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Nov 05, 2025 — Nov 05, 2026
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Section 1416 updated
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1416. (JIRA,THEORY and FEATURE) People Analytics - Unique Identification - Fingerprint recognition - contour kmeans clustering reverted to OpenCV2 drawContours() - 12 April 2023,13 April 2023 - related to 834,1223,1237,1260 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Product Homotopy, Unique identification, Homeomorphism and Topological Deformations, Merit of Large Scale Visuals, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, 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, Cybercrime analytics, Forensics-Predictive Policing, Fraud Analytics, Birthday Paradox, Contextual Name Parsing, PIPL Name syllable based unique person search, Collision resitant hash functions, Non Fungible Tokens
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------------------------------------------------------------------------------------------------------------------------------------------- 1416. (JIRA,THEORY and FEATURE) People Analytics - Unique Identification - Fingerprint recognition - contour kmeans clustering reverted to OpenCV2 drawContours() - 12 April 2023,13 April 2023 - related to 834,1223,1237,1260 and all sections on Merit of Large Scale Visuals, Physique Recognition From Quadrilateral mesh isomorphism,Topological Face and Handwriting recognition, Product Homotopy, Unique identification, Homeomorphism and Topological Deformations, Merit of Large Scale Visuals, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, 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, Cybercrime analytics, Forensics-Predictive Policing, Fraud Analytics, Birthday Paradox, Contextual Name Parsing, PIPL Name syllable based unique person search, Collision resitant hash functions, Non Fungible Tokens -------------------------------------------------------------------------------------------------------------------------------------------- 1416.1 ImageGraph_Keras_Theano.py has been updated to change kmeans cluster image drawing by choosing between 1) OpenCV2 drawContours() which is the obvious and 2) OpenCVs drawContours() with Convex Hull by a flag opencv2drawconvexhull. Existing pyplot code which was implemented to circumvent occassional drawContours() errors has been commented due to a sudden mysterious error that drew blank images (which was a working code so far). 1416.2 Non-trivial changes where required to refactor above code which include defining a new inverted index contour2cluster mapping array_repr() string of each contour to its containing cluster. If opencv2drawconvexhull is False contours are drawn by drawContours() color coded by cluster id. If opencv2drawconvexhull is True all contours within a cluster are bounded by convex hulls showing a cluster boundary and drawn by drawContours() color coded by cluster_id. 1416.3 In addition, contours within a cluster are drawn by polylines() color coded by cluster_id instead of pyplot (which is a non-OpenCV paradigm) 1416.4 This resolves JIRA issue: https://krishnairesearch.atlassian.net/browse/NEUR-26
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+1415. (THEORY and FEATURE) People Analytics - Merit of Large Scale Visuals - Face Recognition by DBSCAN clustering similarity - related to 159,660,714,715,720,1185,1186,1187,1188,1189,1190,1201,1202,1203 and all sections on Topological Face and Handwriting recognition, Product Homotopy, Unique identification, Homeomorphism and Topological Deformations, Merit of Large Scale Visuals, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem, DBSCAN spatial clustering, Histogram analytics - 12 April 2023 ^M
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-------------------------------------------------------------------------------------------------------------------------------------------- 1415. (THEORY and FEATURE) People Analytics - Merit of Large Scale Visuals - Face Recognition by DBSCAN clustering similarity - related to 159,660,714,715,720,1185,1186,1187,1188,1189,1190,1201,1202,1203 and all sections on Topological Face and Handwriting recognition, Product Homotopy, Unique identification, Homeomorphism and Topological Deformations, Merit of Large Scale Visuals, People Analytics, Computational Geometry, Graph Edit Distance and Earth mover distance, Graph Isomorphism (Exact, Approximate and Subgraph),Graph matching,Graph similarity,Image Search,Quadrilateral Mesh Deformation and Isomorphism,Maximum Common Subgraph Problem, DBSCAN spatial clustering, Histogram analytics - 12 April 2023 -------------------------------------------------------------------------------------------------------------------------------------------- 1415.1 python-src/DBSCANClustering.py has been updated to return clustering data from function clustering(). 1415.2 python-src/DeepLearning_ConvolutionNetwork_BackPropagation.py has been changed to import DBSCAN clustering and based on a flag DBSCANClusteringSimilarity, images are DBSCAN clustered and Wasserstein Earth Mover Distance (EMD) is computed on pair of DBSCAN cluster histograms. 1415.3 Example pair of face imagery "testlogs/KSrinivasan_2003.jpg" (plain) and "testlogs/KSrinivasan_2003-tessellated.jpg" (overlaid by tessellation) are compared by DBSCAN clustering Wasserstein EMD - captured in python-src/testlogs/DeepLearning_ConvolutionNetwork_BackPropagation.log.12April2023.
