Manan

 जब तक सपना सपना है वही संसार है, और जब सपना पूरा हो जाए तो सपने से ज्यादा कुछ नहीं 

  The concepts of perplexity and entropy , commonly used in information theory and natural language processing, can be applied to government decision-making and policy formulation in various ways. Here's how: 1. Policy Stability and Predictability Entropy measures uncertainty in a system. High entropy indicates unpredictability, while low entropy suggests stability. In economic policies , governments aim to reduce uncertainty (entropy) to create a predictable environment for businesses and citizens. For example, tax policies with low entropy (stable and predictable) encourage long-term investments. 2. Evaluating Public Opinion and Decision-Making Perplexity (a measure of how well a model predicts data) can help analyze the effectiveness of policy communication. If public reaction to a policy is highly perplexed , it may indicate confusion or miscommunication . Governments can refine messaging to ensure clarity. Social media sentiment analysis can use perplexity to measur...

In Natural Language Processing (NLP), smoothing techniques help handle unseen words by distributing probability mass to rare or unknown tokens, preventing models from overfitting to known data. This ensures better generalization and robustness. A similar approach could be applied to policymaking, where immediate emotional reactions often drive decisions, sometimes leading to policies that are not well-thought-out or have unintended consequences. What if we designed a governance framework that accounts for both historical precedent and future stability using a smoothing-inspired policy model ? The Concept: Future-Activated Policies Any policy drafted today would only come into effect after a predefined time window (e.g., 6 months to 2 years). This allows for public emotions to stabilize , giving policymakers time to reassess if the decision still holds after the initial wave of reactions. It prevents reactionary legislation based solely on current public sentiment , reducing the ris...

कुछ फैला हुआ सा डर है, कुछ छाई है इर्ष्या सी, जीत से नहीं, खुश होते - हार से दूसरों की, ना जाने क्या चाहते हैं, थाली का बैगन हो जैसे, आँखें मूंदे भागते जाते हैं।

Welcome, weary wanderer, step inside,   To Inefficiency's realm, where reason has died.   Absurdity's banner unfurls in the breeze,   Prepare for a journey that's sure to displease! Security Theater Passwords and codes, a labyrinthine maze,   OTPs descend in maddening waves.   "Prove you're not a bot, " the system does say,   Though a robot might find a more logical way. Time Bending In hours so meager, we work without end,    The laws of the cosmos, we twist and we bend.    Thirty-hour tasks compressed into eight,   While physics weeps for its pitiful fate. Money Out of Thin Air Where budgets go missing, and debts reappear,   Finances dance with a whimsical leer.   A conjurer's sleight with each debit and bill,   The rules of accounting surrender their will. Accountability? Never Heard of It What matters are failings, the stumbles, the slips,    Accomplishments fade, like words on your lip...

 In claim report Add DAname, initials or userid Add DA processing datetime separate Reason1, Reason2 In periodicity report: Add DAname, initials, userid Add DAprocessing datetime separate Reason1,Reason2  In Primary detail, Online detail, other detail Add Taskid,  Add name/initials/userid add Da processing datetime In transfer status: replace Slip Type mismatched  with the exact status and office where the request is pending Passbook Show the correct status whether the service has been added in new account of not. Transferred and added to Member id, transferred but not added to member id due to rejection, transferred but pending  

  from wordcloud import WordCloud def plt_word_cloud ( text ):     wordcloud = WordCloud().generate(text)     # take relative word frequencies into account, lower max_font_size     wordcloud = WordCloud(background_color= "white" ,max_words= 120 ,max_font_size= 40 , relative_scaling= 1 ).generate(text)     fig = plt.figure(figsize=( 10 , 8 ))     plt.imshow(wordcloud)     plt.axis( "off" )     plt.show() def prepare_cloud_text ( df , name = 'None' ):     df[ 'message' ]= df[ 'message' ]. str .replace( 'omitted' , ' ' )     df[ 'message' ]= df[ 'message' ]. str .replace( 'media' , ' ' )     text= ''     if name== 'None' :         k = df[ 'message' ]     else :         k = df[df[ 'name' ]==name][ 'message' ]     for i in k:         text = text+i     text = (text.encode( 'ascii' , '...