Data Science Commons^™

Dimension Reduction Techniques In Regression, Pei Wang

Theses and Dissertations--Statistics

Because of the advances of modern technology, the size of the collected data nowadays is larger and the structure is more complex. To deal with such kinds of data, sufficient dimension reduction (SDR) and reduced rank (RR) regression are two powerful tools. This dissertation focuses on these two tools and it is composed of three projects. In the first project, we introduce a new SDR method through a novel approach of feature filter to recover the central mean subspace exhaustively along with a method to determine the dimension, two variable selection methods, and extensions to multivariate response and large p …

Fast And Memory-Efficient Tfidf Calculation For Text Analysis Of Large Datasets, Samah Senbel

School of Computer Science & Engineering Faculty Publications

Term frequency – Inverse Document Frequency (TFIDF) is a vital first step in text analytics for information retrieval and machine learning applications. It is a memory-intensive and complex task due to the need to create and process a large sparse matrix of term frequencies, with the documents as rows and the term as columns and populate it with the term frequency of each word in each document.

The standard method of storing the sparse array is the “Compressed Sparse Row” (CSR), which stores the sparse array as three one-dimensional arrays for the row id, column id, and term frequencies. We …

Neural Representations Of Concepts And Texts For Biomedical Information Retrieval, Jiho Noh

Theses and Dissertations--Computer Science

Information retrieval (IR) methods are an indispensable tool in the current landscape of exponentially increasing textual data, especially on the Web. A typical IR task involves fetching and ranking a set of documents (from a large corpus) in terms of relevance to a user's query, which is often expressed as a short phrase. IR methods are the backbone of modern search engines where additional system-level aspects including fault tolerance, scale, user interfaces, and session maintenance are also addressed. In addition to fetching documents, modern search systems may also identify snippets within the documents that are potentially most relevant to the …

Revisiting Absolute Pose Regression, Hunter Blanton

Theses and Dissertations--Computer Science

Images provide direct evidence for the position and orientation of the camera in space, known as camera pose. Traditionally, the problem of estimating the camera pose requires reference data for determining image correspondence and leveraging geometric relationships between features in the image. Recent advances in deep learning have led to a new class of methods that regress the pose directly from a single image.

This thesis proposes methods for absolute camera pose regression. Absolute pose regression estimates the pose of a camera from a single image as the output of a fixed computation pipeline. These methods have many practical benefits …

Requirements Engineering Education Slr Data Set 1988-2020, Marian Daun, Alicia M. Grubb, Bastian Tenbergen

Data

Requirements Engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or exceeding budgets of software development projects. Therefore, it is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. However, to date there exists no central dataset for RE Education articles. To lay the foundation for this important mission, we conducted a systematic literature review. In this dataset, we present 152 articles from the Requirements Engineering …

Reading Datasets: Strategies For Interpreting The Politics Of Data Signification, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

All datasets emerge from and are enmeshed in power-laden semiotic systems. While emerging data ethics curriculum is supporting data science students in identifying data biases and their consequences, critical attention to the cultural histories and vested interests animating data semantics is needed to elucidate the assumptions and political commitments on which data rest, along with the externalities they produce. In this article, I introduce three modes of reading that can be engaged when studying datasets—a denotative reading (extrapolating the literal meaning of values in a dataset), a connotative reading (tracing the socio-political provenance of data semantics), and a deconstructive reading …

"Who Can Help Me?'': Knowledge Infused Matching Of Support Seekers And Support Providers During Covid-19 On Reddit, Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth

Publications

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs). Currently, knowledgeable human moderators match an SS with a user with relevant experience, i.e, an SP on these subreddits. This unscalable process defers timely care. We present a medical knowledge-infused approach to efficient matching of SS and SPs validated by experts for the users affected by anxiety and depression, in the context of with COVID-19. After matching, each SP to …

Uncovering Object Categories In Infant Views, Naiti S. Bhatt

Scripps Senior Theses

While adults recognize objects in a near-instant, infants must learn how to categorize the objects in their visual environments. Recent work has shown that egocentric head-mounted camera videos contain rich data that illuminate the infant experience (Clerkin et al., 2017; Franchak et al., 2011; Yoshida & Smith, 2008). While past work has focused on the social information in view, in this work, we aim to characterize the objects in infants’ at-home visual environments by modifying modern computer vision models for the infant view. To do so, we collected manual annotations of objects that infants seemed to be interacting within a …

