Answer:
Explanation:
Following are the Semaphores:
Customers: Counts waiting customers;
Barbers: Number of idle barbers (0 or 1)
mutex: Used for mutual exclusion.
Cutting: Ensures that the barber won’t cut another customer’s hair before the previous customer leaves
Shared data variable:
count_cust: Counts waiting customers. ------------copy of customers. As value of semaphores can’t access directly.
// shared data
semaphore customers = 0; semaphore barbers = 0; semaphore cutting = 0; semaphore mutex = 1;
int count_cust= 0;
void barber() {
while(true) { //shop is always open
wait(customers); //sleep when there are no waiting customers
wait(mutex); //mutex for accessing customers1
count_cust= count_cust-1; //customer left
signal(barbers);
signal(mutex);
cut_hair();
}
}
void customer() {
wait(mutex); //mutex for accessing count_cust
if (count_cust< n) {
count_cust= count_cust+1; //new customer
signal(customers); signal(mutex);
wait(barbers); //wait for available barbers get_haircut();
}
else { //do nothing (leave) when all chairs are used. signal(mutex);
}
}
cut_hair(){ waiting(cutting);
}
get_haircut(){
get hair cut for some time; signal(cutting);
}
Answer:
D. Subscriber
Explanation:
Digital Subscriber Line is a family of point-to-point technologies designed to provide high-speed data transmission over traditional telephone lines.
The high speed data transmission property helps to transmit data in a fast and timely manner between two or more points or people during calls, texts and other activities.
Generation Y are the people age 14-37, which means they are born 1977 - 2000.<span /><span>Tweens are the younger edge of gen Y who have never lived without the internet or easy access to cell phones.</span>
<span /><span>
</span>
The two different uses or applications of data that is biases in word embeddings and may cause significant ethical harms are:
- Class immobility
- Systemic racism
<h3>What are the
biases in word embedding?</h3>
Word embeddings is known to be made up of a high level bias such as group stereotypes and prejudice.
The two different uses or applications of data that is biases in word embeddings and may cause significant ethical harms are:
Class immobility
Learn more about biases from
brainly.com/question/24491228
#SPJ1
