2020 International Conference on Data Mining Workshops (ICDMW)最新文献_第6页

Deep Learning-based Critical Infrastructure Simulation Model for Disaster Monitoring 基于深度学习的灾害监测关键基础设施仿真模型

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00136

Parashuram Shourya Rajulapati, N. Nukavarapu, S. Durbha

{"title":"Deep Learning-based Critical Infrastructure Simulation Model for Disaster Monitoring","authors":"Parashuram Shourya Rajulapati, N. Nukavarapu, S. Durbha","doi":"10.1109/ICDMW51313.2020.00136","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00136","url":null,"abstract":"In this paper, we describe a Deep learning-based approach for Real-time Critical Infrastructure Protection in the scenario of a Flood event. This would help us understand the dependencies among various Critical Infrastructures and the severity of the current situation. We propose a Multiagent Deep Reinforcement Learning technique to design the policy and reward functions, which the agent must follow. Further, integrating Reinforcement Learning with GIS aids in putting the model in a spatial context and multiple-layer visualization leading to enhanced awareness of the situation. It also helps in the understanding of the spatiotemporal relationships among them. Each of the Geospatial agents will have its state and a set of actions that it needs to take. The agents will act with respect to their dependence on other related Infrastructures and take the best possible action as the disaster unfolds so that immediate response can reduce the severity of the damage. Real-time information simulation would help disaster response personnel to develop various scenarios in the simulation environment and see how the set of critical infrastructures are responding over time as the severity of the flood increases.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"5 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":"124587043","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: 2

WhoSNext: Recommending Twitter Users to Follow Using a Spreading Activation Network Based Approach whoosnext:使用基于传播激活网络的方法推荐Twitter用户关注

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00018

Marco Siino, M. Cascia, I. Tinnirello

{"title":"WhoSNext: Recommending Twitter Users to Follow Using a Spreading Activation Network Based Approach","authors":"Marco Siino, M. Cascia, I. Tinnirello","doi":"10.1109/ICDMW51313.2020.00018","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00018","url":null,"abstract":"The huge number of modern social network users has made the web a fertile ground for the growth and development of a plethora of recommender systems. To date, recommending a new user profile X to a given user U that could be interested in creating a relationship with X has been tackled using techniques based on content analysis, existing friendship relationships and other pieces of information coming from different social networks or websites. In this paper we propose a recommending architecture - called WhoSNext (WSN) - tested on Twitter and which aim is promoting the creation of new relationships among users. As recent researches show, this is an interesting recommendation problem: for a given user U, find which other user might be proposed to U as a new friend. Instead of conducting a study based on a semantic approach (e.g. analyzing tweet content), the proposed algorithm exploits a graph created from a set of Twitter users called seeds. In this work - and, to the best of our knowledge, for the first time - this issue is addressed using only user ID for building a particular Spreading Activation Network. This network was firstly trained and then tested on a set consisting of over 400,000 real users. Experimental results show that this approach outperforms the results obtained from many well-known state-of-the-art systems, which are much more expensive in terms of either data preprocessing or computational resources.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"53 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":"122705971","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: 5

GCMCSR: A New Graph Convolution Matrix Complete Method with Side-Information Reconstruction GCMCSR:一种新的边信息重构图卷积矩阵完备方法

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00033

Kun Niu, Yicong Yu, Xipeng Cao, Chao Wang

引用次数: 1

A Tree-based Fuzzy Average-Utility Mining Algorithm 基于树的模糊平均效用挖掘算法

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00094

T. Hong, Meng-Ping Ku, Wei-Ming Huang, Shu-Min Li, Chun-Wei Lin

引用次数: 2

MIR_MAD: An Efficient and On-line Approach for Anomaly Detection in Dynamic Data Stream MIR_MAD:一种高效的动态数据流异常在线检测方法

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00065

Chang How Tan, V. C. Lee, Mahsa Salehi

{"title":"MIR_MAD: An Efficient and On-line Approach for Anomaly Detection in Dynamic Data Stream","authors":"Chang How Tan, V. C. Lee, Mahsa Salehi","doi":"10.1109/ICDMW51313.2020.00065","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00065","url":null,"abstract":"Anomaly detection in a dynamic data stream is a challenging task. The endless bound and high arriving rate of data prohibits anomaly detection models to store all observations in memory for processing. In addition, the dynamically moving properties of the data stream exhibit concept drift. While recent studies focus on feature extraction for anomaly detection, majority of them assume data stream are static ignoring the possibility of concept drift occurring. Anomaly detection models must operate efficiently in order to deal with high volume and velocity data, that is to have low complexity and to learn incrementally from each arriving observation. Incremental learning allows the model to adapt to concept drift. In cases where drifting rate is higher than adaptation rate, the capability to detect concept drift and retraining a new model is much preferable to minimize the performance losses. In this paper, we propose MIR_MAD, an approach based on multiple incremental robust Mahalanobis estimators that is efficient, learns incrementally and has the capability to detect concept drift. MIR_MAD is fast, can be initialized with small amount of data, and is able to estimate the drift location on the data stream. Our empirical results show that MIR_MAD achieves state-of-the-art performance and is significantly faster. We also performed a case study to show that detecting concept drift is critical to minimize the reduction in model's performance.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"25 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":"132675629","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

