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The task of constructing test sequences difference characteristics was studied. Its relevance for generating controlled random tests and complexity in finding difference measures for the case of symbolic tests were substantiated. The limitations of using traditional distance characteristics to obtain a measure of the difference between test sets are shown. For the binary case, a new measure of the difference MH(Ti, Tk) of two character test sets Ti and Tk is defined based on the classical Hamming distance. This measure represents n components, each of which is determined by the Hamming distance between the binary set Ti and the pattern Tk cyclically shifted by v bits. The main properties of the proposed dissimilarity measure are reviewed and its effectiveness for classifying test candidates when generating controlled random tests is shown. Experimental results are presented that confirm the effectiveness of the proposed difference measure.

Animals have evolved a wide variety of signals that punctuate social interactions, thus optimizing communication systems. In the study of communicative strategies, real and play fighting are good models, as they are associated with risks and injuries. Therefore, within these two domains, clear 'statement' signals should be recruited to disambiguate messages. We gathered video data (135 h) from 38 wolves from three mixed-age captive groups (Canis lupus arctos, C. l. lupus, C. l. occidentalis) and analysed all the facial expressions in aggressive and playful domains. The analyses revealed the presence of three different threatening faces (Light-, Medium- and High-TF), mainly performed during aggressive encounters, which differed in the degree of mouth opening and lip stretching. We also identified two different relaxed open mouth facial expressions (Full- and Half-ROM) exclusively performed during play and possibly signalling different levels of playful arousal. Interestingly, facial expressions did not differ between groups thus suggesting a hard-wired facial communication system at least in these two domains. The next step will be to test hypotheses on the efficacy of such facial displays in eliciting an appropriate response in the receivers, potentially translating into a fine modulation of behavioural patterns in both play and real fighting. • Wolf facial displays differ strongly in playful and aggressive contexts. • During aggression three different threatening faces can be present. • Wolves display two different playful facial displays. • Playful and aggressive facial displays always differ in the same key elements. • Play and aggressive facial displays were consistent across three wolf groups. [ABSTRACT FROM AUTHOR]

Abstract Background Protein-protein interactions (PPIs) hold significant importance in biology, with precise PPI prediction as a pivotal factor in comprehending cellular processes and facilitating drug design. However, experimental determination of PPIs is laborious, time-consuming, and often constrained by technical limitations. Methods We introduce a new node representation method based on initial information fusion, called FFANE, which amalgamates PPI networks and protein sequence data to enhance the precision of PPIs’ prediction. A Gaussian kernel similarity matrix is initially established by leveraging protein structural resemblances. Concurrently, protein sequence similarities are gauged using the Levenshtein distance, enabling the capture of diverse protein attributes. Subsequently, to construct an initial information matrix, these two feature matrices are merged by employing weighted fusion to achieve an organic amalgamation of structural and sequence details. To gain a more profound understanding of the amalgamated features, a Stacked Autoencoder (SAE) is employed for encoding learning, thereby yielding more representative feature representations. Ultimately, classification models are trained to predict PPIs by using the well-learned fusion feature. Results When employing 5-fold cross-validation experiments on SVM, our proposed method achieved average accuracies of 94.28%, 97.69%, and 84.05% in terms of Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori datasets, respectively. Conclusion Experimental findings across various authentic datasets validate the efficacy and superiority of this fusion feature representation approach, underscoring its potential value in bioinformatics.

Abstract Topic modeling methods proved to be effective for inferring latent topics from short texts. Dealing with short texts is challenging yet helpful for many real-world applications, due to the sparse terms in the text and the high dimensionality representation. Most of the topic modeling methods require the number of topics to be defined earlier. Similarly, methods based on Dirichlet Multinomial Mixture (DMM) involve the maximum possible number of topics before execution which is hard to determine due to topic uncertainty, and many noises exist in the dataset. Hence, a new approach called the Topic Clustering algorithm based on Levenshtein Distance (TCLD) is introduced in this paper, TCLD combines DMM models and the Fuzzy matching algorithm to address two key challenges in topic modeling: (a) The outlier problem in topic modeling methods. (b) The problem of determining the optimal number of topics. TCLD uses the initial clustered topics generated by DMM models and then evaluates the semantic relationships between documents using Levenshtein Distance. Subsequently, it determines whether to keep the document in the same cluster, relocate it to another cluster, or mark it as an outlier. The results demonstrate the efficiency of the proposed approach across six English benchmark datasets, in comparison to seven topic modeling approaches, with 83% improvement in purity and 67% enhancement in Normalized Mutual Information (NMI) across all datasets. The proposed method was also applied to a collected Arabic tweet and the results showed that only 12% of the Arabic short texts were incorrectly clustered, according to human inspection.

