DP Computer Science Questionbank
Option B: Modelling and simulation
Description
[N/A]Directly related questions
-
19M.2.SL.TZ0.4d:
Using the formula, rules and initial data given above, construct the pseudocode that would calculate the year that the area of sea ice will be less than 10 000 km2.
-
19M.2.SL.TZ0.4e:
Identify two ways that this model could be implemented.
-
19M.2.SL.TZ0.5d:
Explain why the model would be converted to a simulation.
-
19M.2.SL.TZ0.4a:
Copy and complete the following table showing each variable’s data type and a suitable range of values that would represent the information shown above.
-
19M.2.SL.TZ0.4b:
Using the rules and initial values above, construct the pseudocode that would enable the area of the sea ice and the sea level rise to be calculated if there was an increase of 0.04 °C in the ocean surface temperature.
-
19M.2.SL.TZ0.4c.i:
Using the information above state the area of the sea ice.
-
19M.2.SL.TZ0.4c.ii:
Using the information above state the change in sea level.
-
19M.2.SL.TZ0.4f:
Explain why the accuracy of the simulation in predicting the area of the sea ice is critical.
-
19M.2.SL.TZ0.6b:
Outline the need for rendering in the creation of the animated 3D characters.
-
19M.2.SL.TZ0.6c:
Explain two technical implications of implementing a 3D animation in this way.
-
19M.2.SL.TZ0.5a:
Describe the main features of a “what-if” model.
-
19M.2.SL.TZ0.5b:
Identify three other variables that could be included in this model.
- 19M.2.SL.TZ0.6a: Define the term visualization.
-
19M.2.HL.TZ0.8b:
Identify two ways in which the neural network could be modified that may improve its performance.
-
19M.2.HL.TZ0.8a:
Describe the difference between a genetic algorithm and a neural network.
-
19M.2.HL.TZ0.8e:
Companies such as MAGS are considering products that use unsupervised learning rather than supervised learning.
Explain the benefits of unsupervised learning in developing products such as A Doll Called Alicia.
-
19M.2.HL.TZ0.8c:
Describe the difference between supervised learning and unsupervised learning.
-
19M.2.HL.TZ0.8d:
Explain why the machine learning capabilities of A Doll Called Alicia may lead to instances when the child and the doll cannot communicate effectively.
-
17N.2.SL.TZ0.5a:
By including examples where appropriate, describe a basic structure for this model.
-
17N.2.SL.TZ0.6d.ii:
With the help of examples, discuss how the development in the way such data is visualized has made the results of these simulations more accessible to the general public.
-
17N.2.SL.TZ0.4c.i:
With clear reference to the ethical issue, describe one example where practical experimentation would not be possible for ethical reasons.
-
17N.2.SL.TZ0.4c.ii:
State three other advantages, apart from ethical reasons, of simulating a computer model rather than constructing a physical one.
-
17N.2.SL.TZ0.6b:
Suggest why forecasts become less accurate the more long range they become.
-
17N.2.SL.TZ0.5c:
The company has established certain profit targets that it wishes to achieve over the next three years.
Explain how this model can be used to investigate different strategies that will reach these targets.
- 17N.2.SL.TZ0.4b: Identify two reasons why some systems are difficult to model successfully.
-
17N.2.SL.TZ0.4a:
Distinguish between a computer model and a computer simulation.
- 17N.2.SL.TZ0.5b: Suggest how the reliability of the model could be tested.
-
17N.2.SL.TZ0.6a:
Suggest two reasons why these simulations have improved both in their accuracy and their range.
-
17N.2.SL.TZ0.6c:
Discuss whether historical data can be accurately used to forecast future weather.
-
17N.2.SL.TZ0.6d.i:
Define the term visualization.
-
17N.2.HL.TZ0.8b:
Describe an appropriate set of data that could be used to train the network.
- 17N.2.HL.TZ0.8c.ii: State the number of neurons that would be in the output layer.
