Joys of a Father and Mother

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She, The mother

The pains she took

Not for a day

Not for a week

For months she caried you inside her

You, The baby

…..

He, The father

The plans he made

The dreams he took

For you to come

To this place

To their lives

The hard work they did

The wishes thy made

The prayers they called

For you, The child

She, The mother

The pains she took

Not for a day

Not for a week

For months she caried you inside her

You, The baby

He, The father

The plans he made

The dreams he took

For you to come

To this place

To their lives

The hard work they did

The wishes thy made

The prayers they called

For you, The child

Full poem coming soon……………HERE !

Climate Change and Flooding

[Many questions for you to answer and some answers from me]

Climate change leads to variety of problems not just droughts, extreme cold, hailstorms but also flash flooding and out-of-season rain causing over flooding.  These are natural phenomenon’s which used to occur even long before when climate change was not triggered by certain requirements which never forecasted such an effect.

All that to say man lived always with nature, so has to be understood by modern generations. One must adept to live with nature. Space must be kept aside for nature, just as space is kept for birds and pets in a city. In the same way, one must understand that the till time equilibrium is reached on by Climate initiatives and even after, rains, water, snow will always be there. So why not give it some space? Why not good drainage systems, which are ready for excess water?

Why not  rain water draining system, be robust enough to handle extra rain waters? Why not canals and tributaries be made to direct excess water, distant places would love to get this water especially deserts.  These can be supplied with treated water from sea in normal times and to be used to dissipate extra rain water when in times of need.

Why not points to fill in water in underground depleting water tables? These are works of town planners, how these can be included in already build up cities and how to make sure such systems are planned in new cities being build.

For already build up places.  Points to fill in water tables can be a good option to be studied and experimented. This won’t require much disruption in plan on with a city is build. All it may need is some pipes going down to water table at right intervals. Not even a full dug well.

All this to say, one should be well prepared given lot of climatic changes happening at all places, these are questions and some answers, which can and should be well worked by town planners with environmentalist and geologists. I am asking these questions as a spectator, so are many folks out there !

AI: New Dataset, CIDER for Common sense Inferencing in Two Person Conversations Dialogues

In this article we discuss an research paper by (Ghosal et al, 2021) [1] named “CIDER: Commonsense Inference for Dialogue Explanation and Reasoning”.   

Aim:

The authors have developed a database for conversation-based text understanding for two person conversations and a deep learning-based models have also been evaluated for performance  for NLP tasks including inference, span detection and multiple-choice span detection.

My Analysis About Article:

The researchers had logically created a database of two-person conversation dialogues in which several key issues have been kept in mind. Firstly, the conversations were annotated by research students who were told to annotate the dialogues between entities in form of triplet of form: cause, action and impact. The triplets can be present in a dialogue between two people in following two ways:

  1. Explicit Triplets- Which can be parsed by parser or basic NLP tools of language understanding.
  2. Implicit Triplets-These triplets have to be extracted using common-sense knowledge apart from dialogue understanding and sometimes may require several steps of intermediate triplet detection.

The annotations by the three research students were verified by each other and the best or majority votes were performed in case of any clashes in annotating a dialogue in form of triplets. There are various kinds of relation and causes that are studied.

Further, the datasets were tested on already developed models for the following tasks.

  1. Natural Language Inferencing or Textual Entailment task performed on DNLI dataset. If finds out if the conclusion is true or false given the dialogue.
  2. Span Generation: Finds out the conclusion part given the relation and premise.
  3. Multiple Step Span Generation: Same as span generation but here it is like a multiple choice question answering and one correct inference is to be selected.

My Comments:

Other such datasets viz. Concept, Glucose, Atomic have been recently been developed but neither this problem was covered fully in these datasets nor such annotated combinations for two-person dialogues especially for common-sense reasoning were there.

This is a great work in conversational analysis especially for implicit relations and can be further expanded.

References

Ghosal, D., Hong, P., Shen, S., Majumder, N., Mihalcea, R., & Poria, S. (2021). CIDER: Commonsense Inference for Dialogue Explanation and Reasoning. arXiv preprint arXiv:2106.00510.

This Beautiful Planet, So Little Words to Express

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This beauty,

So little a word to express the charisma

It holds

This essence here

It makes things move

This beautiful planet

So little words to express

They call it planet

I call it Home

Yes, it’s my home

So little words written here

To express the love for it

Asking not much but little care

To be taken care of

For all it has to give

It shall give back again

This magnetic enchanting place

No amount of silver

Can return its favors’ ….