Dialectometry is a computational, quantitative and statistical approach, in which linguistic differences, in a selected geographical area, are examined by using specific methods and techniques. In the present study, the linguistic distances between varieties, common in the area from the south of Hamadan province to the north of Khuzestan province, and their regional distributions, have been studied by using a novel dialectometric approach. These language varieties are mostly Laki and Lori. This study is done in a library and field work method. Therefore, to do that, the distances between the equivalents of 100 words in 80 locations are measured, using Levenshtein distance which is included in RuG/L04software. After the analyzing of the linguistic distances, the outputs are presented in the forms of interpretable maps, diagrams and statistical analysis. The main results of this study are: 1. The clustering of language varieties and 2. The manner of their linguistic-geographical distribution through a linguistic continuum. Also, the study of phonetic-lexical differences between the collected linguistic data, confirms the nature of continuity of these linguistic varieties. Thus, at one end of this continuum, Laki and Lori Lorestani varieties and at the other end, Lori Bakhtiari varieties are locate Introduction Language has a continuous nature, making it difficult to establish clear-cut boundaries between different language varieties. Unlike traditional and qualitative approaches used in dialectology, which often relied on subjective methods, modern dialectometric methods should be employed due to the continuous nature of language. Hence, it is unfeasible to ascertain precise linguistic demarcations among language variations. Quantitative methods in dialectology differ from traditional methods. Traditional approaches rely on the assumptions and linguistic features of the native speakers to determine the distribution and spread of language varieties in a particular geographic region. One of the disadvantages of these methods is the absence of consistent overlap between isoglosses and the utilization of personal preferences and opinions in choosing bundles of isoglosses. Quantitative approaches offer several advantages over traditional approaches. The advantage of using quantitative approaches compared to traditional approaches is the digital classification of data, automatic measurement of distances and frequencies, digital mapping of outputs and providing statistical analysis of linguistic data, which leads to the analysis of a large amount of linguistic data without the personal preferences of the individual researcher. Consequently, this study involves the aggregate analysis of a vast amount of linguistic data through quantitative methods, specifically linguistic distance measurement. The application of quantitative approaches in dialectology research, along with various dialectometric techniques and the assessment of linguistic distances in non-Iranian studies, can be traced back to the works of Seguy (1973) and Goebel (1982). Over the past ten years, dialectometry in Iran has become a focal point for numerous researchers specializing in Iranian languages and dialects. These Studies have been carried out on various common language varieties found in regions such as East Azarbaijan, West Azarbaijan, Hamadan, Mazandaran, Gorgan, Yazd, Ilam, Cherdaval and Talesh. The preservation of a society's identity and cultural heritage is closely intertwined with the study of languages and dialects. As a result, researchers and linguists must prioritize conducting methodical studies in this field. Historically, studies on Iranian language varieties have been conducted in isolation, focusing solely on specific linguistic aspects such as phonology, morphology or syntax. Thus, numerous studies have been carried out thus far, employing both traditional and scientific methodologies, to explore different facets of language varieties (Lori and Laki) common in the examined geographical region. These investigations have primarily focused on phonology, morphology and syntax. It is important to note that these studies are solely descriptive and qualitative in nature, lacking any comparative or quantitative research elements. Furthermore, up to now, few studies have addressed the matter of comparing different linguistic variations with a comprehensive approach, aiming to establish a systematic correlation between linguistic varieties. Their efforts have focused on creating a linguistic atlas and developing a meticulous, scientific, and well-organized classification system for these variations. Hence, it is imperative to carry out a linguistic investigation employing a dialectometric methodology to employ contemporary analytical-computational techniques on prevalent language variations in Lorestan province and its adjacent provinces, namely Hamadan and Khuzestan. The rationale behind selecting this specific geographical scope is the extensive usage of both Lori and Laki dialects within these territories. Consequently, it holds significant value to ascertain the linguistic-geographical dispersion of these language varieties in the aforementioned regions, disregarding any geographical limitations.Methodology This study is a synchronic descriptive-analytical investigation. The RuG/L04 dialectometry and cartography software package was employed to conduct this study. Initially, 100 lexical entries were gathered from 80 different locations. The research database was sourced from three national dialectology projects in Lorestan and Khuzestan provinces, as well as from the field research conducted by the researchers. The participants in this study encompass both males and females, ranging in age from 20 to 70 years, with an average educational attainment of a high school diploma. After transcribing 8000 lexical forms, the geographical coordinates for each location were determined by utilizing Google Earth software. Subsequently, in order to calculate the linguistic distance index, Levenshtein distance algorithm was applied to the data as one of the aggregate analysis approaches. The resulting distance, obtained from the 80x80 matrix, represents a quantitative index within the range of natural numbers. In the subsequent phase of the study, diverse subprograms were employed to categorize the acquired language types. These categorizations are then presented in the form of diagrams, tables, and maps.Results Upon analyzing the acquired outcomes, it was discovered that the linguistic variations being investigated form a continuous language continuum, devoid of any distinct boundaries (in contrast to traditional dialectology approaches). This continuum commences from the Laki and Lori varieties of Lorestan in the southern region of Hamadan province, extending all the way to the Bakhtiari varieties in the northern part of Khuzestan province. The attribute of continuity is also evident in the phonetic differences and alternations observed among the three primary language varieties, namely Laki, Lori Bakhtiari, and Lori Lorestani.Conclusion The greater language distance and difference between Laki and Lori varieties (as indicated in equation 2.5) confirms the belonging of each of these varieties to a different language family; Also, due to belonging to a common language family (Southwestern Iranian), there is less linguistic distance (equal to 1.5) and more linguistic similarity between two varieties of Lori. The obtained Pearson correlation coefficient between the linguistic varieties under study is r=0.88, indicating a strong and statistically significant correlation percentage. This validates the findings of the research and highlights the effectiveness of utilizing Levenshtein's distance dialectometric approach in identifying the primary linguistic clusters of the examined varieties and confirming the continuity nature of the language.