-
17N.2.HL.TZ0.8d:
Explain the way in which the output from neuron D will be determined.
-
17N.2.HL.TZ0.8e:
The network is set up with initial values. The outputs are compared with the desired outputs.
Identify the steps that now take place to train the network.
- 17N.2.HL.TZ0.8c.i: State the number of neurons that would be in the input layer.
- 17N.2.HL.TZ0.8a: Explain one reason why ANNs are suitable for solving the types of problems found in these areas.
-
17N.2.HL.TZ0.9:
Google Translate is an algorithm whose function is to translate text from one language to another. One of the resources that it uses is the body of documents produced by the United Nations which are routinely translated by humans into various languages.
Discuss the reasons why Google Translate takes a probabilistic approach in preference to a cognitive rule-based approach.
- 18N.2.SL.TZ0.4b: Outline how the yearly supply of water is calculated.
-
18N.2.SL.TZ0.5d:
The same spreadsheet is used by a group of four young people who share a house. The simulation, however, does not reflect their actual situation and the weekly target is often exceeded.
Identify two reasons why the simulation may not reflect the actual situation in this case.
-
18N.2.SL.TZ0.6a:
Outline one advantage and one disadvantage of training medical students with simulation software.
-
18N.2.SL.TZ0.6c:
Define the term visualization.
-
18N.2.SL.TZ0.4c:
Construct the pseudocode that will calculate the annual bill for a household based on the information given above. You should introduce any new variables where necessary.
-
18N.2.SL.TZ0.6d:
The medical school is considering updating its simulation software, which currently displays the simulation in 2D, to one that displays it in 3D.
Discuss whether this change to a 3D display would improve the quality of the training.
-
18N.2.SL.TZ0.6b:
Identify three technical aspects that the gloves have to address so that they can be used in this way for the simulation of operations.
-
18N.2.SL.TZ0.4d:
Identify three possible measures that customers could take to reduce their infrastructure charge.
-
18N.2.SL.TZ0.4a:
Identify the possible range of values and their data type for each of the above variables.
-
18N.2.SL.TZ0.5c:
Outline how a simulation in a spreadsheet can be organized so that the family can ensure a consumption of no more than 2700 litres per week.
-
18N.2.SL.TZ0.5b:
Explain how the choice of data in the table may affect the quality of the simulation.
- 18N.2.HL.TZ0.8a: Genetic algorithms follow an iterative process in developing a solution to a problem. Outline...
- 18N.2.HL.TZ0.8b.i: Outline why neural networks need to be trained before they can be used in this analysis.
-
18N.2.HL.TZ0.8b.ii:
Explain why supervised learning may be preferred to unsupervised learning for training these networks to make predictions on the progress of this parasitic disease.
-
18N.2.HL.TZ0.8c:
Many companies now use an automated response system when answering basic telephone queries from customers. This system uses speech analysis together with recorded messages.
Explain why the content of the messages given by these systems must be carefully chosen.
-
19N.2.SL.TZ0.4a:
Calculate the total value of the investment after two years if the principal sum of $30 000 is invested. The yearly interest rate is 10 % and this rate is compounded at the end of each year.
-
19N.2.SL.TZ0.4b:
Outline, using a diagram or otherwise, a method of calculating the total value of the investment after 12 months.
-
19N.2.SL.TZ0.5b.i:
Identify two data inputs for this computer simulation model.
-
19N.2.SL.TZ0.4c:
Construct an algorithm to calculate the fund value at the end of each month. This algorithm should also calculate the total value of the investment after the tax has been deducted after 12 months.
-
19N.2.SL.TZ0.5b.ii:
Identify two criteria that may be used to determine the best location to build the distribution centre.
-
19N.2.SL.TZ0.4d:
Many investment companies offer alternative investment schemes and use modelling to set the rates of interest.
Explain why the investment company would use modelling when setting the rates of interest.
-
19N.2.SL.TZ0.5a:
Outline two reasons for using a computer simulation in this scenario.
