226 research outputs found
ΠΠΎΠ½ΡΠ΅ΠΏΡΡΠ°Π»ΡΠ½ΡΠ΅ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ½Π°Π½ΡΠΎΠ²ΡΡ ΠΏΠΎΡΠΎΠΊΠΎΠ² ΠΊΠΎΠΌΠΌΠ΅ΡΡΠ΅ΡΠΊΠΈΡ Π±Π°Π½ΠΊΠΎΠ²
Π Π°Π·Π²ΠΈΡΠΈΠ΅ ΡΡΠ½ΠΎΡΠ½ΠΎΠ³ΠΎ Ρ
ΠΎΠ·ΡΠΉΡΡΠ²ΠΎΠ²Π°Π½ΠΈΡ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ Π² ΡΡΠ»ΠΎΠ²ΠΈΡΡ
ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π° ΠΏΡΠΎΡΠΈΠ²ΠΎΡΠ΅ΡΠΈΠΉ, ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ²Π»ΡΡΡΡΡ ΡΠΏΠ΅ΡΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ Π΄Π»Ρ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ³ΠΎ ΡΠΎΡΡΠΎΡΠ½ΠΈΡ ΠΎΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠΉ ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΠΈ. ΠΡΠΎ ΠΏΡΠΎΠΈΡΡΠ΅ΠΊΠ°Π΅Ρ Π² Π·Π½Π°ΡΠΈΡΠ΅Π»ΡΠ½ΠΎΠΉ ΠΌΠ΅ΡΠ΅ ΠΈΠ·-Π·Π° ΡΠ±ΠΎΠ΅Π² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΈ Π² ΡΡΡΠ°Π½Π΅. ΠΠ΅ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Ρ ΠΏΠΎΠΊΠ° Π΅ΡΠ΅ Π΄ΠΎ ΠΊΠΎΠ½ΡΠ° ΠΌΠ΅ΡΡ ΠΈ ΡΠΎΡΠΌΡ Π³ΠΎΡΡΠ΄Π°ΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ Π²ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΡΡΠ²Π° Π² ΡΠΊΠΎΠ½ΠΎΠΌΠΈΠΊΡ.Π ΠΎΠ·Π²ΠΈΡΠΎΠΊ ΡΠΈΠ½ΠΊΠΎΠ²ΠΎΠ³ΠΎ Π³ΠΎΡΠΏΠΎΠ΄Π°ΡΡΠ²Π°Π½Π½Ρ Π²ΡΠ΄Π±ΡΠ²Π°ΡΡΡΡΡ Π² ΡΠΌΠΎΠ²Π°Ρ
Π±Π΅Π·Π»ΡΡΡ ΡΡΠΏΠ΅ΡΠ΅ΡΠ½ΠΎΡΡΠ΅ΠΉ, ΡΠΊΡ Ρ ΡΠΏΠ΅ΡΠΈΡΡΡΠ½ΠΈΠΌΠΈ Π΄Π»Ρ ΡΡΡΠ°ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ°Π½Ρ Π²ΡΡΡΠΈΠ·Π½ΡΠ½ΠΎΡ Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΠΈ. Π¦Π΅ Π²ΠΈΠ½ΠΈΠΊΠ°Ρ Π·Π½Π°ΡΠ½ΠΎΡ ΠΌΡΡΠΎΡ ΡΠ΅ΡΠ΅Π· Π·Π±ΠΎΡ Π΅ΠΊΠΎΠ½ΠΎΠΌΡΡΠ½ΠΎΡ ΠΏΠΎΠ»ΡΡΠΈΠΊΠΈ Π² ΠΊΡΠ°ΡΠ½Ρ. ΠΠ΅ Π²ΠΈΠ·Π½Π°ΡΠ΅Π½Ρ ΠΏΠΎΠΊΠΈ ΡΠΎ Π΄ΠΎ ΠΊΡΠ½ΡΡ ΠΌΡΡΠΈ Ρ ΡΠΎΡΠΌΠΈ Π΄Π΅ΡΠΆΠ°Π²Π½ΠΎΠ³ΠΎ Π²ΡΡΡΡΠ°Π½Π½Ρ Π² Π΅ΠΊΠΎΠ½ΠΎΠΌΡΠΊΡ
Visual Summarization of Scholarly Videos using Word Embeddings and Keyphrase Extraction
Effective learning with audiovisual content depends on many factors. Besides
the quality of the learning resource's content, it is essential to discover the
most relevant and suitable video in order to support the learning process most
effectively. Video summarization techniques facilitate this goal by providing a
quick overview over the content. It is especially useful for longer recordings
such as conference presentations or lectures. In this paper, we present an
approach that generates a visual summary of video content based on semantic
word embeddings and keyphrase extraction. For this purpose, we exploit video
annotations that are automatically generated by speech recognition and video
OCR (optical character recognition).Comment: 12 pages, 5 figure
Localizing the Common Action Among a Few Videos
This paper strives to localize the temporal extent of an action in a long
untrimmed video. Where existing work leverages many examples with their start,
their ending, and/or the class of the action during training time, we propose
few-shot common action localization. The start and end of an action in a long
untrimmed video is determined based on just a hand-full of trimmed video
examples containing the same action, without knowing their common class label.
