Data Science Commons^™

Model Of The State Of Threats To The Access Control System, ‪Durdona Irgasheva

Bulletin of TUIT: Management and Communication Technologies

This article is devoted to the presentation of the threat state model of access control, which allows calculating the probabilities of the impact of threats on the access control system and the probability of opening this system based on taking into account the generalized algorithm for the implementation of external threats, and determines the need to develop additional components of the access control system designed to identify and classify attacks.

Data-Driven Decision-Support For Process Improvement Through Predictions Of Bed Occupancy Rates, Kar Way Tan, Qi You Ng, Francis Ngoc Hoang Long Nguyen, Sean Shao Wei Lam

Research Collection School Of Computing and Information Systems

Managing bed utilization and ensuring the supply keeps up with the demand is not an easy task in a large public hospital with many medical disciplines. The bed managers who makes decisions on reserving and allocating beds centrally require high-dimensional data from several hospital information systems supporting emergency room, specialized clinics and bed management processes. In this work, we put together an automated process for cleaning, consolidating and integrating data from several hospital information systems to several reports required by the bed managers to analyse the bed occupancy situations across more than thirty medical disciplines. To prevent bed crunch situations …

Mathematical And Computer Simulation Of The Processes Of Two-Phase Joint Gas Filtration And Water In A Porous Environment, Elmira Nazirova

Bulletin of TUIT: Management and Communication Technologies

A mathematical model, methods and algorithms for the numerical solution of problems of joint gas-water filtration in porous media are considered. The mathematical model of the process of non-stationary joint gas-water filtration in a porous medium is described by a system of nonlinear differential equations of parabolic type. In the numerical solution of the boundary value problem of gas displacement by water in a porous medium, the differential sweeping method is used for systems of differential-difference equations. The system of differential-difference equations with respect to the gas pressure function is nonlinear, therefore, an iterative method is used for it, based …

Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan

Dissertations

Spatial and temporal dependencies are ubiquitous properties of data in numerous domains. The popularity of spatial and temporal data mining has thus grown with the increasing prevalence of massive data. The presence of spatial and temporal attributes not only provides complementary useful perspectives, but also poses new challenges to the representation and integration into the learning procedure. In this dissertation, the involved spatial and temporal dependencies are explored with three genres: sample-wise, feature-wise, and target-wise. A family of novel methodologies is developed accordingly for the dependency representation in respective scenarios.

First, dependencies among discrete, continuous and repeated observations are studied …

On Properties Of Distance-Based Entropies On Fullerene Graphs, Modjtaba Ghorbani, Matthias Dehmer, Mina Rajabi-Parsa, Abbe Mowshowitz, Frank Emmert-Streib

Publications and Research

In this paper, we study several distance-based entropy measures on fullerene graphs. These include the topological information content of a graph I_a(G), a degree-based entropy measure, the eccentric-entropy If_s(G), the Hosoya entropy H(G) and, finally, the radial centric information entropy H_ecc. We compare these measures on two infinite classes of fullerene graphs denoted by A_12n+4 and B_12n+6. We have chosen these measures as they are easily computable and capture meaningful graph properties. To demonstrate the utility of these measures, we investigate the Pearson correlation between them on the fullerene graphs.

Watersheds For Semi-Supervised Classification, Aditya Challa, Sravan Danda, B. S.Daya Sagar, Laurent Najman

Journal Articles

Watershed technique from mathematical morphology (MM) is one of the most widely used operators for image segmentation. Recently watersheds are adapted to edge weighted graphs, allowing for wider applicability. However, a few questions remain to be answered - How do the boundaries of the watershed operator behave? Which loss function does the watershed operator optimize? How does watershed operator relate with existing ideas from machine learning. In this letter, a framework is developed, which allows one to answer these questions. This is achieved by generalizing the maximum margin principle to maximum margin partition and proposing a generic solution, morphMedian, resulting …

Do Misperceptions Of Peer Drinking Influence Personal Drinking Behavior? Results From A Complete Social Network Of First-Year College Students, Melissa J. Cox, Angelo M. Dibello, Matthew K. Meisel, Miles Q. Ott, Shannon R. Kenney, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

