Pub Date : 2021-12-27DOI: 10.26599/BDMA.2021.9020014
Jiantao Zheng;Cuixiang Lin;Zhenpeng Wu;Hong-Dong Li
Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an experimental comparison of existing IR detection methods. Considering the unavailability of a gold standard dataset of intron retention, we compare the IR detection performance on simulation datasets. Then, we compare the IR detection results with real RNA-Seq data. We also describe the use of differential analysis methods to identify disease-associated IRs and compare differential IRs along with their Gene Ontology enrichment, which is illustrated on an Alzheimer's disease RNA-Seq dataset. We discuss key principles and features of existing approaches and outline their differences. This systematic analysis provides helpful guidance for interrogating transcriptomic data from the point of view of IR.
{"title":"A comparison of computational approaches for intron retention detection","authors":"Jiantao Zheng;Cuixiang Lin;Zhenpeng Wu;Hong-Dong Li","doi":"10.26599/BDMA.2021.9020014","DOIUrl":"https://doi.org/10.26599/BDMA.2021.9020014","url":null,"abstract":"Intron Retention (IR) is an alternative splicing mode through which introns are retained in mature RNAs rather than being spliced in most cases. IR has been gaining increasing attention in recent years because of its recognized association with gene expression regulation and complex diseases. Continuous efforts have been dedicated to the development of IR detection methods. These methods differ in their metrics to quantify retention propensity, performance to detect IR events, functional enrichment of detected IRs, and computational speed. A systematic experimental comparison would be valuable to the selection and use of existing methods. In this work, we conduct an experimental comparison of existing IR detection methods. Considering the unavailability of a gold standard dataset of intron retention, we compare the IR detection performance on simulation datasets. Then, we compare the IR detection results with real RNA-Seq data. We also describe the use of differential analysis methods to identify disease-associated IRs and compare differential IRs along with their Gene Ontology enrichment, which is illustrated on an Alzheimer's disease RNA-Seq dataset. We discuss key principles and features of existing approaches and outline their differences. This systematic analysis provides helpful guidance for interrogating transcriptomic data from the point of view of IR.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"15-31"},"PeriodicalIF":13.6,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663257.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68077702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.26599/BDMA.2021.9020021
Qixiang Shao;Runlong Yu;Hongke Zhao;Chunli Liu;Mengyi Zhang;Hongmei Song;Qi Liu
Intelligent Financial Advisors (IFAs) in online financial applications (apps) have brought new life to personal investment by providing appropriate and high-quality portfolios for users. In real-world scenarios, identifying potential clients is a crucial issue for IFAs, i.e., identifying users who are willing to purchase the portfolios. Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. In this study, we formalize a potential conversion relationship, i.e., user ! activated user ! client and decompose this relationship into three related tasks. Then, we propose a Multitask Feature Extraction Model (MFEM), which can leverage useful information contained in these related tasks and learn them jointly, thereby solving the two problems simultaneously. In addition, we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature fields. Finally, we conduct extensive experiments on a real-world dataset provided by a famous fintech bank. Experimental results clearly demonstrate the effectiveness of MFEM.
{"title":"Toward intelligent financial advisors for identifying potential clients: A multitask perspective","authors":"Qixiang Shao;Runlong Yu;Hongke Zhao;Chunli Liu;Mengyi Zhang;Hongmei Song;Qi Liu","doi":"10.26599/BDMA.2021.9020021","DOIUrl":"https://doi.org/10.26599/BDMA.2021.9020021","url":null,"abstract":"Intelligent Financial Advisors (IFAs) in online financial applications (apps) have brought new life to personal investment by providing appropriate and high-quality portfolios for users. In real-world scenarios, identifying potential clients is a crucial issue for IFAs, i.e., identifying users who are willing to purchase the portfolios. Thus, extracting useful information from various characteristics of users and further predicting their purchase inclination are urgent. However, two critical problems encountered in real practice make this prediction task challenging, i.e., sample selection bias and data sparsity. In this study, we formalize a potential conversion relationship, i.e., user ! activated user ! client and decompose this relationship into three related tasks. Then, we propose a Multitask Feature Extraction Model (MFEM), which can leverage useful information contained in these related tasks and learn them jointly, thereby solving the two problems simultaneously. In addition, we design a two-stage feature selection algorithm to select highly relevant user features efficiently and accurately from an incredibly huge number of user feature fields. Finally, we conduct extensive experiments on a real-world dataset provided by a famous fintech bank. Experimental results clearly demonstrate the effectiveness of MFEM.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"64-78"},"PeriodicalIF":13.6,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663261.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68077805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.26599/BDMA.2021.9020013
Hui Bai;Yan Yang;Jie Wang
Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the correlation between slots, so that the slots can incorporate more slot-value transformer information. Experimental results show that our approach has performed well on the Multi-domain Wizard-Of-Oz (MultiWOZ) 2.0 and MultiWOZ2.1 datasets, demonstrating the effectiveness and necessity of incorporating slot-relevant information.
