检索结果-内蒙古大学图书馆

40th IEEE International Conference on Acoustics, Speech, and Signal processing, ICASSP 2015

作者： Lan, Long Guan, Naiyang Zhang, Xiang Huang, Xuhui Luo, Zhigang Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781467369978

Semi-supervised learning (SSL) utilizes plenty of unlabeled examples to boost the performance of learning from limited labeled examples. Due to its great discriminant power, SSL has been widely applied to various real-world tasks such as information retrieval, pattern recognition, and speech separa- tion. Label propagation (LP) is a popular SSL method which propagates labels through the dataset along high density areas defined by unlabeled examples, LP assumes nearby examples should share the same label, thus, it unavoidably pushes the labels to the wrong examples, especially when different la- beled examples are not strictly separated. Seed K-means uses labeled examples to initialize class centers, and avoid getting stuck in poor local optima comparing to traditional K-means, however the hard constraint of each example's membership makes Seed K-means failed in many real world applications. This paper proposes a novel label walking nonnegative matrix factorization method (LWNMF) to handle labeled examples in SSL based on the framework of NMF. LWNMF decomposes the whole dataset into the product of a basis matrix and a coefficient matrix, and to travel labels to unlabeled examples, LWNMF regards the class indicators of labeled examples as their coefficients and iteratively updates both basis matrix and coefficients of unlabeled examples. Since LWNMF learns comprehensive class centroids, labels iteratively walk to unlabeled examples through these significant centroids. © 2015 IEEE.

关键词： K-means Label propagation Nonnegative matrix factorization Semisupervised learning

来源：评论

学校读者我要写书评

暂无评论

Word Embedding-based Context-sensitive Network Flow Payload Anomaly Detection 3

Word Embedding-based Context-sensitive Network Flow Payload ...

引用

3rd International Conference on Applied Machine Learning, ICAML 2021

作者： Li, Yizhou Wang, Yijie Cheng, Li Xu, Hongzuo Science and Technology on Parallel and Distributed Processing Laboratory College of Computer National University of Defense Technology Changsha China

ISBN: (纸本)9781665421256

Payload anomaly detection can discover malicious beliaviors tiidden in network packets. It is liard to liandle payload due to its various possible characters and complex semantic context, and tlius identifying abnormal payload is also a non-trivial task. Prior art only uses the n-gram language model to extract features, which directly leads to ultra-high-dimensional feature space and also fails to capture the context semantics fully. Accordingly, this paper proposes a word embedding-based context-sensitive network flow payload anomaly detection method (termed WECAD). First, WECAD obtains the initial feature representation of the payload through the word embedding-based method. Then, we propose a corpus pruning algorithm, which appUes the cosine similarity clustering and frequency distribution to prune inconsequential characters. We only keep the essential characters to reduce the calculation space. Subsequently, we propose a context learning algorithm. It employs the co-occurrence matrix transformation technology and introduces the backward step size to consider the order relationship of essential characters. Comprehensive experiments on real-world intrusion detection datasets validate the effectiveness of our method. © 2021 IEEE

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A Novel Deep Neural Network Model for Credit Risk Prediction of Chinese Farmers 6

A Novel Deep Neural Network Model for Credit Risk Prediction...

引用

6th IEEE International Conference on Data science in Cyberspace, DSC 2021

作者： Xie, Yalong Li, Aiping Liu, Ziniu Chen, Kai Tu, Hongkui College Of Computer National University Of Defense Technology Science And Technology On Parallel And Distributed Processing Laboratory Changsha China

ISBN: (纸本)9781665418157

China is a big agricultural county with more than 500 million rural population. In China, farmers usually loan from rural commercial banks or rural credit cooperatives. It is crucial for the national economic development and the improvement of people's standard of living that how to reasonably use funds to subsidize the agricultural population and reduce the risk of rural loans. At present, credit risk prediction of farmers mainly depends on the experience of experts in the business field, and there is little published research on using artificial intelligence methods to solve this problem. This paper presents a complete set of methods, including data collection, feature selection, etc. We propose a novel deep neural network model named DNN-CRP for credit risk prediction of Chinese framers. Experiments on an actual credit loan dataset of Chinese farmers are presented, and experimental results show that the comprehensive performance of the DNN-CRP model is better than current state-of-the-art models. It is believed that the DNN-CRP model proposed in this paper can help banks improve the efficiency of the credit loan business of farmers and reduce credit risks. © 2021 IEEE.

