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Nov 05, 2025 — Nov 05, 2026
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An example Interview Algorithm-Recursive Gloss Overlap TextGraph created from class 12 Computer Science textbook - for demonstrating transition from linear textbooks to knowledge graphs of textbooks for classroom teaching - GRAFIT Opencourseware and BRIHASPATHI Virtual classrooms
Srinivasan Kannan
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Section 1245 updated
Srinivasan Kannan
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Section 1474 updated
Srinivasan Kannan
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Updated Requirments.txt
Srinivasan Kannan
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Updated Requirements.txt
Srinivasan Kannan
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Section 1628 updated
Srinivasan Kannan
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+1628. (THEORY and FEATURE) Music Information Retrieval(MIR) - AI Music Synthesis by Random walks on Music Weighted Automata-Recurrent Neural Network Directed Graph - SignalFlow DSP Music Synthesizer add-on integration and Multiple enhancements - 22 July 2025 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353,1432,1433,1434,1438,1439,1440,1441,1458,1478,1524,1535 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
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---------------------------------------------------------------------------------------------------------------------------------------------- 1628. (THEORY and FEATURE) Music Information Retrieval(MIR) - AI Music Synthesis by Random walks on Music Weighted Automata-Recurrent Neural Network Directed Graph - SignalFlow DSP Music Synthesizer add-on integration and Multiple enhancements - 22 July 2025 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353,1432,1433,1434,1438,1439,1440,1441,1458,1478,1524,1535 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 ---------------------------------------------------------------------------------------------------------------------------------------------- 1628.1 python-src/music_pattern_mining/AudioToBitMatrix.py has been updated to widen the percussions argument in function percussion_synthesis() to include more instruments: def percussion_synthesis(virtual_piano_notes=None,percussions=["Saxophone","Guitar","Trumpet","Violin","SteelDrum","Flute","BassDrum","TenorDrum"],tempo=2000) 1628.2 File ./music21_percussion_for_piano.wav has been reset by os.system("cat /dev/null > ./music21_percussion_for_piano.wav") before fluidsynth invocation. 1628.3 Invocation of pretty_midi_synthesize_notes() for Pretty-MIDI addon_synthesizer clause has been changed for additional argument filetype="MIDI' 1628.4 Function def notes_to_audio(automaton=False, function=None, deterministic=True, samplerate=44100, fractal=True, periodicity=500, weightedautomatadotfile=None,state2notedict=None,genre="WesternClassical",wfaweight_threshold=0.0,instruments=["Saxophone","Guitar","Trumpet","Violin","SteelDrum","Flute","BassDrum","TenorDrum"],format="WAV",playsynthesis=False,tempo=1) has been changed for additional argument "tempo" to remove hardcoded duration in librosa.tone() call. 1628.5 Invocation of notes_to_audio() has been added for synthesizing music by random walks over a Weighted Finite Automaton (RNN) .dot file of "Carnatic" genre notes of which are cast to "WesternClassical" notes and composed (such a cast is prone to errors because of variations in notes convention between Carnatic and WesternClassical): notes_to_audio(automaton=True,weightedautomatadotfile="testlogs/054-SBC-Aanandhamridhakarshini.mp3_MusicWeightedAutomaton.dot",state2notedict={'0':'C','1':'D','2':'E','3':'F','4':'G','5':'A','6':'B','7':'A♯','8':'C♯','9':'D♯','10':'F♯','11':'G♯'},genre="WesternClassical",playsynthesis=True) ====================================================== 1628.6 python-src/music_pattern_mining/MusicSynthesizer_AddOn.py has been updated to import tomita (https://tomita.readthedocs.io/en/latest/tomita.legacy.html#module-tomita.legacy.pysynth) which now replaces legacy PySynth: import tomita.legacy.pysynth as ps 1628.7 New function signalflow_synthesize_notes(oscillator="Sine",nvelop="ADSR",maxiterations=100) has