Jingjing Yao, a PhD candidate at New Jersey Institute of Technology, will present a talk on Tuesday, May 11, at 3:00 p.m,.
In her talk, “Intelligent and Secure Fog-Aided Internet of Drones,” Yao discusses the utilization of energy harvesting technology to charge drone batteries and investigate wireless power control to adjust the drone wireless transmission power to reduce drone energy consumption.
Yao’s research interests include Internet of Things (IoT), Internet of Drones (IoD), Deep Reinforcement Learning, Federated Learning, Cybersecurity, Mobile Edge Computing/Caching, and Energy Harvesting.
Intelligent and Secure Fog-Aided Internet of Drones
Internet of drones (IoD), which deploys several drones in the air to collect ground information and send them to the IoD gateway for further processing, can be applied in traffic surveillance and disaster rescue. Fog-aided IoD provisions future events prediction and image classification by machine learning technologies, where massive training data are collected by drones and analyzed in the fog node. However, the performance of IoD is greatly affected by drones’ battery capacities. Also, aggregating all data in the fog node may incur huge network traffic and drone data privacy leakage. The speaker will share her vision and research to address these two challenges.
In this talk, the speaker utilizes energy harvesting technology to charge drone batteries and investigate wireless power control to adjust the drone wireless transmission power to reduce drone energy consumption. The joint optimization of power control and energy harvesting scheduling is investigated in time-varying IoD networks to minimize the long-term average system energy cost constrained by the drone battery capacities and quality of service (QoS) requirements. A modified actor-critic deep reinforcement learning algorithm is designed to address the joint optimization problem in time-varying IoD networks.
To prevent the privacy leakage of IoD, the speaker utilizes federated learning (FL) by performing local training in drones and sharing all training model parameters in the fog node without uploading drone raw data. However, drone privacy can still be divulged to ground eavesdroppers by wiretapping and analyzing uploaded parameters during the FL training process. The power control problem is hence investigated to maximize the FL system security rate constrained by drone battery capacities and the FL training time requirement. An algorithm with low computational complexity is then designed to tackle the security rate maximization problem and its performance is demonstrated by extensive simulations.
Jingjing Yao is a Ph.D. candidate in Computer Engineering with the Department of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT). She will receive her Ph.D. degree from NJIT in May 2021.
She received the M.E. degree in Information and Communication Engineering from the University of Science and Technology of China (USTC), and the B.E. degree in Information and Communication Engineering from the Dalian University of Technology (DUT).
She has published 13 first-author journal articles and 7 first-author conference papers. Her research interests include Internet of Things (IoT), Internet of Drones (IoD), Deep Reinforcement Learning, Federated Learning, Cybersecurity, Mobile Edge Computing/Caching, and Energy Harvesting.
Tara Salman, a final-year PhD candidate at Washington University in St. Louis, will present a talk on Tuesday, April 27, 2021, at 3:00 p.m.
In her talk, “A Collaborative Knowledge-Based Security Solution using Blockchains,” she will present her work on redesigning the blockchains and building a collaborative, distributed, intelligent, and hostile solution that can be used for security purposes.
A Collaborative Knowledge-Based Security Solution using Blockchains
Artificial intelligence and machine learning have recently gained wide adaptation in building intelligent yet simple and proactive security solutions such as intrusion identification, malware detection, and threat intelligence. With the increased risk and severity of cyber-attacks and the distributed nature of modern threats and vulnerabilities, it becomes critical to pose a distributed intelligent solution that evaluates the systems’ and networks’ security collaboratively. Blockchain, as a decade-old successful distributed ledger technology, has the potential to build such collaborative solutions. However, to be used for such solutions, the technology needs to be extended so that it can intelligently process the stored information and achieve a collective decision about security risks or threats that might target a system.
In this talk, I will present our work on redesigning the blockchains and build a collaborative, distributed, intelligent, and hostile solution that can be used for security purposes. In particular, we will discuss our work on (1) extending blockchains for general collaborative decision-making applications, where knowledge should be made out of decisions, risks, or any information stored on the blockchain; (2) applying the proposed extensions to security applications such as malware detection and threat intelligence.
Tara Salman is a final year Ph.D. candidate at Washington University in St. Louis, where she is advised by Raj Jain. She previously received her MS and BSc degrees from Qatar University in 2015 and 2012, respectively. Her research aims to integrate state-of-the-art technologies to provide scalable, collaborative, and intelligent cybersecurity solutions.
Her recent work focuses on the intersection of artificial intelligence, blockchains, and security applications. The work spans several fields, including blockchain technology, security, machine learning, and deep learning applications, cloud computing, and the Internet of Things. She has been selected for the EECS Rising Star in UC Berkeley 2020. Her research has been published in more than twenty internationally recognized conferences and journals and supported by national and international funds.
Xinyu Lei, a Ph.D. candidate in the Department of Computer Science and Engineering at Michigan State University, will present a talk on Thursday, April 29, 2021, at 3:00 p.m.
In his talk, “Secure and Efficient Queries Processing in Cloud Computing,” Lei will discuss his work developing new techniques to support secure and efficient queries processing in cloud storage.
