检索结果-内蒙古大学图书馆

Multi-layer network embedding on scc-based network with motif

Digital Communications and Networks 2024年第3期10卷 546-556页

作者： Lu Sun Xiaona Li Mingyue Zhang Liangtian Wan Yun Lin Xianpeng Wang Gang Xu Department of Communication Engineering Institute of Information Science TechnologyDalian Maritime UniversityDalian116026China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province DUT School of Software Technology&DUT-RU International School of Information Science and EngineeringDalian University of TechnologyDalian 116620China College of Information and Communication Engineering Harbin Engineering UniversityHarbin 150001China School of Information and Communication Engineering Hainan UniversityHaikou 570228China State Key Laboratory of Millimeter Waves School of Information Science and EngineeringSoutheast UniversityNanjing 210096China

Interconnection of all things challenges the traditional communication methods,and Semantic Communication and computing(SCC)will become new *** is a challenging task to accurately detect,extract,and represent semantic information in the research of SCC-based *** previous research,researchers usually use convolution to extract the feature information of a graph and perform the corresponding task of node ***,the content of semantic information is quite *** graph convolutional neural networks provide an effective solution for node classification tasks,due to their limitations in representing multiple relational patterns and not recognizing and analyzing higher-order local structures,the extracted feature information is subject to varying degrees of ***,this paper extends from a single-layer topology network to a multi-layer heterogeneous topology *** Bidirectional Encoder Representations from Transformers(BERT)training word vector is introduced to extract the semantic features in the network,and the existing graph neural network is improved by combining the higher-order local feature module of the network model representation network.A multi-layer network embedding algorithm on SCC-based networks with motifs is proposed to complete the task of end-to-end node *** verify the effectiveness of the algorithm on a real multi-layer heterogeneous network.

关键词： Semantic communication and computing Multi-layer network Graph neural network Motif

来源：评论

学校读者我要写书评

暂无评论

Non-uniform Compressive Sensing Imaging Based on Image Saliency

引用

Chinese Journal of Electronics 2023年第1期32卷 159-165页

作者： LI Hongliang DAI Feng ZHAO Qiang MA Yike CAO Juan ZHANG Yongdong University of Chinese Academy of Sciences Key Lab of Intelligent Information Processing of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences University of Science and Technology of China

For more effective image sampling, compressive sensing(CS) imaging methods based on image saliency have been proposed in recent years. Those methods assign higher measurement rates to salient regions,but lower measurement rate to non-salient regions to improve the performance of CS imaging. However, those methods are block-based, which are difficult to apply to actual CS sampling, as each photodiode should strictly correspond to a block of the scene. In our work, we propose a non-uniform CS imaging method based on image saliency, which assigns higher measurement density to salient regions and lower density to non-salient regions,where measurement density is the number of pixels measured in a unit size. As the dimension of the signal is reduced, the quality of reconstructed image will be improved theoretically, which is confirmed by our experiments. Since the scene is sampled as a whole, our method can be easily applied to actual CS sampling. To verify the feasibility of our approach, we design and implement a hardware sampling system, which can apply our non-uniform sampling method to obtain measurements and reconstruct the images. To our best knowledge, this is the first CS hardware sampling system based on image saliency.

关键词： Measurement units Density measurement Imaging Image sampling Size measurement Hardware Sensors

来源：评论

学校读者我要写书评

暂无评论

APWF: A Parallel Website Fingerprinting Attack with Attention Mechanism

引用

Computers, Materials & Continua 2025年第2期82卷 2027-2041页

作者： Dawei Xu Min Wang Yue Lv Moxuan Fu Yi Wu Jian Zhao College of Computer Science and Technology Changchun UniversityChangchun130022China School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing100081China Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled Changchun UniversityChangchun130022China China Academy of Information and Communications Technology Beijing100083China Key Laboratory of Mobile Application Innovation and Governance Technology Ministry of Industry and Information TechnologyBeijing100846China

