@article{healey_dinakaran_padia_nie_benson_caira_shaw_catalfu_devarajan_2021, title={Visual Analytics of Text Conversation Sentiment and Semantics}, volume={n/a}, url={https://onlinelibrary.wiley.com/doi/10.1111/cgf.14391}, DOI={https://doi.org/10.1111/cgf.14391}, abstractNote={Abstract}, number={n/a}, journal={Computer Graphics Forum}, author={Healey, Christopher G. and Dinakaran, Gowtham and Padia, Kalpesh and Nie, Shaoliang and Benson, J. Riley and Caira, Dave and Shaw, Dean and Catalfu, Gary and Devarajan, Ravi}, year={2021}, month={Aug} }
 @article{padia_bandara_healey_2019, title={A system for generating storyline visualizations using hierarchical task network planning}, volume={78}, ISSN={["1873-7684"]}, DOI={10.1016/j.cag.2018.11.004}, abstractNote={Existing storyline visualization techniques present narratives as a node-link graph where a sequence of links shows the evolution of causal and temporal relationships between characters in the narrative. These techniques make a number of simplifying assumptions about the narrative structure, however. They assume that all narratives progress linearly in time, with a well-defined beginning, middle, and end. They assume that the narrative is complete prior to visualization. They also assume that at least two participants interact at every event. Finally, they assume that all events in the narrative occur along a single timeline. Thus, while existing techniques are suitable for visualizing linear narratives, they are not well suited for visualizing narratives with multiple timelines, non-linear narratives such as those with flashbacks, or for narratives that contain events with only one participant. In our previous work we presented Yarn, a system for automatic construction and visualization of narratives with multiple timelines. Yarn employs hierarchical task network planning to generate all possible narrative timelines and visualize them in a web-based interface. In this work, we extend Yarn to support non-linear narratives with flashbacks and flash-forwards, and non-linear point-of-view narratives. Our technique supports both single-participant as well as multi-participant events in the narrative, and constructs both linear as well as non-linear narratives. Additionally, it enables pairwise comparison within a group of multiple narrative timelines.}, journal={COMPUTERS & GRAPHICS-UK}, publisher={Pergamon}, author={Padia, Kalpesh and Bandara, Kaveen Herath and Healey, Christopher G.}, year={2019}, month={Feb}, pages={64–75} }
 @misc{leeman-munk_sethi_healey_nie_padia_devarajan_caira_benson_cox_lewis_2019, title={Interactive visualizations for a recurrent neural network}, note={US Patent 10,324,983}, author={Leeman-Munk, Samuel Paul and Sethi, Saratendu and Healey, Christopher Graham and Nie, Shaoliang and Padia, Kalpesh and Devarajan, Ravinder and Caira, David James and Benson, Jordan Riley and Cox, James Allen and Lewis, Lawrence E}, year={2019}, month={Jan} }
 @phdthesis{padia_2019, title={Storyline Visualization Techniques for Linear, Non-Linear, and Diegetic Narratives.}, school={North Carolina State University}, author={Padia, Kalpesh}, year={2019} }
 @misc{leeman-munk_sethi_healey_nie_padia_devarajan_caira_benson_cox_lewis_et al._2019, title={Visualizing convolutional neural networks}, note={US Patent 10,192,001}, author={Leeman-Munk, Samuel Paul and Sethi, Saratendu and Healey, Christopher Graham and Nie, Shaoliang and Padia, Kalpesh and Devarajan, Ravinder and Caira, David James and Benson, Jordan Riley and Cox, James Allen and Lewis, Lawrence E and et al.}, year={2019}, month={Jan} }
 @article{leeman-munk_sethi_healey_nie_padia_devarajan_caira_benson_cox_lewis_et al._2018, title={Interactive visualizations of a convolutional neural network}, note={US Patent 10,048,826}, author={Leeman-Munk, Samuel Paul and Sethi, Saratendu and Healey, Christopher Graham and Nie, Shaoliang and Padia, Kalpesh and Devarajan, Ravinder and Caira, David James and Benson, Jordan Riley and Cox, James Allen and Lewis, Lawrence E and et al.}, year={2018}, month={Aug} }
 @article{nie_healey_padia_leeman-munk_benson_caira_sethi_devarajan_2018, title={Visualizing Deep Neural Networks for Text Analytics}, ISSN={["2165-8765"]}, DOI={10.1109/pacificvis.2018.00031}, abstractNote={Deep neural networks (DNNs) have made tremendous progress in many different areas in recent years. How these networks function internally, however, is often not well understood. Advances in under-standing DNNs will benefit and accelerate the development of the field. We present TNNVis, a visualization system that supports un-derstanding of deep neural networks specifically designed to analyze text. TNNVis focuses on DNNs composed of fully connected and convolutional layers. It integrates visual encodings and interaction techniques chosen specifically for our tasks. The tool allows users to: (1) visually explore DNN models with arbitrary input using a combination of node–link diagrams and matrix representation; (2) quickly identify activation values, weights, and feature map patterns within a network; (3) flexibly focus on visual information of interest with threshold, inspection, insight query, and tooltip operations; (4) discover network activation and training patterns through animation; and (5) compare differences between internal activation patterns for different inputs to the DNN. These functions allow neural network researchers to examine their DNN models from new perspectives, producing insights on how these models function. Clustering and summarization techniques are employed to support large convolutional and fully connected layers. Based on several part of speech models with different structure and size, we present multiple use cases where visualization facilitates an understanding of the models.