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A Brief Overview of ChatGPT:The History,Status Quo and Potential Future Development

IEEE/CAA Journal of Automatica Sinica 2023年第5期10卷 1122-1136页

作者： Tianyu Wu Shizhu He Jingping Liu Siqi Sun Kang Liu Qing-Long Han Yang Tang the Key Laboratory of Smart Manufacturing in Energy Chemical Process East China University of Science and TechnologyShanghai 200237China the Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the School of Information Science and Engineering East China University of Science and TechnologyShanghai 200237China the Research Institute of Intelligent Complex Systems Fudan UniversityShanghai 200437China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia

ChatG PT,an artificial intelligence generated content (AIGC) model developed by OpenAI,has attracted worldwide attention for its capability of dealing with challenging language understanding and generation tasks in the form of *** paper briefly provides an overview on the history,status quo and potential future development of ChatGPT,helping to provide an entry point to think about ***,from the limited open-accessed resources,we conclude the core techniques of ChatGPT,mainly including large-scale language models,in-context learning,reinforcement learning from human feedback and the key technical steps for developing *** further analyze the pros and cons of ChatGPT and we rethink the duality of ChatGPT in various *** it has been widely acknowledged that ChatGPT brings plenty of opportunities for various fields,mankind should still treat and use ChatG PT properly to avoid the potential threat,e.g.,academic integrity and safety ***,we discuss several open problems as the potential development of ChatGPT.

关键词： AIGC ChatGPT GPT-3 GPT-4 human feedback large language models

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Two Birds with One Stone: A Link Prediction Model for Knowledge Hypergraph Based on Fully-Connected Tensor Decomposition 9th

Two Birds with One Stone: A Link Prediction Model for Know...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Pang, Jun Qin, Hong-Chao Liu, Yan Liu, Xiao-Qi Wuhan University of Science and Technology Hubei430065 China Hubei Key Laboratory of Intelligent Information Processing and Realtime Industrial System Hubei430065 China Beijing Institute of Technology Beijing100081 China Wuhan Jiangang Middle School Hubei430050 China

ISBN: (纸本)9783031466632

Knowledge hypergraph link prediction aims to predict missing relationships in knowledge hypergraphs and is one of the effective methods for graph completion. The existing optimal knowledge hypergraph link method based on tensor decomposition, i.e., GETD (Generalized Model based on Tucker Decomposition and Tensor Ring Decomposition), has achieved good performance by extending Tucker decomposition, but there are still two main problems: (1)GETD does not establish operations or connections between any two tensor factors, resulting in limited representation of tensor correlation (referred to as finiteness);(2)The tensor decomposed by GETD is highly sensitive to the arrangement of tensor patterns (referred to as sensitivity). In response to the above issues, we propose a knowledge hypergraph link prediction model, called GETD+, based on fully-connected tensor decomposition(FCTN). By combining Tucker decomposition and FCTN, a multi-linear operation/connection is established for any two factor tensors obtained from tensor decomposition. This not only enhances the representation ability of tensors, but also eliminates sensitivity to tensor pattern arrangement. Finally, the superiority of the GETD+ model was verified through a large number of experiments on real knowledge hypergraph datasets and knowledge graph datasets. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Tensors

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Using Rough Sets to Improve the High-dimensional Data Anomaly Detection Method Based on Extended Isolation Forest 26

Using Rough Sets to Improve the High-dimensional Data Anomal...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Liu, Hanlin Zhou, Jiantao Li, Hua Inner Mongolia University College of Computer Science Hohhot China Natl. Loc. Jt. Eng. Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

ISBN: (纸本)9798350331684

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods. © 2023 IEEE.

关键词： Anomaly detection

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Morphotectonic Analyses of LiDAR-Derived DEMs:Insights into Tectonic Activity of the Xinhua Fault within the Three Gorges Area(Central China)

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Journal of Earth Science 2025年第2期36卷 823-829页

作者： Xiaofeng Wang Xiaohan Yin Tonghui Liu Xuan Li Hongming Wang Yaqi Zhong Gang Rao School of Computer and the Hubei Key Laboratory of Intelligent Geo-Information Processing China University of Geosciences(Wuhan)Wuhan 430078China School of Geoscience and Technology Southwest Petroleum UniversityChengdu 610500China School of Earth Sciences China University of Geosciences(Wuhan)Wuhan 430074China Hubei Communications Planning and Design Institute Co. Ltd.Wuhan 430051China School of Economics and Management Shandong Jiaotong UniversityJinan 250357China

0 *** global availability of digital elevation model(DEM)data,such as 90-m Shuttle Radar Topography Mission(SRTM)DEM and 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model(ASTER GDEM),has been extensively utilized in morphotectonic analyses(e.g.,Wang et al.,2024;Cheng et al.,2018;Pérez-Pe?a et al.,2010;El Hamdouni et al.,2008).

