Category: DataS

Sidike Paheding Publishes Paper in Expert Systems and Applications Journal

A research paper by Assistant Professor Sidike Paheding, Applied Computing, is to be published in the November 2020 issue of the journal, Expert Systems and Applications.

An in-press version of the paper, “Binary Chemical Reaction Optimization based Feature Selection Techniques for Machine Learning Classification Problems,” is available online.

Highlights

  • A chemical reaction optimization (CRO) based feature selection (FS) technique is proposed.
  • The proposed CRO based FS technique is improvised using particle swarm optimization.
  • Performance evaluation of proposed techniques on benchmark datasets gives promising results.

Paper Abstract

Feature selection is an important pre-processing technique for dimensionality reduction of high-dimensional data in machine learning (ML) field. In this paper, we propose a binary chemical reaction optimization (BCRO) and a hybrid binary chemical reaction optimization-binary particle swarm optimization (HBCRO-BPSO) based feature selection techniques to optimize the number of selected features and improve the classification accuracy.

Three objective functions have been used for the proposed feature selection techniques to compare their performances with a BPSO and advanced binary ant colony optimization (ABACO) along with an implemented GA based feature selection approach called as binary genetic algorithm (BGA). Five ML algorithms including K-nearest neighbor (KNN), logistic regression, Naïve Bayes, decision tree, and random forest are considered for classification tasks.

Experimental results tested on eleven benchmark datasets from UCI ML repository show that the proposed HBCRO-BPSO algorithm improves the average percentage of reduction in features (APRF) and average percentage of improvement in accuracy (APIA) by 5.01% and 3.83%, respectively over the existing BPSO based feature selection method; 4.58% and 3.12% over BGA; and 4.15% and 2.27% over ABACO when used with a KNN classifier.

Expert Systems With Applications, published by Science Direct/Elsevier, is a refereed international journal whose focus is on exchanging information relating to expert and intelligent systems applied in industry, government, and universities worldwide. The journal’s Impact factor is 5.4.

Innovative, Active, Effective. Introducing Sidike Paheding, Applied Computing

Be Innovative. Be Active. Be Effective. This is College of Computing Assistant Professor Sidike Paheding’s teaching philosophy.

New to the Department of Applied Computing this fall, Paheding’s teaching interests include digital image processing and machine learning. This academic year he is teaching SAT3812 Cyber Security I.

A member of the Institute of Computing and Cybersystems’s Center for Data Sciences, Paheding’s research seeks to develop novel AI-driven technologies. His primary interests are image/video processing, machine learning, deep learning, computer vision, and remote sensing.

Paheding comes to Michigan Tech from Purdue University Northwest, where he was a visiting assistant professor in the ECE department. Prior to that, he was a postdoctoral research associate and assistant research professor in the Remote Sensing Lab at Saint Louis University from 2017 to 2019.

Paheding is an associate editor of the journals, Signal Image and Video Processing (Springer) and Photogrammetric Engineering and Remote Sensing (ASPRS), and topic editor for Remote Sensing. He completed his Ph.D. in electrical engineering at University of Dayton, Ohio.

Computing is a part of my life.

Sidike Paheding, Assistant Professor, Applied Computing

Active Research

Title: Cybersecurity Modules Aligned with Undergraduate Computer Science and Engineering Curricula
Sponsor: NSF
PI at Michigan Tech
Duration: July 2020 – June 2022
Total Award: $159,417.00

Research Abstract

This project aims to serve the national interest by improving how cybersecurity concepts are taught in undergraduate computing curricula. The need to design and maintain cyber-secure computing systems is increasingly important. As a result, the future technology workforce must be trained to have a security mindset, so that they consider cybersecurity during rather than after system design.

This project aims to achieve this goal by building plug-and-play, hands-on cybersecurity modules for core courses in Computer Engineering, and Computer Science and Engineering. The modules will align with the curricula recommended by the Association for Computing Machinery and will be designed for easy adoption into computing programs nationwide. Modules will be designed for integration into both introductory and advanced courses, thus helping students develop in-depth understanding of cybersecurity as they progress through their computing curriculum. It is expected that the project will encourage more students to pursue careers or higher degrees in the field of cybersecurity.

