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2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP)
 
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2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP), August 21-25, 2018, Lviv, Ukraine. Opening, Conference Hall, «Pivdennyi-EXPO».
Views: 680 Dsmp Conference
Drawbridge ICDM Cross-Device Connections Contest: How is this a Data Mining Problem?
 
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At IEEE's International Conference on Data Mining in 2015, Drawbridge hosted a cross-device connections contest tasking participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, our Senior Data Scientist explains the contest in more depth.
Views: 257 Drawbridge
[ACSIC Speaker Series #5] Writing Research Papers for Premier Forums in Knowledge and Data Engine...
 
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Time: Jan. 22nd, 10:00--11:30am, EST Title:  Writing Research Papers for Premier Forums in Knowledge and Data Engineering Presenter: Xindong Wu is a Professor of Computer Science at the University of Vermont (USA), and a Fellow of the IEEE and the AAAS. He holds a PhD in Artificial Intelligence from the University of Edinburgh, Britain. His research interests include data mining, Big Data analytics, knowledge engineering, and Web systems. He has published over 370 refereed papers in these areas in various journals and conferences, including IEEE TPAMI, TKDE, ACM TOIS, KAIS, DMKD, IJCAI, AAAI, ICML, KDD, ICDM, and WWW, as well as 40 books and conference proceedings. He is Steering Committee Chair of the IEEE International Conference on Data Mining (ICDM), Editor-in-Chief of Knowledge and Information Systems (KAIS, by Springer), and Editor-in-Chief of the Springer Book Series on Advanced Information and Knowledge Processing (AI&KP). He was the Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering (TKDE, by the IEEE Computer Society) between January 1, 2005 and December 31, 2008. He has served as Program Committee Chair/Co-Chair for ICDM '03 (the 2003 IEEE International Conference on Data Mining), KDD-07 (the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), CIKM 2010 (the 19th ACM Conference on Information and Knowledge Management), and ASONAM 2014 (the 2014 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining). Professor Wu is the 2004 ACM SIGKDD Service Award winner and the 2006 IEEE ICDM Outstanding Service Award winner. He received the 2012 IEEE Computer Society Technical Achievement Award "for pioneering contributions to data mining and applications", and the 2014 IEEE ICDM 10-Year Highest-Impact Paper Award.
Views: 1661 Acsic People
ICDM Cross-Device Connections Contest: Who is Drawbridge?
 
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At IEEE's International Conference on Data Mining 2015, Drawbridge hosted a cross-device connections contest that tasked participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, our CTO explains the contest.
Views: 372 Drawbridge
Drawbridge ICDM Cross-Device Connections Contest: Keynote Speaker
 
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At IEEE's International Conference on Data Mining in 2015, Drawbridge hosted a cross-device connections contest tasking participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, our keynote speaker Professor Lise Getoor, provides a 45 minute tutorial on entity resolution for big data and it's place in the ecosystem.
Views: 308 Drawbridge
SIGIR 2018:  Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce
 
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The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval Ann Arbor Michigan, U.S.A. July 8-12, 2018 Title: Turning Clicks into Purchases: Revenue Optimization for Product Search in E-Commerce Abstract: In recent years, product search engines have emerged as a key factor for online businesses. According to a recent survey, over 55% of online customers begin their online shopping journey by searching on an E-Commerce (EC) website like Amazon as opposed to a generic web search engine like Google. Information retrieval research to date has been focused on optimizing search ranking algorithms for web documents while little attention has been paid to product search. There are several intrinsic differences between web search and product search that make the direct application of traditional search ranking algorithms to EC search platforms difficult. First, the success of web and product search is measured differently; one seeks to optimize for relevance while the other must optimize for both relevance and revenue. Second, when using real-world EC transaction data, there is no access to manually annotated labels. In this paper, we address these differences with a novel learning framework for EC product search called LETORIF (LEarning TO Rank with Implicit Feedback). In this framework, we utilize implicit user feedback signals (such as user clicks and purchases) and jointly model the different stages of the shopping journey to optimize for EC sales revenue. We conduct experiments on real-world EC transaction data and introduce a a new evaluation metric to estimate expected revenue after re-ranking. Experimental results show that LETORIF outperforms top competitors in improving purchase rates and total revenue earned. Authors: Liang Wu http://www.public.asu.edu/~liangwu1/ Liang Wu has been a PhD student of Computer Science and Engineering at Arizona State University since August, 2014. He obtained his master's degree from Chinese Academy of Sciences in 2014 and bachelor's from Beijing Univ. of Posts and Telecom., China in 2011. The focus of his research is in the areas of misinformation and content polluter detection, and statistical relational learning. He has published over 20 innovative works in major international conferences in data mining and information retrieval, such as SIGIR, ICDM, SDM, WSDM, ICWSM, CIKM and AAAI. Liang has participated in various competitions and data challenges and won the Honorable Mention Award of KDD Cup 2012 on predicting click-through rate of search sponsored ads, ranking 3rd on leaderboard. He is also an author of 6 patent applications and 2 book chapters, and he is a tutorial speaker at SBP'16 and ICDM'17. He has been a Research Intern at Microsoft Research Asia and a Data Science Intern at Etsy and Airbnb. Diane Hu http://cseweb.ucsd.edu/~dhu/ Liangjie Hong http://www.hongliangjie.com/ Huan Liu http://www.public.asu.edu/~huanliu/
Views: 504 Liang Wu
Vista Analytics at the 2017 IEEE International Conference on Big Data
 
