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rainfall can revive them in a matter of hours. 可以定位到D段，因此本题的正确答案为D选项

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 2with 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.

【46】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. 【47】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

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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.

【48】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. \

【49】Machines also had difficulty adjusting for people who look a lot alike—either doppelgangers (长相极相似的人），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.

\invariant to lighting, pose, age,\

【50】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. \in a dataset,\注意：此部分试题请在答题卡2上作答。 【杀掉拦路虎】

1. notoriously [n??'t?:r??sl?]

2018年6月大学英语六级真题试卷及答案（二）

adv.恶名昭彰地；声名狼藉地；著名地；众所

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周知地

2. quantify [?kw?ntifai]

vt. 确定…的数量;[逻辑学] 用量词限定

3. estimate [?est??meit]

n. 估计，预测;报价，预算书;评价，判断; vt. 估计，估算;评价，评论;估量，估价 4. acquaintance [??kweint?ns]

n. 相识的人，熟人;相识;对…有了解;知识，心得

5. massive [?m?siv]

adj. 大的，重的;大块的，大量的;魁伟的，结实的;大规模的

6. precision [pri?si??n] n. 精确度，准确（性）;[语]精确;

adj. 精确的，准确的，细致的;严守标准的;行动精确的

7. enormous [i?n?:m?s]

adj. 巨大的;庞大的;极恶的;凶暴的

8. surveillance [s?:?ve?l?ns] n.盯梢，监督；[法]管制，监视

9. constraints [k?n'stre?nt]

n.强制( constraint的名词复数 )；限制；约束

10. algorithm [??lg?r?e?m] n.运算法则；演算法；计算程

11. scale up [skeil ?p]

按比例增加[提高]；按某种比例增加

12. invariant [?n?ve?ri?nt] adj.无变化的，不变的 n.不变式，不变量

13. efficiently [?'f??ntl?] adv.效率高地；有效地

14. perceive [p??si:v] v. 意识到;察觉，发觉;理解

15. remarkable [ri?mɑ:k?bl]

adj. 异常的，引人注目的，;卓越的;显著的;非凡的，非常（好）的

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

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D) perceive images invisible to the human eye 【答案】C

【解析】 本题为细节题，由 【46】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. 可知：很难量

化一个人在记忆方面有多好。例如，没有人真正知道一个人能回忆起多少不同的面孔，但根据一个人可能认识的熟人的数量，粗略估计在数千人之间。机器不受这种限制。给计算机一个正确的庞大的人脸数据库，它可以处理它看到的东西-然后以惊人的速度和精确性识别它要找的面孔。题目的问题为：与人类记忆相比，机器可以 _______。四个选项分别为：(A)更有效地识别人脸；B)从熟人中分辨出朋友；C)储存无限数量的人脸；D)感知肉眼看不见的图像。本文第二段说，人类只能记忆数千人头像，第三段开头说，机器不受这种限制，由此可知与人类记忆相比，机器可以储存更多的人像，因此C选项正确。(A)更有效地识别人脸，原文中说的是给计算机一个正确的庞大的人脸数据库，它可以处理它看到的东西-然后以惊人的速度和精确性识别它要找的面孔，并不是和人类记忆相比较的，此选项为干扰选项，因此，本题的正确选项为C项。

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. 【答案】C

【解析】 本题为细节题，由 【47】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. 可知：为了开始弄清楚计算机是如何处理的，华盛顿大学的研

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究人员建立了一个庞大的人脸数据库-他们称之为MegaFace-逐渐增加它们的复杂性，来测试各种面部识别算法。题目的问题为：为什么研究人员要创造MegaFace？四个选项分别为：(A)扩大人脸识别数据库的容量。B)增加面部识别软件的种类。(C)了解计算机在面部识别方面的问题。D)降低人脸识别算法的复杂度。由原文可知，他们为了弄清楚电脑是如何处理面部识别的问题，因此，本题的正确选项为C项。

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. 【答案】D

【解析】 本题为细节题，由【 48】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. 可知：随着数据库的发展，机器的准确性全面下降。例如，

在处理13，000个图像数据库时，95%的算法是正确的-例如，当遇到100万幅图像时，大约有70%的算法是准确的。题目的问题为：关于机器的准确性，这篇文章是怎么说的？四个选项分别为：A)没有达到研究者的预期。(B)随着计算能力的增加，它得到了改进。(C)不同的算法有很大的不同。(D)随着数据库规模的增加，精确度逐渐降低。由此可见。D选项符合原文，因此，本题的正确选项为D项。

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. 【答案】A

【解析】 本题为细节题，由 【49】Machines also had difficulty adjusting for people who

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