Computer Sciences Commons^™

Automated Vehicle Electronic Control Unit (Ecu) Sensor Location Using Feature-Vector Based Comparisons, Gregory S. Buthker

Browse all Theses and Dissertations

In the growing world of cybersecurity, being able to map and analyze how software and hardware interact is key to understanding and protecting critical embedded systems like the Engine Control Unit (ECU). The aim of our research is to use our understanding of the ECU's control flow attained through manual analysis to automatically map and identify sensor functions found within the ECU. We seek to do this by generating unique sets of feature vectors for every function within the binary file of a car ECU, and then using those feature sets to locate functions within each binary similar to their …

Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh

Electrical and Computer Engineering Publications

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based …

Toward An Understanding Of How Post-Deployment User-Developer Interactions Influence System Utilization, Colleen Carraher Wolverton Phd

Department of Management

Although initial adoption of an information system has been shown to influence system success, further value can be obtained when end-users move beyond adoption, utilizing more features of the system and integrating it into their work routines. Organizations can increase the post-deployment utilization of their systems by emphasizing continued interaction between developers and end-users. In this study, we develop a research model investigating the influence of shared understanding, faithfulness of appropriation, and consensus on spirit on post-deployment system utilization. Using a sample from a healthcare organization, we show that increased end-user post-deployment interaction with developers supports a shared understanding between …

Automated Trading Systems Statistical And Machine Learning Methods And Hardware Implementation: A Survey, Boming Huang, Yuziang Huan, Li Da Xu, Lirong Zheng, Zhuo Zou

Information Technology & Decision Sciences Faculty Publications

Automated trading, which is also known as algorithmic trading, is a method of using a predesigned computer program to submit a large number of trading orders to an exchange. It is substantially a real-time decision-making system which is under the scope of Enterprise Information System (EIS). With the rapid development of telecommunication and computer technology, the mechanisms underlying automated trading systems have become increasingly diversified. Considerable effort has been exerted by both academia and trading firms towards mining potential factors that may generate significantly higher profits. In this paper, we review studies on trading systems built using various methods and …

Experimental Investigation On The Effects Of Website Aesthetics On User Performance In Different Virtual Tasks, Meinald T. Thielsch, Russell Haines, Leonie Flacke

Information Technology & Decision Sciences Faculty Publications

In Human-Computer Interaction research, the positive effect of aesthetics on users' subjective impressions and reactions is well-accepted. However, results regarding the influence of interface aesthetics on a user's individual performance as an objective outcome are very mixed, yet of urgent interest due to the proceeding of digitalization. In this web-based experiment (N = 331), the effect of interface aesthetics on individual performance considering three different types of tasks (search, creative, and transfer tasks) is investigated. The tasks were presented on an either aesthetic or unaesthetic website, which differed significantly in subjective aesthetics. Goal orientation (learning versus performance goals) was included …

Software Defined Networks Based Smart Grid Communication: A Comprehensive Survey, Mubashir Husain Rehmani, Alan Davy, Brendan Jennings, Chadi Assi

Publications

The current power grid is no longer a feasible solution due to ever-increasing user demand of electricity, old infrastructure, and reliability issues and thus require transformation to a better grid a.k.a., smart grid (SG). The key features that distinguish SG from the conventional electrical power grid are its capability to perform two-way communication, demand side management, and real time pricing. Despite all these advantages that SG will bring, there are certain issues which are specific to SG communication system. For instance, network management of current SG systems is complex, time consuming, and done manually. Moreover, SG communication (SGC) system is …

Usability Engineering Of A Privacy-Aware Compliance Tracking System, Parameswara Reddy Annapureddy

ETD Archive

Software is useful when it is able to provide useful information to the end user with minimum effort. This thesis is about usability improvements to a privacy-aware human motion tracking system for healthcare professionals. The original system has a number of usability issues: (1) Users need to wear a smartwatch, which will be used to connect to the system; (2) Data are stored in XML, comma-separated-value format which is very difficult to analyze; (3) Data are available only at the local computer and there is no easy way to access them remotely via a Web or mobile interface; (4) Analysis …

Monitoring Social Media Using Machine Learning, Joseph Jinn, Keith Vanderlinden

Summer Research

Our research is an extension of prior work by CSIRO - Commonwealth Scientific and Industrial Research Organization, Australia’s national research laboratory. Our focus is on utilizing Twitter data, Tweets, as a dataset by which we measure the SLO - Social License to Operate - of various mining, gas, and oil companies. SLO is defined as the acceptability of a company’s business operations by its employees, stakeholders, and the general public. The primary purpose of the summer 2019 research project is to investigate and find a methodology by which we can effectively model the topics of all the Tweets in our …

