Artificial Intelligence Laboratory

Resources Page for MIS 464 and MIS 611D

Class Resources for MIS 464, Data Analytics, and MIS 611D, Topics in Data and Web Mining (Spring 2023)

Instructor: Hsinchun Chen, Ph.D., Professor, Management Information Systems Dept, Eller College of Management, University of Arizona

Course Syllabi - Spring 2023

TOPIC 1: Introduction (MIS, CS, BI, Data Science)

  1. University of Arizona MIS Program: Overview, by Dr. Hsinchun Chen
  2. Building the UArizona AZSecure Cybersecurity Program: Advancing Research and Education with "AI for Cybersecurity", by Dr. Hsinchun Chen (2023) -- AZSecure Cybersecurity Program Information Sheet 2023
  3. AI in Cybersecurity - Machine Learning/Deep Learning Data Analytics - HICSS 56 Symposium Materials
  4. Business Analytics Intellectual Landscape , by Dr. Hsinchun Chen, 2020, pdf version
  5. MIS Analytics Curriculum, by Dr. Hsinchun Chen, 2020 pdf version
  6. History and Key Events of AI and Data Analytics , by Dr. Hsinchun Chen, 2020
  7. Ideas for the Future of the IS Field, by G. B. Davis, P. Gray, S. Madnick, J. F. Nunamaker, R. Sprague, and A. Whinston. Transactions on Management Information Systems, Volume 1, Issue 1, pp. 2:1 - 2:15. [PDF copy here]
  8. Design Science, Grand Challenges, and Societal Impacts, by Hsinchun Chen. Transactions on Management Information Systems, Volume 2, Issue 1, pp. 1:1 - 1:10. [PDF copy here]
  9. Journals, Conferences, and Funding Sources for MIS Researchers and Educators: A Resource Guide, by Dr. Hsinchun Chen (Updated 2019)
  10. The H-Index for MIS, January 2024
  11. The H-Index for Computer Science, by Jens Palsberg, 2023
  12. Template for Producing IT Research and Publication, by Dr. Hsinchun Chen
    1. IEEE Template - Word Document
    2. IEEE Paper Examples
      1. Detecting Cyber Threats in Non-English Dark Net Markets: A Cross-Lingual Transfer Learning Approach, by Ebrahimi et al., 2018, IEEE ISI
      2. Identifying, Collecting, and Presenting Hacker Community Data: Forums, IRC, Carding Shops, and DNMs, by Du et al., 2018, IEEE ISI
      3. Labeling Hacker Exploits for Proactive Cyber Threat Intelligence: A Deep Transfer Learning Approach, by Benjamin Ampel, Sagar Samtani, Hongyi Zhu, Steven Ullman, and Hsinchun Chen, 2020, IEEE ISI
      4. Identifying Vulnerable GitHub Repositories and Users in Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach, by Ben Lazarine, Sagar Samtani, Mark Patton, Hongyi Zhu, Steven Ullman, Benjamin Ampel, and Hsinchun Chen, 2020, IEEE ISI
      5. Identifying, Collecting, and Monitoring Personally Identifiable Information: From the Dark Web to the Surface Web, by Yizhi Liu, Fang Yu Lin, Zara Ahmad-Post, Mohammadreza Ebrahimi, Ning Zhang, James Lee Hu, Jingyu Xin, Weifeng Li, and Hsinchun Chen, 2020, IEEE ISI
      6. Smart Vulnerability Assessment for Scientific Cyberinfrastructure: An Unsupervised Graph Embedding Approach, by Steven Ullman, Sagar Samtani, Ben Lazarine, Hongyi Zhu, Mark Patton, and Hsinchun Chen, 2020, IEEE ISI
