Speed Reading Tool Powered by Artificial Intelligence for Students with ADHD, Dyslexia, or Short Attention Span

• URL: http://arxiv.org/abs/2307.14544v1
• Date: Wed, 26 Jul 2023 23:47:14 GMT
• Title: Speed Reading Tool Powered by Artificial Intelligence for Students with ADHD, Dyslexia, or Short Attention Span
• Authors: Megat Irfan Zackry Bin Ismail Ahmad Nazran bin Yusri Muhammad Hafizzul Bin Abdul Manap Muhammad Muizzuddin Bin Kamarozaman
• Abstract summary: This paper presents a novel approach to assist students with dyslexia, ADHD, and short attention span in digesting text-based information more efficiently. The proposed solution utilizes the Multilayer Perceptron (MLP) algorithm for complex text processing and summarization tasks. The paper discusses the methodology, implementation, and results of the AI-based speed reading tool.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: This paper presents a novel approach to assist students with dyslexia, ADHD, and short attention span in digesting any text-based information more efficiently. The proposed solution utilizes the Multilayer Perceptron (MLP) algorithm for complex text processing and summarization tasks. The tool leverages the T5 (Text-to-Text Transfer Transformer) model from Hugging Face, which treats every NLP task as a text generation task. The model is fine-tuned on specific tasks using a smaller dataset. The NLTK's Punkt Sentence Tokenizer is used to divide a text into a list of sentences. The application is served using Flask, a lightweight web server and framework. The tool also applies principles from Bionic Reading to enhance readability, which includes a bolding function and adjustments to line, word, and character spacing. The paper discusses the methodology, implementation, and results of the AI-based speed reading tool.

Related papers

• Scalable and Domain-General Abstractive Proposition Segmentation [20.532804009152255]
  We focus on the task of abstractive proposition segmentation. We propose a scalable, yet accurate, proposition segmentation model. As a key contribution of the paper, we share an easy-to-use API for NLP practitioners to use.
  arXiv  Detail & Related papers  (2024-06-28T10:24:31Z)
• Training LLMs over Neurally Compressed Text [55.11828645767342]
  This paper explores the idea of training large language models (LLMs) over highly compressed text. We propose Equal-Info Windows, a novel compression technique whereby text is segmented into blocks that each compress to the same bit length. We demonstrate effective learning over neurally compressed text that improves with scale, and outperforms byte-level baselines by a wide margin on perplexity and inference speed benchmarks.
  arXiv  Detail & Related papers  (2024-04-04T17:48:28Z)
• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• TextFormer: A Query-based End-to-End Text Spotter with Mixed Supervision [61.186488081379]
  We propose TextFormer, a query-based end-to-end text spotter with Transformer architecture. TextFormer builds upon an image encoder and a text decoder to learn a joint semantic understanding for multi-task modeling. It allows for mutual training and optimization of classification, segmentation, and recognition branches, resulting in deeper feature sharing.
  arXiv  Detail & Related papers  (2023-06-06T03:37:41Z)
• Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learning [51.90524745663737]
  A key innovation is our use of explanations as features, which can be used to boost GNN performance on downstream tasks. Our method achieves state-of-the-art results on well-established TAG datasets. Our method significantly speeds up training, achieving a 2.88 times improvement over the closest baseline on ogbn-arxiv.
  arXiv  Detail & Related papers  (2023-05-31T03:18:03Z)
• Reducing Sequence Length by Predicting Edit Operations with Large Language Models [50.66922361766939]
  This paper proposes predicting edit spans for the source text for local sequence transduction tasks. We apply instruction tuning for Large Language Models on the supervision data of edit spans. Experiments show that the proposed method achieves comparable performance to the baseline in four tasks.
  arXiv  Detail & Related papers  (2023-05-19T17:51:05Z)
• Revisiting the Roles of "Text" in Text Games [102.22750109468652]
  This paper investigates the roles of text in the face of different reinforcement learning challenges. We propose a simple scheme to extract relevant contextual information into an approximate state hash. Such a lightweight plug-in achieves competitive performance with state-of-the-art text agents.
  arXiv  Detail & Related papers  (2022-10-15T21:52:39Z)
• Zero-shot Learning by Generating Task-specific Adapters [38.452434222367515]
  We introduce Hypter, a framework that improves zero-shot transferability by training a hypernetwork to generate task-specific adapters from task descriptions. This formulation enables learning at task level, and greatly reduces the number of parameters by using light-weight adapters.
  arXiv  Detail & Related papers  (2021-01-02T10:50:23Z)
• ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation [0.9542023122304099]
  We present ScrabbleGAN, a semi-supervised approach to synthesize handwritten text images. ScrabbleGAN relies on a novel generative model which can generate images of words with an arbitrary length.
  arXiv  Detail & Related papers  (2020-03-23T21:41:19Z)
• Hybrid Attention-Based Transformer Block Model for Distant Supervision Relation Extraction [20.644215991166902]
  We propose a new framework using hybrid attention-based Transformer block with multi-instance learning to perform the DSRE task. The proposed approach can outperform the state-of-the-art algorithms on the evaluation dataset.
  arXiv  Detail & Related papers  (2020-03-10T13:05:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.