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Analyzed about 3 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
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------------------------------------------------------------------------------------------------------------------------------ 1459. (THEORY and FEATURE) Software Analytics - Kernel Analytics and Scheduler Analytics - Enhanced Berkeley Packet Filter (eBPF) support - bpftrace script implementation - related to 561,1191,619,770,906,911,771,1192,1443 and all sections on Program Analysis, AsFer-USBmd-VIRGO-KingCobra FTrace kernel callgraph analysis - Valgrind-Callgrind-KCacheGrind-Flamegraph userspace callgraph analysis, SATURN control flow analysis of VIRGO linux kernel, Software Analytics, Pandemic model of Botnet-Defense adversarial Game theoretic security, Cybercrime analytics, Anti-Malware,Anti-Ransomware,Anti-Theft and Anti-Plagiarism tools, KML, LAKE,Intrusion Prevention-Detection Systems(IDS and IPS),Anomaly Detection - 17 November 2023 ------------------------------------------------------------------------------------------------------------------------------ 1459.1 This update implements a new bash script wrapper python-src/software_analytics/bpftrace.sh for set of oneline bpftrace commands in bt language of eBPF. 1459.2 This script is derived from eBPF bpftrace documentation at https://github.com/iovisor/bpftrace and https://www.brendangregg.com/blog/2021-07-03/how-to-add-bpf-observability.html to gather performance statistics and observables (*) for the system as a whole, (*) for a process id $1 and cgroupid $2 passed as commandline parameters to bpftrace.sh - individual stats oneliners can be commented or uncommented as deemed necessary 1459.3 bpftrace.sh has been invoked on two process id(s) (977 and 2722) and cgroup /sys/fs/cgroup/cgroup.procs and stats are captured in testlogs/bpftrace.log.17November2023 and testlogs/bpftrace.log2.17November2023 : ./bpftrace.sh 977 /sys/fs/cgroup/cgroup.procs 2>&1 > testlogs/bpftrace.log.17November2023 ./bpftrace.sh 2722 /sys/fs/cgroup/cgroup.procs 2>&1 > testlogs/bpftrace.log2.17November2023 1459.4 Observables captured in above logs can be training dataset for Scheduler Analytics implementation of NeuronRain to learn analytics variables in python-src/software_analytics/virgo_kernel_analytics.conf which is set as /etc/virgo_kernel_analytics.conf and read by VIRGO Linux kernel (kernel_analytics driver) and its analytics variables are exported kernelwide. 1459.5 Presently 3 observables have been enabled in bpftrace.sh - user stack for a process id, files opened in a cgroup id, per-second system call rates - which could be anomaly detectors for Intrusion Prevention-Detection Systems(IDS and IPS) - especially abnormal per-second system call rate is an anomaly (Section 1443).
K.Srinivasan
More... almost 3 years ago
Section 561 updated
K.Srinivasan
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Section 1451 updated
Srinivasan Kannan
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+1458. (THEORY and FEATURE) Music Information Retrieval(MIR) - AI Music Synthesis by Random walks on Music Weighted Automata Directed Graph - Audio Quality - Timbre and Envelope implementations - 6 November 2023 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353,1432,1433,1434,1438,1439,1440,1441 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis from summation of damped sinusoids, Additive Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, Waveshaping, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants,Computational Learning Theory, Boolean Function Composition, Longest Common Subsequence, Suffix Trees - Longest Repeated Substring, MultiFractal Detrended Fluctuation Analysis of Non-stationary timeseries, Ramsey Theory-Van Der Waerden Theorem for coloring of sequences, Pumping Lemma, Normalized Compression Distance-Kolmogorov complexity, Merit of Large Scale Visuals, Recurrent Neural Networks,Neo-Riemannian Theory of Music,Attack-Decay-Sustain-Release model
Srinivasan Kannan
More... about 3 years ago
---------------------------------------------------------------------------------------------------------------------------------------------- 1458. (THEORY and FEATURE) Music Information Retrieval(MIR) - AI Music Synthesis by Random walks on Music Weighted Automata Directed Graph - Audio Quality - Timbre and Envelope implementations - 6 November 2023 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353,1432,1433,1434,1438,1439,1440,1441 and all sections on Music search-Music discovery, Intrinsic Merit of Music, MIR(Music Information Retrieval), Music Similarity and Clustering, AI Music Synthesis from summation of damped sinusoids, Additive Synthesis, fMRI connectomes - OpenNeuro - Psychology of music, Weighted automata, Chaos, Deep Learning, Dynamic Time Warping-Music Timeseries