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17th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022

作者： He, Pengfei Qi, Wenchao Liu, Xiaowei Liu, Linlin You, Dianlong Shen, Limin Chen, Zhen School of Information Science and Engineering Yanshan University Qinhuangdao China National Science Libraries Chinese Academy of Sciences Beijing China Key Laboratory for Computer Virtual Technology and System Integration of Hebei Province Yanshan University Qinhuangdao China

ISBN: (纸本)9789819923557

As more and more service providers encapsulate and publish their data and services on the internet in the form of Web APIs, the number of Web APIs is ever-increasing. For this reason, Web API recommendation is gaining momentum and has achieved high performance in accuracy. However, few studies paid attention to function recommendation. As an indispensable entity in the mashup-API ecosystem, function is not only the in-facto basis of API taxonomy, but also determines the compatibility and the internal construction pattern of mashup composition. Considering that users adopt multiple function invocations during a development cycle and the number of functions is also increasing, we propose the complementary function recommendation(CFR), a function-to-function problem. To solve the CFR, we regard each mashup as a transaction set for frequent pattern mining and propose an association rule-based complementary function recommendation(ARCFR) system, which provides function recommendation and corresponding probability explanation. Our experiments show that ARCFR can recommend complementary function effectively, and we give two applicable scenarios to demonstrate the practical value of our method in more aspects. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Association rules

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MvEchoSeg: One-shot In-context Learning for Multi-view Echocardiography Segmentation

MvEchoSeg: One-shot In-context Learning for Multi-view Echoc...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Guo, Yuting Duan, Ya Song, Wenfeng Li, Nannan Li, Aili Li, Shuai Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China University of Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Beijing Information Science and Technology University School of Computer Science Beijing100192 China Dalian Maritime University School of Information Science and Technology Dalian116000 China China-Japan Friendship Hospital Beijing100029 China

ISBN: (纸本)9798350386226

Echocardiography is the clinical standard for evaluation of cardiac morphology, and function, and providing hemodynamic parameters in patients with known or suspected heart disease. Due to the diversity and complexity of diagnostic tasks, comprehensive interpretation of echocardiography often requires multi-view imaging for integrated metric analysis. Deep learning methods have become the mainstream approach for echocardiography segmentation. However, achieving segmentation of multi-view echocardiography still requires a substantial amount of annotation. To remedy this, we propose a one-shot in-context learning network mvEchoSeg for multi-view echocardiography segmentation. This network requires only one annotated image for each view. Specifically, we propose a Task Prompt Identifier (TPI) module to identify the task type of the image and allocate the most precise task prompt for it, with minimal adaptation of CLIP and few-shot strategies. Additionally, we leverage a unified In-Context Model (ICM) capable of performing a diverse set of echocardiography segmentation tasks automatically. Using fine-tuning and low-rank adapters improved the performance of the pre-trained model, achieving significant results with minimal training cost. Furthermore, we collect a multi-view echocardiography dataset (MVECD) with 8 views to evaluate our method. The results show an improvement of more than 10% in the DICE score compared to the SOTA foundational medical image segmentation models. To our knowledge, this is the first exploration of a one-shot model for multi-view echocardiography segmentation. Our codes and models are available at https://***/stellating/mvEchoSeg. © 2024 IEEE.

关键词： Image segmentation

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FDCPNet: Feature Discrimination and Context Propagation Network for 3D Shape Representation

SSRN

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SSRN 2024年

作者： Shi, Weimin Xiong, Yuan Wang, Qianwen Jiang, Han Zhou, Zhong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Zhongguancun Laboratory Beijing China

3D shape representation using mesh data is essential in various applications, such as virtual reality and simulation technologies. Current methods extracting features from mesh edges or faces struggle with complex 3D models due to edge-based approaches missing global context and face-based methods overlooking variations in adjacent areas, which affects overall precision. To address these issues, we propose the Feature Discrimination and Context Propagation Network (FDCPNet), a novel approach that synergistically integrates local and global features in mesh datasets. FDCPNet is composed of two modules: 1) Feature Discrimination Module (FDM), which employs an attention mechanism to enhance the identification of key local features, 2) Context Propagation Module (CPM) enriches key local features by integrating global contextual information, facilitating a more detailed and comprehensive representation of crucial areas within the mesh model. Experiments on the Manifold40 dataset validate FDCPNet's effectiveness, showing a 1.9% improvement in classification accuracy over the baseline MeshNet, achieving 90.3%. Furthermore, even with reduced mesh face numbers and limited training data, FDCPNet achieves promising results, showing its robustness in variable complexity scenarios. © 2024, The Authors. All rights reserved.

