Physical Sciences and Mathematics Commons^™

Piecing Together Performance: Collaborative, Participatory Research-Through-Design For Better Diversity In Games, Daniel L. Gardner, Louanne Boyd, Reginald T. Gardner

Engineering Faculty Articles and Research

Digital games are a multi-billion-dollar industry whose production and consumption extend globally. Representation in games is an increasingly important topic. As those who create and consume the medium grow ever more diverse, it is essential that player or user-experience research, usability, and any consideration of how people interface with their technology is exercised through inclusive and intersectional lenses. Previous research has identified how character configuration interfaces preface white-male defaults [39, 40, 67]. This study relies on 1-on-1 play-interviews where diverse participants attempt to create “themselves” in a series of games and on group design activities to explore how participants may …

Multi-Scale Attention Networks For Pavement Defect Detection, Junde Chen, Yuxin Wen, Yaser Ahangari Nanehkaran, Defu Zhang, Adan Zeb

Engineering Faculty Articles and Research

Pavement defects such as cracks, net cracks, and pit slots can cause potential traffic safety problems. The timely detection and identification play a key role in reducing the harm of various pavement defects. Particularly, the recent development in deep learning-based CNNs has shown competitive performance in image detection and classification. To detect pavement defects automatically and improve effects, a multi-scale mobile attention-based network, which we termed MANet, is proposed to perform the detection of pavement defects. The architecture of the encoder-decoder is used in MANet, where the encoder adopts the MobileNet as the backbone network to extract pavement defect features. …

Counterventions: A Reparative Reflection On Interventionist Hci, Rua Mae Williams, Louanne E. Boyd, Juan E. Gilbert

Engineering Faculty Articles and Research

Research in HCI applied to clinical interventions relies on normative assumptions about which bodies and minds are healthy, valuable, and desirable. To disrupt this normalizing drive in HCI, we define a “counterventional approach” to intervention technology design informed by critical scholarship and community perspectives. This approach is meant to unsettle normative assumptions of intervention as urgent, necessary, and curative. We begin with a historical overview of intervention in HCI and its critics. Then, through reparative readings of past HCI projects in autism intervention, we illustrate the emergent principles of a counterventional approach and how it may manifest research outcomes that …

Para Cima Y Pa’ Abajo: Building Bridges Between Hci Research In Latin America And In The Global North, Pedro Reynolds-Cuéllar, Marisol Wong-Villacres, Karla A. Badillo-Urquiola, Mayra Donaji Barrera-Machuca, Franceli L. Cibrian, Marianela Ciolfi Felice, Carolina Fuentes, Laura Sanely Gaytán-Lugo, Vivian Genaro Motti, Monica Perusquía-Hernández, Oscar A. Lemus

Engineering Faculty Articles and Research

The Human-computer Interaction (HCI) community has the opportunity to foster the integration of research practices across the Global South and North to begin overcoming colonial relationships. In this paper, we focus on the case of Latin America (LATAM), where initiatives to increase the representation of HCI practitioners lack a consolidated understanding of the practices they employ, the factors that influence them, and the challenges that practitioners face. To address this knowledge gap, we employ a mixed-methods approach, comprising a survey (66 respondents) and in-depth interviews (19 interviewees). Our analyses characterize a set of research perspectives on how HCI is practiced …

Large-Scale Identification And Analysis Of Factors Impacting Simple Bug Resolution Times In Open Source Software Repositories, Elia Eiroa-Lledo, Rao Hamza Ali, Gabriela Pinto, Jillian Anderson, Erik Linstead

Engineering Faculty Articles and Research

One of the most prominent issues the ever-growing open-source software community faces is the abundance of buggy code. Well-established version control systems and repository hosting services such as GitHub and Maven provide a checks-and-balances structure to minimize the amount of buggy code introduced. Although these platforms are effective in mitigating the problem, it still remains. To further the efforts toward a more effective and quicker response to bugs, we must understand the factors that affect the time it takes to fix one. We apply a custom traversal algorithm to commits made for open source repositories to determine when “simple stupid …

Completeness Of Nominal Props, Samuel Balco, Alexander Kurz

Engineering Faculty Articles and Research

We introduce nominal string diagrams as string diagrams internal in the category of nominal sets. This leads us to define nominal PROPs and nominal monoidal theories. We show that the categories of ordinary PROPs and nominal PROPs are equivalent. This equivalence is then extended to symmetric monoidal theories and nominal monoidal theories, which allows us to transfer completeness results between ordinary and nominal calculi for string diagrams.

