Showing papers on "Character (mathematics) published in 2019"
01 Jun 2019
TL;DR: This work proposes a method in which it dynamically aggregate contextualized embeddings of each unique string that the authors encounter and uses a pooling operation to distill a ”global” word representation from all contextualized instances.
Abstract: Contextual string embeddings are a recent type of contextualized word embedding that were shown to yield state-of-the-art results when utilized in a range of sequence labeling tasks. They are based on character-level language models which treat text as distributions over characters and are capable of generating embeddings for any string of characters within any textual context. However, such purely character-based approaches struggle to produce meaningful embeddings if a rare string is used in a underspecified context. To address this drawback, we propose a method in which we dynamically aggregate contextualized embeddings of each unique string that we encounter. We then use a pooling operation to distill a ”global” word representation from all contextualized instances. We evaluate these ”pooled contextualized embeddings” on common named entity recognition (NER) tasks such as CoNLL-03 and WNUT and show that our approach significantly improves the state-of-the-art for NER. We make all code and pre-trained models available to the research community for use and reproduction.
269 citations
TL;DR: In this paper, the authors performed a meta-analysis on the impact of character strength interventions on positive affect or happiness and depression, with a weighted Hedges' g of 0.42 and 0.32, respectively.
Abstract: This meta-analysis investigated the impact of interventions focused on using signature character strengths. The meta-analysis consolidated results of studies examining the effect of signature character strength interventions compared to control conditions. Twenty-nine effect sizes reported in fourteen articles allowed examination of several types of outcomes. Across nine studies investigating the impact of these interventions on increases in positive affect or happiness, signature character strength interventions had a significant impact, with a weighted Hedges’ g of 0.32. Across seven studies, interventions had a significant impact on decreases in depression, with a weighted Hedges’ g of 0.21. Across seven study samples, signature strengths had a significant impact on increasing life satisfaction, with a weighted Hedges’ g of 0.42. Only a small number of studies investigated other outcomes, resulting in low meta-analytic power for effect sizes for these outcomes. Of note is a significant effect size of Hedges’ g of 0.55 for increase in use of signature character strengths, based on just two studies, which suggests that signature character strength interventions do impact strengths as intended. The promising effects shown in existing studies indicate that signature strength interventions have the potential to contribute to beneficial outcomes in various areas of life and that more research on the impact of signature character strength interventions is warranted.
132 citations
17 Oct 2019
TL;DR: This work proposes convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass, and develops an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images.
Abstract: Recent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful one-stage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements of 65.33%->71.08% (with generic lexicon) on ICDAR 2015, and 54.0%->69.23% on Total-Text, on end-to-end text recognition. Code is available at: https://github.com/MalongTech/research-charnet.
121 citations
TL;DR: In this paper, the authors present and organize the literature related to the extraction of character networks from works of fiction, as well as their analysis, and identify the limitations of the existing approaches and the most promising perspectives.
Abstract: A character network is a graph extracted from a narrative in which vertices represent characters and edges correspond to interactions between them. A number of narrative-related problems can be addressed automatically through the analysis of character networks, such as summarization, classification, or role detection. Character networks are particularly relevant when considering works of fiction (e.g., novels, plays, movies, TV series), as their exploitation allows developing information retrieval and recommendation systems. However, works of fiction possess specific properties that make these tasks harder. This survey aims at presenting and organizing the scientific literature related to the extraction of character networks from works of fiction, as well as their analysis. We first describe the extraction process in a generic way and explain how its constituting steps are implemented in practice, depending on the medium of the narrative, the goal of the network analysis, and other factors. We then review the descriptive tools used to characterize character networks, with a focus on the way they are interpreted in this context. We illustrate the relevance of character networks by also providing a review of applications derived from their analysis. Finally, we identify the limitations of the existing approaches and the most promising perspectives.
62 citations
TL;DR: In this article, a review of recent studies that have used ethnographic meto-homographies to explore the penal character of mass supervision as a lived experience is presented. But the focus of this paper is not on mass supervision.
Abstract: This paper aims to contribute to debates about ‘mass supervision’ by exploring its penal character as a lived experience. It begins with a review of recent studies that have used ethnographic metho...
