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arXiv 2024年

作者： Liu, Feng-Lin Fu, Hongbo Lai, Yu-Kun Gao, Lin SCM City University of Hong Kong EMIA HKUST China School of Computer Science and Informatics Cardiff University United Kingdom The Beijing Key Laboratory of Mobile Computing and Pervasive Device Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China

Existing text-based 3D generation methods generate attractive results but lack detailed geometry control. Sketches, known for their conciseness and expressiveness, have contributed to intuitive 3D modeling but are confined to producing texture-less mesh models within predefined categories. Integrating sketch and text simultaneously for 3D generation promises enhanced control over geometry and appearance but faces challenges from 2D-to-3D translation ambiguity and multi-modal condition integration. Moreover, further editing of 3D models in arbitrary views will give users more freedom to customize their models. However, it is difficult to achieve high generation quality, preserve unedited regions, and manage proper interactions between shape components. To solve the above issues, we propose a text-driven 3D content generation and editing method, SketchDream, which supports NeRF generation from given hand-drawn sketches and achieves free-view sketch-based local editing. To tackle the 2D-to-3D ambiguity challenge, we introduce a sketch-based multi-view image generation diffusion model, which leverages depth guidance to establish spatial correspondence. A 3D ControlNet with a 3D attention module is utilized to control multi-view images and ensure their 3D consistency. To support local editing, we further propose a coarse-to-fine editing approach: the coarse phase analyzes component interactions and provides 3D masks to label edited regions, while the fine stage generates realistic results with refined details by local enhancement. Extensive experiments validate that our method generates higher-quality results compared with a combination of 2D ControlNet and image-to-3D generation techniques and achieves detailed control compared with existing diffusion-based 3D editing approaches. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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OPEN-DOMAIN IMPLICIT FORMAT CONTROL FOR LARGE LANGUAGE MODEL GENERATION

arXiv

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arXiv 2024年

作者： Yao, Yiqun Ma, Wenjia Fang, Xuezhi Jiang, Xin Li, Xiang Meng, Xuying Han, Peng Li, Jing Sun, Aixin Wang, Yequan Beijing Academy of Artificial Intelligence Beijing China AstralForge AI Lab Beijing China Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Electronic Science and Technology of China Chengdu China Harbin Institute of Technology Shenzhen China School of Computer Science and Engineering Nanyang Technological University Singapore

Controlling the format of outputs generated by large language models (LLMs) is a critical functionality in various applications. Current methods typically employ constrained decoding with rule-based automata or fine-tuning with manually crafted format instructions, both of which struggle with open-domain format requirements. To address this limitation, we introduce a novel framework for controlled generation in LLMs, leveraging user-provided, one-shot QA pairs. This study investigates LLMs’ capabilities to follow open-domain, one-shot constraints and replicate the format of the example answers. We observe that this is a non-trivial problem for current LLMs. We also develop a dataset collection methodology for supervised fine-tuning that enhances the open-domain format control of LLMs without degrading output quality, as well as a benchmark on which we evaluate both the helpfulness and format correctness of LLM outputs. The resulting datasets, named OIFC-SFT, along with the related code, will be made publicly available at https://***/cofe-ai/OIFC. Copyright © 2024, The Authors. All rights reserved.

关键词： Modeling languages

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Exploiting Federated Learning for EEG-based Brain-computer Interface System

Exploiting Federated Learning for EEG-based Brain-Computer I...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Mohammadnavid Ghader Bahar Farahani Zahra Rezvani Mahyar Shahsavari Mahmood Fazlali Department of Computer Science Shahid Beheshti University Tehran Iran Institute for Research in Fundamental Sciences (IPM) Tehran Iran Department of Computer Science Alzahra University Tehran Iran Donders Institute Radboud University Nijmegen The Netherlands Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfield United Kingdom

Motor imagery (MI) is a widely used technique in brain-computer interface (BCI) systems, which allows users to control external devices using their brain signals. Electroen-cephalogram (EEG) signals are commonly used to detect and classify MI tasks. However, the lack of annotated data hampers the performance of machine learning (ML) algorithms. Federated learning (FL) is a promising approach to address these challenges, as it allows models to be trained on decentralized datasets without exchanging data. This study proposes an FL approach for MI-EEG signal classification using a convolutional neural network (CNN) on the PhysioNet dataset containing EEG recordings of left and right-hand imagery movements. We evaluate the performance of the FL approach using two different aggregation methods, namely FedAvg and FedProx, and compare it to the centralized ML approach. Furthermore, we explore the effect of increasing the number of clients who participate in the learning process on the performance of the model. Our findings demonstrate that FL maintains consistent classification accuracy comparable to the centralized ML approach while reducing data leakage risks. Thus, FL shows great promise as an essential instrument for MI-EEG signal classification and BCI systems, enabling distributed training of a comprehensive model when privacy-sensitive data is dispersed across multiple clients.