Parameter Assignment And Schedulability Analysis For Real-Time Multiframe Task Systems, Bo Peng

Wayne State University Dissertations

Schedulability analysis has been considered as one of the most important subjects in real-time systems. Schedulability analysis decides whether all tasks work correctly and safely in a system. For example, the schedulability analysis of an Air Traffic Control (ATC) system should ensure that all airplanes do not have conflicts on departure lanes and are scheduled on time. In a modern car system, it has been shown that there are more than one hundred engine control units (ECUs), and more than twenty million lines of code in a typical modern car [19]. The scheduling of such complex systems is required to …

Data Driven Approach To Characterize And Forecast The Impact Of Freeway Work Zones On Mobility Using Probe Vehicle Data, Mohsen Kamyab

Wayne State University Dissertations

The presence of work zones on freeways causes traffic congestion and creates hazardous conditions for commuters and construction workers. Traffic congestion resulting from work zones causes negative impacts on traffic mobility (delay), the environment (vehicle emissions), and safety when stopped or slowed vehicles become vulnerable to rear-end collisions. Addressing these concerns, a data-driven approach was utilized to develop methodologies to measure, predict, and characterize the impact work zones have on Michigan interstates. This study used probe vehicle data, collected from GPS devices in vehicles, as the primary source for mobility data. This data was used to fulfill three objectives: develop …

Random Linear Network Coding Simulations, R.J. Pereira-Castillo

Undergraduate Research Posters 2019

Network coding is a widely studied theoretical networking scheme with the potential for improving digital communications. The scheme allows a network node to combine information from multiple edges onto a single edge. Message symbols are represented as elements in a finite field of the form 2n. In a broadcast scenario, network coding achieves information transmission rates at the network's mincut max-flow bound. This potential for improving network efficiency has motivated researchers to consider the practical implications of the scheme.

Empathi: An Ontology For Emergency Managing And Planning About Hazard Crisis, Manas Gaur, Kaeedeh Shekarpour, Amelia Gyrard, Amit P. Sheth

Kno.e.sis Publications

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people.
A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts describing the domain of emergency managing and planning of hazard crises.
Although empathi has a coarse-grained view, it considers the necessary …

Augmenting Flight Imagery From Aerial Refueling, James D. Anderson, Scott Nykl, Thomas Wischgoll

Computer Science and Engineering Faculty Publications

© 2019, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply. When collecting real-world imagery, objects in the scene may be occluded by other objects from the perspective of the camera. However, in some circumstances an occluding object is absent from the scene either for practical reasons or the situation renders it infeasible. Utilizing augmented reality techniques, those images can be altered to examine the affect of the object’s occlusion. This project details a novel method for augmenting real images with virtual objects in a virtual environment. Specifically, images from …

Towards An Open And Scalable Music Metadata Layer, Thomas Hardjono, George Howard, Eric Scace, Mizan Chowdury, Lucas Novak, Meghan Gaudet, Justin Anderson, Nicole D'Avis, Christopher Kulis, Edward Sweeney, Chandler Vaughan

Faculty Works

One of the significant issues in the music supply chain today is the lack of consistent, complete and authoritative information or metadata regarding the creation of a given musical work. In many cases multiple entities in the music supply chain have each created their own version of the metadata for a musical work, often by manually re-entering the same information or through scraping data from other sites. In such cases, the effort to synchronize or to correct the information becomes manually laborious and error-prone. Furthermore, confidential information regarding the legal ownership of the musical work is often commingled in the …

High Concentrations Naturally Lead To Fuzzy-Type Interactions And To Gravitational Wave Bursts, Oscar Galindo, Olga Kosheleva, Vladik Kreinovich

Departmental Technical Reports (CS)

Fuzzy logic is normally used to describe the uncertainty of human knowledge and human reasoning. Physical phenomena are usually described by probabilistic models. In this paper, we show that in extremal conditions, when the concentrations are very large, some formulas describing physical interactions become fuzzy-type. We also show the observable consequences of such fuzzy-type formulas: they lead to bursts of gravitational waves.

