I need help so fast- answer both questions- I have 10 minutes 1- Desc.pdf
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I need help so fast, answer both questions, I have 10 minutes 1. Describe at least one human biological variation that represents an adaptation to environmental factors. Briefly discuss a couple of the 10 things to know about race.??? 2. Discuss the Archaic forms mentioned in the lecture. What do you think helped Homo sapiens thrive and populate the world???.
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I want you to add the output of the F1 score- Precision- ROC AUC- and.pdf
I want you to add the output of the F1 score, Precision, ROC AUC, and Cohen kappa to fit in the
code below. Please don't provide me with another code. Just add the requirement I requested at
the end of my code.
Thank you in advance.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_l.
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I use the below scapy code to sniff and then spoof on the IP addresses.pdf
I use the below scapy code to sniff and then spoof on the IP addresses 1.2.3.4 and 8.8.8.8 and it
works perfectly fine. However, when I ping the IP address 10.9.0.99 , I get the Destionation
Host Unreachable. What is the reason for that? Please give your answer clear and legible
format. The Machine that I ping the addresses has the IP address 10.9.0.6. Thank you!
# code starts here
#!/usr/bin/env python3
from scapy.all import *
def spoof(pkt):
if ICMP in pkt and pkt[ICMP].type == 8:
print("Original Packet.........")
print("Source IP : ", pkt[IP].src)
print("Destination IP :", pkt[IP].dst)
ip = IP(src=pkt[IP].dst, dst=pkt[IP].src, ihl=pkt[IP].ihl)
icmp = ICMP(type=0, id=pkt[ICMP].id, seq=pkt[ICMP].seq)
data = pkt[Raw].load
newpkt = ip/icmp/data
print("Spoofed Packet.........")
print("Source IP : ", newpkt[IP].src)
print("Destination IP :", newpkt[IP].dst)
send(newpkt, verbose=0)
sniff(filter=icmp and src host 10.9.0.6, prn=spoof_pkt)
# code ends here
.
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I need help with the moveStudentsFromChairToLine() method. This method chooses a random
number between 0 and the size of the linked list to remove from the circular linked list. The
random number is chosen by StdRandom.uniform(size), you don't need the std.in class. If the
random number is zero then this is the first node that is removed using pointers and getters and
setters remove the node and simply call insertByHeight(Student studentToBeremoved) to add the
removed node to the singly linked list. If the number is size - 1 which is the last node remove the
last node. Otherwise, if it's another number remove it and call on insertByHeight().
InsertByHeight() takes in a student parameter by using the removedNode.getStudent() which is
represented in the SNode class. Please helo me as I've spent a week trying to figure it out. thank
you! the only file that needs to be updated in classroom.java.
Instructions:
Each student that is eliminated from the musical chairs game is put back in the line (
studentsInLine linked list) by height order.
The eliminated student is given as as parameter
Iterate through studentsInLine searching for a student that is taller than the eliminated student.
You will have 3 cases:
eliminated student is the shortest, insert at the front of the list.
eliminated student is the tallest, insert at the end of the list.
eliminated student is not the shortest or tallest, insert in the middle of the list.
When inserting into the list, a student will be insert AFTER all students of the same height.
Watch this video for more information.
This method is called from moveStudentFromChairsToLine() to insert a student that was
eliminated from the musical chairs game. In the driver, you can test this method independently
using moveStudentFromChairsToLine.
Code:
Classroom.java
package kindergarten;
/**
* This class represents a Classroom, with:
* - an SNode instance variable for students in line,
* - an SNode instance variable for musical chairs, pointing to the last student
* in the list,
* - a boolean array for seating availability (eg. can a student sit in a given
* seat), and
* - a Student array parallel to seatingAvailability to show students filed into
* seats
* --- (more formally, seatingAvailability[i][j] also refers to the same seat in
* studentsSitting[i][j])
*/
public class Classroom {
private SNode studentsInLine; // when students are in line: references the FIRST student in the
LL
private SNode musicalChairs; // when students are in musical chairs: references the LAST
student in the CLL
private boolean[][] seatingLocation; // represents the classroom seats that are available to
students
private Student[][] studentsSitting; // when students are sitting in the classroom: contains the
students
public Classroom(SNode l, SNode m, boolean[][] a, Student[][] s) {
studentsInLine = l;
musicalChairs = m;
seatingLocation = a;
studentsSitting = s;
}
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* This method simulates students standing in line and coming into the classroom
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code below. Please don't provide me with another code. Just add the requirement I requested at
the end of my code.
