检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Zhao, Fangyuan Ren, Xuebin Yang, Shusen Yang, Xinyu School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Lab For Intelligent Networks and Network Security Xi'an Jiaotong University China

Latent Dirichlet Allocation (LDA) is a popular topic modeling technique for discovery of hidden semantic architecture of text datasets, and plays a fundamental role in many machine learning applications. However, like many other machine learning algorithms, the process of training a LDA model may leak the sensitive information of the training datasets and bring significant privacy risks. To mitigate the privacy issues in LDA, we focus on studying privacy-preserving algorithms of LDA model training in this paper. In particular, we first develop a privacy monitoring algorithm to investigate the privacy guarantee obtained from the inherent randomness of the collapsed gibbs sampling (CGS) process in a typical LDA training algorithm on centralized curated datasets. Then, we further propose a locally private LDA training algorithm on crowdsourced data to provide local differential privacy for individual data contributors. The experimental results on real-world datasets demonstrate the effectiveness of our proposed algorithms. Copyright © 2019, The Authors. All rights reserved.

关键词： Statistics

来源：评论

学校读者我要写书评

暂无评论

Robust unsupervised feature selection by nonnegative sparse subspace learning

引用

Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

来源：评论

学校读者我要写书评

暂无评论

SELWasm: A Code Protection Mechanism for WebAssembly

SELWasm: A Code Protection Mechanism for WebAssembly

引用

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Jian Sun DingYuan Cao XiMing Liu ZiYi Zhao WenWen Wang XiaoLi Gong Jin Zhang College of Computer Science Nankai University Tianjin China College of Cyber Science Nankai University Tianjin China Department of Computer Science University of Georgia Athens GA USA Tianjin Key Laboratory of Network and Data Security Technology Tianjin China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

WebAssembly is a novel front-end programming technology that has been wildly supported by most web browsers. The high-level language can be compiled into WebAssembly binary code, and executed by the browser at almost native speed. With the help of WebAssembly, the complex business logic and algorithms can be integrated into the webpage and migrate the computation overhead to the browsers. However, due to the limit of browsers, it is very difficult for developers to ensure that the intellectual property embedded in the front-end code is well protected. Even though the WebAssembly code is a compiler-generated binary file, it is easy to obtain it from the website for reuse or reverse engineering. This paper proposed a new framework, SELWasm, to protect the WebAssembly code. In the framework, three major mechanisms are provided. The Environment Self-checking mechanism can protect the source code from illegally reusing by any unauthorized website. The Encryption&Decryption mechanism can protect the WebAssembly code from being distributed as plaintext. The Lazy-loading mechanism can improve the performance of page loading, mitigate the overhead introduced by the previous mechanisms. Comprehensive experiments are conducted to measure the effectiveness and efficiency of SELWasm. The results show that the behaviors considered from web attackers can be resisted and the overhead involved is negligible.

关键词： Browsers Encryption Servers Runtime Intellectual property Loading

来源：评论

学校读者我要写书评

暂无评论

Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings

arXiv

引用

arXiv 2020年

作者： Yu, Liang Sun, Yi Xu, Zhanbo Shen, Chao Yue, Dong Jiang, Tao Guan, Xiaohong College of Automation & College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing210003 China Systems Engineering Institute Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China college of Internet of Things Nanjing University of Posts and Telecommunications Nanjing210003 China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210003 China Wuhan National Laboratory for Optoelectronics School of Electronic Information and Communications Huazhong University of Science and Technology Wuhan430074 China

In commercial buildings, about 40%-50% of the total electricity consumption is attributed to Heating, Ventilation, and Air Conditioning (HVAC) systems, which places an economic burden on building operators. In this paper, we intend to minimize the energy cost of an HVAC system in a multi-zone commercial building under dynamic pricing with the consideration of random zone occupancy, thermal comfort, and indoor air quality comfort. Due to the existence of unknown thermal dynamics models, parameter uncertainties (e.g., outdoor temperature, electricity price, and number of occupants), spatially and temporally coupled constraints associated with indoor temperature and CO2 concentration, a large discrete solution space, and a non-convex and non-separable objective function, it is very challenging to achieve the above aim. To this end, the above energy cost minimization problem is reformulated as a Markov game. Then, an HVAC control algorithm is proposed to solve the Markov game based on multi-agent deep reinforcement learning with attention mechanism. The proposed algorithm does not require any prior knowledge of uncertain parameters and can operate without knowing building thermal dynamics models. Simulation results based on real-world traces show the effectiveness, robustness and scalability of the proposed algorithm. 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

Efficient attribute-based encryption with blackbox traceability 12th

Efficient attribute-based encryption with blackbox traceabil...

