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Analyzed about 3 hours ago. based on code collected 1 day ago.
Nov 05, 2025 — Nov 05, 2026
Commit Message Contributor Files Modified Lines Added Lines Removed Code Location Date
Section 1376 updated
Srinivasan Kannan
More... over 3 years ago
+1432. (JIRA,THEORY and FEATURE) Music Synthesizer - Refactoring of Music Synthesizer code for finetuned notes sequence - 26 June 2023 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353 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^M
Srinivasan Kannan
More... over 3 years ago
------------------------------------------------------------------------------------------------------------------------------ 1432. (JIRA,THEORY and FEATURE) Music Synthesizer - Refactoring of Music Synthesizer code for finetuned notes sequence - 26 June 2023 - related to 3,110,134,298,1223,1224,1257,1258,1261,1305,1306,1307,1308,1309,1310,1311,1315,1353 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 ------------------------------------------------------------------------------------------------------------------------------ 1432.1 python-src/music_pattern_mining/AudioToBitMatrix.py has been updated to import rstr and re for random notes string generation based on Mersenne twister and regular expressions. 1432.2 Function get_piano_frequencies(virtual_piano_notes,genre="WesternClassical") has been simplified for both "WesternClassical" and "Carnatic" notes generation and some of the processing have been moved out. 1432.3 Function music_synthesis(training_music=None,dur=5,samplerate=44100,polynomial_interpolation=True,polyfeatures=False,virtual_piano_notes=None,tempo=1,amplitude=4096,musicgenre="WesternClassical") has been updated to write separate synthesized WAV files for WesternClassical and Carnatic genres. 1432.4 Function generate_virtual_piano_notes(function="",randomnotesstringfrom=None,length=10, genre="WesternClassical") has been changed to include a new argument for randomnotesstringfrom apart from lambda functions, based on which an if clause has been introduced to synthesize notes for WesternClassical and Carnatic genres by rstr library by passing the notes of respective genres including notes subdivisions in both. String tokenizing and processing loop for # subdivision notes in WesternClassical and 1,2,3 subdivision notes in Carnatic has been newly implemented (requires special delimitation processings for # and 1,2,3 note suffixes). So far note subdivisions in both genres were not properly synthesized which is resolved by this rewrite. 1432.5 There is a Ramsey theoretic aspect to Music notes sequence which could be a key to Music information retrieval and AI music synthesis - quoted from https://en.wikipedia.org/wiki/Van_der_Waerden%27s_theorem: "..... Van der Waerden's theorem states that for any given positive integers r and k, there is some number N such that if the integers {1, 2, ..., N} are colored, each with one of r different colors, then there are at least k integers in arithmetic progression whose elements are of the same color. ...." - Music notes sequences are colored by 12-note octaves usually (r=12) and by Van Der Waerden Theorem there is a lurking arithmetic progression of length k within Music waveform of length N (or) notes at positions x,x+p,x+2p,....,x+kp of periodicity p within N-notes sequence are always the same implying predictability of even randomly synthesized music (or in automata terms the same note state is periodically traversed). The least such N is the Van Der Waerden number W(k,r) which has the present known upperbound of 2^2r^2^(k+9) or 12-notes music waveform has to be atleast W(k,12) long to exhibit arithmetic progression patterns. 1432.6 This update resolved JIRA issue - https://krishnairesearch.atlassian.net/browse/NEUR-31
Srinivasan Kannan
More... over 3 years ago
Section 1431 updated
Srinivasan Kannan
More... over 3 years ago
Cyclone Biparjoy - Night Time Lights on 15 and 16 June 2023
Srinivasan Kannan
More... over 3 years ago
