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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zan Mao Xin Li Ze Luo Yingjuan Tang Dongmei Jiang Computer Network Information Center Chinese Academy of Sciences University of the Chinese Academy of Sciences PengCheng Laboratory National Key Laboratory of Advanced Vehicle Integration and Control Beijing Institute of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Currently, many change detection (CD) methods rely on supervised learning, which necessitates extensive manually annotated data, resulting in significant labor and time requirements. Recently, semi-supervised (SS) approaches have emerged in the CD community, which exploit large amounts of unlabeled data by utilizing consistency regularization. However, these methods do not consider the broader perturbation consistency to confer better generalization of the model. In this paper, we propose a novel SS CD framework with a comprehensive perturbation consistency called CPC, which extends perturbation consistency to the entire learning period. Specifically, our CPC combines the input, feature, and network perturbations for comprehensive perturb space. And, we design two distinct structures in practice, decoupled CPC and coupled CPC. Furthermore, we propose a change-aware input perturbation that introduces expensive annotation information to further expand the input perturb space. Extensive experiments conducted on the WHUCD, and GZ-CD datasets demonstrate that the proposal performs favorably against the state-of-the-art methods.

关键词： Codes Annotations Perturbation methods Supervised learning Signal processing Acoustics Proposals Labeling Speech processing Remote sensing

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Complementary Classifier Induced Partial Label Learning

arXiv

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arXiv 2023年

作者： Jia, Yuheng Si, Chongjie Zhang, Min-Ling Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering Nanjing210096 China MoE Key Lab of Artificial Intelligence AI Institute Shanghai200240 China

In partial label learning (PLL), each training sample is associated with a set of candidate labels, among which only one is valid. The core of PLL is to disambiguate the candidate labels to get the ground-truth one. In disambiguation, the existing works usually do not fully investigate the effectiveness of the non-candidate label set (a.k.a. complementary labels), which accurately indicates a set of labels that do not belong to a sample. In this paper, we use the non-candidate labels to induce a complementary classifier, which naturally forms an adversarial relationship against the traditional PLL classifier, to eliminate the false-positive labels in the candidate label set. Besides, we assume the feature space and the label space share the same local topological structure captured by a dynamic graph, and use it to assist disambiguation. Extensive experimental results validate the superiority of the proposed approach against state-of-the-art PLL methods on 4 controlled UCI data sets and 6 real-world data sets, and reveal the usefulness of complementary learning in PLL. The code has been released in the link https://***/Chongjie-Si/PL-CL. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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Position: From Correlation to Causation: Max-Pooling-Based Multi-Instance Learning Leads to More Robust Whole Slide Image Classification

arXiv

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arXiv 2024年

作者： Liu, Xin Zhang, Weijia Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle NSW2308 Australia

Although attention-based multi-instance learning (MIL) algorithms have achieved impressive performance on slide-level whole slide image (WSI) classification tasks, they are prone to mistakenly focusing on irrelevant patterns such as staining conditions and tissue morphology, leading to incorrect patch-level predictions and unreliable interpretability. In this paper, we analyze why attention-based methods tend to rely on spurious correlations in their predictions. Furthermore, we revisit max-pooling-based approaches and examine the reasons behind the underperformance of existing methods. We argue that well-trained max-pooling-based MIL models can make predictions based on causal factors and avoid relying on spurious correlations. Building on these insights, we propose a simple yet effective max-pooling-based MIL method (FocusMIL) that outperforms existing mainstream attention-based methods on two datasets. In this position paper, we advocate renewed attention to max-pooling-based methods to achieve more robust and interpretable predictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Multi-task learning

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EAT: Towards Long-Tailed Out-of-Distribution Detection

arXiv

引用

arXiv 2023年

作者： Wei, Tong Wang, Bo-Lin Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

Despite recent advancements in out-of-distribution (OOD) detection, most current studies assume a class-balanced in-distribution training dataset, which is rarely the case in real-world scenarios. This paper addresses the challenging task of long-tailed OOD detection, where the in-distribution data follows a long-tailed class distribution. The main difficulty lies in distinguishing OOD data from samples belonging to the tail classes, as the ability of a classifier to detect OOD instances is not strongly correlated with its accuracy on the in-distribution classes. To overcome this issue, we propose two simple ideas: (1) Expanding the in-distribution class space by introducing multiple abstention classes. This approach allows us to build a detector with clear decision boundaries by training on OOD data using virtual labels. (2) Augmenting the context-limited tail classes by overlaying images onto the context-rich OOD data. This technique encourages the model to pay more attention to the discriminative features of the tail classes. We provide a clue for separating in-distribution and OOD data by analyzing gradient noise. Through extensive experiments, we demonstrate that our method outperforms the current state-of-the-art on various benchmark datasets. Moreover, our method can be used as an add-on for existing long-tail learning approaches, significantly enhancing their OOD detection performance. Code is available at: https://***/Stomachache/Long-Tailed-OOD-Detection. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

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Enhanced Practical Byzantine Fault Tolerance for Service Function Chain Deployment:Advancing Big Data Intelligence in Control Systems

