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Drivers’ Response To Scenarios When Driving Connected And Automated Vehicles Compared To Vehicles With And Without Driver Assist Technology, Srinivas S. Pulugurtha, Raghuveer Gouribhatla Jan 2022

Drivers’ Response To Scenarios When Driving Connected And Automated Vehicles Compared To Vehicles With And Without Driver Assist Technology, Srinivas S. Pulugurtha, Raghuveer Gouribhatla

Mineta Transportation Institute

Traffic related crashes cause more than 38,000 fatalities every year in the United States. They are the leading cause of death among drivers up to 54 years in age and incur $871 million in losses each year. Driver errors contribute to about 94% of these crashes. In response, automotive companies have been developing vehicles with advanced driver assistance systems (ADAS) that aid in various driving tasks. These features are aimed at enhancing safety by either warning drivers of a potential hazard or picking up certain driving maneuvers like maintaining the lane. These features are already part of vehicles with Driver …

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Taming The Data In The Internet Of Vehicles, Shahab Tayeb Jan 2022

Taming The Data In The Internet Of Vehicles, Shahab Tayeb

Mineta Transportation Institute

As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and feature selection/reduction. These pre-processing techniques play an important role in training a neural network to optimize …

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Can Californian Households Save Money On Transportation Costs By Living In Transit-Oriented Developments (Tods)?, Hongwei Dong Jan 2022

Can Californian Households Save Money On Transportation Costs By Living In Transit-Oriented Developments (Tods)?, Hongwei Dong

Mineta Transportation Institute

Many residents in large Californian metropolitan areas are heavily burdened by housing costs. Advocates, researchers, and elected officials in California are debating whether transit-oriented development (TOD) could be an effective tool to mitigate the housing affordability problem by increasing housing supply and reducing transportation costs in transit-rich neighborhoods. This study contributes to this debate by estimating how much Californian families can save on transportation costs by living in transit-oriented developments (TODs). By utilizing the confidential version of the 2010–2012 California Household Travel Survey, this study evaluates the impact of TOD on household transportation expenditures by comparing TOD households with two …

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“Don't Downvote A$$$$$$S!!”: An Exploration Of Reddit's Advice Communities, Emily Cannon, Bianca Crouse, Souvick Ghosh, Nicholas Rihn, Kristen Chua Jan 2022

“Don't Downvote A$$$$$$S!!”: An Exploration Of Reddit's Advice Communities, Emily Cannon, Bianca Crouse, Souvick Ghosh, Nicholas Rihn, Kristen Chua

Faculty Research, Scholarly, and Creative Activity

Advice forums are a crowdsourced way to reinforce cultural norms and moral behavior. Sites like Reddit contain massive amounts of natural language human interaction, with rules and norms unique to each individual subreddit community. To explore this data, we created a dataset with top 1000 posts from each of two such forums, r/AmItheAsshole and r/relationships, and extracted natural language features including sentiment, similarity, word frequency, and demographics using both algorithmic and manual methods. Further, we developed a method to extract demographic information from the subreddits, examined how the post authors' self-disclosures reflect the unique communities in which their posts are …

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Tracking The Functions Of Ai As Paradata & Pursuing Archival Accountability, Jeremy Davet, Babak Hamidzadeh, Patricia Franks, Jenny Bunn Jan 2022

Tracking The Functions Of Ai As Paradata & Pursuing Archival Accountability, Jeremy Davet, Babak Hamidzadeh, Patricia Franks, Jenny Bunn

Faculty Research, Scholarly, and Creative Activity

While a familiar term in fields like social science research and digital cultural heritage, 'paradata' has not yet been introduced conceptually into the archival realm. In response to an increasing number of experiments with machine learning and artificial intelligence, the InterPARES Trust AI research group proposes the definition of paradata as 'information about the procedure(s) and tools used to create and process information resources, along with information about the persons carrying out those procedures.' The utilization of this concept in archives can help to ensure that AI-driven systems are designed from the outset to honor the archival ethic, and to …