59 citations
Journal Article•
TL;DR: The authors present a síntesis del Enfoque Ontosemiótico (EOS) sobre el conocimiento y la instrucción matemáticos, resaltando los problemas, principios y métodos de investigación en didáctica de las mateme-ticas que se abordan con este marco teórico.
Abstract: Presentamos una síntesis del Enfoque Ontosemiótico (EOS), sobre el conocimiento y la instrucción matemáticos, resaltando los problemas, principios y métodos de investigación en didáctica de las matemáticas que se abordan con este marco teórico. Se argumenta que la didáctica, como disciplina científica y tecnológica, debe abordar los problemas epistemológico, ontológico, semiótico-cognitivo, educativo-instruccional, ecológico y de optimización de la instrucción, así como la formación de profesores. El EOS asume principios antropológicos, pragmáticos y semióticos para investigar estos problemas, además de principios socioculturales para abordar los problemas educativo-instruccionales. La noción de idoneidad didáctica proporciona un criterio sistémico para tratar el problema de optimización de los procesos de instrucción matemática.
59 citations
TL;DR: This paper provides details of a newly created dataset of Chinese text with about 1 million Chinese characters from 3 850 unique ones annotated by experts in over 30 000 street view images and gives baseline results using state-of-the-art methods.
Abstract: In this paper, we introduce a very large Chinese text dataset in the wild. While optical character recognition (OCR) in document images is well studied and many commercial tools are available, the detection and recognition of text in natural images is still a challenging problem, especially for some more complicated character sets such as Chinese text. Lack of training data has always been a problem, especially for deep learning methods which require massive training data. In this paper, we provide details of a newly created dataset of Chinese text with about 1 million Chinese characters from 3 850 unique ones annotated by experts in over 30 000 street view images. This is a challenging dataset with good diversity containing planar text, raised text, text under poor illumination, distant text, partially occluded text, etc. For each character, the annotation includes its underlying character, bounding box, and six attributes. The attributes indicate the character’s background complexity, appearance, style, etc. Besides the dataset, we give baseline results using state-of-the-art methods for three tasks: character recognition (top-1 accuracy of 80.5%), character detection (AP of 70.9%), and text line detection (AED of 22.1). The dataset, source code, and trained models are publicly available.
56 citations
TL;DR: In this paper, it was shown that most supercuspidal representations of a tamely ramified reductive p-adic group arise from pairs (S,\theta), where S is a tame elliptic maximal torus of G, and \theta is a character of S satisfying a simple root-theoretic property.
Abstract: We show that, in good residual characteristic, most supercuspidal representations of a tamely ramified reductive p-adic group G arise from pairs (S,\theta), where S is a tame elliptic maximal torus of G, and \theta is a character of S satisfying a simple root-theoretic property. We then give a new expression for the roots of unity that appear in the Adler-DeBacker-Spice character formula for these supercuspidal representations and use it to show that this formula bears a striking resemblance to the character formula for discrete series representations of real reductive groups. Led by this, we explicitly construct the local Langlands correspondence for these supercuspidal representations and prove stability and endoscopic transfer in the case of toral representations. In large residual characteristic this gives a construction of the local Langlands correspondence for almost all supercuspidal representations of reductive p-adic groups.
53 citations
01 Jun 2019
TL;DR: A novel word-character LSTM(WC-LSTM) model is proposed to add word information into the start or the end character of the word, alleviating the influence of word segmentation errors while obtaining the word boundary information.
Abstract: A recently proposed lattice model has demonstrated that words in character sequence can provide rich word boundary information for character-based Chinese NER model. In this model, word information is integrated into a shortcut path between the start and the end characters of the word. However, the existence of shortcut path may cause the model to degenerate into a partial word-based model, which will suffer from word segmentation errors. Furthermore, the lattice model can not be trained in batches due to its DAG structure. In this paper, we propose a novel word-character LSTM(WC-LSTM) model to add word information into the start or the end character of the word, alleviating the influence of word segmentation errors while obtaining the word boundary information. Four different strategies are explored in our model to encode word information into a fixed-sized representation for efficient batch training. Experiments on benchmark datasets show that our proposed model outperforms other state-of-the-arts models.
53 citations
TL;DR: In this paper, a simple and complete construction of infinite families of consistent, modular-covariant pairs of characters satisfying the basic requirements to describe two-character RCFT was provided.