….

Full poem in my poem book, will be available free on kindle for some days from 1st July 2021 to 5th July 2021.

Here is the link to amazon book:

 Amazon.com: Short Book on Poems on Life, Positivity, Motivation, Hope, Desires, Meanings: Motivational Poem Book eBook: Yadav, Nidhika: Kindle Store

Climate Change and Temporary Measures for Preservations

Climate changed has paved the way for variety of extreme conditions on Earth, be it drought, flooding, cyclones, typhons, flash flooding or forest fires. Yes, until equilibrium is reached on Earth again, when the atmospheric processes gain back a stable and balanced state, measures should be taken to make resources on Earth safe and life on Earth livable with least economic costs. In this article I am putting out some measure that I think should be answered by Climatologists for applicability to preserve the resources from being damaged by Climate Change, be it soil, water bodies and let the sustained forms of life and processes be preserved with ease.

For all this one must understand the relation that exits between heat, water, ice and air. These are the major factors that define the climate change on or above the earth surface. Right now, due to certain imbalance in most of them, the natural processes are hampered. Hence, one need to study how to artificially imbibe the co-relations among them for temporary solutions. All this till, all is back to normal.

Another thing is water supplies as not just human’s but environment likes to be bit hydrated too. So, the question we have is how water be artificially supplied to areas in need and how it can help to balance the extreme conditions. If we see most climatic change are guided by heat, low, high pressures, rains to mention some. How artificially supplied water can help in curbing these issue ? Climatologists can help you give exact mathematical equations on it and how water flow need to be regulated. They can ask me if thy find it difficult !

Now, the fresh water bodies are depleting in quantities and quality too, hence cannot help much in this process. Seas and oceans seem to be one option of water source to keep the lands-water-air-moisture balance at par. Give sea levels are on rise.

Well, sea water is salty, scientists should find a way to remove salt in a cost-effective way, typically by evaporation process under green house or so. Once salt is separated, only thing that remains is to connect this to supplies and keep lands hydrated, and animals, birds and insects thirsts out.  The excess water, which anyways is increasing the sea levels, can be put back on land, to help in NATURAL EVAPORATION, natural cooling and raining processes. A lot of water may be needed for all this, and at regular intervals. Till the time sea levels are rising this solution may be used. This is a solution that I think of, there are many more solutions to this problem. Hence there wont be supplies problems in this solution as in my previous article on backwaters creation and tributaries constructions, which may take several years to be constructed.

The climatology scientists must study the effect of supplying water from sea to main lands can have on balancing these acts of imbalance between – the key contributors of climate change.  The effect of evaporation causing rain in this process may have on lands. And the controlled way to supply sea water to these places, to curb the rains, heating’s, low-and high pressures and even forest fires.

Well, yes there are other factors such as depletion of ozone layer etc., which have to be dealt separately.  Because there may not be a short-term cure for it, just prevention form further damage, as far as I understand. And there are disturbances below Earth surface which can be fixed in due course of time through right measure.

NLP for Social Good: Research Paper Review

Aim:

In this article I discuss a research paper by  Lin et al (2021) [1] named “How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact”.   

This paper presents an in-depth overview of research areas for NLP in 2020,  effect of research in NLP on targets for a better world and improved society. The analysis for choice of research topic for a productive NLP based application and technology development. Existing NLP  techniques and missing points are analysed too. Focus on Social Good from NLP based technologies.

My Analysis About Article:

 The following are key points as per my readings:

  1. The authors introduces various positive applications of NLP in recent years especially the challenging pandemic times, and how NLP helped us in it.  
  2. The negative impacts of NLP has also being mentioned, which includes bot using anonymous language in comments, privacy issues in various software’s using NLP engines and deep learning based gender and other bias.
  3. AI for social good and ethics in general in AI had been explored. United Nations (UN) sustainable development goals (SDGs) have also been touched in the research.
  4. Mathematical expression of the following has been expressed: “Which best technology can improve Social Impact?”
  5. Connection to answering social good has been linked to theories of philosophy along with their acceptance to the people in this domain (which are philosophers)
  6. ACL 2020, research papers have been analysed for getting the key NLP areas in trend, in which country, what topics and other statistics about the same.
  7. NLP technology development is divided in 4 specific tasks including (1)inception (2) model development (3) application and finally (4) deployment.
  8. Rest of the paper computes impact of contribution in a technology by a researcher.
  9. Finally, discussion has been made for development of deciding research priority given the targets- social impact, SDGs to mention a few.

