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Special Sessions

Tutorial Sessions/Invited Talks

All tutorials and invited talks are free to registered conference attendees of all conferences held at CSCE'17. Those who are interested in attending one or more of the tutorials are to sign up on site at the conference registration desk in Las Vegas. A complete & current list of CSCE Tutorials can be found later on the congress website.

In addition to tutorials at other conferences, DMIN'17 aims at providing a set of tutorials dedicated to Data Mining topics. The 2007 key tutorial was given by Prof. Eamonn Keogh on Time Series Clustering. The 2008 key tutorial was presented by Mikhail Golovnya (Senior Scientist, Salford Systems, USA) on Advanced Data Mining Methodologies. DMIN'09 provided four tutorials presented by Prof. Nitesh V. Chawla on Data Mining with Sensitivity to Rare Events and Class Imbalance, Prof. Asim Roy on Autonomous Machine Learning, Dan Steinberg (CEO of Salford Systems) on Advanced Data Mining Methodologies, and Peter Geczy on Emerging Human-Web Interaction Research. DMIN'10 hosted a tutorial presented by Prof. Vladimir Cherkassky on Advanced Methodologies for Learning with Sparse Data. He was a keynote speaker as well (Predictive Data Modeling and the Nature of Scientific Discovery). In 2011, Gary M. Weiss (Fordham University, USA) presented a tutorial on Smart Phone-Based Sensor Data Mining. Michael Mahoney (Stanford University, USA) gave a tutorial on Geometric Tools for Identifying Structure in Large Social and Information Networks. DMIN'12 hosted a talk given by Sofus A. Macskassy (Univ. of Southern California, USA) on  Mining Social Media: The Importance of Combining Network and Content as well as a talk given by Haym Hirsh (Rutgers University, USA): Getting the Most Bang for Your Buck: The Efficient Use of Crowdsourced Labor for Data Annotation. Professor Hirsh was a congress keynote speaker, too. In addition, we hosted tutorials and invited talks held by Peter Geczy on Web Mining, Data Mining and Privacy: Water and Fire?, and Data Mining in Organizations. DMIN'13 hosted the following tutorials: EXTENSIONS and APPLICATIONS of UNIVERSUM LEARNING presented by Vladimir Cherkassky (Dept. Electrical & Computer Eng., University of Minnesota, Minneapolis, USA), Visualization & Data Mining for High Dimensional Datasets presented by Alfred Inselberg, (School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel) as well as invited talks: Big Data = Big Challenges? given by Peter Geczy (National Institute of Advanced Industrial Science and Technology (AIST), Japan) and The Problem of Induction: When Karl Popper meets Big Data given by Vladimir Cherkassky.

DMIN' 17 will be hosting the following tutorials/invited talks:

Invited Talks

Invited Talk A
Speaker Dr. Peter Geczy
National Institute of Advanced Industrial Science and Technology (AIST), Japan

Topic/Title Data Economy: The New Gold Rush?
Date & Time t.b.a. (Duration approx. 60 min,)
Location t.b.a.

If data is the ‘new gold’, is data mining the new gold rush? Companies and governments are racing to accumulate and exploit as much data as possible. Data about customers, operations, transactions, interactions – and the list continues. Data has been a significant innovation driver for several segments of developed economies. It is among the most prized assets of not only data-driven technology companies but also governments and individuals. Data has a substantial inherent value. Realization of this value drives the rapidly expanding data-oriented technology sector. Data mining technologies have been playing a central role in an increasing spectrum of economic activities. Growing data economy has been manifesting along several dimensions. We shall explore the pertinent dimensions of data economy and trends at the intersections of academic and commercial interests in data-oriented technologies.

Short Bio

Dr. Peter Geczy holds a senior position at the National Institute of Advanced Industrial Science and Technology (AIST). His recent research interests are in information technology intelligence. This multidisciplinary research encompasses development and exploration of future and cutting-edge information technologies. It also examines their impacts on societies, organizations and individuals. Such interdisciplinary scientific interests have led him across domains of technology management and innovation, data science, service science, knowledge management, business intelligence, computational intelligence, and social intelligence. Dr. Geczy received several awards in recognition of his accomplishments. He has been serving on various professional boards and committees, and has been a distinguished speaker in academia and industry. He is a senior member of IEEE and has been an active member of INFORMS and INNS.


Invited Talk B
Speaker Diego Galar, Division of Operation and Maintenance Engineering,
Luleå University of Technology, 971 87 Lulea, Sweden


Industrial data science and black swans

Date & Time t.b.a. (Duration approx. 90 min.)
Location t.b.a.

