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Journal articles on the topic 'Bigraph'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Babić, Ranko, and Lidija Babić. "A New Type of Bipartite Random Graph as a Transform of Seismogram and Its Potential for Organizing Seismic Databases." Applied Sciences 13, no. 18 (2023): 10303. http://dx.doi.org/10.3390/app131810303.

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This paper proposes a method to reduce seismogram variability as a determining factor in its interpretation, processing, and clustering. By introducing the concept of single fluctuations (SFs), the seismogram can be parsed into a sequence of random impulses with subsequent ordering. This rearrangement of SFs, if they are assigned by positive integers, represents the formal mapping of a regular string of integers into a random one, which can be represented with a bipartite random graph (bigraph). Due to its specific randomness, such a bigraph is considered a new type of random balanced bigraph.

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2

Xu, Dong, Gang Zhu, and De Zhen Xu. "Towards Checking Bigraphical Context-Aware Models." Applied Mechanics and Materials 411-414 (September 2013): 545–48. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.545.

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Context-aware computing is an important aspect of Pervasive Computing, Cyber-physical Systems (CPS), Cloud computing, etc. Recently, few works have been reported on formal approaches for this area. Bigraph was proposed as a mathematical structure that is consist of two graphs, a place graph and a link graph, intended for modeling distributed, mobile systems, etc. Bigraphs and its corresponding Bigraphical Reactive Systems (BRSs) present a modern, graphical structure, algebra calculus for the description of the syntax and semantics of systems in accordance with the orthogonal notions of connect

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3

Azhar, Aziz Sangoor. "Bigraph in GraphTheory." Journal of Progressive Research in Mathematics 15, no. 1 (2019): 2585–93. https://doi.org/10.5281/zenodo.3974094.

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In this paper we study bigraph in graph theory and discussed properties bigraph of some type graph, we study odd complete graph and even complete graph has bigraph such that when partition graph into two part 𝐺1 , 𝐺2 , if even complete graph such 𝐺1 is odd complete graph after partition and 𝐺2 is not complete graph, either if odd complete graph such 𝐺1 is even complete graph after partition and 𝐺2 is not complete graph, we study regular graph for me bigraph too we get after partition 𝐺1 either odd complete graph or even complete graph, will we discuss the status every bigraph is disconnected g

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4

Beauquier, Maxime, and Carsten Schürmann. "A Bigraph Relational Model." Electronic Proceedings in Theoretical Computer Science 71 (October 31, 2011): 14–28. http://dx.doi.org/10.4204/eptcs.71.2.

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5

Zelinka, Bohdan. "The bigraph decomposition number of a graph." Časopis pro pěstování matematiky 113, no. 1 (1988): 56–59. http://dx.doi.org/10.21136/cpm.1988.118332.

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6

Niu, Yajie, Chen Peng, and Bolin Liao. "Batch-Wise Permutation Feature Importance Evaluation and Problem-Specific Bigraph for Learn-to-Branch." Electronics 11, no. 14 (2022): 2253. http://dx.doi.org/10.3390/electronics11142253.

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The branch-and-bound algorithm for combinatorial optimization typically relies on a plethora of handcraft expert heuristics, and a research direction, so-called learn-to-branch, proposes to replace the expert heuristics in branch-and-bound with machine learning models. Current studies in this area typically use an imitation learning (IL) approach; however, in practice, IL often suffers from limited training samples. Thus, it has been emphasized that a small-dataset fast-training scheme for IL in learn-to-branch is worth studying, so that other methods, e.g., reinforcement learning, may be used

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7

Jadav, Hareshkumar, Ashita Gupta, and Ranveer Singh. "A construction of random bigraphs and their application to error correction codes^*." Journal of Statistical Mechanics: Theory and Experiment 2025, no. 1 (2025): 013403. https://doi.org/10.1088/1742-5468/ad9c4b.

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Abstract Recently, it has been shown that almost every (c, d)-bigraph is ‘almost’ Ramanujan. Marcus, Spielman, and Srivastava also demonstrated the existence of infinite sequence of (c, d)-bigraphs that are Ramanujan for all c , d ⩾ 3 . In this article, we give a construction of an infinite sequence of random (c, d)-biregular graphs G k , k = 1 , 2 , … , ∞ , where G k + 1 can be constructed from Gk in constant time. Experimental results show that they tend to be Ramanujan graphs in very few iterations. Additionally, we use this sequence of random (c, d)-biregular graphs as an error-correcting

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8

Davies, Hayley L., and Arpita Bose. "Bigraph-syllable blending therapy in deep dyslexia." Aphasiology 34, no. 10 (2019): 1241–69. http://dx.doi.org/10.1080/02687038.2019.1693024.

