Computer Science: An Overview (12th Edition)
12th Edition
ISBN: 9780133760064
Author: Glenn Brookshear, Dennis Brylow
Publisher: PEARSON
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Chapter 9, Problem 8SI
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Information collection by corporation:
A corporation is allowed to collect and record the information of individuals in such a manner that they give their information willingly. They collect information to increase marketing and to increase business efficiency. They analyze the marketing patterns of the individuals, personal data, location, habits and many others to increase their profits.
Sometimes, company makes individuals to sign an agreement related to the terms of service and user signs such agreement without complete analysis due to very long descriptions in the agreement. This enables companies to legally collect data of the individuals even without their consent. A company must not use the information with wrong means which compromises with the security of individuals...
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Chapter 9 Solutions
Computer Science: An Overview (12th Edition)
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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- In the diagram, there is a green arrow pointing from Input C (complete data) to Transformer Encoder S_B, which I don’t understand. The teacher model is trained on full data, but S_B should instead receive missing data—this arrow should not point there. Please verify and recreate the diagram to fix this issue. Additionally, the newly created diagram should meet the same clarity standards as the second diagram (Proposed MSCATN). Finally provide the output image of the diagram in image format .arrow_forwardPlease provide me with the output image of both of them . below are the diagrams code make sure to update the code and mentionned clearly each section also the digram should be clearly describe like in the attached image. please do not provide the same answer like in other question . I repost this question because it does not satisfy the requirment I need in terms of clarifty the output of both code are not very well details 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…arrow_forwardWhy I need ?arrow_forward
- 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 -->…arrow_forwardPlease 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)…arrow_forwardI'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…arrow_forward
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