检索结果-内蒙古大学图书馆

International Conference on big data and Smart computing (bigCOMP)

作者： Teng Zhang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728160344

ISBN: (纸本)9781728160351

Recently a promising research direction of statistical learning has been advocated, i.e., the optimal margin distribution learning, with the central idea of optimizing the margin distribution. As the most representative approach of this new learning paradigm, the optimal margin distribution machine (ODM) considers maximizing the margin mean and minimizing the margin variance simultaneously. The standard ODM exploits the ℓ_2-norm penalty, which gives rise to a dense decision boundary. However, in some situations, the model with parsimonious representation is more preferred, due to the redundant noisy features or limited computing resources. In this paper, we propose the sparse optimal margin distribution machine (Sparse ODM), which aims to achieve better generalization performance with moderate model size. For optimization, we extends an efficient coordinate descent method to solve the final problem since the variables are decoupled. In each iteration, we propose a modified Newton method to solve the one-variable sub-problem. Experimental results on both synthetic and real data sets show the superiority of the proposed method.

关键词： Standards Newton method Noise measurement Optimization Support vector machines Computational modeling Training

来源：评论

学校读者我要写书评

暂无评论

Integrated Public Transport Timetable Coordination and Vehicle Scheduling with Even Headways

SSRN

引用

SSRN 2022年

作者： Liu, Tao Ji, Wen Gkiotsalitis, Konstantinos Cats, Oded National Engineering Laboratory of Integrated Transportation Big Data Application Technology School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China Institute of System Science and Engineering School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China Department of Civil Engineering Faculty of Engineering Technology University of Twente Horst Complex Z222 P.O. Box 217 Enschede 7500 AE Netherlands Department of Transport & Planning Delft University of Technology Netherlands

Timetabling and vehicle scheduling are two important activities in public transport (PT) operations planning. Traditionally, the timetabling problem is solved first before proceeding to the vehicle scheduling problem. The integration of these two problems can help further reduce the total operation cost and improve the level of service, especially when timetables of different bus lines are well-coordinated at transfer stations. This work addresses the integrated PT timetable coordination and vehicle scheduling problem while ensuring that each PT line is dispatched with an even headway. We first separately formulate two integer linear programming models for the timetable coordination and vehicle scheduling problems. Next, the two models are combined into a bi-objective integer linear programming model for the integrated timetable coordination and vehicle scheduling problem. For small size PT networks, the model can be solved by using an ɛ -constraint method, together with off-the-shelf optimization solvers. For large-size problems, two constraint reduction procedures are developed to reduce the number of redundant constraints so as to reduce the computation complexity and improve the solution process. Finally, the models and solution method are applied to a numerical example and a real-world bus rapid transit (BRT) network in Chengdu, China. Computation results show that the solution generated by the sequential optimization approach is usually dominated by the Pareto-optimal solutions generated by the integrated optimization approach. Our findings suggest that it is not a wise decision to use the solution generated by the sequential optimization approach or the solution with the minimum fleet size generated by the integrated optimization approach. For practical implementation, it is recommended to choose the solution that has a fleet size of one more vehicle than the minimum fleet size. © 2022, The Authors. All rights reserved.

关键词： Integer programming

来源：评论

学校读者我要写书评

暂无评论

FUSDREAMER: Label-efficient Remote Sensing World Model for Multimodal data Classification

arXiv

引用

arXiv 2025年

作者： Wang, Jinping Song, Weiwei Chen, Hao Ren, Jinchang Zhao, Huimin School of Computer Sciences Guangdong Polytechnic Normal University Guangzhou510665 China Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangdong Polytechnic Normal University Guangzhou510665 China Peng Cheng Laboratory Shenzhen518000 China Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom School of Computer Sciences Guangdong Polytechnic Normal University Guangzhou510640 China National Subsea Centre School of Computing Engineering and Technology Robert Gordon University AberdeenAB10 7AQ United Kingdom

World models significantly enhance hierarchical understanding, improving data integration and learning efficiency. To explore the potential of the world model in the remote sensing (RS) field, this paper proposes a label-efficient remote sensing world model for multimodal data fusion (FusDreamer). The FusDreamer uses the world model as a unified representation container to abstract common and high-level knowledge, promoting interactions across different types of data, i.e., hyperspectral (HSI), light detection and ranging (LiDAR), and text data. Initially, a new latent diffusion fusion and multimodal generation paradigm (LaMG) is utilized for its exceptional information integration and detail retention capabilities. Subsequently, an open-world knowledge-guided consistency projection (OK-CP) module incorporates prompt representations for visually described objects and aligns language-visual features through contrastive learning. In this way, the domain gap can be bridged by fine-tuning the pre-trained world models with limited samples. Finally, an end-to-end multitask combinatorial optimization (MuCO) strategy can capture slight feature bias and constrain the diffusion process in a collaboratively learnable direction. Experiments conducted on four typical datasets indicate the effectiveness and advantages of the proposed FusDreamer. The corresponding code will be released at https://***/ Cimy-wang/FusDreamer. © 2025, CC BY.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

High Performance DDoS Attack Detection system Based on Distribution Statistics 16th

High Performance DDoS Attack Detection System Based on Distr...