关键词： xinhua fault morphotectonic analyses egwang three gorges area digital elevation model aster lidar derived dems tectonic activity morphotectonic analyses digital elevation model dem datasuch

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NAS-HR:Neural architecture search for heart rate estimation from face videos

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Virtual Reality & intelligent Hardware 2021年第1期3卷 33-42页

作者： Hao LU Hu HAN School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences(CAS)Institute of Computing TechnologyBeijing 100080China Peng Cheng Laboratory Shenzhen 518000China

Background In anticipation of its great potential application to natural human-computer interaction and health monitoring,heart-rate(HR)estimation based on remote photoplethysmography has recently attracted increasing research *** the recent deep-learning-based HR estimation methods have achieved promising performance,their computational costs remain high,particularly in mobile-computing *** We propose a neural architecture search approach for HR estimation to automatically search a lightweight network that can achieve even higher accuracy than a complex network while reducing the computational ***,we define the regions of interests based on face landmarks and then extract the raw temporal pulse signals from the R,G,and B channels in each ***,pulse-related signals are extracted using a plane-orthogonal-to-skin algorithm,which are combined with the R and G channel signals to create a spatial-temporal ***,a differentiable architecture search approach is used for the network-structure *** Compared with the state-of-the-art methods on the public-domain VIPL-HR and PURE databases,our method achieves better HR estimation performance in terms of several evaluation metrics while requiring a much lower computational cost1.

关键词： Heart-rate estimation Plane-orthogonal-to-skin STMap Differentiable architecture search

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SELF-SUPERVISED DIFFUSION MRI DENOISING VIA ITERATIVE AND STABLE REFINEMENT

arXiv

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arXiv 2025年

作者： Wu, Chenxu Kong, Qingpeng Jiang, Zihang Kevin Zhou, S. School of Biomedical Engineering Division of Life Sciences and Medicine USTC China MIRACLE Center Suzhou Institute for Advance Research USTC China State Key Laboratory of Precision and Intelligent Chemistry USTC China Key Laboratory of Intelligent Information Processing of CAS Institute of Computing Technology CAS China

Magnetic Resonance Imaging (MRI), including diffusion MRI (dMRI), serves as a "microscope" for anatomical structures and routinely mitigates the influence of low signal-to-noise ratio scans by compromising temporal or spatial resolution. However, these compromises fail to meet clinical demands for both efficiency and precision. Consequently, denoising is a vital preprocessing step, particularly for dMRI, where clean data is unavailable. In this paper, we introduce Di-Fusion, a fully self-supervised denoising method that leverages the latter diffusion steps and an adaptive sampling process. Unlike previous approaches, our single-stage framework achieves efficient and stable training without extra noise model training and offers adaptive and controllable results in the sampling process. Our thorough experiments on real and simulated data demonstrate that Di-Fusion achieves state-of-the-art performance in microstructure modeling, tractography tracking, and other downstream tasks. Code is available at https://***/FouierL/Di-Fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal to noise ratio

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Accurate interpolation for scattered data through hierarchical residual refinement 23

Accurate interpolation for scattered data through hierarchic...

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Proceedings of the 37th International Conference on Neural information processing Systems

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

Accurate interpolation algorithms are highly desired in various theoretical and engineering scenarios. Unlike the traditional numerical algorithms that have exact zero-residual constraints on observed points, the neural network-based interpolation methods exhibit non-zero residuals at these points. These residuals, which provide observations of an underlying residual function, can guide predicting interpolation functions, but have not been exploited by the existing approaches. To fill this gap, we propose Hierarchical INTerpolation Network (HINT), which utilizes the residuals on observed points to guide target function estimation in a hierarchical fashion. HINT consists of several sequentially arranged lightweight interpolation blocks. The first interpolation block estimates the main component of the target function, while subsequent blocks predict the residual components using observed points residuals of the preceding blocks. The main component and residual components are accumulated to form the final interpolation results. Furthermore, under the assumption that finer residual prediction requires a more focused attention range on observed points, we utilize hierarchical local constraints in correlation modeling between observed and target points. Extensive experiments demonstrate that HINT outperforms existing interpolation algorithms significantly in terms of interpolation accuracy across a wide variety of datasets, which underscores its potential for practical scenarios.

