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In 250 words or more, discuss the risk and costs of compromised data integrity breaches. Focus on integrity not confidentiality. Look at military, education, science, medicine, finance, utilities, municipalities, etc. 

   Laboratory I:           To download additional .arff data sets go to: http://www.hakank.org/weka/ or search the Internet for .arff files required · What's the difference between a "training set" and a "test set"? · Why might a pruned decision tree that doesn't fit the data so well be better than an un-pruned one? · What's the first thing that 1R does when making a rule based on a numeric attribute? · How does 1R avoid overfitting when making a rule based on an enumerated and/or numeric attribute? · What is the difference between Attribute, Instance and Training set?  What      is the difference between ID3 and C4.5? Use the following learning      schemes to analyze the iris data (in iris.arff):     OneR - weka.classifiers.OneR   Decision table - weka.classifiers.DecisionTable -R   C4.5 - weka.classifiers.j48.J48 · Do the decisions made by the classifiers make sense to you? Why? · What can you say about the accuracy of these classifiers? When classifying iris that has not been used for training?  · How did each one of the methods perform? Use the following learning      schemes to analyze the bolts data (bolts.arff without the TIME attribute):          Decision Tree - weka.classifiers.j48.J48   Decision table - weka.classifiers.DecisionTable -R   Linear regression - weka.classifiers.LinearRegression   M5'  - weka.classifiers.M5' · The dataset describes the time needed by a machine to produce and count 20 bolts. (More details can be found in the file containing the dataset.)  · Analyze the data. What adjustments have the greatest effect on the time to count 20 bolts?  · According to each classifier, how would you adjust the machine to get the shortest time to count 20 bolts? Produce      a model for both Weather and Weather.nominal data sets. Which method(s) did you use? What did      the tree(s) look like? Laboratory II:   To download additional .arff data sets go to: weka data folder for BreastTumor.arff http://www.hakank.org/weka/ zoo.arff, wine.arff, bodyfat.arff, sleep.arff, pollution.arff Use the following learning schemes to analyze the zoo      data (in zoo.arff):     OneR - weka.classifiers.OneR   Decision table - weka.classifiers.DecisionTable -R   C4.5 - weka.classifiers.j48.J48   K-means - weka.clusterers.SimpleKMeans Try using reduced error pruning for the C4.5. Did it change the produced model? Why?  For K-means, for the first run, set k=10. Adjust as needed. What was the final number of k? Why? Use the following learning schemes to analyze the      breast tumor data.     Linear regression - weka.classifiers.LinearRegression   M5'  - weka.classifiers.M5'   Regression Tree - weka.classifiers.M5'   K-means clustering - weka.clusterers.SimpleKMeans A) How many leaves did the Model tree produce? Regression Tree? What happens if you change the pruning factor?  How many clusters did you choose for the K-means method? Was that a good choice? Did you try a different value for k? B) Now perform the same analysis on the bodyfat.arff data set. Use a      k-means clustering technique to analyze the iris data set. What did you      set the k value to be? Try several different values. What was the random seed value?      Experiment with different random seed values. How did changing of these values      influence the produced models? Produce      a hierarchical clustering (COBWEB) model for iris data. How many clusters did it produce? Why?      Does it make sense? What did you expect? Change the acuity and cutoff parameters in order to produce a model similar to the one obtained in the book. Use the classes to cluster evaluation – what does that tell you? Laboratory III:   To download additional .arff data sets go to: http://www.hakank.org/weka/ zoo.arff, wine.arff, soybean.arff, zoo2_x.arff,  sunburn.arff, disease.arff 8. Use the following learning schemes to compare the training set and 10-fold stratified cross-validation scores of the disease data (in disease.arff):     Decision table - weka.classifiers.DecisionTable -R   C4.5 - weka.classifiers.j48.J48   Id3 - weka.clusterers.Id3 A) What does the training set evaluation score tell you?  B) What does the cross-validation score evaluate?  C) Which one of these models would you say is the best? Why? 9. Use the following learning schemes to analyze the wine data (in wine.arff).     C4.5 - weka.classifiers.j48.J48   Decision List - weka. classifiers.PART A) What is the most important descriptor (attribute) in wine.arff? B) How well were these two schemas able to learn the patterns in the dataset? How would you quantify your answer? C) Compare the training set and 10-fold cross-validations scores of the two schemas. D) Would you trust these two models? Did they really learn what is important for proper classification of wine? E) Which one would you trust more, even if just very slightly? 10. Perform the same analysis of sunburn.arff as in 2. Instead of 10-fold cross-validations use 5-fold. A)-E) Same as in 2. F) Why could not we use 10-fold evaluation in this example? 11. Choose one of the following three files: soybean.arff, zoo.arff or zoo2_x.arff and use any two schemas of your choice to build and compare the models.