To address this task, we introduce a new 3D convolutional network architecture
able to align representations from the support videos with the relevant query
video segments. The network contains: (\textit{i}) a mutual enhancement module
to simultaneously complement the representation of the few trimmed support
videos and the untrimmed query video; (\textit{ii}) a progressive alignment
module that iteratively fuses the support videos into the query branch; and
(\textit{iii}) a pairwise matching module to weigh the importance of different
support videos. Evaluation of few-shot common action localization in untrimmed
videos containing a single or multiple action instances demonstrates the
effectiveness and general applicability of our proposal.Comment: ECCV 202
Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes
ICD coding from electronic clinical records is a manual, time-consuming and
expensive process. Code assignment is, however, an important task for billing
purposes and database organization. While many works have studied the problem
of automated ICD coding from free text using machine learning techniques, most
use records in the English language, especially from the MIMIC-III public
dataset. This work presents results for a dataset with Brazilian Portuguese
clinical notes. We develop and optimize a Logistic Regression model, a
Convolutional Neural Network (CNN), a Gated Recurrent Unit Neural Network and a
CNN with Attention (CNN-Att) for prediction of diagnosis ICD codes. We also
report our results for the MIMIC-III dataset, which outperform previous work
among models of the same families, as well as the state of the art. Compared to
MIMIC-III, the Brazilian Portuguese dataset contains far fewer words per
document, when only discharge summaries are used. We experiment concatenating
additional documents available in this dataset, achieving a great boost in
performance. The CNN-Att model achieves the best results on both datasets, with
micro-averaged F1 score of 0.537 on MIMIC-III and 0.485 on our dataset with
additional documents.Comment: Accepted at BRACIS 202
COVID-19 therapy target discovery with context-aware literature mining
The abundance of literature related to the widespread COVID-19 pandemic is
beyond manual inspection of a single expert. Development of systems, capable of
automatically processing tens of thousands of scientific publications with the
aim to enrich existing empirical evidence with literature-based associations is
challenging and relevant. We propose a system for contextualization of
empirical expression data by approximating relations between entities, for
which representations were learned from one of the largest COVID-19-related
literature corpora. In order to exploit a larger scientific context by transfer
learning, we propose a novel embedding generation technique that leverages
SciBERT language model pretrained on a large multi-domain corpus of scientific
publications and fine-tuned for domain adaptation on the CORD-19 dataset. The
conducted manual evaluation by the medical expert and the quantitative
evaluation based on therapy targets identified in the related work suggest that
the proposed method can be successfully employed for COVID-19 therapy target
discovery and that it outperforms the baseline FastText method by a large
margin.Comment: Accepted to the 23rd International Conference on Discovery Science
(DS 2020
Multi-channel Transformers for Multi-articulatory Sign Language Translation
Sign languages use multiple asynchronous information channels (articulators),
not just the hands but also the face and body, which computational approaches
often ignore. In this paper we tackle the multi-articulatory sign language
translation task and propose a novel multi-channel transformer architecture.
The proposed architecture allows both the inter and intra contextual
relationships between different sign articulators to be modelled within the
transformer network itself, while also maintaining channel specific
information. We evaluate our approach on the RWTH-PHOENIX-Weather-2014T dataset
and report competitive translation performance. Importantly, we overcome the
reliance on gloss annotations which underpin other state-of-the-art approaches,
thereby removing future need for expensive curated datasets
Supervised phrase-boundary embeddings
We propose a new word embedding model, called SPhrase, that incorporates supervised phrase information. Our method modifies traditional word embeddings by ensuring that all target words in a phrase have exactly the same context. We demonstrate that including this information within a context window produces superior embeddings for both intrinsic evaluation tasks and downstream extrinsic tasks
Detecting Machine-obfuscated Plagiarism
Related dataset is at https://doi.org/10.7302/bewj-qx93 and also listed in the dc.relation field of the full item record.Research on academic integrity has identified online paraphrasing tools as a severe threat to the effectiveness of plagiarism detection systems. To enable the automated identification of machine-paraphrased text, we make three contributions. First, we evaluate the effectiveness of six prominent word embedding models in combination with five classifiers for distinguishing human-written from machine-paraphrased text. The best performing classification approach achieves an accuracy of 99.0% for documents and 83.4% for paragraphs. Second, we show that the best approach outperforms human experts and established plagiarism detection systems for these classification tasks. Third, we provide a Web application that uses the best performing classification approach to indicate whether a text underwent machine-paraphrasing. The data and code of our study are openly available.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152346/1/Foltynek2020_Paraphrase_Detection.pdfDescription of Foltynek2020_Paraphrase_Detection.pdf : Foltynek2020_Paraphrase_Detectio
How Common is Common Human Reason?:The Plurality of Moral Perspectives and Kantβs Ethics
In his practical philosophy, Kant aims to systematize and ground a conception of morality that every human being already in some form is supposedly committed to in virtue of her common human reason. While Kantians especially in the last few years have explicitly acknowledged the central role of common human reason for a correct understanding of Kantβs ethics, there has been very little detailed critical discussion of the very notion of a common human reason as Kant envisages it. Sticker critically discusses in what ways Kant is committed to the notion that there are certain rational insights and rational capacities that all humans share, and thus investigates critically how Kant thinks moral normativity appears to the common human being, the rational agent who did not enjoy special education or philosophical training
- β¦