Towards robust real-world historical handwriting recognition

In this thesis, we make a bridge from the past to the future by using artificial-intelligence methods for text recognition in a historical Dutch collection of the Natuurkundige Commissie that explored Indonesia (1820-1850). In spite of the successes of systems like 'ChatGPT', reading historical handwriting is still quite challenging for AI. Whereas GPT-like methods work on digital texts, historical manuscripts are only available as an extremely diverse collections of (pixel) images. Despite the great results, current DL methods are very data greedy, time consuming, heavily dependent on the human expert from the humanities for labeling and require machine-learning experts for designing the models. Ideally, the use of deep learning methods should require minimal human effort, have an algorithm observe the evolution of the training process, and avoid inefficient use of the already sparse amount of labeled data. We present several approaches towards dealing with these problems, aiming to improve the robustness of current methods and to improve the autonomy in training. We applied our novel word and line text recognition approaches on nine data sets differing in time period, language, and difficulty: three locally collected historical Latin-based data sets from Naturalis, Leiden; four public Latin-based benchmark data sets for comparability with other approaches; and two Arabic data sets. Using ensemble voting of just five neural networks, a level of accuracy was achieved which required hundreds of neural networks in earlier studies. Moreover, we increased the speed of evaluation of each training epoch without the need of labeled data

Effects of technology integration in K-12 settings

This review of literature assessed the use and effect of technology in the K-12 public school setting. Local, state and federal governments annually invest billions of dollars to purchase technology; yet, there is still a great deal of uncertainty and debate about the ability of technology to improve classroom teaching and learning. Several types of technologies are available to enhance student learning in the classroom. Everything from audio and video content to handheld technologies and notebook computing has been used in classrooms, and new WEB 2.0-based technology such as Wikis and Blogs are emerging. While it is impossible for any one researcher to present information for all technologies in use in public classrooms across the United States, the goal of this review is to show what is available, who is in control of the technology and how it can be used in the classroom to enhance the learning process. A primary issue of concern for administrators and policy makers in determining whether or not to implement technology is the lack of statistically significant data indicating the effectiveness of current technologies. While not measured by quantitative analyses of standardized tests, findings suggest that the positive influences of technology integration are revealed through more qualitative research

A distributed knowledge-based approach to flexible automation : the contract-net framework

Includes bibliographical references (p. 26-29)

Headsprout Early Reading for Students At Risk for Reading Failure

This study examined the efficacy of using Headsprout Early Reading (Headsprout, 2007) to supplement a balanced literacy curriculum for kindergarten and first grade students in a suburban public school system. Headsprout, which is an example of computer aided instruction (CAI), provided internet-based, supplemental reading instruction that incorporates the five critical components of reading instruction cited by the National Reading Panel (NRP, 2000). The school system implemented Headsprout as a standard protocol, Tier 2 intervention within their Response to Intervention (RTI) process. The study included kindergarten and first grade students from across the school system who were identified as at risk for reading failure based on fall Dynamic Indicators of Basic Early Literacy (DIBELS) scores. Kindergarten and first grade students identified as at risk for reading failure who participated in Headsprout were compared with matched groups of kindergarten and first grade students who did not participate in Headsprout. Overall, neither kindergarten nor first grade students who participated in Headsprout gained meaningful educational benefit from the CAI instruction provided by Headsprout beyond the benefit they received from participating in the general education, RTI Tier 1, balanced literacy curriculum that was available to all kindergarten and first grade students

How Can Neuroimaging Inform Our Treatment of Reading Disorders in Children With Learning Disabilities?