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DSD-MatchingNet:Deformable sparse-to-dense feature matching for learning accurate correspondences

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Virtual Reality & Intelligent Hardware 2022年第5期4卷 432-443页

作者： Yicheng ZHAO Han ZHANG Ping LU Ping LI Enhua WU Bin SHENG Department of Computer Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China ZTE Corporation Shenzhen 518057China State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen 518057China Department of Computing The Hong Kong Polytechnic UniversityHong Kong 999077China School of Design The Hong Kong Polytechnic UniversityHong Kong 999077China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China Faculty of Science and Technology University of MacaoMacao 999078China

Background Exploring correspondences across multiview images is the basis of various computer vision ***,most existing methods have limited accuracy under challenging *** To learn more robust and accurate correspondences,we propose DSD-MatchingNet for local feature matching in this ***,we develop a deformable feature extraction module to obtain multilevel feature maps,which harvest contextual information from dynamic receptive *** dynamic receptive fields provided by the deformable convolution network ensure that our method obtains dense and robust ***,we utilize sparse-to-dense matching with symmetry of correspondence to implement accurate pixel-level matching,which enables our method to produce more accurate *** Experiments show that our proposed DSD-MatchingNet achieves a better performance on the image matching benchmark,as well as on the visual localization ***,our method achieved 91.3%mean matching accuracy on the HPatches dataset and 99.3%visual localization recalls on the Aachen Day-Night dataset.

关键词： Image matching Deformable convolution network Sparse-to-dense matching

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High-rate discretely-modulated continuous-variable quantum key distribution using quantum machine learning

arXiv

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arXiv 2023年

作者： Liao, Qin Liu, Jieyu Huang, Anqi Huang, Lei Fei, Zhuoying Fu, Xiquan College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

We propose a high-rate scheme for discretely-modulated continuous-variable quantum key distribution (DM CVQKD) using quantum machine learning technologies, which divides the whole CVQKD system into three parts, i.e., the initialization part that is used for training and estimating quantum classifier, the prediction part that is used for generating highly correlated raw keys, and the data-postprocessing part that generates the final secret key string shared by Alice and Bob. To this end, a low-complexity quantum k-nearest neighbor (QkNN) classifier is designed for predicting the lossy discretely-modulated coherent states (DMCSs) at Bob's side. The performance of the proposed QkNN-based CVQKD especially in terms of machine learning metrics and complexity is analyzed, and its theoretical security is proved by using semi-definite program (SDP) method. Numerical simulation shows that the secret key rate of our proposed scheme is explicitly superior to the existing DM CVQKD protocols, and it can be further enhanced with the increase of modulation variance. Copyright © 2023, The Authors. All rights reserved.

关键词： Nearest neighbor search

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Research on complex system fault prediction method for massive data 4

Research on complex system fault prediction method for massi...

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4th International Conference on Advanced Electronic Materials, computers and Software Engineering, AEMCSE 2021

作者： Niu, Wei Cheng, Juan Song, Xiaogang Xi'An Aeronautics Computing Technique Research Institute AVIC Xi'an China Xi'An Institute of Applied Optics Xi'an China Xi'an China Faculty of Computer Science and Engineering Xi'An University of Technology Xi an China

ISBN: (纸本)9781665415965

Fault prediction is a challenging problem of complex system. Furthermore, fault prediction plays a critical role in aircraft engines and the performance of their control systems. Using the robustness analysis of parametric uncertain systems, an improved algorithm is proposed for massive data. The algorithm takes the parametric uncertainties into consideration which can distinguish an actual fault from the model uncertainties. Application to an aircraft engine clearly illustrates the improved performance of the proposed method, the results show that the proposed method helps the system accommodate parametric uncertainties in the model and reduce false alarms and missed detection. © 2021 IEEE.

关键词： Aircraft engines

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Image Security Using Hyperchaos and Multidimensional Playfair Cipher 1

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1st International Conference on Security and Privacy, ICSP 2020

作者： Bhat, Krishnaraj Mahto, Dindayal Yadav, Dilip Kumar Azad, Chandrashekhar Department of Computer Science and Engineering NMAM Institute of Technology Nitte574110 India Department of Information Technology School of Computing SASTRA Deemed to be University Thanjavur613401 India Department of Computer Applications National Institute of Technology Jamshedpur Jamshedpur831014 India

