Tuesday, June 22, 2021

A NOVEL BIT ALLOCATION ALGORITHM IN MULTI-VIEW VIDEO

Author :  Tao Yan

Affiliation :  Putian University

Country :  China

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 05, May, 2020

Abstract :

The difficulty of rate control for Multi-view video coding(MVC) is how to allocate bits between views. The results of our previous research including the bit allocation among viewpoints uses the correlation analysis among viewpoints to predict the weight of each viewpoint. But when the scene changes, this prediction method will produce a lot of errors. Therefore, this article avoids this situation happening through scene detection. The core of the algorithm is to first divide all images into 6 types of encoded frames according to the structural relationship between disparity prediction and motion prediction, and improve the binomial rate distortion model, and then perform inter-view, frame layer, and basic unit based on the encoded information. Layer bit allocation and code rate control. In this paper, a reasonable bit rate is allocated between viewpoints based on the encoded information, and the frame layer bit rate is allocated using frame complexity and time-domain activity. Experimental simulation results show that the algorithm can effectively control the bit rate of MVC, while maintaining efficient coding efficiency, compared with the current MVC using JVT with fixed quantization parameters.

Keyword :  MVC, Quantization parameters, Bit allocation, Rate distortion model, Basic unit layer.

For More Details https://aircconline.com/csit/papers/vol10/csit100512.pdf

Thursday, June 10, 2021

FREE-TEXT AND STRUCTURED CLINICAL TIME SERIES FOR PATIENT OUTCOME PREDICTIONS

Author :  Emilia Apostolova PhD

Affiliation :  Language.ai, Chicago, IL

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 05, May, 2020

Abstract :

While there has been considerable progress in building deep learning models based on clinical time series data, overall machine learning (ML) performance remains modest. Typical ML applications struggle to combine various heterogenous sources of Electronic Medical Record (EMR) data, often recorded as a combination of free-text clinical notes and structured EMR data. The goal of this work is to develop an approach for combining such heterogenous EMR sources for time-series based patient outcome predictions. We developed a deep learning framework capable of representing free-text clinical notes in a low dimensional vector space, semantically representing the overall patient medical condition. The free-text based time-series vectors were combined with time-series of vital signs and lab results and used to predict patients at risk of developing a complex and deadly condition: acute respiratory distress syndrome. Results utilizing early data show significant performance improvement and validate the utility of the approach.

Keyword :  Natural Language Processing; Clinical NLP; Time-series data; Machine Learning; Deep Learning; Free-text and structured data; Clinical Decision Support; ARDS; COVID-19

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100511.pdf

Wednesday, June 9, 2021

VSMBM: A NEW METRIC FOR AUTOMATICALLY GENERATED TEXT SUMMARIES EVALUATION

Author :  Alaidine Ben Ayed

Affiliation :  Université du Québec à Montréal (UQAM)

Country :  Canada

Category :  Computer Science & Information Technology

Abstract :

In this paper, we present VSMbM; a new metric for automatically generated text summaries evaluation. VSMbM is based on vector space modelling. It gives insights on to which extent retention and fidelity are met in the generated summaries. Two variants of the proposed metric, namely PCA-VSMbM and ISOMAP VSMbM, are tested and compared to Recall-Oriented Understudy for Gisting Evaluation (ROUGE): a standard metric used to evaluate automatically generated summaries. Conducted experiments on the Timeline17 dataset show that VSMbM scores are highly correlated to the state-of-the-art Rouge scores.

Volume, Issue, Month, Year :  10, 05, May, 2020

Keyword :  Automatic Text Summarization, Automatic summary evaluation, Vector space modelling.

