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Anomaly Prediction in Passenger Flow with Knowledge Transfer Method

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Anomaly Prediction in Passenger Flow with Knowledge Transfer...

IEEE International Symposium on Dependable, Autonomic and Secure computing (DASC)

作者： Zhipu Xie Weifeng Lv Syed Muhammad Asim Ali Bowen Du Runhe Huang Key State Laboratory of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing Hosei University Tokyo Japan

ISBN: (纸本)9781538675199

Predicting anomaly in travel demand is a crucial finding from smart card data analytics. The output of these predictions is a significant contribution to planning sustainable public transport system and generating possible knowledge for transportation learning models. This paper investigates the anomaly effects of the surge in bus passengers demand and compare it with an increase in taxi demand. Indeed, both short-term and long-term demands reveal different patterns of passengers in uncertain situations. In pursuit of our goal, we estimated the similarity in stations by both selected and latent features where pre-trained knowledge are combined as an ensemble with different weights. We present Surge Prediction and Knowledge Transfer (SPKT) model that uses Seq2Seq method combined with Multi-source Transfer Learning method on travel patterns extracted from smart card data to classify source stations and target station. To illustrate the demands blueprint, we considered multiple source stations as input to the predictor, to develop a mechanism that bridges the knowledge transfer learning with the targeted stations. To exemplify our method, we use a case study of an event with passenger surge. From experiments, we found that transferring knowledge can make the surge prediction better compared to only limited training data for the target stations. The results have proved the effectiveness of surge predictions and knowledge transfer for learning models.

关键词： Surges Knowledge transfer Predictive models Public transportation Feature extraction Real-time systems

Recovery and repair schemes for shift-XOR regenerating codes

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arXiv 2019年

作者： Fu, Ximing Yang, Shenghao Xiao, Zhiqing School of Science and Engineering Chinese University of Hong Kong Shenzhen China University of Science and Technology of China China School of Science and Engineering Chinese University of Hong Kong Shenzhen Guangdong518172 China Shenzhen Key Laboratory of IoT Intelligent Systems and Wireless Network Technology Chinese University of Hong Kong Shenzhen Guangdong518172 China Shenzhen Research Institute of Big Data Shenzhen Guangdong518172 China Tsinghua University

Recovery and repair schemes are proposed for shift-exclusive-or (shift-XOR) product-matrix (PM) regenerating codes, which outperform those of the existing PM codes in terms of both communication and computation costs. In particular, for the minimum bandwidth regenerating (MBR) codes, our recovery and repair schemes have the optimal transmission bandwidth, zero decoding auxiliary data space and lower time complexity;for the minimum storage regenerating (MSR) codes, our recovery and repair schemes have smaller transmission bandwidth, smaller decoding auxiliary data space and lower time complexity. Moreover, our schemes involve only XOR operations in recovery and repair and are practical for system implementation. Technically, a procedure is first proposed for solving a system of shift-XOR equations, which plays a similar fundamental role as Gaussian elimination for solving systems of linear equations, and is of independent interest. The recovery and repair of shift-XOR MBR/MSR codes are then decomposed into a sequence of systems of shift-XOR equations, and hence can be solved by a sequence of calls to the procedure for solving a system of shift-XOR equations. As the decomposition of the recovery and repair depends only on the PM construction, but not the specific shift and XOR operations, our recovery and repair schemes can be extended to other MBR/MSR codes using the PM construction. Copyright © 2019, The Authors. All rights reserved.

关键词： Matrix algebra

Comparison of UAV-based multispectral sensors for detection of Solenopsis invicta Nests

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IOP Conference Series: Earth and Environmental Science 2020年第1期569卷

作者： Yuejun Song Feng Chen Kaitao Liao Jiangxi Institute of Soil and Water Conservation Nanchang 330029 China Jiangxi Key Laboratory of Soil Erosion and Prevention Nanchang 330029 China College of Computer and Information Engineering Xiamen University of Technology Xiamen 361024 China Big Data Institute of Digital Natural Disaster Monitoring in Fujian Xiamen University of Technology Xiamen 361024 China Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University Xiamen 361005 China