      7. Distilling Contextual Embeddings Into A Static Word Embedding For Improving Hacker Forum Analytics, by Benjamin Ampel and Hsinchun Chen, 2021, IEEE ISI
      8. Single-Shot Black-Box Adversarial Attacks Against Malware Detectors: A Causal Language Model Approach, by James Lee Hu, Mohammadreza Ebrahimi, and Hsinchun Chen, 2021, IEEE ISI
      9. Automated PII Extraction from Social Media for Raising Privacy Awareness: A Deep Transfer Learning Approach, by Yizhi Liu, Fang Yu Lin, Mohammadreza Ebrahimi, Weifeng Li, and Hsinchun Chen, 2021, IEEE ISI
  13. Sample Research by Applications, by Dr. Hsinchun Chen
    1. Cybersecurity and Security Analytics
      1. Criminal Network Analysis and Visualization, by Jennifer Xu and Hsinchun Chen , 2005, CACM
      2. AI and Security Informatics, by Hsinchun Chen, (September/October 2010), IEEE IS
      3. CyberGate: A Design Framework and System for Text Analysis of Computer-Mediated Communication, by Abbasi and Chen, December 2008, MISQ
      4. DICE-E: A Framework for Conducting Darknet. Identification, Collection, Evaluation, with Ethics, by Victor Benjamin, Joseph S. Valacich, and Hsinchun Chen, 2019, MISQ
      5. Proactively Identifying Emerging Hacker Threats from the Dark Web: A Diachronic Graph Embedding Framework (D-GEF), by Sagar Samtani, Hongyi Zhu, and Hsinchun Chen, 2020, ACM TOPS
      6. Cross-Lingual Cybersecurity Analytics in the International Dark Web with Adversarial Deep Representation Learning, by Mohammadreza Ebrahimi, Yidong Chai, Sagar Samtani, and Hsinchun Chen, June 2022, MISQ
      7. Linking Exploits from the Dark Web to Known Vulnerabilities for Proactive Cyber Threat Intelligence: An Attention-based Deep Structured Semantic Model, by Sagar Samtani, Yidong Chai, and Hsinchun Chen, June 2022, MISQ
    2. Smart Health
      1. Smart Health and Wellbeing, by Hsinchun Chen (September/October 2011), IEEE IS
      2. AI for Global Disease Surveillance, by Hsinchun Chen and Daniel Zeng (November/December 2009), IEEE IS
      3. Time-To-Event Predictive Modeling for Chronic Conditions Using Electronic Health Records, by Yu-Kai Lin, Hsinchun Chen, Randall A. Brown, Shu-Hsing Li, and Hung-Jen Yang (2014), JBI
      4. Healthcare Predictive Analytics for Risk Profiling in Chronic Care: A Bayesian Multitask Learning Approach, by Yu-Kai Lin et al., 2017, MISQ
      5. Connecting Systems, Data, and People: A Multidiciplinary Research Roadmap for Chronic Disease management, by Indranil Bardhan, Hsinchun Chen, and Elena Karahanna, 2020, MISQ
      6. A Deep Learning Approach for Recognizing Activity of Daily Living (ADL) for Senior Care: Exploiting Interaction Dependency and Temporal Patterns, by Hongyi Zhu, Sagar Samtani, Randall Brown, and Hsinchun Chen, June 2021, MISQ
      7. Wearable Sensor-based Chronic Condition Severity Assessment: An Adversarial Attention-based Deep Multisource Multitask Learning Approach, by Shuo Yu, Yidong Chai, Hsinchun Chen, Scott Sherman, and Randall Brown, September 2022, MISQ
    3. Smart Business and Money