引用

computers, Materials & Continua 2025年第6期83卷 4393-4409页

作者： Peiying Zhang Yihong Yu Jing Liu ChongLv Lizhuang Tan Yulin Zhang Qingdao Institute of Software College of Computer Science and TechnologyChina University of Petroleum(East China)Qingdao266580China Shandong Key Laboratory of Intelligent Oil&Gas Industrial Software Qingdao266580China Library of Shanghai Lixin University of Accounting and Finance Shanghai201209China Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan250014China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer ScienceJinan250014China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance of MOE Minzu University of ChinaBeijing100081China Key Laboratory of Intelligent Game Yangtze River Delta Research Institute of NPUTaicang215400China Key Laboratory of Education Informatization for Nationalities(Yunnan Normal University) Ministry of EducationKunming650092China

As Internet ofThings(IoT)technologies continue to evolve at an unprecedented pace,intelligent big data control and information systems have become critical enablers for organizational digital transformation,facilitating data-driven decision making,fostering innovation ecosystems,and maintaining operational *** this study,we propose an advanced deployment algorithm for Service Function Chaining(SFC)that leverages an enhanced Practical Byzantine Fault Tolerance(PBFT)*** main goal is to tackle the issues of security and resource efficiency in SFC implementation across diverse network *** integrating blockchain technology and Deep Reinforcement Learning(DRL),our algorithm not only optimizes resource utilization and quality of service but also ensures robust security during SFC ***,the enhanced PBFT consensus mechanism(VRPBFT)significantly reduces consensus latency and improves Byzantine node detection through the introduction of a Verifiable Random Function(VRF)and a node reputation grading *** results demonstrate that compared to traditional PBFT,the proposed VRPBFT algorithm reduces consensus latency by approximately 30%and decreases the proportion of Byzantine nodes by 40%after 100 rounds of ***,the DRL-based SFC deployment algorithm(SDRL)exhibits rapid convergence during training,with improvements in long-term average revenue,request acceptance rate,and revenue/cost ratio of 17%,14.49%,and 20.35%,respectively,over existing ***,the CPU resource utilization of the SDRL algorithmreaches up to 42%,which is 27.96%higher than other *** findings indicate that the proposed algorithm substantially enhances resource utilization efficiency,service quality,and security in SFC deployment.

关键词： Big data intelligent transformation heterogeneous networks service function chain blockchain deep reinforcement learning trusted deployment

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D-Router: Decoupled Content Routers with Remote Content Store

D-Router: Decoupled Content Routers with Remote Content Stor...

引用

IEEE International Conference on Communications (ICC)

作者： Shuyi Chen Tian Pan Chunyang Wu Guohao Ruan Jiao Zhang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Named Data networking (NDN) enables efficient content distribution through in-network caching. However, the additional states of network intermediary nodes make NDN forwarding more burdensome, and the unpredictability of cache hits during forwarding leads to uncertain content retrieval latency. To overcome performance bottlenecks at the router's data plane and enhance network determinism, we propose the decoupled content router with remote content store (D-Router). This novel architecture decouples the local content store (CS) from routers and introduces the remote CS device for pooling important content. When Interest packets arrive at a router whose CS is overloaded, we ensure determinism by forwarding them to the remote CS for processing if the requested content is cached there, preventing blocking before the local CS of routers and potential random cache hits along the forwarding path. The dual-path bypass forwarding is supported through the design of routers and a dual-path routing protocol. D-Router is compatible with traditional NDN. Experiments show notable enhancements in data plane performance, including a 30% reduction in round-trip time (RTT), a 25% increase in throughput, improved determinism, and reduced network jitter. Additionally, the decoupling of CS makes it easier for network administrators to deploy network upgrades.

关键词： Performance evaluation Jitter Throughput Routing protocols

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Gaia: Ground Station-Centric Mobility Management for LEO Satellite networks

Gaia: Ground Station-Centric Mobility Management for LEO Sat...

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IEEE International Conference on Communications (ICC)

作者： Xiaxin Zhou Tian Pan Zhaokun Yang Sirui Su Jun Hu Guohao Ruan Junkai Huang Tao Huang Yunjie Liu Beijing University of Posts and Telecommunications Beijing China Purple Mountain Laboratories Nanjing China Key Laboratory of Computer Network and Information Integration Ministry of Education Southeast University China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

For mega-scale low Earth orbit (LEO) satellite constellations, the relative position changes between satellites and ground terminals pose challenges for end-to-end TCP session maintenance, which serves as the substrate for many Internet services. Mobile IP resolves the session maintenance issue by in-troducing a binding mechanism between the care-of address and home address; however, this also leads to inefficient triangular routing. Our recently proposed LISP-LEO, through partition-satellite mapping, routes traffic to the service satellite above the destination terminal's partition, addressing the triangular routing problem. However, due to the corner case of partition-satellite mapping, LISP-LEO introduces the issue of the last-hop route selection, as well as the associated per-terminal registration states on the satellite, making the solution non-scalable. In this work, we propose Gaia, a ground station-centric mobility management scheme for LEO satellite networks. Gaia maintains a precise mapping of each ground terminal's IP and its geographical location. When receiving traffic from a source terminal, the access satellite can, based on the geographical location of the destination terminal carried by the traffic, directly locate the satellite above the destination terminal and tunnel the traffic to it. In addition, to reduce the satellite's burden, we add a DNS-like querying mechanism by offloading the mapping of terminal IPs and geographical locations to the ground station. The evaluation shows that Gaia outperforms Mobile IP and LISP-LEO in both end-to-end latency and on-board resource consumption.