1431. (THEORY and FEATURE) GIS Weather Analytics and Urban Sprawl Analytics - Zoom.Earth EUMETSAT-MeteoSAT-IODC Night Lights - 13 June 2023 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1395,1397,1398,1412,1414,1423,1424,1425,1430 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, Weather Forecast, Climate Analytics, Flood Mitigation
Srinivasan Kannan
More... over 3 years ago
------------------------------------------------------------------------------------------------------------------------------------------- 1431. (THEORY and FEATURE) GIS Weather Analytics and Urban Sprawl Analytics - Zoom.Earth EUMETSAT-MeteoSAT-IODC Night Lights - 13 June 2023 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1395,1397,1398,1412,1414,1423,1424,1425,1430 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, Weather Forecast, Climate Analytics, Flood Mitigation ------------------------------------------------------------------------------------------------------------------------------------------- 1431.1 Night Time Lights data have been so far sourced from NASA VIIRS datasets. GISUrbanSprawlAnalytics.py has been updated to contour segment following 3 imagery from https://zoom.earth/maps/satellite/#view=20.6,78.4,5z/date=2023-06-12,01:00,+5.5 by EUMETSAT-MeteoSAT-IODC which capture both NightTime Lights of urban areas and Weather satellite data thus combining Urban Sprawl Analytics and Climate analytics into a single task: seg13=ImageGraph_Keras_Theano.image_segmentation("testlogs/ZoomEarth_NightLights1_12June2023.jpeg") seg13=ImageGraph_Keras_Theano.image_segmentation("testlogs/ZoomEarth_NightLights2_12June2023.jpeg") seg15=ImageGraph_Keras_Theano.image_segmentation("testlogs/ZoomEarth_WeatherAndNightLights3_12June2023.jpeg") urban_sprawl_from_segments("testlogs/ZoomEarth_NightLights1_12June2023.jpeg",seg13) urban_sprawl_from_segments("testlogs/ZoomEarth_NightLights2_12June2023.jpeg",seg14) urban_sprawl_from_segments("testlogs/ZoomEarth_WeatherAndNightLights1_12June2023.jpeg",seg15) 1431.2 Contour or Convex Hull area to Square Kilometre conversion is already part of Zoom.Earth. Following 3 imagery respectively illustrate: testlogs/ZoomEarth_NightLights1_12June2023.jpeg - Convex Hulls of most luminous regions of Chennai (~4200 sqkm) and Bengaluru (~3320 sqkm) urban sprawls on 12 June 2023 1 AM delineated by Zoom.Earth Convex Hull polygon area tool - correspond to urban centers in GHSL (almost no ribbons) testlogs/ZoomEarth_NightLights2_12June2023.jpeg - Convex Hulls of most luminous regions of Chennai urban sprawl (~4200 sqkm) on 12 June 2023 1 AM - magnified - correspond to urban centers in GHSL (heavy ribbon or transit oriented development) testlogs/ZoomEarth_WeatherAndNightLights3_12June2023.jpeg - Combined Weather satellite and Night Time Light imagery from EUMETSAT-MeteoSAT-IODC on 12 June 2023 1 AM 1431.3 Combined Urban sprawl night lights and Weather satellite imagery within same image is especially necessary in flood mitigation and disaster response - For example, testlogs/ZoomEarth_WeatherAndNightLights1_12June2023.jpeg imagery earlier captures both a trajectory of extremely severe cyclonic storm Biparjoy and India Night time lights on 12 June 2023 1 AM. Change in night time lights before and after the expected landfall is proportional to extent of cyclone's impact on infrastructure. 1431.4 Following are logs and images for chain approximation contours-cannyedge-kmeansclustering-watershed segmentation from above 3 imagery: testlogs/GISUrbanSprawlAnalytics.log.13June2023.xz testlogs/RemoteSensingGIS/ZoomEarth_NightLights1_12June2023_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/ZoomEarth_NightLights1_12June2023_segmented.jpg testlogs/RemoteSensingGIS/ZoomEarth_NightLights2_12June2023_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/ZoomEarth_NightLights2_12June2023_segmented.jpg testlogs/RemoteSensingGIS/ZoomEarth_WeatherAndNightLights3_12June2023_ImageNet_Keras_Theano_Segmentation_FaceGraph.dot testlogs/RemoteSensingGIS/ZoomEarth_WeatherAndNightLights3_12June2023_segmented.jpg testlogs/ZoomEarth_NightLights1_12June2023-cannyedgedetection.jpg testlogs/ZoomEarth_NightLights1_12June2023-contourkmeansclustered.jpg testlogs/ZoomEarth_NightLights1_12June2023-contourlabelled.jpg