7:00 – 4:30 ASRC

• Had some thoughts last night about how flocking at different scales in Hilbert space might work. Flocks built upon flocks. There is some equivalent of mass and velocity, where mass might be influence (positive and negative attraction). Velocity is related to how fast beliefs change.
• Also thought about maps some more, weather maps in particular. A weather map maintains a coordinate frame, even though nothing in that frame is stable. Something like this, with a sense of history (playback of the last X years) could provide an interesting framework for visualization.
• Continuing Novelty Learning via Collaborative Proximity Filtering review. Done! Need to submit both now.
• Adding StrVec to the ARFF outputs – done
• Starting this tutorial on Nonnegative Matrix Factorization
  • These slides are also very nice
• Working on building JSON files for loading CI
• Meeting about Healthdatapalooza

7:00 – 5:00 ASRC

• President-elect Trump. Wow. Just wow.
• Starting Novelty Learning via Collaborative Proximity Filtering review
• Working with Aaron to get the java version of the classifier working
• LibRec (http://www.librec.net) is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms. It consists of three major components: Generic Interfaces, Data Structures and Recommendation Algorithms. This should save a *lot* of work. Remember to thank and cite.
• The forces that drove this election’s media failure are likely to get worse – Lots of stuff on echo chambers and social media

7:00 – 6:30 ASRC

• Get mileage from Porsche!
• Start conference spreadsheet – locations, deadlines, domain, etc
  • CHIIR
  • IUI
  • Journalism Conference
  • Simulation Conference
• Meeting with Don at 4:30
  • Meta Particles -> meta organism = flock
  • Homotopy type theory
  • Christelle Viauroux
• Continuing review of Novelty Learning via Collaborative Proximity Filtering
  • A Painless Q-Learning Tutorial
  • The problem with the paper is that the authors do not consider that they are really identifying two different populations of users rather than the behavior changes in a single population. I think that if the paper addressed this possibility, it could contribute much more.
• Visualizing Data using t-SNE
• Simultaneous Discovery of Common and Discriminative Topics via Joint Nonnegative Matrix Factorization
• An Interactive Visual Testbed System for Dimension Reduction and Clustering of Large-scale High-dimensional Data

• Orthogonality and orthography: introducing measured distance into semanticspace

• Utopian: User-driven topic modeling based on interactive nonnegative matrix factorization

• From frequency to meaning: Vector space models of semantics

• Taste Over Time: The Temporal Dynamics of User Preferences.

• GA Tech – Jigsaw

6:30 – 3:00 ASRC

• Notes from Aaron to discuss today:

  • http://karpathy.github.io/2015/05/21/rnn-effectiveness/?branch_used=true Great article on RNN. Sample code available too.

  • Slider based decisions for clustering topic models where we weight similarity contributions individually, including entities (who the document is about via NLP extraction), BOW comparison, TF-IDF LS comparison, etc. The clusters change based off the combined contribution of each vector of attractors.
• Starting review of Novelty Learning via Collaborative Proximity Filtering
  • Gotta read this!!!  Taste Over Time – the Temporal Dynamics of User Preferences
  • and Mixtape – Direction-based Navigation in Large Media Collections
  • MAchine Learning for LanguagE Toolkit (MALLET)
• LingPipe is tool kit for processing text using computational linguistics. LingPipe is used to do tasks like:
  • Find the names of people, organizations or locations in news
  • Automatically classify Twitter search results into categories
  • Suggest correct spellings of queries
• GATE is open source software capable of solving almost any text processing problem
• Semantic Vectors creates semantic WordSpace models from free natural language text. Such models are designed to represent words and documents in terms of underlying concepts. They can be used for many semantic (concept-aware) matching tasks such as automatic thesaurus generation, knowledge representation, and concept matching.
• LSA-based essay grading – could be good for document classification/spam detection

6:45 – 3:00 ASRC

• Nervous enough about the election to move 1/3 of my retirement into long term treasuries.
• Writing up review of Topic-Relevance Map – Visualization for Improving Search Result Comprehension for IUI 2017. Done!
• Got similarity distance working on retrieved documents using a config file