关键词： Neural network models

来源：评论

学校读者我要写书评

暂无评论

Smoothing Point Adjustment-Based Evaluation of Time Series Anomaly Detection 48

Smoothing Point Adjustment-Based Evaluation of Time Series A...

引用

48th IEEE International Conference on Acoustics, Speech and Signal processing, ICASSP 2023

作者： Liu, Mingyu Wang, Yijie Xu, Hongzuo Zhou, Xiaohui Li, Bin Wang, Yongjun National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory College of Computer Changsha China

ISBN: (纸本)9781728163277

Anomalies in time series appear consecutively, forming anomaly segments. Applying the classical point-based evaluation metrics to evaluate the detection performance of segments leads to considerable underestimation, so most related studies resort to point adjustment. This operation treats all points as true positives within a segment equally when only one individual point alarms, resulting in significant overestimation and creating an illusion of superior performance. This paper proposes smoothing point adjustment, a novel range-based evaluation protocol for time series anomaly detection. Our protocol reflects detection performance impartially by carefully considering the specific location and frequency of alarms in the raw results. It is achieved by smoothly determining the adjustment range and rewarding early detection via a ranging function and a rewarding function. Compared with other evaluation metrics, experiments on different datasets show that our protocol can yield a performance ranking of various methods more consistent with the desired situation. © 2023 IEEE.

关键词： Anomaly Detection Evaluation Protocol Point Adjustment Time Series

来源：评论

学校读者我要写书评

暂无评论

Resource Allocation with Dynamic Substrate Network in Data Centre Networks

引用

China Communications 2013年第9期10卷 130-142页

作者：李小玲王怀民丁博李小勇刘惠 National Key Laboratory for Parallel and Distributed Processing School of Computer ScienceNational University of Defense Technology

The resource allocation problem in data centre networks refers to a map of the workloads provided by the cloud users/tenants to the Substrate Network(SN)which are provided by the cloud *** studies consider the dynamic arrival and departure of the workloads,while the dynamics of the substrate are *** this paper,we first propose the resource allocation with the dynamic SN,and denote it as ***,we propose an efficient mapping algorithm for *** performance of the proposed algorithm is evaluated by performing simulation *** results show that the proposed algorithm can effectively solve the GraphMap-DS.

关键词： resource allocation data centrenetwork workload substrate algorithm

来源：评论

学校读者我要写书评

暂无评论

Large-scale virtual machines provisioning in clouds： challenges and approaches

引用

Frontiers of Computer science 2016年第1期10卷 2-18页

作者： Zhaoning ZHANG Dongsheng LI Kui WU National Laboratory for Parallel and Distributed Processing School of Computer Science National University of Defense Technology Changsha 410073 China Computer Science Department University of Victoria Victoria V8W 2Y2 Canada

The scale of global data center market has been explosive in recent years. As the market grows, the demand for fast provisioning of the virtual resources to support elas- tic, manageable, and economical computing over the cloud becomes high. Fast provisioning of large-scale virtual ma- chines （VMs）, in particular, is critical to guarantee quality of service （QoS）. In this paper, we systematically review the existing VM provisioning schemes and classify them in three main categories. We discuss the features and research status of each category, and introduce two recent solutions, VMThunder and VMThunder＋, both of which can provision hundreds of VMs in seconds.

关键词： cloud computing IaaS large scale virtual ma-chine provisioning

来源：评论

学校读者我要写书评

暂无评论

OHTMA:an optimized heuristic topology-aware mapping algorithm on the Tianhe-3 exascale supercomputer prototype

引用

Frontiers of Information technology & Electronic Engineering 2020年第6期21卷 939-949页

作者： Yi-shui LI Xin-hai CHEN Jie LIU Bo YANG Chun-ye GONG Xin-biao GAN Sheng-guo LI Han XU Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China

With the rapid increase of the size of applications and the complexity of the supercomputer architecture,topology-aware process mapping becomes increasingly *** communication cost has become a dominant constraint of the performance of applications running on the *** avoid a bad mapping strategy which can lead to terrible communication performance,we propose an optimized heuristic topology-aware mapping algorithm(OHTMA).The algorithm attempts to minimize the hop-byte metric that we use to measure the mapping *** incorporates a new greedy heuristic method and pair-exchange-based *** reduces the number of long-distance communications and effectively enhances the locality of the *** results on the Tianhe-3 exascale supercomputer prototype indicate that OHTMA can significantly reduce the communication costs.