 Open the WearEverShoes-01.xlsx start file. If the workbook opens in Protected View, click the Enable Editing button so you can modify it. The file will be renamed automatically to include your name. Change the project file name if directed to do so by your instructor, and save it. Remove a split and freeze panes.  Click the Split button [View tab, Window group]. The split is removed.  Click cell A4 to freeze the column titles at this position.  Click the Freeze Panes button [View tab, Window group].  Choose Freeze Panes. A solid border appears between rows 3 and 4 to mark the pane.  Scroll to row 31 and click cell E31. The column titles are frozen.  Type 3 and press Enter (Figure 1-91).There is a thin border below the cells in row 3 Figure 1-91 Panes frozen at cell A4  Press Ctrl+Home. The insertion point returns to cell A4. Change zoom size and unfreeze the pane.  Click the 100% button [View tab, Zoom group].  Click the Freeze Panes button [View tab, Window group].  Choose Unfreeze Panes.  Click cell A1. Check spelling in the worksheet.  Click the Spelling button [Review tab, Proofing group]. The first occurrence of “Hikin” is found as misspelled.  Select Hiking in the Suggestions list.  Click Change All to correct all occurrences of the misspelled word (Figure 1-92). The correct spelling is H I K I N G in Suggestions list Figure 1-92 Change all occurrences of a misspelled word  Click OK. Use the Fill Handle to copy a formula.  Click H4. The formula displays in the Formula bar. The formula multiples the quantity by the cost to calculate the value of the current stock (Figure 1-93).The formula is E4 times G4 Figure 1-93 Formula to be copied  Point to the Fill Handle for cell H4.  Double-click the Fill pointer. The formula is copied down the entire column.  Press Ctrl+Home.  Click the Themes button [Page Layout tab, Themes group] and choose Ion from the gallery. Merge and center the titles.  Select cells A1:J1 and click the Merge & Center button [Home tab, Alignment group].  Merge and center cells A2:J2. Apply cell styles and adjust row height.  Select cell A1, click the Cell Styles button or the More button [Home tab, Styles group], and select Title in the Titles and Headings category.  Select cell A2 and apply the Heading 4 style.  Click the Font Size arrow [Home tab, Font group] and select 14 pt. The font size change overwrites the font size of the cell style.  Point to the row 1 heading and drag to select row headings 1:2.  Click the Format button [Home tab, Cells group] and choose Row Height.  Type 22 as the new row height and press Enter.  Select cells A3:J3.  From the Cell Styles gallery [Home tab], select Light Yellow, 40% - Accent3.  Apply bold to the selected cells.  Click the row 3 heading.  Point to the bottom border of the row heading to display the resize arrow.  Drag the resize arrow to 21.00 (28 pixels) as the new row height and release the pointer (Figure 1-94). The resize pointer is between the row headings for rows 3 and 4 Figure 1-94 Adjust row height from the row heading  Click the Zoom Out button in the Status bar if necessary so that you can see all rows (1:39). Change alignment, format values, apply borders, and increase the indent.  Select cells D4:F39.  Click the Center button [Home tab, Alignment group].  Select cells G4:H39.  Click the Accounting Number Format button [Home tab, Number group].  Select cells A3:J39.  Click the Borders drop-down arrow [Home tab, Font group] and select All Borders.  Click the Font size drop-down arrow [Home tab, Font group] and choose 12 while the cells are selected.  Select cells B4:C39 and click the Increase Indent button [Home tab, Alignment group] one time. This moves the label away from the border for easier reading.  Click and drag to select columns A:J.  Double-click the border between columns J and K to AutoFit the selected columns (Figure 1-95). Columns A:J are selected and shaded gray Figure 1-95 Select column headings to AutoFit  Select and center align the labels in row 3. Insert a column and create a pattern to fill data.  Right-click column heading B.  Choose Insert from the context menu.  In cell B3, type Disc? and press Enter.  In cell B4, type No and press Enter.  In cell B5, type No and press Enter.  In cell B6, type Yes and press Enter. The pattern is two occurrences of No and one occurrence of Yes.  Select cells B4:B6 and increase the indent twice.  Select cells B4:B6 and double-click the Fill pointer (Figure 1-96).The Fill pointer displays at cell B6 Figure 1-96 Double-click the Fill pointer to complete a series  Select cells A4:A39 and increase the indent one time.  Press Ctrl+Home. Rename the sheet tab.  Double-click the Sheet1 tab.  Type Inventory and press Enter. Use Page Layout view to insert a footer.  Click the Page Layout button in the Status bar.  Click the center header section. The Header & Footer Tools tab is active.  If necessary, click the Header & Footer Tools Design tab to display it.  Click the Go to Footer button [Header & Footer Tools Design tab, Navigation group].  Click the middle section if necessary.  Click the File Name button in the Header & Footer Elements group. The code is &[File].  Click a worksheet cell to see the file name.  Switch to Normal view and press Ctrl+Home. Change page setup options.  Click the Page Layout tab and click the Page Setup launcher.  Display the Page tab and select the Landscape radio button under Orientation.  Select the Fit to radio button and enter 1 page wide by 1 tall.  Click OK.  Switch to Normal view and press Ctrl+Home. Set a print area.  Select cells A1:K11.  Click the Page Layout tab.  Click the Print Area button [Page Setup group] and select Set Print Area.  Press Ctrl+Home.  Click the File tab and select Print to preview the worksheet print area.  Return to the worksheet.  Click the Save button in the Quick Access toolbar to save the workbook changes. The print area is saved.  Close the workbook (Figure 1-97).

