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Using Java programming language solve the following:
SLL CLASS:
//************************** SLL.java *********************************
// a generic singly linked list class
public class SLL {
private class SLLNode {
private T info;
private SLLNode next;
public SLLNode() {
this(null,null);
}
public SLLNode(T el) {
this(el,null);
}
public SLLNode(T el, SLLNode ptr) {
info = el; next = ptr;
}
}
protected SLLNode head, tail;
public SLL() {
head = tail = null;
}
public boolean isEmpty() {
return head == null;
}
public void addToHead(T el) {
head = new SLLNode(el,head);
if (tail == null)
tail = head;
}
public void addToTail(T el) {
if (!isEmpty()) {
tail.next = new SLLNode(el);
tail = tail.next;
}
else head = tail = new SLLNode(el);
}
public T deleteFromHead() { // delete the head and return its info;
if (isEmpty())
return null;
T el = head.info;
if (head == tail) // if only one node on the list;
head = tail = null;
else head = head.next;
return el;
}
public T deleteFromTail() { // delete the tail and return its info;
if (isEmpty())
return null;
T el = tail.info;
if (head == tail) // if only one node in the list;
head = tail = null;
else { // if more than one node in the list,
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for (tmp = head; tmp.next != tail; tmp = tmp.next);
tail = tmp; // the predecessor of tail becomes tail;
tail.next = null;
}
return el;
}
public void delete(T el) { // delete the node with an element el;
if (!isEmpty())
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head = tail = null; // node on the list;
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head = head.next; // and el is in the head node;
else { // if more than one node in the list
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for (pred = head, tmp = head.next;
tmp != null && !tmp.info.equals(el);
pred = pred.next, tmp = tmp.next);
if (tmp != null) { // if el was found;
pred.next = tmp.next;
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tail = pred;
}
}
}
@Override
public String toString() {
if(head == null)
return "[ ]";
String str = "[ ";
SLLNode tmp = head;
while(tmp != null){
str += tmp.info + " ";
tmp = tmp.next;
}
return str+"]";
}
public boolean contains(T el) {
if(head == null)
return false;
SLLNode tmp = head;
while(tmp != null){
if(tmp.info.equals(el))
return true;
tmp = tmp.next;
}
return false;
}
public int size(){
if(head == null)
return 0;
int count = 0;
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while(p != null) {
count++;
p = p.next;
}
return count;
}
}
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##Defining Function to Build Multivariate Data Structure
def multivariate_data(dataset, target, start_index, end_index, history_size,
target_size, step, single_step=False):
data = []
labels = []
start_index = start_index + history_size
if end_index is None:
end_index = len(dataset) - target_size
for i in range(start_index, end_index):
indices = range(i-history_size, i, step)
data.append(dataset[indices])
if single_step:
labels.append(target[i+target_size])
else:
labels.append(target[i:i+target_size])
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def multi_step_plot(history, true_future, prediction):
plt.figure(figsize=(12, 6))
num_in = create_time_steps(len(history))
num_out = len(true_future)
plt.plot(num_in, np.array(history[:, 1]), label='History')
plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'bo',
label='True Future')
if prediction.any():
plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro',
label='Predicted Future')
plt.legend(loc='upper left')
plt.show()
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def plot_train_history(history, title):
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(loss))
plt.figure()
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title(title)
plt.legend()
plt.show()
##How far into the past do you want the model to see?
past_history=
##How far into the future do you want the model to see?
future_target=
##
STEP=
df_val = len(dataset) - past_history - future_target
x_train_multi, y_train_multi = multivariate_data(dataset, dataset[:, 1], 0,
df_train, past_history,
future_target, STEP)
x_val_multi, y_val_multi = multivariate_data(dataset, dataset[:, 1],
df_train, df_val, past_history,
future_target, STEP)
##Define Constants
BATCH_SIZE =
BUFFER_SIZE =
EPOCHS =
EVALUATION_INTERVAL =
##Adding Layers Due to Complexity
train_data_multi = tf.data.Dataset.from_tensor_slices(())
train_data_multi = train_data_multi.cache().shuffle().batch().repeat()
val_data_multi = tf.data.Dataset.from_tensor_slices(())
val_data_multi = val_data_multi.batch().repeat()
multi_step_model = tf.keras.models.Sequential()
multi_step_model.add(tf.keras.layers.LSTM())
multi_step_model.add(tf.keras.layers.LSTM())
multi_step_model.add(tf.keras.layers.Dense(72))
multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae')
##Train the Model
multi_step_history = multi_step_model.fit(train_data_multi, epochs=,
steps_per_epoch=,
validation_data=,
validation_steps=50)
##Predict Future Data
for x, y in .take(3):
multi_step_plot(x[0], y[0], .predict(x)[0])
#Evaluate the model on the test data
test_data_multi = tf.data.Dataset.from_tensor_slices())
test_data_mu.
