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ICDM-2019
 
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International Conference on DISCRETE MATHEMATICS - ICDM 2019 (Jan 7-9th 2019)
Views: 22 Martin Ronald
[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: 1718 Acsic People
Utilizing In-Store Sensors for Revisit Prediction (ICDM'2018)
 
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Title: Utilizing In-Store Sensors for Revisit Prediction Presenter: Sundong Kim Presented at IEEE ICDM'2018, Singapore. (Paper link: https://ieeexplore.ieee.org/document/8594846/)
Views: 33 Sundong Kim
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
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: 2399 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
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: 414 KDD2016 video
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
ICDM2017 Tutorial on Misinformation part1
 
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[Speaker: Liang Wu, Arizona State University. Giovanni Luca Ciampaglia, Indiana University. Huan Liu, Arizona State University.] A rapid increase in social networking services in recent years has enabled people to share and seek information effectively. Meanwhile, the openness and timeliness of social networking sites also allow for the rapid creating and dissemination of misinformation. As witnessed in recent incidents of fake news, misinformation escalates quickly and can impact social media users with undesirable consequences and wreak havoc instantaneously. Despite many people have been aware of that fake news and rumors are misleading the public and even compromising elections, the problem is not going away. In this tutorial, we will discuss how misinformation gains traction in the race for attention, introduce emerging challenges of identifying misinformation, present a comparative survey of current data mining research in tackling the challenges, and suggest available resources and point to directions for future work. ICDM Tutorial, Mining Misinformation in Social Media: Understanding Its Rampant Spread, Harm, and Intervention Speaker: Liang Wu http://www.liangwu.me/ Liang Wu, PhD. is a machine learning data scientist at Airbnb. His research focuses on E-Commerce search, web search, and recommender systems. His dissertation work concentrated on building robust machine learning systems with noisy, inaccurate and biased data. He has published over 30 papers in major artificial intelligence conferences including AAAI, CIKM, ICDM, ICWSM, SDM, SIGIR and WSDM, and his solution has won the third place on the leaderboard in KDD Cup 2012. He serves as a Program Committee member for AAAI, SIGIR, KDD, WSDM, BIGDATA, etc, and a main lecturer for tutorials in SBP'16 and ICDM'17. He obtained his Ph.D. from Arizona State University, and his master and bachelor from Univ. of Chinese Academy of Sciences, and Beijing Univ. of Posts and Telecom, respectively. He is an author of 2 book chapters and 4 patents in China.
Views: 413 Liang Wu
2017 01 PLoSOne rumor video 루머 비루머 전파
 
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Network propagation pattern of a single rumor (left) and a single news article (right). - S. Kwon, M. Cha, and K. Jung. Rumor detection over varying time windows, In PloS One, 2017 https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0168344 - S. Kwon, M. Cha, K. Jung, W. Chen, and Y. Wang. Prominent Features of Rumor Propagation in Online Social Media. In proc. of the IEEE International Conference on Data Mining (ICDM’13), 2013. https://ieeexplore.ieee.org/document/6729605
Views: 7 Meeyoung Cha
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.
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1726 Audiopedia
"The Five Tribes of Machine Learning (And What You Can Learn from Each)," Pedro Domingos
 
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There are five main schools of thought in machine learning, and each has its own master algorithm – a general-purpose learner that can in principle be applied to any domain. The symbolists have inverse deduction, the connectionists have backpropagation, the evolutionaries have genetic programming, the Bayesians have probabilistic inference, and the analogizers have support vector machines. What we really need, however, is a single algorithm combining the key features of all of them. In this webinar I will summarize the five paradigms and describe my work toward unifying them, including in particular Markov logic networks. I will conclude by speculating on the new applications that a universal learner will enable, and how society will change as a result. Presenter: Pedro Domingos, University of Washington in Seattle; SIGKDD Innovation Award Winner Pedro Domingos is a professor of computer science at the University of Washington in Seattle. He is a winner of the SIGKDD Innovation Award, the highest honor in data science. He is a Fellow of the Association for the Advancement of Artificial Intelligence, and has received a Fulbright Scholarship, a Sloan Fellowship, the National Science Foundation’s CAREER Award, and numerous best paper awards. He received his Ph.D. from the University of California at Irvine and is the author or co-author of over 200 technical publications. He has held visiting positions at Stanford, Carnegie Mellon, and MIT. He co-founded the International Machine Learning Society in 2001. His research spans a wide variety of topics in machine learning, artificial intelligence, and data science, including scaling learning algorithms to big data, maximizing word of mouth in social networks, unifying logic and probability, and deep learning. Moderator: Gregory Piatetsky-Shapiro, KDnuggets; SIGKDD Co-Founder Gregory Piatetsky-Shapiro is President of KDnuggets, which provides consulting in the areas of business analytics, data mining, data science, and knowledge discovery. Previously, he led data mining and consulting groups at GTE Laboratories, Knowledge Stream Partners, and Xchange. Gregory is also Editor and Publisher of KDnuggets News and the KDnuggets.com website, leading resources for data mining and analytics news, software, jobs, courses, data, education, and more. Gregory is the founder of Knowledge Discovery in Database (KDD) conferences. He organized and chaired the first three Knowledge Discovery in Databases workshops in 1989, 1991, and 1993, and then chaired the KDD Steering Committee until 1998, when he co-founded ACM SIGKDD, the leading professional organization for Knowledge Discovery and Data Mining. He served as Director (1998-2005) and was elected SIGKDD Chair (2005-2009 term). Gregory has over 60 publications, with over 10,000 citations, including two best-selling books and several edited collections on topics related to data mining and knowledge discovery. Gregory was the first recipient of ACM SIGKDD Service Award (2000). He also received the IEEE ICDM Outstanding Service Award (2007) for contributions to data mining field and community.
ICDM Ebenezer Live Stream
 
