scholarly journals A Review on Arabic Sentiment Analysis: State-of-the-Art, Taxonomy and Open Research Challenges

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 162008-162024 ◽  
Author(s):  
Mohamed Elhag Mohamed Abo ◽  
Ram Gopal Raj ◽  
Atika Qazi
Keyword(s):  
Sentiment Analysis ◽  
State Of The Art ◽  
Research Challenges ◽  
Open Research ◽  
Arabic Sentiment Analysis

Ontologies for information entities: State of the art and open challenges

2021 ◽  
Vol 16 (2) ◽  
pp. 111-135
Author(s):  
Emilio M. Sanfilippo
Keyword(s):  
State Of The Art ◽  
The State ◽  
Literary Studies ◽  
Health Records ◽  
Research Challenges ◽  
Open Research ◽  
Applied Ontology ◽  
Nature Of Information ◽  
Technical Specifications

Information entities are used in ontologies to represent engineering technical specifications, health records, pictures or librarian data about, e.g., narrative fictions, among others. The literature in applied ontology lacks a comparison of the state of the art, and foundational questions on the nature of information entities remain open for research. The purpose of the paper is twofold. First, to compare existing ontologies with both each other and theories proposed in philosophy, semiotics, librarianship, and literary studies in order to understand how the ontologies conceive and model information entities. Second, to discuss some open research challenges that can lead to principled approaches for the treatment of information entities, possibly by getting into account the variety of information entity types found in the literature.

Internet-of-things-based smart environments: state of the art, taxonomy, and open research challenges

2016 ◽  
Vol 23 (5) ◽  
pp. 10-16 ◽  
Author(s):  
Ejaz Ahmed ◽  
Ibrar Yaqoob ◽  
Abdullah Gani ◽  
Muhammad Imran ◽  
Mohsen Guizani
Keyword(s):  
Internet Of Things ◽  
State Of The Art ◽  
Smart Environments ◽  
Research Challenges ◽  
Open Research

Survey on Blockchain Networking

2021 ◽  
Vol 54 (5) ◽  
pp. 1-34
Author(s):  
Maya Dotan ◽  
Yvonne-Anne Pignolet ◽  
Stefan Schmid ◽  
Saar Tochner ◽  
Aviv Zohar
Keyword(s):  
Distributed Systems ◽  
Communication Networks ◽  
State Of The Art ◽  
The State ◽  
Neighbor Discovery ◽  
Protocol Stack ◽  
Application Domain ◽  
Empirical Results ◽  
Research Challenges ◽  
Open Research

Blockchains, in general, and cryptocurrencies such as Bitcoin, in particular, are realized using distributed systems and hence critically rely on the performance and security of the interconnecting network. The requirements on these networks and their usage, however, can differ significantly from traditional communication networks, with implications on all layers of the protocol stack. This article is motivated by these differences and, in particular, by the observation that many fundamental design aspects of these networks are not well-understood today. To support the networking community to contribute to this emerging application domain, we present a structured overview of the field, from topology and neighbor discovery, over block and transaction propagation, to sharding and off-chain networks, also reviewing existing empirical results from different measurement studies. In particular, for each of these domains, we provide the context, highlighting differences and commonalities with traditional networks, review the state-of-the-art, and identify open research challenges. Our article can hence also be seen as a call-to-arms to improve the foundation on top of which blockchains are built.

The Big Data Era

2021 ◽  
pp. 87-103
Author(s):  
Maria K. Krommyda ◽  
Verena Kantere
Keyword(s):  
Big Data ◽  
State Of The Art ◽  
Large Datasets ◽  
The State ◽  
Data Exploration ◽  
Everyday Activities ◽  
Research Challenges ◽  
Open Research ◽  
Big Data Applications ◽  
Scientific Fields

Large datasets pertaining to many scientific fields and everyday activities are becoming available at an increasing rate. Processing, analyzing, and understanding the information that they offer poses significant technical challenges. There are many efforts dedicated to the development of big data exploration, analysis, and visualization applications that will improve the value of the information extracted from these datasets. An analysis of the state-of-the-art in these applications is presented here along with open research challenges that have not yet been tackled sufficiently. Also, specific domains where big data applications are needed are presented, and unique challenges are identified.