The invasive red imported fire ant (Solenopsis invicta Buren) (Hymenoptera: Formicidae) has been continuing to expand its range in China, resulting in adverse ecological impacts to where it has invaded. As such, the reliable detection of Solenopsis invicta Buren nests is needed to effectively manage the species. In this paper, the spectral features of the Solenopsis invicta Buren nests were demonstrated using spectra samples obtained by ASD FieldSpec 4 during field surveys in Shanggao County, Jiangxi Province. Furthermore, channel reflectance estimations for three unmanned aerial vehicle (UAV)-based multispectral sensors were simulated correspondingly. Based on the channel reflectance from individual sensors, the detection of the Solenopsis invicta Buren nests was investigated. Generally, between-class differences in reflectance were relatively significant in both Green channel and Red channel, which suggests the importance of these two channels for the detection of Solenopsis invicta Buren nest. Minor between-sensor differences observed in both Green channel and Red channel suggest that detection results obtained from UAV-based multispectral observations could be comparable among the three sensors, considering only the spectral features. There are significant differences in reflectance between the soil of Solenopsis invicta Buren nests and ordinary soil over the longwave region of near infrared (NIR). It suggests the spectral measurement over the longwave region of NIR could be more useful in distinguishing the soil of Solenopsis invicta Buren nests from ordinary soil. Nevertheless, the multispectral sensors provided with channels covering visible and the shortwave region of NIR, possibly do not meet requirements completely for application. An effective method for detecting Solenopsis invicta Buren nests using UAV-based multispectral sensors is currently being evaluated.

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A java code protection scheme via dynamic recovering runtime instructions 18th

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A java code protection scheme via dynamic recovering runtime...

18th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2018

作者： Jiajia, Sun Jinbao, Gao Yu-an, Tan Yu, Zhang Xiao, Yu School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China China University of Mining and Technology Beijing100083 China Department of Computer Science and Technology Shandong University of Technology ZiboShandong255022 China

ISBN: (纸本)9783030050627

As Android operating system and applications on the device play important roles, the security requirements of Android applications increased as well. With the upgrade of Android system, Android runtime mode (ART mode) has gradually become the mainstream architecture of the Android operating system. ART introduces several improvements in Android, but it also introduces new ways to enhance malicious activities. This paper proposed a confidential finer granularity protection scheme for application programs under ART mode of ROOT Android devices. Taking Java method as the protection granularity, the protection scheme increased the accuracy of protecting targets. In addition, the protection scheme provided a more thorough protection for applications by combining dynamic loading technology and encryption technology in ART mode, and improved the security of Android applications. Experiments showed that the proposed protection scheme is effective. © Springer Nature Switzerland AG 2018.

关键词： Android (operating system)

Self-Evolutionary Neuron Model for Fast-Response Spiking Neural Networks

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TechRxiv

TechRxiv 2019年

作者： Zhang, Anguo Han, Ying Hu, Jing Niu, Yuzhen Gao, Yueming Chen, Zhizhang Zhao, Kai College of Physics and Information Engineering Fuzhou University Fujian350108 China The Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fuzhou350116 China Research Institute of Ruijie Ruijie Networks Co. Ltd Fujian350002 China School of Public Health Xiamen University Xiamen361102 China College of Information and Intelligent Transportation Fujian Chuanzheng Communications College Fuzhou350007 China Fujian Key Laboratory of Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University The Key Laboratory of Spatial Data Mining and Information Sharing Ministry of Education Fujian350108 China College of Physics and information Engineering Fuzhou University the Key Laboratory of Medical Instrumentation Pharmaceutical Technology of Fujian Province Fujian350108 China College of Physics and Information Engineering Fuzhou University 350108 China Faculty of Science and Technology University of Macau 999078 China

We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on the input received from its presynapses and subsequent neuron firing events. We analyze and derive the firing activity homeostatic convergence of the proposed models. We experimentally verify and compare the models on MNIST handwritten digits and FashionMNIST classification tasks. We show that the proposed neuron models significantly increase the response speed to the input signal. Experiment codes are available at https://***/anvien/Evol-SNN. © 2019, CC0.