Using facial recognition software in shelters for victims of domesti.pdf
Using facial recognition software in shelters for victims of domestic violence raises several
ethical concerns that must be carefully considered before pursuing such a project. Firstly, using
this technology in such a sensitive and vulnerable setting raises serious concerns about privacy,
safety, and the potential abuse of power by staff. It is essential to obtain informed consent from
guests and staff and ensure that their personal information is securely stored and not misused.
Additionally, there is a risk that this technology could be used to track and monitor guests'
movements, which could violate their rights and compromise their safety. Secondly, there is the
issue of potential bias in the technology itself, as facial recognition software has been shown to
be less accurate when identifying people of colour and women, which could result in false
accusations and wrongful identification. To address these ethical concerns, engaging in a
thorough ethical review process that involves input from diverse stakeholders, including legal
experts, privacy advocates, and survivors of domestic violence is crucial. This process should
prioritize transparency, accountability, and the protection of guests and staff's rights. Other
ethical issues that designers and developers of such an application would need to address include
the potential for hacking and data breaches, the impact on civil liberties and human rights, and
the potential for unintended consequences and harm.
Response to this article is required, please provide In-Text Citations and credible references with
the response. Thanks!.
Using a bash shell write a script using getops with options that dis.pdf
Using a bash shell write a script using getops with options that display user information based on
the users shell by searching an /etc/passwd-like file. The command will allow these options:
-a Display users with all shells. Cannot be used with -s.
-f FILE Search in FILE. FILE must be formatted like /etc/passwd.
-h Display usage information.
-s SHELL Display users with SHELL shell. SHELL must be in /etc/shells. Cannot be used with -
a..
Use the modified abstract below to answer the following two question.pdf
Use the modified abstract below to answer the following two questions:
OBJECTIVES:
The human immunodeficiency virus (HIV) and M. tuberculosis co-infection is a major global
challenge. It is not properly clear why some HIV-positive people are co-infected with
tuberculosis (TB) while others are not. This study answered this question.
METHODS:
This case-control study was conducted in Tehran, Iran, in June 2004, enrolling 2388 HIV-
positive people. Cases were selected from those who were co-infected with TB and controls from
those without TB. Multiple logistic regression analysis was performed to assess the association
between M. tuberculosis/HIV co-infection and several predictors.
RESULTS:
In this study, 241 cases were compared with 2147 controls. Sex, age, marital status, educational
level, imprisonment, smoking, narcotic addiction, route of HIV transmission, previous TB
infection, isoniazid preventive therapy (IPT), antiretroviral therapy (ART), and CD4 count were
independently associated with M. tuberculosis/HIV co-infection. However, after adjusting for all
other variables in the model, only the association between M. tuberculosis/HIV co-infection and
the following predictors were of importance: imprisoned (OR=3.82), previous TB infection
(OR=5.54).
CONCLUSIONS:
Conclusions: Several predictors are associated with M. tuberculosis/HIV co-infection, but, there
are only a few indicators that significantly affect the risk of M. tuberculosis/HIV co-infection. It
is estimated that a number of predictors of M. tuberculosis/HIV co-infection still remain
unknown and require further investigations.
Original Abstract: Molaeipoor L et al. Predictors of Tuberculosis and Human Immunodeficiency
Virus Co-infection: A Case-Control Study. Epidemiol Health. 2014 Oct 30
Flag question: Question 2
Question 2
Is there enough evidence to conclude if imprisonment or prior TB infection might have caused
co-infection with tuberculosis and HIV?
A.Yes
B. No
Question 3
What additional information would help you determine if the Odds Ratios were significantly
different form the null hypothesis (OR = 1)? Select all that apply.
Group of answer choices
A. A 95% Confidence Interval
B. The study hypothesis
C. A full list of potentially confounding variables
D. The Standard Error or Variance of the Odds Ratios
Question 4
Which of the following measures of effect are statistically significant? Select all that apply.
Group of answer choices
A. Odds Ratio = 5.0, p-value = 0.04
B. Incidence Density Ratio = 2.0, 95% CI [0.9 - 3.1]
C. Attributable Risk = 0.5, 95% CI [0.1 - 1.1]
D. Population Attributable Risk = 0.1, 95% CI = [-0.1, 0.2]
E. Cumulative Incidence Ratio = 0.5, 95% CI = [0.1 - 0.9]
F. Odds Ratio = 20, p-value = 0.2.
Use the info that os provided. I just need basic sketch I H.I. H..1..pdf
Use the info that os provided. I just need basic sketch I H.I. H..1.1.11... IHH. HHHHH1.1..