been defined to integrate SignalFlow DSP music synthesizer as add-on - https://signalflow.dev/ - which takes in arguments for type of envelope (at present implemented for "ADSR") and type of oscillator (at present implemented for "Sine") 1628.8 Function pretty_midi_synthesize_notes(virtual_piano_notes=[],instrument_name="SynthDrum",veloCT=200,notesuffix="1",per_note_duration=0.1,filename="PrettyMIDI_music.wav",samplerate=44100,filetype="wav") has been changed to explicitly take in filetype as argument based on which either "MIDI" (required for AudioToBitMatrix.py) or "wav" (for standalone) files are generated. 1628.9 Changes for AudioToBitMatrix.py and MusicSynthesizer_AddOn.py have been tested and logged to following: testlogs/AudioToBitMatrix.log.22July2025 testlogs/MusicSynthesizer_AddOn.log.22July2025 1628.10 Following are the affected music files due to earlier changes: PySynth_music.wav music21_percussion_for_piano.midi music21_percussion_for_piano.wav music21_piano_with_percussions.wav pretty_midi_music.midi pretty_midi_music.wav virtual_piano_music.WesternClassical.wav PrettyMIDI_music.wav
Srinivasan Kannan
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Section 1255 updated
Srinivasan Kannan
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Section 1623 updated
Srinivasan Kannan
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Section 1623 updated
Srinivasan Kannan
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Section 1623 updated
Srinivasan Kannan
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Updated BestPractices.txt
K.Srinivasan
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Updated Requirements.txt
K.Srinivasan
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Section 1623 updated
K.Srinivasan
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1624. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 8 - While loop LHS-RHS ratio Jacob Ladder real randomwalk and Longest Repeated Substring of LHS-RHS ratio binary randomwalk string - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591,1596,1597,1601,1602,1603,1604 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorandom generator, CPU Branch Prediction, Sparse Indentification of Non-Linear Dynamics (SINDy), Pattern in Distribution of Primes, Longest Repeated Substring, Suffix arrays - 2 July 2025
K.Srinivasan
More... over 1 year ago
------------------------------------------------------------------------------------------------------------------------------- 1624. (THEORY and FEATURE) Analysis of Matiyasevich Python programs for Riemann Hypothesis 8 - While loop LHS-RHS ratio Jacob Ladder real randomwalk and Longest Repeated Substring of LHS-RHS ratio binary randomwalk string - related to 629,751,1191,1217,1354,1357,1443,1459,1477,1552,1553,1554,1556,1591,1596,1597,1601,1602,1603,1604 to all sections on Riemann Zeta Function, Set partitions, Computational geometry, Program analysis/Software analytics/Scheduler Analytics,Master Theorem,Busy Beaver Function, Fractal Hausdorff Dimension-Box counting dimension, Timeseries analysis ,Complement diophantines, Worst case execution time estimation, Chaos, Logistic map, Ulam-Neumann Logistic Map Pseudorandom generator, CPU Branch Prediction, Sparse Indentification of Non-Linear Dynamics (SINDy), Pattern in Distribution of Primes, Longest Repeated Substring, Suffix arrays - 2 July 2025 ------------------------------------------------------------------------------------------------------------------------------- 1624.1 Distribution of Prime numbers have been visualized as Jacob's Ladder Randomwalk (which is the plot of integers that changes direction up-down or down-up whenever prime numbers are encountered) in https://arxiv.org/abs/1801.01540 (Jacob's Ladder: Prime numbers in 2d - explained further in https://blog.computationalcomplexity.org/2025/06/the-distribution-of-prime-numbers.html) 1624.2 python-src/MatiyasevichRH.py has been updated to import StringSearch_LongestRepeatedSubstring.py suffix array implementation which computes longest repeated substring within LHS-RHS ratio as a binary randomwalk string of 1s and 0s. Iterations are set to 3000. 