关键词： Satellite constellations Satellites Web and internet services Low earth orbit satellites Production Routing Maintenance

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DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing 29

DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Pa...

引用

29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Bin Gao, Miao Fan, Yunlong Sataer, Yikemaiti Gao, Zhiqiang Gui, Yaocheng School of Computer Science and Engineering Southeast University Nanjing210096 China Key Laboratory of Computer Network and Information Integration Ministry of Education Nanjing210096 China School and Institute of Modern Posts Nanjing University of Posts and Telecommunications Nanjing210003 China

A recent success in semantic dependency parsing shows that graph neural networks can make significant accuracy improvements, owing to its powerful ability in learning expressive graph representations. However, this work learns graph representations based on a static graph constructed by an existing parser, suffering from two drawbacks: (1) the static graph might be error-prone (e.g., noisy or incomplete), and (2) graph construction stage and graph representation learning stage are disjoint, the errors introduced in the graph construction stage cannot be corrected and might be accumulated to later stages. To address these two drawbacks, we propose a dynamic graph learning framework and apply it to semantic dependency parsing, for jointly learning graph structure and graph representations. Experimental results show that our parser outperforms the previous parsers on the SemEval-2015 Task 18 dataset in three languages (English, Chinese, and Czech). © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Graph neural networks

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A VLAN-based network Testbed for Lightweight Satellite Constellation Emulation

A VLAN-based Network Testbed for Lightweight Satellite Const...

引用

IEEE International Conference on Communications (ICC)

作者： Yan Zheng Tian Pan Yan Zhang Jiang Liu Tao Huang Yunjie Liu Purple Mountain Laboratories Nanjing China Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Considering the high costs of satellite manufacturing and launch, as well as the complexity of in-orbit debugging, pre-launch emulation on the ground will significantly reduce the development costs of low Earth orbit (LEO) satellite networks. LEO satellite network emulation faces challenges in emulating mega-scale constellations in a lightweight and scalable manner, as well as efficiently handling the frequent link on/off switching for both inter-satellite networks and terrestrial access networks. Existing simulation/emulation tools, such as NS-3, Mininet, QualNet, fall short in addressing these issues effectively. In this work, we propose a lightweight satellite emulation testbed based on Docker containers and the VLAN protocol. In the data plane, our testbed uses Docker containers to emulate satellites/terminals, and uses VETH-pairs and bridges to emulate inter-satellite networks and terrestrial access networks. Furthermore, these virtual network elements can horizontally scale across multiple servers for mega-scale constellation emulation. In the control plane, the real-time constellation topology changes are efficiently emulated through the configuration of VLAN segmentation according to satellite movement patterns. Evaluation shows the testbed's low resource occupancy and high efficiency, with 100 nodes consuming only 1000MB memory and 100 links switching in less than 2s.

关键词： Satellite constellations Satellites Costs Emulation Low earth orbit satellites Switches Containers

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Intelligent Optimization Method for Mobile NOMA network using IMBO

Intelligent Optimization Method for Mobile NOMA Network usin...

引用

IEEE International Conference on Electronic information and Communication Technology (ICEICT)

作者： Xingyue Fu Lingwei Xu Zhihe Gao Zhe Chen Yufang Li Shubo Cao Qingdao University of Science & Technology Qingdao China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing China

Non-orthogonal multiple access (NOMA) technology can significantly increase access volume and spectrum efficiency. The introduction of NOMA technology into the mobile communication system can significantly enhance communication performance. The power allocation technology can greatly improve the performance of NOMA systems and has become a research hotspot. However, many research schemes currently have many problems, such as poor system performance and inability to balance between user fairness and service quality. Therefore, this article studies the intelligent optimization for mobile NOMA network using improved Monarch Butterfly Optimization Algorithm(IMBO). First, an $\boldsymbol{N}-\mathbf{Nakagami}$ channel-based mobile cooperative NOMA multi-user network is established. Then, considering the non-ideal communication condition, namely imperfect channel state information (ICSI), the exact expressions of outage probability (OP) for the mobile cooperative NOMA multi-user communication system are derived, and the relation between user power allocation coefficient and the performance of OP is investigated. Finally, an optimization algorithm of power allocation based on the IMBO is put forward, which can get the optimal power allocation coefficient. Simulation results indicate that, compared to the monarch butterfly optimization algorithm(MBO), the artificial fish swarm algorithm(AFSA), and the firefly algorithm(FA), the IMBO is at least 30% more efficient, thereby enhancing the performance of the communication system.

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