Secure and Efficient Queries Processing in Cloud Computing
With the advent of cloud computing, data owners are motivated to outsource their databases to the commercial public cloud for storage. The public cloud with database-as-a-service (DBaaS) model has many benefits (including lower cost, better performance, and higher flexibility). However, hosting the datasets on the commercial public cloud deprives the data owners’ direct control over their databases, which brings in security concerns. For example, the corrupted cloud employees may spy the data owner’s commercial valuable databases and sell them for money. To protect data privacy, the sensitive database must be encrypted before outsourcing to the cloud. However, it becomes hard to perform efficient queries (e.g., keyword query) processing over the encrypted database.
In this talk, I focus on developing new techniques to support secure and efficient queries processing in cloud storage. An index-aid approach is proposed to address the problem. In my approach, the data items are formally encrypted, and a secure index is generated for efficient queries processing. The cloud can perform queries directly over the secure index rather than the encrypted data items. The secure index is constructed based on a new data structure named random Bloom filter. Then, multiple random Bloom filters are organized into a binary tree structure to support fast query processing. The proposed approach can achieve data privacy, index privacy, search token privacy, and fast query processing simultaneously.
Xinyu Lei is a Ph.D. candidate in the Department of Computer science and Engineering at Michigan State University. He once worked as a research assistant in Texas A&M University at Qatar and Ford Motor Company. He has research interests in cybersecurity problems in different computer systems (including IoT, blockchain, cloud computing). He has published 20+ papers with 600+ citations. His work has been published on top-tier conferences and journals such as ACM MobiSys, ACM CodaSpy, IEEE ICDE, IEEE ICDCS, etc.
The Senior Design project completed this semester by four senior-level Electrical Engineering Technology (EET) is the topic of a news story that was aired on WLUC-TV6 (Marquette) on Friday, April 23, 2021. The project was to design and produce a motorized swing set that will help a disabled child enjoy herself and sleep comfortably.
The students are Joe Barbercheck, Seth Cherry, Heather Harris, and Cole Kubick.
Tackling the project top to bottom, the students designed the electrical system, control and drive systems, and portions of the mechanical design. Their top priority was making sure the systems and mechanical structure are safe.
Specifications for the swing include that it be lightweight, reliable, and portable. The unit is battery-operated with a rechargeable lithium-ion battery. The swing will both rock the child to sleep and serve as a play toy for three to four years, although the actual lifetime of the swing will be much longer.
Professor Alex Sergeyev and Lecturer Paniz Hazaveh are co-advisors to the team. “The students are very excited about the project,” Sergeyev says. “It’s very meaningful to them.”
“The skills that we are teaching in the EET and Mechatronics undergraduate programs makes students able to just jump on these kinds of projects,” Sergeyev says. “It’s great to see that their learning can be applied to a project as complex as this one.”
Adversarial Reinforcement Learning, Risk-Averse Game Theory and Online Learning with Applications to Autonomous Vehicles and Financial Investments
In this talk, we discuss:
a) Adversarial attacks on the computation of reinforcement learning: The emergence of cloud, edge, and fog computing has incentivized agents to offload the large-scale computation of reinforcement learning models to distributed servers, giving rise to edge reinforcement learning (RL). By the inherently distributed nature of edge RL, the swift shift to this technology brings a host of new adversarial attack challenges that can be catastrophic in safety-critical applications. A natural malevolent attack could be to contaminate the RL computation such that the contraction property of the Bellman operator is undermined in the value/policy iteration methods. This can result in luring the agent to search among suboptimal policies without improving the true values of policies. We prove that under certain conditions, the attacked value/policy iteration methods converge to the vicinity of the optimal policy with high probability if the number of value/policy evaluation iterations is larger than a threshold that is logarithmic in the inverse of a desired precision.
b) Risk-aversion in games and online learning: The fast-growing market of autonomous vehicles, unmanned aerial vehicles, and fleets in general necessitates the design of smart and automatic navigation systems considering the stochastic latency along different paths in a traffic network. To our knowledge, the existing navigation systems including Google Maps, Waze, MapQuest, Scout GPS, Apple Maps, and others are based on minimizing the expected travel time, ignoring the path delay uncertainty. To put the travel time uncertainty into perspective, we model the decision making of risk-averse travelers in a traffic network by an atomic stochastic congestion game and propose three classes of risk-averse equilibria. We show that the Braess paradox may not occur to the extent presented originally and the price of anarchy can be improved, benefiting the society, when players travel according to risk-averse equilibria rather than the Wardrop/Nash equilibrium. Furthermore, we extend the idea of risk-aversion to online learning; in particular, risk-averse explore-then-commit multi-armed-bandits. We use data from the New York Stock Exchange (NYSE) to show that the classical mean-variance and conditional value at risk approaches can come short in addressing risk-aversion for financial investments. We introduce new venues to study risk-aversion by taking the probability distributions into account rather than the summarized statistics of distributions.
