Digital Commons Network^™

Qualified Immunity And Unqualified Assumptions, Teressa Ravenell, Riley H. Ross Iii

Faculty Publications

No abstract provided.

Why Insurance Needs A Restatement: The Case Of Settlement Decision Law, Chaim Saiman

Faculty Publications

Even before its publication, the Restatement of the Law, Liability Insurance had been subjected to withering wholesale criticism that it creates aspirational and pro-policyholder insurance law. This view continues to be forcefully promoted by insurers and their advocates in the legal literature and by governors and state legislatures in the political areas.

This Article finds these wholesale criticisms unwarranted. Liability insurance law is not a field where law is simply found and restated. In fact, settlement law offers the most vivid examples of why the Restatement of the Law, Liability Insurance is possible, useful, and justified. It is possible because …

Promoting Vaccine Innovation, Mirit Eyal-Cohen, Ana Santos Rutschman

Faculty Publications

As society grows to understand the need to promote innovation, policymakers try to employ an arsenal of policy tools, from traditional intellectual property (“IP”) to newer tools such as grants, regulatory vouchers, and prizes. This Article argues that these frameworks crowd out certain types of investments in innovation projects that have a high social value. Vaccine innovation is a case in point. Despite the immense socioeconomic benefit of vaccines, existing policies have been limited in fostering investments in this space. This is because they fail to directly address the relevant bottleneck issues distinct to vaccine development.

This Article offers a …

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 …

Sieve Or Shield? High Tech Firms And Entrepreneurs And The Impacts Of Covid 19 On North American Border Regions, Kathrine Eileen Richardson, Francesco Cappellano

Faculty Research, Scholarly, and Creative Activity

This study examines the role of international borders in the era of the Covid-19 pandemic, which led to unprecedented national decisions to close borders in order to contain the domestic contagion. The idea that borders act as shields conflicts with the needs of cross-border regions, as they rely on networks straddling the borders for goods and services’ provisions. This paper explores different approaches at individual, local, and regional policy levels used to counterbalance such impacts. As evidenced by North American border closures to most non-citizens seeking entry (shield effects), it is important to understand how professionals, firms, and their networks …

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 …

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 …

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 …

On The Possibility Оf A Dual-Natured Self, Anand Jayprakash Vaidya

Faculty Research, Scholarly, and Creative Activity

In this paper I examine compatibilism and incompatibilism about whether the self can be both a subject and an object in the same awareness at the same time. While this is an old debate that many traditions of philosophy have contributed to, my point of departure is the work of A.C. Mukerji (an Indian philosopher of the modern era) who worked on the possibility of self-awareness by articulating, what he called, the paradox of ego-centricity. I also consider Patañjali (an Indian philosopher of the classical era), Maurice Merleau-Ponty (a phenomenologist), and Arindam Chakrabarti (a contemporary Indian and Analytic philosopher) on …

Spatially Resolved Stellar Spectroscopy Of The Ultra-Diffuse Galaxy Dragonfly 44. Iii. Evidence For An Unexpected Star Formation History Under Conventional Galaxy Evolution Processes, Alexa Villaume, Aaron J. Romanowsky, Jean Brodie, Pieter Van Dokkum, Charlie Conroy, Duncan A. Forbes, Shany Danieli, Christopher Martin, Matt Matuszewski

Faculty Research, Scholarly, and Creative Activity

We use the Keck Cosmic Web Imager integral field unit spectrograph to (1) measure the global stellar population parameters for the ultra-diffuse galaxy (UDG) Dragonfly 44 (DF44) to much higher precision than previously possible for any UDG and (2) for the first time measure spatially resolved stellar population parameters of a UDG. We find that DF44 falls below the mass-metallicity relation established by canonical dwarf galaxies both in and beyond the Local Group. We measure a flat radial age gradient ( log Gyr kpc-1) and a flat to positive metallicity gradient ( dex kpc-1), which are inconsistent with the gradients …

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 …

Word Embeddings For Fake Malware Generation, Quang Duy Tran, Fabio Di Troia

Faculty Research, Scholarly, and Creative Activity

Signature and anomaly-based techniques are the fundamental methods to detect malware. However, in recent years this type of threat has advanced to become more complex and sophisticated, making these techniques less effective. For this reason, researchers have resorted to state-of-the-art machine learning techniques to combat the threat of information security. Nevertheless, despite the integration of the machine learning models, there is still a shortage of data in training that prevents these models from performing at their peak. In the past, generative models have been found to be highly effective at generating image-like data that are similar to the actual data …

