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- Use the first thirteen rules of inference to derive the conclusion of the symbolized argument below. N T 2 MP Dist 1 2 3 DV = ( ) MT DN { } [ 1 HS DS CD Simp Trans Impl Equiv Exp PREMISE ~(N. T) PREMISE T PREMISE CONCLUSION ~N Conj Taut Add ACP DM CP Com AIP Assoc IPThe following questions will be based on the recurrence relation: T(n) = 2T(|n/2|) + 7 if n > 2 T(n) = 1 if n 2 Master.Method.3.a.b.k: What is the value of a, b, and k? Variable Coefficient a k Master. Method.3.simplified: Does the simplified master method apply? Master.Method.3.case: What case of the Master Method applies? Master.Method.3.Asymptotic: What is the asymptotically tight bound? Enter an expressionUse the first eight rules of inference to derive the conclusion of the symbolized argument below. DE MP Dist 1 2 3 4 F G H M DV MT DN ( ) HS DS Trans Impl PREMISE (D v E) (G • H) PREMISE G D ~D PREMISE D. F = PREMISE { } CD Equiv CONCLUSION M [ ] Conj Add ACP Simp Exp Taut DM CP Com Assoc AIP IP
- Given the declarations:m,n, s: ℤa: Authorcurrent: ℙ Accoutsnovelists: ℙ AuthorSay what the type is of each of the following terms:(a) m+n(b) {s, n} (c) {{n},{s}} (d) {current} (e) novelists ∪ {a}(f) {{a}} ∪ {novelists}From a list of bid bonds, find out whether the bonds can be equally divided between 2 people with same value. Output explanation: Sample 1 yes: sum 2,782,351,934, 1 st person: {10022120, 2343520822, 428808992}, 2nd person: {2779943396, 2408538} Sample 2 no: there’s no possibility to equally divide the bid bonds. Design an algorithm using C language! (PS: use dynamic programming algorithm)The following questions will be based on the recurrence relation: T(n) = 2T(|n/2|)+n² for n > 2 T(n) = 1 if n < 2 Master.Method.4.a.b.k: What is the value of a, b, and k? Variable Coefficient a Master.Method.4.simplified: Does the simplified master method apply? Master.Method.4.case: What case of the Master Method applies? Master.Method.4.Asymptotic: What is the asymptotically tight bound? Enter an expression
- Load the soybean diagnosis data set in Weka (found in Weka-3.6/data/soybean.arff), then perform the following: Build a decision tree by selecting J48 as the classifier and 10-way cross-validation. Then fill out the following table: Correctly Classified Instances Incorrectly Classified Instances Kappa statistic Mean absolute error Root mean squared error Relative absolute error Root relative squared error Total Number of Instances Build a Naïve Bayes classifier and select 10-way cross-validation. Then fill out the following table: Correctly Classified Instances Incorrectly Classified Instances Kappa statistic Mean absolute error Root mean squared error Relative absolute error Root relative squared error Total Number of Instances Compare between results in previous two sections (a and b), which algorithm give the better result and…Let A = {a, b, c}, B = {x, y}, and C = {0, 1}. Identify B × B × B.Module 6 Journal. Please complete each of the proofs below. The proofs below may use any of the rules of inference or replacement rules given in Chapter 8. (D→C) • (C→ D), E → -(D → C) :. -E F • (G V H), -F V -H : F. G -U → ~B, S → ~B, ~(U • -S), TV B .:. T 3. (QR) V (-Q • -R), N → ~(Q ↔ R), EV N.. E -X → -Y, -Xv -Y, Z →Y: -Z -M V N, -R→-N : M → R
- Given the following UML diagram: Student - studentID: String + getStudentID(): String + toString(): String Person - lastName: String - firstName: String - dateOfBirth: Date - address: Address + getName(): String + getDOB(): Date + getAddress(): Address + toString(): String Professor - employeeID: String +getEmployelD(): String +toString(): String Write an implementation for the class Professor. DO NOT include or implement any constructor for this class. There are no abstract methods/classes in this example. toString method will return any simple String that contains firstName and employeelD only; no less, no more!! Assume firstName was defined as protected in class Person. You are not allowed to add implementation for any class or methods other than what is listed in the question statement.Correct answer will be upvoted else Multiple Downvoted. Computer science. Berland local ICPC challenge has quite recently finished. There were m members numbered from 1 to m, who contended on a problemset of n issues numbered from 1 to n. Presently the article is going to happen. There are two issue creators, every one of them will tell the instructional exercise to precisely k back to back errands of the problemset. The creators pick the section of k continuous errands for themselves autonomously of one another. The sections can correspond, meet or not cross by any means. The I-th member is keen on paying attention to the instructional exercise of all continuous errands from li to ri. Every member consistently decides to pay attention to just the issue creator that tells the instructional exercises to the most extreme number of assignments he is keen on. Leave this greatest number alone artificial intelligence. No member can pay attention to both of the creators, regardless of…Step 1. Intersection over Union # def intersection_over_union(dt_bbox, gt_bbox): ---> return iou Step 2. Evaluate Sample We now have to evaluate the predictions of the model. To do this, we will write a function that will do the following: Take model predictions and ground truth bounding boxes and labels as inputs. For each bounding box from the prediction, find the closest bounding box among the answers. For each found pair of bounding boxes, check whether the IoU is greater than a certain threshold iou_threshold. If the IoU exceeds the threshold, then we consider this answer as True Positive. Remove a matched bounding box from the evaluation. For each predicted bounding box, return the detection score and whether we were able to match it or not. def evaluate_sample(target_pred, target_true, iou_threshold=0.5): # ground truth gt_bboxes = target_true['boxes'].numpy() gt_labels = target_true['labels'].numpy() # predictions dt_bboxes =…