-
19N.2.SL.TZ0.5c:
Outline why the use of a computer simulation may not be beneficial.
-
19N.2.SL.TZ0.5b.iii:
Discuss how the simulation model will use the data inputs in (b)(i) and the criteria identified in (b)(ii) to generate recommendations.
-
19N.2.SL.TZ0.6c:
Explain why the development of a visualization model was necessary in this scenario.
-
19N.2.SL.TZ0.6a:
Identify four items of data that would be included to create the 2D visualization model.
-
19N.2.SL.TZ0.6b:
Explain why a 2D visualization model would be used rather than a 3D visualization model in this scenario.
-
19N.2.HL.TZ0.8a:
Outline one problem that may lead to printed text characters not being detected correctly.
-
19N.2.HL.TZ0.8c:
Explain how ANN pattern recognition techniques are applied to ensure that the handwritten letter X in Figure 3 is recognized as a letter X.
-
19N.2.HL.TZ0.8f:
Outline two potential problems with training the ANN to suggest appropriate words.
-
19N.2.HL.TZ0.8d:
Identify two features that would be required by the ANN to predict the next word in the sentence.
-
19N.2.HL.TZ0.8b:
Outline why an ANN can be used to overcome the challenges outlined in this scenario.
-
19N.2.HL.TZ0.8e:
Explain how the application uses a neural network to suggest suitable words.
-
20N.2.SL.TZ0.4a:
Identify the data types for the
House_Type
,House_Num
andProfit
variables. -
20N.2.SL.TZ0.4b:
Construct a spreadsheet model that shows the total profit for the chosen type of house. The user must input the
House_Type
andHouse_Num
to calculate the total profit. -
20N.2.SL.TZ0.4c:
Outline two validation tests that should be included in the test plan for this spreadsheet model.
-
20N.2.SL.TZ0.4d:
To finance this project, EnviroBuild took out a bank loan of $400 000 and will be required to pay interest on this loan. The project starts on 1 January 2021.
The following steps are used to calculate the total profit:
- Read the
Profit
variable and theNo_of_Days
variable from the spreadsheet model in (b). - Calculate the number of months that the project will take.
- There are no partial months.
- For example, if a project finished on 1 July 2021, the loan interest rates will include July: the project will last for 7 months.
- The rate of interest on the bank loan of $400 000 is 1 % per month.
- The land tax is $500 per month.
Construct the pseudocode that will calculate the profit after these additional costs have been considered. You can introduce any new variables, if necessary.
- Read the
-
20N.2.SL.TZ0.5a.i:
Outline what is meant by a real-time simulation in the context of a glacier size simulation.
-
20N.2.SL.TZ0.5a.ii:
Outline what is meant by the statement “the VESL simulation is an abstraction of reality”.
-
20N.2.SL.TZ0.6a.i:
Define the term visualization.
-
20N.2.SL.TZ0.5b:
Outline two reasons why predictions of global sea levels from simulations may not be accurate.
-
20N.2.SL.TZ0.6a.ii:
Identify how a 2D visualization could be used in this scenario.
-
20N.2.SL.TZ0.5c:
NASA has decided to make its simulation software available for other scientists as well as members of the public.
Evaluate the social and ethical implications of this decision.
-
20N.2.SL.TZ0.6b:
Explain the benefits of using visualization when simulating rising sea levels.
-
20N.2.SL.TZ0.6c:
Once the 3D visualization has been rendered, when the user drags a slider bar to simulate the amount of ice that has melted, the visualization is re-rendered without any delay.
Figure 4: Slider bar to simulate different percentages of sea ice
[Source: Courtesy NASA/JPL-Caltech.]
-
20N.2.HL.TZ0.8c:
Describe the way an NPC in a game could adapt its behaviour when moving and interacting with its environment.
-
20N.2.HL.TZ0.8a:
Outline two advantages of developing NPCs using genetic algorithms.