关键词： Complex networks

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FEAT: Towards Fast Environment-Adaptive Task Offloading and Power Allocation in MEC 42

FEAT: Towards Fast Environment-Adaptive Task Offloading and ...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Ren, Tao Hu, Zheyuan He, Hang Niu, Jianwei Liu, Xuefeng Institute of Software Chinese Academy of Sciences Laboratory for Internet Software Technologies Beijing China Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Mobile edge computing (MEC) has been proposed to provide mobile devices with both satisfactory computing resources and latency. key issues in MEC include task offloading and power allocation (TOPA), for which deep reinforcement learning (DRL) is becoming a popular methodology. However, most DRL-based TOPA approaches are typically developed in a certain environment, lacking the adaptability to unseen environments. Motivated by this, this paper proposes a Fast Environment-Adaptive TOPA (FEAT) approach that could adapt to unseen environments with little fine-tuning. Specifically, we first split MEC states into the internal state and environmental state. Then, based on these two types of states, we develop two main components of FEAT - a group of internal state-dependent TOPA meta-policies and an environmental state-embedded steerer. Meta-policies learn TOPA skills within the internal state space (allowing reusing meta-policies in different environments), while the steerer learns to choose appropriate meta-policies according to embedded environmental states. When encountering an unseen environment with the same internal state space, FEAT only needs to fine-tune the steerer using the newly embedded environmental state with few internal state explorations. Extensive experimental results on simulation and testbed demonstrate that FEAT outperforms the state-of-the-art by more than 16.4% in terms of fine-tuning speeds. © 2023 IEEE.

关键词： Mobile edge computing

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Pedestrian attribute recognition as label-balanced multi-label learning 24

Pedestrian attribute recognition as label-balanced multi-lab...

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Proceedings of the 41st International Conference on Machine Learning

作者： Yibo Zhou Hai-Miao Hu Yirong Xiang Xiaokang Zhang Haotian Wu Beijing Key Laboratory of Digital Media School of Computer Science and Engineering and State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Beijing Key Laboratory of Digital Media School of Computer Science and Engineering and State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China and Hangzhou Innovation Institute of Beihang University Hangzhou China The University of Manchester UK

Rooting in the scarcity of most attributes, realistic pedestrian attribute datasets exhibit unduly skewed data distribution, from which two types of model failures are delivered: (1) label imbalance: model predictions lean greatly towards the side of majority labels; (2) semantics imbalance: model is easily overfitted on the under-represented attributes due to their insufficient semantic diversity. To render perfect label balancing, we propose a novel framework that successfully decouples label-balanced data re-sampling from the curse of attributes co-occurrence, i.e., we equalize the sampling prior of an attribute while not biasing that of the co-occurred others. To diversify the attributes semantics and mitigate the feature noise, we propose a Bayesian feature augmentation method to introduce true in-distribution novelty. Handling both imbalances jointly, our work achieves best accuracy on various popular benchmarks, and importantly, with minimal computational budget.

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Relational Clustering-Based Parallel Spaces Construction and Embedding for Dynamic Knowledge Graph

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IEEE Transactions on Big Data 2025年

作者： Liu, Yao Zhang, Yongfei Beihang University Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beijing100191 China Beihang University School of Computer Science and Engineering China Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beijing100191 China Pengcheng Laboratory Shenzhen518055 China

With the increasing amount of data in various domains, knowledge graphs (KGs) have become powerful tools for representing complex and heterogeneous information in a structured way, and for extracting valuable information from knowledge graphs through embedding techniques to support downstream tasks such as recommendation and Q&A systems. Knowledge graphs consist of triples that are continuously added as knowledge is updated. However, most existing embedding models are designed for static graphs, requiring the entire model to be retrained for each update, which is time-consuming. Existing global dynamic embedding models focus on exploiting the structural and relational information of the whole graph to achieve embedding quality, resulting in reduced dynamic efficiency. To address this problem, we propose a relational clustering-based parallel space model in which knowledge from different domains is embedded in different subspaces, allowing each subspace to focus on the data characteristics of a specific domain, thereby improving the quality of knowledge. Second, the new data only affects some subspaces but not the performance of other spaces, improving the model's adaptability to dynamics. Furthermore, we employ two incremental approaches based on the type of added data to improve the efficiency of dynamic embedding while ensuring that the added data preserves the characteristics of the parallel space. The experimental results show that the dynamic embedding efficiency of our model is improved by an average of 50.3% compared to the SOTA dynamic embedding model for the link prediction task. Particularly on FB15K, our model not only improves the efficiency by 41% but also increases the accuracy by 7.5%, demonstrating the accuracy and efficiency of our model. © 2015 IEEE.

关键词： Knowledge graph

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Enabling Communication-Efficient Federated Learning via Distributed Compressed Sensing 42

Enabling Communication-Efficient Federated Learning via Dist...

