2020 International Conference on Data Mining Workshops (ICDMW)最新文献_第10页

Electric Energy Demand Forecasting with Explainable Time-series Modeling 基于可解释时间序列模型的电力需求预测

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00101

Jin-Young Kim, Sung-Bae Cho

{"title":"Electric Energy Demand Forecasting with Explainable Time-series Modeling","authors":"Jin-Young Kim, Sung-Bae Cho","doi":"10.1109/ICDMW51313.2020.00101","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00101","url":null,"abstract":"Recently, deep learning models are utilized to predict the energy consumption. However, to construct the smart grid systems, the conventional methods have limitation on explanatory power or require manual analysis. To overcome it, in this paper, we present a novel deep learning model that can infer the predicted results by calculating the correlation between the latent variables and output as well as forecast the future consumption in high performance. The proposed model is composed of 1) a main encoder that models the past energy demand, 2) a sub encoder that models electric information except global active power as the latent variable in two dimensions, 3) a predictor that maps the future demand from the concatenation of the latent variables extracted from each encoder, and 4) an explainer that provides the most significant electric information. Several experiments on a household electric energy demand dataset show that the proposed model not only has better performance than the conventional models, but also provides the ability to explain the results by analyzing the correlation of inputs, latent variables, and energy demand predicted in the form of time-series.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"349 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122041042","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: 3

AttentionFM: Incorporating Attention Mechanism and Factorization Machine for Credit Scoring 基于注意力机制和因子分解机的信用评分

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00056

Ying Liu, Wei Wang, Tianlin Zhang, Zhenyu Cui

引用次数: 0

Building knowledge graphs of homicide investigation chronologies 建立凶杀调查年表的知识图谱

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00115

Ritika Pandey, P. Brantingham, Craig D. Uchida, G. Mohler

{"title":"Building knowledge graphs of homicide investigation chronologies","authors":"Ritika Pandey, P. Brantingham, Craig D. Uchida, G. Mohler","doi":"10.1109/ICDMW51313.2020.00115","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00115","url":null,"abstract":"Homicide investigations generate large and diverse data in the form of witness interview transcripts, physical evidence, photographs, DNA, etc. Homicide case chronologies are summaries of these data created by investigators that consist of short text-based entries documenting specific steps taken in the investigation. A chronology tracks the evolution of an investigation, including when and how persons involved and items of evidence became part of a case. In this article we discuss a framework for creating knowledge graphs of case chronologies that may aid investigators in analyzing homicide case data and also allow for post hoc analysis of the key features that determine whether a homicide is ultimately solved. Our method consists of 1) performing named entity recognition to determine witnesses, suspects, and detectives from chronology entries 2) using keyword expansion to identify documentary, physical, and forensic evidence in each entry and 3) linking entities and evidence to construct a homicide investigation knowledge graph. We compare the performance of several choices of methodologies for these sub-tasks using homicide investigation chronologies from Los Angeles, California. We then analyze the association between network statistics of the knowledge graphs and homicide solvability.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123822929","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: 3

TrustedChain: A Blockchain-based Data Sharing Scheme for Supply Chain TrustedChain:基于区块链的供应链数据共享方案

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00128

Gejun Le, Qifeng Gu, Qingshan Jiang, Weiyi Lin

引用次数: 2

Classification of Dementia Associated Disorders Using EEG based Frequent Subgraph Technique 基于脑电图频繁子图技术的痴呆相关疾病分类

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00087

A. T. Adebisi, V. Gonuguntla, Ho-Won Lee, K. Veluvolu

{"title":"Classification of Dementia Associated Disorders Using EEG based Frequent Subgraph Technique","authors":"A. T. Adebisi, V. Gonuguntla, Ho-Won Lee, K. Veluvolu","doi":"10.1109/ICDMW51313.2020.00087","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00087","url":null,"abstract":"Dementia associated disorders such as vascular dementia, frontotemporal dementia and Alzheimer dementia lead to cognitive impairment. Discrimination of dementia associated disorders has reamined a challenging task as they have overlapping underlying complex structures and display similar clinical features. In this work, we explore an EEG based frequent subgraph searching technique to characterize stages of brain functional networks of mild cognitive impairment (MCI), Alzheimer's disease (AD) and vascular dementia (VD) subjects in comparison with healthy control (HC) subjects. To identify the frequent subgraph related to dementia, we first formulated the brain functional network based on the phase information of EEG with mutual information as a measure. The whole network is then divided into sub-regions and frequent sub-graph search is performed. The identified frequent subgraphs were employed to discriminate the dementia associated disorders from the data recorded from 10 healthy and 32 dementia subjects in various stages. Results show that the proposed method has the potential to quantify the disease progression using brain functional connectivity and the identified networks can aid in the diagnosis of dementia associated disorders.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133765864","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