}, journal={2018 IEEE PACIFIC VISUALIZATION SYMPOSIUM (PACIFICVIS)}, author={Nie, Shaoliang and Healey, Christopher G. and Padia, Kalpesh and Leeman-Munk, Samuel and Benson, Jordan Riley and Caira, Dave and Sethi, Saratendu and Devarajan, Ravi}, year={2018}, pages={180–189} }
 @inproceedings{nie_healey_padia_leeman-munk_benson_caira_sethi_devarajan_2018, title={Visualizing Deep Neural Networks for Text Analytics}, booktitle={2018 IEEE Pacific Visualization Symposium (PacificVis)}, author={Nie, Shaoliang and Healey, Christopher and Padia, Kalpesh and Leeman-Munk, Samuel and Benson, Jordan and Caira, Dave and Sethi, Saratendu and Devarajan, Ravi}, year={2018}, pages={180–189} }
 @article{healey_leeman-munk_nie_padia_devarajan_caira_benson_sethi_cox_lewis_et al._2018, title={Visualizing deep neural networks}, note={US Patent 9,934,462}, publisher={Google Patents}, author={Healey, Christopher Graham and Leeman-Munk, Samuel Paul and Nie, Shaoliang and Padia, Kalpesh and Devarajan, Ravinder and Caira, David James and Benson, Jordan Riley and Sethi, Saratendu and Cox, James Allen and Lewis, Lawrence E and et al.}, year={2018}, month={Apr} }
 @inproceedings{padia_bandara_healey_2018, title={Yarn: Generating Storyline Visualizations Using HTN Planning}, booktitle={Proceedings of Graphics Interface 2018}, author={Padia, Kalpesh and Bandara, Kaveen Herath and Healey, Christopher G}, year={2018}, pages={17–24} }
 @article{devarajan_benson_caira_sethi_cox_healey_dinakaran_padia_2017, title={Automatically constructing training sets for electronic sentiment analysis}, note={US Patent 9,704,097}, publisher={Google Patents}, author={Devarajan, Ravinder and Benson, Jordan Riley and Caira, David James and Sethi, Saratendu and Cox, James Allen and Healey, Christopher G and Dinakaran, Gowtham and Padia, Kalpesh}, year={2017}, month={Jul} }
 @inproceedings{padia_healey_2016, title={Sentiment-based document collection narratives}, author={Padia, K. and Healey, C.G.}, year={2016} }
 @article{devarajan_benson_caira_sethi_cox_healey_dinakaran_padia_nie_others_2016, title={Visualizations for electronic narrative analytics}, note={US Patent App. 15/177,237}, publisher={Google Patents}, author={Devarajan, Ravinder and Benson, Jordan Riley and Caira, David James and Sethi, Saratendu and Cox, James Allen and Healey, Christopher G and Dinakaran, Gowtham and Padia, Kalpesh and Nie, Shaoliang and others}, year={2016}, month={Dec} }
 @article{padia_2016, title={Visualizing Narratives}, journal={North Carolina State University}, author={Padia, Kalpesh}, year={2016} }
 @article{devarajan_benson_caira_sethi_cox_healey_dinakaran_padia_2016, title={Visualizing results of electronic sentiment analysis}, note={US Patent App. 14/966,380}, publisher={Google Patents}, author={Devarajan, Ravinder and Benson, Jordan Riley and Caira, David James and Sethi, Saratendu and Cox, James Allen and Healey, Christopher G and Dinakaran, Gowtham and Padia, Kalpesh}, year={2016}, month={Dec} }
 @inproceedings{padia_alnoamany_weigle_2012, title={Visualizing digital collections at archive-it}, booktitle={Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries}, author={Padia, Kalpesh and AlNoamany, Yasmin and Weigle, Michele C}, year={2012}, pages={15–18} }
 @inproceedings{navuluri_padia_gupta_nadeem_2011, title={Poster: What's on your mind?: a mind-based driving alert system}, booktitle={Proceedings of the 9th international conference on Mobile systems, applications, and services}, author={Navuluri, Karthik and Padia, Kalpesh and Gupta, Ajay and Nadeem, Tamer}, year={2011}, pages={415–416} }
 @inproceedings{vikas_padia_iyer_darshan_prasad_srinivas_khan_gupta_olariu_2009, title={A Dynamic GPS-Free Localization Technique using Progressive Interpolation}, booktitle={International Conference on Computer Networks and Mobile Computing}, author={Vikas, GA and Padia, K and Iyer, HS and Darshan, VR and Prasad, NP Ganesh and Srinivas, A and Khan, H and Gupta, A and Olariu, S}, year={2009} }
 @inproceedings{padia_vikas_iyer_darshan_prasad_srinivas_2009, title={A Localization Algorithm for a GPS-Free System with Static Parameter Tuning}, booktitle={Computer And Network Technology-Proceedings Of The International Conference On Iccnt 2009}, author={PADIA, K and VIKAS, GA and IYER, HS and DARSHAN, VR and PRASAD, NP GANESH and SRINIVAS, A}, year={2009}, pages={37–41} }