Experiencing MIS
7th Edition
ISBN: 9780134380421
Author: KROENKE
Publisher: PEARSON
expand_more
expand_more
format_list_bulleted
Expert Solution & Answer
Chapter 1, Problem 3ARQ
Explanation of Solution
Summary of IS related jobs:
- Spence and Hlatshwayo studied about the employment in the US from 1990 to 2008.
- They found that a tradable job as one that was not dependent on a particular location. And such jobs can be outsourced overseas.
- At that time computer related services had the strongest growth of any job type in that category.
- After the dot-com bust in 2000, the number of jobs dipped substantially...
Explanation of Solution
Growth rate of IS related jobs:
- According to Labour Satistics salary growth done by U.S Bureau, from 2012 to 2014 growth rates of all
information system related jobs are above 7 percent average for all occupation...
Explanation of Solution
How innovations in technology increase the value of IS-related jobs:
- The most significant determinant of employment and salary is the nature of work performed.
- In short, as the price of computer technology plummets, the value of jobs that benefit from it increases dramatically...
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
Note : 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…
Here 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…
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…
Chapter 1 Solutions
Experiencing MIS
Ch. 1.3 - Prob. 1SWCh. 1.3 - Prob. 2SWCh. 1.3 - Prob. 3SWCh. 1.3 - Prob. 4SWCh. 1.3 - Prob. 5SWCh. 1 - Prob. 1EGDQCh. 1 - Prob. 2EGDQCh. 1 - Prob. 3EGDQCh. 1 - Prob. 4EGDQCh. 1 - Prob. 5EGDQ
Ch. 1 - Prob. 6EGDQCh. 1 - Prob. 7EGDQCh. 1 - Prob. 1GDQCh. 1 - Prob. 2GDQCh. 1 - Prob. 3GDQCh. 1 - Prob. 4GDQCh. 1 - Prob. 1ARQCh. 1 - Prob. 2ARQCh. 1 - Prob. 3ARQCh. 1 - WHAT IS MIS? Identify the three important phrases...Ch. 1 - Prob. 5ARQCh. 1 - Prob. 3UYKCh. 1 - Prob. 4CECh. 1 - Prob. 5CECh. 1 - Prob. 6CECh. 1 - Prob. 7CECh. 1 - Prob. 8CECh. 1 - Prob. 9CSCh. 1 - Prob. 10CSCh. 1 - Prob. 11CSCh. 1 - Prob. 12CSCh. 1 - Prob. 13CSCh. 1 - Prob. 14CSCh. 1 - Prob. 15CS
Knowledge Booster
Similar questions
- 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…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_forwardI want a database on MySQL to analyze blood disease analyses with a selection of all its commands, with an ER drawing, and a complete chart for normalization. I want them completely.arrow_forward
- Assignment Instructions: You are tasked with developing a program to use city data from an online database and generate a city details report. 1) Create a new Project in Eclipse called "HW7". 2) Create a class "City.java" in the project and implement the UML diagram shown below and add comments to your program. 3) The logic for the method "getCityCategory" of City Class is below: a. If the population of a city is greater than 10000000, then the method returns "MEGA" b. If the population of a city is greater than 1000000 and less than 10000000, then the method returns "LARGE" c. If the population of a city is greater than 100000 and less than 1000000, then the method returns "MEDIUM" d. If the population of a city is below 100000, then the method returns "SMALL" 4) You should create another new Java program inside the project. Name the program as "xxxx_program.java”, where xxxx is your Kean username. 3) Implement the following methods inside the xxxx_program program The main method…arrow_forwardCPS 2231 - Computer Programming – Spring 2025 City Report Application - Due Date: Concepts: Classes and Objects, Reading from a file and generating report Point value: 40 points. The purpose of this project is to give students exposure to object-oriented design and programming using classes in a realistic application that involves arrays of objects and generating reports. Assignment Instructions: You are tasked with developing a program to use city data from an online database and generate a city details report. 1) Create a new Project in Eclipse called "HW7”. 2) Create a class "City.java" in the project and implement the UML diagram shown below and add comments to your program. 3) The logic for the method "getCityCategory" of City Class is below: a. If the population of a city is greater than 10000000, then the method returns "MEGA" b. If the population of a city is greater than 1000000 and less than 10000000, then the method returns "LARGE" c. If the population of a city is greater…arrow_forwardPlease calculate the average best-case IPC attainable on this code with a 2-wide, in-order, superscalar machine: ADD X1, X2, X3 SUB X3, X1, 0x100 ORR X9, X10, X11 ADD X11, X3, X2 SUB X9, X1, X3 ADD X1, X2, X3 AND X3, X1, X9 ORR X1, X11, X9 SUB X13, X14, X15 ADD X16, X13, X14arrow_forward
