July 06, 2019

Ex-R&AW officers want PM to act against Hamid Ansari’s ‘anti-national’ acts


Abhinandan MishraPublished : July 6, 2019, 6:40 pm |Updated : July 7, 2019, 5:29 AM

New Delhi: Former Research and Analysis Wing (R&AW) officers had sought an inquiry against former Indian Vice President Hamid Ansari for what they have called “damaging R&AW operations” while he was posted as Ambassador in Tehran, Iran. They now hope that Prime Minister Narendra Modi will get into the truth of the entire matter. These officers, who were posted in Tehran during Ansari’s tenure, had first approached the PM in August 2017. Ansari was posted in Tehran from 1990-1992.

In their complaint to the Prime Minister, these officers have claimed that Ansari, while being posted in Tehran, “not only failed to protect India’s national interest, but cooperated with the Iranian government and its intelligence agency SAVAK to cause serious dent to R&AW and its operations. According to them, there were four major incidents when Indian Embassy officials, diplomats were kidnapped by SAVAK and Ansari deliberately failed in his duties to protect India’s interest.

One of the officers, N.K. Sood, who retired from the agency in 2010, told The Sunday Guardian that Ansari even went to the extent of recommending the closing down of R&AW stations in Iran.

Sood listed multiple instances which showed that Ansari, during his tenure in Tehran, did not fulfill his duty as was expected from him.

In May 1991, one Indian official, Sandeep Kapoor, was kidnapped from the Tehran airport, ostensibly by SAVAK. When the issue was brought before Ansari, he played it down despite the R&AW station chief—who was in Dubai when the incident took place, but flew back considering the emergency situationbriefing him personally on the matter. “Ansari did not take any steps to trace Kapoor, but sent a confidential report to the MEA that Kapoor was missing and that his activities were suspected in Iran as he was said to be involved with some local woman. He deliberately failed to mention that R&AW had reported about involvement of SAVAK in this case,” Sood said.

Three days later, an anonymous phone call to the Indian Embassy informed the receiver that Kapoor is lying at a particular place on the road side. He was drugged, the effects of which lasted for several years. Despite R&AW’s advise to report and lodge a protest with the Iranian foreign office, Ansari did not take any action.

In August 1991, R&AW was keeping eyes on Kashmiri youths who were regularly visiting Qom, a religious center of Iran, and were taking arms training. Despite the old R&AW staff advising him not to do so, the new station chief of R&AW told Ansari about his operation. “Ansari gave the name of the officer who was handling this operation, D.B. Mathur, to the Iranian Foreign office, who passed it to SAVAK, and Mathur was picked up by them on a morning while coming to the Indian embassy. By the evening, it was clear that he has been picked up by SAVAK,” Sood recalled.

This incident has also been mentioned in the letter that has been shared with Prime Minister Modi. When Ansari refused to take any concrete action, apart from registering a missing report about Mathur with the Iranian Foreign office and sharing it with Delhi without mentioning that he was likely to be picked by SAVAK, the R&AW officers, on the second day, through a scene out of a spy movie, managed to inform Atal Bihari Vajpayee in Delhi, who then told this to P.V. Narasimha Rao, the then Prime Minister, which led to the release of Mathur from Evin prison, where he was kept, on the fourth day of his kidnapping, but he was given 72 hours to leave the country. Once inside the Indian Embassy, Mathur disclosed what had happened to him and how the SAVAK was already aware of the identity of Sood and the station chief, which the letter says can be attributed to Ansari sharing it with the Iranian Foreign Office.

These officers believe and are extremely hopeful that PM Modi will order a thorough probe into this issue, which damaged India’s strategic capabilities deeply.

The Sunday Guardian also reached out to the office of V.P. Ansari through emails, seeking his response on the charges leveled by the R&AW officers. However, no response was received till the time of the story going to press.

Sood also recalled about an incident involving Mohammad Umar, a security guard posted at the residence of the Indian Ambassador, who was picked by the SAVAK and released after three hours. When Ansari was informed about it, he asked the R&AW station chief to ascertain the facts as he believed that he was “turned” by SAVAK. However, despite the R&AW clearly stating that he was innocent, he was sent back to India with Ansari recommending that he should be barred from foreign postings.

Perhaps the most glaring humiliation that the R&AW suffered in those times, as has been mentioned in the letter, is the beating up of P.K. Venugopal, the R&AW station chief by SAVAK, before being let off. The station chief was picked up while he was on a sightseeing tour with an Iranian woman. Ansari never lodged a complaint with the Iranian authorities. Later, Ansari recommended his recall from Iran and he was later dismissed from service. However, this is where things became interesting. She wanted an Indian visa to meet Venugopal, which was resisted by R&AW; however, Ansari went ahead and gave her the visa.

The letter also mentions how a First Secretary, who was posted in Iran for 10 years, was charging US$ 500 from people who claimed to be students and gave them Indian visa. An inquiry done by R&AW revealed that the recommendation letters allegedly issued by the Indian universities on the basis of which the Iranian nationals were being given visa, did not exist. Despite R&AW putting this in writing in front of Ansari, nothing happened and the matter was forgotten.

The letter mentions about Ansari’s regular and long meetings with Pakistan Ambassador in Tehran, which were not reported to the MEA.

It further goes on to talk about how R&AW’s operations, which were defensive in nature, were kept on hold because of Ansari’s order. “Ansari also brought other Indian Ambassadors in Dubai, Bahrain, Saudi Arabia to target R&AW units in their respective missions. At the time of the Bombay blasts, the R&AW’s capabilities vis-a-vis Gulf countries were in total disarray,” the letter reads.

Sood told The Sunday Guardian that when Ansari was transferred from Iran in mid-1993, there were celebrations in the Indian Embassy.

Sood has asked for a detailed inquiry into why Ansari failed to take proper and appropriate actions when staff members in Tehran were being kidnapped, the role of Ansari in the fake visa being given to Iranian students and the role played by Ansari in damaging R&AW’s operations in Tehran and other Gulf countries.


https://www.sundayguardianlive.com/news/ex-raw-officers-want-pm-act-hamid-ansaris-anti-national-acts

In Modi’s Rule, Rich Have Become Richer, Poor are Poorer

In Modi’s Rule, Rich Have Become Richer, Poor are Poorer

More than half of the country’s wealth is now owned by the richest 1% while over three quarters is owned by the top 10%.

