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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 As a result of these problems, existing methods of estimating agricultural production are ineffective or out of date.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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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...

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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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Joint Channel Estimation and Signal Recovery for RIS-Empowered Multi-User Communications

arXiv

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arXiv 2021年

作者： Wei, Li Huang, Chongwen Guo, Qinghua Yang, Zhaohui Zhang, Zhaoyang Alexandropoulos, George C. Debbah, Mérouane Yuen, Chau Pillar Singapore University of Technology and Design Singapore487372 Singapore College of Information Science and Electronic Engineering Zhejiang University Hangzhou310007 China Hangzhou310007 China International Joint Innovation Center Zhejiang University Haining314400 China Zhejiang-Singapore Innovation and AI Joint Research Lab Hangzhou310027 China Zhejiang Laboratory Hangzhou31121 China School of Electrical Computer and Telecommunications Engineering University of Wollongong WollongongNSW2522 Australia Department of Informatics and Telecommunications National and Kapodistrian University of Athens Panepistimiopolis Ilissia Athens15784 Greece Technology Innovation Institute Masdar City Abu Dhabi9639 United Arab Emirates CentraleSupelec University Paris-Saclay Gif-sur-Yvette91192 France

Reconfigurable intelligent surfaces (RISs) have been recently considered as a promising candidate for energy-efficient solutions in future wireless networks. Their dynamic and low-power configuration enables coverage extension, massive connectivity, and low-latency communications. Due to a large number of unknown variables referring to the RIS unit elements and the transmitted signals, channel estimation and signal recovery in RIS-based systems are the ones of the most critical technical challenges. To address this problem, we focus on the RIS-assisted wireless communication system and present two joint channel estimation and signal recovery schemes based on message passing algorithms in this paper. Specifically, the proposed bidirectional scheme applies the Taylor series expansion and Gaussian approximation to simplify the sum-product procedure in the formulated problem. In addition, the inner iteration that adopts two variants of approximate message passing algorithms is incorporated to ensure robustness and convergence. Two ambiguities removal methods are also discussed in this paper. Our simulation results show that the proposed schemes show the superiority over the state-of-art benchmark method. We also provide insights on the impact of different RIS parameter settings on the proposed schemes. Copyright © 2021, The Authors. All rights reserved.

关键词： Channel estimation

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PiaNet: A pyramid input augmented convolutional neural network for GGO detection in 3D lung CT scans

arXiv

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arXiv 2020年

作者： Liu, Weihua Liu, Xiabi Luo, Xiongbiao Wang, Murong Zheng, Guangyuan Han, Guanghui Beijing Lab of Intelligent Information School of Computer Science Beijing Institute of Technology 5 South Zhongguancun Street Haidian District Beijing100081 China Department of computer science Xiamen University No. 422 Siming South Road XiamenFujian361005 China School of Biomedical Engineering Sun Yat-sen University No.132 Outer Ring Road East University Town Panyu District Guangzhou510006 China

This paper proposes a new convolutional neural network with multiscale processing for detecting ground-glass opacity (GGO) nodules in 3D computed tomography (CT) images, which is referred to as PiaNet for short. PiaNet consists of a feature-extraction module and a prediction module. The former module is constructed by introducing pyramid multiscale source connections into a contracting-expanding structure. The latter module includes a bounding-box regressor and a classifier that are employed to simultaneously recognize GGO nodules and estimate bounding boxes at multiple scales. To train the proposed PiaNet, a two-stage transfer learning strategy is developed. In the first stage, the feature-extraction module is embedded into a classifier network that is trained on a large data set of GGO and non-GGO patches, which are generated by performing data augmentation from a small number of annotated CT scans. In the second stage, the pretrained feature-extraction module is loaded into PiaNet, and then PiaNet is fine-tuned using the annotated CT scans. We evaluate the proposed PiaNet on the LIDC-IDRI data set. The experimental results demonstrate that our method outperforms state-of-the-art counterparts, including the Subsolid CAD and Aidence systems and S4ND and GA-SSD methods. PiaNet achieves a sensitivity of 91.75% with only one false positive per scan. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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A Double Auction for Charging Scheduling among Vehicles Using DAG-Blockchains

arXiv

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arXiv 2020年

作者： Guo, Jianxiong Ding, Xingjian Wu, Weili Du, Ding-Zhu Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China Guangdong Key Lab of AI and Multi-Modal Data Processing BNU-HKBU United International College Zhuhai519087 China Faculty of Information Technology Beijing University of Technology Beijing100124 China Department of Computer Science Erik Jonsson School of Engineering and Computer Science The University of Texas at Dallas RichardsonTX75080 United States

Electric Vehicles (EVs) are becoming more and more popular in our daily life, which replaces traditional fuel vehicles to reduce carbon emissions and protect the environment. EVs need to be charged, but the number of charging piles in a Charging Station (CS) is limited and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on a Directed Acyclic Graph (DAG)-blockchain and double auction mechanism. In a smart area, it attempts to assign EVs to the available CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalability during performing scheduling and completing tradings. In this process, a constrained multi-item double auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve truthfulness as well as system efficiency compared to the existing double auction model. To adapt to it, we propose two algorithms, namely Truthful Mechanism for Charging (TMC) and Efficient Mechanism for Charging (EMC), to determine an assignment between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Blockchain

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