To what extent might the strengths, weakness, limitations, and delimitations of existing studies indicate strengths, weakness, limitations, and delimitations of your study? Explain.
Topic 3 DQ 2
After you have determined what sources to include in your literature review to assess employee contributions in the workplace, you must now compile your review and reflect on the implications of all that you have read in the extant literature and how that applies to your research. To what extent might the strengths, weakness, limitations, and delimitations of existing studies indicate strengths, weakness, limitations, and delimitations of your study? Explain.
Note: Rather than providing insights into the topics of employee rewards/compensation or employee retention, focus your discussion post on the research methodology and design to resolve this applied research example.
What kinds of issues do you think can arise with witness testimony?
Types of Evidence
Discussion: In this lesson, we have looked at the following types of evidence: direct, circumstantial, inculpatory, exculpatory, corroborative, witness, and hearsay evidence. Pick one (1) type of evidence for your discussion post. Define that type of evidence (including a citation) and then discuss what you believe is a challenging aspect of that type of evidence for the investigator. For example, direct evidence often includes witness testimony. What kinds of issues do you think can arise with witness testimony? Or, can exculpatory evidence can change the direction of an investigation and what impact does this have on the case and the investigator’s theory of the case?
Requirements:
- Your initial post should be a minimum of 300 words.
TEXTBOOK LINKS
Brown, Thomas. (2019), “Criminal Investigation”. Virginia Wesleyan University. OERCommons. eBook. Viewed from: https://www.oercommons.org/courses/criminal-investigation/view#
Lewandowski, Carla and Jeff Bumgarner. (2021) “Criminal Justice in America: The Encyclopedia of Crime, Law Enforcement, Courts and Corrections”, Santa Barbara, CA: ABC-CLIO, Volumes 1 and 2: Available Online via EGCC Gale EBooks at: Gale eBooks, link.gale.com/apps/pub/69TZ/GVRL?u=steu43952&sid=bookmark-GVRL
Accessed 14 June 2022.
What are some examples of such mental and emotional consequences faced by victims?
Topic
Crime victimization often results in serious mental and emotional consequences. What are some examples of such mental and emotional consequences faced by victims?
Watch the following clip on the effects of victimization:
Source: https://www.youtube.com/watch?v=Rbw0u9vQa84
Identify some of the pitfalls of file-based storage and introduce some of the advantages gained when using a database.
Compare and contrast the problems associated with file systems with the disadvantages of database systems.
Textbook Material:
Printed Textbook: Database Systems: Design, Implementation, and Management, 13th
edition, Coronel and Morris.
Chapter 1 – Database Systems
Identify some of the pitfalls of file-based storage and introduce some of the advantages gained when using a database.
Chapter 2 – Data Models
An overview of the data models and discussion of the terminology and basic structural concepts.
What is the mission statement of the DOJ and these individual law enforcement agencies?
Chapter 1
Use the information located on the Department of Justice (DOJ) website ( http://www.usdoj.gov/ (Links to an external site.) ) to answer the following questions:
· What are some of the law enforcement agencies included within the DOJ?
· What is the mission statement of the DOJ and these individual law enforcement agencies?
· How does the DOJ contribute to the criminal justice system?
Chapter 2
Compare and contrast the UCR and NCVS. How does each method collect their data?? Is the data of these measures reliable? Explain the strengths and weakness of each method.
Chapter 3
Compare and contrast general deterrence with specific deterrence.
Chapter 4
What is a criminal act? What is a criminal state of mind? When are individuals criminally liable for their actions?
Looking at the decision tree itself, are the rules it applies sensible?
Assignment
(100 Points)
CYBR 7240 – Cyber Analytics and Intelligence Module 03: Decision Trees
DECISION TREES for Risk Assessment
One of the great advantages of decision trees is their interpretability. The rules learnt for classification are easy for a person to follow, unlike the opaque “black box” of many other methods, such as neural networks. We demonstrate the utility of this using a German credit data set. You can read a description of this dataset at the UCI site. The task is to predict whether a loan approval is good or bad credit risk based on 20 attributes. We’ve simplified the data set somewhat, particularly making attribute names and values more meaningful.
1. Download the credit_Dataset.arff dataset and load it to Weka.
2. (5 Points) When presented with a dataset, it is usually a good idea to visualise it first. Go to the Visualise tab. Click on any of the scatter plots to open a new window which shows the scatter plot for two selected attributes. Try visualising a scatter plot of age and duration. Do you notice anything unusual? You can click on any data point to display all it’s values.
3. (5 Points) In the previous point you should have found a data point, which seems to be corrupted, as some of its values are nonsensical. Even a single point like this can significantly affect the performance of a classifier. How do you think it would affect Decision trees? A good way to check this is to test the performance of each classifier before and after removing this datapoint.