Towards next generation coordination infrastructures

2015 ◽  
Vol 30 (4) ◽  
pp. 435-453 ◽  
Author(s):  
Juan A. Rodriguez-Aguilar ◽  
Carles Sierra ◽  
Josep Ll. Arcos ◽  
Maite Lopez-Sanchez ◽  
Inmaculada Rodriguez
Keyword(s):  
Multiagent Systems ◽  
State Of The Art ◽  
Human Interaction ◽  
Agent Technology ◽  
Technology Research ◽  
Next Generation ◽  
Research Issues ◽  
Research Challenges ◽  
Open Research ◽  
On Line

AbstractCoordination infrastructures play a central role in the engineering of multiagent systems. Since the advent of agent technology, research on coordination infrastructures has produced a significant number of infrastructures with varying features. In this paper, we review the the state-of-the-art coordination infrastructures with the purpose of identifying open research challenges that next generation coordination infrastructures should address. Our analysis concludes that next generation coordination infrastructures must address a number of challenges: (i) to becomesocially aware, by facilitating human interaction within a MAS; (ii) to assist agents in their decision making by providingdecision supportthat helps them reduce the scope of reasoning and facilitates the achievement of their goals; and (iii) to increaseopennessto support on-line, fully decentralised design and execution. Furthermore, we identify some promising approaches in the literature, together with the research issues worth investigating, to cope with such challenges.

scholarly journals Optimal Transport for Deep Generative Models: State of the Art and Research Challenges

2021 ◽  
Author(s):  
Viet Huynh ◽  
Dinh Phung ◽  
He Zhao
Keyword(s):  
Systematic Review ◽  
Eighteenth Century ◽  
Transport Theory ◽  
Optimal Transport ◽  
State Of The Art ◽  
Generative Models ◽  
Comprehensive Overview ◽  
Research Challenges ◽  
Open Research ◽  
Optimal Transport Theory

Optimal transport has a long history in mathematics which was proposed by Gaspard Monge in the eighteenth century (Monge, 1781). However, until recently, advances in optimal transport theory pave the way for its use in the AI community, particularly for formulating deep generative models. In this paper, we provide a comprehensive overview of the literature in the field of deep generative models using optimal transport theory with an aim of providing a systematic review as well as outstanding problems and more importantly, open research opportunities to use the tools from the established optimal transport theory in the deep generative model domain.

scholarly journals Deep Bidirectional LSTM Network Learning-Based Sentiment Analysis for Arabic Text

2020 ◽  
Vol 30 (1) ◽  
pp. 395-412
Author(s):  
Hanane Elfaik ◽  
El Habib Nfaoui
Keyword(s):  
Deep Learning ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
State Of The Art ◽  
Contextual Information ◽  
The State ◽  
Great Success ◽  
Bidirectional Lstm ◽  
Lstm Network ◽  
Arabic Sentiment Analysis

Abstract Sentiment analysis aims to predict sentiment polarities (positive, negative or neutral) of a given piece of text. It lies at the intersection of many fields such as Natural Language Processing (NLP), Computational Linguistics, and Data Mining. Sentiments can be expressed explicitly or implicitly. Arabic Sentiment Analysis presents a challenge undertaking due to its complexity, ambiguity, various dialects, the scarcity of resources, the morphological richness of the language, the absence of contextual information, and the absence of explicit sentiment words in an implicit piece of text. Recently, deep learning has obviously shown a great success in the field of sentiment analysis and is considered as the state-of-the-art model in Arabic Sentiment Analysis. However, the state-of-the-art accuracy for Arabic sentiment analysis still needs improvements regarding contextual information and implicit sentiment expressed in different real cases. In this paper, an efficient Bidirectional LSTM Network (BiLSTM) is investigated to enhance Arabic Sentiment Analysis, by applying Forward-Backward encapsulate contextual information from Arabic feature sequences. The experimental results on six benchmark sentiment analysis datasets demonstrate that our model achieves significant improvements over the state-of-art deep learning models and the baseline traditional machine learning methods.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

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