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SMK-means:An Improved Mini Batch K-means Algorithm Based on Mapreduce with Big Data

computers, Materials & Continua 2018年第9期56卷 365-379页

作者： Bo Xiao Zhen Wang Qi Liu Xiaodong Liu Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control Jiangsu Collaborative Innovation Centre of Atmospheric Environment and Equipment TechnologySchool of Environmental Science and EngineeringNanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computer and Software Nanjing University of Information Science and Technology219 Ningliu RoadNanjing210044China School of Computing Edinburgh Napier University10 Colinton RoadEdinburghEH105DTUK

In recent years,the rapid development of big data technology has also been favored by more and more *** data storage and calculation problems have also been *** the same time,outlier detection problems in mass data have also come along with ***,more research work has been devoted to the problem of outlier detection in big ***,the existing available methods have high computation time,the improved algorithm of outlier detection is presented,which has higher performance to detect *** this paper,an improved algorithm is *** SMK-means is a fusion algorithm which is achieved by Mini Batch K-means based on simulated annealing algorithm for anomalous detection of massive household electricity data,which can give the number of clusters and reduce the number of iterations and improve the accuracy of *** this paper,several experiments are performed to compare and analyze multiple performances of the *** analysis,we know that the proposed algorithm is superior to the existing algorithms.

关键词： Big data outlier detection SMK-means Mini Batch K-means simulated annealing

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Improvement and Application of Apriori Algorithm Based on Equalization 4

Improvement and Application of Apriori Algorithm Based on Eq...

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4th IEEE International Conference on Data Science in Cyberspace, DSC 2019

作者： Xu, Libo Qiao, Lin Zhao, Feng Yang, Bo Wang, Qiong Ding, Ping Li, Lei State Grid Liaoning Electric Power Co. Ltd Shenyang Liaoning110005 China Anhui Jiyuan Software Co. Ltd Heifei Anhui230000 China State Grid Gansu Electric Power Co. Ltd Lanzhou Gansu730000 China School of Computer Science and Information Engineering Hefei University of Technology Hefei Anhui230601 China

ISBN: (纸本)9781728145280

In the era of information explosion, data collection and analysis have become more and more important, and association rule mining has become an important research area of the data mining field. As a classic association rule algorithm, Apriori often falls into the problem of frequently scanning the database and generating a large number of candidate sets. Aiming at these problems, an improved Apriori algorithm MD-Apriori based on the idea of equalization is proposed, which adopts the vertical data structure to mine frequent items and then the proposed approach is applied to mining frequent items of massive alarm information. Experimental results show that the proposed algorithm is significantly superior to the current association rule mining algorithms. © 2019 IEEE.

关键词： Association rules

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Monitoring system of agricultural performance based on multisensor data and cross-validation technique in merging temporal images

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Agronomy Journal 2022年第1期115卷

作者： N. P. G. Bhavani Prameeladevi Chillakuru Ramana Lakshmi Adusumilli Roobaea Alroobaea Saeed Rubaiee Abdulkader S. Hanbazazah Suman Mann Saveetha School of Engineering Chennai Tamil Nadu 602105 India Contribution: Data curation Formal analysis Rajalakshmi Engineering College Chennai Tamil Nadu 602105 India Contribution: Investigation Methodology Dep. of Computer Science and Engineering Prasad V Potluri Siddhartha Institute of Technology Kanuru Vijayawada Andhra Pradesh 520007 India Contribution: Resources Software Dep. of Computer Science College of Computers and Information Technology Taif Univ. P. O. Box 11099 Taif 21944 Saudi Arabia Contribution: Funding acquisition Supervision Dep. of Industrial and Systems Engineering College of Engineering Univ. of Jeddah Jeddah Saudi Arabia Contribution: Funding acquisition Visualization Contribution: Funding acquisition Writing - original draft Writing - review & editing Maharaja Surajmal Institute of Technology New Delhi 110058 India Contribution: Conceptualization Writing - original draft

Several approaches to forecasting agricultural production have been used across the country, but they have focused on information widely detected with technology that was not very successful. Unfortunately, because of many difficulties such as climate variables (50% global fog cover) with limited temporal accuracy, the remotely detected information necessary to predict crop production was often insufficient. As a result of these problems, existing methods of estimating agricultural production are ineffective or out of date. Several efforts have been made to overcome these challenges by combining images with high temporal accuracy but poor geographic detail. On the other hand, it is this kind of situation that is most suitable for extremely large and homogeneous agricultural areas. An innovative theoretical framework has developed that explains this absence of high-quality satellite images. This intelligent method was built around this new theoretical framework, which incorporates its use of something like the energy equation to improve the predictions of multiple cultures. Many producers were contacted and data regarding agricultural production were obtained to validate the results of the smart technology. The excellent reliability of this intelligent method has been shown by a comparative contrast between projected crop yields and actual output in various areas.

