Character Alignment Research Articles

Bounding the Softwired Parsimony Score of a Phylogenetic Network

In comparison to phylogenetic trees, phylogenetic networks are more suitable to represent complex evolutionary histories of species whose past includes reticulation such as hybridisation or lateral gene transfer. However, the reconstruction of phylogenetic networks remains challenging and computationally expensive due to their intricate structural properties. For example, the small parsimony problem that is solvable in polynomial time for phylogenetic trees, becomes NP-hard on phylogenetic networks under softwired and parental parsimony, even for a single binary character and structurally constrained networks. To calculate the parsimony score of a phylogenetic network N, these two parsimony notions consider different exponential-size sets of phylogenetic trees that can be extracted from N and infer the minimum parsimony score over all trees in the set. In this paper, we ask: What is the maximum difference between the parsimony score of any phylogenetic tree that is contained in the set of considered trees and a phylogenetic tree whose parsimony score equates to the parsimony score of N? Given a gap-free sequence alignment of multi-state characters and a rooted binary level-k phylogenetic network, we use the novel concept of an informative blob to show that this difference is bounded by k+1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k+1$$\\end{document} times the softwired parsimony score of N. In particular, the difference is independent of the alignment length and the number of character states. We show that an analogous bound can be obtained for the softwired parsimony score of semi-directed networks, while under parental parsimony on the other hand, such a bound does not hold.

Enhancing Historical Narrative through Application of Staging Techniques in 3D Animation “How Islam Spread Around the World”

This research addresses the challenge of effectively conveying the historical narrative of Islam's spread through 3D animation. The primary objective is to explore the potential of staging techniques in enhancing message delivery and audience engagement. Staging, a method that emphasizes scene element arrangement, character placement, and camera perspective, is pivotal for a clear and impactful narrative. Through this technique, elements are organized to guide viewers' attention and emphasize the intended message. This study demonstrates that careful alignment of characters, backgrounds, and camera angles, combined with visual symbolism representing Islamic values, can significantly enhance the narrative's depth and viewer comprehension. Experiments reveal that strategic staging not only strengthens the storyline but also boosts audience understanding. The research underscores the importance of staging in 3D animation, especially for intricate narratives like the spread of Islam. It offers insights into the advantages of staging over traditional methods, emphasizing its role in narrative comprehension, deeper meaning conveyance, and audience engagement.

KESEJAJARAN KARAKTER TOKOH DALAM BUKU “ANAK KAMPUNG PALING FENOMENAL” KARYA DR. M. MUFTI MUBAROK

Character was a person's nature, which refers to a person's personal qualities. The characters in the book “The Most Phenomenal Village Children” are no exception, who have the same characters in this study. The purpose of this study is to find out the form of parallel reality of the characters through the words and behavior of the characters in the book "The Most Phenomenal Village Children" by Dr. M. Mufti Mubarok. While this type of research is qualitative literature research using descriptive methods. The results of the study found several forms of reality of character alignment through the words and behavior of the characters such as: being humble, ambitious, empathetic and caring for others, having a strong character, respecting friends and foes, having stable emotions, being brave, patient, and keeping the spirit up. As for other findings from the alignment of characters, through the same words and behavior is a good attitude, which can be an inspiration and innovation for the community.

Villainous snakes and heroic butterflies, the moral alignment of animal-themed characters in American superhero comic books

ABSTRACT People view animals through a framework of a negative to positive scale, with these views having wide-ranging implications including conservation programmeinvestment and success. Media can change public perception, and one media form that has largely been ignored in this regard is comic books. Within superhero comic books, characters are defined by use of animal monikers, traits, or resemblance. Superhero comic books are a unique medium due to characters existing on a hero to villain dichotomy. Examining animal representation on this hero/villain dichotomy can reveal public perceptions of various taxa. For this study, a total of 558 characters were categorised as heroes, villains, or morally ambiguous. Findings indicate vertebrate-themed characters outnumbered invertebrate-themed characters despite invertebrates making up the majority of fauna biodiversity. Additionally, male comic book characters outnumbered female comic book characters. Amphibians, fish, snakes, and lizards were more frequently portrayed as villains, while mammals, birds, butterflies, and beetles were more frequently portrayed as heroes. Exceptions to heroic mammals were even-toed ungulates and Primates which were more villainous than heroic in their depictions. Investigating animal depictions in media, such as superhero comic books, can help reveal where public perceptions originate and add further context to existing species bias research.

