Questões de Concurso
Sobre interpretação de texto | reading comprehension em inglês
Foram encontradas 13.065 questões
Judge the following items according to the text CB3A1AAA.
In spite of being a longstanding matter, concurrent computation
has been used just by professionals who implement database
management systems.
Judge the following items according to the text CB3A1AAA.
Software construction professionals must be acquainted with
concurrency quickly.
Judge the following items according to the text CB3A1AAA.
Even some applications once seen as sequential are now
demanding concurrent computation.
The American singer Beyoncé included in her song “Flawless” a sample from a speech given by the Nigerian writer Chimamanda Adichie entitled “We Should All Be Feminists”. Read the sample from the song and answer the following activity.
We teach girls to shrink themselves, to make themselves smaller. We say to girls, you can have ambition, but not too much. You should aim to be successful, but not too successful. Otherwise, you will threaten the man. Because I am female, I am expected to aspire to marriage. I am expected to make my life choices always keeping in mind that marriage is the most important. Now marriage can be a source of joy and love and mutual support but why do we teach girls to aspire to marriage and we don’t teach boys the same? We raise girls to see each other as competitors not for jobs or accomplishments, which I think can be a good thing, but for the attention of men. We teach girls that they cannot be sexual beings in the way that boys are. Feminist: the person who believes in the social, political and economic equality of the sexes.
According to the excerpt, the song DOES NOT suggest
that:
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voice-recognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers.
(From http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voice-recognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers.
(From http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voice-recognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers.
(From http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)
TEXT I
Will computers ever truly understand what we’re saying?
Date: January 11, 2016
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human communication, think again. Computers like Siri often get confused because they judge meaning by looking at a word’s statistical regularity. This is unlike humans, for whom context is more important than the word or signal, according to a researcher who invented a communication game allowing only nonverbal cues, and used it to pinpoint regions of the brain where mutual understanding takes place.
From Apple’s Siri to Honda’s robot Asimo, machines seem to be getting better and better at communicating with humans. But some neuroscientists caution that today’s computers will never truly understand what we’re saying because they do not take into account the context of a conversation the way people do.
Specifically, say University of California, Berkeley, postdoctoral fellow Arjen Stolk and his Dutch colleagues, machines don’t develop a shared understanding of the people, place and situation - often including a long social history - that is key to human communication. Without such common ground, a computer cannot help but be confused.
“People tend to think of communication as an exchange of linguistic signs or gestures, forgetting that much of communication is about the social context, about who you are communicating with,” Stolk said.
The word “bank,” for example, would be interpreted one way if you’re holding a credit card but a different way if you’re holding a fishing pole. Without context, making a “V” with two fingers could mean victory, the number two, or “these are the two fingers I broke.”
“All these subtleties are quite crucial to understanding one another,” Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication. “In fact, we can understand one another without language, without words and signs that already have a shared meaning.”
(Adapted from http://www.sciencedaily.com/releases/2016/01/1 60111135231.htm)
TEXT I
Will computers ever truly understand what we’re saying?
Date: January 11, 2016
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human communication, think again. Computers like Siri often get confused because they judge meaning by looking at a word’s statistical regularity. This is unlike humans, for whom context is more important than the word or signal, according to a researcher who invented a communication game allowing only nonverbal cues, and used it to pinpoint regions of the brain where mutual understanding takes place.
From Apple’s Siri to Honda’s robot Asimo, machines seem to be getting better and better at communicating with humans. But some neuroscientists caution that today’s computers will never truly understand what we’re saying because they do not take into account the context of a conversation the way people do.
Specifically, say University of California, Berkeley, postdoctoral fellow Arjen Stolk and his Dutch colleagues, machines don’t develop a shared understanding of the people, place and situation - often including a long social history - that is key to human communication. Without such common ground, a computer cannot help but be confused.
“People tend to think of communication as an exchange of linguistic signs or gestures, forgetting that much of communication is about the social context, about who you are communicating with,” Stolk said.
The word “bank,” for example, would be interpreted one way if you’re holding a credit card but a different way if you’re holding a fishing pole. Without context, making a “V” with two fingers could mean victory, the number two, or “these are the two fingers I broke.”
“All these subtleties are quite crucial to understanding one another,” Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication. “In fact, we can understand one another without language, without words and signs that already have a shared meaning.”
(Adapted from http://www.sciencedaily.com/releases/2016/01/1 60111135231.htm)
Based on the summary provided for Text I, mark the statements below as TRUE (T) or FALSE (F).
( ) Contextual clues are still not accounted for by computers.
( ) Computers are unreliable because they focus on language patterns.
