Category: Published

Article by Alex Sergeyev Published in Journal of Engineering Technology (JET)

Alex Sergeyev

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:

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.

Mark Rouleau Is Co-author of Article in the Journal Landscape and Urban Planning

Mark Rouleau

An article by Mark Rouleau, associate professor of social sciences and member of the ICC’s Center for Data Sciences, was recently published in the journal Landscape and Urban Planning, published by Elsevier. The article, titled, “Agent-based modeling for bioenergy sustainability assessment,” is co-authored by Robert J. Zupko II.

Article Abstract: Woody biomass bioenergy is an important renewable alternative to conventional fossil fuels. However, the negative land-use change impacts of biomass harvesting necessary for bioenergy production can potentially outweigh its positive benefits if poorly managed. In this paper, we explain how Agent-Based Modeling (ABM), a form of computer simulation, can be used to conduct a comprehensive bioenergy sustainability assessment to identify possible gains and trade-offs necessary to develop bioenergy in regions with large numbers of private family forest owners or smallholders who own a significant share of available biomass. We discuss how ABM simulation can overcome the barriers of existing sustainability assessment tools and provide a demonstration of the sustainability assessment capabilities of an ABM using a hypothetical case study that explores the introduction of a bioenergy conversion facility in the Western Upper Peninsula of Michigan, United States. We conduct a series of alternative futures scenarios and compare the sustainability outcomes of three alternative policy regimes using voluntary incentive programs to encourage smallholders to harvest biomass.

Michigan Tech Digital Commons listing:

Recommended Citation: Rouleau, M., & Zupko II, R. J. (2019). Agent-based modeling for bioenergy sustainability assessment. Landscape and Urban Planning, 188, 54-63.

Mari Buche Is Co-author of Article in ACM SIGMIS Database

Mari Buche

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.

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

DOI: 10.1145/3353401.3353407

Ali Ebnenasir is Co-Author of Publication in ACM Transactions on Computational Logic

Ali Ebnenasir

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.

doi: 10.1145/3326456

Tim Havens Is Co-author of Article Published in IEEE Transactions on Fuzzy Systems

Timothy HavensTim 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.