Category: Published

An article co-authored by Tim Havens, associate dean for research, College off Computing, “Soft Overlapping Community Detection in Large-Scale Networks via Fast Fuzzy Modularity Maximization,” was published in the March 2020 issue of IEEE Transactions on Fuzzy Systems.

Havens’s co-authors are Audrey Yazdanparast (ECE) and Mohsen Jamalabdollahi of Cisco Systems.

Article Abstract: Soft overlapping clustering is one of the notable problems of community detection. Extensive research has been conducted to develop efficient methods for non-overlapping and crisp-overlapping community detection in large-scale networks. In this paper, Fast Fuzzy Modularity Maximization (FFMM) for soft overlapping community detection is proposed.

FFMM exploits novel iterative equations to calculate the modularity gain associated with changing the fuzzy membership values of network vertices. The simplicity of the proposed scheme enables efficient modifications, reducing computational complexity to a linear function of the network size and the number of communities. Moreover, to further reduce the complexity of FFMM for very large networks, Multi-cycle FFMM (McFFMM) is proposed.

The proposed McFFMM reduces complexity by breaking networks into multiple sub-networks and applying FFMM to detect their communities. Performance of the proposed techniques are demonstrated with real-world data and the Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks. Moreover, the performance of the proposed techniques is eval- uated versus some state-of-the-art soft overlapping community detection approaches. Results show that the McFFMM produces a remarkable performance in terms of overlapping modularity with fuzzy memberships, computational time, number of detected overlapping nodes, and Overlapping Normalized Mutual Informa- tion (ONMI).

View more info here.

Timothy Havens, director of the Institute of Computing and Cybersystems (ICC), is co-author of the article, “A Similarity Measure Based on Bidirectional Subsethood for Intervals,” published in the March 2020 issue of IEEE Transactions on Fuzzy Systems.

Havens’s co-authors are Shaily Kabir, Christian Wagner, and Derek T. Anderson.

Havens is also associate dean for research, College of Computing, and the William and Gloria Jackson Associate Professor of Computer Systems.

Christian Wagner, an affiliated member of the ICC, was an ICC donor-sponsored visiting professor at Michigan Tech in the 2016-17 academic year. He is now with the School of Computer Science at University of Nottingham.

Shaily Kabir is with the School of Computer Science, University of Nottingham. Derek T. Anderson is with the Electrical Engineering and Computer Science Department, University of Missouri, Columbia.

S. Kabir, C. Wagner, T. C. Havens and D. T. Anderson, “A Similarity Measure Based on Bidirectional Subsethood for Intervals,” in IEEE Transactions on Fuzzy Systems.

https://ieeexplore.ieee.org/document/9019656

Yakov Nekrich, associate professor, Department of Computer Science, has been notified that two scholarly papers he has authored were accepted by the 36th International Symposium on Computational Geometry (SoCG 2020), which takes place June 23-26, 2020, in Zurich, Switzerland.

Nekrich is a member of the ICC’s Center for Data Sciences.

The two papers are “Further Results on Colored Range Searching,” by Timothy M. Chan, Qizheng He, and Nekrich, and “Four-Dimensional Dominance Range Reporting in Linear Space” by Nekrich alone.

The Annual Symposium on Computational Geometry (SoCG) is an academic conference in computational geometry. Founded in 1985, it was originally sponsored by the SIGACT and SIGGRAPH Special Interest Groups of the Association for Computing Machinery (ACM). It dissociated from the ACM in 2014. Since 2015 the conference proceedings have been published by the Leibniz International Proceedings in Informatics Since 2019 the conference has been organized by the Society for Computational Geometry. (Wikipedia)

Visit the SoCG 2020 website.

The Process Safety and Environmental Protection special issue on Advanced Oxidation Process (Elsevier), has accepted for publication a paper by associate professor Daisuke Minakata (CEE), his students Robert Zupko, Divya Kamath, and Erica Coscarelli, and his collaborator and co-PI Mark Rouleau (SS), ICC Center for Data Sciences. pictured at left with Mary Raber. Photo by Daily Mining Gazette.

The paper concerns research supported by the National Science Foundation’s Chemical, Bioengineering, Environmental and Transport Systems (CBET) Division.

Grant Title: Coupling Experimental and Theoretical Molecular-Level Investigations to Visualize the Fate of Degradation of Organic Compounds in Aqueous Phase Advanced Oxidation Systems

Grant Abstract: The lack of an overarching management plan combined with uncertainty about the adverse human health and ecological impacts of trace amounts of known and emerging organic compounds have raised public concerns about water. These issues also present major challenges to next generation water treatment utilities dealing with de facto and planned wastewater reuse. Advanced oxidation processes that produce highly reactive hydroxyl radicals are promising technologies to control trace amounts of organic compounds. Although the initial fate of hydroxyl radical induced reactions with diverse organic compounds have been studied, the mechanisms that produce intermediate radicals and stable-byproducts are not well understood. Significant barriers remain in our understanding of complex multi-channel elementary reaction pathways embedded in peroxyl radical bimolecular decay that produce identical intermediate-radicals and stable-byproducts. The model developed in the course of this research will give researchers and policy makers the ability to predict the likely chemical by-products and alternative options to provide least adverse impact on the general public who will directly consume this water or other ecological organisms who will be exposed indirectly.

