1. Consider two strings A and B. Take string A as your First name and Last name without space in-between (eg: TEJINDERTHIND) and string B as the name of your home town (eg: JALANDHARCITY). Now find the Longest Common Subsequence (LCS) common to strings A and B using Dynamic Programming. 2. Find Binomial Coefficient for 'Cs using dynamic programming approach. Also explain the relevant complexity. 3. Find Matrix Chain multiplication for following four matrices (M1, M2, M3 and M4) using d ynamic programming Technique. Matrix M1 M2 M3 М4 Order of Matrix 4 x 3 3 x 5 5 x 2 2 x 6

1. Consider two strings A and B. Take string A as your First name and Last name without space in-between (eg: TEJINDERTHIND) and string B as the name of your home town (eg: JALANDHARCITY). Now find the Longest Common Subsequence (LCS) common to strings A and B using Dynamic Programming. 2. Find Binomial Coefficient for 'Cs using dynamic programming approach. Also explain the relevant complexity. 3. Find Matrix Chain multiplication for following four matrices (M1, M2, M3 and M4) using d ynamic programming Technique. Matrix M1 M2 M3 М4 Order of Matrix 4 x 3 3 x 5 5 x 2 2 x 6

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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implement program to Finding the Longest Common Substringproblem that lends itself to a dynamic programming solution is finding the longest common substring in two strings. For example, in the words “raven” and “havoc”, the longest common substring is “av”.
By contrasting it with the longest-common-subsequence problem, create a dynamic programming approach for the bigger-is-smarter elephant problem. To accomplish this, the LCS issue must be expanded to include letter weights.
1. Here is a model M:Domain: {1, 2, 3, 4, 5}P : {1, 3, 5}, Q : {2, 4, 5}, R : ∅, S : {3, 4}a : 3, b : 4Is the proposition ∀x((P x ∧ Qx) → Sx) ∨ (Rb ↔ P a) true or false in M? Explain.
Reid and Jamieson (2023) demonstrated that a Minerva 2 model when combined with representations from LSA can account for standard patterns from the DRM paradigm. However, it is unclear as to whether other distributional representations can be used to account for false memory effects. The below figure contains average cosine similarity values for three different comparison types obtained from the BEAGLE model of semantics which we previously examined in the last assignment: 1) randomly selected words, 2) within list words (e.g., hospital-nurse, hospital-medicine, nurse-medicine, etc… for the list for the critical word doctor), and 3) critical word similarity to its associates (e.g., doctor-hospital, doctor-medicine, doctor-nurse, etc…). question1 . Summarize the results of the similarity values contained in the above figure. Given the results of the above simulation, provide a justification for using distributional models as an underlying representation for a model accounting for…
Consider the following CFG with starting variable S and Σ = {1, 2, 3, 4, 5, 6, 7, 8, 9, 0}: S → M U V U → N | ε V → V V | N N → M | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 0 M → 1 | 2 a. Draw a derivation tree for 12956664 b.Is this grammar ambiguous or unambiguous? Briefly explain why. c.Convert the CFG into Chomsky Normal Form.
Question 6 smSuggest an admissible heuristic that could be used when applying A* search to solving a planning problem in the state-variable representation. Show that it is admissible Full explain this question and text typing work only
Is there a benefit to using the Vector Space Model (VSM) rather than the Latent Semantic Analysis (LSA)? Justify.
Consider the following simple Probabilistic Context-Free Grammar (PCFG), which we will use to parse the sentence “student types program with keyboard”. . Find each of the possible parses of the sentence using the grammar, and draw the parse tree for each of these parses. Show the probability at each node. b. For each parse, calculate its probability, which will be the product of the probabilities for all the rules used in generating it. Which one is the most probable and the most likely parse for a human reader?
1.a Using a Cross Product method, construct a minimal DFA of w ∑ (a, b) *, where Number of 'a' in a string w mod 2=0 and number of 'b' in a string w mod 2=0 b.Design a minimal Deterministic Finite Automata (DFA) for ∑ = (a, b)
Suppose propositional sentences and knowledge-base here. We have a knowledge-base called KB. KBPrime is defined as an union of (1) KB and (2) the negation of a propositional predicate P. Answer true/false to the following questions. Note that "|=" is used to denote entailment, and "|-" is used to denote derivation through resolution. a) If KB |= P, then KB |- P. That is, if KB entails P, then KB derives (using resolution) P. b) If KB |- P, then KB |= P. c) If KBPrime |- [], then KBPrime is not satisfiable. d) If KBPrime is not satisfiable, then KBPrime |- [].
Compute the bigram count table, C(w2|w1) for the sentence "I saw Susie sitting in a shoe shine shop. Where Susie sits Susie shines, and where Susie shines Susie sits." Put w1 in the left hand column, and w2 in the top row. Include punctuation, clitics, and sentence start and end markers as individual tokens, and index words using their lemmatized forms
Given R=ABCDEFGand F = {CF→B, B→C, FB→E, CBE→F, E→AG, FA→B,BG→FE, BA→CG}The following is a minimal cover: A. (CFD, FBD, BED, BAD, BDG) B. CFD→BEAG C. CF→B, FB→E, E→AG, FA→B, BA→G D. CF→B, B→C, FB→E, E→AG, FA→B, BG→F, BA→G