Concept explainers
Cohesion:
Cohesion shows the functional strength of any particular module. Module is a small part of any
Coupling:
Coupling shows the interaction between modules. It shows the interdependency and reliability of one module with the other. If any module significantly interacts with the other module, then it is said to be high coupling. This shows that the modules are highly interdependent.
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Chapter 7 Solutions
Computer Science: An Overview (13th Edition) (What's New in Computer Science)
- Define positive autocorrelation?arrow_forwardA robot moves into rooms R1 and R2 and switch the bulbs B1 and B2 on/off. The following are the action schema:1. goto(r, x1, x2) : robot r go to x2 from x12. switchON(s): switchON the bulb s3. switchOFF(s): switchOFF the bulb s 1. Write down preconditions and effects of the above actions. 2. Consider the following: (i) Initial state: < R1;R2;B1;B2 >: Robot is at Room R1 not in Room R2 and both bulbs are off.(ii) Goal state: < R2;B1;B2 >: Robot is at Room R2 and both bulbs are ON.Draw state space diagram for the above by drawing to all possible states.arrow_forwardA learning curve is defined as: O a. A plot of the performance of the classifier on the y axis versus the complexity of the model on the x axis b. None of these reflect the definition of a learning curve O c. A plot of the performance of the classifier on the y axis on a growing test set on the x axis with a fixed size training set O d. A plot of the performance of the classifier on the y axis on a growing test set as the size of the training data grows on the x axis e. A plot of the performance of the classifier on the y axis on a fixed test set as the size of the training data grows on the x axis Clear my choicearrow_forward
- For the confusion matrix shown, what is the precision for classes A. B, and C? What is the recall for classes A, B, and C? What is the overall accuracy of the system? Normalized Confustion Matrix Classifier Output (Computed Classification) Prediction A B C True Class of the Samples (Input) A 0.81 0.08 0.11 B 0.10 0.77 0.13 C 0.11 0.15 0.74arrow_forwardNeurons compute? The activation function and the linear function, which is represented by the equation z = Wx + b, are both calculated by neurons. Both a linear function, represented by the equation z = Wx + b, and an activation function are computed by neurons. When g is present in a neuron, the input x is scaled linearly (Wx + b). Before using the result in an activation function, neurons perform a calculation to get the mean of all of their features.arrow_forwardCan you briefly describe Donald Norman's model of interaction's stages?arrow_forward
- Is it necessary to have a self-transition in every State Diagram? What makes you believe this is the case?arrow_forwardWhen choosing between these two seemingly equivalent operations, what criteria does the linker use?arrow_forwardFind the correct hierarchy at model classification. O A. Dynamic->Linear->Unstable->Static B. Static->Nonlinear->Stable->Transient C. Dynamic->Linear->Stable->Transient D. Abstract->Static->Linear->Dynamicarrow_forward
- Even the tiniest activities may be made easier using the Spiral Model.arrow_forwardTwo distinct ideas exist: the Path-Goal Theory and the Leader-Member Exchange (LMX) Theory. Effectively explain using real-world instances.arrow_forwardIs it possible to entirely alter the interface of one of the Model-View-Controller framework's views without modifying the Model, taking the propagation mechanism into account? Explain why you hold your beliefs.Is it possible to entirely alter the interface of one of the Model-View-Controller framework's views without modifying the Model, taking the propagation mechanism into account? Explain why you hold your beliefs.arrow_forward
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