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The Integer-Antimagic Spectra Of A Disjoint Union Of Hamiltonian Graphs, Uğur Odabaşi, Dan Roberts, Richard M. Low Jan 2022

The Integer-Antimagic Spectra Of A Disjoint Union Of Hamiltonian Graphs, Uğur Odabaşi, Dan Roberts, Richard M. Low

Faculty Research, Scholarly, and Creative Activity

Let A be a nontrivial abelian group. A simple graph G = (V,E) is A-antimagic, if there exists an edge labeling f: E(G) → A\{0} such that the induced vertex labeling (Formula Presented) is a one-to-one map. The integer-antimagic spectrum of a graph G is the set IAM(G) = {k: G is Zk-antimagic and k ≥ 2}. In this paper, we determine the integer-antimagic spectra for a disjoint union of Hamiltonian graphs.

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Antisocial Personality Disorder: A Theoretical Approach Utilizing Three Dimensions Of Power Analysis, Timothy J. Nguyen, Tara Zolnikov Jan 2022

Antisocial Personality Disorder: A Theoretical Approach Utilizing Three Dimensions Of Power Analysis, Timothy J. Nguyen, Tara Zolnikov

Faculty Research, Scholarly, and Creative Activity

Antisocial Personality Disorder (ASPD) is often associated with high costs and burden to the rest of society; however, it remains a disorder with no evidence of effective traditional psychological interventions. This theoretical study aimed to analyze the phenomenon of power and how it may influence the conceptualization of ASPD. Data was collected through a comprehensive and systematic review of the current ASPD literature. Findings suggested ASPD as severely conceptually flawed, highly imbalanced in terms of power dynamics between professionals and people diagnosed with ASPD, absent of participants’ voices, absent of any effective traditional psychotherapy interventions, disproportionately focused on deficit-oriented strategies …

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Spanish-Speaking Caregivers’ Use Of Referential Labels With Toddlers Is A Better Predictor Of Later Vocabulary Than Their Use Of Referential Gestures, Janet Y. Bang, Manuel Bohn, Joel Ramírez, Virginia A. Marchman, Anne Fernald Jan 2022

Spanish-Speaking Caregivers’ Use Of Referential Labels With Toddlers Is A Better Predictor Of Later Vocabulary Than Their Use Of Referential Gestures, Janet Y. Bang, Manuel Bohn, Joel Ramírez, Virginia A. Marchman, Anne Fernald

Faculty Research, Scholarly, and Creative Activity

Variation in how frequently caregivers engage with their children is associated with variation in children's later language outcomes. One explanation for this link is that caregivers use both verbal behaviors, such as labels, and non-verbal behaviors, such as gestures, to help children establish reference to objects or events in the world. However, few studies have directly explored whether language outcomes are more strongly associated with referential behaviors that are expressed verbally, such as labels, or non-verbally, such as gestures, or whether both are equally predictive. Here, we observed caregivers from 42 Spanish-speaking families in the US engage with their 18-month-old …

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Impact Of Location Spoofing Attacks On Performance Prediction In Mobile Networks, Nikhil Sai Kanuri, Sang Yoon Chang, Younghee Park, Jonghyun Kim, Jinoh Kim Jan 2022

Impact Of Location Spoofing Attacks On Performance Prediction In Mobile Networks, Nikhil Sai Kanuri, Sang Yoon Chang, Younghee Park, Jonghyun Kim, Jinoh Kim

Faculty Research, Scholarly, and Creative Activity

Performance prediction in wireless mobile networks is essential for diverse purposes in network management and operation. Particularly, the position of mobile devices is crucial to estimating the performance in the mobile communication setting. With its importance, this paper investigates mobile communication performance based on the coordinate information of mobile devices. We analyze a recent 5G data collection and examine the feasibility of location-based performance prediction. As location information is key to performance prediction, the basic assumption of making a relevant prediction is the correctness of the coordinate information of devices given. With its criticality, this paper also investigates the impact …