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42nd IEEE International Conference on computer Communications, INFOCOM 2023

作者： Guan, Yixuan Liu, Xuefeng Ren, Tao Niu, Jianwei Beihang University State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beijing China Zhongguancun Laboratory Beijing China Chinese Academy of Sciences Laboratory for Internet Software Technologies Institute of Software Beijing China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

ISBN: (纸本)9798350334142

Federated learning (FL) trains a shared global model by periodically aggregating gradients from local devices. Communication overhead becomes a principal bottleneck in FL since participating devices usually suffer from limited bandwidth and unreliable connections in uplink transmission. To address this problem, the gradient compression methods based on compressed sensing (CS) theory have been put forward recently. However, most existing CS-based works compress gradients independently, ignoring the gradient correlations between participants or adjacent communication rounds, which constrains the achievement of higher compression rates. In view of the above observation, we propose a novel gradient compression scheme named FedDCS, guided by distributed compressed sensing (DCS) theory. Following the design philosophy of separate encoding and joint decoding in DCS, FedDCS compresses gradients for participants in each round separately while reconstructing them at the central server jointly via fully exploiting correlated gradients from the previous round, which are known as side information (SI). Benefiting from this design, reconstruction performance is significantly improved with fewer decoding errors also iterations under the identical compression rate, and the total uploading bits to achieve model convergence are considerably reduced. Theoretical analysis and extensive experiments conducted on MNIST and Fashion-MNIST both verify the effectiveness of our approach. © 2023 IEEE.

关键词： Compressed sensing

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Deblurring Neural Radiance Fields with Event-driven Bundle Adjustment

arXiv

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arXiv 2024年

作者： Qi, Yunshan Zhu, Lin Zhao, Yifan Bao, Nan Li, Jia State Key Laboratory of Virtual Reality Technology and Systems SCSE Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

Neural Radiance Fields (NeRF) achieves impressive 3D representation learning and novel view synthesis results with high-quality multi-view images as input. However, motion blur in images often occurs in low-light and high-speed motion scenes, which significantly degrades the reconstruction quality of NeRF. Previous deblurring NeRF methods struggle to estimate pose and lighting changes during the exposure time, making them unable to accurately model the motion blur. The bio-inspired event camera measuring intensity changes with high temporal resolution makes up this information deficiency. In this paper, we propose Event-driven Bundle Adjustment for Deblurring Neural Radiance Fields (EBAD-NeRF) to jointly optimize the learnable poses and NeRF parameters by leveraging the hybrid event-RGB data. An intensity-change-metric event loss and a photo-metric blur loss are introduced to strengthen the explicit modeling of camera motion blur. Experiments on both synthetic and real-captured data demonstrate that EBAD-NeRF can obtain accurate camera trajectory during the exposure time and learn a sharper 3D representations compared to prior works. Copyright © 2024, The Authors. All rights reserved.

关键词： 3D modeling

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Object-level Hyperspectral Target Detection Based on Spectral-Spatial Features Integrated YOLOv4-Tiny Network 2022

Object-level Hyperspectral Target Detection Based on Spectra...

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4th International Conference on Image, Video and Signal Processing, IVSP 2022

作者： Nie, Jinyan Guo, Jian Xu, Qizhi State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China

ISBN: (纸本)9781450387415

The spectral resolution and spatial resolution of hyperspectral remote sensing images are mutually limited. To keep the same signal-to-noise ratio, the spatial resolution will decrease when the spectral resolution improves. The targets in low-resolution hyperspectral image, such as airplanes, cars and ships, appear as several pixels or sub-pixels. Current hyperspectral target detection methods mainly focus on pixel-level targets, which process spectral information and simple neighbourhood-pixel-related information in a pixel-by-pixel detection strategy. The contribution of spatial features is limited, and it takes a long time to train and detect pixel-by-pixel. Inspired by the deep learning-based object detection technologies for RGB images, we designed a hyperspectral image target detection method based on spectral-spatial features integrated YOLOv4-tiny network (SS-YOLONet). The 3D hyperspectral images were directly sent to the detection network, their spectral information and complex spatial features were extracted by channel attention module, spatial attention module and 3D convolution. Considering the small size of targets such as airplanes, we extracted two shallow features for small-scale objects. In the experiment, we used the pansharpened EO-1 hyperspectral images to verify the effectiveness of the proposed algorithm. © 2022 ACM.

关键词： Spectroscopy

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Gaze Pattern Genius: Gaze-Driven VR Interaction Using Unsupervised Domain Adaption

Gaze Pattern Genius: Gaze-Driven VR Interaction Using Unsupe...

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virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Kexin Wang Yang Gao Wenfeng Song Yuecheng Li Aimin Hao State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Computer School Beijing Information Science and Technology University Beijing China

ISBN: (数字)9798350374490

ISBN: (纸本)9798350374506

This research advocates shifting VR interaction to gaze-driven interaction, a more intuitive alternative to traditional controls like VR controllers or gestures. Our focus is on enhancing neural network recognition accuracy, especially with limited user-specific gaze data. We introduce a novel framework for capturing gaze gesture patterns and propose a template dataset concept to boost neural training. Our unsupervised domain adaptation model, blending template depth and sparse user data authenticity, consistently excels in recognizing gaze patterns across diverse users. Rigorous benchmarking against leading architectures consistently shows our method outperforming. Empirical user studies confirm: gaze-driven interactions not only elevate VR experiences but also redefine immersive VR control dynamics.

关键词： Training Adaptation models Three-dimensional displays Conferences Neural networks virtual reality User interfaces

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