Abstract: We provide a simple and complete construction of infinite families of consistent, modular-covariant pairs of characters satisfying the basic requirements to describe twocharacter RCFT. These correspond to solutions of generic second-order modular linear differential equations. To find these solutions, we first construct “quasi-characters” from the Kaneko-Zagier equation and subsequent works by Kaneko and collaborators, together with coset dual generalisations that we provide in this paper. We relate our construction to the Hecke images recently discussed by Harvey and Wu.
02 Jun 2019
TL;DR: The results of a statistical analysis of OCR errors on four document collections are described and several suggestions related to the design and implementation of effective OCR post-processing approaches are given.
Abstract: Post-OCR is an important processing step that follows optical character recognition (OCR) and is meant to improve the quality of OCR documents by detecting and correcting residual errors. This paper describes the results of a statistical analysis of OCR errors on four document collections. Five aspects related to general OCR errors are studied and compared with human-generated misspellings, including edit operations, length effects, erroneous character positions, real-word vs. non-word errors, and word boundaries. Based on the observations from the analysis we give several suggestions related to the design and implementation of effective OCR post-processing approaches.
TL;DR: GlyphGAN as discussed by the authors is a style-consistent font generation method based on GANs, where the input vector for the generator network consists of two vectors: character class vector and style vector.
Abstract: In this paper, we propose GlyphGAN: style-consistent font generation based on generative adversarial networks (GANs). GANs are a framework for learning a generative model using a system of two neural networks competing with each other. One network generates synthetic images from random input vectors, and the other discriminates between synthetic and real images. The motivation of this study is to create new fonts using the GAN framework while maintaining style consistency over all characters. In GlyphGAN, the input vector for the generator network consists of two vectors: character class vector and style vector. The former is a one-hot vector and is associated with the character class of each sample image during training. The latter is a uniform random vector without supervised information. In this way, GlyphGAN can generate an infinite variety of fonts with the character and style independently controlled. Experimental results showed that fonts generated by GlyphGAN have style consistency and diversity different from the training images without losing their legibility.
TL;DR: This work has developed a CNN model from scratch by training the model with the Tamil characters in offline mode and has achieved good recognition results on both the training and testing datasets.
TL;DR: The science of well-being has catalyzed a tremendous amount of research with no area more robust in application and impact than the science of character strengths as mentioned in this paper, and the empirical links between ch...
Abstract: The science of well-being has catalyzed a tremendous amount of research with no area more robust in application and impact than the science of character strengths. As the empirical links between ch...
01 Jun 2019
TL;DR: Li et al. as mentioned in this paper proposed a new deep CNN for determining people Looking At Each Other (LAEO) in videos, which takes spatio-temporal tracks as input and reasons about the whole track.
Abstract: Capturing the ‘mutual gaze’ of people is essential for understanding and interpreting the social interactions between them. To this end, this paper addresses the problem of detecting people Looking At Each Other (LAEO) in video sequences. For this purpose, we propose LAEO-Net, a new deep CNN for determining LAEO in videos. In contrast to previous works, LAEO-Net takes spatio-temporal tracks as input and reasons about the whole track. It consists of three branches, one for each character’s tracked head and one for their relative position. Moreover, we introduce two new LAEO datasets: UCO-LAEO and AVA-LAEO. A thorough experimental evaluation demonstrates the ability of LAEO-Net to successfully determine if two people are LAEO and the temporal window where it happens. Our model achieves state-of-the-art results on the existing TVHID-LAEO video dataset, significantly outperforming previous approaches.
TL;DR: Compared to nonhaptic feedback conditions in which virtual characters bump into the participant who is immersed in a virtual environment, significant differences were found in embodiment, realism of virtual character interaction, and haptic feedback realism.
Abstract: In this study, we compare haptic feedback and nonhaptic feedback conditions in which virtual characters bump into the participant who is immersed in a virtual environment. A questionnaire was developed to determine the influence of haptic feedback on a number of concepts (presence, embodiment, positive and negative affect, interaction realism with virtual character, and haptic feedback realism). Physiological data were also collected using galvanic skin response (GSR) to investigate the influence of haptic feedback on physiological arousal during human–virtual character interaction. Five conditions were developed (no haptic feedback, full intensity, half intensity, incorrect position, and delayed timing) to determine which aspects of haptic feedback are most important in influencing participant responses. Significant differences were found in embodiment, realism of virtual character interaction, and haptic feedback realism. In addition, significant differences were found in GSR amplitude after the first interaction with the virtual character. Implications for further research are discussed.