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Robustness Of Image-Based Malware Analysis, Katrina Tran, Fabio Di Troia, Mark Stamp Jan 2022

Robustness Of Image-Based Malware Analysis, Katrina Tran, Fabio Di Troia, Mark Stamp

Faculty Research, Scholarly, and Creative Activity

In previous work, “gist descriptor” features extracted from images have been used in malware classification problems and have shown promising results. In this research, we determine whether gist descriptors are robust with respect to malware obfuscation techniques, as compared to Convolutional Neural Networks (CNN) trained directly on malware images. Using the Python Image Library (PIL), we create images from malware executables and from malware that we obfuscate. We conduct experiments to compare classifying these images with a CNN as opposed to extracting the gist descriptor features from these images to use in classification. For the gist descriptors, we consider a …

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A Blockchain-Based Tamper-Resistant Logging Framework, Thomas H. Austin, Fabio Di Troia Jan 2022

A Blockchain-Based Tamper-Resistant Logging Framework, Thomas H. Austin, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

Since its introduction in Bitcoin, the blockchain has proven to be a versatile data structure. In its role as an immutable ledger, it has grown beyond its initial use in financial transactions to be used in recording a wide variety of other useful information. In this paper, we explore the application of the blockchain outside of its traditional decentralized, financial domain. We show how, even with only a single “mining” node, a proof-of-work blockchain can be the cornerstone of a tamper resistant logging framework. By attaching a proof-of-work to blocks of logging messages, we make it increasingly difficult for an …

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Twitter Bots’ Detection With Benford’S Law And Machine Learning, Sanmesh Bhosale, Fabio Di Troia Jan 2022

Twitter Bots’ Detection With Benford’S Law And Machine Learning, Sanmesh Bhosale, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

Online Social Networks (OSNs) have grown exponentially in terms of active users and have now become an influential factor in the formation of public opinions. For this reason, the use of bots and botnets for spreading misinformation on OSNs has become a widespread concern. Identifying bots and botnets on Twitter can require complex statistical methods to score a profile based on multiple features. Benford’s Law, or the Law of Anomalous Numbers, states that, in any naturally occurring sequence of numbers, the First Significant Leading Digit (FSLD) frequency follows a particular pattern such that they are unevenly distributed and reducing. This …

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Fake Malware Generation Using Hmm And Gan, Harshit Trehan, Fabio Di Troia Jan 2022

Fake Malware Generation Using Hmm And Gan, Harshit Trehan, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

In the past decade, the number of malware attacks have grown considerably and, more importantly, evolved. Many researchers have successfully integrated state-of-the-art machine learning techniques to combat this ever present and rising threat to information security. However, the lack of enough data to appropriately train these machine learning models is one big challenge that is still present. Generative modelling has proven to be very efficient at generating image-like synthesized data that can match the actual data distribution. In this paper, we aim to generate malware samples as opcode sequences and attempt to differentiate them from the real ones with the …

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Crowdfunding Non-Fungible Tokens On The Blockchain, Sean Basu, Kimaya Basu, Thomas H. Austin Jan 2022

Crowdfunding Non-Fungible Tokens On The Blockchain, Sean Basu, Kimaya Basu, Thomas H. Austin

Faculty Research, Scholarly, and Creative Activity

Non-fungible tokens (NFTs) have been used as a way of rewarding content creators. Artists publish their works on the blockchain as NFTs, which they can then sell. The buyer of an NFT then holds ownership of a unique digital asset, which can be resold in much the same way that real-world art collectors might trade paintings. However, while a deal of effort has been spent on selling works of art on the blockchain, very little attention has been paid to using the blockchain as a means of fundraising to help finance the artist’s work in the first place. Additionally, while …

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Characterizing The Feedback That Learning Assistants Give To Faculty, Sadhana Indukuri, Gina M. Quan Jan 2022

