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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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理科 中学生

(1)のPaの問題を教えてください🙇‍♀️💦

1 力の合成と分解 (2) ― 3 運動とエネルギー 3. 図1のような,ともに質量320gの直方体 A,Bを使って次の実験 ①〜②を行った。これについて, 次の問いに答えなさい。ただし,質量100gの物体にはたらく重力の大きさを1Nとする。 図1 物体A 16cm 物体B 5cm .8cm 机 14cm ・8cm・・・・・・・・ 図2 図3 物体A 図 ばねばかり 物体A ばねばかり 8cm 水そう |水そう 図4 図5 物体B 物体B 物体A ばねばかり C 実験 水そう a d 水そう 水そう b リカの矢印の長さは、力の 大きさを正確に表したも のではない。 a: 物体Aにはたらく浮力 b: 物体Aにはたらく重力 ic: 物体Bにはたらく浮力 d: 物体Bにはたらく重力 ① 図2のように, 物体Aをばねばかりにつるしてゆっくりと水そうの水に入れ, 物体Aの一部が水面より上に出てい る状態で静止させた。 このとき, ばねばかりは1.8Nを示した ② 図2の状態からさらにばねばかりを下したところ, 図3のように物体Aの全体が水中に沈んだ。 このとき, 物体A は水そうの底についておらず, ばねばかりはONより大きい値を示した。 また, 物体Bを静かに水そうの水に沈めた ところ, 図4のように水に浮いた。 (1)図1で,机が物体A, Bから受ける圧力はそれぞれ何Paか。本をの (2)実験①で,物体Aにはたらく浮力は何か。 (3) 実験 ②で, 物体A, B にはたらく浮力と重力を図5のようにa,b,c, d と表す。 aとb, cとd, bとdのそれぞれの大小関係はどのように なるか。 次のア~ウ, エ~カ, キ〜ケからそれぞれ1つずつ選び, その い 31 A co Pa (1) B Pa (2) N aとb (3) cd bed 記号を書け。 100 S aとb: ア a > b イ a<ba = b cとd:エ c> d bとd: キ b>d * c<dc=d b<db = d A = d. -21-

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