MS in Computer Science and Software Engineering

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/25560

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Graph-based Modeling and Simulation of Emergency Services Communication Systems
(2024-04-26) Martinez Jordan, Jardi; Stiber, Michael
Emergency Services Communication Systems (ESCS) are evolving intoInternet Protocol (IP)-based communication networks, promising enhancements to their function, availability, and resilience. This increase in complexity and cyber-attack surface demands a better understanding of these systems' breakdown dynamics under extreme circumstances. Existing ESCS research largely overlooks simulation and the little work that exists focuses primarily on specific cybersecurity threats and neglects critical factors such as the non-stationarity of call arrivals. This paper introduces a robust, adaptable graph-based simulation framework and essential mathematical models for ESCS simulation. The framework uses a graph representation of ESCS networks where each vertex is a communicating finite-state machine that exchanges messages along edges and whose behavior is governed by a discrete event queuing model. Call arrival burstiness and its connection to emergency incidents are modeled through a cluster point process. The model applicability is demonstrated through simulations of the Seattle Police Department ESCS. Ongoing work is developing GPU implementations of these models and exploring the use of simulations in cybersecurity tabletop exercises.
Identifying and Addressing the Gap Between How Students and Professionals Read Code
(2024-04-26) Woerner, Matthew Stephen; Socha, David
This project investigated and addressed the questions of: a) how do students and professional software developers read novel codebases, and b) how can we help students learn to better read code. Our Spring 2023 study, seen in Appendix A, used semi-structured interviews and code reading exercises to identify and quantify several differences in the ways students and professional software developers read novel codebases. Students tended to face more difficulty with these reading tasks than the professionals due to an apparent lack of structured code reading process and an over reliance on making unverified assumptions about the code. We focused on three particular anti-patterns. Our interview data also indicated that the lack of a structured code reading process complicates transitioning into a professional atmosphere post degree, requiring new professional software developers to learn these skills on the job. Based upon the results, we developed a module to teach students a structured way to read code in novel codebases, and to assess their improvement. The module was integrated into the Fall 2023 quarter of CSS 390 (Software Engineering Studio). Students worked their way through a variety of formative exercises leading up to a final summative assessment where they were evaluated on their performance improvement throughout the module as well as how they compared to a prior group of students given a similar assessment in the Spring quarter. Comparing the number of code reading anti-patterns exhibited by both groups, we found that the students who completed the module were much less likely to trace into files outside of the code path, were more likely to follow all stack traces in a code reading challenge, and were less likely to make uncorrected misinterpretations about a codebase.
A System for Secure and Categorized Video-Sharing
(2024-02-12) Prakasam, Neil; Lagesse, Brent
Online video sharing is a phenomenon which continues to be increasingly utilized by the entirepopulation. Preserving the privacy of videos shared online is of utmost importance, but there is one use case that hasn’t yet been covered by current mainstream video sharing platforms. The goal of this project was to provide the ability to categorize whether multiple videos are of the same event, so that users can share them only amongst others who were also present at the event and have video evidence. The main method of categorization will be through DNA sequencing, where video files will be converted into literal dna in order to be categorized into 4 categories. This includes those that are of the same event, space, activity, or are completely different videos. The research has shown rather lackluster results that could potentially be further optimized to categorize videos between the 4 categories, let alone whether or not they are of the same event. This paper will introduce and implement multiple methods of doing so, as well as set the stage for future exploration.
