Transposition invariant pattern matching for multi-track strings

Peer reviewe

A hybrid algorithm for the longest common transposition-invariant subsequence problem

The longest common transposition-invariant subsequence (LCTS) problem is a music information retrieval oriented variation of the classic LCS problem. There are basically only two known efficient approaches to calculate the length of the LCTS, one based on sparse dynamic programming and the other on bit-parallelism. In this work, we propose a hybrid algorithm picking the better of the two algorithms for individual subproblems. Experiments on music (MIDI), with 32-bit and 64-bit implementations, show that the proposed algorithm outperforms the faster of the two component algorithms by a factor of 1.4–2.0, depending on sequence lengths. Similar, if not better, improvements can be observed for random data with Gaussian distribution. Also for uniformly random data, the hybrid algorithm is the winner if the alphabet is neither too small (at least 32 symbols) nor too large (up to 128 symbols). Part of the success of our scheme is attributed to a quite robust component selection heuristic

Correlation function for the Grid-Poisson Euclidean matching on a line and on a circle

We compute the two-point correlation function for spin configurations which are obtained by solving the Euclidean matching problem, for one family of points on a grid, and the second family chosen uniformly at random, when the cost depends on a power $p$ of the Euclidean distance. We provide the analytic solution in the thermodynamic limit, in a number of cases ($p>1$ open b.c.\ and $p=2$ periodic b.c., both at criticality), and analyse numerically other parts of the phase diagram.Comment: 34 pages, 10 figure

Does orthographic processing emerge rapidly after learning a new script?

Epub 2020 Aug 11Orthographic processing is characterized by location-invariant and location-specific processing (Grainger, 2018): (1) strings of letters are more vulnerable to transposition effects than the strings of symbols in same-different tasks (location-invariant processing); and (2) strings of letters, but not strings of symbols, show an initial position advantage in target-in-string identification tasks (location-specific processing). To examine the emergence of these two markers of orthographic processing, we conducted a same-different task and a target-in-string identification task with two unfamiliar scripts (pre-training experiments). Across six training sessions, participants learned to fluently read and write one of these scripts. The post-training experiments were parallel to the pre-training experiments. Results showed that the magnitude of the transposed-letter effect in the same-different task and the serial function in the target-in-string identification tasks were remarkably similar for the trained and untrained scripts. Thus, location-invariant and location-specific processing does not emerge rapidly after learning a new script; instead, they may require thorough experience with specific orthographic structures.This study was supported by the Spanish Ministry of Science, Innovation, and Universities (PRE2018-083922, PSI2017-86210-P) and by the Department of Innovation, Universities, Science and Digital Society of the Valencian Government (GV/2020/074

A Comparative Study for String Metrics and the Feasibility of Joining them as Combined Text Similarity Measures

This paper aims to introduce an optimized Damerau–Levenshtein and dice-coefficients using enumeration operations (ODADNEN) for providing fast string similarity measure with maintaining the results accuracy; searching to find specific words within a large text is a hard job which takes a lot of time and efforts. The string similarity measure plays a critical role in many searching problems. In this paper, different experiments were conducted to handle some spelling mistakes. An enhanced algorithm for string similarity assessment was proposed. This algorithm is a combined set of well-known algorithms with some improvements (e.g. the dice-coefficient was modified to deal with numbers instead of characters using certain conditions). These algorithms were adopted after conducting on a number of experimental tests to check its suitability. The ODADNN algorithm was tested using real data; its performance was compared with the original similarity measure. The results indicated that the most convincing measure is the proposed hybrid measure, which uses the Damerau–Levenshtein and dicedistance based on n-gram of each word to handle; also, it requires less processing time in comparison with the standard algorithms. Furthermore, it provides efficient results to assess the similarity between two words without the need to restrict the word length

Machine Annotation of Traditional Irish Dance Music

The work presented in this thesis is validated in experiments using 130 realworld field recordings of traditional music from sessions, classes, concerts and commercial recordings. Test audio includes solo and ensemble playing on a variety of instruments recorded in real-world settings such as noisy public sessions. Results are reported using standard measures from the field of information retrieval (IR) including accuracy, error, precision and recall and the system is compared to alternative approaches for CBMIR common in the literature

Source Code Retrieval using Case Based Reasoning

Formal verification of source code has been extensively used in the past few years in order to create dependable software systems. However, although formal languages like Spec# or JML are getting more and more popular, the set of verified implementations is very small and only growing slowly. Our work aims to automate some of the steps involved in writing specifications and their implementations, by reusing existing verified programs. That is, for a given implementation we seek to retrieve similar verified code and then reapply the missing specification that accompanies that code. In this thesis, I present the retrieval system that is part of the Arís (Analogical Reasoning for reuse of Implementation & Specification) project. The overall methodology of the Arís project is very similar to Case-Based Reasoning (CBR) and its parent discipline of Analogical Reasoning (AR), centered on the activities of solution retrieval and reuse. CBR’s retrieval phase is achieved using semantic and structural characteristics of source code. API calls are used as semantic anchors and characteristics of conceptual graphs are used to express the structure of implementations. Finally, we transfer the knowledge (i.e. formal specification) between the input implementation and the retrieved code artefacts to produce a specification for a given implementation. The evaluation results are promising and our experiments show that the proposed approach has real potential in generating formal specifications using past solutions