The proposed study will integrate three thrusts to discover the currently unknown fate of the three major degradation pathways. First, we will perform pulse-photolysis kinetic measurement to determine the temperature-dependent overall reaction rate constants for multi-channel peroxyl radical reactions. We will also measure the resulting byproducts using a mass spectrometry. Second, we will employ quantum mechanical theoretical calculations to determine the elementary reaction pathways and associated reaction rate constants. Third, we will then combine our kinetic measurements with our theoretical calculations to develop an agent-based model that will enable us to visualize and predict the fate of organic compounds. With explicitly assigned reaction rules and molecular behavior embedded within a simulated reaction network, the resulting agent-based model will use software agents to represent radical species and organic compounds and then simulate their interactions to predict corresponding consequences (i.e., byproducts) over time and space. Finally, experimental observations will validate the outcomes from the agent-based model.

The Chemical, Bioengineering, Environmental and Transport Systems (CBET) Division supports innovative research and education in the fields of chemical engineering, biotechnology, bioengineering, and environmental engineering, and in areas that involve the transformation and/or transport of matter and energy by chemical, thermal, or mechanical means.

View additional grant info on the NSF website.

Find more information about the Process Safety and Environmental Protection special issue on Advanced Oxidation Process here.

An SAE technical paper, co-authored by Nathir Rawashdeh, assistant professor, CMH Division, College of Computing, has been accepted for publication at the WCX SAE World Congress Experience, April 21-23, 2020, in Detroit, MI.  The title of the paper is “Mobile Robot Localization Evaluations with Visual Odometry in Varying Environments using Festo-Robotino.” 

Abstract: Autonomous ground vehicles can use a variety of techniques to navigate the environment and deduce their motion and location from sensory inputs. Visual Odometry can provide a means for an autonomous vehicle to gain orientation and position information from camera images recording frames as the vehicle moves. This is especially useful when global positioning system (GPS) information is unavailable, or wheel encoder measurements are unreliable. Feature-based visual odometry algorithms extract corner points from image frames, thus detecting patterns of feature point movement over time. From this information, it is possible to estimate the camera, i.e. the vehicle’s motion. Visual odometry has its own set of challenges, such as detecting an insufficient number of points, poor camera setup, and fast passing objects interrupting the scene. This paper investigates the effects of various disturbances on visual odometry. Moreover, it discusses the outcomes of several experiments performed utilizing the Festo-Robotino robotic platform. The experiments are designed to evaluate how changing the system’s setup will affect the overall quality and performance of an autonomous driving system. Environmental effects such as ambient light, shadows, and terrain are also investigated. Finally, possible improvements including varying camera options and programming methods are discussed.

Learn more.

An article co-authored by Aleksandr Sergeyev, College of Computing professor, director of the Mechatronics graduate program, and member of the ICC’s Center for Data Sciences, has been published in the Journal of Engineering Technology (JET).

The conclusive article, titled “A University, Community College, and Industry Partnership: Revamping Robotics Education to Meet 21st century Needs – NSF Sponsored Project Final Report,” summarizes the work funded by a $750K NSF grant received by Servgeyev in 2015 to to promote robotics education.  The paper details the grant-funded achievements in curriculum and educational tools development, dissemination, and implementation at Michigan Tech and beyond.

Co-PIs on the project are  Scott A. Kuhl (Michigan Technological University), Prince Mehandiratta (Michigan Technological University), Mark Highum (Bay de Noc Community College), Mark Bradley Kinney (West Shore Community College), and Nasser Alaraje (The University of Toledo).

A related paper was presented at the 2019 ASEE Annual Conference & Exposition, June 21-24, 2019, in Tampa, FL, as part of the panel “Academe/Industry Collaboration” presented by the Technical Engineering Technology Division, where it was awarded the Best Paper Award in the Engineering Technology Division. Download the conference paper here: https://www.asee.org/public/conferences/140/papers/26234/view.