Recent Publications

Sidike, P., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Shakoor, N., Burken, J., … & Fritschi, F. B. (2019). dPEN: deep Progressively Expanded Neural Network for mapping heterogeneous agricultural landscape using WorldView-3 satellite imagery. Remote Sensing of Environment, 221, 756-772. [Impact Factor: 9.085]

Sidike, P., Asari, V. K., & Sagan, V. (2018). Progressively Expanded Neural Network (PEN Net) for hyperspectral image classification: A new neural network paradigm for remote sensing image analysis. ISPRS journal of photogrammetry and remote sensing, 146, 161-181. [Impact Factor: 7.319]

Sidike, P., Asari, V. K., & Alam, M. S. (2015). Multiclass object detection with single query in hyperspectral imagery using class-associative spectral fringe-adjusted joint transform correlation. IEEE Transactions on Geoscience and Remote Sensing, 54(2), 1196-1208. [Impact Factor: 5.855]

Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., & Fritschi, F. B. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599. [Impact Factor: 9.085]

Tim Havens: Warm and Fuzzy Machine Learning

What are you doing for supper this Monday night at 6? Grab a bite with Dean Janet Callahan and Associate Professor Tim Havens, director of the Michigan Tech’s Institute of Computing and Cybersystems and associate dean for research in the College of Computing. Get the full scoop and register at mtu.edu/huskybites.

“Nearly everyone has heard the term ‘Deep Learning’ at this point, whether to describe the latest artificial intelligence feat like AlphaGo, autonomous cars, facial recognition, or numerous other latest-and-greatest gadgets and gizmos,” says Havens. “But what is Deep Learning? How does it work? What can it really do—and how are Michigan Tech students advancing the state-of-the-art?”

In this session of Husky Bites, Prof. Havens will talk about everyday uses of machine learning—including the machine learning research going on in his lab: explosive hazards detection, under-ice acoustics detection and classification, social network analysis, connected vehicle distributed sensing, and other stuff.

Joining in will be one of Havens’ former students, Hanieh Deilamsalehy, who earned her PhD in electrical engineering at Michigan Tech. She’s now a machine learning researcher at Adobe. Dr. Deilamsalehy graduated from Michigan Tech in 2017 and headed to Palo Alto to work for Ford as an autonomous vehicle researcher. She left the Bay Area for Seattle to take a job at Microsoft, first as a software engineer, and then as a machine learning scientist. In April she accepted a new machine learning position at Adobe, “in the middle of the pandemic!”

Havens is a Michigan Tech alum, too. He earned his BS in ‘99 and MS in Electrical Engineering in ‘00, then went to the MIT Lincoln Laboratory, where he worked on simulation and modeling of the Airborne Laser System, among other defense-related projects. From there it was the University of Missouri for a PhD in Electrical and Computer Engineering, researching machine learning in ontologies and relational data.

Nowadays, Havens is the William and Gloria Jackson Associate Professor and Associate Dean for Research in the College of Computing. In addition to serving as director of Michigan Tech’s ICC, he also heads up the ICC Center for Data Sciences and runs his own PRIME Lab, too (short for Pattern Recognition and Intelligent Machines Engineering).

“An important goal for many mobile platforms—terrestrial, aquatic, or airborne—is reliable, accurate, and on-time sensing of the world around them.”Tim Havens

Havens has spent the past 12 years developing methods to find explosive hazards, working with the US Army and a research team in his lab. According to a United Nations report, more than 10,000 civilians were killed or injured in armed conflict in Afghanistan in 2019, with improvised explosive devices used in 42 percent of the casualties. Havens is working to help reduce the numbers.

“Our algorithms detect and locate explosive hazards using two different systems: a vehicle-mounted multi-band ground-penetrating radar system and a handheld multimodal sensor system,” Havens explains. “Each of these systems employs multiple sensors, including different frequencies of ground penetrating radar, magnetometers and visible-spectrum cameras. We’ve created methods of integrating the sensor information to automatically find the explosive hazards.” 

As a PhD student at Michigan Tech, Deilamsalehy worked alongside Havens as a research assistant in the ECE department’s Intelligent Robotics Lab (IRLab). “My research was focused on sensor fusion, machine learning and computer vision, fusing the data from IMU, LiDAR, and a vision camera for 3D localization and mapping purposes,” she says. “I used data from a sensor platform in the IRLab, mounted on an unmanned aerial vehicle (UAV), to evaluate my proposed fusion algorithm.”