-
20N.2.HL.TZ0.8d:
The last stage of NPC development is verbal communication with player characters and other NPCs. To assist with this process, it was decided to explore research related to chatbots.
Explain the benefits of giving NPCs the functionality of chatbots.
-
20N.2.HL.TZ0.8b:
Explain how supervised learning of a genetic algorithm could be used to support the way NPCs learn.
-
18N.2.SL.TZ0.5a:
With reference to the scenario described above, explain the difference between a model and a simulation.
-
18M.2.SL.TZ0.4b.ii:
Describe two items that would have a calculated value of more than $90.
-
18M.2.SL.TZ0.4e:
Identify two tests that should be included in the test plan for this model.
-
18M.2.SL.TZ0.4a:
Copy and complete the following table showing the variables, each variable’s data type and range of values that would represent the information shown above.
-
18M.2.SL.TZ0.4d:
With the help of a diagram, suggest an appropriate design for a spreadsheet used to calculate the final selling price, following this model.
-
18M.2.SL.TZ0.5e:
The simulation program will group all orders received in an interval of 10 minutes. The program will then produce a sequence of all the cooking steps so that these orders can be completed as quickly as possible. Once the kitchen has completed the orders for one interval, it is ready to accept orders for the next interval.
Customers are impressed by the rapidity of service, but not by the quality of prepared food.
Suggest two elements that the software simulation may have not considered that may lead to complaints from the customers.
-
18M.2.SL.TZ0.4c:
Calculate the selling price of a top brand guitar with a volume of 96 dm3 that was damaged. You should show your working.
-
18M.2.SL.TZ0.5a:
Outline two problems with this method of preparation that could affect the time it takes the two people to prepare an order.
-
18M.2.SL.TZ0.5b:
Describe how the two people could improve the efficiency of their work, without compromising on the quality of service to the customers.
-
18M.2.SL.TZ0.5c:
Explain the difference between a model and a simulation.
-
18M.2.SL.TZ0.5d:
Identify three elements that the simulation software might consider, in addition to the information already described above.
-
18M.2.SL.TZ0.6a.ii:
With relation to the activities that the software of an ECU has to perform, suggest one reason why the auto-diagnostic program in the ECU depends upon the make and model of the vehicle.
-
18M.2.SL.TZ0.6a.i:
With relation to the activities that the software of an ECU has to perform, identify two of the sources that provide input data to the ECU.
-
18M.2.SL.TZ0.6b:
Vehicles are tested for their exhaust gas emissions using simulation software at specialist garages. During a period of 5 minutes, a vehicle with the engine switched on is monitored for emissions of carbon dioxide (CO2) and fine particulates. The software uses 3D visualization techniques to display these parameters on a screen for the whole duration of the test.
Explain how emissions of CO2 and fine particulates could be represented in 3D by the software.
-
18M.2.HL.TZ0.8b:
The following block diagram illustrates a neural network used by a supervized learning algorithm to calculate a resulting value RES, for a given set of inputs.
The neural network in the block diagram contains 60 nodes in the input layer (block I), 36 nodes in the hidden layer (block H) and 6 nodes in the outermost layer (block O). The solid black arrows indicate that all nodes in one layer are feeding all nodes in the successive layer. In particular, the value RES is calculated by using all nodes in O.
Calculate the number of weights that the above neural network uses in producing a value for RES. Show the details of your calculation.
-
18M.2.HL.TZ0.9b:
Discuss the appropriateness of using genetic algorithms to compare the processed images with those stored in the knowledge base.
-
18M.2.HL.TZ0.8a:
Outline the basic steps involved in supervized learning algorithms.
-
18M.2.HL.TZ0.10:
Machine translators are regularly used to translate text from one language to another. In spite of the advances that have been made in this field, the output may still need to be proofread by a human.
Describe two problems that these translators may encounter when translating from one language to another.
-
18M.2.SL.TZ0.4b.i:
Using the above rules, construct the pseudocode that will help Ralph in deciding whether to buy an item.
