检索结果-内蒙古大学图书馆

2023 IEEE International Conference on Memristive Computing and applications, ICMCA 2023

作者： Liu, Aidi Yu, Wenyue Huang, Lili Guo, Jian Lei, Tengfei Zhao, Hui School of Computer and Information Engineering Qilu Institute of Technology Jinan China School of Computer and Information Engineering Qilu Institute of Technology China Collaborative Innovation Center of Memristive Computing Application School of Computer and Information Engineering Qilu Institute of Technology China School of Information Science and Engineering University of Jinan Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan China

ISBN: (纸本)9798350316230

This article mainly conducts a series of research on the synchronization control of Cohen-Grossberg neural networks. Firstly, a memristive Cohen-Grossberg neural network model with time-varying delay was constructed, and an equivalent system was obtained by combining inverse function theory, differential inclusion, and set-valued mapping. Secondly, a new predefined-time stability theorem was proposed, and sufficient conditions were obtained to ensure that the drive-response system achieves predefined-time synchronization by designing appropriate control strategy. The simulation results show that the stability time of the error system can be adjusted by the user, verifying the correctness and of the theory. © 2023 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Change Detection Converter: Using Semantic Segmantation Models to Tackle Change Detection Task

Change Detection Converter: Using Semantic Segmantation Mode...

引用

2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Chen, Ding Ye, Bowei Zhao, Zhicheng Wang, Feihong Xu, Weida Yin, Wenjun Beijing University of Posts and Telecommunications China Beijing Key Laboratory of Networks System and Network Culture China Beijing Insights Value Technology CO. LTD. China Zhongguancun Ruichen Satellite Innovation Application Research Institute

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

In recent years, change detection (CD) in remote sensing images has achieved a huge success by using deep learning, and it is essentially a subtask of semantic segmentation. Both of them are tightly related. However, the complementarity of them has not been fully explored. In this paper, we propose a new change detection converter (CDC), which can be easily inserted into the existing semantic segmentation networks and be transformed into new networks suitable for change detection task. In addition, a new task-specific data augmentation method named Crossover is proposed, which can enhance the feature representation ability in sequence-form features without any additional cost. Extensive experiments demonstrate that the proposed method obtains significantly better performance and more efficiency than previous methods on two public datasets, and can achieve continuous performance improvement than other complex-designed solutions. © 2022 IEEE.

关键词： Change detection

来源：评论

学校读者我要写书评

暂无评论

An Alternative Algorithm for the Symmetry Classification of Ordinary Differential Equations

引用

Sound & Vibration 2022年第1期56卷 65-76页

作者： Yi Tian Jing Pang College of Data Science and Application Inner Mongolia University of TechnologyHohhot010080China Inner Mongolia Autonomous Region Engineering and Technology Research Center of Big Data Based Software Service Hohhot010080China College of Science Inner Mongolia University of TechnologyHohhot010051China Inner Mongolia Key Laboratory of Statistical Analysis Theory for Life Data and Neural Network Modeling Inner Mongolia University of TechnologyHohhot010051China

This is the first paper on symmetry classification for ordinary differential equations(ODEs)based on Wu’s *** carry out symmetry classification of two ODEs,named the generalizations of the Kummer-Schwarz equations which involving arbitrary ***,Lie algorithm is used to give the determining equations of symmetry for the given equations,which involving arbitrary ***,differential form Wu’s method is used to decompose determining equations into a union of a series of zero sets of differential characteristic sets,which are easy to be solved *** branch of the decomposition yields a class of symmetries and associated *** algorithm makes the classification become direct and *** Dimitrov Bozhkov,and Pammela Ramos da Conceição have used the Lie algorithm to give the symmetry classifications of the equations talked in this paper in *** this paper,we can find that the differential form Wu’s method for symmetry classification of ODEs with arbitrary function(parameter)is effective,and is an alternative method.

关键词： Kummer-Schwarz equation ordinary differential equations(ODEs) differential form Wu’s method

来源：评论

学校读者我要写书评

暂无评论

LabelEase: A Semi-Automatic Tool for Efficient and Accurate Trace Labeling in Microservices

LabelEase: A Semi-Automatic Tool for Efficient and Accurate ...