 The following MUST meet the criteria below: 1. Code must work using C++ in Visual Studio 2012 or 2015 2. keep basic and simple. (adding //comments would help alot) 3. Please read attached file CAREFULLY for instructions (make it similar to the example)

Exercise 24.3: (pg no.  971)  (Implement a doubly linked list) The MyLinkedList class used in Listing 24.5 is a one-way directional linked list that enables one-way traversal of the list. Modify the Node class to add the new data field name previous to refer to the previous node in the list, as follows: public class Node { E element; Node next; Node previous; public Node(E e) { element = e; } } Implement a new class named TwoWayLinkedList that uses a doubly linked list to store elements. Define TwoWayLinkedList to implements MyList. You need to implement all the methods defined in MyLinkedList as well as the methods listIterator() and listIterator(int index). Both return an instance of java.util.ListIterator (see Figure 20.4). The former sets the cursor to the head of the list and the latter to the element at the specified index.

   Question 1 see attachment Question 2: List the different WiFi frequency bands and their associated names/standards. Explain how to increase WiFi coverage using different technologies and what security solutions exist to ensure confidentiality. Question 3: Your operator has delivered you a binary software image, viewed with a hex editor, coming from a CPU you are not familiar with. She states that it was from a RISC 32-bit architecture that is commonly used in many devices. What steps would you take to identify the image? Be as specific as possible. What follow on questions would you ask to facilitate your analysis? Question 4: see attachment Question 5: Given the following bash script, explain what the script is doing (Hint: This is on a linux machine) (for addr in {1..254}; do for port in {20..23} 80;  do  nc -nvzw 1 10.25.0.$addr $port 2>&1;  done&  done) | grep 'open' Question 6: Given the network 192.168.2.0/23, an IT department needs three variable-length subnets. For the first subnets, the IT department needs 100, 75, 32 hosts, respectively. Based on the information given, compute the variable-length subnets needed to satisfy the IT department's needs.  Hosts Needed  | Hosts Given | Network ID | SN Mask | 1st Available IP | Last  Available IP | Broadcast IP Question 7: Describe a corporate network infrastructure design with which you’re familiar and define the critical areas that need to be secured. What vulnerabilities exist and what are methods for mitigation? Prioritize what needs to be secured and what the potential impacts would be if not secured properly.