This study considered the influence of misperceptions of typical versus self-identified important peers' heavy drinking on personal heavy drinking intentions and frequency utilizing data from a complete social network of college students. The study sample included data from 1,313 students (44% male, 57% White, 15% Hispanic/Latinx) collected during the fall and spring semesters of their freshman year. Students provided perceived heavy drinking frequency for a typical student peer and up to 10 identified important peers. Personal past-month heavy drinking frequency was assessed for all participants at both time points. By comparing actual with perceived heavy drinking frequencies, measures of misperceptions …

Forecasting The Number Of Monthly Active Facebook And Twitter Worldwide Users Using Arma Model, Qasem Abu Al-Haija, Qian Mao, Kamal Al Nasr

Computer Science Faculty Research

In this study, an Auto-Regressive Moving Average (ARMA) Model with optimal order has been developed to estimate and forecast the short term future numbers of the monthly active Facebook and Twitter worldwide users. In order to pickup the optimal estimation order, we analyzed the model order vs. the corresponding model error in terms of final prediction error. The simulation results showed that the optimal model order to estimate the given Facebook and Twitter time series are ARMA[5, 5] and ARMA[3, 3], respectively, since they correspond to the minimum acceptable prediction error values. Besides, the optimal models recorded a high-level of …

A Grammar For Reproducible And Painless Extract-Transform-Load Operations On Medium Data, Benjamin S. Baumer

Statistical and Data Sciences: Faculty Publications

Many interesting datasets available on the Internet are of a medium size—too big to fit into a personal computer’s memory, but not so large that they would not fit comfortably on its hard disk. In the coming years, datasets of this magnitude will inform vital research in a wide array of application domains. However, due to a variety of constraints they are cumbersome to ingest, wrangle, analyze, and share in a reproducible fashion. These obstructions hamper thorough peer-review and thus disrupt the forward progress of science. We propose a predictable and pipeable framework for R (the state-of-the-art statistical computing environment) …

Exploring And Visualizing Household Electricity Consumption Patterns In Singapore: A Geospatial Analytics Approach, Yong Ying Tan, Tin Seong Kam

Research Collection School Of Computing and Information Systems

Despite being a small country-state, electricity consumption in Singa-pore is said to be non-homogeneous, as exploratory data analysis showed that the distributions of electricity consumption differ across and within administrative boundaries and dwelling types. Local indicators of spatial association (LISA) were calculated for public housing postal codes using June 2016 data to discover local clusters of households based on electricity consumption patterns. A detailed walkthrough of the analytical process is outlined to describe the R packages and framework used in the R environment. The LISA results are visualized on three levels: country level, regional level and planning subzone level. At …

Untapped Potential Of Clinical Text For Opioid Surveillance, Amy L. Olex, Tamas Gal, Majid Afshar, Dmitriy Dligach, Niranjan Karnik, Travis Oakes, Brihat Sharma, Meng Xie, Bridget T. Mcinnes, Julian Solway, Abel Kho, William Cramer, F. Gerard Moeller

Wright Center for Clinical and Translational Research Works

Accurate surveillance is needed to combat the growing opioid epidemic. To investigate the potential volume of missed opioid overdoses, we compare overdose encounters identified by ICD-10-CM codes and an NLP pipeline from two different medical systems. Our results show that the NLP pipeline identified a larger percentage of OOD encounters than ICD-10-CM codes. Thus, incorporating sophisticated NLP techniques into current diagnostic methods has the potential to improve surveillance on the incidence of opioid overdoses.

Radically Simplifying Gated Recurrent Architectures Without Loss Of Performance, Jonathan Boardman, Ying Xie

Published and Grey Literature from PhD Candidates

Long Short-Term Memory (LSTM) units are a family of Recurrent Neural Network (RNN) architectures that have proven incredibly effective at learning from sequence data. They are also extremely complex, making them expensive to train and difficult to understand. A recent trend towards simplification has produced the Gated Recurrent Unit (GRU) and the Minimal Gated Unit (MGU), both of which perform as well as the LSTM (or better) on a variety of tasks. The MGU is one of the simplest gated recurrent architectures at the moment. Our study demonstrates that it is possible to radically simplify the MGU without significant loss …

On The Inability Of Markov Models To Capture Criticality In Human Mobility, Vaibhav Klukarni, Abhijit Mahalunkar, Benoit Garbinato, John Kelleher