Technical and Clinical Approaches for Implementing a Vision Screening Tool
(2023-09-27) Kline-Sharpe, Cameron; Erdly, William
Detecting vision problems in children is a challenging task, especially in large populations. Thisis in part due to the difficulty of obtaining useful indications of vision problems which may cause a child to be sent to an eye doctor. Modern vision screening approaches, intended to solve this problem, are either hard to scale, expensive, or limited in applicability. The aim of this thesis was to continue the development of and clinically test a vision screening mobile application aimed at wide distribution among Washington state school nurses and determine future development and testing plans based on the results of those tests. The QuickCheck vision screening application was tested against the FrACT vision screening application and near vision screening cards using accuracy, specificity, sensitivity, and other statistical measures. Using QuickCheck’s best testing policy the application is currently able to detect subjects with any vision problems with an average sensitivity of 0.91 Â± 0.11, although performance by eye is much lower (average sensitivity of 0.83 Â± 0.06). Accuracy by subject is 0.95 Â± 0.07, and again average accuracy by eye is lower, at 0.84 Â± 0.09. Additionally, QuickCheck is worse at detecting distance vision problems (sensitivity 0.83 Â± 0.11) than near vision problems (sensitivity 0.875 Â± 0.10), although this is more than offset by a corresponding difficulty with detecting a lack of near vision problems as compared to distance vision problems. A modification plan for QuickCheck, including methods to decrease the application’s false negative rate for distance vision tests and decrease acuity test time by half was established. However, given the small sample size of the clinical test, further testing of the QuickCheck application is required to demonstrate its effectiveness to a degree allowing widespread distribution. In addition to gathering more data to confirm the results of this work, future research directions include an examination of how the suggested changes improve performance and how well QuickCheck can detect the vision problems of very young (kindergarten-aged) children.
Data Analysis for Detecting Intracranial Hemorrhage Using Ultrasound Tissue Pulsatility Imaging
(2023-08-14) Yeshlur, Nayana; Parsons, Erika
A Traumatic Brain Injury (TBI) is a type of injury that affects how the brain functions. TBI can lead to short-term problems or more long-term severe problems including various types of intracranial hemorrhage, some of which can even result in death. For this reason, finding ways of detecting intracranial hemorrhages early in patients can help to provide faster, more appropriate care, potentially improving patient outcomes. While CT and MRI are more traditional methods of diagnosing intracranial hemorrhage, they have certain drawbacks which ultrasound imaging can overcome. This work utilizes data collected from experiments on TBI patients using an ultrasound technique known as Tissue Pulsatility Imaging (TPI), specifically displacement data of brain and other tissues over the cardiac cycle. The aim of this research is to use such data to understand the differences between healthy brain displacement and brain displacement of TBI patients (with dangerous bleeding in their brain). In addition, we explore if and how the identification of the points of maximum and minimum displacement can be used to further aid in the identification of intracranial hemorrhage. The identification of these displacement points has emerged as a significant objective in this study, as they hold the potential to uncover crucial distinctions between states of wellness and illness. Furthermore, their utility in future research lies in assessing the consistency of these discoveries when applied to a broader dataset.
Real-Time Hatch Rendering
(2023-01-21) Wang, Luyao; Sung, Kelvin K.S.
University of Washington Abstract Real-Time Hatch Rendering Luyao Wang Chair of the Supervisory Committee:Dr. Kelvin Sung Computer Science and Software Engineering Hatching has been a common and popular artistic drawing style for centuries. In computer graphics rendering, hatching has been investigated as one of the many Non-Photorealistic Rendering solutions. However, existing hatch rendering solutions are typically based on simplistic illumination models, and real-time 3D hatch-rendered applications are rarely seen in interactive systems such as games and animations. This project studies the existing hatch rendering solutions, identifies the most appropriate one, develops a real-time hatch rendering system, and improves upon existing results in three areas: support general illumination and hatch tone computation related to observed artistic styles, unify spatial coherence support for Tonal Art Maps and mipmaps, and demonstrate support for animation. The existing hatch rendering solutions can be categorized into texture-based and primitive-based methods. These solutions can be derived in object or screen space. Based on our background research, we chose to examine the texture-based object-space method presented by Praun et al. The approach inherits the advantage of object-space temporal coherence. The object-space spatial incoherence is addressed by the introduction of the Tonal Art Map (TAM). The texture-based solution ensures that the rendering results resemble actual artists' drawings. The project investigated the solution proposed by Praun et al. based on two major components: TAM generation as an off-line pre-computation and real-time rendering via a Multi-Texture Blending shader. The TAM construction involves building a two-dimensional structure, vertically to address spatial coherence as projected object size changes and horizontally to capture hatch tone changes. This unique structure enables the support for smooth transitions during zoom and illumination changes. We have generalized the levels in the vertical dimension of a TAM to integrate with results from traditional mipmaps to allow customization based on spatial coherence requirements. Our TAM implementation also supports the changing of hatch styles such as 90-degree or 45-degree cross hatching. The Multi-Texture Blending shader reproduced the results from Praun et al. in real time. Our rendered results present objects with seamless hatch strokes and appear natural and resemble those of hand-drawn hatch artwork. Our implementation integrated and supported interactive manipulation of effects from general illumination models including specularity, light source types, variable hatch and object colors, and rendering of surface textures as cross hatch. Additionally, we investigated trade-offs between per-vertex and per-fragment tone computation and discovered that the smoothness in hatching can be better captured in the per-vertex computation with the lower sampling rate and interpolations. Finally, the novel integration of TAMs and traditional mipmaps allow customizable spatial coherence support which allows smooth hatch strokes and texture transitions in animations during object size and illumination changes.
