Dr. James Keller, recently retired Curators’ Distinguished Professor in the EE/CS department at University of Missouri, Columbia, will present his lecture, “Soft Streaming Classification,” on Friday, October 30, 2020, at 3:00 p.m., via Zoom online meeting.
The talk is an Institute of Computing and Cybersystems’ (ICC) Distinguished Lecture Series event.
Join the meeting here.
A Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE), Keller recently received the IEEE Frank Rosenblatt Award for his “fundamental work on fuzzy pattern recognition, fuzzy clustering, and fuzzy technologies in computer vision.” He holds a number of additional professional and academic honors and awards.
Lecture Abstract
As the volume and variety of temporally acquired data continues to grow, increased attention is being paid to streaming analysis of that data. Think of a drone flying over unknown terrain looking for specific objects which may present differently in different environments. Understanding the evolving environments is a critical component of a recognition system.
With the explosion of ubiquitous continuous sensing (something Lotfi Zadeh predicted as one of the pillars of Recognition Technology in the late 1990s), this on-line streaming analysis is normally cast as a clustering problem. However, examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models.
These approaches model existing and newly discovered structures via summary information that we call footprints. Incoming data is routinely assigned crisp labels (into one of the structures) and that structure’s footprints are incrementally updated; the data is not saved for iterative assignments.
The three underlying tenets of static clustering:
- Do you believe there are any clusters in your data?
- If so, can you come up with a technique to find the natural grouping of your data?
- Are the clusters you found good groupings of the data?
These questions do not directly apply to the streaming case. What takes their place in this new frontier?
In this talk, I will provide some thoughts on what questions can substitute for the Big 3, but then focus on a new approach to streaming classification, directly acknowledging the real identity of this enterprise. Because the goal is truly classification, there is no reason that these assignments need to be crisp.
With my friends, I propose a new streaming classification algorithm, called StreamSoNG, that uses Neural Gas prototypes as footprints and produces a possibilistic label vector (typicalities) for each incoming vector. These typicalities are generated by a modified possibilistic k-nearest neighbor algorithm.
Our method is inspired by, and uses components of, a method that we introduced under the nomenclature of streaming clustering to discover underlying structures as they evolve. I will describe the various ingredients of StreamSoNG and demonstrate the resulting algorithm on synthetic and real datasets.
David Watkins (CEE/SFI) is the principal investigator on a project that has received a $190,764 research and development grant from the National Science Foundation (NSF).
The project is titled “RAPID: COVID-19, Consumption, and Multi-dimensional Analysis of Risk (C-CAR)“. Chelsea Schelly (SS/SFI), Robert Handler (ChE/SFI) and Charles Wallace (CS/SFI) are co-PIs on this one-year project.
Extract
The COVID-19 pandemic has transformed household dynamics and dramatically changed food, energy, and water consumption within the home. Stay-at-home orders and social distancing has caused U.S. households to shift to working and schooling from home, curtail outside activities, and stop eating in restaurants. Furthermore, as many households face job loss and increasing home utility and grocery bills, U.S. residents are experiencing the economic impacts of the crisis, while at the same time assessing and responding to health risks. The project team has a unique opportunity to study these shifting household consumption and behavioral responses and quantify the associated economic and environmental impacts. The team will collect household food, energy, and water consumption data as well as survey response data from 180 participating households in one Midwestern county and compare it to data collected before the stay-at-home orders were put in place.
University Professor Timothy Schulz (ECE) will be featured at the Michigan Tech Research Forum (MTRF) at 4:30 p.m. Wednesday, Oct. 14.
Schulz’s presentation is titled “Direct Measurement of Coherent Fields.” Additional details can be found on the MTRF website.
The presentation will be available via Zoom and a limited number of people will be permitted to attend in person, dependent on university guidelines on the date of the event. If you wish to be considered for in-person attendance, complete this form by today (Oct. 9).
Schulz is a member of the ICC’s Center for Data Sciences.
The MTRF is presented by the Office of the Provost in coordination with the Office of the Vice President for Research. The forum showcases and celebrates the work of Michigan Tech researchers and aims to strengthen discussions in our community. All are welcome, including the general public.
A Michigan Tech alumnus with a long history of philanthropy and support of students will receive the University’s highest honor.
At its meeting, Friday, (Oct. 8, 2020), the Michigan Tech Board of Trustees approved awarding the Melvin Calvin Medal of Distinction to Kanwal Rekhi. The native of Punjab, in what was then British India (now Pakistan), earned a master’s in electrical engineering from Michigan Tech in 1969. In the more than half a century since his time on campus, MTU has never been far from Rekhi’s thoughts … and generosity.
After leaving Michigan Tech, Rekhi worked as an engineer and manager before becoming an entrepreneur. In 1982, he co-founded Excelan a company that made Ethernet cards to connect PCs to the fledgling Internet. Excelean became the first Indian-owned company to go public in the U.S. In the early 90s, he became a venture capitalist investing in more than 50 startups and sitting on the board of directors of more than 20 companies.