关键词： High-performance computing Topology mapping Heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

An unsupervised deep learning framework for gene regulatory network inference from single-cell expression data

An unsupervised deep learning framework for gene regulatory ...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Mao, Guo Liu, Jie National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory Changsha410073 China National University of Defense Technology Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Laboratory of Software Engineering for Complex System Changsha410073 China

ISBN: (纸本)9798350337488

Recent advances in single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for reconstruction gene regulation networks (GRNs). At present, many different models have been proposed to infer GRN from a large number of RNA-seq data, but most deep learning models use a priori gene regulatory network to infer potential GRNs. It is a challenge to reconstruct GRNs from scRNA-seq data due to the noise and sparsity introduced by the dropout effect. Here, we propose GAALink, a novel unsupervised deep learning method. It first constructs the gene similarity matrix and then refines it by threshold value. It then learns feature representations of genes through a graphical attention autoencoder that propagates information across genes with different weights. Finally, we use gene feature expression for matrix completion such that the GRNs are reconstructed. Compared with seven existing GRNs reconstruction methods, GAALink achieves more accurate performance on seven scRNA-seq dataset with four ground truth networks. GAALink can provide a useful tool for inferring GRNs for scRNA-seq expression data. © 2023 IEEE.

关键词： Feature representations Gene similarity matrix Graphical attention autoencoder Matrix completion Unsupervised deep learning method

来源：评论

学校读者我要写书评

暂无评论

Merge-Weighted Dynamic Time Warping for Speech Recognition

引用

Journal of Computer science & technology 2014年第6期29卷 1072-1082页

作者：张湘莉兰骆志刚李明 State Key Laboratory of Pathogen and Biosecurity Beijing Institute of Microbiology and Epidemiology Science and Technology on Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology David R.Cheriton School of Computer Science University of WaterlooWaterlooOntario N2L 3G1Canada ACM IEEE

Obtaining training material for rarely used English words and common given names from countries where English is not spoken is difficult due to excessive time, storage and cost factors. By considering personal privacy, language- independent （LI） with lightweight speaker-dependent （SD） automatic speech recognition （ASR） is a convenient option to solve tile problem. The dynamic time warping （DTW） algorithm is the state-of-the-art algorithm for small-footprint SD ASR for real-time applications with limited storage and small vocabularies. These applications include voice dialing on mobile devices, menu-driven recognition, and voice control on vehicles and robotics. However, traditional DTW has several lhnitations, such as high computational complexity, constraint induced coarse approximation, and inaccuracy problems. In this paper, we introduce the merge-weighted dynamic time warping （MWDTW） algorithm. This method defines a template confidence index for measuring the similarity between merged training data and testing data, while following the core DTW process. MWDTW is simple, efficient, and easy to implement. With extensive experiments on three representative SD speech recognition datasets, we demonstrate that our method outperforms DTW, DTW on merged speech data, the hidden Markov model （HMM） significantly, and is also six times faster than DTW overall.

关键词： merge-weighted dynamic time warping natural language processing speech recognition and synthesis tem-plate confidence index

来源：评论

学校读者我要写书评

暂无评论

Learning from internet: Handling uncertainty in robotic environment modeling 17

Learning from internet: Handling uncertainty in robotic envi...

引用

9th Asia-Pacific Symposium on Internetware, Internetware 2017

作者： Li, Yiying Wang, Huaimin Ding, Bo Che, Huimin National Key Laboratory of Parallel and Distributed Processing College of Computer National University of Defense Technology ChangSha China

ISBN: (纸本)1595930361

Uncertainty is a great challenge for environment perception of autonomous robots. For instance, while building semantic maps (i.e., maps with semantic labels such as object names), the robot may encounter unexpected objects of which it has no knowledge. It will lead to inevitable failures in traditional environment modeling software. The abundant knowledge being accumulated on the Internet has the potential to assist robots to handle such kind of uncertainty. However, existing researches have not touched this issue yet. This paper proposes a cloud-based semantic mapping engine named SemaCloud, which can not only augment robot's environment modeling capability by the rich cloud resources but also cope with uncertainty by utilizing the Internet knowledge on necessary. It adopts a state-of-art Deep Neural Network (DNN) for real-time and accurate recognition of pre-trained objects. If an object is beyond the knowledge of this DNN, a special mechanism named QoS-aware cloud phase transition is triggered to seek help from existing recognition services on the Internet. By a set of carefullydesigned algorithms, it can maximize benefits and minimize the negative impacts on the Quality of Service (QoS) properties of robotic applications, which is essential to many robot scenarios. The experiments on both open datasets and real robots show that our work can handle uncertainty successfully in robotic semantic mapping without sacrificing critical real-time constraints. © 2017 Association for Computing Machinery.

关键词： Deep neural networks

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：