Topic modeling methods proved to be effective for inferring latent topics from short texts. Dealing with short texts is challenging yet helpful for many real-world applications, due to the sparse terms in the text and the high dimensionality representation. Most of the topic modeling methods require the number of topics to be defined earlier. Similarly, methods based on Dirichlet Multinomial Mixture (DMM) involve the maximum possible number of topics before execution which is hard to determine due to topic uncertainty, and many noises exist in the dataset. Hence, a new approach called the Topic Clustering algorithm based on Levenshtein Distance (TCLD) is introduced in this paper, TCLD combines DMM models and the Fuzzy matching algorithm to address two key challenges in topic modeling: (a) The outlier problem in topic modeling methods. (b) The problem of determining the optimal number of topics. TCLD uses the initial clustered topics generated by DMM models and then evaluates the semantic relationships between documents using Levenshtein Distance. Subsequently, it determines whether to keep the document in the same cluster, relocate it to another cluster, or mark it as an outlier. The results demonstrate the efficiency of the proposed approach across six English benchmark datasets, in comparison to seven topic modeling approaches, with 83% improvement in purity and 67% enhancement in Normalized Mutual Information (NMI) across all datasets. The proposed method was also applied to a collected Arabic tweet and the results showed that only 12% of the Arabic short texts were incorrectly clustered, according to human inspection. [ABSTRACT FROM AUTHOR]

This paper studies the cardinality of codes correcting insertions and deletions. We give improved upper and lower bounds on code size. Our upper bound is obtained by utilizing the asymmetric property of list decoding for insertions and deletions and can be seen as analogous to the Elias bound in the Hamming metric. Our non-asymptotic bound is better than the existing bounds when the minimum Levenshtein distance is relatively large. The asymptotic bound exceeds the Elias and the MRRW bounds adapted from the Hamming-metric bounds for the binary and the quaternary cases. Our lower bound improves on the bound by Levenshtein, but its effect is limited and vanishes asymptotically. [ABSTRACT FROM AUTHOR]

In this research endeavor, our primary objective revolves around the intricate task of matching schemas across diverse databases. The challenge lies in recognizing the inherent relationships between attributes and employing sophisticated machine learning algorithms as our guide. Doing so aims to identify attributes that harbor comparable or akin values, thus facilitating potential mappings between the two databases for seamless integration. Our investigation delves into the complexities of mapping scenarios, encompassing the conventional one-to-one mappings and the intricate scenarios where a single attribute can map to multiple attributes. Within the scope of this report, an in-depth exploration of one-to-one mapping utilizing the powerful Kohonen Self-Organizing Maps is presented. The theoretical underpinnings of this approach and the experiments conducted to evaluate its efficacy are also elucidated. The outcomes and insights gained from these experiments are meticulously documented and presented. By unraveling the intricate nature of schema matching using machine learning techniques, this research contributes to the ever-growing body of knowledge in this domain. Our findings shed light on the potential of employing advanced algorithms to tackle the challenges of schema matching, paving the way for enhanced database integration. [ABSTRACT FROM AUTHOR]

