Tim Havens (CC/DataS) was quoted extensively in the article, “How to Explain AI in Plain English,”published September 23, 2019, in The Enterprisers Project. https://enterprisersproject.com/article/2019/9/ai-explained-plain-english
College of Computing Professor Alex Sergeyev (DataS) presented his research article, “University, Community College and Industry Partnership: Revamping Robotics Education to Meet 21st Century Workforce Needs – NSF Sponsored Project Final Report,” at the 2019 American Society of Engineering Education (ASEE) annual conference, receiving the Best Paper Award in the Engineering Technology Division.
The conference took place June 16-19 in Tampa, Florida.
Co-authors of the publication are S. Kuhl, N. Alaraje, M. Kinney, M. HIghum, and P. Mehandiratta. The paper will be published in the fall issue of the prestigious Journal of Engineering Technology (JET).
The Institute of Computing and Cybersystems (ICC) and the Michigan Tech Visiting Professor Program will present a seminar by Dr. Anna Wilbik on Thursday, October 3, starting at 3:00 p.m., in ME-EM 112 . A reception will follow and refreshments will be served. The title of Dr. Wilbik’s seminar is, “The explainability challenge in descriptive analytics: do we understand the data?”The seminar is presented by the Institute of Computing and Cybersystems and the Michigan Tech Visiting Professor Program, which is funded by a grant to the Michigan Tech Provost Office from the State of Michigan’s King-Chavez-Parks Initiative.
Dr. Anna Wilbik is an assistant professor in the Information Systems Group of the Department of Industrial Engineering and Innovation Sciences at Eindhoven University of Technology (TU/e) in the Netherlands. She received her PhD in Computer Science from the Systems Research Institute, Polish Academy of Science, Warsaw, Poland, in 2010. In 2011, she was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering at the University of Missouri, Columbia, USA. Her research interests are in business intelligence, especially focused on linguistic summaries and computing with words. With her research she tries to bridge the gap between the fuzzy sets theory and industrial applications. She makes this connection in research projects collaborating with industry both on the national and the European level. She has published over 80 papers in international journals and conferences.
Seminar Abstract: Nowadays, ever more data are collected, for instance in the healthcare domain. The amount of patients’ data has doubled in the previous two years. This exponential growth creates a data flood that is hard to handle by decision makers. In many domains, humans are collaborating with machines for decision making purposes to cope with the resulting data complexity and size. This collaboration can be realized through machine learning, visual analytics, or online analytical processing, where a machine is just a tool – but often used to make important decisions. The question now is: do we really understand the data by using the tool this way?
Explainability is a great challenge in data analytics, with the aim to explain to the user why certain decisions have been recommended or made. This challenge is especially important in predictive and prescriptive analytics. Less attention in this respect is payed to less mature analytics levels, descriptive and diagnostics, although they are the first steps for understanding data.
Data analysis methods use numbers, figures, or mathematical equations to show data, decision recommendations, and patterns. Yet for a human, the natural way of communication is natural language: words, not numbers or figures. This causes a gap between the meaning of data and human understanding. The challenge is: How to make data more understandable for humans?
Fuzzy techniques, or the application of the computing with words paradigm, have the potential to close the gap by using natural language as the communication means. In this talk, I will focus on descriptive analytics and show with a set of examples how fuzzy techniques can provide better insight of data to the user. I pay special attention to the technique of linguistic summaries.
The Department of Mathematical Sciences and the College of Computing will present a lecture on high-performance computing by Dr. Laura Monroe from the Ultrascale Systems Research Center (USRC) at Los Alamos National Laboratory on Tuesday, September 24, from 5:00 to 6:00 p.m., in Fisher Hall, Room 133. The lecture is titled “The Mathematical Analysis of Faults and the Resilience of Applications.” Discussion will follow the lecture, and pizza and refreshments will be served.
Abstract: As the post-Moore’s-Law era advances, faults are expected to increase in number and in complexity on emerging novel devices. This will happen on exascale and post-exascale architectures due to smaller feature sizes, and also on new devices with unusual fault models. Attention to error-correction and resilience will thus be needed in order to use such devices effectively. Known mathematical error-correction methods may not suffice under these conditions, and an ad hoc approach will not cover the cases likely to emerge, so mathematical approaches will be essential. We will discuss the mathematical underpinnings behind such approaches, illustrate with examples, and emphasize the interdisciplinary approaches that combine experimentation, simulation, mathematical theory and applications that will be needed for success.