引用

International Symposium on Software Reliability Engineering (ISSRE)

作者： Shenglin Zhang Zeyu Che Zhongjie Pan Xiaohui Nie Yongqian Sun Lemeng Pan Dan Pei Nankai University Haihe Laboratory of Information Technology Application Innovation Computer Network Information Center Chinese Academy of Sciences Tianjin Key Laboratory of Software Experience and Human Computer Interaction Huawei Beijing National Research Center for Information Science and Technology Tsinghua University

ISBN: (数字)9798350353884

ISBN: (纸本)9798350353891

Trace data is crucial for system observability and maintainability within microservices architectures, and many operation algorithms depend heavily on trace data, including anomaly detection, root cause analysis, etc. However, the actual performance of these algorithms might be unsatisfactory due to the absence of high-quality labeled datasets for effective training and evaluation. Since billions of traces could be generated daily for large-scale microservices, labeling overhead is the main hurdle to obtaining high-quality trace *** this paper, we propose LabelEase, a novel semi-automatic trace labeling tool, which uses active learning techniques to achieve efficient and accurate trace labeling. For anomaly trace labeling, LabelEase clusters similar traces with a graph-based trace representation technique and selects a few representative traces for human labeling, avoiding labeling most of the traces. For root cause labeling, LabelEase aggregates the labeled anomalous traces and identifies the service’s failures for operators to label. Our systematic experiments on two large-scale datasets show that LabelEase achieves over 0.98 F 1 -score in anomaly trace labeling and 0.89 precision of failure detection in root cause labeling, LabelEase can reduce operators’ labeling overhead by more than 99.9%. To the best of our knowledge, we are the first to propose a semi-automatic trace labeling tool capable of achieving efficient and accurate trace labeling.

关键词： Training Location awareness Accuracy Costs Transfer learning Microservice architectures Manuals Data models Labeling Anomaly detection

来源：评论

学校读者我要写书评

暂无评论

Fake the Real: Backdoor Attack on Deep Speech Classification via Voice Conversion

arXiv

引用

arXiv 2023年

作者： Ye, Zhe Mao, Terui Dong, Li Yan, Diqun Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology China Ningbo City College of Vocational Technology Ningbo China

Deep speech classification has achieved tremendous success and greatly promoted the emergence of many real-world applications. However, backdoor attacks present a new security threat to it, particularly with untrustworthy third-party platforms, as pre-defined triggers set by the attacker can activate the backdoor. Most of the triggers in existing speech backdoor attacks are sample-agnostic, and even if the triggers are designed to be unnoticeable, they can still be audible. This work explores a backdoor attack that utilizes sample-specific triggers based on voice conversion. Specifically, we adopt a pre-trained voice conversion model to generate the trigger, ensuring that the poisoned samples does not introduce any additional audible noise. Extensive experiments on two speech classification tasks demonstrate the effectiveness of our attack. Furthermore, we analyzed the specific scenarios that activated the proposed backdoor and verified its resistance against fine-tuning. Copyright © 2023, The Authors. All rights reserved.

关键词： Speech recognition

来源：评论

学校读者我要写书评

暂无评论

A Pseudo-Dual Self-Rectification Framework for Semantic Segmentation

A Pseudo-Dual Self-Rectification Framework for Semantic Segm...

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Huazheng Hao Hui Xiao Li Dong Diqun Yan Dongtai Liang Jiayan Zhuang Chengbin Peng Ningbo University Ningbo China Ningbo Institute of Materials Technology & Engineering Chinese Academy of Sciences Ningbo China Key Laboratory of Mobile Network Application Technology of Zhejiang Province Ningbo China

Semantic segmentation has achieved remarkable success in various applications. However, the training process for such techniques necessitates a significant amount of labeled data. Although semi-supervised frameworks can alleviate this issue, traditional approaches typically require multiple baseline models to form a dual model. To allow a semi-supervised semantic segmentation framework to be used in robotic systems with precious computation and memory resources, we propose a framework utilizing a single baseline model only. The overall framework is composed of three parts: an encoder, a shallow decoder, and a deep decoder. It distills knowledge from the ensemble of two decoders to improve the encoder, which can implicitly form a pseudo-dual model. It also calculates class-wise likelihoods according to the similarity between features and class prototypes learned from different decoders and rectifies low-confidence pseudo-labels. Our framework outperforms state-of-the-art frameworks on benchmark datasets with a significant amount of decrease in using computing resources.

