Data Science Commons^™

Pembagian Tingkat Kecanduan Game Online Menggunakan K-Means Clustering Serta Korelasinya Terhadap Prestasi Akademik, Yudi Prastyo

Elinvo (Electronics, Informatics, and Vocational Education)

Game online tidak hanya memberikan hiburan tetapi juga memberikan tantangan yang menarik untuk diselesaikan sehingga individu bermain game online tanpa memperhitungkan waktu demi mencapai kepuasan. Salah satu metode yang dapat digunakan untuk mengelompokkan tingkat kecanduan game online adalah metode K-Means Clustering. K-Means Clustering merupakan salah satu metode data clustering non hirarki yang berusaha mempartisi data yang ada ke dalam bentuk satu atau lebih cluster/kelompok.Penelitian ini mengambil data sample kuesioner dari mahasiswa di Universitas Ibn Khaldun Bogor dimana isian kuesioner akan diolah sebagai acuan pengelompokkan tingkat kecanduan game online.Hasil clusteringdigunakan untuk mengetahui hubungannya antara tingkat kecanduan game …

Penerapan Data Mining Menggunakan Perbandingan Algoritma Greedy Dengan Algoritma Genetika Pada Prediksi Rentet Waktu Harga Crude Palm Oil, Desy Ika Puspitasari

Elinvo (Electronics, Informatics, and Vocational Education)

Penelitian ini menerapkan data mining pada prediksi harga CPO (Crude Palm Oil) dengan membandingkan pemodelan optimasi seleksi fitur algoritma genetika dan algoritma greedy pada metode neural network (NN). Prediksi harga CPO dilakukan untuk memenuhi kebutuhan investor kelapa sawit, melalui analisa masalah fluktuasi harga CPO time series yang tidak pasti. Guna mempermudah dalam melakukan perhitungan, langkah-langkah dari algoritma Genetika dan algoritma Greedy diimplementasikan dengan program komputer Rapid Miner Studio. Adapun tujuan penelitian ini yaitu mengetahui perbandingan akurasi dengan parameter evaluasi RMSE yang dihasilkan dan waktu eksekusi program yang dibutuhkan oleh algoritma Genetika dan algoritma Greedy dalam menyelesaikan masalah prediksi harga CPO. …

The Utility Of Bioenergetics Modelling In Quantifying Predation Rates Of Marine Apex Predators: Ecological And Fisheries Implications, A. Barnett, M. Braccini, C. L. Dudgeon, N. L. Payne, K. G. Abrantes, M. Sheaves, E. P. Snelling

Fisheries Research Articles

Predators play a crucial role in the structure and function of ecosystems. However, the magnitude of this role is often unclear, particularly for large marine predators, as predation rates are difficult to measure directly. If relevant biotic and abiotic parameters can be obtained, then bioenergetics modelling offers an alternative approach to estimating predation rates, and can provide new insights into ecological processes. We integrate demographic and ecological data for a marine apex predator, the broadnose sevengill shark Notorynchus cepedianus, with energetics data from the literature, to construct a bioenergetics model to quantify predation rates on key fisheries species in …

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 …

Supervised Classification Using Finite Mixture Copula, Sumen Sen, Norou Diawara

Mathematics & Statistics Faculty Publications

Use of copula for statistical classification is recent and gaining popularity. For example, statistical classification using copula has been proposed for automatic character recognition, medical diagnostic and most recently in data mining. Classical discrimination rules assume normality. But in this data age time, this assumption is often questionable. In fact features of data could be a mixture of discrete and continues random variables. In this paper, mixture copula densities are used to model class conditional distributions. Such types of densities are useful when the marginal densities of the vector of features are not normally distributed and are of a mixed …

Content Analysis Of Data Science Graduate Programs In The U.S., Duo Li, Elizabeth Milonas, Qiping Zhang

Publications and Research

Data science is an emerging academic field (Paul & Aithal, 2018), which has its origins in “Big Data/Cloud Computing” and complexity science domains. Data Science is about managing large and complex data (Big Data management) and analytics technologies (Paul & Aithal, 2018). Data, technology, and people are the three pillars of data science. In addition, Data Science is composed of three key areas: analytics, infrastructure, and data curation (Tang & Sae-Lim, 2016). Stanton (2012) defined data science as “an emerging area of work concerned with the collection, preparation, analysis, visualization, management, and preservation of large collections of information (Song & …

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 …

Devious Design: Digital Infrastructure Challenges For Experimental Ethnography, Lindsay Poirier

Statistical and Data Sciences: Faculty Publications

No abstract provided.

