检索结果-内蒙古大学图书馆

International Symposium on High Performance Distributed computing

作者： M.K. Gardner Wu-chun Feng M. Fisk Research & Development in Advanced Network Technology (RADIANT) Computer & Computational Sciences Division Los Alamos National Laboratory Los Alamos NM USA Network Security Team Network Engineering Group Computing Communications & Networking Division Los Alamos National Laboratory Los Alamos NM USA

With the advent of computational grids, networking performance over the wide-area network (WAN) has become a critical component in the grid infrastructure. Unfortunately, many high-performance grid applications only use a small fraction of the available bandwidth because operating systems and their associated protocol stacks are still tuned for yesterdays WAN speeds. As a result, network gurus undertake the tedious process of manually, tuning system buffers to allow TCP flow control to scale to today's WAN grid environments. Although recent research has shown how to set the size of these system buffers automatically at connection set-up, the buffer sizes are only appropriate at the beginning of the connection's lifetime. To address these problems, we describe an automated and scalable technique called dynamic right-sizing. We implement this technique in user space (in particular for bulk-data transfer) so that end users do not have to modify the kernel to achieve a significant increase in throughput.

关键词： Wide area networks Computer networks Grid computing Bandwidth Operating systems Protocols Automatic control Control systems Kernel Throughput

来源：评论

学校读者我要写书评

暂无评论

Multisequence synthesis over an integral domain 1

引用

International Conference on Cryptography and Lattices, CaLC 2001

作者： Wang, Li-Ping Zhu, Yue-Fei State Key Laboratory of Information Security Graduate School University of Science and Technology of China Beijing100039 China Department of Network Engineering Information Engineering University Zhengzhou450002 China

ISBN: (数字)9783540446705

ISBN: (纸本)3540424881

We first give an extension of F[x]-lattice basis reduction algorithm to the polynomial ring R[x] where F is a field and R an arbitrary integral domain. So a new algorithm is presented for synthesizing minimum length linear recurrence (or minimal polynomials) for the given multiple sequences over R. Its computational complexity is O(N2) multiplications in R where N is the length of each sequence. A necessary and sufficient conditions for the uniqueness of minimal polynomials are given. The set of all minimal polynomials is also described. © Springer-Verlag Berlin Heidelberg 2001.

关键词： Polynomials

来源：评论

学校读者我要写书评

暂无评论

Online Learning from Mix-typed, Drifted, and Incomplete Streaming Features

引用

ACM Transactions on Knowledge Discovery from Data 1000年

作者： Shengda Zhuo Di Wu Yi He Shuqiang Huang Xindong Wu College of Cyber Security Jinan University Guangzhou Guangdong China College of Computer and Information Science Southwest University Chongqing China School of Computing Data Science and Physics William & Mary Williamsburg VA USA Key Laboratory of Knowledge Engineering with Big Data the Ministry of Education of China) School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui China

Online learning, where feature spaces can change over time, offers a flexible learning paradigm that has attracted considerable attention. However, it still faces three significant challenges. First, the heterogeneity of real-world data streams with mixed feature types presents challenges for traditional parametric modeling. Second, data stream distributions can shift over time, causing an abrupt and substantial decline in model performance. Additionally, the time and cost constraints make it infeasible to label every data instance in a supervised setting. To overcome these challenges, we propose a new algorithm Online Learning from Mix-typed, Drifted, and Incomplete Streaming Features (OL-MDISF), which aims to relax restrictions on both feature types, data distribution, and supervision information. Our approach involves utilizing copula models to create a comprehensive latent space, employing an adaptive sliding window for detecting drift points to ensure model stability, and establishing label proximity information based on geometric structural relationships. To demonstrate the model’s efficiency and effectiveness, we provide theoretical analysis and comprehensive experimental results.

