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media Power Measuring via Emotional Contagion

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Journal of Social computing 2024年第1期5卷 15-35页

作者： Xue Lin Hong Huang Zongya Li Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China School of Journalism Huazhong University of Science and TechnologyWuhan 430074China

media power,the impact that media have on public opinion and perspectives,plays a significant role in maintaining internal stability,exerting external influence,and shaping international dynamics for nations/regions.However,prior research has primarily concentrated on news content and reporting time,resulting in limitations in evaluating media power.To more accurately assess media power,we use news content,news reporting time,and news emotion simultaneously to explore the emotional contagion between media.We use emotional contagion to measure the mutual influence between media and regard the media with greater impact as having stronger media power.We propose a framework called Measuring media Power via Emotional Contagion(MMPEC)to capture emotional contagion among media,enabling a more accurate assessment of media power at the media and national/regional levels.MMPEC also interprets experimental results through correlation and causality analyses,ensuring explainability.Case analyses confirm the higher accuracy of MMPEC compared to other baseline models,as demonstrated in the context of COVID-19-related news,yielding compelling and interesting insights.

关键词： explainability media power emotional contagion

Deepwalk-aware graph convolutional networks

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Science China(Information Sciences) 2022年第5期65卷 81-95页

作者： Taisong JIN Huaqiang DAI Liujuan CAO Baochang ZHANG Feiyue HUANG Yue GAO Rongrong JI media analytics and computing lab Department of Computer Science and Technology School of InformaticsXiamen University media analytics and computing lab Department of Artificial Intelligence School of InformaticsXiamen University School of Automation Science and Electrical Engineering Beihang University Tencent Youtu lab School of Software Tsinghua University

Graph convolutional networks(GCNs) provide a promising way to extract the useful information from graph-structured data. Most of the existing GCNs methods usually focus on local neighborhood information based on specific convolution operations, and ignore the global structure of the input data. To extract the latent representation for the graph-structured data more effectively, we introduce a deepwalk strategy into GCNs to efficiently explore the global graph information. This strategy can complement the local neighborhood information of a graph, resulting in the more robust representation for the graph data.The fusion of the local neighboring and global structured information of a graph can further facilitate deep feature learning at the output layer of GCNs for node classification. Experimental results show that the proposed model has achieved state-of-the-art results on three benchmark datasets including Cora, Citeseer,and Pubmed citation networks.

关键词： graph convolutional networks global information fusion node classification

Comparative analysis of SIMILAR to RCD ONE(SRO)family from tetraploid cotton species and their diploid progenitors depict their significance in cotton growth and development

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Journal of Cotton Research 2024年第1期7卷 45-57页

作者： SHABAN Muhammad TABASSUM Riaz RANA Iqrar Ahmad ATIF Rana Muhammad AZMAT Muhammad Abubakkar IQBAL Zubair MAJEED Sajid AZHAR Muhammad Tehseen Department of Plant Breeding and Genetics University of Agriculture FaisalabadSub-Campus BurewalaVehari 61010Pakistan Department of Chemistry and Biochemistry University of AgricultureFaisalabad 38400Pakistan Centre of Agricultural Biochemistry and Biotechnology University of AgricultureFaisalabad 38400Pakistan Department of Plant Breeding and Genetics University of AgricultureFaisalabad 38400Pakistan Precision Agriculture and analytics lab National Centre in Big Data and Cloud ComputingCentre for Advanced Studies in Agriculture and Food SecurityUniversity of Agriculture FaisalabadFaisalabad 38400Pakistan Department of Plant Pathology University of CaliforniaDavisCA 95616USA State Key laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural SciencesAnyangHenan 455000China Federal Seed Certification and Registration Department Ministry of National Food Security and ResearchIslamabad 44090Pakistan School of Agriculture Sciences Zhengzhou UniversityZhengzhou 450000China

Background SRO(Similar to RCD1)genes family is largely recognized for their importance in the growth,develop-ment,and in responding to environmental stresses.However,genome-wide identification and functional characteri-zation of SRO genes from cotton species have not been reported so far.Results A total of 36 SRO genes were identified from four cotton species.Phylogenetic analysis divided these genes into three groups with distinct structure.Syntenic and chromosomal distribution analysis indicated uneven distribu-tion of GaSRO,GrSRO,GhSRO,and GbSRO genes on A2,D5 genomes,Gh-At,Gh-Dt,Gb-At,and Gb-Dt subgenomes,respectively.Gene duplication analysis revealed the presence of six duplicated gene pairs among GhSRO genes.In promoter analysis,several elements responsive to the growth,development and hormones were found in GhSRO genes,implying gene induction during cotton growth and development.Several miRNAs responsive to plant growth and abiotic stress were predicted to target 12 GhSRO genes.Organ-specific expression profiling demonstrated the roles of GhSRO genes in one or more tissues.In addition,specific expression pattern of some GhSRO genes dur-ing ovule development depicted their involvement in these developmental processes.Conclusion The data presented in this report laid a foundation for understanding the classification and functions of SRO genes in cotton.