Dr. Monroe has spent most of her career focused on unconventional approaches to difficult computing problems, specifically researching new technologies to enable better performance as processor-manufacturing techniques reach the limits of the atomic scale, also known as the end of Moore’s Law. Dr. Monroe received her PhD in the theory of error-correcting codes, working with Dr.Vera Pless. She worked at NASA Glenn, then joined Los Alamos National Laboratory in 2000. She has contributed on the design teams on the LANL Cielo and Trinity supercomputers, and originated and leads the Laboratory’s inexact computing project that is meant to address Moore’s Law challenges in a unique way. She also provides mathematical and theoretical support to LANL’s HPC Resilience project.
The Alliance for Computing, Information, and Automation (ACIA) and Michigan Tech Career Services invite students to a casual networking mixer with industry employment recruiters on Tuesday, September 24, 2019, from 4:00 to 6:00 p.m., in the Rozsa Center lobby. The event is free and appetizers and refreshments will be served.Students in the following majors are encouraged to attend: Computer Network and System Administration (CNSA), Computer Engineering, Computer Science, Cybersecurity, Data Sciences, Electrical Engineering, Electrical Engineering Technology (EET), and Software Engineering.
Recruiters interested in hiring Michigan Tech students and graduates in the above majors will be in attendance.
The Alliance for Computing, Information, and Automation (ACIA) at Michigan Technological University is a collaborative effort between the Department of Electrical and Computer Engineering and the College of Computing. The mission of the ACIA is to provide faculty and students the opportunity to work across organizational boundaries to create an environment that is a reflection of contemporary technological innovation. The research arm of the ACIA is the Institute of Computing and Cybersystems (ICC).
Recruiters interested in hiring Michigan Tech students and graduates in the above majors will be in attendance. Invited companies include the following:
3M
Amcor (fka Bemis)
ArcelorMittal
Black & Veatch
Caterpillar
Amcor (fka Bemis)
ArcelorMittal
Black & Veatch
Caterpillar
CCI Iron Mountain
Continental
Cummins
Denso
Dow
DTE Energy
Fiat Chrysler Automobiles (FCA)
Ford Motor Company
Georgia-Pacific
Gerdau
Greenheck
Kimberly-Clark
Kohler
Continental
Cummins
Denso
Dow
DTE Energy
Fiat Chrysler Automobiles (FCA)
Ford Motor Company
Georgia-Pacific
Gerdau
Greenheck
Kimberly-Clark
Kohler
Leidos
Los Alamos National Lab (LANL)
Marathon Petroleum
Mercury Marine
Michigan Scientific Corporation
Marathon Petroleum
Mercury Marine
Michigan Scientific Corporation
Milwaukee Tool
National Air and Space Intelligence Center
Nexteer Automotive
Nucor
Oshkosh Corporation
Nexteer Automotive
Nucor
Oshkosh Corporation
Palantir
Plexus
Schneider
Superior Technologies
Systems Control
Plexus
Schneider
Superior Technologies
Systems Control
Zhen Liu, associate professor of civil and environmental engineering and member of the ICC’s Center for Cyber-Physical Systems (CPS), is co-author of the article, “A multivariate freezing-thawing depth prediction model for spring load restriction,” which was published August 6, 2019, in the journal Cold Regions Science and Technology, which is published by Elsevier. Co-authors of the article are Ting Bio and John Bland.
Abstract: Road damages induced by heavily loaded truck traffic during the spring thaw are a major road distress in cold regions. To minimize these damages, Spring Load Restriction (SLR) is widely applied in the U.S., Canada, and other countries during the early thawing season by controlling the movement of freight-carrying trucks and heavy equipment travel until the thawing ends. Most SLR policies rely on the Freezing Depth (FD) and Thawing Depth (TD), especially the latter one. Therefore, accurate predictions of FD and TD are important to prevent both the extensive damage to the pavement due to the late placement or early removal of SLR and the economic loss of road users due to an unnecessarily long SLR period. Here, we propose a new multivariate model for predicting FD and TD in support of SLR decision-making. The model gives a curving surface of FD and TD in a 3-dimensional space, instead of 2-dimensional in traditional methods, by considering both the freezing and thawing indices in the entire freeze-thaw cycle. For model evaluations, yearly field data measured at five typical sites from 104 sites in Michigan were adopted. The evaluation results showed that the proposed model is accurate in predicting FD and TD for most sites. Compared to the previous TD predictions in the existing study, the TD predictions with the proposed model have been significantly improved. In addition, this study provides field data that have not been reported earlier in the literature and that can be used for validating other prediction models. The reported work is ready for practice for roadways in cold regions to support SLR decision-making.
