Physical Sciences and Mathematics Commons^™

Linking Taxonomic Diversity And Trophic Function: A Graph-Based Theoretical Approach, Marcella M. Jurotich, Kaitlyn Dougherty, Barbara Hayford, Sally Clark

Transactions of the Nebraska Academy of Sciences and Affiliated Societies

The purpose of this study is to develop a novel, visual method in analyzing complex functional trait data in freshwater ecology. We focus on macroinvertebrates in stream ecosystems under a gradient of habitat degradation and employ a combination of taxonomic and functional trait diversity analyses. Then we use graph theory to link changes in functional trait diversity to taxonomic richness and habitat degradation. We test the hypotheses that: 1) taxonomic diversity and trophic functional trait diversity both decrease with increased habitat degradation; 2) loss of taxa leads to a decrease in trophic function as visualized using a bipartite graph; and …

Ordinal Convolutional Neural Networks For Predicting Rdoc Positive Valence Psychiatric Symptom Severity Scores, Anthony Rios, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task.

Objective—Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are …

Predicting Mental Conditions Based On "History Of Present Illness" In Psychiatric Notes With Deep Neural Networks, Tung Tran, Ramakanth Kavuluru

Computer Science Faculty Publications

Background—Applications of natural language processing to mental health notes are not common given the sensitive nature of the associated narratives. The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) changed this scenario by providing the first set of neuropsychiatric notes to participants. This study summarizes our efforts and results in proposing a novel data use case for this dataset as part of the third track in this shared task.

Objective—We explore the feasibility and effectiveness of predicting a set of common mental conditions a patient has based on the short textual description of patient’s history …

Testing The Independence Hypothesis Of Accepted Mutations For Pairs Of Adjacent Amino Acids In Protein Sequences, Jyotsna Ramanan, Peter Revesz

School of Computing: Faculty Publications

Evolutionary studies usually assume that the genetic mutations are independent of each other. However, that does not imply that the observed mutations are independent of each other because it is possible that when a nucleotide is mutated, then it may be biologically beneficial if an adjacent nucleotide mutates too. With a number of decoded genes currently available in various genome libraries and online databases, it is now possible to have a large-scale computer-based study to test whether the independence assumption holds for pairs of adjacent amino acids. Hence the independence question also arises for pairs of adjacent amino acids within …

Discovering Explanatory Models To Identify Relevant Tweets On Zika, Roopteja Muppalla, Michele Miller, Tanvi Banerjee, William L. Romine

Kno.e.sis Publications

Zika virus has caught the worlds attention, and has led people to share their opinions and concerns on social media like Twitter. Using text-based features, extracted with the help of Parts of Speech (POS) taggers and N-gram, a classifier was built to detect Zika related tweets from Twitter. With a simple logistic classifier, the system was successful in detecting Zika related tweets from Twitter with a 92% accuracy. Moreover, key features were identified that provide deeper insights on the content of tweets relevant to Zika. This system can be leveraged by domain experts to perform sentiment analysis, and understand the …

A Knowledge Graph Framework For Detecting Traffic Events Using Stationary Cameras, Roopteja Muppalla, Sarasi Lalithsena, Tanvi Banerjee, Amit Sheth

Kno.e.sis Publications

With the rapid increase in urban development, it is critical to utilize dynamic sensor streams for traffic understanding, especially in larger cities where route planning or infrastructure planning is more critical. This creates a strong need to understand traffic patterns using ubiquitous sensors to allow city officials to be better informed when planning urban construction and to provide an understanding of the traffic dynamics in the city. In this study, we propose our framework ITSKG (Imagery-based Traffic Sensing Knowledge Graph) which utilizes the stationary traffic camera information as sensors to understand the traffic patterns. The proposed system extracts image-based features …

Eassistant: Cognitive Assistance For Identification And Auto-Triage Of Actionable Conversations, Hamid R. Motahari Nezhad, Kalpa Gunaratna, Juan Cappi