Towards Qos-Based Embedded Machine Learning, Tom Springer, Erik Linstead, Peiyi Zhao, Chelsea Parlett-Pelleriti

Engineering Faculty Articles and Research

Due to various breakthroughs and advancements in machine learning and computer architectures, machine learning models are beginning to proliferate through embedded platforms. Some of these machine learning models cover a range of applications including computer vision, speech recognition, healthcare efficiency, industrial IoT, robotics and many more. However, there is a critical limitation in implementing ML algorithms efficiently on embedded platforms: the computational and memory expense of many machine learning models can make them unsuitable in resource-constrained environments. Therefore, to efficiently implement these memory-intensive and computationally expensive algorithms in an embedded computing environment, innovative resource management techniques are required at the …

A Large-Scale Sentiment Analysis Of Tweets Pertaining To The 2020 Us Presidential Election, Rao Hamza Ali, Gabriela Pinto, Evelyn Lawrie, Erik J. Linstead

Engineering Faculty Articles and Research

We capture the public sentiment towards candidates in the 2020 US Presidential Elections, by analyzing 7.6 million tweets sent out between October 31st and November 9th, 2020. We apply a novel approach to first identify tweets and user accounts in our database that were later deleted or suspended from Twitter. This approach allows us to observe the sentiment held for each presidential candidate across various groups of users and tweets: accessible tweets and accounts, deleted tweets and accounts, and suspended or inaccessible tweets and accounts. We compare the sentiment scores calculated for these groups and provide key insights into the …

Machine Learning Based Medical Image Deepfake Detection: A Comparative Study, Siddharth Solaiyappan, Yuxin Wen

Engineering Faculty Articles and Research

Deep generative networks in recent years have reinforced the need for caution while consuming various modalities of digital information. One avenue of deepfake creation is aligned with injection and removal of tumors from medical scans. Failure to detect medical deepfakes can lead to large setbacks on hospital resources or even loss of life. This paper attempts to address the detection of such attacks with a structured case study. Specifically, we evaluate eight different machine learning algorithms, which include three conventional machine learning methods (Support Vector Machine, Random Forest, Decision Tree) and five deep learning models (DenseNet121, DenseNet201, ResNet50, ResNet101, VGG19) …

A Deep Learning-Based Approach To Extraction Of Filler Morphology In Sem Images With The Application Of Automated Quality Inspection, Md. Fashiar Rahman, Tzu-Liang Bill Tseng, Jianguo Wu, Yuxin Wen, Yirong Lin

Engineering Faculty Articles and Research

Automatic extraction of filler morphology (size, orientation, and spatial distribution) in Scanning Electron Microscopic (SEM) images is essential in many applications such as automatic quality inspection in composite manufacturing. Extraction of filler morphology greatly depends on accurate segmentation of fillers (fibers and particles), which is a challenging task due to the overlap of fibers and particles and their obscure presence in SEM images. Convolution Neural Networks (CNNs) have been shown to be very effective at object recognition in digital images. This paper proposes an automatic filler detection system in SEM images, utilizing a Mask Region-based CNN architecture. The proposed system …

Three Wave Mixing In Epsilon-Near-Zero Plasmonic Waveguides For Signal Regeneration, Nicholas Mirchandani, Mark C. Harrison

