Category: CS

Grad Students Take 6th Place in Navy’s AI Tracks at Sea Challenge

by Karen S. Johnson, Communications Director, College of Computing


The Challenge

Four Michigan Tech graduate students recently took 6th place in the U.S. Navy’s Artificial Intelligence (AI) Tracks at Sea Challenge, receiving a $6,000 prize.

The Challenge solicited software solutions to automatically generate georeferenced tracks of maritime vessel traffic based on data recorded from a single electro-optical camera imaging the traffic from a moving platform.

Each Challenge team was presented with a dataset of recorded camera imagery of vessel traffic, along with the recorded GPS track of a vessel of interest that is seen in the imagery.

Graduate students involved in the challenge were Zach DeKraker and Nicholas Hamilton, both Computer Science majors advised by Tim Havens; Evan Lucas, Electrical Engineering, advised by Zhaohui Wang; and Steven Whitaker, Electrical Engineering.

Submitted solutions were evaluated against additional camera data not included in the competition testing set in order to verify generalization of the solutions. Judging was based on track accuracy (70%) and overall processing time (30%).

“We never got our final score, but we were the “first runner up” team,” says Lucas. “Based on our testing before sending it, we think it worked well most of the time and occasionally tracked a seagull or the wrong boat.”

The total $200,000 prize was distributed among five winning teams, which submitted full working solutions, and three runners-up, which submitted partial working solutions.

The Challenge was sponsored by the Naval Information Warfare Center (NIWC) Pacific and the Naval Science, Technology, Engineering, and Mathematics (STEM) Coordination Office, and managed by the Office of Naval Research. Its goal was to engage with the workforce of tomorrow on challenging and relevant naval problems, with the immediate need to augment unmanned surface vehicles’ (USVs’) maritime contact tracking capability.

The Problem

“The problem presented was to find a particular boat in a video taken of a harbor, and track its GPS coordinates.,” says Zach DeKraker. “We were provided with samples of other videos along with the target boat’s GPS coordinates for that video, which we were able to use to come up with a mapping from pixels to GPS coordinates.”

“Basically, we wanted to track boats with a video camera,” adds ECE graduate student Steven Whitaker. “Our team used machine learning and computer vision to do this. At weekly meetings we brainstormed approaches to tackling the problem, and at regular work sessions, together we programmed it all and produced a white paper with the technical details.”

Whitaker says the competition tied in pretty closely to work the students have already done. “We had a good majority of the code already written. We just needed to fit everything together and add in a few more details and specialize it for the AI Tracks at Sea research,” he explains.

Competitions like this one often connect directly or indirectly with a student’s academic and career goals.

“It’s good to not be pigeon-holed, and to use our knowledge in a different scenario,” Steven Whitaker says of these opportunities. “This helps us remember that there are other things in the world other than our small section of research.”

Dividing Responsibilities

The team knew that there were two primary issues at hand. First, how can the pixel coordinates be translated into GPS coordinates? And second, how can the boat be located so that GPS pixel coordinates can be determined?

“Once we broke it down into these two subproblems, it became pretty clear how to solve each half,” DeKraker says. “Steven had already done a significant amount of work mapping pixel coordinates into GPS coordinates, so we had a pretty quick answer to subproblem one.”

AI Tracks at Sea Flowchart

The team met weekly to discuss their ideas for the project and compare and contrast how effective they would be as solutions to the problem at hand. Then, they got together on Fridays or during the weekends to work together on the project.

“Dr. Havens would come in to our weekly meetings and nudge us in the right direction or give tips on what we should do and what we should avoid,” Whitaker adds.

For subproblem two, after some discussion the group decided it was probably best to use a machine learning approach, as that promised the most significant gains for the least amount of effort, which was important given the tight schedule.

“We tried some different sub-projects independently and then worked together to combine the parts we thought worked best,” Evan Lucas says.

The Solution

To identify the boat and track its movement, the team used a simple neural network and a computer vision technique called optical flow, which made the analysis much faster and cleaner. They used a pre-built algorithm, adding a bit of optical flow so that the boat’s position didn’t have to be verified every time.

