no code implementations • 16 Aug 2023 • Yuhao Yang, Jun Wu, Yue Wang, Guangjian Zhang, Rong Xiong
Traditional geometric registration based estimation methods only exploit the CAD model implicitly, which leads to their dependence on observation quality and deficiency to occlusion.
no code implementations • 14 Aug 2023 • Lianfa Li, Roxana Khalili, Frederick Lurmann, Nathan Pavlovic, Jun Wu, Yan Xu, Yisi Liu, Karl O'Sharkey, Beate Ritz, Luke Oman, Meredith Franklin, Theresa Bastain, Shohreh F. Farzan, Carrie Breton, Rima Habre
Atmospheric nitrogen oxides (NOx) primarily from fuel combustion have recognized acute and chronic health and environmental effects.
no code implementations • 9 Aug 2023 • Yuxin Qi, Xi Lin, Jun Wu
We propose NAP-GNN, a node-importance-grained privacy-preserving GNN algorithm with privacy guarantees based on adaptive differential privacy to safeguard node information.
no code implementations • 25 Jun 2023 • Jun Wu, Weijie Yuan, Lajos Hanzo
The real-time unmanned aerial vehicle (UAV) trajectory design of secure integrated sensing and communication (ISAC) is optimized.
no code implementations • 13 Jun 2023 • Haochen Mei, Gaolei Li, Jun Wu, Longfei Zheng
In this paper, we propose a novel privacy inference-empowered stealthy backdoor attack (PI-SBA) scheme for FL under non-IID scenarios.
1 code implementation • 10 Jun 2023 • Wenxuan Bao, Haohan Wang, Jun Wu, Jingrui He
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized.
no code implementations • 30 May 2023 • Jun Wu, Xuesong Ye
We evaluate our system on a public dataset and demonstrate the effectiveness of incorporating swarm features in fake news identification, achieving an f1-score and accuracy of over 97% by combining all three types of swarm features.
1 code implementation • 19 May 2023 • Xuesong Ye, Jun Wu, Chengjie Mou, Weinan Dai
Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment.
no code implementations • 24 Apr 2023 • Jun Wu, Xuesong Ye, Chengjie Mou, Weinan Dai
To address this issue, we propose FINEEHR, a system that utilizes two representation learning techniques, namely metric learning and fine-tuning, to refine clinical note embeddings, while leveraging the intrinsic correlations among different health statuses and note categories.
no code implementations • 6 Apr 2023 • Jun Wu, Xuesong Ye, Yanyuet Man
Second, we demonstrate that metric learning techniques can be applied in this context to refine raw embeddings and improve classification performance.
no code implementations • 17 Mar 2023 • Jun Wu, Xuesong Ye, Chengjie Mou
An essential topic in online social network security is how to accurately detect bot accounts and relieve their harmful impacts (e. g., misinformation, rumor, and spam) on genuine users.
1 code implementation • 15 Dec 2022 • Jun Wu, Jingrui He, Elizabeth Ainsworth
To bridge the gap, in this paper, we propose rigorous generalization bounds and algorithms for cross-network transfer learning from a source graph to a target graph.
no code implementations • 4 Nov 2022 • Jingchang Zhuge, Huiyuan Liang, Yiming Zhang, Shichao Li, Xinyu Yang, Jun Wu
Aircraft taxiing conflict is a threat to the safety of airport operations, mainly due to the human error in control command infor-mation.
1 code implementation • 12 Oct 2022 • Xuecheng Xu, Sha Lu, Jun Wu, Haojian Lu, Qiuguo Zhu, Yiyi Liao, Rong Xiong, Yue Wang
In addition, we derive sufficient conditions of feature extractors for the representation preserving the roto-translation invariance, making RING++ a framework applicable to generic multi-channel features.
1 code implementation • 5 Jul 2022 • Jun Wu, Jingrui He
Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task.
no code implementations • 1 Jul 2022 • Jun Wu, Lilu Liu, Yue Wang, Rong Xiong
We ascertain the Mid- Fusion approach is the best approach to restore the most precise 3D keypoints useful for object pose estimation.
no code implementations • 23 Jun 2022 • Honglei Zhang, Fangyuan Luo, Jun Wu, Xiangnan He, Yidong Li
Federated recommender system (FRS), which enables many local devices to train a shared model jointly without transmitting local raw data, has become a prevalent recommendation paradigm with privacy-preserving advantages.
no code implementations • 14 Jan 2022 • Jun Wu, Elizabeth A. Ainsworth, Sheng Wang, Kaiyu Guan, Jingrui He
Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth.
no code implementations • 25 Sep 2021 • Jun Wu, Lilu Liu, Yue Wang, Rong Xiong
Current monocular-based 6D object pose estimation methods generally achieve less competitive results than RGBD-based methods, mostly due to the lack of 3D information.
no code implementations • 2 Sep 2021 • Liping Yang, Xiaxia Niu, Jun Wu
Given the complex problem of feature engineering, the classic model RFM in the field of customer relationship management is improved, and an improved model is proposed to describe the characteristics of customer buying behaviour, which includes five indicators.
no code implementations • NeurIPS 2021 • Feiping Nie, Shenfei Pei, Rong Wang, Liang Zhang, Jun Wu, Qinglong Chang, Xuelong Li
We also developed a general model that unified LKM, KSUMS, and SC, and discussed the connection among them.
no code implementations • 15 Mar 2021 • Ge Ren, Jun Wu, Gaolei Li, Shenghong Li
The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users.
no code implementations • 26 Feb 2021 • Zun Li, Congyan Lang, Liqian Liang, Tao Wang, Songhe Feng, Jun Wu, Yidong Li
With the aim of matching a pair of instances from two different modalities, cross modality mapping has attracted growing attention in the computer vision community.
