2. Give an example of algorithm instructions that would violate the following criteria: (a) precision: a =

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1 CSC105, Introduction to Computer Science Exercises NAME DIRECTIONS. Complete each set of problems. Provide answers and supporting work as prescribed I. Algorithms. 1. Write a pseudocoded algorithm for the following problem. (Remember to assume the basic processor model that we studied.) Given a sequence of ten test scores input from a list, find the highest score in the sequence and output the answer. Here is an example. (Write out the algorithm instructions below.) List = { 93, 74, 80, 65, 94, 58, 79, 83, 80, 71 } 2. Give an example of algorithm instructions that would violate the following criteria: (a) precision: (b) effectiveness II. Digital Representations. 1. Given the following binary values for these ASCII characters, show their equivalent Unicode form (16- bit hex). C = a = \t =

2 2. Suppose that you invented a character code that used a precision of 20 bits. How many possible codes would be available for representing characters. Explain. 3. Suppose that you had a document with 100,000 characters in it. If you encoded with your new character code, how big a file (plaintext) would it take to store it (in bytes). 4. Which of these units expresses the closest order of magnitude for your file? (Circle) kilobytes (KB) megabytes (MB) gigabytes (GB) 5. Examine the following images. Each of these commits a digitization error. For each, identify the type of error and explain how it might be remedied (if digitized again). (a) cowpoke A. error: Explanation: (b) Riley Hall B. error: Explanation: - 2-

3 III. Search Engines. 1. Suppose that you are supplied with the following simple document sets. Document Text (Optimism by Geoffrey Stapleton) 1 Let s be optimistic 2 If the planet were destroyed 3 We d be gone but so would all 4 The people we avoid Create an inverted index that shows keywords, frequency, and document location in the table below. Term Frequency; Documents (table size is arbitrary) - 3-

4 2. The table illustrates values used to calculate the inverse document frequency (IDF) of the use of term in a collection of documents. In the last column, the IDF is weighted by log10. Why is this desirable especially given that IDF is usually combined with term frequency (TF)? Explain. term dft N / dft idft calpurnia 1 1,000, animal , sunday 1,000 1, fly 10, under 100, the 1,000, Calculate the TF- IDF for Google s top ranked page for the keyword search = link analysis. A text file version of the Wikipedia page can be found in lab materials folder in the csc105 folder in your Box account. Look for the exc01 folder. (a) Using Word, you can count the total number of words in the document (Tools/Word Count). Using Find, you can count the occurrences of each word. Here is a table to help you keep track of these occurrences. (Fill in all values and find the totals.) page # of link # of analysis totals (b) Separate Google searches can find the total number of pages that match link as a single keyword and analysis as a single keyword. hits for link hits for analysis - 4-

5 (c) We will use de Kunder s method to estimate the size of Google s index. The keyword the should appear in approximately 67.61% of the pages in the index given its normal frequency. So, find the total number of pages that match the keyword the. hits for the (divide that value by.6761 to find the estimated size of the index.) Google s index = (d) These values should give you all that you need to calculate the TF- IDF of each term and then their total TF- IDF. (Copy your answers here, but show your calculations below.) TF link = IDF link = TF- IDF link = TF analysis = IDF analysis = TF- IDF analysis = total TF- IDF = calculations: - 5-