Task 1

Chapter 7:
1. Using a Web browser, perform some research on a newer malware variant
that has been reported by a major malware containment vendor. Using a
search engine, go to the vendor’s Web site; this could be Symantec, McAfee,
or any of their competitors. Visit one malware prevention software vendor.
Search for the newest malware variants and pick one. Note its name and try
to understand how it works. Now look for information about that same malware from at
least one other vendor. Were you able to see this malware at both vendors? If so, are there
any differences in how they are reported between the two vendors?

need min 2 to 3 pages paper excluding title and reference page

Task 2

Include at least 250 words in your posting and at least 250 words in your reply. Indicate at least one source or reference in your original post. Please see syllabus for details on submission requirements.

Search “scholar.google.com” or your textbook. Include at least 250 words in your reply. Indicate at least one source or reference in your original post. Discuss ways organizations have built a CSIRT. What are the components to building an effective and successful CSIRT team?

main post -300 words

2 secondary post -each 200 words

For this task, prepare a tri-fold Pamphlet suitable for distribution to parents, teachers, and caregivers of young children, about physical development in childhood or adolescence. Include material on how caregivers can promote positive physical development in children or adolescence. Be sure to include at least one real-life resource (i.e., a camp, a club, an organization, website) in Oklahoma that promotes physical development for children or adolescents. You may include appropriate graphs and visual enhancements to make your brochure engaging. Keep in mind your intended audience–parents, educators, and caregivers. This should be easy to read and visually appealing.

Length: 2-4 pages, not including title and reference pages

References: Include a minimum of five scholarly resources.

Your pamphlet should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards.

Length: 2-4 pages, not including title and reference pages

References: Include a minimum of five scholarly resources.

Your pamphlet should demonstrate thoughtful consideration of the ideas and concepts presented in the course by providing new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards.

1st:

Excel activity: Lunch order

Our Women Designers are ordering lunch. FAQ has tips!

Download the spreadsheet and use the Subtotal (outline) to answer these questions:

1. What does everyone owe in FOOD?
2. What do they owe in TAX? (L.A. County tax rate 9.5%)
3. What do they owe for TIP? (Christine’s minimum tip amount 20%, what percent will you give in tip?)
4. What is the GRAND TOTAL all orders together?

In-Class Activity – Ordering Lunch-1.xlsx

Assignment help: see HELPFUL FUNCTIONS video for refresher on SUBTOTAL/OUTLINE. Also, Textbook Module 8 section Create Subtotals, and FAQ discussion.

EVALUATION (SEE RUBRIC):

• When working on course assignments your number one priority is following directions, this is what you will be graded on. Following directions will achieve the SLOs.

✏️ Student Learning Outcomes

1. 1. Demonstrate competency in using spreadsheet technology (Excel) entering appropriate retail math formulas.
   2. Exhibit strong information literacy skills by looking up values in a table.
   3. Show expertise in planning and executing a macro to maintain table data.
   4. Demonstrate technical proficiency by enhancing charts to improve the user’s understating of the information displayed.
   5. Combine strong critical thinking skills with information literacy by using the data in PivotTables to make decisions.

   2nd:Excel Activity: Gradebook EnhancementObjective: Enhance your Grdebook spreadsheet to reflect the skills learned in MRCH.2760. Purpose:This quarter you have boosted your Excel skills and this is your chance to show them off! This enhanced gradebook report will showcase your advanced reporting techniques. This spreadsheet is not your official gradebook, so grades missing from Canvas garadebook at the time of assignment submission are not required, but the spreadsheet still needs to have proper formulas present so when the data is entered, the spreadsheet calculates and meets the assignment criteria.The goal of your Dashboard is to tell the story of your data – we usually call this analyzing data.Each chart used within your dashboard is a visual display of the data analysis. A dashboard report has THREE layersData (think table)Analysis of the data (data with formulas and functions applied; data made into information)Presentation or visual display and organization of the report (your charts)Remember in MRCH.1950 where we created the two small data sets to analyzed the greadebook data?They were the Min, Max, Ave data set and the data set displaying an calculating your final grades.These two part of the MRCH.1950 gradebook (and probably the gradebook from earlier in this quarter) are the data analysis layer of the gradebook. The data portion was made of the gradebook item names, points earned, and points possible. Criteria:Read 10ChartDesignPrinciples_AND_ChartTypesExplained.pdf (click link to download the file or scroll down to read in Canvas)Begin with the Gradebook file submitted in Week 2 and complete the following criteria: Add a pivot table (you will need to create a table based on PivotTable rules with your gradebook data)Add the following data:Due date for each gradebook itemDate you submitted the workInclude how many times you submitted/revised each gradebook itemInclude your engagement time Time spent in MindTap for the assignment (MindTp provides an engagement report, how can you report this data in your table?Time spent on the Excel Spreadsheet reports and Textbook work (you track throughout the quarter)Add PivotTable Chart to the dashboardAdd Sparklines to a table within your dashboard (see textbook module 10)Add at least one Macro Button that takes us the location in the workbook holding raw data for a chart)Edit all charts in your dashboard using the skills learned in Textbook Module 10Would your charts benefit from customizing the data series?Which chart elements can support can you use to achieve a report that is impactfulEvaluate the chart axis, can this improve your chart? Pay close attention to Max and Minimum values.Would combination chart be an effective update to your dashboard? Add or change chart to a combo chart.
   Gradebook I uploaded is gradebook file submitted in Week 2