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MAC-AC: A Novel Distributed MAC Protocol for Accessing Channel in Vehicular Ad Hoc Networks

MAC-AC: A Novel Distributed MAC Protocol for Accessing Chann...

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IEEE Conference on Vehicular technology (VTC)

作者： Baozhu Li Fen Hou Changyue Zhang Shaohua Li Shujuan Ji Shanzhi Chen School of Information Science and Engineering University of Jinan Jinan China Shandong Provincial Key Laboratory of Network-based Intelligent Computing Jinan China Faculty of Science and Technology University of Macau Macau China Automotive Research Institute China National Heavy Duty Truck Group Co. Ltd Jinan China Shandong Key Laboratory of Wisdom Mine Information Technology Qingdao China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China State Key Lab of Wireless Mobile Communication Beijing China China Academy of Telecommunication Technology Beijing China

ISBN: (数字)9781728194844

ISBN: (纸本)9781728194851

As a promising paradigm, VANET has been attracting more and more attention from the industry and academia recently. However, due to rapid movement of vehicles and highly dynamic topology, designing efficient MAC protocol for VANET is still challenging. In this paper, we propose MAC-AC, a novel TDMA-based distributed MAC protocol designed specifically for a vehicular ad hoc network. In MAC-AC, when multiple vehicles compete for the same time slot within their two-hop communication range, one of contending vehicle can obtain this time slot by a simple method, which increases the success probability of vehicles accessing channel. Analysis results are presented to demonstrate the efficiency of MAC-AC and compare it to ADHOC MAC, an existing MAC protocol based on TDMA.

关键词： Vehicular and wireless technologies Time division multiple access Network topology Vehicular ad hoc networks Media Access Protocol Topology Vehicle dynamics

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Defensive Few-shot Learning

arXiv

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arXiv 2019年

作者： Li, Wenbin Wang, Lei Zhang, Xingxing Qi, Lei Huo, Jing Gao, Yang Luo, Jiebo The State Key Laboratory for Novel Software Technology Nanjing University 210023 China The School of Computing and Information Technology University of Wollongong Australia The Department of Computer Science Tsinghua University China The School of Computer Science and Engineering Southeast University China The Department of Computer Science University of Rochester United States

This paper investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting. To address this situation, we develop a general defensive few-shot learning (DFSL) framework to answer the following two key questions: (1) how to transfer adversarial defense knowledge from one sample distribution to another? (2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting? To answer the first question, we propose an episode-based adversarial training mechanism by assuming a task-level distribution consistency to better transfer the adversarial defense knowledge. As for the second question, within each few-shot task, we design two kinds of distribution consistency criteria to narrow the distribution gap between clean and adversarial examples from the feature-wise and prediction-wise perspectives, respectively. Extensive experiments demonstrate that the proposed framework can effectively make the existing few-shot models robust against adversarial attacks. Code is available at https://***/WenbinLee/***. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Simultaneous spectral-spatial feature selection and extraction for hyperspectral images

arXiv

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arXiv 2019年

作者： Zhang, Lefei Zhang, Qian Du, Bo Huang, Xin Tang, Yuan Yan Tao, Dacheng State Key Laboratory of Software Engineering School of Computer Wuhan University Wuhan430072 China Beijing Samsung Telecom R&D Center Beijing100028 China School of Remote Sensing and Information Engineering Wuhan University Wuhan430072 China Department of Computer and Information Science University of Macau Macau999078 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation hasn't efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address the these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectralspatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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Correlating MSM's Mental Health with Usage Behaviors on MSM-Specific Social Applications

Correlating MSM's Mental Health with Usage Behaviors on MSM-...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Xin Gao Chaohe Zhang Liantao Ma Yasha Wang Jiangtao Wang Daqing Zhang Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China Peking University Information Technology Institute (Tianjin Binhai) Tianjin China National Engineering Research Center for Software Engineering Peking University Beijing China School of Computing and Communications Lancaster University UK

ISBN: (数字)9781728140346

ISBN: (纸本)9781728140353

Homophobia and discrimination towards men who have sex with men (MSM) make the mental health of this community a severe concern. The prevalence of mobile social apps for MSM provides a new channel to study their mental health issues. However, the correlation between the psychological states of MSM and their behaviors on social apps are still uninvestigated. In this paper, we conduct a case study of 103 MSM in China to explore whether the User Generated Content, Profile, Mobility information of MSM on social apps correlate with their depression, loneliness, anxiety and stress level. The analysis results indicate that the mental health disorder level of MSM is correlated with the emotion expressed in posts, whether the user has filled in self-description blank, the social relationship among online friends, frequency of visiting popular gays-meeting places. Our findings imply future opportunities in early psychological problems detection and intervention.

关键词： Psychology Stress Correlation Atmospheric measurements Particle measurements Privacy User-generated content

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Recruiting the K-most influential prospective workers for crowdsourcing platforms

Recruiting the K-most influential prospective workers for cr...

