Category: Publications

Bo Chen’s Research on COVID-19 Prevention Method to be Published in IEEE IoT Magazine

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.

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.

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.

Tony Pinar

Tim Havens

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.

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.

ICC Releases FY19 Annual Report

The Institute of Computing and Cybersystems has released its FY 19 Annual Report, which can be viewed and downloaded on the ICC website.

We had a strong year in 2018-19,” says Timothy Havens, director of the ICC and associate dean for research, College of Computing.

“In FY20, new awards and research expenditures were even stronger, and I look forward to sharing more accomplishments with you in the coming months.”

Tim Havens, ICC Director

Nathir Rawashdeh Publishes Paper in BioSciences Journal

A paper co-authored by Assistant Professor Nathir Rawashdeh (DataS, Applied Computing) on Skin Cancer Image Feature Extraction, has been published this month in the EurAsian Journal of BioSciences.

View the open access article, “Visual feature extraction from dermoscopic colour images for classification of melanocytic skin lesions,” here.

Additional authors are Walid Al-Zyoud, Athar Abu Helou, and Eslam AlQasem, all with the Department of Biomedical Engineering, German Jordanian University, Amman, Jordan.

Citation: Al-Zyoud, Walid et al. “Visual feature extraction from dermoscopic colour images for classification of melanocytic skin lesions”. Eurasian Journal of Biosciences, vol. 14, no. 1, 2020, pp. 1299-1307.

Rawashdeh’s interests include unmanned ground vehicles, electromobility, robotics, image analysis, and color science. He is a senior member of the IEEE.

Sergeyev, Students Earn ASEE Conference Awards

Professor Aleksandr Segeyev (DataS), Applied Computing, and a group of Michigan Tech students presented two papers at the 2020 American Society for Engineering Education (ASEE) Gulf-Southwest Annual conference, which was conducted online April 23-24, 2020. Both papers received conference awards.

Faculty Paper Award

“Pioneering Approach for Offering the Convergence MS Degree in Mechatronics and Associate Graduate Certificate”
by Sergeyev, Professor and Associate Chair John Irwin (MMET), and Dean Adrienne Minerick (CC).

Student Paper Award

“Efficient Way of Converting outdated Allen Bradley PLC-5 System into Modern ControlLogix 5000 suit”, by Spencer Thompson (pictured), Larry Stambeck, Andy Posa, Sergeyev, and Lecturer Paniz Hazaveh, Applied Computing.

Sergeyev is director of the Michigan Tech Mechatronics Graduate Program and FANUC Certified Industrial Robotics Training Center.

Founded in 1893, the American Society for Engineering Education is a nonprofit organization of individuals and institutions committed to furthering education in engineering and engineering technology.

Bo Chen, Grad Students Present Posters at Security Symposium

College of Computing Assistant Professor Bo Chen, Computer Science, and his graduate students presented two posters at the 41st IEEE Symposium on Security and Privacy, which took place online May 18 to 21, 2020.

Since 1980, the IEEE Symposium on Security and Privacy has been the premier forum for presenting developments in computer security and electronic privacy, and for bringing together researchers and practitioners in the field.

Chen leads the Security and Privacy (SnP) lab at Michigan Tech. He is a member of Michigan Tech’s Institute of Computing and Cybersystems (ICC) Center for Cybersecurity (CyberS).

Chen’s research focuses on applied cryptography and data security and he investigates novel techniques to protect sensitive data in mobile devices/flash storage media and cloud infrastructures. Chen is also interested in designing novel techniques to ensure security and privacy of big data.

Chen will serve as general chair for the First EAI International Conference on Applied Cryptography in Computer and Communications (AC3), which will be held in Xiamen, China, in May 2021.

Visit Bo Chen’s faculty webpage here.

Poster: A Secure Plausibly Deniable System for Mobile Devices against Multi-snapshot Adversaries
Authors: Bo Chen, Niusen Chen
Abstract: Mobile computing devices have been used broadly to store, manage and process critical data. To protect confidentiality of stored data, major mobile operating systems provide full disk encryption, which relies on traditional encryption and requires keeping the decryption keys secret. This however, may not be true as an active attacker may coerce victims for decryption keys. Plausibly deniable encryption (PDE) can defend against such a coercive attacker by disguising the secret keys with decoy keys. Leveraging concept of PDE, various PDE systems have been built for mobile devices. However, a practical PDE system is still missing which can be compatible with mainstream mobile devices and, meanwhile, remains secure when facing a strong multi- snapshot adversary. This work fills this gap by designing the first mobile PDE system against the multi-snapshot adversaries.

Poster: Incorporating Malware Detection into Flash Translation Layer
Authors: Wen Xie, Niusen Chen, Bo Chen
Abstract: OS-level malware may compromise OS and obtain root privilege. Detecting this type of strong malware is challeng- ing, since it can easily hide its intrusion behaviors or even subvert the malware detection software (or malware detector). Having observed that flash storage devices have been used broadly by computing devices today, we propose to move the malware detector to the flash translation layer (FTL), located inside a flash storage device. Due to physical isolation provided by the FTL, the OS-level malware can neither subvert our malware detector, nor hide its access behaviors from our malware detector.

The 41st IEEE Symposium on Security and Privacy was sponsored by the IEEE Computer Society Technical Committee on Security and Privacy in cooperation with the International Association for Cryptologic Research. The Symposium was May 18-20, 2020, and the Security and Privacy Workshops were May 21, 2020.

Havens, Yazdanparast Publish Article in IEEE Transactions on Big Data

Timothy Havens

An article by Audrey Yazdanparast (2019, PhD, Electrical Engineering) and Dr. Timothy Havens, “Linear Time Community Detection by a Novel Modularity Gain Acceleration in Label Propagation,” has been accepted for publication in the journal, IEEE Transactions on Big Data.

The paper presents an efficient approach for detecting self-similar communities in weighted graphs, with applications in social network analysis, online commodity recommendation systems, user clustering, biology, communications network analysis, etc.

Paper Abstract: Community detection is an important problem in complex network analysis. Among numerous approaches for community detection, label propagation (LP) has attracted a lot of attention. LP selects the optimum community (i.e., label) of a network vertex by optimizing an objective function (e.g., Newman’s modularity) subject to the available labels in the vicinity of the vertex. In this paper, a novel analysis of Newman’s modularity gain with respect to label transitions in graphs is presented. Here, we propose a new form of Newman’s modularity gain calculation that quantifies available label transitions for any LP based community detection.

The proposed approach is called Modularity Gain Acceleration (MGA) and is simplified and divided into two components, the local and global sum-weights. The Local Sum-Weight (LSW) is the component with lower complexity and is calculated for each candidate label transition. The General Sum-Weight (GSW) is more computationally complex, and is calculated only once per each label. GSW is updated by leveraging a simple process for each node-label transition, instead of for all available labels. The MGA approach leads to significant efficiency improvements by reducing time consumption up to 85% relative to the original algorithms with the exact same quality in terms of modularity value which is highly valuable in analyses of big data sets.

Timothy Havens is director of Michigan Tech’s Institute of Computing and Cybersystems (ICC), the associate dean for research for the College of Computing , and the William and Gloria Jackson Associate Professor of Computer Systems.

View the article abstract here.

Article by Tim Havens in IEEE Transactions on Fuzzy Systems

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.

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

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.

Two Papers by Yakov Nekrich Accepted by SoCG 2020 Conference

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.