ADVANCED RATE CONTROL TECHNOLOGIES FOR MVC

 Author :  Tao Yan

Affiliation :  Putian University

Country :  China

Category :  Computer Science & Information Technology

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

Abstract :

After analyzing the research status and existing problems of multi-view video coding and bit rate control, we found that in addition to achieving higher coding efficiency, scalability characteristics, and quality consistency, reasonable bit rate control is urgent What needs further research. The paper proposes a multi-view video coding rate control algorithm based on the quadratic rate distortion (RD) model is presented. There are already many rate control algorithms. However, the testing work is very important, and different sequences need to be tested to effectively judge the effectiveness of the algorithm. Experimental simulation results show that the algorithm can effectively control the bit rate of multi-view video coding, while maintaining efficient coding efficiency, compared with the current MVC using JVT with fixed quantization parameters.

Keyword :  MVC(multi-view video coding), Rate control, Bit allocation, Basic unit layer

For More Details :  https://aircconline.com/csit/papers/vol10/csit100514.pdf

A SEMI-SUPERVISED LEARNING APPROACH TO FORECAST CPU USAGES UNDER PEAK LOAD IN AN ENTERPRISE ENVIRONMENT

Author :  Nitin Khosla

Affiliation :  University of Canberra

Country :  Austrlia

Category :  Computer Science & Information Technology

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

Abstract :

The aim of a semi-supervised neural net learning approach in this paper is to apply and improve the supervised classifiers and to develop a model to predict CPU usages under unpredictable peak load (under stress conditions) in a large enterprise applications environment with several hundred applications hosted and with large number of concurrent users. This method forecasts the likelihood of extreme use of CPU because of a burst in web traffic mainly due to web-traffic from large number of concurrent users. This model predicts the CPU utilization under extreme load (stress) conditions.

Keyword :  Semi-supervised learning, Performance Engineering, Stress testing, Neural Nets, Machine learning applications.

For More Details :  https://aircconline.com/csit/papers/vol10/csit100513.pdf

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

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 Details :  https://aircconline.com/csit/papers/vol10/csit100511.pdf

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 Details :  https://aircconline.com/csit/papers/vol10/csit100510.pdf

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 Details :  https://aircconline.com/csit/papers/vol10/csit100509.pdf

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 Details :  https://aircconline.com/csit/papers/vol10/csit100508.pdf