https://digitalcommons.mtu.edu/michigantech-p/406
Citation: Bao, T., Liu, Z., & Bland, J. (2019). A multivariate freezing-thawing depth prediction model for spring load restriction. Cold Regions Science and Technology, 167.http://dx.doi.org/10.1016/j.coldregions.2019.102856
Timothy Havens (CC/ICC), the William and Gloria Jackson Associate Professor of Computer Systems and director of the Institute of Computing and Cybersystems (ICC), was quoted extensively in the article “How to make a career switch into AI: 8 tips,” which was published September 5, 2019, on The Enterprisers Project blog.
https://enterprisersproject.com/article/2019/9/ai-career-path-how-make-switch
Bo Chen, assistant professor of computer science and member of the Institute of Computing and Cybersystems Center for Cybersecurity, is the principal investigator on a project that has received a $249,918 research and development grant from the National Science Foundation. The project is entitled, “SaTC: CORE: Small: Collaborative: Hardware-Assisted Plausibly Deniable System for Mobile Devices.” This is a potential three-year project.
Abstract: Mobile computing devices typically use encryption to protect sensitive information. However, traditional encryption systems used in mobile devices cannot defend against an active attacker who can force the mobile device owner to disclose the key used for decrypting the sensitive information. This is particularly of concern to dissident users who are targets of nation states. An example of this would be a human rights worker collecting evidence of untoward activities in a region of oppression or conflict and storing the same in an encrypted form on the mobile device, and then being coerced to disclose the decryption key by an official. Plausibly Deniable Encryption (PDE) has been proposed to defend against such adversaries who can coerce users into revealing the encrypted sensitive content. However, existing techniques suffer from several problems when used in flash-memory-based mobile devices, such as weak deniability because of the way read/write/erase operations are handled at the operating systems level and at the flash translation layer, various types of side channel attacks, and computation and power limitations of mobile devices. This project investigates a unique opportunity to develop an efficient (low-overhead) and effective (high-deniability) hardware-assisted PDE scheme on mainstream mobile devices that is robust against a multi snapshot adversary. The project includes significant curriculum development activities and outreach activities to K-12 students.
This project fundamentally advances the mobile PDE systems by leveraging existing hardware features such as flash translation layer (FTL) firmware and TrustZone to achieve a high deniability with a low overhead. Specifically, this project develops a PDE system with capabilities to: 1) defend against snapshot attacks using raw flash memory on mobile devices; and 2) eliminate side-channel attacks that compromise deniability; 3) be scalable to deploy on mainstream mobile devices; and 4) efficiently provide usable functions like fast mode switching. This project also develops novel teaching material on PDE and cybersecurity for K-12 students and the Regional Cybersecurity Education Collaboration (RCEC), a new educational partnership on cybersecurity in Michigan.
Publications related to this research:
[DSN ’18] Bing Chang, Fengwei Zhang, Bo Chen, Yingjiu Li, Wen Tao Zhu, Yangguang Tian, Zhan Wang, and Albert Ching. MobiCeal: Towards Secure and Practical Plausibly Deniable Encryption on Mobile Devices. The 48th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN ’18), June 2018 (Acceptance rate: 28%)
[Cybersecurity ’18] Qionglu Zhang, Shijie Jia, Bing Chang, Bo Chen. Ensuring Data Confidentiality via Plausibly Deniable Encryption and Secure Deletion – A Survey. Cybersecurity (2018) 1: 1.
