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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 高校生

(1)の下から2行目、(2)の式変形、(3)の最後の行が分からないので教えてください🙇🏻‍♀️

ここがポイント 11 投げた位置を原点として,水平方向に x 軸を、 鉛直方向下向きに軸をとる。 小球の運動は 向には、初速度の水平成分 v COS 30° の等速直線運動、 鉛直方向には、 初速度の鉛直成分 vosin 30 直投げ下ろし運動となる。 各方向ごとに速度の式, 変位の式を立ててみる。 Vox x 1 解答 初速度の x, y 成分は √3 ~30° Vox = VoCOS 30° Vo Voy Vo 2 11 Vo (5) Vox 30° Voy 2 Vo 1 2 Voy= Vosin 30° (1) y 軸方向には初速度voy の鉛直 投げ下ろし運動をする。 「y=cnt + 1/2gt2」より h = 1/1 vot vo=√gh を代入して整理すると 0x 水面 h Vy sin 30° cos 30°= 12 √3 2 2 別解 2次方程式 公式より h 8h + y g g g t= 2 h t² 2+√1-24-0 =0 g g より(1-1+2=0 h2 t> 0 であるから t= g AA h ± 3. 20 h 11 斜方投射 知 図のように, 水面からの高さんの位置 から 小球を水平に対して30°の角度で斜め下方に速さ ghで投げ出した。 g は重力加速度の大きさを表す。 次の問いに,h, g を用いて答えよ。 (1) 小球が水面に達するまでの時間を求めよ。 (2) 小球を投げた位置から着水点までの水平距離を求めよ。 (3) 着水する瞬間の小球の速さを求めよ。 ➡ 5,6,7 h Vo 130° 水面

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