引用

12th International Conference on Provable security, ProvSec 2018

作者： Xu, Shengmin Yang, Guomin Mu, Yi Liu, Ximeng Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong Wollongong Australia School of Information Systems Singapore Management University Singapore Singapore College of Mathematics and Computer Science Fuzhou University FuzhouFujian China Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Normal University Fuzhou China

ISBN: (纸本)9783030014452

Traitor tracing scheme can be used to identify a decryption key is illegally used in public-key encryption. In CCS’13, Liu et al. proposed an attribute-based traitor tracing (ABTT) scheme with blackbox traceability which can trace decryption keys embedded in a decryption blackbox/device rather than tracing a well-formed decryption key. However, the existing ABTT schemes with blackbox traceability are based on composite order group and the size of the decryption key depends on the policies and the number of system users. In this paper, we revisit blackbox ABTT and introduce a new primitive called attribute-based set encryption (ABSE) based on key-policy ABE (KP-ABE) and identity-based set encryption (IBSE), which allows aggregation of multiple related policies and reduce the decryption key size in ABTT to be irrelevant to the number of system users. We present a generic construction of the ABTT scheme from our proposed ABSE scheme and fingerprint code based on the Boneh-Naor paradigm in CCS’08. We then give a concrete construction of the ABSE scheme which can be proven secure in the random oracle model under the decisional BDH assumption and a variant of q-BDHE assumption. © Springer Nature Switzerland AG 2018.

关键词： Public key cryptography

来源：评论

学校读者我要写书评

暂无评论

Towards Privacy-preserving Incentive for Mobile Crowdsensing Under An Untrusted Platform

Towards Privacy-preserving Incentive for Mobile Crowdsensing...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Zhibo Wang Jingxin Li Jiahui Hu Ju Ren Zhetao Li Yanjun Li State Key Laboratory for Novel Software Technology Nanjing University P. R. China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University P. R. China School of Information Science and Engineering Central South University P. R. China Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education College of Information Engineering Xiangtan University P. R. China School of Computer Science and Technology Zhejiang University of Technology P. R. China

Reverse auction-based incentive mechanisms have been commonly proposed to stimulate mobile users to participate in crowdsensing, where users submit bids to the platform to compete for tasks. Recent works pointed out that bid is a private information which can reveal sensitive information of users (e.g., location privacy), and proposed bid-preserving mechanisms with differential privacy against inference attack. However, all these mechanisms rely on a trusted platform, and would fail in bid protection completely when the platform is untrusted (e.g., honest-but-curious). In this paper, we focus on the bid protection problem in mobile crowdsensing with an untrusted platform, and propose a novel privacy-preserving incentive mechanism to protect users' true bids against the honest-but-curious platform while minimizing the social cost of winner selection. To this end, instead of uploading the true bid to the platform, a differentially private bid obfuscation function is designed with the exponential mechanism, which helps each user to obfuscate bids locally and submit obfuscated task-bid pairs to the platform. The winner selection problem with the obfuscated task-bid pairs is formulated as an integer linear programming problem and proved to be NP-hard. We consider the optimization problem at two different scenarios, and propose a solution based on Hungarian method for single measurement and a greedy solution for multiple measurements, respectively. The proposed incentive mechanism is proved to satisfy ε-differential privacy, individual rationality and γ-truthfulness. The extensive experiments on a real-world data set demonstrate the effectiveness of the proposed mechanism against the untrusted platform.

关键词： Task analysis Privacy Differential privacy Sensors Mobile handsets Cryptography Integer linear programming

来源：评论

学校读者我要写书评

暂无评论

A data glove-based KEM dynamic gesture recognition algorithm

引用

International Journal of Performability Engineering 2018年第11期14卷 2589-2600页

作者： Han, Rui Feng, Zhiquan Ai, Changsheng Xie, Wei Wang, Kang School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China School of Mechanical Engineering University of Jinan Jinan250022 China School of Information and Electrical Engineering Harbin Institute of Technology at Weihai WeiHai264209 China

Data gloves-based gesture recognition plays a very important role in the virtual reality interaction system. A new dynamic gesture recognition method, that is, K-means clustering dimensionality reduction and Euclidean metric template matching algorithm based on data glove (KEM algorithm), is proposed in this paper. First, high-dimensional data is clustered in the K-means clustering algorithm to achieve dimensionality reduction. Then, the low-dimensional data is put into the template matching method based on Euclidean metric to get the distance that matches all the templates. Finally, the corresponding gesture is identified according to the template matching. The main innovations of the proposed KEM algorithm are as follows: (a) K-means clustering is applied to dynamic gesture recognition for the first time to achieve real-time recognition, (b) the classical K-means method is optimized, and (c) the template matching process is more reasonable. Experiments show that the proposed KEM method can achieve 99.42% in recognition rate. The validity of the KEM method has been verified in a 3D Intelligent Teaching System. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Template matching

