Traditional Belief-Rule-Based(BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems particip...Traditional Belief-Rule-Based(BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems participating in ensemble learning increases, a large amount of redundant sub-BRB systems are generated because of the diminishing difference between subsystems. This drastically decreases the prediction speed and increases the storage requirements for BRB systems. In order to solve these problems, this paper proposes BRBCS-PAES: a selective ensemble learning approach for BRB Classification Systems(BRBCS) based on ParetoArchived Evolutionary Strategy(PAES) multi-objective optimization. This system employs the improved Bagging algorithm to train the base classifier. For the purpose of increasing the degree of difference in the integration of the base classifier, the training set is constructed by the repeated sampling of data. In the base classifier selection stage, the trained base classifier is binary coded, and the number of base classifiers participating in integration and generalization error of the base classifier is used as the objective function for multi-objective optimization. Finally,the elite retention strategy and the adaptive mesh algorithm are adopted to produce the PAES optimal solution set.Three experimental studies on classification problems are performed to verify the effectiveness of the proposed method. The comparison results demonstrate that the proposed method can effectively reduce the number of base classifiers participating in the integration and improve the accuracy of BRBCS.展开更多
Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)algorithms.The effect of this subsampling on the computing time and accuracy of CF is not fully understo...Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)algorithms.The effect of this subsampling on the computing time and accuracy of CF is not fully understood,and clear guidelines for selecting optimal or even appropriate subsampling levels are not available.In this paper,we present a Density-based Random Stratified Subsampling using Clustering(DRSC)algorithm in which the desired Fraction of Users Dropped(FUD)and Fraction of Items Dropped(FID)are specified,and the overall density during subsampling is maintained.Subsequently,we develop simple models of the Training Time Improvement(TTI)and the Accuracy Loss(AL)as functions of FUD and FID,based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens,Yahoo Music Rating,and Amazon Automotive data.Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods.The TTI linear regression of a CF method appears to be same for all datasets.Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only,but AL requires considering additional dataset characteristics.The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL.A simple sub-optimal approximation was found,in which the optimal AL is proportional to the optimal Training Time Reduction Factor(TTRF)for higher values of TTRF,and the optimal subsampling levels,like optimal FID/(1-FID),are proportional to the square root of TTRF.展开更多
Big Data Mining and Analytics,an academic journal sponsored by Tsinghua University,is published quarterly.This journal aims at presenting the up-to-date scientific achievements with high creativity and great significa...Big Data Mining and Analytics,an academic journal sponsored by Tsinghua University,is published quarterly.This journal aims at presenting the up-to-date scientific achievements with high creativity and great significance in big data mining and analytics.Contributions all over the world are welcome.Big Data Mining and Analytics is indexed by EEEE Xplore.展开更多
With the enhancement of data collection capabilities,massive streaming data have been accumulated in numerous application scenarios.Specifically,the issue of classifying data streams based on mobile sensors can be for...With the enhancement of data collection capabilities,massive streaming data have been accumulated in numerous application scenarios.Specifically,the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors.Existing incremental learning methods are often single-task single-view,which cannot learn shared representations between relevant tasks and views.An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges,utilizing the idea of multi-task multi-view learning.Specifically,the attention mechanism is first used to align different sensor data of different views.In addition,MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning.Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.展开更多
To protect consumers and those who manufacture and sell the products they enjoy,it is important to develop convenient tools to help consumers distinguish an authentic product from a counterfeit one.The advancement of ...To protect consumers and those who manufacture and sell the products they enjoy,it is important to develop convenient tools to help consumers distinguish an authentic product from a counterfeit one.The advancement of deep learning techniques for fine-grained object recognition creates new possibilities for genuine product identification.In this paper,we develop a Semi-Supervised Attention(SSA)model to work in conjunction with a large-scale multiple-source dataset named YSneaker,which consists of sneakers from various brands and their authentication results,to identify authentic sneakers.Specifically,the SSA model has a self-attention structure for different images of a labeled sneaker and a novel prototypical loss is designed to exploit unlabeled data within the data structure.The model draws on the weighted average of the output feature representations,where the weights are determined by an additional shallow neural network.This allows the SSA model to focus on the most important images of a sneaker for use in identification.A unique feature of the SSA model is its ability to take advantage of unlabeled data,which can help to further minimize the intra-class variation for more discriminative feature embedding.To validate the model,we collect a large number of labeled and unlabeled sneaker images and perform extensive experimental studies.The results show that YSneaker together with the proposed SSA architecture can identify authentic sneakers with a high accuracy rate.展开更多