Conference papers

We examine the non-Markovian nature of human mobility by exposing the inability of Markov models to capture criticality in human mobility. In particular, the assumed Markovian nature of mobility was used to establish an upper bound on the predictability of human mobility, based on the temporal entropy. Since its inception, this bound has been widely used for validating the performance of mobility prediction models. We show that the variants of recurrent neural network architectures can achieve significantly higher prediction accuracy surpassing this upper bound. The central objective of our work is to show that human-mobility dynamics exhibit criticality characteristics which …

Enrollment And Assessment Of A First-Year College Class Social Network For A Controlled Trial Of The Indirect Effect Of A Brief Motivational Intervention, Nancy P. Barnett, Melissa A. Clark, Shannon R. Kenney, Graham Diguiseppi, Matthew K. Meisel, Sara Balestrieri, Miles Q. Ott, John Light

Statistical and Data Sciences: Faculty Publications

Heavy drinking and its consequences among college students represent a serious public health problem, and peer social networks are a robust predictor of drinking-related risk behaviors. In a recent trial, we administered a Brief Motivational Intervention (BMI) to a small number of first-year college students to assess the indirect effects of the intervention on peers not receiving the intervention. Objectives: To present the research design, describe the methods used to successfully enroll a high proportion of a first-year college class network, and document participant characteristics. Methods: Prior to study enrollment, we consulted with a student advisory group and campus stakeholders …

The Global Disinformation Order: 2019 Global Inventory Of Organised Social Media Manipulation, Samantha Bradshaw, Philip N. Howard

Copyright, Fair Use, Scholarly Communication, etc.

Executive Summary

Over the past three years, we have monitored the global organization of social media manipulation by governments and political parties. Our 2019 report analyses the trends of computational propaganda and the evolving tools, capacities, strategies, and resources.

1. Evidence of organized social media manipulation campaigns which have taken place in 70 countries, up from 48 countries in 2018 and 28 countries in 2017. In each country, there is at least one political party or government agency using social media to shape public attitudes domestically.

2.Social media has become co-opted by many authoritarian regimes. In 26 countries, computational propaganda …

Relationships Between Social Network Characteristics, Alcohol Use, And Alcohol-Related Consequences In A Large Network Of First-Year College Students: How Do Peer Drinking Norms Fit In?, Graham T. Diguiseppi, Matthew K. Meisel, Sara G. Balestrieri, Miles Q. Ott, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

A burgeoning area of research is using social network analysis to investigate college students' substance use behaviors. However, little research has incorporated students' perceived peer drinking norms into these analyses. The present study investigated the association between social network characteristics, alcohol use, and alcohol-related consequences among first-year college students (N 1,342; 81% of the first-year class) at one university. The moderating role of descriptive norms was also examined. Network characteristics and descriptive norms were derived from participants' nominations of up to 10 other students who were important to them; individual network characteristics included popularity (indegree), network expansiveness (outdegree), relationship reciprocity, …

On The Exactitude Of Big Data: La Bêtise And Artificial Intelligence, Noel Fitzpatrick, John D. Kelleher

Articles

This article revisits the question of ‘la bêtise’ or stupidity in the era of Artificial Intelligence driven by Big Data, it extends on the questions posed by Gille Deleuze and more recently by Bernard Stiegler. However, the framework for revisiting the question of la bêtise will be through the lens of contemporary computer science, in particular the development of data science as a mode of analysis, sometimes, misinterpreted as a mode of intelligence. In particular, this article will argue that with the advent of forms of hype (sometimes referred to as the hype cycle) in relation to big data and …

Strategic Players For Identifying Optimal Social Network Intervention Subjects, Miles Q. Ott, John M. Light, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

We present a method whereby social network ties are used to identify behavioral leaders who are situated in the network such that these individuals are: 1) able to influence other individuals who are in need of and most receptive to intervention, thereby optimizing the impact of the intervention; and 2) not embedded with ties to individuals that are likely to be behaviorally antagonistic to the intervention or that would compromise the optimal impact of intervention. In this study we developed a method that we call Strategic Players, which is a solution for identifying a set of players who are close …