Subodh Varma

20 Oct 2018

There is much celebratory high-fiving among the elite of the country at the latest Global Wealth Report 2018 published by Credit Suisse, a Switzerland based investment bank. It says that India now has 343 dollar-millionaires, that is, those who have wealth of more than a million dollars. That’s roughly about Rs.7 crore.

But the same report also has this chilling statistic: nearly 52% of the total wealth of the country is now controlled by the top 1% of the population while the remaining 99% have to be content with just over 48% of the wealth.

Strikingly, this enormous imbalance in wealth distribution – otherwise known as inequality – has worsened since the BJP government led by Narendra Modi came to power in 2014. In that year, the shares were reverse with the top 1% owning about 49% and the rest 99% owning 51% of the wealth, as per the 2014 Credit Suisse report.

In case you think the difference is not much, here is another way of looking at it: while dollar-millionaires increased from 0.02% of the population to 0.04% under Modi’s rule, the number of adults owning wealth less than Rs.7 lakh ($10,000) decreased from 94.5% to 90.8% of the population. As a result, there are still over 77 crore adults who own less than Rs.7 lakh of wealth, out of a total number of adults estimated by the report at about 85 crore.

What this report shows is that a very very small minority of adults – a handful, really – have gained enormously in the past five years while the rest of the people, especially the poor, have become poorer or at best remained as they were.

This fact is also reflected in a measure of inequality called the Gini coefficient. India’s Gini coefficient has worsened from 81.4% in 2014 to 85.4% in 2018. Note that a Gini coefficient of 100% represents ‘perfect’ inequality while 0 represents no inequality.

All these numbers relate to wealth not income. It represents all the wealth that the individual may have accumulated or inherited over the years. Wealth may consist of financial assets (like shares and bonds), non-financial assets (land, house) or debt. Wealth is related to income in the sense that if you have high income, you will be accumulating wealth because you cannot spend all that you earn. Conversely, a poor person will spend most of his or her income on current consumption – food, rent, transport, etc. – and end up not adding any wealth. Credit Suisse does not track incomes in this report.

Incomes are tracked by another database called the World Inequality Database (WID), which recently reported that, in India, average inflation adjusted income of the bottom 50% of the population was just Rs.45,000 per year per adult compared with Rs.33 lakh per year per adult for the top 1% of the population.

This staggering chasm of incomes between the haves and the havenots in India is complementary to the similar chasm in wealth. The latter is perpetuated by the former – in fact, wealth inequality is growing because of this income inequality.

These revelations decisively remove any doubts about the way the Modi government  has managed the country – it has benefitted the corporate entities and big property owners while squeezing the poor mercilessly. It is possible that some sections of the middle class may have gained some income increments or their wealth may have increased because of property prices or stock market gambling. But the bitter truth remains that Modi has betrayed his biggest, most lucrative promise – that of achhe din (good days) coming soon.


https://www.newsclick.in/modis-rule-rich-have-become-richer-poor-are-poorer

July 05, 2019

In Modi’s Rule, Rich Have Become Richer, Poor are Poorer

More than half of the country’s wealth is now owned by the richest 1% while over three quarters is owned by the top 10%.

Subodh Varma

20 Oct 2018

There is much celebratory high-fiving among the elite of the country at the latest Global Wealth Report 2018 published by Credit Suisse, a Switzerland based investment bank. It says that India now has 343 dollar-millionaires, that is, those who have wealth of more than a million dollars. That’s roughly about Rs.7 crore.

But the same report also has this chilling statistic: nearly 52% of the total wealth of the country is now controlled by the top 1% of the population while the remaining 99% have to be content with just over 48% of the wealth.

Strikingly, this enormous imbalance in wealth distribution – otherwise known as inequality – has worsened since the BJP government led by Narendra Modi came to power in 2014. In that year, the shares were reverse with the top 1% owning about 49% and the rest 99% owning 51% of the wealth, as per the 2014 Credit Suisse report.

In case you think the difference is not much, here is another way of looking at it: while dollar-millionaires increased from 0.02% of the population to 0.04% under Modi’s rule, the number of adults owning wealth less than Rs.7 lakh ($10,000) decreased from 94.5% to 90.8% of the population. As a result, there are still over 77 crore adults who own less than Rs.7 lakh of wealth, out of a total number of adults estimated by the report at about 85 crore.

What this report shows is that a very very small minority of adults – a handful, really – have gained enormously in the past five years while the rest of the people, especially the poor, have become poorer or at best remained as they were.

This fact is also reflected in a measure of inequality called the Gini coefficient. India’s Gini coefficient has worsened from 81.4% in 2014 to 85.4% in 2018. Note that a Gini coefficient of 100% represents ‘perfect’ inequality while 0 represents no inequality.

All these numbers relate to wealth not income. It represents all the wealth that the individual may have accumulated or inherited over the years. Wealth may consist of financial assets (like shares and bonds), non-financial assets (land, house) or debt. Wealth is related to income in the sense that if you have high income, you will be accumulating wealth because you cannot spend all that you earn. Conversely, a poor person will spend most of his or her income on current consumption – food, rent, transport, etc. – and end up not adding any wealth. Credit Suisse does not track incomes in this report.

Incomes are tracked by another database called the World Inequality Database (WID), which recently reported that, in India, average inflation adjusted income of the bottom 50% of the population was just Rs.45,000 per year per adult compared with Rs.33 lakh per year per adult for the top 1% of the population.

This staggering chasm of incomes between the haves and the havenots in India is complementary to the similar chasm in wealth. The latter is perpetuated by the former – in fact, wealth inequality is growing because of this income inequality.

These revelations decisively remove any doubts about the way the Modi government  has managed the country – it has benefitted the corporate entities and big property owners while squeezing the poor mercilessly. It is possible that some sections of the middle class may have gained some income increments or their wealth may have increased because of property prices or stock market gambling. But the bitter truth remains that Modi has betrayed his biggest, most lucrative promise – that of achhe din (good days) coming soon.


https://www.newsclick.in/modis-rule-rich-have-become-richer-poor-are-poorer

The Unrealistic Optimism of Indians on Prospects of Upward Social Mobility

ECONOMY

To rectify this gap between perception and reality, inequality of opportunities and the lack of emphasis on primary education need to feature more prominently in Indian policy discussions.

