency of the overall object, resulting in a dual achievement of customization and consistency. Consequently, DreamView empowers artists to design 3D objects creatively, fostering the creation of more innovative and diverse 3D assets. Code and model will be released at here. Copyright © 2024, The Authors. All rights reserved.

关键词： Digital elevation model

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ESNet: Evolution and Succession Network for High-Resolution Salient Object Detection 41

ESNet: Evolution and Succession Network for High-Resolution ...

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41st International Conference on Machine Learning, ICML 2024

作者： Liu, Hongyu Cong, Runmin Li, Hua Xu, Qianqian Huang, Qingming Zhang, Wei Institute of Information Science Beijing Jiaotong University Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing China School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Computer Science and Technology Hainan University Hainan China Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China

Preserving details and avoiding high computational costs are the two main challenges for the High-Resolution Salient Object Detection (HRSOD) task. In this paper, we propose a two-stage HRSOD model from the perspective of evolution and succession, including an evolution stage with Low-resolution Location Model (LrLM) and a succession stage with High-resolution Refinement Model (HrRM). The evolution stage achieves detail-preserving salient objects localization on the low-resolution image through the evolution mechanisms on supervision and feature;the succession stage utilizes the shallow high-resolution features to complement and enhance the features inherited from the first stage in a lightweight manner and generate the final high-resolution saliency prediction. Besides, a new metric named Boundary-Detail-aware Mean Absolute Error (MAEBD) is designed to evaluate the ability to detect details in high-resolution scenes. Extensive experiments on five datasets demonstrate that our network achieves superior performance at real-time speed (49 FPS) compared to state-of-the-art methods. Our code is publicly available at: https://***/rmcong/ESNet_ICML24. Copyright 2024 by the author(s)

关键词： Miocene

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GraphSR: A Data Augmentation Algorithm for Imbalanced Node Classification

arXiv

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arXiv 2023年

作者： Zhou, Mengting Gong, Zhiguo State Key Laboratory of Internet of Things for Smart City University of Macau China Guangdong-Macau Joint Laboratory for Advanced and Intelligent Computing China

Graph neural networks (GNNs) have achieved great success in node classification tasks. However, existing GNNs naturally bias towards the majority classes with more labelled data and ignore those minority classes with relatively few labelled ones. The traditional techniques often resort over-sampling methods, but they may cause overfitting problem. More recently, some works propose to synthesize additional nodes for minority classes from the labelled nodes, however, there is no any guarantee if those generated nodes really stand for the corresponding minority classes. In fact, improperly synthesized nodes may result in insufficient generalization of the algorithm. To resolve the problem, in this paper we seek to automatically augment the minority classes from the massive unlabelled nodes of the graph. Specifically, we propose GraphSR, a novel self-training strategy to augment the minority classes with significant diversity of unlabelled nodes, which is based on a Similarity-based selection module and a Reinforcement Learning(RL) selection module. The first module finds a subset of unlabelled nodes which are most similar to those labelled minority nodes, and the second one further determines the representative and reliable nodes from the subset via RL technique. Furthermore, the RL-based module can adaptively determine the sampling scale according to current training data. This strategy is general and can be easily combined with different GNNs models. Our experiments demonstrate the proposed approach outperforms the state-of-the-art baselines on various class-imbalanced datasets. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Novel Feature Selection Strategy Based on Salp Swarm Algorithm for Plant Disease Detection

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Plant Phenomics 2023年第2期5卷 209-225页

作者： Xiaojun Xie Fei Xia Yufeng Wu Shouyang Liu Ke Yan Huanliang Xu Zhiwei Ji College of Artificial Intelligence Nanjing Agricultural UniversityNanjingJiangsu 210095China Center for Data Science and Intelligent Computing Nanjing Agricultural UniversityNanjingJiangsu 210095China State Key Laboratory for Crop Genetics and Germplasm Enhancement Bioinformatics CenterAcademy for Advanced Interdisciplinary StudiesNanjing Agricultural UniversityNanjingJiangsu 210095China Academy for Advanced Interdisciplinary Studies Nanjing Agricultural UniversityNanjingJiangsu 210095China Department of the Built Environment College of Design and EngineeringNational University of Singapore4 Architecture DriveSingapore 117566Singapore

Deep learning has been widely used for plant disease recognition in smart agriculture and has proven to be a powerful tool for image classification and pattern ***,it has limited interpretability for deep *** the transfer of expert knowledge,handcrafted features provide a new way for personalized diagnosis of plant ***,irrelevant and redundant features lead to high *** this study,we proposed a swarm intelligence algorithm for feature selection[salp swarm algorithm for feature selection(SSAFS)]in image-based plant disease *** is employed to determine the ideal combination of handcrafted features to maximize classification success while minimizing the number of *** verify the effectiveness of the developed SSAFS algorithm,we conducted experimental studies using SSAFS and 5 metaheuristic *** evaluation metrics were used to evaluate and analyze the performance of these methods on 4 datasets from the UCI machine learning repository and 6 plant phenomics datasets from *** results and statistical analyses validated the outstanding performance of SSAFS compared to existing state-of-the-art algorithms,confirming the superiority of SSAFS in exploring the feature space and identifying the most valuable features for diseased plant image *** computational tool will allow us to explore an optimal combination of handcrafted features to improve plant disease recognition accuracy and processing time.