Conference Paper Abstract: Recently, educators have worked to improve STEM education at all levels, but challenges remain. Capitalizing on the appeal of robotics is one strategy proposed to increase STEM interest. The interdisciplinary nature of robots, which involve motors, sensors, and programs, make robotics a useful STEM pedagogical tool. There is also a significant need for industrial certification programs in robotics. Robots are increasingly used across industry sectors to improve production throughputs while maintaining product quality. The benefits of robotics, however, depend on workers with up-to-date knowledge and skills to maintain and use existing robots, enhance future technologies, and educate users. It is critical that education efforts respond to the demand for robotics specialists by offering courses and professional certification in robotics and automation. This NSF sponsored project introduces a new approach for Industrial Robotics in electrical engineering technology (EET) programs at University and Community College. The curriculum and software developed by this collaboration of two- and four-year institutions match industry needs and provide a replicable model for programs around the US. The project also addresses the need for certified robotic training centers (CRTCs) and provides curriculum and training opportunities for students from other institutions, industry representatives, and displaced workers. Resources developed via this project were extensively disseminated through a variety of means, including workshops, conferences, and publications. In this article, authors provide final report on project outcomes, including various curriculum models and industry certification development, final stage of the “RobotRun” robotic simulation software, benefits of professional development opportunities for the faculty members from the other institutions, training workshops for K-12 teachers, and robotic one-day camps for high school students.

The Journal of Engineering Technology® (JET) is a refereed journal published semi-annually, in spring and fall, by the Engineering Technology Division (ETD) of the American Society for Engineering Education (ASEE). The aim of JET is to provide a forum for the dissemination of original scholarly articles as well as review articles in all areas related to engineering technology education. engtech.org/jet

Mari Buche (DataS), School of Business and Economics associate dean and professor of management information systems, is co-author of the article, “He Said, She Said: Communication Theory of Identity and the Challenges Men Face in the Information Systems Workplace,” which was published in the August 2019 issue of the newsletter ACM SIGMIS Database: the DATABASE for Advances in Information Systems.

Co-authors of the article are Cynthia K. Riemenschneider, Baylor University, and Deb Armstrong, Florida State University.

Abstract: The preponderance of the academic research focused on diversity in the IS field has emphasized the perspectives of women and racioethnic minorities. Recent research has found that following the appointment of a female CEO, white male top managers provided less help to colleagues, particularly those identified as minority-status (McDonald, Keeves, & Westphal, 2018). Additionally, Collison and Hearn (1994) assert that white men’s universal status and their occupancy of the normative standard state have rendered them invisible as objects of analysis. To develop a more holistic view of the IS workplace, we expand the academic exploration by looking at the challenges men face in the Information Systems (IS) workplace. Using a cognitive lens, we evoke the challenges men perceive they face at work and cast them into revealed causal maps. We then repeat the process evoking women’s perspectives of men’s challenges. The findings are analyzed using the Communication Theory of Identity (CTI) to determine the areas of overlap and identity gaps. This study advances our understanding of the cognitive overlap (and lack thereof) regarding the challenges facing men in the IS field, and provides another step toward developing a more inclusive IS work environment.

Citation:
ACM SIGMIS Database: the DATABASE for Advances in Information Systems
Volume 50 Issue 3, August 2019
Pages 85-115
ACM New York, NY, USA

https://dl.acm.org/citation.cfm?id=3353407

DOI: 10.1145/3353401.3353407

An article co-authored by Ali Ebnenasir (SAS/CS) and Alex Klinkhamer, “Verification of Livelock-Freedom and Self-Stabilization on Parameterized Rings,” was recently published in ACM Transactions on Computational Logic.

Abstract: This article investigates the verification of livelock-freedom and self-stabilization on parameterized rings consisting of symmetric, constant space, deterministic, and self-disabling processes. The results of this article have a significant impact on several fields, including scalable distributed systems, resilient and self-* systems, and verification of parameterized systems. First, we identify necessary and sufficient local conditions for the existence of global livelocks in parameterized unidirectional rings with unbounded (but finite) number of processes under the interleaving semantics. Using a reduction from the periodic domino problem, we show that, in general, verifying livelock-freedom of parameterized unidirectional rings is undecidable (specifically, Π10-complete) even for constant space, deterministic, and self-disabling processes. This result implies that verifying self-stabilization for parameterized rings of self-disabling processes is also undecidable. We also show that verifying livelock-freedom and self-stabilization remain undecidable under (1) synchronous execution semantics, (2) the FIFO consistency model, and (3) any scheduling policy. We then present a new scope-based method for detecting and constructing livelocks in parameterized rings. The proposed semi-algorithm behind our scope-based verification is based on a novel paradigm for the detection of livelocks that totally circumvents state space exploration. Our experimental results on an implementation of the proposed semi-algorithm are very promising as we have found livelocks in parameterized rings in a few microseconds on a regular laptop. The results of this article have significant implications for scalable distributed systems with cyclic topologies.

https://dl.acm.org/citation.cfm?id=3326456&dl=ACM&coll=DL

doi: 10.1145/3326456

Tim Havens (CS/ICC) coauthored the article, “Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks,” which was accepted for publication in the journal IEEE Transactions on Fuzzy Systems.

Citation: M.A. Islam, D.T. Anderson, A. Pinar, T.C. Havens, G. Scott, and J.M. Keller. Enabling explainable fusion in deep learning with fuzzy integral neural networks. Accepted, IEEE Trans. Fuzzy Systems.

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 multi-layer 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 AI (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.