Submitted in partial fulfillment of the requirements of the Master of Arts Special Education degree at the University of Alaska SoutheastNeuroimaging technology in the last two decades has allowed a direct 3 dimensional view of the processing activity in an individual’s brain while completing a particular cognitive task enabling the characterization of functional brain areas and typical processing pathways. This meta-synthesis examines current studies of the neuroimaging of reading in both typical proficient readers, and individuals with developmental dyslexia and examines how these studies can inform our treatment of reading disorders. Functional Imaging studies with fMRI, DTI, MEG, and EEG techniques have documented that the brains of individuals with dyslexia have distinct physical differences and an atypical processing of reading tasks when compared to their normal reading peers. These differences in both form and function can be determined in young pre-reading age children, enabling the early identification (with 90% accuracy) of individuals that will later struggle with the disability. Researchers in the field indicate that DD is an evolving progressive disorder beginning with a distinct phonological disorder and evolves into semantic word recognition disorder as the child ages. The underlying causes for DD that are being currently advocated are a Magnocellular/vision deficit, a cerebellar deficit, and/or a phonological deficit. Studies indicate that more than one of these deficits may be contributing factors, however 90% of individuals presenting with the DD have a phonological deficit as a major contributor making this the target area of most early interventions. Many studies have contrasted the functional scans of DD readers before, and after phonological interventions in an attempt to characterize a neuro-plastic change resulting from the intervention. These contrast studies indicate that many individuals with dyslexia will normalize their atypical processing of written information to appear to process written text much like their proficient reading peers. However, there are still many individuals with dyslexia who do not respond to interventions with normalization, but instead compensate for their atypical processing of written text by recruiting disparate areas in the brain to accomplish the same task. These researchers’ results indicate central challenge of developing interventions guided by the neurology. These interventions should target activation of a given brain system identified to be the source of the deficit in an individual’s Dyslexia with the intent to induce a neuro plastic, normalizing change in brain

Rethinking the Agreement in Human Evaluation Tasks

Human evaluations are broadly thought to be more valuable the higher the inter-annotator agreement. In this paper we examine this idea. We will describe our experiments and analysis within the area of Automatic Question Generation. Our experiments show how annotators diverge in language annotation tasks due to a range of ineliminable factors. For this reason, we believe that annotation schemes for natural language generation tasks that are aimed at evaluating language quality need to be treated with great care. In particular, an unchecked focus on reduction of disagreement among annotators runs the danger of creating generation goals that reward output that is more distant from, rather than closer to, natural human-like language. We conclude the paper by suggesting a new approach to the use of the agreement metrics in natural language generation evaluation tasks

Resource-lean modeling of coherence in commonsense stories

We present a resource-lean neural recognizer for modeling coherence in commonsense stories. Our lightweight system is inspired by successful attempts to modeling discourse relations and stands out due to its simplicity and easy optimization compared to prior approaches to narrative script learning. We evaluate our approach in the Story Cloze Test demonstrating an absolute improvement in accuracy of 4.7% over state-of-the-art implementations

SMAN : Stacked Multi-Modal Attention Network for cross-modal image-text retrieval

This article focuses on tackling the task of the cross-modal image-text retrieval which has been an interdisciplinary topic in both computer vision and natural language processing communities. Existing global representation alignment-based methods fail to pinpoint the semantically meaningful portion of images and texts, while the local representation alignment schemes suffer from the huge computational burden for aggregating the similarity of visual fragments and textual words exhaustively. In this article, we propose a stacked multimodal attention network (SMAN) that makes use of the stacked multimodal attention mechanism to exploit the fine-grained interdependencies between image and text, thereby mapping the aggregation of attentive fragments into a common space for measuring cross-modal similarity. Specifically, we sequentially employ intramodal information and multimodal information as guidance to perform multiple-step attention reasoning so that the fine-grained correlation between image and text can be modeled. As a consequence, we are capable of discovering the semantically meaningful visual regions or words in a sentence which contributes to measuring the cross-modal similarity in a more precise manner. Moreover, we present a novel bidirectional ranking loss that enforces the distance among pairwise multimodal instances to be closer. Doing so allows us to make full use of pairwise supervised information to preserve the manifold structure of heterogeneous pairwise data. Extensive experiments on two benchmark datasets demonstrate that our SMAN consistently yields competitive performance compared to state-of-the-art methods