Sub sections and their related questions
B.1 The basic model
-
18M.2.SL.TZ0.4a:
Copy and complete the following table showing the variables, each variable’s data type and range of values that would represent the information shown above.
-
18M.2.SL.TZ0.4d:
With the help of a diagram, suggest an appropriate design for a spreadsheet used to calculate the final selling price, following this model.
-
18M.2.SL.TZ0.4e:
Identify two tests that should be included in the test plan for this model.
-
18M.2.SL.TZ0.6a.i:
With relation to the activities that the software of an ECU has to perform, identify two of the sources that provide input data to the ECU.
-
18M.2.SL.TZ0.6a.ii:
With relation to the activities that the software of an ECU has to perform, suggest one reason why the auto-diagnostic program in the ECU depends upon the make and model of the vehicle.
-
19M.2.SL.TZ0.4a:
Copy and complete the following table showing each variable’s data type and a suitable range of values that would represent the information shown above.
-
19M.2.SL.TZ0.5b:
Identify three other variables that could be included in this model.
- 17N.2.SL.TZ0.4b: Identify two reasons why some systems are difficult to model successfully.
-
17N.2.SL.TZ0.5a:
By including examples where appropriate, describe a basic structure for this model.
- 17N.2.SL.TZ0.5b: Suggest how the reliability of the model could be tested.
-
17N.2.SL.TZ0.5c:
The company has established certain profit targets that it wishes to achieve over the next three years.
Explain how this model can be used to investigate different strategies that will reach these targets.
-
18N.2.SL.TZ0.4a:
Identify the possible range of values and their data type for each of the above variables.
-
19N.2.SL.TZ0.5b.i:
Identify two data inputs for this computer simulation model.
-
19N.2.SL.TZ0.5b.ii:
Identify two criteria that may be used to determine the best location to build the distribution centre.
-
20N.2.SL.TZ0.4a:
Identify the data types for the
House_Type
,House_Num
andProfit
variables.
B.2 Simulations
-
18M.2.SL.TZ0.4b.i:
Using the above rules, construct the pseudocode that will help Ralph in deciding whether to buy an item.
-
18M.2.SL.TZ0.4b.ii:
Describe two items that would have a calculated value of more than $90.
-
18M.2.SL.TZ0.4c:
Calculate the selling price of a top brand guitar with a volume of 96 dm3 that was damaged. You should show your working.
-
18M.2.SL.TZ0.5a:
Outline two problems with this method of preparation that could affect the time it takes the two people to prepare an order.
-
18M.2.SL.TZ0.5b:
Describe how the two people could improve the efficiency of their work, without compromising on the quality of service to the customers.
-
18M.2.SL.TZ0.5c:
Explain the difference between a model and a simulation.
-
18M.2.SL.TZ0.5d:
Identify three elements that the simulation software might consider, in addition to the information already described above.
-
18M.2.SL.TZ0.5e:
The simulation program will group all orders received in an interval of 10 minutes. The program will then produce a sequence of all the cooking steps so that these orders can be completed as quickly as possible. Once the kitchen has completed the orders for one interval, it is ready to accept orders for the next interval.
Customers are impressed by the rapidity of service, but not by the quality of prepared food.
Suggest two elements that the software simulation may have not considered that may lead to complaints from the customers.
-
19M.2.SL.TZ0.4b:
Using the rules and initial values above, construct the pseudocode that would enable the area of the sea ice and the sea level rise to be calculated if there was an increase of 0.04 °C in the ocean surface temperature.
-
19M.2.SL.TZ0.4c.i:
Using the information above state the area of the sea ice.
-
19M.2.SL.TZ0.4c.ii:
Using the information above state the change in sea level.
-
19M.2.SL.TZ0.4d:
Using the formula, rules and initial data given above, construct the pseudocode that would calculate the year that the area of sea ice will be less than 10 000 km2.
-
19M.2.SL.TZ0.4e:
Identify two ways that this model could be implemented.