Improving Space Efficiency Of Deep Neural Networks, Aliakbar Panahi

Theses and Dissertations

Language models employ a very large number of trainable parameters. Despite being highly overparameterized, these networks often achieve good out-of-sample test performance on the original task and easily fine-tune to related tasks. Recent observations involving, for example, intrinsic dimension of the objective landscape and the lottery ticket hypothesis, indicate that often training actively involves only a small fraction of the parameter space. Thus, a question remains how large a parameter space needs to be in the first place — the evidence from recent work on model compression, parameter sharing, factorized representations, and knowledge distillation increasingly shows that models can be …

Full Interpretable Machine Learning Method With In-Line Coordinates, Hoang Phan

All Master's Theses

This thesis explores a new approach for machine learning classification task in 2-dimensional space (2-D ML) with In-line Coordinates. This is a full machine learning approach that does not require to deal with n-dimensional data in n-dimensional space. In-line coordinates method allows discovering n-D patterns in 2-D space without loss of n-D information using graph representation of n-D data in 2-D. Specifically, this thesis shows that it can be done with In-line Based Coordinates in different modifications, which are defined, including static and dynamic ones. Some classification and regression algorithms based on these In-line Coordinates were explored. Two successful cases …

A Multi-Resolution Graph Convolution Network For Contiguous Epitope Prediction, Lisa Oh

Dartmouth College Master’s Theses

Computational methods for predicting binding interfaces between antigens and antibodies (epitopes and paratopes) are faster and cheaper than traditional experimental structure determination methods. A sufficiently reliable computational predictor that could scale to large sets of available antibody sequence data could thus inform and expedite many biomedical pursuits, such as better understanding immune responses to vaccination and natural infection and developing better drugs and vaccines. However, current state-of-the-art predictors produce discontiguous predictions, e.g., predicting the epitope in many different spots on an antigen, even though in reality they typically comprise a single localized region. We seek to produce contiguous predicted epitopes, …

Automatic Hierarchy Expansion For Improved Structure And Chord Evaluation, Katherine M. Kinnaird, Brian Mcfee

Statistical and Data Sciences: Faculty Publications

No abstract provided.

Moving Ethnography: Infrastructuring Doubletakes And Switchbacks In Experimental Collaborative Methods, Aalok Khandekar, Brandon Costelloe-Kuehn, Lindsay Poirier, Alli Morgan, Alison Kenner, Kim Fortun, Mike Fortun

Statistical and Data Sciences: Faculty Publications

In this article, we describe how our work at a particular nexus of STS, ethnography, and critical theory—informed by experimental sensibilities in both the arts and sciences—transformed as we built and learned to use collaborative workflows and supporting digital infrastructure. Responding to the call of this special issue to be “ethnographic about ethnography,” we describe what we have learned about our own methods and collaborative practices through building digital infrastructure to support them. Supporting and accounting for how experimental ethnographic projects move—through different points in a research workflow, with many switchbacks, with project designs constantly changing as the research develops—was …

Searching Harder, Localizing Better, Classifying Faster: Optimizing Fast Radio Burst Detection And Analysis, Kshitij Aggarwal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Fast Radio Bursts (or FRBs) are millisecond-duration transients of extragalactic origin. They exhibit dispersion caused by propagation through an ionized medium, and quantified by Dispersion Measure (DM). Around 800 FRBs (24 repeaters) have been discovered; so far, 24 FRBs have been confidently associated with a host galaxy. In this thesis, we discuss multiple new FRB search and analysis techniques and the corresponding tools that enable us to search for FRBs harder, localize them better, and classify candidates faster.

