Problem #1 (15 points) FOR SECTIONS 590-12 and 590-53 (undergraduate) ONLY Problem #2 (15 points) Create a 2-dimensional data set with 20 samples that has the following properties:Explain why the K-Means cannot generate the correct clusters.What kind of linkage is needed for the Agglomerative algorithm to cluster the data correctly? Problem #3 (15 points)Create a 2-dimensional data set with 22 samples that has the following properties:Explain why the K-Means and the Agglomerative algorithms cannot generate the correct clusters.Explain why the DBSCAN is the appropriate algorithm for this dataset. Problem #4 (15 points)List three different reasons for trying to reduce the number of features prior to applying a machine learning algorithm. Justify and explain each reason. Problem #5 (15 points)Identify two cases where accuracy may be an inadequate measure to evaluate the performance of a classification algorithm. Explain the reasons.For each case, provide an alternative scoring measure and explain why it is more reliable than the accuracy. Problem #6 (10 points)Given the following pseudo-code that is supposed to train and test an SVM classifier on the Iris data. Is the above algorithm logically correct? If not, identify the problem and correct it. Problem #7 (15 points)Suppose that we have 3 classification algorithms. Each algorithm has two parameters: P1 and P2.After performing a grid search for each algorithm (using {0.01, 0.1, 1, 10} for each parameter), we obtain the following accuracy results: Did we use the correct range of values for each parameter? Justify your answer.If the answer is no, then what other values for P1 and P2 do you recommend exploring? Did we use the correct range of values for each parameter? Justify your answer.If the answer is no, then what other values for P1 and P2 do you recommend exploring? Did we use the correct range of values for each parameter? Justify your answer.If the answer is no, then what other values for P1 and P2 do you recommend exploring?
Create a 2-dimensional data set with 30 samples that has the following properties a) Samples should belong to 2 classes (15 samples per class)b) Using a Logistic Regression classifier, all samples from both classes can be correctly classified c) Using a K-NN classifier, with K=3, two samples from each class will always be misclassified.The remaining 26 can be classified correctly. Generate a scatter plot of your data. Use a different color/symbol for each class. Indicate the 4 samples that cannot be classified correctly using the KNN and explain the reasons. Note: This data should be generated manually and you do not need to run any code on itCreate a 2-dimensional data set with 30 samples that has the following properties a) Samples should belong to 2 classes (15 samples per class) b) All samples can be correctly classified using a decision tree classifier with only 2 levels c) The data cannot be perfectly classified using a linear classifier.If it is not possible to generate such data, explain why. Otherwise, generate a scatter plot of your data using a different color/symbol for each class. Indicate the samples that cannot be classified correctly using a linear classifier.Display your 2-level decision tree indicating the feature/threshold used at each non-leaf node and the number of samples at each leaf node. Note: This data should be generated manually and you do not need to run any code on itFor this problem, you need to use the built-in sklearn California Housing dataset. You can load this data using from sklearn.datasets import fetch_california_housing cal_housing = fetch_california_housing()Divide the data into training and test sets using train_test_split and random_state=38 The goal is to experiment with few regression algorithms and compare their performance on this data.a) Build and train a LASSO Regression model. Vary the constraint parameter α and analyze the results by identifying cases of overfitting and underfitting. Select the optimal value of α and justify your choice.b) Build and train a Decision Tree regression model. Vary the pruning parameter and analyze the results by identifying cases of overfitting and underfitting. Select the optimal pruning and justify your choice.c) Compare the accuracy of the 2 methods and the relevant features identified by each method and comment on the results.For this problem, you need to use the built-in sklearn digits dataset. You can load this data using Sklearn.datasets.load_digits (*, n_class=10,return_X_y=False, as_frame=False) Divide the data into training and test sets using train_test_split and random_state=0 The goal is to train a Random Forest classifier and optimize its performance on this data.a) Identify the most important parameters that affect the performance of the Random Forest classifier and outline your experimental design (using 4-fold cross validation) to learn the optimal values for these parameters. b) Analyze the results of the classifier using its optimal parameters and comment on its generalization capability.c) Visualize and explain the relevant features identified by the Random Forest classifier. • Create a white 8×8 image that represents the original 64 features. Map each identified relevant feature to this 2D image and display it using a grey scale that reflects its importance (e.g. 0 most relevant feature and 255 least relevant feature).d) Identify one misclassified sample from each class (if they exist). Visualize each misclassified sample as an 8×8 image, and use its nearest neighbors and the learned important features to explain why it was misclassified.Hint: for examples on how to read this data and visualize it, check https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glrauto-examples-classification-plot-digits-classification-py
This project has two parts. Part A is to have you utilize any face detection algorithm available in opencv library (version = 4.5.4. You MUST use this version for Project #3!) and perform face detection. Part B is to CROP the detected faces and cluster them using k-means algorithm. The goal of part B is to cluster faces with the same identity so they end up in the same cluster.Given a face detection dataset composed of hundreds of images, the goal is to detect faces contained in the images. The detector should be able to locate the faces in any testing image. Figure 2 shows an example of performing face detection. We will use a subset (will be provided) of FDDB [1] as the dataset for this project. You can use any face detection modules available in OpenCV.2.1 Libraries permitted and prohibited • Any API provided by OpenCV. You may NOT use any internet examples that directly focuses on face detection using the OpenCV APIs. • You may NOT use any APIs that can perform face detection or machine learning from other libraries outside of OpenCV2.2 Data and Evaluation You will be given “Project3 data.zip” which contains 100 images along with