A web-based Java programming learning assistant system (JPLAS) has been developed for novice students to study Java programming by themselves while enhancing code reading and code writing skills. One type of the implemented exercise problem is code writing problem (CWP), which asks students to create a source code that can pass the given test code. The correctness of this answer code is validated by running them on JUnit. In previous works, a Python-based answer code validation program was implemented to assist teachers. It automatically verifies the source codes from all the students for one test code, and reports the number of passed test cases by each code in the CSV file. While this program plays a crucial role in checking the correctness of code behaviors, it cannot detect code plagiarism that can often happen in programming courses. In this paper, we implement a code plagiarism checking function in the answer code validation program, and present its application results to a Java programming course at Okayama University, Japan. This function first removes the whitespace characters and the comments using the regular expressions. Next, it calculates the Levenshtein distance and similarity score for each pair of source codes from different students in the class. If the score is larger than a given threshold, they are regarded as plagiarism. Finally, it outputs the scores as a CSV file with the student IDs. For evaluations, we applied the proposed function to a total of 877 source codes for 45 CWP assignments submitted from 9 to 39 students and analyzed the results. It was found that (1) CWP assignments asking for shorter source codes generate higher scores than those for longer codes due to the use of test codes, (2) proper thresholds are different by assignments, and (3) some students often copied source codes from certain students. [ABSTRACT FROM AUTHOR]

A web-based Java programming learning assistant system (JPLAS) has been developed for novice students to study Java programming by themselves while enhancing code reading and code writing skills. One type of the implemented exercise problem is code writing problem (CWP), which asks students to create a source code that can pass the given test code. The correctness of this answer code is validated by running them on JUnit. In previous works, a Python-based answer code validation program was implemented to assist teachers. It automatically verifies the source codes from all the students for one test code, and reports the number of passed test cases by each code in the CSV file. While this program plays a crucial role in checking the correctness of code behaviors, it cannot detect code plagiarism that can often happen in programming courses. In this paper, we implement a code plagiarism checking function in the answer code validation program, and present its application results to a Java programming course at Okayama University, Japan. This function first removes the whitespace characters and the comments using the regular expressions. Next, it calculates the Levenshtein distance and similarity score for each pair of source codes from different students in the class. If the score is larger than a given threshold, they are regarded as plagiarism. Finally, it outputs the scores as a CSV file with the student IDs. For evaluations, we applied the proposed function to a total of 877 source codes for 45 CWP assignments submitted from 9 to 39 students and analyzed the results. It was found that (1) CWP assignments asking for shorter source codes generate higher scores than those for longer codes due to the use of test codes, (2) proper thresholds are different by assignments, and (3) some students often copied source codes from certain students.

Establishing the phonological Levenshtein distance (PLD) of words and pseudowords is useful for various psycholinguistic research applications, such as generating stimuli for experiments on language processing or analysing the PLD between erroneous and intended utterances. PLD2flex is a tool for establishing the PLD of (pseudo)words in pairwise, one-to-many and many-to-many comparisons of orthographic word forms, including one-to-many comparisons of (pseudo)word forms with databases of words generated from corpora. Specifically, one-to-many comparisons can be used to determine the average distance of a word to its 20 closest neighbours (PLD20), in analogy to the OLD20-measure for written words. PLD2flex makes use of the BAS Web Services API and several third-party python libraries. PLD2flex is freely available at https://github.com/FelixTheodor/PLD2flex.

Arabic Handwritten Character Recognition (AHCR) systems encounter various challenges arising from the unique characteristics of the Arabic language and the limited availability of public databases. Consequently, numerous research endeavors have aimed to enhance the recognition accuracy of AHCR. In this study, we propose a solution inspired by the intricate functions of the human visual cortex and hippocampus. Our proposed system employs a segmentation method to break down Arabic characters, and a Convolutional Neural Network (CNN) is then utilized for character recognition. To recognize entire words, we employ the Levenshtein distance with a personalized database containing an extensive collection of Arabic words. Experimental results demonstrate that our system yields a word error rate ranging from 1% to 25%, contingent upon the number of accurately recognized characters. [ABSTRACT FROM AUTHOR]

