Prove the following argument. Be sure to define predicates that you use. The domain is all people. Please indicate the law or rule being used.  Everyone who likes football and lives in Seattle likes the Seahawks. Only the people who live in Seattle like the Mariners. Ella likes the Mariners but not the Seahawks. Therefore, there is at least one person who lives in Seattle that does not like football. P(x): x likes football Q(x): x lives in Seattle R(x): x likes the Seahawks S(x): x likes the mariners

Using the predicate symbols shown and appropriate quantifiers, write each English language statement as a predicate wff. (The domain is the whole world.) D(x): x is a day S(x): x is sunny R(x): x is rainy M: Monday T: Tuesday 1. All animals are dogs.  2.Some animals are not cats. 3.Every animal that is a dog is not a cat. 4.Some animals are dogs and cats. 5.No animal is both a dog and a cat. 6. An animal is always a dog only if it is a cat. 7.No animals are dogs. 8.Meg is a cat; all animals are cats. 9.Meg and Alex are both cats. 10.If one animal is a cat, then every animal will be a cat.

a. The following pair of concepts are related in some way and different in another.  Explain how the concepts are similar and how they are different:  GROUP BY / ORDER BY b. The following pair of concepts are related in some way and different in another.  Explain how the concepts are similar and how they are different: WHERE / HAVING

Question 5 Suppose you want to use a GNN to classify the nodes of a social network (for example, the classes may be: "Democrat", "Republican", “Undecided", "Other"). Suppose that you know the correct label (i.e., the political affiliation) for some of the nodes. Additionally, you know some additional attributes (gender, age, education level) for all nodes. Which of the following statements is/are correct? The known node attributes, together with the political affiliation data, can be used to form the embedding of each node at layer-O. O The training data should consist of all nodes for which we know the political affiliation. O The larger the number of layers (depth) of the network, the more susceptive the network is to overfitting. O The network can be trained with the cross-entropy loss function we presented in class.

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Consider the following predicate: P = A or B or C.       What are the Unique True Points and Near False Points for this predicate?

Synthesize the domain model of vending machine which has the following characteristics. Products can be purchased by inserting the correct number of coins into the machine. A user selects a product from a list of available products, adds coins, and either gets the product or gets the coins returned if insufficient money was supplied or if the product is sold out. Products can be restocked and money removed by an operator. Requirements for this system: Product Selection: Users will be able to choose a product from a list of available items. The system must display the product list with corresponding prices. Coin Insertion: Users can insert coins into the machine to pay for selected products. The machine will accept common denominations. Purchase Process: Upon sufficient coin insertion, the selected product will be dispensed. If the inserted coins are insufficient, the machine should return the coins. In case of a sold-out product, the machine will return coins. Restocking: Operators…

• Clearly explain the difference between the inclusive 'or' and the exclusive 'or. Give a clear example of each and explain how the example works to be either the inclusive or the exclusive 'or. Note: you must use your own examples, not examples from the text. O Explain how negation differs from the other three truth functional connectives you have learned. . Do you think a double negation is possible? If so, how do you think double negation works? Make sure to provide an example.

4. Briefly explain the difference between a singular term and a predicate.

Develop a system/application in Prolog that will allow grader to: load a set of student’s grades (see samples from the Table C below) query student’s final letter grade (A, B, C, etc.) by a given student’s name. query student(s) whose grade matches to the input letter grade (e.g., A). For example, show student(s) whose final letter grade is A. There are 3 types of grades for each student—HWs, Exams, and Project, with a weight of 20%, 40% and 40%, respectively, to the 100-point final. Table A and B show how the final grades (in points and letter) are computed. Table C shows samples of students’ grades. You will define the execution instructions to answer the following questions so that grader can test out your application in Prolog: how to load your source code to Prolog runtime/compiler how to load students’ grades to Prolog runtime/compiler how to query student’s letter grade how to query student(s) whose letter grade matches to an input letter grade Please DO NOT hard code any…