TL;DR: Character education is a growing discipline in recent times with the intent of optimizing student's ethical behaviour as mentioned in this paper. The outcome of character education has been seen in the continuous encouragement and preparation of a solid background of the leaders of tomorrow.
TL;DR: Due to low physical workout, high-calorie intake, and bad behavioral character, people were affected by cardiological disorders.
Abstract: Due to low physical workout, high-calorie intake, and bad behavioral character, people were affected by cardiological disorders. Every instant, one out of four deaths are due to heart-related ailme...
TL;DR: A novel radical aggregation network (RAN) is proposed for few-shot/zero-shot offline handwritten Chinese character recognition and experiments show that the method can effectively recognize unseen handwritten characters given few support samples, while maintaining a high performance on seen characters.
TL;DR: In this article, a review of current literature and existing programs on young children's creativity, creative character, and creative problem solving was conducted to develop and evaluate a creative problem-solving program for enhancing creativity and character.
Posted Content•
TL;DR: CharNet as discussed by the authors directly outputs bounding boxes of words and characters, with corresponding character labels, and uses character as basic element, allowing them to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch.
Abstract: Recent progress has been made on developing a unified framework for joint text detection and recognition in natural images, but existing joint models were mostly built on two-stage framework by involving ROI pooling, which can degrade the performance on recognition task. In this work, we propose convolutional character networks, referred as CharNet, which is an one-stage model that can process two tasks simultaneously in one pass. CharNet directly outputs bounding boxes of words and characters, with corresponding character labels. We utilize character as basic element, allowing us to overcome the main difficulty of existing approaches that attempted to optimize text detection jointly with a RNN-based recognition branch. In addition, we develop an iterative character detection approach able to transform the ability of character detection learned from synthetic data to real-world images. These technical improvements result in a simple, compact, yet powerful one-stage model that works reliably on multi-orientation and curved text. We evaluate CharNet on three standard benchmarks, where it consistently outperforms the state-of-the-art approaches [25, 24] by a large margin, e.g., with improvements of 65.33%->71.08% (with generic lexicon) on ICDAR 2015, and 54.0%->69.23% on Total-Text, on end-to-end text recognition. Code is available at: this https URL.
TL;DR: The authors argue that character education should take account of intersubjective relationships in schools and the wider social context within which character is shaped, as an empathetic connection to others arising from their intrinsic worth is a prerequisite for moral action.
Abstract: As part of a revival of interest in character education, English schools are required to teach the new ‘three Rs’: resilience; respect for ‘fundamental British values’; and responsibility for one’s own well-being. School inspectors evaluate children’s resilience, whilst the Department for Education has offered financial incentives to schools that ‘instil’ mental toughness and ‘grit’. However, this approach may prove counterproductive because it relies on teaching about desirable character traits and neglects the interpersonal relations within which ‘character’ develops. This paper argues for an alternative ‘fourth R’ of character education, based on Honneth’s theory of recognition. As an empathetic connection to others arising from their intrinsic worth, recognition precedes cognition and a detached, neutral stance. Recognition of others as a prerequisite for moral action provides a foundation for an approach to character education that takes account of intersubjective relationships in schools and the wider social context within which character is shaped.
TL;DR: In this article, the authors used projective mapping with 3D shapes and colors, along with a wine label matching study, to test whether chardonnay odors of different character (buttery, citrus, floral, smoky, and vegetable) were associated with certain colors and shapes.
TL;DR: The proposed technique utilizes effective Tamil character recognition by means of optimal artificial neural network, which is used for recognizing the characters from scanned input digital image and converting them into machine editable form.