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Yihua Shi Astle presents "Application of Dynamic Logistic Regression with Unscented Kalman Filter in Predictive Coding" which is a paper accepted by the conference co-authored with Vista co-founder Craig Freeman and Dr. Xuning Tang
Views: 171 Vista Analytics
Connecting devices to cookies via filtering, feature engineering, and boosting
 
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By: Michael Sungjun Kim, Jiwei Liu, Xiasozhou Wang, Wei Yang At IEEE's International Conference on Data Mining in 2015, Drawbridge hosted a cross-device connections contest tasking participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, a contest participant explains his procedure.
Views: 300 Drawbridge
PBC 2009 ICDM Brain Connectivity Competition Overview Sept 8
 
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Overview of the IEEE International Conference on Data Mining Pittsburgh Brain Competition Brain Connectivity Challenge. This details the data, the challenges, how to download and submit, and the awards
Views: 2397 schneiderlab
Drawbridge ICDM Cross-Device Connections Contest: What is it?
 
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At IEEE's International Conference on Data Mining in 2015, Drawbridge hosted a cross-device connections contest tasking participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, our Director of Data Science explains why we decided to host the contest and what we hope to accomplish.
Views: 101 Drawbridge
Tutorial Pittsburgh Brain Connectivity Competition ICDM 2009 Part 1 of 3 ( 9:09 min)
 
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Tutorial of Pittsburgh Brain Competition (PBC) IEEE International Conference on Data Mining. Part 1 of 3 describes how to enter the competition and microtutorials on High Definition Fiber Tracking (HDF), neuroanatomy for the data miner seeking to map the cables of the brain. It includes how data were collected, what the data represent, micro-tutorial on human brain neuroanatomy, micro-tutorial on applications of HDFT to tract segmentation. It is 9:09 long of a total of 25 minutes. For information see http://www.braincompetition.org
Views: 1027 schneiderlab
Tutorial Pittsburgh Brain Connectivity Competition ICDM 2009 Part 2 of 3 (9:15 min)
 
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Tutorial of Pittsburgh Brain Competition (PBC) IEEE International Conference on Data Mining. Part 2 of 3 describes limitations of the data a data miner should be aware of and details of the data and how to approach the problem. It is 9:15 long of a total of 25 minutes. For information see http://www.braincompetition.org
Views: 391 schneiderlab
Tutorial Pittsburgh Brain Connectivity Competition ICDM 2009 Part 3 of 3 (6:45 min)
 
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Tutorial of Pittsburgh Brain Competition (PBC) IEEE International Conference on Data Mining. Part 3 of 3 concludes the tutorial covering what is submitted, resources, and how you can contribute. It is 6:45 long of a total of 25 minutes. For information see http://www.braincompetition.org
Views: 325 schneiderlab
Recovering cross-device connections via mining IP footprints with ensemble learning
 
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By: Weiyue Huang, Xuezhi Cao, Yong Yu At IEEE's International Conference on Data Mining in 2015, Drawbridge hosted a cross-device connections contest tasking participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, a contest participant explains his procedure.
Views: 330 Drawbridge
Fałszywe opinie w Internecie - jak je odróżnić?
 