We discuss five open-source software suites that can be used in FRB analysis. These suites are used to distinguish between FRBs and …

Topic Modeling And Cultural Nature Of Citations, Marie Coraline Dumaz

Graduate Theses, Dissertations, and Problem Reports (ETD)

Ever since the beginning of research journals, the number of academic publications has been increasing steadily. Nowadays, especially, with the new importance of online open-access journals and databases, research papers are more easily available to read and share. It also becomes harder to keep up with novelties and grasp an idea of the general impact of a given researcher, institution, journal, or field. For this reason, different bibliometric indicators are now routinely used to classify and evaluate the impact or significance of individual researchers, conferences, journals, or entire scientific communities. In this thesis, we provide tools to study trends in …

Estimating The Azimuthal Mode Structure Of Ultra Low Frequency Waves And Its Effects On The Radial Diffusion Of Radiation Belt Electrons, Mohammad Barani

Graduate Theses, Dissertations, and Problem Reports (ETD)

Characterizing the azimuthal mode number �� of Ultra Low Frequency (ULF) waves is critical to quantifying the radial diffusion of radiation belt electrons. A Wavelet cross-spectral technique is applied to the compressional ULF waves observed by multiple pairs of GOES and MMS satellites to estimate the mode structure of ULF waves. A more realistic distribution of mode numbers is achieved by inclusion of the modes corresponding to different wave propagation directions as well as at �� higher than fundamental mode number. For the event study of a geomagnetic storm using GOES data, ULF wave power is found to dominate at …

Analysis And Classification Of Software Fault-Proneness And Vulnerabilities, Mohammad Jamil Ahmad

Graduate Theses, Dissertations, and Problem Reports (ETD)

Software bugs are expensive to fix and can lead to catastrophic consequences. Therefore, their analysis and the use of machine learning for prediction are of the utmost importance. Many prediction models have been proposed and different factors affecting the prediction performance have been extensively studied. This work addresses four topics in two areas in software engineering: software fault-proneness prediction and analysis and classification of security-related bug reports. The first topic focuses on the effect of the learning approach (i.e., the way software fault-proneness prediction models are trained and tested) on the performance of software fault-proneness prediction which lacks extensive research …

A Comparison Of Exhaustive And Non-Lattice-Based Methods For Auditing Hierarchical Relations In Gene Ontology, Rashmie Abeysinghe, Fengbo Zheng, Licong Cui

Faculty, Staff and Student Publications

Uncovering and fixing errors in biomedical terminologies is essential so that they provide accurate knowledge to downstream applications that rely on them. Non-lattice-based methods have been applied to identify various kinds of inconsistencies in different biomedical terminologies. In previous work, we have introduced two inference-based approaches that were applied in an exhaustive manner to audit hierarchical relations in the Gene Ontology: (1) Lexical-based inference framework, and (2) Subsumption-based sub-term inference framework. However, it is unclear how effective these exhaustive approaches perform compared with their corresponding non-lattice-based approaches. Therefore, in this paper, we implement the non-lattice versions of these two exhaustive …

A Review And Evaluation Of Techniques For Improved Feature Detection In Mass Spectrometry Data, Annika R. Tostengard, Rob Smith

Graduate Student Theses, Dissertations, & Professional Papers

Mass spectrometry (MS) is used in analysis of chemical samples to identify the molecules present and their quantities. This analytical technique has applications in many fields, from pharmacology to space exploration. Its impacts on medicine are particularly significant, since MS aids in the identification of molecules associated with disease; for instance, in proteomics, MS allows researchers to identify proteins that are associated with autoimmune disorders, cancers, and other conditions. Since the applications are so wide-ranging and the tool is ubiquitous across so many fields, it is critical that the analytical methods used to collect data are sound.

Data analysis in …

Inference Of Surface Velocities From Oblique Time Lapse Photos And Terrestrial Based Lidar At The Helheim Glacier, Franklyn T. Dunbar Ii

Graduate Student Theses, Dissertations, & Professional Papers

Using time dependent observations derived from terrestrial LiDAR and oblique
time-lapse imagery, we demonstrate that a Bayesian approach to glacial motion es-
timation provides a concise way to incorporate multiple data products into a single
motion estimation procedure effectively producing surface velocity estimates with
an associated uncertainty. This approach brings both improved computational effi-
ciency, and greater scalability across observational time-frames when compared to
existing methods. To gauge efficacy, we apply these methods to a set of observa-
tions from the Helheim Glacier, a critical actor in contemporary mass loss trends
observed in the Greenland Ice Sheet. We find that …