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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Chengjia Jiang Tao Wang Sien Li Jinyang Wang Shirui Wang Antonios Antoniou Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China The Key Laboratory of Cognitive Computing and Intelligent Information Processing Fujian Education Institutions Wuyi University Wuyishan China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Engineering European University Cyprus Nicosia Cyprus

We address the problem of learning new classes for semantic segmentation models from few examples, which is challenging because of the following two reasons. Firstly, it is difficult to learn from limited novel data to capture the underlying class distribution. Secondly, it is challenging to retain knowledge for existing classes and to avoid catastrophic forgetting. For learning from limited data, we propose a pseudo-labeling strategy to augment the few-shot training annotations in order to learn novel classes more effectively. Given only one or a few images labeled with the novel classes and a much larger set of unlabeled images, we transfer the knowledge from labeled images to unlabeled images with a coarse-to-fine pseudo-labeling approach in two steps. Specifically, we first match each labeled image to its nearest neighbors in the unlabeled image set at the scene level, in order to obtain images with a similar scene layout. This is followed by obtaining pseudo-labels within this neighborhood by applying classifiers learned on the few-shot annotations. In addition, we use knowledge distillation on both labeled and unlabeled data to retain knowledge on existing classes. We integrate the above steps into a single convolutional neural network with a unified learning objective. Extensive experiments on the Cityscapes and KITTI datasets validate the efficacy of the proposed approach in the self-driving domain. Code is available from https://***/ChasonJiang/FSCILSS.

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Traffic engineering in Dynamic Hybrid Segment Routing Networks

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Computers, Materials & Continua 2021年第7期68卷 655-670页

作者： Yingya Guo Kai Huang Cheng Hu Jiangyuan Yao Siyu Zhou College of Mathematics and Computer Science Fuzhou UniversityFuzhou350000China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou UniversityFuzhou350000China Key Laboratory of Spatial Data Mining&Information Sharing Ministry of EducationFuzhou350003China School of Information Science and Technology Guangdong University of Foreign StudiesGuangzhou510006China School of Computer Science and Cyberspace Security Hainan UniversityHaiKou570228China Tandon School of Engineering New York UniversityNew York10012USA Department of Computing Hong Kong Polytechnic UniversityHong Hom999077Hong Kong

The emergence of Segment Routing(SR)provides a novel routing paradigm that uses a routing technique called source packet *** SR architecture,the paths that the packets choose to route on are indicated at the ingress *** with shortest-path-based routing in traditional distributed routing protocols,SR can realize a flexible routing by implementing an arbitrary flow splitting at the ingress *** the advantages of SR,it may be difficult to update the existing IP network to a full SR deployed network,for economical and technical *** partial of the traditional IP network to the SR network,thus forming a hybrid SR network,is a preferable *** the traffic is dynamically changing in a daily time,in this paper,we propose a Weight Adjustment algorithm WASAR to optimize routing in a dynamic hybrid SR *** algorithm can be divided into three steps:firstly,representative Traffic Matrices(TMs)and the expected TM are obtained from the historical TMs through ultrascalable spectral clustering ***,given the network topology,the initial network weight setting and the expected TM,we can realize the link weight optimization and SR node deployment optimization through a Deep Reinforcement Learning(DRL)***,we optimize the flow splitting ratios of SR nodes in a centralized online manner under dynamic traffic demands,in order to improve the network *** the evaluation,we exploit historical TMs to test the performance of the obtained routing configuration in *** extensive experimental results validate that our proposed WASAR algorithm has superior performance in reducing Maximum Link Utilization(MLU)under the dynamic traffic.