Recently, artificial intelligence and data science have witnessed dramatic progress and rapid growth, especially Automatic Speech Recognition (ASR) technology based on Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs). Consequently, new end-to-end Recurrent Neural Network (RNN) toolkits were developed with higher speed and accuracy that can often achieve a Word Error Rate (WER) below 10%. These toolkits can nowadays be deployed, for instance, within aircraft cockpits and Air Traffic Control (ATC) systems in order to identify aircraft and display recognized voice messages related to flight data, especially for airports not equipped with radar. Hence, the performance of air traffic controllers and pilots can ultimately be improved by reducing workload and stress and enforcing safety standards. Our experiment conducted at Tangier's International Airport ATC aimed to build an ASR model that is able to recognize aircraft call signs in a fast and accurate way. The acoustic and linguistic models were trained on the Ibn Battouta Speech Corpus (IBSC), resulting in an unprecedented speech dataset with approved transcription that includes real weather aerodrome observation data and flight information with a call sign captured by an ADS-B receiver. All of these data were synchronized with voice recordings in a structured format. We calculated the WER to evaluate the model's accuracy and compared different methods of dataset training for model building and adaptation. Despite the high interference in the VHF radio communication channel and fast-speaking conditions that increased the WER level to 20%, our standalone and low-cost ASR system with a trained RNN model, supported by the Deep Speech toolkit, was able to achieve call sign detection rate scores up to 96% in air traffic controller messages and 90% in pilot messages while displaying related flight information from ADS-B data using the Fuzzy string-matching algorithm. [ABSTRACT FROM AUTHOR]

Dialectometry is a computational, quantitative and statistical approach, in which linguistic differences, in a selected geographical area, are examined by using specific methods and techniques. In the present study, the linguistic distances between varieties, common in the area from the south of Hamadan province to the north of Khuzestan province, and their regional distributions, have been studied by using a novel dialectometric approach. These language varieties are mostly Laki and Lori. This study is done in a library and field work method. Therefore, to do that, the distances between the equivalents of 100 words in 80 locations are measured, using Levenshtein distance which is included in RuG/L04 software. After the analyzing of the linguistic distances, the outputs are presented in the forms of interpretable maps, diagrams and statistical analysis. The main results of this study are: 1. The clustering of language varieties and 2. The manner of their linguisticgeographical distribution through a linguistic continuum. Also, the study of phoneticlexical differences between the collected linguistic data, confirms the nature of continuity of these linguistic varieties. Thus, at one end of this continuum, Laki and Lori Lorestani varieties and at the other end, Lori Bakhtiari varieties are located. [ABSTRACT FROM AUTHOR]

Nonhuman primate vocalizations have been traditionally described as stereotyped and most likely genetically determined. However, there is increasing evidence of flexibility with a wide variety of species demonstrating the ability to change aspects of their calls, such as note order and phrase duration. We assess patterns of variation in note order and temporal features in wild siamang (Symphalangus syndactylus) phrases. We used three types of analyses on 1,015 ululating scream phrases (US-II), containing 21,609 notes, produced by ten siamang groups at three field sites. First, to assess similarity or differences in the organization of notes in the US-II phrase, we calculated the Levenshtein distance (LD), which quantifies the similarity of sequences of strings through the number of insertions, deletions, and substitutions required to transform one into the other. Second, we used a supervised classification approach to see how well we could assign US-II phrases to their groups and sites. Third, we compared five unsupervised clustering algorithms to investigate the tendency to cluster in US-II phrase types. The note order of the US-II phrase was variable shown by relatively large LD values; there also were significant mean differences between groups. Supervised classification using support vector machine and leave-one-out cross-validation returned 89.5% accuracy for site and 53.1% for group. For unsupervised clustering, the most stable solution based on the Silhouette coefficient returned four unique clusters or classes of the US-II phrase in our dataset. Our findings indicate high levels of intragroup variation in the US-II phrase and are consistent with previous reports on siamang vocalizations being flexible. Quantifying variation in the siamang US-II phrase provides a crucial first step in understanding the evolutionary forces that shaped these signals and the potential information they convey. [ABSTRACT FROM AUTHOR]

Regular Expressions (RegEx) can be employed as a technique for supervised learning to define and search for specific patterns inside text. This work devised a method that utilizes regular expressions to convert the reference style of academic papers into several styles, dependent on the specific needs of the target publication or conference. Our research aimed to detect distinctive patterns of reference styles using RegEx and compare them with a dataset including various reference styles. We gathered a diverse range of reference format categories, encompassing seven distinct classes, from various sources such as academic papers, journals, conference proceedings, and books. Our approach involves employing RegEx to convert one referencing format to another based on the user's specific preferences. The proposed model demonstrated an accuracy of 57.26% for book references and 57.56% for journal references. We used the similarity ratio and Levenshtein distance to evaluate the dataset's performance. The model achieved a 97.8% similarity ratio with a Levenshtein distance of 2. Notably, the APA style for journal references yielded the best results. However, the effectiveness of the extraction function varies depending on the reference style. For APA style, the model showed a 99.97% similarity ratio with a Levenshtein distance of 1. Overall, our proposed model outperforms baseline machine learning models in this task. This study introduces an automated program that utilizes regular expressions to modify academic reference formats. This will enhance the efficiency, precision, and adaptability of academic publishing.