-
19M.2.SL.TZ0.4f:
Explain why the accuracy of the simulation in predicting the area of the sea ice is critical.
-
19M.2.SL.TZ0.5a:
Describe the main features of a “what-if” model.
-
19M.2.SL.TZ0.5d:
Explain why the model would be converted to a simulation.
-
17N.2.SL.TZ0.4a:
Distinguish between a computer model and a computer simulation.
-
17N.2.SL.TZ0.4c.i:
With clear reference to the ethical issue, describe one example where practical experimentation would not be possible for ethical reasons.
-
17N.2.SL.TZ0.4c.ii:
State three other advantages, apart from ethical reasons, of simulating a computer model rather than constructing a physical one.
-
17N.2.SL.TZ0.6a:
Suggest two reasons why these simulations have improved both in their accuracy and their range.
-
17N.2.SL.TZ0.6b:
Suggest why forecasts become less accurate the more long range they become.
-
17N.2.SL.TZ0.6c:
Discuss whether historical data can be accurately used to forecast future weather.
- 18N.2.SL.TZ0.4b: Outline how the yearly supply of water is calculated.
-
18N.2.SL.TZ0.4c:
Construct the pseudocode that will calculate the annual bill for a household based on the information given above. You should introduce any new variables where necessary.
-
18N.2.SL.TZ0.4d:
Identify three possible measures that customers could take to reduce their infrastructure charge.
-
18N.2.SL.TZ0.5a:
With reference to the scenario described above, explain the difference between a model and a simulation.
-
18N.2.SL.TZ0.5b:
Explain how the choice of data in the table may affect the quality of the simulation.
-
18N.2.SL.TZ0.5c:
Outline how a simulation in a spreadsheet can be organized so that the family can ensure a consumption of no more than 2700 litres per week.
-
18N.2.SL.TZ0.5d:
The same spreadsheet is used by a group of four young people who share a house. The simulation, however, does not reflect their actual situation and the weekly target is often exceeded.
Identify two reasons why the simulation may not reflect the actual situation in this case.
-
18N.2.SL.TZ0.6a:
Outline one advantage and one disadvantage of training medical students with simulation software.
-
18N.2.SL.TZ0.6b:
Identify three technical aspects that the gloves have to address so that they can be used in this way for the simulation of operations.
-
19N.2.SL.TZ0.4a:
Calculate the total value of the investment after two years if the principal sum of $30 000 is invested. The yearly interest rate is 10 % and this rate is compounded at the end of each year.
-
19N.2.SL.TZ0.4b:
Outline, using a diagram or otherwise, a method of calculating the total value of the investment after 12 months.
-
19N.2.SL.TZ0.4c:
Construct an algorithm to calculate the fund value at the end of each month. This algorithm should also calculate the total value of the investment after the tax has been deducted after 12 months.
-
19N.2.SL.TZ0.4d:
Many investment companies offer alternative investment schemes and use modelling to set the rates of interest.
Explain why the investment company would use modelling when setting the rates of interest.
-
19N.2.SL.TZ0.5a:
Outline two reasons for using a computer simulation in this scenario.
-
19N.2.SL.TZ0.5b.iii:
Discuss how the simulation model will use the data inputs in (b)(i) and the criteria identified in (b)(ii) to generate recommendations.
-
19N.2.SL.TZ0.5c:
Outline why the use of a computer simulation may not be beneficial.
-
20N.2.SL.TZ0.4b:
Construct a spreadsheet model that shows the total profit for the chosen type of house. The user must input the
House_Type
andHouse_Num
to calculate the total profit. -
20N.2.SL.TZ0.4c:
Outline two validation tests that should be included in the test plan for this spreadsheet model.
-
20N.2.SL.TZ0.4d:
To finance this project, EnviroBuild took out a bank loan of $400 000 and will be required to pay interest on this loan. The project starts on 1 January 2021.