Havens is also co-advisor to students in the SENSE (Strategic Education through Naval Systems Experience) Enterprise team at Michigan Tech, along with ME-EM Professor Andrew Barnard. Students in SENSE design, build, and test engineering systems in all domains: space, air, land, sea, and undersea. Like all Enterprise teams, SENSE is open to students in any major. 

Prof. Havens, when did you first get into engineering? What sparked your interest?

I first became an engineer at Michigan Tech in the late 90s. What really sparked my interest in what-I-do-now was my introductory signal processing courses. The material in these courses was the first stuff that really ‘spoke’ to me. I have always been a serious musician and the mathematics of waves and filters was so intuitive because of my music knowledge. I loved that this field of study joined together the two things that I really loved: music and math. And I’ve always been a computer geek. I was doing programming work in high school to make extra money; so that side of me has always led me to want to solve problems with computers.

Hometown, Hobbies, Family?

I grew up in Traverse City, Michigan, and came to Tech as a student in the late 90s. I’ve always wanted to come back to the Copper Country; so, it’s great that I was able to return to the institution that gave me the jump start in my career. I live (and currently work from home) in Hancock with my partner, Dr. Stephanie Carpenter (an author and MTU professor), and our two fur children, Rick Slade, the cutest ginger in the entire world, and Jaco, the smartest cat in the entire world. I have a grown son, Sage, who enjoys a fast-paced life in Traverse City. Steph and I enjoy exploring the greater Keweenaw and long discussions about reality television, and I enjoy playing music with all the local talent, fishing (though catching is a challenge), and gradually working through the lumber pile in my garage.

Dr. Deilamsalehy, how did you find engineering? What sparked your interest?

I was born and raised in Tehran, Iran. I have always been into robotics. I was a member of our robotics team in high school and that led me to engineering. I decided to apply to Michigan Tech sort of by chance when a friend of mine told me about it. I looked at the programs in the ECE department, and felt they aligned with my interests. Then soon after I first learned about Michigan Tech, I found out that one of my undergraduate classmates went there. I talked to him, and he also encouraged me to apply. And that’s how I was able to join Michigan Tech for my PhD program. My degree is in electrical engineering but my focus at Michigan Tech involved computer science and designing Machine Learning solutions.

Hobbies and Interests?

I now live in Seattle, famous for outdoor activities—kind of like the UP, but without the cold—so I do lots of mountaineering, biking, rock climbing, and in the winter, skiing. I learned how to ski at Michigan Tech, up on Mont Ripley. It’s steep, and it’s cold! Once you learn skiing on Ripley, you’re good. You can ski just about anywhere.
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Tim Schulz to Present Michigan Tech Research Forum Oct. 14

Timothy Schulz

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.

Paper by Yakov Nekrich Accepted for ACM-SIAM SODA21 Symposium

A paper by Associate Professor Yakov Nekrich, Computer Science, has been accepted for the 61st ACM-SIAM Symposium on Discrete Algorithms 2021 (SODA21), which will take place virtually January 10-13, 2021.

Nekrich is sole author of the accepted article, “New Data Structures for Orthogonal Range Reporting and Range Minima Queries.” An extended version of the paper is available for download on ArXiv.

The annual ACM-SIAM Symposium on Discrete Algorithms (SODA) is an academic conference in the fields of algorithm design and discrete mathematics. It is considered among the top conferences for research in algorithms.


Paper Abstract

In this paper we present new data structures for two extensively studied variants of the orthogonal range searching problem.
First, we describe a data structure that supports two-dimensional orthogonal range minima queries in O(n) space and O(logεn) time, where n is the number of points in the data structure and ε is an arbitrarily small positive constant. Previously known linear-space solutions for this problem require O(log1+εn) (Chazelle, 1988) or O(lognloglogn) time (Farzan et al., 2012). A modification of our data structure uses space O(nloglogn) and supports range minima queries in time O(loglogn). Both results can be extended to support three-dimensional five-sided reporting queries.

Next, we turn to the four-dimensional orthogonal range reporting problem and present a data structure that answers queries in optimal O(logn/loglogn+k) time, where k is the number of points in the answer. This is the first data structure that achieves the optimal query time for this problem. Our results are obtained by exploiting the properties of three-dimensional shallow cuttings.