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 …

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 …

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 …

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 …

Blackcurrants: A Nutrient-Rich Source For The Development Of Functional Foods For Improved Athletic Performance, Ali Ali Redha, Shahida Anusha Siddiqui, Reza Zare, Daniele Spadaccini, Silvia Guazzotti, Xi Feng, Nur Alim Bahmid, Yuan Seng Wu, Fathima Zahraa Ozeer, Rotimi E. Aluko

Faculty Research, Scholarly, and Creative Activity

Blackcurrants are nutrient-rich fruits with a significant amount of bioactive compounds including vitamin C and polyphenols, especially anthocyanins. The high phytochemical content of blackcurrants promotes this fruit to become a valuable functional food ingredient with varying health-promoting activities targeting different consumers including athletes. Athletes experience oxidative stress during intense exercise, which can result in inflammation and reduced exercise performance. Antioxidants such as vitamin C and polyphenols can restore the regular oxidative status of the body. Blackcurrant supplementation has shown potential ergogenic activity to improve athlete performance during high-intensity training. Clinical trials have evaluated the effectiveness of blackcurrant supplementation on exercise …

Race Car Aerodynamics Simulation And Transmitted Tire Reaction Forces, Mark T.J. Lin, Periklis Papadopoulos

Faculty Research, Scholarly, and Creative Activity

Often in Computational Fluid Dynamics integral forces and restoring moments are calculated, but the location where they should be applied may not always be clear. This is because the location is not intuitive: it is dependent on the vehicle’s velocity and its orientation (e.g. sideslip angle). A technique has been developed to translate the aerodynamic forces to the reaction forces on the vehicle’s tires. In this study, CFD simulation is performed with a 7.5° yaw to look at the effect of cornering. To understand aerodynamic effect on vehicle dynamics one must know the location at which this force is applied. …

2022 Silicon Valley Pain Index, Scott Myers-Lipton, William Armaline

Faculty Research, Scholarly, and Creative Activity

No abstract provided.

On Fatigue Detection For Air Traffic Controllers Based On Fuzzy Fusion Of Multiple Features, Yi Hu, Zhuo Liu, Aiqin Hou, Chase Wu, Wenbin Wei, Yanjun Wang, Min Liu

Faculty Research, Scholarly, and Creative Activity

Fatigue detection for air traffic controllers is an important yet challenging problem in aviation safety research. Most of the existing methods for this problem are based on facial features. In this paper, we propose an ensemble learning model that combines both facial features and voice features and design a fatigue detection method through multifeature fusion, referred to as Facial and Voice Stacking (FV-Stacking). Specifically, for facial features, we first use OpenCV and Dlib libraries to extract mouth and eye areas and then employ a combination of M-Convolutional Neural Network (M-CNN) and E-Convolutional Neural Network (E-CNN) to determine the state of …

Stress-Associated Metabolites Vary With Both Season And Habitat Across Populations Of A Climate Sentinel Species, A. L. Whipple, C. Ray, J. Varner, J. N. Kitchens, A. A. Hove, J. A. Castillo Vardaro, J. L. Wilkening

Faculty Research, Scholarly, and Creative Activity

Relating physiological stress to habitat quality could refine conservation efforts. Habitat quality, which is often inferred from patch occupancy or demographic rates, might be measured in a more timely and nuanced way using metrics of physiological stress. To understand whether stress-associated hormones vary with metrics of habitat quality, we measured fecal glucocorticoid metabolite (FGM) levels in the American pika (Ochotona princeps), a small mammal with well-defined habitat (talus), which can vary in quality depending on the presence of subsurface ice features. In spring and fall 2018, we collected feces noninvasively from pika territories in taluses “with” or “without” subsurface ice …

Flexible And Robust Real-Time Intrusion Detection Systems To Network Dynamics, Kicho Yu, Khanh Nguyen, Younghee Park

Faculty Research, Scholarly, and Creative Activity

Deep learning-based intrusion detection systems have advanced due to their technological innovations such as high accuracy, automation, and scalability to develop an effective network intrusion detection system (NIDS). However, most of the previous research has focused on model generation through intensive analysis of feature engineering instead of considering real environments. They have limitations to applying the previous methods for a real network environment to detect real-time network attacks. In this paper, we propose a new flexible and robust NIDS based on Recurrent Neural Network (RNN) with a multi-classifier to generate a detection model in real time. The proposed system adaptively …

