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Frontiers of Architectural Research 2024年第1期13卷 37-56页

作者： Narmin Ariannia Negar Naseri Mansour Yeganeh Digital Architecture and artificial intelligence lab Department of ArchitectureTarbiat Modares UniversityTehranIran Architectural Design Modeling and Fabrication LabDepartment of ArchitectureTarbiat Modares UniversityTehranIran

The present research studies the relationship between place attachment and the perception of form’s visual quality in fifteen outstanding contemporary Iranian architectural cultural *** study puts forward the hypothesis that there is a correlation between the quality of building form and the sense of place attachment,in the sense that creating high visual quality through enhancing the quality of building form increases citizens’initial satisfaction with and subsequent attachment to the *** visual quality influences people’s experience of the environment and improves the quality of *** attachment highlights how people,on a personal level,recreate a sense of place for *** present study adopts the descriptive-analytical method as its theoretical framework and the survey as the empirical *** were developed using the Likert scale and distributed among experts and ordinary *** analysis using SPSS and the adoption of descriptive-analytical statistics,correlation analysis,and regression showed the relationship among the characteristics of *** results show a positive correlation between form and place attachment mediated through visual quality,and they are causal conditions for one *** addition,only some of the buildings under study evoke the same level of place attachment.

关键词： Visual preferences Environmental quality Environmental psychology Mental schemas Contemporary architecture

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Prescribed Performance Tracking Control of Time-Delay Nonlinear Systems With Output Constraints

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1557-1565页

作者： Jin-Xi Zhang Kai-Di Xu Qing-Guo Wang IEEE the State Key lab.ratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China Institute of artificial intelligence and Future Networks Beijing Normal UniversityZhuhai 519087 Guangdong Key lab.ratory of artificial intelligence and Multi-Modal Data Processing Beijing Normal University-Hong Kong Baptist University United International CollegeZhuhai 519087

The problem of prescribed performance tracking control for unknown time-delay nonlinear systems subject to output constraints is dealt with in this paper. In contrast with related works, only the most fundamental requirements, i.e., boundedness and the local Lipschitz condition, are assumed for the allowable time delays. Moreover, we focus on the case where the reference is unknown beforehand, which renders the standard prescribed performance control designs under output constraints infeasible. To conquer these challenges, a novel robust prescribed performance control approach is put forward in this ***, a reverse tuning function is skillfully constructed and automatically generates a performance envelop for the tracking error. In addition, a unified performance analysis framework based on proof by contradiction and the barrier function is established to reveal the inherent robustness of the control system against the time delays. It turns out that the system output tracks the reference with a preassigned settling time and good accuracy,without constraint violations. A comparative simulation on a two-stage chemical reactor is carried out to illustrate the above theoretical findings.

关键词： Nonlinear systems output constraints prescribed performance reference tracking time delays

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Human-robot object handover:Recent progress and future direction

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Biomimetic intelligence & Robotics 2024年第1期4卷 1-13页

作者： Haonan Duan Yifan Yang Daheng Li Peng Wang Institute of Automation Chinese Academy of SciencesBeijing 100190China School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China Centre for artificial intelligence and Robotics Hong Kong Institute of Science and InnovationChinese Academy of SciencesHong Kong 999077China

Human-robot object handover is one of the most primitive and crucial capabilities in human-robot *** is of great significance to promote robots to truly enter human production and life scenarios and serve human in numerous *** progressions in the field of human-robot object handover have been made by *** article reviews the recent literature on human-robot object *** this end,we summarize the results from multiple dimensions,from the role played by the robot(receiver or giver),to the end-effector of the robot(parallel-jaw gripper or multi-finger hand),to the robot abilities(grasp strategy or motion planning).We also implement a human-robot object handover system for anthropomorphic hand to verify human-robot object handover *** review aims to provide researchers and developers with a guideline for designing human-robot object handover methods.

关键词： Human-robot interactions Object handover

Robust Regularization Design of Graph Neural Networks Against Adversarial Attacks Based on Lyapunov Theory

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Chinese Journal of Electronics 2024年第3期33卷 732-741页

作者： Wenjie YAN Ziqi LI Yongjun QI School of artificial intelligence Hebei University of Technology School of Computer Science and Engineering of North China Institute of Aerospace Engineering

The robustness of graph neural networks（GNNs） is a critical research topic in deep *** researchers have designed regularization methods to enhance the robustness of neural networks,but there is a lack of theoretical analysis on the principle of *** order to tackle the weakness of current robustness designing methods,this paper gives new insights into how to guarantee the robustness of GNNs.A novel regularization strategy named Lya-Reg is designed to guarantee the robustness of GNNs by Lyapunov *** results give new insights into how regularization can mitigate the various adversarial effects on different graph *** experiments on various public datasets demonstrate that the proposed regularization method is more robust than the state-of-theart methods such as L1-norm,L2-norm,L2-norm,Pro-GNN,PA-GNN and GARNET against various types of graph adversarial attacks.