( ) A game has been invented based on the words people use.
The statements are, respectively:
TEXT I
Will computers ever truly understand what we’re saying?
Date: January 11, 2016
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human communication, think again. Computers like Siri often get confused because they judge meaning by looking at a word’s statistical regularity. This is unlike humans, for whom context is more important than the word or signal, according to a researcher who invented a communication game allowing only nonverbal cues, and used it to pinpoint regions of the brain where mutual understanding takes place.
From Apple’s Siri to Honda’s robot Asimo, machines seem to be getting better and better at communicating with humans. But some neuroscientists caution that today’s computers will never truly understand what we’re saying because they do not take into account the context of a conversation the way people do.
Specifically, say University of California, Berkeley, postdoctoral fellow Arjen Stolk and his Dutch colleagues, machines don’t develop a shared understanding of the people, place and situation - often including a long social history - that is key to human communication. Without such common ground, a computer cannot help but be confused.
“People tend to think of communication as an exchange of linguistic signs or gestures, forgetting that much of communication is about the social context, about who you are communicating with,” Stolk said.
The word “bank,” for example, would be interpreted one way if you’re holding a credit card but a different way if you’re holding a fishing pole. Without context, making a “V” with two fingers could mean victory, the number two, or “these are the two fingers I broke.”
“All these subtleties are quite crucial to understanding one another,” Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication. “In fact, we can understand one another without language, without words and signs that already have a shared meaning.”
(Adapted from http://www.sciencedaily.com/releases/2016/01/1 60111135231.htm)
The actress Viola Davis made history for becoming the first African-American actress to win an Emmy in the best drama actress category. On the ceremony, she gave a polemical speech. Read the excerpt below and answer the following activity.
Viola Davis’s Emmy Speech
‘In my mind, I see a line. And over that line, I see green fields and lovely flowers and beautiful white women with their arms stretched out to me, over that line. But I can’t seem to get there no how. I can’t seem to get over that line.’
That was Harriet Tubman in the 1800s. And let me tell you something: The only thing that separates women of color from anyone else is opportunity.
You cannot win an Emmy for roles that are simply not there. So here’s to all the writers, the awesome people that are Ben Sherwood, Paul Lee, Peter Nowalk, Shonda Rhimes, people who have redefined what it means to be beautiful, to be sexy, to be a leading woman, to be black. And to the Taraji P. Hensons, the Kerry Washingtons, the Halle Berrys, the Nicole Beharies, the Meagan Goods, to Gabrielle Union: Thank you for taking us over that line. Thank you to the Television Academy. Thank you.
(Extracted from URL <http://www.nytimes.com/live/emmys-2015/viola-daviss-emotional-emmys-acceptance-speech/> Retrieved on February 09 2016.)
Based on Davis’s speech, what alternative best
describes the purpose of her speech:
Read carefully the following text.
THE OTHER MINISTER
It was nearing midnight and the Prime Minister was sitting alone in his office, reading a long memo that was slipping through his brain without leaving the slightest trace of meaning behind. He was waiting for a call from the President of a far distant country, and between wondering when the wretched man would telephone, and trying to suppress unpleasant memories of what had been a very long, tiring, and difficult week, there was not much space in his head for anything else. The more he attempted to focus on the print on the page before him, the more clearly the Prime Minister could see the gloating face of one of his political opponents. This particular opponent had appeared on the news that very day, not only to enumerate all the terrible things that had happened in the last week (as though anyone needed reminding) but also to explain why each and every one of them was the government's fault.
(Extracted from Chapter One, Harry Potter and the Half-Blood Prince written by J.K.Rowling and published in 2005)
According to the text, it is NOT correct to infer that:
Read the text below and answer the following activity.
The Boy Who Lived
Mr. and Mrs. Dursley, of number four, Privet Drive, were proud to say that they were perfectly normal, thank you very much. They were the last people you'd expect to be involved in anything strange or mysterious, because they just didn't hold with such nonsense.
Mr. Dursley was the director of a firm called Grunnings, which made drills. He was a big, beefy man with hardly any neck, although he did have a very large mustache. Mrs. Dursley was thin and blonde and had nearly twice the usual amount of neck, which came in very useful as she spent so much of her time craning over garden fences, spying on the neighbors. The Dursleys had a small son called Dudley and in their opinion there was no finer boy anywhere.
