Digital Commons Network^™

Breeding And Feeding Habitat Selection By An Island Endemic Bird May Increase Its Vulnerability To Climate Change, Paul Radley, Eddie Van Etten, David Blake, Robert Davis

Research Datasets

Characterising patterns of habitat use is an important first step for effective conservation planning. Species restricted to low-lying islands are at greatest risk from climate change-related sea level rise, and requirements for breeding and foraging habitat may determine their risk from tidal inundation. The endangered Micronesian Scrubfowl (Megapodius laperouse senex) is a model species for understanding these impacts. This species faces the cumulative challenges of tourist visitation, invasive species, and rising sea levels, yet little is understood about its habitat use in the Rock Islands Southern Lagoon Conservation Area (RISL) of Palau. We studied the habitat requirements of …

Fmri Feature Extraction Model For Adhd Classification Using Convolutional Neural Network, Senuri De Silva, Sanuwani Udara Dayarathna, Gangani Ariyarathne, Dulani Meedeniya, Sampath Jayarathna

Computer Science Faculty Publications

Biomedical intelligence provides a predictive mechanism for the automatic diagnosis of diseases and disorders. With the advancements of computational biology, neuroimaging techniques have been used extensively in clinical data analysis. Attention deficit hyperactivity disorder (ADHD) is a psychiatric disorder, with the symptomology of inattention, impulsivity, and hyperactivity, in which early diagnosis is crucial to prevent unwelcome outcomes. This study addresses ADHD identification using functional magnetic resonance imaging (fMRI) data for the resting state brain by evaluating multiple feature extraction methods. The features of seed-based correlation (SBC), fractional amplitude of low-frequency fluctuation (fALFF), and regional homogeneity (ReHo) are comparatively applied to …

Automated Filtering Of Eye Movements Using Dynamic Aoi In Multiple Granularity Levels, Gavindya Jayawardena, Sampath Jayarathna

Computer Science Faculty Publications

Eye-tracking experiments involve areas of interest (AOIs) for the analysis of eye gaze data. While there are tools to delineate AOIs to extract eye movement data, they may require users to manually draw boundaries of AOIs on eye tracking stimuli or use markers to define AOIs. This paper introduces two novel techniques to dynamically filter eye movement data from AOIs for the analysis of eye metrics from multiple levels of granularity. The authors incorporate pre-trained object detectors and object instance segmentation models for offline detection of dynamic AOIs in video streams. This research presents the implementation and evaluation of object …

De Novo Prediction Of Drug–Target Interactions Using Laplacian Regularized Schatten P-Norm Minimization, Gaoyan Wu, Mengyun Yang, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

In pharmaceutical sciences, a crucial step of the drug discovery is the identification of drug–target interactions (DTIs). However, only a small portion of the DTIs have been experimentally validated. Moreover, it is an extremely laborious, expensive, and time-consuming procedure to capture new interactions between drugs and targets through traditional biochemical experiments. Therefore, designing computational methods for predicting potential interactions to guide the experimental verification is of practical significance, especially for de novo situation. In this article, we propose a new algorithm, namely Laplacian regularized Schatten p-norm minimization (LRSpNM), to predict potential target proteins for novel drugs and potential drugs for …

A Survey Of Enabling Technologies For Smart Communities, Amna Iqbal, Stephan Olariu

Computer Science Faculty Publications

In 2016, the Japanese Government publicized an initiative and a call to action for the implementation of a "Super Smart Society" announced as Society 5.0. The stated goal of Society 5.0 is to meet the various needs of the members of society through the provisioning of goods and services to those who require them, when they are required and in the amount required, thus enabling the citizens to live an active and comfortable life. In spite of its genuine appeal, details of a feasible path to Society 5.0 are conspicuously missing. The first main goal of this survey is to …

Vehicular Crowdsourcing For Congestion Support In Smart Cities, Stephan Olariu

Computer Science Faculty Publications

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various …

Smart Parking Systems: Reviewing The Literature, Architecture And Ways Forward, Can Biyik, Zaheer Allam, Gabriele Pieri, Davide Moroni, Muftah O' Fraifer, Eoin O' Connell, Stephan Olariu, Muhammad Khalid

