Open Hub
Open Hub

 Settings |  Report Duplicate

0
I Use This!
Very Low Activity

Commits : Listings

Analyzed about 2 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 1552 updated
K.Srinivasan
More... over 1 year ago
Section 1552 updated
K.Srinivasan
More... over 1 year ago
Section 1552 updated
K.Srinivasan
More... over 1 year ago
Section 461 updated
K.Srinivasan
More... over 1 year ago
Section 1582 updated
K.Srinivasan
More... over 1 year ago
Section 1582 updated
K.Srinivasan
More... over 1 year ago
Section 1272 updated
K.Srinivasan
More... over 1 year ago
Section 1215 updated
K.Srinivasan
More... over 1 year ago
Section 1437 updated
K.Srinivasan
More... over 1 year ago
1585. (THEORY and FEATURE) Nachiketas Formal LLM - User defined PoS-to-Vocabulary from Kaggle word_pos.csv dataset - Query related to Drug Discovery, New WSJ datasets and correction to defaultdict - Changes to Wikipedia Question-Answering algorithm for better accuracy 19 - 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 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 - 14 February 2025
Srinivasan Kannan
More... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1585. (THEORY and FEATURE) Nachiketas Formal LLM - User defined PoS-to-Vocabulary from Kaggle word_pos.csv dataset - Query related to Drug Discovery, New WSJ datasets and correction to defaultdict - Changes to Wikipedia Question-Answering algorithm for better accuracy 19 - 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 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 - 14 February 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1585.1 QuestionAnswering.py has been updated to make correction in Kaggle user defined PoS-to-Vocabulary dict by converting it to defaultdict(list) from defaultdict() to avert overwriting of words with same PoS tags. Two new Penn Treebank datasets wsj_0082.mrg and wsj_0074.mrg have been used to extract grammar trees. 1585.2 Query on medication has been tried: python3.9 QuestionAnswering.py "treatment for coronavirus 2019" and sentences-phrases ranked by perplexity have been logged to testlogs/QuestionAnswering.log.14February2025
Srinivasan Kannan
More... over 1 year ago
Section 1581 updated
K.Srinivasan
More... over 1 year ago
1584. (THEORY and FEATURE) Nachiketas Formal LLM - Schematic Diagram - 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 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 - 14 February 2025
K.Srinivasan
More... over 1 year ago
Section 1488 updated
Srinivasan Kannan
More... over 1 year ago
1583. (THEORY and FEATURE) Nachiketas Formal LLM - User defined PoS-to-Vocabulary from Kaggle word_pos.csv dataset - Changes to Wikipedia Question-Answering algorithm for better accuracy 18 - 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 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 - 13 February 2025
Srinivasan Kannan
More... over 1 year ago
GPTzero and LlaMa perplexity scores for section 1583
Srinivasan Kannan
More... over 1 year ago
-------------------------------------------------------------------------------------------------------------------------------------------- 1583. (THEORY and FEATURE) Nachiketas Formal LLM - User defined PoS-to-Vocabulary from Kaggle word_pos.csv dataset - Changes to Wikipedia Question-Answering algorithm for better accuracy 18 - 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 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 - 13 February 2025 -------------------------------------------------------------------------------------------------------------------------------------------- 1583.1 python-src/NeuronRainApps/QuestionAnswering/QuestionAnswering.py has been updated to import python csv reader. 1583.2 Small error in itertools.product() code has been resolved by encompassing rwtexttok within square brackets to make it 1 element array. 1583.3 Kaggle word_pos.csv dataset - https://www.kaggle.com/datasets/ruchi798/part-of-speech-tagging?resource=download - containing 370000 words in inflected forms (example: entente,ententes,detente,detentes,come,came,coming,...) which maps English words to their part-of-speech tag names has been read by csvreader and user defined kaggle_corpus_PoS2Vocabulary_dict is populated for filling the semifilled sentence template. This vocabulary pretty much covers entire Oxford dictionary vocabulary. 