8,835 research outputs found
In Pursuit of Experience: The Authentic Documentation of Experience in Beat Generation Literature
Throughout their lives the authors of The Beat Generation sought an escape from the conformity of mid-century American life, in favour of fresh thrilling experiences to influence their writing. The writers of the Beat Generation developed writing methods that authentically document their real-life experiences. Therefore, this thesis examines the documentary nature of literature that came out of this Generation. The first section of the essay explores Beat literature as memoir; arguing that Kerouac's prose is based on his own first-hand experience recollected after the event. This section also argues that due to its fast pace and lack of revision, the Spontaneous Prose Method can be used by authors as a form suited to the authentic documentation of experience.
The second chapter looks at the use of transcription methods to document a moment, or specific event, written during the experience. This chapter compares Gary Snyder's Riprap and Cold Mountain Poems, Ginsberg's 'Wichita Vortex Sutra', and Kerouac's Blues Poems as poetry that authentically portrays a moment of experience to the reader. The final chapter explores the more experimental methods of documentation, and whether any authenticity was lost to experimentation. The chapter also explores the Beat use of drugs on the content and form of the literature
GenAssist: Making Image Generation Accessible
Blind and low vision (BLV) creators use images to communicate with sighted
audiences. However, creating or retrieving images is challenging for BLV
creators as it is difficult to use authoring tools or assess image search
results. Thus, creators limit the types of images they create or recruit
sighted collaborators. While text-to-image generation models let creators
generate high-fidelity images based on a text description (i.e. prompt), it is
difficult to assess the content and quality of generated images. We present
GenAssist, a system to make text-to-image generation accessible. Using our
interface, creators can verify whether generated image candidates followed the
prompt, access additional details in the image not specified in the prompt, and
skim a summary of similarities and differences between image candidates. To
power the interface, GenAssist uses a large language model to generate visual
questions, vision-language models to extract answers, and a large language
model to summarize the results. Our study with 12 BLV creators demonstrated
that GenAssist enables and simplifies the process of image selection and
generation, making visual authoring more accessible to all.Comment: For accessibility tagged pdf, please refer to the ancillary fil
Writing Facts: Interdisciplinary Discussions of a Key Concept in Modernity
"Fact" is one of the most crucial inventions of modern times. Susanne Knaller discusses the functions of this powerful notion in the arts and the sciences, its impact on aesthetic models and systems of knowledge. The practice of writing provides an effective procedure to realize and to understand facts. This concerns preparatory procedures, formal choices, models of argumentation, and narrative patterns. By considering "writing facts" and "writing facts", the volume shows why and how "facts" are a result of knowledge, rules, and norms as well as of description, argumentation, and narration. This approach allows new perspectives on »fact« and its impact on modernity
Augmented Behavioral Annotation Tools, with Application to Multimodal Datasets and Models: A Systematic Review
Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, as well as automation employed to amplify human efforts. Recent developments in machine learning models, such as Transformers, allow for training upon very large and sophisticated multimodal datasets and enable generalization across domains of knowledge. These models also herald an increasing emphasis on prompt engineering to provide qualitative fine-tuning upon the model itself, adding a novel emerging layer of direct machine learning annotation. These capabilities enable machine intelligence to recognize, predict, and emulate human behavior with much greater accuracy and nuance, a noted shortfall of which have contributed to algorithmic injustice in previous techniques. However, the scale and complexity of training data required for multimodal models presents engineering challenges. Best practices for conducting annotation for large multimodal models in the most safe and ethical, yet efficient, manner have not been established. This paper presents a systematic literature review of crowd and machine learning augmented behavioral annotation methods to distill practices that may have value in multimodal implementations, cross-correlated across disciplines. Research questions were defined to provide an overview of the evolution of augmented behavioral annotation tools in the past, in relation to the present state of the art. (Contains five figures and four tables)
ChartSumm: A Comprehensive Benchmark for Automatic Chart Summarization of Long and Short Summaries
Automatic chart to text summarization is an effective tool for the visually