Remote Onboarding for Software Engineers: From “Forming” to “Performing”
(2022-09-23) Syed, Abdullah; Socha, David
Onboarding is defined as the process when a new employee joins, learns about, integrates into and becomes a contributing member of a team. A successful onboarding is essential for moving a team from Forming to Performing stage. It helps increase the new hire’s job satisfaction, improve the team’s performance, and reduce turnovers (which bring the team back to the forming stage). With remote work being the new norm in software engineering, remote onboarding brings a unique set of challenges.In this project, I aim to identify the main challenges faced during remote onboarding for software engineers, specifically for role-specific onboarding that happens in the team domain, and provide recommendations on improving this onboarding process. To achieve these aims, I conducted a qualitative interview study and activity exercise with software engineers who have gone through remote onboarding. Nine interviews were conducted with software engineers ranging from junior software engineers to senior software engineers and software engineering managers. I analyzed these interviews to gain insights into factors affecting onboarding. From the interviews, I identified a hierarchy of needs, in which I classified the needs of the new hire into basic needs and needs required for excellence. Needs such as access to tools, clarity of tasks and knowledge were categorized as basic needs to do the work, whereas mentorship, relationship building, and collaboration transform the onboarding into an excellent experience. I then further linked these needs to 5 main themes that emerged during the interviews for having an effective onboarding: (i) having an effective onboarding buddy; (ii) the ability to create relationships with team members and other stakeholders; (iii) being provided with up to date and organized documentation and onboarding plan; (iv) the manager’s ability to listen and adapt to remote needs; and (v) a team culture which enables team members to communicate effectively and get unblocked quickly. Based on the interviews’ analysis together with insights from the literature, I developed checklists for recommended best practices for effective onboarding. A checklist was developed for each of the main onboarding stakeholders i.e., manager, onboarding buddy and new hire, along with a template of an onboarding plan. Using these checklists will help improve the effectiveness and consistency of remote onboarding for software engineering new hires.
Deep Learning Methods to Identify Human Cranium, Brain Ventricles, and Intracranial Hemorrhage Using Tissue Pulsatility Ultrasound Imaging
(2022-07-14) Phan, Nhut Minh; Parsons, Erika
Traumatic Brain Injury (TBI) is a serious medical condition when a person experiences trauma in the head, resulting in intracranial hemorrhage (bleeding) and potential deformation of head-enclosed anatomical structures. Detecting these abnormalities early is the key to saving lives and improving survival outcomes. Standard methods of detecting intracranial hemorrhage are Computed Tomography (CT) and Magnetic Resonant Imaging (MRI). However, they are not readily available on the battlefield and in low-income settings. A team of researchers from the University of Washington developed a novel ultrasound signal processing technique called Tissue Pulsatility Imaging (TPI) that operates on raw ultrasound data collected using a hand-held tablet-like ultrasound device. This research work aims to build segmentation deep-learning models that take the input TPI data and detect the skull, ventricles, and intracranial hemorrhage in a patient's head. We employed the U-Net architecture and four of its variants for this purpose. Results show that the proposed methods can segment the brain-enclosing skull and is relatively successful in ventricle detection, while more work is needed to produce a model that can reliably segment intracranial hemorrhage.