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Updated BestPractices.txt
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+1414. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Official Overlay of GHSL and Expanded Chennai Metropolitan Area limits - Density based coloring of DBSCAN clusters - 11 April 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 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 ^M
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-------------------------------------------------------------------------------------------------------------------------------------------- 1414. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Official Overlay of GHSL and Expanded Chennai Metropolitan Area limits - Density based coloring of DBSCAN clusters - 11 April 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 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1414.1 python-src/DBSCANClustering.py has been updated to define a new dictionary clusterdict to store number of pixels per DBSCAN cluster which is populated by iterating through dbscandict cluster labelling of pixels within function clustering(). 1414.2 write_clustered_image(self,neuralnetwork=False,fraction=1) has been changed to color code each cluster proportional to density of pixels within DBSCAN cluster. 1414.3 DBSCAN image from Official Overlay of GHSL and Expanded Chennai Metropolitan Area limits (http://www.cmavision.in) python-src/image_pattern_mining/ImageNet/testlogs/ExpandedCMA_map_cmavision_dbscanclustered.jpg has been rewritten by coloring the DBSCAN clusters based on their pixel density. Clusters of same color are identically populated which is an essential prerequisite in automatic delineation of urban extent. 1414.4 Cluster labelling of pixels and densities are printed to python-src/image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.11April2023 1414.5 python-src/image_pattern_mining/ImageNet/testlogs/ExpandedCMA_map_cmavision_dbscanclustered.jpg shows 3 concentric layers of densities (by pixel coloring) - 1) interiors of suburbs colored dark 2) peripheries of suburbs colored light and 3) lot of black square clusters surrounding the suburbs 1414.6 DBSCAN clustering has applications beyond GIS geospatial analytics - for example, face recognition could be implemented based on DBSCAN clustering by following algorithm: 1414.6.1 DBSCAN cluster two face images and compute cluster statistics - clusterimage1, clusterimage2 1414.6.2 Earth mover distance between the two clusters - clusterimage1 and clusterimage2 - is a face similarity measure which could complement Graph Isomorphism/Approximate Graph Matching similarity of face image Trimesh-Quadmesh. 1414.7 This update resolves JIRA issue : https://krishnairesearch.atlassian.net/browse/NEUR-25
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Section 1409 updated
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Section 1404 updated
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Updated index.rst
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-------------------------------------------------------------------------------------------------------------------------------------------- 1413. (JIRA,THEORY and FEATURE) Recursive Lambda Function Growth - Updates to Transformers Degree Attention Model - related to 249,707,829,1133,1134,1204,1263,1269,1284,1285,1405 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 - 6 April 2023 ------------------------------------------------------------------------------------------------------------------------------------------- 1413.1 python-src/RecursiveLambdaFunctionGrowth.py has been updated to import NumPy and defaultdict for Transformers degree attention model updates. 1413.2 Query,Key,Value matrices of the transformers attention model have been initialized as ndarray pre-filled to 0.5 and their shape has been set to 10% of textgraph vertices as perceptron gradient descent computation for full graph is intensive. Query-Key-Value triples were hardcoded earlier within machine_translation() and now the transformers degree attention computation has been made essential part of Recursive Lambda Function Growth algorithm. JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-24 has been raised for this. 1413.3 Query-Key-Value for every word pair edge in textgraph have been populated in 3 respective dictionaries which are sorted. 