Theories about the genetic origins of altruism suggest that evolution should only favor altruism when it is directed toward family members. Assuming such theories are correct, which of the following choices best explains the fact that people nonetheless do behave altruistically, sometimes astonishingly so, toward strangers?

While at the dog park with his family, Joel heroically jumped into a fight between two dogs and saved the life of a stranger’s pet. According to Freud, which aspect of Joel’s personality took over in this moment?

A solar concentrator is a type of solar collector that collects light over a certain area and focuses it onto smaller area.

What is solar insolation?

What is the difference between the lens and mirror solar concentrators? Which one is more popular?

Question Set 3 – 3.5 Referring to Figure 3.3, which of the following attack scenarios are presented based on the given AG? (select all that apply) Figure 3.3. Network Setup and Attack Scenario Computer network attack diagram with an attacker, firewall, router, FTP, and a database server, connected by a flowchart of probable attack steps reaching a “local_bof()” function with numerical probabilities.  

Question Set 4 – 4.2 Recall that one of the key features of FNN is forward propagation. Which of the following statements correctly describes this feature? (select all that apply)

Question Set 3 – 3.3 Referring back to Figure 3.1, which of the following statements are correct? (select all that apply)The temporal metric can be summarized as shown in Figure 3.1 below: (Defined (X) in RC should be Undefined (X)) Figure 3.1. Temporal Metrics  Flowchart titled “Temporal Metrics” with three columns indicating stages of exploit code maturity, remediation level, and report confidence.

The subclass inherits the public members of the superclass only.

Question Set 4 – 4.4 The list below presents the procedural steps of FNN. Which of the answer options presents their correct order? STEP A: Input the first observation of your dataset in the input layer, each feature in one input node. STEP B:Compare the predicted result to the actual result. Measure the generated error. STEP C: Forward-propagation: from left to right, the neurons are activated in a way that the impact of each neuron’s activation is limited by the weights. Propagate the activations until getting the predicted result y. STEP D: Randomly initialize the network, set weights to small numbers close to 0 (but not 0). STEP E: Repeat previous steps and update the weights after each observation. Or, repeat previous steps but update the weights only after a batch of observations (Batch Learning). STEP F:Back-Propagation: From right to left, the error is back-propagated. Update the weights according to how much they are responsible for the error. The learning rate decides by how much we update the weights.