关键词： Traffic engineering routing optimization segment routing deep reinforcement learning ultra-scalable spectral clustering

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Applying hybrid clustering in pulsar candidate sifting with multi-modality for FAST survey

arXiv

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arXiv 2023年

作者： You, Zi-Yi Pan, Yun-Rong Ma, Zhi Zhang, Li Xiao, Shuo Zhang, Dan-Dan Dang, Shi-Jun Zhao, Ru-Shuang Wang, Pei Dong, Ai-Jun Jiang, Jia-Tao Leng, Ji-Bing Li, Wei-An Li, Si-Yao School of Physics and Electronic Science Guizhou Normal University Guiyang550025 China College of Big Data and Information Engineering Guizhou University Guiyang550025 China CAS Key Laboratory of FAST National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550025 China School of Big data and Computer Science Guizhou Normal University Guiyang550025 China Guizhou Software Engineering Research Center Guiyang550000 China Key Laboratory of Information and Computing Guizhou Province Guizhou Normal University Guiyang550001 China

Pulsar search is always the basis of pulsar navigation, gravitational wave detection and other research topics. Currently, the volume of pulsar candidates collected by Five-hundred-meter Aperture Spherical radio Telescope (FAST) shows an explosive growth rate that has brought challenges for its pulsar candidate filtering System. Particularly, the multi-view heterogeneous data and class imbalance between true pulsars and non-pulsar candidates have negative effects on traditional single-modal supervised classification methods. In this study, a multi-modal and semi-supervised learning based pulsar candidate sifting algorithm is presented, which adopts a hybrid ensemble clustering scheme of density-based and partition-based methods combined with a feature-level fusion strategy for input data and a data partition strategy for parallelization. Experiments on both HTRU (The High Time Resolution Universe Survey) 2 and FAST actual observation data demonstrate that the proposed algorithm could excellently identify the pulsars: On HTRU2, the precision and recall rates of its parallel mode reach 0.981 and 0.988. On FAST data, those of its parallel mode reach 0.891 and 0.961, meanwhile, the running time also significantly decrease with the increment of parallel nodes within limits. So, we can get the conclusion that our algorithm could be a feasible idea for large scale pulsar candidate sifting of FAST drift scan observation. Copyright © 2023, The Authors. All rights reserved.

关键词： Clustering algorithms

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Towards Intelligent Transportation with Pedestrians and Vehicles In-the-Loop: A Surveillance Video-Assisted Federated Digital Twin Framework

arXiv

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arXiv 2025年

作者： Li, Xiaolong Wei, Jianhao Wang, Haidong Dong, Li Chen, Ruoyang Yi, Changyan Cai, Jun Niyato, Dusit Shen, Xuemin The Xiangjiang Laboratory Hunan Provincial General University Key Laboratory of IoT Intelligent Sensing and Distributed Collaborative Optimization Hunan University of Technology and Business Changsha410205 China The College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China The Department of Electrical and Computer Engineering University of Concordia Montreal Canada The College of Computing and Data Science Nanyang Technological University Singapore The Department of Electrical and Computer Engineering University of Waterloo Canada

In intelligent transportation systems (ITSs), incorporating pedestrians and vehicles in-the-loop is crucial for developing realistic and safe traffic management solutions. However, there is falls short of simulating complex real-world ITS scenarios, primarily due to the lack of a digital twin implementation framework for characterizing interactions between pedestrians and vehicles at different locations in different traffic environments. In this article, we propose a surveillance video assisted federated digital twin (SV-FDT) framework to empower ITSs with pedestrians and vehicles in-the-loop. Specifically, SVFDT builds comprehensive pedestrian-vehicle interaction models by leveraging multi-source traffic surveillance videos. Its architecture consists of three layers: (i) the end layer, which collects traffic surveillance videos from multiple sources;(ii) the edge layer, responsible for semantic segmentation-based visual understanding, twin agent-based interaction modeling, and local digital twin system (LDTS) creation in local regions;and (iii) the cloud layer, which integrates LDTSs across different regions to construct a global DT model in realtime. We analyze key design requirements and challenges and present core guidelines for SVFDT’s system implementation. A testbed evaluation demonstrates its effectiveness in optimizing traffic management. Comparisons with traditional terminal-server frameworks highlight SV-FDT’s advantages in mirroring delays, recognition accuracy, and subjective evaluation. Finally, we identify some open challenges and discuss future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

arXiv

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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

