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Cost Sensitive Matrix Factorization for Face Recognition 1

引用

18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Wan, Jianwu Yang, Ming Wang, Hongyuan Changzhou Univ Sch Informat Sci & Engn Changzhou Peoples R China Nanjing Normal Univ Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China

ISBN: (数字)9783319689357

ISBN: (纸本)9783319689357;9783319689340

In this paper, we propose a cost sensitive matrix factorization (CSMF) for face recognition. To make the face representation cost sensitive, CSMF adopts a more flexible feature embedding strategy. It contains two main steps: (1) matrix factorization for the learning of latent semantic representation and (2) cost sensitive latent semantic regression. In this way, the face images are embedded into their label space with the misclassification loss minimized. the experimental results on Extended Yale B and ORL demonstrate its effectiveness.

关键词： Cost sensitive learning Matrix factorization Face recognition Latent semantic regression

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Understanding Matching data through their Partial Components 18th

Understanding Matching Data Through Their Partial Components

引用

18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Alvarez de Toledo, Pablo Nunez, Fernando Usabiaga, Carlos Tallon-Ballesteros, Antonio J. Univ Seville Dept Ind Org & Business Management 1 Seville Spain Pablo Olavide Univ Dept Econ Quantitat Methods & Econ Hist Seville Spain Univ Seville Dept Languages & Comp Syst Seville Spain

ISBN: (纸本)9783319689357;9783319689340

In this paper we develop a previous work on matching data [2], inserting their contents in the more general framework of contingency tables and dealing with the dimensions problem generated by the combination of the multiple characteristics that define each row and column category. Two concepts related to the matching process are defined: propensity to match and similarity in the matching. Both measures can be divided into partial components which allow a better understanding of the underlying structure of the data. We illustrate our methodology taking as an example a labor market where each worker category and each job category is defined by the combination of two attributes: location and occupational level.

关键词： Contingency tables Propensity to match Factor decomposition Clustering Labor matching data

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Energy Cooperation in Co MP System Based on Q-learning 11

Energy Cooperation in Co MP System Based on Q-learning

引用

2017 11th IEEE international conference on Anti-counterfeiting,Security,and Identification(ASID)

作者： Yabo Lv Baogang Li Yuanbin Yao Dandan Guo Department of Electric and Communication Engineering North China Electric Power University

ISBN: (纸本)9781538605349;9781538605332

this paper studies energy harvesting communication systems in which the base station sends data packets using the energy harvested from the surrounding natural environment. We assume that at each time slot only information about the current and past state of the base station is available, modeling the scenario as a markov decision process and propose a reinforcement learning approach based on Q-learning for the transmitter to learn to cooperation trough energy sharing. For the problem of continuous state space in reinforcement learning, we propose an online algorithm based on approximate linear dependence to sparse sample pool. Furthermore, we use a neural network as approximator for the value function to improve the generalization ability. Numerical results show that our proposed algorithm can significantly improve the system performance even when the future environment change and energy harvested is uncertain for the BS.

关键词： Q-learning approximate linear dependence energy cooperation coordinated multipoint

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Anonymous data Sharing Between Organisations with Elliptic Curve Cryptography 16

Anonymous Data Sharing Between Organisations with Elliptic C...

引用

16th IEEE international conference on Trust, Security and Privacy in Computing and Communications / the 11th IEEE international conference on Big data Science and engineering / the 14th IEEE international conference on Embedded Software and Systems

作者： Will, Mark A. Ko, Ryan K. L. Schlickmann, Silvino J. Univ Waikato Cyber Secur Lab Hamilton New Zealand INTERPOL Global Complex Innovat IGCI Innovat Directorate Singapore Singapore

ISBN: (纸本)9781509049066

Promoting data sharing between organisations is challenging, without the added concerns over having actions traced. Even with encrypted search capabilities, the entities digital location and downloaded information can be traced, leaking information to the hosting organisation. this is a problem for law enforcement and government agencies, where any information leakage is not acceptable, especially for investigations. Anonymous routing is a technique to stop a host learning which agency is accessing information. Many related works for anonymous routing have been proposed, but are designed for Internet traffic, and are over complicated for internal usage. A streaming design for circuit creation is proposed using elliptic curve cryptography. Allowing for a simple anonymous routing solution, which provides fast performance with source and destination anonymity to other organisations.