IHH.1.1.H1HHH..11 HI IHI 31 IH 11 I.1.1.1 II 1 i) 1111 IH!t!l! IIH H II 1 I If
III 11 11111 HithHI..H1...I.H1.1...11.. 11H IIIII. IHH1. .HH II HI II fif
ShImu!llumu!lluhiluIlIIIIIII fintimentintititu it.
Tory and Cathy entered into a contract under which Cathy was suppose.pdf
Tory and Cathy entered into a contract under which Cathy was supposed to provide the floral
arrangements for Torys wedding on May 23. The day of the wedding arrives, but Cathy does not
deliver the floral arrangements. Cory is devastated and feels that her wedding was ruined by the
lack of floral arrangements. She sues Cathy successfully for breach of contract and then asks for
punitive damages because her wedding was ruined. The evidence presented at court only shows
that Cathy did not deliver the arrangements because she had accidentally written the wrong
delivery date on her calendar. Answer following the IRAC method..
use Selenium to write a Java code that will Scrape data from a podca.pdf
use Selenium to write a Java code that will Scrape data from a podcast site, such as episode titles,
descriptions, and ratings. Java program should do the following: Open the website in a web
browser using Selenium. Find and interact with various elements on the page (e.g., links,
buttons, text boxes) using Selenium commands. Extract data from the page using Selenium
commands, such as finding and storing text, images, or other content. Save the scraped data in a
CSV file or other format of podcast site..
বাংলাদেশ অর্থনৈতিক সমীক্ষা (Economic Review) ২০২৪ UJS App.pdf
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
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//************************** SLL.java *********************************
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public class SLL {
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tmp = tmp.next;
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return str+"]";
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if(head == null)
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SLLNode tmp = head;
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return false;
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labels = []
start_index = start_index + history_size
if end_index is None:
end_index = len(dataset) - target_size
for i in range(start_index, end_index):
indices = range(i-history_size, i, step)
data.append(dataset[indices])
if single_step:
labels.append(target[i+target_size])
else:
labels.append(target[i:i+target_size])
return np.array(data), np.array(labels)
##Define the Multi-step Plotting Structure
def multi_step_plot(history, true_future, prediction):
plt.figure(figsize=(12, 6))
num_in = create_time_steps(len(history))
num_out = len(true_future)
plt.plot(num_in, np.array(history[:, 1]), label='History')
plt.plot(np.arange(num_out)/STEP, np.array(true_future), 'bo',
label='True Future')
if prediction.any():
plt.plot(np.arange(num_out)/STEP, np.array(prediction), 'ro',
label='Predicted Future')
plt.legend(loc='upper left')
plt.show()
##Define Function to Plot training and Validation over Time
def plot_train_history(history, title):
loss = history.history['loss']
val_loss = history.history['val_loss']
epochs = range(len(loss))
plt.figure()
plt.plot(epochs, loss, 'b', label='Training loss')
plt.plot(epochs, val_loss, 'r', label='Validation loss')
plt.title(title)
plt.legend()
plt.show()
##How far into the past do you want the model to see?
past_history=
##How far into the future do you want the model to see?
future_target=
##
STEP=
df_val = len(dataset) - past_history - future_target
x_train_multi, y_train_multi = multivariate_data(dataset, dataset[:, 1], 0,
df_train, past_history,
future_target, STEP)
x_val_multi, y_val_multi = multivariate_data(dataset, dataset[:, 1],
df_train, df_val, past_history,
future_target, STEP)
##Define Constants
BATCH_SIZE =
BUFFER_SIZE =
EPOCHS =
EVALUATION_INTERVAL =
##Adding Layers Due to Complexity
train_data_multi = tf.data.Dataset.from_tensor_slices(())
train_data_multi = train_data_multi.cache().shuffle().batch().repeat()
val_data_multi = tf.data.Dataset.from_tensor_slices(())
val_data_multi = val_data_multi.batch().repeat()
multi_step_model = tf.keras.models.Sequential()
multi_step_model.add(tf.keras.layers.LSTM())
multi_step_model.add(tf.keras.layers.LSTM())
multi_step_model.add(tf.keras.layers.Dense(72))
multi_step_model.compile(optimizer=tf.keras.optimizers.RMSprop(clipvalue=1.0), loss='mae')
##Train the Model
multi_step_history = multi_step_model.fit(train_data_multi, epochs=,
steps_per_epoch=,
validation_data=,
validation_steps=50)
##Predict Future Data
for x, y in .take(3):
multi_step_plot(x[0], y[0], .predict(x)[0])
#Evaluate the model on the test data
test_data_multi = tf.data.Dataset.from_tensor_slices())
test_data_mu.
Using facial recognition software in shelters for victims of domesti.pdf
Using facial recognition software in shelters for victims of domestic violence raises several
ethical concerns that must be carefully considered before pursuing such a project. Firstly, using
this technology in such a sensitive and vulnerable setting raises serious concerns about privacy,
safety, and the potential abuse of power by staff. It is essential to obtain informed consent from
guests and staff and ensure that their personal information is securely stored and not misused.