ground-truth annotations (validation folder). You can evaluate each of your models performances on this validation set or use it to further improve your detection. During testing, you need to report results on another 100 images (testFigure 1: An example of performing face detection. The detected faces are annotated using gray bounding boxes. folder) without ground truth annotations.NOTE : Please read all the readme files present in“Project3 data.zip”. Please refer to the script in readme.md in root directory for running and validating your code. YOUR implementation should be in the function detect faces(), in the file UBFaceDetector.py.The function should detect faces in all the images in input path and return the bounding boxes of the detected faces in a list.The result list should have the following format: [{“iname”: “img.jpg”, “bbox”: [x, y, width, height]}, …] “img.jpg” is an example of the name of an image; x and y are the top-left corner of the bounding box; width and height are the width and height of the bounding box, respectively. x, y, width and height should be integers. Consider origin (0,0) to be the top-left corner of the image and x increases to the right and y increases to the bottom.We will provide ComputeFBeta.py, a python code that computes fβ using detection result and groundtruth. You can refer to the sample json provided to you for more information. There is also a piece of example code regarding how to create json file.2.3 Evaluation Rubric Rubric: 40 points – 30 points (F1 score of the detector), 10 points (report) For F1 score computed using ComputeFBeta.py. (30 points for the F1 score.) • F1 > 0.80: 30 points • F1 > 0.75: 25 points Page 2 of 6 CSE 473/573 Project #3 • F1 > 0.70: 20 points • F1 > 0.60: 10 points • F1 > 0.01: 5 points • F1 < 0.01: 0 pointsReport (10 points): • Concise description of what algorithms tried and ended up using (5-10 bullet points) (5 points) • Discussion of the results and implementation challenges (5-10 bullet points) (5 points)You will be building on top of the above face detection code to do face clustering. You will be using images present in faceCluster K folder (in Project3 data.zip) for part B (i.e., face clustering). Each image will only contain one face in it. K in faceCluster K folder name provides you the unique number of face clusters present.3.1 Steps Involved. • Step 1: Use Part A and OpenCV functions to crop the detected faces from images present in faceCluster K folder in Project3 data.zip.• Step 2: pip (or pip3) install face-recognition to install a library. Use the function face recognition.face encodings(img, boxes) to get 128 dimensional vector for each cropped face.img is the image (after cv2.imread(‘img’) or any variants). boxes is a list of found face-locations from Step 1. Each face-location is a tuple (top, right, bottom, left). top = y, left = x, bottom = y + height, right = x + width. . So, boxes would be something like [(top, right, bottom, left)]. face recognition.face encodings(img, boxes) would return a list of 128 dimension numpy.ndarrafor each face present in the image ‘img’.• Step 3: Using these computed face vectors, you need to code a k-means or other relevant clustering algorithm. If you use K-Means, or another algorithm requiring a pre-defined number of clusters, that number will be K from faceCluster K. You may not use OpenCV APIs that have ‘face’, ‘kmeans’, ‘knn’ or ‘cluster’ in their name. • Rubric : 50 points (Accuracy), 10 points (Report)3.2 Evaluation Rubric – 60 Points • Accuracy will be based on the number of faces with the same identity present in a cluster. • Rubric : 50 points (Accuracy), 10 points (code)Report (10 points): • Concise description of what algorithms tried and ended up using (5-10 bullet points) (5 points) • You need to display each of faces in a cluster for all the clusters as follows. To achieve this, you may not use any external libraries other than OpenCV or numpy or matplotlib (5 points) Cluster 0 Cluster 1 Cluster K-1 Figure 2: Face clusters.3.3 Code and Report Please refer to the script in readme.md in root directory for running your code. YOUR implementation should be in the function cluster faces() in the file UBFaceDetector.py. The function should cluster faces present in all of the images in input path into K clusters. The function should return all the clusters and corresponding image names in a list.The result list should have the following format: [{“cluster no”: 0, “elements”: [“img1.jpg”, “img2.jpg”, …]}, {“cluster no”: 1, “elements”: [“img5.jpg”, “img6.jpg”, …]}, … , {“cluster no”: K-1, “elements”: [“img12.jpg”, “img13.jpg”, …]}] Note that cluster no for say K clusters starts from 0 and ends at K-1.4 Code and Report Submission package requirements for file UBID Project3.zip. You should submit this to ”Project 3 code” on UBlearns system. • UBFaceDetector.py You must not change the file name.• results.json You must not change this name. results.json is the resultant files generated by your FaceDetection.pycode on the test folder images present in “Project3 data.zip”. • clusters.json You must not change this name. clusters.json is the resultant files generated by your FaceCluster.py code on the faceClusters K folder present in “Project3 data.zip”.You should submit your report to ”Project 3 report” on UBlearns system. You must use this name Report.pdf. The report should contain Your name and your UBID at the top. The result of the report should contain a description of what algorithms tried and ended up using, a discussion of the results, face cluster images and anything you learned. You do not need to upload any of the test images during submission.5 Submission Folder structure To ”Project 3 code” on UBlearns system • UBID project3.zip UBFaceDetector.py results.json clusters.json To ”Project 3 report” on UBlearns system• Report.pdf We will be running an automated script. Any variations to the above submission folder structure will result in a ZERO for the project. Also, there is no need to use any hard-coded local paths in your project. Usage of such paths, would break when we run your code and will result in a ZERO.6 Submission Guidelines • Unlimited number of submissions is allowed and only the latest submission will be used for grading. • Identical code will be treated as plagiarism. Please work it out independently. • For code raising “RuntimeError”, the grade will be ZERO for the project if it can not be corrected • Late submissions guidelines apply for this project. • You will be permitted to submit a prelimiary verison of your code early by May 4th 11:59 PM on UBlearns for a dry run. We will provide feedback on whether your code has RUNTIME issues or not. No feedback would be provided on F1 scores or accuracy. • The final submission will be the one that is graded on the May 11th deadline.References [1] V. Jain and E. L. Miller, “Fddb: a benchmark for face detection in unconstrained settings,” 2010.
