Monthly Archives: January 2024

In 250 words

Use the Web to search for methods to prevent XSS attacks.

Write a brief description of more than one method

Submit a one-page outline with your proposed term paper title, thesis statement, and an outline of the subtopics you will cover in your paper. You may choose any computer-related crime. When choosing a specific computer crime, make sure that you are able to find relevant cases to support your topic. ALL TOPICS MUST BE APPROVED BY SUBMISSION IN WEEK 1!

Include at least 3 references supporting your paper research.

Complete all the tasks with Jupyter Notebook.

Submit your notebook 

Given the relational model below, using your ADU ID and SQL statements.

List all your courses of the last term [0.5 pt.]

List the instructors’ names of these courses in descending order [0.5 pt.]

List all courses of the last term that have no prerequisite [0.5 pt.]

List all courses of the last term with grade above ‘C’ [0.5 pt.]

Determine the CSIT department of the last term courses [1 pt.]

Define the courses of the last term that have more than one section [1 pt.]

Display the sections of the courses above that are offered in your location (‘Abu Dhabi’ or ‘Al-Ain’) [1 pt.]

Please attach your last semester schedule details for crosschecking. 

1. Transient Overloads: One drawback of RM algorithm is that task priorities are defined by their periods. Sometimes, we must change the task priorities to ensure that all critical tasks get completed. With average execution times, all the critical and non-critical tasks may be RM-schedulable. But, if we consider worst-case execution time we wish to schedule so that all critical tasks meet the deadline and the non-critical task may miss the dead line. The solution is to boost the priority of some critical tasks higher than the non-critical tasks. This is done by altering the “Period” of some critical tasks so that their priorities increase. For example: If we reduce the period of a task by k and have k number of tasks each small sub task will have period divided by K, and execution time divided by k. When the period is smaller, the tasks automatically get higher priority. We can also lengthen the period of non-critical tasks and make it larger than the largest critical task and increase the non-critical task’s execution time also.Carry out a period transformation for this task set to ensure that all the critical tasks will meet the dead line even with worst case execution timing.
   Draw for your modified table of tasks, timing diagram to show schedulability for average execution time and another diagram to show for worst-case execution time. Identify if any tasks miss the dead line.
2. Give explanation of your period transformations and solution.
3. What is CPU Utilization for average and worst-case execution times.
4. Considering Context switch time to be 1 ms, redraw the execution time line for this system for both average and worst case timing.
5. What is the system time-loading (CPU utilization) factor with the context switching included?

1. Write a 2-3 page paper where you compare and contrast the differences between Identity as a Service, Infrastructure as a Service and Identity Access Management. The submission needs to include a minimal of 3 scholarly resources in APA format.

2. Define and describe Platform as a Service. In the description be sure to list the benefits and potential disadvantages. Locate an article that supports the benefits of PaaS in an organization and/or the failures of PaaS implementation. Be sure to cite in APA format. 

No plagiarism

Objective 

● list and string handling: split, indexing, len() 

● list comprehension, with if and else 

● if statement 

● file reading, csv file handling (csv: comma separated values) 

● nan: not a number. matplotlib will auto ignore all ‘nan’ entries. 

● matplotlib: make line plots, line/dot style, label, saving figure 

● self learning. (google and follow example)

For this assignment research and discuss the various security related components that must be addressed when implementing any system (HINT: the first is policy).  Include citations and sources in APA style. 

500 words.

 Topic – Spam Email Detection

Research content (at least 1000 words and 6 references – 3 must be scholarly peer-reviewed articles)

Create visualizations using R Language as applicable, discuss findings

**Must be APA formatted** **College Level Writing****No grammar issues and no  spelling issues**

• Title Page – Include Group number and names of all contributors from the group
• No abstract is to be included
• Document body with citations (rewrite all information used from sources)
• Reference Page

In this individual assignment, you will perform an exploratory analysis with What-If Tool, to better understand the structure of datasets, investigate initial questions, and develop preliminary insights and hypotheses. Your final submission will take the form of a report consisting of key insights gained during your analysis.

Step 1: Dataset Selection and Initial Questions

Pick two datasets. These can be ones that are available for demo at https://pair-code.github.io/what-if-tool/explore/ (Links to an external site.). But we’ll give you additional points if you choose to use datasets that are not available there.  

After selecting datasets – but prior to analysis – write down an initial set of three questions you’d like to investigate about the datasets and prediction results from ML models.

Part 2: Exploratory Visual Analysis

Next, you will perform an exploratory analysis of your dataset and results from ML models using What-If Tool. You can either use their web demo if you use their provided datasets. You can also use notebooks and revise them with your datasets and models.

You should consider two different phases of exploration.

In the first phase, you should seek to gain an overview of the structure of your datasets and results from their models. What is the structure of datasets? Which features are used? Are there any notable issues with the distributions of datasets? What is the model performance? What features contributed the most? Are there any surprising relationships among subsets of data and model results? Are there any fairness issues?

In the second phase, you should investigate your initial questions, as well as any new questions that arise during your exploration. For each question, playing with the visualizations in What-If Tool, that might provide a useful answer. Interact with their functionalities (e.g., datapoint editors, dropdown menus, fairness analysis) to develop better perspectives, explore unexpected observations, or sanity check your assumptions. You should repeat this process for each of your questions, and also feel free to revise your questions or branch off to explore new questions.

What to submit?

You’ll submit a single PDF as a form of a report. For each dataset, you will provide 10 most interesting or surprising findings (or “insights”) with details and screenshots. Your “insights” can include important surprises or issues (such as skewed data distributions, critical fairness issues) as well as responses to your analysis questions. Each finding will consist of a title and 2-4 sentence descriptions, and screenshots. Provide sufficient detail so that anyone could read through your report and understand what you’ve learned. You are free, but not required, to annotate your images to draw attention to specific features of the data. 

Do not submit a report cluttered with everything little thing you tried. Submit a clean, succinct report that highlights the most interesting, insightful observations. You don’t need to tell us how the tool works — we already know that. Think of this like a report to your manager who wants to know what the datasets look like and how the model worked. 

The structure of the report will be:

• Dataset 1
  • Which dataset?
  • Three initial questions
  • 10 most interesting findings
• Dataset 2
  • Which dataset?
  • Three initial questions
  • 10 most interesting findings

Grading

• Clear questions applicable to the chosen datasets
• Clearly written, understandable descriptions that communicate primary insights
• Sufficient breadth of analysis, exploring multiple questions
• Sufficient depth of analysis, with appropriate follow-up questions
• Interesting insights that are worth reporting