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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

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数学 高校生

問題3枚目、図・表1.2枚目です。問題の2.3.4.が分からないです。わかる所だけでも解説よろしくお願いします。

20 TV 34 2019 年度 総合問題 次の文章を読んで、後の問1~問5に答えなさい。 図1は、経済協力開発機構(OECD) 印度でいるのが国の相対的武術の タである。 相対的貧困率とは、各国の所得分布における中央値の50%に満たない 人々の総人口に占める割合である。 20% 18% 16% 14% 12% 10% 8% 6% 4% 2% 0% チェコ フィンランド フランス アイスランド デンマーク 5 オランダ ノルウェー スロバキア オーストリア スウェーデン スイス ベルギー スロベニア アイルランド イギリス ドイツ ハンガリー ルクセンブルク ニュージーランド ポーランド 5-5 OECD平均 福山市立大・柳瀬 韓国 カナダ イタリア ポルトガル オーストラリア ギリシア スペイン 図1 相対的貧困率の国際比較」 スエチ エ 日本 チリ リトアニア 「ラトビア ストニア トルコ イスラエル アメリカ 福山市立大 表 世帯総 平均世帯 相対的 平坦 中 15.7 注1) 各国のデータは,2012年~2016年のデータの中で最新のデータをもとにし ている。 出典:経済協力開発機構 (2018), Income distribution, OECD Social and Welfare Statistics (database), https://doi.org/10.1787/data-00654-en をもとに作成 ETUT ROB09229 表1は,日本における世帯数と世帯人員,各世帯の所得などの年次推移を示してい る。表2は,各国の絶対的な貧困率を示すデータである。絶対的な貧困率とは、経済 的な理由のために,食料が買えない,医療を受けられない、衣服が買えないなどの状 態に,過去1年間に陥ったことがある割合を示している。 torn at T som med sin blunded vonom an

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英語 高校生

英文がわからないです心の優しい方、英文の解き方を教えて欲しいです🙇‍♀️

35 15 20 signatures in business. However, no one used fingerprints in crime work until the late In ancient times, people used fingerprints to identify people. They also used them as 1880s. Three men, working in three different areas of the world, made this possible. (1) The first man who collected a large number of fingerprints was William Herschel. He worked for the British government in India. He took fingerprints when people (7) official papers. For many years, he collected the same people's fingerprints several times. He made an important discovery. Fingerprints do not change over time. At about the same time, a Scottish doctor in Japan began to study fingerprints. Henry Faulds was looking at ancient Japanese pottery* one day when he noticed small It occurred to him that the lines were 2,000-year-old fingerprints. Faulds wondered, "Are fingerprints unique to each person?" He began to take fingerprints of all his friends, co-workers, and students at his medical school. Each print was (). He also wondered, "Can you change your fingerprints?” shaved the fingerprints off his fingers with a razor to find out. Would they grow back lines on the pots. (2) He the same? They did. One day, there was a theft in Faulds's medical school. Some alcohol was missing. Faulds found fingerprints on the bottle. He compared the fingerprints to the ones in his records, and he found a match. The thief was one of his medical students. By examining fingerprints, Faulds solved the crime. Both Herschel and Faulds collected fingerprints, but there was a problem. It was very difficult to use their collections to identify a specific fingerprint. Francis Galton in England made it easier. He noticed common patterns in fingerprints. He used these to help classify fingerprints. These features, called "Galton details," made it easier for police to search through fingerprint records. The system is still in use today. When 25 police find a fingerprint, they look at the Galton details. Then they search for other fingerprints with similar features. (4) Like Faulds, Galton believed that each person had a unique fingerprint. According to Galton, the chance of two people with the same fingerprint was 1 in 64 billion. Even the fingerprints of identical twins are ( ). Fingerprints were the perfect tool to 30 identify criminals. For mo than 100 years, no one found two people with the same prints. Then, in 2004, terrorists (I) a crime in Madrid, Spain. Police in Madrid found a fingerprint. They used computers to search databases of fingerprint records all over the world. Three fingerprint experts agreed that a man on the West Coast of the United States was one of the criminals. Police arrested him, but the experts were wrong. The man was innocent. Another man was (). Amazingly, the two men who were 6,000 5 10 136 Lesson 日本大学 470 words 22 (3) 23 024 25 26

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英語 高校生

ELEMENT2 lesson3のページです。Cのリスニングの解答わかる方、教えて欲しいです

B Reading for details : Fill in the blanks with the words in the box below, and then choo, To Comprehension, daLbaboan d Vocabulary Reading for main ideas: Choose the best answer. orfw tong 00g 1. What is the main idea of the passage? A Choose the correct SS a 1. The report he m space. mitaoo poY 2. I felt an overw ©A professor kindly agreed to let a promising teenager use his lab 2. Teenagers can make the world a better place by @ living up to their uncle's words bgetting a lab to carry out experiments working hard and going through many trials 3. Although there out his plans. 4.I am disappoir 5. It takes a lot c SS the correct title for each part. C d > Pancreatic cancer tends to be diagnosed too late due to poor detection (1. >A high school student named Jack Andraka decided to come up with a 1 way to detect it when his close friend (2.g1 ) the disease. Part 1 B Choose the c > Jack found a database of (3 Protelns number of proteins to be checked was no less than 8,000. S which are related to pancreatic cancer, but ih 1019o6 1. I'm sorry, Part 2 > He went through the list one by one and found a protein that can be used o. (4.moyke) to detect the cancer. 2. They were 3. Most peo > Jack wrote to 200 professors and asked them to let him use their lab space fr further experiments, but almost all of them (5k0eyhis request. Part 3 (よ) anterected. C Fill in the > One professor was (6. in his plans and invited him for an interview. > Jack managed to answer all the (7. 0esting 1. There w , Ther ) asked at the interview and gu space in a lab. > Several months later, he successfully came up witha new detection method which is faster, cheaper and more (8. new method. 一→ Part 4 dnie 2.I found →I ca ). He changed the world with the accurceと 3. It tool Titles → Itt Words 9 O Finding the Marker Protein D His Success and a Great Detection Method 4. I am interested / proteins / accurate got / questions / rejected marker / methods →I © Jack Andraka's Decision © Trying to Get Lab Space 5. She c Listening for details : Listen to the statements and answer T(true) or F(false).. Tips pa D Retelling the story : Look at the pictures on pages 48-49, and retell the story. 味で 52 A Teenager To Change the World 8-

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