Characterizing The Feedback That Learning Assistants Give To Faculty, Sadhana Indukuri, Gina M. Quan

Faculty Research, Scholarly, and Creative Activity

Learning assistants are undergraduate peer educators that help facilitate learning in a university classroom environment. Jardine (2019) found that learning assistant feedback to faculty roughly fell into three categories: course logistics, student behavior, and student understanding. We built from this previous work by further characterizing the feedback given to faculty by learning assistants and found the following categories: student experience, classroom content, classroom structure, accessibility, empathy, and broad feedback. Using interview data with learning assistants and faculty working with learning assistants, we created a preliminary framework for the types of feedback and examples by learning assistants. This framework may be …

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Leveraging Hispanic-Serving Institutions Within Physics Education Research, Brianne Gutmann, Rebecca Rosenblatt Jan 2022

Leveraging Hispanic-Serving Institutions Within Physics Education Research, Brianne Gutmann, Rebecca Rosenblatt

Faculty Research, Scholarly, and Creative Activity

Hispanic serving institutions (HSIs) are an increasingly large set of higher education institutions in the United States. From 2010-2020 the number of HSIs increased from 311 to 569. Within the physics education research (PER) community, research perspectives from HSIs have provided critical insights into how to support racially and ethnically diverse students. Within the last decade, research from HSIs made up approximately 20% of publications from US higher education institutions in common PER journals. These publications do not always fully name and leverage their HSI context, though the work done at HSIs still more consistently centers students’ identities as compared …

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Abortion Attempts Without Clinical Supervision Among Transgender, Nonbinary And Gender-Expansive People In The United States, Heidi Moseson, Laura Fix, Caitlin Gerdts, Sachiko Ragosta, Jen Hastings, Ari Stoeffler, Eli A. Goldberg, Mitchell R. Lunn, Annesa Flentje, Matthew R. Capriotti, Micah E. Lubensky, Juno Obedin-Maliver Jan 2022

Abortion Attempts Without Clinical Supervision Among Transgender, Nonbinary And Gender-Expansive People In The United States, Heidi Moseson, Laura Fix, Caitlin Gerdts, Sachiko Ragosta, Jen Hastings, Ari Stoeffler, Eli A. Goldberg, Mitchell R. Lunn, Annesa Flentje, Matthew R. Capriotti, Micah E. Lubensky, Juno Obedin-Maliver

Faculty Research, Scholarly, and Creative Activity

Background Transgender, nonbinary and gender-expansive (TGE) people face barriers to abortion care and may consider abortion without clinical supervision. Methods In 2019, we recruited participants for an online survey about sexual and reproductive health. Eligible participants were TGE people assigned female or intersex at birth, 18 years and older, from across the United States, and recruited through The PRIDE Study or via online and in-person postings. Results Of 1694 TGE participants, 76 people (36% of those ever pregnant) reported considering trying to end a pregnancy on their own without clinical supervision, and a subset of these (n=40; 19% of those …

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Design, Construction And Operation Of The Protodune-Sp Liquid Argon Tpc, A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, D. Adams, M. Adinolfi, A. Aduszkiewicz, J. Aguilar, Z. Ahmad, J. Ahmed, B. Ali-Mohammadzadeh, T. Alion, K. Allison, S. Alonso Monsalve, M. Alrashed, Athanasios Hatzikoutelis, For Full Author List, See Comments Below Jan 2022

Design, Construction And Operation Of The Protodune-Sp Liquid Argon Tpc, A. Abed Abud, B. Abi, R. Acciarri, M. A. Acero, M. R. Adames, G. Adamov, D. Adams, M. Adinolfi, A. Aduszkiewicz, J. Aguilar, Z. Ahmad, J. Ahmed, B. Ali-Mohammadzadeh, T. Alion, K. Allison, S. Alonso Monsalve, M. Alrashed, Athanasios Hatzikoutelis, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful …

