@misc{wu2019detectron2,
  author =       {Yuxin Wu and Alexander Kirillov and Francisco Massa and
                  Wan-Yen Lo and Ross Girshick},
  title =        {Detectron2},
  howpublished = {\url{https://github.com/facebookresearch/detectron2}},
  year =         {2019},
note = {Accessed on 2023-10-09}
}

@article{wang2022yolov7,
  title={{{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors}},
  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
  journal={arXiv preprint arXiv:2207.02696},
  year={2022}
}

@misc{reactGitHub,
  author = {{Meta Platforms, Inc.}},
  title = {{React - A JavaScript library for building user interfaces}},
  year = {2023},
  url = {https://github.com/facebook/react/},
  note = {Accessed on 2023-12-13}
}

@article{McInnes2017, doi = {10.21105/joss.00205}, url = {https://doi.org/10.21105/joss.00205}, year = {2017}, publisher = {The Open Journal}, volume = {2}, number = {11}, pages = {205}, author = {Leland McInnes and John Healy and Steve Astels}, title = {{hdbscan: Hierarchical density based clustering}}, journal = {Journal of Open Source Software}, note = {Accessed on 2023-09-28} } 

@inproceedings{terragni2020octis,
    title={{{OCTIS}: Comparing and Optimizing Topic Models is Simple!}},
    author={Terragni, Silvia and Fersini, Elisabetta and Galuzzi, Bruno Giovanni and Tropeano, Pietro and Candelieri, Antonio},
    year={2021},
    booktitle={Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations},
    month = apr,
    year = "2021",
    publisher = "Association for Computational Linguistics",
    url = "https://www.aclweb.org/anthology/2021.eacl-demos.31",
    pages = "263--270",
note = {Accessed on 2023-09-09.}
}

@book{grinberg2018flask,
  title={{Flask Web Development: Developing web applications with Python}},
  author={Grinberg, Miguel},
  year={2018},
  publisher={``O'Reilly Media, Inc."}
}

@article{LDA,
author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
title = {{Latent Dirichlet Allocation}},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
issn = {1532-4435},
abstract = {We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.},
journal = {J. Mach. Learn. Res.},
month = {Mar},
pages = {993–1022},
numpages = {30}
}


@article{srivastava2017autoencoding2,
      title={{Autoencoding Variational Inference for Topic Models}}, 
      author={Akash Srivastava and Charles Sutton},
      year={2017},
      journal={arXiv arXiv:1703.01488},
      primaryClass={stat.ML}
}

@article{srivastava2017autoencoding,
  title={Autoencoding Variational Inference for Topic Models},
  author={Srivastava, Akash and Sutton, Charles},
  year={2017},
  journal={arXiv preprint arXiv:1703.01488},
  type={Preprint},
  note={Affiliations: Informatics Forum, University of Edinburgh, Edinburgh, EH89AB, UK. Additional affiliation for Charles Sutton: Alan Turing Institute, British Library, London, UK. Accessed on 2023-9-01.},
  url={https://arxiv.org/abs/1703.01488},
  publisher={arXiv.org, Cornell University}
}


@inproceedings{10.5555/2976248.2976267,
author = {Blei, David M. and Lafferty, John D.},
title = {{Correlated Topic Models}},
year = {2005},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
abstract = {Topic models, such as latent Dirichlet allocation (LDA), can be useful tools for the statistical analysis of document collections and other discrete data. The LDA model assumes that the words of each document arise from a mixture of topics, each of which is a distribution over the vocabulary. A limitation of LDA is the inability to model topic correlation even though, for example, a document about genetics is more likely to also be about disease than x-ray astronomy. This limitation stems from the use of the Dirichlet distribution to model the variability among the topic proportions. In this paper we develop the correlated topic model (CTM), where the topic proportions exhibit correlation via the logistic normal distribution [1]. We derive a mean-field variational inference algorithm for approximate posterior inference in this model, which is complicated by the fact that the logistic normal is not conjugate to the multinomial. The CTM gives a better fit than LDA on a collection of OCRed articles from the journal Science. Furthermore, the CTM provides a natural way of visualizing and exploring this and other unstructured data sets.},
booktitle = {Proceedings of the 18th International Conference on Neural Information Processing Systems},
pages = {147–154},
numpages = {8},
location = {Vancouver, British Columbia, Canada},
series = {NIPS'05}
}

