Lightweight Fingerprints for Fast Approximate Keyword Matching Using Bitwise Operations

Measuring the similarity or difference of two strings is a fundamental task in computer science and engineering. Among the various measures, Hamming distance and Levenshtein distance have been widely utilized. In this paper, we present neural networks for computing these distances, employing the Rectified Linear Unit function as the activation function. We show that the number of units in the networks is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(n)</i> for the Hamming distance between two strings of length <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i>, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O(mn)</i> for the Levenshtein distance between two strings of lengths <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i>. These complexities align with those of traditional algorithms. While the depth of the latter network is large (O(m+n)), we show that the depth can be significantly reduced at the expense of employing a larger number of units. Furthermore, we show that the networks can be modified for learning a string from a set of strings along with their respective distances to the target string. We performed computational experiments to evaluate the potential usufulness of our proposed networks. The results suggest that these networks can efficiently compute distances in a time proportional to the network size, and can proficiently learn a string when utilizing the Hamming distance.

Prevention and detection of plagiarism are crucial. There are several algorithms that can be used to detect plagiarism in documents, including the Cosine Similarity, Rabin-Karp, Levenshtein Distance, Hamming Distance, Euclidean Distance, Edit Distance, Ratcliff/Obershelp, etc. Based on the literature review from previous research, three best algorithms were identified: Cosine Similarity, Rabin-Karp, and Levenshtein Distance. However, there has been no study analyzing the comparison of these three algorithms. Therefore, this study will compare the performance of each algorithm and determine the best algorithm for plagiarism detection in documents based on similarity scores and execution time. The research objects use a sample of documents consisting of titles and abstracts from Indonesian-language informatics journals. Cosine Similarity algorithm is superior to others for plagiarism detection in documents, as it produces the highest average similarity score with a relatively fast execution time. The similarity values of Cosine Similarity, Rabin-Karp with 4 k-grams, and Levenshtein Distance are 48.80%, 47.13%, 20.61%, respectively. The average execution time of Cosine Similarity, Rabin-Karp with 4 k-grams, and Levenshtein Distance are 0.22 s, 0.45 s, and 39.15 s, respectively

Approximate pattern matching is a fundamental problem in the bioinformatics and information retrieval applications. The problem involves different matching relations such as Hamming distance, edit distances, and the wildcards matching problem. The input is usually a text of length n over a fixed alphabet of length Σ, a pattern of length m, and an integer k. The output is to find all positions that have ≤ k Hamming distance, edit distance, or wildcards matching with P. Many algorithms and indexes have been proposed to solve the problems more efficiently, but due to the space and time complexities of the problems, most tools adopted heuristics approaches based on, for instance, suffix tree, suffix array, or Burrows Wheeler Transform to reach practical implementations. Error Tree is a novel tree structure that is mainly oriented to solve the approximate pattern matching problems, using less space and faster computation time. The algorithm proposes for Hamming distance and wildcards matching a tree structure that needs [Formula: see text] words and takes [Formula: see text] in the average case) of query time for any online/offline pattern, where occ is the number of outputs. In addition, a tree structure of [Formula: see text] words and [Formula: see text] in the average case) query time for edit distance for any online/offline pattern.

This paper presents an efficient IrisCode classifier, built from phase features which uses AdaBoost for the selection of Gabor wavelets bandwidths. The final iris classifier consists of a weighted contribution of weak classifiers. As weak classifiers we use 3-split decision trees that identify a candidate based on the Levenshtein distance between phase vectors of the respective iris images. Our experiments show that the Levenshtein distance has better discrimination in comparing IrisCodes than the Hamming distance. Our process also differs from existing methods because the wavelengths of the Gabor filters used, and their final weights in the decision function, are chosen from the robust final classifier, instead of being fixed and/or limited by the programmer, thus yielding higher iris recognition rates. A pyramidal strategy for cascading filters with increasing complexity makes the system suitable for realtime operation.

