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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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31 B 残高試算表 土 3,000 支払 受取 受取 仕 7,000 給 2,000 600 支払保険料 200 通 150 支払 利 28,200 201年12月31日 借 方 2.350 現 1,900 当座預 1,200 1.800 売掛 1,000 2,000 5,000 建 受取手 繰越 商 備 日日金金形金品品物地形金金額額金金上賃代入料料費息 家地 貸 方 手 1,250 借入 5,000 貸倒引当金 100 備品減価償却累計額 建物減価償却累計額 資 本 720 900 8,000 繰越利益剰余金 売 1,000 10,000 700 530 保信 品 | 取得原価 減価償却累計額 当期減価償却額 物 取得原価 減価償却累計額 当期減価償却額 前払保険料支払保険料前払高 (4ヵ月分) 前未未 受 家賃 家賃前受高(2ヵ月分) 息 利息未払高 (6ヵ月分) 代 地代未収高(3ヵ月分) 手 未使用高 家利地切 2,000 2017080 360 5,000 920 900/50 150 3,950 200 100 150 210 80 収 郵便 テストで 解答 (借) 雑 (借) 仕 (借) 繰越商品 (借)貸倒引当金繰入 (借) 減価償却費 損 100 入 1,000 900 (貸)仕 20 (貸)現 (貸)繰 越商 (貸)貸倒引当金 金品 100 1,000 入 900 20 510 (貸) 備品減価償却累計額 360 建物減価償却累計額 150 (借)前払保険料 28,200 (借)受 (借)支 取払収 家利地 蔵 料賃息代品 200 (貸)支払保険料 100 (貸)前 受 150 (貸) 未 払 210 (貸)受 取 80 (貸)通 信 料賃息代費 家利地 200 100 150 210 80 (借) 未 棚卸表 (借)貯 勘定科目 20×1年12月31日 摘要 内訳 現 金 帳簿残高 金額 2,350 不足額(原因不明) 100 越商品 A商品 @ ¥1060個 2,250 600 B商品 @¥1520個 せいさんひょう 300 受取手形 期末残高 900 1,200 貸倒引当金残高 ¥40, 第2節 8桁精算表の作成 精算表は,決算振替仕訳によって帳簿決算を行う前に決算手続の妥当性 を概観するためや損益勘定を作成する前に当期純利益の金額を事前に把握 売掛 貸倒引当金 (受取手形残高の4%) ¥8 金 期末残高 48 1,152 1,800 貸倒引当金残高 ¥60 けたせいさんひょう | 貸倒引当金(売掛金残高の4%) ¥12 72 1,728 ある。 8桁精算表の作成の手順は、次のとおりである。 するためなど,決算手続を行うときの参考資料として作成される作業表で 貸倒引当金 100 120

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