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Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection

arXiv

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arXiv 2024年

作者： Zhang, Yaning Li, Qiufu Yu, Zitong Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Guangdong Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Guangdong Shenzhen518060 China School of Computing and Information Technology Great Bay University Guangdong Dongguan523000 China

Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible to capturing local forgery patterns generated by various manipulation methods. Though transformer-based detectors exhibit improvements in modeling global dependencies, they are not good at exploring local forgery artifacts. Hybrid transformer-based networks are designed to capture local and global manipulated traces, but they tend to suffer from the attention collapse issue as the transformer block goes deeper. Besides, soft labels are rarely available. In this paper, we propose a distilled transformer network (DTN) to capture both rich local and global forgery traces and learn general and common representations for different forgery faces. Specifically, we design a mixture of expert (MoE) module to mine various robust forgery embeddings. Moreover, a locally-enhanced vision transformer (LEVT) module is proposed to learn locally-enhanced global representations. We design a lightweight multi-attention scaling (MAS) module to avoid attention collapse, which can be plugged and played in any transformer-based models with only a slight increase in computational costs. In addition, we propose a deepfake self-distillation (DSD) scheme to provide the model with abundant soft label information. Extensive experiments show that the proposed method surpasses the state of the arts on five deepfake datasets. Copyright © 2024, The Authors. All rights reserved.

关键词： Face recognition

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Federated Learning in STAR-RIS Aided SAGINs

IEEE Communications Standards Magazine

引用

IEEE Communications Standards Magazine 2025年

作者： Wu, Min Guo, Kefeng Dev, Kapal Hamadi, Hussam Al Singh, Keshav Khowaja, Sunder Ali Li, Xingwang Space Engineering University School of Space Information Beijing101407 China Munster Technological University CONNECT Centre Department of Computer Science Ireland College of Engineering and IT University of Dubai United Arab Emirates National Sun Yat-sen University Institute of Communications Engineering Kaohsiung Taiwan Technological University Dublin School of Computing Faculty of Computing Digital and Data Ireland Henan Polytechnic University School of Physics and Electronic Information Engineering Jiaozuo454000 China Jiaozuo Key Laboratory of Crow-Sensing Network Jiaozuo454003 China

Satellite-aerial-ground integrated network (SAGIN) is considered to be an advanced architecture that offers seamless coverage, flexible access, and efficient connectivity. However, several challenges emerge during the construction of SAGINs, which include complex propagation environments and limited spectrum resources. The aforementioned challenges can be addressed in a cost effective manner by integrating simultaneous transmission and reflecting reconfigurable intelligent surfaces (STAR-RISs). The STAR-RISs are capable of reflecting and transmitting the incident signals passively in order to serve users on both sides of the panel. Given the self-organizing nature of STAR-RIS-aided SAGINs, meticulous resource allocation and transmission strategy adaptation are imperative to accommodate changes in multiple communication node environments. Recently, federated learning has been envisioned as a pioneering distributed machine learning (ML) approach for managing the multi-objective optimization problem in STAR-RIS-aided SAGINs. This approach strives to balance the trade-offs between wireless communication performance and model efficiency. In this work, we present the advantages and potential directions for employing federated learning in STAR-RIS-aided SAGINs through multi-objectives optimization. Furthermore, to illustrate the practical application of federated learning in STAR-RIS-aided SAGINs, we present two case studies utilizing federated deep reinforcement learning, based on the non-orthogonal multiple access (NOMA) scheme. © 2017 IEEE.