Human Cranium, Brain Ventricle and Blood Detection Using Machine Learning on Ultrasound Data
(2022-07-14) Thomas, William; Parsons, Erika
Any head related injury can be very serious and may be classified as a traumatic brain injury (TBI), which can be a result of intracranial hemorrhaging. TBI is one of the most common injuries in or around a battlefield, which can be caused by both direct and indirect impacts. While assessing a brain injury in a well-equipped hospital is typically a trivial task, the same cannot be said about a TBI assessment in a non-hospital environment. Typically, a computer tomography (CT) machine is used to diagnose TBI. However, this project demonstrates the use of ultrasound and how it can be used to predict where skull, ventricles, and bleeding occur. The Pulsatility Research Group at the University of Washington has conducted three years of data collection and research to create a procedure that diagnoses TBI in a field situation. In this paper, machine learning methodologies will be used to predict these CT derived features. The result of this research shows that with adequate data and collection methods skull, ventricles, and potentially blood can be detected while applying machine learning to ultrasound obtained data.
Emulated Autoencoder: A Time-Efficient Image Denoiser for Defense of Convolutional Neural Networks against Evasion Attacks
(2022-07-14) Le, Dat Tien; Lagesse, Brent
As Convolutional Neural Networks (CNN) have become essential to modern applications such as image classification on social networks or self-driving vehicles, evasion attacks targeting CNNs can lead to damage for users. Therefore, there has been a rising amount of research focusing on defending against evasion attacks. Image denoisers have been used to mitigate the impact of evasion attacks; however, there is not a sufficiently broad view of the use of image denoisers as adversarial defenses in image classification due to a lack of trade-off analysis. Thus, image denoisers' costs, including training time, image reconstruction time, and loss of benign F1 scores of CNN classifiers, are explored in this thesis. Additionally, Emulated Autoencoder (EAE), which is the proposed method of this thesis to optimize trade-offs for high volume classification tasks, is evaluated alongside state-of-the-art image denoisers in the gray-box and white-box threat models. EAE outperforms most image denoisers in both the gray-box and white-box threat models while drastically reducing training and image reconstruction time compared to the state-of-the-art denoisers. As a result, EAE is more appropriate for securing high-volume classification applications of images.
Demonstrating Software Reusability: Simulating Emergency Response Network Agility with a Graph-Based Simulator
(2021-10-29) Salvatore, Victoria; Stiber, Michael
This research validates the re-engineering of a neural network simulator to implement other graph-based scenarios. Most of the simulator’s components were abstracted to increase reusability and maintainability through strategic refactoring decisions. This paper demonstrates how the simulator, developed at the University of Washington Bothell, can be adapted for other graph-based problems. By separating the neurospecific components from the core architecture of the simulator, this research verifies its functionality as reusable software. The scenario used to test the new architecture is the resilience of the US’s Next-Generation 911 (NG-911) system in the face of a crisis. Existing research acknowledges that both neural networks and emergency response networks are complex networks that exhibit self-organizing behavior. Initial results from this small-scale test-case demonstrate that when a crisis destroys critical parts of emergency response infrastructure, NG-911 can reroute calls to keep communities connected with resources. This supports the conjecture that self-organizing patterns will emerge from the interconnected events of a full-scale network simulation. The success of this configuration provides evidence that the simulator can serve a broad spectrum of graph-based scenarios. Its growth potential is further substantiated by the simulator’s improved long-term maintainability and overall software quality.