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9

Changat, Manoj. "On order and geodesic alignment of a connected bigraph." Czechoslovak Mathematical Journal 41, no. 4 (1991): 713–15. http://dx.doi.org/10.21136/cmj.1991.102501.

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10

CHANG, Zhi-Ming, Xin-Jun MAO, and Zhi-Chang QI. "Applying Bigraph Theory to Self-Adaptive Software Architecture." Chinese Journal of Computers 32, no. 1 (2009): 97–106. http://dx.doi.org/10.3724/sp.j.1016.2009.00097.

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11

Delaurentis, J. "Appearance of complex components in a random bigraph." Random Structures and Algorithms 7, no. 4 (1995): 311–35. http://dx.doi.org/10.1002/rsa.3240070405.

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12

Qiang, Qianyao, Bin Zhang, Chen Jason Zhang, and Feiping Nie. "Adaptive bigraph-based multi-view unsupervised dimensionality reduction." Neural Networks 188 (August 2025): 107424. https://doi.org/10.1016/j.neunet.2025.107424.

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13

Portmann, Nicole. "How Many Steps Are Necessary to Separate a Bigraph?" Journal of Combinatorial Theory, Series B 84, no. 1 (2002): 126–29. http://dx.doi.org/10.1006/jctb.2001.2067.

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14

Stallmann, Matthias, Franc Brglez, and Debabrata Ghosh. "Heuristics, Experimental Subjects, and Treatment Evaluation in Bigraph Crossing Minimization." ACM Journal of Experimental Algorithmics 6 (December 31, 2001): 8. http://dx.doi.org/10.1145/945394.945402.

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15

Lu, Chao-Ze, Guo-Sun Zeng, and Wen-Juan Liu. "Software Evolution Rules with Condition Constrains to Support Component Type Matching Based on Bigraph." International Journal of Software Engineering and Knowledge Engineering 28, no. 10 (2018): 1429–53. http://dx.doi.org/10.1142/s0218194018500419.

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With the gradual maturity of component oriented software development method, component-based software evolution technology has become hot research in academia and industry. Although many evolution rules are designed, they rarely consider component type-mismatched problem in evolution rules. This has led to evolution rules that often run error in software evolution execution. Hence, focusing on the mismatch problem of component type in software evolution, this paper addresses various evolution rules with condition constrains to support component type matching. First, we use the bigraph theory t

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16

YUAN, Yao, Yu-Cheng ZHANG, Wen-Xia DONG, Ru-Song ZHENG, Yu-Bo YANG, and Jing-Lin SHI. "Multi-Flow Network Fairness Selection Scheme Based on Weighted Bigraph Model." Journal of Software 21, no. 6 (2010): 1378–90. http://dx.doi.org/10.3724/sp.j.1001.2010.03519.

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17

Zhang, Jing, Mingzhe Chen, Yunzuo Hu, Xinzhou Zhang, and Zhe Wang. "BMPCN: A Bigraph Mutual Prototype Calibration Net for few-shot classification." Pattern Recognition 156 (December 2024): 110841. http://dx.doi.org/10.1016/j.patcog.2024.110841.

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18

Cherfia, Taha Abdelmoutaleb, Faïza Belala, and Kamel Barkaoui. "A bigraph-based framework for specification and analysis of context-aware systems." International Journal of Critical Computer-Based Systems 6, no. 4 (2016): 322. http://dx.doi.org/10.1504/ijccbs.2016.081808.

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19

Lu, Chao-ze, Guo-sun Zeng, and Ying-jie Xie. "Bigraph specification of software architecture and evolution analysis in mobile computing environment." Future Generation Computer Systems 108 (July 2020): 662–76. http://dx.doi.org/10.1016/j.future.2020.02.008.

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20

Stary, Chris, and Dominik Wachholder. "System-of-systems support — A bigraph approach to interoperability and emergent behavior." Data & Knowledge Engineering 105 (September 2016): 155–72. http://dx.doi.org/10.1016/j.datak.2015.12.001.