AI Tracks at Sea Neural Net Summary

“These two tools allowed us to find the pixel coordinates of the boat and turn them into GPS coordinates,” DeKraker says, whose primary role in the project was integrating the two tools and packaging it for testing.

“Part of my PhD is to map out a snowmobile’s GPS coordinates with a camera,” Whitaker says. “This is extremely similar to mapping out a boat’s GPS coordinates. I could even say that it was exactly the same. I don’t believe I’ll add anything new, but I’ve tweaked it to work for my research.”

Whitaker sums up the team’s division of responsibilities like this: “Evan detects all the boats in the picture; Nik detects which of those boats is our boat; Steven takes our boat position and converts it to GPS coordinates, Zach glued all of our pieces together.”

DeKraker says, “One of the things the judges stressed was the ease of implementing the solution. Since that falls under what I would consider user experience (UX) or user interface (UI), it was pretty natural for me to take these tasks on, having studied software engineering for my undergrad,” DeKraker says.

A primary focus was speed. “Using machine learning for object detection tends to be slow, so to mitigate that we used the boat detector only once every 5 seconds,” DeKraker explains.

“Most of the tracking was done using a very fast technique called optical flow, which looks at the difference between two consecutive frames of a video to track motion,” DeKraker says. “It tended to drift from the target though, so we decided on running the boat detector every 5 seconds to keep optical flow on target. “

“The end result is that our solution could run nearly in real-time,” he says. “The accuracy wasn’t the best, but given a little bit more time and more training data, the neural network could be significantly improved.”

AI Tracks at Sea Homography Transform

Zach DeKraker

DeKraker’s graduate studies focus heavily on various machine learning techniques, He says that this opportunity to integrate machine learning into our solution was a fantastic experience.

“First, it sounded like an interesting challenge. I don’t get to do a lot of software design these days, and this challenge sounded like a great opportunity to do just that,” he explains.

“Second, it looked like a great opportunity to build up my resume a little bit. Saying that you won thousands of dollars for your university in a nationwide competition sounds really good. And finally, I really wanted the chance to see a practical application of machine learning in action.”

DeKraker completed a BS in Software Engineering at Michigan Tech in 2018. He returned to Michigan Tech the next year to complete his master’s degree. He says the biggest reason he did so was to learn more about machine learning.

“Before embarking on this journey, I really didn’t know anything about it,” he says of machine learning. “Having this chance to actually solve a problem, to integrate a neural network into a fully realized boat tracker using nothing but a video helped me see how machine learning can be used practically, rather than merely understanding how it works.”

And although it was a fascinating exploration into the practical side of machine learning and computer vision, DeKraker says it’s rather tangential to his main research focus right now, which is on comparing different network architectures to evaluate which one performs best given particular data and the problem being solved.

DeKraker believes that the culture is the most magnetizing thing about Tech. “Everybody here is cut from the same cloth. We’re all nerds and proud of it,” he explains. “You can have a half-hour conversation with a complete stranger about singularities, the economics of fielding a fleet of star destroyers, or how Sting was forged.”

And the most appealing thing about Michigan Tech was its size. DeKraker says. “When I looked at a ranking of the top universities in Michigan, Tech was number 3, but still extremely small. It was a perfect blend of being a small but very good school.”

And he says the second-best thing about Tech is the location. “The Keweenaw is one of the most beautiful places on earth.”

DeKraker has many ideas about where he’d like to take his career. For instance, he’d love the chance to work for DARPA, Los Alamos National Laboratory, or NASIC. He also intends to commission into the Air Force in the next couple of years, “if they have a place for programmers like me.”

Evan Lucas

Evan Lucas is a PhD candidate in the Electrical Engineering department., advised by Zhaohui Wang. Lucas completed both a bachelor’s and master’s in Mechanical Engineering at Tech in 2012 and 2014,

Lucas, whose research interests are in applying machine learning methods to underwater acoustic communication systems, worked on developing a classifier to separate the boat of interest from the many other boats in the image. Although the subject of the competition is tangential to Lucas’s graduate studies, as computer vision isn’t his area, there was some overlap in general machine learning concepts. respectively.