As a huge number of satellites revolve around the earth,a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis.Therefore,cla...As a huge number of satellites revolve around the earth,a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis.Therefore,classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones.In this article,a classification approach is proposed using Deep Convolutional Neural Network(DCNN),comprising numerous layers,which extract the features through a downsampling process for classifying satellite cloud images.DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy.Delivery time decreases for testing images,whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances.The satellite images are taken from the Meteorological&Oceanographic Satellite Data Archival Centre,the organization is responsible for availing satellite cloud images of India and its subcontinent.The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.展开更多
Graph data mining has been a crucial as well as inevitable area of research.Large amounts of graph data are produced in many areas,such as Bioinformatics,Cheminformatics,Social Networks,etc.Scalable graph data mining ...Graph data mining has been a crucial as well as inevitable area of research.Large amounts of graph data are produced in many areas,such as Bioinformatics,Cheminformatics,Social Networks,etc.Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities.Frequent subgraph mining is one such area where the task is to find overly recurring patterns/subgraphs.To tackle this problem,many main memory-based methods were proposed,which proved to be inefficient as the data size grew exponentially over time.In the past few years,several research groups have attempted to handle the Frequent Subgraph Mining(FSM)problem in multiple ways.Many authors have tried to achieve better performance using Graphic Processing Units(GPUs)which has multi-fold improvement over in-memory while dealing with large datasets.Later,Google’s MapReduce model with the Hadoop framework proved to be a major breakthrough in high performance large batch processing.Although MapReduce came with many benefits,its disk I/O and noniterative style model could not help much for FSM domain since subgraph mining process is an iterative approach.In recent years,Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability.This is a right fit solution for iterative style of programming as well.In this survey,we cover how high-performance computing has helped in improving the performance tremendously in the transactional directed and undirected aspect of graphs and performance comparisons of various FSM techniques are done based on experimental results.展开更多
Unsupervised learning algorithms can effectively solve sample imbalance.To address battery consistency anomalies in new energy vehicles,we adopt a variety of unsupervised learning algorithms to evaluate and predict th...Unsupervised learning algorithms can effectively solve sample imbalance.To address battery consistency anomalies in new energy vehicles,we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three vehicles using charging fragment data from actual operating conditions.We extract battery-related features,such as the mean of maximum difference,standard deviation,and entropy of batteries and then apply principal component analysis to reduce the dimensionality and record the amount of preserved information.We then build models through a collection of unsupervised learning algorithms for the anomaly detection of cell consistency faults.We also determine whether unsupervised and supervised learning algorithms can address the battery consistency problem and document the parameter tuning process.In addition,we compare the prediction effectiveness of charging and discharging features modeled individually and in combination,determine the choice of charging and discharging features to be modeled in combination,and visualize the multidimensional data for fault detection.Experimental results show that the unsupervised learning algorithm is effective in visualizing and predicting vehicle core conformance faults,and can accurately predict faults in real time.The“distance+boxplot”algorithm shows the best performance with a prediction accuracy of 80%,a recall rate of 100%,and an F1 of 0.89.The proposed approach can be applied to monitor battery consistency faults in real time and reduce the possibility of disasters arising from consistency faults.展开更多
This is a review of quantum methods for machine learning problems that consists of two parts.The first part,"quantum tools",presents the fundamentals of qubits,quantum registers,and quantum states,introduces...This is a review of quantum methods for machine learning problems that consists of two parts.The first part,"quantum tools",presents the fundamentals of qubits,quantum registers,and quantum states,introduces important quantum tools based on known quantum search algorithms and SWAP-test,and discusses the basic quantum procedures used for quantum search methods.The second part,"quantum classification algorithms",introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification.展开更多
Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.How...Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.However,these definitions could hardly reach universal acceptance.Rather than providing a formal definition of the Metaverse,we list four must-have characteristics of the Metaverse:socialization,immersive interaction,real world-building,and expandability.These characteristics not only carve the Metaverse into a novel and fantastic digital world,but also make it suffer from all security/privacy risks,such as personal information leakage,eavesdropping,unauthorized access,phishing,data injection,broken authentication,insecure design,and more.This paper first introduces the four characteristics,then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors.Based on the four characteristics and the findings of the current progress,the security and privacy issues in the Metaverse are investigated.We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics.Lastly,the paper also raises some other concerns regarding society and humanity.展开更多
