Web Development and Design Foundations with HTML5 (9th Edition) (What's New in Computer Science)
Web Development and Design Foundations with HTML5 (9th Edition) (What's New in Computer Science)
9th Edition
ISBN: 9780134801148
Author: Terry Felke-Morris
Publisher: PEARSON
Question
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Chapter 14, Problem 2WR
Program Plan Intro

Program Plan:

  • Include the HTML tag using “<html>”.
    • Include the heading tag using “<head>”.
      • Open the title tag using “<title>” with appropriate title and close the tag.
      • Set the “meta” tag using “<meta>”.
      • Open the style tag “<style>” to make style for HTML tags.
        • Make a style for “input” type.
          • Set the display as “block”.
          • Set the “margin-bottom” as “1em”.
        • Make a style for “label” element and set the properties of label.
        • Make a style for “submit” element with margin-left property.
        • Make a style for “h2” with background-color property.
    • Open the script tag using “<script>”.
      • Define the function named “validateForm()” to validate the user name, age and city.
        • Check the condition for user name, if user name is “Null” then display the alert message and return “false”.
        • Check the condition for age. If user’s age is less than 18 display the alert message and return “false”.
        • Check the condition for city input. If the input box for city is “Null” then display the alert message “City field cannot be empty” and return “false”.
      • Display the alert message for valid inputs and return “true”.
    • Close the script tag using “</script>”.
    • Close the head tag using “</head>”.
    • Include the body tag using “<body>”.
      • Open the heading tag with appropriate heading using “<h1>” tag and close it.
      • Descript the purpose of form validation in the paragraph tag “<p>”.
      • Include the unordered list using “<ul>” tag.
        • Create the list of URL of the websites in the list tag “<li>” with anchor tag “<a>”.
        • Close each list with anchor tag using “</li>”.
      • Close the unordered list “</ul>”.
      • Open the form tag with “post” method, when the “onsubmit” event handler clicks by the user the “validateForm()” function called to validate the inputs.
      • Include the inputs using “<input>” tag and label using “<label>” tag.
      • Close the form tag using “</form>”.
      • Include the anchor tag for the email link using “<a>”.
    • Close the body tag using “</body>”.
  • Close the file using “</html>” tag.

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Here are two diagrams. Make them very explicit, similar to Example Diagram 3 (the Architecture of MSCTNN). graph LR subgraph Teacher_Model_B [Teacher Model (Pretrained)] Input_Teacher_B[Input C (Complete Data)] --> Teacher_Encoder_B[Transformer Encoder T] Teacher_Encoder_B --> Teacher_Prediction_B[Teacher Prediction y_T] Teacher_Encoder_B --> Teacher_Features_B[Internal Features F_T] end subgraph Student_B_Model [Student Model B (Handles Missing Labels)] Input_Student_B[Input C (Complete Data)] --> Student_B_Encoder[Transformer Encoder E_B] Student_B_Encoder --> Student_B_Prediction[Student B Prediction y_B] end subgraph Knowledge_Distillation_B [Knowledge Distillation (Student B)] Teacher_Prediction_B -- Logits Distillation Loss (L_logits_B) --> Total_Loss_B Teacher_Features_B -- Feature Alignment Loss (L_feature_B) --> Total_Loss_B Partial_Labels_B[Partial Labels y_p] -- Prediction Loss (L_pred_B) --> Total_Loss_B Total_Loss_B -- Backpropagation -->…
Please provide me with the output  image of both of them . below are the diagrams code I have two diagram : first diagram code  graph LR subgraph Teacher Model (Pretrained) Input_Teacher[Input C (Complete Data)] --> Teacher_Encoder[Transformer Encoder T] Teacher_Encoder --> Teacher_Prediction[Teacher Prediction y_T] Teacher_Encoder --> Teacher_Features[Internal Features F_T] end subgraph Student_A_Model[Student Model A (Handles Missing Values)] Input_Student_A[Input M (Data with Missing Values)] --> Student_A_Encoder[Transformer Encoder E_A] Student_A_Encoder --> Student_A_Prediction[Student A Prediction y_A] Student_A_Encoder --> Student_A_Features[Student A Features F_A] end subgraph Knowledge_Distillation_A [Knowledge Distillation (Student A)] Teacher_Prediction -- Logits Distillation Loss (L_logits_A) --> Total_Loss_A Teacher_Features -- Feature Alignment Loss (L_feature_A) --> Total_Loss_A Ground_Truth_A[Ground Truth y_gt] -- Prediction Loss (L_pred_A)…
I'm reposting my question again please make sure to avoid any copy paste from the previous answer because those answer did not satisfy or responded to the need that's why I'm asking again  The knowledge distillation part is not very clear in the diagram. Please create two new diagrams by separating the two student models: First Diagram (Student A - Missing Values): Clearly illustrate the student training process. Show how knowledge distillation happens between the teacher and Student A. Explain what the teacher teaches Student A (e.g., handling missing values) and how this teaching occurs (e.g., through logits, features, or attention). Second Diagram (Student B - Missing Labels): Similarly, detail the training process for Student B. Clarify how knowledge distillation works between the teacher and Student B. Specify what the teacher teaches Student B (e.g., dealing with missing labels) and how the knowledge is transferred. Since these are two distinct challenges…
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