Engineering Faculty Articles and Research

Vast improvements in communications technology are possible if the conversion of digital information from optical to electric and back can be removed. Plasmonic devices offer one solution due to optical computing’s potential for increased bandwidth, which would enable increased throughput and enhanced security. Plasmonic devices have small footprints and interface with electronics easily, but these potential improvements are offset by the large device footprints of conventional signal regeneration schemes, since surface plasmon polaritons (SPPs) are incredibly lossy. As such, there is a need for novel regeneration schemes. The continuous, uniform, and unambiguous digital information encoding method is phase-shift-keying (PSK), so …

Applications Of Unsupervised Machine Learning In Autism Spectrum Disorder Research: A Review, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis R. Dixon, Erik J. Linstead

Engineering Faculty Articles and Research

Large amounts of autism spectrum disorder (ASD) data is created through hospitals, therapy centers, and mobile applications; however, much of this rich data does not have pre-existing classes or labels. Large amounts of data—both genetic and behavioral—that are collected as part of scientific studies or a part of treatment can provide a deeper, more nuanced insight into both diagnosis and treatment of ASD. This paper reviews 43 papers using unsupervised machine learning in ASD, including k-means clustering, hierarchical clustering, model-based clustering, and self-organizing maps. The aim of this review is to provide a survey of the current uses of …

Feel And Touch: A Haptic Mobile Game To Assess Tactile Processing, Ivonne Monarca, Monica Tentori, Franceli L. Cibrian

Engineering Faculty Articles and Research

Haptic interfaces have great potential for assessing the tactile processing of children with Autism Spectrum Disorder (ASD), an area that has been under-explored due to the lack of tools to assess it. Until now, haptic interfaces for children have mostly been used as a teaching or therapeutic tool, so there are still open questions about how they could be used to assess tactile processing of children with ASD. This article presents the design process that led to the development of Feel and Touch, a mobile game augmented with vibrotactile stimuli to assess tactile processing. Our feasibility evaluation, with 5 children …

Let's Read: Designing A Smart Display Application To Support Codas When Learning Spoken Language, Katie Rodeghiero, Yingying Yuki Chen, Annika M. Hettmann, Franceli L. Cibrian

Engineering Faculty Articles and Research

Hearing children of Deaf adults (CODAs) face many challenges including having difficulty learning spoken languages, experiencing social judgment, and encountering greater responsibilities at home. In this paper, we present a proposal for a smart display application called Let's Read that aims to support CODAs when learning spoken language. We conducted a qualitative analysis using online community content in English to develop the first version of the prototype. Then, we conducted a heuristic evaluation to improve the proposed prototype. As future work, we plan to use this prototype to conduct participatory design sessions with Deaf adults and CODAs to evaluate the …

Hierarchical Scheduling For Real-Time Periodic Tasks In Symmetric Multiprocessing, Tom Springer, Peiyi Zhao

Engineering Faculty Articles and Research

In this paper, we present a new hierarchical scheduling framework for periodic tasks in symmetric multiprocessor (SMP) platforms. Partitioned and global scheduling are the two main approaches used by SMP based systems where global scheduling is recommended for overall performance and partitioned scheduling is recommended for hard real-time performance. Our approach combines both the global and partitioned approaches of traditional SMP-based schedulers to provide hard real-time performance guarantees for critical tasks and improved response times for soft real-time tasks. Implemented as part of VxWorks, the results are confirmed using a real-time benchmark application, where response times were improved for soft …

On-Device Deep Learning Inference For System-On-Chip (Soc) Architectures, Tom Springer, Elia Eiroa-Lledo, Elizabeth Stevens, Erik Linstead

Engineering Faculty Articles and Research

As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can …

Exploring The Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, And Autism Quotient To Identify Eating Disorder Vulnerability: A Cluster Analysis, Natalia Stewart Rosenfield, Erik Linstead

Engineering Faculty Articles and Research

Eating disorders are very complicated and many factors play a role in their manifestation. Furthermore, due to the variability in diagnosis and symptoms, treatment for an eating disorder is unique to the individual. As a result, there are numerous assessment tools available, which range from brief survey questionnaires to in-depth interviews conducted by a professional. One of the many benefits to using machine learning is that it offers new insight into datasets that researchers may not previously have, particularly when compared to traditional statistical methods. The aim of this paper was to employ k-means clustering to explore the Eating Disorder …

