Abstract: The purpose of this chapter is to develop a formalism within which psychologists can develop detailed information processing models of the reading process. I argue that such a formalism is necessary because the usual formalisms tend to lead most naturally to bottom-up, serial, stage-by-stage models of reading. Moreover, I argue that there is a good deal of evidence suggesting that reading is best characterized as a process of applying simultaneous constraints at all levels and thereby coming up with the most probable interpretation of the input string. Although it is probably not impossible to use the usual flow chart formalisms to represent such models (have arrows pointing back from higher levels to lower levels) it is not especially natural and when carried to the extreme of a completely interacting system is not very informative (two way arrows between every pair of levels). I suggest that the formalisms designed for parallel computing applications are the best substitutions. Finally, I develop a model based on HEARSAY II and GSP and argue that such a model has many very promising features.

Abstract: This chapter synthesizes the scientific research on children's reading comprehension difficulties to address the critical issues. Reading comprehension problems must be considered within a developmental context. First, relations between different skills and comprehension proficiency may be developmentally limited such that different skills may be more or less predictive of proficiency at different points in development. Second, the language and cognitive skills that support reading comprehension may share reciprocal relations over time. The majority of work investigating the extent and source of reading comprehension difficulties has focused on children who exhibit weak reading comprehension in the presence of word identification skills. Longitudinal studies of poor comprehenders, and reading and language development more generally, provide insight into the potential for early detection of comprehension difficulties, as well as the developmental trajectories of poor comprehenders.

Abstract: Offers comprehensive guidance for practitioners, students, and researchers in psychology, psychiatry, and counseling to teach relaxation to clients. Two clinical psychologists widely known for their writings on relaxation present state-of-the-art methods for teaching clients to ease muscle and mind tension to deal with stress and anxiety disorders, as well as other conditions where stress and anxiety play a role. Bernstein and Hazlett-Stevens explain who the targets for Progressive Relaxation Training (PRT) are; the rationale, basic procedures, and variations of PRT; the setting and possible problems and solutions of PRT; and how to assess a client's progress. They also address hypnosis, drugs, and PRT, as well as PRT used in a mindfulness-based clinical practice. Case studies and evaluative research in PRT are also included. Students and practitioners in psychology, psychiatry, and counseling will find this work of interest. This book may also be useful supplemental reading for behavior modification courses and practicum courses in behavior therapy.

Abstract: This article intends to describe the effectiveness of using Canva as an MI/SD science learning medium. Globalization brings major changes in the way people live. These changes are driven by the growing development of science and technology, which has an impact on various sectors of life, including in the field of education. The design of learning media at this time not only utilizes objects that can be found in everyday life but also utilizes the digital world. Canva is an application that can be developed in the process of making science learning media that really needs the media as an introduction to information from the content of abstract learning materials. The Canva application provides a variety of interesting features that can make it easier for teachers to create learning media, one of which is the availability of various templates that can be used in the process of designing learning media, one of which is science subjects in MI/SD. In carrying out this research, the literature review method was used, namely the process of placing, obtaining, reading, and evaluating various research literature related to or related to the issue to be studied. The data described are the results of research on the effectiveness of using the Canva application that has been carried out by previous researchers.

Abstract: The COVID-19 pandemic disrupted classes in spring 2020. Temporary school closures supposedly led to a considerable learning loss, particularly for low-achieving students. Teachers faced challenges of remote learning environments. Students spent less time learning. The present study investigates the competencies of fifth graders in Baden-Württemberg, Germany, using large-scale assessments in reading and mathematics from annual mandatory tests in September (each n > 80,000). Competence scores were slightly lower in 2020 (after 2 months of school closures) compared with the three previous years (–0.07 SD for reading comprehension, –0.09 for operations, and –0.03 for numbers). Regarding mathematics, low-achieving students seem to have a learning backlog that deserves attention in future education. School characteristics such as the average sociocultural capital and the proportion of students with a migration background played a minor role in mediating the schools’ learning loss. Still, lower sociocultural capital was positively associated with larger learning loss in mathematics.

Abstract: This chapter reviews evidence about how best to teach children to read. It utilizes the term decoding to mean the use of a set of principles to access the pronunciation and the meaning of printed words. The chapter considers three questions that are central to research, practice, and policy. First, what are the principles and evidence concerning how best to teach decoding in whole-class settings? Second, how might we best support children with dyslexia to overcome problems in developing decoding skills? Third, how can we support reading comprehension in children with wider language needs? Helpful recent development has been the contribution of classroom research funded by nongovernmental bodies and charities that support evidence-based practice. Evidence already considered from meta-analyses in the National Reading Panel report and from randomized controlled trials by Torgerson et al. suggests phonics is efficacious for children with reading difficulties.