“It sounded like a fun challenge to put together an entry and learn more about computer vision,” Lucas says. “Working with the rest of the team was a really good opportunity to learn from people who have experience making software that is used by other people.”

Following completion of his doctoral degree, hopefully in spring 2023, Lucas plans to return to industry in a research focused role that applies some of the work he did in his PhD.


Steven Whitaker

Steven Whitaker’s research interests are in machine learning and acoustics. He tracks and locates the position of on-ice vehicles, like snowmobiles, based on acoustics. He says he has used some of the results from this competition project in his PhD research.

Whitaker’s machine learning research is experiment-based., and that’s why he chose Michigan Tech. “There aren’t many opportunities in academia to do experiment-based research,” he says. “Most machine learning is very software-focused using pre-made datasets. I love doing the experiments myself. Research is fun. I enjoy getting paid to do what I normally would do in my free time.”

In 2019, Whitaker completed his BS in Electrical Engineering at Michigan Tech. He expects to complete his master’s degree in Electrical Engineering at the end of the summer 2021 semester, and his PhD in summer 2022. His advisors are Tim Havens and Andrew Barnard.

Whitaker would love to be a university professor one day, but first he wants to work in industry.


Background Info

Timothy Havens is associate dean for research, College of Computing; the William and Gloria Jackson Associate Professor of Computer Systems; and director of the Institute of Computing and Cybersystems (ICC). His research interests are in pattern recognition and machine learning, signal and image processing, sensor and data fusion, heterogeneous data mining, and explosive hazard detection.

Michael Roggeman is a professor in the Electrical and Computer Engineering department. His research interests include optics, image reconstruction and processing, pattern recognition, and adaptive and atmospheric optics.

Zhaohui Wang is an associate professor in the Electrical and Computer Engineering department. Her research interests are in communications, signal processing, communication networks, and network security, with an emphasis on underwater acoustic applications.

The Naval Information Warfare Center (NIWC) Pacific and the Naval Science, Technology, Engineering, and Mathematics (STEM) Coordination Office, managed by the Office of Naval Research are conducting the Artificial Intelligence (AI) Tracks at Sea challenge.

View more details about the Challenge competition here: https://www.challenge.gov/challenge/AI-tracks-at-sea/

Watch a Navy webinar about the Challenge here: https://www.youtube.com/watch?v=MjZwvCX4Tx0.

Challenge.gov is a web platform that assists federal agencies with inviting ideas and solutions directly from the public, or “crowd.” This is called crowdsourcing, and it’s a tenet of the Challenge.gov program. The website enables the U.S. government to engage citizen-solvers in prize competitions for top ideas and concepts as well as breakthrough software, scientific and technology solutions that help achieve their agency missions.

This site also provides a comprehensive toolkit, a robust repository of considerations, best practices, and case studies on running public-sector prize competitions as developed with insights from prize experts across government.


GenCyber Cybersecurity Teacher Camp Is July 19-23

by Yu Cai, College of Computing

A GenCyber Cybersecurity Teacher Camp for K-12 teachers will be held at Michigan Tech during the week of July 19 – 23. Participants will learn cyber hygiene and fundamental security knowledge including email phishing, password management, and cyber ethics. Participants will also learn how to develop lesson plans to teach cybersecurity in K-12.

This is a residential camp (commuting optional), and is offered at NO COST to all participants. Room and board is included. Each teacher participant will receive a stipend of $500 for attending and completing camp activities. Camp activities will count for 25 State Continuing Education Clock Hours (SCECH).

Click here for more information and to apply. The application deadline is May.

Funding for the camp is provided jointly by the National Security Agency (NSA) and the National Science Foundation (NSF) through an award led by Yu Cai and Tim Van Wagner from the College of Computing.


Graduate Research Colloquium 2021

by Graduate Student Government

This year’s Graduate Research Colloquium organized by the Graduate Student Government was hosted virtually due to COVID restrictions. There were in total 48 presentations — 17 poster presenters and 31 oral presenters.

Poster presentations took place in a pre-recorded video style and the oral sessions were hosted live via Zoom. You can watch all the poster videos and recordings for the oral sessions here. Each presentation was scored by two judges from the same field of research.