There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human c...There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human cognitive process, this paper proposes a complex tasks decomposition mechanism based on Density Peaks Clustering(DPC) to address complex tasks with an unsupervised process, which simulates the multi-granular observation and analysis of human being. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. We also define subtask centers set and the granulation rules to guide the multi-granularity decomposing procedure. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. Furthermore, comprehensive experiments are presented to verify the applicability and veracity of our proposed method in community-detection tasks with several benchmark complex social networks.The results show that our method outperforms other four state-of-the-art approaches.展开更多
At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,...At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,security strategies,cost per customer,and reputation in the market.Thus,software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities.Thus,there is a need to evaluate various cloud service providers(CSPs)and platforms before choosing a suitable vendor.Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes.However,they require more time to collect data,simulate and evaluate the vendor.The proposed work compares various CSPs in terms of major metrics,such as establishment,services,infrastructure,tools,pricing models,market share,etc.,based on the comparison,parameter ranking,and weightage allocated.Furthermore,the parameters are categorized depending on the priority level.The weighted average is calculated for each CSP,after which the values are sorted in descending order.The experimental results show the unbiased selection of CSPs based on the chosen parameters.The proposed parameter-ranking priority level weightage(PRPLW)algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.展开更多
Big Data Mining and Analytics is an international academic journal sponsored by Tsinghua University and published quarterly.It features on technologies to enable and accelerate big data discovery.All of papers publish...Big Data Mining and Analytics is an international academic journal sponsored by Tsinghua University and published quarterly.It features on technologies to enable and accelerate big data discovery.All of papers published are on the IEEE Xplore Digital Library with the open access mode.A complex system is a nonlinear system consisting of a set of intelligent agents that can not only process their local data separately but also collaborate with each other globally.Generally,through analyzing the decision-making data collected by distributed agents.展开更多
The COVID-19 pandemic has hit the world hard.The reaction to the pandemic related issues has been pouring into social platforms,such as Twitter.Many public officials and governments use Twitter to make policy announce...The COVID-19 pandemic has hit the world hard.The reaction to the pandemic related issues has been pouring into social platforms,such as Twitter.Many public officials and governments use Twitter to make policy announcements.People keep close track of the related information and express their concerns about the policies on Twitter.It is beneficial yet challenging to derive important information or knowledge out of such Twitter data.In this paper,we propose a Tripartite Graph Clustering for Pandemic Data Analysis(TGC-PDA)framework that builds on the proposed models and analysis:(1)tripartite graph representation,(2)non-negative matrix factorization with regularization,and(3)sentiment analysis.We collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth data.Our framework can detect the communities of Twitter users and analyze the topics that are discussed in the communities.The extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions,which would provide policy makers useful information and statistics for decision making.展开更多
In recent years,Apache Spark has become the de facto standard for big data processing.SparkSQL is a module offering support for relational analysis on Spark with Structured Query Language(SQL).SparkSQL provides conven...In recent years,Apache Spark has become the de facto standard for big data processing.SparkSQL is a module offering support for relational analysis on Spark with Structured Query Language(SQL).SparkSQL provides convenient data processing interfaces.Despite its efficient optimizer,SparkSQL still suffers from the inefficiency of Spark resulting from Java virtual machine and the unnecessary data serialization and deserialization.Adopting native languages such as C++could help to avoid such bottlenecks.Benefiting from a bare-metal runtime environment and template usage,systems with C++interfaces usually achieve superior performance.However,the complexity of native languages also increases the required programming and debugging efforts.In this work,we present LotusSQL,an engine to provide SQL support for dataset abstraction on a native backend Lotus.We employ a convenient SQL processing framework to deal with frontend jobs.Advanced query optimization technologies are added to improve the quality of execution plans.Above the storage design and user interface of the compute engine,LotusSQL implements a set of structured dataset operations with high efficiency and integrates them with the frontend.Evaluation results show that LotusSQL achieves a speedup of up to 9 in certain queries and outperforms Spark SQL in a standard query benchmark by more than 2 on average.展开更多
Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.How...Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.展开更多
