Data Science Commons^™

Reducing Uncertainty In Sea-Level Rise Prediction: A Spatial-Variability-Aware Approach, Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, Aneesh Subramanian

I-GUIDE Forum

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or …

Instagram Travel Influencers Coping With Covid-19 Travel Disruption, Andrei Kirilenko, Katarzyna Emin, Karen Tavares

ITSA 2022 Gran Canaria - 9th Biennial Conference: Corporate Entrepreneurship and Global Tourism Strategies After Covid 19

A significant portion of today’s marketing is done through social media influencers, that is, through bloggers with established online credibility in a certain area who are recognized and followed by a sizable online audience. In the travel and hospitality industry, the influencer marketing is primarily done through Instagram due to its emphasis on visual images rather than texts. Covid-19 related travel restrictions and shrinking social media advertisement in travel industry have heavily impacted travel influencers, reducing their income and forcing many out of business. We present the outcomes of a study of the top 150 online travel influencers. The analysis …

Automated Delineation Of Visual Area Boundaries And Eccentricities By A Cnn Using Functional, Anatomical, And Diffusion-Weighted Mri Data, Noah C. Benson, Bogeng Song, Toshikazu Miyata, Hiromasa Takemura, Jonathan Winawer

MODVIS Workshop

Delineating visual field maps and iso-eccentricities from fMRI data is an important but time-consuming task for many neuroimaging studies on the human visual cortex because the traditional methods of doing so using retinotopic mapping experiments require substantial expertise as well as scanner, computer, and human time. Automated methods based on gray-matter anatomy or a combination of anatomy and functional mapping can reduce these requirements but are less accurate than experts. Convolutional Neural Networks (CNNs) are powerful tools for automated medical image segmentation. We hypothesize that CNNs can define visual area boundaries with high accuracy. We trained U-Net CNNs with ResNet18 …

Toward A Manifold Encoding Neural Responses, Luciano Dyballa, Andra M. Rudzite, Mahmood S. Hoseini, Mishek Thapa, Michael P. Stryker, Greg D. Field, Steven W. Zucker

MODVIS Workshop

Understanding circuit properties from physiological data presents two challenges: (i) recordings do not reveal connectivity, and (ii) stimuli only exercise circuits to a limited extent. We address these challenges for the mouse visual system with a novel neural manifold obtained using unsupervised algorithms. Each point in our manifold is a neuron; nearby neurons respond similarly in time to similar parts of a stimulus ensemble. This ensemble includes drifting gratings and flows, i.e., patterns resembling what a mouse would “see” running through fields.

Regarding (i), our manifold differs from the standard practice in computational neuroscience: embedding trials in neural coordinates. Topology …

Polarimetric Radar And Vhf Lightning Observations In A Significantly Tornadic Supercell, Jacob Bruss

The Journal of Purdue Undergraduate Research

No abstract provided.

Supporting The Protect Initiative, Josh Lefton, Jackson Murray, Ahmed Thabet, Sriram Baireddy, Prakash Shukla, Mridul Gupta, Reagan Becker, Julie Ertle, Tony Doan, Aerin Yang

Purdue Journal of Service-Learning and International Engagement

Recently, medication dosage errors have received more political and media attention. Dosage errors are the most common medical errors, affecting about 1.5 million people annually.

Furthermore, U.S. poison-control centers reported more than 200,000 cases per year of medication errors. These cases result in medical costs of around $3.5 billion, and children under 6 years old constitute approximately 30% of these cases.

The PROTECT Initiative (Preventing Overdoses and Treatment Errors in Children Taskforce) was launched in 2008 as a collaborative effort between public health agencies and patient advocates to minimize dosage errors.

