Algorithm and Data Structure for Improving of Operation Layer Performance in NETCONF Protocol

2014 ◽  
Vol 20 (10) ◽  
pp. 543-548
Author(s):  
Yang-Min Lee ◽  
Jae-Kee Lee
Keyword(s):  
Data Structure ◽  
Algorithm And Data Structure

Pyramid Search: Skip Ring Data Structure-Based New Searching Algorithm

2018 ◽  
Vol 27 (14) ◽  
pp. 1850218
Author(s):  
Mustafa Aksu ◽  
Ali Karcı
Keyword(s):  
Data Structure ◽  
Time Complexity ◽  
Linear Search ◽  
Data Set ◽  
Linked List ◽  
Insertion And Deletion ◽  
Skip List ◽  
Algorithm And Data Structure ◽  
Text Element ◽  
Linked Lists

Our new algorithm and data structure, pyramid search (PS) and skip ring, were created with the help of circular linked list and skip list algorithms and data structures. In circular linked list, operations were performed on a single circular list. Our new data structure consists of circular linked lists formed in layers which were linked in a pyramid way. Time complexity of searching, insertion and deletion algorithms equal to [Formula: see text] (lg[Formula: see text]) in an [Formula: see text]-element skip ring data structure. Therefore, skip ring data structure is employed more effectively ([Formula: see text](lg[Formula: see text])) in circumstances where circular linked lists ([Formula: see text]) are used. The priority is determined based on the searching frequency in PS which was developed in this study. Thus, the time complexity of searching is almost [Formula: see text](1) for [Formula: see text] records data set. In this paper, the applications of searching algorithms like linear search (LS), binary search (BS) and PS were realized and the obtained results were compared. The obtained results demonstrated that the PS algorithm is superior to the BS algorithm.

scholarly journals Initial Suspicion on Detecting Code Plagiarism and Collusion in Academia: Case Study of Algorithm and Data Structure Courses

2021 ◽  
Vol 6 (1) ◽  
pp. 9
Author(s):  
Mewati Ayub ◽  
Oscar Karnalim ◽  
Maresha Caroline Wijanto ◽  
Risal Risal
Keyword(s):  
Data Structure ◽  
Engineering Education ◽  
Academic Integrity ◽  
Program Code ◽  
Similarity Detection ◽  
Detection Tool ◽  
Algorithm And Data Structure

In engineering education, some assessments require the students to submit program code, and since that code might be a result of plagiarism or collusion, a similarity detection tool is often used to filter excessively similar programs. To improve the scalability of such a tool, it is suggested to initially suspect some programs and only compare those programs to others (instead of exhaustively compare all programs one another). This paper compares the ef-fectiveness of two common techniques to raise such initial suspicion: focusing on the submissions of smart students (as they are likely to be copied), or the submissions of slow-paced students (since those students are likely to breach academic integrity to get higher assessment mark). Our study shows that the latter statistically outperforms the former by 13% in terms of precision; slow-paced students are likely to be the perpetrators, but they fail to get the submissions of smart students.

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