Additionally, there is a risk that this technology could be used to track and monitor guests'
movements, which could violate their rights and compromise their safety. Secondly, there is the
issue of potential bias in the technology itself, as facial recognition software has been shown to
be less accurate when identifying people of colour and women, which could result in false
accusations and wrongful identification. To address these ethical concerns, engaging in a
thorough ethical review process that involves input from diverse stakeholders, including legal
experts, privacy advocates, and survivors of domestic violence is crucial. This process should
prioritize transparency, accountability, and the protection of guests and staff's rights. Other
ethical issues that designers and developers of such an application would need to address include
the potential for hacking and data breaches, the impact on civil liberties and human rights, and
the potential for unintended consequences and harm.
Response to this article is required, please provide In-Text Citations and credible references with
the response. Thanks!.
Using a bash shell write a script using getops with options that dis.pdf
Using a bash shell write a script using getops with options that display user information based on
the users shell by searching an /etc/passwd-like file. The command will allow these options:
-a Display users with all shells. Cannot be used with -s.
-f FILE Search in FILE. FILE must be formatted like /etc/passwd.
-h Display usage information.
-s SHELL Display users with SHELL shell. SHELL must be in /etc/shells. Cannot be used with -
a..
Use the modified abstract below to answer the following two question.pdf
Use the modified abstract below to answer the following two questions:
OBJECTIVES:
The human immunodeficiency virus (HIV) and M. tuberculosis co-infection is a major global
challenge. It is not properly clear why some HIV-positive people are co-infected with
tuberculosis (TB) while others are not. This study answered this question.
METHODS:
This case-control study was conducted in Tehran, Iran, in June 2004, enrolling 2388 HIV-
positive people. Cases were selected from those who were co-infected with TB and controls from
those without TB. Multiple logistic regression analysis was performed to assess the association
between M. tuberculosis/HIV co-infection and several predictors.
RESULTS:
In this study, 241 cases were compared with 2147 controls. Sex, age, marital status, educational
level, imprisonment, smoking, narcotic addiction, route of HIV transmission, previous TB
infection, isoniazid preventive therapy (IPT), antiretroviral therapy (ART), and CD4 count were
independently associated with M. tuberculosis/HIV co-infection. However, after adjusting for all
other variables in the model, only the association between M. tuberculosis/HIV co-infection and
the following predictors were of importance: imprisoned (OR=3.82), previous TB infection
(OR=5.54).
CONCLUSIONS:
Conclusions: Several predictors are associated with M. tuberculosis/HIV co-infection, but, there
are only a few indicators that significantly affect the risk of M. tuberculosis/HIV co-infection. It
is estimated that a number of predictors of M. tuberculosis/HIV co-infection still remain
unknown and require further investigations.
Original Abstract: Molaeipoor L et al. Predictors of Tuberculosis and Human Immunodeficiency
Virus Co-infection: A Case-Control Study. Epidemiol Health. 2014 Oct 30
Flag question: Question 2
Question 2
Is there enough evidence to conclude if imprisonment or prior TB infection might have caused
co-infection with tuberculosis and HIV?
A.Yes
B. No
Question 3
What additional information would help you determine if the Odds Ratios were significantly
different form the null hypothesis (OR = 1)? Select all that apply.
Group of answer choices
A. A 95% Confidence Interval
B. The study hypothesis
C. A full list of potentially confounding variables
D. The Standard Error or Variance of the Odds Ratios
Question 4
Which of the following measures of effect are statistically significant? Select all that apply.
Group of answer choices
A. Odds Ratio = 5.0, p-value = 0.04
B. Incidence Density Ratio = 2.0, 95% CI [0.9 - 3.1]
C. Attributable Risk = 0.5, 95% CI [0.1 - 1.1]
D. Population Attributable Risk = 0.1, 95% CI = [-0.1, 0.2]
E. Cumulative Incidence Ratio = 0.5, 95% CI = [0.1 - 0.9]
F. Odds Ratio = 20, p-value = 0.2.
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Tory and Cathy entered into a contract under which Cathy was supposed to provide the floral
arrangements for Torys wedding on May 23. The day of the wedding arrives, but Cathy does not
deliver the floral arrangements. Cory is devastated and feels that her wedding was ruined by the
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use Selenium to write a Java code that will Scrape data from a podca.pdf
use Selenium to write a Java code that will Scrape data from a podcast site, such as episode titles,
descriptions, and ratings. Java program should do the following: Open the website in a web
browser using Selenium. Find and interact with various elements on the page (e.g., links,
buttons, text boxes) using Selenium commands. Extract data from the page using Selenium
commands, such as finding and storing text, images, or other content. Save the scraped data in a
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