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Cody : An Interactive Machine Learning System for Qualitative Coding

Tim Rietz, Peyman Toreini, Alexander Maedche
2020
In this work, we present Cody, a system for semi-automated qualitative coding that suggests codes based on human-defined coding rules and supervised machine learning (ML).  ...  Cody is the first coding system to allow users to define query-style code rules in combination with supervised ML.  ...  In this paper, we introduce "Cody", a system to increase the speed and quality of qualitative coding through interactive machine learning (IML).  ... 
doi:10.5445/ir/1000122802 fatcat:dapvnlbylzghbef5ogvgtwmcj4

Towards the Design of an Interactive Machine Learning System for Qualitative Coding

Tim Rietz, Alexander Maedche
2020
To accelerate this process, qualitative coding systems increasingly utilize machine learning (ML) to automatically recommend codes.  ...  We present an ongoing design science research project to design an IML system for qualitative coding. First, we discover several issues that hinder the success of current ML-based coding systems.  ...  question: Which design principles should an interactive machine learning system for qualitative coding follow in order to establish trust in and perceived control of the system in order to increase coding  ... 
doi:10.5445/ir/1000124563 fatcat:n476yqxhbvdzzitv5a6dbgftiq

On Learning Meaningful Code Changes via Neural Machine Translation [article]

Michele Tufano, Jevgenija Pantiuchina, Cody Watson, Gabriele Bavota, Denys Poshyvanyk
2019 arXiv   pre-print
Our goal is to make this first important step by quantitatively and qualitatively investigating the ability of a Neural Machine Translation (NMT) model to learn how to automatically apply code changes  ...  Our results pave the way for novel research in the area of DL on code, such as the automatic learning and applications of refactoring.  ...  RQ1: Can Neural Machine Translation be employed to learn meaningful code changes?  ... 
arXiv:1901.09102v1 fatcat:6eac2mbu4zcvzhev6jt46ki63i

Human modeling for human–robot collaboration

Laura M Hiatt, Cody Narber, Esube Bekele, Sangeet S Khemlani, J Gregory Trafton
2017 The international journal of robotics research  
Here, we discuss, compare and contrast the many techniques available for modeling human cognition and behavior, and evaluate their benefits and drawbacks in the context of human-robot collaboration.  ...  Conventional Approaches to Machine Learning Traditional or conventional machine learning models are common and effective ways of learning human behavior.  ...  large datasets compared to machine learning techniques.  ... 
doi:10.1177/0278364917690592 fatcat:teebme5ntvewdcjau3anwajjne

NETCDL: The Network Certification Description Language

Cody Hanson, Kristen R. Walcott
2018 International Journal of Computational Science Information Technology and Control Engineering  
These mistakes can have real monetary and operational efficiency costs for organizations that maintain large networks.  ...  When installing and maintaining networks without a standard for describing their behavior, teams find themselves prone to making configuration mistakes.  ...  NETCDL aims to bring the expressiveness of Cucumber to a simple imperative language that does not require the user to interact with computer code at all.  ... 
doi:10.5121/ijcsitce.2018.5301 fatcat:tidwitjlyfg3bb4nlpx5sgeyky

Data Driven Vulnerability Exploration for Design Phase System Analysis [article]

Georgios Bakirtzis, Brandon J. Simon, Aidan G. Collins, Cody H. Fleming, Carl R. Elks
2019 arXiv   pre-print
Among others there are two significant challenges in this area: (1) the need for models that can characterize a realistic system in the absence of an implementation and (2) an automated way to associate  ...  We propose the cybersecurity body of knowledge (CYBOK), which takes in sufficiently characteristic models of systems and acts as a search engine for potential attack vectors.  ...  The process itself is an iterative learning experience, allowing circumspection into how a system behaves in response to potential exploits. IX.  ... 
arXiv:1909.02923v1 fatcat:2hlxnlp4ara7bfkigok2mgqf6m

The integration of business intelligence and knowledge management

W. F. Cody, J. T. Kreulen, V. Krishna, W. S. Spangler
2002 IBM Systems Journal  
*Trademark or registered trademark of International Business Machines Corporation. Cited references  ...  Acknowledgments The authors gratefully acknowledge the contributions of Dharmendra Modha, Ray Strong, Justin Lessler, Thomas Brant, Iris Eiron, Hamid Pirahesh, Shivakumar Vaithyanathan, and Anant Jhingran for  ...  This interaction model and its underlying information model is an area for our current research. Consider again the example in Figure 4 .  ... 
doi:10.1147/sj.414.0697 fatcat:gfxmaedhsngdpfqc7ti2nnr6g4

Using Disaster Outcomes to Validate Components of Social Vulnerability to Floods: Flood Deaths and Property Damage across the USA

Beth Tellman, Cody Schank, Bessie Schwarz, Peter D. Howe, Alex de Sherbinin
2020 Sustainability  
Vulnerability indices are rarely validated with disaster outcome data at broad spatial scales, making it difficult to develop effective national scale strategies to mitigate loss for vulnerable populations  ...  Regression models identify sociodemographic factors associated with variation in outcomes from 11,629 non-coastal flood events in the USA (2008–2012), controlling for flood intensity using stream gauge  ...  Contact the lead author for copies of r code, models or additional data which is available upon request.  ... 
doi:10.3390/su12156006 fatcat:rsxgtfajlnhmva55dlpscyjqli

