9,492 research outputs found
Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 212
A bibliography listing 146 reports, articles, and other documents introduced into the NASA scientific and technical information system is presented. The subject coverage concentrates on the biological, psychological, and environmental factors involved in atmospheric and interplanetary flight. Related topics such as sanitary problems, pharmacology, toxicology, safety and survival, life support systems, and exobiology are also given attention
Discovering topic structures of a temporally evolving document corpus
In this paper we describe a novel framework for the discovery of the topical content of a data corpus, and the tracking of its complex structural changes across the temporal dimension. In contrast to previous work our model does not impose a prior on the rate at which documents are added to the corpus nor does it adopt the Markovian assumption which overly restricts the type of changes that the model can capture. Our key technical contribution is a framework based on (i) discretization of time into epochs, (ii) epoch-wise topic discovery using a hierarchical Dirichlet process-based model, and (iii) a temporal similarity graph which allows for the modelling of complex topic changes: emergence and disappearance, evolution, splitting, and merging. The power of the proposed framework is demonstrated on two medical literature corpora concerned with the autism spectrum disorder (ASD) and the metabolic syndrome (MetS)—both increasingly important research subjects with significant social and healthcare consequences. In addition to the collected ASD and metabolic syndrome literature corpora which we made freely available, our contribution also includes an extensive empirical analysis of the proposed framework. We describe a detailed and careful examination of the effects that our algorithms’s free parameters have on its output and discuss the significance of the findings both in the context of the practical application of our algorithm as well as in the context of the existing body of work on temporal topic analysis. Our quantitative analysis is followed by several qualitative case studies highly relevant to the current research on ASD and MetS, on which our algorithm is shown to capture well the actual developments in these fields.Publisher PDFPeer reviewe
An Empirical Study of AI Generated Text Detection Tools
Since ChatGPT has emerged as a major AIGC model, providing high-quality
responses across a wide range of applications (including software development
and maintenance), it has attracted much interest from many individuals. ChatGPT
has great promise, but there are serious problems that might arise from its
misuse, especially in the realms of education and public safety. Several AIGC
detectors are available, and they have all been tested on genuine text.
However, more study is needed to see how effective they are for multi-domain
ChatGPT material. This study aims to fill this need by creating a multi-domain
dataset for testing the state-of-the-art APIs and tools for detecting
artificially generated information used by universities and other research
institutions. A large dataset consisting of articles, abstracts, stories, news,
and product reviews was created for this study. The second step is to use the
newly created dataset to put six tools through their paces. Six different
artificial intelligence (AI) text identification systems, including "GPTkit,"
"GPTZero," "Originality," "Sapling," "Writer," and "Zylalab," have accuracy
rates between 55.29 and 97.0%. Although all the tools fared well in the
evaluations, originality was particularly effective across the board.Comment: 15 Pages, 4 Figures, 2 Tables, 42 Reference
La estructura retórica del resumen (abstract) en las disciplinas arte y diseño : un estudio descriptivo
Maestría en Inglés con Orientación en Lingüística AplicadaAs an effective means of representing the research article, the abstract has increasingly
become an essential part of this genre. For that reason, understanding the rhetorical
conventions that govern abstract writing in their respective fields may help students and
novice researchers acquire reading and writing skills in their fields of specialization. Recent
research on the rhetorical features of abstracts has revealed broad patterns of regularity as
well as disciplinary variation. Although several investigations have focused their analysis
on a variety of disciplines, no study appears to have explored the rhetorical structure of
abstracts in the fields of Art and Design. The present research, therefore, examines the
rhetorical moves and main linguistic features of Art and Design abstracts, and proposes a
schema for the abstract genre in each of these disciplinary domains. To conduct the study, a
corpus of 30 abstracts from four high-impact journals was compiled, and subjected to a
move analysis (Swales, 1981, 1990) using the analytical framework proposed by Pho
(2008), and the methodology suggested by Dudley-Evans (1994) and Holmes (1997). The
results reveal that although Art and Design abstracts bear some similarities, they also show
some differences that result in distinct emerging patterns. Based on these findings, two
models are proposed of the rhetorical elements that are constitutive of each discipline. The
outcome of this research has pedagogical implications for students, novice researchers and
teachers within ESP (English for Specific Purposes) contexts.Fil: Caturegli, Alicia. Universidad Nacional de Córdoba. Facultad de Lenguas; Argentina
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Advancing Artificial Intelligence in Sensors, Signals, and Imaging Informatics.
ObjectiveTo identify research works that exemplify recent developments in the field of sensors, signals, and imaging informatics.MethodA broad literature search was conducted using PubMed and Web of Science, supplemented with individual papers that were nominated by section editors. A predefined query made from a combination of Medical Subject Heading (MeSH) terms and keywords were used to search both sources. Section editors then filtered the entire set of retrieved papers with each paper having been reviewed by two section editors. Papers were assessed on a three-point Likert scale by two section editors, rated from 0 (do not include) to 2 (should be included). Only papers with a combined score of 2 or above were considered.ResultsA search for papers was executed at the start of January 2019, resulting in a combined set of 1,459 records published in 2018 in 119 unique journals. Section editors jointly filtered the list of candidates down to 14 nominations. The 14 candidate best papers were then ranked by a group of eight external reviewers. Four papers, representing different international groups and journals, were selected as the best papers by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board.ConclusionsThe fields of sensors, signals, and imaging informatics have rapidly evolved with the application of novel artificial intelligence/machine learning techniques. Studies have been able to discover hidden patterns and integrate different types of data towards improving diagnostic accuracy and patient outcomes. However, the quality of papers varied widely without clear reporting standards for these types of models. Nevertheless, a number of papers have demonstrated useful techniques to improve the generalizability, interpretability, and reproducibility of increasingly sophisticated models
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