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Views: 164 ICDM Ebenezer
Ashok N. Srivastava receives 2010 IEEE Computer Society Technical Achievement Award
 
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The IEEE Computer Society presented its 2010 Technical Achievement Award to Ashok N. Srivastava for his pioneering contributions to intelligent information systems. The Technical Achievement Award honors outstanding and innovative contributions to computer and information science and engineering, usually within the past 10 years. Dr. Srivastava accepted his award at the Computer Society's 9 June 2010 awards ceremony in Denver, Colorado. Ashok N. Srivatava is the Principal Investigator for the Integrated Vehicle Health Management research project at NASA. His current research focuses on the development of data mining algorithms for anomaly detection in massive data streams, kernel methods in machine learning, and text mining algorithms. Dr. Srivastava is also the leader of the Intelligent Data Understanding group at NASA Ames Research Center. The group performs research and development of advanced machine learning and data mining algorithms in support of NASA missions. For more information about Ashok N. Srivastava: http://www.computer.org/portal/web/awards/srivastava For more information about IEEE Computer Society Awards: http://www.computer.org/awards
Views: 361 ieeeComputerSociety
Meta Paths and Meta Structures: Analysing Large Heterogeneous Information Networks
 
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How to analyze large heterogeneous information from social networks like Facebook and Twitter? Dr. Reynold Cheng, Associate Professor of University of Hong Kong, shared his thoughts in Tech [email protected] #BigData Follow us! Get first hand and in-depth information about Alibaba Technology: Medium: https://medium.com/@alitech_2017 Facebook: https://www.facebook.com/AlibabaTechnology/ Twitter: https://twitter.com/AliTech2017 Meta Paths and Meta Structures: Analysing Large Heterogeneous Information Networks A heterogeneous information network (HIN) is a graph model in which objects and edges are annotated with types. Large and complex databases, such as YAGO and DBLP, can be modeled as HINs. A fundamental problem in HINs is the computation of closeness, or relevance, between two HIN objects. Relevance measures, such as PCRW, PathSim, and HeteSim, can be used in various applications, including information retrieval, entity resolution, and product recommendation. These metrics are based on the use of meta-paths, essentially a sequence of node classes and edge types between two nodes in a HIN. In this tutorial, we will give a detailed review of meta-paths, as well as how they are used to define relevance. In a large and complex HIN, retrieving meta paths manually can be complex, expensive, and error-prone. Hence, we will explore systematic methods for finding meta paths. In particular, we will study a solution based on the Query-by-Example (QBE) paradigm, which allows us to discovery meta-paths in an effective and efficient manner. We further generalise the notion of meta path to "meta structures", which is a directed acyclic graph of object types with edge types connecting them. Meta structure, which is more expressive than the meta path, can describe complex relationship between two HIN objects (e.g., two papers in DBLP share the same authors and topics). We develop three relevance measures based on meta structure. Due to the computational complexity of these measures, we also study an algorithm with data structures proposed to support their evaluation. Finally, we will examine solutions for performing query recommendation based on meta-paths. We will also discuss future research directions in HINs. Dr. Reynold Cheng's Bio: Dr. Reynold Cheng is an Associate Professor of the Department of Computer Science in the University of Hong Kong. He was an Assistant Professor in HKU in 2008-11. He received his BEng ( Computer Engineering ) in 1998, and MPhil ( Computer Science and Information Systems ) in 2000, from the Department of Computer Science in the University of Hong Kong. He then obtained his MSc and PhD from Department of Computer Science of Purdue University in 2003 and 2005 respectively. Dr. Cheng was an Assistant Professor in the Department of Computing of the Hong Kong Polytechnic University during 2005-08. He was a visiting scientist in the Institute of Parallel and Distributed Systems in the University of Stuttgart during the summer of 2006. Dr. Cheng was granted an Outstanding Young Researcher Award 2011-12 by HKU. He was the recipient of the 2010 Research Output Prize in the Department of Computer Science of HKU. He also received the U21 Fellowship in 2011. He received the Performance Reward in years 2006 and 2007 awarded by the Hong Kong Polytechnic University. He is the Chair of the Department Research Postgraduate Committee, and was the Vice Chairperson of the ACM ( Hong Kong Chapter ) in 2013. He is a member of the IEEE, the ACM, the Special Interest Group on Management of Data ( ACM SIGMOD ), and the UPE (Upsilon Pi Epsilon Honor Society). He is an editorial board member of TKDE, DAPD and IS, and was a guest editor for TKDE, DAPD, and Geoinformatica. He is an area chair of ICDE 2017, a senior PC member for DASFAA 2015, PC co-chair of APWeb 2015, area chair for CIKM 2014, area chair for Encyclopedia of Database Systems, program co-chair of SSTD 2013, and a workshop co-chair of ICDE 2014. He received an Outstanding Service Award in the CIKM 2009 conference. He has served as PC members and reviewer for top conferences (e.g., SIGMOD, VLDB, ICDE, EDBT, KDD, ICDM, and CIKM) and journals (e.g., TODS, TKDE, VLDBJ, IS, and TMC).
Views: 365 AlibabaTech
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: 225 SPARK Deakin
Dynamic view expansion for Minimally Invasive Surgery
 