      1. Business and Market Intelligence 2.0, by Hsinchun Chen (January/February 2010), IEEE IS
      2. Smart Market and Money, by Hsinchun Chen (November/December 2011), IEEE IS
      3. AI and Opinion Mining, by Hsinchun Chen and David Zimbra (May/June 2010), IEEE IS
      4. Web Media and Stock Markets : A Survey and Future Directions from a Big Data Perspective, by Qing Li, Yan Chen, Jun Wang, Yuanzhu Chen, and Hsinchun Chen, 2018, IEEE TKDE
      5. A Multimodal Event-driven LSTM Model for Stock Prediction Using Online News, by Qing Li, Jinghua Tan, Jun Wang, and Hsinchun Chen, 2021, IEEE TKDE
    4. Sports and Games Analytics
      1. Expert Prediction, Symbolic Learning, and Neural Networks-An Experiment on Greyhound Racing, by Hsinchun Chen et al., IEEE Expert (December 1994), IEEE Expert
      2. Sports Data Mining, by Robert Schumaker, Osama Solieman, and Hsinchun Chen, 2010, Springer
      3. AI, Virtual Worlds, and Massively Multiplayer Online Games, by Hsinchun Chen and Yulei Zhang (January/February 2011), IEEE IS
  14. Sample NSF Proposal for Cybersecurity , by Sagar Samtani, 2019
  15. Publishing in Major Journals & Getting Major Grants Consistently, by Dr. Hsinchun Chen, 2023
  16. Design Science in Information Systems Research, by Alan R. Hevner, Salvatore T. March, Jinsso Park, and Sudha Ram. MIS Quarterly, Volume 28, Number 1, pp. 75-105, March 2004.
  17. Positioning and Presenting Design Science Research for Maximum Impact, by Shirley Gregor and Alan R. Hevner. MIS Quarterly, Volume 37, Number 2, pp. 337-355, June 2013.
  18. Editor's Comments: Diversity of Design Science Research, by Arun Rai, Andrew Burton-Jones, Hsinchun Chen, Alok Gupta, Alan R. Hevner, and Wolfgang Ketter. MIS Quarterly, Volume 41, Number 1, pp. iii-xviii, March 2017.
  19. MISQ BI Special Issue: Business Intelligence and Analytics: From Big Data to Big Impact, by Hsinchun Chen et al. (2012).
  20. UC Berkeley’s Fastest-Growing Class Is Data Science 101, by Douglas Belkin, WSJ, November 2, 2018
  21. The 50 Best Jobs in America for 2019 (Glassdoor Ranking), January 23, 2019.
  22. The State of Data Science and Machine Learning - Kaggle Survey 2017, 2017. [Interactive Online Version]
  23. The State of Data Science and Machine Learning - Kaggle Survey 2022, 2022.
  24. Is Data Scientist Still the Sexiest Job of the 21st Century?, by Thomas Davenport, 2022, HBR
  25. CyberGate: A Design Framework and System for Text Analysis of CMC, by Abbasi and Chen, 2008 - PPT
  27. Python Overview for Data Analytics
    1. Python for Data Analytics, by Benjamin Ampel and Steven Ullman, 2023
    2. Python Tutorial - Colab Notebook 2023
    3. Python Data Analytics Tutorial - Colab Notebook 2023
  28. Tableau Overview and Publicly Available Data Sources (Sagar Samtani and Hsinchun Chen, with updates from Hongyi Zhu and Steven Ullman, 2023)
    1. Sample NFL Dataset for Visualization
  29. Dark Web and Privacy Analytics Research: Hands-on Training and Planning, by Ebrahimi et. al., 2020
  30. Smart Vulnerability Assessment for OS/VM, GitHub, IoT: An Overview, by Ullman et. al., 2020

