Archive for month: January, 2024

1. Label the box below to create a two-pass schedule legend.

Ans:

3. In the example below, label which activities are predecessors and which activities are successors.

Ans:

Predecessor: An activity which comes before another activity. So, it is activity “A” and activity “B”.

Successor: An activity that must occur after another activity. So, it is activity “C”.

5. Calculate early start, early finish, late start, late finish, and float for each of the activities in the network below. The duration of each activity is given.

Based on the analysis for the two-pass schedule:

A B C D

Duration: 12 Duration: 4 Duration: 1 Duration: 7

Early start: 0 Early start: 12 Early start: 16 Early start: 12

Early finish: 12 Early finish: 16 Early finish: 17 Early finish:19

Late start: 0 Late start: 14 Late start: 18 Late start: 12

Late finish: 12 Late finish: 18 Late finish: 19 Late finish: 19

Float: 0 Float: 2 Float: 2 Float: 0

E

Duration: 3 Early finish: 22 Late finish: 22

Early start:19 Late start: 19 Float: 0

6. Identify the critical path for the network in Exercise 5. How long should the project take?

Ans: The critical path is:

A – D – E, which has the longest path of 22 days.

9. Given the information below, create the project schedule network. Then, using the enumeration method, calculate and show all of the paths through the network. Show how long each path will take. Identify the critical path. Show the schedule on a Gantt chart showing critical and noncritical activities and float.

The critical path is

B – A – C – F – H – I, which takes 25 days.

Other paths:

B – E – H – I = 20 days

B – A – D – G – I = 23 days

Part 2 Scheduling Problem (50 points)

Complete the following:

Your child is a member of the school theater group and have been asked to prepare the schedule, etc. for a skit to be performed during the 4th of July celebration in the local village square.  As a volunteer, you offer to serve as the project controller because of your expertise in schedule management.

1. Construct an AON diagram similar to the format used in the Youtube videos found in the commentary using an MS Excel spreadsheet.  Ensure that you include a Start activity and a Finish activity.  Include a legend (example is the one completed in Question 1, Part 1.

1. Calculate the estimated time for each activity using PERT techniques for the 3 estimates provided.  Include a sample calculation.

1. Prepare a table that identifies all paths and their lengths.  Which path is the critical path?

1. During the execution phase of the skit project, one of the activities on the critical path is delayed by one day.  Is this serious?

1. Using the activity estimates for optimistic, pessimistic and most likely time to complete activities on the critical path, qualitatively discuss the estimate uncertainty of the critical path.

ANS: 1

2.

Different Paths are:

1. : 1-5-10-13-15

1. : 1-4-9-13-15

1. : 1-4-6-8-12-15

1. : 2-6-8-12-15

1. : 2-3-8-12-15

1. : 2-3-7-11

1. : 2-3-7-14

3.

The critical path is 1-4-6-8-12-15 = 4+10+6+7+8+17 = 52

4.

If one activity in the critical path is delayed for one day, the critical path will delay one day and the whole project will delay for a day too.

5.

If we use optimistic time for the critical path, that is 41 days, the pessimistic path is 63 days, which means 22 days of uncertainty can be saved.

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1-  collect data using an API for TMDb

2- construct a graph representation of this data that will show which actors have acted together in various movies

3- complete all tasks according to the instructions found in the file ‘submissions_og2.py’ to complete the Graph class, the TMDbAPIUtils class, and the two global functions. The Graph class will serve as a re-usable way to represent and write out your collected graph data. The TMDbAPIUtils class will be used to work with the TMDB API for data retrieval.

4- Create a TMDb account to obtain an authentication token.

5- Producing correct nodes.csv and edges.csv

ps. other files are examples found in the intenret (not sure if can help)