引用

16th IFIP WG 10.3 International Conference on Network and Parallel computing, NPC 2019

作者： Xie, Xia Li, Jinpeng Hu, Xiaoyang Jin, Hai Chen, Hanhua Ma, Xiaojing Huang, Hong National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783030307080

Nowadays, web servers often face the threat of distributed denial of service attacks and their intrusion prevention systems cannot detect those attacks effectively. Many existing intrusion prevention systems detect attacks by the state of per-flow and current processing speed cannot fulfill the requirements of real-time detection due to the high speed traffic. In this paper, we propose a powerful system TreeSketchShield which can improve sketch data structure and detect attacks quickly. First, we discuss a novel structure TreeSketch to obtain statistics of network flow, which utilizes the stepped structure of binary tree to map the distribution and reduces the complexity of the statistic calculation. Second, we present a two-level detection scheme that could make a compromise between the detection speed and detection accuracy. Experimental results show that our method can process more than 100,000 records per second. The false alarm rate can achieve 2% to 25% performance improvement. © 2019, IFIP International Federation for Information Processing.

关键词： Binary trees

来源：评论

学校读者我要写书评

暂无评论

Failure order: A missing piece in disk failure processing of data centers 21

Failure order: A missing piece in disk failure processing of...

引用

21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and systems, HPCC/SmartCity/DSS 2019

作者： Yi, Yusheng Xiao, Jiang Wu, Song Li, Huichuwu Jin, Hai National Engineering Research Center for Big Data Technology System Services Computing Technology System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728120584

To avoid data loss, data centers adopt disk failure prediction (DFP) technology to raise warnings ahead of actual disk failures, and process the warnings in the order they are raised, i.e., a first-in-first-out (FIFO) warning order. The FIFO-guided warning order can process warnings timely when disk failures are rare in data centers. With the growing scale of data centers, the increasing number of disk failures leads to a complex situation that multiple warnings are raised simultaneously, where the FIFO-guided warning order neither processes warnings timely, nor manages warnings properly due to lack of the priority of warnings. Thus, a real-time and finer-grained priority guidance for warning order management is an urgent need. To this end, we turn our attention to the failures since each warning corresponds to a fail event. The key insight is that the interdependence of failures, i.e., the order failure occurred, indicates the order of warning processing. With an accurate failure order, data centers can decrease the probability of data loss and the downtime of latency-sensitive applications by processing urgent warnings in advance. In this paper, we predict the failure order with a LambdaMART model, which is a state-of-the-art ranking algorithm in information retrieval. To avoid overly concerning on the correctness of high-rank warnings in information retrieval, we design a symmetric metric to evaluate the prediction evaluation of failure order. Experiment on a public dataset, provided by the Backblaze company, shows that our model outperforms the FIFO order and the order from previous DFP models. © 2019 IEEE.

关键词： Information retrieval

来源：评论

学校读者我要写书评

暂无评论

data Anonymization for big Crowdsourcing data

Data Anonymization for Big Crowdsourcing Data

引用

2019 INFOCOM IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2019

作者： Deng, Xiaofeng Zhang, Fan Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9781728118789

In traditional database systems, data anonymization has been extensively studied, it provides an effective solution for data privacy preservation, and multidimensional anonymization scheme among them is widely used. However, without delicate parameter settings, these technologies may cause uncontrollable information loss and decrease the accuracy of data analytic tasks. Furthermore, crowdsourcing data is usually huge in amount and must be distributed stored in clouds, which makes the conventional data anonymization technologies not applicable. In this paper, we propose a framework that uses MapReduce to anonymize large-scale data before disseminating them to human workers. In order to guarantee the number and distribution of data records to be similar in all nodes, our framework first redistributes the original data to all participating nodes. Then a heuristic two-phase anonymization schema, which can be seamlessly integrated into the framework, is proposed. Experimental results show that with the same objective of privacy, our approach is scalable for large-scale data and can improve the average accuracy of human worker's analytic tasks. © 2019 IEEE.