AN USER PROGRAMMABLE SYSTEM AGNOSTIC REAL-TIME RAY TRACING FRAMEWORK
(2021-10-29) Leung, Cho Chark Joe; Sung, Kelvin
Ray tracing rendering is a well-understood rendering technique that can produce photorealistic images or complex visual effects. Until recently, customizing a ray tracing pipeline posed challenges because of the lack of infrastructure to program such a pipeline. Industries have been proposing their own ray tracing frameworks to address the customizability issue by supporting user programs integration. However, most of the existing solutions target proprietary platforms, which limits the access to ray tracing for the general public. In this project, we propose a platform-agnostic ray tracing framework that is based on the general programming capacity found in commodity graphical processing units (GPUs). The framework supports six types of customizable user programs, including custom geometries, shading, and lighting. The framework also provides the developers with utilities such as built-in acceleration structure, secondary ray generation, and material instancing. Our framework has been demonstrated that it is able to fulfill the common functional requirements found in existing commercial products without the platform limitations. We also identified tradeoffs in developing a flexible, cross-platform ray tracing framework running on GPUs. Our results provide insights into the future development of a similar ray tracing framework.
Method of Adding Color Information to Spatially-Enhanced, Bag-of-Visual-Words Models
(2021-08-26) Laurenson, Robert; Olson, Clark F
This thesis provides a late-fusing method, based on the HoNC (Histogram of Normalized Colors) descriptor, for combining color with shape in spatially-enhanced-BOVW models to improve predictive accuracy for image classification. The HoNC descriptor is a pure color descriptor that has several useful properties, including the ability to differentiate achromatic colors (e.g., white, grey, black), which are prevalent in real-world images, and to provide illumination intensity invariance. The method is distinguishable from prior late-fusing methods that utilize alternative descriptors, e.g., hue and color names descriptors, that are lacking with respect to one or both of these properties. The method is shown to boost the predictive accuracy by between about 1.9% - 3.2% for three different spatially-enhanced BOVW model types, selected for their suitability for real-time use cases, when tested against two datasets (i.e., Caltech101, Caltech256), across a range of vocabulary sizes. The method adds between about 150 – 190 mS to the models’ total inference time.
Enabling Deep Geometric Learning on Cryo-EM Maps Using Neural Representation
(2021-08-26) Ranno, Nathan; Si, Dong
Advances in imagery at atomic and near-atomic resolution, such as cryogenic electron microscopy (cryo-EM), have led to an influx of high resolution images of proteins and other macromolecular structures to data banks worldwide. Deep geometric learning is intriguing for use in structure segmentation, but the native voxel format of cryo-EM maps is unsuitable as input to such methods. We present a novel data format called the neural cryo-EM map that accurately parameterizes cryo-EM maps and provides native, spatially continuous density and gradient data to serve as the basis for a graph-based interpretation of cryo-EM maps. Density values interpolated using the non-linear neural cryo-EM format are more accurate than conventional tri-linear interpolation. Our graph-based interpretations of 115 experimental cryo-EM maps from 1.15 to 4.0 Angstrom resolution provide high coverage of the underlying amino acid residue locations, while accuracy of nodes is correlated with resolution. The nodes of graphs created from atomic resolution maps (higher than 1.6 Angstrom) provide greater than 99% residue coverage as well as 85% full atomic coverage with a mean of 0.19 Angstrom root mean squared deviation (RMSD). Other graphs have a mean 84% residue coverage with less specificity of the nodes due to experimental noise and differences of density context at lower resolutions. Graphs created from atomic resolution maps may serve as input to downstream deep geometric learning applications and may be generalized to transform any 3D grid-based data format into non-linear, continuous, and differentiable format.
Associate-Degree-Plan scheduling and Recommendation system for Virtual Academic Advisor system
(2021-08-26) Hariharan, Iswarya; Parsons, Erika
Community college students come from diverse backgrounds and experience levels. They begin their education path pursuing a degree in a major of their choice. Most students aim to get transferred to certain universities, an academic path that demands to fulfill specific requirements, which makes students eligible for the transfer. Academic advisors at community colleges help students in creating academic plans trying their best to incorporate students’ interests, life constraints, and background. Being a heavily manual process that demands experience and familiarity with the process, there is a clear need to automate this process.The Virtual Academic Advisor (VAA) system aims to address the problem of automating academic plan creation for community colleges. The VAA is a research project paired with the development of an interactive software system that supports creating and displaying academic plans based on the needs and preferences of students. Work previously done by various students, focused on automated recommendation of core courses for targeted majors. However, no research or development has been done to incorporate selection of elective-course choices when generating an academic plan, nor a clear strategy on how to integrate elective-recommendation with the VAA system has been outlined. Incorporating electives opens up a whole new research aspect of automated scheduling. Furthermore, elective-course selection is crucial for scheduling associate degrees plans. Associate degrees are offered by community colleges and students can earn such a degree before/without getting transferred to a university. In this thesis, we incorporate the logic and functionality of scheduling elective courses along with the core courses to generate associate degree schedules for the intended major and university of the student. We gather and collect the necessary data for the elective courses and test our scheduler for the associate degree schedules. This project also addresses the research and implementation necessary to generate alternative-schedule recommendations and its integration with the VAA system using APIs. This will assist students in exploring alternate academic paths.