1413.4 course_material/NeuronRain/LinuxKernelAndCloud/code/Transformers_PerceptronAndGradient.py in Grafit repositories has been updated to change compute_perceptron(weights, variables, outputs, threshold=0.5) to include threshold value as an argument and make thresholding based on absolute value 1413.5 Query-Key-Value matrices of word vertex degree attention are as equally important as dense subgraph document classifiers since degree attention is another spectacle to view subgraph core numbers (attention by a word vertex to another = incoming degree). Existing generative pretrained transformer models consider a text as one dimensional sequence of words while transformer degree attention makes it deeper by concentrating on degrees of textgraph obtained from flat text - in other words Generative AI meets formal languages and algorithmic graph theory. Tree adjoining grammar parse trees could be integrated into transformer degree attention model by recursively assigning an attention query-key-value to each auxiliary tree adjoined to an initial tree nonterminal. An example is described in 1413.7. 1413.6 Following are updated text,DOT and logs for this update - textgraph obtained for updated RecursiveLambdaFunctionGrowth.txt shows Gross Domestic Product as prominent word vertex of high core number and degree attention (Query,Key,Value matrices): new file: testlogs/RecursiveLambdaFunctionGrowth.log.6April2023 modified: RecursiveLambdaFunctionGrowth.dot modified: RecursiveLambdaFunctionGrowth.txt
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-------------------------------------------------------------------------------------------------------------------------------------------- 1413. (JIRA,THEORY and FEATURE) Recursive Lambda Function Growth - Updates to Transformers Degree Attention Model - related to 249,707,829,1133,1134,1204,1263,1269,1284,1285,1405 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 - 6 April 2023 ------------------------------------------------------------------------------------------------------------------------------------------- 1413.1 python-src/RecursiveLambdaFunctionGrowth.py has been updated to import NumPy and defaultdict for Transformers degree attention model updates. 1413.2 Query,Key,Value matrices of the transformers attention model have been initialized as ndarray pre-filled to 0.5 and their shape has been set to 10% of textgraph vertices as perceptron gradient descent computation for full graph is intensive. Query-Key-Value triples were hardcoded earlier within machine_translation() and now the transformers degree attention computation has been made essential part of Recursive Lambda Function Growth algorithm. JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-24 has been raised for this. 1413.3 Query-Key-Value for every word pair edge in textgraph have been populated in 3 respective dictionaries which are sorted. 1413.4 course_material/NeuronRain/LinuxKernelAndCloud/code/Transformers_PerceptronAndGradient.py in Grafit repositories has been updated to change compute_perceptron(weights, variables, outputs, threshold=0.5) to include threshold value as an argument and make thresholding based on absolute value 1413.5 Query-Key-Value matrices of word vertex degree attention are as equally important as dense subgraph document classifiers since degree attention is another spectacle to view subgraph core numbers (attention by a word vertex to another = incoming degree). Existing generative pretrained transformer models consider a text as one dimensional sequence of words while transformer degree attention makes it deeper by concentrating on degrees of textgraph obtained from flat text - in other words Generative AI meets formal languages and algorithmic graph theory. Tree adjoining grammar parse trees could be integrated into transformer degree attention model by recursively assigning an attention query-key-value to each auxiliary tree adjoined to an initial tree nonterminal. An example is described in 1413.7. 1413.6 Following are updated text,DOT and logs for this update - textgraph obtained for updated RecursiveLambdaFunctionGrowth.txt shows Gross Domestic Product as prominent word vertex of high core number and degree attention (Query,Key,Value matrices): new file: testlogs/RecursiveLambdaFunctionGrowth.log.6April2023 modified: RecursiveLambdaFunctionGrowth.dot modified: RecursiveLambdaFunctionGrowth.txt
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+1412. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Official Overlay of GHSL and Expanded Chennai Metropolitan Area limits - Corrections to DBSCAN cluster image - 4 April 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 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^M