Website fingerprinting (WF) attacks can reveal information about the websites users browse by de-anonymizing encrypted traffic. Traditional website fingerprinting attack models, focusing solely on a single spatial feature, are inefficient regarding training time. When confronted with the concept drift problem, they suffer from a sharp drop in attack accuracy within a short period due to their reliance on extensive, outdated training data. To address the above problems, this paper proposes a parallel website fingerprinting attack (APWF) that incorporates an attention mechanism, which consists of an attack model and a fine-tuning method. Among them, the APWF model innovatively adopts a parallel structure, fusing temporal features related to both the front and back of the fingerprint sequence, along with spatial features captured through channel attention enhancement, to enhance the accuracy of the attack. Meanwhile, the APWF method introduces isomorphic migration learning and adjusts the model by freezing the optimal model weights and fine-tuning the parameters so that only a small number of the target, samples are needed to adapt to web page changes. A series of experiments show that the attack model can achieve 83% accuracy with the help of only 10 samples per category, which is a 30% improvement over the traditional attack model. Compared to comparative modeling, APWF improves accuracy while reducing time costs. After further fine-tuning the freezing model, the method in this paper can maintain the accuracy at 92.4% in the scenario of 56 days between the training data and the target data, which is only 4% less loss compared to the instant attack, significantly improving the robustness and accuracy of the model in coping with conceptual drift.

关键词： Website fingerprinting attack transfer learning concept drift

来源：评论

学校读者我要写书评

暂无评论

Time-Varying Formation Tracking Control of Heterogeneous Multi-Agent Systems With Intermittent Communications and Directed Switching Networks

引用

IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 294-296页

作者： Yuhan Wang Zhuping Wang Hao Zhang Huaicheng Yan the Shanghai Research Institute for Intelligent Autonomous Systems Tongji University the National Key Laboratory of Autonomous Intelligent Unmanned Systems the Frontiers Science Center for Intelligent Autonomous System of Ministry of Education the College of Electronic and Information Engineering the Department of Control Science and Engineering Tongji University the Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education the School of Information Science and Engineering East China University of Science and Technology

Dear Editor,This letter is concerned with the problem of time-varying formation tracking for heterogeneous multi-agent systems(MASs) under directed switching networks. For this purpose, our first step is to present so... 详细信息

关键词： Switches Topology Observers Formation control Event detection Switched systems Multi-agent systems Control theory Autonomous systems Stability

来源：评论

学校读者我要写书评

暂无评论

Pseudo-Centroid Representation Learning for Cross-domain Few-shot Classification from Remote Sensing Imagery 2

Pseudo-Centroid Representation Learning for Cross-domain Few...

引用

2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Li, Can Xie, Jianlin Chen, He Zhuang, Yin Li, Jiahao Chen, Liang Beijing Institute of Technology National Key Laboratory of Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Cross-domain few-shot classification is gaining significant attention due to its potential to address challenges in remote sensing applications, such as large domain gaps and limited labeled data. Recent approaches have explored leveraging unlabeled target domain data for semi-supervised learning, in addition to source labeled data, to improve cross-domain training. However, these techniques often struggle with getting discriminative representation for distinguishing categories in novel target domains, particularly when facing new categories and limited training examples. To overcome these issues, we propose a novel cross-domain pseudo-centroid representation (CDPCR) framework, designed to generate more discriminative and adaptable class representations for improved cross-domain learning. The framework consists of a semi-supervised cross-domain structure that integrates both source labeled data and a portion of target unlabeled data to establish the relationship between domains. A consistency regularization branch is introduced to stabilize model outputs by aligning predictions on the target unlabeled data under input perturbations. Additionally, a pseudo-centroid contrastive representation learning module is incorporated to enhance intra-category feature consistency while improving the separability of inter-category representations, aiding the model in adapting to classification tasks across diverse domains. The effectiveness of the proposed CDPCR framework is validated through comprehensive experiments conducted across 8 cross-domain scenarios using remote sensing scene classification datasets. © 2024 IEEE.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

A Hippocampus Spatial Cognition Model based on Predictive Coding 43

A Hippocampus Spatial Cognition Model based on Predictive Co...