Learning Disentangled Representation of Residential Power Demand Peak via Convolutional-Recurrent Triplet Network 基于卷积-循环三重网络的住宅电力需求峰值解纠缠表征

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00110

Hyung-Jun Moon, Seok-Jun Bu, Sung-Bae Cho

引用次数: 2

Design of Neural Network-based Boost Charging for Reducing the Charging Time of Li-ion Battery 缩短锂离子电池充电时间的神经网络升压充电设计

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00109

Sue Hyang Lim, S. Kim, Hyeong Min Lee, Sijun Kim, Y. Shin

{"title":"Design of Neural Network-based Boost Charging for Reducing the Charging Time of Li-ion Battery","authors":"Sue Hyang Lim, S. Kim, Hyeong Min Lee, Sijun Kim, Y. Shin","doi":"10.1109/ICDMW51313.2020.00109","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00109","url":null,"abstract":"Rapid charging of Li-ion batteries is vital for the commercialization of electric propulsion systems. But, during the fast-charging process, reduction in the battery capacity and temperature increases must be considered in real-time. Most Li-ion battery chargers follow the charging profile of an open-loop system, which has been determined based on prior knowledge. However, such a system does not reflect the temperature change of the battery and the degree of aging. Therefore, in this study, we propose a neural network-based charging profile model by applying a closed-loop system to reflect the various states of batteries; we also show two battery-state characteristics in addition to temperature. Consequently, we show battery characteristics other than those shown in the past, such as the battery voltage and temperature trends. In addition to the design of the charging current, an improvement of approximately 22 ∼ 50% based on the mean absolute error (MAE) is achieved. By considering the various characteristics, the long short-term memory performance is determined to be better when compared to the feed-forward neural network, and this performance is improved by 35% based on MAE.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123358265","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

Using Deep Generative Models to Boost Forecasting: A Phishing Prediction Case Study 使用深度生成模型促进预测:一个网络钓鱼预测案例研究

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00073

Syed Hasan Amin Mahmood, A. Abbasi

{"title":"Using Deep Generative Models to Boost Forecasting: A Phishing Prediction Case Study","authors":"Syed Hasan Amin Mahmood, A. Abbasi","doi":"10.1109/ICDMW51313.2020.00073","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00073","url":null,"abstract":"Time series predictions are important for various application domains. However, effective forecasting can be challenging in noisy contexts devoid of time series data encompassing stationarity, cyclicality, completeness, and non-sparseness. Cyber-security is a good example of such context. In organizational security settings, predicting time series related to emerging attacks could enhance cyber threat intelligence, resulting in timely and actionable insights at the operational, tactical, and strategic levels. In order to explore this gap, we propose a deep generative model-based framework for time series forecasting in noisy data environments. The proposed framework incorporates a novel ensembling strategy where generative adversarial networks and recurrent variational autoencoders are leveraged in unison with base predictors for enhanced regularization of time series predictive models. The framework is extensible, supporting different model combinations and analytical or iterative model fusion strategies. Using a test bed encompassing 10 years of weekly phishing attack volume data from 5 organizations in the technology, financial services, and social networking sectors, we show that the framework can boost predictive power for various standard time series models. Additional results reveal that the framework outperforms generative data augmentation approaches designed to enrich the input time series data matrices. Collectively, our findings suggest that utilizing generative models in more robust end-to-end setup can improve prediction in cyber threat intelligence contexts, as well as related problems involving challenging time series data.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115744481","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

Restructuring of Hoeffding Trees for Trapezoidal Data Streams 梯形数据流Hoeffding树的重构

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00064

Christian Schreckenberger, Tim Glockner, H. Stuckenschmidt, Christian Bartelt

引用次数: 4

Efficient Mining of Non-Dominated High Quantity-Utility Patterns 非支配型高数量效用模式的有效挖掘

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00097

J. Wu, Qian Teng, Gautam Srivastava, Matin Pirouz, Chun-Wei Lin

引用次数: 0