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 …

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 …

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 …

Multiple Sediment Incorporation Events In A Continental Magmatic Arc: Insight From The Metasedimentary Rocks Of The Northern North Cascades, Washington (Usa), Ann E.H. Hanson, Stacia M. Gordon, Kyle T. Ashley, Robert B. Miller, Elizabeth Langdon-Lassagne

Faculty Research, Scholarly, and Creative Activity

The rheology and composition of arc crust and the overall evolution of continental magmatic arcs can be affected by sediment incorporation events. The exhumed Cretaceous–Eocene North Cascades arc exposes abundant meta­ sedimentary rocks that were incorporated into the arc during multiple events. This study uses field relationships, detrital zircon geochronology, bulk rock geo­ chemistry, geothermometry, and quartz­in­garnet geobarometry to distinguish approximate contacts and emplacement depths for different metasedimentary units to better understand their protolith incorporation history and impact on the arc. The Skagit Gneiss Complex is one of the main deep crustal units of the North Cascades arc. It includes …

In-Vehicle Human Machine Interface: Investigating The Effects Of Tactile Displays On Information Presentation In Automated Vehicles, Kimberly D. Martinez, Gaojian Huang

Faculty Research, Scholarly, and Creative Activity

Background: Semi-autonomous vehicles still require human drivers to take over when the automated systems can no longer perform the driving task. Objective: The goal of this study was to design and test the effects of six meaningful tactile signal types, representing six driving scenarios (i.e., navigation, speed, surrounding vehicles, over the speed limit, headway reductions, and pedestrian status) respectively, and two pattern durations (lower and higher urgencies), on drivers' perception and performance during automated driving. Methods: Sixteen volunteers participated in an experiment utilizing a medium-fidelity driving simulator presenting vibrotactile signals via 20 tactors embedded in the seat back, pan, and …

Prediction Of Length Of Stay In The Emergency Department For Covid-19 Patients: A Machine Learning Approach, Egbe Etu Etu, Leslie Monplaisir, Suzan Arslanturk, Sara Masoud, Celestine Aguwa, Ihor Markevych, Joseph Miller

Faculty Research, Scholarly, and Creative Activity

The coronavirus disease (COVID-19) outbreak has become a global public health threat. The influx of COVID-19 patients has prolonged the length of stay (LOS) in the emergency department (ED) in the United States. Our objective is to develop a reliable prediction model for COVID-19 patient ED LOS and identify clinical factors, such as age and comorbidities, associated with LOS within a '4-hour target.' Data were collected from an urban, demographically diverse hospital in Detroit for all COVID-19 patients' ED presentations from March 16 to December 29, 2020. We trained four machine learning models, namely logistic regression (LR), gradient boosting (GB), …

Research Handling Effects On Stress Hormones, Blood Parameters, And Heart Rate In Juvenile Northern Elephant Seals (Mirounga Angustirostris), Lauren A. Cooley

Master's Theses

Wildlife researchers must balance the need to safely capture and handle their study animals to sample tissues, collect morphological measurements, and attach dataloggers while simultaneously ensuring their results are not confounded by stress artifacts caused by handling. To determine the physiological effects of research activities including chemical immobilization, transport, instrumentation with biologgers, and overnight holding on a model marine mammal species, I collected hormone, blood chemistry, hematology, and heart rate data from 19 juvenile northern elephant seals (Mirounga angustirostris) throughout a translocation experiment. Across my six sampling timepoints, cortisol and aldosterone data revealed a moderate hormonal stress response to handling …

Coordination Protocols For Verifiable Consistency In Distributed Storage Systems, Ashwin Ramaswamy

Master's Theses

Achieving consistency in a highly available distributed storage system has been formally proven to be an impossible task when the system faces network partitions and faulty processes. The complexity is exacerbated when the system allows concurrent processes to send transactions to all the other servers and coordinate the consistent commitment of different transactions. In the event of a partition, each server may allow clients to request updates involving the current state of the data, which makes achieving replicated consistency challenging. To solve the inconsistency problems, several consensus protocols are used, but have strict requirements in order to make progress and …