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English Senior High

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Exercises 上ができる」能力・可 「~してもよい」許 「ただけますか?」依頼 1 日本語に合うように,( )に適切な語を入れなさい。 AB 1. 私は今日, 妹の世話をすることができない。 I ( ) take care of my sister today. 金曜日にはカジュアルな服装で仕事をしてもよい。 You ( ) work in casual clothes on Fridays. 2. 3. コップ1杯の水をいただけますか。 ( ) ( ) have a glass of water? Anything ( ) happen in life. 4. 人生にはどんなことも起こり得る。 5. 彼が理由なく欠席するはずがない。 病気かもしれないね。 ) be absent without any reason. He ( He ( 6.ミキは昨日、100メートルを泳ぐことができた。 はあり得る」可能性 強い否定的な推 まわれる場合は 一定文で表す場合は hours. I~? は「~して ) be sick. Miki ( ) ( ) ( ) swim 100 meters yesterday. 内に入る適切な語句を下の[ 2 次の英文 ってはいけません。 BC ] から選びなさい。 ただし, 語句は一度しか使 Lesson 8 もよい」 許可 1. You ( ) want to try some delicious dishes here in Rome. <推量〉 2. “Can I touch these animals?” “No, you ( 3. Alice had a fever yesterday. She ( 4. The concert will start in ten minutes. We ( 5. We ( ). Some of them are poisonous.” 〈禁止> ) come to the party today. 〈推量〉 l) hurry, or we'll miss it! 〈義務 必要〉 )wear a jacket because it's already 32 degrees. 〈不必要〉 [don't have to / may not / must not / must/ may ] 現在の推量 3次の英文を( 内の指示に従って書きかえなさい。 総合 in. 1. He has to see a doctor. (疑問文に) 使う。 2. I can go shopping with you. (next week を加えた文に) 度は低い。 3. It must be a joke. (反対の意味の文に) 4. She must take care of the children all day. (yesterday を加えた文に) 5. Can I eat lunch here? (丁寧な言い方に) aho ・必要 禁止 必要 6. She is in trouble. (「~かもしれない」 という現在の推量を表す文に) 4 次の英文を日本語に直しなさい。 |総合 1. You must not eat just before bedtime. 2. Yuta may not agree with your opinion. hitooria 必要 信 3. Emma hasn't been able to get information about it yet. 4. You don't have to answer that question in a hurry. 5. Where can I get a bus ticket? 6. “Can you lend me your notebook for a few days?” “I'm sorry I can't.” る。 e to ■Writing Skills 1.彼はすばらしい野球選手に違いない。彼はたくさんのホームランを打つ。 2. 彼女がおなかが空いているはずがない。 ちょうど昼食を食べたところだ。

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TOEIC・English Undergraduate

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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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