EXPERIENCING MIS >CUSTOM<
7th Edition
ISBN: 9781323518731
Author: KROENKE
Publisher: PEARSON C
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Chapter 4, Problem 4ARQ
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GNU:
The abbreviation for GNU is GNU’s Not Unix.
- It is a design which is used to create free Unix-like
operating system but different from UNIX. - The main aim of this design is to give freedom to all users to copy, modify, develop, design, and study the software.
- This goal was accomplished in the year of 1992 at the time of release of LINUX under the GNU General Public License.
GPL:
The abbreviation for GPL is General Public License.
- It uses free software license which is written by Richard Stallman.
- It helps the software to be used, modified, and restructured by anybody.
Three successful open source projects:
Open Office:
- The Open office which is similar to the Microsoft office can run on Linux operating system is one of the most popular open source projects and is also very useful in many organizations because it’s free and open source in nature.
Hadoop:
- Another successful open source project is Hadoop which is considered one of the latest
programming tools for data scientists for dealing with big data; it means that the dataset is very large ,which is not possible to handle using the traditionaldatabase management system. - Hadoop is known as one of the big data tools and it is free and a successful open source project.
MySQL:
- MySQL is one of the most popular database servers and one of the successful open source projects that has been used in many websites including Wikipedia, Facebook etc.
Apache Spark:
- It can also be considered as one of the latest successful open source project that has distributed data processing capabilities for processing huge amount of data sets within a small period of time for dealing with big data.
Four reasons for programmers to contribute to open source projects:
- Improved coding skills can be achieved.
- The programmers will get more experience and can get chance to learn new things from other developers who are currently working in the project.
- Another reason for contributing to the open source software is freedom to make new things.
- Another reason for contributing to open source software is to increase the peer recognition and to enjoy while developing any software product based on some specific needs and requirements...
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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…
details 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…
We 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…
Chapter 4 Solutions
EXPERIENCING MIS >CUSTOM<
Ch. 4.2 - Prob. 1SWCh. 4.2 - Prob. 2SWCh. 4.2 - Prob. 3SWCh. 4.2 - Prob. 4SWCh. 4.2 - Prob. 5SWCh. 4.2 - Prob. 6SWCh. 4 - Prob. 1EGDQCh. 4 - Prob. 2EGDQCh. 4 - Prob. 3EGDQCh. 4 - Prob. 4EGDQ
Ch. 4 - Prob. 1GDQCh. 4 - Prob. 2GDQCh. 4 - Prob. 3GDQCh. 4 - Prob. 4GDQCh. 4 - Prob. 1ARQCh. 4 - Prob. 2ARQCh. 4 - Prob. 3ARQCh. 4 - Prob. 4ARQCh. 4 - Prob. 1UYKCh. 4 - Prob. 2UYKCh. 4 - Prob. 3UYKCh. 4 - Prob. 4CECh. 4 - Prob. 5CECh. 4 - Prob. 6CECh. 4 - Prob. 7CECh. 4 - Prob. 8CECh. 4 - Prob. 9CSCh. 4 - Prob. 10CSCh. 4 - Prob. 11CSCh. 4 - Prob. 12CSCh. 4 - Prob. 13CSCh. 4 - Prob. 14MMLCh. 4 - Prob. 15MML
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