Starting Out with C++ from Control Structures to Objects (9th Edition)
9th Edition
ISBN: 9780134498379
Author: Tony Gaddis
Publisher: PEARSON
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Chapter 12, Problem 16RQE
How do you determine the number of bytes that a file contains?
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Chapter 12 Solutions
Starting Out with C++ from Control Structures to Objects (9th Edition)
Ch. 12.1 - Which file access flag would you use if you want...Ch. 12.1 - Prob. 12.2CPCh. 12.1 - Assuming diskInfo is an fstream object, write a...Ch. 12.1 - Assuming diskInfo is an fstream object, write a...Ch. 12.1 - Assuming diskInfo is an fstream object, write a...Ch. 12.1 - Write a statement that defines an fstream object...Ch. 12.5 - Assume the file input.txt contains the following...Ch. 12.5 - Describe the difference between reading a file...Ch. 12.5 - What will be stored in the file out.txt after the...Ch. 12.10 - Describe the difference between the seekg and the...
Ch. 12.10 - Describe the difference between the tellg and the...Ch. 12.10 - Describe the meaning of the following file access...Ch. 12.10 - What is the number of the first byte in a file?Ch. 12.10 - Briefly describe what each of the following...Ch. 12.10 - Describe the mode that each of the following...Ch. 12 - What capability does the fstream data type provide...Ch. 12 - Which file access flag do you use to open a file...Ch. 12 - Assume the file data.txt already exists, and the...Ch. 12 - How do you combine multiple file access flags when...Ch. 12 - Should file stream objects be passed to functions...Ch. 12 - Under what circumstances is a file stream objects...Ch. 12 - Under what circumstances is a file stream objects...Ch. 12 - Under what circumstances is a file stream objects...Ch. 12 - How do you read the contents of a text file that...Ch. 12 - What arguments do you pass to a file stream...Ch. 12 - What arguments do you pass to a file stream...Ch. 12 - Prob. 12RQECh. 12 - Prob. 13RQECh. 12 - How do you get the byte number of a files current...Ch. 12 - If a program has read to the end of a file, what...Ch. 12 - How do you determine the number of bytes that a...Ch. 12 - How do you rewind a sequential-access file?Ch. 12 - The _____ file stream data type is for output...Ch. 12 - If a file fails to open, the file stream object...Ch. 12 - The same formatting techniques used with...Ch. 12 - The _____ function reads a line of text from a...Ch. 12 - The ____________ member function reads a single...Ch. 12 - The ________member function writes a single...Ch. 12 - Prob. 24RQECh. 12 - __________ files contain data formatted as...Ch. 12 - Prob. 26RQECh. 12 - Prob. 27RQECh. 12 - The ___________ member function writes raw binary...Ch. 12 - The __________ member function reads raw binary...Ch. 12 - Prob. 30RQECh. 12 - In ___________ file access, the contents of the...Ch. 12 - In __________ file access, the contents of a file...Ch. 12 - The _____________ member function moves a files...Ch. 12 - The ___________ member function moves a files...Ch. 12 - The __________ member function returns a files...Ch. 12 - The ___________ member function returns a files...Ch. 12 - The __________ mode flag causes an offset to be...Ch. 12 - The __________ mode flag causes an offset to be...Ch. 12 - The ________ mode flag causes an offset to be...Ch. 12 - A negative offset causes the files read or write...Ch. 12 - Write a statement that defines a file stream...Ch. 12 - Write two statements that use a file stream object...Ch. 12 - Write two statements that use a file stream object...Ch. 12 - Write two statements that use a file stream object...Ch. 12 - Write a program segment that defines a file stream...Ch. 12 - Write code that opens the file data.txt for both...Ch. 12 - Write code that determines the number of bytes...Ch. 12 - The infoFile file stream object is used to...Ch. 12 - T F Different operating systems have different...Ch. 12 - T F fstream objects are only capable of performing...Ch. 12 - T F ofstream objects, by default, delete the...Ch. 12 - T F ifstream objects, by default, create a file if...Ch. 12 - T F Several file access flags may be joined by...Ch. 12 - T F A file may be opened in the definition of the...Ch. 12 - T F If a file is opened in the definition of the...Ch. 12 - T F A file stream objects fail member function may...Ch. 12 - T F The same output formatting techniques used...Ch. 12 - T F The operator expects data to be delimited by...Ch. 12 - T F The getline member function can be used to...Ch. 12 - T F It is not possible to have more than one file...Ch. 12 - T F Binary files contain unformatted data, not...Ch. 12 - T F Binary is the default mode in which files are...Ch. 12 - T F The tellp member function tells a file stream...Ch. 12 - T F It is possible to open a file for both input...Ch. 12 - fstream file(ios::in | ios::out);...Ch. 12 - ofstream file; file.open (info.dat, ios::tin); if...Ch. 12 - fstream file("info.dat"); if (!file) { cout ...Ch. 12 - fstream dataFile("info.dat", ios:in | ios:binary);...Ch. 12 - Prob. 69RQECh. 12 - fstream dataFi1e("info.dat", ios:in); char...Ch. 12 - Prob. 71RQECh. 12 - fstream inFile("info.dat", ios:in); int x;...Ch. 12 - File Head Program Write a program that asks the...Ch. 12 - File Display Program Write a program that asks the...Ch. 12 - Punch Line Write a program that reads and prints a...Ch. 12 - Tail Program Write a program that asks the user...Ch. 12 - Line Numbers (This assignment could be done as a...Ch. 12 - String Search Write a program that asks the user...Ch. 12 - Sentence Filter Write a program that asks the user...Ch. 12 - Array/File Functions Write a function named...Ch. 12 - File Encryption Filter File encryption is the...Ch. 12 - File Decryption Filter Write a program that...Ch. 12 - Prob. 11PCCh. 12 - Prob. 12PCCh. 12 - Inventory Program Write a program that uses a...Ch. 12 - Inventory Screen Report Write a program that reads...Ch. 12 - Average Number of Words If you have downloaded...Ch. 12 - Customer Accounts This program should be designed...
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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
- 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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