[ComSec ’18 ] Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen Tao Zhu, Yingjiu Li, and Zhan Wang. User-Friendly Deniable Storage for Mobile Devices. Elsevier Computers & Security, vol. 72, pp. 163-174, January 2018
[CCS ’17] Shijie Jia, Luning Xia, Bo Chen, and Peng Liu. DEFTL: Implementing Plausibly Deniable Encryption in Flash Translation Layer. 2017 ACM Conference on Computer and Communications Security (CCS ’17), Dallas, Texas, USA, Oct 30 – Nov 3, 2017 (Acceptance rate: 18%)
[ACSAC ’15] Bing Chang, Zhan Wang, Bo Chen, and Fengwei Zhang. MobiPluto: File System Friendly Deniable Storage for Mobile Devices. 2015 Annual Computer Security Applications Conference (ACSAC ’15), Los Angeles, California, USA, December 2015 (Acceptance rate: 24.4%)
[ISC ’14] Xingjie Yu, Bo Chen, Zhan Wang, Bing Chang, Wen Tao Zhu, and Jiwu Jing. MobiHydra: Pragmatic and Multi-Level Plausibly Deniable Encryption Storage for Mobile Devices. The 17th Information Security Conference (ISC ’14), Hong Kong, China, Oct. 2014
Link to more information about this project: https://snp.cs.mtu.edu/research/index.html#pde
Soner Onder, professor of computer science, was recently awarded $246,329 for the first year of a four-year NSF grant for his project, “SHF: Medium: Collaborative Research: Statically Controlled Asynchronous Lane Execution (SCALE).” The project is in collaboration with Prof. David Whalley of Florida State University. Michigan Tech is the lead institution in the project, it is expected to total $1.2 million, with Michigan Tech receiving $600,000.
Abstract: Enabling better performing systems benefits applications that span those running on mobile devices to large data applications running on data centers. The efficiency of most applications is still primarily affected by single thread performance. Instruction-level parallelism (ILP) speeds up programs by executing instructions of the program in parallel, with ‘superscalar’ processors achieving maximum performance. At the same time, energy efficiency is a key criteria to keep in mind as such speedup happens, with these two being conflicting criteria in system design. This project develops a Statically Controlled Asynchronous Lane Execution (SCALE) approach that has the potential to meet or exceed the performance of a traditional superscalar processor while approaching the energy efficiency of a very long instruction word (VLIW) processor. As implied by its name, the SCALE approach has the ability to scale to different types and levels of parallelism. The toolset and designs developed in this project will be available as open-source and will also have an impact on both education and research. The SCALE architectural and compiler techniques will be included in undergraduate and graduate curricula.
The SCALE approach supports separate asynchronous execution lanes where dependencies between instructions in different lanes are statically identified by the compiler to provide inter-lane synchronization. Providing distinct lanes of instructions allows the compiler to generate code for different modes of execution to adapt to the type of parallelism that is available at each point within an application. These execution modes include explicit packaging of parallel instructions, parallel and pipelined execution of loop iterations, single program multiple data (SPMD) execution, and independent multi-threading.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1901005&HistoricalAwards=false
George Anderson and Sally Sutherland of the US Naval Undersea Warfare Center (NUWC)-Newport will present talks on Tuesday, September 17, 2019, from 3:00 to 4:00 pm, in Room 202 of the Michigan Tech Great Lakes Research Center. A reception will follow and refreshments will be served.
George Anderson will present his talk from 3:00 – 3:30 pm. Titled “Classification of Personnel and Vehicle Activity Using a Sensor System With Numerous Array Elements,” Anderson’s talk will present the performance of a hybrid discriminative/generative classifier using experimental data collected from a scripted field test.
Sally Sutherland, NEEC Director, NAVSEA Headquarters, whose talk is 3:30-4:00 pm, will present, “An Overview of the Naval Engineering Education Consortium (NEEC) Program,” in which she will share information about the Navy’s Naval Engineering Education Consortium (NEEC) program, whose mission is to educate and develop world-class naval engineers and scientists to become part of the Navy’s civilian science and engineering workforce.
One of two divisions of the Naval Undersea Warfare Center, NUWC Division Newport is the Navy’s full-spectrum research, development, test and evaluation, engineering, and fleet support center for submarine warfare systems and many other systems associated with the undersea battlespace. It provides the technical foundation that enables the conceptualization, research, development, fielding, modernization, and maintenance of systems that ensure our Navy’s undersea superiority. The NUWC Division Newport is responsible, cradle to grave, for all aspects of systems under its charter, and is engaged in efforts ranging from participation in fundamental research to the support of evolving operational capabilities in the U.S. Navy fleet. The major thrust of NUWC Division Newport’s activities is in applied research and system development.
This event is sponsored by the Great Lakes Research Center (GLRC) and the Institute of Computing and Cybersystems (ICC).
Please contact Tim Havens (906-487-3115, thavens@mtu,edu) or Andrew Barnard (906-487-2412, arbarnar@mtu.edu) for additional information.Visit the NAVSEA online at: https://www.navsea.navy.mil/Home/Warfare-Centers/NUWC-Newport/ and Facebook: https://www.facebook.com/NUWCNewport/
https://doi.org/10.1016/j.coldregions.2019.102856