The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translatin...The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook.The role of antecedents of attitude,namely enjoyment,credibility,and peer communication was also explored.The main purpose was to analyze the F-commerce pervasiveness(retail purchases through Facebook)among Generation Z in India and how could it be materialized effectively.A conceptual fac¸ade was proposed after trotting out germane and urbane literature.The study focused exclusively on Generation Z population.The data were statistically analyzed using partial least squares structural equation modelling.The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce.Enjoyment,credibility,and peer communication were proved to be good predictors of attitude(R^(2)=0.589)and furthermore attitude was found to be a stellar antecedent to purchase intentions(R^(2)=0.540).展开更多
Circular RNA(circRNA)is a novel non-coding endogenous RNAs.Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions.Although increasing numbe...Circular RNA(circRNA)is a novel non-coding endogenous RNAs.Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions.Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies,these techniques are still time-consuming and costly.In this study,we propose a computational method to predict circRNA-disesae associations which is based on metapath2 vec++and matrix factorization with integrated multiple data(called PCD MVMF).To construct more reliable networks,various aspects are considered.Firstly,circRNA annotation,sequence,and functional similarity networks are established,and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks.Secondly,metapath2 vec++is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score.Finally,we use matrix factorization,take similarity as a constraint,and optimize it to obtain the final prediction results.Leave-one-out cross-validation,five-fold cross-validation,and f-measure are adopted to evaluate the performance of PCD MVMF.These evaluation metrics verify that PCD MVMF has better prediction performance than other methods.To further illustrate the performance of PCD MVMF,case studies of common diseases are conducted.Therefore,PCD MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.展开更多
Human Action Recognition(HAR)attempts to recognize the human action from images and videos.The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A...Human Action Recognition(HAR)attempts to recognize the human action from images and videos.The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral filters.The proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral information.To create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are combined.Linear Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate data.Finally,a closest neighbor method is used to classify the dataset.Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well.The average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,respectively.The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,respectively.From the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.展开更多
With the development of the Internet,technology,and means of communication,the production of tourist data has multiplied at all levels(hotels,restaurants,transport,heritage,tourist events,activities,etc.),especially w...With the development of the Internet,technology,and means of communication,the production of tourist data has multiplied at all levels(hotels,restaurants,transport,heritage,tourist events,activities,etc.),especially with the development of Online Travel Agency(OTA).However,the list of possibilities offered to tourists by these Web search engines(or even specialized tourist sites)can be overwhelming and relevant results are usually drowned in informational"noise",which prevents,or at least slows down the selection process.To assist tourists in trip planning and help them to find the information they are looking for,many recommender systems have been developed.In this article,we present an overview of the various recommendation approaches used in the field of tourism.From this study,an architecture and a conceptual framework for tourism recommender system are proposed,based on a hybrid recommendation approach.The proposed system goes beyond the recommendation of a list of tourist attractions,tailored to tourist preferences.It can be seen as a trip planner that designs a detailed program,including heterogeneous tourism resources,for a specific visit duration.The ultimate goal is to develop a recommender system based on big data technologies,artificial intelligence,and operational research to promote tourism in Morocco,specifically in the Daraa-Tafilalet region.展开更多
基金supported by the National Natural Science Foundation of China (Nos. 71501047 and 61773123)the Natural Science Foundation of Fujian Province (No. 2019J01647)
文摘Traditional Belief-Rule-Based(BRB) ensemble learning methods integrate all of the trained sub-BRB systems to obtain better results than a single belief-rule-based system. However, as the number of BRB systems participating in ensemble learning increases, a large amount of redundant sub-BRB systems are generated because of the diminishing difference between subsystems. This drastically decreases the prediction speed and increases the storage requirements for BRB systems. In order to solve these problems, this paper proposes BRBCS-PAES: a selective ensemble learning approach for BRB Classification Systems(BRBCS) based on ParetoArchived Evolutionary Strategy(PAES) multi-objective optimization. This system employs the improved Bagging algorithm to train the base classifier. For the purpose of increasing the degree of difference in the integration of the base classifier, the training set is constructed by the repeated sampling of data. In the base classifier selection stage, the trained base classifier is binary coded, and the number of base classifiers participating in integration and generalization error of the base classifier is used as the objective function for multi-objective optimization. Finally,the elite retention strategy and the adaptive mesh algorithm are adopted to produce the PAES optimal solution set.Three experimental studies on classification problems are performed to verify the effectiveness of the proposed method. The comparison results demonstrate that the proposed method can effectively reduce the number of base classifiers participating in the integration and improve the accuracy of BRBCS.