The following steps are used to calculate the total profit:
- Read the
Profit
variable and theNo_of_Days
variable from the spreadsheet model in (b). - Calculate the number of months that the project will take.
- There are no partial months.
- For example, if a project finished on 1 July 2021, the loan interest rates will include July: the project will last for 7 months.
- The rate of interest on the bank loan of $400 000 is 1 % per month.
- The land tax is $500 per month.
Construct the pseudocode that will calculate the profit after these additional costs have been considered. You can introduce any new variables, if necessary.
- Read the
-
20N.2.SL.TZ0.5a.i:
Outline what is meant by a real-time simulation in the context of a glacier size simulation.
-
20N.2.SL.TZ0.5a.ii:
Outline what is meant by the statement “the VESL simulation is an abstraction of reality”.
-
20N.2.SL.TZ0.5b:
Outline two reasons why predictions of global sea levels from simulations may not be accurate.
-
20N.2.SL.TZ0.5c:
NASA has decided to make its simulation software available for other scientists as well as members of the public.
Evaluate the social and ethical implications of this decision.
B.3 Visualization
-
18M.2.SL.TZ0.6b:
Vehicles are tested for their exhaust gas emissions using simulation software at specialist garages. During a period of 5 minutes, a vehicle with the engine switched on is monitored for emissions of carbon dioxide (CO2) and fine particulates. The software uses 3D visualization techniques to display these parameters on a screen for the whole duration of the test.
Explain how emissions of CO2 and fine particulates could be represented in 3D by the software.
- 19M.2.SL.TZ0.6a: Define the term visualization.
-
19M.2.SL.TZ0.6b:
Outline the need for rendering in the creation of the animated 3D characters.
-
19M.2.SL.TZ0.6c:
Explain two technical implications of implementing a 3D animation in this way.
-
17N.2.SL.TZ0.6d.i:
Define the term visualization.
-
17N.2.SL.TZ0.6d.ii:
With the help of examples, discuss how the development in the way such data is visualized has made the results of these simulations more accessible to the general public.
-
18N.2.SL.TZ0.6c:
Define the term visualization.
-
18N.2.SL.TZ0.6d:
The medical school is considering updating its simulation software, which currently displays the simulation in 2D, to one that displays it in 3D.
Discuss whether this change to a 3D display would improve the quality of the training.
-
19N.2.SL.TZ0.6a:
Identify four items of data that would be included to create the 2D visualization model.
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19N.2.SL.TZ0.6b:
Explain why a 2D visualization model would be used rather than a 3D visualization model in this scenario.
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19N.2.SL.TZ0.6c:
Explain why the development of a visualization model was necessary in this scenario.
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20N.2.SL.TZ0.6a.i:
Define the term visualization.
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20N.2.SL.TZ0.6a.ii:
Identify how a 2D visualization could be used in this scenario.
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20N.2.SL.TZ0.6b:
Explain the benefits of using visualization when simulating rising sea levels.
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20N.2.SL.TZ0.6c:
Once the 3D visualization has been rendered, when the user drags a slider bar to simulate the amount of ice that has melted, the visualization is re-rendered without any delay.
Figure 4: Slider bar to simulate different percentages of sea ice
[Source: Courtesy NASA/JPL-Caltech.]
B.4 Communication modelling and simulation
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18M.2.HL.TZ0.8a:
Outline the basic steps involved in supervized learning algorithms.
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18M.2.HL.TZ0.8b:
The following block diagram illustrates a neural network used by a supervized learning algorithm to calculate a resulting value RES, for a given set of inputs.
The neural network in the block diagram contains 60 nodes in the input layer (block I), 36 nodes in the hidden layer (block H) and 6 nodes in the outermost layer (block O). The solid black arrows indicate that all nodes in one layer are feeding all nodes in the successive layer. In particular, the value RES is calculated by using all nodes in O.
Calculate the number of weights that the above neural network uses in producing a value for RES. Show the details of your calculation.
