Humans are startlingly bad at detecting fraud. Even when we're on the lookout for signs of deception, studies show, our accuracy is hardly better than chance. Technology has opened the door to new and more pervasive forms of fraud: Americans lose an estimated $ 50 billion a year to con artists a-round the world, according to the Financial Fraud Research Center at Stanford University. But because computers aren't subject to the foibles of emotion and what we like to call "intuition," they can also help protect us. Here's how leading fraud researchers, neuroscientists, psychiatrists, and computer scientists think technology can be put to work to fight fraud however it occurs—in person, online, or over the phone. Spam filters are supposed to block e-mail scams from ever reaching us, but criminals have learned to circumvent them by personalizing their notes with information gleaned from the Internet and by grooming victims over time. In response, a company called ZapFraud is turning to natural-language analytics; Instead of flagging key words, it looks for narrative patterns symptomatic of fraud. For instance, a message could contain a statement of surprise, the mention of a sum of money, and a call to action. "Those are the hallmark expressions of one particular fraud e-mail," Markus Jakobsson, the company's founder, told me. "There's a tremendous number of[spam]e-mails, but a small number of story lines. " A similar approach could help combat fraud by flagging false statements on social media. Kalina Bontcheva, a computer scientist who researches natural-language processing at the University of Sheffield, in England, is leading a project that examines streams of social data to identify rumors and esti mate their veracity by analyzing the semantics, cross-referencing information with trusted sources, identifying the point of origin and pattern of dissemination, and the like. Bontcheva is part of a research collaboration which plans to flag misleading tweets and posts and classify them by severity: speculation, controversy, misinformation, or disinformation.
单选题 What does Paragraph 1 say about fraud?
【正确答案】 C
【答案解析】解析:细节题。文章第一段“即使在诈骗迹象初现端倪时,我们就保持警觉状态,但是我们判断诈骗的准确度不比我们的运气好多少。科技为更新、更普遍的诈骗形式打开了方便之门。”[A]crack down on“镇压,制裁”文章未提及。[B]“很容易质疑诈骗”与文中意思相反。[D]与文意不符。
单选题 As far as fraud is concerned, technology______.
【正确答案】 D
【答案解析】解析:细节题。由technology定位到第—段第三句。文中提到“科技为更新、更普遍的诈骗形式打开了方便之门”,“科技也可以为我们保驾护航”。[A]和[B]文中未提及。[C]与文中意思相反。
单选题 Para. 2 suggests that spam filters will function properly if______.
【正确答案】 D
【答案解析】解析:推理题。题干“垃圾邮件过滤软件将会正常运转如果……”,第二段提到诈骗分子通过搜集个人信息,发送个性化邮件的形式来进行诈骗。所以[D]“网民的隐私得以被保护不落人骗子的手中”为正确选项。[A]“诈骗邮件被精确地发送给接收者”和[B]“基本的安全漏洞没有被修补”及[C]“详尽的受害者名单被提供给黑客”文章均未提及。
单选题 By saying "a tremendous number of[spam]e-mails, but a small number of story lines" , Mr. Jakobsson probably means that______.
【正确答案】 C
【答案解析】解析:细节题。[A]“诈骗手段数量惊人并且有很完善的理论”文章未提到完善的理论。[B]“诈骗手段数量惊人但是受到了规则的限制”文章未提到规则限制了这样的邮件。[D]“犯罪分子不断想出新点子去行骗”。文中说到“垃圾邮件数不胜数,但编故事的套路却是掐指可数”,所以[C]为正确选项。
单选题 Natural-language analytics can do the followings except______.
【正确答案】 A
【答案解析】解析:细节题。本题问“自然语言分析学能做以下的事情但除了……之外”。[A]尤指“出庭作证”不合题意。[B]“标记”;[C]“分类”;[D]“对比”。