4. (10 Points) To remove this instance from the dataset we will use a filter. We want to remove all instances, where the age of an applicant is lower than 0 years, as this suggests that the instance is corrupted. In the Preprocess tab click on Choose in the Filter pane. Select filters > unsupervised > instance > RemoveWithValues. Click on the text of this filter to change the parameters. Set the attribute index to 13 (Age) and set the split point at 0. Click Ok to set the parameters and Apply to apply the filter to the data. Visualise the data again to verify that the invalid data point was removed.
5. (20 Points) On the Classify tab, select the Percentage split test option and change its value to 90%. This way, we will train the classifiers using 90% of the training data and evaluate their performance on the remaining 10%. First, train a decision tree classifier with default options. Select classifiers > trees > J48 and click Start. J48 is the Weka implementation of the C4.5 algorithm, which uses the normalized information gain criterion to build a decision tree for classification.
6. (20 Points) After training the classifier, the full decision tree is output for your perusal; you may need to scroll up for this. The tree may also be viewed in graphical form by right-clicking in the Result list and selecting Visualize tree; unfortunately this format is very cluttered for large trees. Such a tree accentuates one of the strengths of decision tree algorithms: they produce classifiers which are understandable to humans. This can be an important asset in real life applications (people are seldom prepared to do what a computer program tells them if there is no clear explanation). Observe the output of the classifier and try to answer the following questions:
· How would you assess the performance of the classifier? Is the Percentage of Correctly Classified Instances a sufficient measure in this case? Why? Hint: check the number of good and bad cases in the test sample, using the confusion matrix. Each column of the matrix represents the instances in a predicted class, while each row represents the instances in an actual class. For example let us define an experiment from P positive instances and N negative instances. The four outcomes can be formulated in a 2 by 2 contingency table or confusion matrix. One benefit of a confusion matrix is that it is easy to see if the system is confusing two classes (i.e. commonly mislabeling one as another).
· Looking at the decision tree itself, are the rules it applies sensible? Are there any branches which appear absurd? At what depth of the tree? What does this suggest? Hint: Check the rules applied after following the paths: (a) CheckingAccount = <0, Foreign = yes, Duration >11, Job = skilled, OtherDebtors = none, Duration <= 30 and (b) CheckingAccount = <0, Foreign = yes, Duration >11, Job = unskilled.
· How does the decision tree deal with classification in the case where there are zero instances in the training set corresponding to that particular path in the tree (e.g. those leaf nodes that have (0:0))?
7. (20 Points) Now, explore the effect of the confidenceFactor option. You can find this by clicking on the Classifer name (to the right of the Choose button on the Classify tab). On the Classifier options window, click on the More button to find out what the confidence factor controls. Try the values 0.1, 0.2, 0.3 and 0.5. What is the performance of the classifier at each case? Did you expect this given your observations in the previous questions? Why do you think this happens?
8. (20 Points) Suppose that it is worse to classify a customer as good when they are bad, than it is to classify a customer as bad when they are good. Which value would you pick for the confidence factor? Which performance measure would you base your decision on?
9. (20 Points)Finally we will create a random decision forest and compare the performance of this classifier to that of the decision tree and the decision stump. The random decision forest is an ensemble classifier that consists of many decision trees and outputs the class that is the mode of the class’s output by individual trees. Again set the test option Percentage split to 90%. Select classifiers > trees > RandomForest and hit Start. Again, observe the output. How high can you get the performance of the classifier by changing the number of trees (numTrees) parameter? How does the random decision forest compare performance wise to the decision tree and decision stump?
Deliverable:
· Your report including the screenshots of your implementation for each section and the results.
Discuss why it’s important to understand the impact of extraneous cognitive load when designing instruction.
Please respond to the following:
- Discuss why it’s important to understand the impact of extraneous cognitive load when designing instruction.
- Explain how your instructional message supports germane cognitive load.
- Be sure to include the title of your project and a brief description to provide your classmates with context for their responses.
My topic is “The race gap in standardize testing”
Why is IT project success often so difficult to assess?
Consider the six criteria for successful IT projects. Why is IT project success often so difficult to assess? Make a case for some factors being more important than others.
Textbook:
Project Management: Achieving Competitive Advantage (5th ed.) Jeffery K. Pinto
Define the fruit of the poisonous tree doctrine.
Please respond to the following in a substantive post:
- Define the fruit of the poisonous tree doctrine.
- Explain how effective you think the fruit of poisonous tree doctrine is in protecting defendants’ Fourth Amendment rights.
Why is it essential for the early childhood educator to understand the characteristics of a gifted student?
Why is it essential for the early childhood educator to understand the characteristics of a gifted student? Where can an early childhood professional find additional support when it is possible there might be a gifted student in his or her class? (150 words and please cite)