Programming For Data Science Csc 310, Amanda Izenstark

Library Impact Statements

No abstract provided.

Data Science Program, Amanda Izenstark

Library Impact Statements

No abstract provided.

1st Analytics Without Borders Conference Program, Bentley University

Analytics Without Borders

No abstract provided.

How China Is Preparing For An Ai-Powered Future, Yujia He

Patterson School of Diplomacy and International Commerce Faculty Publications

U.S. leadership in artificial intelligence (AI) research and development (R&D) may be challenged by China’s recent and centralized policy planning aimed at rapidly developing AI-related technologies. The Chinese government supports AI as a strategic area supported by high-level policies with ambitious and quantifiable targets, inter-ministry coordination, government funding for research and development, support for workforce development, and suggestions for international collaboration and expansion. Still, as a relative latecomer in the global AI race, China faces numerous challenges to implement its policy planning, including short-term skills gap and long-term institutional constraints.

Survey Results: Complexities And Overlaps In Existing Citizen Science Mosquito Projects, Yujia He, Elizabeth Tyson

Patterson School of Diplomacy and International Commerce Faculty Publications

Currently, multiple projects worldwide engage the public in the scientific process of learning about mosquito biodiversity or monitoring mosquitoes that carry diseases like Dengue, Chikungunya, Zika and Malaria. These projects are executed at different invasion-stages and different scenarios of epidemiological risk based on their country of origin. Through surveying 12 international research teams, this brief report illuminates the differences and the overlaps in the protocols and tools used by existing citizen science mosquito monitoring projects. The analysis covers project goals, requirements and support for participants, policy impact, digital tools and data privacy.

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.

So What Are You Going To Do With That? The Promises And Pitfalls Of Massive Data Sets, Sigrid Anderson Cordell, Melissa Gomis

University of Nebraska-Lincoln Libraries: Faculty Publications

This article takes as its case study the challenge of data sets for text mining, sources that offer tremendous promise for digital humanities (DH) methodology but present specific challenges for humanities scholars. These text sets raise a range of issues: What skills do you train humanists to have? What is the library’s role in enabling and supporting use of those materials? How do you allocate staff? Who oversees sustainability and data management? By addressing these questions through a specific use case scenario, this article shows how these questions are central to mapping out future directions for a range of library …

The Politics Of Citations At The Ecj: Policy Preferences Of E.U. Member State Governments And The Citation Behavior Of Judges At The European Court Of Justice, Jens Frankenreiter

Scholarship@WashULaw

This paper investigates the relationship between the political preferences of EU Member States and the behavior of judges at the European Court of Justice (ECJ) by analyzing their citation behavior. It shows that judges at the ECJ are more likely to cite judgments authored by judges appointed by Member State governments with similar preferences regarding European integration. Analogous with the context of U.S. courts, non-random opinion assignment potentially threatens the validity of these results. To overcome this problem, I exploit the unique institutional setting at the ECJ to develop an improved identification strategy which builds on comparing the citations in …

Pushback: Critical Data Designers And Pollution Politics, Kim Fortun, Lindsay Poirier, Alli Morgan, Brandon Costelloe-Kuehn, Mike Fortun

Statistical and Data Sciences: Faculty Publications

In this paper, we describe how critical data designers have created projects that ‘push back’ against the eclipse of environmental problems by dominant orders: the pioneering pollution database Scorecard, released by the US NGO Environmental Defense Fund in 1997; the US Environmental Protection Agency’s EnviroAtlas that brings together numerous data sets and provides tools for valuing ecosystem services; and the Houston Clean Air Network’s maps of real-time ozone levels in Houston. Drawing on ethnographic observations and interviews, we analyse how critical data designers turn scientific data and findings into claims and visualisations that are meaningful in contemporary political terms. The …

A Bayesian Framework For The Classification Of Microbial Gene Activity States, Craig Disselkoen, Brian Greco, Kaitlyn Cook, Kristin Koch, Reginald Lerebours, Chase Viss, Joshua Cape, Elizabeth Held, Yonatan Ashenafi, Karen Fischer, Allyson Acosta, Mark Cunningham, Aaron A. Best, Matthew Dejongh, Nathan Tintle