analysis, Music Evoked Autobiographical Memories, Waveshaping, ThoughtNet, Majority Voting (Enjoyable music) Vs Intrinsic (Quality music) - Condorcet Jury Theorem variants,Computational Learning Theory, Boolean Function Composition, Longest Common Subsequence, Suffix Trees - Longest Repeated Substring, MultiFractal Detrended Fluctuation Analysis of Non-stationary timeseries, Ramsey Theory-Van Der Waerden Theorem for coloring of sequences, Pumping Lemma, Normalized Compression Distance-Kolmogorov complexity, Merit of Large Scale Visuals, Recurrent Neural Networks,Neo-Riemannian Theory of Music,Attack-Decay-Sustain-Release model ---------------------------------------------------------------------------------------------------------------------------------------------- 1458.1 Updated python-src/music_pattern_mining/AudioToBitMatrix.py to import PyTimbre2 for Timbre statistics computation of an audio waveform: from pytimbre2.audio_files.wavefile import WaveFile from pytimbre2.spectral.spectra import SpectrumByFFT 1458.2 Function def audio_features(audiofilename=None,signal_bitmap=None,frame_size=1024,hop_length=256) has been updated to add more arguments (audiofilename,frame size and hop length) for audio timbre and envelope computation. Envelope of the audio signal is already computed as onset strength while an overall amplitude envelope code has been added in this update to chunk the waveform into frames of predefined size and sampled at intervals of hop length from which maximum amplitude per frame is found constituting an envelope array. 1458.3 Envelope array is printed for Attack-Decay-Sustain-Release (ADSR) model. Timbre statistics are obtained from PyTimbre2 timbre toolbox FFT spectrum roll off and average metrics (ADSR). Full timbre average features are computationally intensive and are printed based on a boolean flag timbreaveragefeatures. 1458.4 Example Envelope and Timbre quality measures are computed for AI synthesized waveform virtual_piano_music.WesternClassical.wav. Merit of an audio-visual has 2 connotations - (1) Quality of the content within the audio-music or the visual (2) Quality of audio-music or visual itself (clarity of the audio or resolution of the visual which depends more on the device capacity) - latter is more objective than the former in which judging the content of an audio-visual is subjective (requires human jury) and NeuronRain implements ImageNet deeplearning predictions based EventNet Tensor Product algorithm for objectively quantifying quality of the content. 1458.5 Following are the logs and audio waveforms for this update: modified: music21_percussion_for_piano.midi modified: music21_percussion_for_piano.wav modified: music21_piano_with_percussions.mp3 modified: virtual_piano_music.WesternClassical.wav testlogs/AudioToBitMatrix.log.6November2023 testlogs/AudioToBitMatrix.log2.6November2023 testlogs/AudioToBitMatrix.log3.6November2023
Srinivasan Kannan
More... about 3 years ago
Updated Requirements.txt
Srinivasan Kannan
More... about 3 years ago
Section 1441 updated
Srinivasan Kannan
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Updated Requirements.txt
Srinivasan Kannan
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1456. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 31 October 2023 - N-Body gravitational acceleration analysis of date range 7-20 November 2023 - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules
Srinivasan Kannan
More... about 3 years ago
---------------------------------------------------------------------------------------------------------------------------------------- 1456. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 31 October 2023 - N-Body gravitational acceleration analysis of date range 7-20 November 2023 - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules ---------------------------------------------------------------------------------------------------------------------------------------- 1456.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to invoke climate_analytics() and gaussian_ensemble_forecast_rainfall_timeseries() for date range 7-20 November 2023 over selected celestial body conjunctions - Gaussian Mixture Ensemble model has been trained on fictitious training and forecast rainfall timeseries of NorthEast Monsoon due to lack of data: climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2023,11,7,17,30,00),'dateto':(2023,11,20,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Jupiter",'angularsepbounds':('0d','30d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2023,11,7,17,30,00),'dateto':(2023,11,20,17,30,00),'loc':'@0','bodyconjunctions':"Mercury-Venus",'angularsepbounds':('0d','30d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2023,11,7,17,30,00),'dateto':(2023,11,20,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Moon",'angularsepbounds':('0d','30d')},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]}) gaussian_ensemble_forecast_rainfall_timeseries(predEWEparams={'datefrom':(2023,11,7,17,30,00),'dateto':(2023,11,20,17,30,00),'loc':'@0','bodyconjunctions':"Venus-Mercury-Sun-Jupiter",'angularsepbounds':('0d','30d')},days=5,forecast_seasonal_rainfall=10,historic_training_timeseries=nem_rainfall_timeseries) 