Computer Engineering Commons^™

Concolic Execution Of Nmap Scripts For Honeyfarm Generation, Zhe Li, Bo Chen, Wu-Chang Feng, Fei Xie

Computer Science Faculty Publications and Presentations

Attackers rely upon a vast array of tools for automating attacksagainst vulnerable servers and services. It is often the case thatwhen vulnerabilities are disclosed, scripts for detecting and exploit-ing them in tools such asNmapandMetasploitare released soonafter, leading to the immediate identification and compromise ofvulnerable systems. Honeypots, honeynets, tarpits, and other decep-tive techniques can be used to slow attackers down, however, such approaches have difficulty keeping up with the sheer number of vulnerabilities being discovered and attacking scripts that are being released. To address this issue, this paper describes an approach for applying concolic execution on attacking scripts in Nmap in …

Digitally Reporting Trail Obstructions In Forest Park, Colton S. Maybee

REU Final Reports

The inclusion of technology on the trail can lead to better experiences for everyone involved in the hobby. Hikers can play a more prominent role in the maintenance of the trails by being able to provide better reports of obstructions while directly on the trail. This paper goes into the project of revamping the obstruction report system applied at Forest Park in Portland, Oregon. Most of my contributions to the project focus on mobile app development with some research into path planning algorithms related to the continuations of this project.

Forest Park Trail Monitoring, Adan Robles, Colton S. Maybee, Erin Dougherty

REU Final Reports

Forest Park, one of the largest public parks in the United States with over 40 trails to pick from when planning a hiking trip. One of the main problems this park has is that there are too many trails, and a lot of the trails extend over 3 miles. Due to these circumstances’ trails are not checked frequently and hikers are forced to hike trails in the area with no warnings of potential hazards they can encounter. In this paper I researched how Forest Park currently monitors its trails and then set up a goal to solve the problem. We …

Multi-Agent Radiation Localization, Teresa Nguyen

REU Final Reports

Advancement of radiation detection technology is an ongoing process, and adjustments are made based on pre-existing conditions of radiation presence--both natural and man made. Tools that are currently used for safely detecting radiation in urban environments exist in several forms: drones, robots, or handheld radiation detection devices. This is a harm reductive way to explore radiation-infected environments while preserving human health as best as possible. In order for these autonomous platforms to successfully detect radiation sources, an algorithm needs to be created that is capable of gathering crucial data on its own with little to no human interference. Machine learning …

On The (Im)Practicality Of Adversarial Perturbation For Image Privacy, Arezoo Rajabi, Rakesh B. Bobba, Mike Rosulek, Charles Wright, Wu-Chi Feng

Computer Science Faculty Publications and Presentations

Image hosting platforms are a popular way to store and share images with family members and friends. However, such platforms typically have full access to images raising privacy concerns. These concerns are further exacerbated with the advent of Convolutional Neural Networks (CNNs) that can be trained on available images to automatically detect and recognize faces with high accuracy.

Recently, adversarial perturbations have been proposed as a potential defense against automated recognition and classification of images by CNNs. In this paper, we explore the practicality of adversarial perturbation based approaches as a privacy defense against automated face recognition. Specifically, we first …

Creating A 3d Printed Bipedal Robot’S Ankle And Foot With Human-Like Motion, Tylise E. Fitzgerald

Undergraduate Research & Mentoring Program

Humanoid robots are being created to replace humans in dangerous situations, assist overworked humans, and improve our quality of life by completing chores. However, current bipedal robots haven’t matched the performance of humans and are still impractical for commercial use.

One of the Agile and Adaptive Robotics Lab’s goals is to create a humanoid robot whose anatomy is similar to the human body. If this can be accomplished, we can have a functioning model of the human body that we can adjust to improve both humanoid robots’ functions and the functionality of our own human bodies. This specific project looks …

Material Parameter Estimation Of Thin Wafers With Terahertz Time-Domain Spectroscopy, Kirk R. Jungles

Undergraduate Research & Mentoring Program

Terahertz Time Domain Spectroscopy(THz TDS) is a spectroscopic technique that can be implemented to perform non destructive material parameter extraction on a variety of materials. Accuracy of these material parameters is often limited by statistical variation between measurements and insufficient knowledge of the thickness of the slabs being measured.