TOPIC 2: Web Mining (Surface Web, Deep Web, Social Web)

  1. Inside Internet Search Engines
    1. Fundamentals, by Jan Pedersen and William Chang (SIGIR 1999)
    2. Spidering and Indexing, by Jan Pedersen and William Chang (SIGIR 1999)
    3. Search, by Jan Pedersen and William Chang (SIGIR 1999)
    4. Products, by William Chang and Jan Pedersen (SIGIR 1999)
    5. Business, by William Chang and Jan Pedersen (SIGIR 1999)
  2. Search Engines and Their Algorithms, by C. Lee Giles (2018) (33M)
  3. The Anatomy of a Large-Scale Hypertextual Web Search Engine, S. Brin and L. Page (1998)
  4. Google Architecture and Technologies, by Hsinchun Chen (2020)
  5. Page Rank and Google Story, by Vise and Malseed, 2005
  6. AI, Chapter 4. Search Algorithm, Winston (1984)
  7. Search Algorithms with Examples, by Hsinchun Chen and Mohammadreza Ebrahimi (2020)
  8. GA Handout (27M)
  9. Network Science by Sagar Samtani, Weifeng Li, Hsinchun Chen, 2016
  10. The Long Tail, by Chris Anderson, WIRED Magazine (December 2004)
  11. Web 2.0 ... The Machine is Us/ing Us (YouTube)
  12. What Is Web 2.0? Design Patterns and Business Models for the Next Generation of Software, by Tim O'Reilly (2005)
  13. Web 2.0: Introduction, by Hsinchun Chen, 2009
  14. Communications of the ACM (2011):
    1. Reflecting on the DARPA Red Balloon Challenge, by John C. Tang et al. (April 2011)
    2. Crowdsourcing Systems on the World-Wide Web, by Anhai Doan et al. (April 2011)
    3. The Topology of Dark Networks, by Jennifer Xu and Hsinchun Chen (October 2008)
  15. The Netflix Recommender System: Algorithms, Business Value, and Innovation (Uribe and Hunt, 2015)
  16. Matrix Factorization Techniques for Recommender Systems (Koren, Bell, and Volinsky, 2009)
  17. Harvard Business Review (October 2012)
    1. Big Data: The Management Revolution (from HBR 12/12)
    2. Data Scientist: The Sexiest Job Of the 21st Century (from HBR 12/12)
    3. Making Advanced Analytics Work For You (from HBR 12/12)
  18. The Economist A Special Report on Social Networking---A World of Connections (January 30th 2010):
    1. A world of connections (from The Economist 1/30/10)
    2. Global swap shops (from The Economist 1/30/10)
    3. Twitter's transmitters (from The Economist 1/30/10)
    4. Profiting from friendship (from The Economist 1/30/10)
  19. The Economist, Data, Data, Everywhere: A Special Report on Managing Information (February 25th 2010); includes the following pieces:
    1. The data deluge
    2. Data, data everywhere
    3. All too much
    4. A different game
    5. Show me
    6. Needle in a haystack
    7. New rules for big data
    8. Clicking for gold
    9. Handling the cornucopia
    10. The open society
    11. Sources and acknowledgments
  20. The Economist, A Special Report on Personal Technology (October 8th 2011). Includes the following sections:
    1. Beyond the PC
    2. The Power of Many
    3. The Beauty of Bite-sized Software
    4. IT's Arab Spring
    5. Up Close
  21. The Economist, Special Report, Cyber-Security, July 12, 2014: Defending the Digital Frontier. Includes the following sections:
    1. Cybercrime: Hackers, Inc.
    2. Vulnerabilities: Zero-day game
    3. Business: Digital disease control
    4. Critical infrastructure: Crashing the system
    5. Market failures: Not my problem
    6. The Internet of Things: Home, hacked home
    7. Remedies: Prevention is better than cure
  22. The Economist, September 9, 2017: Facial Industry. Includes the following sections:
    1. The facial-industry complex
    2. Keeping a straight face
    3. Making faces from DNA
  23. The Economist, Special Report, Autonomous Vehicles, March 3, 2018: Reinventing Wheels. Includes the following sections:
    1. From here to autonomy
    2. Selling rides, not cars
    3. The new autopia
    4. A different world
    5. Rules of the road
  24. World (Patent) War - Bloomberg, 2012
  25. Cyber Threat Intelligence, by Sagar Samtani and Hsinchun Chen, 2019
  26. Looking to the Future of Cybersecurity, by Fang Yu Lin and Hsinchun Chen, 2019
  27. Computational Propaganda and Political Disinformation, by Zara AhmadPost and Steve Ullman, 2019.
  28. Introduction to Web Application and APIs (Revised by Jonathan Jiang and Julian Guo):
    1. Flickr Photo Search API Sample Code
    2. Amazon Product Advertising API Sample Code
    3. YouTube Data API Sample Code
    4. Yelp API Sample Code
  29. Big Data Technology - Hadoop, MapReduce, and Spark (Jonathan Jiang, with updates from Sagar Samtani and Shuo Yu, 2019)

