学年

質問の種類

情報:IT 高校生

この問題をわかりやすく解説して欲しいです🙇

今の さい。 (1) ①~② に当てはまる語句または数値を答えなさい。 メモリの実効アクセス時間は、 実際の1アクセスに要する平均時 間である。 キャッシュメモリ上に求めるデータがある確率(ヒット率) をHとすると、 この平均時間は、 (1) ① 主記憶のアクセス時間× ( ② ) ② (キャッシュメモリのアクセス時間× ( ① )) + で表される。 (2) あるプログラムをコンピュータA で実行したときのキャッシュメモ リのヒット率と実効アクセス時間は, コンピュータBで実行したと きと同じになった。 この時のキャッシュメモリのヒット率を答えな さい。 14 ◆コンピュータの動作 以下は、仮想プログラミング言語にしたがって, 乗算 (xXy=z)の計算をして13番地に結果 (z) を書き込むための プログラムである。 乗算命令は無いので, 加算命令を繰り返すことで(x をy回加算) 実現する。 ①~③に当てはまる命令を答えなさい。 なお, AレジスタとBレジスタを使うものとする。 (2) 仮想プログラミング言語命令一覧 番地 主記憶装置 READ r. (adr) adr番地のメモリから 1 READ A, (13) r レジスタに読み出し 2 READ B, (12) WRITE (adr),r rレジスタから adr 番 地のメモリに書き込み 3 (①) Ir レジスタとadr 番地 (2) ADDr. (adr)の和を計算 4 r=r + adr 番地の値 or レジスタとadr 番地 5 JNZ (3) SUBr, (adr)の差を計算 ③ r=radr 番地の値 6 (③) 直前の計算結果が零の 場合は何もせず 7 STOP JNZ (adr)零の時だけ (adr) 番地 の命令へ順番を戻す (ジャンプする) 10 10 STOP プログラムの停止 11 7 X 12 3 13 y Z

未解決 回答数: 1
TOEIC・英語 大学生・専門学校生・社会人

この長文問題の答えと解説をお願いします。

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

回答募集中 回答数: 0