Thank you in advance.
import os
import numpy as np
import pandas as pd
import keras
import tensorflow as tf
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import load_img
from keras.utils import to_categorical
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import random
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, MaxPool2D , Dropout, Flatten, Dense,
Activation, BatchNormalization
from keras.callbacks import EarlyStopping, ReduceLROnPlateau
from sklearn.utils import class_weight
import keras,os
from keras.models import Sequential
from keras.layers import Dense, Conv2D, MaxPool2D , Flatten
from keras.preprocessing.image import ImageDataGenerator
import numpy as np
from keras.optimizers import Adam
from keras.callbacks import ModelCheckpoint, EarlyStopping
from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score,
cohen_kappa_score, roc_auc_score
print(os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages"))
# Define Constants
FAST_RUN = False
IMAGE_WIDTH=256 # 150 accept maybe 256
IMAGE_HEIGHT=256 # maybe 256
IMAGE_SIZE=(IMAGE_WIDTH, IMAGE_HEIGHT)
IMAGE_CHANNELS=3 # maybe not need
physical_devices = tf.config.experimental.list_physical_devices('GPU')
print(physical_devices)
if physical_devices:
tf.config.experimental.set_memory_growth(physical_devices[0], True)
# Prepare Traning Data
filenames = os.listdir("D:\RansomSecondApproach\Ransomware_Detection_using
_CNN\MixImages")
categories = []
for filename in filenames:
category = filename.split('l')[0]
if category == 'image_benign_':
categories.append(0)
else:
categories.append(1)
df = pd.DataFrame({
'filename': filenames,
'category': categories
})
print(df.head())
print(df.tail())
# in collab it will work
df['category'].value_counts().plot.bar()
model = Sequential()
model.add(Conv2D(16, (3, 3), activation='relu', input_shape=(IMAGE_WIDTH,
IMAGE_HEIGHT, IMAGE_CHANNELS)))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(BatchNormalization())
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(512, activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax')) # 2 because we have cat and dog classes
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
# Callbacks
## Early Stop To prevent over fitting we will stop the learning after 10 epochs and val_l.
I would appreciate it if you can explain how you got the answers- Than (3).pdf
I would appreciate it if you can explain how you got the answers. Thank you!
Accounting for Organization Costs Perry Inc. was organized in the current year. Cash
expenditures included the following. Required Prepare a summary journal entry to record these
cash expenditures related to the startup of the new company..
I use the below scapy code to sniff and then spoof on the IP addresses.pdf
I use the below scapy code to sniff and then spoof on the IP addresses 1.2.3.4 and 8.8.8.8 and it
works perfectly fine. However, when I ping the IP address 10.9.0.99 , I get the Destionation
Host Unreachable. What is the reason for that? Please give your answer clear and legible
format. The Machine that I ping the addresses has the IP address 10.9.0.6. Thank you!
# code starts here
#!/usr/bin/env python3
from scapy.all import *
def spoof(pkt):
if ICMP in pkt and pkt[ICMP].type == 8:
print("Original Packet.........")
print("Source IP : ", pkt[IP].src)
print("Destination IP :", pkt[IP].dst)
ip = IP(src=pkt[IP].dst, dst=pkt[IP].src, ihl=pkt[IP].ihl)
icmp = ICMP(type=0, id=pkt[ICMP].id, seq=pkt[ICMP].seq)
data = pkt[Raw].load
newpkt = ip/icmp/data
print("Spoofed Packet.........")
print("Source IP : ", newpkt[IP].src)
print("Destination IP :", newpkt[IP].dst)
send(newpkt, verbose=0)
sniff(filter=icmp and src host 10.9.0.6, prn=spoof_pkt)
# code ends here
.
I try to change the port number ut this error appears(MongoServerError.pdf
I try to change the port number ut this error appears(MongoServerError: bad auth : authentication
failed
at Connection.onMessage )
and I want to delete every thing by write this in terminal (
how can I write this (
)
in windows terminal to kill the port
or any other solution
//dealing with static files app.use(express.static('public')) // bring ejs app.set('view engine', 'ejs') //
move between pages app.get('/', (req, res) { res.redirect("/html") \}) app.get('/html', (req, res) {
\}); res.render("addnew") app.listen(port, () { console.log('Example app listening on port $ { p or
t } ) \} ).
I tried graphing each function on Desmos but I'm still confused as to.pdf
I tried graphing each function on Desmos but I'm still confused as to which functions grow from
smallest to fastest in Big O. Please help!
Arrange the following functions in a single list so that each function is Big- O of the function
below it. Functions Functions in increasing big- O order.
I need some help with part A and B- Thank you! 4) Use truth tables to.pdf
I need some help with part A and B. Thank you!
4) Use truth tables to establish which of the statement forms are tautologies and which are
contradictions. A. ( p q ) ( p ( p q )) B. ( p q ) ( p q ).