Computer Science Faculty Publications

The Internet of Things (IoT) has come of age, and complex solutions can now be implemented seamlessly within urban governance and management frameworks and processes. For cities, growing rates of car ownership are rendering parking availability a challenge and lowering the quality of life through increased carbon emissions. The development of smart parking solutions is thus necessary to reduce the time spent looking for parking and to reduce greenhouse gas emissions. The principal role of this research paper is to analyze smart parking solutions from a technical perspective, underlining the systems and sensors that are available, as documented in the …

Large Scale Subject Category Classification Of Scholarly Papers With Deep Attentive Neural Networks, Bharath Kandimalla, Shaurya Rohatgi, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Subject categories of scholarly papers generally refer to the knowledge domain(s) to which the papers belong, examples being computer science or physics. Subject category classification is a prerequisite for bibliometric studies, organizing scientific publications for domain knowledge extraction, and facilitating faceted searches for digital library search engines. Unfortunately, many academic papers do not have such information as part of their metadata. Most existing methods for solving this task focus on unsupervised learning that often relies on citation networks. However, a complete list of papers citing the current paper may not be readily available. In particular, new papers that have few …

Understanding The Impact Of Encrypted Dns On Internet Censorship, Lin Jin, Shuai Hao, Haining Wang, Chase Cotton

Computer Science Faculty Publications

DNS traffic is transmitted in plaintext, resulting in privacy leakage. To combat this problem, secure protocols have been used to encrypt DNS messages. Existing studies have investigated the performance overhead and privacy benefits of encrypted DNS communications, yet little has been done from the perspective of censorship. In this paper, we study the impact of the encrypted DNS on Internet censorship in two aspects. On one hand, we explore the severity of DNS manipulation, which could be leveraged for Internet censorship, given the use of encrypted DNS resolvers. In particular, we perform 7.4 million DNS lookup measurements on 3,813 DoT …

Ranked List Fusion And Re-Ranking With Pre-Trained Transformers For Arqmath Lab, Shaurya Rohatgi, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

This paper elaborates on our submission to the ARQMath track at CLEF 2021. For our submission this year we use a collection of methods to retrieve and re-rank the answers in Math Stack Exchange in addition to our two-stage model which was comparable to the best model last year in terms of NDCG’. We also provide a detailed analysis of what the transformers are learning and why is it hard to train a math language model using transformers. This year’s submission to Task-1 includes summarizing long question-answer pairs to augment and index documents, using byte-pair encoding to tokenize formula and …

Parallel Anisotropic Unstructured Grid Adaptation, Christos Tsolakis, Nikos Chrisochoides, Michael A. Park, Adrien Loseille, Todd Michal

Computer Science Faculty Publications

Computational fluid dynamics (CFD) has become critical to the design and analysis of aerospace vehicles. Parallel grid adaptation that resolves multiple scales with anisotropy is identified as one of the challenges in the CFD Vision 2030 Study to increase the capacity and capability of CFD simulation. The study also cautions that computer architectures are undergoing a radical change, and dramatic increases in algorithm concurrency will be required to exploit full performance. This paper reviews four different methods to parallel anisotropic grid adaptation. They cover both ends of the spectrum: 1) using existing state-of-the-art software optimized for a single core and …

Combining Cryo-Em Density Map And Residue Contact For Protein Secondary Structure Topologies, Maytha Alshammari, Jing He

Computer Science Faculty Publications

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set …

See-Trend: Secure Traffic-Related Event Detection In Smart Communities, Stephan Olariu, Dimitrie C. Popescu

Computer Science Faculty Publications

It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traffic-related information along the roadway to help the driving public make informed …

Adaptive Physics-Based Non-Rigid Registration For Immersive Image-Guided Neuronavigation Systems, Fotis Drakopoulos, Christos Tsolakis, Angelos Angelopoulos, Yixun Liu, Chengjun Yao, Kyriaki Rafailia Kavazidi, Nikolaos Foroglou, Andrey Fedorov, Sarah Frisken, Ron Kikinis, Alexandra Golby, Nikos Chrisochoides