7:00 – 3:00 ASRC

• Continuing to review Topic-Relevance Map – Visualization for Improving Search Result Comprehension for IUI 2017. I think parts of this are effectively plagiarized. Need to talk to someone about this
• How Google Shapes the News You See About the Candidates
• The Linear Algebra Behind Search Engines Online tutorial from 2005, which means there’s a chance I can follow. Loo
• Matrices, Vector Spaces, and Information Retrieval. Good examples. Looks readable. More on this later.
• Jaegul Choo
  • Weakly supervised nonnegative matrix factorization for user-driven clustering
  • L-EnsNMF: Boosted Local Topic Discovery via Ensemble of Nonnegative Matrix Factorization
  • Simultaneous discovery of common and discriminative topics via joint nonnegative matrix factorization
  • DemographicVis: Analyzing demographic information based on user generated content
  • Nonnegative matrix factorization for interactive topic modeling and document clustering
  • And so on. Need to spend some time looking at this.
• A human-machine collaborative system for identifying rumors on twitter

7:00 – 4:00 ASRC

• Reviewing Topic-Relevance Map – Visualization for Improving Search Result Comprehension for IUI 2017
• This looks important: Directing exploratory search: reinforcement learning from user interactions with keywords
• More (work) proposal writing. Done with first draft.

7:00 – 5:00 ASRC

• Playing around with using dissertation to search from. Interesting and different results for non–and-equalized docs and single counts.
  • baseline: information model behavior agent result pattern search system between
  • equalized docs: information search behavior design system result source document provide
  • single counts: information result provide system between search process example behavior
  • equalized + single: information result provide between search system process approach design
• Finishing survey and sending out – done!
• Tested the new CSE
• Back to Vector space models of semantics
• Worked on proposal with Aaron.

7:00 – 5:00 ASRC

• Working on survey
• Reading Vector space models of semantics. Fifty pages. Ow!
• Useful neural net blog
• Meeting with Wayne
  • Add an open comments section “is there anything that you’d like to add”
  • UI+AI to determine online trustworthiness
  • Ran my proposal (with equalized docs – better!) through LMN. More words in the query mean more specificity, so I went up to 9.
    • The Scholar result is here, set for no patents.
    • Scholar, with ‘single counts’ is here.
    • The standard result, which is also interesting is here.
    • Standard, with single counts (better?) is here.
  • Need to start on a spreadsheet of venues. (Get these off the laptop)
    • ICWSM 2017 – Abstracts Jan 6, full papers Jan 13
    • ICPSR is a site containing lots of data. Looking for qualitative corpora. Using ‘interview’ seems to bring up a lot, but you need to log in to use.

7:00 – 5:00 ASRC

• Listening to BBC Business Daily this morning on trusting algorithms.
  • This site: Computational Legal Studies. Seems very relevant for all kinds of reasons. Does Aaron Massey know about them?
    • Daniel Martin Katz: “Research Interests include legal futurism, legal informatics,  law & entrepreneurship, quantitative modeling of litigation and jurisprudence, quantitative finance, computational legal studies, big data and the law, economics of the legal profession, positive legal theory, technology aided access to justice, legal complexity and the overall impact of information technology, analytics and automation on the future of the legal profession.”
    • Jon Zelner “My research is focused on using spatial and social network analysis to prevent infectious diseases, with a focus on tuberculosis and diarrheal disease, and to understand social and epidemiological systems characterized by complex spatiotemporal dynamics. “
  • Cathy O’Neil, author of Weapons of Math Destruction. MathBabe.
• Interview with Judea Pearl on Bayesian computation: “We are losing the transparency now with deep learning. I am talking to users who say “it works well” but they don’t know why. Once you unleash it, it has its own dynamics, it does its own repair and its own optimization, and it gives you the right results most of the time. But when it doesn’t, you don’t have a clue as to what went wrong and what should be fixed.”
• Working on survey questions. Reading Internet, Mail, and Mixed Mode Surveys, chapter 6,
• Need to add a section on ‘Transparent’ Cognitive Computing.
• Took a look at Theresa’s slides. Need to call out some ML/AI word salad
• Working on adding a key. Slow UI. Done, I think…
• Add credit card to semrush
• Meeting with Shimei. We started talking about recommender systems, but wound up talking about neural word/sentence/paragraph/topic embedding. Trained against something like the Wikipedia and its outbound links, this might be a good way of having a knowledge coordinate frame that velocity and position can be determined.
  • This looks like a good introduction: From frequency to meaning: Vector space models of semantics
  • And this looks good too. Check references!