Abstract: Educators have indicated that social approaches to reading such as book talk activities are helpful for promoting students' interest in reading. However, it is not possible for teachers to interact with all students to talk about the books they have read as they have different language proficiency levels and different topics of interest. This study thus aimed to understand the affordances of a chatbot built with artificial intelligence techniques as a book talk companion, and to explore the role of the interaction in students' engagement and interest in reading. Adopting AI techniques, the chatbot in this study had basic understanding of 157 books. While students could choose any of the books to read and interact with the chatbot, the chatbot provided book talk and social affective cues to facilitate the book talk. Multiple data sources from 68 students participating in a 6-week reading activity were collected and analyzed. It was found that students perceived a high level of social connection with the chatbot. In particular, students talking with the chatbot maintained a stable level of situational interest in the value dimension, while the interest of those who did not participate in the book talk with the chatbot faded significantly. Students' perceptions of the social connection with the chatbot were closely related to their engagement in the reading activity and correlated with both their triggered-situational interest and maintained situational interest. The results provide insights into how a chatbot with AI techniques can create a positive reading experience to sustain students’ interest in learning. • AI-enabled chatbots can act as reading companions. • Chatbots maintained high level of social connection with students. • Social connection with chatbots was closely related to situational interest. • AI techniques can be an alternative to facilitate the social reading practice.

Abstract: Scientific studies of language behavior need to grapple with a large diversity of languages in the world and, for reading, a further variability in writing systems. Yet, the ability to form meaningful theories of reading is contingent on the availability of cross-linguistic behavioral data. This paper offers new insights into aspects of reading behavior that are shared and those that vary systematically across languages through an investigation of eye-tracking data from 13 languages recorded during text reading. We begin with reporting a bibliometric analysis of eye-tracking studies showing that the current empirical base is insufficient for cross-linguistic comparisons. We respond to this empirical lacuna by presenting the Multilingual Eye-Movement Corpus (MECO), the product of an international multi-lab collaboration. We examine which behavioral indices differentiate between reading in written languages, and which measures are stable across languages. One of the findings is that readers of different languages vary considerably in their skipping rate (i.e., the likelihood of not fixating on a word even once) and that this variability is explained by cross-linguistic differences in word length distributions. In contrast, if readers do not skip a word, they tend to spend a similar average time viewing it. We outline the implications of these findings for theories of reading. We also describe prospective uses of the publicly available MECO data, and its further development plans.

Abstract: Taking a fresh thematic approach to politics and society in Latin America, this introductory textbook analyzes the region's past and present in an accessible and engaging style well-suited to undergraduate students. The book provides historical insights into modern states and critical issues they are facing, with insightful analyses that are supported by empirical data, maps and timelines. Drawing upon cutting-edge research, the text considers critical topics relevant to all countries within the region such as the expansion of democracy and citizenship rights and responses to human rights abuses, corruption, and violence. Each richly illustrated chapter contains a compelling and cohesive narrative, followed by thought-provoking questions and further reading suggestions, making this text a vital resource for anyone encountering the complexities of Latin American politics for the first time in their studies.

Abstract: Video recordings of speech contain correlated audio and visual information, providing a strong signal for speech representation learning from the speaker's lip movements and the produced sound. We introduce Audio-Visual Hidden Unit BERT (AV-HuBERT), a self-supervised representation learning framework for audio-visual speech, which masks multi-stream video input and predicts automatically discovered and iteratively refined multimodal hidden units. AV-HuBERT learns powerful audio-visual speech representation benefiting both lip-reading and automatic speech recognition. On the largest public lip-reading benchmark LRS3 (433 hours), AV-HuBERT achieves 32.5% WER with only 30 hours of labeled data, outperforming the former state-of-the-art approach (33.6%) trained with a thousand times more transcribed video data (31K hours). The lip-reading WER is further reduced to 26.9% when using all 433 hours of labeled data from LRS3 and combined with self-training. Using our audio-visual representation on the same benchmark for audio-only speech recognition leads to a 40% relative WER reduction over the state-of-the-art performance (1.3% vs 2.3%). Our code and models are available at https://github.com/facebookresearch/av_hubert

Abstract: Drawing on poststructuralist approaches, Craig Martin outlines a theory of discourse, ideology, and domination that can be used by scholars and students to understand these central elements in the study of culture. The book shows how discourses are used to construct social institutions—often classist, sexist, or racist—and that those social institutions always entail a distribution of resources and capital in ways that capacitate some subject positions over others. Such asymmetrical power relations are often obscured by ideologies that offer demonstrably false accounts of why those asymmetries exist or persist. The author provides a method of reading in order to bring matters into relief, and the last chapter provides a case study that applies his theory and method to racist ideologies in the United States, which systematically function to discourage white Americans from sympathizing with poor African Americans, thereby contributing to reinforcing the latter’s place at the bottom of a racial hierarchy that has always existed in the US.