关键词： Anonymous Routing Elliptic Curve Cryptography Light-weight data Sharing Secure Processing

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Co-clustering with Manifold and Double Sparse Representation 18th

Co-clustering with Manifold and Double Sparse Representation

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18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Li, Fang Zhang, Sanyuan Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Guilin Univ Elect Technol Key Lab Image & Graph Intelligent Proc Higher Edu Guilin Peoples R China Guilin Univ Elect Technol Guangxi Key Lab Trusted Software Guilin Peoples R China

ISBN: (纸本)9783319689357;9783319689340

Clustering is a fundamental tool that has been applied in dealing with huge volumes of text documents and images. For extracting relevant information from the enormous volumes of available data, some co-clustering algorithms have been proposed and shown to be superior to traditional one-side clustering. In this paper, we proposed a novel co-clustering approach called double sparse manifold learning (DSML). We based our formulation on double sparse constraints and manifold learning which use a modified version of mutual k-nearest neighbor graph to capture the underlying structure, modeled sample-feature relationship from the data reconstruction perspective. We developed an iterative procedure to get the solution. Our method preserves local geometrical structure better. Experiments on three benchmark datasets show that our method can get more promising performance on all analyzed data-sets.

关键词： Co-clustering Sparse representation Manifold learning Double sparse Mutual kNN

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Malware Detection in Cloud Infrastructures Using Convolutional Neural Networks

Malware Detection in Cloud Infrastructures Using Convolution...

引用

IEEE international conference on Cloud Computing, CLOUD

作者： Mahmoud Abdelsalam Ram Krishnan Yufei Huang Ravi Sandhu Department of Computer Science Institute for Cyber Security and Center for Security and Privacy Enhanced Cloud Computing Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio Texas USA

A major challenge in Infrastructure as a Service (IaaS) clouds is its exposure to malware. Malware can spread rapidly within a datacenter and can cause major disruption to a cloud service provider and its clients. this paper introduces and discusses an effective malware detection approach in cloud infrastructure using Convolutional Neural Network (CNN), a deep learning approach. We initially employ a standard 2d CNN by training on metadata available for each of the processes in a virtual machine (VM) obtained by means of the hypervisor. We enhance the CNN classifier accuracy by using a novel 3d CNN (where an input is a collection of samples over a time interval), which greatly helps reduce mislabelled samples during data collection and training. Our experiments are performed on data collected by running various malware (mostly Trojans and Rootkits) on VMs. the malware used in our experiments are randomly selected. this reduces the selection bias of known-to-be highly active malware for easy detection. We demonstrate that our 2d CNN model reaches an accuracy of ≃ 79%, and our 3d CNN model significantly improves the accuracy to ≃ 90%.

关键词： Malware Cloud computing Measurement Feature extraction Static analysis Training Machine learning

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Deep learning Approach for QRS Wave Detection in ECG Monitoring 11

Deep Learning Approach for QRS Wave Detection in ECG Monitor...

引用

11th IEEE international conference on Application of Information and Communication Technologies (AICT)

作者： Mitrokhin, Maxim Kuzmin, Andrey Mitrokhina, Natalia Zakharov, Sergey Rovnyagin, Mikhail Penza State Univ Polytech Inst Penza Russia Natl Res Nucl Univ Intelligent Cybernet Syst Inst MEPhI Moscow Russia

ISBN: (纸本)9781538605011

Paper describes an approach of deep learning for QRS wave detection for using in mobile heart monitoring systems. Authors analyze a deep learning approach and its advantages in the field of feature extraction and detection, and deep network architecture. Two different variants of deep network are proposed. ECG data processing scheme that includes a neural network is described. It presumes preprocessing, filtering, windowing of ECG signal, buffering, QRS wave detection and analysis. Network training process is mathematically founded. Two variants of neural network are experimentally tested. Training sets and test sets are obtained from free ECG data bank ***. Experimental results show that network with decreasing number of neurons in hidden layers has a better generalization capability. Next steps of research will include experiments with training set size and determining of its' influence on the quality of detection.