对话状态跟踪（DST）旨在从对话中提取当前状态，在对话系统中发挥着重要作用。现有的方法通常独立地预测每个时隙的值，而不考虑时隙之间的相关性，这将由于候选值的数量增加而加剧数据稀疏性问题。在本文中，我们提出了一个集成时隙相关信息的多域DST模型。特别地，不同域中的槽之间可能存在某些连接，并且可以通过显式或隐式推理来获得它们对应的值。因此，我们使用图邻接矩阵来确定槽之间的相关性，以便槽可以包含更多的槽值变换器信息。实验结果表明，我们的方法在多域Wizard Of Oz（MultiWOZ）2.0和MultiWOZ2.1数据集上表现良好，证明了引入时隙相关信息的有效性和必要性。
{"title":"Exploiting more associations between slots for multi-domain dialog state tracking","authors":"Hui Bai;Yan Yang;Jie Wang","doi":"10.26599/BDMA.2021.9020013","DOIUrl":"https://doi.org/10.26599/BDMA.2021.9020013","url":null,"abstract":"Dialog State Tracking (DST) aims to extract the current state from the conversation and plays an important role in dialog systems. Existing methods usually predict the value of each slot independently and do not consider the correlations among slots, which will exacerbate the data sparsity problem because of the increased number of candidate values. In this paper, we propose a multi-domain DST model that integrates slot-relevant information. In particular, certain connections may exist among slots in different domains, and their corresponding values can be obtained through explicit or implicit reasoning. Therefore, we use the graph adjacency matrix to determine the correlation between slots, so that the slots can incorporate more slot-value transformer information. Experimental results show that our approach has performed well on the Multi-domain Wizard-Of-Oz (MultiWOZ) 2.0 and MultiWOZ2.1 datasets, demonstrating the effectiveness and necessity of incorporating slot-relevant information.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"41-52"},"PeriodicalIF":13.6,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663259.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68077701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.26599/BDMA.2021.9020016
Amanpreet Kaur Sandhu
With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, Amazon Web Services, International Business Machine cloud, Hortonworks, and MapR. A comparative analysis of various cloud-based big data frameworks is also performed. Various research challenges are defined in terms of distributed database storage, data security, heterogeneity, and data visualization.
随着计算机技术的进步，可用的数据量与日俱增。然而，过多的数据给用户带来了巨大的挑战。同时，云计算服务提供了一个强大的环境来存储大量数据。它们消除了各种要求，例如专用空间和维护昂贵的计算机硬件和软件。处理大数据是一项耗时的任务，需要大型计算集群来确保成功的数据存储和处理。在这项工作中，讨论了大数据的定义、分类和特征，以及各种云服务，如Microsoft Azure、Google cloud、Amazon Web services、International Business Machine cloud、Hortonworks和MapR。还对各种基于云的大数据框架进行了比较分析。从分布式数据库存储、数据安全、异构性和数据可视化等方面定义了各种研究挑战。
{"title":"Big data with cloud computing: Discussions and challenges","authors":"Amanpreet Kaur Sandhu","doi":"10.26599/BDMA.2021.9020016","DOIUrl":"https://doi.org/10.26599/BDMA.2021.9020016","url":null,"abstract":"With the recent advancements in computer technologies, the amount of data available is increasing day by day. However, excessive amounts of data create great challenges for users. Meanwhile, cloud computing services provide a powerful environment to store large volumes of data. They eliminate various requirements, such as dedicated space and maintenance of expensive computer hardware and software. Handling big data is a time-consuming task that requires large computational clusters to ensure successful data storage and processing. In this work, the definition, classification, and characteristics of big data are discussed, along with various cloud services, such as Microsoft Azure, Google Cloud, Amazon Web Services, International Business Machine cloud, Hortonworks, and MapR. A comparative analysis of various cloud-based big data frameworks is also performed. Various research challenges are defined in terms of distributed database storage, data security, heterogeneity, and data visualization.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"32-40"},"PeriodicalIF":13.6,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663258.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68077700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new trusted service evaluation model consists of the blockchain-based identity authentication scheme, evaluation submission module, and evaluation publicity module. In the proposed evaluation model, only users who have successfully been authenticated can submit reviews to service vendors. The registration and authentication records of users' identity and the reviews for service vendors are all stored in the blockchain network. The security analysis shows that this model can ensure the credibility of users' reviews for service vendors, and other users can obtain credible reviews of service vendors via the review publicity module. The experimental results also show that the proposed model has a lower review submission delay than other models.