1456.2 N-Body variable gravity climate model relies on varying gravitational constant due to celestial bodies (1451.9) and complements deep learning models (which are Vision Tokenization and Convolution Neural Network based) that predict future weather variables from past ERA5 historic visual terrestrial climate data. 1456.3 Following are the Gravity Plots and Logs for the celestial body pairs that show a peak Mercury gravity from 7 November 2023 onwards upto 20 November 2023 which could cause a gravity induced atmospheric cooling: testlogs/GISWeatherAnalytics.Gravity.Mercury-Jupiter.2023-11-7#2023-11-20.jpg testlogs/GISWeatherAnalytics.Gravity.Mercury-Sun.2023-11-7#2023-11-20.jpg testlogs/GISWeatherAnalytics.Gravity.Mercury-Venus.2023-11-7#2023-11-20.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Jupiter.2023-11-7#2023-11-20.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Moon.2023-11-7#2023-11-20.jpg testlogs/GISWeatherAnalytics.Gravity.Venus-Jupiter.2023-11-7#2023-11-20.jpg testlogs/GISWeatherAnalytics.Gravity.Venus-Mercury.2023-11-7#2023-11-20.jpg testlogs/GISWeatherAnalytics.Gravity.Venus-Sun.2023-11-7#2023-11-20.jpg testlogs/GISWeatherAnalytics.log.31October2023
Srinivasan Kannan
More... about 3 years ago
Section 507 updated
Srinivasan Kannan
More... about 3 years ago
Section 1451 updated
Srinivasan Kannan
More... about 3 years ago
Section 1280 updated
K.Srinivasan
More... about 3 years ago
Section 1451 updated
K.Srinivasan
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Section 1451 updated - Fourcastnet
K.Srinivasan
More... about 3 years ago
Section 1451 updated
K.Srinivasan
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Section 682 updated
K.Srinivasan
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Section 1288 updated
K.Srinivasan
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Section 368 updated
K.Srinivasan
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Section 1449 updated
K.Srinivasan
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Section 1288 updated
K.Srinivasan
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Section 1449 updated
K.Srinivasan
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Section 1454 updated
K.Srinivasan
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Section 1443 updated
K.Srinivasan
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Section 1288 updated
K.Srinivasan
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1455. (THEORY and FEATURE) Recursive Lambda Function Growth - Fill-In-The-Blanks word prediction by Transformers Degree Attention Model - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413 and all sections on Intrinsic Merit of Texts, Question-Answering, Embedding in vectorspace, Machine Translation, Transformers Attention Model,Named Entity Recognition and Meaning Representation, Complexity models of Question-Answering (multiple choice,open-ended,adaptive and static) - LTF,PTF,Switching Circuits,TQBF,Markov Chains, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 9 October 2023
Srinivasan Kannan
More... about 3 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1455. (THEORY and FEATURE) Recursive Lambda Function Growth - Fill-In-The-Blanks word prediction by Transformers Degree Attention Model - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413 and all sections on Intrinsic Merit of Texts, Question-Answering, Embedding in vectorspace, Machine Translation, Transformers Attention Model,Named Entity Recognition and Meaning Representation, Complexity models of Question-Answering (multiple choice,open-ended,adaptive and static) - LTF,PTF,Switching Circuits,TQBF,Markov Chains, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression - 9 October 2023 ------------------------------------------------------------------------------------------------------------------------------------------- 1455.1 python-src/RecursiveLambdaFunctionGrowth.py has been updated to include a flag for enabling-disabling intrinsic merit statistics. 1455.2 Function def grow_lambda_function3(self, text="None", level=3, textgraph=None, predict_word=False, fillintheblankstxt=None) has been enhanced to add two arguments - predict_word boolean flag and fillingtheblanktxt text file for Fill-In-The-Blanks algorithm described in 1413.7. 1455.3 If predict_word is set to True, transformer degree attention is computed and algorithm 1413.7 implemented by function fill_in_the_blanks() is executed. 