1624.3 Two randomwalks - real and binary - are iteratively computed by python3.12 commandline "/home/ksrinivasan/venv/bin/python3.12 MatiyasevichRH.py 2>&1 > testlogs/MatiyasevichRH.log.2July2025" over the course of the while loop: (1) If LHS-RHS ratio in next iteration exceeds previous iteration real randomwalk value is incremented by a pre-defined step and decremented otherwise by same step (2) If LHS-RHS ratio in next iteration exceeds previous iteration binary randomwalk string is appended by "1" and by "0" otherwise 1624.4 Real randomwalk of length 3000 of while loop LHS-RHS ratio is plotted for step 0.1 alongside other values in : python-src/testlogs/MatiyasevichRH.LHSRHSRatio.2July2025.jpg - original size python-src/testlogs/MatiyasevichRH.LHSRHSRatio.magnified.2July2025.jpg - zoomed for real randomwalk and loop iterations are logged to python-src/testlogs/MatiyasevichRH.log.2July2025 (compressed to .xz) 1624.5 Binary randomwalk of while loop LHS-RHS ratio has been written to python-src/StringSearch_Pattern.txt and following is the Longest Repeated Substring within binary randomwalk of length 3000 from python-src/testlogs/MatiyasevichRH.log.2July2025 - Repeated substring Pattern in binary randomwalk of LHS-RHS ratio timeseries is an alternative measure of randomness within a sequence: ========================================================= Longest Repeated Substring= 10111010101110111010101010101110101010111010101010111110101 ========================================================= ========================================================= Occurrences of Longest Repeated Substring at indices(Binary Changepoints where trend reverses)= [757, 2737] Average Periodicity of Longest Repeated Substring(Average distance between Binary Changepoints)= 1747.0 ========================================================= 1624.6 Randomwalks over LHS-RHS ratio series of Riemann Hypothesis program and Randomwalks over Prime distribution could be related because truth of Riemann Hypothesis (if program MatiyasevichRH.py doesn't terminate) bounds the deviation in Prime Number Theorem average estimate (refer 1624.8)
K.Srinivasan
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Section 636 updated
Srinivasan Kannan
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Section 1481 updated
Srinivasan Kannan
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Section 1553 updated
Srinivasan Kannan
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1623. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Negative values error handling in Aggregate computation and WorldPop Population Counts 2020 Raster Dataset - Population Estimate of Chennai Metropolitan Area Bounding Box (for year 2020) - 29 June 2025 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching
Srinivasan Kannan
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-------------------------------------------------------------------------------------------------------------------------------------------- 1623. (THEORY and FEATURE) GIS Urban Sprawl Analytics - Negative values error handling in Aggregate computation and WorldPop Population Counts 2020 Raster Dataset - Population Estimate of Chennai Metropolitan Area Bounding Box (for year 2020) - 29 June 2025 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1387,1395,1397,1398,1412,1414,1423,1448,1449,1462,1471,1472,1533,1559,1560,1608 and all sections on GIS Urban Sprawl Analytics, Merit of Large Scale Visuals, Gridded Population Estimators (SEDAC GPW4 and GHSL convex hull estimators), DBSCAN and Neural Network Urban Extent Learners, Computational Geometry, Intrinsic Merit Rankings, Verlhulste-R Ricker Population Estimation, Community detection, Social Network Analytics, Space filling by Curves, Unequal Circle and Polynomial packing, Computational Geometry and Polygonal packing by Voronoi Tessellation, Four color theorem, Economic Merit, Computational Economics - GDP estimation from Urban sprawl demographics, electricity consumption, Poverty alleviation, Preferential Attachment, Automatic Delineation of Urban Sprawls, Cellular Automata-SLEUTH-Polya Urn-Dynamic Facegraph Urban Growth Models,Expander Graphs,Timeseries analysis,Graph Edit Distance and Earth mover distance, Face recognition,Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem and Approximate Topological Matching -------------------------------------------------------------------------------------------------------------------------------------------- 1623.