The Society for Industrial and Applied Mathematics (SIAM) is an international community of 14,500+ individual members. Almost 500 academic, manufacturing, research and development, service and consulting organizations, government, and military organizations worldwide are institutional members.

SOSSEC / US Army ERDC Award to Study Adaptive AI

Dr. Timothy Havens, College of Computing, and Dr. Anthony Pinar, Electrical and Computer Engineering, have been awarded a two-year, $428,707 project by the SOSSEC Inc. / U.S. Army ERDC to investigate “Modeling and Algorithm Development for Adaptive Adversarial AI for Complex Autonomy.”

The project will study how autonomous systems operate in complex and unstructured environments, focusing on sensing, processing, and decision-making capabilities.

Havens and Pinar are members of the Institute of Computing and Cybersystem’s Center for Data Sciences.

Tim 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.

Tony Pinar is a lecturer and senior design coordinator in the Electrical and Computer Engineering department.

The SOSSEC Consortium was specifically formed to address the needs of the Department of Defense (DoD). It was founded on a simple concept: that collaboration, innovation, and cooperation among a broad spectrum of industry, academia and non-profit entities vastly improves the products and services delivered to its clients, according to the organization’s website.

The mission of the US Army Engineer Research and Development Center (ERDC), an integral component of the Office of the Assistant Secretary of Defense for Research and Engineering, is to help solve the nation’s most challenging problems in civil and military engineering, geospatial sciences, water resources, and environmental sciences for the benefit of the Army, the Department of Defense, civilian agencies, and the public good, according to the organizations’s website.

The Institute of Computing and Cyberersystems (ICC) promotes collaborative, cross-disciplinary research and learning experiences through six research centers in the areas of computing education, cyber-physical systems, cybersecurity, data sciences, human-centered computing, and scalable architectures and systems, for the benefit of Michigan Technological University and society at large.

The ICC’s 55 members represent more than 20 academic disciplines at Michigan Tech. Member scientists are collaborating to conduct impactful research, make valuable contributions in the field of computing, and solve problems of critical national importance.

ICC’s Center for Data Sciences (DataS) focuses on the research of data sciences education, algorithms, mathematics, and applications. DataS fosters interdisciplinary collaborations by bringing together diverse faculty and students from varied disciplines to discover new knowledge and exciting research opportunities in the field of data sciences.

$243K DURIP Award will Multiply Michigan Tech Research Capabilities

Dr. Timothy Havens (ICC), Dr. Andrew Barnard (GLRC), Dr. Guy Meadows (GLRC), and Dr. Gowtham (IT/ECE) have been awarded an Office of Naval Research DURIP grant titled, “Acoustic Sensing System and High-Throughput Computing Environment and Threat Monitoring in Naval Environments Using Machine Learning.”

The $243,169 award will fund procurement of new high throughput computing and underwater acoustic sensing systems for use by researchers at Michigan Tech.

The Defense University Research Instrumentation Program (DURIP) supports universities through awards meant to build the infrastructures necessary for relevant, high-quality Navy research.

We believe that these resources will considerably multiply our capability and productivity in assisting the U.S. Navy, and DoD at large, to move forward on numerous fronts. We have excellent resources, but lack some infrastructure capabilities to make a leap in theory and applications.

Timothy Havens, Director, Institute of Computing and Cybersystems

Havens says that the award supports two active U.S. Navy projects in particular, “ONR Graduate Traineeship Award: Multi-Modal, Near-Shore, Ice-Covered Arctic Acoustic Propagation Measurements and Analysis (ONR #N00014-18-1-2592)” and “Localization, Tracking, and Classification of On-Ice and Underwater Noise Sources Using Machine Learning (US NSWC #N00174-19-1-0004).”

“With this new equipment we can begin to conduct more detailed, realistic, and repeatable sensor/target experiments, and facilitate expansion of current research into related areas of interest to the DoD, such as deep learning with digital phased arrays and persistent, distributed sensing with sensor arrays,” Havens notes.

“The equipment will significantly enhance Michigan Tech capabilities for six other Department of Defense (DoD)-funded projects as well, including NGA, SPAWAR, and DARPA awards,” he adds.

Finally, through graduate student participation in the research, and collaboration with the undergraduate SENSE Enterprise at Michigan Tech (Strategic Education through Naval Systems Experiences), the equipment will augment Navy STEM education and future workforce development.

