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10. I was surprised that John offered help to Mary. He was ( thing, as they usually don't get along with each other. ) I expected to do such a the last 1 the first person 3 the best person no 100を含まない否定表現 彼は決して内気ではない 11. He is ( bart 2 the last person 最も~しそうにない名詞 4 the right person 〈文教大〉 but shy. anything but A 決してAではない not ①none ②anything 3 nor tobige something <宮崎大 > 12. Unfortunately, the result of their experiments turned out to be ( ) being what you would call a great success. far from A Aからはほどとおい 1 almost next to 2 despite 13. I have ( ) meet a person as dedicated to her job as Maria. have yet to do "~7 in 〈立教大〉 2 known to 14. Little ( ) how important these documents are. 1 already 3 never 24 yet to 彼女はこれらの記録がどのくらい重要なのかほとんど理解していない。 ・10g 否定の意味の副詞句が文頭に 1 she realizes ③far from 4 nothing but 〈金沢医科大 置 ■ 15. ( (大養薬 3 she does realize ) attended many international issues during these meetings, too. Not only he has He only has ② realizes she くるとういろは倒置形になる 4 does she realize <京都精華大〉 conferences, but he has expressed his views on many Not only A but BAだけでなくBもまた AとBどちらにも文がはいるときAに入る文だけ倒置 2 Not only has he 4 He used to only 16. Only when you pass the examination ( (韓国 17. ( <北里大〉 ) a reward. Only +副詞節が文頭にくると 2 can you get you can get 3 could you get you get ) amaldong ) he got on the bus did John realize that he had left his wallet at home. 1 When 2 Once 3 Not till (1) <松山大 4 As 否定の意味の〈日本大〉 副詞節が頭にくると うしろは倒置になる 85

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TOEIC・英語 大学生・専門学校生・社会人

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15 語数: 398 語 出題校 法政大 5 We are already aware that our every move online is tracked and analyzed. But you 2-53 couldn't have known how much Facebook can learn about you from the smallest of social interactions - a 'like'*. (1) Researchers from the University of Cambridge designed (2) a simple machine-learning 2-54 system to predict Facebook users' personal information based solely on which pages they had liked. E "We were completely surprised by the accuracy of the predictions," says Michael 2-55 Kosinski, lead researcher of the project. Kosinski and colleagues built the system by scanning likes for a sample of 58,000 volunteers, and matching them up with other 10 profile details such as age, gender, and relationship status. They also matched up those likes with the results of personality and intelligence tests the volunteers had taken. The team then used their model to make predictions about other volunteers, based solely on their likes. The system can distinguish between the profiles of black and white Facebook users, 15 getting it right 95 percent of the time. It was also 90 percent accurate in separating males and females, Democrats and Republicans. Personality traits like openness and intelligence were also estimated based on likes, and were as accurate in some areas as a standard personality test designed for the task. Mixing what a user likes with many kinds of other data from their real-life activities could improve these predictions even more. 20 Voting records, utility bills and marriage records are already being added to Facebook's database, where they are easier to analyze. Facebook recently partnered with offline data companies, which all collect this kind of information. This move will allow even deeper insights into the behavior of the web users. 25 30 (3) - Sarah Downey, a lawyer and analyst with a privacy technology company, foresees insurers using the information gained by Facebook to help them identify risky customers, and perhaps charge them with higher fees. But there are potential benefits for users, too. Kosinski suggests that Facebook could end up as an online locker for your personal information, releasing your profiles at your command to help you with career planning. Downey says the research is the first solid example of the kinds of insights that can be made through Facebook. "This study is a great example of how the little things you do online show so much about you,” she says. "You might not remember liking things, " but Facebook remembers and (4) it all adds up.", * a 'like': フェイスブック上で個人の好みを表示する機能。 日本語版のフェイスブックでは「いいね!」 と表記される。 2-56 2-57 2-58 36

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