关键词： Cotton SRO miRNAs Gene duplications Gene expression Ovule development

A Survey on Event Tracking in Social media Data Streams

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Big Data Mining and analytics 2024年第1期7卷 217-243页

作者： Zixuan Han Leilei Shi Lu Liu Liang Jiang Jiawei Fang Fanyuan Lin Jinjuan Zhang John Panneerselvam Nick Antonopoulos School of Computer Science and Communication Engineering and Jiangsu Key laboratory of Security Technology for Industrial Cyberspace Jiangsu UniversityZhenjiang 212013China School of computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Ocean College Jiangsu University of Science and TechnologyZhenjiang 212100China University Executive Office Edinburgh Napier UniversityEdinburghEH114BNUK

Social networks are inevitable parts of our daily life,where an unprecedented amount of complex data corresponding to a diverse range of applications are generated.As such,it is imperative to conduct research on social events and patterns from the perspectives of conventional sociology to optimize services that originate from social networks.Event tracking in social networks finds various applications,such as network security and societal governance,which involves analyzing data generated by user groups on social networks in real time.Moreover,as deep learning techniques continue to advance and make important breakthroughs in various fields,researchers are using this technology to progressively optimize the effectiveness of Event Detection(ED)and tracking algorithms.In this regard,this paper presents an in-depth comprehensive review of the concept and methods involved in ED and tracking in social networks.We introduce mainstream event tracking methods,which involve three primary technical steps:ED,event propagation,and event evolution.Finally,we introduce benchmark datasets and evaluation metrics for ED and tracking,which allow comparative analysis on the performance of mainstream methods.Finally,we present a comprehensive analysis of the main research findings and existing limitations in this field,as well as future research prospects and challenges.

关键词： Event Detection(ED) event propagation event evolution social networks

Advances in wireless sensor networks under AI-5 G for augmented reality

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Virtual Reality & Intelligent Hardware 2022年第3期4卷 I0001-I0003页

作者： Muhammad KHAN Visual analytics for Knowledge laboratory(VIS 2 KNOW lab) Department of Applied Artificial IntelligenceSchool of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul 03063Republic of Korea

Due to the recent rapid development in the 5 G technology,the usage of sensor networks especially wireless sensor networks(WSNs)has boosted advances in the augmented reality(AR),supporting decision making in AR environments.Such decision-making needs support and consideration of artificial intelligence(AI)techniques capable of adapting to changes in AR environments for creating systems that evolve autonomously over time.Currently,it is important to apply new information fusion techniques that allow for the processing of information at low and high levels to improve the accuracy of such systems.

关键词： networks evolve sensor

Machine Learning and Synthetic Minority Oversampling Techniques for Imbalanced Data: Improving Machine Failure Prediction

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Computers, Materials & Continua 2023年第6期75卷 4821-4841页

作者： Yap Bee Wah Azlan Ismail Nur Niswah Naslina Azid Jafreezal Jaafar Izzatdin Abdul Aziz Mohd Hilmi Hasan Jasni Mohamad Zain Institute for Big Data analytics and Artificial Intelligence(IBDAAI) Kompleks Al-KhawarizmiUniversiti Teknologi MARA(UiTM)Shah Alam40450SelangorMalaysia School of computing Sciences College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)40450Shah AlamSelangorMalaysia Mathematical Sciences Studies College of ComputingInformatics and MediaUniversiti Teknologi MARA(UiTM)Kelantan BranchMachang CampusBukit Ilmu18500MachangKelantan Darul NaimMalaysia Centre for Research in Data Science(CeRDaS) Department of Computer and Information Sciences(DCIS)Universiti Teknologi PETRONAS(UTP)Seri Iskandar32610PerakMalaysia UNITAR International University Jalan SS6/3SS6Petaling Jaya47301SelangorMalaysia

Prediction of machine failure is challenging as the dataset is often imbalanced with a low failure rate.The common approach to han-dle classification involving imbalanced data is to balance the data using a sampling approach such as random undersampling,random oversampling,or Synthetic Minority Oversampling Technique(SMOTE)algorithms.This paper compared the classification performance of three popular classifiers(Logistic Regression,Gaussian Naïve Bayes,and Support Vector Machine)in predicting machine failure in the Oil and Gas industry.The original machine failure dataset consists of 20,473 hourly data and is imbalanced with 19945(97%)‘non-failure’and 528(3%)‘failure data’.The three independent variables to predict machine failure were pressure indicator,flow indicator,and level indicator.The accuracy of the classifiers is very high and close to 100%,but the sensitivity of all classifiers using the original dataset was close to zero.The performance of the three classifiers was then evaluated for data with different imbalance rates(10%to 50%)generated from the original data using SMOTE,SMOTE-Support Vector Machine(SMOTE-SVM)and SMOTE-Edited Nearest Neighbour(SMOTE-ENN).The classifiers were evaluated based on improvement in sensitivity and F-measure.Results showed that the sensitivity of all classifiers increases as the imbalance rate increases.SVM with radial basis function(RBF)kernel has the highest sensitivity when data is balanced(50:50)using SMOTE(Sensitivitytest=0.5686,Ftest=0.6927)compared to Naïve Bayes(Sensitivitytest=0.4033,Ftest=0.6218)and Logistic Regression(Sensitivitytest=0.4194,Ftest=0.621).Overall,the Gaussian Naïve Bayes model consistently improves sensitivity and F-measure as the imbalance ratio increases,but the sensitivity is below 50%.The classifiers performed better when data was balanced using SMOTE-SVM compared to SMOTE and SMOTE-ENN.

