scholarly journals A Comprehensive Survey on Image Dehazing Based on Deep Learning

2021 ◽  
Author(s):  
Jie Gui ◽  
Xiaofeng Cong ◽  
Yuan Cao ◽  
Wenqi Ren ◽  
Jun Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Loss Functions ◽  
Evaluation Metrics ◽  
Future Research ◽  
Image Dehazing ◽  
The Core ◽  
Comprehensive Survey ◽  
Open Issues

The presence of haze significantly reduces the quality of images. Researchers have designed a variety of algorithms for image dehazing (ID) to restore the quality of hazy images. However, there are few studies that summarize the deep learning (DL) based dehazing technologies. In this paper, we conduct a comprehensive survey on the recent proposed dehazing methods. Firstly, we conclude the commonly used datasets, loss functions and evaluation metrics. Secondly, we group the existing researches of ID into two major categories: supervised ID and unsupervised ID. The core ideas of various influential dehazing models are introduced. Finally, the open issues for future research on ID are pointed out.

Citizen Deliberation Online

2019 ◽  
pp. 689-712
Author(s):  
Patrícia Rossini ◽  
Jennifer Stromer-Galley
Keyword(s):  
Political Engagement ◽  
Future Research ◽  
The Internet ◽  
Digital Platforms ◽  
The Core ◽  
Political Conversation ◽  
Political Talk ◽  
Citizen Deliberation ◽  
The Impact

Political conversation is at the heart of democratic societies, and it is an important precursor of political engagement. As society has become intertwined with the communication infrastructure of the Internet, we need to understand its uses and the implications of those uses for democracy. This chapter provides an overview of the core topics of scholarly concern around online citizen deliberation, focusing on three key areas of research: the standards of quality of communication and the normative stance on citizen deliberation online; the impact and importance of digital platforms in structuring political talk; and the differences between formal and informal political talk spaces. After providing a critical review of these three major areas of research, we outline directions for future research on online citizen deliberation.

scholarly journals Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

2020 ◽  
Author(s):  
Md Jalil Piran
Keyword(s):  
Cognitive Radio ◽  
Channel Coding ◽  
Multimedia Communication ◽  
Radio Spectrum ◽  
Multimedia Communications ◽  
Future Research ◽  
Wireless Multimedia ◽  
Comprehensive Survey ◽  
Feasible Solutions

The stringent requirements of wireless multimedia<br>transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the<br>issue with spectrum availability is not scarcity, but the inefficient<br>utilization. Unique characteristics of cognitive radio (CR) such<br>as flexibility, adaptability, and interoperability, particularly have<br>contributed to it being the optimum technological candidate to<br>alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR<br>networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and<br>loss-tolerant service, exposes several severe challenges specially<br>to guarantee quality of service (QoS) and quality of experience<br>(QoE). As a result, to date, different schemes based on source and<br>channel coding, multicast, and distributed streaming, have been<br>examined to improve the QoS/QoE in MCRNs. In this paper,<br>we survey QoS/QoE provisioning schemes in MCRNs. We first<br>discuss the basic concepts of multimedia communication, CRNs,<br>QoS and QoE. Then, we present the advantages of utilizing CR<br>for multimedia services and outline the stringent QoS and QoE<br>requirements in MCRNs. Next, we classify the critical challenges<br>for QoS/QoE provisioning in MCRNs including spectrum sensing,<br>resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for<br>each challenge highlighting performance issues, strengths, and<br>weaknesses. Furthermore, we discuss several important open<br>research problems and provide some avenues for future research. <br>

scholarly journals Service Level Agreement (SLA) in Utility Computing Systems

2012 ◽  
pp. 1-25 ◽  
Author(s):  
Linlin Wu ◽  
Rajkumar Buyya
Keyword(s):  
Service Level Agreement ◽  
Service Level ◽  
Future Research ◽  
Fundamental Issue ◽  
Utility Computing ◽  
Computing Systems ◽  
Comprehensive Survey ◽  
Autonomy Management ◽  
State Of Art

In recent years, extensive research has been conducted in the area of Service Level Agreement (SLA) for utility computing systems. An SLA is a formal contract used to guarantee that consumers’ service quality expectation can be achieved. In utility computing systems, the level of customer satisfaction is crucial, making SLAs significantly important in these environments. Fundamental issue is the management of SLAs, including SLA autonomy management or trade off among multiple Quality of Service (QoS) parameters. Many SLA languages and frameworks have been developed as solutions; however, there is no overall classification for these extensive works. Therefore, the aim of this chapter is to present a comprehensive survey of how SLAs are created, managed and used in utility computing environment. We discuss existing use cases from Grid and Cloud computing systems to identify the level of SLA realization in state-of-art systems and emerging challenges for future research.