1.1 Goals The goal of this task is to implement an optical character recognition system. You will experiment with connected component and matching algorithms and your goal is both detection and recognition of various characters.The first input will be a directory with an arbitrary number of target characters as individual image files. Each will represent a character to recognize in the from of a ”template”. Code will be provided to read these images into an array of matrices. You will need to enroll them in your system by extracting appropriate features.The second input will be a gray scale test image containing characters to recognize. The input image will have characters that are darker than the background. The background will be defined by the color that is touching the boundary of the image. All characters will be separated from each other by at least one background pixel but may have different gray levels.1.2 Implementation The OCR system will contain three parts, Enrollment, Detection and Recognition. 1. Enrollment • Code will be provided to read in a set of test images from a provided directory• You process an enrollment set of target characters from the provided directory and generate features for each suitable for classification/recognition in Part 3 (Recognition). • You may store these features in any way you want to in an intermediate file, that is read in by the recognizer. The file you store should NOT be the same as an image file. The reason for the intermediate file is so you do not have to run enrollment every time you want to run detection and recognition on a new image.• Rubric: 20 points – 10 points (code) and 10 points (features).• Code will be provided to read in a test image. • Once you have read in the test image, you will need to use connected component labeling (that you implement) to detect various candidate characters in the image. • You should identify ALL possible candidates in the test image even if they do not appear in the list of enrolled characters.• The characters can be between 1/2 and 2x the size of the enrolled images. • Once you have detected the character positions, you are free to generate any features or resize those areas of the image in any way you want in preparation for recognition. • The detection results should be stored for output with the recognition results in part 3, and should be in the original image coordinates. • Rubric: 30 points – 10 points (code) and 20 points (evaluation)• Taking each of the candidate characters detected from previous part, and your features for each of the enrolled characters, you are required to implement a recognition or matching function that determines the identity of the character if it is in the enrollment set or UNKNOWN if it is not. • Rubric: 50 points – 10 points (code), 40 points (evaluation)1.3 Output For the output, you are expected to generate an output file ‘results.json’. It will be a list with each entry as {“bbox” : [x (integer), y (integer), w (integer), h (integer)],“name”: (string)} : • x, y are the top-left coordinates of the detected character. Consider origin (0,0) to be the top-left corner of the test image and x increases to the right and y increases to the bottom. • the h is the height, and w is the width of detection (from part 2), • matching enrollment character identify from part 3 (if any). Use “UNKNOWN” for characters that are not in the enrollment set.The order of detected characters should follow English text reading pattern, i.e.,list should start from top left, then move from left to right. After finishing the first line, go to the next line and continue.Note that • the final code should read target enrollment images from the directory “characters”, read the file “test img” from the main directory, and write the output file“results.json” to the main directory.• the size of the test characters MAY differ from the size of the enrollment images. You also need to submit a report “report.pdf” (approximately 1 page) explaining how you computed features, performed detection, and performed recognition.1.4 Evaluation • For part 1 (20 points), computing appropriate features for the task would fetch you points. • For part 2 (30 points), we will proportionately assign a score based on the number of characters detected. A character is considered to be detected if more than 50% of the character is covered by the bounding box.• For part 3 (50 points), The F1 measure is the harmonic mean of Precision and Recall, with precision being the # of true positives / # one says are positive, and the recall is the # of true positive / # of positives that exist. The F1 will be used as the metric. you can assume that if you get an F1 measure > 0.6 you will get full credit. Note that “UNKNOWN” detections will not be counted towards f1 score.• Irrespective of the F1 score, using template matching without generating features would result in a maximum of 90 points for the project.• We have provided “groundtruth.json” for the data in “data” folder. You may use it to compute F1 score with ‘evaluate.py’. However, for final evaluation, we will use similar but different test image and character group (i.e., English alphabets and numbers).2 Project Guidelines and Submission • Do not modify the code provided. • All work should be your own. You are not permitted to copy code from the internet. • You are free to use any opencv (cv2 version 3.4.5 ONLY) and numpy (np) function for generating features. For other parts of your code (especially connected components and template matching), you cannot use any API provided by opencv (cv2) and numpy (np) (except “np.sqrt()”, “np.zeros()”, “np.ones()”, “np.multiply()”, “np.divide()”, “cv2.imread()”, “cv2.imshow()”, “cv2.imwrite()”, “cv2.resize()”, any basic “cv2” and “np” API).• You may use opencv version 3.4.5 ONLY, no other versions• Do not import any additional libraries (function, module, etc.) except native Python packages, e.g., pdb, os, sys.• Compress the python files, i.e., “task1.py”, “data” folder, features folder (from part 1), “results.json” and “report.pdf”, into a zip file, name it as “UBID.zip” (replace “UBID” with your eight-digit UBID, e.g., 50305566) and upload it to UBLearns before the due date.• Late submission guidelines apply for this project. • Not following the project guidelines may result in penalty.
At the completion of this project, you should be able to: o Implement a hybrid algorithm for solving TSP which combines Genetic Algorithm with a Wisdom of Crowds approach. o Be able to evaluate a novel algorithm for solving an NP-Complete problem.o Read: “Wisdom of the Crowds in Traveling Salesman Problems” by Sheng Kung Michael Yi, Mark Steyvers, Michael D. Lee and Matthew J. Dry. o Modify you GA from Programming Assignment 4 to utilize the Wisdom of Crowds o Test data will be supplied, but also generate your own test cases• Hints o Take a certain percentage (experimentally determine what percentage) of the fittest individuals in the population of solutions (let’s call them experts) to the TSP and combine their solutions to produce a better solution.o Regardless of which approach you take to combine opinions of the experts make sure your final solution visits all nodes and does not visit any node multiple times (except the starting node).o If you detect that the resulting solution does not satisfy requirements of a TSP solution use a greedy algorithm of your choice to get it into the proper form.• Deliverables o Well-commented source code for your project. You can use any language you like, but I reserve the right to ask you to demo performance of your algorithm on a new dataset.o Include a GUI with visual representation of the solutions for this project and incorporate snapshots in your report.o Project report (5-6 pages). • The following should be included in your report: o Describe in detail the algorithm you used to aggregate opinions. Did you have to alter the combined solution to make it a valid TSP solution? o On average how well did the Wisdom of Crowds approach perform compared to the standard unenhanced GA?o Comparison charts for GA vs. (GA & WOC) on same problems in terms of performance, speed, optimality of discovered solutions, etc.o Does the size of the problem make a difference? What is the largest you tested? o Report results of your experiments with multiple graphs, tables and figures. Look at: “A Hybrid Heuristic for the Traveling Salesman Problem” (included with the assignment) for some ideas on how to present results of your experiments.
At the completion of this project, you should be able to: o Implement a genetic algorithm for an intractable problem. o Be able to discuss aspects of the GA that control success in finding good solutions.o A Traveling Salesperson Problem (TSP) is an NP-complete problem. A salesman is given a list of cities and a cost to travel between each pair of cities (or a list of city locations). The salesman must select a starting city and visit each city exactly one time and return to the starting city. His problem is to find the route (also known as a Hamiltonian Cycle) that will have the lowest cost. (See http://www.tsp.gatech.edu for more info)o You will be expected to implement a GA for a TSP dataset with up to 100 cities o Data for each problem will be supplied• Hints o Analyze the results of two different settings for two different parameters. For example you can use two different types of crossover methods and two different mutation rates. In that case, you need to collect four sets of data: o Crossover A Crossover B Mutation 1 Dataset 1A Dataset 1B Mutation 2 Dataset 2A Dataset 2B • For each of the four datasets, you will need to run your code several times to develop performance statistics. • I use the example of two crossovers and two mutations. You can select any modification of GA so long as you can have (at least) 2 options for each modification. For example you could choose two different sizes of populations. You could choose two different selection methods, etc. • Don’t forget, if you run your code a 100 times on a large dataset, you will likely wind up with nearly 100 different solutions.• Deliverable o Well-commented source code for your project. You can use any language you like, but I reserve the right to ask you to demo performance of your algorithm on a new dataset.o Include a GUI with visual (and dynamic) representation of the solutions for this project. o Project report (3-4 pages). o Define your crossover and mutation methods. Or else define which ever two GA elements you chose to investigate. Clearly identify if your methods were your idea or else the source from which you got the idea.o Define your stopping criteria. o Clearly identify your best solution for the solved problem. o Analyze the effectiveness of the two chosen variations in GA. To do this you will likely need to run your code several times and provide the min, max, average and standard deviation of each set.o Briefly discuss how long a typical run took for each of the datasets. o Graphically present your improvement curves.o Critical thinking section Describe the biggest problem that you had in the implementation and analysis of this project. Describe the one thing you would change if you did this project again. Please elaborate on what you learned from using GA. At least one paragraph, please. Give your overall impressions of GA as problem solving technique.
