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Analyzed 1 day ago. based on code collected 2 days ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
------------------------------------------------------------------------------------------------------ Social Network Analysis with Twitter - Changes to Belief Propagation Potential Computation ------------------------------------------------------------------------------------------------------ 1. Exception handling for UnicodeError has been added in SentimentAnalyzer 2. Belief Propagation Potential computation for the RGO graph constructed has been changed to do plain summation of positivity and negativity scores for DFS of K-Core rather than multiplication which heuristically appears to predict sentiments better 3. An example for tweets sentiments analysis for 2 search keywords has been logged and committed in testlogs/
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
flag changed for invoking VIRGO memory system calls from python
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
test log for boost::python virgo_clone() invocation with use_as_kingcobra_service=0
Srinivasan Kannan
More... over 10 years ago
virgo_clone() system call has been included in switch-case for cpython invocation of VIRGO linux kernel
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
virgo_clone() system call has been included in switch-case and invokes an exported kernel module function in kernelspace
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Updated Requirements.txt
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
---------------------------------------------------------------------------------------------- OAuth2 authentication and credentials storage in MongoDB/Redis with passlib sha256 encryption ---------------------------------------------------------------------------------------------- 1.Login Handler has been augmented with OAuth2 authentication by python-oauth2 library. 2.MongoDB is used as OAuth2 credentials storage backend and Redis for token storage backend 3.Passlib is used to encrypt the password with SHA256 hash digest and stored once in MongoDB (code is commented) 4.Login page password is verified with passlib generated hash in MongoDB queried for a username argument 5.With this minimal standard authentication has been implemented for NeuronRain cloud deployment. 6.Prerequisite is to populate MongoDB neuronrain_oauth database and neuronrain_users collections with passlib encypted JSON by uncommenting the collections.insert_one() code.
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Rewritten Tornado GUI with Login Template and redirect to Algorithms Execution Template. Presently implemented for only a root user by cookie
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
------------------------------------------------------------------------------------------------------ Binary String Encoding of Time Series - Commits - 27 July 2016 ------------------------------------------------------------------------------------------------------ 1. New python script to encode a time series datastream as binary string has been added to repository. 2. This writes to StringSearch_Pattern.txt which is read by StringSearch_LongestRepeatedSubstring.py 3. Encoding scheme: If next data point is smaller, write 0 else if greater write 1. This captures the fluctuations in dataset irrespective of the amplitudes at each point. 4. For example a bitonic sequence would have been encoded as ....111111110000000.... (ascends and descends) 5. Suffix Array + LCP algorithm on this sequence finds the Longest Repeated Substring. For a specific example of Stock ticker time series data this amounts to frequently recurring fluctuation pattern. 6. Logs for the above have been committed to testlogs/
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
------------------------------------------------------------------------------------------------------------ String Search - Longest Common Substring - Suffix Trees(Suffix Arrays+LCP) Implementation ------------------------------------------------------------------------------------------------------------ This implementation finds the most repeated substrings within a larger string by constructing Suffix Trees indirectly with Suffix Array and Longest Common Prefix (LCP) datastructures. An ideal application for String Search is in Bioinformatics, Streamed Data analysis etc., For example, a Time Series data with fluctuating curve can be encoded as a huge binary string by mapping ebb and tide to 0 and 1. Thus a function graph time series is projected on to {0,1} alphabet to create blob. Usual Time Series analysis mines for Trends, Cycles, Seasons and Irregularities over time. This binary encoding of time series gives and alternative spectacle to look at the trends (highs and lows). Longest repeating pattern in this binary encoding is a cycle. Suffix Array by [UdiManber] has been implemented over Suffix Trees implementations of [Weiner] and [Ukkonen] because of simplicity of Suffix Arrays and Suffix Trees=Suffix Arrays + LCPs.
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
-------------------------------------------------------------------------------------------- Software Analytics - Cyclomatic Complexity from SATURN .dot graphs -------------------------------------------------------------------------------------------- New Python-Spark implementation that reads the SATURN program analyzer generated .dot graph files processes them with Spark MapReduce to find the number of edges and vertices in the graph represented by .dot file for each snippet. From this Cyclomatic Complexity is calculated (which is Edges - Vertices + 2) - a standard Function Point Estimator for code complexity. There were some JVM crashes frequently while starting up spark-submit logs for which are also committed in testlogs along with the two successful Spark computed Cyclomatic Complexity measures for two .dot files. These .dot files are sourced from VIRGO Linux saturn program analysis kernel driver.
Srinivasan Kannan
More... over 10 years ago
Updated AsFer Design Document
Srinivasan Kannan
More... over 10 years ago
---------------------------------------------------------------------------------------------------------------------- Music Pattern Mining - Jensen-Shannon Divergence Distance between two FFT Frequency Distributions for similarity ---------------------------------------------------------------------------------------------------------------------- FFTs of 2 audio files are parsed and written to *_trimmed.txt by awk to contain only the frequencies for each sample. These files are read by JensenShannonDivergence.py to compute the JS Distance between these two FFT frequency distributions. Preprocessing is done so as to normalize the frequency to map to a probability (frequency/sum_of_frequencies) which gives probability distribution from frequency distribution. Jensen-Shannon Distance which is the weighted average of bidirectional Kullback-Leibler Divergence measures of the two distribution, indicates similarity or distance between two music samples quantitatively.
Srinivasan Kannan
More... over 10 years ago
FFTs for Music Pattern Mining
Srinivasan Kannan
More... over 10 years ago