Forestsim: Spatially Explicit Agent-Based Modeling Of Non-Industrial Forest Owner Policies, R. Zupko, M. Rouleau

Michigan Tech Publications, Part 1

This paper describes ForestSim, an agent-based modeling (ABM) platform for forest management policy experimentation and bioenergy sustainability assessment. ForestSim integrates tools and techniques from biomass estimation, ABM, sustainability assessment, and forest-growth modeling to simulate the harvest activities of thousands of decentralized private forest owners responding to alternative forest management policies to determine the impacts on locally derived sustainability indicators. ForestSim is relatively easy to modify for those interested in exploring more nuanced aspects of non-industrial private forest owner decision-making, forest growth dynamics, forest management policy alternatives, and sustainability assessment criteria tailored to their own research design purposes or specific study …

Integrating Heuristics To Support Impact Analysis In Software Evolution, Yibin Wang

Wayne State University Dissertations

Iterative impact analysis (IIA) is a process that allows developers to estimate the impacted units of a software change. Starting from a single impacted unit, the developers inspect its interacting units via program dependencies to identify the ones that are also impacted, and this process continues iteratively. Experience has shown that developers often miss impacted units and inspect many irrelevant units.

In order to enhance IIA, first we put forward a new program representation that provides more precise dependencies for software change propagation. Our study showed that the precision of IIA was indeed improved using such a program representation while …

Web-Based Medical Data Visualization And Information Sharing Towards Application In Distributed Diagnosis, Qi Zhang

Faculty Publications - Information Technology

Network based medical data computing and collaborative visualization have been commonly used in remote medicine and distributed diagnosis, where visualizing 3D medical data on web browsers and sharing medical information on internet are critically important. However, due to the lack of efficient algorithms and compatible graphics hardware support, there are still some major technical challenges in web based medical data visualization and information exploration on internet. To address these practical issues, we created a new network based medical data rendering and information sharing system, where an Apache HTTP Server was applied to handle data information, and MySQL and PHP were …

Work-In-Progress Reports Submitted To The Library Of Congress As Part Of Digital Libraries, Intelligent Data Analytics, And Augmented Description, Chulwoo Pack, Yi Liu, Leen-Kiat Soh, Elizabeth Lorang

School of Computing: Technical Reports

This document includes work-in-progress reports submitted to the Library of Congress as part of the Aida digital libraries research team's work on Digital Libraries, Intelligent Data Analytics, and Augmented Description: A Demonstration Project. These work-in-progress reports provide a snapshot glimpse, as well as underlying rationale and decision-making, at various points in the development of the project and its machine learning explorations. Reports cover explorations on historic newspapers, minimally-processed manuscript collections, materials digitized from physical originals and those digitized from microform surrogates, and investigate challenges related to image segmentation and document zoning, classification, document image quality analysis, metadata generation, and more.

Statistical Analysis Of Tandem Queues With Markovian Passages In Porous Mediums, Gboyega David Adepoju

Theses, Dissertations and Capstones

Queuing theory is the mathematical study of queues or waiting lines. A queue is formed whenever the demand for service exceeds the capacity to provide service at that point in time. In this thesis, the birth-and-death process is used to model the movement of customers or units into and out of a network of queues in tandem. We start with the theoretical analysis of M/M/1 queues with Poisson arrival and exponential service time with first-come first-served (FCFS) discipline and one service station. We derive the global balance equation for each network. Using both the iterative and the probability generating function, …

High-Speed Video From Asynchronous Camera Array (Poster), Si Lu

Computer Science Faculty Publications and Presentations

Poster presented at: 2019 IEEE Winter Conference on Applications of Computer Vision (WACV)

Good Similar Patches For Image Denoising (Poster), Si Lu

Computer Science Faculty Publications and Presentations

Patch-based denoising algorithms like BM3D have achieved outstanding performance. An important idea for the success of these methods is to exploit the recurrence of similar patches in an input image to estimate the underlying image structures....

Context-Aware Synthesis For Video Frame Interpolation, Simon Niklaus, Feng Liu

Computer Science Faculty Publications and Presentations

Video frame interpolation algorithms typically estimate optical flow or its variations and then use it to guide the synthesis of an intermediate frame between two consecutive original frames. To handle challenges like occlusion, bidirectional flow between the two input frames is often estimated and used to warp and blend the input frames. However, how to effectively blend the two warped frames still remains a challenging problem. This paper presents a context-aware synthesis approach that warps not only the input frames but also their pixel-wise contextual information and uses them to interpolate a high-quality intermediate frame. Specifically, we first use a …

Stock Returns And Investor Sentiment: Textual Analysis And Social Media, Zachary Mcgurk, Adam Nowak, Joshua C. Hall

Economics Faculty Working Papers Series

The behavioral finance literature has found that investor sentiment has predictive ability for equity returns. This differs from standard finance theory, which provides no role for investor sentiment. We examine the relationship between investor sentiment and stock returns by employing textual analysis on social media posts. We find that our investor sentiment measure has a positive and significant effect on abnormal stock returns. These findings are consistent across a number of different models and specifications, providing further evidence against non-behavioral theories.

