2018年6月英语六级考试真题及答案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(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.

"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.

【参考答案】

46-50：ACDAB

【参考译文】

众所周知，人类的记忆是不可靠的。即使是面部识别能力最强的人也只能记住这么多。

很难量化一个人的记忆力有多好。例如，没有人真正知道一个人能回忆起多少张不同的脸，但根据一个人可能认识的熟人的数量，各种各样的估计往往停留在千分之一上下。

机器不受这种方式的限制。给正确的计算机一个巨大的人脸数据库，它就能以惊人的速度和精度处理它看到的东西——然后识别它被告知要找到的面孔。这种技能正是21世纪面部识别软件的巨大潜力所在。这也是现代监控系统如此可怕的原因。

问题是，机器在面部识别方面仍然有局限性。科学家们才刚刚开始了解这些约束条件是什么。开始找出电脑是挣扎,华盛顿大学的研究人员创造了一个巨大的数据库,他们称之为MegaFace-and测试各种人脸识别算法(算法)在扩大规模时的复杂性。这个想法是在一个数据库上测试这些机器，这个数据库包含了近70万不同人的100万张不同的图像，而不仅仅是一个大数据库，其中有相对较少的不同面孔，这与其他研究中使用的方法更加一致。

随着数据库的增长，机器的精确度全面下降。例如，在处理1.3万张图像数据库时，算法准确率高达95%，但在处理100万张图像时，准确率仅为70%。其中一名研究人员Ira Kemelmacher-Shlizerman说，这仍然很好。“比我们预期的要好得多，”她说。

机器也难以调整的人看起来很alike-either出现(长相极相似的人),机器将无法识别的两个独立的人,或在不同的年龄相同的人出现在不同的照片或在不同的照明,机器会错误地认为独立的人。

Kemelmacher-Shlizerman说:“一旦我们扩大规模，算法必须对身份的微小变化敏感，同时对光照、姿势、年龄不变。”

问题是，对于许多想要设计系统来应对这些挑战的研究人员来说，用于实验的大量数据集根本不存在——至少，没有学术研究人员能够访问的格式。像谷歌和Facebook这样的培训都是私人的。没有包含数百万张面孔的公共数据库。MegaFace的创建者说，这是目前最大的公开面部识别数据集。

研究人员写道:“一个终极的人脸识别算法应该能在一个数据集中对数十亿人进行识别。”

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2018年6月英语六级考试真题及答案汇总（第2套）