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JNMR: Joint Non-Linear Motion Regression for Video Frame Interpolation

IEEE Transactions on Image Processing 2023年 32卷 5283-5295页

作者： Liu, Meiqin Xu, Chenming Yao, Chao Lin, Chunyu Zhao, Yao Beijing Jiaotong University Institute of Information Science Beijing100044 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China

Video frame interpolation (VFI) aims to generate predictive frames by motion-warping from bidirectional references. Most examples of VFI utilize spatiotemporal semantic information to realize motion estimation and interpolation. However, due to variable acceleration, irregular movement trajectories, and camera movement in real-world cases, they can not be sufficient to deal with non-linear middle frame estimation. In this paper, we present a reformulation of the VFI as a joint non-linear motion regression (JNMR) strategy to model the complicated inter-frame motions. Specifically, the motion trajectory between the target frame and multiple reference frames is regressed by a temporal concatenation of multi-stage quadratic models. Then, a comprehensive joint distribution is constructed to connect all temporal motions. Moreover, to reserve more contextual details for joint regression, the feature learning network is devised to explore clarified feature expressions with dense skip-connection. Later, a coarse-to-fine synthesis enhancement module is utilized to learn visual dynamics at different resolutions with multi-scale textures. The experimental VFI results show the effectiveness and significant improvement of joint motion regression over the state-of-the-art methods. The code is available at https://***/ruhig6/JNMR. © 1992-2012 IEEE.

关键词： Interpolation

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A Location Recommendation Model Based on User Behavior and Sequence Influence 1

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8th International Conference on Internet on Things, ICIOT 2023, held as part of Services Conference Federation SCF 2023

作者： Mao, Weixuan Wen, Ruwang Cheng, Zhengxiang Mei, Zhuolin Wang, Haibin Wu, Bin Shi, Jiaoli School of Computer and Big Data Science Jiangxi Jiujiang China Jiujiang Key Laboratory of Network and Information Security Jiangxi Jiujiang China Institute of Information Security Jiujiang University Jiangxi Jiujiang China

ISBN: (数字)9783031517341

ISBN: (纸本)9783031517334

There are evident sequential patterns in human visits to different locations in daily life, which has led to the emergence of numerous sequence-based geographic location recommendation methods. However, previous research has mainly focused on considering the sequential patterns of check-ins as a whole, while overlooking the fact that users’ interactive behaviors at check-in locations are likely to influence their preferences for those locations, and subsequently impact their future check-in decisions. The problem of how to fully leverage a user’s overall location sequence and location interaction data to provide more personalized recommendations is still unresolved. To address this issue, we propose a geographic location recommendation model called UBSI (User Behavior and Sequence Influence) in this article. Our model is constructed based on check-in sequence, weight model, collaborative filtering, and n th-order additive Markov chain. First, UBSI collects users’ check-in sequence and interaction behavior data, generates interest weight using a weight model, integrates personalized data using collaborative filtering method, and finally applies the weighted n th-order additive Markov chain to mine sequential pattern of user check-ins for location recommendations. Finally, we conducted extensive experiments to demonstrate the effectiveness of our approach. The experimental results indicate that our UBSI solution can effectively improve the accuracy of recommendations. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Collaborative filtering

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An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs

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Science China(information Sciences) 2025年第5期68卷 170-193页

作者： Xianxin SONG Yuan FANG Feng WANG Zixiang REN Xianghao YU Ye ZHANG Fan LIU Jie XU Derrick Wing Kwan NG Rui ZHANG Shuguang CUI School of Science and Engineering (SSE) Shenzhen Future Network of Intelligence Institute (FNii-Shenzhen)and Guangdong Provincial Key Laboratory of Future Networks of Intelligence The Chinese University of Hong Kong Department of Electrical Engineering City University of Hong Kong School of Information Engineering Guangdong University of Technology Key Laboratory of Wireless-Optical Communications Chinese Academy of Sciences School of Information Science and Technology University of Science and Technology of China Computer School Beijing Information Science and Technology University National Mobile Communications Research Laboratory School of Information Science and Engineering Southeast University School of Electrical Engineering and Telecommunications University of New South Wales School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