关键词： Online Learning Mix-Typed Streaming Feature

来源：评论

学校读者我要写书评

暂无评论

Adaptive Alignment Contrastive Learning of Degradation Prediction for Blind Image Super-Resolution

引用

ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Xianhong Wen Sheng Ren Bin Hu Xiangyuan Zhu Tianyu Chen Kehua Guo School of Computer Science and Engineering Central South University China School of Computer and Electrical Engineering Hunan University of Arts and Science China School of Computer and Communication Engineering Changsha University of Science and Technology China Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police Academy China School of Computing Engineering and Mathematical Sciences La Trobe University Australia School of Computer Science and Engineering Central South University China and Furong Laboratory Central South University China

Blind super-resolution (BSR) is entering a new era focused on diverse and complex applications, where the trade-off between generalization and performance prevents models from performing as they should. Model performance decreases when trained on multiple degraded images due to the inter-class and intra-class imbalances in degradation prediction, which consists of degradation sampling and estimation. The inter-class imbalance in degradation estimation causes inaccurate estimates, leading to severe artifacts in images. The intra-class imbalance in degradation sampling causes a long-tail problem, leading to model collapse and satisfactory results only in specific applications. To tackle these challenges, we propose adaptive alignment contrastive learning (AACL), which includes adaptive degradation sampling (ADS) and \(\sigma\)-alignment. ADS utilizes nonlinear sampling by weighting the parameters of the degradation process for training uniformly degraded images, avoiding the long-tail problem. \(\sigma\)-alignment controls the standard deviation among positive samples, we identify a subset with small degraded distance, which aids contrastive learning in extracting representations more effectively. We extend AACL to several CNN-based and Transformer-based methods by coming up with a 6\(\times\)6 fair architecture with degradation representation fusion block (DRFB) and degradation representation fusion group (DRFG). DRFB and DRFG are designed for degradation representation fusion and image reconstruction, respectively. We evaluate on 6 types of degradation, and the improvement experiments on synthesized images show that our method balances performance and generalization, and is applicable to networks with different architectures. The comparison experiments show that our improved methods achieve promising results compared to SOTA methods. Code is available at: https://***/para999/AACL.

关键词： Blind super-resolution contrastive learning degradation prediction

来源：评论

学校读者我要写书评

暂无评论

GAHE: Geometry-Aware Embedding for Hyper-Relational Knowledge Graph Representation

引用

ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Zongsheng Cao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security SKLOIS) Institute of Information Engineering Chinese Academy of Sciences China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Security Department of Alibaba Group China School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University China

Knowledge graphs have proven highly effective for learning representations of entities and relations, with hyper-relational knowledge graphs (HKGs) gaining increased attention due to their enhanced representation capabilities. Each fact in an HKG consists of a main triple supplemented by attribute-value qualifiers that provide additional contextual information. Due to the complexity of hyper-relations, HKGs typically contain complex geometric structures, such as hierarchical, ring, and chain structures, often mixed together. However, previous work mainly embeds HKGs into Euclidean space, limiting their ability to capture these complex geometric structures simultaneously. To address this challenge, we propose a novel model called Geometry Aware Hyper-relational Embedding (GAHE). Specifically, GAHE adopts a multi-curvature geometry-aware approach by modeling HKGs in Euclidean space (zero curvature), hyperbolic space (negative curvature), and hyperspherical space (positive curvature) in a unified framework. In this way, it can integrate space-invariant and space-specific features to accurately capture the diverse structures in HKGs. In addition, GAHE introduces a module termed hyper-relational subspace learning, which allocates multiple sub-relations for each hyper-relation. It enables the exploitation of abundant latent semantic interactions and facilitates the exploration of fine-grained semantics between attribute-value pairs and hyper-relations across multiple subspaces. Furthermore, we provide theoretical guarantees that GAHE is fully expressive and capable of modeling a wide range of semantic patterns for hyper-relations. Empirical evaluations demonstrate that GAHE achieves state-of-the-art results on both hyper-relational and binary-relational benchmarks.