A Fortran-Keras Deep Learning Bridge For Scientific Computing, Jordan Ott, Mike Pritchard, Natalie Best, Erik Linstead, Milan Curcic, Pierre Baldi

Engineering Faculty Articles and Research

Implementing artificial neural networks is commonly achieved via high-level programming languages such as Python and easy-to-use deep learning libraries such as Keras. These software libraries come preloaded with a variety of network architectures, provide autodifferentiation, and support GPUs for fast and efficient computation. As a result, a deep learning practitioner will favor training a neural network model in Python, where these tools are readily available. However, many large-scale scientific computation projects are written in Fortran, making it difficult to integrate with modern deep learning methods. To alleviate this problem, we introduce a software library, the Fortran-Keras Bridge (FKB). This two-way …

Exploring The Efficacy Of Transfer Learning In Mining Image‑Based Software Artifacts, Natalie Best, Jordan Ott, Erik J. Linstead

Engineering Faculty Articles and Research

Background

Transfer learning allows us to train deep architectures requiring a large number of learned parameters, even if the amount of available data is limited, by leveraging existing models previously trained for another task. In previous attempts to classify image-based software artifacts in the absence of big data, it was noted that standard off-the-shelf deep architectures such as VGG could not be utilized due to their large parameter space and therefore had to be replaced by customized architectures with fewer layers. This proves to be challenging to empirical software engineers who would like to make use of existing architectures without …

A Machine Learning Approach To Delineating Neighborhoods From Geocoded Appraisal Data, Rao Hamza Ali, Josh Graves, Stanley Wu, Jenny Lee, Erik Linstead

Engineering Faculty Articles and Research

Identification of neighborhoods is an important, financially-driven topic in real estate. It is known that the real estate industry uses ZIP (postal) codes and Census tracts as a source of land demarcation to categorize properties with respect to their price. These demarcated boundaries are static and are inflexible to the shift in the real estate market and fail to represent its dynamics, such as in the case of an up-and-coming residential project. Delineated neighborhoods are also used in socioeconomic and demographic analyses where statistics are computed at a neighborhood level. Current practices of delineating neighborhoods have mostly ignored the information …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Learning In The Machine: To Share Or Not To Share?, Jordan Ott, Erik Linstead, Nicholas Lahaye, Pierre Baldi

Engineering Faculty Articles and Research

Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization …

Vrsensory: Designing Inclusive Virtual Games With Neurodiverse Children, Ben Wasserman, Derek Prate, Bryce Purnell, Alex Muse, Kaitlyn Abdo, Kendra Day, Louanne Boyd

Engineering Faculty Articles and Research

We explore virtual environments and accompanying interaction styles to enable inclusive play. In designing games for three neurodiverse children, we explore how designing for sensory diversity can be understood through a formal game design framework. Our process reveals that by using sensory processing needs as requirements we can make sensory and social accessible play spaces. We contribute empirical findings for accommodating sensory differences for neurodiverse children in a way that supports inclusive play. Specifically, we detail the sensory driven design choices that not only support the enjoyability of the leisure activities, but that also support the social inclusion of sensory-diverse …

Low-Energy Acceleration Of Binarized Convolutional Neural Networks Using A Spin Hall Effect Based Logic-In-Memory Architecture, Ashkan Samiee, Payal Borulkar, Ronald F. Demara, Peiyi Zhao, Yu Bai

Engineering Faculty Articles and Research

Deep Learning (DL) offers the advantages of high accuracy performance at tasks such as image recognition, learning of complex intelligent behaviors, and large-scale information retrieval problems such as intelligent web search. To attain the benefits of DL, the high computational and energy-consumption demands imposed by the underlying processing, interconnect, and memory devices on which software-based DL executes can benefit substantially from innovative hardware implementations. Logic-in-Memory (LIM) architectures offer potential approaches to attaining such throughput goals within area and energy constraints starting with the lowest layers of the hardware stack. In this paper, we develop a Spintronic Logic-in-Memory (S-LIM) XNOR neural …