Content-based features predict social media influence operations

Meysam Alizadeh, Jacob N. Shapiro, Cody Buntain, Joshua A. Tucker
2020 Science Advances  
We study how easy it is to distinguish influence operations from organic social media activity by assessing the performance of a platform-agnostic machine learning approach.  ...  To assess how well content-based features distinguish these influence operations from random samples of general and political American users, we train and test classifiers on a monthly basis for each campaign  ...  Acknowledgments: We thank R3 for suggesting task 5. We thank seminar participants at NYU  ... 
doi:10.1126/sciadv.abb5824 pmid:32832674 pmcid:PMC7439640 fatcat:7qe2k42wqjgrnhi5fchzafa5oa

Processing single-cell RNA-seq data for dimension reduction-based analyses using open-source tools

Bob Chen, Marisol A. Ramirez-Solano, Cody N. Heiser, Qi Liu, Ken S. Lau
2021 STAR Protocols  
While commercial platforms can serve as "one-stop shops" for data analysis, they relinquish the flexibility required for customized analyses and are often inflexible between experimental systems.  ...  For instance, there is no universal solution for the discrimination of informative or uninformative encapsulated cellular material; thus, pipeline flexibility takes priority.  ...  For automated droplet filtering in Python, dropkick is a machine learning tool that builds a probabilistic model of single-cell barcode transcriptome quality and returns a score for all barcodes in the  ... 
doi:10.1016/j.xpro.2021.100450 pmid:33982010 pmcid:PMC8082116 fatcat:bu5rmdpuvfckrorirqvqx3jeti

Supplementary material from Preferences and constraints: the value of economic games for studying human behaviour

Anne C. Pisor, Matthew M. Gervais, Benjamin G. Purzycki, Cody T. Ross
2020 Figshare  
Site description, methods, models, and results for Colombian data  ...  Rather than attending to the highly constrained products of individuals interacting in social systems, economic games are often designed to measure the comparatively less constrained preferences of individuals  ...  Ability to work is a binary measure based on a qualitative assessment by CTR.  ... 
doi:10.6084/m9.figshare.12376928.v1 fatcat:2rwbvpcz2vds5hs7v66uxbl7je

Cody: An AI-Based System to Semi-Automate Coding for Qualitative Research

Tim Rietz, Alexander Maedche
2021
Further, little is known about the interaction of researchers with AI-based coding assistance. We introduce Cody, an AI-based system that semi-automates coding through code rules and supervised ML.  ...  Existing AI-based approaches for partially automating coding, like supervised machine learning (ML) or explicit knowledge represented in code rules, require high technical literacy and lack transparency  ...  We thank our colleagues for their participation in various pre-tests of Cody and our anonymous reviewers for valuable feedback on drafts.  ... 
doi:10.5445/ir/1000128503 fatcat:xxxqjeug5rfybb6dyc5ye475wa

Grand Challenges in Immersive Analytics

Barrett Ens, Benjamin Bach, Maxime Cordeil, Ulrich Engelke, Marcos Serrano, Wesley Willett, Arnaud Prouzeau, Christoph Anthes, Wolfgang Büschel, Cody Dunne, Tim Dwyer, Jens Grubert (+12 others)
2021 Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems  
It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version will be published in CHI 2021, May 8-13, 2021, Yokohama, Japan.  ...  Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption.  ...  As the field continues to grow and bridge to related fields, such as interaction design, psychology, machine learning, or computer vision, it becomes important to develop a unified research agenda to facilitate  ... 
doi:10.1145/3411764.3446866 fatcat:byvz63jylzdvhn7tlbfj7v23hy

Detecting and Summarizing GUI Changes in Evolving Mobile Apps [article]

Kevin Moran, Cody Watson, John Hoskins, George Purnell, Denys Poshyvanyk
2018 arXiv   pre-print
In this paper, we present a fully automated approach, called GCAT, for detecting and summarizing GUI changes during the evolution of mobile apps.  ...  Due to this rapid pace of evolution, developers need automated support for documenting the changes made to their apps in order to aid in program comprehension.  ...  This problem could be mitigated by asking developers to annotate certain screen content as dynamic, or through automatic recognition of dynamic content via machine learning.  ... 
arXiv:1807.09440v3 fatcat:dcg7s3wlrzgq7bhfj3mmj4rkaq

Evaluation of multi-level barriers and facilitators in a large diabetic retinopathy screening program in federally qualified health centers: a qualitative study

Ana Bastos de Carvalho, S. Lee Ware, Tamara Belcher, Franceska Mehmeti, Eric B. Higgins, Rob Sprang, Cody Williams, Jamie L. Studts, Christina R. Studts
2021 Implementation Science Communications  
Addressing this gap, this qualitative study aimed to identify and evaluate multi-level perceived determinants of TDRS in Federally Qualified Health Centers (FQHCs), to inform the development of targeted  ...  Constructs influencing TDRS implementation were rated for each study site and compared across sites by the investigators.  ...  Jing Li for their insightful suggestions on study design.  ... 
doi:10.1186/s43058-021-00157-2 pmid:34022946 fatcat:ui65gvplungzxbcb2pu3ep2npe
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