关键词： Knowledge Graph Hyper-relational Learning Multicurvature Spaces Geometry Aware

来源：评论

学校读者我要写书评

暂无评论

A Systematic Literature Review of Robust Federated Learning: Issues, Solutions, and Future Research Directions

引用

ACM computing Surveys 1000年

作者： Md Palash Uddin Yong Xiang Mahmudul Hasan Jun Bai Yao Zhao Longxiang Gao School of Information Technology Deakin University Burwood Australia Deakin University Burwood Australia Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Shandong Academy of Sciences Qilu University of Technology Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China

Federated Learning (FL) has emerged as a promising paradigm for training machine learning models across distributed devices while preserving their data privacy. However, the robustness of FL models against adversarial data and model attacks, noisy updates, and label-flipped data issues remain a critical concern. In this paper, we present a systematic literature review using the PRISMA framework to comprehensively analyze existing research on robust FL. Through a rigorous selection process using six key databases (ACM Digital Library, IEEE Xplore, ScienceDirect, Springer, Web of Science, and Scopus), we identify and categorize 244 studies into eight themes of ensuring robustness in FL: objective regularization, optimizer modification, differential privacy employment, additional dataset requirement and decentralization orchestration, manifold, client selection, new aggregation algorithms, and aggregation hyperparameter tuning. We synthesize the findings from these themes, highlighting the various approaches and their potential gaps proposed to enhance the robustness of FL models. Furthermore, we discuss future research directions, focusing on the potential of hybrid approaches, ensemble techniques, and adaptive mechanisms for addressing the challenges associated with robust FL. This review not only provides a comprehensive overview of the state-of-the-art in robust FL but also serves as a roadmap for researchers and practitioners seeking to advance the field and develop more robust and resilient FL systems.

关键词： Federated Learning Robustness Attack Countermeasure Defence.

来源：评论

学校读者我要写书评

暂无评论

IBAQ: Frequency-Domain Backdoor Attack Threatening Autonomous Driving via Quadratic Phase

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Jinghan Qiu Honglong Chen Junjian Li Yudong Gao Junwei Li Xingang Wang College of Control Science and Engineering China University of Petroleum China the Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology China

The rapid evolution of backdoor attacks has emerged as a significant threat to the security of autonomous driving models. An attacker injects a backdoor into the model by adding triggers to the samples, which can be activated to manipulate the model’s inference. Backdoor attacks can lead to severe consequences, such as misidentifying traffic signs during autonomous driving, posing a risk of causing traffic accidents. Recently, there has been a gradual evolution of frequency-domain backdoor attacks. However, since the change of both amplitude and its corresponding phase will significantly affect image appearance, most of the existing frequency-domain backdoor attacks change only the amplitude, which results in a suboptimal efficacy of the attack. In this work, we propose an attack called IBAQ, to solve this problem by blurring semantic information of the trigger image through the quadratic phase. Initially, we convert the trigger and benign sample to YCrCb space. Then, we perform the fast Fourier transform on the Y channel, blending the trigger image’s amplitude and quadratic phase linearly with the benign sample’s amplitude and phase. IBAQ achieves covert injection of trigger information within amplitude and phase, enhancing the attack effect. We validate the effectiveness and stealthiness of IBAQ through comprehensive experiments.

关键词： backdoor attack quadrature phase artificial intelligence security

来源：评论

学校读者我要写书评

暂无评论

End-to-End Explainable Fake News Detection Via Evidence-Claim Variational Causal Inference

引用

ACM Transactions on Information Systems 1000年

作者： Jinguang Wang Shengsheng Qian Jun Hu Wenxiang Dong Xudong Huang Richang Hong School of Public Security and Emergency Management Anhui University of Science and Technology China and State Key Laboratory of Digital Intelligent Technology for Unmanned Coal Mining Anhui University of Science and Technology China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China and School of Artificial Intelligence University of Chinese Academy of Sciences China School of Computing National University of Singapore Singapore Institute of Dataspace Hefei Comprehensive National Science Center China School of Computer Science and Information Engineering Hefei University of Technology China