{"title":"BCSE: Blockchain-based trusted service evaluation model over big data","authors":"Fengyin Li;Xinying Yu;Rui Ge;Yanli Wang;Yang Cui;Huiyu Zhou","doi":"10.26599/BDMA.2020.9020028","DOIUrl":"https://doi.org/10.26599/BDMA.2020.9020028","url":null,"abstract":"The blockchain, with its key characteristics of decentralization, persistence, anonymity, and auditability, has become a solution to overcome the overdependence and lack of trust for a traditional public key infrastructure on third-party institutions. Because of these characteristics, the blockchain is suitable for solving certain open problems in the service-oriented social network, where the unreliability of submitted reviews of service vendors can cause serious security problems. To solve the unreliability problems of submitted reviews, this paper first proposes a blockchain-based identity authentication scheme and a new trusted service evaluation model by introducing the scheme into a service evaluation model. The new trusted service evaluation model consists of the blockchain-based identity authentication scheme, evaluation submission module, and evaluation publicity module. In the proposed evaluation model, only users who have successfully been authenticated can submit reviews to service vendors. The registration and authentication records of users' identity and the reviews for service vendors are all stored in the blockchain network. The security analysis shows that this model can ensure the credibility of users' reviews for service vendors, and other users can obtain credible reviews of service vendors via the review publicity module. The experimental results also show that the proposed model has a lower review submission delay than other models.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"1-14"},"PeriodicalIF":13.6,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663256.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68077704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.26599/BDMA.2021.9020018
He Wang;Zhoujian Cao;Yue Zhou;Zong-Kuan Guo;Zhixiang Ren
Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called "curse of dimensionality" and the complex joint distributions of these dimensions. This is a particularly profound issue for high-dimensional gravitational wave data analysis where one requires to conduct Bayesian inference and estimate joint posterior distributions. In this study, we incorporate prior physical knowledge by sampling from desired interim distributions to develop the training dataset. Accordingly, the more relevant regions of the high-dimensional feature space are covered by additional data points, such that the model can learn the subtle but important details. We adapt the normalizing flow method to be more expressive and trainable, such that the information can be effectively extracted and represented by the transformation between the prior and target distributions. Once trained, our model only takes approximately 1 s on one V100 GPU to generate thousands of samples for probabilistic inference purposes. The evaluation of our approach confirms the efficacy and efficiency of gravitational wave data inferences and points to a promising direction for similar research. The source code, specifications, and detailed procedures are publicly accessible on GitHub.
{"title":"Sampling with prior knowledge for high-dimensional gravitational wave data analysis","authors":"He Wang;Zhoujian Cao;Yue Zhou;Zong-Kuan Guo;Zhixiang Ren","doi":"10.26599/BDMA.2021.9020018","DOIUrl":"https://doi.org/10.26599/BDMA.2021.9020018","url":null,"abstract":"Extracting knowledge from high-dimensional data has been notoriously difficult, primarily due to the so-called \"curse of dimensionality\" and the complex joint distributions of these dimensions. This is a particularly profound issue for high-dimensional gravitational wave data analysis where one requires to conduct Bayesian inference and estimate joint posterior distributions. In this study, we incorporate prior physical knowledge by sampling from desired interim distributions to develop the training dataset. Accordingly, the more relevant regions of the high-dimensional feature space are covered by additional data points, such that the model can learn the subtle but important details. We adapt the normalizing flow method to be more expressive and trainable, such that the information can be effectively extracted and represented by the transformation between the prior and target distributions. Once trained, our model only takes approximately 1 s on one V100 GPU to generate thousands of samples for probabilistic inference purposes. The evaluation of our approach confirms the efficacy and efficiency of gravitational wave data inferences and points to a promising direction for similar research. The source code, specifications, and detailed procedures are publicly accessible on GitHub.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"53-63"},"PeriodicalIF":13.6,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663260.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68077806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.26599/BDMA.2021.9020025
Satellite images are humungous sources of data that require efficient methods for knowledge discovery. The increased availability of earth data from satellite images has immense opportunities in various fields. However, the volume and heterogeneity of data poses serious computational challenges. The development of efficient techniques has the potential of discovering hidden information from these images. This knowledge can be used in various activities related to planning, monitoring, and managing the earth resources. Deep learning are being widely used for image analysis and processing. Deep learning based models can be effectively used for mining and knowledge discovery from satellite images.