TOPIC 3: Data Mining and Machine Learning

  1. Predictive Analytics for Data Mining (Weifeng Li, Sagar Samtani, Hsinchun Chen, 2023)
  2. Publicly Available Data Sources (Sagar Samtani and Hsinchun Chen, with updates from Hongyi Zhu, 2019)
  3. Logistic Regression and Elastic Net (Weifeng Li, Hsinchun Chen, 2016)
  4. Pattern Recognition using Support Vector Machine: Text Classification and Cybersecurity (Ahmed Abbasi, Hsinchun Chen, 2020)
  5. Clustering for Data Mining: Overview and Examples (Dr. Hsinchun Chen, 2020)
  6. WEKA Overview (Sagar Samtani, Weifeng Li, and Hsinchun Chen, with updates from Shuo Yu, 2019)
    1. iris-train, iris-test, houses-train, houses-test
  7. Scikit-Learn: An End-to-End Tutorial For Building Machine Learning Models in Python (Benjamin Ampel and Hsinchun Chen, 2022)
    1. Scikit-Learn Colab Notebook
  8. Top 10 Algorithms in Data Mining (PDF)
  9. ID3 Handout
  10. Backpropagation Neural Network Handout
  11. Self-organizing maps: an introduction
  12. K-means algorithm
  13. Expert Prediction, Symbolic Learning, and Neural Networks-An Experiment on Greyhound Racing, by Hsinchun Chen et al., IEEE Expert (December 1994)
  14. Detecting Fake Websites: The Contribution of Statistical Learning Theory, by Abbasi et al., September 2010, MISQ
  15. Introduction to Support Vector Machine (SVM) and Conditional Random Field (CRF) (Long Version, Short Version)
  16. Machine Learning: Trends, Perspectives, and Prospects (Science, Jordan and Mitchell, 2015)