Industrial systems are complex with respect to technology and operations with involvement in a wide range of human actors, organizations and technical solutions. For the operations and control of such complex environments, a viable solution is to apply intelligent computerized systems, such as computerized control systems, or advanced monitoring and diagnostic systems. Moreover, assets cannot compromise the safety of the users by applying operation and maintenance activities. Industry 4.0 is a term that describes the fourth generation of industrial activity which is enabled by smart systems and Internet-based solutions. Two of the characteristic features of Industry 4.0 are computerization by utilizing cyber-physical systems and intelligent factories that are based on the concept of "internet of things". Maintenance is one of the application areas, referred to as maintenance 4.0, in form of self-learning and smart systems that predicts failure, makes diagnosis and triggers maintenance by making use of “internet of things”.

Thus, for complex assets, much information needs to be captured and mined to assess the overall condition of the whole system. Therefore the integration of asset information is required to get an accurate health assessment of the whole system, and determine the probability of a shutdown or slowdown. Moreover, the data collected are not only huge but often dispersed across independent systems that are difficult to access, fuse and mine due to disparate nature and granularity. If the data from these independent systems are combined into a common correlated data source, this new set of information could add value to the individual data sources by the means of data mining.

However the data collected are not sufficient due to the black swan effect which pop up by the means of rare events not considered by the data driven models. The black swan events is a metaphor that describes an event that comes as a surprise, has a major effect, and is often inappropriately rationalized after the fact with the benefit of hindsight. The term is based on an ancient saying which presumed black swans did not exist, but the saying was rewritten after black swans were discovered in the wild.

This talk will discuss the possibilities that lie within applying the maintenance 4.0 concept in the industry and the positive effects on technology, organization and operations from a systems perspective and its limitations if black swans are neglected.
Short Bio
Prof. Diego Galar holds a M.Sc. in Telecommunications and a PhD degree in Design and Manufacturing from the University of Saragossa. He has been Professor in several universities, including the University of Saragossa or the European University of Madrid, researcher in the Department of Design and Manufacturing Engineering in the University of Saragossa, researcher also in I3A, Institute for engineering research in Aragon, director of academic innovation and subsequently pro-vice-chancellor.

He has authored more than two hundred journal and conference papers, books and technical reports in the field of maintenance, working also as member of editorial boards, scientific committees and chairing international journals and conferences.

In industry, he has been technological director and CBM manager of international companies, and actively participated in national and international committees for standardization and R&D in the topics of reliability and maintenance.

Currently, he is Professor of Reliability and Maintenance in Skovde University, holding the VOLVO chair for maintenance, and Professor of Condition Monitoring in the Division of Operation and Maintenance Engineering at LTU, Luleå University of Technology, where he is coordinating several EU-FP7 projects related to different maintenance aspects, and was also involved in the SKF UTC center located in Lulea focused in SMART bearings. He is also actively involved in national projects with the Swedish industry and also funded by Swedish national agencies like Vinnova.

In the international arena, he has been visiting Professor in the Polytechnic of Braganza (Portugal), University of Valencia and NIU (USA), currently, University of Sunderland (UK) and University of Maryland (USA). He is also guest professor in the Pontificia Universidad Católica de Chile.


Tutorial A
Gary M. Weiss
Interim Chair, Associate Professor & Director of Wireless Sensor Data Mining (WISDM) Lab, Dept. of Computer and Information Science, Fordham University, Bronx NY, USA
Andrew H. Johnston
WISDM Lab, Fordham University, Bronx  NY, USA

Terrorists, Hackers, and Criminals: Understanding the Darknet

Date & Time t.b.a. (Duration: approx. 90 min.)
Location t.b.a.

The Darknet is the area of the internet that terrorists, child pornographers, hackers, and insider traders call home. Utilizing technologies like TOR, I2P, and ZeroNet, the darknet’s anonymity and distributed nature make law enforcement operations all but impossible. Likewise, the covert and secretive nature of most sites means that most sites are known only to their users. For those not a part of the underworld, the darknet represents an interesting research opportunity. Otherwise hard-to-find underworld groups roam freely, and there are many opportunities to generate interesting data sets. 