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18M.2.HL.TZ0.9b:
Discuss the appropriateness of using genetic algorithms to compare the processed images with those stored in the knowledge base.
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18M.2.HL.TZ0.10:
Machine translators are regularly used to translate text from one language to another. In spite of the advances that have been made in this field, the output may still need to be proofread by a human.
Describe two problems that these translators may encounter when translating from one language to another.
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19M.2.HL.TZ0.8a:
Describe the difference between a genetic algorithm and a neural network.
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19M.2.HL.TZ0.8b:
Identify two ways in which the neural network could be modified that may improve its performance.
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19M.2.HL.TZ0.8c:
Describe the difference between supervised learning and unsupervised learning.
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19M.2.HL.TZ0.8d:
Explain why the machine learning capabilities of A Doll Called Alicia may lead to instances when the child and the doll cannot communicate effectively.
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19M.2.HL.TZ0.8e:
Companies such as MAGS are considering products that use unsupervised learning rather than supervised learning.
Explain the benefits of unsupervised learning in developing products such as A Doll Called Alicia.
- 17N.2.HL.TZ0.8a: Explain one reason why ANNs are suitable for solving the types of problems found in these areas.
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17N.2.HL.TZ0.8b:
Describe an appropriate set of data that could be used to train the network.
- 17N.2.HL.TZ0.8c.i: State the number of neurons that would be in the input layer.
- 17N.2.HL.TZ0.8c.ii: State the number of neurons that would be in the output layer.
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17N.2.HL.TZ0.8d:
Explain the way in which the output from neuron D will be determined.
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17N.2.HL.TZ0.8e:
The network is set up with initial values. The outputs are compared with the desired outputs.
Identify the steps that now take place to train the network.
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17N.2.HL.TZ0.9:
Google Translate is an algorithm whose function is to translate text from one language to another. One of the resources that it uses is the body of documents produced by the United Nations which are routinely translated by humans into various languages.
Discuss the reasons why Google Translate takes a probabilistic approach in preference to a cognitive rule-based approach.
- 18N.2.HL.TZ0.8a: Genetic algorithms follow an iterative process in developing a solution to a problem. Outline...
- 18N.2.HL.TZ0.8b.i: Outline why neural networks need to be trained before they can be used in this analysis.
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18N.2.HL.TZ0.8b.ii:
Explain why supervised learning may be preferred to unsupervised learning for training these networks to make predictions on the progress of this parasitic disease.
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18N.2.HL.TZ0.8c:
Many companies now use an automated response system when answering basic telephone queries from customers. This system uses speech analysis together with recorded messages.
Explain why the content of the messages given by these systems must be carefully chosen.
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19N.2.HL.TZ0.8a:
Outline one problem that may lead to printed text characters not being detected correctly.
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19N.2.HL.TZ0.8b:
Outline why an ANN can be used to overcome the challenges outlined in this scenario.
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19N.2.HL.TZ0.8c:
Explain how ANN pattern recognition techniques are applied to ensure that the handwritten letter X in Figure 3 is recognized as a letter X.
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19N.2.HL.TZ0.8d:
Identify two features that would be required by the ANN to predict the next word in the sentence.
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19N.2.HL.TZ0.8e:
Explain how the application uses a neural network to suggest suitable words.
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19N.2.HL.TZ0.8f:
Outline two potential problems with training the ANN to suggest appropriate words.
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20N.2.HL.TZ0.8a:
Outline two advantages of developing NPCs using genetic algorithms.
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20N.2.HL.TZ0.8b:
Explain how supervised learning of a genetic algorithm could be used to support the way NPCs learn.
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20N.2.HL.TZ0.8c:
Describe the way an NPC in a game could adapt its behaviour when moving and interacting with its environment.
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20N.2.HL.TZ0.8d:
The last stage of NPC development is verbal communication with player characters and other NPCs. To assist with this process, it was decided to explore research related to chatbots.
Explain the benefits of giving NPCs the functionality of chatbots.