关键词： Training Deep learning Garnets Robustness Graph neural networks

Brain-inspired dual-pathway neural network architecture and its generalization analysis

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Science China Technological Sciences 2024年第8期67卷 2319-2330页

作者： DONG SongLin TAN ChengLi ZUO ZhenTao HE YuHang GONG YiHong ZHOU TianGang LIU JunMin ZHANG JiangShe College of artificial intelligence Xi'an Jiaotong University School of Mathematics and Statistics Xi'an Jiaotong University State Key lab.ratory of Brain and Cognitive Science Institute of Biophysics Chinese Academy of Sciences Institute of artificial intelligence Hefei Comprehensive National Science Center University of Chinese Academy of Sciences

In this study, we explored the neural mechanism of global topological perception in the human visual system. We showed strong evidence that the retinotectal pathway in the archicortex of the human brain is responsible for global topological perception, and for modulating the local feature processing in the classical ventral visual pathway. Inspired by this recent cognitive discovery,we developed a novel CogNet architecture to emulate the global-local dichotomy of human visual cognitive mechanisms. The thorough experimental results indicate that the proposed CogNet not only significantly improves image classification accuracies but also effectively addresses the texture bias problem observed in baseline CNN models. We have also conducted mathematical analysis for the generalization gap for general neural networks. Our theoretical derivations suggest that the Hurst parameter, a measure of the curvature of the loss landscape, can closely bind the generalization gap. A larger Hurst parameter corresponds to a better generalization ability. We found that our proposed CogNet achieves a lower test error and attains a larger Hurst parameter,strengthening its superiority over the baseline CNN models further.

关键词： global topological perception dual-pathway generalization gap analysis Hurst parameter

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A Knowledge-enhanced Two-stage Generative Framework for Medical Dialogue Information Extraction

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Machine intelligence Research 2024年第1期21卷 153-168页

作者： Zefa Hu Ziyi Ni Jing Shi Shuang Xu Bo Xu School of artificial intelligence University of Chinese Academy of SciencesBeijing 100049China Institute of Automation Chinese Academy of SciencesBeijing 100190China

This paper focuses on term-status pair extraction from medical dialogues(MD-TSPE),which is essential in diagnosis dia-logue systems and the automatic scribe of electronic medical records(EMRs).In the past few years,works on MD-TSPE have attracted increasing research attention,especially after the remarkable progress made by generative ***,these generative methods output a whole sequence consisting of term-status pairs in one stage and ignore integrating prior knowledge,which demands a deeper un-derstanding to model the relationship between terms and infer the status of each *** paper presents a knowledge-enhanced two-stage generative framework(KTGF)to address the above *** task-specific prompts,we employ a single model to com-plete the MD-TSPE through two phases in a unified generative form:We generate all terms the first and then generate the status of each generated *** this way,the relationship between terms can be learned more effectively from the sequence containing only terms in the first phase,and our designed knowledge-enhanced prompt in the second phase can leverage the category and status candidates of the generated term for status ***,our proposed special status"not mentioned"makes more terms availab.e and en-riches the training data in the second phase,which is critical in the low-resource *** experiments on the Chunyu and CMDD datasets show that the proposed method achieves superior results compared to the state-of-the-art models in the full training and low-re-sourcesettings.

关键词： Medical dialogue understanding information extraction text generation knowledge-enhanced prompt low-resource setting dataaugmentation

Dynamic SLAM Visual Odometry Based on Instance Segmentation:A Comprehensive Review

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Computers, Materials & Continua 2024年第1期78卷 167-196页

作者： Jiansheng Peng Qing Yang Dunhua Chen Chengjun Yang Yong Xu Yong Qin College of Automation Guangxi University of Science and TechnologyLiuzhou545000China Department of artificial intelligence and Manufacturing Hechi UniversityHechi547000China

Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous ***,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map *** the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic *** to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s *** article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and *** comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic ***,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM *** recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance ***,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.