Computer Science Faculty Publications

Objective: In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a safe resection of brain tumors in eloquent areas of the brain. However, the brain deforms during surgery, particularly in the presence of tumor resection. Non-Rigid Registration (NRR) of the preoperative image data can be used to create a registered image that captures the deformation in the intraoperative image while maintaining the quality of the preoperative image. Using clinical data, this paper reports the results of a comparison of the accuracy and performance among several non-rigid registration methods for handling brain deformation. A …

Extraction And Evaluation Of Statistical Information From Social And Behavioral Science Papers, Sree Sai Teja Lanka, Sarah Rajtmajer, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

With substantial and continuing increases in the number of published papers across the scientific literature, development of reliable approaches for automated discovery and assessment of published findings is increasingly urgent. Tools which can extract critical information from scientific papers and metadata can support representation and reasoning over existing findings, and offer insights into replicability, robustness and generalizability of specific claims. In this work, we present a pipeline for the extraction of statistical information (p-values, sample size, number of hypotheses tested) from full-text scientific documents. We validate our approach on 300 papers selected from the social and behavioral science literatures, and …

A Tool For Segmentation Of Secondary Structures In 3d Cryo-Em Density Map Components Using Deep Convolutional Neural Networks, Yongcheng Mu, Salim Sazzed, Maytha Alshammari, Jiangwen Sun, Jing He

Computer Science Faculty Publications

Although cryo-electron microscopy (cryo-EM) has been successfully used to derive atomic structures for many proteins, it is still challenging to derive atomic structures when the resolution of cryo-EM density maps is in the medium resolution range, such as 5–10 Å. Detection of protein secondary structures, such as helices and β-sheets, from cryo-EM density maps provides constraints for deriving atomic structures from such maps. As more deep learning methodologies are being developed for solving various molecular problems, effective tools are needed for users to access them. We have developed an effective software bundle, DeepSSETracer, for the detection of protein secondary structure …

Ssentiaa: A Self-Supervised Sentiment Analyzer For Classification From Unlabeled Data, Salim Sazzed, Sampath Jayarathna

Computer Science Faculty Publications

In recent years, supervised machine learning (ML) methods have realized remarkable performance gains for sentiment classification utilizing labeled data. However, labeled data are usually expensive to obtain, thus, not always achievable. When annotated data are unavailable, the unsupervised tools are exercised, which still lag behind the performance of supervised ML methods by a large margin. Therefore, in this work, we focus on improving the performance of sentiment classification from unlabeled data. We present a self-supervised hybrid methodology SSentiA (Self-supervised Sentiment Analyzer) that couples an ML classifier with a lexicon-based method for sentiment classification from unlabeled data. We first introduce LRSentiA …

Understanding And Predicting Retractions Of Published Work, Sai Ajay Modukuri, Sarah Rajtmajer, Anna Cinzia Squicciarini, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Recent increases in the number of retractions of published papers reflect heightened attention and increased scrutiny in the scientific process motivated, in part, by the replication crisis. These trends motivate computational tools for understanding and assessment of the scholarly record. Here, we sketch the landscape of retracted papers in the Retraction Watch database, a collection of 19k records of published scholarly articles that have been retracted for various reasons (e.g., plagiarism, data error). Using metadata as well as features derived from full-text for a subset of retracted papers in the social and behavioral sciences, we develop a random forest classifier …

Recognizing Figure Labels In Patents, Ming Gong, Xin Wei, Diane Oyen, Jian Wu, Martin Gryder

Computer Science Faculty Publications

Scientific documents often contain significant information in figures. The United States Patent and Trademark Office (USPTO) awards thousands of patents each week, with each patent containing on the order of a dozen figures. The information conveyed by these figures typically include a drawing or diagram, a label, caption and reference text within the document. Yet associating the short bits of text to the figure is challenging when labels are embedded within the figure, as they typically are in patents. Using patents as a testbench, this paper highlights an open challenge in analyzing all of the information presented in scientific/technical documents …

Analysis Of Subtelomeric Rextal Assemblies Using Quast, Tunazzina Islam, Desh Ranjan, Mohammad Zubair, Eleanor Young, Ming Xiao, Harold Riethman