The goal of this project was to develop an in house procedure that would allow us to perform THz TDS on thin wafers using an up to date signal processing algorithm that would provide accurate predictions for the thickness of the wafers, reliable estimations of the wafer’s material parameters, and demonstration …

Omni-Gravity Hydroponics System For Spacecraft, Tara M. Prevo

Undergraduate Research & Mentoring Program

Effective omni-gravity hydroponics will allow astronauts to supplement nutrition and further close the life cycle of water in orbit, lunar, and Martian conditions. This project determines the operational limits of the test cells for the Plant Water Management Hydroponics mission. A scaled 1-g channel was designed by Rihana Mungin to mimic full-scale performance in microgravity that could be tested terrestrially. This project sought to find the limits of operation of the 1-g test cells and identify failure modes that could pose a safety risk in space. The cells were filled at increments of 20% and cycled from 0.184 to 8.33 …

Simulation Of Human Balance Control Using An Inverted Pendulum Model, Joshua E. Caneer

Undergraduate Research & Mentoring Program

The nervous system that human beings use to control balance is remarkably adaptable to a wide variety of environments and conditions. This neural system is likely a combination of many inputs and feedback control loops working together. The ability to emulate this system of balance could be of great value in understanding and developing solutions to proprioceptive disorders and other diseases that affect the human balance control system. Additionally, the process of emulating the human balance system may also have widespread applications to the locomotion capabilities of many types of robots, in both bipedal and non-bipedal configurations.

The goal of …

The Applications Of Grid Cells In Computer Vision, Keaton Kraiger

Undergraduate Research & Mentoring Program

In this study we present a novel method for position and scale invariant object representation based on a biologically-inspired framework. Grid cells are neurons in the entorhinal cortex whose multiple firing locations form a periodic triangular array, tiling the surface of an animal’s environment. We propose a model for simple object representation that maintains position and scale invariance, in which grid maps capture the fundamental structure and features of an object. The model provides a mechanism for identifying feature locations in a Cartesian plane and vectors between object features encoded by grid cells. It is shown that key object features …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods

Undergraduate Research & Mentoring Program

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

No-Reference Image Denoising Quality Assessment, Si Lu

Computer Science Faculty Publications and Presentations

A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a noreference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and …

Keyword-Based Patent Citation Prediction Via Information Theory, Farshad Madani, Martin Zwick, Tugrul U. Daim

Engineering and Technology Management Faculty Publications and Presentations

Patent citation shows how a technology impacts other inventions, so the number of patent citations (backward citations) is used in many technology prediction studies. Current prediction methods use patent citations, but since it may take a long time till a patent is cited by other inventors, identifying impactful patents based on their citations is not an effective way. The prediction method offered in this article predicts patent citations based on the content of patents. In this research, Reconstructability Analysis (RA), which is based on information theory and graph theory, is applied to predict patent citations based on keywords extracted from …

Combining Algorithms For More General Ai, Mark Robert Musil

Undergraduate Research & Mentoring Program

Two decades since the first convolutional neural network was introduced the AI sub-domains of classification, regression and prediction still rely heavily on a few ML architectures despite their flaws of being hungry for data, time, and high-end hardware while still lacking generality. In order to achieve more general intelligence that can perform one-shot learning, create internal representations, and recognize subtle patterns it is necessary to look for new ML system frameworks. Research on the interface between neuroscience and computational statistics/machine learning has suggested that combined algorithms may increase AI robustness in the same way that separate brain regions specialize. In …

Automating Knife-Edge Method Of Thz Beam Characterization, Christopher Charles Faber

Undergraduate Research & Mentoring Program

The goal of this project is to create a time and cost-effective solution for THz beam profiling.

The knife edge method of beam characterization is a technique to verify the intensity profile of a beam involving traveling a blade orthogonal to the beam path and measuring transmission in successive steps. We use a vector network analyzer (VNA) to measure S21 transmission from a THz source. Manual implementation of this method was time-consuming and inefficient.