testlogs/ZoomEarth_NightLights1_12June2023-contoursegmented.jpg testlogs/ZoomEarth_NightLights2_12June2023-cannyedgedetection.jpg testlogs/ZoomEarth_NightLights2_12June2023-contourkmeansclustered.jpg testlogs/ZoomEarth_NightLights2_12June2023-contourlabelled.jpg testlogs/ZoomEarth_NightLights2_12June2023-contoursegmented.jpg testlogs/ZoomEarth_WeatherAndNightLights3_12June2023-cannyedgedetection.jpg testlogs/ZoomEarth_WeatherAndNightLights3_12June2023-contourkmeansclustered.jpg testlogs/ZoomEarth_WeatherAndNightLights3_12June2023-contourlabelled.jpg testlogs/ZoomEarth_WeatherAndNightLights3_12June2023-contoursegmented.jpg
Srinivasan Kannan
More... over 3 years ago
1430. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from arbitrary URL content specialized to capture twitter trends - 9 June 2023 - related to Section 588,1380,1406,1407,1408,1409,1410,1411 and all sections on Majority Voting, Approximate Majority,Election analytics-Psephology-Opinion polls,Reservoir Sampling,Topic detection and tracking,Social network analysis and People analytics from Twitter-Facebook-LinkedIn-PIPL streams, Large Scale Visuals-Analyzing Audio-Visuals for sentiment (Movies,YouTube) by LIWC-Empath, Drone Electronic Voting Machines-Multipolar voting
Srinivasan Kannan
More... over 3 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1430. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from arbitrary URL content specialized to capture twitter trends - 9 June 2023 - related to Section 588,1380,1406,1407,1408,1409,1410,1411 and all sections on Majority Voting, Approximate Majority,Election analytics-Psephology-Opinion polls,Reservoir Sampling,Topic detection and tracking,Social network analysis and People analytics from Twitter-Facebook-LinkedIn-PIPL streams, Large Scale Visuals-Analyzing Audio-Visuals for sentiment (Movies,YouTube) by LIWC-Empath, Drone Electronic Voting Machines-Multipolar voting -------------------------------------------------------------------------------------------------------------------------------------------- 1430.1 python-src/SocialNetworkAnalysis_OpinionMining.py has been updated to import BeautifulSoup4,requests and defaultdict. New function opinion_mining_from_url() has been defined that retrieves the text content of any URL by requests.get(), parses the HTML by BeautifulSoup4, and searches the content for Twitter trends to consolidate the data into a Twitter poll - number of tweets for each trending hashtag. 1430.2 Scraping by BeautifulSoup has been preferred to Twitter API (e.g Tweepy) as there is no auth token preprocessing required for accessing Twitter developer account and only the collective trends (voting) are captured. 1430.3 Regular expressions for "Tweet" "Number of Tweets","Trending" etc., in https://twitter.com/i/trends format are parsed from URL text in a for loop and a Tweet dictionary ballot is populated as <HashTag>:<NumberOfTweets> [Every hashtag is considered a candidate and number of tweets for it the votes polled] 1430.4 Sentiment polarity of trending twitter hashtags are analyzed by VADER. Twitter trends from https://trendlistz.com/india are analyzed and twitter poll dictionary is populated from tweets. 1430.5 An example twitter trends is cast as ballot in testlogs/SocialNetworkAnalysis_OpinionMining.log.9June2023 for following trending twitter hashtags (BTS TakeTwo KPop dominates the trends): Votes for tweets - tweetdict: defaultdict(None, {'#PollstersSurveyForMP': -1, '#मुख्यमंत्री_लाड़ली_बहना_योजना': -1, '#BirsaMunda': '15K', 'भगवान': '#9YearsOfTechForGrowth': '39K', '#OMG2': '14K', 'KARANTLY': '25K', 'WELCOME': '43K', 'महान': -1, 'कानपुर': -1, 'सिंह': -1, 'Manusmriti': -1, 'U': -1, 'चिंतन': -1, 'शरद': -1, 'जनजातीय': -1, 'BIRTH': '42K', 'Supporters': '14K', 'प्यारी': -1, 'संजय': -1, 'CSAT': -1, 'वनवासी': -1, 'रक्षा': -1 '2216K', 'Einstein': '12K', 'अजीत': -1, 'Bharpai': '63K', 'सुधीर': '34K', 'No': '66K', 'Jai': -1, 'Bangtan': '122K', 'Jaishankar': '21K', '#TaTwo': '1253K', '#instagramdown': -1, '#Animal': -1, '#GodKabir_Comes_In_All_4Yugas': '29K', '#AkshayKumar': -1, '#Gadar2': -1, '#MissWorld2023': -1, '#SharadPawar': -1, '#Pathaan': -1, '#BloodyDaddy': -1, '#BSF_BGB': -1, '#SonamKapoor': -1, '#1000DaysOfInjustice': -1, '#TereIshqMeinGhayal': '24K', '#FridayFeeling': -1})