Computer Science Faculty Publications

Genomic regions of high segmental duplication content and/or structural variation have led to gaps and misassemblies in the human reference sequence, and are refractory to assembly from whole-genome short-read datasets. Human subtelomere regions are highly enriched in both segmental duplication content and structural variations, and as a consequence are both impossible to assemble accurately and highly variable from individual to individual. Recently, we developed a pipeline for improved region-specific assembly called Regional Extension of Assemblies Using Linked-Reads (REXTAL). In this study, we evaluate REXTAL and genome-wide assembly (Supernova) approaches on 10X Genomics linked-reads data sets partitioned and barcoded using the …

Extractive Research Slide Generation Using Windowed Labeling Ranking, Athar Sefid, Prasenjit Mitra, Jian Wu, C. Lee Giles

Computer Science Faculty Publications

Presentation slides generated from original research papers provide an efficient form to present research innovations. Manually generating presentation slides is labor-intensive. We propose a method to automatically generates slides for scientific articles based on a corpus of 5000 paper-slide pairs compiled from conference proceedings websites. The sentence labeling module of our method is based on SummaRuNNer, a neural sequence model for extractive summarization. Instead of ranking sentences based on semantic similarities in the whole document, our algorithm measures the importance and novelty of sentences by combining semantic and lexical features within a sentence window. Our method outperforms several baseline methods …

Systematizing Confidence In Open Research And Evidence (Score), Nazanin Alipourfard, Beatrix Arendt, Daniel M. Benjamin, Noam Benkler, Michael Bishop, Mark Burstein, Martin Bush, James Caverlee, Yiling Chen, Chae Clark, Anna Dreber Almenberg, Timothy M. Errington, Fiona Fidler, Nicholas Fox, Aaron Frank, Hannah Fraser, Scott Friedman, Ben Gelman, James Gentile, Jian Wu, Et Al., Score Collaboration

Computer Science Faculty Publications

Assessing the credibility of research claims is a central, continuous, and laborious part of the scientific process. Credibility assessment strategies range from expert judgment to aggregating existing evidence to systematic replication efforts. Such assessments can require substantial time and effort. Research progress could be accelerated if there were rapid, scalable, accurate credibility indicators to guide attention and resource allocation for further assessment. The SCORE program is creating and validating algorithms to provide confidence scores for research claims at scale. To investigate the viability of scalable tools, teams are creating: a database of claims from papers in the social and behavioral …

Automatic Metadata Extraction Incorporating Visual Features From Scanned Electronic Theses And Dissertations, Muntabir Hasan Choudhury, Himarsha R. Jayanetti, Jian Wu, William A. Ingram, Edward A. Fox

Computer Science Faculty Publications

Electronic Theses and Dissertations (ETDs) contain domain knowledge that can be used for many digital library tasks, such as analyzing citation networks and predicting research trends. Automatic metadata extraction is important to build scalable digital library search engines. Most existing methods are designed for born-digital documents, so they often fail to extract metadata from scanned documents such as ETDs. Traditional sequence tagging methods mainly rely on text-based features. In this paper, we propose a conditional random field (CRF) model that combines text-based and visual features. To verify the robustness of our model, we extended an existing corpus and created a …

Associations Between Fruit Intake And Risk Of Diabetes In The Ausdiab Cohort: Supplementary Material, Nicola P. Bondonno, Raymond J. Davey, Kevin Murray, Simone Radavelli-Bagatini, Catherine P. Bondonno, Lauren C. Blekkenhorst, Marc Sim, Dianna J. Magliano, Robin M. Daly, Jonathan E. Shaw, Joshua R. Lewis, Jonathan M. Hodgson

Research Datasets

Supplementary Material accompanying manuscript entitled ‘Associations between fruit intake and risk of diabetes in the AusDiab cohort’.