Metapragmatic stereotypes and humour: interpreting and perceiving linguistic homogeneity in mass culture texts

The present study concentrates on the potential of mass culture texts to impose specific metapragmatic stereotypes (Agha 2007) through humour on the wider audience. Metapragmatic stereotypes constitute speakers’ internal models of how language should or should not be used; such models guide speakers’ own language use and enable them to make evaluations about their own language behaviour or that of others. In this context, I explore the dominant metapragmatic stereotypes for humorous mass culture texts’ interpretation and perception. To this end, I analyse a humorous Greek TV advertisement of a telecommunications company. Drawing upon Coupland’s (2007) conceptualization of style and the General Theory of Verbal Humour (Attardo 2001), I intend to show that humour reflects, sustains, and reproduces the dominant metapragmatic stereotypes of linguistic homogenization and monolingualism (Blommaert & Rampton 2011). Then, I explore how the audience perceives the representation of stylistic choices in mass culture texts and more specifically in the analysed advertisement. My informants were 96 students of the last two grades of elementary school. The recipients’ responses show that their metapragmatic stereotypes are aligned with the dominant ones: they approach stylistic choices as strictly-defined systems used in specific social contexts and they expect the alignment of TV fictional characters with linguistic homogeneity. My findings suggest that humour stigmatises specific styles as well as that the audience perceives the respective (and reinforced through humour) metapragmatic stereotypes as guidelines for “correct” stylistic use. Furthermore, it seems that through humour such stereotypes usually go unnoticed in mass culture texts and may even become naturalised, as they are framed in a “trivial” and “non-serious” manner.

Manifold multi-view learning for cartoon alignment

Cartoon alignment is a key to retrieve cartoon characters and synthesise new cartoon clips. To successfully achieve the tasks, it is necessary to extract visual features that comprehensively denote cartoon characters and to align the feature points accurately between cartoon characters. In this paper, Speed Up Robust Feature (SURF) and Shape Context (SC) are introduced to characterise the cartoon character from multi-view. To increase accuracy rate of cartoon character alignment, semi-supervised alignment and Procrustes alignment require predetermining the correspondence. To overcome the flaw, we propose a Manifold Multi-View Learning (MML) to align cartoon characters. MML learns a projection that maps data instance (from cartoon characters with different dimensionality) to a lower-dimensional space, which simultaneously matches the local geometry and preserves the neighbourhood relationship within each cartoon character. The matching relationship can be obtained from local geometry structure. Experimental results show the good performance.

Manifold multi-view learning for cartoon alignment

Cartoon alignment is a key to retrieve cartoon characters and synthesise new cartoon clips. To successfully achieve the tasks, it is necessary to extract visual features that comprehensively denote cartoon characters and to align the feature points accurately between cartoon characters. In this paper, Speed Up Robust Feature (SURF) and Shape Context (SC) are introduced to characterise the cartoon character from multi-view. To increase accuracy rate of cartoon character alignment, semi-supervised alignment and Procrustes alignment require predetermining the correspondence. To overcome the flaw, we propose a Manifold Multi-View Learning (MML) to align cartoon characters. MML learns a projection that maps data instance (from cartoon characters with different dimensionality) to a lower-dimensional space, which simultaneously matches the local geometry and preserves the neighbourhood relationship within each cartoon character. The matching relationship can be obtained from local geometry structure. Experimental results show the good performance.

Word-character attention model for Chinese text classification

Recent progress in applying neural networks to image classification has motivated the exploration of their applications to text classification tasks. Unlike the majority of these researches devoting to English corpus, in this paper, we focus on Chinese text, which is more intricate in semantic representations. As the basic unit of Chinese words, character plays a vital role in Chinese linguistic. However, most existing Chinese text classification methods typically regard word features as the basic unit of text representation but ignore the beneficial performance of character features. Besides, existing approaches compress the entire word features into a semantic representation, without considering attention mechanism which allows for capturing salient features. To tackle these issues, we propose the word-character attention model (WCAM) for Chinese text classification. This WCAM approach integrates two levels of attention models: word-level attention model captures salient words which have closer semantic relationship to the text meaning, and character-level attention model selects discriminative characters of text. Both are jointly employed to learn representation of texts. Meanwhile, the word-character constraint model and character alignment are introduced in our proposed approach to ensure the highly representative of selected characters as well as enhance their discrimination. Both are jointly employed to exploit the subtle and local differences for distinguishing the text classes. Extensive experiments on two benchmark datasets demonstrate that our WCAM approach achieves comparable or even better performance than the state-of-the-art methods for Chinese text classification.