{"title":"Call for papers: Special issue on deep learning and evolutionary computation for satellite imagery","authors":"","doi":"10.26599/BDMA.2021.9020025","DOIUrl":"10.26599/BDMA.2021.9020025","url":null,"abstract":"Satellite images are humungous sources of data that require efficient methods for knowledge discovery. The increased availability of earth data from satellite images has immense opportunities in various fields. However, the volume and heterogeneity of data poses serious computational challenges. The development of efficient techniques has the potential of discovering hidden information from these images. This knowledge can be used in various activities related to planning, monitoring, and managing the earth resources. Deep learning are being widely used for image analysis and processing. Deep learning based models can be effectively used for mining and knowledge discovery from satellite images.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"79-79"},"PeriodicalIF":13.6,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663262.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44723605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-12-27DOI: 10.26599/BDMA.2021.9020026
Artificial Intelligence of Things (AIoT) is experiencing unimaginable fast booming with the popularization of end devices and advanced machine learning and data processing techniques. An increasing volume of data is being collected every single second to enable Artificial Intelligence (AI) on the Internet of Things (IoT). The explosion of data brings significant benefits to various intelligent industries to provide predictive services and research institutes to advance human knowledge in data-intensive fields. To make the best use of the collected data, various data mining techniques have been deployed to extract data patterns. In classic scenarios, the data collected from IoT devices is directly sent to cloud servers for processing using diverse methods such as training machine learning models. However, the network between cloud servers and massive end devices may not be stable due to irregular bursts of traffic, weather, etc. Therefore, autonomous data mining that is self-organized by a group of local devices to maintain ongoing and robust AI services plays a growing important role for critical IoT infrastructures. Privacy issues become more concerning in this scenario. The data transmitted via autonomous networks are publicly accessible by all internal participants, which increases the risk of exposure. Besides, data mining techniques may reveal sensitive information from the collected data. Various attacks, such as inference attacks, are emerging and evolving to breach sensitive data due to its great financial benefits. Motivated by this, it is essential to devise novel privacy-preserving autonomous data mining solutions for AIoT. In this Special Issue, we aim to gather state-of-art advances in privacy-preserving data mining and autonomous data processing solutions for AIoT. Topics include, but are not limited to, the following: • Privacy-preserving federated learning for AIoT • Differentially private machine learning for AIoT • Personalized privacy-preserving data mining • Decentralized machine learning paradigms for autonomous data mining using blockchain • AI-enhanced edge data mining for AIoT • AI and blockchain empowered privacy-preserving big data analytics for AIoT • Anomaly detection and inference attack defense for AIoT • Privacy protection measurement metrics • Zero trust architectures for privacy protection management • Privacy protection data mining and analysis via blockchain-enabled digital twin.