Instructions

• Exercise 1 focuses on Deep Neural Networks and is mandatory
• Package your answers (Jupyter notebook, Prism model files) into zip and share it with us

●Exercise 2 focuses on Probabilistic Model Checking and is ‘best-effort’

Exercise 1: Deep Neural Networks

Objectives

1.Run and fine-tune a pre-trained pix2pix (DNN1) that can translate images from a satellite view to a flood segmentation map (sat2map) network. This result is DNN2.

2.Evaluate the model on a hand-made labelled dataset on both DNN1 and DNN2.

• Report IoU metric (to be implemented) for 10 different cross validation splits of our 87 images xBD2seg.zip dataset.

Given

• Existing implementation of the pix2pix model designed for paired image-to-image translation task and associated data.
• xBD2seg.zip dataset: a dataset of images (1024 x1024 pixels) extended from xBD (Building damage satellite imagery) as input X, and a segmentation of floods in the output image for each image input. The dataset is small and manually annotated.

Tasks

1.Run the pretrained pix2pix DNN (DNN1)

2.Implement and briefly describe two fine tuning strategies, i.e., one should be adding one-two final layers to DNN1 and train on the xBD2seg-train set to finetune DNN2 to segment xBD2seg-test set.

3.Implement the Intersection-over-union (IoU) metric to quantify ability of the network at segmenting floods from satellite images of size.

4.Report IoU for 10 different splits train/test of xBD2Seg dataset.

5.According to literature, an IoU of 0.5-0.65 is the state of the art on segmentation tasks over high resolution satellite imagery. Can fine tuning of DNN2 achieve this performance?

6.Compare your fine tuned network to the results you obtain from training a model from scratch using 256×256 cropos over our 1024×1024 xBD2seg dataset.

Help

You may report visual results for real image A, real image B and Generated image B with pix2pix sat2map model in a similar grid as the one below

but where the fake images should be segmented areas of flood:

Expected output

The provided segmented dataset evaluated together with the ground truth labelled data:

The IoU metric reported for each image in the given dataset in a pdf of the final Jupyter notebook: IoU of sample #1 is…

IoU of sample #2 is …

IoU of sample #3 is …

IoU of sample #n

Exercise 2: Probabilistic Model Checking

Objective

1.Use probabilistic modelling to model a medical rescue scenario involving drones that have to deliver supplies to a first responder team treating an injured runner in the park (location 4 in the figure). Two nearby healthcare centers (locations 1 and 2) are equipped to dispatch drones of different types (A and B, respectively).

• Verify properties of interest for the modelled scenario

Scenario

When moving to the location where the first-responder team is placed, drones have to cover some distance, and that consumes energy of their batteries. Drones A and B have different energy efficiency, meaning that drone A consumes 1000 mwh per kilometer, while drone B consumes 2000 mwh per kilometer.

Moreover, the air space is not entirely safe and drones might collide against obstacles when they are on their way to deliver their payload. In particular, the probability of collision of the drones are:

●Drone A: 0.3 when moving between locations 1 and 2;

●Drone B: 0.6 when moving between locations 2 and 3;

●Drones A and B: 0.1 when moving between locations 3 and 4.

Finally, municipal regulations also prescribe that, at any given time, only a single drone can be flying within the park’s airspace. Hence, your model should be constrained so that the situation in which more than one drone is within park premises (i.e., in location 4) at the same time never occurs.

Tasks

1.Model the described scenario as a DTMC using the probabilistic modelling language Prism

2.Write and verify a probabilistic computational tree logic (PCTL) propertyto calculate the overall probability that the payload will be eventually delivered to the first responder team

3.Write and verify a PCTL property to calculate the overall probability that any of the drones will collide before arriving at the park.

For Tasks (2) and (3), provide:

• the PCTL formula you have written to check the property.
• A brief, informal description of the PCTL property, including the adequacy of temporal operators employed, the meaning of the different elements in the formula (relating them e.g., to the meaning of variables and other elements in your model, if needed), and how they work together to achieve the desired result.
• A screenshot of the result of checking the formula in PRISM, along with a brief explanation of your interpretation of the results (i.e., what does the result mean?).

Help

• You can download Prism from http://prismmodelchecker.org/download.php
• A DTMC Prism model of a fair 6-sided die with PCTL properties that can be evaluated using this model are available at http://prismmodelchecker.org/tutorial/die.php

The data set file is too long that is why cannot uploaded here. the data set will be given later through google drive