Tim 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.

Andrew Barnard is director of the Great Lakes Research Center,
associate professor, Mechanical Engineering—Engineering Mechanic, and Faculty advisor to the undergraduate SENSE Enterprise.

Guy Meadows is director of the Marine Engineering Laboratory, the Robbins Professor of Sustainable Marine Engineering, and a research professor in the Department of Mechanical Engineering-Engineering Mechanics.

Gowtham is director of research computing for Michigan Tech’s Information Technology department; an adjunct assistant professor, Physics; a research associate professor, Electrical and Computer Engineering; and an NSF XSEDE Campus Champion.

The Institute of Computing and Cyberersystems (ICC) promotes collaborative, cross-disciplinary research and learning experiences through six research centers in the areas of computing education, cyber-physical systems, cybersecurity, data sciences, human-centered computing, and scalable architectures and systems, for the benefit of Michigan Technological University and society at large.

The ICC’s 55 members represent more than 20 academic disciplines at Michigan Tech. Member scientists are collaborating to conduct impactful research, make valuable contributions in the field of computing, and solve problems of critical national importance.

The Great Lakes Research Center (GLRC) provides state-of-the-art laboratories to support research on a broad array of topics. Faculty members from many departments across Michigan Technological University’s campus collaborate on interdisciplinary research, ranging from air–water interactions to biogeochemistry to food web relationships.

One of the GLRC’s most important functions is to educate the scientists, engineers, technologists, policymakers, and stakeholders of tomorrow about the Great Lakes basin. The Center for Science and Environmental Outreach provides K–12 student, teacher, and community education/outreach programs, taking advantage of the Center’s many teaching labs.

The GLRC also contains a lake-level marine facility and convenient deep-water docking, providing a year-round home for Michigan Tech’s surface and sub-surface fleet of marine vehicles.


Yu Cai is PI of 2-year NSA GenCyber Project

Professor Yu Cai, Applied Computing, a member of the ICC’s Center for Cybersecurity, is the principal investigator on a two-year project that has received a $99,942 grant from the National Security Agency (GenCyber). The project is titled, “GenCyber Teacher Camp at Michigan Tech. ”

Lecturer Tim Van Wagner (AC) and Assistant Professor Bo Chen (CS, DataS) are Co-PIs. Cai will serve as the camp director, Tim Van Wagner as lead instructor.

This GenCyber project aims to host a week-long, residential summer camp for twenty K-12 STEM teachers in 2021 at Michigan Tech. Target educators are primarily from Michigan and surrounding states.

The objectives of the camp are to teach cybersecurity knowledge and safe online behavior, develop innovative teaching methods for delivering cybersecurity content, and provide professional development opportunities so participants will return to their home schools with contagious enthusiasm about teaching cybersecurity.

The GenCyber camp will be offered at no cost to camp participants. Room and board will be provided. Teacher participants will receive a stipend of $500 for attending and completing camp activities.

Read about the 2019 Michigan Tech GenCyber camps for teachers and students here.

Tim Havens, Tony Pinar Co-Authors of Article in IEEE Trans. Fuzzy Systems

An article by Anthony Pinar (DataS/ECE) and Timothy Havens (DataS/CC), in collaboration with University of Missouri researchers Muhammad Islam, Derek Anderson, Grant Scott, and Jim Keller, all of University of Missouri, has been published in the July 2020 issue of the journal IEEE Transactions on Fuzzy Systems.

The article is titled, “Enabling explainable fusion in deep learning with fuzzy integral neural networks.” Link to the article here.

Abstract:
Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multilayer network, referred to hereafter as ChIMP.

We also put forth an improved ChIMP (iChIMP) that leads to a stochastic-gradient-descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables explainable artificial intelligence (XAI). Synthetic validation experiments are provided, and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy, and our previously established XAI indices shed light on the quality of our data, model, and its decisions.

Citation
M. Islam, D. T. Anderson, A. J. Pinar, T. C. Havens, G. Scott and J. M. Keller, “Enabling Explainable Fusion in Deep Learning With Fuzzy Integral Neural Networks,” in IEEE Transactions on Fuzzy Systems, vol. 28, no. 7, pp. 1291-1300, July 2020, doi: 10.1109/TFUZZ.2019.2917124.