Description

• Submit the pdf and LATEX versions inside an archive of type tar, zip, or rar
• Add any referenced article to the archive
• Follow the group submission guidelines
• Any work that fails to comply with the previous requirements will be ignored
Ex. 1 — Tenses
Rewrite the following paragraph twice: using (i) past and (ii) present tenses.
By the time Tom (notice) the doorbell, it already (ring) three times. As usual, he (listen) to loud music on his stereo. He (turn) the stereo down and (stand up) to (answer) the door.
An old man (stand) on the steps. The man (begin) to (speak) slowly, (ask) for directions.
Ex. 2 — Passive voice
Convert each sentence to the passive voice:
1. I would like to thank you for your kindness.
4. The customer raised several objections to the decision.
5. We have processed your application.
Ex. 3 — Positive tone
What is the problem with the following sentences? Rewrite them, improving the tone.
1. Your misunderstanding of our letter caused you to make this mistake.
2. Your comment on the second paragraph of page four indicates that you know absolutely nothing about our field of research.
3. The results are incoherent for one-tenth of the experiments.
4. Since not enough care was taken for the experimental setup the results cannot be as precise as they could have been.
5. As many other authors [5, 12, 14, 15] we failed to achieve a 100% purity level as we only reached a level of 99.7%.
Ex. 4 — LATEX
Create a bibTEX database including at least 10 articles or books of your choice. Then write a random text (e.g. copy/paste the “lorem ipsum” text) and add at least four references to elements of the database.
Note: do not include the referenced items in the final archive.
Ex. 5 — Group exercise
The following text was extracted form an anonymous draft. It contains many issues of any kind (grammar, conjugation, long and unclear sentences, transitions…). Rewrite it.
In traditional two round-trip secure search method, the trapdoor of each keyword list are generated by the data owner or his agent and the user will search them. This will be a time-consuming job for mobile device and will degrade the user experience significantly. Here in our method each trapdoor can be generated on its own device.
The user side stores the noise set and a frequently-used cache. The cache originally store the keywords which are commonly used in the corpus and corresponding characteristic bits. If the user input a keyword, it will do stemming process first and then search the cache, and if hit it will return the intermediate trapdoor immediately, otherwise it will calculate the characteristic bits and and the keyword to the cache for next use. The whole process can be presented as Fig. 4. By introducing cache, the trapdoor generation time can be efficiently reduced.