Structural Off-line Handwriting Character Recognition Using Approximate Subgraph Matching and Levenshtein Distance

A k-nearest neighbor (k-NN) query retrieves k objects from a database that are considered to be the closest to a given query point. Numerous techniques have been proposed in the past for supporting efficient k-NN searches in continuous data spaces. No such work has been reported in the literature for k-NN searches in a non-ordered discrete data space (NDDS). Performing k-NN searches in an NDDS raises new challenges. The Hamming distance is usually used to measure the distance between two vectors (objects) in an NDDS. Due to the coarse granularity of the Hamming distance, a k-NN query in an NDDS may lead to a large set of candidate solutions, creating a high degree of non-determinism for the query result. We propose a new distance measure, called granularity-enhanced Hamming (GEH) distance, that effectively reduces the number of candidate solutions for a query. We have also considered using multidimensional database indexing for implementing k-NN searches in NDDSs. Our experiments on synthetic and genomic data sets demonstrate that our index-based k-NN algorithm is effective and efficient in finding k-NNs in NDDSs.

Volume formula and growth rates of the balls of strings under the edit distances

We consider the streaming complexity of a fundamental task in approximate pattern matching: the $k$-mismatch problem. It asks to compute Hamming distances between a pattern of length $n$ and all length-$n$ substrings of a text for which the Hamming distance does not exceed a given threshold $k$. In our problem formulation, we report not only the Hamming distance but also, on demand, the full \emph{mismatch information}, that is the list of mismatched pairs of symbols and their indices. The twin challenges of streaming pattern matching derive from the need both to achieve small working space and also to guarantee that every arriving input symbol is processed quickly. We present a streaming algorithm for the $k$-mismatch problem which uses $O(k\log{n}\log\frac{n}{k})$ bits of space and spends \ourcomplexity time on each symbol of the input stream, which consists of the pattern followed by the text. The running time almost matches the classic offline solution and the space usage is within a logarithmic factor of optimal. Our new algorithm therefore effectively resolves and also extends an open problem first posed in FOCS'09. En route to this solution, we also give a deterministic $O( k (\log \frac{n}{k} + \log |\Sigma|) )$-bit encoding of all the alignments with Hamming distance at most $k$ of a length-$n$ pattern within a text of length $O(n)$. This secondary result provides an optimal solution to a natural communication complexity problem which may be of independent interest.

The need for approximate rather than exact search arises in numerous compare-intensive applications, from networking to computational genomics. This brief presents a novel sensing approach for approximate matching content-addressable memory (CAM) designed to handle large Hamming distances (HDs) between the query pattern and stored data. The proposed matchline sensing scheme (MLSS) employs a replica mechanism and a 12-transistor positive feedback sense amplifier to effectively resolve the approximate match operation. The MLSS was integrated into a 4kB approximate CAM array and fabricated in a 65nm CMOS technology. With an overall area footprint of 0.0048mm, which includes 512 sense amplifiers and the replica mechanism, the MLSS allows a flexible and dynamic adjustment of the HD tolerance threshold via several design variables. Experimental measurements demonstrate the efficiency of our sensing scheme in tolerating very large HDs with the highest sensitivity.

Genomic data mining and knowledge extraction is an important problem in bioinformatics. Some research work has been done on unknown genome identification and is based on exact pattern matching of n-grams. In most of the real world biological problems exact matching may not give desired results and the problem in using n-grams is exponential explosion. In this paper we propose a method for genome data classification based on approximate matching. The algorithm works by selecting random samples from the genome database. Tolerance is allowed by generating candidates of varied length to query from these sample sequences. The Levenshtein distance is then checked for each candidate and whether they are k-fuzzily equal. The total number of fuzzy matches for each sequence is then calculated. This is then classified using the data mining techniques namely, naive Bayes, support vector machine, back propagation and also by nearest neighbor. Experiment results are provided for different tolerance levels and they show that accuracy increases as tolerance does. We also show the effect of sampling size on the classification accuracy and it was observed that classification accuracy increases with sampling size. Genome data of two species namely Yeast and E. coli are used to verify proposed method.