关键词： ECG signal analysis data processing QRS wave cardiology neural networks

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Sparse Representation Based on Discriminant Locality Preserving Dictionary learning for Face Recognition 18th

Sparse Representation Based on Discriminant Locality Preserv...

引用

18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Feng, Guang Li, Hengjian Dong, Jiwen Chen, Xi Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China Guizhou Normal Univ Sch Bigdata & Comp Sci Guiyang Guizhou Peoples R China

ISBN: (纸本)9783319689357;9783319689340

A novel discriminant locality preserving dictionary learning (DLPDL) algorithm for face recognition is proposed in this paper. In order to achieve better performance and less computation, dimensionality reduction is applied on original image samples. Most of the proposed dictionary learning methods learn features and dictionary, however, the inner structure of feature is hardly considered. therefore, by incorporating discriminant locality preserving criteria into dictionary learning, the margin of coefficients distance between between-class and within-class is encourage to be large in order to enhance the classification ability and gain discriminative information. What is more, the local structure of the feature is also preserved, which is very vital in face recognition performance. Our experiments on Extended Yale B, AR and CMU face database demonstrated the proposed algorithm has higher recognition performance than other dictionary learning based classification methods.

关键词： Dictionary learning Sparse representation Discriminant locality preserving criterion Face recognition

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A Pay as You Use Resource Security Provision Approach Based on data Graph, Information Graph and Knowledge Graph 18th

A Pay as You Use Resource Security Provision Approach Based ...

引用

18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Shao, Lixu Duan, Yucong Cui, Lizhen Zou, Quan Sun, Xiaobing Hainan Univ Coll Informat Sci & Technol State Key Lab Marine Resource Utilizat South Chin Haikou Hainan Peoples R China Shandong Univ Coll Comp Sci & Technol Jinan Shandong Peoples R China Tianjin Univ Coll Comp Sci Tianjin Peoples R China Yangzhou Univ Sch Informat Engn Yangzhou Jiangsu Peoples R China

ISBN: (纸本)9783319689357;9783319689340

With the development of data mining technology, lack of private resource protection has become a serious challenge. We propose to clarify the expression of Knowledge Graph in three layers including data Graph, Information Graph and Knowledge Graph and illustrate the representation of data Graph, Information Graph and Knowledge Graph respectively. We elaborate a pay as you use resource security provision approach based on data Graph, Information Graph and Knowledge Graph in order to ensure that resources will not be used, tampered with, lost and destroyed in unauthorized situations.

关键词： Resource modelling Knowledge graph Security provision

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Consensus-based Parallel Algorithm for Robust Convex Optimization with Scenario Approach in Colored Network 18th

Consensus-based Parallel Algorithm for Robust Convex Optimiz...

引用

18th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Feng, Fan Cao, Feilong China Jiliang Univ Coll Sci Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9783319689357;9783319689340

this paper mainly proposes an parallel distributed learning algorithm for the robust convex optimization (RCO). Firstly, the scenario approach is used to transform RCO into its probabilistic approximation Scenario Problem (SP), which is distributively solved by multiprocessors to lighten the computational burden. Secondly, each processor (node) of the colored network processes the local optimization via a primal-dual subgradient algorithm (PDSA) to obtain an optimal solution called a local variable. Finally, a consensus method named the Colored Distributed Average Consensus (CDAC), which is based on Distributed Average Consensus (DAC), is proposed to act on the whole local variables to obtain the global optimal solution. Experimental results show that CDAC has an advantage in terms of computational time over DAC, while they have the same results.

关键词： Distributed learning Robust convex optimization Scenario approach Primal-dual subgradient algorithm Distributed average consensus Colored network

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