检索结果-内蒙古大学图书馆

Optimal Feature Manipulation Attacks against Linear Regression

学校读者我要写书评

暂无评论

Optimal Feature Manipulation Attacks against Linear Regressi...

作者： Li, Fuwei Lai, Lifeng Cui, Shuguang Department of Electrical and Computer Engineering University of California DavisCA95616 United States School of Science and Engineering Shenzhen Research Institute of Big Data and Future Network of Intelligence Institute The Chinese University of Hong Kong Shenzhen518172 China

In this paper, we investigate how to manipulate the coefficients obtained via linear regression by adding carefully designed poisoning data points to the dataset or modifying the original data points. Given the energy budget, we first provide the closed-form solution of the optimal poisoning data point when our target is modifying one designated regression coefficient. We then extend the analysis to a more challenging scenario where the attacker aims to change one particular regression coefficient while making others to be changed as small as possible. For this scenario, we introduce a semidefinite relaxation method to design the best attack scheme. Finally, we study a more powerful adversary who can perform a rank-one modification on the feature matrix. We propose an alternating optimization method to find the optimal rank-one modification matrix. Numerical examples are provided to illustrate the analytical results obtained in this paper. © 1991-2012 IEEE.

关键词： Linear regression

EFFICIENTLY PARAMETERIZED NEURAL METRIPLECTIC SYSTEMS

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Gruber, Anthony Lee, Kookjin Lim, Haksoo Park, Noseong Trask, Nathaniel Center for Computing Research Sandia National Laboratories AlbuquerqueNM United States School of Computing and Augmented Intelligence Arizona State University TempeAZ United States Big Data Analytics Laboratory Yonsei University Seoul Korea Republic of Big Data Analytics Laboratory Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Engineering and Applied Science University of Pennsylvania PhiladelphiaPA United States

Metriplectic systems are learned from data in a way that scales quadratically in both the size of the state and the rank of the metriplectic data. Besides being provably energy conserving and entropy stable, the proposed approach comes with approximation results demonstrating its ability to accurately learn metriplectic dynamics from data as well as an error estimate indicating its potential for generalization to unseen timescales when approximation error is low. Examples are provided which illustrate performance in the presence of both full state information as well as when entropic variables are unknown, confirming that the proposed approach exhibits superior accuracy and scalability without compromising on model expressivity. Copyright © 2024, The Authors. All rights reserved.

关键词：

Rethinking Resource Management in Edge Learning: A Joint Pre-training and Fine-tuning Design Paradigm

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Lyu, Zhonghao Li, Yuchen Zhu, Guangxu Xu, Jie Poor, H. Vincent Cui, Shuguang Shenzhen China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Baidu Inc. Beijing China Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China The Department of Electrical and Computer Engineering Princeton University Princeton United States

In some applications, edge learning is experiencing a shift in focusing from conventional learning from scratch to new two-stage learning unifying pre-training and task-specific fine-tuning. This paper considers the problem of joint communication and computation resource management in a two-stage edge learning system. In this system, model pre-training is first conducted at an edge server via centralized learning on local pre-stored general data, and then task-specific fine-tuning is performed at edge devices based on the pre-trained model via federated edge learning. For the two-stage learning model, we first analyze the convergence behavior (in terms of the average squared gradient norm bound), which characterizes the impacts of various system parameters such as the number of learning rounds and batch sizes in the two stages on the convergence rate. Based on our analytical results, we then propose a joint communication and computation resource management design to minimize an average squared gradient norm bound, subject to constraints on the transmit power, overall system energy consumption, and training delay. The decision variables include the number of learning rounds, batch sizes, clock frequencies, and transmit power control for both pre-training and fine-tuning stages. Finally, numerical results are provided to evaluate the effectiveness of our proposed design. It is shown that the proposed joint resource management over the pre-training and fine-tuning stages well balances the system performance trade-off among the training accuracy, delay, and energy consumption. The proposed design is also shown to effectively leverage the inherent tradeoff between pre-training and fine-tuning, which arises from the differences in data distribution between pre-stored general data versus real-time task-specific data, thus efficiently optimizing overall system performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Learning systems

FDSR-INT: A Flexible On-Demand In-Band Telemetry Approach for Aerial Computing Networks

