When a temperature sensor surpasses a threshold, your local nuclear power facility alarms. Core temperature is gauged. Consider the Boolean variables A (alarm sounds), FA (faulty alarm), and FG (faulty gauge) and the multivalued nodes G (gauge reading) and T (real core temperature). Since the gauge is more likely to fail at high core temperatures, draw a Bayesian network for this area. c. Is your network polytree? Why/why not?

When someone presses SEND on a cellular phone, the phone attempts to set up a call bytransmitting a “set-up” message to a nearby base station. The phone waits for a response and ifnone arrives with 0.5 second, it tried again. If it doesn’t get a response after n = 6 tries, the phonestops transmitting messages and generates a busy signal.(a) Draw a tree diagram that describes the call set up procedure.(b) If all transmissions are independent and the probability is “p” that a set-up message will getthrough, what is the PMF of “K”, the number of messages transmitted in a call attempt ?(c) What is the probability that the phone will generate a busy signal ?(d) As manager of a cellular phone system, you want the probability of a busy signal to be lessthan 0.02. If p = 0.9, what is the minimum value of n necessary to achieve this goal ?

1. Consider the method m1() in Question 2. Consider the AOR[1] and the ROR[2] mutation operators. a. What is the minimum number of mutants necessary for coverage comparable to the strongest graph coverage criteria? Justify. b. List such minimum set of mutants. c. Pick one of your mutants and provide the reachability conditions, infection conditions, propagation conditions, and test case values to kill that mutant. 1. public int m1() { 2. 3. outVal = 1; 4. 5. 6. 7. 8. 9. 10. } 11. 12. } 13. 14. 02 = new C2(); GPA 02.m2(); (stub) if GPA > 3.5 { outVal outVal * 10 + 10%; www wwwwwwwwww if major "ICS" { outVal = outVal * 2; wwwwwwwww outVal outVal / 2; wwwwwwww wwwwwwwwwwww elsif GPA 2.5 { wwwwww outVal = 60 30 ✶ outVal; wwwwwww - wwwwwwwww 15. } 16. else { 17. 18. } 19. outVal = 20 outVal + 20; wwwwwwww return outVal; wwwwwwwwww wwwwww 20. }

Consider the hypothesis space defined over instances shown below, we characterize each hypothesis (apple taste) by 4-tuples. Please hand trace the ID3 classifier to build a decision tree, then predict the target value Taste=Sweet/Tart for the following instances: a) <Red, High, Some, No> b) <Red, Low, Some, Yes> c) <Yellow, Low, Some, No> d) <Green, High, None, No> e) <Green, Mid, Some, Yes> Now suppose the actual taste of the five apples above are actually “Sweet, Sweet, Sweet, Tart, Tart”, what is the accuracy of the decision tree? Please show all the steps and include the corresponding confusion matrix for accuracy calculation. (please................ as soon as give correct solution)