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-------------------------------------------------------------------------------------------------------------------------------------------- 1412. (JIRA,THEORY and FEATURE) GIS Urban Sprawl Analytics - Official Overlay of GHSL and Expanded Chennai Metropolitan Area limits - Corrections to DBSCAN cluster image - 4 April 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 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1412.1 python-src/DBSCANClustering.py has been updated to introduce error handling clause for ndarray bounds check while writing DBSCAN clustered imagery (JIRA issue: https://krishnairesearch.atlassian.net/browse/NEUR-23) which was earlier causing truncated cluster image. 1412.2 Official GHSL overlay map of Expanded Chennai Metropolitan Area from http://www.cmavision.in has been analyzed by DBSCAN clustering and a better clustered image has been written by write_clustered_image(). Following are the logs,official GHSL overlay and DBSCAN imagery for this update: image_pattern_mining/ImageNet/testlogs/ExpandedCMA_map_cmavision.jpg image_pattern_mining/ImageNet/testlogs/ExpandedCMA_map_cmavision_dbscanclustered.jpg image_pattern_mining/ImageNet/testlogs/ExpandedCMA_map_cmavision_neuralnetworkclustered.jpg image_pattern_mining/ImageNet/testlogs/GISUrbanSprawlAnalytics.log.4April2023 1412.3 image_pattern_mining/ImageNet/testlogs/ExpandedCMA_map_cmavision_dbscanclustered.jpg shows well-marked DBSCAN clusters for urbanized pixels interspersed by periurban pixels. Each cluster is a color-coded rectangular neighbourhood.
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1411. (JIRA,THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from Google trends news article sentiments 3 - correction to multipolar sentiment votes - theory of ternary majority gate - 3 April 2023 - related to Section 588,1380,1406,1407,1408,1409,1410 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
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-------------------------------------------------------------------------------------------------------------------------------------------- 1411. (JIRA,THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from Google trends news article sentiments 3 - correction to multipolar sentiment votes - theory of ternary majority gate - 3 April 2023 - related to Section 588,1380,1406,1407,1408,1409,1410 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1411.1 python-src/SocialNetworkAnalysis_OpinionMining.py has been updated to rectify computation of nett multipolar sentiment vote by subtracting negative polarity scores - every article is a voter on the candidate search query and multipolar vote (like-neutral-dislike) by an article is computed as positive-negative+objective sentiment scores from various algorithms. 1411.2 Nett sentiment vote was earlier computed as positive+negative+objective which has been corrected to subtract negative sentiment. JIRA issue https://krishnairesearch.atlassian.net/browse/NEUR-22 has been raised for this error. 1411.3 Sentiment analyzer based multipolar vote envisages a fundamental change to conventional ballot besign where vote button against every candidate is of 3 columns (in percentage) - like %, neutrality %, dislike % (or) in quantum computation terms every vote is a superposed state of amp1<like| + amp2<neutral| + amp3<dislike| for respective complex amplitudes amp1,amp2 and amp3 and classical probabilities are obtained by Born's rule (classical probability = |complex amplitude|^2). Conventional boolean (bipolar) majority gate gets converted to ternary majority gate of leaves -1(dislike),0(neutral),+1(like) in complexity theoretic version of multipolar majority. Ternary majority gate discards 0(neutral) votes if they are less than 50% of total and calculates majority from -1(dislike) and +1(like) leaves. If number of 0(neutral) votes are > 50%, ternary majority gate fails with Godelian design defect(described in 1408.2). Thus boolean majority gate is a special case of ternary majority gate. 1411.4 New boolean flag UseNeuronRainSentimentAnalyzer has been introduced in opinion_mining() to optionally include votes from NeuronRain recursive gloss overlap textgraph sentiment analyzers based on sentiwordnet,belief propagation and markov random fields which are not normalized to 0 to 1 decimal fraction scale. By default UseNeuronRainSentimentAnalyzer is set to False and multipolar sentiment votes for news articles are based only on TextBlob,VADER,Empath with VADER being best suited for social media. 1411.5 Above changes were tested for trending Google searches and captured in testlogs/SocialNetworkAnalysis_OpinionMining.log.3April2023