关键词： Dynamic SLAM instance segmentation visual odometry

Hyperspectral image super resolution using deep internal and self-supervised learning

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CAAI Transactions on intelligence Technology 2024年第1期9卷 128-141页

作者： Zhe Liu Xian-Hua Han Graduate School of Science and Technology for Innovation Yamaguchi UniversityYamaguchiJapan Graduate School of artificial intelligence and Science Rikkyo UniversityTokyoJapan

By automatically learning the priors embedded in images with powerful modelling ca-pabilities,deep learning-based algorithms have recently made considerable progress in reconstructing the high-resolution hyperspectral(HR-HS)*** previously collected large-amount of external data,these methods are intuitively realised under the full supervision of the ground-truth ***,the database construction in merging the low-resolution(LR)HS(LR-HS)and HR multispectral(MS)or RGB image research paradigm,commonly named as HSI SR,requires collecting corresponding training triplets:HR-MS(RGB),LR-HS and HR-HS image simultaneously,and often faces dif-ficulties in *** learned models with the training datasets collected simultaneously under controlled conditions may significantly degrade the HSI super-resolved perfor-mance to the real images captured under diverse *** handle the above-mentioned limitations,the authors propose to leverage the deep internal and self-supervised learning to solve the HSI SR *** authors advocate that it is possible to train a specific CNN model at test time,called as deep internal learning(DIL),by on-line preparing the training triplet samples from the observed LR-HS/HR-MS(or RGB)images and the down-sampled LR-HS ***,the number of the training triplets extracted solely from the transformed data of the observation itself is extremely few particularly for the HSI SR tasks with large spatial upscale factors,which would result in limited reconstruction *** solve this problem,the authors further exploit deep self-supervised learning(DSL)by considering the observations as the unlab.lled training ***,the degradation modules inside the network were elab.rated to realise the spatial and spectral down-sampling procedures for transforming the generated HR-HS estimation to the high-resolution RGB/LR-HS approximation,and then the reconstruction errors of the observations were formulated for mea

关键词： computer vision deep learning deep neural networks hyperspectral image enhancement

Unstructured Big Data Threat intelligence Parallel Mining Algorithm

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Big Data Mining and Analytics 2024年第2期7卷 531-546页

作者： Zhihua Li Xinye Yu Tao Wei Junhao Qian School of artificial intelligence and Computer Science Jiangnan UniversityWuxi 214122China School of IoT Engineering Jiangnan UniversityWuxi 214122China

To efficiently mine threat intelligence from the vast array of open-source cybersecurity analysis reports on the web,we have developed the Parallel Deep Forest-based Multi-lab.l Classification(PDFMLC)***,open-source cybersecurity analysis reports are collected and converted into a standardized text ***,five tactics category lab.ls are annotated,creating a multi-lab.l dataset for tactics *** the limitations of low execution efficiency and scalab.lity in the sequential deep forest algorithm,our PDFMLC algorithm employs broadcast variables and the Lempel-Ziv-Welch(LZW)algorithm,significantly enhancing its acceleration ***,our proposed PDFMLC algorithm incorporates lab.l mutual information from the established dataset as input *** captures latent lab.l associations,significantly improving classification ***,we present the PDFMLC-based Threat intelligence Mining(PDFMLC-TIM)*** results demonstrate that the PDFMLC algorithm exhibits exceptional node scalab.lity and execution ***,the PDFMLC-TIM method proficiently conducts text classification on cybersecurity analysis reports,extracting tactics entities to construct comprehensive threat *** a result,successfully formatted STIX2.1 threat intelligence is established.

关键词： unstructured big data mining parallel deep forest multi-lab.l classification algorithm threat intelligence

Index-free triangle-based graph local clustering

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Frontiers of Computer Science 2024年第3期18卷 143-153页

作者： Zhe YUAN Zhewei WEI Fangrui LV Ji-Rong WEN The Country and Area Studies Academy Beijing Foreign Studies UniversityBeijing 100089China Gaoling School of artificial intelligence Renmin University of ChinaBeijing 100872China

Motif-based graph local clustering(MGLC)is a popular method for graph mining tasks due to its various ***,the traditional two-phase approach of precomputing motif weights before performing local clustering loses locality and is impractical for large *** some attempts have been made to address the efficiency bottleneck,there is still no applicable algorithm for large scale graphs with billions of *** this paper,we propose a purely local and index-free method called Index-free Triangle-based Graph Local Clustering(TGLC^(*))to solve the MGLC problem w.r.t.a ***^(*)directly estimates the Personalized PageRank(PPR)vector using random walks with the desired triangleweighted distribution and proposes the clustering result using a standard sweep *** demonstrate TGLC^(*)’s scalab.lity through theoretical analysis and its practical benefits through a novel visualization ***^(*)is the first algorithm to solve the MGLC problem without precomputing the motif *** experiments on seven real-world large-scale datasets show that TGLC^(*)is applicable and scalab.e for large graphs.

关键词： graph local clustering triangle motif index-free sampling method visualization