import http.client import json import csv ############################################################################################################################# # cse6242 # All instructions, code comments, etc. contained within this notebook are part of the assignment instructions. # Portions of this file will auto-graded in Gradescope using different sets of parameters / data to ensure that values are not # hard-coded. # # Instructions: Implement all methods in this file that have a return # value of ‘NotImplemented’. See the documentation within each method for specific details, including # the expected return value # # Helper Functions: # You are permitted to write additional helper functions/methods or use additional instance variables within # the `Graph` class or `TMDbAPIUtils` class so long as the originally included methods work as required. # # Use: # The `Graph` class is used to represent and store the data for the TMDb co-actor network graph. This class must # also provide some basic analytics, i.e., number of nodes, edges, and nodes with the highest degree. # # The `TMDbAPIUtils` class is used to retrieve Actor/Movie data using themoviedb.org API. We have provided a few necessary methods # to test your code w/ the API, e.g.: get_movie_cast(), get_movie_credits_for_person(). You may add additional # methods and instance variables as desired (see Helper Functions). # # The data that you retrieve from the TMDb API is used to build your graph using the Graph class. After you build your graph using the # TMDb API data, use the Graph class write_edges_file & write_nodes_file methods to produce the separate nodes and edges # .csv files for use with the Argo-Lite graph visualization tool. # # While building the co-actor graph, you will be required to write code to expand the graph by iterating # through a portion of the graph nodes and finding similar artists using the TMDb API. We will not grade this code directly # but will grade the resulting graph data in your Argo-Lite graph snapshot. # ############################################################################################################################# class Graph: # Do not modify def __init__(self, with_nodes_file=None, with_edges_file=None): “”” option 1: init as an empty graph and add nodes option 2: init by specifying a path to nodes & edges files “”” self.nodes = [] self.edges = [] if with_nodes_file and with_edges_file: nodes_CSV = csv.reader(open(with_nodes_file)) nodes_CSV = list(nodes_CSV)[1:] self.nodes = [(n[0], n[1]) for n in nodes_CSV] edges_CSV = csv.reader(open(with_edges_file)) edges_CSV = list(edges_CSV)[1:] self.edges = [(e[0], e[1]) for e in edges_CSV] def add_node(self, id: str, name: str) -> None: “”” add a tuple (id, name) representing a node to self.nodes if it does not already exist The graph should not contain any duplicate nodes “”” return NotImplemented def add_edge(self, source: str, target: str) -> None: “”” Add an edge between two nodes if it does not already exist. An edge is represented by a tuple containing two strings: e.g.: (‘source’, ‘target’). Where ‘source’ is the id of the source node and ‘target’ is the id of the target node e.g., for two nodes with ids ‘a’ and ‘b’ respectively, add the tuple (‘a’, ‘b’) to self.edges “”” return NotImplemented def total_nodes(self) -> int: “”” Returns an integer value for the total number of nodes in the graph “”” return NotImplemented def total_edges(self) -> int: “”” Returns an integer value for the total number of edges in the graph “”” return NotImplemented def max_degree_nodes(self) -> dict: “”” Return the node(s) with the highest degree Return multiple nodes in the event of a tie Format is a dict where the key is the node_id and the value is an integer for the node degree e.g. {‘a’: 8} or {‘a’: 22, ‘b’: 22} “”” return NotImplemented def print_nodes(self): “”” No further implementation required May be used for de-bugging if necessary “”” print(self.nodes) def print_edges(self): “”” No further implementation required May be used for de-bugging if necessary “”” print(self.edges) # Do not modify def write_edges_file(self, path=”edges.csv”)->None: “”” write all edges out as .csv :param path: string :return: None “”” edges_path = path edges_file = open(edges_path, ‘w’, encoding=’utf-8′) edges_file.write(“source” + “,” + “target” + “\n”) for e in self.edges: edges_file.write(e[0] + “,” + e[1] + “\n”) edges_file.close() print(“finished writing edges to csv”) # Do not modify def write_nodes_file(self, path=”nodes.csv”)->None: “”” write all nodes out as .csv :param path: string :return: None “”” nodes_path = path nodes_file = open(nodes_path, ‘w’, encoding=’utf-8′) nodes_file.write(“id,name” + “\n”) for n in self.nodes: nodes_file.write(n[0] + “,” + n[1] + “\n”) nodes_file.close() print(“finished writing nodes to csv”) class TMDBAPIUtils: # Do not modify def __init__(self, api_key:str): self.api_key=api_key def get_movie_cast(self, movie_id:str, limit:int=None, exclude_ids:list=None) -> list: “”” Get the movie cast for a given movie id, with optional parameters to exclude an cast member from being returned and/or to limit the number of returned cast members documentation url: https://developers.themoviedb.org/3/movies/get-movie-credits :param string movie_id: a numerical movie_id :param integer limit: maximum number of returned cast members by their ‘order’ attribute e.g., limit=5 will attempt to return the 5 cast members having ‘order’ attribute values between 0-4 If after excluding, there are fewer cast members than the specified limit, then return the remaining members (excluding the ones whose order values are outside the limit range). If cast members with ‘order’ attribute in the specified limit range have been excluded, do not include more cast members to reach the limit. If after excluding, the limit is not specified, then return all remaining cast members.” e.g., if limit=5 and the actor whose id corresponds to cast member with order=1 is to be excluded, return cast members with order values [0, 2, 3, 4], not [0, 2, 3, 4, 5] :param list exclude_ids: a list of ints containing ids (not cast_ids) of cast members that should be excluded from the returned result e.g., if exclude_ids are [353, 455] then exclude these from any result. :rtype: list return a list of dicts, one dict per cast member with the following structure: [{‘id’: 97909 # the id of the cast member ‘character’: ‘John Doe’ # the name of the character played ‘credit_id’: ’52fe4249c3a36847f8012927′ # id of the credit, …}, … ] Note that this is an example of the structure of the list and some of the fields returned by the API. The result of the API call will include many more fields for each cast member. Important: the exclude_ids processing should occur prior to limiting output. “”” return NotImplemented def get_movie_credits_for_person(self, person_id:str, vote_avg_threshold:float=None)->list: “”” Using the TMDb API, get the movie credits for a person serving in a cast role documentation url: https://developers.themoviedb.org/3/people/get-person-movie-credits :param string person_id: the id of a person :param vote_avg_threshold: optional parameter to return the movie credit if it is >= the specified threshold. e.g., if the vote_avg_threshold is 5.0, then only return credits with a vote_avg >= 5.0 :rtype: list return a list of dicts, one dict per movie credit with the following structure: [{‘id’: 97909 # the id of the movie credit ‘title’: ‘Long, Stock and Two Smoking Barrels’ # the title (not original title) of the credit ‘vote_avg’: 5.0 # the float value of the vote average value for the credit}, … ] “”” return NotImplemented ############################################################################################################################# # # BUILDING YOUR GRAPH # # Working with the API: See use of http.request: https://docs.python.org/3/library/http.client.html#examples # # Using TMDb’s API, build a co-actor network for the actor’s/actress’ highest rated movies # In this graph, each node represents an actor # An edge between any two nodes indicates that the two actors/actresses acted in a movie together # i.e., they share a movie credit. # e.g., An edge between Samuel L. Jackson and Robert Downey Jr. indicates that they have acted in one # or more movies together. # # For this assignment, we are interested in a co-actor network of highly rated movies; specifically, # we only want the top 3 co-actors in each movie credit of an actor having a vote average >= 8.0. # Build your co-actor graph on the actor ‘Laurence Fishburne’ w/ person_id 2975. # # You will need to add extra functions or code to accomplish this. We will not directly call or explicitly grade your # algorithm. We will instead measure the correctness of your output by evaluating the data in your argo-lite graph # snapshot. # # GRAPH SIZE # With each iteration of your graph build, the number of nodes and edges grows approximately at an exponential rate. # Our testing indicates growth approximately equal to e^2x. # Since the TMDB API is a live database, the number of nodes / edges in the final graph will vary slightly depending on when # you execute your graph building code. We take this into account by rebuilding the solution graph every few days and # updating the auto-grader. We establish a bound for lowest & highest encountered numbers of nodes and edges with a # margin of +/- 100 for nodes and +/- 150 for edges. e.g., The allowable range of nodes is set to: # # Min allowable nodes = min encountered nodes – 100 # Max allowable nodes = max allowable nodes + 100 # # e.g., if the minimum encountered nodes = 507 and the max encountered nodes = 526, then the min/max range is 407-626 # The same method is used to calculate the edges with the exception of using the aforementioned edge margin. # ———————————————————————————————————————- # BEGIN BUILD CO-ACTOR NETWORK # # INITIALIZE GRAPH # Initialize a Graph object with a single node representing Laurence Fishburne # # BEGIN BUILD BASE GRAPH: # Find all of Laurence Fishburne’s movie credits that have a vote average >= 8.0 # FOR each movie credit: # | get the movie cast members having an ‘order’ value between 0-2 (these are the co-actors) # | # | FOR each movie cast member: # | | using graph.add_node(), add the movie cast member as a node (keep track of all new nodes added to the graph) # | | using graph.add_edge(), add an edge between the Laurence Fishburne (actress) node # | | and each new node (co-actor/co-actress) # | END FOR # END FOR # END BUILD BASE GRAPH # # # BEGIN LOOP – DO 2 TIMES: # IF first iteration of loop: # | nodes = The nodes added in the BUILD BASE GRAPH (this excludes the original node of Laurence Fishburne!) # ELSE # | nodes = The nodes added in the previous iteration: # ENDIF # # FOR each node in nodes: # | get the movie credits for the actor that have a vote average >= 8.0 # | # | FOR each movie credit: # | | try to get the 3 movie cast members having an ‘order’ value between 0-2 # | | # | | FOR each movie cast member: # | | | IF the node doesn’t already exist: # | | | | add the node to the graph (track all new nodes added to the graph) # | | | ENDIF # | | | # | | | IF the edge does not exist: # | | | | add an edge between the node (actor) and the new node (co-actor/co-actress) # | | | ENDIF # | | END FOR # | END FOR # END FOR # END LOOP # # Your graph should not have any duplicate edges or nodes # Write out your finished graph as a nodes file and an edges file using: # graph.write_edges_file() # graph.write_nodes_file() # # END BUILD CO-ACTOR NETWORK # ———————————————————————————————————————- # Exception handling and best practices # – You should use the param ‘language=en-US’ in all API calls to avoid encoding issues when writing data to file. # – If the actor name has a comma char ‘,’ it should be removed to prevent extra columns from being inserted into the .csv file # – Some movie_credits may actually be collections and do not return cast data. Handle this situation by skipping these instances. # – While The TMDb API does not have a rate-limiting scheme in place, consider that making hundreds / thousands of calls # can occasionally result in timeout errors. If you continue to experience ‘ConnectionRefusedError : [Errno 61] Connection refused’, # – wait a while and then try again. It may be necessary to insert periodic sleeps when you are building your graph. def return_name()->str: “”” Return a string containing your GT Username e.g., gburdell3 Do not return your 9 digit GTId “”” return NotImplemented def return_argo_lite_snapshot()->str: “”” Return the shared URL of your published graph in Argo-Lite “”” return NotImplemented # You should modify __main__ as you see fit to build/test your graph using the TMDBAPIUtils & Graph classes. # Some boilerplate/sample code is provided for demonstration. We will not call __main__ during grading. if __name__ == “__main__”: graph = Graph() graph.add_node(id=’5064′, name=’Meryl Streep’) tmdb_api_utils = TMDBAPIUtils(api_key=’YOURKEY’) # call functions or place code here to build graph (graph building code not graded) # Suggestion: code should contain steps outlined above in BUILD CO-ACTOR NETWORK graph.write_edges_file() graph.write_nodes_file() # If you have already built & written out your graph, you could read in your nodes & edges files # to perform testing on your graph. # graph = Graph(with_edges_file=”edges.csv”, with_nodes_file=”nodes.csv”)

