Scientific Journals
Jung, S., Kwon, H. 2026. Literary Systems as Complex Adaptive Systems: A Computational Study of Motif Self-Organization in Agatha Christie’s Detective Fiction. Digital Scholarship in the Humanities. (2026).
Jung, S., Kwon, H. 2026. The Narrative Function of Ending Speech and Hermeneutic Complexity in Aesopic Fables: A Computational Analysis of 600-Fable Corpus. Computational Humanities Research. (2026).
Jung, S. and Salado, A. 2026. State of Systems Engineering Graduate Education as of 2023. Systems Engineering. (2026).
Jung, S. and Salado, A. 2025. Accuracy of Citation Patterns in the Field of Systems Engineering: Benefits of MBSE. Systems Engineering. (2025).
Jung, S., Kwon, H. 2025. Analyzing Contents of Screenwriting Manual Books with Topic Modeling. International Journal of Contents. (2025).
Wach, P., Topcu, T. G., Jung, S., Sandman, B., Kulkarni, A. U., and A. Salado. 2024. A systematic literature review on the mathematical underpinning of model-based systems engineering. Systems Engineering. (2024).
Jung, S. and Salado, A. 2024. Emergent knowledge patterns in verification artifacts. Systems Engineering. (2024). DOI: https://doi.org/10.1002/sys.21771.
Segev, A., Jung, S. 2023. Common Knowledge Processing Patterns in Networks of Different Systems. PLOS ONE. 18.10 (2023).
Jung, S., Segev, A. 2022. Identifying a Common Pattern within Ancestors of Emerging Topics for Pan-domain Topic Emergence Prediction. Knowledge-Based Systems (2022): 1-17. DOI: https://doi.org/10.1016/j.knosys.2022.110020.
Jung, S., Segev, A. 2022. DAC: Descendant-Aware Clustering Algorithm for Network-Based Topic Emergence Prediction. Journal of Informetrics. 16.3 (2022). DOI: https://doi.org/10.1016/j.joi.2022.101320.
Jung, S. and Segev. A. 2022. Analyzing the Generalizability of the Network-based Topic Emergence Identification Method. Semantic Web Preprint: 1-17 (2022). DOI: https://doi.org/10.3233/sw-212951.
Jung, C., Yoon, W.C., Datta, R. and Jung, S. 2021. Knowledge Base Driven Automatic Text Summarization using Multi-objective Optimization. International Journal of Advanced Computer
Science and Applications (IJACSA). 12, 8 (Aug 2021). DOI: https://doi.org/10.14569/IJACSA.2021.0120895.
Jung, S. and Yoon, W.C. 2020. An Alternative Topic Model based on Common Interest Authors for Topic Evolution Analysis. Journal of Informetrics. 14, 3 (Aug. 2020), 101040. DOI: https://doi.org/10.1016/j.joi.2020.101040.
Jung, S., Reddy Kandadi, R., Datta, R., Benton, R. and Segev, A. 2020. Identification of Technology-relevant Entities Based on Trend Curves and Semantic Similarities. International journal of Web & Semantic Technology. 11, 3 (Jul. 2020), 1–16. DOI: https://doi.org/10.5121/ijwest.2020.11301.
Segev, A., Curtis, D., Balili, C. and Jung, S. 2020. Neuronless Knowledge Processing in Forests. Applied Sciences. 10, 7 (Apr. 2020), 2509. DOI: https://doi.org/10.3390/app10072509.
Segev, A., Curtis, D., Jung, S. and Chae, S. 2016. Invisible Brain: Knowledge in Research Works and Neuron Activity. PLOS ONE. 11, 7 (Jul. 2016), e0158590. DOI: https://doi.org/10.1371/journal.pone.0158590.
Segev, A., Jung, S. and Choi, S. 2015. Analysis of Technology Trends Based on Diverse Data Sources. IEEE Transactions on Services Computing. 2015 Vol.8, 06 (Dec. 2015), 903–915. DOI: https://doi.org/10.1109/TSC.2014.2338855.
Jung, S. and Segev, A. 2014. Analyzing Future Communities in Growing Citation Networks. Knowledge-Based Systems. 69, (Oct. 2014), 34–44. DOI: https://doi.org/10.1016/j.knosys.2014.04.036.
Jang, J., Jung, S., Lee, S., Jung, C., Yoon, W.C. and Yi, M. 2014. Learning Material Bookmarking Service based on Collective Intelligence. Journal of Intelligence and Information Systems. 20, 2 (Jun. 2014), 179-192.
Conference/Workshops
Jung, S. & Salado, A. 2025. Application of A Verification Complexity Framework. INCOSE International Symposium (2025).
