EBK COMPUTER SCIENCE: AN OVERVIEW
12th Edition
ISBN: 8220102744196
Author: BRYLOW
Publisher: PEARSON
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Chapter 9, Problem 1SI
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To justify if release of
Release of DNA database may or may not be ethical depending on the database that is being released. Release of DNA database to public is not good because it may be used for some illegal practices. Release of database is good if it is done in the following conditions:
- The DNA database can be used for forensic purposes. The DNA sample can be collected from crime scene and it can be compared with the DNAs available in the database. This activity is helpful to identify suspects in investigation. If a prisoner released from prison executes a crime, it will be quite easy to identify the criminal with the DNA found from crime scene.
- The DNA database can be used for medical research...
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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…
Chapter 9 Solutions
EBK COMPUTER SCIENCE: AN OVERVIEW
Ch. 9.1 - Identify two departments in a manufacturing plant...Ch. 9.1 - Prob. 2QECh. 9.1 - Summarize the roles of the application software...Ch. 9.2 - Prob. 1QECh. 9.2 - Prob. 2QECh. 9.2 - Prob. 4QECh. 9.2 - Prob. 5QECh. 9.2 - Prob. 6QECh. 9.3 - Prob. 1QECh. 9.3 - What is a persistent object?
Ch. 9.3 - Identify some classes as well as some of their...Ch. 9.3 - Prob. 4QECh. 9.4 - Prob. 1QECh. 9.4 - Prob. 2QECh. 9.4 - Prob. 3QECh. 9.4 - Prob. 4QECh. 9.4 - Prob. 5QECh. 9.4 - Prob. 6QECh. 9.5 - Prob. 1QECh. 9.5 - Prob. 2QECh. 9.5 - Prob. 3QECh. 9.5 - Prob. 4QECh. 9.5 - Prob. 5QECh. 9.5 - Prob. 6QECh. 9.5 - Prob. 7QECh. 9.6 - Prob. 1QECh. 9.6 - Give an additional example of a pattern that might...Ch. 9.6 - Prob. 3QECh. 9.6 - How does data mining differ from traditional...Ch. 9.7 - Prob. 1QECh. 9.7 - Prob. 2QECh. 9.7 - Prob. 3QECh. 9.7 - Prob. 4QECh. 9 - Prob. 1CRPCh. 9 - Prob. 2CRPCh. 9 - Prob. 3CRPCh. 9 - Prob. 4CRPCh. 9 - Prob. 5CRPCh. 9 - Prob. 6CRPCh. 9 - Prob. 7CRPCh. 9 - Prob. 8CRPCh. 9 - Prob. 9CRPCh. 9 - Prob. 10CRPCh. 9 - Prob. 11CRPCh. 9 - Prob. 12CRPCh. 9 - Using the commands SELECT, PROJECT, and JOIN,...Ch. 9 - Answer Problem 13 using SQL. PROBLEM 13 13. Using...Ch. 9 - Prob. 15CRPCh. 9 - Prob. 16CRPCh. 9 - Prob. 17CRPCh. 9 - Prob. 18CRPCh. 9 - Prob. 19CRPCh. 9 - Empl Id Name Address SSN Job Id Job Title Skill...Ch. 9 - Empl Id Name Address SSN Job Id Job Title Skill...Ch. 9 - Prob. 22CRPCh. 9 - Prob. 23CRPCh. 9 - Prob. 24CRPCh. 9 - Prob. 25CRPCh. 9 - Write a sequence of instructions (using the...Ch. 9 - Prob. 27CRPCh. 9 - Prob. 28CRPCh. 9 - Prob. 29CRPCh. 9 - Prob. 30CRPCh. 9 - Prob. 31CRPCh. 9 - Prob. 32CRPCh. 9 - Prob. 33CRPCh. 9 - Prob. 34CRPCh. 9 - Prob. 35CRPCh. 9 - Prob. 36CRPCh. 9 - Prob. 37CRPCh. 9 - Prob. 38CRPCh. 9 - Prob. 39CRPCh. 9 - Prob. 40CRPCh. 9 - Prob. 41CRPCh. 9 - Prob. 42CRPCh. 9 - Prob. 43CRPCh. 9 - Prob. 44CRPCh. 9 - Prob. 45CRPCh. 9 - Prob. 46CRPCh. 9 - Prob. 47CRPCh. 9 - Prob. 48CRPCh. 9 - Prob. 49CRPCh. 9 - Prob. 50CRPCh. 9 - Prob. 51CRPCh. 9 - Prob. 52CRPCh. 9 - Prob. 53CRPCh. 9 - Prob. 54CRPCh. 9 - Prob. 55CRPCh. 9 - Prob. 56CRPCh. 9 - Prob. 57CRPCh. 9 - Prob. 58CRPCh. 9 - Prob. 59CRPCh. 9 - Prob. 60CRPCh. 9 - Prob. 61CRPCh. 9 - Prob. 62CRPCh. 9 - Prob. 1SICh. 9 - Prob. 2SICh. 9 - Prob. 3SICh. 9 - Prob. 4SICh. 9 - Prob. 5SICh. 9 - Prob. 6SICh. 9 - Prob. 7SICh. 9 - Prob. 8SICh. 9 - Prob. 9SICh. 9 - Prob. 10SI