来源：评论

学校读者我要写书评

暂无评论

Dual Graph-Laplacian PCA: A closed-form solution for bi-clustering to find "checkerboard" structures on gene expression data

arXiv

引用

arXiv 2019年

作者： Liu, Jin-Xing Feng, Chun-Mei Kong, Xiang-Zhen Xu, Yong School of Information Science and Engineering Qufu Normal University Rizhao Shandong276826 China Bio-Computing Research Center Shenzhen Graduate School Harbin Institute of Technology Key Laboratory of Network Oriented Intelligent Computation Shenzhen Guangdong518055 China

In the context of cancer, internal "checkerboard" structures are normally found in the matrices of gene expression data, which correspond to genes that are significantly up- or down-regulated in patients with specific types of tumors. In this paper, we propose a novel method, called dual graph-regularization principal component analysis (DGPCA). The main innovation of this method is that it simultaneously considers the internal geometric structures of the condition manifold and the gene manifold. Specifically, we obtain principal components (PCs) to represent the data and approximate the cluster membership indicators through Laplacian embedding. This new method is endowed with internal geometric structures, such as the condition manifold and gene manifold, which are both suitable for bi-clustering. A closed-form solution is provided for DGPCA. We apply this new method to simultaneously cluster genes and conditions (e.g., different samples) with the aim of finding internal "checkerboard" structures on gene expression data, if they exist. Then, we use this new method to identify regulatory genes under the particular conditions and to compare the results with those of other state-of-the-art PCA-based methods. Promising results on gene expression data have been verified by extensive experiments. Copyright © 2019, The Authors. All rights reserved.

关键词： Principal component analysis

来源：评论

学校读者我要写书评

暂无评论

network Traffic Outlier Detection Based on Full Convolutional network

Network Traffic Outlier Detection Based on Full Convolutiona...

引用

International Conference on Computational Intelligence and security

作者： Jingrong Mo Yong Wang Miao Ye School of Computer Science and Information Security Guilin University of Electronic Technology Guilin Guangxi China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin Guangxi China Guangxi Colleges and Universities Key Laboratory of Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin Guangxi China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi China

ISBN: (数字)9781728160924

ISBN: (纸本)9781728160931

network traffic flow can be represented as time series. Outliers of these time series is important for the computer network security. The existing methods for the time series anomaly detection mainly operate on the entire time series to detect the abnormal series, seldom find out the position of the abnormal point. To solve this defect, we propose a method to discover the location of the outliers in time series based on a full convolutional network. We first map the time series into two-dimensional grayscale images by sliding window approach, and then feed them into the full convolutional network. In the classified stage, the feature maps of the last convolution layer are restored to the same size as the input image by up-sampling. Finally, we predict the class for each pixel and calculate the loss on a pixel-by-pixel basis. We tested the model on Yahoo Webscope S5 anomaly benchmark dataset, and the results show that our proposed method can achieve a mean pixel accuracy of 99.4% and mean IOU of 97% on the test dataset.

关键词： Time series analysis Telecommunication traffic Anomaly detection Convolution Feature extraction Gray-scale Machine learning

来源：评论

学校读者我要写书评

暂无评论

Fake Colorized Image Detection with Channel-wise Convolution based Deep-learning Framework

Fake Colorized Image Detection with Channel-wise Convolution...

引用

Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA)

作者： Long Zhuo Shunquan Tan Jishen Zeng Bin Lit Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen China

ISBN: (纸本)9781728102436;9789881476852

Colorization is one remarkable emerging image manipulating technique, which maybe potentially used for illegal purpose. In this paper, we introduce WISERNet (Wider Separate-then-reunion network), a recently proposed deep-learning based data-driven color image steganalyzer in the field of fake colorized image detection. We believe that statistical inconsistencies introduced by different automatic colorization methods can be captured by advanced deep-learning based data-driven color-image steganalyzers such as WISERNet. Experimental evidences reported in this paper supports our claims: the detection performance of our proposed detector obviously outperforms FCID-HIST and FCID-FE, two state-of-the-art hand-crafted features specific to fake colorized image detection. Please note that in our approach we have never explicitly utilized information from the specific channels other than the ordinary red, green, and blue color channel, which is completely different from prior works in this field.

关键词： Detectors Training Image color analysis Error analysis Convolution Forensics

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：