no code implementations • 1 Jan 2021 • Yao Zhou, Jun Wu, Jingrui He
In federated learning, data is distributed among local clients which collaboratively train a prediction model using secure aggregation.
no code implementations • 1 Jan 2021 • Jun Wu, Jingrui He
One major challenge associated with continuous transfer learning is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time.
no code implementations • 29 Dec 2020 • Qianqian Pan, Jun Wu, Xi Zheng, Jianhua Li, Shenghong Li, Athanasios V. Vasilakos
The ever-increasing data traffic, various delay-sensitive services, and the massive deployment of energy-limited Internet of Things (IoT) devices have brought huge challenges to the current communication networks, motivating academia and industry to move to the sixth-generation (6G) network.
no code implementations • 12 Dec 2020 • Yao Zhou, Jianpeng Xu, Jun Wu, Zeinab Taghavi Nasrabadi, Evren Korpeoglu, Kannan Achan, Jingrui He
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
no code implementations • 2 Nov 2020 • Pei Wang, Xiaoyu Zhou, Qingteng Zhao, Jun Wu, Qiuguo Zhu
Autonomous navigation has played an increasingly significant role in quadruped robot system.
Autonomous Navigation
Motion Planning
Robotics
no code implementations • 1 Nov 2020 • Anqiao Li, Zhicheng Wang, Jun Wu, Qiuguo Zhu
Bounding is one of the important gaits in quadrupedal locomotion for negotiating obstacles.
1 code implementation • 24 Oct 2020 • Weitong Hua, Zhongxiang Zhou, Jun Wu, Huang Huang, Yue Wang, Rong Xiong
Object 6D pose estimation is a fundamental task in many applications.
1 code implementation • 18 Sep 2020 • Yao Zhou, Jun Wu, Haixun Wang, Jingrui He
In this work, we show that this paradigm might inherit the adversarial vulnerability of the centralized neural network, i. e., it has deteriorated performance on adversarial examples when the model is deployed.
no code implementations • 10 Jun 2020 • Carlos Améndola, Philipp Dettling, Mathias Drton, Federica Onori, Jun Wu
We consider the problem of structure learning for linear causal models based on observational data.
no code implementations • 5 Jun 2020 • Jun Wu, Jingrui He
To bridge this gap, in this paper, we study a novel continuous transfer learning setting with a time evolving target domain.
2 code implementations • 28 Apr 2020 • Tim Kuipers, Eugeni L. Doubrovski, Jun Wu, Charlie C. L. Wang
In this paper we present a framework which supports multiple schemes to generate toolpaths with adaptive width, by employing a function to decide the number of beads and their widths.
Graphics Robotics Systems and Control Systems and Control J.6
no code implementations • 17 Apr 2020 • Xin Chen, Lingxi Xie, Jun Wu, Longhui Wei, Yuhui Xu, Qi Tian
We alleviate this issue by training a graph convolutional network to fit the performance of sampled sub-networks so that the impact of random errors becomes minimal.
2 code implementations • 23 Dec 2019 • Xin Chen, Lingxi Xie, Jun Wu, Qi Tian
With the rapid development of neural architecture search (NAS), researchers found powerful network architectures for a wide range of vision tasks.
no code implementations • 20 Dec 2019 • Shujie Han, Jun Wu, Erci Xu, Cheng He, Patrick P. C. Lee, Yi Qiang, Qixing Zheng, Tao Huang, Zixi Huang, Rui Li
To provide proactive fault tolerance for modern cloud data centers, extensive studies have proposed machine learning (ML) approaches to predict imminent disk failures for early remedy and evaluated their approaches directly on public datasets (e. g., Backblaze SMART logs).
no code implementations • 20 Nov 2019 • Fei Ding, Gang Yang, Jinlu Liu, Jun Wu, Dayong Ding, Jie Xv, Gangwei Cheng, Xirong Li
Unlike previous self-attention based methods that capture context information from one level, we reformulate the self-attention mechanism from the view of the high-order graph and propose a novel method, namely Hierarchical Attention Network (HANet), to address the problem of medical image segmentation.
1 code implementation • 5 Jun 2019 • Jun Wu, Jingrui He, Jiejun Xu
Graph data widely exist in many high-impact applications.
Ranked #1 on
Node Classification
on BlogCatalog
4 code implementations • ICCV 2019 • Xin Chen, Lingxi Xie, Jun Wu, Qi Tian
Recently, differentiable search methods have made major progress in reducing the computational costs of neural architecture search.
no code implementations • 29 Dec 2018 • Lianfa Li, Ying Fang, Jun Wu, Jinfeng Wang
To have a superior generalization, a deep learning neural network often involves a large size of training sample.
1 code implementation • 24 Apr 2018 • Jun Wu, Jingrui He, Yongming Liu
Then, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, in which context sampling uses VDRW and the label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs in different classes.
Social and Information Networks
no code implementations • 11 Mar 2017 • Shenglan Liu, Jun Wu, Lin Feng, Feilong Wang
This paper proposed a new explicit nonlinear dimensionality reduction using neural networks for image retrieval tasks.
no code implementations • 24 Sep 2016 • Shenglan Liu, Jun Wu, Lin Feng, Yang Liu, Hong Qiao, Wenbo Luo Muxin Sun, Wei Wang
Incompatibility of image descriptor and ranking is always neglected in image retrieval.
no code implementations • 24 Sep 2016 • Shenglan Liu, Muxin Sun, Lin Feng, Yang Liu, Jun Wu
Multi-feature fusion ranking can be utilized to improve the ranking list of query.
no code implementations • 15 Aug 2016 • Jun Wu, Niels Aage, Ruediger Westermann, Ole Sigmund
Our method builds upon and extends voxel-wise topology optimization.
Graphics