In this tutorial, we introduce the audience to the different technologies and explore what type of security features are used by the different underworld groups. We will also briefly cover different attacks and potential attacks that could be used to break the security features provided by the different darknet networks. We will also cover some new research that attempts to recognize terrorist content from benign content, and its use as a tool for finding terrorist content on both the darknet and the regular internet. We will discuss how similar models can be made using text, image, and graph mining techniques.

This tutorial is suitable for a general  audience, but is especially recommended for data scientists, researchers, and cybersecurity practitioners who have an interest in cultivating data from criminal and terrorist enterprises, exploiting anonymity networks, or data mining on such networks. 

Short Bio
Gary Weiss is an associate professor, and interim department chair, in the Department of Computer and Information Science at Fordham University in New York City. He is the director of the Wireless Sensor Data Mining (WISDM) Lab, which explores how smartphones, smartwatches, and other mobile sensors can be used to support human activity recognition, biometrics, and other sensor-based applications. More recently he has started research on the Darknet and the use of text mining to identify terrorist sentiment. His work is funded by the US National Science Foundation, Google, and several other industry partners. He has published over fifty papers in machine learning and data mining.

Andrew Johnston is with Fordham University's Department of Computer Science and is a member of the Wireless Sensor Data Mining (WISDM) Lab. Andrew specializes in using data mining and artificial intelligence techniques to explore and improve security systems. His recent research has focused on using data mining techniques to create the first gait-based biometric system using a smartwatch. He is a coauthor of “Mobile Biometrics”, the first textbook on the topic, scheduled for release in September 2017. Andrew has worked with City of Hope Hospitals, LaQuinta Hotels, Staples, and with the FBI employing a data-driven approach to improving cybersecurity.

Tutorial B
Ulf Johansson, Department of Computer Science and Informatics, Jönköping University, Sweden,

Predicting with Confidence

Date & Time t.b.a. (Duration: approx. 2 hours)
Location t.b.a.

How good is your prediction? In risk-sensitive applications, it is crucial to be able to assess the quality of a prediction, but traditional classification and regression models don't provide their users with any information regarding prediction trustworthiness.

Conformal predictors, on the other hand, are predictive models that associate each of their predictions with a precise measure of confidence. Given a user-defined significance level E, a conformal predictor outputs, for each test pattern, a multivalued prediction region (class label set or real-valued interval) that, under relatively weak assumptions, contains the test pattern’s true output value with probability 1-E. In other words, given a significance level E, a conformal predictor makes an erroneous prediction with probability E. The conformal prediction framework allows any traditional classification or regression model to be transformed into a confidence predictor with little extra work, both in terms of implementation and computational complexity.

Some key properties of conformal prediction are:
• We obtain probabilities/error bounds per instance
• Probabilities are well-calibrated: 95% means 95%
• We don't need to know the priors
• We make a single assumption - that the data is exchangeable ~ i.i.d.
• We can apply it to any machine learning algorithm
• It is rigorously proven and straightforward to implement
• There is no magic involved – only mathematics and algorithms

Hence, confidence predictors is an important tool that every data scientist should carry in their toolboxes, and conformal prediction represents a straight-forward way of associating the predictions of any predictive machine learning algorithm with confidence measures.

This tutorial aims to provide an introduction and an example-oriented exposition of the conformal prediction framework, directed at machine learning researchers and professionals. A publicly available Python library, developed by one of the authors of the tutorial, will be used for the running examples.

The goal of the tutorial is to provide attendees with the knowledge necessary for implementing functional conformal predictors, and to highlight current research on the subject.

Short Bio
Prof. Ulf Johansson holds a M.Sc. in Computer Engineering and Computer Science from Chalmers University of Technology, and a PhD degree in Computer Science from the Institute of Technology, Linköping University, Sweden.
Ulf Johansson’s research focuses on developing machine learning algorithms for data analytics. Most of the research is applied, and often co-produced with industry. Application areas include drug discovery, health science, marketing, high-frequency trading, game AI, sales forecasting and gambling. In 2011, he had his 15 minutes of fame when called as an expert witness in the Swedish Supreme Court regarding whether Poker is a game of skill or chance. In the court, Prof. Johansson argued that skill predominates over chance using, among other sources, his paper “Fish or Shark – Data Mining Online Poker”, originally presented at DMIN 2009.
Ulf Johansson has published extensively in the fields of artificial intelligence, machine learning, soft computing and data mining. He is also a regular program committee member of the leading conferences in computational intelligence and machine learning. During the last few years, Prof. Johansson has published several papers on conformal prediction, some presented in top-tier venues like the Machine Learning journal and the ICDM conference.












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