2022 IEEE International Conference on Data Mining Workshops (ICDMW)最新文献_第5页

Adversarial Removal of Population Bias in Genomics Phenotype Prediction 基因组学表型预测中种群偏差的对抗性去除

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00052

Honggang Zhao, Wenlu Wang

引用次数: 1

Online Deep Knowledge Tracing 在线深度知识追踪

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00047

Wenxin Zhang, Yupei Zhang, Shuhui Liu, Xuequn Shang

引用次数: 1

Backdoor Poisoning of Encrypted Traffic Classifiers 加密流量分类器的后门中毒

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00080

J. Holodnak, Olivia Brown, J. Matterer, Andrew Lemke

引用次数: 2

Above Ground Biomass Estimation of a Cocoa Plantation using Machine Learning 利用机器学习估算可可种植园地上生物量

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00147

Sabrina Sankar, Marvin B. Lewis, Patrick Hosein

{"title":"Above Ground Biomass Estimation of a Cocoa Plantation using Machine Learning","authors":"Sabrina Sankar, Marvin B. Lewis, Patrick Hosein","doi":"10.1109/ICDMW58026.2022.00147","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00147","url":null,"abstract":"The rapid increase in carbon dioxide in the atmosphere and its associated effects on climate change and global warming has raised the importance of monitoring carbon sequestration levels. Estimating above ground biomass (AGB) is one way of monitoring carbon sequestration in forested areas. Quantifying above ground biomass using direct methods is costly, time-consuming and, in many cases, impractical. However, remote sensing technologies such as LiDAR (Light Detection And Ranging) captures three dimensional information which can be used to perform this estimation. In particular, LiDAR can be used to estimate the diameter of a tree at breast height (DBH) and from this we can estimate its AGB. For this research we used LiDAR data, along with various Machine Learning (ML) algorithms (Multiple Linear Regression, Random Forest, Support Vector Regression and Regression Tree) to estimate DBH of cocoa trees. Various feature selection methods were used to select the most significant features for our model. The best performing algorithm was Random Forest which achieved an R2 value of 0.83 and Root Mean Square Estimate (RMSE) value of 0.062. This algorithm then estimated an AGB value of 28.75 ± 2.34 Mg/ha (Megagram per hectare). We compared this result with that obtained from locally-developed allometric equations for the same cocoa plot. The comparison proved our estimate to be 14.7% lower than the allometric equation. The results demonstrated that using ML with LiDAR measurements for AGB estimation is quite promising.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114107989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

End-to-End Modeling of Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow-based Reconciliation 基于自回归变压器和条件归一化流调节的分层时间序列端到端建模

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00141

Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, Yang Zheng, Lei Lei, Yun Hu

{"title":"End-to-End Modeling of Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow-based Reconciliation","authors":"Shiyu Wang, Fan Zhou, Yinbo Sun, Lintao Ma, James Zhang, Yang Zheng, Lei Lei, Yun Hu","doi":"10.1109/ICDMW58026.2022.00141","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00141","url":null,"abstract":"Multivariate time series forecasting with hierarchi-cal structure is pervasive in real-world applications, demanding not only predicting each level of the hierarchy, but also recon-ciling all forecasts to ensure coherency, i.e., the forecasts should satisfy the hierarchical aggregation constraints. Moreover, the disparities of statistical characteristics between levels can be huge, worsened by non-Gaussian distributions and non-linear correlations. To this extent, we propose a novel end-to-end hierarchical time series forecasting model, based on conditioned normalizing flow-based autoregressive transformer reconciliation, to represent complex data distribution while simultaneously reconciling the forecasts to ensure coherency. Unlike other state-of-the-art methods, we achieve the forecasting and reconciliation simultaneously without requiring any explicit post-processing step. In addition, by harnessing the power of deep model, we do not rely on any assumption such as unbiased estimates or Gaussian distribution. Our evaluation experiments are conducted on four real-world hierarchical datasets from different industrial domains (three public ones and a dataset from the application servers of Alipay11Alipay is the world's leading company in payment technology. https:/len.wikipedia.org/wiki/Alipay) and the preliminary results demonstrate efficacy of our proposed method.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124347423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Hybrid ConvLSTM Deep Neural Network for Noise Reduction and Data Augmentation for Prediction of Non-linear Dynamics of Streamflow 基于混合ConvLSTM深度神经网络的流场非线性动力学降噪与数据增强预测

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00146

J. Rochac, N. Zhang, T. Deksissa, Jiajun Xu, Lara A. Thompson

{"title":"A Hybrid ConvLSTM Deep Neural Network for Noise Reduction and Data Augmentation for Prediction of Non-linear Dynamics of Streamflow","authors":"J. Rochac, N. Zhang, T. Deksissa, Jiajun Xu, Lara A. Thompson","doi":"10.1109/ICDMW58026.2022.00146","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00146","url":null,"abstract":"Long Short-Term Memory (LSTM) models are at the cutting edge of artificial learning and ecoinformatics in regards to water quantity prediction. However, one driver for more accuracy, efficient, and robust, water pollution perdition methods is climate change, and in particular global sea level rising. Statistical systems are no longer reliable and new prediction models need to be explored due to the increasing nonlinearity of streamflow predictors and extremes sea level changes. Another driver is that, in places with legacy infrastructure, updated water monitoring systems and unreliable forecasting framework, state-of-the-art LSTM -based models suffer due to the presence of noisy data. This paper proposes multiple LSTM-based models with Scharr filtering to improve the streamflow prediction accuracy against noise. A hybrid ConvLSTM approach is realized to overcome the nonlinearity of the main predictors and the noises. The evaluation results demonstrate that the proposed hybrid ConvLSTM model can effectively improve the overall prediction accuracy for both real-world data and the noise-augmented data. The hybrid ConvLSTM model also obtained competitive and even better performance compared with several state-of-the-art methods. In addition, our proposed design achieves comparable performance in terms of prediction time.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123266297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EnD: Enhanced Dedensification for Graph Compressing and Embedding 结束:图形压缩和嵌入的增强去密度化