I need help with the moveStudentsFromChairToLine() method- This method.pdf
I need help with the moveStudentsFromChairToLine() method. This method chooses a random
number between 0 and the size of the linked list to remove from the circular linked list. The
random number is chosen by StdRandom.uniform(size), you don't need the std.in class. If the
random number is zero then this is the first node that is removed using pointers and getters and
setters remove the node and simply call insertByHeight(Student studentToBeremoved) to add the
removed node to the singly linked list. If the number is size - 1 which is the last node remove the
last node. Otherwise, if it's another number remove it and call on insertByHeight().
InsertByHeight() takes in a student parameter by using the removedNode.getStudent() which is
represented in the SNode class. Please helo me as I've spent a week trying to figure it out. thank
you! the only file that needs to be updated in classroom.java.
Instructions:
Each student that is eliminated from the musical chairs game is put back in the line (
studentsInLine linked list) by height order.
The eliminated student is given as as parameter
Iterate through studentsInLine searching for a student that is taller than the eliminated student.
You will have 3 cases:
eliminated student is the shortest, insert at the front of the list.
eliminated student is the tallest, insert at the end of the list.
eliminated student is not the shortest or tallest, insert in the middle of the list.
When inserting into the list, a student will be insert AFTER all students of the same height.
Watch this video for more information.
This method is called from moveStudentFromChairsToLine() to insert a student that was
eliminated from the musical chairs game. In the driver, you can test this method independently
using moveStudentFromChairsToLine.
Code:
Classroom.java
package kindergarten;
/**
* This class represents a Classroom, with:
* - an SNode instance variable for students in line,
* - an SNode instance variable for musical chairs, pointing to the last student
* in the list,
* - a boolean array for seating availability (eg. can a student sit in a given
* seat), and
* - a Student array parallel to seatingAvailability to show students filed into
* seats
* --- (more formally, seatingAvailability[i][j] also refers to the same seat in
* studentsSitting[i][j])
*/
public class Classroom {
private SNode studentsInLine; // when students are in line: references the FIRST student in the
LL
private SNode musicalChairs; // when students are in musical chairs: references the LAST
student in the CLL
private boolean[][] seatingLocation; // represents the classroom seats that are available to
students
private Student[][] studentsSitting; // when students are sitting in the classroom: contains the
students
public Classroom(SNode l, SNode m, boolean[][] a, Student[][] s) {
studentsInLine = l;
musicalChairs = m;
seatingLocation = a;
studentsSitting = s;
}
/**
* This method simulates students standing in line and coming into the classroom
* (not.
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min_temperature = float(row[5])
avg_temperature = float(row[6])
heating_degree_days = float(row[7])
cooling_degree_days = float(row[8])
sunrise = row[9]
sunset = row[10]
length_of_day = row[11]
key = getKey(date)
weatherDB[key] = Weather(date, precipitation, snowfall, snow_depth, max_temperature,
min_temperature, avg_temperature, heating_degree_days, cooling_degree_days, sunrise, sunset,
length_of_day)
def getKey(date):
parts = date.split('/')
month = parts[0].rjust(2, '0')
day = parts[1].rjust(2, '0')
return f"{month}/{day}"
def printHeader():
print(f"{'Date':<10}{'Precipitation':<15}{'Snowfall':<10}{'Snow Depth':<10}{'Max
Temp.':<10}{'Min Temp.':<10}{'Avg
Temp.':<10}{'HDD':<10}{'CDD':<10}{'Sunrise':<10}{'Sunset':<10}{'Length of Day':<15}")
def printAllRecords():
printHeader()
for key in weatherDB:
weather = weatherDB[key]
print(f"{weather.date:<10}{weather.precipitation:<15}{weather.snowfall:<10}{weather.snow_depth:<10}{weather.max_temperature:<10}{weather.min_temperature:<10}{weather.avg_temperature:<10}{weather.heating_degree_days:<10}{weather.cooling_degree_days:<10}{weather.sunrise:<10}{weather.sunset:<10}{weather.length_of_day:<15}")
def printRecordsFromSingleMonth():
month = input("Enter month (MM): ")
printHeader()
for key in weatherDB:
if key.startswith(month):
weather = weatherDB[key]
print(f"{weather.date:<10}{weather.precipitation:<15}{weather.snowfall:<10}{weather.snow_depth:<10}{weather.max_temperature:<10}{weather.min_temperature:<10}{weather.avg_temperature:<10}{weather.heating_degree_days:<10}{weather.cooling_degree_days:<10}{weather.sunrise:<10}{weather.sunset:<10}{weather.length_of_day:<15}")
def printRecordWithHighestTemperature():
"""
Prints the record with the highest recorded temperature in the weather database.
"""
max_temp = -float('inf')
for key in weatherDB:
weather = weatherDB[key]
if weather.max_temperature > max_temp:
max_temp = weather.max_temperature
max_temp_record = weather
printHeader()
print(f"{max_temp_record.date:<10}{max_temp_record.precipitation:<15}{max_temp_record.snowfall:<10}{max_temp_record.snow_depth:.
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