检索结果-内蒙古大学图书馆

Interactive Web API Recommendation for Mashup Development based on Light Neural Graph Collab.rative Filtering

学校读者我要写书评

暂无评论

Interactive Web API Recommendation for Mashup Development ba...

International Conference on Computer Supported Cooperative Work in Design

作者： Jiayan Xiang Wanjun Chen Yang Wang Bowen Liang Zihao Liu Guosheng Kang School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology HNUST Xiangtan China

With the development of Mashup technique, the number of Web APIs released on the Web continues to grow year by year. However, it is a challenging issue to find and select the desirable Web APIs among the large amount of Web APIs. Consequently, interactive Web API recommendation is used to alleviate the difficulty of service selection, when users or developers try to invoke Web APIs for solving their business requirements or software development requirements. Currently, there are several collab.rative filtering based approaches proposed for Web API recommendation, while their recommendation performance is limited on both optimality and scalab.lity. This paper proposes a light neural graph collab.rative filtering based Web API recommendation approach, named LNGCF. Specifically, LNGCF learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted summation of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train. A set of experiments are conducted on a real-world dataset. Experimental results demonstrate the substantial improvements on both optimality and scalab.lity over the baselines.

关键词：

Suppress Content Shift: Better Diffusion Features via Off-the-Shelf Generation Techniques 38

学校读者我要写书评

暂无评论

Suppress Content Shift: Better Diffusion Features via Off-th...

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Yang, Zhiyong Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Computer Science and Tech. University of Chinese Academy of Sciences China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are powerful generative models, and this capability can also be applied to discrimination. The inner activations of a pre-trained diffusion model can serve as features for discriminative tasks, namely, diffusion feature. We discover that diffusion feature has been hindered by a hidden yet universal phenomenon that we call content shift. To be specific, there are content differences between features and the input image, such as the exact shape of a certain object. We locate the cause of content shift as one inherent characteristic of diffusion models, which suggests the broad existence of this phenomenon in diffusion feature. Further empirical study also indicates that its negative impact is not negligible even when content shift is not visually perceivable. Hence, we propose to suppress content shift to enhance the overall quality of diffusion features. Specifically, content shift is related to the information drift during the process of recovering an image from the noisy input, pointing out the possibility of turning off-the-shelf generation techniques into tools for content shift suppression. We further propose a practical guideline named GATE to efficiently evaluate the potential benefit of a technique and provide an implementation of our methodology. Despite the simplicity, the proposed approach has achieved superior results on various tasks and datasets, validating its potential as a generic booster for diffusion features. Our code is availab.e at this url. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features 38

学校读者我要写书评

暂无评论

Not All Diffusion Model Activations Have Been Evaluated as D...

38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Meng, Benyuan Xu, Qianqian Wang, Zitai Cao, Xiaochun Huang, Qingming Institute of Information Engineering CAS China School of Cyber Security University of Chinese Academy of Sciences China Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China Peng Cheng Laboratory China School of Cyber Science and Tech. Shenzhen Campus of Sun Yat-sen University China School of Computer Science and Tech. University of Chinese Academy of Sciences China Key Laboratory of Big Data Mining and Knowledge Management CAS China

Diffusion models are initially designed for image generation. Recent research shows that the internal signals within their backbones, named activations, can also serve as dense features for various discriminative tasks such as semantic segmentation. Given numerous activations, selecting a small yet effective subset poses a fundamental problem. To this end, the early study of this field performs a large-scale quantitative comparison of the discriminative ability of the activations. However, we find that many potential activations have not been evaluated, such as the queries and keys used to compute attention scores. Moreover, recent advancements in diffusion architectures bring many new activations, such as those within embedded ViT modules. Both combined, activation selection remains unresolved but overlooked. To tackle this issue, this paper takes a further step with a much broader range of activations evaluated. Considering the significant increase in activations, a full-scale quantitative comparison is no longer operational. Instead, we seek to understand the properties of these activations, such that the activations that are clearly inferior can be filtered out in advance via simple qualitative evaluation. After careful analysis, we discover three properties universal among diffusion models, enabling this study to go beyond specific models. On top of this, we present effective feature selection solutions for several popular diffusion models. Finally, the experiments across multiple discriminative tasks validate the superiority of our method over the SOTA competitors. Our code is availab.e at this url. © 2024 Neural information processing systems foundation. All rights reserved.

关键词：

Data Privacy Protection for Maritime Mobile Terminals 13

学校读者我要写书评

暂无评论

Data Privacy Protection for Maritime Mobile Terminals

13th International Conference on Wireless Communications and Signal Processing, WCSP 2021

作者： Jiang, Su Su, Xin Zhou, Yiqing College of IOT Engineering Hohai University Jiangsu Changzhou213022 China Beijing Key Lab. of Mobile Computing and Pervasive Devices Institute of Computing Technology University of Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781665407854

Mobile edge computing (MEC) can effectively support various high reliability and low delay maritime applications, but security risks in computing tasks offloading exist. We describe a maritime network (MN) model based on MEC, and analyze the risk of data privacy leakage of maritime mobile terminal (MMT) during the offloading tasks. For abnormal maritime monitoring data (MMD), a data cleaning algorithm of sample density-based local outlier factor (SD-LOF) is proposed. It can effectively save the cost of data privacy protection (DPP) and improve the quality of MEC service. For data privacy leakage of MMT, an attribute sensitivity-based differential privacy (AS-DP) algorithm is proposed to balance security and availab.lity of data and improve the differential privacy protection utility. Experimental results have verified the validity of the proposed two algorithms. © 2021 IEEE.