1583.4 Penn Treebank datasets have been changed to wsj_0032.mrg and wsj_0054.mrg. WikipediaRLFGTransformersQuestionAnswering() is invoked twice for dictionary sentence PoS template and array sentence PoS template respectively for query - python3.9 QuestionAnswering.py "define odd integers" - logged to testlogs/QuestionAnswering.log.13February2025: ... WikipediaRLFGTransformersQuestionAnswering(question,wsheading=True,answerslice=0.01,bothvertices_intersection=False,sentence_type="textgraph_random_walk",number_of_words_per_sentence=50,std_sentence_PoS_dict=sentencePoSdict,number_of_cores_per_random_walk=3,number_of_random_walks=3,blanks=False,treenode_type="tag",user_defined_PoS2Vocabulary_dict=kaggle_corpus_PoS2Vocabulary_dict) ... WikipediaRLFGTransformersQuestionAnswering(question,wsheading=True,answerslice=0.01,bothvertices_intersection=False,sentence_type="textgraph_random_walk",number_of_words_per_sentence=10,std_sentence_PoS_dict={},number_of_cores_per_random_walk=5,number_of_random_walks=5,blanks=False,treenode_type="tag",sentence_tuple_array=True, sentence_PoS_array=sentencePoSarray, randomwalk_to_sentence_template_ratio=3,user_defined_PoS2Vocabulary_dict=kaggle_corpus_PoS2Vocabulary_dict) 1583.5 Following is a paragraph on some of the phrases-sentences synthesized without blanks through dictionary PoS sentence template that has been analyzed in GPTzero AI detector and LlaMa perplexity score computation - "related quantity mathematics sciences one dealing. related quantity mathematics sciences one dealing. related quantity integers sciences one dealing . related parity mathematics sciences one dealing . related group mathematics sciences one dealing . related parity mathematics sciences one dealing" : 1583.5.1 LlaMa perplexity score per sentence - 6.00 and paragraph as a whole - 1.51 1583.5.2 GPTzero AI detector - above text is human created with 96% probability and AI created with 4% probability. 1583.6 Sentences synthesized through array sentence PoS template are filled for PoS blanks if any through earlier PoS-word tagged dataset and there might be blanks still left depending on efficacy of PoS tagged dataset - Dictionary PoS sentence template almost always produces blank-free phrases in trials so far.
Srinivasan Kannan
More... over 1 year ago
Section 1582 updated
Srinivasan Kannan
More... over 1 year ago
Section 1581 updated
Srinivasan Kannan
More... over 1 year ago
1582. (THEORY and FEATURE) Fraud Analytics - Label Propagation implementation - related to 693 and all sections on Fraud Analytics, Anti-Money Laundering (AML), Money Trail Graph, Cybercrime analytics, Clustering, Community Detection, Correlated Majority Voting, Collaborative Filtering, Social network analysis - 12 February 2025
Srinivasan Kannan
More... over 1 year ago
--------------------------------------------------------------------------------------------------------------------------------------------- 1582. (THEORY and FEATURE) Fraud Analytics - Label Propagation implementation - related to 693 and all sections on Fraud Analytics, Anti-Money Laundering (AML), Money Trail Graph, Cybercrime analytics, Clustering, Community Detection, Correlated Majority Voting, Collaborative Filtering, Social network analysis - 12 February 2025 --------------------------------------------------------------------------------------------------------------------------------------------- 1582.1 New python script LabelPropagation.py has been added to NeuronRain repositories that implements following Label Propagation algorithm: 1582.1.1 Every node in a NetworkX graph is assigned an initial label 1582.1.2 At each iteration every node in the graph is assigned to a label that is maximum-assigned to its neighbours found by a majority function over neighbours (or) each node is swayed by majority voted choice of its neighbours - parallels could be drawn to Collaborative filtering that recommends to a user a choice which is maximum preferred by similar users. In case of voter graph (voter vertices a and b are related if they are social media or geographic neighbours), each node is initialized to an independent choice of the voter vertex and label propagated clustering of vertices, after convergence or predefined number of iterations, is a measure of how voters could be influenced by their neighbours or voters are correlated in their choice due to a Bandwagon effect. 1582.1.3 Step 1582.1.2 is repeated for maximum iterations or till label assignment converges. 1582.2 Label Propagation is especially useful in financial (money trail) graphs where payments are made amongst trusted communities of vertices - any anomalous vertex that is not part of a trusted payment cluster is flagged before a transaction involving it.