Learning objectives. At the completion of this project, you should be able to o Implement a greedy algorithm for solving TSPo A Traveling Salesperson Problem (TSP) is an NP-complete problem. A salesman is given a list of cities and a cost to travel between each pair of cities (or a list of city locations). The salesman must select a starting city and visit each city exactly one time and return to the starting city. His problem is to find the route (also known as a Hamiltonian Cycle) that will have the lowest cost. (See http://www.tsp.gatech.edu for more info)o Solve an instance of a TSP problem by using a variant of the greedy heuristic o Build tour by starting with one vertex, and inserting vertices one by one. o Always insert vertex that is closest to an edge already in tour. o Data for each problem will be supplied in a .tsp file (a plain text file). Figure from: www.cs.uu.nl/docs/vakken/an/an-tsp.ppt Hints o An instance of TSP with < 4 nodes is trivially solvable. Deliverables o Project report (3-4 pages). Describe how you selected initial nodes and generated the list of edges to consider. How did you selected the “next” node to be added. Show the route and the cost of the best tour for each provided dataset as well as order in which nodes have been added to the tour. How does this algorithm compare to other approaches for solving TSP you have tried so far? How quick is this method?o Well-commented source code for your project. You can use any language you like, but I reserve the right to ask you to demo performance of your algorithm on a new dataset.o You have to include a GUI with visual representation of the solutions for this project. Equation of a line (http://webmath.com/equline1.html) Distance from Point to a Line (http://mathworld.wolfram.com/Point-LineDistance2-Dimensional.html)
For this lab we are looking at a special case of TSP in which not all cities are connected and the salesperson only needs to find the best path to a target city not visit all cities. Table 1: Cities connected by a one way path of Euclidian distance (left = from, top = to).
• Learning objectives. At the completion of this project, you should be able to: o Implement a method to generate all permutations of a set of numbers. o Trace a Hamiltonian path through an undirected graph. o Use brute force to find the minimum cost solution to a Traveling Salesperson Problem.• Background o A Traveling Salesperson Problem (TSP) is an NP-complete problem. A salesman is given a list of cities and a cost to travel between each pair of cities (or a list of city locations). The salesman must select a starting city and visit each city exactly one time and return to the starting city. His problem is to find the route (also known as a Hamiltonian Cycle) that will have the lowest cost. (See http://www.tsp.gatech.edu for more info)• Problem o You will be expected to use brute force to calculate the minimum cost paths for a series of problems. o Data for each problem will be supplied in a .tsp file (a plain text file).• Hints o Be careful of exponential explosion. 10 cities will have over 3 million possible paths. o The largest data set will be 12 cities. o I strongly urge you to create reusable code. It will be needed for the future projects. o In the future projects, we will be dealing with MUCH larger datasets. So large that brute force approach would not be possible.• Deliverables o Project report (2-3 pages). Describe how you generated the permutations representing the tours. Show the route and the cost of the optimal tour for each provided dataset.o Well-commented source code for your project (You can use any language you like, but I reserve the right to ask you to demo performance of your algorithm on a new dataset). o You don’t have to include a GUI with visual representation of the solutions for this project, but it might be useful for your future TSP related projects in this course.Data Format: You can read about standard TSP problem files here: http://www.iwr.uni-heidelberg.de/groups/comopt/software/TSPLIB95 Data files for development and testing of your code will be generated using Concorde: http://www.tsp.gatech.edu/concorde/index.htmlSample data file with 7 cities: NAME: concorde7 TYPE: TSP COMMENT: Generated by CCutil_writetsplib COMMENT: Write called for by Concorde GUI DIMENSION: 7 EDGE_WEIGHT_TYPE: EUC_2D NODE_COORD_SECTION 1 87.951292 2.658162 2 33.466597 66.682943 3 91.778314 53.807184 4 20.526749 47.633290 5 9.006012 81.185339 6 20.032350 2.761925 7 77.181310 31.922361 Distance Formula:
Learning objectives. At the completion of this project, you should be able to: o Implement a hybrid GA+WoC algorithm for solving an NP complete problem o Be able to evaluate a novel algorithm for solving an NP-Complete problem. Problem o By now you have a great deal of experience in solving TSP problem using different methodologies. TSP was chosen as a classical NP-complete problem, but the skills you have learned are equally valuable for other NP-complete problems. This project is designed to give you a greater degree of scientific independence. For this project YOU will have to decide on a specific NP-complete problem to attempt to solve.o You will also need to be able to generate test data for your experiments based on the problem you select o A large list of potential problems is available at: http://en.wikipedia.org/wiki/List_of_NP-complete_problems Feel free to consult with your instructor regarding your options … but do not choose: Light Up, Sudoku, Knapsack, Mastermind, Battleship, Kakuro, Crossword Puzzle, Graph Coloring or TSP Hints o This is our last project; it is the largest and most time demanding. Allocate sufficient time to do an excellent job on it. There will be no extensions given since we are approaching the end of the semester and other deliverables will be based on Proj. 6. o Due in two weeks.o Your presentation and research paper (to be assigned later) will also be based on the results of your experiments from this project so a large percentage of your grade will be based on this project directly or indirectly.o Look at project 5 assignment for specific requirements/experiments to include in your report. Make sure you conduct enough different experiments to generate results sufficient for a research paper about solving your chosen NP-complete problem using the WoC approach. Deliverables o Well-commented source code for your project. You can use any language you like, but I reserve the right to ask you to demo performance of your algorithm on a new dataset.o Include a GUI with visual representation of the solutions for this project and incorporate snapshots in your report.o Project report (9-10 pages). o Make sure to describe the problem you are addressing in great detail. o Make sure you explain how test data was generated (format, etc.) o Research paper (details will be announced). Presentation (details will be announced)