A comprehensive model for code readability

AbstractUnreadable code could compromise program comprehension, and it could cause the introduction of bugs. Code consists of mostly natural language text, both in identifiers and comments, and it is a particular form of text. Nevertheless, the models proposed to estimate code readability take into account only structural aspects and visual nuances of source code, such as line length and alignment of characters. In this paper, we extend our previous work in which we use textual features to improve code readability models. We introduce 2 new textual features, and we reassess the readability prediction power of readability models on more than 600 code snippets manually evaluated, in terms of readability, by 5K+ people. We also replicate a study by Buse and Weimer on the correlation between readability and FindBugs warnings, evaluating different models on 20 software systems, for a total of 3M lines of code. The results demonstrate that (1) textual features complement other features and (2) a model containing all the features achieves a significantly higher accuracy as compared with all the other state‐of‐the‐art models. Also, readability estimation resulting from a more accurate model, ie, the combined model, is able to predict more accurately FindBugs warnings.

Image based book cover recognition and retrieval

In this we are developing a graphical user interface using MATLAB for the users to check the information related to books in real time. We are taking the photos of the book cover using GUI, then by using MSER algorithm it will automatically detect all the features from the input image, after this it will filter bifurcate non-text features which will be based on morphological difference between text and non-text regions. We implemented a text character alignment algorithm which will improve the accuracy of the original text detection. We will also have a look upon the built in MATLAB OCR recognition algorithm and an open source OCR which is commonly used to perform better detection results, post detection algorithm is implemented and natural language processing to perform word correction and false detection inhibition. Finally, the detection result will be linked to internet to perform online matching. More than 86% accuracy can be obtained by this algorithm.

文字配置と背景着色の工夫がもたらす日本語電子リーダーの読み速度向上

文字配置と背景着色の工夫がもたらす日本語電子リーダーの読み速度向上

OCR- The 3 Layered Approach for Decision Making State and Identification of Telugu Hand Written and Printed Consonants and Conjunct Consonants by Using Advanced Fuzzy Logic Controller

Optical Character recognition is the method of digitalization of hand and type written or printed text into machine-encoded form and is superfluity of the various applications of envision of human’s life. In present human life OCR has been successfully using in finance, legal, banking, health care and home need appliances. India is a multi cultural, literature and traditional scripted country. Telugu is the southern Indian language, it is a syllabic language, symbol script represents a complete syllable and formed with the conjunct mixed consonants in their representation. Recognition of mixed conjunct consonants is critical than the normal consonants, because of their variation in written strokes, conjunct maxing with pre and post level of consonants. This paper proposes the layered approach methodology to recognize the characters, conjunct consonants, mixed- conjunct consonants and expressed the efficient classification of the hand written and printed conjunct consonants. This paper implements the Advanced Fuzzy Logic system controller to take the text in the form of written or printed, collected the text images from the scanned file, digital camera, Processing the Image with Examine the high intensity of images based on the quality ration, Extract the image characters depends on the quality then check the character orientation and alignment then to check the character thickness, base and print ration. The input image characters can classify into the two ways, first way represents the normal consonants and the second way represents conjunct consonants. Digitalized image text divided into three layers, the middle layer represents normal consonants and the top and bottom layer represents mixed conjunct consonants. Here recognition process starts from middle layer, and then it continues to check the top and bottom layers. The recognition process treat as conjunct consonants when it can detect any symbolic characters in top and bottom layers of present base character otherwise treats as normal consonants. The post processing technique applied to all three layered characters. Post processing of the image: concentrated on the image text readability and compatibility, if the readability is not process then repeat the process again. In this recognition process includes slant correction, thinning, normalization, segmentation, feature extraction and classification. In the process of development of the algorithm the pre-processing, segmentation, character recognition and post-processing modules were discussed. The main objectives to the development of this paper are: To develop the classification, identification of deference prototyping for written and printed consonants, conjunct consonants and symbols based on 3 layered approaches with different measurable area by using fuzzy logic and to determine suitable features for handwritten character recognition.