Project hardware includes an Arduino, a motor shield, and a ball screw linear rail with stepper motor actuator. Software was created in LabView and data is …

Binder Free Graphene Hybridized Fe3o4 Nanoparticles For Supercapacitor Applications, Nathan D. Jansen

Undergraduate Research & Mentoring Program

In a world with increasing energy demands, the need for safe and mobile energy storage grows. There are a number of renewable energy sources that can be harvested, however peak demand and peak production times tend to not overlap. As the capabilities of collecting the energy grows so does the need to store the energy for later consumption. The two promising methods of storing energy are batteries or supercapacitors. Both technologies employ an electrode consisting of an active material bound to a current collector. This material participates in a redox reaction, storing charge electrochemically to later be used as energy, …

Factors Affecting Big Data Technology Adoption, Nayem Rahman

Student Research Symposium

With the advancement of computer science, hardware and software engineering, and computing power, and later with the advent of the internet, social networking tools and other sources such as sensors data growth has increased significantly. These data are called big data which are mostly unstructured, generated in large volumes, data need to be captured in near real-time. To handle big data a completely new set of tools and technologies are being emerged. I have studied big data literature to identify the factors that might influence big data adoption. I was able to list quite a few factors or attributes that …

High-Performance Computing For Drought Prediction, Henry Cooney

Undergraduate Research & Mentoring Program

In recent decades, there has been considerable interest in using satellite soil moisture data to examine the global water-energy cycle and manage water resources. Current satellites are limited in their sensing depth, and can only directly measure top soil layers. Using a particle filter, this data may be fused with the output of a hydrologic simulation to improve simulation results, and characterize a hydrologic system at the watershed level. However, this approach increases computational requirements dramatically, and requires rethinking to accommodate data scaling and achieve good performance.

We present a detailed performance study of several alternative implementations of the hybrid …

Computational Capacity And Energy Consumption Of Complex Resistive Switch Networks, Jens Bürger, Alireza Goudarzi, Darko Stefanovic, Christof Teuscher

Electrical and Computer Engineering Faculty Publications and Presentations

Resistive switches are a class of emerging nanoelectronics devices that exhibit a wide variety of switching characteristics closely resembling behaviors of biological synapses. Assembled into random networks, such resistive switches produce emerging behaviors far more complex than that of individual devices. This was previously demonstrated in simulations that exploit information processing within these random networks to solve tasks that require nonlinear computation as well as memory. Physical assemblies of such networks manifest complex spatial structures and basic processing capabilities often related to biologically-inspired computing. We model and simulate random resistive switch networks and analyze their computational capacities. We provide a …

System-Wide Performance Analysis For Virtualization, Deron Eugene Jensen

Dissertations and Theses

With the current trend in cloud computing and virtualization, more organizations are moving their systems from a physical host to a virtual server.

Although this can significantly reduce hardware, power, and administration costs, it can increase the cost of analyzing performance problems. With virtualization, there is an initial performance overhead, and as more virtual machines are added to a physical host the interference increases between various guest machines. When this interference occurs, a virtualized guest application may not perform as expected. There is little or no information to the virtual OS about the interference, and the current performance tools in …

Building A Better Computer For The Aging Generation, Gretchen Baird, Boryeon Kwon, Dylan Lasner, Parker Swensen, Zane Ross, Adrik Gurganus

PSU High School Innovation Challenge

The problem which we have ventured out to solve is that the aging community does not have easily accessible computers and technologies.

Optimal Network Topologies And Resource Mappings For Heterogeneous Networks-On-Chip, Haera Chung

Dissertations and Theses

Communication has become a bottleneck for modern microprocessors and multi-core chips because metal wires don't scale. The problem becomes worse as the number of components increases and chips become bigger. Traditional Systems-on-Chips (SoCs) interconnect architectures are based on shared-bus communication, which can carry only one communication transaction at a time. This limits the communication bandwidth and scalability. Networks-on-Chip (NoC) were proposed as a promising solution for designing large and complex SoCs. The NoC paradigm provides better scalability and reusability for future SoCs, however, long-distance multi-hop communication through traditional metal wires suffers from both high latency and power consumption. A radical …

Calibration Of Complex System Dynamics Models: A Practioner's Report, Rod Walker, Wayne W. Wakeland

Wayne W. Wakeland

This paper is not a typical academic paper that is solidly grounded in the literature. Instead, this paper reports practitioner’s experiences in rebuilding and calibrating a very large system dynamics model. A prior version of this model had been in use for over 10 years in an ongoing executive training simulation. That model had never worked correctly in several key areas, requiring the outputs to be manually adjusted by very experienced facilitators during the course of the simulation. The present project rebuilt the system dynamics model, redesigned the parts that weren’t working, and calibrated the resulting model to match the …