An Algorithm For Calculating Top-Dimensional Bounding Chains, J. Frederico Carvalho​, Mikael Vejdemo-Johansson, Danica Kragic, Florian T. Pokorny

Publications and Research

We describe the Coefficient-Flow algorithm for calculating the bounding chain of an (n-1)-boundary on an n-manifold-like simplicial complex S. We prove its correctness and show that it has a computational time complexity of O(|S(n−1)|) (where S(n−1) is the set of (n-1)-faces of S). We estimate the big-O coefficient which depends on the dimension of S and the implementation. We present an implementation, experimentally evaluate the complexity of our algorithm, and compare its performance with that of solving the underlying linear system.

Multi-Point Vibration Measurement And Mode Magnification Of Civil Structures Using Video-Based Motion Processing, Zhexiong Shang, Zhigang Shen

Department of Construction Engineering and Management: Faculty Publications

Image-based vibration measurement has gained increased attentions in civil and construction communities. A recent video-based motion magnification method was developed to measure and visualize small structure motions. This new approach presents a potential for low-cost vibration measurement and mode shape identification. Pilot studies using this approach on simple rigid body structures were reported. Its validity on complex outdoor structures has not been investigated. In this study, a non-contact video-based approach for multi-point vibration measurement and mode magnification is introduced. The proposed approach can output a full-field vibration map that increases the efficiency of the current structural health monitoring (SHM) practice. …

Summary Of The Special Issue “Neutrosophic Information Theory And Applications” At “Information” Journal, Florentin Smarandache, Jun Ye

Branch Mathematics and Statistics Faculty and Staff Publications

Over a period of seven months (August 2017–February 2018), the Special Issue dedicated to “Neutrosophic Information Theory and Applications” by the “Information” journal (ISSN 2078-2489), located in Basel, Switzerland, was a success. The Guest Editors, Prof. Dr. Florentin Smarandache from the University of New Mexico (USA) and Prof. Dr. Jun Ye from the Shaoxing University (China), were happy to select—helped by a team of neutrosophic reviewers from around the world, and by the “Information” journal editors themselves—and publish twelve important neutrosophic papers, authored by 27 authors and coauthors. There were a variety of neutrosophic topics studied and used by the …

Non-Linear Machine Learning With Active Sampling For Mox Drift Compensation, Tamara Matthews, Muhammad Iqbal, Horacio Gonzalez-Velez

Conference papers

Abstract—Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, …

Mapping Opioid Mortality Rates Across Treatment Capacity To Identify Need And Access, Garrett K. Wong, Justin R. Chang, Chase Greco, Yadunandan Pillai, Mohammad A. Shahrezaei, Melissa H. Burton, Rob Lawrence, Alan Dow

Graduate Research Posters

Background: The opioid and heroin overdose epidemic is a public health emergency in the state of Virginia, resulting in the death of more than 1,100 people in 2016. In order to overcome this epidemic, we need to match the places with the greatest need for services related to substance use disorders with the appropriate healthcare workforce.

Aims: As the data about the overdose outbreak and related socioeconomic factors grow in size and complexity, data scientists have attempted to utilize big data techniques to identify communities and risk factors contributing to addiction.

Methods: Using data obtained from the …

Visualizing The Opioid Overdose With A Dynamic Heat Map To Identify And Predict Vulnerable Communities, Justin R. Chang, Garrett K. Wong, Chase Greco, Yadunandan Pillai, Mohammad A. Shahrezaei, Melissa H. Burton, Rob Lawrence, Alan Dow

Graduate Research Posters

Background: Opioid and heroin overdose epidemic is a public health emergency in the state of Virginia. In order to prevent overdose deaths, we need the target expertise in substance use disorders to areas with high rates of overdose. In particular, an area with an acute spike in overdoses might represent an urgent need for intervention.