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- 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 (missing values vs. missing labels), they should not be combined in the same diagram. Instead, create two separate diagrams for clarity. For reference, I will attach a second image…arrow_forwardNote : please avoid using AI answer the question by carefully reading it and provide a clear and concise solutionHere is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns…arrow_forwardHere is a clear background and explanation of the full method, including what each part is doing and why. Background & Motivation Missing values: Some input features (sensor channels) are missing for some samples due to sensor failure or corruption. Missing labels: Not all samples have a ground-truth RUL value. For example, data collected during normal operation is often unlabeled. Most traditional deep learning models require complete data and full labels. But in our case, both are incomplete. If we try to train a model directly, it will either fail to learn properly or discard valuable data. What We Are Doing: Overview We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing…arrow_forward
- here is a diagram code : graph LR subgraph Inputs [Inputs] A[Input C (Complete Data)] --> TeacherModel B[Input M (Missing Data)] --> StudentA A --> StudentB end subgraph TeacherModel [Teacher Model (Pretrained)] C[Transformer Encoder T] --> D{Teacher Prediction y_t} C --> E[Internal Features f_t] end subgraph StudentA [Student Model A (Trainable - Handles Missing Input)] F[Transformer Encoder S_A] --> G{Student A Prediction y_s^A} B --> F end subgraph StudentB [Student Model B (Trainable - Handles Missing Labels)] H[Transformer Encoder S_B] --> I{Student B Prediction y_s^B} A --> H end subgraph GroundTruth [Ground Truth RUL (Partial Labels)] J[RUL Labels] end subgraph KnowledgeDistillationA [Knowledge Distillation Block for Student A] K[Prediction Distillation Loss (y_s^A vs y_t)] L[Feature Alignment Loss (f_s^A vs f_t)] D -- Prediction Guidance --> K E -- Feature Guidance --> L G --> K F --> L J -- Supervised Guidance (if available) --> G K…arrow_forwarddetails explanation and background We solve this using a Teacher–Student knowledge distillation framework: We train a Teacher model on a clean and complete dataset where both inputs and labels are available. We then use that Teacher to teach two separate Student models: Student A learns from incomplete input (some sensor values missing). Student B learns from incomplete labels (RUL labels missing for some samples). We use knowledge distillation to guide both students, even when labels are missing. Why We Use Two Students Student A handles Missing Input Features: It receives input with some features masked out. Since it cannot see the full input, we help it by transferring internal features (feature distillation) and predictions from the teacher. Student B handles Missing RUL Labels: It receives full input but does not always have a ground-truth RUL label. We guide it using the predictions of the teacher model (prediction distillation). Using two students allows each to specialize in…arrow_forwardWe are doing a custom JSTL custom tag to make display page to access a tag handler. Write two custom tags: 1) A single tag which prints a number (from 0-99) as words. Ex: <abc:numAsWords val="32"/> --> produces: thirty-two 2) A paired tag which puts the body in a DIV with our team colors. Ex: <abc:teamColors school="gophers" reverse="true"> <p>Big game today</p> <p>Bring your lucky hat</p> <-- these will be green text on blue background </abc:teamColors> Details: The attribute for numAsWords will be just val, from 0 to 99 - spelling, etc... isn't important here. Print "twenty-six" or "Twenty six" ... . Attributes for teamColors are: school, a "required" string, and reversed, a non-required boolean. - pick any four schools. I picked gophers, cyclones, hawkeyes and cornhuskers - each school has two colors. Pick whatever seems best. For oine I picked "cyclones" and red text on a gold body - if…arrow_forward
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