Paper Prototyping Comfortable Vr Play For Diverse Sensory Needs, Louanne E. Boyd, Kendra Day, Ben Wasserman, Kaitlyn Abdo, Gillian Hayes, Erik J. Linstead

Engineering Faculty Articles and Research

We co-designed paper prototype dashboards for virtual environments for three children with diverse sensory needs. Our goal was to determine individual interaction styles in order to enable comfortable and inclusive play. As a first step towards an inclusive virtual world, we began with designing for three sensory-diverse children who have labels of neurotypical, ADHD, and autism respectively. We focused on their leisure interests and their individual sensory profiles. We present the results of co-design with family members and paper prototyping sessions conducted by family members with the children. The results contribute preliminary empirical findings for accommodating different levels of engagement …

A Virtual Reality System For Practicing Conversation Skills For Children With Autism, Natalia Stewart Rosenfield, Kathleen Lamkin, Jennifer Re, Kendra Day, Louanne E. Boyd, Erik J. Linstead

Engineering Faculty Articles and Research

We describe a virtual reality environment, Bob’s Fish Shop, which provides a system where users diagnosed with Autism Spectrum Disorder (ASD) can practice social interactions in a safe and controlled environment. A case study is presented which suggests such an environment can provide the opportunity for users to build the skills necessary to carry out a conversation without the fear of negative social consequences present in the physical world. Through the repetition and analysis of these virtual interactions, users can improve social and conversational understanding.

Applications Of Supervised Machine Learning In Autism Spectrum Disorder Research: A Review, Kayleigh K. Hyde, Marlena N. Novack, Nicholas Lahaye, Chelsea Parlett-Pelleriti, Raymond Anden, Dennis R. Dixon, Erik Linstead

Engineering Faculty Articles and Research

Autism spectrum disorder (ASD) research has yet to leverage "big data" on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as …

Exploring Age-Related Metamemory Differences Using Modified Brier Scores And Hierarchical Clustering, Chelsea Parlett-Pelleriti, Grace C. Lin, Masha R. Jones, Erik Linstead, Susanne M. Jaeggi

Engineering Faculty Articles and Research

Older adults (OAs) typically experience memory failures as they age. However, with some exceptions, studies of OAs’ ability to assess their own memory functions—Metamemory (MM)— find little evidence that this function is susceptible to age-related decline. Our study examines OAs’ and young adults’ (YAs) MM performance and strategy use. Groups of YAs (N = 138) and OAs (N = 79) performed a MM task that required participants to place bets on how likely they were to remember words in a list. Our analytical approach includes hierarchical clustering, and we introduce a new measure of MM—the modified Brier—in order to adjust …

Features Of Agent-Based Models, Reiko Heckel, Alexander Kurz, Edmund Chattoe-Brown

Engineering Faculty Articles and Research

The design of agent-based models (ABMs) is often ad-hoc when it comes to defining their scope. In order for the inclusion of features such as network structure, location, or dynamic change to be justified, their role in a model should be systematically analysed. We propose a mechanism to compare and assess the impact of such features. In particular we are using techniques from software engineering and semantics to support the development and assessment of ABMs, such as graph transformations as semantic representations for agent-based models, feature diagrams to identify ingredients under consideration, and extension relations between graph transformation systems to …

The Positivication Of Coalgebraic Logics, Fredrik Dahlqvist, Alexander Kurz

Engineering Faculty Articles and Research

We present positive coalgebraic logic in full generality, and show how to obtain a positive coalgebraic logic from a boolean one. On the model side this involves canonically computing a endofunctor T': Pos->Pos from an endofunctor T: Set->Set, in a procedure previously defined by the second author et alii called posetification. On the syntax side, it involves canonically computing a syntax-building functor L': DL->DL from a syntax-building functor L: BA->BA, in a dual procedure which we call positivication. These operations are interesting in their own right and we explicitly compute posetifications and positivications in the case …