Fabrication and band engineering of Cu-doped CdSe0.6Te0.4-alloyed quantum dots for solar cells

A great deal of attention has been directed towards the development of facile synthetic methods for semiconductor materials that allow for doping. In this work, Cu-doped CdSeTe-alloyed quantum dots (QDs) were synthesized via a non-injection one pot synthesis route and used as a sensitizer in solar cells. The lattice incorporation of CdSeTe QDs with Cu2+ was evidenced by X-ray diffraction (XRD) analysis and ab initio simulation. We have performed characterization on the relaxed geometry structure, bonding character, and band alignment of the Cu-doped CdSeTe QDs based on rigorous ab initio calculations. Our main objective is to fully understand the effect of the Cu2+ dopant on the physical properties of CdSeTe QDs, which would be very useful to further understand the mechanism of photoelectric conversion. HR-TEM analysis showed ~7nm and ~9nm particles for CdSeTe and Cu-doped CdSeTe nanocrystals (NCs), respectively. It was found that the open circuit voltage (Voc) of Cu-doped CdSeTe NCs decreases with respect to that of the undoped ones. The QD-based solar cell with Cu-doped Cu0.2Cd0.8Se0.6Te0.4 exhibited a photo-conversion efficiency of 1.25% (VOC=0.593V, JSC=3.78mA/cm2, FF=55.5%), which was higher than the reference CdSeTe NCs under AM1.5G at 100mW/cm2 simulated sunlight radiation conditions.

Modeling Monolingual Character Alignment for Automatic Evaluation of Chinese Translation

Automatic evaluation of machine translations is an important task. Most existing evaluation metrics rely on matching the same word or letter n -grams. This strategy leads to poor results on Chinese translations because one has to rely merely on matching identical characters. In this article, we propose a new evaluation metric that allows different characters with the same or similar meaning to match. An Indirect Hidden Markov Model (IHMM) is proposed to align the Chinese translation with human references at the character level. In the model, the emission probabilities are estimated by character similarity, including character semantic similarity and character surface similarity, and transition probabilities are estimated by a heuristic distance-based distortion model. When evaluating the submitted output of English-to-Chinese translation systems in the IWSLT’08 CT-EC and NIST’08 EC tasks, the experimental results indicate that the proposed metric has a significantly better correlation with human evaluation than the state-of-the-art machine translation metrics (i.e., BLEU, Meteor Universal, and TESLA-CELAB). This study shows that it is important to allow different characters to match in the evaluation of Chinese translations and that the IHMM is a reasonable approach for the alignment of Chinese characters.

Optical Character Recognition (OCR)-Decision Making State and Identification of Telugu Hand Written Touching Conjuct Consonants and Mixed Consonants by Using Advanced Fuzzy Logic Controller With Layered Approach

Optical Character recognition is superfluity of the various applications of envision of human's life. In present life OCR has been successfully using in finance, legal, banking, health care and home need appliances. OCR is the method of digitalization of hand and type written or printed text into machine-encoded form. India is a multi cultural, literature and traditional scripted country. 18 official scripted languages are formed and have many local regional languages in India. Telugu is official language of the southern Indian states of Telangana and Andhra Pradesh and many people spoken in all over in Malaysia, Bahrain, Oman, Singapore, Fiji, UAE and Mauritius. Telugu is a syllabic language, symbol script represents a complete syllable and formed with the conjunct mixed consonants in their representation. Recognition of mixed conjunct consonants is critical than the normal consonants, because of their variation in written strokes, conjunct maxing with pre and post level of consonants. This paper proposes the layered approach methodology to recognize the characters, conjunct consonants, mixed-conjunct consonants and expressed the efficient classification of the hand written and printed conjunct consonants. This paper implements the Advanced Fuzzy Logic system controller to take the text in the form of written or printed, collected the text images from the scanned file, digital camera, Processing the Image with Examine the high intensity of images based on the quality ration, Extract the image characters depends on the quality then check the character orientation and alignment then to check the character thickness, base and print ration. The input image characters can classify into the two ways, first way represents the normal consonants and the second way represents conjunct consonants. Digitalized image text divided into three layers, the middle layer represents normal consonants and the top and bottom layer represents mixed conjunct consonants. Here recognition process starts from middle layer, and then it continues to check the top and bottom layers. The recognition process treat as conjunct consonants when it can detect any symbolic characters in top and bottom layers of present base character otherwise treats as normal consonants. The post processing technique applied to all three layered characters. Post processing of the image: concentrated on the image text readability and compatibility, if the readability is not process then repeat the process again. In this recognition process includes slant correction, thinning, normalization, segmentation, feature extraction and classification. In the process of development of the algorithm the pre-processing, segmentation, character recognition and post-processing modules were discussed. The main objectives to the development of this paper are: To develop the classification, identification of deference prototyping for written and printed consonants, conjunct consonants and symbols based on 3 layered approaches with different measurable area by using fuzzy logic and to determine suitable features for handwritten character recognition.

