Essentials of MIS (12th Edition)
12th Edition
ISBN: 9780134238241
Author: Kenneth C. Laudon, Jane P. Laudon
Publisher: PEARSON
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Chapter 12, Problem 2RQ
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Alternative methods of building
An information system is a system which provides the necessary information to an organization to achieve the goals. The following alternative methods are using to building an information system...
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Traditional systems life cycle:
It is a formal methodology for managing the development of systems in development life cycle. It has the six step process to develop the system. They are:
- Analyzing the
system - Design the system
- Programming
- Test the system
- Implementation of system
- Maintenance of system.
Advantages for systems building:
The advantages of traditional system life cycle for system building is as follows:
- It is simple to implement and easy to understand...
Explanation of Solution
Information system prototyping:
It is an alternative model for building information system. It has a prototype which represents the requirements of the user.
Benefits of information system prototyping:
- The benefit of information system prototyping is exactly matches to the customer requirements and these model is mainly used to enhance the bigger projects.
- It is useful for routine problems and this information system is very structured and well-understood.
- It reduces the implementation cost of the project.
Limitations of information system prototyping:
- It is not appropriate for massive amounts of data...
Explanation of Solution
End-user development:
End-user development of information system is refers the programmers and system analysts. It gives the direct control to the project. By using the end-user development model, the project is rapidly developed with standard programming tools.
Advantages of end-user development:
- It takes minimal time to finish the software and the customer requirements in project.
- The tools are used by professional programmers in productivity levels...
Explanation of Solution
Application software packages and cloud software services (SaaS):
- The software packages is common tool for all business organizations for its daily process like payroll, inventory control, etc. Many organizations use cloud services for its higher security.
- SaaS is one of the cloud software services in cloud computing. In SaaS, the software is deployed from a cloud provider; internet is used to deliver the service and the subscribers access the service through the browser.
Advantages:
- Using application software packages, the organization got a solution in quick and easy way...
Explanation of Solution
Outsourcing:
Outsourcing means taking a company for contract to complete the specific task of some other company project using the in-house employees.
Advantages of outsourcing:
- Company takes specialized skills.
- Provides labor flexibility.
- Cost effective.
Drawbacks of outsourcing:
- Increases the confidence on third parties.
- Lacking the knowledge of in-house for complex business operations.
Favorable circumstances for the outsourcing strategy are as given below:
Lack in experience:
- Whenever there are less experienced professionals in any organization, then for a new system design, the organization always prefer the outsourcing strategy.
- Hence, the product is designed by the other firm.
Inadequate resources:
- If an organization does not have adequate resources, then outsourcing of the project must be done.
- Organization can hire different vendors, developers, or suppliers to meet their needs...
Explanation of Solution
Explanation of developing e-business applications:
- Developing e-business application is a needed one for every business for its development. RAD is the technology of process for developing systems in short period of time...
Explanation of Solution
Development of the mobile application:
- Smart phone tend to become more important part in every person’s day to day life routine.
- Every process required to be made in a smart manner and it is considered to be more important in case of the business process.
- Every customer requires process that is made should be effective a...
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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 12 Solutions
Essentials of MIS (12th Edition)
Ch. 12.2 - Prob. 1CQ1Ch. 12.2 - Prob. 2CQ1Ch. 12.2 - Prob. 3CQ1Ch. 12.4 - Prob. 1CQ2Ch. 12.4 - Prob. 2CQ2Ch. 12.4 - Prob. 3CQ2Ch. 12.4 - Prob. 4CQ2Ch. 12 - What are the core problem-solving steps for...Ch. 12 - Prob. 2RQCh. 12 - Prob. 3RQ
Ch. 12 - 12-4 How should information systems projects be...Ch. 12 - Prob. 5DQCh. 12 - Prob. 6DQCh. 12 - Prob. 7DQCh. 12 - The Warm and Toasty Heating Oil Company used to...Ch. 12 - Prob. 9HMPCh. 12 - Prob. 11HMPCh. 12 - Prob. 13CSQCh. 12 - Prob. 14CSQCh. 12 - Prob. 15CSQCh. 12 - Prob. 16CSQCh. 12 - Prob. 17CSQCh. 12 - Prob. 18MLMCh. 12 - Prob. 19MLM
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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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