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Navigation during Minimally Invasive Surgery (MIS) has recognized difficulties due to limited field-of-view, off-axis visualization and loss of direct 3D vision. This can cause visual-spatial disorientation when exploring complex in vivo structures. In this video, we present an approach to dynamic view expansion which builds a 3D textured model of the MIS environment to facilitate in vivo navigation. With the proposed technique, no prior knowledge of the environment is required and the model is built sequentially while the laparoscope is moved. The method was presented at the International Conference of the IEEE Engineering in Medicine and Biology Society in 2009. The paper explaining the techniques behing this is avaliable at http://tinyurl.com/2cd53nq
Views: 627 peter mountney
Xindong Wu receives 2012 IEEE Computer Society Technical Achievement Award
 
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The IEEE Computer Society presented its 2012 Technical Achievement Award to Xindong Wu for his pioneering contributions to data mining and applications. The Technical Achievement Award honors outstanding and innovative contributions to computer and information science and engineering, usually within the past 10 years. Dr. Wu accepted his award at the Computer Society's 13 June 2012 awards ceremony in Seattle, Washington. Xindong Wu is a Professor of Computer Science at the University of Vermont, a Yangtze River Scholar in the School of Computer Science and Information Engineering at the Hefei University of Technology, and an IEEE Fellow. Dr. Wu's research interests include data mining, knowledge-based systems, and Web information exploration. His research has been supported by the U.S. National Science Foundation (NSF), the U.S. Department of Defense (DOD), the National Natural Science Foundation of China (NSFC), and the Ministry of Science and Technology of China, as well as industrial companies including Microsoft Research and U.S. West Advanced Technologies. For more information about Xindong Wu: http://www.computer.org/portal/web/awards/ta-xindong-wu For more information about IEEE Computer Society Awards: http://www.computer.org/awards
Views: 2655 ieeeComputerSociety
Simulated Ocular Surgery: Radial Keratotomy
 
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A semester project of UM-SJTU JI course VM467 (Intro to Robotics) The S.M.O.S robot is performing a ocular surgery. Simulated results are based on the paper by A. Guerrouad and P. Vidal, "S.M.O.S. : Stereotaxical Microtelemanipulator for Ocular Surgery," in Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), Seattle, WA, USA, 1989, pp. 879-880.
Views: 638 Kai Xu
SNS Tutorial / Beat Broadcast 1 twitter 3/3 (Micro Blog)
 
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社内SNSのパイオニア、Beat Communicationによるtwitterについての説明その3 Tutorial on how to use twitter! We are Beat Communication, Social Technology Solution (SNS) Company. For more, please visit our web site. http://www.beat.co.jp
Views: 92 BEATBROADCAST
Configuration management database
 
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A configuration management database (CMDB) is a repository that acts as a data warehouse for information technology (IT) organizations. Its contents are intended to hold a collection of IT assets that are commonly referred to as configuration items (CI), as well as descriptive relationships between such assets. When populated, the repository becomes a means of understanding how critical assets such as information systems are composed, what their upstream sources or dependencies are, and what their downstream targets are. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1007 Audiopedia