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21

Zhang, Fan, Changhu Wang, Zebang Cheng, et al. "DREAM: Decoupled Discriminative Learning with Bigraph-aware Alignment for Semi-supervised 2D-3D Cross-modal Retrieval." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 12 (2025): 13206–14. https://doi.org/10.1609/aaai.v39i12.33441.

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With the burst of big data, 2D-3D cross-modal retrieval has received increasing attention, which aims to retrieve relevant data from one modality given the query from the other modality. In this paper, we study an underexplored yet practical problem of semi-supervised 2D-3D cross-modal retrieval, which could suffer from serious label scarcity in real-world applications. Moreover, the huge heterogeneous gap could deteriorate the process of learning from unlabeled data. In this work, we propose a novel approach named Decoupled Discriminative Learning with Bigraph-aware Alignment (DREAM) for semi

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22

Babych, Vyacheslav, and Nataliya Golovashchuk. "Galois coverings of one-sided bimodule problems." Proceedings of the International Geometry Center 14, no. 2 (2021): 93–116. http://dx.doi.org/10.15673/tmgc.v14i2.1768.

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Applying geometric methods of 2-dimensional cell complex theory, we construct a Galois covering of a bimodule problem satisfying some structure, triangularity and finiteness conditions in order to describe the objects of finite representation type. Each admitted bimodule problem A is endowed with a quasi multiplicative basis. The main result shows that for a problem from the considered class having some finiteness restrictions and the schurian universal covering A', either A is schurian, or its basic bigraph contains a dotted loop, or it has a standard minimal non-schurian bimodule subproblem.

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23

Bowes, Kelly, and Nadine Martin. "Longitudinal study of reading and writing rehabilitation using a bigraph–biphone correspondence approach." Aphasiology 21, no. 6-8 (2007): 687–701. http://dx.doi.org/10.1080/02687030701192117.

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24

Lu, Jiawei, Huan Zhou, Haotian Zhu, Yuanming Zhang, Qianhui Liang, and Gang Xiao. "DCEM: A data cell evolution model for service composition based on bigraph theory." Future Generation Computer Systems 112 (November 2020): 330–47. http://dx.doi.org/10.1016/j.future.2020.05.006.

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25

Grzelak, Dominik. "BiGGer: A Model Transformation Tool written in Java for Bigraph Rewriting in GrGen.NET." Journal of Open Source Software 9, no. 98 (2024): 6491. http://dx.doi.org/10.21105/joss.06491.

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26

Gupta, Ishu, and Ashutosh Kumar Singh. "A Probabilistic Approach for Guilty Agent Detection using Bigraph after Distribution of Sample Data." Procedia Computer Science 125 (2018): 662–68. http://dx.doi.org/10.1016/j.procs.2017.12.085.

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27

Lu, Chaoze, and Qifeng Zou. "Dynamic Evolution Method and Symmetric Consistency Analysis for Big Data-Oriented Software Architecture Based on Extended Bigraph." Symmetry 17, no. 4 (2025): 626. https://doi.org/10.3390/sym17040626.

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With the development of artificial intelligence technology, there are increasingly high requirements for processing big data systems. Big data systems have undergone rapid evolution in response to changing demands. Due to the complex structural connections and dispersed component positions of big data processing systems, traditional formal methods find it difficult to dynamically model their structure and position simultaneously. To address this issue, this study proposes a formal modeling framework that extends Bigraph to support the dynamic evolution of big data software architecture. This m

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28

Savi, Alexander O., Benjamin E. Deonovic, Maria Bolsinova, der Maas Han L. J. Van, and Gunter K. J. Maris. "Tracing Systematic Errors to Personalize Recommendations in Single Digit Multiplication and Beyond." Journal of Educational Data Mining 13, no. 4 (2021): 1–30. https://doi.org/10.5281/zenodo.5806832.