Ranjan Ray

ECONOMY

EDUCATION

LABOUR

POLITICAL ECONOMY

14/JUN/2019

We are constantly reminded of how Narendra Modi started from humble beginnings by selling tea and rose to occupy the position of the prime minister of India. Irrespective of which side of the political divide one is on, there is no disputing the fact that this is an incredible trajectory and an impressive achievement.

Modi is, of course, not alone since Margaret Thatcher, born into a family of grocery shop owners, assumed the office of the prime minister of the UK. By sheer coincidence though, both Modi and Thatcher rose to head their respective governments as leaders of right-wing parties wedded to free enterprise and the protection of privileges handed down from one generation to another.

Though Modi and Thatcher’s achievements are isolated and extreme examples, they nevertheless have encouraged the perception that there are no barriers to upward mobility in their respective countries. What is the evidence for this?

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While in recent years there has been considerable attention paid by economists such as François Bourguignon, Thomas Piketty, Branko Milanović, Joseph Stiglitz and others to the topic of income inequality, the issue of inequality of opportunities associated with the lack of social mobility has not figured as prominently in the discussions. Yet, inequality of income is a result of inequality of opportunities which is linked largely but not exclusively to one’s ‘accident of birth’.

By focussing on income, which is an output, and not on the range of inputs that generate the income, we are in danger of overlooking the source of the problem. There was an early recognition of this in the Human Development Report (HDR), 1990 that introduced the measure, Human Development Index (HDI), used to rank countries.

HDI downplayed the exclusive role played by income in per capita income based comparisons by reducing its weightage from one to a third and bringing in education – which is widely regarded as one of the most effective means of achieving upward mobility.

Also read: Cost of Inequality in India May Be High, but Doing Nothing About It Is More Expensive

Though HDI has recently been extended to the more sophisticated concept of the multi-dimensional poverty index (MPI) in HDR, 2010, we are yet to see a satisfactory measure of social mobility of socioeconomic groups embedded in such measures that are used in international comparisons.

The gap between perception and reality

What then is the evidence for social mobility? There are of course several aspects to ‘social mobility’. In this piece, we restrict the discussion to ‘intergenerational social mobility’ on the back of mobility between jobs and income advancement from one generation to another, though in the case of Modi rising from a tea seller to the Indian prime minister – the mobility is within one generation.

Before turning to India, let us examine some data from international statistics on economic mobility. Some of the evidence is both surprising and interesting. In a report, produced by Julia Isaacs from the Brookings Institution, the ‘land of opportunities’ – the US – ranks quite low in social mobility amongst the affluent countries based on the correlation between father’s and son’s income.

In a list of 27 affluent countries grouped into ‘low’, ‘middle’ and ‘high’ mobility countries, both the US and Thatcher’s UK fall into the ‘low mobility’ category. In the author’s words, “the earnings of American men are more closely tied to the earnings of their fathers than are those of men in other countries”.

This is in spite of the fact that this report also found that ‘Americans are more optimistic than others about their chances of getting ahead’.

High-rise residential towers under construction are pictured behind an old residential building in central Mumbai. Photo: Vivek Prakash/Reuters

This was confirmed in a report in the Economist that ‘Americans overestimate social mobility in their country’. Perception couldn’t be further from reality. A recent report in the Guardian found that social mobility in the UK has been virtually stagnant since 2014. At the other end, Canada, Norway, Finland and Denmark fall into the category of ‘high mobility countries’ where “parental earnings had the least effect on sons’ earnings” and ‘less than 20% of income advantages are passed onto children’.

To add to the mismatch between perception and reality, the same report also found that ‘in Europe climbing the ladder is easier than most people believe’. It is perhaps significant that the Scandinavian countries belonging to the ‘high mobility’ group are among those with the lowest income inequality on a 2018 international ranking based on Gini inequality.

Social mobility in India

Let us now turn to India. According to a recent survey by the World Economic Forum in 27 countries,  India was the top-ranked country graded on the residents’ belief in upward social mobility – ‘Indians, more than any other nationality, believe it is common for someone in their country to start poor, work hard, and ultimately become rich’.

Also read: As India’s Inequality Spirals, How can the Government Step in and Intervene?

India is in good company in this misperception since it is closely followed by Saudi Arabia, Pakistan and Egypt. The mismatch between perception and reality is also evident in the finding that India is among the top countries that believe that people have access to quality education, though the UN Human Development Index for 2017, ranks India at a low 132 among 189 countries when it comes to the years of schooling received by individuals in the country. An average Indian, with 12 years of schooling, is unlikely to have a college degree.

What is the evidence for social mobility in India?  Unfortunately, the lack of jobs and income data from different generations of the same family have forced researchers to base their evidence on education as measured by years of schooling received by successive generations in the same family. As in the US and UK, perception is at odds with reality in India.

In a recent study by US-based researchers, Paul Novosad, Charlie Rafkin and Sam Asher, the authors found that “India has seen little change in the rate of upward mobility since Independence…Indians born in the 1980s have only about as much chance of outstripping their parents in socioeconomic rank as those born in the 1950s”.

This report also highlights some interesting findings on the differences between socioeconomic groups in India on social mobility. Contrary to the widely held belief, Dalits and Adivasis have seen some improvement, the scheduled castes have seen considerable improvement, the forward castes and the OBCs were in the same situation as they were in the 1950s, but the group for whom the upward mobility has fallen significantly has been Muslims.

Since the early 1980s, India’s growth has been unevenly distributed within the top 10% group. Photo: Reuters/Vijay Mathur

The last feature of this study is quite significant since while the plight of the Muslims under the Modi regime has attracted considerable media attention, in fairness to the present regime, Muslims have fared poorly on social mobility under all the regimes.

There has been a lack of recognition under all the previous regimes including UPA-I and UPA-II of the plight of the religious minorities and the absence of concerted policy action in breaking the intergenerational ink in their socio-economic backwardness.

What is also quite revealing is that ‘while Muslims are the least upwardly mobile group, they are not the worst in terms of living standards. As per data from the National Sample Survey, Muslims are above Dalits and Adivasis in metrics such as wages, consumption and income’. This calls for different policy initiatives at improving the plight of the Dalits, Adivasis and Muslims recognising the heterogeneity in their circumstances. It is not helpful to brand these groups together as ‘marginalised communities’.