In this paper, the MEA algorithm is applied to the initial optimization of the weight threshold of BP neural network, which improves the stability of the classification of course resources through a two-stage optimization search process. Secondly, the effective recognition of knowledge point entities of teaching resources is realized by combining the LSTM-CRF entity recognition model, the BERT pre-trained language model and the text data enhancement technique based on the maximum bi-directional matching algorithm. In addition, the article proposes a candidate knowledge point entity generation method based on Levenshtein Distance and an entity characterization method based on BERT-TextCNN, which helps to alleviate the noise problem of the text data of curriculum resources and enhance the entity linking effect. The results of the study show that this newly constructed online course resource repository can significantly improve the allocation level of educational resources and reduce the variability of resource allocation among colleges and universities. The coefficient of educational resource allocation variation for SN217 in the university district was reduced from 0.764 to 0.518.In addition, the mean values of this repository in terms of learning path, perceived usefulness, course resources, and intention to use ranged from 1.45 to 1.66.The study in this paper provides valuable methods and insights for optimizing online educational resources.

Regular Expressions (RegEx) can be employed as a technique for supervised learning to define and search for specific patterns inside text. This work devised a method that utilizes regular expressions to convert the reference style of academic papers into several styles, dependent on the specific needs of the target publication or conference. Our research aimed to detect distinctive patterns of reference styles using RegEx and compare them with a dataset including various reference styles. We gathered a diverse range of reference format categories, encompassing seven distinct classes, from various sources such as academic papers, journals, conference proceedings, and books. Our approach involves employing RegEx to convert one referencing format to another based on the user's specific preferences. The proposed model demonstrated an accuracy of 57.26% for book references and 57.56% for journal references. We used the similarity ratio and Levenshtein distance to evaluate the dataset's performance. The model achieved a 97.8% similarity ratio with a Levenshtein distance of 2. Notably, the APA style for journal references yielded the best results. However, the effectiveness of the extraction function varies depending on the reference style. For APA style, the model showed a 99.97% similarity ratio with a Levenshtein distance of 1. Overall, our proposed model outperforms baseline machine learning models in this task. This study introduces an automated program that utilizes regular expressions to modify academic reference formats. This will enhance the efficiency, precision, and adaptability of academic publishing. [ABSTRACT FROM AUTHOR]

A large body of research seeking to explore how form affects lexical processing in bilinguals has suggested that orthographically similar translations (e.g., English-Portuguese "paper-papel") are responded to more quickly and accurately than words with little to no overlap (e.g., English-Portuguese "house-casa"). One of the most prominent algorithms to estimate orthographic similarity, the normalized Levenshtein distance (NLD), returns an index of the proportion of identical characters of two strings, and is an efficient and invaluable tool for the selection, manipulation, and control of verbal stimuli. Notwithstanding its many advantages for second-language research, the absence of a comparable measure for phonology has resulted in the adoption of different strategies to assess the degree of interlanguage phonological similarity across the literature, with profound implications for the interpretation of results on the relative role of orthographic and phonological similarity in bilingual lexical access. In the present work, we introduce PHOR-in-One, a multilingual lexical database with a set of phonological and orthographic NLD estimates for 6160 translation equivalents in American and British English, European Portuguese, German and Spanish in a total of 30,800 words. We also propose a new measure of phonographic NLD, a pooled index of orthographic and phonological similarity, particularly useful for researchers interested in controlling for and/or manipulating both estimates at once. PHOR-in-One includes a comprehensive characterization of its lexical entries, namely Part-of-Speech-dependent and independent frequency counts, number of letters and phonemes, and phonetic transcription. PHOR-in-One is thus a valuable tool to support bilingual and multilingual research. [ABSTRACT FROM AUTHOR]

This study focuses on the extraction and semantic analysis of data from tables, emphasizing the importance of understanding the semantics of tables to obtain useful information. The main goal was to develop a technology using the ontology for the semantic analysis of tables. An iterative algorithm has been proposed that can parse the contents of a table and determine cell types based on the ontology. The study presents an automated method for extracting data in various languages in various fields, subject to the availability of an appropriate ontology. Advanced techniques such as cosine distance search and table subject classification based on a neural network have been integrated to increase efficiency. The result is a software application capable of semantically classifying tabular data, facilitating the rapid transition of information from tables to ontologies. Rigorous testing, including 30 tables in the field of water resources and socio-economic indicators of Kazakhstan, confirmed the reliability of the algorithm. The results demonstrate high accuracy with a notable triple extraction recall of 99.4%. The use of Levenshtein distance for matching entities and ontology as a source of information was key to achieving these metrics. The study offers a promising tool for efficiently extracting data from tables. [ABSTRACT FROM AUTHOR]