A System Dynamics Model Of Pharmaceutical Opioids: Medical Use, Diversion, And Nonmedical Use, Teresa D. Schmidt, Wayne W. Wakeland, J. David Haddox

Wayne W. Wakeland

Abstract: A dramatic rise in the nonmedical of pharmaceutical opioids has presented the United States with a substantial public health problem. Nonmedical use of prescription pain relievers has become increasingly prevalent in the US over the last two decades, and diversion of medicines obtained by prescription is assumed to be a major source of supply for nonmedical opioid use. Policymakers striving to protect population health by ameliorating the adverse outcomes of nonmedical use of opioid analgesics could benefit from a systems-level model which reflects the complexity of the system and incorporates the full range of available data. To address this …

Calibration Of Complex System Dynamics Models: A Practioner's Report, Rod Walker, Wayne Wakeland

Systems Science Faculty Publications and Presentations

This paper is not a typical academic paper that is solidly grounded in the literature. Instead, this paper reports practitioner’s experiences in rebuilding and calibrating a very large system dynamics model. A prior version of this model had been in use for over 10 years in an ongoing executive training simulation. That model had never worked correctly in several key areas, requiring the outputs to be manually adjusted by very experienced facilitators during the course of the simulation. The present project rebuilt the system dynamics model, redesigned the parts that weren’t working, and calibrated the resulting model to match the …

A System Dynamics Model Of Pharmaceutical Opioids: Medical Use, Diversion, And Nonmedical Use, Teresa Schmidt, Wayne Wakeland, J. David Haddox

Systems Science Faculty Publications and Presentations

A dramatic rise in the nonmedical of pharmaceutical opioids has presented the United States with a substantial public health problem. Nonmedical use of prescription pain relievers has become increasingly prevalent in the US over the last two decades, and diversion of medicines obtained by prescription is assumed to be a major source of supply for nonmedical opioid use. Policymakers striving to protect population health by ameliorating the adverse outcomes of nonmedical use of opioid analgesics could benefit from a systems-level model which reflects the complexity of the system and incorporates the full range of available data. To address this need, …

Resizable, Scalable, Concurrent Hash Tables, Josh Triplett, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

We present algorithms for shrinking and expanding a hash table while allowing concurrent, wait-free, linearly scalable lookups. These resize algorithms allow the hash table to maintain constant-time performance as the number of entries grows, and reclaim memory as the number of entries decreases, without delaying or disrupting readers.

We implemented our algorithms in the Linux kernel, to test their performance and scalability. Benchmarks show lookup scalability improved 125x over readerwriter locking, and 56% over the current state-of-the-art for Linux, with no performance degradation for lookups during a resize.

To achieve this performance, this hash table implementation uses a new concurrent …

Resizable, Scalable, Concurrent Hash Tables Via Relativistic Programming, Josh Triplett, Paul E. Mckenney, Jonathan Walpole

Computer Science Faculty Publications and Presentations

Presentation focusing on software synchronization, thread locking, transactional memory, and relativistic programming. Hash table algorithms are presented with examples of relativistic list insertion and removal, and related data structures. Existing approaches are compared to new methodologies and future work with relativistic data structures.

Efficient Support Of Consistent Cyclic Search With Read-Copy-Update And Parallel Updates, Jonathan Walpole, Paul E. Mckenney

Computer Science Faculty Publications and Presentations

A method, system and computer program product for supporting concurrent updates to a shared data element group while preserving group integrity on behalf of one or more readers that are concurrently referencing group data elements without using locks or atomic instructions. Two or more updaters may be invoked to generate new group data elements. Each new data element created by the same up dater is assigned a new generation number that is different than a global generation number associated with the data element group and which allows a reader of the data element group to determine whether the new data …

A Relativistic Enhancement To Software Transactional Memory, Philip William Howard, Jonathan Walpole

Computer Science Faculty Publications and Presentations

Relativistic Programming is a technique that allows low overhead, linearly-scalable concurrent reads. It also allows joint access parallelism between readers and a writer. Unfortunately, it has so far been limited to a single writer so it does not scale on the write side. Software Transactional Memory (STM) is a technique that allows programs to take advantage of disjoint access parallelism on both the read-side and write-side. Unfortunately, STM systems have a higher overhead than many other synchronization mechanisms so although STM scales, STM starts from a lower baseline. We propose combining relativistic programming and software transactional memory in a way …