Srinivasan Kannan
More... over 3 years ago
Section 1419 updated
Srinivasan Kannan
More... over 3 years ago
Updated section 1426
Srinivasan Kannan
More... over 3 years ago
Section 1192 updated
Srinivasan Kannan
More... over 3 years ago
Section 1419 updated
Srinivasan Kannan
More... over 3 years ago
Section 1419 updated
Srinivasan Kannan
More... over 3 years ago
Section 1419 updated
Srinivasan Kannan
More... over 3 years ago
Section 1426 updated
Srinivasan Kannan
More... over 3 years ago
Section 1426 updated
Srinivasan Kannan
More... over 3 years ago
Section 1426 updated
Srinivasan Kannan
More... over 3 years ago
Section 1426 updated
Srinivasan Kannan
More... over 3 years ago
1428. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Four colored morphological settlement classification from R2023A GHS-BUILT-C dataset - Expanded Chennai Metropolitan Area Bounding Box - 3-Colored GHSL MSZ split into 4-colored MSZ by clearing height - 26 May 2023 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1395,1397,1398,1412,1414,1423,1424,1425 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
More... over 3 years ago
------------------------------------------------------------------------------------------------------------------------------------------- 1428. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Four colored morphological settlement classification from R2023A GHS-BUILT-C dataset - Expanded Chennai Metropolitan Area Bounding Box - 3-Colored GHSL MSZ split into 4-colored MSZ by clearing height - 26 May 2023 - related to 823,1163,1164,1174,1175,1177,1179,1194,1211,1226,1234,1290,1291,1292,1295,1296,1297,1301,1302,1304,1312,1313,1320,1323,1326,1328,1342,1343,1349,1350,1351,1362,1363,1377,1386,1395,1397,1398,1412,1414,1423,1424,1425 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 ------------------------------------------------------------------------------------------------------------------------------------------- 1428.1 python-src/image_pattern_mining/ImageNet/GISUrbanSprawlAnalytics.py has been updated to add clearing height as a parameter to four_colored_morphological_settelement_zones(heightrasterdata,longx,latx,longy,laty,clearingheight=10.9727999833) which is set to default 36 feet or 10.97 meters (and varies upto 32 feet). 1428.2 Based on clearingheight 3-colored GHSL R2023A SMOD MSZ is split into 4-colored MSZ by including Manufacturing-Industrial as additional segment if height is in range 6 to 15 meters: if k==23 and clearingheight > 6 and clearingheight < 15: print("23 : Manufacturing-Industrial MSZ, built spaces, non-residential(Manufacturing-Industrial), 6m < building height <= 15m:",v) 1428.3 Earth Mover Distance similarity between R2022A and R2023A Degree of Urbanization Visuals is found by ImageGraph_Keras_Theano.histogram_partition_distance_similarity(): ImageGraph_Keras_Theano.histogram_partition_distance_similarity("testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation.jpg","testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2023A_GHS_SMOD_DegreeOfUrbanisation.jpg") 1428.4 Problems in JPG file testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2023A_GHS_SMOD_DegreeOfUrbanisation.jpg from GHSL R2023A SMOD Visualization https://ghsl.jrc.ec.europa.eu/visualisation.php has been corrected (JIRA issue:https://krishnairesearch.atlassian.net/browse/NEUR-30) 1248.5 Following statistics are captured in testlogs/GISUrbanSprawlAnalytics.log.26May2023 for 4-color MSZ and EMD similarity between the two imagery from GHSL SMOD R2022A and R2023A - EMD similarity for CMA bounding box is 63.95% or a visual change of 36% in one year which more or less coincides with ~25% growth between 2022 and 2023 found from GHSL SMOD rasters in 1423: -------- urbansprawl_gini_coefficient(): Gini Index of the dataset = 0.9399500643202778 Morphological settlement zones : defaultdict(<class 'int'>, {0: 11017, 11: 265, 2: 229, 12: 58, 3: 223, 5: 43, 13: 213, 1: 105, 23: 8, 22: 1, 14: 36, 24: 