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作者： Shahsavari, Maryam Golpayegani, Alireza Hashemi Saberi, Morteza Hussain, Farookh Khadeer Department of Computer Engineering and Information Technology Amirkabir University of Technology Tehran Iran School of Business Australian Defence Forces Academy University of New South Wales Canberra CanberraACT Australia Faculty of Engineering and Information Technology School of Software and Centre for Artificial Intelligence University of Technology Sydney UltimoNSW2007 Australia

Viral marketing is widely used by businesses to achieve their marketing objectives using social media. In this work, we propose a customized crowdsourcing approach for viral marketing which aims at efficient marketing based on information propagation through a social network. We term this approach the social community-based crowdsourcing platform and integrate it with an information diffusion model to find the most efficient crowd workers. We propose an intelligent viral marketing framework (IVMF) comprising two modules to achieve this end. The first module identifies the K-most influential users in a given social network for the platform using a novel linear threshold diffusion model. The proposed model considers the different propagation behaviors of the network users in relation to different contexts. Being able to consider multiple topics in the information propagation model as opposed to only one topic makes our model more applicable to a diverse population base. Additionally, the proposed content-based improved greedy (CBIG) algorithm enhances the basic greedy algorithm by decreasing the total amount of computations required in the greedy algorithm (the total influence propagation of a unique node in any step of the greedy algorithm). The highest computational cost of the basic greedy algorithm is incurred on computing the total influence propagation of each node. The results of the experiments reveal that the number of iterations in our CBIG algorithm is much less than the basic greedy algorithm, while the precision in choosing the K influential nodes in a social network is close to the greedy algorithm. The second module of the IVMF framework, the multi-objective integer optimization model, is used to determine which social network should be targeted for viral marketing, taking into account the marketing budget. The overall IVMF framework can be used to select a social network and recruit the K-most influential crowd workers. In this paper, IVMF is exemplifi

关键词： Crowdsourcing

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Dynamic strategy based parallel ant colony optimization on GPUs for TSPs

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Science China(information Sciences) 2017年第6期60卷 260-262页

作者： Yi ZHOU Fazhi HE Yimin QIU State Key Laboratory of Software Engineering School of Computer Science Wuhan University School of Computer Science and Technology Wuhan University of Science and Technology School of Information Science and Engineering Wuhan University of Science and Technology

Metaheuristics are a type of approximate optimization algorithms for solving hard and complex problems in science and engineering[1].They can be defined as algorithm templates that can be easily adapted to solve specific optimization *** by search behavior,most researchers divide metaheuristics into two classes:trajectory-based and population-based[1].Ant

关键词： GPU Dynamic strategy based parallel ant colony optimization on GPUs for TSPs TSP

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Targeted Malicious Email Detection using Hypervisor-based Dynamic Analysis and Ensemble Learning

Targeted Malicious Email Detection using Hypervisor-based Dy...

引用

IEEE Global Communications Conference

作者： Jian Zhang Zhaojun Gu Wenzhen Li Liangyi Gong Jeffrey Wu School of Computer Science and Engineering Tianjin University of Technology College of Cyber Science Nankai University Tianjin China School of Computer Science and Engineering Tianjin University of Technology Tianjin China School of Software and BNRist Tsinghua University Beijing China Information Security Evaluation Center of Civil Aviation Civil Aviation University of China Tianjin China Jiaxing Chenxiang Information Technology Co. Ltd. Jiaxing China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

At present, email is still one of the most frequently used communication tools for organizations and individuals. With the leakage of personal privacy information, targeted malicious email (TME) is becoming a prominent targeted cyber attack vector in today's Internet. This type of attack often uses personal information, about an individual, group of individuals, or an organization, to make a TME more believable and personalized. TME is effective to penetrate email defense system because it is fundamentally difficult for traditional email security method to distinguish legitimate emails from malicious emails. And TMEs often contain malicious URLs or malicious attachments, which are extremely aggressive and destructive. In order to effectively deal with this new type of malicious email attack, this paper proposes a dynamic detection method for malicious email. We simulate the recipient opening the email in the virtual machine (VM), accessing the URL and activating the attachment. And we use the virtual machine introspection (VMI) and memory forensics analysis (MFA) technology to obtain the dynamic features of the email by the out-of-VM. Then we use AdaBoostM1 ensemble learning method and Voting combination strategy to combine three base classifiers such as BayesNet, SMO and J48 to build a powerful classification model for detecting TME attacks. The AdaBoostM1 classifier achieved the high detection rates, with an AUC of 0.997, true positive rate (TPR) of 0.997, and false positive rate (FPR) of 0.015. In addition, our proposed detection method is superior to the 56 anti-virus engines on VirusTotal and most of the existing research works.

关键词： Electronic mail Feature extraction Virtual machining Unsolicited e-mail Uniform resource locators Learning systems

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