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A Blockchain-Based Retribution Mechanism For Collaborative Intrusion Detection, Wenjun Fan, Shubham Kumar, Sang Yoon Chang, Younghee Park Jan 2022

A Blockchain-Based Retribution Mechanism For Collaborative Intrusion Detection, Wenjun Fan, Shubham Kumar, Sang Yoon Chang, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Collaborative intrusion detection approach uses the shared detection signature between the collaborative participants to facilitate coordinated defense. In the context of collaborative intrusion detection system (CIDS), however, there is no research focusing on the efficiency of the shared detection signature. The inefficient detection signature costs not only the IDS resource but also the process of the peer-to-peer (P2P) network. In this paper, we therefore propose a blockchain-based retribution mechanism, which aims to incentivize the participants to contribute to verifying the efficiency of the detection signature in terms of certain distributed consensus. We implement a prototype using Ethereum blockchain, which instantiates …

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Visual Task Classification Using Classic Machine Learning And Cnns, Devangi Vilas Chinchankarame, Noha Elfiky, Nada Attar Jan 2022

Visual Task Classification Using Classic Machine Learning And Cnns, Devangi Vilas Chinchankarame, Noha Elfiky, Nada Attar

Faculty Research, Scholarly, and Creative Activity

Our eyes actively perform tasks including, but not limited to, searching, comparing, and counting. This includes tasks in front of a computer, whether it be trivial activities like reading email, or video gaming, or more serious activities like drone management, or flight simulation. Understanding what type of visual task is being performed is important to develop intelligent user interfaces. In this work, we investigated standard machine and deep learning methods to identify the task type using eye-tracking data-including both raw numerical data and the visual representations of the user gaze scan paths and pupil size. To this end, we experimented …

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Leveraging Initial Cognitive Load To Predict User Response To Complex Visual Tasks, Reem Albaghli, Yaman Jandali, Sarah Almahmid, Nada Attar Jan 2022

Leveraging Initial Cognitive Load To Predict User Response To Complex Visual Tasks, Reem Albaghli, Yaman Jandali, Sarah Almahmid, Nada Attar

Faculty Research, Scholarly, and Creative Activity

In this study, we were able to show how cognitive load measurement during the initiation of a complex visual task can predict user response. We measured cognitive load using pupil size and microsaccade rate. The initial phase of task was defined as the first 25 percent of the trial Reaction Time (RT), which was variable up to 50 seconds. The complex visual task entailed a set of twelve words that could be grouped based into 1, 2, or 3 categories or sets, e.g., the words bed, pillow, headboard, etc. can be grouped into a single set that is bedroom. We …

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Constructing Integer-Magic Graphs Via The Combinatorial Nullstellensatz, Richard M. Low, Dan Roberts Jan 2022

Constructing Integer-Magic Graphs Via The Combinatorial Nullstellensatz, Richard M. Low, Dan Roberts

Faculty Research, Scholarly, and Creative Activity

Let A be a nontrivial additive abelian group and A* = A \ {0}. A graph is A-magic if there exists an edge labeling f using elements of A∗ which induces a constant vertex labeling of the graph. Such a labeling f is called an A-magic labeling and the constant value of the induced vertex labeling is called an A-magic value. In this paper, we use the Combinatorial Nullstellensatz to construct nontrivial classes of Zp-magic graphs, prime p ≥ 3. For these graphs, some lower bounds on the number of distinct Zp-magic labelings are also established.

