Web and Complex Systems Lab

Dept. of Computer Science and Engineering, Wright State U., Dayton, OH

Web and Complex Systems Lab
Dept. of CSE, Wright State University

We develop and apply novel network science, deep learning, statistical graph, and nonparametric methods to model and analyze web, computing, and logical complex systems. We tackle a variety of machine learning and data science research problems and emphasize method, model, and systems development. Our focus is to develop novel (machine learning) techniques that are theoretically developed but practically transferrable to studies and evaluations of physical, logical, geospatial, and sociotechnolgical complex systems.

Representative Projects
(See current project details here)

Web Systems Cache Development

Integration of Bayesian models with deep recurrent neural networks to build better web server cache prefetchers. []

Explainable Deep Learning

Meta-analysis of deep neural network activations to classify types of semantically meaningful input concepts. []

Network Traffic Classification

Soft computing theory and algorithms to accuractely classify human, benign crawlers, and malicious bot traffic. []

Geospatial Traffic Dynamics

Intrinsic PoI recovery [] and new dynamic stochastic blockmodel formulations [] to model macro-scale geospatial dynamics.

Sociotechnological Systems

Measurement-based studies and analysis of activities in emerging sociotechnological systems [].

Emoji at Work

New semantics-based measures of Emoji similarity [] and building open resources for Emoji semantics [].

People

Alumni (@ last known whereabouts)

MS Alumni
Lakshika Balasuriya (2017) @ Data Science R&D, Gracenote
Nathan Rude (2016) @ Systems Engineer, LexisNexis Special Services
Samir Yelne (2016) @ Data Scientist, Cisco

Undergrad Alumni
Scott Duberstein (2017) @ Research Engineer, Ball Aerospace
Nripesh Trivedi (2015) @ PhD Student, UC Riverside

4/16/18: dbscan: Fast Density Based Clustering in R accepted for publication in the Journal of Statistical Software. This article documents the development of the dbscan open source R package for density based clustering. Matt Piekenbrock implemented state-of-the-art density based clustering methods in the package and was instrumental in making dbscan the fastest library for density based clustering available today. Congratulations Matt!

4/9/18: Jace successfully defends his thesis on a new statistical dynamic network model incorporating seasonal dynamics. He will be joining Purdue as a Ph.D. student in the Fall. **Congratulations Jace!**

3/28/18: Multiple WaCS students have secured research internships this summer: Ning heads to NEC Labs in Princeton to tackle visual reasoning problems by deep learning; Kyle will be at AFRL and the Wright Brothers Institute working on topological data analysis and applications to DoD problems; Matt leaves for NASA to explore unsupervised learning problems for explainable AI; Jace leaves for Tenet3 to develop new machine learning methods over networks for cybersecurity use cases; and Jameson leaves for AFRL to develop software for satellites. Congratulations everyone!

1/20/18: Lakshika successfully defends her thesis on studying gang member profiles on Twitter. She will be joining Gracenote as a research engineer in Okland, CA this summer. **Congratulations Lakshika!**

1/16/18: Some (non-)Universal Features of Web Robot Traffic accepted at IEEE Conference on Information Sciences and Systems. This study identifies statistical properties of web robot traffic that are common and contrasting across multiple web servers around the world. Great job Mahdieh!

1/14/18: Contrasting Web Robot and Human Behaviors with Network Models accepted for publication in the Journal of Communications. The article contrasts web robot and human visititation behaviors through the lens of network analytics. Congratulations Kyle!

11/15/17: Relating Input Concepts to Convolutional Neural Network Decisions accepted at NIPS Workshop on Explainable Machine Learning. This work develops a novel algorithm to find distributed representations of input concepts in a convolutional neural network. It then studies how types of representations could affect the decisions the deep learning system makes. Well done Ning!

11/3/17: EmojiNet, our machine readable sense inventory for Emoji co-developed by Lakshika, has been selected for publication as a Kaggle dataset. Check it out here. If you play around with it let us know what you find! And, an article on EmojiNet has been picked up by Psychology Today. Neat!