@inproceedings{roboflow,
  added-at = {2017-05-22T00:00:00.000+0200},
  author = {Alexandrova, Sonya and Tatlock, Zachary and Cakmak, Maya},
  biburl = {https://www.bibsonomy.org/bibtex/287645ab0422efa683fa79daaf4b4acde/dblp},
  booktitle = {ICRA},
  crossref = {conf/icra/2015},
  ee = {https://doi.org/10.1109/ICRA.2015.7139973},
  interhash = {e08aee21f941dced30cd6825d8bc2866},
  intrahash = {87645ab0422efa683fa79daaf4b4acde},
  isbn = {978-1-4799-6923-4},
  keywords = {dblp},
  pages = {5537-5544},
  publisher = {IEEE},
  timestamp = {2019-10-17T13:12:36.000+0200},
  title = {{RoboFlow: A flow-based visual programming language for mobile manipulation tasks.}},
  url = {http://dblp.uni-trier.de/db/conf/icra/icra2015.html#AlexandrovaTC15},
  year = 2015,
   note = {Accessed on 2023-09-30}
}

@article{bertopic,
  title={{BERTopic: Neural topic modeling with a class-based TF-IDF procedure}},
  author={Grootendorst, Maarten},
  journal={arXiv preprint arXiv:2203.05794},
  year={2022}
}



@article{paszke2019pytorch2,
      title={{PyTorch: An Imperative Style, High-Performance Deep Learning Library}}, 
      author={Adam Paszke and Sam Gross and Francisco Massa and Adam Lerer and James Bradbury and Gregory Chanan and Trevor Killeen and Zeming Lin and Natalia Gimelshein and Luca Antiga and Alban Desmaison and Andreas Köpf and Edward Yang and Zach DeVito and Martin Raison and Alykhan Tejani and Sasank Chilamkurthy and Benoit Steiner and Lu Fang and Junjie Bai and Soumith Chintala},
      year={2019},
      journal={arXiv arXiv:1912.01703},
      primaryClass={cs.LG}
}

@misc{paszke2019pytorch,
  title={{PyTorch: An Imperative Style, High-Performance Deep Learning Library}},
  author={Paszke, Adam and others},
  year={2019},
  howpublished={arXiv preprint arXiv:1912.01703},
  note={Accessed on 2023-09-30.},
  url={https://arxiv.org/abs/1912.01703},
  publisher={arXiv.org, Cornell University}
}


@article{grootendorst2022bertopic,
  title={{BERTopic: Neural topic modeling with a class-based TF-IDF procedure}},
  author={Grootendorst, Maarten},
  journal={arXiv preprint arXiv:2203.05794},
  year={2022}
}

@article{detectronImage2,
  title={{Deep Learning for Computer Vision — Lecture 15, Object Detection}},
  author={Justin, Johnson},
  journal= {\url{https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/}},
note = {Accessed on 2023-10-11},
  year={2019}
}

@misc{detectronImage,
  title={{Deep Learning for Computer Vision — Lecture 15, Object Detection}},
  author={Johnson, Justin},
  year={2019},
  note={Lecture 15 from EECS 498-007 / 598-005, University of Michigan, Ann Arbor, MI. Delivered in Fall 2019 term. Accessed on 2023-10-11},
  howpublished={\url{https://web.eecs.umich.edu/~justincj/teaching/eecs498/WI2022/}},
  organization={University of Michigan, Department of Electrical Engineering and Computer Science}
}

@article{yolov8Image,
  title = {{YOLOv8 Ultralytics: State-of-the-Art YOLO Models}},
  author = {Sovith Rath},
  journal = {\url{https://learnopencv.com/ultralytics-yolov8/}},
  year = {2023},
  note = {Accessed on 2023-10-11}
}


@article{yolov7Image,
  title= {{YOLOv7 Object Detection Paper Explanation and Inference}},
  author={Sovith Rath and Kukil},
  journal= {\url{https://learnopencv.com/yolov7-object-detection-paper-explanation-and-inference/}},
  year={2022},
note = {Accessed on 2023-10-11}
}

@article{ren2016faster2,
      title={{Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks}}, 
      author={Shaoqing Ren and Kaiming He and Ross Girshick and Jian Sun},
      year={2016},
journal={arXiv arXiv:1506.01497},
      primaryClass={cs.CV}
}