引用

43rd Chinese Control Conference, CCC 2024

作者： Yu, Naigong Lv, Zexuan Yan, Jinhan Beijing University of Technology Faculty of information Technology Beijing100021 China Beijing Key Laboratory of Computing Intelligence and Intelligent System Beijing100021 China

ISBN: (纸本)9789887581581

In current computation neuroscience, it is widely accepted that the mammalian entorhinal-hippocampus complex provides representations of spatial environment, exhibiting specific responses to particular locations and directions. Various computation models of spatial cell firing have been proposed to unravel the mysteries of spatial cognition representation in the brain. However, an increasing body of experimental evidence suggests that the brain operates more akin to a predictive system. Actively acquiring and utilizing limited environmental information, the brain constructs internal models during interaction with the external world, generating predictive representations of internal states following environmental changes. Building upon this premise, we propose a predictive coding theory-based entorhinal-hippocampus spatial cognition model. This model incorporates vestibular proprioceptive cues and border vector cells discharge responses to initially derive unimodal place cell discharge fields, followed by hippocampal place cells projecting to the entorhinal cortex via excitatory-inhibitory synaptic connections, ultimately generating periodic hexagonal grid cell discharge fields. Experimental results demonstrate that the neuronal discharge patterns obtained by this model align with physiological research findings, thereby providing new insights for further investigations into brain cognitive mechanisms. © 2024 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Mammals

来源：评论

学校读者我要写书评

暂无评论

Exploring key Point Analysis with Pairwise Generation and Graph Partitioning

Exploring Key Point Analysis with Pairwise Generation and Gr...

引用

2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Li, Xiao Jiang, Yong Huang, Shen Xie, Pengjun Cheng, Gong Huang, Fei State Key Laboratory for Novel Software Technology Nanjing University China Institute for Intelligent Computing Alibaba Group China

ISBN: (纸本)9798891761148

key Point Analysis (KPA), the summarization of multiple arguments into a concise collection of key points, continues to be a significant and unresolved issue within the field of argument mining. Existing models adapt a two-stage pipeline of clustering arguments or generating key points for argument clusters. This approach rely on semantic similarity instead of measuring the existence of shared key points among arguments. Additionally, it only models the intra-cluster relationship among arguments, disregarding the inter-cluster relationship between arguments that do not share key points. To address these limitations, we propose a novel approach for KPA with pairwise generation and graph partitioning. Our objective is to train a generative model that can simultaneously provide a score indicating the presence of shared key point between a pair of arguments and generate the shared key point. Subsequently, to map generated redundant key points to a concise set of key points, we proceed to construct an arguments graph by considering the arguments as vertices, the generated key points as edges, and the scores as edge weights. We then propose a graph partitioning algorithm to partition all arguments sharing the same key points to the same subgraph. Notably, our experimental findings demonstrate that our proposed model surpasses previous models when evaluated on both the ArgKP and QAM datasets. © 2024 Association for Computational Linguistics.

关键词： Graph theory

来源：评论

学校读者我要写书评

暂无评论

In-Context Compositional Generalization for Large Vision-Language Models

In-Context Compositional Generalization for Large Vision-Lan...

