Create a process graph for the following program. int main () { int x = 4; if (fork() == 0) printf("printf1: x=%d\n", ++x); printf("printf2: x=%d\n", --x); exit 0; } Solution (may be hand-drawn): Create a process graph for the following program. int main () { fork(); printf("hello\n"); fork(); printf("world\n"); exit 0; } Solution (may be hand-drawn):

Create a process graph for the following program. int main () { int x = 4; if (fork() == 0) printf("printf1: x=%d\n", ++x); printf("printf2: x=%d\n", --x); exit 0; } Solution (may be hand-drawn): Create a process graph for the following program. int main () { fork(); printf("hello\n"); fork(); printf("world\n"); exit 0; } Solution (may be hand-drawn):

Systems Architecture

7th Edition

ISBN:9781305080195

Author:Stephen D. Burd

Publisher:Stephen D. Burd

Chapter10: Application Development

Section: Chapter Questions

Problem 14VE

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The first known correct software solution to the critical-section problem for two processes was developed by Dekker. The two processes, P0 and P1, share the following variables: boolean flag[2]; /* initially false */ int turn;The structure of process Pi (i == 0 or 1) is shown below; the other process is Pj (j == 1 or 0). Prove that the algorithm satisfies all three requirements for the critical-section problem.while (true) { flag[i] = true; while (flag[j]) {if (turn == j) { flag[i] = false; while (turn == j) ; /* do nothing */ flag[i] = true;} }/* critical section */ turn = j;flag[i] = false;/* remainder section */ }
121. The circular wait condition can be prevented by defining a linear ordering of a. Program Type b. User Type c. Resource Type d. Process Type
The language must be in python. Neural Network Units weights of [-1.2, -1.1, 3.3, -2.1] two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples
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Pls help.me answer the following , show neat and whole solution and kindly send a screenshot of simulation.
The language must be in python. The code for parts 1-4 is below. Just do parts 5-7 Neural Network Units Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1] Calculate the outputs for two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative? is example 2 positive or negative? Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples import numpy as np import math def forward(x,w): t = np.dot(x.T,w) t = 1/(1+math.e**(-t))…
The language for coding must be in python Neural Network Units Implement a single sigmoid neural network unit with weights of [-1.2, -1.1, 3.3, -2.1] Calculate the outputs for two training examples:Example 1: [0.9, 10.0, 3.1, 1]Example 2: [0.9, 2.1, 3.7, 1] Note that you don't have to explicitly include a threshold or bias since the examples include a last element of 1 which means that the last weight effectively operates as a threshold. Assuming that a sigmoid unit response >0.5 denotes a positive class and <0.5 is negative class, is example 1 positive or negative? is example 2 positive or negative? Create a single ReLU unit and provide the outputs for those examples. Calculate the derivative of the sigmoid with respect to net input for both examples Calculate the derivative of the ReLU with respect to net input for both examples
Explore the bakery algorithm for the N-process critical problem. Explain all the conditions for good solution to a critical section problem against the bakery algorithm. Your explanation needs to have a proper scenario you can discuss the scenarios either diagrammatically or descriptively.
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Compare mathematical programming versus discrete event simulation approaches to the critical path home construction problem, discussing advantages and disadvantages of each approach. If you ran your own construction company, which approach would you use?
Explore the bakery algorithm for the N-process critical problem. Explain all the conditions for good solution to a critical section problem against the bakery algorithm. Your explanation needs to have a proper scenario you can discuss the scenarios either diagrammatically or descriptively. Note : Code Must be in c++
Step 1. Develop a Java/Python/C++ program that will build a dynamic database of consumer network activity. You must use data collected from your home network. Or you can collect this data from the lab (Room 108). Step 2. Develop a Java/Python/C++ program that will identify deviations from the consumer usage patterns to detect hostile activities. Step 3. Develop test data for simulation and provide visualizations of results and output data. use python, tell me the complier used, include your dataset so i can use it to test it. explain the steps