Aims: The CDC urges the use of near real-time surveillance to effectively identify overdose incidence, and to coordinate community responses in the states affected by the epidemic, including Virginia. However, current opioid overdose datasets for Virginia lack adequate consistency, granularity, and temporality for …

Privacy In Iot Cloud, Aftab Ahmad, Ravi Mukkamala, Karthik Navuluri

Computer Science Faculty Publications

We present a framework for privacy preservation in an information cloud of IoT devices. We contend that privacy provisioning should be located in the user device and must protect the user, the information, and the device from breaches in privacy. We elaborate on how the layered privacy model can ensure such privacy provisioning, and justify the device being the provisioning point instead of the cloud alone. We present the point of view that, due to resource limitations of the IoT devices in general, the privacy preserving measures need to be hard-coded in the device technology. We fall short of suggesting …

Interactive Visual Analytics Application For Spatiotemporal Movement Data Vast Challenge 2017 Mini-Challenge 1: Award For Actionable And Detailed Analysis, Yifei Guan, Tin Seong Kam

Research Collection School Of Computing and Information Systems

The Visual Analytics Science and Technology (VAST) Challenge 2017 Mini-Challenge 1 dataset mirrored the challenging scenarios in analysing large spatiotemporal movement tracking datasets. The datasets provided contains a 13-month movement data generated by five types of sensors, for six types of vehicles passing through the Boonsong Lekagul Nature Preserve. We present an application developed with the market leading visualisation software Tableau to provide an interactive visual analysis of the multi-dimensional spatiotemporal datasets. Our interactive application allows the user to perform an interactive analysis to observe movement patterns, study vehicle trajectories and identify movement anomalies while allowing them to customise the …

Ancr—An Adaptive Network Coding Routing Scheme For Wsns With Different-Success-Rate Links †, Xiang Ji, Anwen Wang, Chunyu Li, Chun Ma, Yao Peng, Dajin Wang, Qingyi Hua, Feng Chen, Dingyi Fang

Department of Computer Science Faculty Scholarship and Creative Works

As the underlying infrastructure of the Internet of Things (IoT), wireless sensor networks (WSNs) have been widely used in many applications. Network coding is a technique in WSNs to combine multiple channels of data in one transmission, wherever possible, to save node’s energy as well as increase the network throughput. So far most works on network coding are based on two assumptions to determine coding opportunities: (1) All the links in the network have the same transmission success rate; (2) Each link is bidirectional, and has the same transmission success rate on both ways. However, these assumptions may not be …

Constructing Interactive Visual Classification, Clustering And Dimension Reduction Models For N-D Data, Boris Kovalerchuk, Dmytro Dovhalets

Computer Science Faculty Scholarship

The exploration of multidimensional datasets of all possible sizes and dimensions is a long-standing challenge in knowledge discovery, machine learning, and visualization. While multiple efficient visualization methods for n-D data analysis exist, the loss of information, occlusion, and clutter continue to be a challenge. This paper proposes and explores a new interactive method for visual discovery of n-D relations for supervised learning. The method includes automatic, interactive, and combined algorithms for discovering linear relations, dimension reduction, and generalization for non-linear relations. This method is a special category of reversible General Line Coordinates (GLC). It produces graphs in 2-D that represent …

Taking A Byte Out Of Corruption: A Data Analytic Framework For Cities To Fight Fraud, Cut Costs, And Promote Integrity, Center For The Advancement Of Public Integrity

Center for the Advancement of Public Integrity (Inactive)

In recent years, the emerging science of data analytics has equipped law enforcement agencies and urban policymakers with game-changing tools. Many leaders and thinkers in the public integrity community believe such innovations could prove equally transformational for the fight against public corruption. However, corruption control presents unique challenges that must be addressed before city watchdog agencies can harness the power of big data. City governments need to improve data collection and management practices and develop new models to leverage available data to better monitor corruption risks.

To bridge this gap and pave the way for a potential data breakthrough in …

Special Issue: Neutrosophic Theories Applied In Engineering, Florentin Smarandache, Jun Ye

Branch Mathematics and Statistics Faculty and Staff Publications

Neutrosophic sets and logic are generalizations of fuzzy and intuitionistic fuzzy sets and logic. Neutrosophic sets and logic are gaining significant attention in solving many real life decision making problems that involve uncertainty, impreciseness, vagueness, incompleteness, inconsistent, and indeterminacy. They have been applied in computational intelligence, multiple criteria decision making, image processing, medical diagnoses, etc. This Special Issue presents original research papers that report on state-of-the-art and recent advancements in neutrosophic sets and logic in soft computing, artificial intelligence, big and small data mining, decision making problems, and practical achievements.