Project 1 is designed to introduce the student to Assembly and C programming as important components in microcomputer and embedded design. One important goal to achieve from project 1 is to understand the relationship between C and its equivalent assembly code. For example, examining an assembly code is a great way to debug its equivalent C code for logical errors and with respect to the target hardware.Professional C–compilers allow for the mixing of assembly source code with C source code using inline code and/or external calling conventions. A processor‟s assembly language/instruction set and addressing modes must be mastered in order to help one understand the processor‟s internal architecture and behavior – a behavior that extends out to the rest of the system during run-time. Project 1 is also designed to demonstrate the programming and proper use of the ARM Cortex A53 processor used in the Raspberry PI 3 Model B computer. The student is required to research areas that are unfamiliar as well as ask questions.Part 1: Using a Raspberry PI lab computer, explore GCC, GAS, GDB, SSH, and the ARM programming model. When exploring SSH, you can optionally install, if necessary, SmarTTY onto your laptop to be able to SSH to a Raspberry PI computer in the lab (need to be on Belknap campus WIFI). Note: The latest Windows 10 won‟t need a third-party app such as SmarTTY in order to SSH, simply bring up a commandline window (run „CMD‟) and use the SSH command at the command line. Apple OSX and Linux computers also have the SSH command built-in, simply launch a Terminal window and use.After connected to a RPI computer, you will find that GCC, GAS, and GDB are already installed (native) by default in the Raspbian OS. See these excellent references: http://www.microdigitaled.com/ARM/ASM_ARM/Software/ARM_Assembly_Programming_Usin g_Raspberry_Pi_GUI.pdf https://www.gnu.org/software/gnu-c-manual/gnu-c-manual.pdf https://www.gnu.org/software/gdb/documentation/ https://sourceware.org/binutils/docs/as/And of course, programming on the Raspberry PI computer can be accomplished directly using its own keyboard and mouse instead of remotely via WIFI and SSH.Lab Tip: One helpful way of learning a processor’s architecture and operational behavior is by first learning its programming model including its Assembly instruction set and addressing modes and how these things are implemented according to the manufacturer’s specifications or guidelines. A good starting point is the C compiler because a C compiler written for a particular processor is designed based on the manufacturers specifications or guidelines.Therefore, when a C program is written and compiled and the Assembly listing is examined, the manufacturer’s specifications or guidelines are revealed. Note: You can also find or determine the specifications/guidelines in the manufacturer’s technical data sheets.Understanding how the compiler generates the correct Assembly code will help you to do the same. If you “think” like the C compiler when you are writing Assembly source code then you will be following the manufacturer’s specifications or guidelines and your Assembly code will be 100% compatible however you are using it. So, learn to think like the C compiler by always examining the Assembly it generates. Sooner or later, you won’t need to examine the Assembly unless for a quick reference or debugging logical errors. And as a C and Assembly systems programmer, you will benefit greatly by making this lab tip a habit.Refer to the ARM Info Center for information about ARM Assembly Instructions, and Addressing Modes, and Calling Conventions, basically everything about the ARM technology can be found there.Note: We are using the ARM Cortex A53 64-bit processor but the current Raspbian is a 32-bit OS that comes with the ARMv6 GCC toolchain for 32-bit builds. The A53 processor is designed to be backwards compatible with 32-bit software but with some decrease in performance. No 64-bit ARMv8 Raspbian exists at the time of this writing. However, there is a 64-bit SUSE Linux OS that runs on the Raspberry PI and supports ARMv8 64-bit builds.Enter the following code in your favorite editor and save as P1-1.c, I will use nano. The Raspbian Text Editor and Leafpad are good built-in editors too. You may also choose to install and use the Code::Blocks editor on the Raspberry PI by entering ‘sudo apt-get install codeblocks’. Compile by entering ‘gcc P1-1.c’//Eugene Rockey, Copyright 2018, All rights reserved. //Project 1 Part 1 //Compile on Raspberry PI using the command ‘gcc P1-1.c’ to make sure there are no errors. //Compile on Raspberry PI using the command ‘gcc -S P1-1.c’ to generate Assembly listing. //Open the generated Assembly file named P1-1.s and fulfill part 1 requirements. //Global Data Types signed char var1 = 1; unsigned char var2 = 2; signed int var3 = 3; unsigned int var4 = 4; const int num = -10; char wave[10]=”goodbye!!!”; void main() { //Local Data Type int var5 = 5; //Various Loop Types for (var5;var5>0;var5–) { var1*=var1; var1/=var1; var1+=var1; var1-=var1; } do { var4-=1; }while(var4>0); while(var3 == 3) { var2 = var2; break; } }After compiling on the Raspberry PI, generate an Assembly file listing (P1-1.s) by entering ‘gcc -S P1-1.c’. In your report, discuss every unique Assembler directive in that listing, discuss every equivalent assembly section corresponding to each line of C code, comment all the Assembly lines of code describing the operation and addressing modes of each instruction.Part 2: Use a Raspberry PI lab computer to continue to explore the ARM programming model. Enter and compile the following code with the name P!-2.c. After compiling on the Raspberry PI, generate an Assembly file listing (P1-1.s). In your report, discuss every new and unique Assembler directive in that listing, discuss every equivalent assembly section corresponding to each line of C code, comment all the Assembly lines of code describing the operation and addressing modes of each new and unique instruction.//Eugene Rockey, Copyright 2018, All rights reserved. //Project 1 Part 2 //Compile on Raspberry PI using the command ‘gcc P1-2.c’ to make sure there are no errors. //Compile on Raspberry PI using the command ‘gcc -S P1-2.c’ to generate Assembly listing. //Open the generated Assembly file P1-2.s and fulfill part 2 requirements. //volatile modifier for variables that chage due to hardware interrupts, RTC, etc… volatile int var; //Function with Pointers void swap(int *x, int *y) { int temp; temp = *x; *x = *y; *y = temp; } //A stack frame (fp) is used here for the subroutine(function) call. //Also, the stack is used when switching between OS and main() too. int main() { //local variables int a, b; a = 10; b = 20; swap(&a, &b); return 0; }Part 3: A) Use a Raspberry PI computer to explore the ARM Calling Conventions(an industry standard). Calling Conventions are about how the C compiler utilizes the processor’s registers and memory(stack) to pass input and output parameters between main() and subroutines or functions and between subroutines or functions. The processor’s manufacturer specifies guidelines on how this should be done so that all other manufacturer’s who implement said processor are on the same page making all their products compatible with each other.Note: If you do not want your product to be compatible then do not follow the industry standard Calling Conventions, use the registers and memory in some other way. However, this means you will also have to write your own custom C compiler in accordance with your custom register and memory usage or just program in 100% Assembly. We use Rasbian, which implements the industry standard ARM Calling Conventions; therefore, we must also take into account the ARM Calling Conventions in this Lab.Enter and compile the following code with the name P1-3A.c. After compiling on the Raspberry PI, generate an Assembly file listing (P1-3A.s). In your report, discuss every new and unique Assembler directive in that listing, discuss every equivalent assembly section corresponding to each line of C code, comment all the Assembly lines of code describing the operation and addressing modes of each new and unique instruction. Comment and discuss the ARM Calling Conventions detected in the Assembly listing, specifying register and or memory(stack) used to pass parameters between main() and the subroutine or function.