引用

2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Li, Chuanhao Jing, Chenchen Li, Zhen Zhai, Mingliang Wu, Yuwei Jia, Yunde Beijing Key Laboratory of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology China Guangdong Laboratory of Machine Perception and Intelligent Computing Shenzhen MSU-BIT University China School of Computer Science Zhejiang University Hangzhou China

ISBN: (纸本)9798891761643

Recent work has revealed that in-context learning for large language models exhibits compositional generalization capacity, which can be enhanced by selecting in-context demonstrations similar to test cases to provide contextual information. However, how to exhibit in-context compositional generalization (ICCG) of large vision-language models (LVLMs) is non-trival. Due to the inherent asymmetry between visual and linguistic modalities, ICCG in LVLMs faces an inevitable challenge-redundant information on the visual modality. The redundant information affects in-context learning from two aspects: (1) Similarity calculation may be dominated by redundant information, resulting in sub-optimal demonstration selection. (2) Redundant information in in-context demonstrations brings misleading contextual information to in-context learning. To alleviate these problems, we propose a demonstration selection method to achieve ICCG for LVLMs, by considering two key factors of demonstrations: content and structure, from a multimodal perspective. Specifically, we design a diversity-coverage-based matching score to select demonstrations with maximum coverage, and avoid selecting demonstrations with redundant information via their content redundancy and structural complexity. We build a GQA-ICCG dataset to simulate the ICCG setting, and conduct experiments on GQA-ICCG and the VQA v2 dataset. Experimental results demonstrate the effectiveness of our method. © 2024 Association for Computational Linguistics.

关键词： Visual languages

来源：评论

学校读者我要写书评

暂无评论

Efficient On-board Remote Sensing Scene Classification Using FPGA With Ternary Weight 2

Efficient On-board Remote Sensing Scene Classification Using...

引用

2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Qi, Guijie Qiao, Tingting Yu, Jingchi Xie, Yizhuang Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Remote Sensing Scene Classification (RSSC) is essential for applications such as environmental monitoring and catastrophe management, which often have stringent time constraints requiring real-time processing. On-board processing significantly enhances real-time performance but is challenged by limited computational resources and strict power requirements. To address these challenges, this study proposes an FPGA-based accelerator designed for Ternary Weight Networks (TWNs), which use ternary values {+1, 0, -1} for weights. By adopting TWNs, the multiplication operations in convolution are eliminated, resulting in a significant reduction in computing and power needs. Experimental results show that TWNs significantly reduce network parameters while retaining classification accuracy comparable to Full-precision Weight Networks (FPWNs). The FPGA-based accelerator achieves an energy-efficiency ratio of 434.11 GOP/W, outperforming most existing CNN accelerators, hence meeting the requirements for on-board RSSC. © 2024 IEEE.

关键词： Image thinning

来源：评论

学校读者我要写书评

暂无评论

Image Editing based on Diffusion Model for Remote Sensing Image Change Captioning 2

Image Editing based on Diffusion Model for Remote Sensing Im...

引用

2nd IEEE International Conference on Signal, information and Data Processing, ICSIDP 2024

作者： Cai, Miaoxin Chen, He Li, Can Gan, Shuyu Chen, Liang Zhuang, Yin Beijing Institute of Technology National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing China

ISBN: (纸本)9798331515669

Remote Sensing Image Change Captioning (RSICC) is a task that utilizes natural language to describe changes in remote sensing images of the same area captured at different times. However, the significant temporal intervals between multi-temporal images, the infrequency of observable changes, and the limitations on observation locations make it difficult to acquire and annotate a large and diverse dataset for analyzing change in multi-temporal images. The scarcity of labeled data hinders the training of RSICC models, leading to poor generalization. Compared to annotated registered bi-temporal image, single-temporal data is easier to obtain. Therefore, to tackle the issue of poor generalization of RSICC models under limited annotated sample conditions, a text-guided image pairs generation (TGIPG) method is proposed to create synthetic RSICC datasets from single-temporal data and randomly sampled text instructions via a diffusion-based controllable image editing model. This approach generates more valid pairs of multi-temporal samples to address the constraints of limited change information. Specifically, this method utilizes language instructions to introduce change information into the diffusion process, gradually transforming the pre-phase image into the post-phase image. Our experiments on the LEVIR-CC dataset show that synthetic data can significantly enhance the performance of any RSICC model, with a restricted number of training samples, by employing this plug-and-play TGIPG method. © 2024 IEEE.

关键词： Spatio-temporal data

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：