Accepted Tutorials

(1)  Tutorial 1: Introduction to Digital Libraries 

Time: 7:30am-12:00pm, 06/03/2018

Edward A. Fox, Virginia Tech (fox@vt.edu

Abstract: This tutorial is a thorough and deep introduction to the Digital Libraries (DL) field, providing a firm foundation: covering key concepts and terminology, as well as services, systems, technologies, methods, standards, projects, issues, and practices. It introduces and builds upon a firm theoretical foundation (starting with the `5S' set of intuitive aspects: Streams, Structures, Spaces, Scenarios, Societies), giving careful definitions and explanations of all the key parts of a `minimal digital library', and expanding from that basis to cover key DL issues. Illustrations come from a set of case studies, including from multiple current projects, including with webpages, tweets, and social networks. Attendees will be exposed to four Morgan and Claypool books that elaborate on 5S, published 2012-2014. Complementing the coverage of `5S' will be an overview of key aspects of the DELOS Reference Model and DL.org activities. Further, use of a Hadoop cluster supporting big data DLs will be described.

 

(2)  Tutorial 2: Introduction to Digital Humanities 

Time: 1:30pm-5:00pm, 06/03/2018

Luis Meneses, University of Victoria (ldmm@uvic.ca)

Richard Furuta, Texas A&M University (furuta@cse.tamu.edu)

Abstract: Digital Humanities is an area of inquiry and scholarship that combines the procedural methodologies from the Sciences with the reflection that is carried out in the Humanities. The Digital Humanities is characterized by its new forms of scholarship involving collaborative, transdisciplinary, and computationally-engaged research, teaching, and publishing. In this half-day tutorial, which is sponsored in part by the Digital Humanities Summer Institute (http://www.dhsi.org), we introduce the concepts of Digital Humanities from the perspective of participants that are familiar with Digital Libraries. We focus on four core areas: concepts, the tools used, their implications, and the future of the discipline. By the end of the tutorial the participants should be able to articulate some of the benefits and the drawbacks of using digital tools to approach the research questions in the Humanities, and critically interrogate the way they use the Internet to get information, produce content and interact with others.

 

(3)  Tutorial 3: Improving Search and Retrieval in Digital Libraries by Leveraging Keyphrase Extraction Systems 

Time: 7:30am-12:00pm, 06/03/2018

Wei Jin, University of North Texas (wei.jin@unt.edu)

Corina Florescu, University of North Texas (corinaflorescu@my.unt.edu)

Abstract: In this tutorial, we will focus on recent developments in the keyphrase extraction task using research papers as a case study. In particular, we will discuss a wide range of keyphrase extraction models ranging from the representative supervised approaches such as KEA and GenEx to more recent ones that make use of the advances in artificial intelligence. Beyond introducing the outstanding approaches in this domain, we will discuss how keyphrases can significantly improve the search and retrieval of information in digital libraries and hence, leads to an improved organization, search, retrieval, and recommendation of scientific documents. Participants will learn about existing approaches, challenges and future trends in the keyphrase extraction task, and how they can be applied to digital library applications.

 

(4)  Tutorial 4: Introduction to Machine Learning for Digital Library Applications 

Time: 1:30pm-5:00pm, 06/03/2018

Rodney Nielsen, University of North Texas (Rodney.Nielsen@unt.edu)

Abstract: This tutorial begins with an overview of the major branches of machine learning (ML) and then provides more thorough coverage of deep neural networks. It covers key concepts, tools, experimental methods, applications, evaluation measures and associated issues for supervised learning (regression and classification), unsupervised learning (clustering and dimensionality reduction), semi-supervised and active learning (which combine the former approaches), and reinforcement learning. The deep neural network discussion covers convolutional neural networks (CNNs), recurrent neural networks (RNNs), word embeddings and related techniques. The discussion will be grounded on digital library (DL) - related applications and will highlight issues, techniques and tools associated with processing big data.