//Eugene Rockey, Copyright 2018, All rights reserved. //Project 1 Part 3A //Compile on Raspberry PI using the command ‘gcc P1-3A.c’ to make sure no errors exist. //Run and test the program by entering ‘./a.out’ //Generate the Assembly listing by entering ‘gcc -S P1-3A.c’ //Open the generated Assembly file ‘P1-3A.s’ and fulfill part 3A requirements. #include unsigned char next_char(char in) { return in + 1; } void main() { printf(“Next Character= %c ”,next_char(‘A’)); }B) Enter and compile the following source files with the names P1-3B.c and P1-3BASM.s. After compiling on the Raspberry PI, generate an Assembly file listing (P1-3B.s). In your report, discuss every new and unique Assembler directive in that listing, discuss every equivalent assembly section corresponding to each line of C code, comment all the Assembly lines of code describing the operation and addressing modes of each new and unique instruction. Comment and discuss the ARM Calling Conventions detected in the Assembly listing, specifying register and or memory(stack) used to pass parameters between main() and the subroutine or function.Put the following C source code into its own P1-3B.c source file… //Eugene Rockey, Copyright 2018, All rights reserved. //Project 1 Part 3B //Compile the program by entering ‘gcc P1-3B.c P1-3BASM.s’ and check for errors. //Run the program by entering ‘./a.out’ at the command line. //Generate Assembly listing by entering ‘gcc -S P1-3B.c P1-3BASM.s’ //Open the generated equivalent Assembly file ‘P1-3B.s’ and fulfill part 3B requirements. #include unsigned char next_char(char in); void main() { printf(“Next Character= %c ”,next_char(‘A’)); } Put the following Assembly source code into its own P1-3BASM.s file… //Assembly Subroutine //Eugene Rockey, Copyright 2018, All Rights Reserved .section “.text” .global next_char next_char: ADD r0,#1 //How is the ARM calling convention obeyed here? MOV pc,lr .end This document is subject to change.
1. (20 points) Solve the following problem (Ch. 11). Write a program to enter employee data including his or her name, Social Security number, and salary, into an array.The maximum number of employees is 100, but your program should also work for any number of employees less than 100. Your program should use two exception classes, one for the case when the Social Security number length is not exactly nine characters (without dashes or spaces), and another when the Social Security number includes any character that is not a digit, as defined below.(a) Create a class called Employee which has three instance variables, Name, Salary, and SSNumber. The Employee class has one constructor that accepts values for initializing the Name, Salary, and SSNumber. Include accessor methods, mutator methods, equals method and toString method. The constructor method, or the mutator method for the SSNumber, should throw exceptions when a Social Security number does not satisfy the required format. The equals method returns true if the two employee objects have either the same name or the same Social Security number, and false otherwise.(b) Define two exception classes. One is called SSNLengthException, for the case when the Social Security number entered without dashes or spaces is not exactly nine characters. The other is called SSNCharacterException, for the case when any character in the Social Security number is not a digit. The two exception classes should display appropriate messages, when they are thrown, telling the user what has been entered and why they were not appropriate.Write a client class, called EmplyeeBuilder, that builds a list of Employee objects, using an array of maximum size of 100. The client allows a user to enter values for an employee. The client class catches and handles the exceptions if they are thrown due to creating unacceptable Employee object that does not satisfy the Social Security number format. Besides, the program should not allow two employees of the same name or Social Number to be added to the built list of Employee objects. No employee record should be added to the list if it does not satisfy the conditions. After all data has been entered, your program should display the records for all employees, with an annotation stating whether the employee’s salary is above or below average.2. (10 points, optional bonus problem) Write a JavaFX application that calculates the registration fees for a conference. The general conference fee is $800 per person, and student registration is $450 per person. There is also an optional opening night dinner with a keynote speech for $30 per person. In addition, the optional preconference workshops listed below are available.Workshop Fee Introduction to E-commerce $350 The Future of Web $300 Advanced Java Programming $400 Network Security $450 The application should allow the user to select the registration type using two radio buttons, the optional opening night dinner using a check box, and one preconference workshop as desired using either a choice box, a spinner, or a list view. The total cost should be displayed on the screen window.Submission Requirements: Your presentation in your report reflects great deal about you, your understanding of the assignment and on how much this course means to you. I try very hard to look at the substance of the report but I will be lying if I said that presentation does not influence my judgment. So, I expect your reports to be well organized and conform to the following rules:All reports must be submitted in PDF format. Each assignment should contain the following: 1. Title page with your name, assignment number and the day you are actually submitting this report (Not the assignment due date). 2. A brief description of your solution of each problem of the assignment separately, you can also explain your solution using a pseudocode. Number your descriptions according to the problem numbers. 3. A comprehensive set of snapshots showing the inputs submitted, outputs obtained in the case of a successful output or a failure, including required output formatting, prompts, and messages. 4. Java source files that contain your solutions. It must be a “*.java” file. Source programs should contain meaningful comments and variable names.5. Please zip both the PDF document with the source code and submit one zipped file. Please name your zipped file as “HWx_firstname_lastname.zzz”. Where, “firstname” and “lastname” refer to your first and last names, “x” refers to the homework number (e.g., 1, 2, etc), “zzz” refers to the file name extension for the software used for archiving.6. Submissions after the due date are accepted with a penalty of 25% per day (weekend days are counted as one-day delay). Grading Table Summary Problem Item Points Problem 1 (20 points) Report (Description of solution, and pseudocode) 2.0 Output snapshots (input/output, formatting) 1.0 Java Source Code Implementation Exception classes 1.0 Employee Class 9.0 EmployeeBuilder Class 7.0 Bonus Problem 2 (10 points) Report (Description of solution) 1 Output snapshots (input/output, formatting) 1 Java Source Code Implementation Correct implementation satisfying requirements 8 Total 30
1. (12 points) Solve the following problem. (a) Define an abstract class, called UtilityCustomer, that has one Integer instance variable, an account number, and an abstract method, called calculateBill, that returns the bill amount as double. The UtilityCustomer class implements the Comparable interface, and includes a constructor that is passed a parameter for initializing the instance variable. It includes also accessor and mutator methods for the instance variable, and a toString method that returns a string for displaying the account number. The UtilityCustomer class has two non-abstract subclasses that inherit from the UtilityCustomer: GasCustomer and ElectricCustomer. (b) GasCustomer class has two additional fields, the cubicMetersUsed instance variable, and a constant for the price of gas per cubic meter of $2.75. Include appropriate constructor for initializing the class instance variable, accessor and mutator methods for class’s instance variable, and implement the calculateBill method. Write a toString method, that calls the toString method of the UtilityCustomer a formatted string to display the gas customer’s account number, the gas consumption, and the amount charged. (c) ElectricCustomer class has three additional fields, kWattHourUsed instance variable, along with two constants, one for the price of electricity per kilowatt hour, and the other for a flat power delivery fee of $30 that is added to every bill. Include appropriate constructor for initializing the class instance variable, and accessor and mutator methods for class’s instance variable. Write a toString method, that calls the toString method of the UtilityCustomer, and implement the calculateBill method. (d) Write a client class, called CollectionOfUcustomers, that maintains a list of UtilityCustomer objects created by a user. The client class prompts a user for the type of a UtilityCustomer and its corresponding parameters to create. The program allows the user to specify up to 10 UtilityCustomer objects to be stored in an array. The program should display the information of UtilityCustomer objects in descending order based on their account numbers. 