Ali Yekkehkhany is a postdoctoral scholar with the Department of Industrial Engineering and Operations Research, University of California, Berkeley. He received his PhD and MSc degrees in Electrical and Computer Engineering from the University of Illinois, Urbana-Champaign (UIUC) in 2020 and 2017, respectively, and BSc degree in Electrical Engineering from Sharif University of Technology in 2014.
He is the recipient of the “best poster award in recognition of high-quality research, professional poster, and outstanding presentation” in the 15th CSL Student Conference, 2020, and the “Harold L. Olesen award for excellence in undergraduate teaching by graduate students” in the 2019-2020 academic year at UIUC. He was chosen as “teachers ranked as excellent” twice and “teachers ranked as excellent and outstanding” twice at UIUC.
His research interests include machine/reinforcement learning, queueing theory, applied probability theory and stochastic processes.
A team of Michigan Tech students competed last week in the International Collegiate Programming Contest (ICPC) North America Division Championships, placing 28th out of 42 teams in the Central Division.
To qualify for the Championships, a Michigan Tech student team placed 14th out of more than 80 teams in the regional ICPC contest this February. Students on that team were Alex Gougeon (Software Engineering), Ben Wireman (Mathematics), and Dominika Bobik.
Students interested in the programming competitions are encouraged to contact Dr. Laura Brown, Computer Science. Additional programming contests and events take place throughout the year.
The International Collegiate Programming Contest is the premier world-wide, algorithmic programming contest for college students.
In ICPC competitions, teams of three students work to solve the most real-world problems efficiently and correctly. Teams represent their university in multiple levels of competition: regionals, divisionals, championships, and world finals.
Dr. Dukka KC, Electrical Engineering and Computer Science, Wichita State University, will present a talk on Wednesday, May 5, 2021, at 3:00 p.m.
Dr. KC will discuss some past and ongoing projects in his lab related to machine learning/deep learning-based approaches for an important problem in Bioinformatics: protein post-translational modification.
Bioinformatics as an emerging field of Data Science: Protein post-translation modification prediction using Deep Learning
In this talk, I will be presenting about some of the past and ongoing projects in my lab especially related to Machine Learning/Deep Learning based approaches for one of the important problems in Bioinformatics – protein post-translational modification.
Especially, I will focus on our endeavors to get away from manual feature extraction (hand-crafted feature extraction) from protein sequence, use of notion of transfer learning to solve problems where there is scarcity of labeled data in the field, and stacking/ensemble-based approaches.
I will also summarize our future plans for using multi-label, multi-task and multi-modal learning for the problem. I will highlight some of the ongoing preliminary works in disaster resiliency. Finally, I will provide my vision for strengthening data science related research, teaching, and service for MTU’s college of computing.
Dr. Dukka KC is the Director of Data Science Lab, Director of Data Science Efforts, Director of Disaster Resilience Analytics Center and Associate Professor of Electrical Engineering and Computer Science (EECS) in the Department of EECS at Wichita State University. His current efforts are focused on application of various computing/data science concepts including but not limited to Machine Learning, Deep Learning, HPC, etc. for elucidation of protein sequence, structure, function and evolution relationship among others.
He has received grant funds totaling $4.25M as PIs or Co-PIs, spanning 17 funded grants. He was the PI on the $499K NSF Excellence in Research project focused on developing Deep Learning based approaches for Protein Post-translational modification sites.
He received his B.E. in computer science in 2001, his M.Inf. in 2003 and his Ph.D. in Informatics (Bioinformatics) in 2006 from Kyoto University, Japan. Subsequently he did a postdoc at Georgia Institute of Technology working on refinement algorithms for protein structure prediction. He then moved to UNC-Charlotte and did another postdoc working on functional site predictions in proteins. He was a CRTA Fellow in National Cancer Institute at National Institutes of Health where he was working on intrinsically symmetric domains.
Prior to his arrival at WSU, he was associate professor and graduate program director in the Department of Computational Science and Engineering at North Carolina A&T State University.
Dr. KC has published more than 30 journal and 20 conference papers in the field and is associate editor for two leading journals (BMC Bioinformatics and Frontiers in Bioinformatics) in the field. He also dedicates much of his efforts to K-12 education, STEM workforce development, and increasing diversity in engineering and science.
by Karen S. Johnson, Communications Director, College of Computing
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.
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
“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.
like this one often connect directly or indirectly with a student’s academic
and career goals.
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.”
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?
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.”
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.
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.
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.
tried some different sub-projects independently and then worked together to
combine the parts we thought worked best,” Evan Lucas says.
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.
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.
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.”
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.”
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.
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.
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
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.”
graduate studies focus heavily on various machine learning techniques, He says
that this opportunity to integrate machine learning into our solution was a
“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.”
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.
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.”
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.
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.”
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
he says the second-best thing about Tech is the location. “The Keweenaw is one
of the most beautiful places on earth.”
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 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.
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.”
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
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.
would love to be a university professor one day, but first he wants to work in
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.
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.
We are looking for volunteers to take part in a study exploring how people may interact with future Augmented Reality (AR) interfaces. During the study, you will record videos of yourself tapping on a printed keyboard. The study takes approximately one hour, and you will be paid $15 for your time. You will complete the study at your home.
To participate you must meet the following requirements: