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“Efficient Way of Converting outdated Allen Bradley PLC-5 System into Modern ControlLogix 5000 suit”, by Spencer Thompson (pictured), Larry Stambeck, Andy Posa, Sergeyev, and Lecturer Paniz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.
Professor Thomas Oommen (DataS, GMES, EPSSI) is the principal investigator on a one-year project that has been awarded a $41K research and development contract with the University of Nebraska-Omaha.
The project is titled “Flood Hazard Map to Water Management & Planning.”
Oommen’s research focuses on developing improved susceptibility characterization and documentation of geo-hazards (e.g. earthquakes, landslides) and spatial modeling of georesource (e.g. mineral deposits) over a range of spatial scales and data types.
To achieve his research interests, he has adopted an inter-disciplinary research approach joining aerial/satellite based remote sensing for obtaining data, and artificial intelligence/machine learning based methods for data processing and modeling.
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.
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 College of Computing is pleased to announce that it has awarded five faculty seed grants, which will provide immediate funding in support of research projects addressing critical needs during the current global pandemic.
Tim Havens, College of Computing associate dean for research, said that the faculty seed grants will enable progress in new research that has the potential to make an impact on the current research. Additional details will be shared soon.
Congratulations to the winning teams!
Guy Hembroff (AC, HI): “Development of a Novel Hospital Use Resource Prediction Model to Improve Local Community Pandemic Disaster Planning”
Bo Chen (CS): “Mobile Devices Can Help Mitigate Spreading of Coronavirus”
Nathir Rawashdeh (AC, MERET): “A Tele-Operated Mobile Robot for Sterilizing Indoor Space Using UV Light” (A special thanks to Paul Williams, who’s generous gift to support AI and robotics research made this grant possible)
Weihua Zhou(AC, HI) andJinshan Tang (AC, MERET): “KD4COVID19: An Open Research Platform Using Feature Engineering and Machine Learning for Knowledge Discovery and Risk Stratification of COVID-19″
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.
The U.S. Department of Defense, Office of Naval Research, has awarded Michigan Tech faculty researchers a $249,000 grant that supports the creation of an ROTC undergraduate science and engineering research program at Michigan Tech. The primary goal of the program is to supply prepared cadets to all military branches to serve as officers in Cyber commands.
The principal investigator (PI) of the project is Andrew Barnard, Mechanical Engineering-Engineering Mechanics. Co-PIs are Timothy Havens, College of Computing; Laura Brown , Computer Science, and Yu Cai, Applied Computing. The title of the project is, “Defending the Nation’s Digital Frontier: Cybersecurity Training for Tomorrow’s Officers.”
The curriculum will be developed over the summer, and instruction associated with the award will begin in the fall 2020 semester. Cadets interested in joining the new program are urged to contact Andrew Barnard.
Initially, the program will focus on topics in cybersecurity, machine learning and artificial intelligence, data science, and remote sensing systems, all critical to the The Naval Science and Technology (S&T) Strategic Plan and the Navy’s Force of the Future, and with equal relevance in all branches of the armed forces.
The plan of work focuses on on engaging ROTC students in current and on-going Cyber research, and supports recruitment of young ROTC engineers and scientists to serve in Navy cybersecurity and cyber-systems commands. The program will compel cadets to seek positions within Cyber commands upon graduation, or pursue graduate research in Cyber fields.
“Our approach develops paid, research-based instruction for ROTC students through the existing Michigan Tech Strategic Education Naval Systems Experiences (SENSE) program,” said principal investigator Andrew Barnard, “ROTC students will receive one academic year of instruction in four Cyber domains: cybersecurity, machine learning and artificial intelligence (ML/AI), data science, and remote sensing systems.”
Barnard says the cohort-based program will enrich student learning through deep shared research experiences. He says the program will be designed with flexibility and agility in mind to quickly adapt to new and emerging Navy science and technology needs in the Cyber domain.
Placement of officers in Cyber commands is of critical long-term importance to the Navy (and other DoD branches) in maintaining technological superiority, says the award abstract, noting that technological superiority directly influences the capability and safety of the warfighter.
Also closely involved in the project are Michigan Tech Air Force and Army ROTC officers Lt. Col. John O’Kane and LTC Christian Thompson, respectively.
“Unfortunately, many ROTC cadets are either unaware of Cyber related careers, or are unprepared for problems facing Cyber officers,” said Lt. Col. O’Kane. “This proposal aims to provide a steady flow of highly motivated and trained uniformed officers to the armed-services, capable of supporting the warfighter on day-one.”
Andrew Barnard is director of Michigan Tech’s Great Lakes Research Center, an associate professor of Mechanical Engineering-Engineering Mechanics, and faculty advisor to the SENSE Enterprise.
Tim Havens is director of the Institute of Computing and Cybersystems, associate dean for research, College of Computing, and the William and Gloria Jackson Associate Professor of Computer Systems.
Laura Brown is an associate professor, Computer Science, director of the Data Science graduate program, and a member of the ICC’s Center for Data Sciences.
Yu Cai is a professor of Applied Computing, an affiliated professor of Computational Science and Engineering, a member of the ICC’s Center for Cybersecurity, and faculty advisor for the Red Team, which competes in the National Cyber League (NCL).
The Great Lakes Research Center (GLRC) provides state-of-the-art laboratories to support research on a broad array of topics. Faculty members from many departments across Michigan Technological University’s campus collaborate on interdisciplinary research, ranging from air–water interactions to biogeochemistry to food web relationships.
The Army and Air Force have active ROTC programs on Michigan Tech’s campus.
The Office of Naval Research (ONR) coordinates, executes, and promotes the science and technology programs of the United States Navy and Marine Corps.
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).
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.