2. (8 points) Write a JavaFX application that allows a user to enter the food charge for a meal at a restaurant using a text field. The application provides the user with four options for tip percentages to select from: 0%, 15%, 18%, and 20%. You can use controls for determining the tip percentage such as radio buttons, or choice box. The application provides a button that is used to calculate and display the amount of tip on the meal charge, an 8% sales tax amount, and the total of all three amounts using Text objects. You can choose any layout to organize the control elements. (Note: Radio buttons are introduced in section 5.10, and choice boxes are introduced in section 8.9.)Submission Requirements: Your presentation in your report reflects great deal about you, your understanding of the assignment and on how much this course means to you. I try very hard to look at the substance of the report but I will be lying if I said that presentation does not influence my judgment. So, I expect your reports to be well organized and conform to the following rules:All reports must be submitted in PDF format. Each assignment should contain the following: 1. Title page with your name, assignment number and the day you are actually submitting this report (Not the assignment due date).2. A brief description of your solution of each problem of the assignment separately, you can also explain your solution using a pseudocode. Number your descriptions according to the problem numbers.3. A comprehensive set of snapshots showing the inputs submitted, outputs obtained in the case of a successful output or a failure, including required output formatting, prompts, and messages.4. Java source files that contain your solutions. It must be a “*.java” file. Source programs should contain meaningful comments and variable names.5. Please zip both the PDF document with the source code and submit one zipped file. Please name your zipped file as “HWx_firstname_lastname.zzz”. Where, “firstname” and “lastname” refer to your first and last names, “x” refers to the homework number (e.g., 1, 2, etc), “zzz” refers to the file name extension for the software used for archiving.6. Submissions after the due date are accepted with a penalty of 25% per day (weekend days are counted as one-day delay). Grading Table Summary Problem Item Points Problem 1 Report (Description of solution, discussion, & analysis) 1 Output snapshots (input/output, formatting) 1 Java Source Code Implementation UtilityCustomer Class 2 GasCustomer Class 2 ElectricCustomer Class 2 CollectionOfUcustomers Class 4 Problem 2 Report (Description of solution, discussion, & analysis) 1 Output snapshots (input/output, formatting) 1 Java Source Code Implementation start Method 4 Handler Methods 2 Total 20
1. (20 points) Solve the following problem (a) When a new user logs in for the first time on a website, the user has to submit personal information such as user_id, password, name, email address, telephone number, and so forth. Typically, there are two fields for passwords, requiring the user to enter the password twice, to ensure that the user did not make a typo in the first password field.Write a class encapsulating the concept of processing a form, called ProcessForm, with the following elements: User_id, name, password, re-enter password, email address, and telephone number. The ProcessForm class has one instance variable, called UserInfo. It is an array of 6 elements (in this case) of Strings, containing the user’s personal information elements. The ProcessForm class contains the following methods: A constructor with one parameter, that is a one dimensional array of 6 words representing the user’s information, used to initialize the ProcessForm class instance variable. The constructor must check that the passed array length matches the array length of the instance variable. If the two arrays do not have the same length, the instance variable array elements are initialized with empty strings, and warning message is displayed on the screen. Accessor method, getUserInfo, that returns an array of Strings for the user’s information. A method, called SetInfo, that accepts two parameters to set or update an element of the UserInfo array, a string value and an index related to the position of the element in the array. toString method that returns a string of the user’s information nicely formatted, containing all the personal information in a printable form. However, the user’s password elements should be hidden. A method, called CheckPasswords, which checks if the two Strings representing the passwords are identical. If they are, it returns true; if not, it returns false. A method, called CheckEmailAdd, which used to check if the String representing the email address actually “looks like” an email address. For simplicity, we assume that an email address contains one and only one “@” character, and contains one or more periods after the @ character. The method returns true if the email address element looks like a valid one; otherwise, it returns false. A method, called CheckPhone, which is used to check if the String representing the phone number satisfies the format of a phone number in North America. The phone number format is specified as follows: Local number (7-digits subscriber): Nxx-xxxx, where N is for digits 2-9. Domestic (10-digits): NPA-Nxx-xxxx, where NPA refers to 3-digit area code. The method returns true if the phone number element is valid; otherwise, it returns false. .(b) Write a client class, called Website_User, that allows a user to enter his/her personal information for a website. The program should prompt the user to enter each data element. The entered information is used to create an object of the ProcessForm class. Accordingly, the client program should check the validity of user’s password, email address, and phone number, and allow the user to reenter each invalid information till a valid one is accepted. Finally, the program should display the user’s information on the screen.Report and Program Submission Guidelines It is expected that your report to be well written and organized. The report reflects your understanding of the assignment and its solution. So, you must assume that your marks assigned to the report part is related to how is the report is written and presented. All reports must be submitted in PDF format. Each assignment should contain the following:1. Report 1.1. Title page with your name, assignment number and the day you are actually submitting this report (Not the assignment due date). 1.2. A brief description of your solution of each problem of the assignment, you can also explain your solution using a pseudocode. Number your descriptions according to the problem numbers. 1.3. A comprehensive set of snapshots showing the inputs submitted, outputs obtained in the case of a successful output or a failure, including required output formatting, prompts, and messages.2. Submission Procedure: 2.1. Java source files that contain your solutions. They must be a “*.java” files. Source programs should contain meaningful comments and variable names. 2.2. Please zip both the PDF document with the source codes and submit one zipped file. Please name your zipped file as “HWx_firstname_lastname.zzz”. Where, “firstname” and “lastname” refer to your first and last names, “x” refers to the homework number (e.g., 1, 2, etc), “zzz” refers to the file name extension for the software used for archiving. 2.3. Submissions after the due date are accepted with a penalty of 25% per day (weekend days are counted as one-day delay).Grading Table Problem Item Points Problem 1 Report (Description of solution) 2 Output snapshots (input/output, formatting) 1 Java Source Code Implementation ProcessForm Class 12 Website_User Class 5 Total 20