关键词： Crowdsourcing

来源：评论

学校读者我要写书评

暂无评论

Predicting Friendship Using a Unified Probability Model 7th

Predicting Friendship Using a Unified Probability Model

引用

7th CCF Academic Conference on bigdata, CCF bigdata 2019

作者： Kou, Zhijuan Wang, Hua Yuan, Pingpeng Jin, Hai Xie, Xia National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789811518980

Now, it is popular for people to share their feelings, activities tagged with geography and temporal information in Online Social Networks (OSNs). The spatial and temporal interactions occurred in OSNs contain a wealth of information to indicate friendship between persons. Existing researches generally focused on single dimension: spatial or temporal dimension. The simplified model only works in limited scenarios. Here, we aim to understand the probability of friendship and the place and time of interactions. First, spatial similarity of interactions is defined as a vector of places where persons checked in. Second, we employ exponential functions to characterize the change of strength of interactions as time goes on. Finally, a unified probability model to predict friendship between two persons is given. The model contains two sub-models based on spatial similarity and temporal similarity respectively. The experimental results on four data sets including spatial data sets (Gowalla and Weeplaces) and temporal data sets (Higgs Twitter data set, High school Call data set) show that our model works as expected. © Springer Nature Singapore Pte Ltd 2019.

关键词： Social networking (online)

来源：评论

学校读者我要写书评

暂无评论

RNC: Reliable network property classifier based on graph embedding 20

RNC: Reliable network property classifier based on graph emb...

引用

20th International Conference on Parallel and Distributed computing, Applications and Technologies, PDCAT 2019

作者： Liao, Hao Liu, Qixin Vidmer, Alexandre Zhou, Mingyang Mao, Rui National Engineering Laboratory for Big Data System Computing Technology Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (数字)9781728126166

ISBN: (纸本)9781728126166

In the past two decades, analyzing the information network has been intensively studied from various disciplines. Small world property and scale-free property prevail in network science research. The comparison and classification of different kinds of graphs are extremely important. However, how to design a robust and accurate classification with deep learning techniques for network property still lack enough attention, which is a vital task in various application scenarios. In this paper, we proposed the reliable network property classifier based on graph embedding(RNC) to classify the network property (scale free or small world property). In order to process non-euclidean data, we embedded each network into an image and use dimensional reduction, rasterization, and convolutional neural networks to complete the classification problem. The method can effectively accomplish classification tasks in not only artificial networks but also real networks. Besides, RNC wins greatly in terms of robustness on real networks, showing the robustness of RNC against the incomplete structure of the network. © 2019 IEEE.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

µVulDeePecker: A deep learning-based system for multiclass vulnerability detection

arXiv

引用

arXiv 2020年

作者： Zou, Deqing Wang, Sujuan Xu, Shouhuai Li, Zhen Jin, Hai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518057 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

Fine-grained software vulnerability detection is an important and challenging problem. Ideally, a detection system (or detector) not only should be able to detect whether or not a program contains vulnerabilities, but also should be able to pinpoint the type of a vulnerability in question. Existing vulnerability detection methods based on deep learning can detect the presence of vulnerabilities (i.e., addressing the binary classification or detection problem), but cannot pinpoint types of vulnerabilities (i.e., incapable of addressing multiclass classification). In this paper, we propose the first deep learning-based system for multiclass vulnerability detection, dubbed µVulDeePecker. The key insight underlying µVulDeePecker is the concept of code attention, which can capture information that can help pinpoint types of vulnerabilities, even when the samples are small. For this purpose, we create a dataset from scratch and use it to evaluate the effectiveness of µVulDeePecker. Experimental results show that µVulDeePecker is effective for multiclass vulnerability detection and that accommodating control-dependence (other than data-dependence) can lead to higher detection capabilities. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Asynchronous control for Markov jump systems subject to actuator saturation

Asynchronous control for Markov jump systems subject to actu...

引用

第33届中国控制与决策会议

作者： Chao Wang Lei Liu Wei Wang Xiaobing Luo Huijin Fan the National Key Laboratory of Science and Technology of Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and Technology the School of Automation Science and Electrical Engineering and the Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University School of Computer Technology and Software Engineering Wuhan Polytechnic

In this paper,the asynchronous controller problem is considered for Markov jump systems(MJSs) subject to actuator *** asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are *** on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique,a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic ***,a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

关键词： Asynchronous control actuator saturation Markov jump systems

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：