Also read: In Terms of Educational Mobility, India’s Muslims Worse off Than African-Americans

The same report also draws attention to sharp spatial disparities in social mobility in India. North India does quite badly, the worst performer being Bihar, but Gujarat does not do all that well either. In contrast, the Southern states of Kerala and Tamil Nadu do quite well.

At a time when we are obsessed with growth rates, the Gujarat experience is that of “a state with very high economic growth but relatively low mobility”. Clearly, the economic performance of Gujarat has done relatively little for Gujaratis. An equally significant result is that liberalisation has not improved upward mobility in India.

Policy lessons

There are, principally, three messages that follow from this discussion. First, the focus needs to shift from inequality of income to inequality of opportunities. A key aspect of the latter is primary education which has been a great promoter of upward social mobility. Yet, primary education has not featured much in Indian policy discussions.

According to the Legatum Prosperity Index, India fares quite poorly in primary education, ranking a lowly 92 in a list of 145 countries. Yet, one of the first policy pronouncements of the re-elected Modi government has been to set up 100 ‘centres of eminence’, when what we need to focus on is in improving the dire state of primary schooling in India. Our neighbour, China, is featuring increasingly in international lists of high quality universities and research institutes and it has been able to do that on the back of significant improvements in primary and secondary schooling.

Second, we need to have better data for keeping a track on social mobility by providing information on the education levels of, and income earned by, members of the same family from different generations. The recent decision to merge the CSO and the NSSO is a backward step, in this regard, since the NSSO is uniquely placed to provide such information through its household surveys, a feature that may be lost in the merger.

Finally, the a-temporal cross-sectional comparisons between households need to be supplemented by intergenerational comparisons between the same families. Unlike comparable emerging economies, such as China, Indonesia and Vietnam, India lacks panel data that can allow researchers to track household welfare over time. To allow increased intertemporal comparisons of inequality of opportunities, it is vital to have panel data covering a host of information.

Also read: Global Inequality Is Getting Worse, but Fewer People Than Ever Are Aware of It

Let us recognise the promotion of social mobility as a key policy objective and the importance of good schools and teachers in facilitating upward social mobility in backward communities. As concerns mount over rising income inequality, it is necessary to go behind the symptom and address the core issue of inequality of opportunities.

As India seeks upward mobility in the international income ladder, she must make every effort to achieve upward social mobility in the domestic ladder as well.

Let the rise of Modi from tea seller to PM act as a catalyst for this.

Ranjan Ray is a professor of economics at Monash University.

https://thewire.in/economy/india-upward-social-mobility-inequality-economy-education

Pro-Independence leader Dr. Allah Nizar Baloch’s open letter to US President Donald Trump

Pro-Independence leader Dr. Allah Nizar Baloch’s open letter to US President Donald Trump

AN OPEN LETTER TO PRESIDENT DONALD TRUMP

Dear Mr. Trump,

Imagine what would have become of what is now the United States if in 1776 France and other European powers had designated George Washington and others as terrorists and joined the United Kingdom to suppress the settlers. The United States would have remained a colony rather than becoming the beacon of democracy and progress.

If George Washington was not a terrorist, neither are the Baloch.

We, the Baloch, have been resisting Pakistan’s state terrorism with knives and Kalashnikovs for the last seven decades. Armed with the most sophisticated weaponry, courtesy of U.S. military aid, Pakistan military has kidnapped, tortured and murdered thousands of Baloch. They trade the organs of our dead, rape our women and burn our villages.

Dear Mr. Trump,

The fighter jets and other weaponry which Pakistan is using against the Baloch civilians have been provided by your predecessors. But you’ve outdone even them. Your administration branded us terrorists. Because we are not ready to let Pakistan and China plunder our resources? Well, we won’t. Even if your administration branded every single Baloch as a terrorist, we would keep fighting with our knives and Kalashnikovs.

It’s a shame that the champions of a secular and liberal world have failed to extend a helping hand to the Baloch, the only people in the region successfully resisting religious extremism.

Dear Mr. Trump

What does the world expect from us? You fight bitter wars for every extra cent and we are expected to remain silent even as our gold, copper, gas, are looted with both hands. Our port line is being used to build someone else's empire. Yet, we are the terrorists.

What shocks me the most is the fact that we have never hurt a single US citizen, and yet we get banned by your administration. Maybe the ban is aimed at luring the Taliban to the negotiation table, or whatever. It’s part of the politics. I understand.

Nevertheless, the world leader shouldn’t be compromising on secular values for such petty gains. Once you begin sidelining natural allies like the Baloch for petty interests, your enemies would sideline you in no time. There is no problem with your desire for making friends with Pakistan, but do not compromise on your basic values and the essence of your country’s constitution.

Dear Mr. Trump

Let me end this letter by telling you what I like most about your country. I love the sense of resistance among the American people. The U.S. citizens have shown to the world – once in 1941 and then in 2001 – that they do not like to be killed.

Do you know what I like about the Baloch? They don’t like to be killed either.

Yours Truly

Dr. Allah Nizar Baloch - a freedom fighter from Balochistan

Date: July 5, 2019  http://sangarpublication.com/home/page/1086.html

July 04, 2019

Toiling for Telia Rumaal in Hyderabad


It takes over a month to create Telia Rumaal, and with the help of Department of Handloom and Textiles and TSCO, the weavers have already begun the work

Published: 03rd July 2019 10:30 PM  |   Last Updated: 04th July 2019 09:36 AM  

A worker works on Telia Rumaal

By Srividya Palaparthi

Express News Service

HYDERABAD: A month ahead of the National Handloom Day, hardly any of us think about what the ancient art. The Department of Handloom and Textile, Telangana State Handloom Weavers Cooperative Society (TSCO) and weavers have been toiling to bring together the show that is promised.

Shailaja Rama Iyer, the Managing Director of TSCO gives us a peek into what has been going on behind the scenes for the celebration of the National Handloom Day coming up in August. “Government of Telangana has been actively promoting and celebrating Handloom Day since two years. They have been using this day as the foundation to promote handloom particularly among the youth,” says Shailaja.