A COMPREHENSIVE SURVEY OF THE REVIEWER ASSIGNMENT PROBLEM

2010 ◽  
Vol 09 (04) ◽  
pp. 645-668 ◽  
Author(s):  
FAN WANG ◽  
NING SHI ◽  
BEN CHEN
Keyword(s):  
Academic Writing ◽  
Operations Research ◽  
Assignment Problem ◽  
Future Research ◽  
Intelligence Operations ◽  
Comprehensive Survey ◽  
Matching Degree ◽  
Two Phases ◽  
Three Phases

Reviewer Assignment Problem (RAP) is an important issue in peer-review of academic writing. This issue directly influences the quality of the publication and as such is the brickwork of scientific authentication. Due to the obvious limitations of manual assignment, automatic approaches for RAP is in demand. In this paper, we conduct a survey on those automatic approaches appeared in academic literatures. In this paper, regardless of the way reviewer assignment is structured, we formally divide the RAP into three phases: reviewer candidate search, matching degree computation, and assignment optimization. We find that current research mainly focus on one or two phases, but obviously, these three phases are correlative. For each phase, we describe and classify the main issues and methods for addressing them. Methodologies in these three phases have been developed in a variety of research disciplines, including information retrieval, artificial intelligence, operations research, etc. Naturally, we categorize different approaches by these disciplines and provide comments on their advantages and limitations. With an emphasis on identifying the gaps between current approaches and the practical needs, we point out the potential future research opportunities, including integrated optimization, online optimization, etc.

Practical Implementation of Assessment Activities for Deep Learning

2020 ◽  
pp. 84-105
Author(s):  
Noor Liza Adnan ◽  
Wan Karomiah Wan Abdullah ◽  
Rokiah Muda ◽  
Nur Raihana Mohd Sallem
Keyword(s):  
Deep Learning ◽  
Student Assessment ◽  
Future Research ◽  
Practical Implementation ◽  
Classroom Activities ◽  
Effective Assessment ◽  
Assessment Strategies ◽  
Assessment Activities

Student assessment would influence the quality of the graduates produced. However, many assessment strategies are found to inhibit this intention. As such, this chapter proposes a few assessment activities, along with their practical implementation, that may encourage deep learning among students in the learning of management accounting subjects. This chapter reviews previous literature, focusing on the characteristics of effective assessment activities that suit the nature of the Millennial. Five assessment activities, namely test/quiz, case study, field study, simulated enterprise, and classroom activities, are proposed. A questionnaire evaluating the preferences of the students and lecturers on how the proposed activities could be implemented was adapted. The chapter elaborates on the practical implementation of the five proposed assessment activities believed to engage students' learning so they become deep learners. A future research project is also put forth.

scholarly journals Deep Learning for Generic Object Detection: A Survey

2019 ◽  
Vol 128 (2) ◽  
pp. 261-318 ◽  
Author(s):  
Li Liu ◽  
Wanli Ouyang ◽  
Xiaogang Wang ◽  
Paul Fieguth ◽  
Jie Chen ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Rapid Evolution ◽  
Feature Representation ◽  
Future Research ◽  
Context Modeling ◽  
Feature Representations ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Object Feature

Abstract Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

scholarly journals Neural Machine Reading Comprehension: Methods and Trends

2019 ◽  
Vol 9 (18) ◽  
pp. 3698 ◽  
Author(s):  
Shanshan Liu ◽  
Xin Zhang ◽  
Sheng Zhang ◽  
Hui Wang ◽  
Weiming Zhang
Keyword(s):  
Reading Comprehension ◽  
Deep Learning ◽  
Research Field ◽  
The Past ◽  
Learning Techniques ◽  
Comprehensive Survey ◽  
Recent Trends ◽  
General Architecture ◽  
Machine Reading ◽  
Open Issues

