Methodology for the Assessment of the Text Similarity of Documents in the CORE Open Access Data Set of Scholarly Documents

This paper describes the methodology of data preparation and analysis of the text similarity required for plagiarism detection on the CORE data set. Firstly, we used the CrossREF API and Microsoft Academic Graph data set for metadata enrichment and elimination of duplicates of doc-uments from the CORE 2018 data set. In the second step, we used 4-gram sequences of words from every document and transformed them into SHA-256 hash values. Features retrieved using hashing algorithm are compared, and the result is a list of documents and the percentages of cov-erage between pairs of documents features. In the third step, called pairwise feature-based ex-haustive analysis, pairs of documents are checked using the longest common substring.

Linear trace similarity matching based on improved longest common substring

The linear trace indicates the external morphological structure of the contact portion of clamping and cutting tools, which is not easy to be destroyed, has a high occurrence rate and high significant on identification. It is of great significance for prosecutor to determine the nature of the case and determine the tools used in the crime so as to find the criminals. The traditional linear trace analyzing methods include microscopy, manual comparison of characteristics, image recognition and three-dimensional scanning methods. The single-point laser picks up the toolmark detection signal, and the longest common substring is obtained after noise reduction. In addition, the improved dynamic programming algorithm calculates and generates matching results. Finally, the effectiveness of the algorithm is verified by the actual detection data.

Dynamic and Internal Longest Common Substring

Given two strings S and T, each of length at most n, the longest common substring (LCS) problem is to find a longest substring common to S and T. This is a classical problem in computer science with an $$\mathcal {O}(n)$$ O ( n ) -time solution. In the fully dynamic setting, edit operations are allowed in either of the two strings, and the problem is to find an LCS after each edit. We present the first solution to the fully dynamic LCS problem requiring sublinear time in n per edit operation. In particular, we show how to find an LCS after each edit operation in $$\tilde{\mathcal {O}}(n^{2/3})$$ O ~ ( n 2 / 3 ) time, after $$\tilde{\mathcal {O}}(n)$$ O ~ ( n ) -time and space preprocessing. This line of research has been recently initiated in a somewhat restricted dynamic variant by Amir et al. [SPIRE 2017]. More specifically, the authors presented an $$\tilde{\mathcal {O}}(n)$$ O ~ ( n ) -sized data structure that returns an LCS of the two strings after a single edit operation (that is reverted afterwards) in $$\tilde{\mathcal {O}}(1)$$ O ~ ( 1 ) time. At CPM 2018, three papers (Abedin et al., Funakoshi et al., and Urabe et al.) studied analogously restricted dynamic variants of problems on strings; specifically, computing the longest palindrome and the Lyndon factorization of a string after a single edit operation. We develop dynamic sublinear-time algorithms for both of these problems as well. We also consider internal LCS queries, that is, queries in which we are to return an LCS of a pair of substrings of S and T. We show that answering such queries is hard in general and propose efficient data structures for several restricted cases.