This study presents an overview on intelligent reflecting surface(IRS)-enabled sensing and communication for the forthcoming sixth-generation(6G) wireless networks, in which IRSs are strategically deployed to proactively reconfigure wireless environments to improve both sensing and communication(S&C) performance. First, we exploit a single IRS to enable wireless sensing in the base station's(BS's) non-line-of-sight(NLoS) area. In particular, we present three IRS-enabled NLoS target sensing architectures with fully-passive, semi-passive, and active IRSs, respectively. We compare their pros and cons by analyzing the fundamental sensing performance limits for target detection and parameter estimation. Next, we consider a single IRS to facilitate integrated sensing and communication(ISAC), in which the transmit signals at the BS are used for achieving both S&C functionalities, aided by the IRS through reflective beamforming. We present joint transmit signal and receiver processing designs for realizing efficient ISAC, and jointly optimize the transmit beamforming at the BS and reflective beamforming at the IRS to balance the fundamental performance tradeoff between S&C. Furthermore, we discuss multi-IRS networked ISAC, by particularly focusing on multi-IRS-enabled multi-link ISAC, multi-region ISAC, and ISAC signal routing, respectively. Finally, we highlight various promising research topics in this area to motivate future work.

关键词： integrated sensing and communication (ISAC) intelligent reflecting surface (IRS) non-line-of-sight (NLoS) sensing sensing and communication tradeoff

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A Dirty Data Identification Method in Mechanical Equipment Condition Monitoring 10

A Dirty Data Identification Method in Mechanical Equipment C...

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10th IEEE Smart World Congress, SWC 2024

作者： Hao, Huijuan Zhao, Hongge Zhu, Sijian Feng, Yue Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China JinanChina

ISBN: (纸本)9798331520861

The rise of the Industrial Internet of Things (IIOT) has brought new opportunities for the mechanical equipment condition monitoring. The trend towards intelligent and automated production of mechanical equipment has become mainstream. However, in the actual operational environment, mechanical equipment is often mixed with a large amount of dirty data unrelated to equipment failures, which seriously affects equipment fault diagnosis and wastes maintenance resources. To improve the reliability of mechanical equipment health status analysis, this paper proposes a new method for dirty data identification suitable for mechanical equipment condition monitoring. First, utilizing the time-space position encoding (TSPE) to map positional relationships between sequences. Next, designing a time-dimension attention (TDA) mechanism to capture the correlation between multiple temporal attribute features and the temporal dependencies between preceding and subsequent data. Then, building a dirty data identification model using an improved Transformer network and Generative Adversarial network (GAN). Finally, employing an adaptive thresholding method to dynamically set thresholds, determining the degree of data anomalies by comparing anomaly scores with the threshold. The effectiveness of the proposed method are validated through one simulation experiment and one real-world example. The F1 Score of this method on the simulated missing data and drifting data is 0.9874 and 0.9838. On the drive-end and fan-end is 0. 9 7 0 0 and 0.9882 at Case Western Reserve University (CWRU). Compared to other dirty data identification methods in the field, this method has better dirty data identification capability. © 2024 IEEE.

关键词： Reliability analysis

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Text Guided Road Marking Point Cloud Completion 6

Text Guided Road Marking Point Cloud Completion

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： He, Xu Xu, Qihong Luo, Huan Fuzhou University College of Computer and Data Science Fuzhou China Fuzhou University Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou China

ISBN: (纸本)9798350388374

With the continuous development of intelligent transportation technologies such as autonomous driving and navigation, accurate perception of road markings becomes crucial. However, due to limitations in sensor perspectives and obstacles blocking the view, the scanned point cloud of road markings is often incomplete, potentially leading to erroneous decisions in intelligent transportation systems. Therefore, it becomes imperative to recover complete road markings from these incomplete point clouds. This paper proposes a text-guided road marking completion method, which integrates text information with point cloud data using attention mechanisms. By leveraging text information to guide the network in completing road marking point clouds, the proposed method aims to enhance the perception accuracy and completeness of road markings. Experimental validation on road marking datasets demonstrates the effectiveness and feasibility of the proposed approach. © 2024 IEEE.