AGENT-BASED COMPUTATIONAL GEOMETRY
(2021-08-26) Paronyan, Satine; Fukuda, Munehiro
The Multi-Agent Spatial Simulation (MASS) library is a parallel programming library that uses agent-based modeling (ABM) parallelization approach over a distributed cluster. The MASS library contains several applications solving computational geometry problems using ABM algorithms. This research aims to build additional four ABM algorithm-based applications: (1) range search, (2) point location, (3) largest empty circle, and (4) Euclidean shortest path. This research presents ABM solutions implemented with MASS library as well as divide and conquer (D&C) solutions to four problems implemented with big data parallelization platforms MapReduce and Spark. In this paper, we discuss design approaches used in solutions for the four problems. We present ABM and D&C algorithms with MASS, MapReduce, and Spark platforms. We provide a detailed analysis of programmability and execution performance metrics of ABM algorithm-based implementations with MASS against D&C algorithm-based versions with MapReduce and Spark. Results showed that the MASS library provides an intuitive approach to developing parallel solutions to computational geometry problems. We observed that ABM MASS solutions produce competitive performance results when performing computations in-memory over distributed structured datasets.
A Joint Model Provisioning and Request Dispatch Solution for Mobile Inference Serving at the Edge
(2021-08-26) Prasad, Anish Nagendra; Peng, Yang
With the advancement of machine learning (ML), a growing number of mobile clients rely onML inference for making time-sensitive and safety-critical decisions. Therefore, the demand for high-quality and low-latency inference services at the network edge has become the key to the modern intelligent society. This thesis proposes a novel solution that jointly provisions inference models and dispatches inference requests for reducing the latency of mobile inference serving on edge nodes. Unlike existing solutions that either direct inference requests to the nearest edge node or balance the workload between edge nodes, the solution we propose provisions each edge node with the optimal type and the number of inference serving instances under a holistic consideration of networking, computing, and memory resources. Mobile clients can thus utilize ML inference services on edge nodes that offer minimal inference serving latency. In this work, we implement the proposed solution using TensorFlow Serving and Kubernetes on a cluster of edge nodes, including Nvidia Jetson Nano and Jetson Xavier. We further demonstrate the proposed solution’s effectiveness in reducing the overall inference latency under various system parameters and practical system settings through simulation and testbed experiments, respectively.