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100510.pdf

Monday, June 7, 2021

QUANTUM CRITICISM: A TAGGED NEWS CORPUS ANALYSED FOR SENTIMENT AND NAMED ENTITIES

Author :  Ashwini Badgujar

Affiliation :  University of San Francisco

Country :  USA

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 05, May, 2020

Abstract :

In this project, we continuously collect data from the RSS feeds of traditional news sources. We apply several pre-trained implementations of named entity recognition (NER) tools, quantifying the success of each implementation. We also perform sentiment analysis of each news article at the document, paragraph and sentence level, with the goal of creating a corpus of tagged news articles that is made available to the public through a web interface. Finally, we show how the data in this corpus could be used to identify bias in news reporting.

Keyword :  Content Analysis, Named Entity Recognition, Sentiment Analysis.

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100509.pdf

Thursday, June 3, 2021

TOPIC DETECTION FROM CONVERSATIONAL DIALOGUE CORPUS WITH PARALLEL LATENT DIRICHLET ALLOCATION MODEL AND ELBOW METHOD

Author :  Haider Khalid

Affiliation :  University of Dublin

Country :  Ireland

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 05, May, 2020

Abstract :

A conversational system needs to know how to switch between topics to continue the conversation for a more extended period. For this topic detection from dialogue corpus has become an important task for a conversation and accurate prediction of conversation topics is important for creating coherent and engaging dialogue systems. In this paper, we proposed a topic detection approach with Parallel Latent Dirichlet Allocation (PLDA) Model by clustering a vocabulary of known similar words based on TF-IDF scores and Bag of Words (BOW) technique. In the experiment, we use K-mean clustering with Elbow Method for interpretation and validation of consistency within-cluster analysis to select the optimal number of clusters. We evaluate our approach by comparing it with traditional LDA and clustering technique. The experimental results show that combining PLDA with Elbow method selects the optimal number of clusters and refine the topics for the conversation.

Keyword :  Conversational dialogue, latentDirichlet allocation, topic detection, topic modelling, textclassification

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100508.pdf

Sunday, May 30, 2021

HATE SPEECH DETECTION OF ARABIC SHORTTEXT

Author :  Abdullah Aref

Affiliation :  University for Technology

Country :  Jordan

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 05, May, 2020

Abstract :

The aim of sentiment analysis is to automatically extract the opinions from a certain text and decide its sentiment. In this paper, we introduce the first publicly-available Twitter dataset on Sunnah and Shia (SSTD), as part of a religious hate speech which is a sub problem of the general hate speech. We, further, provide a detailed review of the data collection process and our annotation guidelines such that a reliable dataset annotation is guaranteed. We employed many stand-alone classification algorithms on the Twitter hate speech dataset, including Random Forest, Complement NB, DecisionTree, and SVM and two deep learning methods CNN and RNN. We further study the influence of word embedding dimensions FastText and word2vec. In all our experiments, all classification algorithms are trained using a random split of data (66% for training and 34% for testing). The two datasets were stratified sampling of the original dataset. The CNN-FastText achieves the highest F-Measure (52.0%) followed by the CNN-Word2vec (49.0%), showing that neural models with FastText word embedding outperform classical feature-based mode.

Keyword :  HateSpeech, Dataset, Text classification, Sentiment analysis.

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100507.pdf

Friday, May 28, 2021

LINKING SOCIAL MEDIA POSTS TO NEWS WITH SIAMESE TRANSFORMERS

Author :  Jacob Danovitch

Affiliation :  Carleton University

Country :  Canada

Category :  Computer Science & Information Technology

Volume, Issue, Month, Year :  10, 05, May, 2020

Abstract :

Many computational social science projects examine online discourse surrounding a specific trending topic. These works often involve the acquisition of large-scale corpora relevant to the event in question to analyze aspects of the response to the event. Keyword searches present a precision-recall trade-off and crowd-sourced annotations, while effective, are costly. This work aims to enable automatic and accurate ad-hoc retrieval of comments discussing a trending topic from a large corpus, using only a handful of seed news articles.

Keyword :  Deep Learning, Natural Language Processing, Information Retrieval, Social Media, News Articles.

For More Detailshttps://aircconline.com/csit/papers/vol10/csit100506.pdf