Statistical and Data Sciences: Faculty Publications

Numerous methods for classifying gene activity states based on gene expression data have been proposed for use in downstream applications, such as incorporating transcriptomics data into metabolic models in order to improve resulting flux predictions. These methods often attempt to classify gene activity for each gene in each experimental condition as belonging to one of two states: active (the gene product is part of an active cellular mechanism) or inactive (the cellular mechanism is not active). These existing methods of classifying gene activity states suffer from multiple limitations, including enforcing unrealistic constraints on the overall proportions of active and inactive …

Tools And Techniques For Computational Reproducibility, Stephen Piccolo, Michael B. Frampton

Faculty Publications

When reporting research findings, scientists document the steps they followed so that others can verify and build upon the research. When those steps have been described in sufficient detail that others can retrace the steps and obtain similar results, the research is said to be reproducible. Computers play a vital role in many research disciplines and present both opportunities and challenges for reproducibility. Computers can be programmed to execute analysis tasks, and those programs can be repeated and shared with others. The deterministic nature of most computer programs means that the same analysis tasks, applied to the same data, will …

The Global Rock-Art Database Project Towards Machine Learning: Building A Collaborative Open Source Platform For Heritage Management From Information Structure To Information Visualization Using Australian Heritage Examples, Robert Haubt

Staff Scholarship - Australia & Dubai

This guest talk, presented at Lava Lab at the University of Hawaiʻi, explores the intersection of collaboration, data ontology, and information visualization in advancing machine learning within the Global Rock Art Database project. Drawing on insights from the project’s first four years, the talk emphasizes the critical need for cultural heritage preservation by systematically recording and structuring global rock art data in accessible and sustainable ways. This effort not only supports public education on rock art but also facilitates scholarly research.

Key discussions include advancements in data ontology using the CIDOC Conceptual Reference Model (CIDOC CRM) for semantic data management …

Short Distance Structure, L. B. Weinstein, S. E. Kuhn

Physics Faculty Publications

Over the last fifteen years of operation, the Jefferson Lab CLAS Collaboration has performed many experiments using nuclear targets. Because the CLAS detector has a very large acceptance and because it used a very open (i.e., nonspecific) trigger, there is a vast amount of data on many different reaction channels yet to be analyzed.

The goal of the Jefferson Lab Nuclear Data Mining grant was to (1) collect the data from nuclear target experiments using the CLAS detector, (2) collect the associated cuts and corrections used to analyze that data, (3) provide non-expert users with a software environment for easy …

Marine Ecoregion And Deepwater Horizon Oil Spill Affect Recruitment And Population Structure Of A Salt Marsh Snail, Steven C. Pennings, Scott Zengel, Jacob Oehrig, Merryl Alber, T. Dale Bishop, Donald R. Deis, Donna Devlin, A. Randall Hughes, John J. Hutchens, Jr., Whitney M. Kiehn, Caroline R. Mcfarlin, Clay L. Montague, Sean P. Powers, C. Edward Proffitt, Nicholle Rutherford, Camille L. Stagg, Keith Walters

University Faculty and Staff Publications

Marine species with planktonic larvae often have high spatial and temporal variation in recruitment that leads to subsequent variation in the ecology of benthic adults. Using a combination of published and unpublished data, we compared the population structure of the salt marsh snail, Littoraria irrorata, between the South Atlantic Bight and the Gulf Coast of the United States to infer geographic differences in recruitment and to test the hypothesis that the Deepwater Horizon oil spill led to widespread recruitment failure of L. irrorata in Louisiana in 2010. Size-frequency distributions in both ecoregions were bimodal, with troughs in the distributions consistent …

Using Data Analytics To Further Understand The Role That Boredom, Loneliness, Social Anxiety, Social Gratification, And Social Relationships (Brag) Play In A Driver’S Decision To Text, Nathan White, Yair Levy, Steven R. Terrell, Steve Bronsburg