{"title":"Call for papers: Special issue on privacy-preserving data mining for artificial intelligence of things","authors":"","doi":"10.26599/BDMA.2021.9020026","DOIUrl":"10.26599/BDMA.2021.9020026","url":null,"abstract":"Artificial Intelligence of Things (AIoT) is experiencing unimaginable fast booming with the popularization of end devices and advanced machine learning and data processing techniques. An increasing volume of data is being collected every single second to enable Artificial Intelligence (AI) on the Internet of Things (IoT). The explosion of data brings significant benefits to various intelligent industries to provide predictive services and research institutes to advance human knowledge in data-intensive fields. To make the best use of the collected data, various data mining techniques have been deployed to extract data patterns. In classic scenarios, the data collected from IoT devices is directly sent to cloud servers for processing using diverse methods such as training machine learning models. However, the network between cloud servers and massive end devices may not be stable due to irregular bursts of traffic, weather, etc. Therefore, autonomous data mining that is self-organized by a group of local devices to maintain ongoing and robust AI services plays a growing important role for critical IoT infrastructures. Privacy issues become more concerning in this scenario. The data transmitted via autonomous networks are publicly accessible by all internal participants, which increases the risk of exposure. Besides, data mining techniques may reveal sensitive information from the collected data. Various attacks, such as inference attacks, are emerging and evolving to breach sensitive data due to its great financial benefits. Motivated by this, it is essential to devise novel privacy-preserving autonomous data mining solutions for AIoT. In this Special Issue, we aim to gather state-of-art advances in privacy-preserving data mining and autonomous data processing solutions for AIoT. Topics include, but are not limited to, the following: • Privacy-preserving federated learning for AIoT • Differentially private machine learning for AIoT • Personalized privacy-preserving data mining • Decentralized machine learning paradigms for autonomous data mining using blockchain • AI-enhanced edge data mining for AIoT • AI and blockchain empowered privacy-preserving big data analytics for AIoT • Anomaly detection and inference attack defense for AIoT • Privacy protection measurement metrics • Zero trust architectures for privacy protection management • Privacy protection data mining and analysis via blockchain-enabled digital twin.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"5 1","pages":"80-80"},"PeriodicalIF":13.6,"publicationDate":"2021-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9663253/09663263.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41991676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-26DOI: 10.26599/BDMA.2021.9020008
Jintao Zhang;Quan Xu
As a powerful tool for elucidating the embedding representation of graph-structured data, Graph Neural Networks (GNNs), which are a series of powerful tools built on homogeneous networks, have been widely used in various data mining tasks. It is a huge challenge to apply a GNN to an embedding Heterogeneous Information Network (HIN). The main reason for this challenge is that HINs contain many different types of nodes and different types of relationships between nodes. HIN contains rich semantic and structural information, which requires a specially designed graph neural network. However, the existing HIN-based graph neural network models rarely consider the interactive information hidden between the meta-paths of HIN in the poor embedding of nodes in the HIN. In this paper, we propose an Attention-aware Heterogeneous graph Neural Network (AHNN) model to effectively extract useful information from HIN and use it to learn the embedding representation of nodes. Specifically, we first use node-level attention to aggregate and update the embedding representation of nodes, and then concatenate the embedding representation of the nodes on different meta-paths. Finally, the semantic-level neural network is proposed to extract the feature interaction relationships on different meta-paths and learn the final embedding of nodes. Experimental results on three widely used datasets showed that the AHNN model could significantly outperform the state-of-the-art models.
{"title":"Attention-aware heterogeneous graph neural network","authors":"Jintao Zhang;Quan Xu","doi":"10.26599/BDMA.2021.9020008","DOIUrl":"https://doi.org/10.26599/BDMA.2021.9020008","url":null,"abstract":"As a powerful tool for elucidating the embedding representation of graph-structured data, Graph Neural Networks (GNNs), which are a series of powerful tools built on homogeneous networks, have been widely used in various data mining tasks. It is a huge challenge to apply a GNN to an embedding Heterogeneous Information Network (HIN). The main reason for this challenge is that HINs contain many different types of nodes and different types of relationships between nodes. HIN contains rich semantic and structural information, which requires a specially designed graph neural network. However, the existing HIN-based graph neural network models rarely consider the interactive information hidden between the meta-paths of HIN in the poor embedding of nodes in the HIN. In this paper, we propose an Attention-aware Heterogeneous graph Neural Network (AHNN) model to effectively extract useful information from HIN and use it to learn the embedding representation of nodes. Specifically, we first use node-level attention to aggregate and update the embedding representation of nodes, and then concatenate the embedding representation of the nodes on different meta-paths. Finally, the semantic-level neural network is proposed to extract the feature interaction relationships on different meta-paths and learn the final embedding of nodes. Experimental results on three widely used datasets showed that the AHNN model could significantly outperform the state-of-the-art models.","PeriodicalId":52355,"journal":{"name":"Big Data Mining and Analytics","volume":"4 4","pages":"233-241"},"PeriodicalIF":13.6,"publicationDate":"2021-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8254253/9523493/09523497.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"68020434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-08-26DOI: 10.26599/BDMA.2021.9020006
Changjie Wang;Zhihua Li;Benjamin Sarpong
Identity-recognition technologies require assistive equipment, whereas they are poor in recognition accuracy and expensive. To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. Experimental results show that the proposed algorithms perform well in recognition accuracy, the confusion matrix, and the kappa statistic, and they have better recognition scores and robustness than the compared algorithms; thus, the proposed algorithm has prominent promise in practice.
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