impaired people along with providing precise insights of tabular data in
natural language to the user. A large and well-structured dataset is always a
key part for data driven models. In this paper, we propose ChartSumm: a
large-scale benchmark dataset consisting of a total of 84,363 charts along with
their metadata and descriptions covering a wide range of topics and chart types
to generate short and long summaries. Extensive experiments with strong
baseline models show that even though these models generate fluent and
informative summaries by achieving decent scores in various automatic
evaluation metrics, they often face issues like suffering from hallucination,
missing out important data points, in addition to incorrect explanation of
complex trends in the charts. We also investigated the potential of expanding
ChartSumm to other languages using automated translation tools. These make our
dataset a challenging benchmark for future research.Comment: Accepted as a long paper at the Canadian AI 202
CAMEL: Communicative Agents for "Mind" Exploration of Large Scale Language Model Society
The rapid advancement of conversational and chat-based language models has
led to remarkable progress in complex task-solving. However, their success
heavily relies on human input to guide the conversation, which can be
challenging and time-consuming. This paper explores the potential of building
scalable techniques to facilitate autonomous cooperation among communicative
agents and provide insight into their "cognitive" processes. To address the
challenges of achieving autonomous cooperation, we propose a novel
communicative agent framework named role-playing. Our approach involves using
inception prompting to guide chat agents toward task completion while
maintaining consistency with human intentions. We showcase how role-playing can
be used to generate conversational data for studying the behaviors and
capabilities of chat agents, providing a valuable resource for investigating
conversational language models. Our contributions include introducing a novel
communicative agent framework, offering a scalable approach for studying the
cooperative behaviors and capabilities of multi-agent systems, and
open-sourcing our library to support research on communicative agents and
beyond. The GitHub repository of this project is made publicly available on:
https://github.com/lightaime/camel
Self-supervised learning techniques for monitoring industrial spaces
Dissertação de mestrado em Matemática e ComputaçãoEste documento é uma Dissertação de Mestrado com o título ”Self-Supervised Learning Techniques for
Monitoring Industrial Spaces”e foi realizada e ambiente empresarial na empresa Neadvance - Machine Vision
S.A. em conjunto com a Universidade do Minho.
Esta dissertação surge de um grande projeto que consiste no desenvolvimento de uma plataforma de
monitorização de operações específicas num espaço industrial, denominada SMARTICS (Plataforma tecnoló gica para monitorização inteligente de espaços industriais abertos). Este projeto continha uma componente
de investigação para explorar um paradigma de aprendizagem diferente e os seus métodos - self-supervised
learning, que foi o foco e principal contributo deste trabalho. O supervised learning atingiu um limite, pois
exige anotações caras e dispendiosas. Em problemas reais, como em espaços industriais nem sempre é
possível adquirir um grande número de imagens. O self-supervised learning ajuda nesses problemas, ex traindo informações dos próprios dados e alcançando bom desempenho em conjuntos de dados de grande
escala. Este trabalho fornece uma revisão geral da literatura sobre a estrutura de self-supervised learning e
alguns métodos. Também aplica um método para resolver uma tarefa de classificação para se assemelhar
a um problema em um espaço industrial.This document is a Master’s Thesis with the title ”Self-Supervised Learning Techniques for Monitoring
Industrial Spaces” and was carried out in a business environment at Neadvance - Machine Vision S.A.
together with the University of Minho.
This dissertation arises from a major project that consists of developing a platform to monitor specific
operations in an industrial space, named SMARTICS (Plataforma tecnológica para monitorização inteligente
de espaços industriais abertos). This project contained a research component to explore a different learning
paradigm and its methods - self-supervised learning, which was the focus and main contribution of this work.
Supervised learning has reached a bottleneck as they require expensive and time-consuming annotations.
In real problems, such as in industrial spaces it is not always possible to require a large number of images.
Self-supervised learning helps these issues by extracting information from the data itself and has achieved
good performance in large-scale datasets. This work provides a comprehensive literature review of the self supervised learning framework and some methods. It also applies a method to solve a classification task to
resemble a problem in an industrial space and evaluate its performance
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