1 . 短语汉译英
1. 为老百姓提供花小钱买高质商品的机会
2. 年轻时尽可能多学技能以备不时之需
3. 有助于缓解城市交通拥挤
4. 轻而易举获得教育、医疗和其他各种资源
5. 上交质量远远不尽如人意的寒假作业
6. 提醒自己在感到焦虑的时候放松
7. 留下一张解释来龙去脉的纸
8. 能否按时完成任务，拭目以待
9. 采取比预期更有效的措施

2 . Directions: Fill in each blank with a proper word chosen from the box. Each word can be used only once. Note that there is one word more than you need.

A. consisted 　B. composing 　C. measures　 D. account　 E. patterns　 F. limitations G. moderately　 H. progressed　 I. distribution　 J. significantly　 K. complement

AI can distinguish between bots and humans based on Twitter activity

Artificial intelligence is being used to spot the difference between human users and fake accounts on Twitter.

Emilio Ferrara at the University of Southern California in the US, and his colleagues have trained an AI to detect bots on Twitter based on differences in     1     of activity between real and fake accounts.

The team analysed two separate datasets of Twitter users, which had been classified either manually or by a pre-existing algorithm as either bot or human.

The manually verified dataset     2     of 8.4 million tweets from 3500 human accounts, and 3.4 million tweets from 5000 bots.

The researchers found that human users replied four to five times more often to other tweets than bots did. Real users gradually become more interactive, with the fraction of replies increasing over the course of an hour-long session of Twitter use.

The length of tweets by human users also decreased as sessions     3    . “The amount of information that is exchanged diminishes,” says Ferrara. He believes that the change may result from a cognitive depletion over time, in which people become less likely to expend mental effort    4     original content.

Bots, on the other hand, show no changes in their interactivity or the length of information they tweet over time.

The team also analysed the amount of time between any two consecutive (连读的) tweets from a single user. When this     5     is plotted, bots showed spikes for certain time gaps, such as tweeting at 30-minute or 60-minute intervals.

The team then combined these     6     to train an existing bot-detection algorithm, called Botometer, on the difference in activity patterns. The AI was     7     more likely to accurately detect to fake accounts than when it was not taking into     8     the timing of posts.

The algorithm could be used to     9     other bot-detection tools that analyse the language within posts, says Ferrara.

One of the study’s     10     is that the Twitter data the team analysed is from three years ago. In that time, it’s possible that bots have become more human-like in their activity patterns.