Pelican: Continual Adaptation for Phishing Detection 鹈鹕:持续适应网络钓鱼检测

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00067

Wernsen Wong, G. Dobbie

{"title":"Pelican: Continual Adaptation for Phishing Detection","authors":"Wernsen Wong, G. Dobbie","doi":"10.1109/ICDMW51313.2020.00067","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00067","url":null,"abstract":"An increasing number of people are using social media services and with it comes a more attractive outlet for phishing attacks. Our initial focus is to analyze Twitter as it is one of the most popular social media services. Phishers on Twitter curate tweets that lead users to websites that download malware. This is a major issue as phishers can then gain access to the user's digital identity and perform malicious acts. Phishing attacks have the potential to be similar in different regions, perhaps at different times. We have developed a novel semi-supervised machine learning algorithm, which we call Pelican, that detects potential phishing attacks in real-time on Twitter. Pelican can be used for early detection of potential phishing attacks and is able to detect potential new attacks without pre-existing assumptions about the type of data or understanding of the characteristics of the attacks. The technique uses ensembles and sampling methods to handle class imbalances in real-world applications. The technique continuously detects unusual behaviour or changes in Twitter. We have investigated changes in trends across Twitter to detect changes in online behaviour of potential phishing links. The technique uses a change detector that enables automatic retraining when there is unusual behaviour detected. Pelican is a novel technique that adapts to changes within phishing attacks in real-time. The technique detects 93.94% of the phishing tweets in real-world data that we collected over a 9 month period, which is higher than benchmark algorithms.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"333 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":"114963509","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

20th IEEE International Conference on Data Mining Workshops ICDMW 2020 2020年第20届IEEE数据挖掘国际会议

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/icdmw51313.2020.00001

引用次数: 0

An Examination of the State-of-the-Art for Multivariate Time Series Classification 多元时间序列分类研究进展

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00042

Bhaskar Dhariyal, T.P Le Nguyen, S. Gsponer, Georgiana Ifrim

{"title":"An Examination of the State-of-the-Art for Multivariate Time Series Classification","authors":"Bhaskar Dhariyal, T.P Le Nguyen, S. Gsponer, Georgiana Ifrim","doi":"10.1109/ICDMW51313.2020.00042","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00042","url":null,"abstract":"The UEA Multivariate Time Series Classification (MTSC) archive released in 2018 provides an opportunity to evaluate many existing time series classifiers on the MTSC task. Nevertheless, although many new TSC approaches were proposed recently, a comprehensive overview and empirical evaluation of techniques for the MTSC task is currently missing from the time series literature. In this work, we investigate the state-of-the-art for multivariate time series classification using the UEA MTSC benchmark. We compare recent methods originally developed for univariate TSC, to bespoke methods developed for MTSC, ranging from the classic DTW baseline to very recent linear classifiers (e.g., MrSEQL, ROCKET) and deep learning methods (e.g., MLSTM-FCN, TapNet). We aim to understand whether there is any benefit in learning complex dependencies across different time series dimensions versus treating dimensions as independent time series, and we analyse the predictive accuracy, as well as the efficiency of these methods. In addition, we propose a simple statistics-based time series classifier as an alternative to the DTW baseline. We show that our simple classifier is as accurate as DTW, but is an order of magnitude faster. We also find that recent methods that achieve state-of-the-art accuracy for univariate TSC, such as ROCKET, also achieve high accuracy on the MTSC task, but recent deep learning MTSC methods do not perform as well as expected.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"1 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":"130025889","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: 11

Data Management System based on Blockchain Technology for Agricultural Supply Chain 基于区块链技术的农业供应链数据管理系统

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00130

Chenxue Yang, Zhiguo Sun

{"title":"Data Management System based on Blockchain Technology for Agricultural Supply Chain","authors":"Chenxue Yang, Zhiguo Sun","doi":"10.1109/ICDMW51313.2020.00130","DOIUrl":"https://doi.org/10.1109/ICDMW51313.2020.00130","url":null,"abstract":"In order to better realize agricultural informatization, a data management system for agricultural supply chain, which has the characteristics of safe, credible, stable, traceable, information-sharing, and large-throughput, is needed to construct. To date, the information management method for China's agricultural supply chain is usually stored in a centralized database and file system, with weak information management capabilities, leading to problems such as theft, tampering, deletion, and inconsistencies. In light of these problems, we introduce blockchain technology which is a cutting-edge technology in digital finance and has developed rapidly. This paper proposes a data management system based on blockchain technology and affords efficient data extraction, management and access control for heterogeneous forms of data across the agricultural supply chain. The data management system includes four credible data management platforms: agricultural production information, recording transportation information, farmer-consumer transaction information, and consumer credit information. It also can add more platforms according to later needs and combine an interstellar distributed file system and smart contract technology, making it possible to conduct information security research throughout the agricultural supply chain. The proposed system effectively protects information about supply chain activities including agricultural product production, warehousing, transportation, distribution, and sales. It implements seamless connection between agricultural product production and marketing, enabling the transforming and upgrading of agriculture, thereby helping farmers increase their income and eliminate poverty.","PeriodicalId":426846,"journal":{"name":"2020 International Conference on Data Mining Workshops (ICDMW)","volume":"20 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":"125346741","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: 8

Wavelet Denoised-ResNet CNN and LightGBM Method to Predict Forex Rate of Change 小波去噪- resnet CNN和LightGBM方法预测外汇变化率

2020 International Conference on Data Mining Workshops (ICDMW) Pub Date : 2020-11-01 DOI: 10.1109/ICDMW51313.2020.00060

Yiqi Zhao, Matloob Khushi

引用次数: 21