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00092

Tanvir Hossain, Esra Akbas, Muhammad Ifte Khairul Islam

{"title":"EnD: Enhanced Dedensification for Graph Compressing and Embedding","authors":"Tanvir Hossain, Esra Akbas, Muhammad Ifte Khairul Islam","doi":"10.1109/ICDMW58026.2022.00092","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00092","url":null,"abstract":"Graph representation learning is essential in applying machine learning methods on large-scale networks. Several embedding approaches have shown promising outcomes in recent years. Nonetheless, on massive graphs, it may be time-consuming and space inefficient for direct applications of existing embedding methods. This paper presents a novel graph compression approach based on dedensification called Enhanced Dedensification with degree-based compression (EnD). The principal goal of our system is to assure decent compression of large graphs that eloquently favor their representation learning. For this purpose, we first compress the low-degree nodes and dedensify them to reduce the high-degree nodes' loads. Then, we embed the compressed graph instead of the original graph to decrease the representation learning cost. Our approach is a general meta-strategy that attains time and space efficiency over the original graph by applying the state-of-the-art graph embedding methods: Node2vec, DeepWalk, RiWalk, and xNetMf. Comprehensive ex-periments on large-scale real-world graphs validate the viability of our method, which shows sound performance on single and multi-label node classification tasks without losing accuracy.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127091399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks 时空图神经网络预测未观测节点状态

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00101

Andreas Roth, T. Liebig

{"title":"Forecasting Unobserved Node States with spatio-temporal Graph Neural Networks","authors":"Andreas Roth, T. Liebig","doi":"10.1109/ICDMW58026.2022.00101","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00101","url":null,"abstract":"Forecasting future states of sensors is key to solving tasks like weather prediction, route planning, and many others when dealing with networks of sensors. But complete spatial coverage of sensors is generally unavailable and would practically be infeasible due to limitations in budget and other resources during deployment and maintenance. Currently existing approaches using machine learning are limited to the spatial locations where data was observed, causing limitations to downstream tasks. Inspired by the recent surge of Graph Neural Networks for spatio-temporal data processing, we investigate whether these can also forecast the state of locations with no sensors available. For this purpose, we develop a framework, named Forecasting Unobserved Node States (FUNS), that allows forecasting the state at entirely unobserved locations based on spatio-temporal correlations and the graph inductive bias. FUNS serves as a blueprint for optimizing models only on observed data and demonstrates good generalization capabilities for predicting the state at entirely unobserved locations during the testing stage. Our framework can be combined with any spatio-temporal Graph Neural Network, that exploits spatio-temporal correlations with surrounding observed locations by using the network's graph structure. Our employed model builds on a previous model by also allowing us to exploit prior knowledge about locations of interest, e.g. the road type. Our empirical evaluation of both simulated and real-world datasets demonstrates that Graph Neural Networks are well-suited for this task.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130665518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

MultiAspectEmo: Multilingual and Language-Agnostic Aspect-Based Sentiment Analysis MultiAspectEmo:多语言和语言不可知论的基于方面的情感分析

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00065

Joanna Szolomicka, Jan Kocoń

引用次数: 1

Deep Heterogeneous Graph Neural Networks via Similarity Regularization Loss and Hierarchical Fusion 基于相似性正则化损失和层次融合的深度异构图神经网络

2022 IEEE International Conference on Data Mining Workshops (ICDMW) Pub Date : 2022-11-01 DOI: 10.1109/ICDMW58026.2022.00104

Zhilong Xiong, Jia Cai

{"title":"Deep Heterogeneous Graph Neural Networks via Similarity Regularization Loss and Hierarchical Fusion","authors":"Zhilong Xiong, Jia Cai","doi":"10.1109/ICDMW58026.2022.00104","DOIUrl":"https://doi.org/10.1109/ICDMW58026.2022.00104","url":null,"abstract":"Recently, Graph Neural Networks (GNNs) have emerged as a promising and powerful method for tackling graph-structured data. However, most real-world graph-structured data contains distinct types of objects (nodes) and links (edges), which is called heterogeneous graph. The heterogeneity and rich semantic information indeed increase the difficulties in handling heterogeneous graph. Most of the current heterogeneous graph neural networks (HeteGNNs) can only build on a very shallow structure. This is caused by a phenomenon called semantic confusion, where the node embeddings become indistinguishable with the growth of model depth, leading to the degradation of the model performance. In this paper, we address this problem by proposing a similarity regularization loss and hierarchical fusion based heterogeneous graph neural networks (SHGNN). The hierarchical fusion strategy is utilized to fuse the features of the node embeddings at each layer, which can improve the expressive power of the model, and then a similarity regularization loss is introduced, by which the problem of indistinguishability among nodes can be alleviated. Our approach is flexible to combine various HeteGNNs effectively. Experimental results on real-world heterogeneous graph-structured data demonstrate the state-of-the-art performance of the proposed approach, which can efficiently mitigate the semantic confusion problem.","PeriodicalId":146687,"journal":{"name":"2022 IEEE International Conference on Data Mining Workshops (ICDMW)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134336843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0