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            Source: 恒星英語學習網    2015-11-24  我要投稿   論壇   Favorite  

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            Section I Use of English

            Directions:Read the following text. Choose the best word(s) for each numbered blank and mark A,B,C or D on ANSWER SHEET 1.(10 points)

            An European Union directive is making shoppers feel like they have extra ammunition to return faulty goods for up to two years. It is not quite__1__simple. There has been an increase in people using the__2__EU directive to get retailers to refund or__3__faulty goods, even after the stated__4__periods have ended.

            The EU rule allowing the__5__of goods up to two years after purchase is at__6__with the returns policies adopted by most__7__shops. However, as this is a directive only partially adopted by the UK, its use is a __8__area. Despite this, many shops have__9__refunded items when presented with the directive and its argument,__10__it could potentially improve your position.

            Most big retailers will have a stated returns policy that__11__with UK consumer law. Those__12__can see the exact wording of the Sale of Goods act,__13__put simply the law says that retailers must sell goods that are ‘as described, __14__for purpose and of satisfactory quality’. If a defect is__15__when, or in a reasonable period of time after, the sale is made, __16__buyers can demand a full refund. However, the rules get fuzzier when faults__17__over time and a buyer has to return goods after__18__them for a longer period.

            Under UK law, buyers in England and Wales can get a partial refund or full repair up to six year after the__19__was made. The refund should take into account how much use the customer has already had__20__a product. Ultimately, a county court would decide this.

            1. [A] ever [B] that [C] rather [D] very

            2. [A] little-known [B] well-made [C] badly-designed [D] warmly-accepted

            3. [A] repair [B] destroy [C] replace [D] remove

            4. [A] processing [B] guarantee [C] permission [D] attainment

            5. [A] upkeep [B] refusal [C] upturn [D] return

            6. [A] work [B] peace [C] ends [D] odds

            7. [A] major [B] franchised [C] new [D] fashionable

            8. [A] forbidden [B] grey [C] lonely [D] disturbed

            9. [A] completely [B] hardly [C] inevitably [D] willingly

            10. [A] because [B] though [C] so [D] but

            11. [A] complied [B] accompanies [C] deals [D] disagrees

            12. [A] worried [B] pleased [C] interested [D] confused

            13. [A] if [B] but [C] and [D] while

            14. [A] fit [B] possible [C] ready [D] best

            15. [A] remedied [B] concealed [C] avoided [D] detected

            16. [A] soon [B] then [C] even [D] hence

            17. [A] disappear [B] occur [C] develop [D] remain

            18. [A] buying [B] protecting [C] trying [D] possessing

            19. [A] complaint [B] purchase [C] request [D] agreement

            20. [A] of [B] in [C] from [D] over

            Section II Reading Comprehension

            Part A


            Read the following four texts. Answer the questions below each text by choosing A, B, C or D. Mark your answers on ANSWER SHEET 1.(40 points)

            Text 1

            When one of the handles of Karineh Gurjian-Angelo’s Yves Saint Laurent bag broke, she took the tote to a YSL boutique to have it fixed. Instead of repairing it, the sales associate told her it was fake. He pointed out all the subtle ways he could tell it wasn’t authentic, including the bag’s improper lining and lack of embossing on the bottom.She was mortified.

            “I felt like I was back in school in the principal’s office,”said Ms. Gurjian-Angelo, a New York photographer who often shoots accessories and is familiar with high-end handbags. She had been thrilled to get the shiny black YSL bag on eBay for $300. The low price made her suspect it might be fake, but when it arrived with price tags that looked authentic, she said she thought, “Wow, it is real.”

            Ms. Gurjian-Angelo fell victim to a new generation of fake fashion goods, offering much more convincing reproductions of actual products. They are a far cry from cheap knockoffs, with “Prado”or “Cucci”logos sold out of trash bags on street corners to consumers who know they are buying fakes. The goods are made of high-quality materials, with zippers and grommets boasting the brand name, and are stamped with what appears to be the proper manufacturing location and date. They’re fooling even shrewd shoppers, especially online.

            A fakes Hermes bag imitates the real bag’s leather‘veining,’but doesn’t feel as supple, says Elizabeth Bernstein, an expert in authenticating luxury goods.Other signs of a fake:hardware that feels lightweight and zippers that catch. Vendors selling fake merchandise can easily set up legitimate-looking ecommerce sites, with full product descriptions as well as marketing images and logos that look like those on websites selling authentic goods. They also buy keyword advertisements on search engines to lure in bargain-hunting shoppers, said Frederick Felman, chief marketing officer at MarkMonitor, a firm that helps companies protect their brands.