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Black⁃box adversarial attacks with imperceptible fake user profiles for recommender systems

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南京大学学报(自然科学版) 2024年第6期60卷 881-899页

作者： Qian Fulan Liu Jinggang Chen Hai Chen Wenbin Zhao Shu Zhang Yanping Artificial Intelligence Institute Anhui UniversityHefei230601China Key Laboratory of Intelligent Computing and Signal Processing Ministry of EducationAnhui UniversityHefei230601China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui UniversityHefei230601China

Attackers inject the designed adversarial sample into the target recommendation system to achieve illegal goals,seriously affecting the security and reliability of the recommendation *** is difficult for attackers to obtain detailed knowledge of the target model in actual scenarios,so using gradient optimization to generate adversarial samples in the local surrogate model has become an effective black‐box attack ***,these methods suffer from gradients falling into local minima,limiting the transferability of the adversarial *** reduces the attack's effectiveness and often ignores the imperceptibility of the generated adversarial *** address these challenges,we propose a novel attack algorithm called PGMRS‐KL that combines pre‐gradient‐guided momentum gradient optimization strategy and fake user generation constrained by Kullback‐Leibler ***,the algorithm combines the accumulated gradient direction with the previous step's gradient direction to iteratively update the adversarial *** uses KL loss to minimize the distribution distance between fake and real user data,achieving high transferability and imperceptibility of the adversarial *** results demonstrate the superiority of our approach over state‐of‐the‐art gradient‐based attack algorithms in terms of attack transferability and the generation of imperceptible fake user data.

关键词： recommendation systems adversarial examples transferability imperceptible

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DragEntity:Trajectory Guided Video Generation using Entity and Positional Relationships

arXiv

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arXiv 2024年

作者： Wan, Zhang Tang, Sheng Wei, Jiawei Zhang, Ruize Cao, Juan Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Zhengzhou University Zhengzhou China Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

In recent years, diffusion models have achieved tremendous success in the field of video generation, with controllable video generation receiving significant attention. However, existing control methods still face two limitations: Firstly, control conditions (such as depth maps, 3D Mesh) are difficult for ordinary users to obtain directly. Secondly, it’s challenging to drive multiple objects through complex motions with multiple trajectories simultaneously. In this paper, we introduce DragEntity, a video generation model that utilizes entity representation for controlling the motion of multiple objects. Compared to previous methods, DragEntity offers two main advantages: 1) Our method is more user-friendly for interaction because it allows users to drag entities within the image rather than individual pixels. 2) We use entity representation to represent any object in the image, and multiple objects can maintain relative spatial relationships. Therefore, we allow multiple trajectories to control multiple objects in the image with different levels of complexity simultaneously. Our experiments validate the effectiveness of DragEntity, demonstrating its excellent performance in fine-grained control in video generation. Copyright © 2024, The Authors. All rights reserved.

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Building Semantic Segmentation Through an Ensemble Architecture of UNet and Graph Convolution Networks 22

Building Semantic Segmentation Through an Ensemble Architect...

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Proceedings of the 2022 6th International Conference on Electronic information technology and Computer Engineering

作者： Yuxin Shang Hao Cuib Haitao Guo Shandong Institute of Commerce and Technology China Shandong Institute of Petroleum and Chemical Technology Key Laboratory of Intelligent Information Processing China

ISBN: (纸本)9781450397148

Extracting building from remote sensing images remains a challenge because some obstacles, such as trees, cars, farmland, and shadows, disturb the identification accuracy. In recnet years, deep convolution neural networks have shown significant improvements in computer vision, however it is still difficult to extract irregular and small buildings. To develop the extraction accuracy of building, we propose a deep learning model, which combined UNet and graph convolution network, to capture the long-range information. Seven deep convolution neural networks were used on the Massachusetts Buildings Dataset for comparison. And the proposed model, which performed best among these compared models, achieved a building Intersection over Union (IOU) of 71.10%, mean IOU of 81.88%, recall of 81.90%, precision 84.36% and F1 score of 83.11%. This result indicated that the proposed model can accurately extract building objects. The visualization results showed that the proposed model performs better in extracting irregular and small buildings and caneffectively extracts buildings from high-resolution remote-sensing images.

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