ISBN: (数字)9789813367814

ISBN: (纸本)9789813367807

Image is utilized in diverse fields due to its vividness, intuitiveness, and copiousness. Existing methods for image encryption have limitations such as only support for 24-bit pixel images, greater cipher image sizes, low resistance against differential attacks, high correlation between cipher images obtained by using a 1-bit modification of the plain images, restriction on the sizes of the secret images, and overhead of sharing secret key among participants for every secret image. The proposed encryption method uses five-dimensional hyperchaos-based triple permutation, XORing, and multidimensional Playfair encryption that overcomes the limitations of the existing methods. The statistical analysis is performed in respect of the parameters: correlation coefficient, encryption quality, diffusion characteristics, histogram analysis, key and search space analysis, and computational complexity. The empirical results indicate that the proposed method has the potential to be an effective method for image encryption. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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GACL-Net:Hybrid Deep Learning Framework for Accurate Motor Imagery Classification in Stroke Rehabilitation

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computers, Materials & Continua 2025年第4期83卷 517-536页

作者： Chayut Bunterngchit Laith H.Baniata Mohammad H.Baniata Ashraf ALDabbas Mohannad A.Khair Thanaphon Chearanai Sangwoo Kang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing100190China School of Computing Gachon UniversitySeongnam13120Republic of Korea Computer Science Department Faculty of Information TechnologyThe World Islamic Sciences and Education UniversityAmman11947Jordan Intelligent Systems Department Faculty of Artificial IntelligenceAl-Balqa Applied UniversityAl-Salt19117Jordan IT Infrastructure Department Qatrana Cement CompanyAmman11821Jordan Division of Industrial and Logistics Engineering Technology Faculty of Engineering and TechnologyKing Mongkut’s University of Technology North BangkokRayong CampusRayong21120Thailand

Stroke is a leading cause of death and disability worldwide,significantly impairing motor and cognitive *** rehabilitation is often hindered by the heterogeneity of stroke lesions,variability in recovery patterns,and the complexity of electroencephalography(EEG)signals,which are often contaminated by *** classification of motor imagery(MI)tasks,involving the mental simulation of movements,is crucial for assessing rehabilitation strategies but is challenged by overlapping neural signatures and patient-specific *** address these challenges,this study introduces a graph-attentive convolutional long short-term memory(LSTM)network(GACL-Net),a novel hybrid deep learning model designed to improve MI classification accuracy and ***-Net incorporates multi-scale convolutional blocks for spatial feature extraction,attention fusion layers for adaptive feature prioritization,graph convolutional layers to model inter-channel dependencies,and bidi-rectional LSTM layers with attention to capture temporal *** on an open-source EEG dataset of 50 acute stroke patients performing left and right MI tasks,GACL-Net achieved 99.52%classification accuracy and 97.43%generalization accuracy under leave-one-subject-out cross-validation,outperforming existing state-of-the-art ***,its real-time processing capability,with prediction times of 33–56 ms on a T4 GPU,underscores its clinical potential for real-time neurofeedback and adaptive *** findings highlight the model’s potential for clinical applications in assessing rehabilitation effectiveness and optimizing therapy plans through precise MI classification.

关键词： Motor imagery EEG stroke rehabilitation deep learning brain-computer interface

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Multi-Grained Feature Pruning for Video-Based Human Pose Estimation

Multi-Grained Feature Pruning for Video-Based Human Pose Est...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Zhigang Fan, Shaojing Liu, Zhenguang Wu, Zheqi Wu, Sifan Jiao, Yingying College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatio-temporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset. © 2025 IEEE.

关键词： Large datasets

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A Joint Gradient and Loss Based Clustered Federated Learning Design

A Joint Gradient and Loss Based Clustered Federated Learning...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Licheng Lin Zhaohui Yang Yusen Wu Yuchen Liu Mingzhe Chen Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Frost Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, a novel clustered FL framework that enables distributed edge devices with non-IID data to independently form several clusters in a distributed manner and implement FL training within each cluster is proposed. In particular, our designed clustered FL algorithm must overcome two challenges associated with FL training. First, the server has limited FL training information (i.e., the parameter server can only obtain the FL model information of each device) and limited computational power for finding the differences among a large amount of devices. Second, each device does not have the data information of other devices for device clustering and can only use global FL model parameters received from the server and its data information to determine its cluster identity, which will increase the difficulty of device clustering. To overcome these two challenges, we propose a joint gradient and loss based distributed clustering method in which each device determines its cluster identity considering the gradient similarity and training loss. The proposed clustering method not only considers how a local FL model of one device contributes to each cluster but also the direction of gradient descent thus improving clustering speed. By delegating clustering decisions to edge devices, each device can fully leverage its private data information to determine its own cluster identity, thereby reducing clustering overhead and improving overall clustering performance. Simulation results demonstrate that our proposed clustered FL algorithm can reduce clustering iterations by up to 99% compared to the existing baseline.

关键词： Training Performance evaluation Federated learning Clustering methods Computational modeling Simulation Clustering algorithms Distributed databases Data models Servers

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