2, 4: 2, 25: 1, 15: 1}) 11 : Residential MSZ, built spaces, residential, building height <= 3m: 265 02 : Greenery-Waterbody MSZ, open spaces, medium vegetation surfaces 0.3 < NDVI <=0.5: 229 12 : Residential MSZ, built spaces, residential, 3m < building height <= 6m: 58 03 : Greenery-Waterbody MSZ, open spaces, high vegetation surfaces NDVI > 0.5: 223 05 : Non-Residential MSZ, open spaces, road surfaces: 43 13 : Residential MSZ, built spaces, residential, 6m < building height <= 15m: 213 01 : Greenery-Waterbody MSZ, open spaces, low vegetation surfaces NDVI <= 0.3: 105 23 : Manufacturing-Industrial MSZ, built spaces, non-residential(Manufacturing-Industrial), 6m < building height <= 15m: 8 22 : Non-Residential MSZ, built spaces, non-residential(commercial,IT-ITES), 3m < building height <= 6m: 1 14 : Residential MSZ, built spaces, residential, 15m < building height <= 30m: 36 24 : Commercial MSZ, built spaces, non-residential(commercial,IT-ITES), 15m < building height <= 30m: 2 04 : Greenery-Waterbody MSZ, open spaces, water surfaces LAND < 0.5: 2 25 : Commercial MSZ, built spaces, non-residential(commercial,IT-ITES), building height > 30m: 1 15 : Residential MSZ, built spaces, residential, building height > 30m: 1 ('EMD Histogram distance similarity between images - testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2022A_GHS_SMOD_DegreeOfUrbanisation.jpg and testlogs/RemoteSensingGIS/ChennaiMetropolitanArea_GHSL_R2023A_GHS_SMOD_DegreeOfUrbanisation.jpg :', (0.6395461559295654, None, array([[111., 0., 0., ..., 0., 0., 0.], [ 0., 111., 0., ..., 0., 0., 0.], [ 0., 0., 111., ..., 0., 0., 0.], ..., [ 0., 0., 0., ..., 0., 0., 0.], [ 0., 0., 0., ..., 0., 0., 0.], [ 0., 0., 0., ..., 0., 0., 0.]], dtype=float32)))
Srinivasan Kannan
More... over 3 years ago
Section 1426 updated
Srinivasan Kannan
More... over 3 years ago
Section 1192 updated
Srinivasan Kannan
More... over 3 years ago
Section 1427 updated
Srinivasan Kannan
More... over 3 years ago
Section 1318 updated
Srinivasan Kannan
More... over 3 years ago
1427. (THEORY and FEATURE) People Analytics - Social Network Analysis (Search Engine Results) - Spearman Rank Correlation and Partial Ordered Rankings - related to 14,365,626,711,753,876,1127,1128,1129,1130,1204,1263 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium
Srinivasan Kannan
More... over 3 years ago
------------------------------------------------------------------------------------------------------------------------------------------- 1427. (THEORY and FEATURE) People Analytics - Social Network Analysis (Search Engine Results) - Spearman Rank Correlation and Partial Ordered Rankings - related to 14,365,626,711,753,876,1127,1128,1129,1130,1204,1263 and all sections on People Analytics, Intrinsic Merit Versus Fame Equilibrium, BKS Conjecture, Stability and Noise Sensitivity of Interview LTF/PTF,Peres Theorem,Unique Identification,HR Analytics and Automated Recruitment,Question-Answering,Majority Voting,Condorcet Jury Theorem Variants,Margulis-Ruzzo Thresholds,Novelty Detection,Recommender Systems,AI Question-Answering, Partial Ordered Ranking, Fame-Merit equilibrium ------------------------------------------------------------------------------------------------------------------------------------------- 1427.1 python-src/SocialNetworkAnalysis_PeopleAnalytics.py has been updated to define a small function to compute Spearman Rank Correlation Coefficient between rankings of search results from two search engines for a query (Search engines are the obvious sources to locate a person by name or domain). 