关键词： Neural networks

Dynamic network embedding via incremental skip-gram with negative sampling

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arXiv 2019年

作者： Peng, Hao Li, Jianxin Yan, Hao Gong, Qiran Wang, Senzhang Liu, Lin Wang, Lihong Ren, Xiang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China Department of Computer Science University of Southern California Los Angeles United States

Network representation learning, as an approach to learn low dimensional representations of vertices, has attracted considerable research attention recently. It has been proven extremely useful in many machine learning tasks over large graph. Most existing methods focus on learning the structural representations of vertices in a static network, but cannot guarantee an accurate and efficient embedding in a dynamic network scenario. The fundamental problem of continuously capturing the dynamic properties in an efficient way for a dynamic network remains unsolved. To address this issue, we present an efficient incremental skip-gram algorithm with negative sampling for dynamic network embedding, and provide a set of theoretical analyses to characterize the performance guarantee. Specifically, we first partition a dynamic network into the updated, including addition/deletion of links and vertices, and the retained networks over time. Then we factorize the objective function of network embedding into the added, vanished and retained parts of the network. Next we provide a new stochastic gradient-based method, guided by the partitions of the network, to update the nodes and the parameter vectors. The proposed algorithm is proven to yield an objective function value with a bounded difference to that of the original objective function. The first order moment of the objective difference converges in order of O( 1/n2 ), and the second order moment of the objective difference can be stabilized in order of O(1). Experimental results show that our proposal can significantly reduce the training time while preserving the comparable performance. We also demonstrate the correctness of the theoretical analysis and the practical usefulness of the dynamic network embedding. We perform extensive experiments on multiple real-world large network datasets over multi-label classification and link prediction tasks to evaluate the effectiveness and efficiency of the proposed framework, and up

关键词： Classification (of information)

Research on the Brain-inspired Cross-modal Neural Cognitive computing Framework

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arXiv 2018年

作者： Liu, Yang Key Laboratory of Big Data Analysis and Processing of Henan province Intelligent Technology and Application Engineering Research Centre of Henan province College of Computer Science and Information Engineering Henan University Kaifeng475004 China

To address modeling problems of brain-inspired intelligence, this thesis is focused on researching in the semantic-oriented framework design for multimedia and multimodal information. The Multimedia Neural Cognitive computing (MNCC) model was designed based on the nervous mechanism and cognitive architecture. Furthermore, the semantic-oriented hierarchical Cross-modal Neural Cognitive computing (CNCC) framework was proposed based on MNCC model, and formal description and analysis for CNCC framework was given. It would effectively improve the performance of semantic processing for multimedia and cross-modal information, and has far-reaching significance for exploration and realization brain-inspired computing. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

Correction to: Incentive mechanisms for mobile crowd sensing based on supply-demand relationship

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Peer-to-Peer Networking and Applications 2019年第3期13卷 1069-1069页

作者： Xu, Jia Lu, Wei Xu, Lijie Yang, Dejun Li, Tao Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China Department of Computer Science Colorado School of Mines Golden USA

Correction is needed in the original article. The university name in affiliation (1) is changed from “University of Posts and Telecommunications” to “Nanjing University of Posts and Telecommunications”.

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Impact of Prior Knowledge and data Correlation on Privacy Leakage: A Unified Analysis

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arXiv 2019年

作者： Li, Yanan Ren, Xuebin Yang, Shusen Yang, Xinyu National Engineering Laboratory for Big Data Analytics [NEL-BDA Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an Shaanxi710049 China School of Electronic and Information Engineering Xi’an Jiaotong University Xi’an Shaanxi710049 China Ministry of Education Key Lab for Intelligent Networks and Network Security [MOE KLINNS Lab Xi’an Jiaotong University Xi’an Shaanxi710049 China

It has been widely understood that differential privacy (DP) can guarantee rigorous privacy against adversaries with arbitrary prior knowledge. However, recent studies demonstrate that this may not be true for correlated data, and indicate that three factors could influence privacy leakage: the data correlation pattern, prior knowledge of adversaries, and sensitivity of the query function. This poses a fundamental problem: what is the mathematical relationship between the three factors and privacy leakage? In this paper, we present a unified analysis of this problem. A new privacy definition, named prior differential privacy (PDP), is proposed to evaluate privacy leakage considering the exact prior knowledge possessed by the adversary. We use two models, the weighted hierarchical graph (WHG) and the multivariate Gaussian model to analyze discrete and continuous data, respectively. We demonstrate that positive, negative, and hybrid correlations have distinct impacts on privacy leakage. Considering general correlations, a closed-form expression of privacy leakage is derived for continuous data, and a chain rule is presented for discrete data. Our results are valid for general linear queries, including count, sum, mean, and histogram. Numerical experiments are presented to verify our theoretical analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning