Category Archives: Phil

Phil 11.23.16

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!!

Phil 11.22.16

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.

Phil 11.21.16

6:45 – 4:45 ASRC

  • Continuing Opinion Dynamics With Decaying Confidence: Application to Community Detection in Graphs. Details here.
  • 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

Phil 11.17.16

7:00 – 10:00, 10:30 – 5:30 ASRC

Phil 11.16.16

7:00 – 4:00 ASRC

Phil 11.14.16

7:00 – 5:00 ASRC

Phil 11.11.16

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.

Phil 11.10.16

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
  • Working on building JSON files for loading CI
  • Meeting about Healthdatapalooza

Phil 11.9.16

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

Phil 11.8.16

7:00 – 6:30 ASRC

Phil 11.7.16

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
  • 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

Phil 11.4.16

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

doccluster