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In learning, errors are ubiquitous and inevitable. As these errors may signal otherwise latent cognitive processes, tutors - and students alike - can greatly benefit from the information they provide. In this paper, we introduce and evaluate the Systematic Error Tracing (SET) model that identifies the possible causes of systematically observed errors in domains where items are susceptible to most or all causes and errors can be explained by multiple causes. We apply the model to single-digit multiplication, a domain that is very suitable for the model, is well-studied, and allows us to analyze

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29

Aimmanee, Pakinee, and Thanaruk Theeramunkong. "Improving the Retrieval Performance by Using the Distance-Based Bigrams." ECTI Transactions on Electrical Engineering, Electronics, and Communications 8, no. 1 (2009): 106–12. http://dx.doi.org/10.37936/ecti-eec.201081.172043.

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In this paper, we discuss a new way to improve retrieval performance using a special type of bigrams called Distance-based Bigram (DB). DB is a word pair whose distance between the two components is greater than or equal to one. DB allows us to find documents that express a phrase or a sentence differently from a query. The results show that DB combined with unigram performs significantly better than the unigram and bigram when the first few correct documents are needed.

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30

Zivkovic, Miodrag, Sasa Malkov, Snezana Zaric, et al. "Statistical dependence of protein secondary structure on amino acid bigrams." Chemical Industry and Chemical Engineering Quarterly 12, no. 1 (2006): 82–85. http://dx.doi.org/10.2298/ciceq0601082z.

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The statistical dependence of protein secondary structure on amino acid bigram frequencies was studied. Proteins in the PDBSELECT subset of the Protein Data Bank database were investigated. Protein secondary structures were determined using DSSP software. The conditional probabilities of protein secondary structures were calculated and presented. The results on bigrams show the frequencies of all the possible bigrams in all secondary structure types. These results elucidate some factors important for the prediction of the secondary structures of proteins based on the amino acid sequence.

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31

Crossley, Scott, and Max M. Louwerse. "Multi-dimensional register classification using bigrams." International Journal of Corpus Linguistics 12, no. 4 (2007): 453–78. http://dx.doi.org/10.1075/ijcl.12.4.02cro.

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A corpus linguistic analysis investigated register classification using frequency of bigrams in nine spoken and two written corpora. Four dimensions emerged from a factor analysis using bigram frequencies shared across corpora: (1) Scripted vs. Unscripted Discourse, (2) Deliberate vs. Unplanned Discourse, (3) Spatial vs. Non-Spatial Discourse, and (4) Directional vs. Non-Directional Discourse. These findings were replicated in a second analysis. Both analyses demonstrate the strength of bigrams for classifying spoken and written registers, especially in locating distinct collocations among spo

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32

Dang, Edward Kai Fung, Robert Wing Pong Luk, and Qing Li. "A Study of Word Bigrams for Pseudo-relevance Feedback in Information Retrieval." JUCS - Journal of Universal Computer Science 30, no. 11 (2024): 1511–28. http://dx.doi.org/10.3897/jucs.112725.

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Traditional information retrieval models mostly adopt a term independence assumption and are based on single terms or unigrams. Past efforts have attempted to go beyond this assumption, such as by using contiguous terms (i.e. word n-grams) or terms appearing in proximity. One such approach employs pseudo-relevance feedback (PRF) in an extended BM25 model, with an expanded query containing bigrams and proximity word pairs besides unigrams. However, the benefit of this approach over the traditional unigram PRF remains inconclusive. We speculate the uncertain effectiveness of bigram PRF in this p

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Dang, Edward Kai Fung, Robert Wing Pong Luk, and Qing Li. "A Study of Word Bigrams for Pseudo-relevance Feedback in Information Retrieval." JUCS - Journal of Universal Computer Science 30, no. (11) (2024): 1511–28. https://doi.org/10.3897/jucs.112725.

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Traditional information retrieval models mostly adopt a term independence assumption and are based on single terms or unigrams. Past efforts have attempted to go beyond this assumption, such as by using contiguous terms (i.e. word n-grams) or terms appearing in proximity. One such approach employs pseudo-relevance feedback (PRF) in an extended BM25 model, with an expanded query containing bigrams and proximity word pairs besides unigrams. However, the benefit of this approach over the traditional unigram PRF remains inconclusive. We speculate the uncertain effectiveness of bigram PRF in this p

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34

Sardinha, Tony Berber. "A historical characterisation of American and Brazilian cultures based on lexical representations." Corpora 15, no. 2 (2020): 183–212. http://dx.doi.org/10.3366/cor.2020.0194.