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“We Are About Life-Changing Research”: Community Partner Perspectives On Community-Engaged Research Collaborations, Rebecca A. London, Ronald David Glass, Ethan Chang, Sheeva Sabati, Saugher Nojan Jan 2022

“We Are About Life-Changing Research”: Community Partner Perspectives On Community-Engaged Research Collaborations, Rebecca A. London, Ronald David Glass, Ethan Chang, Sheeva Sabati, Saugher Nojan

Faculty Research, Scholarly, and Creative Activity

This study examines the ethics and politics of knowledge across 15 distinctive community-engaged research projects. We focus our analysis on interviews with community partners and consider their perceptions of research, academic research partners, motivations for partnering, and the benefits and challenges of community-engaged research. We highlight three themes: Community partners’ (1) motivations to know better and more systematically what they already know, (2) interests in legitimating community-based knowledge (i.e., knowledge produced beyond the academy), and (3) efforts to navigate often inflexible university timelines and budgetary processes. Our findings highlight concerns at various ethical, political, and epistemic intersections and connect to …

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Verification Of Nand Flash Controller, M. J. Prajwala, Kush Desai, Lili He Jan 2022

Verification Of Nand Flash Controller, M. J. Prajwala, Kush Desai, Lili He

Faculty Research, Scholarly, and Creative Activity

NAND structure-based Flash memory-made SSDs are turning more popular than HDDs in several applications notably in consumer microelectronics devices and enterprise memory storage devices. The NAND controller is the heart of this system. It acts as a bridge for communication between the host system (usually the Personal Computer system) and the NAND Flash memory storage device. Unarguably, it can be said that it is an essential part of the NAND flash-based memory device and it helps to manage the directory containing file systems. The controller is also accountable for managing system features like wear levelling, error code correction (through ECC), …

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Presence 5 For Racial Justice Workshop: Fostering Dialogue Across Medical Education To Disrupt Anti-Black Racism In Clinical Encounters, Megha Shankar, Kelsey Henderson, Raquel Garcia, Gabrielle Li, Ke Andrea Titer, Rhonda Graves Acholonu, Utibe R. Essien, Cati Brown-Johnson, Joy Cox, Jonathan G. Shaw, Marie Christine Haverfield, Kenji Taylor, Sonoo Thadaney Israni, Donna Zulman Jan 2022

Presence 5 For Racial Justice Workshop: Fostering Dialogue Across Medical Education To Disrupt Anti-Black Racism In Clinical Encounters, Megha Shankar, Kelsey Henderson, Raquel Garcia, Gabrielle Li, Ke Andrea Titer, Rhonda Graves Acholonu, Utibe R. Essien, Cati Brown-Johnson, Joy Cox, Jonathan G. Shaw, Marie Christine Haverfield, Kenji Taylor, Sonoo Thadaney Israni, Donna Zulman

Faculty Research, Scholarly, and Creative Activity

Introduction: Anti-Black racism has strong roots in American health care and medical education. While curricula on social determinants of health are increasingly common in medical training, curricula directly addressing anti-Black racism are limited. Existing frameworks like the Presence 5 framework for humanism in medicine can be adapted to develop a novel workshop that promotes anti-racism communication. Methods: We performed a literature review of anti-racism collections and categorized anti-racism communication practices using the Presence 5 framework to develop the Presence 5 for Racial Justice Workshop. Implementation included an introductory didactic, a small-group discussion, and a large-group debrief. Participants evaluated the workshop …

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Spartan Face Mask Detection And Facial Recognition System, Ziwei Song, Kristie Nguyen, Tien Nguyen, Catherine Cho, Jerry Gao Jan 2022

Spartan Face Mask Detection And Facial Recognition System, Ziwei Song, Kristie Nguyen, Tien Nguyen, Catherine Cho, Jerry Gao

Faculty Research, Scholarly, and Creative Activity

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, the Spartan Face Detection …

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Predicting Cervical Cancer Biopsy Results Using Demographic And Epidemiological Parameters: A Custom Stacked Ensemble Machine Learning Approach, Krishnaraj Chadaga, Srikanth Prabhu, Niranjana Sampathila, Rajagopala Chadaga, S. Swathi, Saptarshi Sengupta Jan 2022

Predicting Cervical Cancer Biopsy Results Using Demographic And Epidemiological Parameters: A Custom Stacked Ensemble Machine Learning Approach, Krishnaraj Chadaga, Srikanth Prabhu, Niranjana Sampathila, Rajagopala Chadaga, S. Swathi, Saptarshi Sengupta