文摘Dropping fractions of users or items judiciously can reduce the computational cost of Collaborative Filtering(CF)algorithms.The effect of this subsampling on the computing time and accuracy of CF is not fully understood,and clear guidelines for selecting optimal or even appropriate subsampling levels are not available.In this paper,we present a Density-based Random Stratified Subsampling using Clustering(DRSC)algorithm in which the desired Fraction of Users Dropped(FUD)and Fraction of Items Dropped(FID)are specified,and the overall density during subsampling is maintained.Subsequently,we develop simple models of the Training Time Improvement(TTI)and the Accuracy Loss(AL)as functions of FUD and FID,based on extensive simulations of seven standard CF algorithms as applied to various primary matrices from MovieLens,Yahoo Music Rating,and Amazon Automotive data.Simulations show that both TTI and a scaled AL are bi-linear in FID and FUD for all seven methods.The TTI linear regression of a CF method appears to be same for all datasets.Extensive simulations illustrate that TTI can be estimated reliably with FUD and FID only,but AL requires considering additional dataset characteristics.The derived models are then used to optimize the levels of subsampling addressing the tradeoff between TTI and AL.A simple sub-optimal approximation was found,in which the optimal AL is proportional to the optimal Training Time Reduction Factor(TTRF)for higher values of TTRF,and the optimal subsampling levels,like optimal FID/(1-FID),are proportional to the square root of TTRF.
文摘Big Data Mining and Analytics,an academic journal sponsored by Tsinghua University,is published quarterly.This journal aims at presenting the up-to-date scientific achievements with high creativity and great significance in big data mining and analytics.Contributions all over the world are welcome.Big Data Mining and Analytics is indexed by EEEE Xplore.
文摘With the enhancement of data collection capabilities,massive streaming data have been accumulated in numerous application scenarios.Specifically,the issue of classifying data streams based on mobile sensors can be formalized as a multi-task multi-view learning problem with a specific task comprising multiple views with shared features collected from multiple sensors.Existing incremental learning methods are often single-task single-view,which cannot learn shared representations between relevant tasks and views.An adaptive multi-task multi-view incremental learning framework for data stream classification called MTMVIS is proposed to address the above challenges,utilizing the idea of multi-task multi-view learning.Specifically,the attention mechanism is first used to align different sensor data of different views.In addition,MTMVIS uses adaptive Fisher regularization from the perspective of multi-task multi-view learning to overcome catastrophic forgetting in incremental learning.Results reveal that the proposed framework outperforms state-of-the-art methods based on the experiments on two different datasets with other baselines.
基金supported by the National Key R&D Program of China(No.2018YFB1004300)the National Natural Science Foundation of China(Nos.61773198,61632004,and 61751306)+2 种基金the National Natural Science Foundation of China-Korea Research Foundation Joint Research Project(No.61861146001)Collaborative Innovation Center of Novel Software Technology and IndustrializationPostgraduate Research&Practice Innovation Program of Jiangsu Province(No.KYCX180045).
文摘To protect consumers and those who manufacture and sell the products they enjoy,it is important to develop convenient tools to help consumers distinguish an authentic product from a counterfeit one.The advancement of deep learning techniques for fine-grained object recognition creates new possibilities for genuine product identification.In this paper,we develop a Semi-Supervised Attention(SSA)model to work in conjunction with a large-scale multiple-source dataset named YSneaker,which consists of sneakers from various brands and their authentication results,to identify authentic sneakers.Specifically,the SSA model has a self-attention structure for different images of a labeled sneaker and a novel prototypical loss is designed to exploit unlabeled data within the data structure.The model draws on the weighted average of the output feature representations,where the weights are determined by an additional shallow neural network.This allows the SSA model to focus on the most important images of a sneaker for use in identification.A unique feature of the SSA model is its ability to take advantage of unlabeled data,which can help to further minimize the intra-class variation for more discriminative feature embedding.To validate the model,we collect a large number of labeled and unlabeled sneaker images and perform extensive experimental studies.The results show that YSneaker together with the proposed SSA architecture can identify authentic sneakers with a high accuracy rate.
文摘As a huge number of satellites revolve around the earth,a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis.Therefore,classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones.In this article,a classification approach is proposed using Deep Convolutional Neural Network(DCNN),comprising numerous layers,which extract the features through a downsampling process for classifying satellite cloud images.DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy.Delivery time decreases for testing images,whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances.The satellite images are taken from the Meteorological&Oceanographic Satellite Data Archival Centre,the organization is responsible for availing satellite cloud images of India and its subcontinent.The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.
文摘Graph data mining has been a crucial as well as inevitable area of research.Large amounts of graph data are produced in many areas,such as Bioinformatics,Cheminformatics,Social Networks,etc.Scalable graph data mining methods are getting increasingly popular and necessary due to increased graph complexities.Frequent subgraph mining is one such area where the task is to find overly recurring patterns/subgraphs.To tackle this problem,many main memory-based methods were proposed,which proved to be inefficient as the data size grew exponentially over time.In the past few years,several research groups have attempted to handle the Frequent Subgraph Mining(FSM)problem in multiple ways.Many authors have tried to achieve better performance using Graphic Processing Units(GPUs)which has multi-fold improvement over in-memory while dealing with large datasets.Later,Google’s MapReduce model with the Hadoop framework proved to be a major breakthrough in high performance large batch processing.Although MapReduce came with many benefits,its disk I/O and noniterative style model could not help much for FSM domain since subgraph mining process is an iterative approach.In recent years,Spark has emerged to be the De Facto industry standard with its distributed in-memory computing capability.This is a right fit solution for iterative style of programming as well.In this survey,we cover how high-performance computing has helped in improving the performance tremendously in the transactional directed and undirected aspect of graphs and performance comparisons of various FSM techniques are done based on experimental results.