关键词： Mobile edge computing

Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Zhou, Ziqi Li, Minghui Liu, Wei Hu, Shengshan Zhang, Yechao Wan, Wei Xue, Lulu Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab. China Cluster and Grid Computing Lab. China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the attackers. In this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six

关键词： Topology

A Multi-Layer Regression based Predicable Function Fitting Network

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wan, Changlin Shi, Zhongzhi Huizhou University Guangdong516007 China CAS Key Lab. of IIP Institute of Computing Technology Beijing100190 China

Function plays an important role in mathematics and many science branches. As the fast development of computer technology, more and more study on computational function analysis, e.g., Fast Fourier Transform, Wavelet Transform, Curve Function, are presented in these years. However, there are two main problems in these approaches: 1) hard to handle the complex functions of stationary and non-stationary, periodic and non-periodic, high order and low order;2) hard to generalize the fitting functions from training data to test data. In this paper, a multiple regression based function fitting network that solves the two main problems is introduced as a predicable function fitting technique. This technique constructs the network includes three main parts: 1) the stationary transform layer, 2) the feature encoding layers, and 3) the fine tuning regression layer. The stationary transform layer recognizes the order of input function data, and transforms nonstationary function to stationary function. The feature encoding layers encode the raw input sequential data to a novel linear regression feature that can capture both the structural and the temporal characters of the sequential data. The fine tuning regression layer then fits the features to the target ahead values. The fitting network with the linear regression feature layers and a non-linear regression layer come up with high quality fitting results and generalizable predictions. The experiments of both mathematic function examples and the real word function examples verifies the efficiency of the proposed technique. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear regression

REASSIGNER: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Cui, Haochuan Sheng, Junjie Jin, Bo Hu, Yiqiu Su, Li Zhu, Lei Zhou, Wenli Wang, Xiangfeng School of Computer Science and Technology East China Normal University Shanghai200062 China Algorithm Innovation Lab. Huawei Cloud Computing Technologies Co. Shaanxi Xi’an710049 China Computing Service Product Dept. Huawei Cloud Computing Technologies Co. Shaanxi Xi’an710049 China

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (REASSIGNER), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of REASSIGNER is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. REASSIGNER can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from Huawei cloud, the proposed REASSIGNER achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods. Copyright © 2022, The Authors. All rights reserved.

关键词： cloud computing

Online Midgress-Sensitive Traffic Allocation for Percentile Charging in Pracitcal CDNs

学校读者我要写书评

暂无评论

Online Midgress-Sensitive Traffic Allocation for Percentile ...

International Workshop on Quality of Service

作者： Huiyou Zhan Haisheng Tan Huang Xu Chi Zhang Hongqiu Ni Pengfei Zhang Weihua Shan Xiang-Yang Li CAS Key Lab of Wireless-Optical Communications University of Science and Technology of China China USTC-Deqing Alpha Innovation Research Institute Zhejiang China Algorithm Innovation Lab Huawei Cloud Computing Technologies Co. Ltd. Xi'an China

The traffic bandwidth costs comprise a significant amount of operating expenditure in CDNs, induced by the traffic from the end-users to edge servers (edge cost) and from the edge to the center servers (midgress cost). Traffic allocation is the main approach to minimizing the total bandwidth cost. The joint optimization of the total costs is challenging, specifically when the percentile charging mechanism as well as some other practical issues are considered, such as the dynamicity of midgress traffic and the granularity of traffic allocation. In this work, based on our novel miss ratio prediction mechanism, we propose the first online framework, named Iris, jointly optimizing the edge and midgress costs under the 95th percentile charging in commercial CDNs. Iris can theoretically achieve a competitive ratio of $1+\frac{p_{e}}{\beta\cdot p_{c}}$ , when the miss ratio of all domains is set as $\beta$ . Here $p_{e}$ and $p_{c}$ are the unit bandwidth price of the edge and midgress cost, respectively. Iris is tolerant to prediction errors which can be deployed in practical CDN systems. Extensive experiments based on real data indicate that Iris can dramatically reduce bandwidth costs by about 8.149% compared with the SOTA schemes, potentially saving millions of dollars per month for our large-scale commercial CDN collab.rator.

关键词：

AUCSeg: AUC-oriented Pixel-level Long-tail Semantic Segmentation

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Han, Boyu Xu, Qianqian Yang, Zhiyong Bao, Shilong Wen, Peisong Jiang, Yangbangyan Huang, Qingming Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS China School of Computer Science and Tech. University of Chinese Academy of Sciences China Peng Cheng Laboratory China Key Laboratory of Big Data Mining and Knowledge Management CAS China

The Area Under the ROC Curve (AUC) is a well-known metric for evaluating instance-level long-tail learning problems. In the past two decades, many AUC optimization methods have been proposed to improve model performance under long-tail distributions. In this paper, we explore AUC optimization methods in the context of pixel-level long-tail semantic segmentation, a much more complicated scenario. This task introduces two major challenges for AUC optimization techniques. On one hand, AUC optimization in a pixel-level task involves complex coupling across loss terms, with structured inner-image and pairwise inter-image dependencies, complicating theoretical analysis. On the other hand, we find that mini-batch estimation of AUC loss in this case requires a larger batch size, resulting in an unaffordable space complexity. To address these issues, we develop a pixel-level AUC loss function and conduct a dependency-graph-based theoretical analysis of the algorithm’s generalization ability. Additionally, we design a Tail-Classes Memory Bank (T-Memory Bank) to manage the significant memory demand. Finally, comprehensive experiments across various benchmarks confirm the effectiveness of our proposed AUCSeg method. The code is availab.e at https://***/boyuh/AUCSeg. © 2024, CC BY.

关键词： Semantic Segmentation