            EBay says it combats fakes aggressively, in part through a program which gives brands or other intellectual property rights owners special tools to report listings, When brands flag a listing as inauthentic, it is removed within hours, the company said. EBay also independently scans its millions of listings for fake products. In a statement, eBay’s Dan Dougherty, associate general counsel, said,“In the rare cases when a fake item appears on the site, buyers are covered for eligible purchases through our Buyer Protection programs.”The programs enable buyers to return an item if it wasn’t what the seller promised. Some manufacturers are also fighting back by embedding hidden security devices into products and regulating the Web to attempt to stop unauthorized sites selling their products.

            21. The word “mortified”(Line 4, Paragraph 1) is closest in meaning to___________.

            [A] disgusted

            [B] puzzled

            [C] embarrassed

            [D] astonished

            22. It can be inferred from Paragraph 3 that___________.

            [A] recent fakes are more expensive than the previous ones

            [B] recent fakes look much more authentic than cheap fakes

            [C] cheap knockoffs are sold with famous logos

            [D] fakes with inferior workmanship will decrease online

            23. Vendors may promote their fake merchandise by____________.

            [A] buying fake products from some popular websites

            [B] providing the same descriptions used for authentic goods

            [C] paying search engines to put on their advertisements

            [D] bargaining with customers hunting for luxury goods

            24. Which of the following can be a measure to fight fakes?

            [A] Manufacturers stop providing products for the website sellers.

            [B] Customers learn to identify fake products.

            [C] The government gives permission to return fake products.

            [D] Shopping websites keep removing inauthentic items.

            25. Which of the following would be the best title for the text?

            [A] EBay’s Combat against Fakes

            [B] Position of Fakes in Online Market

            [C] The Finer Art of Faking

            [D] Ways to Spot Fakes

            Text 2

            Computer scientists have long tried to foist order on the explosion of data that is on the internet, One obvious way is to group information by topic, but tagging it all comprehensively by hand is impossible. David Blei, of Princeton University, has therefore been trying to teach machines to do the job.

            He starts with defining topics as sets of words that tend to crop up in the same document. For example, “Big Bang”and “black hole”often will co-occur, but not as often as each does with “galaxy”. Neither, however, would be expected to pop up next to “genome”. This captures the intuition that the first three terms, but not the fourth , are part of a single topic. Of course, much depends on how narrow you want a topic to be. But Dr Blei’s model, which he developed with John Lafferty, of Carnegie Mellon University, allows for that.

            The user decides how fine-grained he wants the analysis to be by picking the number of topics. The computer then creates a virtual bin for each topic and begins to read the documents to be analyzed. After removing common words that it finds evenly spread through the original documents, it assigns each of the remaining ones, at random, to a bin. The computer then selects pairs of words in a bin to see if they co-occur more often than they would by chance in the original documents. If so, the association is preserved. If not, the words (together with others to which they have already been tied) are dropped at random into another bin. Repeat this process and networks of linked words will emerge. Repeat it enough and each network will correspond with a single bin.

            And it works. When Dr Blei and Dr Lafferty asked their software to find 50 topics in papers published in Science between 1980 and 2002, the words it threw up as belonging together were instantly recognisable as being related. One topic included “orbit”, “dust”, “Jupiter”, “line”, “system”, “solar”,“gas”, “atmospheric”, “Mars”and “field”. Another contained “computer”, “methods”, “number”, “two”, “principle”, “design”, “access”and “processing”.

            All of which is interesting as a way of dealing with information overload, and tagging papers so that they can be searched in a more useful way. But Dr Blei found himself wondering if his method could yield any truly novel insights into the scientific method. And he thinks it can. In cooperation with Sean Gerrish, a doctoral student at Princeton, he has now produced a version that not only peruses text for topics, but also tracks how these topics evolve, by looking at how the patterns in each topic bin change from year to year.

            26. According to the first paragraph, David Blei has been trying to_________.

            [A] search for the wanted information by topic

            [B] tag the numerous data on the internet by hand

            [C] use computer to categorize the information by topic

            [D] invent a special computer to group net work information

            27. Which of the following can be inferred from Paragraph 2?

            [A] “Big Bang”, “black hole”and “galaxy”always appear at the same time.

            [B] “Black hole”would never co-occur with “genome”in the same paper.

            [C] Dr Blei’s model selects key words according to the range of a topic.

            [D] Words co-occurring in the same paper always belong to a single topic.

            28. In the process of classifying information, the computer___________.

            [A] determines the way of analysis and the number of topics

            [B] groups all the related words in a single topic

            [C] analyses the common words in the documents

            [D] builds networks of linked words with higher co-occurrence frequency

            29. Which of the following is true of Dr Blei’s model?

            [A] It is the first creative method for information classification.

            [B] It provides a more effective way of searching internet information.

            [C] It can only be used to find some specific topics in papers.

            [D] It is developed with the purpose of calculating the number of papers.

            30. Which of the following would be the subject of the text?

            [A] How to use the computer to group information on the internet by topic.

            [B] Why online information cannot be classified by hand.

            [C] How Dr Blei developed the model of tagging internet information.

            [D] How to use the web to understand the way topics evolve.

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