In the past few decades, Rekhi has been a tireless supporter and benefactor to Michigan Tech. He developed and funded the Rekhi Innovation Challenge a crowdfunding competition to help promote and support student innovation. He provided major funding for the Silicon Valley Experience, an immersive tour during spring break of San Francisco area companies that includes meetings with entrepreneurs and Michigan Tech alumni, and is a sponsor of the 14 Floors Entrepreneur Alumni Mentoring Sessions.
“Kanwal and his accomplishments epitomize the values we share as an institution. His passion for Michigan Tech is unparalleled and he is most deserving of this award,” said Rick Koubek, President.
While the Melvin Calvin Medal of Distinction is Michigan Tech’s highest honor, it is far from the first recognition the University has given Rekhi. He has received the Distinguished Alumni Award, the Board of Control Silver Medal, an honorary Doctorate in Business and Engineering, and was inducted into the Electrical Engineering Academy.
Additionally, every student who has walked the Michigan Tech campus in the past 15 years has passed the Kanwal and Ann Rekhi Computer Science Hall, dedicated in April of 2005.
The Melvin Calvin Medal of Distinction is bestowed on individuals associated with the University who have exhibited especially distinguished professional and personal accomplishments. It is named for 1931 Michigan Tech alumnus Melvin Calvin, who won the Nobel Prize in Chemistry for unraveling the biochemical secrets of photosynthesis. The series of biochemical reactions Calvin identified is known as the Calvin Cycle.
Associate Professor Zhen Liu, Civil and Environmental Engineering, is the principal investigator on a project that has received a $689,239 research and development contract from the U.S. Department of Transportation, Federal Highway Administration.
Liu is a member of the ICC’s Center for Cyber-Physical Systems.
The project is titled, “Autonomous Winter Road Maintenance Decision Making Enabled by Boosting Existing Transportation Data Infrastructure with Deep and Reinforcement Learning.”
Yongchao Yang (ME-EM), Tim Colling (CEE) and Michael Billmire (MTRI) are Co-PI’s on this three-year project.
Associate Professor Kuilin Zhang, Civil and Environmental Engineering and affiliated associate professor, Computer Science, was featured in a recent article on Michigan Tech News. The article appears below. Link to the original article here.
By Kelley Christensen, September 28, 2020.
Networked data-driven vehicles can adapt to road hazards at longer range, increasing safety and preventing slowdowns.
Vehicle manufacturers offer smart features such as lane and braking assist to aid drivers in hazardous situations when human reflexes may not be fast enough. But most options only provide immediate benefits to a single vehicle. What if entire groups of vehicles could respond? What if instead of responding solely to the vehicle immediately in front of us, our cars reacted proactively to events happening hundreds of meters ahead?
What if, like a murmuration of starlings, our cars and trucks moved cooperatively on the road in response to each vehicle’s environmental sensors, reacting as a group to lessen traffic jams and protect the humans inside?
This question forms the basis of Kuilin Zhang’s National Science Foundation CAREER Award research. Zhang, an associate professor of civil and environmental engineering at Michigan Technological University, has published “A distributionally robust stochastic optimization-based model predictive control with distributionally robust chance constraints for cooperative adaptive cruise control under uncertain traffic conditions” in the journal Transportation Research Part B: Methodological.
The paper is coauthored with Shuaidong Zhao ’19, now a senior quantitative analyst at National Grid, where he continues to conduct research on the interdependency between smart grid and electric vehicle transportation systems.
Vehicle Platoons Operate in Sync
Creating vehicle systems adept at avoiding traffic accidents is an exercise in proving Newton’s First Law: An object in motion remains so unless acted on by an external force. Without much warning of what’s ahead, car accidents are more likely because drivers don’t have enough time to react. So what stops the car? A collision with another car or obstacle — causing injuries, damage and in the worst case, fatalities.
But cars communicating vehicle-to-vehicle can calculate possible obstacles in the road at increasing distances — and their synchronous reactions can prevent traffic jams and car accidents.
“On the freeway, one bad decision propagates other bad decisions. If we can consider what’s happening 300 meters in front of us, it can really improve road safety. It reduces congestion and accidents.”Kuilin Zhang
Zhang’s research asks how vehicles connect to other vehicles, how those vehicles make decisions together based on data from the driving environment and how to integrate disparate observations into a network.
Zhang and Zhao created a data-driven, optimization-based control model for a “platoon” of automated vehicles driving cooperatively under uncertain traffic conditions. Their model, based on the concept of forecasting the forecasts of others, uses streaming data from the modeled vehicles to predict the driving states (accelerating, decelerating or stopped) of preceding platoon vehicles. The predictions are integrated into real-time, machine-learning controllers that provide onboard sensed data. For these automated vehicles, data from controllers across the platoon become resources for cooperative decision-making.
CAREER Award
Kuilin Zhang won an NSF CAREER Award in 2019 for research on connected, autonomous vehicles and predictive modeling.
