2018年6月英語六級考試真題及答案4（第2套）

Section C

Directions:There are 2 passages in this section.Each passage is followed by some questions or unfinished statements.For each of them there are four choices marked A),B),C)and D).You should decide on the best choice and mark the corresponding letter on Answer Sheet 2 with a single line through the centre.

Passage One

Questions 46 to 50 are based on the following passage.

Human memory is notoriously unreliable.Even people with the sharpest facial-recognition skills can only remember so much.

It's tough to quantify how good a person is at remembering.No one really knows how many different faces someone can recall,for example,but various estimates tend to hover in the thousands—based on the number of acquaintances a person might have.

Machines aren't limited this way.Give the right computer a massive database of faces,and it can process what it sees—then recognize a face it's told to find—with remarkable speed and precision.This skill is what supports the enormous promise of facial-recognition software in the 21st century.It's also what makes contemporary surveillance systems so scary.

The thing is,machines still have limitations when it comes to facial recognition.And scientists are only just beginning to understand what those constraints are.To begin to figure out how computers are struggling,researchers at the University of Washington created a massive database of faces—they call it MegaFace—and tested a variety of facial-recognition algorithms(算法)as they scaled up in complexity.The idea was to test the machines on a database that included up to 1 million different images of nearly 700,000 different people—and not just a large database featuring a relatively small number of different faces,more consistent with what's been used in other research.

As the databases grew,machine accuracy dipped across the board.Algorithms that were right 95%of the time when they were dealing with a 13,000-image database,for example,were accurate about 70%of the time when confronted with 1 million images.That's still pretty good,says one of the researchers,Ira Kemelmacher-Shlizerman."Much better than we expected,"she said.

Machines also had difficulty adjusting for people who look a lot alike—either doppelgangers(長相極相似的人),whom the machine would have trouble identifying as two separate people,or the same person who appeared in different photos at different ages or in different lighting,whom the machine would incorrectly view as separate people.

"Once we scale up,algorithms must be sensitive to tiny changes in identities and at the same time invariant to lighting,pose,age,"Kemelmacher-Shlizerman said.

The trouble is,for many of the researchers who'd like to design systems to address these challenges,massive datasets for experimentation just don't exist—at least,not in formats that are accessible to academic researchers.Training sets like the ones Google and Facebook have are private.There are no public databases that contain millions of faces.MegaFace's creators say it's the largest publicly available facial-recognition dataset out there.

"An ultimate face recognition algorithm should perform with billions of people in a dataset,"the researchers wrote.

46．Compared with human memory,machines can ________.

A．identify human faces more efficiently

B．tell a friend from a mere acquaintance

C．store an unlimited number of human faces

D．perceive images invisible to the human eye

47．Why did researchers create MegaFace?

A．To enlarge the volume of the facial-recognition database.

B．To increase the variety of facial-recognition software.

C．To understand computers'problems with facial recognition.

D．To reduce the complexity of facial-recognition algorithms.

48．What does the passage say about machine accuracy?

A．It falls short of researchers'expectations.

B．It improves with added computing power.

C．It varies greatly with different algorithms.

D．It decreases as the database size increases.

49．What is said to be a shortcoming-of facial-recognition machines?

A．They cannot easily tell apart people with near-identical appearances.

B．They have difficulty identifying changes in facial expressions.

C．They are not sensitive to minute changes in people's mood.

D．They have problems distinguishing people of the same age.

50．What is the difficulty confronting researchers of facial-recognition machines?

A．No computer is yet able to handle huge datasets of human faces.

B．There do not exist public databases with sufficient face samples.

C．There are no appropriate algorithms to process the face samples.

D．They have trouble converting face datasets into the right format.

【參考答案】

46-50：ACDAB

【參考譯文】

眾所周知，人類的記憶是不可靠的。即使是面部識別能力最強(qiáng)的人也只能記住這么多。

很難量化一個人的記憶力有多好。例如，沒有人真正知道一個人能回憶起多少張不同的臉，但根據(jù)一個人可能認(rèn)識的熟人的數(shù)量，各種各樣的估計往往停留在千分之一上下。

機(jī)器不受這種方式的限制。給正確的計算機(jī)一個巨大的人臉數(shù)據(jù)庫，它就能以驚人的速度和精度處理它看到的東西——然后識別它被告知要找到的面孔。這種技能正是21世紀(jì)面部識別軟件的巨大潛力所在。這也是現(xiàn)代監(jiān)控系統(tǒng)如此可怕的原因。

問題是，機(jī)器在面部識別方面仍然有局限性?？茖W(xué)家們才剛剛開始了解這些約束條件是什么。開始找出電腦是掙扎,華盛頓大學(xué)的研究人員創(chuàng)造了一個巨大的數(shù)據(jù)庫,他們稱之為MegaFace-and測試各種人臉識別算法(算法)在擴(kuò)大規(guī)模時的復(fù)雜性。這個想法是在一個數(shù)據(jù)庫上測試這些機(jī)器，這個數(shù)據(jù)庫包含了近70萬不同人的100萬張不同的圖像，而不僅僅是一個大數(shù)據(jù)庫，其中有相對較少的不同面孔，這與其他研究中使用的方法更加一致。

隨著數(shù)據(jù)庫的增長，機(jī)器的精確度全面下降。例如，在處理1.3萬張圖像數(shù)據(jù)庫時，算法準(zhǔn)確率高達(dá)95%，但在處理100萬張圖像時，準(zhǔn)確率僅為70%。其中一名研究人員Ira Kemelmacher-Shlizerman說，這仍然很好?！氨任覀冾A(yù)期的要好得多，”她說。

機(jī)器也難以調(diào)整的人看起來很alike-either出現(xiàn)(長相極相似的人),機(jī)器將無法識別的兩個獨(dú)立的人,或在不同的年齡相同的人出現(xiàn)在不同的照片或在不同的照明,機(jī)器會錯誤地認(rèn)為獨(dú)立的人。

Kemelmacher-Shlizerman說:“一旦我們擴(kuò)大規(guī)模，算法必須對身份的微小變化敏感，同時對光照、姿勢、年齡不變?！?/p>

問題是，對于許多想要設(shè)計系統(tǒng)來應(yīng)對這些挑戰(zhàn)的研究人員來說，用于實驗的大量數(shù)據(jù)集根本不存在——至少，沒有學(xué)術(shù)研究人員能夠訪問的格式。像谷歌和Facebook這樣的培訓(xùn)都是私人的。沒有包含數(shù)百萬張面孔的公共數(shù)據(jù)庫。MegaFace的創(chuàng)建者說，這是目前最大的公開面部識別數(shù)據(jù)集。

研究人員寫道:“一個終極的人臉識別算法應(yīng)該能在一個數(shù)據(jù)集中對數(shù)十億人進(jìn)行識別?！?/p>

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2018年6月英語六級考試真題及答案匯總（第2套）