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Internet szczególnie w okresie przedświątecznym wypełniają setki komentarzy zachwalających przeróżne produkty, które możemy zechcieć zakupić. Niestety wybierając produkt będziemy w wielu wypadkach musieli posłużyć się opiniami Internatów. Nauczmy się jak je odróżniać. Nie dajmy się zwieść podczas świątecznych zakupów. Więcej informacji znajdziesz tutaj: http://marszalkowski.org/ https://www.facebook.com/Neurolution Źródła: Jing Wang, Clement. T. Yu, Philip S. Yu, Bing Liu, Weiyi Meng. “Diversionary comments under blog posts." Accepted. ACM Transactions on the Web (TWEB), 2015. Huayi Li, Zhiyuan Chen, Arjun Mukherjee, Bing Liu and Jidong Shao. "Analyzing and Detecting Opinion Spam on a Large-scale Dataset via Temporal and Spatial Patterns." Short paper at ICWSM-2015, 2015. Huayi Li, Arjun Mukherjee, Bing Liu, Rachel Kornfieldz and Sherry Emery. Detecting Campaign Promoters on Twitter using Markov Random Fields. to appear in Proceedings of IEEE International Conference on Data Mining (ICDM-2014), December 14-17, 2014. Huayi Li, Zhiyuan Chen, Bing Liu, Xiaokai Wei and Jidong Shao. Spotting Fake Reviews via Collective Positive-Unlabeled Learning. to appear in Proceedings of IEEE International Conference on Data Mining (ICDM-2014, short paper), December 14-17, 2014. Tieyun Qian, Bing Liu. Identifying Multiple Userids of the Same Author. To appear in Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP-2013), October 18-21, 2013, Seattle, USA.
2018 Crypto Valley Conference: Data mining for detecting Bitcoin Ponzi schemes
 
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The 2018 Crypto Valley Conference in collaboration with Lucerne University of Applied Sciences and Arts has invited leading speakers, researchers and academics to share their latest discoveries in the domain of blockchain technology. This annual event includes a business and academic track. The scientific publications, presented at the conference can be found on IEEE Xplore Digital Library.
Views: 45 Crypto Valley
Learning to rank for cross-device identification By: Jeremy Walthers, Aaron Davis
 
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At IEEE's International Conference on Data Mining in 2015, Drawbridge hosted a cross-device connections contest tasking participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, the contest winner explains his procedure.
Views: 1073 Drawbridge
VLDB Day 2 Designing for Interaction Panel
 
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VLDB is a premier annual international forum for data management and database researchers, vendors, practitioners, application developers, and users. The conference will feature research talks, tutorials, demonstrations, and workshops. It will cover current issues in data management, database, and information systems research. Data management and databases remain among the main technological cornerstones of the applications of the twenty-first century. With the emergence of Big Data, data-related technologies are becoming more important than ever before. VLDB 2015 will take place at the Hilton Waikoloa Village on the beautiful Kohala Coast on the northwestern side of the Big Island of Hawai‘i.
Views: 499 VLDB 2015
Cloud-based Data Mining Tools part 1
 
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Cloud-based Data Mining Tools for Storage, Distributed Processing, and Machine Learning Systems for Scientific Data (part 1) Author: Dennis Gannon, Computer Science Department, Indiana University Vani Mandava, Microsoft Research Abstract: This hands-on training is intended to familiarize researchers and data scientists with the services Azure offers to aid them in their research, especially with regard to high-performance computing, big-data analysis, and analyzing data streaming from Internet-of-Things (IoT) devices. https://a4ronline.azurewebsites.net/ More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 119 KDD2017 video
A Big Data Perspective (ACM SIGKDD 2016 Innovation Award)
 
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Author: Philip S. Yu, Department of Computer Science, College of Engineering, University of Illinois at Chicago More on http://www.kdd.org/kdd2016/ KDD2016 Conference is published on http://videolectures.net/
Views: 369 KDD2016 video
Multiple-layer classification
 
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By: Mark Landry, Sudalai Rajkumar, Robert Chong At IEEE's International Conference on Data Mining in 2015, Drawbridge hosted a cross-device connections contest tasking participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, a contest participant explains his procedure.
Views: 399 Drawbridge
VLDB Day 2 40 Years VLDB Panel
 
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VLDB is a premier annual international forum for data management and database researchers, vendors, practitioners, application developers, and users. The conference will feature research talks, tutorials, demonstrations, and workshops. It will cover current issues in data management, database, and information systems research. Data management and databases remain among the main technological cornerstones of the applications of the twenty-first century. With the emergence of Big Data, data-related technologies are becoming more important than ever before. VLDB 2015 will take place at the Hilton Waikoloa Village on the beautiful Kohala Coast on the northwestern side of the Big Island of Hawai‘i.
Views: 443 VLDB 2015
Paper (pp. 46-54) Presentation at SIAM Data Mining 2018
 
