Ques 6 Your goal is to navigate a robot out of a maze. The robot starts in the center of the maze facing north. You can turn the robot to face north, east, south, or west. You can direct the robot to move forward a certain distance although it will stop after hitting a wall. a) Formulate this problem. This means you will have to describe initial state, goal test,successor function. and cost function. Successor function is a description of the robots successive actions after the initial state. We’ll define the coordinate system so that the center of the maze is at (0, 0), and the maze itself is a square from (-1,–1) to (1, 1)

22. Many CPU-scheduling algorithms are parameterized. For example, the RR algorithm requires a parameter to indicate the time slice. Multilevel feedback queues require parameters to define the number of queues, the scheduling algorithms for each queue, the criteria used to move processes between queues, and so on. These algorithms are thus really sets of algorithms (for example, the set of RR algorithms for all time slices, and so on). One set of algorithms may include another (for example, the FCFS algorithm is the RR algorithm with an infinite time quantum). What (if any) relation holds between the following pairs of algorithm sets? a. Priority and SJF b. Multilevel feedback queues and FCFS c. Priority and FCFS

23. Suppose that a CPU scheduling algorithm favors those processes that have used the least processor time in the recent past. Why will this algorithm favor I/O-bound programs and yet notpermanently starve CPU-bound programs?

1) In a computer instruction format, the instruction length is 11 bits and the size of an address field is 4 bits. Is it possible to have: 5 two-address instructions 45 one-address instructions 32 zero-address instructions using the specified format? Justify your answer. b) Assume that a computer architect has already designed 6 two-address and 24 zero address instructions using the instruction format above. What is the maximum number of one-address instructions that can be added to the instruction set?

5. What is an IP address? What is a mnemonic address? Using the 32-bit representation,how many different domains are allowed? And how many machines in a domain are allowed? (8 points)

Assignment 7: Exploring 3D Sinusoidal Data using Artificial Neural Networks DTSC 680: Applied Machine Learning Name: Directions and Overview The main purpose of this assignment is for you to gain experience using artificial neural networks to solve simple regression problems. In this assignment, you will fit a neural network to a noisy 3D sinusoidal data set. You will use a Sequential model that can be trained very quickly on the supplied data, so I want you to manually adjust hyperparameter values and observe their influence on the model's predictions. That is, you should manually sweep the hyperparameter space and try to hone in on the reasonable hyperparameter values, again, manually. (Yep, that means guess-and-check: pick some values, train the model, observe the prediction curve, repeat.) So, play around and build some models. When you are done playing with hyperparameter values, you should finish by building an ANN that models the data reasonably well! You should be able to train a model and use it to predict a curve at least as good as mine, but your goal should be to obtain a smoother and less erratic curve. (Side Note: Achieving a less erratic prediction curve could be done either by building a better model, OR by sorting the data more intelligently thereby plotting a prediction curve that looks better. I propose the ideal line is created by sorting the data in such a way that the resulting line minimizes the arc length of the curve. You don't need to worry about any of this, however you do need to generate a figure with a descent-looking prediction curve superimposed on the data.)

1. Vary the value of k from 1 through 10. Based on the continuous (kNN regression) prediction error measures, what is the optimal number of nearest neighbors employ? a. 1 b. 2 c. 3 d. 4 e. 5 f. 6 g. 7 h. 8 i. 9 j. 10/n2. Based on the confusion matrix you observe when k = 5, calculate the prediction accuracy of the KNN classifier in cell B30 (Hint: overall accuracy is the proportion of the 20 predictions that were correct, i.e., on the diagonal of the confusion matrix). a) 65% b) 35% c) 40% d) We cannot answer this question without more information./n3.Vary the value of k from 1 through 9. Considering the discrete, KNN classifier's prediction accuracy, what is the optimal number of nearest neighbors employ, i.e., what value of k maximizes the prediction accuracy? a. 1 b. 2 c. 3 d. 4 e. 5 f. 6 60 h. i. 7 8 9/n4. Yes, or no? Would either of these kNN predictive models be useful for generating recommendations related to a new song that has just been released, i.e., which has not yet been rated by any users, or for a new user, i.e., a user who has not yet entered any song ratings? (Hint: you can assume that you would have details on the new user or the new song). a. YES b. NO

13) Suppose a RISC machine uses overlapping register windows with: 10 global registers 6 input parameter registers 10 local registers 6 output parameter registers How large is each overlapping register window?

Question 5. Let R = (A, B,C, D) be a relation schema and let F = {C →D,C → A, B → C} be a set of FDs. Is R in Boyce-Codd normal form (BCNF) with respect to F? If it is explain why, otherwise decompose R into a set of schemas in BCNF.

3) Given 16-bit instructions, is it possible to use expanding opcodes to allow the following to be encoded assuming we have a total of 32 registers? If so, show the encoding. If not, explain why it is not possible. 60 instructions with 2 register operands 30 instructions with 1 register operand 3 instructions with one 10-bit address 26 instructions with zero operands