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Section 727 updated
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+1410. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from Google trends news article sentiments 2 - multipolar sentiment votes - 31 March 2023 - related to Section 588,1380,1406,1407,1408,1409 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 ^M
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-------------------------------------------------------------------------------------------------------------------------------------------- 1410. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from Google trends news article sentiments 2 - multipolar sentiment votes - 31 March 2023 - related to Section 588,1380,1406,1407,1408,1409 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1410.1 Updated python-src/SocialNetworkAnalysis_OpinionMining.py to populate multipolarvotes ballot by triples of positive,negative and objective sentiment votes for each news article searched by trending Google searches. 1410.2 Logs for trending Google searches for India and tripolar sentiment analysis ballots for some of the news articles are captured in SocialNetworkAnalysis_OpinionMining.log.31March2023 and SocialNetworkAnalysis_OpinionMining.log2.31March2023
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Section 1408 updated
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Updated Requirements.txt
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1409. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from Google trends news article sentiments - 29 March 2023 - related to Section 588,1380,1406,1407,1408 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
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-------------------------------------------------------------------------------------------------------------------------------------------- 1409. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from Google trends news article sentiments - 29 March 2023 - related to Section 588,1380,1406,1407,1408 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1409.1 python-src/SentimentAnalyzer.py has been updated to comment networkx drawing of Recursive Gloss Overlap textgraph. 1409.2 python-src/SocialNetworkAnalysis_OpinionMining.py has been updated to import pytrends python library for Google search trends. Existing implementation of Twitter sentiment analyis in python-src/SocialNetworkAnalysis_Twitter.py is most likely broken due to discontinuation of free access to Twitter API from February 2023 - https://www.forbes.com/sites/jenaebarnes/2023/02/03/twitter-ends-its-free-api-heres-who-will-be-affected/?sh=eaa1f0e62664. 1409.3 Multipolar sentiment votes (like-neutral-dislike opinion triples) by each article for a query are added to an array multipolarvotes which serves as the ballot box for concept Receipt-based Drone VREPAT Electronic Voting Machine implementation in NeuronRain. Drone EVMs differ from Mobile phone voting by ensuring software independent geographic uniqueness of a voter at h(is/er) address at the time of voting which is a precondition in [Rivest] - https://people.csail.mit.edu/rivest/RivestWack-OnTheNotionOfSoftwareIndependenceInVotingSystems.pdf. Non-digital currency UUID mandated for electors at the time voting is another software independent authentication improving the nationwide uniqueness guarantee. Mobile voting might be able to verify geographic location of voter by GPS but yet not software independent and mobile devices belong to users which are not under control of an authority - Drone's flight to voter's address and Paper currency UUID OTP are non-digital. User Mobile devices cannot create an encrypted paper receipt similar to PoS terminals. 1409.4 New function opinion_mining_from_google_trends(query=None,fromdate='01/01/2023',todate='01/03/2023',maxtrends=2,region='IN') has been defined to iterate through realtime trending google searches for a region and analyze sentiments for news articles matching those trending queries (limited by maximum number of trends and maximum number of articles). 1409.5 Logs for sentiment voting of few realtime Google trends for a daterange in python-src/testlogs/SocialNetworkAnalysis_OpinionMining.log.29March2023 present the ballots for mixed genre of queries.