学校读者我要写书评

暂无评论

IEEE Internet of Things Journal 2025年

作者： Xu, Jinghan Xu, Xiaolong Zhao, Juan Gao, Honghao Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210023 China Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing The School of Computer Science Nanjing210023 China Jinling Institute of Technology School of Computer Science Nanjing211169 China Shanghai University School of Computer Engineering and Science Shanghai200444 China Gachon University College of Future Industry Gyeonggi-Do 461-701 Korea Republic of

In-band network telemetry (INT) is a new network measurement technique that provides real-time, fine-grained packet-level network measurements. However, standard INT lacks the flexibility to perform configurable on-demand network measurements. In this work, we propose a flexible on-demand network measurement mechanism (FDSR-INT) based on a dual bitmap by combining the programmability of segment routing based on IPv6 (SRv6) and the telemetry efficiency of the INT to achieve customizable network measurements. By designing the dual bitmap, the telemetry information of personalized probe nodes is supported, and the telemetry efficiency is improved. SRv6 is employed to direct measurement probe packets, ensuring coverage of a specified set of network targets. Its inherent programmability enables INT to conduct customized per-hop network measurements. The designed flexible INT field structure, which appends traceability information along with telemetry information, reduces the information traceability overhead in the control plane and improves the network compatibility of the mechanism. We solve the optimal probing path by solving a traveler problem in an auxiliary graph and design a greedy pathcutting algorithm to maximize the number of nodes for single packet probing while satisfying the packet length constraints to improve the success rate of the probing task. Finally, we implemented FDSR-INT using P4 and verified its performance experimentally in the constructed *** integrated simulation dynamic environment. FDSR-INT saves 30% of the data plane bandwidth and 54% of the data plane bandwidth on *** interfaces compared with SONM-SR-INT, etc. Furthermore, it has low control plane processing overhead and probe path transmission overhead. © 2014 IEEE.

关键词： Bandwidth

RSRP Measurement Based Channel Autocorrelation Estimation for IRS-Aided Wideband Communication

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Sun, He Zhu, Lipeng Mei, Weidong Zhang, Rui Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore School of Science and Engineering Shenzhen Research Institute of Big Data China The Chinese University of Hong Kong Guangdong Shenzhen518172 China National Key Laboratory of Wireless on Communications University of Electronic Science and Technology of China 611731 China

The passive and frequency-flat reflection of IRS, as well as the high-dimensional IRS-reflected channels, have posed significant challenges for efficient IRS channel estimation, especially in wideband communication systems with significant multi-path channel delay spread. To address these challenges, we propose a novel neural network (NN)-empowered framework for IRS channel autocorrelation matrix estimation in wideband orthogonal frequency division multiplexing (OFDM) systems. This framework relies only on the easily accessible reference signal received power (RSRP) measurements at users in existing wideband communication systems, without requiring additional pilot transmission. Based on the estimates of channel autocorrelation matrix, the passive reflection of IRS is optimized to maximize the average user received signal-to-noise ratio (SNR) over all subcarriers in the OFDM system. Numerical results verify that the proposed algorithm significantly outperforms existing power-measurement-based IRS reflection designs in wideband channels. Copyright © 2024, The Authors. All rights reserved.

关键词： Channel estimation

Sparse Aggregation-Based Channel Estimation For Massive Mimo Systems With Decentralized Baseband Processing

学校读者我要写书评

暂无评论

Sparse Aggregation-Based Channel Estimation For Massive Mimo...

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

作者： Yanqing Xu Enbin Song Qingjiang Shi Tsung-Hui Chang School of Science & Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China College of Mathematics Sichuan University Chengdu China School of Software Engineering Tongji University Shanghai China

To cope with the bottlenecks of the high computational complexity and excessive inter-connection communication in the conventional centralized baseband processing architecture, the decentralized baseband processing (DBP) architecture has been proposed, where the antennas are partitioned into multiple clusters, each connected to a local baseband unit (BBU). In this paper, we are interested in the distributed channel estimation (DCE) method under such DBP architecture, which is rarely studied in the literature. Our goal is to devise a DCE algorithm that can perform as well as the centralized scheme but with a small inter-connection communication cost. Specifically, based on the low-complexity diagonal minimum mean square error channel estimator, we propose an aggregate-then-estimate based DCE algorithm. In contrast to the existing DCE algorithm which requires iterative information exchanges among BBUs, our algorithm only requires one round-trip communication between the nodes. Experiment results are presented to demonstrate the efficacy of the proposed DCE algorithm.