*******Need in 2 hours******* Answer 10 questions from the attached sheet. Subject :Data Science

Who Wants to Be a Millionaire  Your final project will be to create a quiz game similar to “Who Wants to be a Millionaire?”  Requirements:  • Your game will include 5 questions. • You will ask the user multiple choice questions (in increasing difficulty if possible).  • Each question, if answered correctly, will have a point value attached to it. The point values should be as follows: 100, 200, 500, 1000, 2000.  • Unlike the game on T.V., the score will be an accumulation of the points earned throughout the game and the user may continue if he/she answers incorrectly. Show the user’s point total after each question.  • At the end of the game, the user should be given the option to play again if he/she chooses.  • Include a text file called “highscore.txt” that stores the highest scoring player’s first name and score. Update the text file if the user scores the highest score.  • Include at least three functions in your program.  Input Validation: The user must choose valid options from menus.  Grading Criteria:  1. Algorithm  2. Text File manipulation       -Read from file        -Compare to score in file        -Write to file  3. Loops/If Structures       -Game ends at correct place        -Play as many times as user wishes        -Game and score resets o Input Validation  4. Score Accumulation  5. Functions 

A picture of the assignment, and a text file with the quiz questions are included. I need this app ASAP! it must be done in the next 4 hours, please help!!!

  QUESTION 1 RStudio Data Analysis - Upload a screen shot of the RStudio commands and result How many females under the weight of 60 exist in the dataset? (Use Dataset: dataset_self_reports_of_height.csv) RStudio Data Analysis - Upload a screen shot of the RStudio commands and result What proportion of females do not exercise and smoke in the dataset? (Use Dataset: dataset_student_survey_data.csv)   Match the following:   Visual hierarchy – layout:                       Visual hierarchy – size: Absolute positioning:  A. How to manage the hierarchy of element sizes?  B. Where specifically should certain elements be placed? C. How to arrange the position of elements? 4.  RStudio Data Analysis - Upload a screen shot of the RStudio commands and result What proportion of males and females never smoked and exercise frequently in the dataset? (Use Dataset: dataset_student_survey_data.csv) 5.  Match the following:   Chart size: Chart scales:   Chart orientation:  Chart value sorting:  A. Don’t be afraid to shrink charts, so long as any labels are still readable, and especially embrace the power of small multiple.  B. what are the most meaningful range of values given the nature of the data? C..Which way is best? D. Consider the most meaningful sorting arrangement for your data and editorial focus, based on the LATCH acronym. 6.   Composition is divided into the following: (Select all that apply) Project composition Level composition Chart composition 7.  RStudio Data Analysis - Upload a screen shot of the RStudio commands and result How many males smokers eat dinner in the dataset? (Use Dataset: dataset_tipping_data.csv) 8.  RStudio Data Analysis - Upload a screen shot of the RStudio commands and result What percentage of species have a petal length greater than and equal to 1.2? (Use Dataset: dataset_edgar_anderson_iris_data.csv) 9.   RStudio Data Analysis - Upload a screen shot of the RStudio commands and result What percentage of males paid for meals on Saturday that was over $17.50 are in the dataset? (Use Dataset: dataset_tipping_data.csv) 10.  RStudio Data Analysis - Upload a screen shot of the RStudio commands and result What percentage of males purchased a 1st class ticket and survived in the dataset? (Use Dataset: dataset_survival_of_passengers_on_the_titanic.csv)