Abstract: Nowadays, recognition of machine printed or hand printed document is essential part in applications. Optical character recognition is one of the techniques which are used to convert the printed or hand written file into its corresponding text format. Tamil is the south Indian language spoken widely in Tamil Nadu. It has the longest unbroken literary tradition amongst Dravidian language. Tamil character recognition (TCR) is one of the challenging tasks in optimal character recognition. It is used for recognizing the characters from scanned input digital image and converting them into machine editable form. Recognition of handwritten in Tamil character is very difficult, due to variations in size, style and orientation angle. Character editing and reprinting of text document that were printed on paper are time consuming and low accuracy. In order to overcome this problem, the proposed technique utilizes effective Tamil character recognition. The proposed method has four main process such as preprocessing process, segmentation process, feature extraction process and recognition process. For preprocessing, the input image is fed to Gaussian filter, Binarization process and skew detection technique. Then the segmentation process is carried out, here line and character segmentation is done. From the segmented output, the features are extracted. After that the feature extraction, the Tamil character is recognized by means of optimal artificial neural network. Here the traditional neural network is modified by means of optimization algorithm. In neural network, the weights are optimized by means of Elephant Herding Optimization. The performance of the proposed method is assessed with the help of the metrics namely Sensitivity, Specificity and Accuracy. The proposed approach is experimented and its results are analyzed to visualize the performance. The proposed approach will be implemented in MATLAB.
12 May 2019
TL;DR: This article proposed a representation mixing method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference.
Abstract: Recent character and phoneme-based parametric TTS systems using deep learning have shown strong performance in natural speech generation. However, the choice between character or phoneme input can create serious limitations for practical deployment, as direct control of pronunciation is crucial in certain cases. We demonstrate a simple method for combining multiple types of linguistic information in a single encoder, named representation mixing, enabling flexible choice between character, phoneme, or mixed representations during inference. Experiments and user studies on a public audiobook corpus show the efficacy of our approach.
TL;DR: In this paper, the authors completely characterize the injectivity of the Borel map by means of the theory of proximate orders, showing that a growth index ω(M ) turns out to put apart the values of the opening of the sector for which injectivity holds or not.
TL;DR: Experimental results on benchmark license plate databases, as well as video databases, namely, ICDAR 2015, YVT video, and natural scene data, show that the proposed technique is effective and useful.
Abstract: Developing an automatic license plate recognition system that can cope with multiple factors is challenging and interesting in the current scenario. In this paper, we introduce a new concept called partial character reconstruction to segment characters of license plates to enhance the performance of license plate recognition systems. Partial character reconstruction is proposed based on the characteristics of stroke width in the Laplacian and gradient domain in a novel way. This results in character components with incomplete shapes. The angular information of character components determined by PCA and the major axis are then studied by considering regular spacing between characters and aspect ratios of character components in a new way for segmenting characters. Next, the same stroke width properties are used for reconstructing the complete shape of each character in the gray domain rather than in the gradient domain, which helps in improving the recognition rate. Experimental results on benchmark license plate databases, namely, MIMOS, Medialab, UCSD data, Uninsbria data Challenged data, as well as video databases, namely, ICDAR 2015, YVT video, and natural scene data, namely, ICDAR 2013, ICDAR 2015, SVT, MSRA, show that the proposed technique is effective and useful.
TL;DR: The position of animals that share our lives is a growing area of interest, as we recognise and value the interconnected character of our environment as mentioned in this paper, and we want to go further in this direction.
Abstract: The position of animals that share our lives is a growing area of interest, as we recognise and value the interconnected character of our environment. Radhika Govindrajan asks us to go further in o...
30 Jun 2019
TL;DR: In this paper, the authors emphasized that the family is the central pillar as the first means for instilling character education so that a solid Muslim personality is shaped, and that failure of family failure to instill character values towards a child will complicate other institutions outside of the family (including schools) in an effort to improve the character of the child.
Abstract: Indonesia as a nation-state and ethnic group that the majority is Muslim, has many of heritage value that can shape its personality into a superior character. Seeing the phenomenon of the industrial era 4.0 as if forgetting the character values as human identity which inevitably lost the divine values. To build the nation's next generation with good character is the responsibility of all lines of life, because education is truly our shared responsibility, for sure this is not an easy matter, therefore awareness of all parties is needed that character education is very important to be implemented. Although all parties are responsible for character education, but the family is the central pillar as the first means for instilling character education so that a solid Muslim personality is shaped. Family failure in instilling character values towards a child will complicate other institutions outside of the family (including schools) in an effort to improve the character of the child. Transfer of habituation based values is an alternative process that must be carried out continuously, so that the formation of children's character individually, restoring divine values in Muslim personality. thus it will form socio-culture in society and socio-cultural shape the country that has character.