All Faculty Scholarship for the College of Education and Professional Studies

Texting while driving is a growing problem that current efforts have failed to curtail. This behavior has serious, and sometimes fatal, consequences, and the factors that cause a driver to text are not well understood. This study investigates the influence that boredom, social relationships, social anxiety, and social gratification (BRAG) have upon the texting driver. A survey instrument was used to collect data from 297 respondents at a mid-sized regional university in the Pacific Northwest of the United States. The data was evaluated with PLS-SEM, which indicated that social gratification plays a very significant role in a driver’s decision to …

A Hierarchical Statistical Engineering Modeling Methodology, Teddy Steven Cotter

Engineering Management & Systems Engineering Faculty Publications

In the ASEM-IAC 2015, Cotter (2015) proposed a systemic joint deterministic-stochastic dynamic causal Bayesian statistical engineering model that addressed the knowledge gap needed to integrate deterministic mathematical engineering models within a stochastic framework. However, Cotter did not specify the modeling methodology through which statistical engineering models could be developed, diagnosed, and applied to predict systemic mission performance. This paper updates research into the development a hierarchical statistical engineering modeling methodology and sets forth the initial theoretical foundation for the methodology.

Two Influential Primate Classifications Logically Aligned, Nico M. Franz, Naomi M. Pier, Deeann M. Reeder, Mingmin Chen, Shizhuo Yu, Parisa Kianmajd, Shaun Bowers, Bertram Ludäscher

Computer Science Faculty Scholarship

Classifications and phylogenies of perceived natural entities change in the light of new evidence. Taxonomic changes, translated into Code-compliant names, frequently lead to name:meaning dissociations across succeeding treatments. Classification standards such as the Mammal Species of the World (MSW) may experience significant levels of taxonomic change from one edition to the next, with potential costs to long-term, large-scale information integration. This circumstance challenges the biodiversity and phylogenetic data communities to express taxonomic congruence and incongruence inways that both humans and machines can process, that is, to logically represent taxonomic alignments across multiple classifications.We demonstrate that such alignments are feasible for …

Names Are Not Good Enough: Reasoning Over Taxonomic Change In The Andropogon Complex, Nico M. Franz, Mingmin Chen, Parisa Kianmajd, Shizhuo Yu, Shaun Bowers, Alan S. Weakley, Bertram Ludäscher

Computer Science Faculty Scholarship

We present a novel, logic-based solution to the challenge of reconciling the meanings of taxonomic names across multiple biological taxonomies. The challenge arises due to limitations inherent in using type-anchored taxonomic names as identifiers of granular semantic similarities and differences being expressed in original and revised taxonomic classifications. We address this challenge through: (1) the use of taxonomic concept labels – thereby individuating name usages according to particular sources and allowing each taxonomy to be recognized separately; (2) sets of user-provided Region Connection Calculus articulations among concepts (RCC-5: congruence, proper inclusion, inverse proper inclusion, overlap, exclusion); and (3) the use …

A Multistep Approach To Single Nucleotide Polymorphism-Set Analysis: An Evaluation Of Power And Type I Error Of Gene-Based Tests Of Association After Pathway-Based Association Tests, Alessandra Valcarcel, Kelsey Grinde, Kaitlyn Cook, Alden Green, Nathan Tintle

Statistical and Data Sciences: Faculty Publications

The aggregation of functionally associated variants given a priori biological information can aid in the discovery of rare variants associated with complex diseases. Many methods exist that aggregate rare variants into a set and compute a single p value summarizing association between the set of rare variants and a phenotype of interest. These methods are often called gene-based, rare variant tests of association because the variants in the set are often all contained within the same gene. A reasonable extension of these approaches involves aggregating variants across an even larger set of variants (eg, all variants contained in genes within …

A General Method For Combining Different Family-Based Rare-Variant Tests Of Association To Improve Power And Robustness Of A Wide Range Of Genetic Architectures, Alden Green, Kaitlyn Cook, Kelsey Grinde, Alessandra Valcarcel, Nathan Tintle

Statistical and Data Sciences: Faculty Publications

Current rare-variant, gene-based tests of association often suffer from a lack of statistical power to detect genotype-phenotype associations as a result of a lack of prior knowledge of genetic disease models combined with limited observations of extremely rare causal variants in population-based samples. The use of pedigree data, in which rare variants are often more highly concentrated than in population-based data, has been proposed as 1 possible method for enhancing power. Methods for combining multiple gene-based tests of association into a single summary p value are a robust approach to different genetic architectures when little a priori knowledge is available …