Kno.e.sis Publications

The browser and screen have been the main user interfaces of the Web and mobile apps. The notification mechanism is an evolution in the user interaction paradigm by keeping users updated without checking applications. Conversational agents are posed to be the next revolution in user interaction paradigms. However, without intelligence on the triage of content served by the interaction and content differentiation in applications, interaction paradigms may still place the burden of information overload on users. In this paper, we focus on the problem of intelligent identification of actionable information in the content served by applications, and in particular in …

What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, And Prevention, Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William L. Romine, Amit Sheth

Kno.e.sis Publications

Background: In order to harness what people are tweeting about Zika, there needs to be a computational framework that leverages machine learning techniques to recognize relevant Zika tweets and, further, categorize these into disease-specific categories to address specific societal concerns related to the prevention, transmission, symptoms, and treatment of Zika virus.

Objective: The purpose of this study was to determine the relevancy of the tweets and what people were tweeting about the 4 disease characteristics of Zika: symptoms, transmission, prevention, and treatment.

Methods: A combination of natural language processing and machine learning techniques was used to determine what people were …

A Proposed Frequency-Based Feature Selection Method For Cancer Classification, Yi Pan

Masters Theses & Specialist Projects

Feature selection method is becoming an essential procedure in data preprocessing step. The feature selection problem can affect the efficiency and accuracy of classification models. Therefore, it also relates to whether a classification model can have a reliable performance. In this study, we compared an original feature selection method and a proposed frequency-based feature selection method with four classification models and three filter-based ranking techniques using a cancer dataset. The proposed method was implemented in WEKA which is an open source software. The performance is evaluated by two evaluation methods: Recall and Receiver Operating Characteristic (ROC). Finally, we found the …

Homestead National Monument Of America, Bat Acoustic Monitoring, September 2016, Daniel S. Licht

United States National Park Service: Publications

Abstract

Homestead National Monument of America is a 211-acre park located in an agrarian landscape in southeastern Nebraska. From September 16 to October 1, 2016, park staff deployed acoustic monitors at three sites in the park for purposes of monitoring night-time bat activity. The three sites averaged 179, 48, and 33 bat detections per night. Night-time bat activity was generally highest in the 1-2 hours following sunset.

Based on the acoustic surveys the big brown (Eptesicus fuscus), eastern red (Lasiurus borealis), northern long-eared (Myotis septentrionalis) and evening bats (Nycticeius humeralis) were present at the …

Identifying Depressive Disorder In The Twitter Population, Goonmeet Bajaj, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth

Kno.e.sis Publications

Depression is a highly prevalent public health challenge and a major cause of disability across the globe.

• Annually 6.7% of Americans (that is, more than 16 million).
• Traditional approaches to curb depression involve survey·based methods via phone or online questionnaires.
• Large temporal gaps and cognitive bias.

Social media provides a method for learning users' feelings, emotions, behaviors, and decisions in real-time.

Gpu-Pcc: A Gpu Based Technique To Compute Pairwise Pearson’S Correlation Coefficients For Big Fmri Data, Taban Eslami, Muaaz Gul Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Functional Magnetic Resonance Imaging (fMRI) is a non-invasive brain imaging technique for studying the brain’s functional activities. Pearson’s Correlation Coefficient is an important measure for capturing dynamic behaviors and functional connectivity between brain components. One bottleneck in computing Correlation Coefficients is the time it takes to process big fMRI data. In this paper, we propose GPU-PCC, a GPU based algorithm based on vector dot product, which is able to compute pairwise Pearson’s Correlation Coefficients while performing computation once for each pair. Our method is able to compute Correlation Coefficients in an ordered fashion without the need to do post-processing reordering …

An Out-Of-Core Gpu Based Dimensionality Reduction Algorithm For Big Mass Spectrometry Data And Its Application In Bottom-Up Proteomics, Muaaz Awan, Fahad Saeed