Abstract: This study examines whether text characteristics in branded Facebook image posts associate with consumer engagement and brand awareness. The examined text characteristics include i) readability indices, ii) text length, and iii) number of hashtags. A dataset of 135 image posts with description texts was exported from a Fashion retail Facebook business page providing post performance metrics in terms of engagement (expressed in likes) and awareness (expressed in reaches and impressions). Positive associations were indicated between all performance metrics and the text's length, as well as the number hashtags. The readability index of Gunning Fog revealed strong associations with both engagement and awareness, while the Flesch Kincaid reading ease index was associated only with awareness metrics of reaches and impressions. Overall, the results revealed that, the posts’ text which is easy to read, long (more than 31 words, or more than 321 characters), and contains many hashtags tends to achieve higher performance of engagement and awareness. This research contributes to prior literature by shedding light on the role of text characteristics of branded messaging in social media and offering insights for brand communication and social media message strategies.

Abstract: Assistive technology would be an immense benefit for hearing impaired people by using Audio Visual Speech Recognition (AVSR). Around 466 million people worldwide suffer from hearing loss. Hearing impaired student rely on lip reading for understanding the speech. Lack of trained sign language facilitators and high cost of assistive devices are some of the major challenges faced by hearing impaired students. In this work, we have identified a visual speech recognition technique using cutting edge deep learning models. Moreover, the existing VSR techniques are erroneous. Hence to address the gaps identified, we propose a novel technique by fusion the results from audio and visual speech. This study proposes a new deep learning based audio visual speech recognition model for efficient lip reading. In this paper, an effort has been made to improve the performance of the system significantly by achieving a lowered word error rate of about 6.59% for ASR system and accuracy of about 95% using lip reading model.

Abstract: Understanding reading abilities and their development is fundamental for language comprehension and human cognition. Now in its second edition, this book draws on research from multiple disciplines to explain reading abilities in both L1 and L2, and shows how this research can be applied in practice in order to support reading development. Research into reading has progressed a great deal since the first edition was published, so this edition has been completely updated and revised, in order to reflect these advances. All chapters present updated research studies, and completely new chapters are included on the neurocognition of reading, reading-writing relationships, and digital reading. If you want to know how reading works, no matter the language(s) involved, as well as how it can be taught effectively, this book provides a persuasive research foundation and many practical insights. It is essential reading for academic researchers and students in Applied Linguistics and TESOL.

Abstract: Those who teach children to read are continually seeking answers to several questions that will improve their approaches to helping children learn. THE SCIENCE OF READING

Abstract: Although citations are widely used to measure the influence of scientific works, research shows that many citations serve rhetorical functions and reflect little-to-no influence on the citing authors. If highly cited papers disproportionately attract rhetorical citations then their citation counts may reflect rhetorical usefulness more than influence. Alternatively, researchers may perceive highly cited papers to be of higher quality and invest more effort into reading them, leading to disproportionately substantive citations. We test these arguments using data on 17,154 randomly sampled citations collected via surveys from 9,380 corresponding authors in 15 fields. We find that most citations (54%) had little-to-no influence on the citing authors. However, citations to the most highly cited papers were 2–3 times more likely to denote substantial influence. Experimental and correlational data show a key mechanism: displaying low citation counts lowers perceptions of a paper's quality, and papers with poor perceived quality are read more superficially. The results suggest that higher citation counts lead to more meaningful engagement from readers and, consequently, the most highly cited papers influence the research frontier much more than their raw citation counts imply.