My Comments:

The paper presents an in-depth analysis of the authors for NLP for social good and global challenges. They have also provided mathematical representation of the computation of suitability of a research topic as a viable topic for an improved socially impactable and relevant NLP research.  But the experiments related to these models were missing. Though statistics were presented for ACL 2020 papers, in a well-documented tabular form. Conclusions were drawn of what is missing from NLP research and how to include it, for instance, the gender bias in NLP tasks. Further, case studies were provided of several key NLP areas including Green NLP (which saves major energy resources in model development) to NLP for social media. Overall a good read.

References:

[1]. Jin, Z., Chauhan, G., Tse, B., Sachan, M., & Mihalcea, R. (2021). How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact. arXiv preprint arXiv:2106.02359.

What’s there in a poem

What’s there in a poem?

Few words, to say a lot

Which can twist the thoughts

And those that can bind the hearts

Mend the souls

And rejoice the sprits

…………

Do read my full poems in my poem books, will be available free on kindle for days from 1st July 2021 to 5th July 2021.

Here is the link to amazon book:

 Amazon.com: Short Book on Poems on Life, Positivity, Motivation, Hope, Desires, Meanings: Motivational Poem Book eBook: Yadav, Nidhika: Kindle Store

Book on Poems: Amazon.com: Collection of love filled poems on love, life, passion!: Poems on Love, Life eBook: Yadav, Nidhika: Kindle Store

Let you be envied, my dear

.

You are special

You are worthy

You are original,

Not easy to be duplicated

By anything

You are as unique as the lines in your hands

You are as special as the life itself

And as remarkable as you yourselves

.

Full poem in my poem book, will be available free on kindle for some days from 1st July 2021 to 5th July 2021.

Here is the link to amazon book:

 Amazon.com: Short Book on Poems on Life, Positivity, Motivation, Hope, Desires, Meanings: Motivational Poem Book eBook: Yadav, Nidhika: Kindle Store

It Pained Me and You Still Pain me- Not anymore!

It pained me, when you hurted me first

I still remember it, the pains you gave me

It pained me so badly,

When you hurted me again and again

Each time I gave

It pains in head

It pains in soul

It pains a lot

I am better without you…………………

Full poem here….Check out my book for full poem. Here!

Full poem in my poem book, will be available free on kindle from 1st July 2021 to 5th July 2021.

Here is the link to amazon book:

 Amazon.com: Short Book on Poems on Life, Positivity, Motivation, Hope, Desires, Meanings: Motivational Poem Book eBook: Yadav, Nidhika: Kindle Store

AI: Deep Learning for Semantic Similarity

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In this article I discuss an research paper by (Adrian Sanborn and  Jacek Skryzalin) [1] named “Deep Learning for Semantic Similarity”.   

Aim: Given two sentences or small text fragments, are they similar? If so, how much similar or dis-similar?

Technique:

The authors have proposed use of AI technique of deep learning in particular –Recurrent Neural Networks and Recursive Neural Networks. Recurrent Neural Network use the previous states in a learning mechanism. The model learned here is a non-linear function of previous states plus the new inputs. The semantic similarity model works by learning two set of words, one for each sentence. This is the learning or model building part. Deep Neural Networks require a considerable sized training data, each word here is represented by its word embedding. While in Recursive Neural Network based semantic similarity, a binary tree is fed into the model, the tree being the parse tree of the sentence.  The results obtained by the research were comprehended by authors as well in comparison to the constraints in the experimentations performed. Further, the similarity scores have been classified into six categories.

My Comments:

Semantic similarity can be used in various applications as suggested by authors as well. Once such a technique is well developed is becomes handy to compute the similarity between two comments on twitter, LinkedIn, Facebook or any social media platform. It can be used as a statistics called “statistics for comments” and can be helpful for both social media businesses and individuals too, especially those who gets lot of comments and want to get statistics of their comments, not just number of likes and dislikes. 

References

[1] Sanborn, A., & Skryzalin, J. (2015). Deep learning for semantic similarity. CS224d: Deep Learning for Natural Language Processing Stanford, CA, USA: Stanford University.