- Outline the overall steps for configuring and securing Linux servers Consider and describe how a mixed Operating System environment will affect what you have to do to protect the company assets Describe at least three technologies that will help to protect CIA of data on Linux systemsarrow_forwardNode.js, Express, Nunjucks, MongoDB, and Mongoose There are a couple of programs similar to this assignment given in the lecture notes for the week that discusses CRUD operations. Specifically, the Admin example and the CIT301 example both have index.js code and nunjucks code similar to this assignment. You may find some of the other example programs useful as well. It would ultimately save you time if you have already studied these programs before giving this assignment a shot. Either way, hopefully you'll start early and you've kept to the schedule in terms of reading the lecture notes. You will need to create a database named travel using compass, then create a collection named trips. Use these names; your code must work with my database. The trips documents should then be imported unto the trips collection by importing the JSON file containing all the data as linked below. The file itself is named trips.json, and is available on the course website in the same folder as this…arrow_forwardusing r languagearrow_forward
- using r languagearrow_forwardusing r languagearrow_forwardWrite a short paper (1 page/about 500 words) summarizing what we as System Admins can do to protect the CIA of our servers. Outline the overall steps for configuring and securing Linux servers Consider and describe how a mixed Operating System environment will affect what you have to do to protect the company assets Describe at least three technologies that will help to protect CIA of data on Linux systems Required Resourcesarrow_forward
arrow_back_ios
SEE MORE QUESTIONS
arrow_forward_ios
Recommended textbooks for you
Enhanced Discovering Computers 2017 (Shelly Cashm...Computer ScienceISBN:9781305657458Author:Misty E. Vermaat, Susan L. Sebok, Steven M. Freund, Mark Frydenberg, Jennifer T. CampbellPublisher:Cengage Learning
Principles of Information Systems (MindTap Course...Computer ScienceISBN:9781285867168Author:Ralph Stair, George ReynoldsPublisher:Cengage Learning
Principles of Information Systems (MindTap Course...Computer ScienceISBN:9781305971776Author:Ralph Stair, George ReynoldsPublisher:Cengage Learning
Fundamentals of Information SystemsComputer ScienceISBN:9781305082168Author:Ralph Stair, George ReynoldsPublisher:Cengage Learning
Principles of Information Security (MindTap Cours...Computer ScienceISBN:9781337102063Author:Michael E. Whitman, Herbert J. MattordPublisher:Cengage Learning
Fundamentals of Information SystemsComputer ScienceISBN:9781337097536Author:Ralph Stair, George ReynoldsPublisher:Cengage Learning
Enhanced Discovering Computers 2017 (Shelly Cashm...
Computer Science
ISBN:9781305657458
Author:Misty E. Vermaat, Susan L. Sebok, Steven M. Freund, Mark Frydenberg, Jennifer T. Campbell
Publisher:Cengage Learning
Principles of Information Systems (MindTap Course...
Computer Science
ISBN:9781285867168
Author:Ralph Stair, George Reynolds
Publisher:Cengage Learning
Principles of Information Systems (MindTap Course...
Computer Science
ISBN:9781305971776
Author:Ralph Stair, George Reynolds
Publisher:Cengage Learning
Fundamentals of Information Systems
Computer Science
ISBN:9781305082168
Author:Ralph Stair, George Reynolds
Publisher:Cengage Learning
Principles of Information Security (MindTap Cours...
Computer Science
ISBN:9781337102063
Author:Michael E. Whitman, Herbert J. Mattord
Publisher:Cengage Learning
Fundamentals of Information Systems
Computer Science
ISBN:9781337097536
Author:Ralph Stair, George Reynolds
Publisher:Cengage Learning