1455.4 Function def fill_in_the_blanks(self,prevtok,nexttok,definitiongraph,QKV) populates the argmax dictionaries for left and right context bigrams by iterating through path degree attention QKV data of the textgraph - these argmax dictionaries create a probability distribution on maximum likely word that might fill in the blanks. All simple paths between every s-t pair of word vertices in textgraph are looped through and a path degree attention for s-t path is populated (as sum of degree attentions of constituent vertices in each path) in 3 dictionaries for path QKV. 1455.5 grow_lamda_function3() is invoked by enabling predict_word and passing a text file RecursiveLambdaFunctionGrowth_FillInTheBlanks.txt whch has some blank words to be filled: lambdafn.grow_lambda_function3(textread,predict_word=True, fillintheblankstxt="RecursiveLambdaFunctionGrowth_FillInTheBlanks.txt") 1455.6 Following are the probable words that might fill in the blanks for the text in RecursiveLambdaFunctionGrowth_FillInTheBlanks.txt - exact match for the blanks is the word "area" while degree attention maximum likelihood has guessed the word to be "conterminous" and "location" respectively in two executions which is indirectly related to "area" or a contiguous geographic boundary (excerpts from 2 XZ compressed log files): ----------------------------------------------------------- testlogs/RecursiveLambdaFunctionGrowth.log.8October2023.xz: ----------------------------------------------------------- fitbtoks: ['Recent', 'estimates', 'of', 'the', 'economy', 'of', 'the', 'Chennai', 'metropolitan', [{'United_States': 36.18630580357142, 'conte rminous': 74.82794642857142}, {'United_States': 36.18630580357142, 'conterminous': 74.82794642857142}], '(which', 'includes', 'the', 'industri al', 'zones)', 'is', 'over', 'US$200', 'billion', '(PPP', 'GDP),', 'making', 'it', 'the', 'third', 'most', 'productive', 'metro', 'area', 'of' , 'India,', 'and', 'the', 'third', 'highest', 'by', 'GDP', 'per', 'capita.', 'Previously,', 'the', 'metropolitan', 'area', "didn't", 'include' , 'the', 'nearby', 'industrial', 'zones', 'of', 'Siruseri,', 'Oragadam,', 'and', 'Sriperumbudur,', 'due', 'to', 'which', 'the', 'economy', 'of ', 'these', 'regions', "didn't", 'get', 'included', 'in', 'the', 'economy', 'of', 'Chennai.', 'Now,', 'post', 'expansion', 'of', 'the', 'Chenn ai', 'metropolitan', [{'United_States': 36.18630580357142, 'conterminous': 74.82794642857142}, {'United_States': 36.18630580357142, 'contermin ous': 74.82794642857142}], 'in', '2022,', 'these', 'regions', 'are', 'included'] ----------------------------------------------------------- testlogs/RecursiveLambdaFunctionGrowth.log2.8October2023.xz: ----------------------------------------------------------- fitbtoks: ['Recent', 'estimates', 'of', 'the', 'economy', 'of', 'the', 'Chennai', 'metropolitan', [{'geographical': 8.930849609375, 'location' : 15.29462884756235}, {'geographical': 8.930849609375, 'location': 15.29462884756235}], '(which', 'includes', 'the', 'industrial', 'zones)', ' is', 'over', 'US$200', 'billion', '(PPP', 'GDP),', 'making', 'it', 'the', 'third', 'most', 'productive', 'metro', 'area', 'of', 'India,', 'and ', 'the', 'third', 'highest', 'by', 'GDP', 'per', 'capita.', 'Previously,', 'the', 'metropolitan', 'area', "didn't", 'include', 'the', 'nearby ', 'industrial', 'zones', 'of', 'Siruseri,', 'Oragadam,', 'and', 'Sriperumbudur,', 'due', 'to', 'which', 'the', 'economy', 'of', 'these', 'reg ions', "didn't", 'get', 'included', 'in', 'the', 'economy', 'of', 'Chennai.', 'Now,', 'post', 'expansion', 'of', 'the', 'Chennai', 'metropolit an', [{'geographical': 8.930849609375, 'location': 15.29462884756235}, {'geographical': 8.930849609375, 'location': 15.29462884756235}], 'in', '2022,', 'these', 'regions', 'are', 'included']
Srinivasan Kannan
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iSection 1405 updated
K.Srinivasan
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iSection 1288 updated
root
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1454. (THEORY and FEATURE) Computational Geometric Integer Factorization - Python 3.11.5 + PySpark 3.5.0 Upgrade - Single node clustering benchmark - related to 498,601,1226,1259,1303,1319,1321,1323,1324,1325,1355,1365,1366,1369,1379,1381,1384,1385,1401,1419 and all sections on Integer Factorization (DMRC MapReduce PySpark and NC HPC Rust implementations),Computational Geometry,Set Partitions-to-Tile Cover reduction by Lagranges Four Square Theorem and ILP,Primality Testing,RSA Semiprimes,Almost-Square Bounding Boxes in Large Scale Visual Recognition,Pell's Equation,Brahmagupta's Chakravala,Approximate Factoring by Newton-Raphson Rayshooting queries,Algebraic Geometry of Computational Geometric Factorization,Quantum computation and Derandomization of BQP Factorization by Condorcet Jury Theorem Majority-BQPVoterSAT boolean composition - 7 October 2023
root
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