TOPIC 4: Deep Learning & Artificial Intelligence

  1. The Economist, Special Report, AI in Business, March 31, 2018: GrAIt Expectations. Includes the following sections:
    1. In algorithms we trust
    2. Here to help
    3. Hire education
    4. Simile, you're on camera
    5. Leave it to the experts
    6. Two faced
  2. A Special Report on Artificial Intelligence. The New York Times, October 19, 2018. Includes the following articles:
    1. Workers Beware, by David Kaufman, October 18, 2018
    2. What Comes After the Roomba? by John Markoff, October 21, 2018
    3. The Computerized Chauffeur, by Norman Mayersohn, October 19, 2018
    4. A.I. Is Begining to Assist Novelists, by David Streitfeld, October 18, 2018
    5. The A.I. Wave Is Here, by Steve Lohr, October 21, 2018
    6. Acknowledging the Pitfalls, Too, by Cade Metz, October 22, 2018
    7. Will There Be a Ban on Killer Robots? by Adam Satariano, October 19, 2018
    8. Breaking Big Tech's Hold on A.I., by Nathaniel Popper, October 20, 2018
  3. Google masters Go (Nature, Elizabeth Gibney, January 28, 2016)
  4. Artificial Intelligence Go Showdown (The Economist, March 12, 2016)
  5. Mastering the game of Go with deep neural networks and tree search (Nature, Silver et al., 2016)
  6. Deep Learning (Nature, LeCun et al., 2015)
  7. Editorial: Chess, a Drosophila of Reasoning (Science, Kasparov, 2018)
  8. One Giant Step for a Chess-Playing Machine (New York Times, Strogatz, 2018)
  9. A General Reinforcement Learning Algorithm That Masters Chess, Shogi, and Go Through Self-play (Science, Silver et al., 2018)
  10. The Malicious Use of Artificial Intelligence: Forecasting, Prevention, and Mitigation, by Miles Brundage et al., February 2018.
  11. Neural Networks: Feedforward Backpropagation NN and Self-Organizing Map (Dr. Hsinchun Chen, 2020)
  12. Deep Learning: An Overview (Hsinchun Chen, 2020)
  13. An Introduction to Convolutional Neural Networks: Overview, Implementation, and Example (Shuo Yu and Hsinchun Chen, 2023)
  14. An Introduction to Recurrent Neural Networks: Overview, Implementation, and Application (Hongyi Zhu and Hsinchun Chen, 2023)
  15. An Introduction to Graph Neural Networks: Overview, Implementation, and Application (Steven Ullman and Hsinchun Chen, 2023)
  16. An Introduction to Transformers: Overview, Implementation, and Application (Benjamin Ampel and Hsinchun Chen, 2023)
  17. Generative Pre-trained Transformers: An Overview of Large Language Models and Use Cases (Benjamin Ampel, Henry Yang, James Hu, Tony Zhang, and Hsinchun Chen, 2023)
  18. Autoencoders: Overviews and Selected Application (Sagar Samtani and Hsinchun Chen, 2018)
  19. An Introduction to Deep Transfer Learning (Mohammadreza Ebrahimi and Hsinchun Chen, 2018)
  20. Deep Generative Models: An Overview (Yidong Chai, Weifeng Li, and Hsinchun Chen, 2018)
  21. Artificial Intelligence and Deep Learning (Lee Giles, 2018)
  22. Representation Learning (Alexander G. Ororbia II and Lee Giles, 2018)
  23. Attention Is All You Need (Vaswani et al., NeurIPS, 2017)
  24. GPT-2 (Radford et al., OpenAI, 2018)
  25. BERT (Devlin et al., NAACL, 2019)
  26. GPT-3 (Brown et al., NeurIPS, 2020)
  27. SimCLR (Chen et al., ICML, 2020)
  28. DALL-E (Ramesh et al., ICML, 2021)
  29. IEEE Spectrum, Special Report: Why Is AI So Dumb?, October 2021. Includes the following articles:
    1. The Turbulent Past and Uncertain Future of AI, by Eliza Strickland
    2. How Deep Learning Works, by Samuel K. Moore, David Schneider, & Eliza Strickland
    3. How to Train an All-Purpose Robot, by Tom Chivers
    4. 7 Revealing Ways AIs Fail, by Charles Q. Choi
    5. A Human in the Loop, by Rodney Brooks
    6. Deep Learning's Diminishing Returns, by Neil C. Thompson, Kristjan Greenewald, Keeheon Lee, & Gabriel F. Manso
    7. Deep Learning Goes to Boot Camp, by Evan Ackerman
  30. The Economist, Big Tech's Supersized Ambitions, January 2022.
  31. Foundation Models (Bommasani et al., Stanford, 2022)
  32. Introduction to AI-enabled Analytics Bootcamp (Sagar Samtani 2022)
  33. Research Bootcamp: Conducting AI-enabled Analytics (Sagar Samtani 2022)
  34. AI-enabled Analytics: A Brief Overview and An Open-Source Tools Inventory (Sagar Samtani 2022)
  35. The Economist, The World that BERT Built, June 2022.
  36. New York Times, AI is Getting Good. What Happens Next, August 25, 2022.

TOPIC 5: Text Mining (Sentiment Analysis, Topic Modeling, Visualization)

  1. Text Mining: Techniques, Tools, Ontologies and Shared Tasks (Xiao Liu, Shuo Yu, Hsinchun Chen, 2020)
  2. An Overview of Topic Modeling (Weifeng Li and Hsinchun Chen, 2018)
  3. Topic Modeling and Latent Dirichlet Allocation: An Overview (Weifeng Li, Sagar Samtani, and Hsinchun Chen, 2016)
  4. Information Visualization
  5. Information Visualization for Digital Library (2.21M)
  6. Visualizing Data: Frameworks and Examples (Hongyi Zhu, Sagar Samtani, Hsinchun Chen, Spring 2019)

MISCELLANEOUS RESOURCES

AI Lab Website

Photo provided through courtesy of DARPA and available through Wikimedia Commons.