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to compute population aggregate for Chennai metropolitan area bounding box in 2020 from WorldPop - https://hub.worldpop.org/ - Open Spatial Demographic Data Research 1km resolution dataset - https://hub.worldpop.org/geodata/summary?id=31771 downloaded to testlogs/ind_ppp_2020_1km_Aggregated.tif : r1data=urban_sprawl_from_raster(79.271851,12.439259,80.351257,13.568572,"testlogs/ind_ppp_2020_1km_Aggregated.tif",dt="WorldPop population estimates") 1623.2 Earlier WorldPop raster assigns negative values to unoccupied grid cells which have been handled by following if clause: if values[0] >= 0: urbansprawlstatisticsaggregate += values[0] 1623.3 Aggregate population count for earlier bounding box and Gini coefficient computation have been logged to testlogs/GISUrbanSprawlAnalytics.log.29June2025 - 2020 population count of earlier bounding box is aggregated as below from grid pixels which varies widely from GHSL R2025A UNDP GeoHub raster in section 1608 but accounting for 17% error in accuracy of estimates similar to GHSL R2025A aligns the projections along expected lines (computed in 1623.4): urban_sprawl_from_raster(): urbansprawlstatisitcsaggregate = [12373164.] 1623.4 Compounded annual growth rate of 2.4% for 5 years from 2020 yields an estimated population of 12373164 × 1.024^5 = 13930944.19 in 2025 and adjustment for +17% error (based on assumption that WorldPop error is same as GHSL R2025A and -17% is ruled out due to reasons described in 1608.4) yields population of 13930944 * 1.17 = 16299204.71 in 2025 for earlier bounding box.
Srinivasan Kannan
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Section 1621 updated
K.Srinivasan
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Section 1621 updated
K.Srinivasan
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Section 1621 updated
Srinivasan Kannan
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section 1577 updated
Srinivasan Kannan
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1621. (THEORY and FEATURE) Nachiketas Formal LLM - Quantification of meaningfulness from (1) graph core complexity measures of textgraph (2) and WordNet path - Changes to Wikipedia Question-Answering algorithm for better accuracy 27 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550,1568,1572,1573,1574,1575,1576,1578,1579,1583,1585,1587,1595,1611,1620 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression, Drug discovery, Universal Chomsky Sentence Synthesizer, Formal AI, Perplexity in Statistical AI - 19 June 2025
Srinivasan Kannan
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-------------------------------------------------------------------------------------------------------------------------------------------- 1621. (THEORY and FEATURE) Nachiketas Formal LLM - Quantification of meaningfulness from (1) graph core complexity measures of textgraph (2) and WordNet path - Changes to Wikipedia Question-Answering algorithm for better accuracy 27 - related to 2,249,707,829,1133,1134,1204,1263,1269,1284,1285,1405,1413,1455,1468,1469,1536,1537,1538,1539,1540,1541,1542,1543,1550,1568,1572,1573,1574,1575,1576,1578,1579,1583,1585,1587,1595,1611,1620 and all sections on Intrinsic Merit of Texts, Recursive Lambda Function Growth and Recursive Gloss Overlap textgraph algorithms, 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, Large Language Models, Mildly Context Sensitive Grammars-Tree Adjoining Grammars for Natural Languages,Text restoration-Vowelless text (de)compression, Drug discovery, Universal Chomsky Sentence Synthesizer, Formal AI, Perplexity in Statistical AI - 19 June 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1621.1 python-src/RecursiveGlossOverlap_Classifier.py has been updated to change def RecursiveGlossOverlap_Classify(text,maxfractionclasses=1) to accept additional argument maxfractionclasses which takes decimal value from 0 to 1 for fraction of top core number vertices considered in textgraph for a text. If-else clause which had hardcoded fraction earlier has been replaced by condition top_percentile <= no_of_classes*maxfractionclasses. 