7:30 – 10:30 ASRC

• Wrote up notes from yesterday’s meetings with Don and Shimei.
• Really just getting ready for T-day, but I ran my list of recipies through the TF-IDF and LMN tools and now I have a nice, sparse matrix that I can try the NMF on.
• Finish Matrix dot-product code and promote to Labled2DMatrix – done!!

7:00 – 5:00 ASRC

• Worked on getting the spreadsheet of conferences, journals and grant started
• Continuing Opinion Dynamics With Decaying Confidence: Application to Community Detection in Graphs. Details here.
  • When δ increases, the communities become smaller but more densely connected.
  • It should be very interesting to look at belief velocity at different scales.
• A Plethora of Data Set Repositories
• More NMF. Getting closer
• Installing Python on the laptop for discussion with Don
• Got everything working in java! Need to move the dot product code into Labeled2DMatrix and flesh out the other cases.

  rMat
   , D1, D2, D3, D4, 
  U1, 5, 3, 0, 1, 
  U2, 4, 0, 0, 1, 
  U3, 1, 1, 0, 5, 
  U4, 1, 0, 0, 4, 
  U5, 0, 1, 5, 4, 
  
  rowMat
  
  U1, 0.67, 0.89, 
  U2, 0.36, 0.47, 
  U3, 0.51, 0.27, 
  U4, 0.11, 0.84, 
  U5, 0.23, 0.88, 
  
  colMat
  
  D1, 0.36, 0.68, 
  D2, 0.84, 0.06, 
  D3, 0.07, 0.06, 
  D4, 0.65, 0.16, 
  
  steps = 5000
  
  P
  Array2DRowRealMatrix{{0.1714659334,2.4334642215},{0.2222526463,1.8424266034},{1.8809519431,0.3877676639},{1.5002592207,0.3319796716},{1.398228183,1.5413729554}}
  
  Q
  Array2DRowRealMatrix{{0.1642944844,0.083284122,1.152720993,2.6155442597},{2.0998133805,1.0434120295,2.0884233062,0.228777745}}
  
  rowMat
  
  U1, 0.17, 2.43, 
  U2, 0.22, 1.84, 
  U3, 1.88, 0.39, 
  U4, 1.5, 0.33, 
  U5, 1.4, 1.54, 
  
  colMat
  
  D1, 0.16, 2.1, 
  D2, 0.08, 1.04, 
  D3, 1.15, 2.09, 
  D4, 2.62, 0.23, 
  
  newMat
   , D1, D2, D3, D4, 
  U1, 5.14, 2.55, 5.28, 1.01, 
  U2, 3.91, 1.94, 4.1, 1, 
  U3, 1.12, 0.56, 2.98, 5.01, 
  U4, 0.94, 0.47, 2.42, 4, 
  U5, 3.47, 1.72, 4.83, 4.01,

• Meeting with Don.
  • Looked through the modelling and UTOPIAN papers, and walked through some of the math. We’ll meet next Friday to try to convert some of the equations into java code
• Meeting with Shimei
  • There are ways of getting better stability with LDA. Still ok to do NMF, though there may be issues with scaling. That’s where a stable version of LDA might make sense.

6:45 – 4:45 ASRC

• Continuing Opinion Dynamics With Decaying Confidence: Application to Community Detection in Graphs. Details here.
  • Bubbles at scales? “Stability measures the quality of a partition by giving a positive contribution to communities from which a random walker is unlikely to escape within the given time scale. For small values of t, this gives more weights to small communities whereas for larger values of t , larger communities are favored. Thus, by searching the partitions maximizing the stability for several values of , one can detect communities at several scales.“
  • The algebraic connectivity of a graph G is the second-smallest eigenvalue of the Laplacian matrix of G
  • Data sources for the paper:
    • The political blogosphere and the 2004 US election: divided they blog

    • An information flow model for conflict and fission in small groups

• More NMF

  P = [[ 0.67503659  0.89795272]
   [ 0.36939303  0.47816356]
   [ 0.51019257  0.27772317]
   [ 0.1130504   0.84860109]
   [ 0.23238542  0.88222005]]
  
  Q = [[ 0.36692407  0.6844149 ]
   [ 0.84469693  0.06331073]
   [ 0.07366106  0.06603799]
   [ 0.65677669  0.16947152]]
  
  nP = [[ 0.16286496  2.42456084]
   [ 0.21647521  1.83981127]
   [ 1.9047257   0.39049035]
   [ 1.52103295  0.33509559]
   [ 1.41350212  1.51711067]]
  
  nQ = [[ 0.15875994  2.09665688]
   [ 0.08334172  1.04818927]
   [ 1.16320811  2.09280482]
   [ 2.56431807  0.24424636]]
  
  nQt = [[ 0.15875994  0.08334172  1.16320811  2.56431807]
   [ 2.09665688  1.04818927  2.09280482  0.24424636]]
  