Automated Vulnerability Prediction in Software Systems and Lightweight Identification of Design Patterns in Source Code
(2021-08-26) Poozhithara, Jeffy Jahfar; Asuncion, Hazeline
Software development companies put a heavy investment in fixing security vulnerabilities in their products after code development. This demands an automated mechanism to identify security vulnerabilities during and after software development. One approach is to include possible solutions like security design patterns during design. This reduces system-wide architectural changes required and enables efficient documentation and maintenance of the software systems. Further, identifying which design patterns already exist in source code can help maintenance engineers determine if new requirements can be satisfied. The current techniques for design pattern identification require either manually labeling training datasets or manually specifying rules or queries for each pattern. As part of this research, we took a two-pronged approach: 1. Pre-implementation: predict vulnerabilities before any source code is written, to increase awareness of possible risks while developing the system. 2. Post-implementation: check the source code to identify any missing security patterns, based on the identified vulnerabilities. For the first approach, we created a Keyword Extraction-based Vulnerability Identification System (KEVIS) that uses natural language processing techniques to extract keywords and n-grams from software documentation to predict security vulnerabilities in software systems. We analyzed the correlation of certain keywords and n-grams with the occurrence of various security vulnerabilities as well as the correlation between different vulnerabilities. Additionally, we analyzed the performance of classification algorithms (Logistic Regression, Support Vector Machines, K-Nearest Neighbors, Multi-level perception, and Random Forest) in the prediction. To enable the analysis, we also created a dataset by mapping over 200,000 vulnerability reports on the CVE website with technical/functional documentation of 3602 products. The preliminary analysis shows that the performance of KEVIS is comparable or better than the prediction using source code as well as other static analysis methods. For the second approach, we introduced PatternScout, a technique for automatically generating SPARQL queries by parsing UML diagrams of design patterns, ensuring that pattern characteristics are matched. We discuss key concepts and the design of PatternScout. Our results indicate that PatternScout can automatically generate queries for the three types of design patterns (i.e., creational, behavioral, structural), with accuracy that is comparable, or perform better than, existing techniques. Due to the difference in concepts used for both approaches and ease of explanation, the background, literature review, method, results, and discussions corresponding to each approach is discussed separately in their own sections (Approach 1 - Automated Vulnerability Prediction in Software Systems, and Approach 2 - Lightweight Identification of Design Patterns in Source Code, respectively).
Deciphering Protein Complex Structures from Cryo-electron Microscopy Maps using Deep Learning
(2020-10-26) Pfab, Jonas; Si, Dong
Information about macromolecular structure of protein complexes such as SARS-CoV-2, and related cellular and molecular mechanisms can assist the search for vaccines and drug development processes. To obtain such structural information, we present DeepTracer, a fully automatic deep learning-based method for fast de novo multi-chain protein complex structure determination from high-resolution cryo-electron microscopy (cryo-EM) density maps. We applied DeepTracer on a previously published set of 476 raw experimental density maps and compared the results with a current state of the art method. The residue coverage increased by over 30% using DeepTracer and the RMSD value improved from 1.29Ã to 1.18Ã . Additionally, we applied DeepTracer on a set of 62 coronavirus-related density maps, among them 10 with no deposited structure available in EMDataResource. We observed an average residue match of 84% with the deposited structures and an average RMSD of 0.93Ã . Additional tests with related methods further exemplify DeepTracer's competitive accuracy and efficiency of structure modeling. DeepTracer allows for exceptionally fast computations, making it possible to trace around 60,000 residues in 350 chains within only two hours. The web service is globally accessible at https://deeptracer.uw.edu.
A Study of Correlations Between Trait Affect and Phishing Susceptibility
(2020-10-26) Smith, Samantha Emily; Dupuis, Marc J
Although phishing emails have been in use for decades, these social engineering attacks are still prevalent because they keep working; in fact, they are a leading cause of data breaches. In this research, I attempt to discern how an individual’s trait affect levels are related to their susceptibility to clicking on links in phishing emails, with particular attention on how this relationship may vary based on the type of phishing email employed. Trait Affect is a term from psychology that references a subset of one’s disposition and tendency towards certain moods and emotions. Trait Affect is further broken down into positive affect and negative affect, which are largely independent. Positive Affect reflects one’s tendency to act, while Negative Affect reflects one’s tendency towards experiencing negative emotions. Trait Affect has been shown to influence user’s behaviors and risk perception. Additionally, it is generally stable over an individual’s lifetime, making it a useful metric with which to model behavior. Being able to model an individual’s behavior in response to phishing is important to lowering the rates of phishing. While the creation of such models is outside the scope of this paper, the relationships examined will prove useful in future attempts to model such behaviors. To obtain data as close to a real-world scenario as possible, phishing susceptibility was measured on a click-through basis of emails sent to participant’s personal emails. This process caused some difficulty in managing to make emails that were both compelling and capable of passing automated email filtering. The process was further complicated by legal concerns surrounding the real-world approach to phishing. It is important to note, however, that no user data was taken – all measurements were based around a user clicking on a link rather than entering any information.

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