关键词： Baseband Signal processing algorithms Channel estimation Computer architecture Mean square error methods Massive MIMO Signal processing

Movable-Antenna Position Optimization: A Graph-based Approach

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Mei, Weidong Wei, Xin Ning, Boyu Chen, Zhi Zhang, Rui National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Guangdong Shenzhen518172 China Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Fluid antennas (FAs) and movable antennas (MAs) have emerged as promising technologies in wireless communications, which offer the flexibility to improve channel conditions by adjusting transmit/receive antenna positions within a spatial region. In this letter, we focus on an MA-enhanced multiple-input single-output (MISO) communication system, aiming to optimize the positions of multiple transmit MAs to maximize the received signal power. Unlike the prior works on continuously searching for the optimal MA positions, we propose to sample the transmit region into discrete points, such that the continuous antenna position optimization problem is transformed to a discrete sampling point selection problem based on the point-wise channel information. However, such a point selection problem is combinatory and challenging to be optimally solved. To tackle this challenge, we ingeniously recast it as an equivalent fixed-hop shortest path problem in graph theory and propose a customized algorithm to solve it optimally in polynomial time. To further reduce the complexity, a linear-time sequential update algorithm is also proposed to obtain a high-quality suboptimal solution. Numerical results demonstrate that the proposed algorithms can yield considerable performance gains over the conventional fixed-position antennas with/without antenna selection. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph theory

Incomplete Multi-view Clustering Based on Joint Concept Decomposition and Anchor Graph Learning 11th

学校读者我要写书评

暂无评论

Incomplete Multi-view Clustering Based on Joint Concept Dec...

11th International Joint Conference on Rough Sets, IJCRS 2025

作者： Li, Zhuowen Chen, Hongmei Xiang, Biao Yuan, Zhong Luo, Chuan Li, Tianrui School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Engineering Research Center of Sustainable Urban Intelligent Transportation Ministry of Education Chengdu611756 China Manufacturing Industry Chains Collaboration and Information Support Technology Key Laboratory of Sichuan Province Southwest Jiaotong University Chengdu611756 China College of Computer Science Sichuan University Chengdu610065 China

ISBN: (纸本)9783031927430

The main objective of incomplete multi-view clustering is to effectively utilize the existing view information to fill in the missing data and to mine the complementary information and potential associations between multiple views to effectively group samples. The existing primary means for recovering missing information are classified into two parts: one uses matrix decomposition or low-rank constraints to fill in the missing views, and the other constructs multiple graph structures and uses graph regularization to recover the corresponding missing parts. However, the two methods are relatively independent and are not used jointly to improve the model performance. This paper proposes an incomplete multi-view clustering method with complete clustering metrics and complete anchor graphs (CFAG). Specifically, each view’s missing samples are recovered by a matrix of consistent clustering metrics and represented by an anchor graph. The recovered complete data performs anchor graph construction and anchor learning and utilizes the orthogonal variation principle to learn the consistency structure of multiple anchor graphs to mine the complementary information among multiple views. The clustering metrics are obtained based on the consistency structure, and the consistency of the clustering metrics is also considered to obtain the final clustering results. The effectiveness of the proposed method is verified on multiple datasets. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Graphic methods

GAUSSIAN KERNEL-BASED CROSS MODAL NETWORK FOR SPATIO-TEMPORAL VIDEO GROUNDING

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Xiong, Zeyu Liu, Daizong Zhou, Pan The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology China Wangxuan Institute of Computer Technology Peking University China

Spatial-Temporal Video Grounding (STVG) is a challenging task which aims to localize the spatio-temporal tube of the interested object semantically according to a natural language query. Most previous works not only severely rely on the anchor boxes extracted by Faster R-CNN, but also simply regard the video as a series of individual frames, thus lacking their temporal modeling. Instead, in this paper, we are the first to propose an anchor-free framework for STVG, called Gaussian Kernel-based Cross Modal Network (GKCMN). Specifically, we utilize the learned Gaussian Kernel-based heatmaps of each video frame to locate the query-related object. A mixed serial and parallel connection network is further developed to leverage both spatial and temporal relations among frames for better grounding. Experimental results on VidSTG dataset demonstrate the effectiveness of our proposed GKCMN. Copyright © 2022, The Authors. All rights reserved.

关键词： Gaussian distribution