  R = [[5 3 0 1]
   [4 0 0 1]
   [1 1 0 5]
   [1 0 0 4]
   [0 1 5 4]]
  
  nR = [[ 5.10932861  2.55497211  5.26357846  1.00982771]
   [ 3.89182055  1.94651185  4.10217161  1.00447849]
   [ 1.12111842  0.56805092  3.03281247  4.97969837]
   [ 0.94405957  0.4780091   2.47056752  3.98225815]
   [ 3.40526805  1.70802283  4.81921366  3.99521777]]

  • Hard coded the random values for gradient descent to compare python and java
  • Stepping h
• Sprint stuff?
  • Scrum
  • Sent Jeremy the svn file names for my Vistronix code
• Fika
• Meeting with Wayne? Basic catching up. started the spreadsheet of conferences and grants

7:00 – 4:00 ASRC

• Continuing Opinion Dynamics With Decaying Confidence: Application to Community Detection in Graphs. Details here.
• Working my way through Matrix Factorization for Movie Recommendations – CME 510
• 
• Still working the NMF math, particularly the gradient descent. This is still the best version I can find
• Mark Zuckerberg on fake news

7:00 – 10:00, 10:30 – 5:30 ASRC

• Tenure review meeting at 10:00? Show up and see, I guess
• Continuing Opinion Dynamics With Decaying Confidence: Application to Community Detection in Graphs. Details here.
• We prove, under some conditions, the existence of a solution to the system dynamics, convergence to clusters, and a non-trivial lower bound on the
  distance between clusters. Huh. So bubbles must exist at a certain minimum information distance from each other??
• Inverse matrices are needed because we can’t divide by a matrix but we can multiply by its reciprocal.
• Lemma – a subsidiary or intermediate theorem in an argument or proof.
• (From Wikipedia) In measure theory, the Lebesgue measure, named after French mathematician Henri Lebesgue, is the standard way of assigning a measure to subsets of n-dimensional Euclidean space. For n = 1, 2, or 3, it coincides with the standard measure of length, area, or volume. In general, it is also called n-dimensional volume, n-volume, or simply volume.^[1] It is used throughout real analysis, in particular to define Lebesgue integration. Sets that can be assigned a Lebesgue measure are called Lebesgue measurable; the measure of the Lebesgue measurable set A is denoted by λ(A).
• The backward slash is kind of the set theory equivalent of subtracting, i.e.,
  A∖B={a∈A∣a∉B}.
• Group doc on how to fix fake news
• Back to working through NMF.
• Looks like we’ll watch videos tomorrow morning

7:00 – 4:00 ASRC

• Continuing Opinion Dynamics With Decaying Confidence: Application to Community Detection in Graphs. My notes are here.
• Clean data and apis from propublica
• Ulrich Krause
  Professor of Mathematics, Bremen University
  Positive dynamical systems, opinion dynamics, algebra
  • Opinion dynamics and bounded confidence models, analysis, and simulation – looks incredibly clear and helpful.
  • Opinion dynamics under the influence of radical groups, charismatic leaders, and other constant signals: A simple unifying model – derived from the above.
• From Is there negative social influence? Disentangling effects of dissimilarity and disliking on opinion shifts
  •  This finding implies for models of opinion dynamics that a complex non-linear social influence function might be unnecessary to characterize the relationship between similarity and opinion change. Our results suggest that not only for the sake of simplicity, but also for the sake of realism, model builders should be cautioned against resorting too readily to a more complex assumption than a simple linear influence function.
• I need to add recursion to the QueryComponent (content list and child list) and work through the combinations that way and get rid of the lists. Done! Had an issue with the BufferedWriter not flushing.
• BRC kickoff meeting
• Made a new arff for Aaron of the BRC doctor data I tagged. Should be enough for a starting junk filter.
• Finished the Utopian paper. Need to get up to speed on NMF.

7:00 – 3:30 ASRC

• Continuing Opinion Dynamics With Decaying Confidence: Application to Community Detection in Graphs. My notes are here.
• Working on getting the crawl payload builder. I got messed up with permutations. Tomorrow I need to add recursion to the QueryComponent (content list and child list) and work through the combinations that way and get red of the lists.

7:00 – 5:00 ASRC

• Pick up printer paper!
• My intuition is that there is a form of information ‘flocking behavior’ with respect to information space. There wouldn’t be quite the same physics as birds or fish in motion, but there do seem to be rules.
  Surprisingly, when I started to look at the literature, many of my hits came back from swarm robotics, for example Stable social foraging swarms in a noisy Environment. This is particularly interesting since information search behavior has long been equated with foraging behavior.
• The Max Planck Department of Collective Behaviour: “If it’s collective, and a great system for asking questions, then it is of interest to us.”
• So, Reading up on flocking.
  • Found Stable social foraging swarms in a noisy Environment
  • On Krause’s multi-agent consensus model with state-dependent connectivity
  • Opinion dynamics with decaying confidence: Application to community detection in graphs <- going to start with this one.
• Add wayne to my resume
• Quick meetings with Shimei and Aaron

8:00 – 12:00 – UMBC

• Finished the IUI reviews
• Doing Shimei’s review
• Setting up meeting with Christelle Viauroux
• Too frazzled to do coding. Reading Last Place on Earth.

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