1427.2 Following two example search engine rankings of URLs are correlated by SciPy Stats Spearman coefficient: searchresults1=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20] searchresults2=[20,1,2,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,18,19] hranal.search_engine_rank_correlation(searchresults1,searchresults2) 1427.3 Earlier two searchresults are first 20 numeric ranking values for query "big data linux kernel cloud machine learning" in Google and Microsoft Bing respectively (added to testlogs at python-src/testlogs/GoogleSearch_bigdatalinuxkernelcloudmachinelearning_23May2023.pdf and python-src/testlogs/MicrosoftBingSearch_bigdatalinuxkernelcloudmachinelearning_23May2023.pdf) 1427.4 Earlier results simulate the conflict between intrinsic merit (Bing SPTAG search intent) versus majority voting (PageRank voting). Theoretically both rankings must have concurred attaining Fame-Merit equilibrium. New Partial Ordered Ranking could be evolved (section 626) by merging the two search results arrays which are totally ordered into a single POSET array of feature tuples (e.g ((pagerank1,sptag1),(pagerank2,sptag2),......) - some or all ordinates of feature tuples within the partial order may not be comparable by < relation. 1427.5 Logs testlogs/SocialNetworkAnalysis_PeopleAnalytics.log.24May2023 show following Spearman correlation and pvalue for the 2 rankings: search_engine_rank_correlation(): res = SpearmanrResult(correlation=-0.12781954887218044, pvalue=0.5912462398946646)
Srinivasan Kannan
More... over 3 years ago
+1426. (THEORY and FEATURE) Asymptotic approximation of Condorcet Jury Theorem - C++ implementation - related to 400,572,666,725,730,688,1136,1137,1152 and all sections on Intrinsic Merit Versus Majority Voting, Derandomization, P=BPP ("Maybe" tends to "Exists") in the context of Condorcet Jury Theorem and its derivatives, Margulis-Russo Threshold for boolean majority, Histogram analytics, Set Partition Theoretical Electronic Voting Machines, Edit distance to Earth Mover Distance reduction, Boolean function composition, KRW Conjecture, Communication complexity, Hardness amplification, Bounded Rationality, Gibbard-Satterthwaite Theorem, Ramsey coloring of sequences, Infinite Pigeonhole Principle, Voting Analytics (pre-poll and post-poll),Epsilon bias spaces - 18 May 2023^M
Srinivasan Kannan
More... over 3 years ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1426. (THEORY and FEATURE) Asymptotic approximation of Condorcet Jury Theorem - C++ implementation - related to 400,572,666,725,730,688,1136,1137,1152 and all sections on Intrinsic Merit Versus Majority Voting, Derandomization, P=BPP ("Maybe" tends to "Exists") in the context of Condorcet Jury Theorem and its derivatives, Margulis-Russo Threshold for boolean majority, Histogram analytics, Set Partition Theoretical Electronic Voting Machines, Edit distance to Earth Mover Distance reduction, Boolean function composition, KRW Conjecture, Communication complexity, Hardness amplification, Bounded Rationality, Gibbard-Satterthwaite Theorem, Ramsey coloring of sequences, Infinite Pigeonhole Principle, Voting Analytics (pre-poll and post-poll),Epsilon bias spaces - 18 May 2023 -------------------------------------------------------------------------------------------------------------------------------------------- 1426.1 cpp-src/miscellaneous/pgood.cpp has been updated to include <cmath> headers and has been enhanced to implement a new function condorcet_jury_theorem() which computes asymptotic appoximation of CJT when p is very close to 0.5 or voters are epsilon-bias spaces. New flag iterative chooses either the binomial series iterative evaluation of CJT or asymptotic approximation of CJT from expression mentioned in https://en.wikipedia.org/wiki/Condorcet%27s_jury_theorem#Asymptotics based on user input of voter size n,probability p and constants c2,c3,c4. 1426.2 Condorcet Jury Theorem is central to lot of majority voting consensus related theory mentioned in NeuronRain design and is used as asymptotic derandomization gadget for BP* classes, particularly asymptotic derandomization of Shor BQP factorization by majority voting in section 1152 (https://neuronrain-documentation.readthedocs.io/en/latest/) in which every BPQ voter factorizes an integer with 67% accuracy and by CJT p-bias=0.67 making the group decision tend to 100% correctness asymptotically (Margulis Ruzzo Sharp Threshold). 1426.3 Some example asymptotic approximation of CJT for various population sizes and probabilities have been captured in cpp-src/miscellaneous/testlogs/pgood.log.18May2023
Srinivasan Kannan
More... over 3 years ago
Section 666 updated
root
More... over 3 years ago