Parallel Computing and Data Science Lab Technical Reports

Modern high resolution Mass Spectrometry instruments can generate millions of spectra in a single systems biology experiment. Each spectrum consists of thousands of peaks but only a small number of peaks actively contribute to deduction of peptides. Therefore, pre-processing of MS data to detect noisy and non-useful peaks are an active area of research. Most of the sequential noise reducing algorithms are impractical to use as a pre-processing step due to high time-complexity. In this paper, we present a GPU based dimensionality-reduction algorithm, called G-MSR, for MS2 spectra. Our proposed algorithm uses novel data structures which optimize the memory and …

Biosimp: Using Software Testing Techniques For Sampling And Inference In Biological Organisms, Mikaela Cashman, Jennie L. Catlett, Myra B. Cohen, Nicole R. Buan, Zahmeeth Sakkaff, Massimiliano Pierobon, Christine A. Kelley

CSE Conference and Workshop Papers

Years of research in software engineering have given us novel ways to reason about, test, and predict the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. In this paper we reverse the direction and present BioSIMP, a process that models and predicts the behavior of biological organisms to aid in the emerging field of systems biology. It utilizes techniques from testing and modeling of highly-configurable software systems. Using both experimental and simulation data we show …

A Novel Approach For Classifying Gene Expression Data Using Topic Modeling, Soon Jye Kho, Himi Yalamanchili, Michael L. Raymer, Amit Sheth

Kno.e.sis Publications

Understanding the role of differential gene expression in cancer etiology and cellular process is a complex problem that continues to pose a challenge due to sheer number of genes and inter-related biological processes involved. In this paper, we employ an unsupervised topic model, Latent Dirichlet Allocation (LDA) to mitigate overfitting of high-dimensionality gene expression data and to facilitate understanding of the associated pathways. LDA has been recently applied for clustering and exploring genomic data but not for classification and prediction. Here, we proposed to use LDA inclustering as well as in classification of cancer and healthy tissues using lung cancer …

Road Accidents Bigdata Mining And Visualization Using Support Vector Machines, Usha Lokala, Srinivas Nowduri, Prabhakar K. Sharma

Kno.e.sis Publications

Useful information has been extracted from the road accident data in United Kingdom (UK), using data analytics method, for avoiding possible accidents in rural and urban areas. This analysis make use of several methodologies such as data integration, support vector machines (SVM), correlation machines and multinomial goodness. The entire datasets have been imported from the traffic department of UK with due permission. The information extracted from these huge datasets forms a basis for several predictions, which in turn avoid unnecessary memory lapses. Since data is expected to grow continuously over a period of time, this work primarily proposes a new …

Relatedness-Based Multi-Entity Summarization, Kalpa Gunaratna, Amir Hossein Yazdavar, Krishnaprasad Thirunarayan, Amit Sheth, Gong Cheng

Kno.e.sis Publications

Representing world knowledge in a machine processable format is important as entities and their descriptions have fueled tremendous growth in knowledge-rich information processing platforms, services, and systems. Prominent applications of knowledge graphs include search engines (e.g., Google Search and Microsoft Bing), email clients (e.g., Gmail), and intelligent personal assistants (e.g., Google Now, Amazon Echo, and Apple’s Siri). In this paper, we present an approach that can summarize facts about a collection of entities by analyzing their relatedness in preference to summarizing each entity in isolation. Specifically, we generate informative entity summaries by selecting: (i) inter-entity facts that are similar and …

A Semantics-Based Measure Of Emoji Similarity, Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran

Kno.e.sis Publications

Emoji have grown to become one of the most important forms of communication on the web. With its widespread use, measuring the similarity of emoji has become an important problem for contemporary text processing since it lies at the heart of sentiment analysis, search, and interface design tasks. This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base. Using emoji descriptions, emoji sense labels and emoji sense definitions, and with different training corpora obtained from Twitter and Google News, we develop and test …