The Dursleys had everything they wanted, but they also had a secret, and their greatest fear was that somebody would discover it. They didn't think they could bear it if anyone found out about the Potters. Mrs. Potter was Mrs. Dursley's sister, but they hadn't met for several years; in fact, Mrs. Dursley pretended she didn't have a sister, because her sister and her good-for-nothing husband were as unDursleyish as it was possible to be. The Dursleys shuddered to think what the neighbors would say if the Potters arrived in the street. The Dursleys knew that the Potters had a small son, too, but they had never even seen him. This boy was another good reason for keeping the Potters away; they didn't want Dudley mixing with a child like that.
(Extracted from Chapter One Harry Potter and the Philosopher's Stone written by J.K.Rowlling and published in 1997)
According to the text, which of the following information
is FALSE:
History and Debate of Internet Censorship
Censorship refers to any action taken by a society to control access to ideas and information. Throughout history, many different types of societies, including democracies, have used censorship in various ways. The issue is increasingly important due to the rapid development of new communication technology. As innovators continue to create new ways for people to share information, many people are now arguing over the issue of censorship.
Pros and Cons of the Internet Censorship Debate
For the proponents of censorship, restricting the access of information is something that can provide benefits to society. By censoring pornography on the internet, children are less likely to encounter it. By censoring certain types of images and videos, society can prevent offensive or vulgar material from offending those that it targets. For example, some would argue that society should censor material that is insulting to a particular religion in order to maintain societal harmony. In this way, censorship is viewed as a way to protect society as a whole or certain segments of society from material that is seen as offensive or damaging.
Some argue that censorship is necessary to preserve national security. Without using any kind of censorship, they argue that it is impossible to maintain the secrecy of information necessary for protecting the nation. For this purpose, censorship protects a state's military or security secrets from its enemies who can use that information against the state.
Those who are against censorship argue that the practice limits the freedoms of speech, the press and expression and that these limitations are ultimately a detriment to society. By preventing free access to information, it is argued that society is fostering ignorance in its citizens. Through this ignorance, citizens are more easily controlled by special interest groups, and groups that are able to take power are able to use censorship to maintain themselves. Additionally, they argue that censorship limits a society's ability to advance in its understanding of the world.
Another main issue for those who are against censorship is a history of censorship abuse. Those who argue against censorship can point to a number of examples of dictators who used censorship to create flattering yet untrue images of themselves for the purpose of maintaining control over a society. They argue that people should control the government instead of the government controlling its people.
(SOURCE: http://www.debate.org/internet-censorship/ accessed
on 19/02/16 at 3:10 pm).
Busy air traffic control facilities lack enough controllers
WASHINGTON — Thirteen of America's busiest air traffic control facilities are suffering from a shortage of air traffic controllers, a problem that demands “urgent attention," a government watchdog told lawmakers on Tuesday. The facilities also are under stress because a large share of their controllers are still being trained and are not yet competent to work on their own, he said. Many of their experienced controllers also are eligible to retire, Hampton said.
Officials with the National Air Traffic Controllers Association, the union representing controllers, also complained about the difficulty in moving an experienced controller from a less-busy workplace to a busy one. Managers are reluctant to let workers go for fear they won't be readily replaceable, he said. And employees may oppose moving to an area where the cost of living is higher — New York, for example.
Washington Post 6/12/15 [adapted]
Busy air traffic control facilities lack enough controllers
WASHINGTON — Thirteen of America's busiest air traffic control facilities are suffering from a shortage of air traffic controllers, a problem that demands “urgent attention," a government watchdog told lawmakers on Tuesday. The facilities also are under stress because a large share of their controllers are still being trained and are not yet competent to work on their own, he said. Many of their experienced controllers also are eligible to retire, Hampton said.
Officials with the National Air Traffic Controllers Association, the union representing controllers, also complained about the difficulty in moving an experienced controller from a less-busy workplace to a busy one. Managers are reluctant to let workers go for fear they won't be readily replaceable, he said. And employees may oppose moving to an area where the cost of living is higher — New York, for example.
Washington Post 6/12/15 [adapted]
Busy air traffic control facilities lack enough controllers
WASHINGTON — Thirteen of America's busiest air traffic control facilities are suffering from a shortage of air traffic controllers, a problem that demands “urgent attention," a government watchdog told lawmakers on Tuesday. The facilities also are under stress because a large share of their controllers are still being trained and are not yet competent to work on their own, he said. Many of their experienced controllers also are eligible to retire, Hampton said.
Officials with the National Air Traffic Controllers Association, the union representing controllers, also complained about the difficulty in moving an experienced controller from a less-busy workplace to a busy one. Managers are reluctant to let workers go for fear they won't be readily replaceable, he said. And employees may oppose moving to an area where the cost of living is higher — New York, for example.
Washington Post 6/12/15 [adapted]
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