Proving-Grounds Ready
The next phase of Zhang’s CAREER Award-supported research is to test the model’s simulations using actual connected, autonomous vehicles. Among the locations well-suited to this kind of testing is Michigan Tech’s Keweenaw Research Center, a proving ground for autonomous vehicles, with expertise in unpredictable environments.
Ground truthing the model will enable data-driven, predictive controllers to consider all kinds of hazards vehicles might encounter while driving and create a safer, more certain future for everyone sharing the road.
Tomorrow Needs Mobility
Michigan Technological University is a public research university, home to more than 7,000 students from 54 countries. Founded in 1885, the University offers more than 120 undergraduate and graduate degree programs in science and technology, engineering, forestry, business and economics, health professions, humanities, mathematics, and social sciences. Our campus in Michigan’s Upper Peninsula overlooks the Keweenaw Waterway and is just a few miles from Lake Superior.
About the Researcher: Kuilin Zhang
- Data-driven optimization and control models for connected and automated vehicles (CAVs)
- Big traffic data analytics using machine learning
- Mobile and crowd sensing of dynamic traffic systems
- Dynamic network equilibrium and optimization
- Modeling and simulation of large-scale complex systems
- Freight logistics and supply chain systems
- Impact of plug-in electric vehicles to smart grid and transportation network systems
- Interdependency and resiliency of large-scale networked infrastructure systems
- Vehicular Ad-hoc Networks (VANETs)
- Smart Cities
- Cyber-Physical Systems
Associate Professor Guy Hembroff, director of Michigan Tech’s Health Informatics graduate program, presented an invited virtual talk to physicians, residents, and medical students at the Bahiana Medical School, Salvador, Brazil, on September 25, 2020.
Hembroff spoke about, “The Challenges and Opportunities of Artificial Intelligence in Disease Prevention and Monitoring.”
BAHIANA (Bahia School of Medicine and Public Health) is a private, nonprofit, educational, cultural, scientific and healthcare institution. Its main purpose is “teaching, research and the spread of knowledge and special services in the fields of health, science and culture in general.” Learn more here.
The College of Computing and the Institute of Computing and Cybersystems (ICC) are the subjects of an article published today (Sept. 2, 2020) on HostingAdvice.com, a website and blog that educates visitors to the site about the world of web hosting.
The article, for which College of Computing Dean Adrienne Minerick was interviewed, provides a close look at the new College, its well-established Computer Science and Software Engineering degree programs (B.S., M.S., and Ph.D.), new Cybersecurity and Mechatronics undergraduate programs, as well as faculty research and the ICC.
Special emphasis is placed on the Computer Network and Systems Administration undergraduate degree program, in which students prepare for careers as network and computer systems administrators, commonly referred to as a “sysadmins.”
Read the full article here.
“Our readers know that a lot goes into finding the best providers of shared, dedicated, and virtual private servers,” said Sean Garrity, managing editor at HostingAdvice.com. “The article provides information about how to prepare if you want to to break into the industry as a professional, not just a consumer.”
A paper authored by Michigan Tech Assistant Professor Bo Chen, Computer Science, and Data Science master’s student Shashank Reddy Danda, has been accepted for publication in the IEEE Internet of Things Magazine special issue on Smart IoT Solutions for Combating COVID-19 Pandemic. The special issue will be published in September 2020.
The paper focuses on Chen’s research of COVID-19 prevention through the leveraging of computing technology. The project is currently supported by a Michigan Tech College of Computing seed grant, and external funding for further development is being pursued.
Chen is a member of the ICC’s Center for Cybersecurity.
Download a preprint of the paper here.
Abstract:
Recently, the impact of coronavirus has been witnessed by almost every country around the world. To mitigate spreading of coronavirus, a fundamental strategy would be reducing the chance of healthy people from being exposed to it. Having observed the fact that most viruses come from coughing/sneezing/runny nose of infected people, in this work we propose to detect such symptom events via mobile devices (e.g., smartphones, smart watches, and other IoT devices) possessed by most people in modern world and, to instantly broadcast locations where the symptoms have been observed to other people. This would be able to significantly reduce risk that healthy people get exposed to the viruses. The mobile devices today are usually equipped with various sensors including microphone, accelerometer, and GPS, as well as network connection (4G, LTE, Wi-Fi), which makes our proposal feasible. Further experimental evaluation shows that coronavirus-like symptoms (coughing/sneezing/runny nose) can be detected with an accuracy around 90%; in addition, the dry cough (more likely happening to COVID-19 patients) and wet cough can also be differentiated with a high accuracy.
Bo Chen is an assistant professor in the Department of Computer Science. His areas of expertise include mobile device security, cloud computing security, named data networking security, big data security, and blockchain.
Shashank Reddy Danda is an MS student in Data Science. He is currently working as a research assistant in MTU Security and Privacy (SnP) Lab under the supervision of Dr. Bo Chen.
IEEE Internet of Things Magazine (IEEE IoTM) is a publication of the IEEE Internet of Things Initiative, a Multi-Society Technical Group.