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Presentation at SIAM Data Mining 2018 Paper title: Mixtures of Block Models for Brain Networks Authors: Zilong Bai, Peter Walker, Ian Davidson DOI: https://epubs.siam.org/doi/10.1137/1.9781611975321.6 Presenter: Zilong Bai (first author of the paper) Thanks to Sikun Li for recording the talk!
Views: 53 zilong bai
ICDM Ebenezer Live Stream
 
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Views: 156 ICDM Ebenezer
Cross-Device Consumer Identification  By: Girma Kejela
 
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At IEEE's International Conference on Data Mining in 2015, Drawbridge hosted a cross-device connections contest tasking participants with identifying a set of user connections across different devices without using common user handle information, for the purpose of proving that a technological, probabilistic approach to cross-device identity is a viable alternative to relying on deterministic user handle information. Here, a contest participant explains his procedure.
Views: 271 Drawbridge
What Will Our Society Be Like When A.I. Is Everywhere?
 
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What will our society be like when A.I. is everywhere? How will it affect the way we build businesses and engage with products and services? This event is hosted by SPARK Deakin and the Applied Artificial Intelligence Institute (A²I²) with special guests Goeff Webb, Svetha Venkatesh and Rajesh Vasa. You do not want to miss this one. SPARK Deakin is Deakin University's hub for likeminded problem solvers who are entrepreneurial in spirit and in practice. SPARK Deakin hosts a number of events throughout the year and provides opportunities for funding, co-working space membership and mentorship. The Applied Artificial Intelligence Institute (A²I²) collaborates with industry to act as a catalyst for change through the use of Artificial Intelligence. Their team of research fellows, data scientists and software engineers contribute to the development of human-in-the-loop artificial intelligence and AI experimentation for sectors such as defence, health care, security, social media, advanced manufacturing and more. Let's meet our guests: Svetha Venkatesh is an ARC Australian Laureate Fellow, Alfred Deakin Professor and Director of Centre for Pattern Recognition and Data Analytics (PRaDA) at Deakin University. She was elected a Fellow of the International Association of Pattern Recognition in 2004 for contributions to formulation and extraction of semantics in multimedia data, and a Fellow of the Australian Academy of Technological Sciences and Engineering in 2006. In 2017, Professor Venkatesh was appointed an Australian Laureate Fellow, the highest individual award the Australian Research Council can bestow. Professor Venkatesh and her team have tackled a wide range of problems of societal significance, including the critical areas of autism, security and aged care. The outcomes have impacted the community and evolved into publications, patents, tools and spin-off companies. This includes 554 publications, 3 full patents, 3 start-up companies (iCetana.com, Virtual Observer.com) and a significant product (TOBY Playpad). Geoff Webb is Director of the Monash University Centre for Data Science. He is a leading data scientist and the only Australian to have been Program Committee Chair of the two leading Data Mining conferences, ACM SIGKDD and IEEE ICDM. He received the 2016 Australian Computer Society's ICT Researcher of the Year Award, the 2016 Australasian Artificial Intelligence Distinguished Research Contributions Award, a 2014 Australian Research Council Discovery Outstanding Researcher Award and the 2013 IEEE ICDM Service Award and was elevated to IEEE Fellow in 2015. Geoff was editor in chief of Data Mining and Knowledge Discovery from 2005 to 2014. He has been Program Committee Chair of both ACM SIGKDD and IEEE ICDM, as well as General Chair of IEEE ICDM. He is a Technical Advisor to BigML Inc, who have incorporated his best of class association discovery software, Magnum Opus, as a core component of their cloud-based Machine Learning service. He developed many of the key mechanisms of support-confidence association discovery in the 1980s. Rajesh Vasa currently leads the innovation effort at Deakin Software and Technology Innovation Lab (DSTIL). He builds intelligent homes, improving flow of traffic to alleviate congestion, predicting high risk events based on historical behaviour (data science), improving patient care in hospitals, rapid mobile application development using DSLs, collecting data using low-cost sensors (IoT), and helping build better software. Keaton Okkonen is a technical co-founder and CEO of Black.ai, a venture-backed Australian startup working to solve various information problems surrounding the future of robotic automation. Black.ai have worked closely with the self-driving research teams of Volkswagen and Audi in Germany, and have helped the City of Toronto with smart-city development alongside Alphabet company Sidewalk Labs on the Toronto waterfront.
Views: 191 SPARK Deakin