Code of ethics: The “ethics” word comes from the Greek term “duty”. The code of ethics means all the responsibilities that professional must respect when carrying out their work. It also contains the core value of the work and the behavior which should be adopted. Example: Professional and confidentiality secrecy are responsibilities contained in the code of ethics and conduct in numerous professions like accountants, lawyers, doctors and journalists. Intent of the code of ethics: The intent of the code of ethics established by a professional order as a way to safeguard public and the status of the professionals. The employee who breach their code of ethics suffer disciplinary actions that can range from a warning or rebuke to dismissal or ejection from their professional order. Privacy policy: A privacy policy denotes a statement that discloses some or all ways an organization would gather, use, disclose and manage data of customers as well as clients. It fulfills a legal requirement for protecting privacy of customers as well as clients. Personal information denotes anything that could be used to identify an individual. It represents a generalized treatment that tends to be more specific and detailed. The exact content of certain privacy policy will depend upon applicable law and need to address requirements across all boundaries.

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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 -->…

Answer 1

Question

Chapter 5, Problem 1EDM

Program Plan Intro

Code of ethics:

The “ethics” word comes from the Greek term “duty”.

The code of ethics means all the responsibilities that professional must respect when carrying out their work.

It also contains the core value of the work and the behavior which should be adopted.

Example:

Professional and confidentiality secrecy are responsibilities contained in the code of ethics and conduct in numerous professions like accountants, lawyers, doctors and journalists.

Intent of the code of ethics:

The intent of the code of ethics established by a professional order as a way to safeguard public and the status of the professionals.

The employee who breach their code of ethics suffer disciplinary actions that can range from a warning or rebuke to dismissal or ejection from their professional order.

Privacy policy:

A privacy policy denotes a statement that discloses some or all ways an organization would gather, use, disclose and manage data of customers as well as clients.

It fulfills a legal requirement for protecting privacy of customers as well as clients.

Personal information denotes anything that could be used to identify an individual.

It represents a generalized treatment that tends to be more specific and detailed.

The exact content of certain privacy policy will depend upon applicable law and need to address requirements across all boundaries.

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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…