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Project Scenario

Previously, the CEO told you about the breach at a rival company and his concerns for security at Mercury USA. In light of these developments, Mercury USA has decided to conduct vulnerability assessments. An outside firm has conducted penetration tests on Mercury’s systems and provided the reports. Now, it is up to you as the cyber threat analyst to look at those reports, apply them to the specific company setup and business needs, and recommend changes.

Mercury USA’s concerns:

· protecting customer information

· protecting proprietary business data

· protecting dangerous loads/hazardous materials during transportation

Before you came onboard, a third-party penetration tester was brought in to assess Mercury USA’s security posture and network. One of the findings from the pen test report was the realization that an important part of a VM process is a vulnerability scanner. The pen tester provided a scan report using the free tool Open Vulnerability Assessment Scanner (OpenVAS). Judy has provided you with the OpenVAS report for your initial analysis.

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Week 4 Assignment Part 1:

Create a document that contains answers to the following questions:

1. My long-term career goal (3-5 years) is to be a (an):

a. Find this job on a company website or job board and include a link to it or copy and paste the job description.

b. To obtain this position, I foresee myself having to:

c. The date I have set to obtain this career goal is:

Week 4 Assignment Part 2:

2. My ultimate career goal is to be a (an):

a. Find this job on a company website or job board and include a link to it or copy and paste the job description.

b. In this position, I will perform the following tasks:

c. The date I have set to obtain this career tool is:

Your submission should be a minimum of two pages of content in length. Please type all work, save it as a doc or pdf and upload here. All work must be submitted in APA format.

Assignments with a similarity report greater than 10% will not be graded. 

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