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The goal of this study is to detect the historical distribution of representations of the United States and Brazil formed around the use of the nationality adjectives American and Brazilian. To achieve this goal, the study used a pre-existing multi-dimensional analysis of representations based on bigrams from the half-a-trillion-word Google Books bigram dataset of English writing ( Berber Sardinha, 2019 ), which provided the major representations of both cultures. The method was based on the text type approach developed by Biber (1989) , which uses cluster analysis to identify the groupings of

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35

Adyatma, Adrian Dwinanda, Lasmedi Afuan, and Eddy Maryanto. "THE EFFECT OF UNIGRAM AND BIGRAM IN THE NAÏVE BAYES MULTINOMIAL FOR ANALYZING OF COMMENT SENTIMENT OF GOJEK APPLICATION IN GOOGLE PLAY STORE." Jurnal Teknik Informatika (Jutif) 4, no. 6 (2023): 1535–40. http://dx.doi.org/10.52436/1.jutif.2023.4.6.1310.

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In sentiment classification systems that use Naïve Bayes Classifier, a commonly used feature extraction method is TF-IDF with unigram and bigram, where the two is used separately. In the reality, most of texts contain single or composed word,so it is needed to use the combination of unigram and bigram to maximize the accuracy of the classification results. In this research, the impact and performance improvement between classification systems using unigram or bigram solely and those using a combination of both are studied. Using 1000 data of reviews with ratings 1 (negative) and 5 (positive) f

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36

Alsharman, Nesreen Mohammad, and Inna V. Pivkina. "Generating Summaries Through Unigram and Bigram." International Journal of Information Technology and Web Engineering 15, no. 1 (2020): 64–74. http://dx.doi.org/10.4018/ijitwe.2020010105.

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This article describes a new method for generating extractive summaries directly via unigram and bigram extraction techniques. The methodology uses the selective part of speech tagging to extract significant unigrams and bigrams from a set of sentences. Extracted unigrams and bigrams along with other features are used to build a final summary. A new selective rule-based part of speech tagging system is developed that concentrates on the most important parts of speech for summarizations: noun, verb, and adjective. Other parts of speech such as prepositions, articles, adverbs, etc., play a lesse

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Garner, James, Scott Crossley, and Kristopher Kyle. "Beginning and intermediate L2 writer’s use of N-grams: an association measures study." International Review of Applied Linguistics in Language Teaching 58, no. 1 (2020): 51–74. http://dx.doi.org/10.1515/iral-2017-0089.

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AbstractAcommon approach to analyzing phraseological knowledge in first language (L1) and second language (L2) learners is to employ raw frequency data. Several studies have also analyzed n-gram use on the basis of statistical association scores. Results from n-gram studies have found significant differences between L1 and L2 writers and between intermediate and advanced L2 writers in terms of their bigram use. The current study expands on this research by investigating the connection between bigram and trigram association measures and human judgments of L2 writing quality. Using multiple stat

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38

Shabahang, Kevin D., Hyungwook Yim, and Simon J. Dennis. "Generalization at Retrieval Using Associative Networks with Transient Weight Changes." Computational Brain & Behavior 5, no. 1 (2022): 124–55. http://dx.doi.org/10.1007/s42113-022-00127-4.

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Abstract Without having seen a bigram like “her buffalo”, you can easily tell that it is congruent because “buffalo” can be aligned with more common nouns like “cat” or “dog” that have been seen in contexts like “her cat” or “her dog”—the novel bigram structurally aligns with representations in memory. We present a new class of associative nets we call Dynamic-Eigen-Nets, and provide simulations that show how they generalize to patterns that are structurally aligned with the training domain. Linear-Associative-Nets respond with the same pattern regardless of input, motivating the introduction

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39

Grohmann, Davide, and Marino Miculan. "Directed Bigraphs." Electronic Notes in Theoretical Computer Science 173 (April 2007): 121–37. http://dx.doi.org/10.1016/j.entcs.2007.02.031.

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40

Krivine, Jean, Robin Milner, and Angelo Troina. "Stochastic Bigraphs." Electronic Notes in Theoretical Computer Science 218 (October 2008): 73–96. http://dx.doi.org/10.1016/j.entcs.2008.10.006.

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Ó Conchúir, Shane. "Kind Bigraphs." Electronic Notes in Theoretical Computer Science 225 (January 2009): 361–77. http://dx.doi.org/10.1016/j.entcs.2008.12.086.