关键词： Plant image redundant

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BuildEnVR: An Immersive Analysis System for Environmental Field 30

BuildEnVR: An Immersive Analysis System for Environmental Fi...

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30th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2024

作者： Zhou, Zhenghan Liu, Kebin Xie, Yantong Jin, Hangxu Liu, Shi Wang, Ruiqing Zhao, Haitian Lin, Borong Mu, Xiaofang Qi, Hui Tsinghua University Global Innovation Exchange China Fuzhou Fuyao Institute for Advanced Study China Carnegie Mellon University School of Architecture United States Tsinghua University School of Architecture China Taiyuan Normal University College of Computer Science and Technology China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology China

ISBN: (纸本)9798331515966

Amidst global warming and escalating extreme weather events, indoor environmental quality's impact on human health and public hygiene gains prominence. Environmental parameters exist essentially as fields, which are characterized by high dimensionality, density and complexity, and contain massive amounts of information in space. To facilitate visualization and analysis of indoor environmental field, we design and implement BuildEnVR, an immersive analysis system by virtual reality, enabling remote analysis of real-time and historical environmental field data. Grounded in user needs and cognitive psychology, three visualization modes emerge: the Virtual Sensor mode enables users to access perceptual data in real-time at any 3D coordinates in ambient space, the 4D Heatmap mode visualizes spatial variations and trends over time in environmental field data, and the Synaesthesia mode realizes the fusion display of multi-dimensional environmental field data, allowing users to quickly understand the overall condition of the indoor environment with a low cognitive load. Extensive user surveys validate BuildEnVR's intuitiveness and precision, and it is suitable for both experts and general users. © 2024 IEEE.

关键词： Indoor air pollution

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Multiscale Bilateral Attention Fusion Network for Pansharpening

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5828-5843页

作者： Guo, Zhongyuan Li, Jiawei Lei, Jia Liu, Jinyuan Zhou, Shihua Wang, Bin Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Dalian University of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand University of Ulster Intelligent Systems Research Center LondonderryBT48 7JL United Kingdom Bulgarian Academy of Sciences IICT Sofia1000 Bulgaria

High-resolution multispectral (HRMS) images combine spatial and spectral information originating from panchromatic (PAN) and reduced-resolution multispectral (LRMS) images. Pansharpening performs well and is widely used to obtain HRMS images. However, most pansharpening approaches determine the ratio of PAN and LRMS images through direct interpolation, which may introduce artifacts and distort the color of the fused results. To address this issue, an unsupervised progressive pansharpening framework, MSBANet, is proposed, which adopts a multistage fusion strategy. Each stage contains an attention interactive extraction module (AIEM) and a multiscale bilateral fusion module (MBFM). The AIEM extracts spatial and spectral features from input images and captures the correlations between features. The MBFM can efficiently integrate information from the AIEM and improve MSBANet context awareness. We design a hybrid loss function that enhances the ability of the fusion network to store spectral and texture details. In qualitative and quantitative experimental studies on four datasets, MSBANet outperformed state-of-the-art pansharpening techniques. The code will be released. © 2024 IEEE.

关键词： Data mining

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Positive-Guided Knowledge Distillation for Document-Level Relation Extraction with Noisy Labeled Data 12th

Positive-Guided Knowledge Distillation for Document-Level R...

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12th National CCF Conference on Natural Language Processing and Chinese computing, NLPCC 2023

作者： Zeng, Daojian Zhu, Jianling Jiang, Lincheng Dai, Jianhua Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China Institute of AI and Targeted International Communication Hunan Normal University Changsha China College of Advanced Interdisciplinary Studies National University of Defense Technology Changsha China

ISBN: (纸本)9783031446924

Since one entity may have multiple mentions and relations between entities may stretch across multiple sentences in a document, the annotation of document-level relation extraction datasets becomes a challenging task. Many studies have identified that these datasets contain a large number of noisy labels, hindering performance improvement for the document-level relation extraction task. The previous and most straightforward method is denoising noisy labeled data from a data annotation perspective. However, this time-consuming approach is not suitable for large-scale datasets. In this paper, we propose a novel Positive-Guided Knowledge Distillation (PGKD) model to address the noisy labeled data problem for document-level relation extraction. We design a new teacher-student architecture. The teacher model trained with only positive samples can partially supervise the student model. The positive-guided knowledge distillation algorithm transfers the clean positive-class patterns from the teacher model to the student model. In this way, the student model trained with all samples can efficiently prevent the interference of false negative samples. Extensive experiments on Mix-DocRED demonstrate that PGKD achieves state-of-the-art effectiveness for document-level relation extraction with noisy labeled data. Moreover, PGKD also surpasses other baselines even on the well-annotated Re-DocRED. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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