Semi-supervised cluster-and-label with feature based re-clustering to reduce noise in Thai document images

Noise components are a major cause of poor performance in document analysis. To reduce undesired components, most recent research works have applied an image processing technique. However, the effectiveness of these techniques is suitable only for a Latin script document but not a non-Latin script document. The characteristics of the non-Latin script document, such as Thai, are considerably more complicated than the Latin script document and include many levels of character alignment, no word or sentence separator, and variability in a character’s size. When applying an image processing technique to a Thai document, we usually remove the characters that are relatively close to noise. Hence, in this paper, we propose a novel noise reduction method by applying a machine learning technique to classify and reduce noise in document images. The proposed method uses a semi-supervised cluster-and-label approach with an improved labeling method, namely, feature selected sub-cluster labeling. Feature selected sub-cluster labeling focuses on the clusters that are incorrectly labeled by conventional labeling methods. These clusters are re-clustered into small groups with a new feature set that is selected according to class labels. The experimental results show that this method can significantly improve the accuracy of labeling examples and the performance of classification. We compared the performance of noise reduction and character preservation between the proposed method and two related noise reduction approaches, i.e., a two-phased stroke-like pattern noise (SPN) removal and a commercial noise reduction software called ScanFix Xpress 6.0. The results show that semi-supervised noise reduction is significantly better than the compared methods of which an F-measure of character and noise is 86.01 and 97.82, respectively.

Noise-aware Character Alignment for Extracting Transliteration Fragments

Noise-aware Character Alignment for Extracting Transliteration Fragments

Towards improving searches for optimal phylogenies.

Finding the optimal evolutionary history for a set of taxa is a challenging computational problem, even when restricting possible solutions to be "tree-like" and focusing on the maximum-parsimony optimality criterion. This has led to much work on using heuristic tree searches to find approximate solutions. We present an approach for finding exact optimal solutions that employs and complements the current heuristic methods for finding optimal trees. Given a set of taxa and a set of aligned sequences of characters, there may be subsets of characters that are compatible, and for each such subset there is an associated (possibly partially resolved) phylogeny with edges corresponding to each character state change. These perfect phylogenies serve as anchor trees for our constrained search space. We show that, for sequences with compatible sites, the parsimony score of any tree [Formula: see text] is at least the parsimony score of the anchor trees plus the number of inferred changes between [Formula: see text] and the anchor trees. As the maximum-parsimony optimality score is additive, the sum of the lower bounds on compatible character partitions provides a lower bound on the complete alignment of characters. This yields a region in the space of trees within which the best tree is guaranteed to be found; limiting the search for the optimal tree to this region can significantly reduce the number of trees that must be examined in a search of the space of trees. We analyze this method empirically using four different biological data sets as well as surveying 400 data sets from the TreeBASE repository, demonstrating the effectiveness of our technique in reducing the number of steps in exact heuristic searches for trees under the maximum-parsimony optimality criterion.

Image Capturing and Segmentation Method for Characters Marked on Hot Billets

Real-time detecting information marked on billets is important for automatically manufacturing and management in steelworks. But due to the tough production environments in steel enterprises, capturing and identifying characters marked on hot billets have many challenges. This paper presents a real-time image capturing and segmenting method with machine vision for characters marked on hot billets, and characters area is located based on color information of images. Furthermore, considering the marked characters are often slant, we proposed a kind of characters skew correction method to adjust the alignment of characters, and then segment characters into singles for recognition. Finally, with the proposed method, we have conducted some experiments in Baosteel Company. The result shows that our method can achieve 97% segmentation rate if we select proper image acquisition device and preprocessing algorithm. Additionally, it provides a new way for steel enterprise real-time capturing and segmenting marked characters image.

Noise-aware Character Alignment for Extracting Transliteration Fragments

Noise-aware Character Alignment for Extracting Transliteration Fragments