Participants were able to gain valuable feedback from these judges before presenting their research at an actual conference. It was stiff competition amongst all presenters. Following are the winners for each of these sessions.

Of the many presentations were the following by two graduate students affiliated with the College of Computing.

Simulating the Spread of Infectious Diseases
Meara Pellar-Kosbar, Data Science

This simulation is designed to show how a fictional viral illness could spread among people in a virtual room. Over the course of the virtual simulation, a number of automatic simulated people called subjects will move about an adjustable virtual grid. During this time, subjects will come into contact with each other and with item cells in the virtual room. Subjects will be exposed to this fictional virus via contact with other subjects, items, and via the air when within a certain distance of a contagious subject. The viral counts of each subject will be tracked and shown as the simulation runs, showing how the actions of the subjects’ affects their viral counts.

Cultural Competence Effects of Repeated Implicit Bias Training
Karen Colbert, Social Sciences

Karen Colbert is a PhD student in the Computational Sciences and Engineering department.

Abstract: Diversity training literature suggests that mandatory and recurrent sessions should maximize training efficacy, but research has primarily focused on single, brief training sessions that are often voluntary. Michigan Tech is one of few universities to implement required and repeated diversity training for all faculty who serve on search, tenure, and promotion committees. The goal of this study is to evaluate the training’s effectiveness, as well as to fill the gap in research on mandatory recurring diversity training. To do this, we anonymously surveyed faculty members on their knowledge, attitudes, and skills related to content from the Diversity Literacy program and scored responses to create a single composite score for each participant. We hypothesized that composite Cultural Competency Score (CCS) would be higher for faculty who 1) have taken more refresher trainings, and 2) completed training more recently. This study included 130 total respondents (large sample), 69 of whom provided their Diversity Literacy completion information anonymously through Human Resources (small sample). Composite CCS did not differ significantly by frequency of training, H(2)=3.78, p=.151. CCS did differ significantly by years since last training, F(2,63)=4.436, p=.016. Results from both large and small groups showed no statistical significant relationship between CCS and faculty committee service. CCS was negatively correlated with years employed at Tech in both the large (r=-0.363, p=0.002) and small (r = -0.258, p=0.01) samples. This relationship between low CCS and longer employment at Tech may additionally be related to the Diversity Literacy program’s implementation in 2010. Qualitative responses were also collected regarding training material that faculty found most memorable (N=102) and most confident to put into practice (N=93).

View all the Research Colloquium abstracts here.


Call for Manuscripts: Fault Tolerance in Cloud/Edge/Fog Computing

Call for Manuscripts:

Special Issue on Fault Tolerance in Cloud/Edge/Fog Computing in Future Internet, an international peer-reviewed open access monthly journal published by MDPI.

Informational Flyer

https://blogs.mtu.edu/icc/files/2021/04/ali-ebnenasir-call-for-papers-032521-sm.pdf

Deadline

April 20, 2021

Author Notification

June 10, 2021

Website

mdpi.com/journal/futureinternet/special_issues/FT_CEFC

Collection Editors

Keywords

  • Fault tolerance
  • Cloud computing
  • Edge computing
  • Resource-constrained devices
  • Distributed protocols
  • State replication

Topics

Including, but not limited to:

  • Faults and failures in cloud and edge computing.
  • State replication on edge devices under the scarcity of resources.
  • Fault tolerance mechanism on the edge and in the cloud.
  • Models for the predication of service latency and costs in distributed fault-tolerant protocols on the edge and in the cloud.
  • Fault-tolerant distributed protocols for resource management of edge devices.
  • Fault-tolerant edge/cloud computing.
  • Fault-tolerant computing on low-end devices.
  • Load balancing (on the edge and in the cloud) in the presence of failures.
  • Fault-tolerant data intensive applications on the edge and the cloud.
  • Metrics and benchmarks for the evaluation of fault tolerance mechanisms in cloud/edge computing.

Background

The Internet of Things (IoT) has brought a new era of computing that permeates in almost every aspect of our lives. Low-end IoT devices (e.g., smart sensors) are almost everywhere, monitoring and controlling the private and public infrastructure (e.g., home appliances, urban transportation, water management system) of our modern life. Low-end IoT devices communicate enormous amount of data to the cloud computing centers through intermediate devices, a.k.a. edge devices, that benefit from stronger computational resources (e.g., memory, processing power).