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Updated Requirements.txt
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1408. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from news article sentiments - support for VADER and Empath Sentiment Analysis added - 28 March 2023 - related to Section 588,1380,1406,1407 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
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-------------------------------------------------------------------------------------------------------------------------------------------- 1408. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Poll from news article sentiments - support for VADER and Empath Sentiment Analysis added - 28 March 2023 - related to Section 588,1380,1406,1407 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1408.1 python-src/SocialNetworkAnalysis_OpinionMining.py has been updated to import VADER(Valence Aware Dictionary for Sentiment Reasoning) Sentiment analyzer from NLTK and Empath sentiment analyzer. 1408.2 sentiment_analyzer() function has been updated to add clauses for 2 more sentiment analyzers - VADER and Empath - and implement a virtual opinion poll from sentiments of news articles returned for a user query. Set of news articles constitute the sample population and weighted numeric sentiment of the article computed by an ensemble of algorithms (RGO_Belief_Propagation,RGO_Belief_Propagation_MRF,TextBlob,SentiWordNet,VADER and Empath) is construed as vote of the article on the query. Sum of votes (numeric sentiments) from sample population (all news article) is the outcome of opinion poll. Traditional definition of a vote is usually unipolar binary (whether a voter chooses a candidate or not) while sentiment voting is tripolar (a voter has fractions of like,dislike and neutrality towards a candidate - lovehate). Neutral voters are theoretically indispensable - Some voting machines support "None of the above" button for neutrality or abstention. A Godelian design defect in democracy could be theoretically demonstrated by neutral voters who abstain from majority voting - Majority is effected by more than 50% votes and if more than 50% of an electorate abstains from an election definition of "Majority weilding power" is violated - Example: If 51 of 100 voters abstain and remaining 49 vote to elect a leader, resulting outcome is non-majoritarian (not voted for by more than 50 voters) whatever be the voting pattern of 49 who voted. 1408.3 Earlier Google News based sentiment analysis could be extended to Google trends and Twitter trending hashtags. Function opinion_mining(query,fromdate,todate,maxpages=2,maxarticles=10,articlefraction=0.2) has been changed to add an argument articlefraction by which only a fractional summary of a big (TL;DR) article could be easily parsed for sentiments. 1408.4 News articles (population sample) for an example query "Stock market volatility" from 1 January 2023 to 1 March 2023 are sentiment analyzed and votes of the opinion poll are computed. 1408.5 Logs of virtual opinion poll are captured in testlogs/SocialNetworkAnalysis_OpinionMining.log.28March2023: Opinion mining - polled votes on the query [ Stock market volatility ] for population of size 10 : 16451.839150636566
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Updated Section 1407 and Requirements.txt
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-------------------------------------------------------------------------------------------------------------------------------------------- 1407. (THEORY and FEATURE) Social network analysis and People analytics - Opinion Mining - Sentiment Analysis - 24 March 2023 - related to Section 1380,1406 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 -------------------------------------------------------------------------------------------------------------------------------------------- 1407.1 python-src/SocialNetworkAnalysis_OpinionMining.py has been updated to import TextBlob and NeuronRain SentimentAnalyzer. 1407.2 New function sentiment_analyzer(text,algorithm=None) has been defined to analyze Opinion in every result from Google News for a query by following algorithms: 1407.2.1 Recursive Gloss Overlap Belief Propagation 1407.2.2 Recursive Gloss Overlap Belief Propagation Markov Random Fields 1407.2.3 TextBlob Sentiment Analyzer 1407.2.4 SentiWordNet 1407.3 Function opinion_mining(query,fromdate,todate,maxpages=2,maxarticles=3) has been redefined to limit the maximum number of articles by argument maxarticles 1407.4 Conflated opinion which is a concatenation of summaries from all Newspaper3k article texts is printed as "summarizedopinion". 1407.5 Sentiment analysis by above algorithms for an example news article is captured in logs: Opinion-RGO-BeliefPropagation-SentimentAnalysis.txt Opinion-RGO-MRF-BeliefPropagation-SentimentAnalysis.txt testlogs/SocialNetworkAnalysis_OpinionMining.log2.24March2023
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Section 1404 updated
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