关键词： Deep reinforcement learning

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Boosting Fine-Grained visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial Learning 39

Boosting Fine-Grained Visual Anomaly Detection with Coarse-K...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Fang, Qingqing Su, Qinliang Lv, Wenxi Xu, Wenchao Yu, Jianxing School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Artificial Intelligence Sun Yat-sen University Guangdong China Pazhou Lab Guangzhou 510330 China

ISBN: (纸本)157735897X

Many unsupervised visual anomaly detection methods train an auto-encoder to reconstruct normal samples and then leverage the reconstruction error map to detect and localize the anomalies. However, due to the powerful modeling and generalization ability of neural networks, some anomalies can also be well reconstructed, resulting in unsatisfactory detection and localization accuracy. In this paper, a small coarsely-labeled anomaly dataset is first collected. Then, a coarse-knowledge-aware adversarial learning method is developed to align the distribution of reconstructed features with that of normal features. The alignment can effectively suppress the auto-encoder’s reconstruction ability on anomalies and thus improve the detection accuracy. Considering that anomalies often only occupy very small areas in anomalous images, a patch-level adversarial learning strategy is further developed. Although no patch-level anomalous information is available, we rigorously prove that by simply viewing any patch features from anomalous images as anomalies, the proposed knowledge-aware method can also align the distribution of reconstructed patch features with the normal ones. Experimental results on four medical datasets and two industrial datasets demonstrate the effectiveness of our method in improving the detection and localization performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Adversarial machine learning

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LLM4RSR: Large Language Models as data Correctors for Robust Sequential Recommendation 39

LLM4RSR: Large Language Models as Data Correctors for Robust...

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Sun, Yatong Yang, Xiaochun Sun, Zhu Wang, Yan Wang, Bin Qu, Xinghua School of Computer Science and Engineering Northeastern University China School of Computing Macquarie University Australia Singapore University of Technology and Design Singapore National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China Bytedance(Seed) Singapore

ISBN: (纸本)157735897X

Sequential Recommenders (SRs) are trained to predict the next item as the target given its preceding items as the input, assuming every input-target pair is matched and is reliable for training. However, users can be induced by external distractions to click on items inconsistent with their true preferences, resulting in unreliable training instances with mismatched input-target pairs. To resist unreliable data, researchers attempt to develop Robust SRs (RSRs). However, our data analysis unveils that existing RSRs are data-driven. That is, for most instances formed by infrequently co-occurred items, existing RSRs are uncertain about their reliability. To fill this gap, we propose a generic framework - LLM4RSR (Large Language Models for Robust Sequential Recommendation) to semantically complement data-driven RSRs by correcting uncertain instances into reliable ones based on LLMs' semantic comprehension of items beyond co-occurrence. In this way, RSRs can be re-trained with the corrected data for better accuracy. This is a selective knowledge distillation procedure, where the LLM acts as a teacher guiding student RSRs via uncertain instances. To align LLMs with the data correction task and mitigate inherent hallucinations, we equip the LLM with profile, plan, and memory modules, which are automatically optimized via textual gradient descent, eliminating the need for human effort and expertise. Experiments on four real-world datasets spanning eight backbones verify the generality, effectiveness, and efficiency of LLM4RSR. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Recommender systems

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Style Content Decomposition-based data Augmentation for Domain Generalizable Medical Image Segmentation

arXiv

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arXiv 2025年

作者： Shen, Zhiqiang Cao, Peng Yang, Jinzhu Zaiane, Osmar R. Chen, Zhaolin School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada Monash Biomedical Imaging Monash University Australia Department of Data Science and AI Monash University Australia

Due to the domain shifts between training and testing medical images, learned segmentation models often experience significant performance degradation during deployment. In this paper, we first decompose an image into its style code and content map and reveal that domain shifts in medical images involve: style shifts (i.e., differences in image appearance) and content shifts (i.e., variations in anatomical structures), the latter of which has been largely overlooked. To this end, we propose StyCona, a style content decomposition-based data augmentation method that innovatively augments both image style and content within the rank-one space, for domain generalizable medical image segmentation. StyCona is a simple yet effective plug-and-play module that substantially improves model generalization without requiring additional training parameters or modifications to the segmentation model architecture. Experiments on cross-sequence, cross-center, and cross-modality medical image segmentation settings with increasingly severe domain shifts, demonstrate the effectiveness of StyCona and its superiority over state-of-the-arts. The code is available at https://***/Senyh/StyCona. Copyright © 2025, The Authors. All rights reserved.

关键词： Medical imaging

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