attributes that increment the probability that the document belongs to a class‚ are included in the classification model.\looseness=-1 The LSPTAN heuristic initially builds the model based on Naive Bayes and initializes a set of orphans $O$‚ inserting into $O$ all the terms of the vocabulary. Then‚ for each test document‚ the technique evaluates each term as a Super Parent ($a_{sp}$) and‚ at the end‚ it selects as $a_{sp}$ the term that has the highest probability $P(c_i | d_t‚ a_{sp})$. Thus
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because the WS may consist of multiple operations. Thus‚ the authors adopted finite state machine to determine the Web service (WS) operations invocation sequences. Additionally‚ the authors defined an aggregated reliability (AR) to measure the probabilities of leading to successful execution in a failure-prone environment. Then‚ the authors had proved that the AR computation equals to the calculation of the eigenvector. They proposed to derive AR using a power method. Besides‚ the authors proposed
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in this case‚ the sample size is 64-bottles of beers are large enough to assume the distribution for a probability is approximately normal nπ=64(0.5)=32>5n1-π=641-0.5=32>5 Among the three new machines‚ the population mean was obtained at 16 ounces with a standard deviation of .16 ounces. By obtaining a Z-score at -.35 for the sampling distribution‚ it’s believed that the probability of the new machines producing a group of 64 bottles with a mean of 15.993 ounces will be 36.32% In another
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18. Compute the following probabilities: i) P (AB)=? [4 points] P (AB)=P[A]+P[B]-P[AB]=0.35+0.40-0.18=0.57 ii) P(AB)=? P[A B] P[ A B] 0.18 0.45 P[ B] 0.40 1 of 6 [4 points] Name: Problem 2 A diagnostic test for a certain disease is said to be 90% accurate in that‚ if a person has the disease‚ the test will detect it with probability 0.9. Also‚ if a person does not have the disease‚ the test will report that he or she does not have it with probability 0.9. Only 1% of the population
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applications: Probability Sampling: Simple Random Sampling‚ Stratified Random Sampling‚ Multi-Stage Sampling * What is each and how is it done? * How do we decide which to use? * How do we analyze the results differently depending on the type of sampling? Non-probability Sampling: Why don’t we use non-probability sampling schemes? Two reasons: * We can’t use the mathematics of probability to analyze the results. * In general‚ we can’t count on a non-probability sampling scheme
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and graphs; algebra and coordinate geometry; | |simultaneous linear equations; polynomial and quadratic functions and equations; calculus‚ including bilinear‚ exponential and logarithmic | |functions; two dimensional vectors and matrices; and probability. | | | |MPS Adopted Textbook/Instructional
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Zero Knowledge Authentication Scheme based on Discrete Log Vineeth Pillai Department of Computer Science‚ Illinois Institute of Technology‚ Chicago‚ USA vipillai@hawk.iit.edu‚ CWID: A20260824 April 26‚ 2012 Abstract: This paper details a variant of the parallel version of zero knowledge proof of identity which tries to optimize the space usage and number of iteration by not sacrificing the soundness factor. This protocol could a suitable candidate for smart card based authentication schemes
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could affect Nicolas balance standing up. Probability:3 Severity:4 Total risk: 12 Take a different route or assess the path/road way‚ and guide her right around the glass‚ giving her a safe route. Observe Nicola right around the glass thoroughly. Uneven Ground Due to Nicola being partially sighted and needing a Zimmer frame for steadiness‚ she may not be able to see and feel that the pavement isn’t flat and has bumps and indents and could trip. Probability: 4 Severity:3 Total risk: 12 Watch Nicolas
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15 percent probability of a boom‚ a 75 percent chance of a normal economy‚ and a 10 percent chance of a recession. What is your expected rate of return on this stock? a. 5.00 percent b. 6.45 percent c. 7.30 percent d. 7.65 percent e. 8.30 percent EXPECTED RETURN a 61. The Inferior Goods Co. stock is expected to earn 14 percent in a recession‚ 6 percent in a normal economy‚ and lose 4 percent in a booming economy. The probability of a boom is
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Analyzing Waiting Lines Most people find waiting lines irritating – waiting is idle and nonproductive time. From a service system perspective‚ however‚ a line represents a demand for service. Think of a restaurant on a Friday night. As a customer it is an irritation to have to wait 40 plus minutes for a table‚ but from the restaurant’s perspective‚ if there is not a line‚ then that means there are empty tables. Idle services are not good. So management must balance waiting time with the
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