文摘Unsupervised learning algorithms can effectively solve sample imbalance.To address battery consistency anomalies in new energy vehicles,we adopt a variety of unsupervised learning algorithms to evaluate and predict the battery consistency of three vehicles using charging fragment data from actual operating conditions.We extract battery-related features,such as the mean of maximum difference,standard deviation,and entropy of batteries and then apply principal component analysis to reduce the dimensionality and record the amount of preserved information.We then build models through a collection of unsupervised learning algorithms for the anomaly detection of cell consistency faults.We also determine whether unsupervised and supervised learning algorithms can address the battery consistency problem and document the parameter tuning process.In addition,we compare the prediction effectiveness of charging and discharging features modeled individually and in combination,determine the choice of charging and discharging features to be modeled in combination,and visualize the multidimensional data for fault detection.Experimental results show that the unsupervised learning algorithm is effective in visualizing and predicting vehicle core conformance faults,and can accurately predict faults in real time.The“distance+boxplot”algorithm shows the best performance with a prediction accuracy of 80%,a recall rate of 100%,and an F1 of 0.89.The proposed approach can be applied to monitor battery consistency faults in real time and reduce the possibility of disasters arising from consistency faults.
基金supported in part by the Russian Science Foundation(No.19-19-00656)Natural Science Foundation of Guangdong Province,China(No.2019A1515011721).
文摘This is a review of quantum methods for machine learning problems that consists of two parts.The first part,"quantum tools",presents the fundamentals of qubits,quantum registers,and quantum states,introduces important quantum tools based on known quantum search algorithms and SWAP-test,and discusses the basic quantum procedures used for quantum search methods.The second part,"quantum classification algorithms",introduces several classification problems that can be accelerated by using quantum subroutines and discusses the quantum methods used for classification.
基金supported by the National Natural Science Foundation of China(No.1912753).
文摘Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021.There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse.However,these definitions could hardly reach universal acceptance.Rather than providing a formal definition of the Metaverse,we list four must-have characteristics of the Metaverse:socialization,immersive interaction,real world-building,and expandability.These characteristics not only carve the Metaverse into a novel and fantastic digital world,but also make it suffer from all security/privacy risks,such as personal information leakage,eavesdropping,unauthorized access,phishing,data injection,broken authentication,insecure design,and more.This paper first introduces the four characteristics,then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors.Based on the four characteristics and the findings of the current progress,the security and privacy issues in the Metaverse are investigated.We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics.Lastly,the paper also raises some other concerns regarding society and humanity.
基金supported by the National Natural Science Foundation of China (No. 61572091)Chongqing Postgraduate Scientific Research and Innovation Project (No. CYB16106)
文摘There are many algorithms for solving complex problems in supervised manner. However, unsupervised tasks are more common in real scenarios. Inspired by the idea of granular computing and the characteristics of human cognitive process, this paper proposes a complex tasks decomposition mechanism based on Density Peaks Clustering(DPC) to address complex tasks with an unsupervised process, which simulates the multi-granular observation and analysis of human being. Firstly, the DPC algorithm is modified to nullify its essential defects such as the difficulty of locating correct clustering centers and classifying them accurately. Then, the improved DPC algorithm is used to construct the initial decomposition solving space with multi-granularity theory. We also define subtask centers set and the granulation rules to guide the multi-granularity decomposing procedure. These rules are further used to decompose the solving space from coarse granules to the optimal fine granules with a convergent and automated process. Furthermore, comprehensive experiments are presented to verify the applicability and veracity of our proposed method in community-detection tasks with several benchmark complex social networks.The results show that our method outperforms other four state-of-the-art approaches.
