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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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物理 大学生・専門学校生・社会人

(1)〜(4)の解き方って合っていますでしょうか。また、(5)の問題が分からなかったので教えていただきたいです🙇左が問題、右が解いたものです。

問4 軽いバネの片端を壁に固定し、 他端に質量mの物体をつけて粗い床面に置いた、水平パネ振り子を考 える。 バネが自然長の時の物体の位置を=0とし、 バネが伸びる向きに軸正をとる。 物体は床面から速度 と逆向きの抵抗力-bu を受ける (6は比例定数)。時刻 t = 0 に、 原点にある物体に正の初速度 vo を与える と、バネ定数にがん=だったため、このパネ振り子は臨界減衰振動をした。 この時、任意の時刻 t におけ る物体の位置(t) は右下のグラフのようになり、y=を用いて以下の式で表せる。 (t)=votent 以下の間に、mo, のうち、 必要な記号を用いて答えよ。 (自然対数の底eは数字なので、当然使用可。) (1) 最初に物体の速さが0となる時刻 to を求めよ。 (2) 時刻 to の物体の位置 z (to) を求めよ。 (3) 時刻 to までにバネが物体にする仕事 W を求めよ。 (4) 時刻 to までに床からの抵抗力が物体にする仕事 Wa を、 (3) の結果を用いて求めよ。 (5) 【チャレンジ問題】 前問で求めたW を、 以下の積分を実行することで導け。 rx(to) = to) (-kv)dz = Wa= ・to sto (-kv)dr = √ (-bv) vdt = √ (-bv²) dt 位置 時刻

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