Abstract: The goal of this paper is to learn strong lip reading models that can recognise speech in silent videos. Most prior works deal with the open-set visual speech recognition problem by adapting existing automatic speech recognition techniques on top of trivially pooled visual features. Instead, in this paper, we focus on the unique challenges encountered in lip reading and propose tailored solutions. To this end, we make the following contributions: (1) we propose an attention-based pooling mechanism to aggregate visual speech representations; (2) we use sub-word units for lip reading for the first time and show that this allows us to better model the ambiguities of the task; (3) we propose a model for Visual Speech Detection (VSD), trained on top of the lip reading network. Following the above, we obtain state-of-the-art results on the challenging LRS2 and LRS3 benchmarks when training on public datasets, and even surpass models trained on large-scale industrial datasets by using an order of magnitude less data. Our best model achieves 22.6% word error rate on the LRS2 dataset, a performance unprecedented for lip reading models, significantly reducing the performance gap between lip reading and automatic speech recognition. Moreover, on the AVA-ActiveSpeaker benchmark, our VSD model surpasses all visual-only baselines and even outperforms several recent audio-visual methods.

Abstract: Reading of alphabetic writing systems, such as English, has been extensively studied and most theories and models of reading are based on findings from these studies. This practice raises a practical question regarding whether findings from alphabetic writing systems can be extended to other writing systems, such as Korean or Chinese, and a more fundamental question about the universality of reading mechanisms. In this Review, we discuss how findings from different writing systems contribute to an understanding of the universal mechanisms of reading. We first describe the unique properties of different writing systems. Then we review evidence that points to universal mechanisms common to all writing systems, followed by evidence suggesting that readers of different writing systems develop specific perceptual and cognitive mechanisms for efficient reading. These findings suggest that computational models developed for alphabetic reading cannot always account for reading in other scripts. We conclude that studies in non-alphabetic writing systems are valuable in understanding the universal and script-specific mechanisms of reading. Different languages use distinct writing systems, including the alphabetic system used for English, syllabic system for Korean, and logographic system for Chinese. In this Review, Li and colleagues discuss the similarities and differences among writing systems and consider the consequences for universal cognitive mechanisms for reading.

Abstract: The goal of this work is to recognise phrases and sentences being spoken by a talking face, with or without the audio. Unlike previous works that have focussed on recognising a limited number of words or phrases, we tackle lip reading as an open-world problem - unconstrained natural language sentences, and in the wild videos. Our key contributions are: (1) we compare two models for lip reading, one using a CTC loss, and the other using a sequence-to-sequence loss. Both models are built on top of the transformer self-attention architecture; (2) we investigate to what extent lip reading is complementary to audio speech recognition, especially when the audio signal is noisy; (3) we introduce and publicly release a new dataset for audio-visual speech recognition, LRS2-BBC, consisting of thousands of natural sentences from British television. The models that we train surpass the performance of all previous work on a lip reading benchmark dataset by a significant margin.

Abstract: To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile reading on both molecule structure and biomedical text information, we propose a knowledgeable machine reading system that bridges both types of information in a unified deep-learning framework for comprehensive biomedical research assistance. We solve the problem that existing machine reading models can only process different types of data separately, and thus achieve a comprehensive and thorough understanding of molecule entities. By grasping meta-knowledge in an unsupervised fashion within and across different information sources, our system can facilitate various real-world biomedical applications, including molecular property prediction, biomedical relation extraction and so on. Experimental results show that our system even surpasses human professionals in the capability of molecular property comprehension, and also reveal its promising potential in facilitating automatic drug discovery and documentation in the future.

Abstract: In the medical field, a doctor must have a comprehensive knowledge by reading and writing narrative documents, and he is responsible for every decision he takes for patients. Unfortunately, it is very tiring to read all necessary information about drugs, diseases and patients due to the large amount of documents that are increasing every day. Consequently, so many medical errors can happen and even kill people. Likewise, there is such an important field that can handle this problem, which is the information extraction. There are several important tasks in this field to extract the important and desired information from unstructured text written in natural language. The main principal tasks are named entity recognition and relation extraction since they can structure the text by extracting the relevant information. However, in order to treat the narrative text we should use natural language processing techniques to extract useful information and features. In our paper, we introduce and discuss the several techniques and solutions used in these tasks. Furthermore, we outline the challenges in information extraction from medical documents. In our knowledge, this is the most comprehensive survey in the literature with an experimental analysis and a suggestion for some uncovered directions.

Abstract: This paper introduces the CommonLit Ease of Readability (CLEAR) corpus, which provides unique readability scores for ~ 5000 text excerpts along with information about the excerpt's year of publishing, genre, and other metadata. The CLEAR corpus will provide researchers interested in discourse processing and reading with a resource from which to develop and test readability metrics and to model text readability. The CLEAR corpus includes a number of improvements in comparison to previous readability corpora including size, breadth of the excerpts available, which cover over 250 years of writing in two different genres, and unique readability criterion provided for each text based on teachers' ratings of text difficulty for student readers. This paper discusses the development of the corpus and presents reliability metrics for the human ratings of readability.