1621.2 Estimating meaningfulness of a text through pure formal language and linguistic measures is still an unsolved Formal AI problem while meaningfulness in Statistical AI depends heavily on pretrained datasets, n-gram contexts and neural network models e.g Perplexity. 1621.3 In this update a graph theoretic intrinsic merit and meaningfulness measure "Core Meaningfulness" is introduced for Formal AI which is defined as: (a) Core Meaningfulness of a textgraph for text = CoreNumber1 * CoreNumber2 * ... * CoreNumberN for N top percentile k-core vertices (or) (b) Core Meaningfulness of a textgraph for text = (Average Core Number of a Textgraph)^(Number of top core numbered vertices in textgraph) or Core Meaningfulness of a textgraph for text = [(CoreNumber1 + CoreNumber2 + ... + CoreNumberN)/N]^N 1621.4 K-core is the maximal subgraph that contains vertices of degree k or more and captures the most important vertices within a textgraph that contribute the most to the meaning of the text which in a sense unifies atomism and holism notions into one - refer section 1542.10 - Yaska's atomism gives weightage to individual words in sentence while Panini's holism gives weightage to connectivity between words in a sentence. Another prominent graph complexity measure Betweenness Centrality, which has already been implemented for Recursive Gloss Overlap textgraphs, is an important meaningfulness metric because it is the fraction of those shortest s-t paths that pass through a vertex v and total number of s-t paths. 1621.5 Core Meaningfulness measure in 1621.3 tries to quantify meaning formally which depends both on number of vertices in textgraph and their core numbers - Meaning in a text is enriched by increasing both the number of vertices of its textgraph and their core numbers. This update implements definition 1621.3(a) only and 1621.3(b) is its average equivalent. 1621.6 Following two sentences one from set CHOMSKY and the other from set MEANINFUL are analyzed for core meaningfulness: 1621.6.1 "Colorless green ideas sleep furiously" - Oft cited meaningless but grammatical sentence 1621.6.2 "unattractive raw ideas are kept in abeyance for long" - Sentence 1621.6.1 has been reworded semantically by replacing (*) "clorless" by "unattractive" (*) "Green" by "raw" (*) "sleep furiously" by "kept in abeyance for long" to increase readability and meaning. 1621.7 Logs testlogs/RecursiveGlossOverlap_Classifier.log.CHOMSKY.19June2025 and testlogs/RecursiveGlossOverlap_Classifier.log.MEANINGFUL.19June2025 analyze 1621.6.1 and 1621.6.2 respectively and following are the core meaningfulness measures which rank 1621.6.2 to be more meaningful than 1621.6.1: ============================================================================================================ testlogs/RecursiveGlossOverlap_Classifier.log.CHOMSKY.19June2025: ============================================================================================================= Unsupervised Classification based on top percentile Core numbers of the definition graph(subgraph of WordNet) ============================================================================================================= ('This document belongs to class:', 'lacking', ',core number=', 2) ('This document belongs to class:', 'colorless', ',core number=', 2) ('This document belongs to class:', 'cognition', ',core number=', 2) ('This document belongs to class:', 'idea', ',core number=', 2) ('This document belongs to class:', 'deep', ',core number=', 2) ('This document belongs to class:', 'sleep', ',core number=', 2) ('This document belongs to class:', 'pigment', ',core number=', 2) ('This document belongs to class:', 'green', ',core number=', 2) ('This document belongs to class:', 'interest', ',core number=', 2) ('This document belongs to class:', 'thinking', ',core number=', 2) ('This document belongs to class:', 'stormy', ',core number=', 2) ('This document belongs to class:', 'furiously', ',core number=', 2) ('This document belongs to class:', 'color', ',core number=', 2) ('This document belongs to class:', 'thing', ',core number=', 2) ('This document belongs to class:', 'growing', ',core number=', 2) ('This document belongs to class:', 'content', ',core number=', 2) ('This