Publications

2018

  1. C1. Y. Li, D.W. Kim, J. Zhang, and D. Doran. “TeaFilter: Detecting Suspicious Members in an Online Emotional Support Service”, EAI Intl. Conference on Security and Privacy in Communication Networks, Singapore, Aug. 2018
  2. M. Hashler, M. Piekenbrock, and D. Doran. “dbscan: Fast Density-based Clustering Algorithms in R”, Journal of Statistical Software, 2018 [pdf]
  3. K. Brown and D. Doran. “Contrasting Web Robot and Human Behaviors with Network Models’, Journal of Communications, ETPub, 2018 (accepted for publication)
  4. D. Doran. “Data Scientist”, Encyclopedia of Big Data, Springer, L. Schintler, C. McNeely (Eds.), 2018
  5. M. Zabihimayvan and D. Doran. “Some (Non-)Universal Properties of Web Robot Traffic”, IEEE Conference on Information Sciences and Systems, Princeton, NJ, March 2018

2017

  1. D. Doran. “Graph and Link Mining”, Encyclopedia of Big Data, Springer, L. Schintler, C. McNeely (Eds.), 2017
  2. M. Zabihimayvan, R. Sadeghi, and D. Doran. "An Integrated Approach for Benign and Malicious Web Robot Detection", Expert Systems With Applications, 2017
  3. N. Xie, M. K. Sarker, D. Doran, P. Hitzler, and M. Raymer. “Relating Input Concepts to Convolutional Neural Network Decisions”, NIPS Workshop on Interpreting, Explaining, and Visualizing Deep Learning, Long Beach, CA, Dec. 2017 [pdf]
  4. K. Brown and D. Doran. “Realistic Traffic Generation for Web Robots”, Proc. of IEEE Intl. Conference on Machine Learning and Applications, Dec. 2017
  5. D. Doran, S. Schulz, and T. Besold. “What Does Explainable AI Really Mean? A New Conceptualization of Perspectives”, Proc. Of Intl. Workshop on Comprehensibility and Explainability in Artificial Intelligence and Machine Learning, Bari, Italy, Nov. 2017
  6. J. Robinson and D. Doran. “Seasonality in Dynamic Stochastic Blockmodels”, Proc. of ACM/IEEE Intl. Conference on Web Intelligence, Leipzig, Germany, Aug. 2017 [pdf]
  7. S. Wijeratne, L. Balasuriya, A. Sheth, and D. Doran. “A Semantics-Based Measure of Emoji Similarity”, Proc. Of IEEE/WIC/ACM Intl. Conference on Web Intelligence, Leipzig, Germany, Aug. 2017
  8. M. K. Sarker, N. Xie, D. Doran, M. Raymer, and P. Hitzler. “Explaining Trained Neural Networks with Semantic Web Technologies: First Steps”, Proc. Of 12th Intl. Workshop on Neural-Symbolic Learning and Reasoning, London, United Kingdom, Jul. 2017 [pdf]
  9. N. Xie, K. Brown, N. Rude, and D. Doran. “A Soft Computing Prefetcher to Mitigate Cache Degradation by Web Robots”, Proc. Of Intl. Symposium on Neural Networks, Sapporo, Japan, Jun. 2017 [pdf]
  10. S. Wijeratne, L. Balasuriya, A. Sheth, and D. Doran. “EmojiNet: An Open Service and API for Emoji Sense Discovery”, Proc. Of AAAI Intl. Conference on Weblogs and Social Media, Vancouver, CA, May 2017