Android Application For Mnist Handwritten Digits Classification, Mina Gabriel

Project Topics and Ideas

Use Neural Network architecture to classify MNIST handwritten digits dataset, student/s should implement a phone application (Android) to demonstrate their work, application will then be published to the app store for other students and CISC faculty members for evaluation and feedback.

Regulation Of Artificial Intelligence In Selected Jurisdictions, Jenny Gesley, Tariq Ahmad, Edouardo Soares, Ruth Levush, Gustavo Guerra, James Martin, Kelly Buchanan, Laney Zhang, Sayuri Umeda, Astghik Grigoryan, Nicolas Boring, Elin Hofverberg, Clare Feikhert-Ahalt, Graciela Rodriguez-Ferrand, George Sadek, Hanibal Goitom

Copyright, Fair Use, Scholarly Communication, etc.

Comparative Summary

This report examines the emerging regulatory and policy landscape surrounding artificial intelligence (AI) in jurisdictions around the world and in the European Union (EU). In addition, a survey of international organizations describes the approach that United Nations (UN) agencies and regional organizations have taken towards AI. As the regulation of AI is still in its infancy, guidelines, ethics codes, and actions by and statements from governments and their agencies on AI are also addressed. While the country surveys look at various legal issues, including data protection and privacy, transparency, human oversight, surveillance, public administration and services, autonomous vehicles, …

Cyber Intrusion Detection By Using Deep Neural Networks With Attack-Sharing Loss, Boxiang Dong, Hui Wendy Wang, Aparna S. Varde, Dawei Li, Bharath K. Samanthula, Weifeng Sun, Liang Zhao

Department of Computer Science Faculty Scholarship and Creative Works

Cyber attacks pose crucial threats to computer system security, and put digital treasuries at excessive risks. This leads to an urgent call for an effective intrusion detection system that can identify the intrusion attacks with high accuracy. It is challenging to classify the intrusion events due to the wide variety of attacks. Furthermore, in a normal network environment, a majority of the connections are initiated by benign behaviors. The class imbalance issue in intrusion detection forces the classifier to be biased toward the majority/benign class, thus leave many attack incidents undetected. Spurred by the success of deep neural networks in …

Interpretable Distance Metric Learning For Handwritten Chinese Character Recognition, Boxiang Dong, Aparna S. Varde, Danilo Stevanovic, Jiayin Wang, Liang Zhao

Department of Computer Science Faculty Scholarship and Creative Works

Handwriting recognition is of crucial importance to both Human Computer Interaction (HCI) and paperwork digitization. In the general field of Optical Character Recognition (OCR), handwritten Chinese character recognition faces tremendous challenges due to the enormously large character sets and the amazing diversity of writing styles. Learning an appropriate distance metric to measure the difference between data inputs is the foundation of accurate handwritten character recognition. Existing distance metric learning approaches either produce unacceptable error rates, or provide little interpretability in the results. In this paper, we propose an interpretable distance metric learning approach for handwritten Chinese character recognition. The learned …

Approximate In-Memory Computing On Rerams, Salman Anwar Khokhar

Electronic Theses and Dissertations

Computing systems have seen tremendous growth over the past few decades in their capabilities, efficiency, and deployment use cases. This growth has been driven by progress in lithography techniques, improvement in synthesis tools, architectures and power management. However, there is a growing disparity between computing power and the demands on modern computing systems. The standard Von-Neuman architecture has separate data storage and data processing locations. Therefore, it suffers from a memory-processor communication bottleneck, which is commonly referred to as the 'memory wall'. The relatively slower progress in memory technology compared with processing units has continued to exacerbate the memory wall …

Correctness And Progress Verification Of Non-Blocking Programs, Christina Peterson

Electronic Theses and Dissertations

The progression of multi-core processors has inspired the development of concurrency libraries that guarantee safety and liveness properties of multiprocessor applications. The difficulty of reasoning about safety and liveness properties in a concurrent environment has led to the development of tools to verify that a concurrent data structure meets a correctness condition or progress guarantee. However, these tools possess shortcomings regarding the ability to verify a composition of data structure operations. Additionally, verification techniques for transactional memory evaluate correctness based on low-level read/write histories, which is not applicable to transactional data structures that use a high-level semantic conflict detection. In …