Explainable Fake News Detection (EFND) is a new challenge that aims to verify news authenticity and provide clear explanations for its decisions. Traditional EFND methods often treat the tasks of classification and explanation as separate, ignoring the fact that explanation content can assist in enhancing fake news detection. To overcome this gap, we present a new solution: the End-to-end Explainable Fake News Detection network (\(EExpFND\)). Our model includes an evidence-claim variational causal inference component, which not only utilizes explanation content to improve fake news detection but also employs a variational approach to address the distributional bias between the ground truth explanation in the training set and the prediction explanation in the test set. Additionally, we incorporate a masked attention network to detail the nuanced relationships between evidence and claims. Our comprehensive tests across two public datasets show that \(EExpFND\) sets a new benchmark in performance. The code is available at https://***/r/EExpFND-F5C6.

关键词： Social media Explainable fake news detection Causal inference

来源：评论

学校读者我要写书评

暂无评论

Edge-Adaptive Dynamic Scalable Convolution for Efficient Remote Mobile Pathology Analysis

引用

ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Peng Xiao Dajiang Chen Zhen Qin Mingsheng Cao Ruidong Chen Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China China School of Computer Science & Engineering University of Electronic Science and Technology of China China

With the emergence of edge computing, there’s a growing need for advanced technologies capable of real-time, efficient processing of complex data on edge devices, particularly in mobile health systems handling pathological images. On edge computing devices, the lightweighting of models and reduction of computational requirements not only save resources but also increase inference speed. Although many lightweight models and methods have been proposed in recent years, they still face many common challenges. This paper introduces a novel convolution operation, Dynamic Scalable Convolution (DSC), which optimizes computational resources and accelerates inference on edge computing devices. DSC is shown to outperform traditional convolution methods in terms of parameter efficiency, computational speed, and overall performance, through comparative analyses in computer vision tasks like image classification and semantic segmentation. Experimental results demonstrate the significant potential of DSC in enhancing deep neural networks, particularly for edge computing applications in smart devices and remote healthcare, where it addresses the challenge of limited resources by reducing computational demands and improving inference speed. By integrating advanced convolution technology and edge computing applications, DSC offers a promising approach to support the rapidly developing mobile health field, especially in enhancing remote healthcare delivery through mobile multimedia communication.

关键词： Internet of things edge computing dynamic scalable convolution deep learning lightweight

来源：评论

学校读者我要写书评

暂无评论

S3 Agent: Unlocking the Power of VLLM for Zero-Shot Multi-modal Sarcasm Detection

引用

ACM Transactions on Multimedia computing, Communications, and Applications 1000年

作者： Peng Wang Yongheng Zhang Hao Fei Qiguang Chen Yukai Wang Jiasheng Si Wenpeng Lu Min Li Libo Qin School of Computer Science and Engineering Central South University China National University of Singapore Singapore Harbin Institute of Technology China Key Laboratory of Computing Power Network and Information Security Ministry of Education Qilu University of Technology (Shandong Academy of Sciences) China

Multi-modal sarcasm detection involves determining whether a given multi-modal input conveys sarcastic intent by analyzing the underlying sentiment. Recently, vision large language models have shown remarkable success on various of multi-modal tasks. Inspired by this, we systematically investigate the impact of vision large language models in zero-shot multi-modal sarcasm detection task. Furthermore, to capture different perspectives of sarcastic expressions, we propose a multi-view agent framework, S3 Agent, designed to enhance zero-shot multi-modal sarcasm detection by leveraging three critical perspectives: superficial expression, semantic information, and sentiment expression. Our experiments on the MMSD2.0 dataset, which involves six models and four prompting strategies, demonstrate that our approach achieves state-of-the-art performance. Our method achieves an average improvement of 13.2% in accuracy. Moreover, we evaluate our method on the text-only sarcasm detection task, where it also surpasses baseline approaches.

关键词： Natural language processing Multi-modal sarcasm detection Vision Large language model

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：