Machine reading comprehension (MRC), which requires a machine to answer questions based on a given context, has attracted increasing attention with the incorporation of various deep-learning techniques over the past few years. Although research on MRC based on deep learning is flourishing, there remains a lack of a comprehensive survey summarizing existing approaches and recent trends, which motivated the work presented in this article. Specifically, we give a thorough review of this research field, covering different aspects including (1) typical MRC tasks: their definitions, differences, and representative datasets; (2) the general architecture of neural MRC: the main modules and prevalent approaches to each; and (3) new trends: some emerging areas in neural MRC as well as the corresponding challenges. Finally, considering what has been achieved so far, the survey also envisages what the future may hold by discussing the open issues left to be addressed.

scholarly journals You can Try without Visiting: A Comprehensive Survey on Virtually Try-on Outfits

2021 ◽  
Author(s):  
Hajer Ghodhbani ◽  
Adel Alimi ◽  
Mohamed Neji ◽  
Imran Razzak
Keyword(s):  
Deep Learning ◽  
Literature Review ◽  
Research Field ◽  
Future Research ◽  
Fashion Industry ◽  
Research Directions ◽  
Comprehensive Literature Review ◽  
Benchmark Datasets ◽  
Comprehensive Survey ◽  
Future Research Directions

<p>Our work aims to conduct a comprehensive literature review of deep learning methods applied in the fashion industry and, especially, the image-based virtual fitting task by citing research works published in the last years. We have summarized their challenges, their main frameworks, the popular benchmark datasets, and the different evaluation metrics. Also, some promising future research directions are discussed to propose improvements in this research field.</p>

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

scholarly journals Deep Learning for Cyber Security Applications: A Comprehensive Survey

2021 ◽  
Author(s):  
vinayakumar R ◽  
Mamoun Alazab ◽  
Soman KP ◽  
Sriram Srinivasan ◽  
Sitalakshmi Venkatraman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Cyber Security ◽  
Smart Cities ◽  
Critical Discussion ◽  
Future Research ◽  
Next Generation ◽  
Security Applications ◽  
The Past ◽  
Comprehensive Survey

Deep Learning (DL), a novel form of machine learning (ML) is gaining much research interest due to its successful application in many classical artificial intelligence (AI) tasks as compared to classical ML algorithms (CMLAs). Recently, DL architectures are being innovatively modelled for diverse applications in the area of cyber security. The literature is now growing with DL architectures and their variations for exploring different innovative DL models and prototypes that can be tailored to suit specific cyber security applications. However, there is a gap in literature for a comprehensive survey reporting on such research studies. Many of the survey-based research have a focus on specific DL architectures and certain types of malicious attacks within a limited cyber security problem scenario of the past and lack futuristic review. This paper aims at providing a well-rounded and thorough survey of the past, present, and future DL architectures including next-generation cyber security scenarios related to intelligent automation, Internet of Things (IoT), Big Data (BD), Blockchain, cloud and edge technologies. <br>This paper presents a tutorial-style comprehensive review of the state-of-the-art DL architectures for diverse applications in cyber security by comparing and analysing the contributions and challenges from various recent research papers. Firstly, the uniqueness of the survey is in reporting the use of DL architectures for an extensive set of cybercrime detection approaches such as intrusion detection, malware and botnet detection, spam and phishing detection, network traffic analysis, binary analysis, insider threat detection, CAPTCHA analysis, and steganography. Secondly, the survey covers key DL architectures in cyber security application domains such as cryptography, cloud security, biometric security, IoT and edge computing. Thirdly, the need for DL based research is discussed for the next generation cyber security applications in cyber physical systems (CPS) that leverage on BD analytics, natural language processing (NLP), signal and image processing and blockchain technology for smart cities and Industry 4.0 of the future. Finally, a critical discussion on open challenges and new proposed DL architecture contributes towards future research directions.

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