Featuring a significant part of the Ikat tradition, Telia Rumaal this year, the DHT and TSCO don’t want to leave any stone unturned. She laments, “This is an ancient craft and not many of us have even heard of it. It was a form that originated in Chirala and then the weavers of course migrated to Pochampalli and Puttapaka. There are barely any weavers left who are working on this process. And it is our responsibility to bring back their glory.”

Telia Rumaal involves soaking the yarn in castor oil up until the fabric completely absorbs the oil. It is then dyed, designed, and weaved into rumaals traditionally. However, these fabrics have also been made into other garments over the years. “Because they were infused with castor oil, they made for a perfect headdress in hot regions. They kept you cool,” Shailaja adds.

For a batch of rumaals to be completed might take well over a week and add to that the dying, weaving processes, it would take over a month to get the garments ready.  Featuring the Telia Rumaal this year for Handloom Day, TSCO wants to revive this ancient art as well. “The main motive of Handloom Day is to promote the craft and create a branding for these various looms. By drawing the attention towards them through promotion, ambassadors and celebrations like these, it penetrates the mind of the general public and influences them to opt for handloom which is more eco-friendly, sustainable and also fashionable,” she elaborates.

The motive is not only to promote the craft but also the effort that goes into it. She adds, “Unless the process is explained and the effort of the weavers is put forth, the consumers will not know the worth of what they are getting. It also increases the value of the weavers, their products and their dignity as well.”    

Shailaja Rama Iyer also points out that there are barely one or two families that still know the accurate process of Telia Rumaal that has been passed down from generations. “These families have been holding on to the craft and keeping it alive. We have convinced them to share the knowledge with other weavers and handloom workers so that it grows instead of going extinct. They too have been accommodating of the same as they also believe it is an art form that needs to be passed down,” she shares. 

While the regular urban consumers returning towards handloom for the aggressive branding that the Telangana Government has been doing for the same, it was a conscious decision to use and promote handloom through influencers. “KTR himself wears handloom and makes sure he suggests anyone influential person he meets to try out handloom. Mahesh Babu was one such person who also took to handloom after that. Samantha Ruth Prabhu was the handloom ambassador for a while and it drew a lot of attention to the weavers and their work.

Such promotion helps not only benefit the small businesses but also the weavers quality of life.” shares Shailaja. She also shares that the Government has been actively providing schemes and subsidies for weavers to ease their effort. “The Government provides 40 per cent subsidies on the yarn that weavers have to purchase. And these subsidies not only accommodate master weavers but also the ancillary workers. Similarly, we have been training handloom workers in dying, weaving and other processes which help them in their businesses.”

This year, before holding an exhibition by the weavers for the Handloom Day, Rina Singh and six other designers will showcase their work in handloom to promote sustainable, eco-friendly fashion.

The writer can be contacted at  srividya.palaparthi@newindianexpress.com 

Twitter- @PSrividya53

July 03, 2019

The 10 Best Machine Learning Algorithms for Data Science Beginners

The 10 Best Machine Learning Algorithms for Data Science Beginners

Source: 

https://www.dataquest.io/blog/top-10-machine-learning-algorithms-for-beginners/

Interest in learning machine learning has skyrocketed in the years since Harvard Business Reviewarticle named ‘Data Scientist’ the ‘Sexiest job of the 21st century’. But if you’re just starting out in machine learning, it can be a bit difficult to break into. That’s why we’re rebooting our immensely popular post about good machine learning algorithms for beginners.

(This post was originally published on KDNuggets as The 10 Algorithms Machine Learning Engineers Need to Know. It has been reposted with permission, and was last updated in 2019).

This post is targeted towards beginners. If you’ve got some experience in data science and machine learning, you may be more interested in this more in-depth tutorial on doing machine learning in Python with scikit-learn, or in our machine learning courses, which start here.

Machine learning algorithms are programs that can learn from data and improve from experience, without human intervention. Learning tasks may include learning the function that maps the input to the output, learning the hidden structure in unlabeled data; or ‘instance-based learning’, where a class label is produced for a new instance by comparing the new instance (row) to instances from the training data, which were stored in memory. ‘Instance-based learning’ does not create an abstraction from specific instances.

Types of Machine Learning Algorithms

There are 3 types of machine learning (ML) algorithms:

Supervised Learning Algorithms:

Supervised learning uses labeled training data to learn the mapping function that turns input variables (X) into the output variable (Y). In other words, it solves for f in the following equation:

Y = f (X)

This allows us to accurately generate outputs when given new inputs.

We’ll talk about two types of supervised learning: classification and regression.

Classification is used to predict the outcome of a given sample when the output variable is in the form of categories. A classification model might look at the input data and try to predict labels like “sick” or “healthy.”

Regression is used to predict the outcome of a given sample when the output variable is in the form of real values. For example, a regression model might process input data to predict the amount of rainfall, the height of a person, etc.

The first 5 algorithms that we cover in this blog – Linear Regression, Logistic Regression, CART, Naïve-Bayes, and K-Nearest Neighbors (KNN) — are examples of supervised learning.

Ensembling is another type of supervised learning. It means combining the predictions of multiple machine learning models that are individually weak to produce a more accurate prediction on a new sample. Algorithms 9 and 10 of this article — Bagging with Random Forests, Boosting with XGBoost — are examples of ensemble techniques.

Unsupervised Learning Algorithms:

Unsupervised learning models are used when we only have the input variables (X) and no corresponding output variables. They use unlabeled training data to model the underlying structure of the data.

We’ll talk about three types of unsupervised learning:

Association is used to discover the probability of the co-occurrence of items in a collection. It is extensively used in market-basket analysis. For example, an association model might be used to discover that if a customer purchases bread, s/he is 80% likely to also purchase eggs.

Clustering is used to group samples such that objects within the same cluster are more similar to each other than to the objects from another cluster.

Dimensionality Reduction is used to reduce the number of variables of a data set while ensuring that important information is still conveyed. Dimensionality Reduction can be done using Feature Extraction methods and Feature Selection methods. Feature Selection selects a subset of the original variables. Feature Extraction performs data transformation from a high-dimensional space to a low-dimensional space. Example: PCA algorithm is a Feature Extraction approach.

Algorithms 6-8 that we cover here — Apriori, K-means, PCA — are examples of unsupervised learning.