document belongs to class:', 'torpid', ',core number=', 2) ('This document belongs to class:', 'resembling', ',core number=', 2) ('This document belongs to class:', 'state', ',core number=', 2) ('This document belongs to class:', 'manner', ',core number=', 2) ('This document belongs to class:', 'variety', ',core number=', 2) ('This document belongs to class:', 'main', ',core number=', 2) ('This document belongs to class:', 'grass', ',core number=', 2) ('This document belongs to class:', 'elements', ',core number=', 2) ('This document belongs to class:', 'wild', ',core number=', 2) Core meaningfulness of the text: 33554432 ============================================================================================================= testlogs/RecursiveGlossOverlap_Classifier.log.MEANINGFUL.19June2025 ============================================================================================================= Unsupervised Classification based on top percentile Core numbers of the definition graph(subgraph of WordNet) ============================================================================================================= ('This document belongs to class:', 'keep', ',core number=', 2) ('This document belongs to class:', 'cognition', ',core number=', 2) ('This document belongs to class:', 'idea', ',core number=', 2) ('This document belongs to class:', 'activity', ',core number=', 2) ('This document belongs to class:', 'charm', ',core number=', 2) ('This document belongs to class:', 'unattractive', ',core number=', 2) ('This document belongs to class:', 'relatively', ',core number=', 2) ('This document belongs to class:', 'long', ',core number=', 2) ('This document belongs to class:', 'cessation', ',core number=', 2) ('This document belongs to class:', 'abeyance', ',core number=', 2) ('This document belongs to class:', 'main', ',core number=', 2) ('This document belongs to class:', 'lacking', ',core number=', 2) ('This document belongs to class:', 'nakedness', ',core number=', 2) ('This document belongs to class:', 'raw', ',core number=', 2) ('This document belongs to class:', 'state', ',core number=', 2) ('This document belongs to class:', 'position', ',core number=', 2) ('This document belongs to class:', 'temporary', ',core number=', 2) ('This document belongs to class:', 'thinking', ',core number=', 2) ('This document belongs to class:', 'duration', ',core number=', 2) ('This document belongs to class:', 'content', ',core number=', 2) ('This document belongs to class:', 'informal', ',core number=', 2) ('This document belongs to class:', 'beauty', ',core number=', 2) ('This document belongs to class:', 'thing', ',core number=', 2) ('This document belongs to class:', 'terms', ',core number=', 2) ('This document belongs to class:', 'speech', ',core number=', 2) ('This document belongs to class:', 'syllables', ',core number=', 2) ('This document belongs to class:', 'suspension', ',core number=', 2) ('This document belongs to class:', 'sounds', ',core number=', 2) ('This document belongs to class:', 'e.g.', ',core number=', 2) ('This document belongs to class:', 'certain', ',core number=', 2) Core meaningfulness of the text: 1073741824 1621.8 This update also defines a new function def wordnet_path_meaningfulness(sentence) which invokes path_between() on consecutive pairs of words in a text and concatenates them to compute WordNet path of a text which is a flat measure of how words in a text are connected within WordNet (doesn't require a textgraph of text). Following are the wordnet_path_meaningfulness() measures for sentences in 1621.6 from testlogs/RecursiveGlossOverlap_Classifier.log.CHOMSKY.19June2025 and testlogs/RecursiveGlossOverlap_Classifier.log.MEANINGFUL.19June2025 : 1621.8.1 "Colorless green ideas sleep furiously" - wordnet path meaningfulness: 0 1621.8.2 "unattractive raw ideas are kept in abeyance for long" - wordnet path meaningfulness: 11 which shows a marked separation of the two sentences in terms of their meaningful content and 1621.8.2 is ranked more meaningful than 1621.8.1
Srinivasan Kannan
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Section 1620 updated
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Section 1608 updated
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