1. (12 points) Solve the following problem (a) Write a class called BabysittingJob that represents the concept of a babysitting service. The class contains the following instance variables: A job number that contains six digits. The first two digits represent the year, and the last four digits represent a sequential number. For example, the first job in 2019 has a job number of 190001. A code representing the employee assigned to the job. Assume that the code will always be 1, 2, or 3. A name based on the babysitting code. Assume the service currently has three babysitters: (1) Cindy, (2) Greg, and (3) Marcia. The number of children to be watched. Assume that this number is always greater than zero. The number of hours in the job. Assume that all hour values are whole numbers. The fee for the job is based on the babysitter and the number of children are watched. Cindy is paid $7 per hour per child. Greg and Marcia are paid $9 an hour for the first child, and $4 per additional hour for each additional child. For example, if Greg watches three children for two hours, he makes $17 per hour, or $34 for the two hours.Create a constructor for the BabysittingJob class that accepts arguments for the job number, babysitter code, number of children, number of hours. The constructor determines the babysitter name and fee for the job. Also include a toString method that returns a nicely formatted string containing the details of a babysittingJob object.(b) Write a client class that prompts the user for entering the data for a babysitting job. The application should keep prompting the user for each of the following values until they are valid: A four-digit year between 2019 and 2025 inclusive. A job number for the year between 1 and 9999 inclusive. A babysitter code of 1, 2, or 3. A number of children for the job between 1 and 9 inclusive. A number of hours between 1 and 12 inclusive.When all the data entries are valid, construct a job number from the last two digits of the year and a four-digit sequential number, which might require adding leading zeros. Accordingly, create a BabysittingJob object, and display its values.2. (8 points) Solve the following JavaFX application. Write a JavaFX application that analyzes a word. The user would type the word in a text field, and the application provides three buttons for the following: One button, when clicked, displays the length of the word. Another button, when clicked, displays the number of vowels in the word. Another button, when clicked, displays the number of uppercase letters in the word. (Hints: Use the GridPane or HBox and VBox to organize the GUI controls. See the Listings 4.8, 4.9, and 5.12 for related examples.)Report and Program Submission Guidelines It is expected that your report to be well written and organized. The report reflects your understanding of the assignment and its solution. So, you must assume that your marks assigned to the report part is related to how is the report is written and presented. All reports must be submitted in PDF format. Each assignment should contain the following:1. Report 1.1. Title page with your name, assignment number and the day you are actually submitting this report (Not the assignment due date).1.2. A brief description of your solution of each problem of the assignment, you can also explain your solution using a pseudocode. Number your descriptions according to the problem numbers. 1.3. A comprehensive set of snapshots showing the inputs submitted, outputs obtained in the case of a successful output or a failure, including required output formatting, prompts, and messages.2. Submission Procedure: 2.1. Java source files that contain your solutions. They must be a “*.java” files. Source programs should contain meaningful comments and variable names.2.2. Please zip both the PDF document with the source codes and submit one zipped file. Please name your zipped file as “HWx_firstname_lastname.zzz”. Where, “firstname” and “lastname” refer to your first and last names, “x” refers to the homework number (e.g., 1, 2, etc), “zzz” refers to the file name extension for the software used for archiving.2.3. Submissions after the due date are accepted with a penalty of 25% per day (weekend days are counted as one-day delay). Grading Table Problem Item Points Problem 1 Report (Description of solution) 2 Output snapshots (input/output, formatting) 1 Java Source Code Implementation BabysittingJob class 5 Client Class 4 Problem 2 Report (Description of solution) 1 Output snapshots (input/output, formatting) 1 Java Source Code Implementation Start method 4 Handler methods 2 Total 20
Write a menu driven application that is going to use the hierarchy that you have written for homework 8 and the functions that you have written for homework 9. You will keep track of the collection of Files in a vector of File pointers (File pointers, since File is an abstract class). The menu should contain the following options:Here are the contents of the file.txt that you can use for testing purposes. txtstudents500 txtfiles120 gifpicture1100 x 2008 gifpicture2200 x 30016 txttestfile450 Submit your project to your lab instructor.
Implement a Roster Management Program using the classes that you developed in homework assignments 2-6. The program should be command-line interfaced.In Roster Management Program you will have two Modes: “Supervisor Mode” and “User Mode”. In “Supervisor Mode” you have options to:In a “User Mode” you can only do the following: 1) Select a Roster to perform following operations: a) Insert new Student to a Roster b) Remove a Student from a Roster c) Update a Student in a RosterIn Main menu you will have following options: A) Supervisor Mode that will be password protected. The user can be authenticated against a database.txt file entries which would contain user/password pair B) User Mode C) ExitNote: When you start off the program the rosters must be read in from a file called rosters.txt. When you exit a program the rosters must be stored back to a file called rosters.txt. Internally in your program rosters are stored in a growable array of rosters. The format of the rosters.txt file is: course1-name | course1-code | number-credits | professor-name student1 first name | student1 last name|sid|standing|credits|gpa|mm/dd/yyyy|mm/dd/yyyy student2 first name | student2 last name|sid|standing|credits|gpa|mm/dd/yyyy|mm/dd/yyyy end_roster| course2-name | course2-code | number-credits | professor-name student1 first name | student1 last name|sid|standing|credits|gpa|mm/dd/yyyy|mm/dd/yyyy end_roster|
The aim of this homework assignment is to practice writing recursive functions.For this homework assignment write two recursive functions:The first one receives a vector of File pointers and recursively outputs properties of every file stored inside this vector.The second function receives a vector of File pointers and a string which represents a type of the file (gif or txt). The function should return another vector of File pointers containing only Image files or Text files, depending on the second parameter. The function should be recursive.Please submit this assignment to your lab instructor.