The problem of application of the dissimilarity measures for binary test sequences is investigated. Their relevance in generating controlled random tests is substantiated. Dissimilarity measure AD(Ti, Tk) between test sets Ti and Tk is considered, using the characteristic of the distance D(ti,j, tk,r) between ti,j and tk,r, which is based on the determination of independent pairs of identical data ti,j = tk,r belonging to two patterns Ti and Tk. This measure AD(Ti, Tk) allows us to estimate the degree of difference between two test sets Ti and Tk, which may be indistinguishable when using other difference measures, including the Hamming distance. Upper and lower estimates for the measurement of dissimilarity are obtained for the case of inverse test patterns and arbitrary test patterns Ti and Tk with different combinations of their weights wi and wk. Examples of calculating the boundary values of the specified dissimilarity measure and the ratio of their values are given. Experimental results confirm the correctness of the obtained boundary values of the indicated dissimilarity measure AD(Ti, Tk) and show the possibility of their application for its evaluation.

Abstract Motivation Clustering of genetic sequences is one of the key parts of bioinformatics analyses. Resulting phylogenetic trees are beneficial for solving many research questions, including tracing the history of species, studying migration in the past, or tracing a source of a virus outbreak. At the same time, biologists provide more data in the raw form of reads or only on contig-level assembly. Therefore, tools that are able to process those data without supervision need to be developed. Results In this paper, we present a tool for reference-free phylogeny capable of handling data where no mature-level assembly is available. The tool allows distance calculation for raw reads, contigs, and the combination of the latter. The tool provides an estimation of the Levenshtein distance between the sequences, which in turn estimates the number of mutations between the organisms. Compared to the previous research, the novelty of the method lies in a newly proposed combination of the read and contig measures, a new method for read-contig mapping, and an efficient embedding of contigs.

Featured Application: Railroad Infrastructure Detection. This paper presents an automated high-speed rolling mark recognition system for railroad rails utilizing image processing techniques. Rolling marks, which consist of numbers, letters, and special characters, were engraved into the rail web as 3D information. These rolling marks provide crucial details regarding the rail manufacturer, steel quality, year of production, and rail profile. As a result, they empower rail infrastructure managers to gain valuable insights into their infrastructure. The rolling marks were captured using a standard color camera under dark field illumination. The recognition of individual numbers, letters, and special characters was achieved through state-of-the-art deep neural network object detection, specifically employing the YOLO architecture. By leveraging reference rolling marks, the detected characters can then be accurately interpreted and corrected. This correction process involves calculating a weighted Levenshtein distance, ensuring that the system can identify and rectify partially misidentified rolling marks. Through the proposed system, the accurate and reliable identification of rolling marks was achieved, even in cases in which there were partial errors in the detection process. This novel system thus has the potential to substantially improve the management and maintenance of railroad infrastructure. [ABSTRACT FROM AUTHOR]

The bibliometrics field has appeared out of the need of indexing and managing metadata of published scientific materials so this data could be analyzed. Its goal is the evaluation of the researchersț scientific activity. With the increasing number of published scientific papers, the techniques and the methods used in the bibliometrics domain are facing harder and harder challenges. To overcome those challenges, new technological solutions are required to be developed to realize all the procedures of bibliometrics in an as correct and efficient manner as possible. A solution that aims to solve the problem of the evaluation of research activity should have great consistency. It should include all the necessary steps involved in the evaluation process, from the data collection stage to the analysis and the interpretation of the results stage. This paper presents an application that aims to fulfill all of the before mentioned requirements and produce an efficient solution for the addressed problem. [ABSTRACT FROM AUTHOR]

Grammatical cases of nouns are expressed by inflectional endings in Slovak. Therefore, nouns have several word forms, with the nominative considered the basic form. In addition to the endings, in some word forms there are morphophonological changes also in stems. The differences between the basic form and inflected forms are evaluated using the Levenshtein distance. We show that word forms more similar to the basic form occur more often. The category of animacy plays a very important role in the frequency behaviour of masculine nouns. The overall idea of the present paper is to give some insights into phenomena of noun inflection and to test various factors influencing and regulating the overall complexity of expressing certain required morphological information. It seems that word frequency, but also word length plays an important role. Moreover, it is shown that also gender and animacy has to be considered to understand noun declension in Slovak properly. [ABSTRACT FROM AUTHOR]