To enhance the throughput and resiliency of such a three-tier architecture (i.e., low-end devices, edge devices and the cloud), it is desirable to perform some tasks (e.g., storing shared objects) on edge devices instead of delegating everything to the cloud. Moreover, any sort of failure in this three-tier architecture would undermine the quality of service and the reliability of services provided to the end users.

Scope

Theoretical and experimental methods that incorporate fault tolerance in cloud and edge computing, which have the potential to improve the overall robustness of services in three-tier architectures.

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website (https://www.mdpi.com/user/login/). Once you are registered, click here to go to the submission form (https://susy.mdpi.com/user/manuscripts/upload/?journal=futureinternet).

Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page.

Please visit the Instructions for Authors page before submitting a manuscript.

The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English.

Authors may use MDPI’s English editing service prior to publication or during author revisions.


Our Stories: Dr. Robert Pastel, Assoc. Prof., Computer Science

This is part of a series of short introductions about College students, faculty, and staff that we would like to include in the Weekly Download. Would you like to be featured? Send a photo and some background info about yourself to computing@mtu.edu.

Dr. Robert Pastel, Associate Professor of Computer Science

  • Advisor to Humane Interface Design Enterprise (HIDE)
  • Has been teaching at Michigan Tech for about 20 years, and teaching for 30 years.
  • Researcher with the Human-Centered Computing group of the Institute of Computing and Cybersystems (ICC)

Education

  • PhD, University of New Mexico, Physics
  • MS, Computer Science, Michigan Tech

Faculty Profile


Classes Dr. Pastel teaches: 
o    CS5760 – Human-Computer Interaction – Usability Evaluation and Testing 
o    CS4791 and CS4792 – Senior Design
o    ENT1960 – ENT5960 – Humane Interface Design Enterprise

The “coolest” class you teach, and why: All my classes are “cool” because they all involve making applications that will be used by people. The “coolest” class is CS4760 – User Interface – Design and Implementation where students work with scientists across the world to make citizen science applications.

The importance of your class topics to the overall understanding of Computing and your discipline: In all my classes, students learn to design and implement usable applications for people.

Your teaching philosophy: My teaching philosophy is that students learn best by experience and working with others. Consequently students work in teams on project for clients. 

Research projects in which students are assisting: 

  • StreamCLIMES – Large collaborative project studying bio diversity of intermittent streams. I’m responsible for developing a web applications monitoring the stream.
  • FloodAware – Large collaborative project recording and modelling flooding in urban areas. I’m responsible for developing the citizen science effort.
  • KeTT – Keweenaw Time Traveler – Historical geospatial information citizen science website for user to record region’s history and explore the maps and stories. 

Interests beyond teaching and research: The outdoors: skiing, biking and hiking. Every summer, he takes a one-month backpacking trip. 


PhD Defense: Jinxiang Liu, Monday, April 2, 1-3 pm

PhD candidate Jinxiang Liu, Computer Science, will present his PhD Defense on Monday, April 12, 2021, from 1:00 to 3:00 p.m.

The title of Liu’s dissertation is, “Prediction of Coincident Peak Days in Electricity System: A Case Study for Classification on Imbalanced Data.”

Dissertation Abstract

To guarantee sufficient electricity supply for its highest demands, many regional organizations surcharge their customers during coincident peaks (CPs), a time of highest demand across the system or region of interest. Therefore, the accurate prediction of these coincident peaks would be helpful not only for companies to ensure sufficient generation is available, but also for customers who may try to avoid electricity consumption and consequent additional cost.

This dissertation focuses on the prediction of the top five coincident peak days (5CPs) in a year. We used classification models to solve this imbalanced prediction problem (around 1.3\% for positive cases) by classifying the next day as 5CP days or non-5CP days.

We analyze six sets of actual historical data from different regions of Canada and the United States. We explore the effect of forecast accuracy on 5CP days prediction through four cases: I – knowing tomorrow’s power demand and weather condition exactly (an oracle), II & III – knowing some information about tomorrow (an oracle + increasing noise), and IV – no knowledge of future.