Srinivasan Kannan
More... over 1 year ago
Section 1581 updated
Srinivasan Kannan
More... over 1 year ago
astroquery has been upgraded to 0.4.7 and clear_cache() has been invoked on Horizons objects due to SSLconnection errors in astroquery NASA JPL Horizons requests to ssd.jpl.nasa.gov:443
Srinivasan Kannan
More... over 1 year ago
1581. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 11 February 2025 - VARUNA N-Body Climate Model - N-Body gravity analysis of date range 1/1/2025-10/2/2025 for 4-body system Sun-Moon-Mercury-Mars - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562,1567 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules, Global warming
Srinivasan Kannan
More... over 1 year ago
---------------------------------------------------------------------------------------------------------------------------------------- 1581. (THEORY and FEATURE) GIS Weather Analytics and Climate Analytics - 11 February 2025 - VARUNA N-Body Climate Model - N-Body gravity analysis of date range 1/1/2025-10/2/2025 for 4-body system Sun-Moon-Mercury-Mars - related to 148,172,313,445,446,463,1165,1171,1176,1266,1289,1316,1318,1327,1358,1359,1360,1361,1364,1367,1368,1388,1404,1455,1456,1463,1479,1480,1515,1526,1527,1528,1548,1558,1561,1562,1567 and all sections on Correlation between gravitational effects of Mined Celestial N-Body Patterns and terrestrial extreme weather events (or Gravitational effects on climate change), Drone natural obstacle avoidance-Drone autonomous online shopping delivery-Drone Electronic Voting Machine, Artificial Intelligence models for Climate Change-Adaptive Fourier Neural Operator-FourCastNet, Astronomical algorithms, Multifractals, Timeseries analysis, Sequence Mining-Class Association Rules, Global warming ---------------------------------------------------------------------------------------------------------------------------------------- 1581.1 python-src/image_pattern_mining/ImageNet/GISWeatherAnalytics.py has been updated to analyze N-Body gravity in date range 1 January 2025-10 February 2025 for 4-body system Sun-Moon-Mercury-Mars: climate_analytics(datasource="precipitation_GaussianMixture",predict_EWE_params={'datefrom':(2025,1,1,17,30,00),'dateto':(2025,2,10,17,30,00),'loc':'@0','bodyconjunctions':"Sun-Moon-Mercury-Mars",'angularsepbounds':('0d','30d'),'longlat':['223@399'],'seasurfacelonglat':(95,12)},precipitation_timeseries={"timeseries":nem_rainfall_timeseries,"averageseasonalrainfall":100,'forecast_timeseries':[10,2,12,25,30,1,2,3,4,10]},computepressuregradient=False) 1581.2 Dates between last week of January 2025 and first week of February 2025 saw lot of seismic activity. Following log and gravity graph plots of planetary pairs show (*) a steep V-shaped fluctuation in Mercury's gravity (*) small decline in gravity of Mars (*) sinusoidal fluctuation in gravity of Moon (corresponding to Full Moon-New Moon cycles) during 1 January 2025 - 10 February 2025: testlogs/GISWeatherAnalytics.Gravity.Mercury-Mars.2025-1-1#2025-2-10.jpg testlogs/GISWeatherAnalytics.Gravity.Moon-Mars.2025-1-1#2025-2-10.jpg testlogs/GISWeatherAnalytics.Gravity.Moon-Mercury.2025-1-1#2025-2-10.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mars.2025-1-1#2025-2-10.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Mercury.2025-1-1#2025-2-10.jpg testlogs/GISWeatherAnalytics.Gravity.Sun-Moon.2025-1-1#2025-2-10.jpg testlogs/GISWeatherAnalytics.log.11February2025 1581.3 Pairs from earlier 4-body system are prominent in SequenceMined USGS historical dataset (Section 147): Sun+Mercury , Sun+Mars , Mars+Mercury , Moon+Mercury
Srinivasan Kannan
More... over 1 year ago
Section 693 updated
Srinivasan Kannan
More... over 1 year ago
Section 561 updated
Srinivasan Kannan
More... over 1 year ago
ORCID profile of publications
K.Srinivasan
More... over 1 year ago
Section 1488 updated
K.Srinivasan
More... over 1 year ago
Updated BestPractices.txt
K.Srinivasan
More... over 1 year ago