Reinforcement learning:

Reinforcement learning is a type of machine learning algorithm that allows an agent to decide the best next action based on its current state by learning behaviors that will maximize a reward.

Reinforcement algorithms usually learn optimal actions through trial and error. Imagine, for example, a video game in which the player needs to move to certain places at certain times to earn points. A reinforcement algorithm playing that game would start by moving randomly but, over time through trial and error, it would learn where and when it needed to move the in-game character to maximize its point total.

Quantifying the Popularity of Machine Learning Algorithms

Where did we get these ten algorithms? Any such list will be inherently subjective. Studies such as these have quantified the 10 most popular data mining algorithms, but they’re still relying on the subjective responses of survey responses, usually advanced academic practitioners. For example, in the study linked above, the persons polled were the winners of the ACM KDD Innovation Award, the IEEE ICDM Research Contributions Award; the Program Committee members of the KDD ’06, ICDM ’06, and SDM ’06; and the 145 attendees of the ICDM ’06.

The top 10 algorithms listed in this post are chosen with machine learning beginners in mind. They are are primarily algorithms I learned from the ‘Data Warehousing and Mining’ (DWM) course during my Bachelor’s degree in Computer Engineering at the University of Mumbai. I have included the last 2 algorithms (ensemble methods) particularly because they are frequently used to win Kaggle competitions.

Without Further Ado, The Top 10 Machine Learning Algorithms for Beginners:

1. Linear Regression

In machine learning, we have a set of input variables (x) that are used to determine an output variable (y). A relationship exists between the input variables and the output variable. The goal of ML is to quantify this relationship.

Figure 1: Linear Regression is represented as a line in the form of y = a + bx. Source

In Linear Regression, the relationship between the input variables (x) and output variable (y) is expressed as an equation of the form y = a + bx. Thus, the goal of linear regression is to find out the values of coefficients a and b. Here, a is the intercept and b is the slope of the line.

Figure 1 shows the plotted x and y values for a data set. The goal is to fit a line that is nearest to most of the points. This would reduce the distance (‘error’) between the y value of a data point and the line.

2. Logistic Regression

Linear regression predictions are continuous values (i.e., rainfall in cm), logistic regression predictions are discrete values (i.e., whether a student passed/failed) after applying a transformation function.

Logistic regression is best suited for binary classification: data sets where y = 0 or 1, where 1 denotes the default class. For example, in predicting whether an event will occur or not, there are only two possibilities: that it occurs (which we denote as 1) or that it does not (0). So if we were predicting whether a patient was sick, we would label sick patients using the value of 1 in our data set.

Logistic regression is named after the transformation function it uses, which is called the logistic function h(x)= 1/ (1 + ex). This forms an S-shaped curve.

In logistic regression, the output takes the form of probabilities of the default class (unlike linear regression, where the output is directly produced). As it is a probability, the output lies in the range of 0-1. So, for example, if we’re trying to predict whether patients are sick, we already know that sick patients are denoted as 1, so if our algorithm assigns the score of 0.98 to a patient, it thinks that patient is quite likely to be sick.

This output (y-value) is generated by log transforming the x-value, using the logistic function h(x)= 1/ (1 + e^ -x) . A threshold is then applied to force this probability into a binary classification.

Figure 2: Logistic Regression to determine if a tumor is malignant or benign. Classified as malignant if the probability h(x)>= 0.5. Source

In Figure 2, to determine whether a tumor is malignant or not, the default variable is y = 1 (tumor = malignant). The x variable could be a measurement of the tumor, such as the size of the tumor. As shown in the figure, the logistic function transforms the x-value of the various instances of the data set, into the range of 0 to 1. If the probability crosses the threshold of 0.5 (shown by the horizontal line), the tumor is classified as malignant.

The logistic regression equation P(x) = e ^ (b0 +b1x) / (1 + e(b0 + b1x))can be transformed into ln(p(x) / 1-p(x)) = b0 + b1x.

The goal of logistic regression is to use the training data to find the values of coefficients b0 and b1 such that it will minimize the error between the predicted outcome and the actual outcome. These coefficients are estimated using the technique of Maximum Likelihood Estimation.

3. CART

Classification and Regression Trees (CART) are one implementation of Decision Trees.

The non-terminal nodes of Classification and Regression Trees are the root node and the internal node. The terminal nodes are the leaf nodes. Each non-terminal node represents a single input variable (x) and a splitting point on that variable; the leaf nodes represent the output variable (y). The model is used as follows to make predictions: walk the splits of the tree to arrive at a leaf node and output the value present at the leaf node.

The decision tree in Figure 3 below classifies whether a person will buy a sports car or a minivan depending on their age and marital status. If the person is over 30 years and is not married, we walk the tree as follows : ‘over 30 years?’ -> yes -> ’married?’ -> no. Hence, the model outputs a sports car.

Figure 3: Parts of a decision tree. Source

4. Naïve Bayes

To calculate the probability that an event will occur, given that another event has already occurred, we use Bayes’s Theorem. To calculate the probability of hypothesis(h) being true, given our prior knowledge(d), we use Bayes’s Theorem as follows:

P(h|d)= (P(d|h) P(h)) / P(d)

where:

P(h|d) = Posterior probability. The probability of hypothesis h being true, given the data d, where P(h|d)= P(d1| h) P(d2| h)….P(dn| h) P(d)P(d|h) = Likelihood. The probability of data d given that the hypothesis h was true.P(h) = Class prior probability. The probability of hypothesis h being true (irrespective of the data)P(d) = Predictor prior probability. Probability of the data (irrespective of the hypothesis)

This algorithm is called ‘naive’ because it assumes that all the variables are independent of each other, which is a naive assumption to make in real-world examples.

Figure 4: Using Naive Bayes to predict the status of ‘play’ using the variable ‘weather’.

Using Figure 4 as an example, what is the outcome if weather = ‘sunny’?

To determine the outcome play = ‘yes’ or ‘no’ given the value of variable weather = ‘sunny’, calculate P(yes|sunny) and P(no|sunny) and choose the outcome with higher probability.