We proposed a three-phase model to predict 5CP days: first, clustering is applied to filter some negative cases, second, an all convolutional neural network that estimates the probability of being a 5CP day for the remaining cases is learned, and third, an adaptive method is used determines thresholds.

This three-phase model exhibits promising performances with the highest mean recall of 1.00, mean precision of 0.56, and mean F1 score of 0.72. Finally, we explored the use of a few-short learning framework to this problem. A triplet network is implemented for the 2-way-5-shot classifications. The prediction results have the highest mean recall of 1.00, mean precision of 0.67, and mean F1 score of 0.79.


Spend 1010 Minutes with Gorkem Asilioglu


You are invited to spend one-zero-one-zero—that is, ten—minutes with Gorkem Asilioglu, Computer Science, on Thurday, March 18, from 5:30 to 5:40 p.m. EST.

Asilioglu is a lecturer in the Department of Computer Science. His research interests include computer architecture, high-performance computing, programming languages, and CS education.

Join the Zoom meeting here.

We look forward to spending 1010 minutes with you!

Visit the 1010 with … webpage here.


CS Lecture: Kelly Steelman, CLS, March 19, 3 pm

The Department of Computer Science will present a lecture by Dr. Kelly Steelman, Cognitive and Learning Sciences, on Friday, March 19, 2021, at 3:00 p.m.

The title of the lecture is, “Keeping Up with Tech.”

Join the virtual lecture here.

Steelman is interim department chair and associate professor in the Department of Cognitive and Learning Sciences. Her research interests include basic and applied attention, models of attention, human performance in aviation, display design, tech adoption, and technology training.

Lecture Title

“Keeping Up with Tech”

Lecture Abstract

COVID has revealed much in the past year, including our dependence on technology and the challenges that many of us experience trying to keep up with it. Dr. Kelly Steelman has spent the past 15 years studying human attention and applying it to support the introduction of new technologies in contexts ranging from aviation to education.

In her presentation, Steelman will provide an overview of her research, using examples from Next Gen Aviation and the BASIC Digital Literacy Training Program to illustrate how understanding human attention can help us predict the consequences of introducing new technology, improve the design of technology, and support training to help people keep up with the rapid pace of technological change.



CS Dept. Lecture: Tim Frick, Mightybytes

The Department of Computer Science will present a lecture by Tim Frick, founder and president of Mightybytes, on Friday, April 9, 2021, at 3:00 p.m.

In his talk, “People, Planet, Pixels: Toward Sustainable Digital Products and Practices,” Frick will discuss how sustainable web design and responsible digital practices can help create an internet that is clean, efficient, open, honest, regenerative, and resilient.

Join the Zoom meeting here.

Lecture Title

“People, Planet, Pixels: Toward Sustainable Digital Products and Practices”

Speaker Bio

Tim Frick started his digital agency Mightybytes in 1998 to help purpose-driven companies, social enterprises, and large nonprofits solve problems, amplify their impact, and drive measurable results. He is the author of four books, including Designing for Sustainability: A Guide to Building Greener Digital Products and Services. Tim regularly presents at conferences and offers workshops on sustainable design, measuring impact, and problem solving in the digital economy.

Lecture Abstract

The internet has a larger environmental impact than the commercial airline industry. It currently produces approximately 3.8% of global carbon emissions, which are rising in line with our hunger to consume more data. Increasingly, web technologies are also being used to sow discontent, erode privacy, prompt unethical decisions, and, in some countries, undermine personal freedoms and the well-being of society. Web technology has the potential to bring huge benefits to society and the environment, but only if we use it wisely.

In this talk, author and digital agency owner Tim Frick will discuss how sustainable web designand responsible digital practices can help us create an internet that is clean, efficient, open, honest, regenerative, and resilient—principles outlined in the Sustainable Web Manifesto, of which Tim is a co-author. Elements of this talk are also based on Tim’s book, Designing for Sustainability: A Guide to Building Greener Digital Products and Services. Creating an internet that works for people and planet is possible. The methods described in this talk will show you how.