文摘At present,hundreds of cloud vendors in the global market provide various services based on a customer’s requirements.All cloud vendors are not the same in terms of the number of services,infrastructure availability,security strategies,cost per customer,and reputation in the market.Thus,software developers and organizations face a dilemma when choosing a suitable cloud vendor for their developmental activities.Thus,there is a need to evaluate various cloud service providers(CSPs)and platforms before choosing a suitable vendor.Already existing solutions are either based on simulation tools as per the requirements or evaluated concerning the quality of service attributes.However,they require more time to collect data,simulate and evaluate the vendor.The proposed work compares various CSPs in terms of major metrics,such as establishment,services,infrastructure,tools,pricing models,market share,etc.,based on the comparison,parameter ranking,and weightage allocated.Furthermore,the parameters are categorized depending on the priority level.The weighted average is calculated for each CSP,after which the values are sorted in descending order.The experimental results show the unbiased selection of CSPs based on the chosen parameters.The proposed parameter-ranking priority level weightage(PRPLW)algorithm simplifies the selection of the best-suited cloud vendor in accordance with the requirements of software development.
文摘Big Data Mining and Analytics is an international academic journal sponsored by Tsinghua University and published quarterly.It features on technologies to enable and accelerate big data discovery.All of papers published are on the IEEE Xplore Digital Library with the open access mode.A complex system is a nonlinear system consisting of a set of intelligent agents that can not only process their local data separately but also collaborate with each other globally.Generally,through analyzing the decision-making data collected by distributed agents.
文摘The COVID-19 pandemic has hit the world hard.The reaction to the pandemic related issues has been pouring into social platforms,such as Twitter.Many public officials and governments use Twitter to make policy announcements.People keep close track of the related information and express their concerns about the policies on Twitter.It is beneficial yet challenging to derive important information or knowledge out of such Twitter data.In this paper,we propose a Tripartite Graph Clustering for Pandemic Data Analysis(TGC-PDA)framework that builds on the proposed models and analysis:(1)tripartite graph representation,(2)non-negative matrix factorization with regularization,and(3)sentiment analysis.We collect the tweets containing a set of keywords related to coronavirus pandemic as the ground truth data.Our framework can detect the communities of Twitter users and analyze the topics that are discussed in the communities.The extensive experiments show that our TGC-PDA framework can effectively and efficiently identify the topics and correlations within the Twitter data for monitoring and understanding public opinions,which would provide policy makers useful information and statistics for decision making.
文摘In recent years,Apache Spark has become the de facto standard for big data processing.SparkSQL is a module offering support for relational analysis on Spark with Structured Query Language(SQL).SparkSQL provides convenient data processing interfaces.Despite its efficient optimizer,SparkSQL still suffers from the inefficiency of Spark resulting from Java virtual machine and the unnecessary data serialization and deserialization.Adopting native languages such as C++could help to avoid such bottlenecks.Benefiting from a bare-metal runtime environment and template usage,systems with C++interfaces usually achieve superior performance.However,the complexity of native languages also increases the required programming and debugging efforts.In this work,we present LotusSQL,an engine to provide SQL support for dataset abstraction on a native backend Lotus.We employ a convenient SQL processing framework to deal with frontend jobs.Advanced query optimization technologies are added to improve the quality of execution plans.Above the storage design and user interface of the compute engine,LotusSQL implements a set of structured dataset operations with high efficiency and integrates them with the frontend.Evaluation results show that LotusSQL achieves a speedup of up to 9 in certain queries and outperforms Spark SQL in a standard query benchmark by more than 2 on average.
文摘Industrial Internet of Things(IIoT)represents the expansion of the Internet of Things(IoT)in industrial sectors.It is designed to implicate embedded technologies in manufacturing fields to enhance their operations.However,IIoT involves some security vulnerabilities that are more damaging than those of IoT.Accordingly,Intrusion Detection Systems(IDSs)have been developed to forestall inevitable harmful intrusions.IDSs survey the environment to identify intrusions in real time.This study designs an intrusion detection model exploiting feature engineering and machine learning for IIoT security.We combine Isolation Forest(IF)with Pearson’s Correlation Coefficient(PCC)to reduce computational cost and prediction time.IF is exploited to detect and remove outliers from datasets.We apply PCC to choose the most appropriate features.PCC and IF are applied exchangeably(PCCIF and IFPCC).The Random Forest(RF)classifier is implemented to enhance IDS performances.For evaluation,we use the Bot-IoT and NF-UNSW-NB15-v2 datasets.RF-PCCIF and RF-IFPCC show noteworthy results with 99.98%and 99.99%Accuracy(ACC)and 6.18 s and 6.25 s prediction time on Bot-IoT,respectively.The two models also score 99.30%and 99.18%ACC and 6.71 s and 6.87 s prediction time on NF-UNSW-NB15-v2,respectively.Results prove that our designed model has several advantages and higher performance than related models.