In alignment with the PROTECT Initiative effort, this project …

Towards A Burden-Free Implicit Authentication For Wearable Device Users, Bryan Lee, Sudip Vhaduri

Discovery Undergraduate Interdisciplinary Research Internship

The state of current knowledge-based wearable authentication systems requires users to physically interact with a device to initiate and validate their presence, thereby imposing a burden on the user. However, with the recent advancements of sensor technologies in consumer smart wearables (e.g., Fitbit and Apple watches), we were able to utilize vectors of statistical features extracted from the continuous stream of data from these IoT devices to implicitly validate a user's activities and its spatiotemporal context via the use of machine learning techniques. To improve the performance of our models, additional soft biometric data (i.e., respiratory sounds) was collected, and …

Physics-Informed Machine Learning To Predict Extreme Weather Events, Rthvik Raviprakash, Jonathan Buchanan, Mahdi Bu Ali

Discovery Undergraduate Interdisciplinary Research Internship

Extreme weather events refer to unexpected, severe, or unseasonal weather events, which are dynamically related to specific large-scale atmospheric patterns. These extreme weather events have a significant impact on human society and also natural ecosystems. For example, natural disasters due to extreme weather events caused more than $90 billion global direct losses in 2015. These extreme weather events are challenging to predict due to the chaotic nature of the atmosphere and are highly correlated with the occurrence of atmospheric blocking. A key aspect for preparedness and response to extreme climate events is accurate medium-range forecasting of atmospheric blocking events.

Unlike …

Automated Data Processing: Making Community Indicators Possible For Lafayette, Indiana, Jace T. Newell, Eli W. Coltin, Eric D. Flaningam

The Journal of Purdue Undergraduate Research

No abstract provided.

Data Is Personal: We Should Treat It As Such, Kaleb Dunn

Student Papers in Public Policy

The rise of the internet as a fact of daily life is the defining element of the modern age. Widespread use of the internet has fundamentally altered entire industries, and much of American life has migrated online. Dating is augmented by online dating; shopping by online shopping; television by internet streaming.

The digitization of American life has brought with it considerable benefits, including great convenience and innumerable efficiencies, but it has not come without a cost. Although there are many business models used by internet companies, many of the now-largest companies in the world have converged on one entity upon …

Implement Multi-Factor Authentication On All Federal Systems Now, Megan Walsh

Student Papers in Public Policy

The White House Office of Management and Budget recorded 31,107 information security incidents in fiscal year 2018. The most common attacks to gain access to a user’s login credentials were e-mail/phishing, web-based attack, and brute force entering of username/password combinations. Given this high number of incidents, strong reliance on computers for everyday business, and common attacks that target passwords, information security should be a priority for information technology administrators working in federal agencies.

Removing Racially Biased Algorithms In Policing, Andie Lee

Student Papers in Public Policy

Local police departments use algorithm-based programs to do police work and predict crime. Technology has created the police tactic of predictive crime prevention. Police work, however, requires social skills, assessment of the environment, and most importantly human interaction. Automated policing lacks these characteristics. Moreover, the algorithms used to make crime predictions and risk assessments have disproportionately affected minorities.

The Case For Online Ranked-Choice Voting, Rayyan Khan

Student Papers in Public Policy

Maine was the first to embrace ranked-choice voting on a statewide level in 2018, using it for all state and general elections. Maine voters will be the first to use ranked-choice voting in a presidential election in 2020. This system differs from traditional voting in that voters rank candidates rather than choose just one. Supporters of ranked-choice voting tout it as a better model for accurately representing the values of the voting population; however, a study conducted in San Francisco details a potential shortfall referred to as “ballot fatigue” that the theoretically-ideal system may face as it struggles to deal …

Potential For Participatory Big Data Ethics And Algorithm Design: A Scoping Mapping Review, Madisson Whitman, Chien-Yi Hsiang, Kendall Roark

Libraries Faculty and Staff Scholarship and Research

Ubiquitous networked data collection and algorithm-based information systems have the potential to disparately impact lives around the planet and pose a host of emerging ethical challenges. One response has been a call for more transparency and democratic control over the design and implementation of such systems. This scoping mapping review focuses on participatory approaches to the design, governance, and future of these systems across a wide variety of contexts and domains.