关键词： Machine failure machine learning imbalanced data SMOTE classification

Unsupervised image translation with distributional semantics awareness

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Computational Visual media 2023年第3期9卷 619-631页

作者： Zhexi Peng He Wang Yanlin Weng Yin Yang Tianjia Shao State Key lab of CAD&CG Zhejiang UniversityHangzhou 310058China School of computing University of LeedsLeedsUK School of computing Clemson UniversityClemsonUSA

Unsupervised image translation(UIT)studies the mapping between two image domains.Since such mappings are under-constrained,existing research has pursued various desirable properties such as distributional matching or two-way consistency.In this paper,we re-examine UIT from a new perspective:distributional semantics consistency,based on the observation that data variations contain semantics,e.g.,shoes varying in colors.Further,the semantics can be multi-dimensional,e.g.,shoes also varying in style,functionality,etc.Given two image domains,matching these semantic dimensions during UIT will produce mappings with explicable correspondences,which has not been investigated previously.We propose distributional semantics mapping(DSM),the first UIT method which explicitly matches semantics between two domains.We show that distributional semantics has been rarely considered within and beyond UIT,even though it is a common problem in deep learning.We evaluate DSM on several benchmark datasets,demonstrating its general ability to capture distributional semantics.Extensive comparisons show that DSM not only produces explicable mappings,but also improves image quality in general.

关键词： generative adversarial networks(GANs) manifold alignment unsupervised learning image-to-image translation distributional semantics

Detection of Copy-Move Forgery in Digital Images Using Singular Value Decomposition

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Computers, Materials & Continua 2023年第2期74卷 4135-4147页

作者： Zaid Nidhal Khudhair Farhan Mohamed Amjad Rehman Tanzila Saba Saeed Ali bahaj Faculty of Engineering School of ComputingUniversity Technology of MalaysiaJohar BahruMalaysia UTM-IRDA MaGICX Institute of Human Centered EngineeringUniversiti Teknologi MalaysiaMalaysia Artificial Intelligence&Data analytics lab CCIS Prince Sultan University Riyadh Saudi Arabia Computer Techniques Engineering Department Faculty of Information TechnologyImam Ja’afar Al-Sadiq UniversityBaghdadIraq

This paper presents an improved approach for detecting copy-move forgery based on singular value decomposition(SVD).It is a block-based method where the image is scanned from left to right and top to down by a sliding window with a determined size.At each step,the SVD is determined.First,the diagonal matrix’s maximum value(norm)is selected(representing the scaling factor for SVD and a fixed value for each set of matrix elements even when rotating thematrix or scaled).Then,the similar norms are grouped,and each leading group is separated into many subgroups(elements of each subgroup are neighbors)according to 8-adjacency(the subgroups for each leading group must be far from others by a specific distance).After that,a weight is assigned for each subgroup to classify the image as forgery or not.Finally,the F1 score of the proposed system is measured,reaching 99.1%.This approach is robust against rotation,scaling,noisy images,and illumination variation.It is compared with other similarmethods and presents very promised results.

关键词： Forgery image forensic image processing region duplication SVD transformation technological development

Erroneous pixel prediction for semantic image segmentation

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Computational Visual media 2022年第1期8卷 165-175页

作者： Lixue Gong Yiqun Zhang Yunke Zhang Yin Yang Weiwei Xu State Key lab of CAD&CG Zhejiang UniversityHangzhou 310058China School of computing Clemson University South Carolina29634USA

We consider semantic image segmentation.Our method is inspired by Bayesian deep learning which improves image segmentation accuracy by modeling the uncertainty of the network output.In contrast to uncertainty,our method directly learns to predict the erroneous pixels of a segmentation network,which is modeled as a binary classification problem.It can speed up training comparing to the Monte Carlo integration often used in Bayesian deep learning.It also allows us to train a branch to correct the labels of erroneous pixels.Our method consists of three stages:(i)predict pixel-wise error probability of the initial result,(ii)redetermine new labels for pixels with high error probability,and(iii)fuse the initial result and the redetermined result with respect to the error probability.We formulate the error-pixel prediction problem as a classification task and employ an error-prediction branch in the network to predict pixel-wise error probabilities.We also introduce a detail branch to focus the training process on the erroneous pixels.We have experimentally validated our method on the Cityscapes and ADE20K datasets.Our model can be easily added to various advanced segmentation networks to improve their performance.Taking Deeplabv3+as an example,our network can achieve 82.88%of mloU on Cityscapes testing dataset and 45.73%on ADE20K validation dataset,improving corresponding Deeplabv3+results by 0.74%and 0.13%respectively.

关键词： erroneous pixel prediction image segmentation deep learning