2016

  1. N. Trivedi, D. Asamoah, and D. Doran. “Keep the Conversation Going: Engagement-Based Customer Segmentation for Online Social Service Platforms”, Information Systems Frontiers, 2016
  2. D. Asamoah, R. Sharda, N. Rude, and D. Doran. “RFID-Based Information Visibility for Hospital Operations: Exploring its Positive Effects using Discrete Event Simulation”, Healthcare Management Science, Springer, pp. 1-12, 2016
  3. D. Doran and S. Gokhale. “An Integrated Method for Real-Time and Offline Web Robot Detection”, Expert Systems, Wiley, 2016
  4. D. Doran, K.Severin, S. Gokhale, and A. Dagnino. “Social Media Enabled Human Sensing for Smart Cities”, AI Communications, IOS Press, Vol. 29, pp. 57-76, 2016
  5. D. Doran and A. Fox. “Operationalizing Central Place and Central Flow Theory with Mobile Phone Data”, Annals of Data Science, Springer, Vol. 3, No. 1, pp. 1-24, 2016
  6. L. Balasuriya, S. Wijeratne, D. Doran, and A. Sheth. “Signals Revealing Street Gang Members on Twitter”, 2016 ChASM workshop on Computational Approaches to Social Modeling, Seattle, WA, Nov. 2016
  7. S. Wijeratne, L. Balasuriya, A. Sheth, and D. Doran. “EmojiNet: Building a Machine Readable Sense Inventory for Emoji”, Proc. Of International Conference on Social Informatics, pp. 527-541, Seattle, WA, Nov. 2016
  8. L. Balasuriya, S. Wijeratne, D. Doran, and A. Sheth. “Finding Street Gang Members on Twitter”, Proc. of IEEE/ACM Intl. Conference on Advances in Social Network Analysis and Mining, pp. 685-692, San Francisco, CA, Aug. 2016
  9. M.C. Calzarossa, L. Massari, D. Doran, S. Yelne, N. Trivedi, and G. Moriarty. “Measuring the Users and Conversations of a Vibrant Online Emotional Support System”, Proc. Of IEEE Symposium on Computers and Communications, pp. 1193-1199, Messina, Italy, Jul. 2016
  10. S. Wijeratne, L. Balasuriya, D. Doran, and A. Sheth. “Word Embeddings to Enhance Twitter Gang Member Profile Identification”, 3rd Workshop on Semantic Machine Learning at Intl. Joint Conference on Artificial Intelligence, pp. 18-24, New York, NY, Jul. 2016
  11. M.C. Calzarossa, L. Massari, D. Doran, S. Yelne, N. Trivedi, and G. Moriarty. “Measuring the Users and Conversations of a Vibrant Online Emotional Support System”, Proc. Of IEEE Symposium on Computers and Communications, pp. 1193-1199, Messina, Italy, Jul. 2016
  12. M. Piekenbrock and D. Doran. "Exploring Information-Optimal Network Discretization for Dynamic Network Analysis", INSNA Sunbelt Conference, Newport Beach, CA, Apr. 2016

2015

  1. D. Doran. “On the Discovery of Social Roles in Large Scale Social Systems”, Social Network Analysis and Mining, Springer, Vol. 5, No. 49, 2015
  2. N. Rude and D. Doran. “Request Type Prediction for Web Robot and Internet of Things Traffic”, Proc. Of IEEE Intl. Conference on Machine Learning and Applications, pp.988-993, Miami, FL, Dec. 2015
  3. D. Asamoah, D. Doran, and S. Schiller. “Teaching the Foundations of Data Science: An Interdisciplinary Approach”, SIGDSA Pre-ICIS Business Analytics Congress, Fort Worth, TX, Dec. 2015
  4. M. Maurice, M. Piekenbrock, and D. Doran. "WAMINet: An Open Source Library for Dynamic Geospace Analysis Using WAMI", Proc. of IEEE Intl. Symposium on Multimedia, Miami, Florida, Dec. 2015
  5. D. Doran, S. Yelne, L. Massari, M.C. Calzarossa, L. Jackson, and G. Moriarty. “Stay Awhile and Listen: User Interactions in a Crowdsourced System Offering Emotional Support”, Proc. of IEEE/ACM Intl. Conference on Advances in Social Network Analysis and Mining, pp. 667-674, Paris, France, Aug. 2015
  6. S. Kumar, P. Kapanipathi, D. Doran, P. Jain, and A. Sheth. “Entity Recommendations Using Hierarchical Knowledge Bases”, Proc. Of Intl. Workshop on Knowledge Discovery and Data Mining Meets Linked Open Data at European Semantic Web Conference, Portoroz, Slovenia, Jun. 2015
  7. S. Wijeratne, D. Doran, A. Sheth, and J. Dustin. “Analyzing the Social Media Footprint of Street Gangs”, Proc. Of IEEE Intl. Conference on Intelligence Security Informatics, pp. 91-96, Baltimore, MD, May 2015
  8. D. Doran, A. Fox, and V. Mendiratta. “Where do we Develop? Discovering Regions for Urban Investment in Senegal”, Intl. Conf. on the Analysis of Mobile Phone Datasets Data for Development Challenge Book of Abstracts: Scientific Papers, pp. 530-540, Cambridge, MA, Apr. 2015
  9. H. Alzhami, S. Gokhale, and D. Doran. “Understanding Social Effects in Online Networks”, Proc. Of IEEE Intl. Symposium on Social Computing and Semantic Data Mining, pp. 863-868, Anaheim, CA, Feb. 2015