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42

Denk, Michaela, Peter Hackl, and Norbert Rainer. "String Matching Techniques: An Empirical Assessment Based on Statistics Austria's Business Register." Austrian Journal of Statistics 34, no. 3 (2016): 235–49. http://dx.doi.org/10.17713/ajs.v34i3.415.

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The maintenance and updating of Statistics Austria's business register requires a regularly matching of the register against other data sources; one of them is the register of tax units of the Austrian Federal Ministry of Finance. The matching process is based on string comparison via bigrams of enterprise names and addresses, and a quality class approach assigning pairs of register units into classes of different compliance (i.e., matching quality) based on bigram similarity values and the comparison of other matching variables, like the NACE code or the year of foundation.Based on methodolog

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43

Rahimi, Fatemeh. "Splitting of Bigraphs." IOSR Journal of Mathematics 5, no. 3 (2013): 70–73. http://dx.doi.org/10.9790/5728-0537073.

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44

Sevegnani, Michele, and Muffy Calder. "Bigraphs with sharing." Theoretical Computer Science 577 (April 2015): 43–73. http://dx.doi.org/10.1016/j.tcs.2015.02.011.

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45

Jensen, Ole Høgh, and Robin Milner. "Bigraphs and transitions." ACM SIGPLAN Notices 38, no. 1 (2003): 38–49. http://dx.doi.org/10.1145/640128.604135.

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46

Chang, Gerard Jennhwa, Ton Kloks, and Sheng-Lung Peng. "Probe interval bigraphs." Electronic Notes in Discrete Mathematics 19 (June 2005): 195–201. http://dx.doi.org/10.1016/j.endm.2005.05.027.

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47

Birkedal, Lars, Troels Christoffer Damgaard, Arne John Glenstrup, and Robin Milner. "Matching of Bigraphs." Electronic Notes in Theoretical Computer Science 175, no. 4 (2007): 3–19. http://dx.doi.org/10.1016/j.entcs.2007.04.013.

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48

Neacsu, Teodor, Teodor Poncu, Stefan Ruseti, and Mihai Dascalu. "DoubleStrokeNet: Bigram-Level Keystroke Authentication." Electronics 12, no. 20 (2023): 4309. http://dx.doi.org/10.3390/electronics12204309.

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Keystroke authentication is a well-established biometric technique that has gained significant attention due to its non-intrusive and continuous characteristics. The method analyzes the unique typing patterns of individuals to verify their identity while interacting with the keyboard, both virtual and hardware. Current deep-learning approaches like TypeNet and TypeFormer focus on generating biometric signatures as embeddings for the entire typing sequence. The authentication process is defined using the Euclidean distances between the new typing embedding and the saved biometric signatures. Th

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Hadwan, Mohammed, Mohammed A. Al-Hagery, Maher Al-Sanabani, and Salah Al-Hagree. "Soft Bigram distance for names matching." PeerJ Computer Science 7 (April 21, 2021): e465. http://dx.doi.org/10.7717/peerj-cs.465.

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Background Bi-gram distance (BI-DIST) is a recent approach to measure the distance between two strings that have an important role in a wide range of applications in various areas. The importance of BI-DIST is due to its representational and computational efficiency, which has led to extensive research to further enhance its efficiency. However, developing an algorithm that can measure the distance of strings accurately and efficiently has posed a major challenge to many developers. Consequently, this research aims to design an algorithm that can match the names accurately. BI-DIST distance is

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50

van Kesteren, Ron, Ton Dijkstra, and Koenraad de Smedt. "Markedness effects in Norwegian–English bilinguals: Task-dependent use of language-specific letters and bigrams." Quarterly Journal of Experimental Psychology 65, no. 11 (2012): 2129–54. http://dx.doi.org/10.1080/17470218.2012.679946.

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This study investigates how bilinguals use sublexical language membership information to speed up their word recognition process in different task situations. Norwegian–English bilinguals performed a Norwegian–English language decision task, a mixed English lexical decision task, or a mixed Norwegian lexical decision task. The mixed lexical decision experiments included words from the nontarget language that required a “no” response. The language specificity of the Bokmål (a Norwegian written norm) and English (non)words was varied by including language-specific letters (“smør”, “hawk”) or big

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