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英語 高校生

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Lesson 01 演習問題 (want など + (人) +to不定詞) 4) 私 I de nobu ansvare won a adquod ① 日本語に合うように,( Id 内から適切なものを選ぼう。 1)I want ani / ask) Emi to read this booked suse I a 5) あ Dic Dic (私はエミにこの本を読んでほしい。) 2)I wanted / asked my sister to turn off the TV.digianaco (私は妹にテレビを消すように頼んだ。 ) 3) Please ( tell / want) Tom to attend the meeting tomorrow. (トムに明日会議に出席するように言ってください。)sis po 4) I don't ( ask / want ) my son to eat junk food. (私は息子にジャンクフードを食べてほしくない。) 5) Did you ( want / ask) Sam to call me? (私に電話するようにサムに頼みましたか。) ② 日本語に合うように、( 1) I( SHA 内に適切な語を入れよう。 ) Ryota ( ) ( ) here at nine. (私はリョウタに9時にここに来るように言いました。) 2) I ( ) Kate ( (私はケイトにあなたを助けるように頼みました。) 3) Do you ( ) me ( (私にお皿を洗ってほしいのですか。) 4) I( ) my brother ( ) ( ) ( (私は弟にこの部屋を掃除するように言いました。) 5) Mr. Yamada ( ) Kana ( )( (山田先生はカナに窓を開けるように頼みました。) 6) Jiro ( ) us ( ) ( ジロウは私たちにいっしょに遊んでほしかった。) ) you. ) the dishes? (5) ) this room. ) the window. ) with him. ③ 日本語に合うように,[ []内の語句を正しい順に並べかえよう。 1) 私はあなたにこの歌を歌ってほしい。 Ⅰ [ to sing/ want / you ] this song. I this song. 兄は私にテレビをつけるように頼んだ。 My brother [ asked / to turn on / me ] the TV. My brother 3) アツシにチケットを2枚買うように言ってください。 Please [ tell / to buy / Atsushi ] two tickets. Please the TV. two tickets.

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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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