Cornejo, S., Jung, S. & Salado, A. 2025. A Double-Helix Model for the V&V of Physical and Digital Twins. INCOSE International Symposium (2025).
(Accepted) Jung, S. and Salado, A. 2025. Application of A Verification Complexity Framework. INCOSE International Symposium (2025).
Jung, S. and Salado, A. 2025. Factors of Verification Complexity: A Theoretical Exploration. Conference on Systems Engineering Research (CSER) (2025).
Vinarcik, M., Jung, S. & Salado, A. 2024. Automating Rule-Checking to Identify SysML Modeling Errors: A Preliminary Study in a Classroom Environment. INCOSE International Symposium (2024).
Jung, S. and Jung, A. 2024. Cognitive Load Management for Planning and Executing Verification Strategies. INCOSE International Conference on Human Systems Integration (2024).
Jung, S. and Jung, A. 2024. Verification Complexity: Definitions, Measurements, and Indicators. INCOSE International Conference on Human Systems Integration (2024).
Jung, S. and Salado, A. 2024. Exploring A Verification Complexity Framework. INCOSE International Conference on Human Systems Integration (2024).
Jung, S. and Salado, A. 2024. Exploring the Notion of Verification Complexity. INCOSE International Symposium (2024).
Vinarcik, M., Jung, S. and Salado, A. 2024. Automating Rule-Checking to Identify SysML Modeling Errors: A Preliminary Study in a Classroom Environment. INCOSE International Symposium (2024).
Jung, S. and Salado, A. 2024. Graph Complexity Measures as Indicators of Verification Complexity. Conference on Systems Engineering Research (CSER) (2024).
Jung, S. and Salado, A. 2023. Verification Complexity: An Initial Look at Verification Artifacts. 2023 Conference on Systems Engineering Research (Mar. 2023).
Jung, S. and Segev, A. 2022. Optimizing the Descendant-Aware Clustering Parameters. 2022 IEEE. International Conference on Big Data (Big Data) Second Workshop on Knowledge Graphs and Big Data (Dec. 2022).
Jung, S. and Segev, A. 2022. Semantic Similarity Analysis between Future Topics and Their Neighbors in Topic Networks for Network-based Topic Evolution. 2022 IEEE International Conference on Big Data (Big Data) 8th Special Session on Intelligent Data Mining (Dec. 2022).
Jung, S., Datta, R. and Segev, A. 2020. An Automatic Classification of the Primary and the Corresponding Authors in Research Articles. 2020 IEEE International Conference on Big Data (Big Data) (Dec. 2020).
Jung, S., Datta, R. and Segev, A. 2020. Identification and Prediction of Emerging Topics through Their Relationships to Existing Topics. 2020 IEEE International Conference on Big Data (Big Data) (Dec. 2020).
Jung, S., Kandadi, R.R., Datta, R., Benton, R. and Segev, A. 2020. Identification of Technology-Relevant Entities Based on Trend Curves. 9th International Conference on Information Technology Convergence and Services
(ITCSE 2020) (May 2020), 1–13.
Jung, S. and Yoon, W.C. 2019. Citation-Based Author Contribution Measure for Byline-Independency. 2019 IEEE International Conference on Big Data (Big Data) (Dec. 2019), 6086–6088.
Jung, S., Lai, T.M. and Segev, A. 2016. Analyzing Future Nodes in a Knowledge Network. 2016 IEEE International Congress on Big Data (BigData Congress) (Jun. 2016), 357–360.
Kwon, H., Jung, S., Kwon, H.T. and Yoon, W.C. 2014. A Knowledge Distribution Model to Support an Author in Narrative Creation. Human Interface and the Management of Information. Information and Knowledge in Applications and Services (Jun. 2014), 511–522.
Jang, J., Lee, S., Jung, S., Jung, C., Yoon, W. and Yi, M. 2014. WeStudy : Implementation and Evaluation of Collective Intelligence based Learning Material Bookmarking Service. Proceedings in Korea Intelligent Information System Society (May 2014), 257–263.
Jung, S., Lee, S. and Jung, C. 2014. Reviewing the Influence of Structural Differences of Various Social Networks on Influence Analysis. HCI Korea (Feb. 2014), 427–430.
Segev, A., Jung, C. and Jung, S. 2013. Analysis of Technology Trends Based on Big Data. 2013 IEEE International Congress on Big Data (BigData Congress) (Jun. 2013), 419–420.
Jung, S. and Segev, A. 2013. Analyzing Future Communities in Growing Citation Networks. Proceedings of ACM International Conference on Information and Knowledge Management (CIKM 2013) International Workshop on Mining Unstructured Big Data using Natural Language Processing (New York, NY, USA, Oct. 2013), 15–22.