7:00 – ASRC

• Get a new copy of SPSS? My license has expired.
• Some thoughts about librarians in the internet age from PBS. This is more of the guide instead of recommender thing for Friday.
• Start writing up NIH proposal
• Working on Shimei’s evaluation. Oops – getting some kind of parse exception. Ah. I was calling the PDF parser. Cut and paste error.
  • Took terms from ratemyprofessor.com
    • Top three comments that were longer than a sentance for top three professors rated 5.0 and below, 4.0 and below, 3.0 and below. Giving a corpora consisting of 27 entries
    • Broken into three texts, sorted by LSI, with top 25% terms used
    • Centrality calculated using PageRank algorithm with terms as links
    • Culled out terms such as ‘there’ and ‘about’
    • Reweighted documents based on overall counts
    • Remaining sorted terms were:
      

      project

      write

      group

      stupid

      slide

      curve

      clearly

      member

      helpful

      workload

      unrealistic

      worst

      experience

      midterm

      school

      homework

      great

      exactly

      knowledgable

      science

      flexible

    • Build likert scale questionnaire grounded in the top x terms?
    • I can use the terms pointing into the text to ground the questions (e.g. “Three easy exams and two easy projects. It would be hard not to get an A.”
  • Wrote up response to RFI with Aaron

7:00 – 4:30 ASRC

• Wrote up notes from yesterday
• Getting started on the review for Barb. Sent her an email with Wayne’s questions.
• Added multithreading to the research browser based on this page: http://docs.oracle.com/javafx/2/threads/jfxpub-threads.htm
• Added text file processing so that I can use the ratemyprofessor data.

PhD proposal! Passed!Here are the slides.

• Shimei brought up a point about recommender systems. I think that as we currently have them, recommenders are like advisors. Maybe we need a guide more than a recommender? Could be an interesting study.
• Aaron M. wants me to be clear on the differences between True and Antibubble
• Aaron D. brought up the thought that all bubbles are bad. Need to point him at the section in the Law of Group Polarization that shows how small bubbles can allow nascent ideas to mature. I think that this is an important part of the agent model. I also think that agents should be able to ‘see’ how another agent feels about them (third belief/antibelief model)
• Need to follow up with everyone to get their thoughts before everything fades.
  • Wayne – Monday
  • Don
  • Aaron
  • Shimei
  • Thom

7:00 – 4:00 ASRC

• More slide prep.
• Got the centrality calculations working in the research browser. Gotta get multithreading working
• I’m now working on IRAD, not BRC
• Meeting with Wayne

7:00 – 4:00 ASRC

• Fixing slides
  • Add breadcrumbs in the lower border throughout – done
  • Add timeline that starts back at the initial politifact paper – done
  • Future
    • app dev takes 4-6 months
    • Running experiments/data gathering takes about a month
    • Analysis tools take about two months to build/learn/produce results
    • Writing papers takes about two months
    • Total time for each step = 9-11 months
    • RQ1 – August 2016 – May 2017
    • RQ2 – March 2017 – Dec 2017
    • RQ3 – Oct 2017 – Jul 2018
    • RQ4 – May 2018 – Feb 2019
    • RQ5 – Dec 2018 – Sept 2019
• RateMyProfessor UMBC IS: https://www.ratemyprofessors.com/campusRatings.jsp?sid=1244
  • Build a corpora (by rating?)
  • Central terms for each rating
  • Survey questions built from those