@article{ren2016faster,
  title={{Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks}},
  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
  journal={arXiv preprint arXiv:1506.01497},
  year={2016},
  note={Affiliations: University of Science and Technology of China, Hefei, China; Visual Computing Group, Microsoft Research; Facebook AI Research. Accessed on  2023-09-30.},
  url={https://ar5iv.org/abs/1506.01497},
  publisher={arXiv.org, Cornell University}
}


@article{camelot,
    title= Camelot,
    url= "https://camelot-project.readthedocs.io/en/latest/yourorganisation.html" 
}

@phdthesis{AAhuja,
  author = {Aman Ahuja},
  title = {Analyzing and Navigating Electronic Theses and Dissertations},
  school = {Virginia Tech Computer Science},
  year = {2023},
  note = {Accessed on 2023-11-10},
  url = {http://hdl.handle.net/10919/115817}
}




@misc{OD_cs4624,
  author = {Kevin Dinh and Brian Dinh and Andrew Leavitt and Annie Tran},
  title = {{Object Detection}},
  year = 2022,
  howpublished = {\url{http://hdl.handle.net/10919/114082}},
  note = {Virginia Tech CS4624 team term project submission, Dec. 2022, Virginia Polytechnic Institute and State University, Accessed on 2023-11-10}
}




@misc{od-cluster,
  author = {Fall 2023 CS5604 Team 3},
  title = {{Team3-endeavour-object detection-code}},
  howpublished = {\url{https://team3-container-1.endeavour.cs.vt.edu/notebooks/ceph_copy_f23/team3/fall2023/freshcopy/ObjectDetectionAccessibility-main/}},
note = {{Endeavour cluster Object Detection code repository, Virginia Polytechnic Institute and State University, Accessed on 2023-12-10}}
}

@misc{tm-cluster,
  author = {Fall 2023 CS5604 Team 3},
  title = {{Team3-endeavour-topic modeling-code}},
  howpublished = {\url{https://team3-container-1.endeavour.cs.vt.edu/tree/ceph_copy_f23/team3/fall2023/TopicModelling/DocumentBrowsingToolkit-main}},
note = {{Endeavour cluster Topic Modeling code repository, Virginia Polytechnic Institute and State University, Accessed on 2023-12-10}}
}

 @misc{brammer_2010, 
title={{Una Revolucion Ni mas ni Menos: The Role of the Enlightenment in the Supreme Juntas in Quito, 1765-1822}}, 
author={Brammer, Beau J.}, 
year={2010},
howpublished = {\url{http://rave.ohiolink.edu/etdc/view?acc_num=osu1267625671}},
note = {{Accessed on 2023-10-12}}
}

 @article{Huang20173, 
title={{Immigration, Regional Resilience, and Local Economic Development Policy}},
journal={Public Management and Policy Dissertations},
author={Huang, Xi},
year={2017},
month={Dec},
howpublished = {\url{https://doi.org/10.57709/11008718}},
note = {{Accessed on 2023-10-12}}
}

@phdthesis{Huang2017,
  title={{Immigration, Regional Resilience, and Local Economic Development Policy}},
  author={Huang, Xi},
  year={2017},
  month={Dec},
  school={Georgia State University},
  department={Public Management and Policy},
  type={Dissertation},
  url={https://doi.org/10.57709/11008718},
  note={{Accessed on 2023-10-12}}
}

@phdthesis{Huang20172,
    author = {Huang, Xi},
    title = {Immigration, Regional Resilience, and Local Economic Development Policy},
    year = {2017},
    month = {Dec},
    note = {Accessed on 2023-10-12},
    type = {Public Management and Policy Dissertations}
}


@misc{OD-cs5604-f22,
author = {Alan Devera and Raj Sahu and 
 Nila Masrourisaadat and 
 Nirmal Amirthalingam and
Chenyu Mao},
title = {{Team 3: Object Detection and Topic Modeling (Objects&Topics) CS 5604 F2022}},
year = 2022,
howpublished = {\url{http://hdl.handle.net/10919/114081}},
note = {Virginia Tech CS5604 Team 3 term project submission, Dec. 2022, Virginia Polytechnic Institute and State University, Accessed on 2023-9-01}
}