->P(yes|sunny)= (P(sunny|yes) * P(yes)) / P(sunny) = (3/9 * 9/14 ) / (5/14) = 0.60

-> P(no|sunny)= (P(sunny|no) * P(no)) / P(sunny) = (2/5 * 5/14 ) / (5/14) = 0.40

Thus, if the weather = ‘sunny’, the outcome is play = ‘yes’.

5. KNN

The K-Nearest Neighbors algorithm uses the entire data set as the training set, rather than splitting the data set into a training set and test set.

When an outcome is required for a new data instance, the KNN algorithm goes through the entire data set to find the k-nearest instances to the new instance, or the k number of instances most similar to the new record, and then outputs the mean of the outcomes (for a regression problem) or the mode (most frequent class) for a classification problem. The value of k is user-specified.

The similarity between instances is calculated using measures such as Euclidean distance and Hamming distance.

Unsupervised learning algorithms

6. Apriori

The Apriori algorithm is used in a transactional database to mine frequent item sets and then generate association rules. It is popularly used in market basket analysis, where one checks for combinations of products that frequently co-occur in the database. In general, we write the association rule for ‘if a person purchases item X, then he purchases item Y’ as : X -> Y.

Example: if a person purchases milk and sugar, then she is likely to purchase coffee powder. This could be written in the form of an association rule as: {milk,sugar} -> coffee powder. Association rules are generated after crossing the threshold for support and confidence.

Figure 5: Formulae for support, confidence and lift for the association rule X->Y.

The Support measure helps prune the number of candidate item sets to be considered during frequent item set generation. This support measure is guided by the Apriori principle. The Apriori principle states that if an itemset is frequent, then all of its subsets must also be frequent.

7. K-means

K-means is an iterative algorithm that groups similar data into clusters.It calculates the centroids of k clusters and assigns a data point to that cluster having least distance between its centroid and the data point.

Figure 6: Steps of the K-means algorithm. Source

Here’s how it works:

We start by choosing a value of k. Here, let us say k = 3. Then, we randomly assign each data point to any of the 3 clusters. Compute cluster centroid for each of the clusters. The red, blue and green stars denote the centroids for each of the 3 clusters.

Next, reassign each point to the closest cluster centroid. In the figure above, the upper 5 points got assigned to the cluster with the blue centroid. Follow the same procedure to assign points to the clusters containing the red and green centroids.

Then, calculate centroids for the new clusters. The old centroids are gray stars; the new centroids are the red, green, and blue stars.

Finally, repeat steps 2-3 until there is no switching of points from one cluster to another. Once there is no switching for 2 consecutive steps, exit the K-means algorithm.

8. PCA

Principal Component Analysis (PCA) is used to make data easy to explore and visualize by reducing the number of variables. This is done by capturing the maximum variance in the data into a new coordinate system with axes called ‘principal components’.

Each component is a linear combination of the original variables and is orthogonal to one another. Orthogonality between components indicates that the correlation between these components is zero.

The first principal component captures the direction of the maximum variability in the data. The second principal component captures the remaining variance in the data but has variables uncorrelated with the first component. Similarly, all successive principal components (PC3, PC4 and so on) capture the remaining variance while being uncorrelated with the previous component.

Figure 7: The 3 original variables (genes) are reduced to 2 new variables termed principal components (PC’s). Source

Ensemble learning techniques:

Ensembling means combining the results of multiple learners (classifiers) for improved results, by voting or averaging. Voting is used during classification and averaging is used during regression. The idea is that ensembles of learners perform better than single learners.

There are 3 types of ensembling algorithms: Bagging, Boosting and Stacking. We are not going to cover ‘stacking’ here, but if you’d like a detailed explanation of it, here’s a solid introduction from Kaggle.

9. Bagging with Random Forests

The first step in bagging is to create multiple models with data sets created using the Bootstrap Sampling method. In Bootstrap Sampling, each generated training set is composed of random subsamples from the original data set.

Each of these training sets is of the same size as the original data set, but some records repeat multiple times and some records do not appear at all. Then, the entire original data set is used as the test set. Thus, if the size of the original data set is N, then the size of each generated training set is also N, with the number of unique records being about (2N/3); the size of the test set is also N.

The second step in bagging is to create multiple models by using the same algorithm on the different generated training sets.

This is where Random Forests enter into it. Unlike a decision tree, where each node is split on the best feature that minimizes error, in Random Forests, we choose a random selection of features for constructing the best split. The reason for randomness is: even with bagging, when decision trees choose the best feature to split on, they end up with similar structure and correlated predictions. But bagging after splitting on a random subset of features means less correlation among predictions from subtrees.

The number of features to be searched at each split point is specified as a parameter to the Random Forest algorithm.

Thus, in bagging with Random Forest, each tree is constructed using a random sample of records and each split is constructed using a random sample of predictors.

10. Boosting with AdaBoost

Adaboost stands for Adaptive Boosting. Bagging is a parallel ensemble because each model is built independently. On the other hand, boosting is a sequential ensemble where each model is built based on correcting the misclassifications of the previous model.

Bagging mostly involves ‘simple voting’, where each classifier votes to obtain a final outcome– one that is determined by the majority of the parallel models; boosting involves ‘weighted voting’, where each classifier votes to obtain a final outcome which is determined by the majority– but the sequential models were built by assigning greater weights to misclassified instances of the previous models.

Figure 9: Adaboost for a decision tree. Source

In Figure 9, steps 1, 2, 3 involve a weak learner called a decision stump (a 1-level decision tree making a prediction based on the value of only 1 input feature; a decision tree with its root immediately connected to its leaves).

The process of constructing weak learners continues until a user-defined number of weak learners has been constructed or until there is no further improvement while training. Step 4 combines the 3 decision stumps of the previous models (and thus has 3 splitting rules in the decision tree).

First, start with one decision tree stump to make a decision on one input variable.

The size of the data points show that we have applied equal weights to classify them as a circle or triangle. The decision stump has generated a horizontal line in the top half to classify these points. We can see that there are two circles incorrectly predicted as triangles. Hence, we will assign higher weights to these two circles and apply another decision stump.

Second, move to another decision tree stump to make a decision on another input variable.

We observe that the size of the two misclassified circles from the previous step is larger than the remaining points. Now, the second decision stump will try to predict these two circles correctly.

As a result of assigning higher weights, these two circles have been correctly classified by the vertical line on the left. But this has now resulted in misclassifying the three circles at the top. Hence, we will assign higher weights to these three circles at the top and apply another decision stump.

