2019-09-09: Information Reuse & Integration for Data Science (IRI) 2019 Trip Report

The 20th IEEE Information Reuse and Integration for Data Science (IRI) 2019 was held in Los Angles, CA this year. Given the emerging global Information-centric IT landscape that has tremendous social and economic implications, effectively processing and integrating humungous volumes of information from diverse sources to enable effective decision making and knowledge generation have become one of the most significant challenges of current times. Information Reuse and Integration for Data Science (IRI) seeks to maximize the reuse of information by creating simple, rich, and reusable knowledge representations and consequently explores strategies for integrating this knowledge into systems and applications. The IEEE IRI conference serves as a forum for researchers and practitioners from academia, industry, and government to present, discuss, and exchange ideas that address real-world problems with real-world solutions. Theoretical and applied papers are both included. The conference program includes special sessions, open forum workshops, panels and keynote speeches.

Day 1 (July 30)
This year the conference had 69 submissions and only 16 papers accepted (23%) as regular papers.

Keynote 1
The first day of the conference started with a keynote by Dr. Huan Liu, Professor, School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Texas, USA titled, "Some New Data Science Challenges for Data Science."
Professor Liu brought fresh perspective to the idea of paper acceptance with three key ideas.
So we had a nice chat at the end.  He was the adviser of my good friend and the creator of the popular Auto keras framework Xia "Ben" Hu at Texas A&M.
In the first session of the morning, Machine Learning and AI, Fafael Almedia presented, "Combining Data Mining techniques for Evolutionary Analysis of Programming Languages."  His work examines whether the changes made a positive or negative impact on the community.
Next, Dr. Khoshgoftaar from Florida Atlantic University presented an important case study in the area of Medicare fraud detection.  He said he met multiple federal government officials in Washington, DC during the last couple of months.
For the final paper of the morning session, Brian Blake presented his group's work on crowd-sourced sentiment for decision making.  He talked about subjective influencing on any category as well as the correlation and significance based on the data sets of presidential debate, election night, and post election protest.
Keynote 2
The second keynote speaker for day 1,  Dr. Matthew C. Stafford Chief Learning Officer, US Air Force’s Air Education and Training Command, Joint Base San Antonio, Texas, USA, spoke about “Chameleons” – Actors Who Can “Play Any Part”: Your Data Can Have a Starring Role, too!
The key insights of Dr. Stafford's talk included ideas about the mistrust in machines.  When do you trust and when you don't trust them?  Specially in machine learning, the conflict and have to put the human element in data. Data scientist, they don't understand the data and the human side of the knowledge.  Data scientists need to bring both sides to table from the machine learning side and human side.

The evening session started the session on Novel Data Mining and Machine Learning Applications. The first talk was on GO-FOR: A Goal-Oriented Framework for Ontology Reuse by Cássio Reginato.

The first day sessions concluded with a panel on On Expanding the Impact of Data Science on the Theory of Intelligence and Its Applications,j chaired by IRI 2019 general chair Stuart H. Rubin and the panelists were Chengcui Zhang, University of Alabama at Birmingham, USA, Taghi Khoshgoftaar, Florida Atlantic University and Matthew C. Stafford, Chief Learning Officer, US Air Force’s Air Education and Training Command, Joint Base San Antonio, Texas, USA
Day 2 (July 31)

Despite a few morning clouds, it turned out to be another beautiful day in southern California.  After attendees arrived and got their morning fill of coffee and pastries, they once again made their way into the main conference room.

Keynote 3
The second day of the of the conference started with the final keynote address by Aidong Zhang, WilliamWulf Faculty Fellow and Professor of Computer Science at the University of Virginia (UVA), titled, "Graph Neural Networks for Supporting Knowledge-Data Integrated Machine Learning."  Many were surprised at the findings presented (use of a small sample to train a deep learning model) and wanted more information regarding the research being performed by Dr. Zhang's group at the UVA.

Soon after the keynote address, participants dispersed, making their way to different rooms for one of three breakout sessions:  visual analytics, biomedical applications, and big data applications.  ODU’s own Dr. Sampath Jayarathna (@OpenMaze) started things off in the biomedical applications session by presenting the best student paper nominated work, "Analysis of Temporal relationships between ASD and Brain Activity through EEG and Machine Learning."
The next presentation was on how deep multi-task learning for interpretable glaucoma detection could overcome some of the challenges faced in the field.  After Nooshin Moab finished addressing questions following her presentation, most conference participants would part ways to find lunch.
After the break, an overview of MIT’s Lottery Ticket hypothesis for training neural networks was discussed.  Afterwards, we remained in the main conference room for the Novel Data Mining and Machine Learning Applications II breakout session.  The session began with a presented of machine learning models for airfare prediction.  Dr. Jayarathna (@OpenMaze) then returned to discuss, "Eye Tracking Area of Interest (AOI) in the Context of Working Memory Capacity Tasks."  The discussion focused on how proper utilization of the AOI could allow the capture of eye gaze metrics that could predict Attention-Deficit/Hyperactivity Disorder (ADHD) in humans.

Next, Mohammed Kuko would presented a review of his machine learning method for single and clustered cervical cell classification in support of an automated Pap smear screening system.  Bathsheba Farrow would finish that afternoon’s breakout session with a discussion on the different techniques used for post-traumatic stress disorder (PTSD) detection, which marked the path of her future research.
There was not a heavy concentration of posters during the poster session.  There was an interesting poster presentation on graph visualizations of Asian music and another on hand gesture recognition with convolution neural networks.  Many attendees took a few minutes to take in the poster presentations then found an opportunity to network with the other presenters in the hotel foyer.

The second day of the conference ended with a hotel banquet for the conference goers.  Dinner and dessert were followed by an awards ceremony.  Several conference organizers received awards for their work on various committees.  There was definitely excitement in the air when it was announced that the Best Student Paper award would go to our own Yasith Jayawardana (@yasithmilinda), Dr. Mark Jamie, and Dr. Sampath Jayarathna (@OpenMaze) for their work on the "Analysis of Temporal Relationships between ASD and Brain Activity through EEG and Machine Learning."
The best paper award went for the work of "Data Clustering using Online Variational Learning of Finite Scaled Dirichlet Mixture Models" by Hieu Nguyen, Meeta Kalra, Muhammad Azam and Nizar Bouguila.

Other Resources
Check out the pictures of the event available at:

-- Bathsheba Nelson (@sheissheba) and Sampath Jayarathna (@openmaze)