Law paper

1. Describe Types of Offer.

Definition (Section 2(a), Indian Contract Act, 1872):
When one person signifies to another his willingness to do or abstain from doing something, with a view to obtaining the assent of that other person to such act or abstinence, he is said to make a proposal or offer.

Explanation:
An offer is the first step in the formation of a contract. It expresses a person’s readiness to enter into a legal relationship on specific terms.

Types of Offers:

1. Express Offer: Made by...

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BCHM 322 Cheat Sheet

Lactic Acid Bacteria (LAB)

  • Gram-positive, non-motile, and non-spore forming bacteria that are strict fermenters.

  • They are catalase and oxidase negative and can't synthesize cytochromes.

  • LAB can be either homofermentative or heterofermentative. The type of fermentation is determined by the enzymes produced by the organism.

    • Homofermentative: Converts glucose into two lactic acid molecules, with 85-95% of the glucose carbon ending up as lactate. This pathway uses the glycolytic (Embden-Meyerhof)

...

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MOD 6

124. Design recommendation system using collaborative filtering (movie streaming) (10 marks — 10 points)

Input: users, movies, ratings → construct utility matrix.

Step 1: compute similarity between users (user-based) or movies (item-based).

Step 2: nearest neighbors identification using cosine/Pearson similarity.

Step 3: predict ratings for unseen movies via weighted average of neighbors’ ratings.

Step 4: recommend top-N movies with highest predicted ratings.

Handle sparsity with matrix factorization

...

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MOD 5

99. Given documents — compute i) Jaccard, ii) Cosine, iii) Hamming (detailed) (10 marks — 10 points)

Documents (after lowercasing and tokenizing words, remove punctuation):

  • D1: “the night is dark and the moon is red”

  • D2: “the moon in the night is red”

  • D3: “i can see moon is red the night is dark”

Step A — construct word sets / vectors (unified vocabulary):
Vocabulary (unique words across D1–D3):
{the, night, is, dark, and, moon, red, in, i, can, see} (11 words)

i) Jaccard

...

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HKHJVV

79. Explain MapReduce algorithm for matrix multiplication.

(10 Marks)

  1. Input matrices A and B stored in HDFS.

  2. Map Phase: Emits key-value pairs for each element multiplication.

  3. Mapper emits (i,j), value = A[i][k]×B[k][j].

  4. Shuffle Phase: Groups values by (i,j).

  5. Reduce Phase: Sums all values for each key (i,j).

  6. Final result = Σ A[i][k]×B[k][j].

  7. Each Mapper processes partial multiplication.

  8. Reducer aggregates partial results.

  9. Data locality optimizes performance.

  10. Scalable to huge

...

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arte 1

Il Museo
Il termine "museo" deriva dal greco antico Mouseion (Μουσεῖον), che significa "luogo sacro alle Muse". Le
Muse erano le figlie di Zeus e protettrici delle arti e delle scienze, considerate le ispiratrici di ogni forma di
creazione intellettuale. Il primo Mouseion è stato la famosa Biblioteca di Alessandria d'Egitto, fondata nel III
secolo a.C. da Tolomeo I, che ospitava una vasta collezione di libri e manoscritti, dove studiosi, poeti e
filosofi...

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tybms short

Diffusion Theory of PR – Short Note

Diffusion Theory explains how new ideas, innovations, or messages spread among members of a social system over time. In Public Relations, this theory helps understand how people adopt new information or behavior communicated by an organization.

Key Points:

  • Developed by Everett Rogers in his book “Diffusion of Innovations” (1962).

  • It describes how people move through stages before accepting an idea or innovation.

Five Stages of Adoption:

  1. Awareness:

...

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#include <stdio.h>

int linearsearch(int arr[], int size, int key) {

    int i;

    for (i = 0; i < size; i++) {

        if (arr[i] == key) {

            return i; // return index if key is found

        }

    }

    return -1; // return -1 if key not found

}

int main() {

    int arr[] = {10, 50, 20, 90, 60, 40, 30, 80, 70};

    int size = sizeof(arr) / sizeof(arr[0]);

    int key = 50; // element to be searched

    int result = linearsearch(arr, size, key);

...

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Marketing Models

⚙️ 1️⃣ LOGISTIC REGRESSION – CHURN PREDICTION
Predict P(churn = 1) via logit link → ln(p/(1-p)) = β₀ + βx
Metric Meaning Trade-off / Exam Tip
Precision = TP/(TP+FP) “How accurate are my churn flags?” ↑ Precision → few false alarms
Recall = TP/(TP+FN) “How many real churners caught?” ↑ Recall → catch more churners
AUC Prob(model ranks churner > non-churner) 0.5 = random, 1 = perfect
ROC Curve TPR (Recall) vs FPR (1-Specificity) Bows top-left = better
...

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