Faculty Research, Scholarly, and Creative Activity

The human papillomavirus (HPV) is responsible for most cervical cancer cases worldwide. This gynecological carcinoma causes many deaths, even though it can be treated by removing malignant tissues at a preliminary stage. In many developing countries, patients do not undertake medical examinations due to the lack of awareness, hospital resources and high testing costs. Hence, it is vital to design a computer aided diagnostic method which can screen cervical cancer patients. In this research, we predict the probability risk of contracting this deadly disease using a custom stacked ensemble machine learning approach. The technique combines the results of several machine …

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Malview: Interactive Visual Analytics For Comprehending Malware Behavior, Huyen N. Nguyen, Faranak Abri, Vung Pham, Moitrayee Chatterjee, Akbar Siami Namin, Tommy Dang Jan 2022

Malview: Interactive Visual Analytics For Comprehending Malware Behavior, Huyen N. Nguyen, Faranak Abri, Vung Pham, Moitrayee Chatterjee, Akbar Siami Namin, Tommy Dang

Faculty Research, Scholarly, and Creative Activity

Malicious applications are usually comprehended through two major techniques, namely static and dynamic analyses. Through static analysis, a given malicious program is parsed, and some representative artifacts (e.g., control-flow graphs) are produced without any execution; whereas, the given malicious application needs to be executed when conducting dynamic analysis. These two mainstream techniques for analyzing the given software are effective in detecting certain classes of malware. More specifically, through static analysis, the patterns and signature of the malware are exposed, helping in detecting any known malicious payload hidden in or injected into the code. On the other hand, behavioral and run-time …

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Eocene Dike Orientations Across The Washington Cascades In Response To A Major Strike-Slip Faulting Episode And Ridge-Trench Interaction, Robert B. Miller, Kathleen I. Bryant, Brigid Doran, Michael P. Eddy, Franco P. Raviola, Nicholas Sylva, Paul J. Umhoefer Jan 2022

Eocene Dike Orientations Across The Washington Cascades In Response To A Major Strike-Slip Faulting Episode And Ridge-Trench Interaction, Robert B. Miller, Kathleen I. Bryant, Brigid Doran, Michael P. Eddy, Franco P. Raviola, Nicholas Sylva, Paul J. Umhoefer

Faculty Research, Scholarly, and Creative Activity

The northern Cascade Mountains in Washington (USA) preserve an exceptional shallow to mid-crustal record of Eocene transtension marked by dextral strike-slip faulting, intrusion of dike swarms and plutons, rapid non-marine sedimentation, and ductile flow and rapid cooling in parts of the North Cascades crystalline core. Transtension occurred during ridge-trench interaction with the formation of a slab window, and slab rollback and break-off occurred shortly after collision of the Siletzia oceanic plateau at ca. 50 Ma. Dike swarms intruded a >1250 km2 region between ca. 49.3 Ma and 44.9 Ma, and orientations of more than 1500 measured dikes coupled with geochronologic …

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An Overview Of Carbon Footprint Mitigation Strategies. Machine Learning For Societal Improvement, Modernization, And Progress, Vishnu S. Pendyala, Saritha Podali Jan 2022

An Overview Of Carbon Footprint Mitigation Strategies. Machine Learning For Societal Improvement, Modernization, And Progress, Vishnu S. Pendyala, Saritha Podali

Faculty Research, Scholarly, and Creative Activity

Among the most pressing issues in the world today is the impact of globalization and energy consumption on the environment. Despite the growing regulatory framework to prevent ecological degradation, sustainability continues to be a problem. Machine learning can help with the transition toward a net-zero carbon society. Substantial work has been done in this direction. Changing electrical systems, transportation, buildings, industry, and land use are all necessary to reduce greenhouse gas emissions. Considering the carbon footprint aspect of sustainability, this chapter provides a detailed overview of how machine learning can be applied to forge a path to ecological sustainability in …

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