Investigating Long-Term Physical, Chemical And Biological Changes In Narragansett Bay Using Bayesian Multivariate Dynamic Linear Models, Jacob P. Strock

Open Access Master's Theses

Within the past 50 years, Narragansett Bay has undergone major physical and chemical changes including climate-induced warming and policy-driven reductions in anthropogenic nutrient pollution. These long-term changes have the capacity to transform the ecological function of Narragansett Bay, but may also represent a case study for global oceanic changes. Despite its importance, the long-term response of ecosystems to climatological change remains uncertain, as well as the consistency of biological interactions with the environment over time. I explore these uncertainties here using Bayesian dynamic linear models (DLMs) to investigate the Narragansett Bay Long-Term Plankton Time Series. In a first stage, DLMs …

A Phd Filter Based Relative Localization Filter For Robotic Swarms, Rupasinghe Thivanka Perera

Open Access Master's Theses

In this thesis, we present a Probability Hypothesis Density (PHD) filter based relative localization system for robotic swarms. The system is designed to use only local information collected by onboard lidar and camera sensors to identify and track other swarm members within proximity. The multi-sensor setup of the system accounts for the inability of single sensors to provide enough information for the simultaneous identification of teammates and estimation of their position. However, it also requires the implementation of sensor fusion techniques that do not employ complex computer vision or recognition algorithms, due to robots' limited computational capabilities. The use of …

Real-World Utilization And Expenditure Of Top-Down And Step-Up Therapy In Inflammatory Bowel Disease, Kanya K. Shah

Open Access Master's Theses

Introduction:

Inflammatory bowel disease (IBD) describes gastrointestinal inflammatory diseases Crohn’s disease (CD) and ulcerative colitis (UC). The inflammation associated with CD can penetrate deep layers of gastrointestinal tissue anywhere along the gastrointestinal tract. Pharmaceutical therapy options for CD include aminosalicylates, corticosteroids, antimicrobials, immunomodulators, and biologics. The step-up (SU) treatment strategy for CD begins with anti-inflammatory agents (aminosalicylates, corticosteroids, and antimicrobials), and progresses to immunomodulators and biologics if disease control is not achieved. Conversely, with top-down (TD) therapy strategy, patients initiate CD medication treatment with immunomodulators and biologics and use anti-inflammatory agents as second-line options. Presently, limited evidence exists characterizing the …

Characterization Of A New Sediment Porewater Passive Sampler For Hydrophobic Organic Contaminants, Samuel D. Katz

Open Access Master's Theses

Passive samplers have been proven as a useful tool for sampling the bioavailable fraction of hydrophobic organic contaminants (HOCs) in sediment. Polyethylene (PE) sheets used as passive samplers enable the quantification of a wide range of the bioavailable HOCs in situ and ex situ. The sediment of Newark Bay and Passaic River historically is a source of legacy HOCs such as polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and polychlorinated biphenyls (PCBs) as well as emerging contaminates such as organophosphate esters (OPEs). Here we examine the distribution of PCBs, PCDD/Fs, and OPEs in Newark Bay utilizing a new form of passive sampling …

Identifying Novel Isotopic Tracers Of Marine Primary Producers To Study Food Web Carbon Cycles, Angela Stahl

Open Access Master's Theses

Eukaryotic microalgae play critical roles in global biogeochemical cycling, the efficacy of the biological pump, and the structure and function of most marine food webs. Compound-specific isotope analysis of amino acids (CSIA-AA) couples molecular-level biomarkers with stable isotope analyses to track the contribution of primary producers to food webs. Previous work has developed δ13CAA fingerprints for major groups of primary producers but categorized all marine algae together despite their vast taxonomic and ecological diversity. This work characterizes the δ13CAA fingerprints of four major groups of eukaryotic microalgae — diatoms, dinoflagellates, raphidophytes, and prasinophytes — in controlled cultures, and quantifies the …

The Effects Of Message Framing On Response To Plastic Reduction Policies, Courtney Milley

Open Access Master's Theses

Academic literature to date has effectively proven the scale of plastic pollution’s harm to the environment. Some scholars have even argued that it should be discussed broadly in society as a crisis (Mæland & Staupe‐Delgado 2019). To reduce plastic pollution, potential policies need widespread support. Existing literature shows framing manipulations can influence opinion and care for plastic pollution but have not been tied to policy support. This study looked to assess the difference in policy support between groups framed with plastic pollution contributing to climate change causing emissions, plastic pollution endangering charismatic animals, or a control framing with simple information …