Category: Published

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.

Abstract:
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.

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.

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.

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.

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.

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.