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The problem of constructing characteristics of the difference between test sequences is investigated. Its relevance for generating controlled random tests and the complexity of finding difference measures for symbolic tests are substantiated. The limitations of using the Hamming and Damerau–Levenshtein distances to obtain a measure of the difference between test patterns are shown. For an arbitrary case, a new measure of the difference between two symbolic test sets is determined based on the interval used in the theory of the chain of successive events. The distance D(Ti, Tk) between test patterns Ti and Tk, using the interval characteristic, is based on determining independent pairs of identical (equal) symbols belonging to two patterns and calculating the intervals between them. The combinatorial nature of the calculation, the proposed difference measure for symbolic test patterns of an arbitrary alphabet and dimension, is shown. An example of calculating this measure is given and its possible modifications and limitations are shown. The application of the measure of difference is considered for the case of multi-run testing of memory devices based on address sequences pA with even p repetition of addresses. For the case p = 2, mathematical relations are given for calculating intervals and distances D(Ti, Tk) for address sequences 2A used for controlled random testing of memory devices. Experimental results are presented confirming the effectiveness of the proposed difference measure.

Motivation: Clustering of genetic sequences is one of the key parts of bioinformatics analyses. Resulting phylogenetic trees are beneficial for solving many research questions, including tracing the history of species, studying migration in the past, or tracing a source of a virus outbreak. At the same time, biologists provide more data in the raw form of reads or only on contig-level assembly. Therefore, tools that are able to process those data without supervision need to be developed. Results: In this paper, we present a tool for reference-free phylogeny capable of handling data where no mature-level assembly is available. The tool allows distance calculation for raw reads, contigs, and the combination of the latter. The tool provides an estimation of the Levenshtein distance between the sequences, which in turn estimates the number of mutations between the organisms. Compared to the previous research, the novelty of the method lies in a newly proposed combination of the read and contig measures, a new method for read-contig mapping, and an efficient embedding of contigs. [ABSTRACT FROM AUTHOR]

The aim of this research is to discuss the groundwork of building an Islamic Banking Document Screening Prototype based on a serverless architecture framework. This research first forms an algorithm for document matching based Vector Space Model (VCM) and adopts Levenshtein Distance for similarity setting. Product proposals will become a query, and policy documents by the central bank will be a corpus or database for document matching. Both the query and corpus went through preprocessing stage prior to similarity analysis. One set of queries with two sets of corpora is tested in this research to compare similarity values. Finally, a prototype of Shariah Document Screening is built based on a serverless architecture framework and ReactJS interface. This research is the first attempt to introduce a Shariah document screening prototype based on a serverless architecture technology that would be useful to the Islamic financial industry towards achieving a Shariah-compliant business. Given the development of Fintech, the output of this research study would be a complement to the existing Fintech applications, which focus on ensuring the Islamic nature of the businesses. [ABSTRACT FROM AUTHOR]

Large phonetic corpora are frequently used to investigate language variation and change in dialects, but these corpora are often constructed by many researchers in a collaborative effort. This typically results in inter-transcriber issues that may impact the reliability of analyses using these data. This problem is exacerbated when multiple phonetic corpora are compared when investigating real time dialect change. In this study, we therefore propose a method to automatically and iteratively merge phonetic symbols used in the transcriptions to obtain a more coarse-grained, but better comparable, phonetic transcription. Our approach is evaluated using two large phonetic Netherlandic dialect corpora in an attempt to estimate sound change in the area in the 20th century. The results are discussed in the context of the available literature about dialect change in the Netherlandic area. [ABSTRACT FROM AUTHOR]

This study focuses on the extraction and semantic analysis of data from tables, emphasizing the importance of understanding the semantics of tables to obtain useful information. The main goal was to develop a technology using the ontology for the semantic analysis of tables. An iterative algorithm has been proposed that can parse the contents of a table and determine cell types based on the ontology. The study presents an automated method for extracting data in various languages in various fields, subject to the availability of an appropriate ontology. Advanced techniques such as cosine distance search and table subject classification based on a neural network have been integrated to increase efficiency. The result is a software application capable of semantically classifying tabular data, facilitating the rapid transition of information from tables to ontologies. Rigorous testing, including 30 tables in the field of water resources and socio-economic indicators of Kazakhstan, confirmed the reliability of the algorithm. The results demonstrate high accuracy with a notable triple extraction recall of 99.4%. The use of Levenshtein distance for matching entities and ontology as a source of information was key to achieving these metrics. The study offers a promising tool for efficiently extracting data from tables.