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英語 高校生

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" 8.日本語に合うように、空所に適切な英語を入れなさい。 (1) この店ではりんごはみかんより人気があります。 Apples are mare Popular than (2) 東京スカイツリーは日本で最も高い建物です。 the highest Tokyo Skytree is (3) 兄は私よりもたくさんの本を持っています。 My older brother has more books most beautiful (4) これは5つの中で最も美しい絵です 。 This is the oranges in this shop. building in Japan. than I do. painting of the five. 9-1. 次の日本語に合うように,( )に適切な英語を入れなさい。 (1) 私たちの教室は毎日そうじされます。 Our classroom ( is (2) このいすは木で作られています。 This chair ( )( cleaned ) every day. made ) ( of ) wood. (fregsuawttg) (3)これら2つの部屋はあまり使われないです。 These two rooms (aven't much. )(ofler 9-2( )内の英語を適切な形に変えなさい。(ただし, 1語になるとは限らな (1) I am (old) than my sister. older good (2). Your room is (big) than mine. bigger (3) This question was (difficult) than the others. more difficult 9.3例にならって,各単語を比較級と最上級にしよう。 (例1) long (longer) (longest) - (2) beautiful - (more beautiful) - (most beautiful) colder 1) cold - ( 2) safe - ( Safer )-( coldest ) )-( Safest )(happiest ) )-( biggest ) )-( best 3) happy (happier 4) big - ( bigger 5) good - (better 6) many/much - ( more 7) difficult - (more difficult 8) exciting (more exciting )-(most) )-(most difficult ) )-(most exciting)

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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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