关键词：

来源：评论

学校读者我要写书评

暂无评论

CGNet: Classification-Guided Multi-Task Interactive network for Hyperspectral and Multispectral Image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

引用

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

作者： Guohua Lv Yanlong Xu Yongbiao Gao Guixin Zhao Guotao Wang Xiangcheng Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing Image fusion Information exchange Hyperspectral imaging

来源：评论

学校读者我要写书评

暂无评论

Gradient Sign Inversion: Making an Adversarial Attack a Good Defense

Gradient Sign Inversion: Making an Adversarial Attack a Good...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Xiaojian Ji Li Dong Rangding Wang Diqun Yan Yang Yin Jinyu Tian Department of Computer Science Ningbo University Zhejiang Key Laboratory of Mobile Network Application Technology Geely Auto Central Research Institute School of Computer Science and Engineering Macau University of Science and Technology

Deep neural networks have been proven vulnerable to deliberately crafted adversarial example, which cause serious safety and security concerns. Many defense approaches were proposed to resist such threats. However, existing defenses such as pre-compression or adversarial training would degrade the model performance on clean images or incur heavy computational costs. In this work, we propose a plug-and-play defensive module Gradient Sign Inversion (GSI) to defend gradient-based attack. Essentially, GSI attempts to inverse the direction of the backpropagated gradient for the victim model, disturbing the adversarial example generation of the attacking while retaining the performance of the vanilla network on genuine inputs. Specifically, an additive model based on periodic trigonometric function is established by investigating the necessary conditions that a suitable defensive module should have. By enforcing constraints on the defensive module, the parameters of GSI are determined, accompanied by a theoretical justification. Interestingly, we observe that the proposed GSI not only prevents the gradient-based adversarial attack, but can even improve the confidence of the ground-truth label when initiating an attack, making the attack betray as a defense. Source code is publicly available at https://***/JidaDiao/GSI.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Enhancing source code summarization from structure and semantics

Enhancing source code summarization from structure and seman...

引用

International Joint Conference on Neural networks (IJCNN)

作者： Xurong Lu Jun Niu Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China Zhejiang Key Laboratory of Mobile Network Application Technology Ningbo China Zhejiang Engineering Research Center of Advcanced Mass Spectrometry and Clinical Application Ningbo China

Source code summarization aims to generate concise and high-quality natural language descriptions for code snippets. High-quality code summaries can help developers better understand source codes. Researchers have made great efforts to generate more accurate summaries; however, due to the lack of preservation of source code structure and semantics, previous approaches have struggled to generate summaries that accurately describe the functionality or other major characteristics of codes. In this paper, we propose a novel approach called Code Structure and Semantic Fusion (CSSF) for automatically generating summaries for source code. CSSF can utilize both the structural and semantic information of source codes. To achieve this, we extract the overall structure of the Abstract Syntax Tree (AST) by expanding the AST and using a heterogeneous graph attention network. Furthermore, we use an additional sequence model to obtain the semantic information of the code fragment. Finally, we fuse the two kinds of information through a novel modality fusion method. We evaluate our approach on a widely used Java dataset; experimental results confirm that our approach outperforms existing methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Graph Data Augmentation based on Adaptive Graph Convolution for Skeleton-based Action Recognition 9

Graph Data Augmentation based on Adaptive Graph Convolution ...

引用

9th IEEE International Conference on Computer Science and network technology, ICCSNT 2021

作者： Yu, Lubin Tian, Lianfang Du, Qiliang School of Automation Science And Engineering South China University And Technology Guangzhou China Zhuhai China Key Laboratory of Marine Environmental Survey Technology And Application Ministry of Natural Resources Guangzhou China Key Laboratory of Autonomous Systems Network Control of Ministry of Education Guangzhou China

ISBN: (纸本)9781665443647

Recently, action recognition based on graph data has received widespread attention. Graph convolutional neural networks need a large amount of graph data support. Therefore, graph data augmentation has significant research value. Existing graph data augmentation methods are generally based on graph structure or graph node features to add or remove graph edges, leading to problems in generality and effectiveness. To improve the above issues, this paper utilizes adaptive graph convolution to design an algorithm that obtains the optimal graph connection for graph data augmentation as graph convolution proceeds, which greatly improves the generalization and effectiveness. The results on NTU-RGBD and Kinetics-Skeleton datasets for skeleton-based action recognition prove that our proposed method reveals better results than existing methods. © 2021 IEEE.

关键词： Convolution

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：