Learning content-based metrics for music similarity

In this abstract, we propose a method to learn application-specific content-based metrics for music similarity using unsupervised feature learning and neighborhood components analysis. Multiple-timescale features extracted from music audio are embedded into a Euclidean metric space, so that the distance between songs reflects their similarity. We evaluated the method on the GTZAN and Magnatagatune datasets

Application of the Ring Theory in the Segmentation of Digital Images

Ring theory is one of the branches of the abstract algebra that has been broadly used in images. However, ring theory has not been very related with image segmentation. In this paper, we propose a new index of similarity among images using Zn rings and the entropy function. This new index was applied as a new stopping criterion to the Mean Shift Iterative Algorithm with the goal to reach a better segmentation. An analysis on the performance of the algorithm with this new stopping criterion is carried out. The obtained results proved that the new index is a suitable tool to compare images.Comment: Very interesting new index to compute the similarity among images. arXiv admin note: substantial text overlap with arXiv:1306.262

Abstract Symbolic Automata: Mixed syntactic/semantic similarity analysis of executables

We introduce a model for mixed syntactic/semantic approximation of programs based on symbolic finite automata (SFA). The edges of SFA are labeled by predicates whose semantics specifies the denotations that are allowed by the edge. We introduce the notion of abstract symbolic finite automaton (ASFA) where approximation is made by abstract interpretation of symbolic finite automata, acting both at syntactic (predicate) and semantic (denotation) level. We investigate in the details how the syntactic and semantic abstractions of SFA relate to each other and contribute to the determination of the recognized language. Then we introduce a family of transformations for simplifying ASFA. We apply this model to prove properties of commonly used tools for similarity analysis of binary executables. Following the structure of their control flow graphs, disassembled binary executables are represented as (concrete) SFA, where states are program points and predicates represent the (possibly infinite) I/O semantics of each basic block in a constraint form. Known tools for binary code analysis are viewed as specific choices of symbolic and semantic abstractions in our framework, making symbolic finite automata and their abstract interpretations a unifying model for comparing and reasoning about soundness and completeness of analyses of low-level code

Zero energy correction method for non-Hermitian Harmonic oscillator with simultaneous transformation of co-ordinate and momentum: Wave function analysis under Iso-spectral condition

We present a complete analysis on energy and wave function of Harmonic oscillator with simultaneous non-hermitian transformation of co-ordinate ($(x \rightarrow \frac{(x+ i\lambda p)}{\sqrt{(1+\beta \lambda)}}$ and momentum $(p \rightarrow \frac{(p+ i\beta x)}{\sqrt{(1+\beta \lambda)}}$ for getting energy eigenvalue using perturbation theory under iso-spectral condition. Further we notice that two different frequency of oscillation ($w_{1}, w_{2}$)correspond to same energy eigenvalue, which can also be verified using Lie algebraic approach [Zhang et.al J.Math.Phys 56 ,072103 (2015)]. Interestingly wave function analysis using similarity transformation [F.M. Fernandez, Int. J. Theo. Phys. (2015)(in Press)] refers to a very special case.Comment: This paper for replacement .(i) Minor change in title reflecting wave function analysis(ii) Abstract-chaed suitably to refect wave function (iii) Text original work with information on wave function ,comparison and slight modification in references.Kindly accep

Information and Experience in Metaphor: A Perspective From Computer Analysis

Novel linguistic metaphor can be seen as the assignment of attributes to a topic through a vehicle belonging to another domain. The experience evoked by the vehicle is a significant aspect of the meaning of the metaphor, especially for abstract metaphor, which involves more than mere physical similarity. In this article I indicate, through description of a specific model, some possibilities as well as limitations of computer processing directed toward both informative and experiential/affective aspects of metaphor. A background to the discussion is given by other computational treatments of metaphor analysis, as well as by some questions about metaphor originating in other disciplines. The approach on which the present metaphor analysis model is based is consistent with a theory of language comprehension that includes both the intent of the originator and the effect on the recipient of the metaphor. The model addresses the dual problem of (a) determining potentially salient properties of the vehicle concept, and (b) defining extensible symbolic representations of such properties, including affective and other connotations. The nature of the linguistic analysis underlying the model suggests how metaphoric expression of experiential components in abstract metaphor is dependent on the nominalization of actions and attributes. The inverse process of undoing such nominalizations in computer analysis of metaphor constitutes a translation of a metaphor to a more literal expression within the metaphor-nonmetaphor dichotomy

Stemming Influence on Similarity Detection of Abstract Written in Indonesia

In this paper we would like to discuss about stemming effect by using Nazief and Adriani algorithm against similarity detection result of Indonesian written abstract. The contents of the publication abstract similarity detection can be used as an early indication of whether or not the act of plagiarism in a writing. Mostly in processing the text adding a pre-process, one of it which is called a stemming by changing the word into the root word in order to maximize the searching process. The result of stemming process will be changed as a certain word n-gram set then applied an analysis of similarity using Fingerprint Matching to perform similarity matching between text. Based on the F1-score which used to balance the precision and recall number, the detection that implements stemming and stopword removal has a better result in detecting similarity between the text with an average is 42%. It is higher comparing to the similarity detection by using only stemming process (31%) or the one that was done without involving the text pre-process (34%) while applying the bigram

Systematic Characterizations of Text Similarity in Full Text Biomedical Publications

Computational methods have been used to find duplicate biomedical publications in MEDLINE. Full text articles are becoming increasingly available, yet the similarities among them have not been systematically studied. Here, we quantitatively investigated the full text similarity of biomedical publications in PubMed Central.72,011 full text articles from PubMed Central (PMC) were parsed to generate three different datasets: full texts, sections, and paragraphs. Text similarity comparisons were performed on these datasets using the text similarity algorithm eTBLAST. We measured the frequency of similar text pairs and compared it among different datasets. We found that high abstract similarity can be used to predict high full text similarity with a specificity of 20.1% (95% CI [17.3%, 23.1%]) and sensitivity of 99.999%. Abstract similarity and full text similarity have a moderate correlation (Pearson correlation coefficient: -0.423) when the similarity ratio is above 0.4. Among pairs of articles in PMC, method sections are found to be the most repetitive (frequency of similar pairs, methods: 0.029, introduction: 0.0076, results: 0.0043). In contrast, among a set of manually verified duplicate articles, results are the most repetitive sections (frequency of similar pairs, results: 0.94, methods: 0.89, introduction: 0.82). Repetition of introduction and methods sections is more likely to be committed by the same authors (odds of a highly similar pair having at least one shared author, introduction: 2.31, methods: 1.83, results: 1.03). There is also significantly more similarity in pairs of review articles than in pairs containing one review and one nonreview paper (frequency of similar pairs: 0.0167 and 0.0023, respectively).While quantifying abstract similarity is an effective approach for finding duplicate citations, a comprehensive full text analysis is necessary to uncover all potential duplicate citations in the scientific literature and is helpful when establishing ethical guidelines for scientific publications