关键词： Road and street markings

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An SDN Traffic Engineering Approach Based on Traffic Unsupervised Contrastive Representation and Reinforcement Learning 6

An SDN Traffic Engineering Approach Based on Traffic Unsuper...

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6th International Conference on Next Generation Data-Driven networks, NGDN 2024

作者： Yang, Ruiyu Tang, Qi Guo, Yingya College of Computer and Data Science Fuzhou University Fuzhou China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9798350388374

With the rapid development of Internet technology and the continuous explosive growth of network traffic, Traffic Engineering (TE), as a viable method for optimizing network traffic distribution and improving network performance, attracts widespread attention from both industry and academia. Software Defined networks (SDN), which decouples the data plane and the control plane, realizes a flexible routing and improves the TE performance. Existing TE approaches in SDN mainly utilize Reinforcement Learning (RL) methods to learn the mapping relationship between network traffic and routing policies. However, due to the continuous expansion of network size and dynamic changes in traffic, the enlargement of traffic state space hinders RL from converging to the optimal routing policy, leading to a decline in network performance. To address these issues, this paper presents a TE method based on unsupervised contrastive representation and RL. This method first shrinks the original traffic state space by efficiently extracting traffic features through Contrastive Learning (CL), aiding quick convergence of RL. It then uses RL to directly learn the mapping from traffic features to traffic splitting policies. Finally, through numerous experiments on real network traffic and topology, it demonstrates that the proposed TE method can effectively achieve load balancing of network traffic under complex and volatile dynamic traffic demands, thereby enhancing network performance. © 2024 IEEE.

关键词： Reinforcement learning

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An Approach to New Bearing Fault Diagnosis Utilizing Deep Transfer Learning 10

An Approach to New Bearing Fault Diagnosis Utilizing Deep Tr...

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10th IEEE Smart World Congress, SWC 2024

作者： Hao, HuiJuan Feng, Yue Zhao, HongGe Zhu, SiJian Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China Jinan China

ISBN: (纸本)9798331520861

Bearings, serving as crucial components in mechanical equipment, exert a direct influence on the overall performance of the system. Deep transfer learning has garnered significant attention in the realm of intelligent fault diagnosis for machines. Nevertheless, extant deep transfer learning algorithms encounter challenges when detecting newly emerging faults within the target domain. To address this limitation, this study introduces a novel bearing fault diagnosis approach for newly emerging faults, leveraging deep transfer learning principles. Initially, a multi-grained hierarchical feature extraction network is constructed to effectively capture intricate data characteristics. Subsequently, these extracted features are employed to train a fault classifier, thereby establishing a fault diagnosis network grounded in deep transfer learning. This network is capable of discriminating unlabeled new faults from labeled known faults with remarkable precision. Furthermore, an adaptive hierarchical clustering algorithm, informed by the elbow method, is incorporated to categorize the newly emerging faults. The clustered samples are then utilized to train the online sequential extreme learning machine (OS-ELM), facilitating the construction of a new fault diagnosis network tailored for diagnosing these faults in the target domain. The efficacy of the proposed methodology was rigorously verified through experiments executed on both the CWRU and XJTU-SY datasets. The outcomes conclusively demonstrated an accuracy of 9 8. 7 5 % on the CWRU dataset and 9 7. 9 2 % on the XJTU-SY dataset. This model portends immense potential in tackling fault diagnosis transfer scenarios characterized by the emergence of multiple novel faults within the target domain, thereby markedly broadening the applicability of deep transfer learning. © 2024 IEEE.