Third, train another decision tree stump to make a decision on another input variable.

The three misclassified circles from the previous step are larger than the rest of the data points. Now, a vertical line to the right has been generated to classify the circles and triangles.

Fourth, Combine the decision stumps.

We have combined the separators from the 3 previous models and observe that the complex rule from this model classifies data points correctly as compared to any of the individual weak learners.

Conclusion:

To recap, we have covered some of the the most important machine learning algorithms for data science:

5 supervised learning techniques- Linear Regression, Logistic Regression, CART, Naïve Bayes, KNN.3 unsupervised learning techniques- Apriori, K-means, PCA.2 ensembling techniques- Bagging with Random Forests, Boosting with XGBoost.

Editor’s note: This was originally posted on KDNuggets, and has been reposted with permission. Author Reena Shaw is a developer and a data science journalist.

July 01, 2019

Iconic London landmarks emblazoned with projection raising human rights issue in Pakistan

Iconic London landmarks emblazoned with projection raising human rights issue in Pakistan.
0'July 2019

(Current Balochistan)

Last night, the World Baloch Organisation and Baloch Republican Party organised a projection over iconic landmarks in London including the UK Houses of Parliament, Marble Arch and Shoreditch High Street. The projection read “Help end enforced disappearances in Pakistan”. The projection also included a video of Pakistan’s Prime minister Imran Khan in which he vowed to resign if he was unable to put an end to the practice of Enforced Disappearances in Pakistan , holding those involved responsible.

The groups believes that the projection will serve as a key message and reminder to the international community of their responsibility to speak out against enforced disappearances and extra- judicial killings in Pakistan . The projection also aims to raise awareness amongst Londoners on the dire situation that desperately calls for international attention and support.
 
It is worth mentioning that the group had earlier organised an aerial banner to circle a cricket stadium in Headingly, Leeds has it hosted a World Cup clash between Pakistan and Afghanistan. The banner that caught the attention of the international media read “Help End Disappearances in Pakistan” and “Justice for Balochistan”. Despite protests from the Pakistani government over the incident, the group reaffirmed their resolve to peacefully and rightfully continue their campaign to highlight worsening human rights situation.
 
The new initiative comes as part of a successful a campaign in UK that will focuses on the issue of Enforced Disappearances in Pakistan, where thousands have been forcibly disappeared by Pakistani authorities. Most are extra-judicially killed and their bodies dumped on roadsides bearing signs of extreme torture, others stay missing forever, Amnesty International has called it the “Kill and dump” policy. Victims include activists, teachers, students, doctors, intellectuals and journalists who have voiced their opinions against the military’s iron grip over the country. The military’s control over the local media is such that anyone reporting such incidents risks falling victim themselves.

International NGO’s and Journalists are not given access to Balochistan where most of the cases of disappearances have been registered.

Families of the abducted victims have long been protesting for the safe recovery of their loved ones in the provincial capital Quetta, and their protest camp has now completed more than 3500 days.

The organizers of the campaign have long been engaged in efforts to highlight the worsening human rights situation in Balochistan at international platforms, organising events around Europe and in the United States, focusing on advocacy activities in the European Parliament, the US parliamentary houses, and the United Nations.

The WBO and the BRP are non-violent and democratic organisations led by Baloch individuals, dedicated to raising awareness of the dire situation of human rights in Balochistan.

http://cubalochistan.home.blog/2019/07/01/iconic-london-landmarks-emblazoned-with-projection-raising-human-rights-issue-in-pakistan/

June 30, 2019

Convocation robes out, students will now wear traditional attire ‘made of Indian handloom’

UGC has issued a circular to all public and private universities under it, asking them to go traditional during their convocation ceremonies.

KRITIKA SHARMAUpdated: 26 June, 2019 2:59 pm IST

Students of Panjab University (representational image) | Commons

New Delhi: The Western graduation robe worn by students at convocation ceremonies will soon be a thing of the past in Indian universities, according to a circular issued by the University Grants Commission (UGC). The Narendra Modi government wants students to dress in traditional attire made up of Indian handloom when they receive their degrees at convocations.

The UGC circular released earlier this month has asked all universities to go traditional. It said that, “using handloom garments would give a sense of pride of being Indian”. The commission has also asked for an ‘Action Taken Report’ from the universities on this.

The letter is addressed to all private and public universities that are under the commission.

“With changing times, everything changes. Indian universities have been carrying on the British style of wearing a robe during convocations. It’s high time that we change the tradition and make it localised,” a senior UGC official said.

Wearing the Western convocation outfit — black robe and cap — wasn’t mandatory in Indian universities, but some institutions followed the practice.

‘Idea is to make convocation robe regional’

The UGC official quoted above said the idea is to make convocation attires more regional.

For example, women in Punjab could wear traditional salwar-kurtas, those in Kerala and Tamil Nadu could wear their traditional sarees and students in Himachal Pradesh could don the traditional cap and costume of the region, the official added.

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A month ago, during the convocation ceremony of National Institute of Technology, Hamirpur, in Himachal Pradesh, students had worn traditional outfits.

Also read: Better data can improve public education in India – draft National Education Policy says it too

Previous Modi govt also promoted desi convocation attire

Former HRD Minister Prakash Javadekar had also promoted the idea of wearing traditional Indian outfits during convocations and some institutes, including the Indian Institutes of Technology (IITs), had implemented it last year.

IITs in Roorkee, Bombay and Kanpur had last year switched to traditional Indian attire — sarees for women and kurtapajama for men. Even when Jawaharlal Nehru University (JNU) held its convocation earlier this year, after a gap of several years, students were seen dressed in sarees and kurtas.

However, not all universities were following the Indian dress code. But, now after the UGC circular, all varsities will have to adhere to it.

Besides the BJP, the Congress was also against the Western convocation robe.

In 2010, former UPA minister Jairam Ramesh had called the Western convocation robe a “barbaric colonial relic”. “I still have not been able to figure out after 60 years of Independence why we stick to these barbaric colonial relics,” he had said at the 7th convocation of Indian Institute of Forest Management.

https://theprint.in/india/convocation-robes-out-students-will-now-wear-traditional-attire-made-of-indian-handloom/254630/