In this paper we consider the problem of non-metric/qualitative environment representation for frescoes based autonomous robot navigation. Within this framework, the problem of selecting meaningful set of frescoes and some possible solutions are analysed and compared. Experimental results using six different approaches (resemblance, barycentre, Hamming and Levenshtein distances, cross-correlation and selforganizing feature map neural network) are also provided

SID channels are discrete channels represented by expressions that involve combinations of the error types substitution, insertion, and deletion. Based on the SID channel model, a simple distance is defined that generalizes the Hamming and Levenshtein distances. For a certain class of SID channels, the distance is used to obtain a unifying necessary and sufficient condition for the error correcting capability that corresponds to the channel in question. Moreover, it is shown that for many SID channels whose expressions include the insertion type their error-correcting codes coincide with those for SID channels whose expressions result by removing the insertion type or by replacing it with the deletion type.

On the bottleneck tree alignment problems

In the approximate pattern matching problem, given a text $T$ , a pattern $P$ , and a threshold $k$ , the task is to find (the starting positions of) all substrings of $T$ that are at distance at most $k$ from $P$ . We consider the two most fundamental string metrics: Under the Hamming distance, we search for substrings of $T$ that have at most $k$ mismatches with $P$ , while under the edit distance, we search for substrings of $T$ that can be transformed to $P$ with at most $k$ edits. Exact occurrences of $P$ in $T$ have a very simple structure: If we assume for simplicity that $\vert P\vert and that $P$ occurs both as a prefix and as a suffix of $T$ , then both $P$ and $T$ are periodic with a common period. However, an analogous characterization for occurrences with up to $k$ mismatches was proved only recently by Bringmann et al. [SODA'19]: Either there are $\mathcal{O}(k^{2}) k$ -mismatch occurrences of $P$ in $T$ , or both $P$ and $T$ are at Hamming distance $\mathcal{O}(k)$ from strings with a common string period of length $\mathcal{O}(m/k)$ . We tighten this characterization by showing that there are $\mathcal{O}(k) k$ -mismatch occurrences in the non-periodic case, and we lift it to the edit distance setting, where we tightly bound the number of $k$ -edit occurrences by $\mathcal{O}(k^{2})$ in the non-periodic case. Our proofs are constructive and let us obtain a unified framework for approximate pattern matching for both considered distances. In particular, we provide meta-algorithms that only rely on a small set of primitive operations. We showcase the generality of our meta-algorithms with results for the fully compressed setting, the dynamic setting, and the standard setting.

Named entity recognition (NER) plays a vital role in extracting critical medical entities from health care records, facilitating applications such as clinical decision support and data mining. Developing robust NER models for low-resource languages, such as Estonian, remains a challenge due to the scarcity of annotated data and domain-specific pretrained models. Large language models (LLMs) have proven to be promising in understanding text from any language or domain. This study addresses the development of medical NER models for low-resource languages, specifically Estonian. We propose a novel approach by generating synthetic health care data and using LLMs to annotate them. These synthetic data are then used to train a high-performing NER model, which is applied to real-world medical texts, preserving patient data privacy. Our approach to overcoming the shortage of annotated Estonian health care texts involves a three-step pipeline: (1) synthetic health care data are generated using a locally trained GPT-2 model on Estonian medical records, (2) the synthetic data are annotated with LLMs, specifically GPT-3.5-Turbo and GPT-4, and (3) the annotated synthetic data are then used to fine-tune an NER model, which is later tested on real-world medical data. This paper compares the performance of different prompts; assesses the impact of GPT-3.5-Turbo, GPT-4, and a local LLM; and explores the relationship between the amount of annotated synthetic data and model performance. The proposed methodology demonstrates significant potential in extracting named entities from real-world medical texts. Our top-performing setup achieved an F1-score of 0.69 for drug extraction and 0.38 for procedure extraction. These results indicate a strong performance in recognizing certain entity types while highlighting the complexity of extracting procedures. This paper demonstrates a successful approach to leveraging LLMs for training NER models using synthetic data, effectively preserving patient privacy. By avoiding reliance on human-annotated data, our method shows promise in developing models for low-resource languages, such as Estonian. Future work will focus on refining the synthetic data generation and expanding the method's applicability to other domains and languages.