关键词： Deep Transfer Learning Detection of New Faults Multi-grained Feature Extraction OS-ELM

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Forward Legal Anonymous Group Pairing-Onion Routing for Mobile Opportunistic networks

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IEEE Transactions on Mobile Computing 2025年第7期24卷 6595-6612页

作者： Zhu, Xiuzhen Lin, Limei Huang, Yanze Wang, Xiaoding Hsieh, Sun-Yuan Wu, Jie Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701 Taiwan Temple University Department of Computer and Information Science 1925 N. 12th St PhiladelphiaPA19122 United States

Mobile Opportunistic networks (MONs) often experience frequent interruptions in end-to-end connections, which increases the likelihood of message loss during delivery and makes users more susceptible to various cyber attacks. However, most currently proposed anonymous routing protocols are primarily designed for networks with stable connections, making it challenging to protect user identities in MONs. To address these challenges, we propose FLAG-POR (Forward Legal Anonymous Group Pairing-Onion Routing), a novel anonymous routing protocol specifically tailored to enhance message delivery anonymity and security in MONs. Specifically, we abstract the mobile opportunistic network as a contact graph. By introducing the concept of "groups» into the pairing-onion routing protocol, which encrypts messages and relay nodes layer by layer, we develop a novel group-based pairing-onion routing protocol. This protocol ensures message confidentiality and relay node anonymity, while also improving message forwarding rates, as any node within a group can potentially act as a relay. To ensure message authenticity, we employ the efficient SM2 signing algorithm to generate signatures for the message source. Furthermore, by incorporating parameters such as the public key validity period and master key validity period into the group pairing-onion routing protocol, we achieve forward security in message delivery. We conduct a thorough theoretical analysis of the protocol's security and performance. The experimental results demonstrate that our FLAG-POR protocol outperforms baseline anonymous protocols in terms of delivery success rate, traceability rate, path anonymity, and node anonymity. Additionally, the FLAG-POR scheme effectively resists three potential threats to the routing system: collusion attack threat, node identification threat, and path identification threat, in any situation. © 2002-2012 IEEE.

关键词： Anonymity

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Stereoscopic Video Retargeting Based on Camera Motion Classification 48

Stereoscopic Video Retargeting Based on Camera Motion Classi...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Cai, Linghui Tang, Zhenhua Guangxi University School of Computer and Electronics Information Nanning China Guangxi Key Laboratory of Multimedia Communication and Network Technology Nanning China

ISBN: (纸本)9781728163277

The existing stereo video retargeting algorithms commonly use a same methodology to perform resizing without considering different videos with various features, leading to the low quality of reconstructed videos. To address this issue, we propose a stereo video retargeting method based on camera motion classification, which employs different retargeting strategies to rescale stereo videos. We also design an adaptive stereo video classification method which determines the types of camera motion according to the distribution of motion vectors extracted from the left view of stereo videos. Besides, we develop a motion saliency detection method to eliminate the jittering of moving objects during video resizing. Experimental results show that the qualities of retargeted videos produced by our method are significantly superior to those of existing methods. © 2023 IEEE.

关键词： carmera motion motion saliency detection stereo video classification Stereo video retargeting

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Graph Contrastive Learning with Learnable Graph Augmentation 48

Graph Contrastive Learning with Learnable Graph Augmentation

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Pu, Xinyan Zhang, Ke Shu, Huazhong Coatrieux, Jean Louis Kong, Youyong Southeast University Ministry of Education Key Laboratory of Computer Network and Information Integration Nanjing China Southeast University Jiangsu Provincial Joint International Research Laboratory of Medical Information Processing School of Computer Science and Engineering Nanjing China Universit de Rennes 1 Ltsi Rennes France Centre de Recherche en Information BioMdicale Sino-Franais Rennes France

ISBN: (纸本)9781728163277

Graph contrastive learning has gained popularity due to its success in self-supervised graph representation learning. Augmented views in contrastive learning greatly determine the quality of the learned representations. Handcrafted data augmentations in previous work require tedious trial-and- errors per dataset, which is time-consuming and resource-intensive. Here, we propose a Graph Contrastive learning framework with Learnable graph Augmentation called GraphCLA. Specifically, learnable graph augmentation trains augmented views by minimizing the mutual information (MI) between the input graphs and their augmented graphs. This paper designs a kernel contrastive loss function based on an end-to-end differentiable graph kernel to learn augmented views. In addition, this paper utilizes a min-max optimization strategy to learn challenging augmented graphs and to learn discriminative representations adversarially. Finally, we compared GraphCLA with state-of-the-art self-supervised learning baselines and experimentally validate the effectiveness of GraphCLA. © 2023 IEEE.

关键词： Contrastive learning data augmentation graph representation learning

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