文摘The objective of this paper is to provide a better rendition of Generation Z purchase intentions of retail products through Facebook.The study gyrated around the favorable attitude formation of Generation Z translating into intentions to purchase retail products through Facebook.The role of antecedents of attitude,namely enjoyment,credibility,and peer communication was also explored.The main purpose was to analyze the F-commerce pervasiveness(retail purchases through Facebook)among Generation Z in India and how could it be materialized effectively.A conceptual fac¸ade was proposed after trotting out germane and urbane literature.The study focused exclusively on Generation Z population.The data were statistically analyzed using partial least squares structural equation modelling.The study found the proposed conceptual model had a high prediction power of Generation Z intentions to purchase retail products through Facebook verifying the materialization of F-commerce.Enjoyment,credibility,and peer communication were proved to be good predictors of attitude(R^(2)=0.589)and furthermore attitude was found to be a stellar antecedent to purchase intentions(R^(2)=0.540).
基金supported by the National Natural Science Foundation of China(Nos.61972451,61672334,and 61902230)the Fundamental Research Funds for the Central Universities,Shaanxi Normal University(Nos.GK201901010 and 2018TS079)
文摘Circular RNA(circRNA)is a novel non-coding endogenous RNAs.Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions.Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies,these techniques are still time-consuming and costly.In this study,we propose a computational method to predict circRNA-disesae associations which is based on metapath2 vec++and matrix factorization with integrated multiple data(called PCD MVMF).To construct more reliable networks,various aspects are considered.Firstly,circRNA annotation,sequence,and functional similarity networks are established,and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks.Secondly,metapath2 vec++is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score.Finally,we use matrix factorization,take similarity as a constraint,and optimize it to obtain the final prediction results.Leave-one-out cross-validation,five-fold cross-validation,and f-measure are adopted to evaluate the performance of PCD MVMF.These evaluation metrics verify that PCD MVMF has better prediction performance than other methods.To further illustrate the performance of PCD MVMF,case studies of common diseases are conducted.Therefore,PCD MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.
文摘Human Action Recognition(HAR)attempts to recognize the human action from images and videos.The major challenge in HAR is the design of an action descriptor that makes the HAR system robust for different environments.A novel action descriptor is proposed in this study,based on two independent spatial and spectral filters.The proposed descriptor uses a Difference of Gaussian(DoG)filter to extract scale-invariant features and a Difference of Wavelet(DoW)filter to extract spectral information.To create a composite feature vector for a particular test action picture,the Discriminant of Guassian(DoG)and Difference of Wavelet(DoW)features are combined.Linear Discriminant Analysis(LDA),a widely used dimensionality reduction technique,is also used to eliminate duplicate data.Finally,a closest neighbor method is used to classify the dataset.Weizmann and UCF 11 datasets were used to run extensive simulations of the suggested strategy,and the accuracy assessed after the simulations were run on Weizmann datasets for five-fold cross validation is shown to perform well.The average accuracy of DoG+DoW is observed as 83.6635%while the average accuracy of Discrinanat of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 80.2312%and 77.4215%,respectively.The average accuracy measured after the simulation of proposed methods over UCF 11 action dataset for five-fold cross validation DoG+DoW is observed as 62.5231%while the average accuracy of Difference of Guassian(DoG)and Difference of Wavelet(DoW)is observed as 60.3214%and 58.1247%,respectively.From the above accuracy observations,the accuracy of Weizmann is high compared to the accuracy of UCF 11,hence verifying the effectiveness in the improvisation of recognition accuracy.
文摘With the development of the Internet,technology,and means of communication,the production of tourist data has multiplied at all levels(hotels,restaurants,transport,heritage,tourist events,activities,etc.),especially with the development of Online Travel Agency(OTA).However,the list of possibilities offered to tourists by these Web search engines(or even specialized tourist sites)can be overwhelming and relevant results are usually drowned in informational"noise",which prevents,or at least slows down the selection process.To assist tourists in trip planning and help them to find the information they are looking for,many recommender systems have been developed.In this article,we present an overview of the various recommendation approaches used in the field of tourism.From this study,an architecture and a conceptual framework for tourism recommender system are proposed,based on a hybrid recommendation approach.The proposed system goes beyond the recommendation of a list of tourist attractions,tailored to tourist preferences.It can be seen as a trip planner that designs a detailed program,including heterogeneous tourism resources,for a specific visit duration.The ultimate goal is to develop a recommender system based on big data technologies,artificial intelligence,and operational research to promote tourism in Morocco,specifically in the Daraa-Tafilalet region.
