Data-driven Crowd Modeling Techniques: A Survey

2022 ◽  
Vol 32 (1) ◽  
pp. 1-33
Author(s):  
Jinghui Zhong ◽  
Dongrui Li ◽  
Zhixing Huang ◽  
Chengyu Lu ◽  
Wentong Cai
Keyword(s):  
Game Design ◽  
State Of The Art ◽  
Crowd Simulation ◽  
Data Driven ◽  
Future Research ◽  
Crowd Modeling ◽  
The Past ◽  
Modeling Techniques ◽  
Comprehensive Survey ◽  
Data Driven Modeling

Data-driven crowd modeling has now become a popular and effective approach for generating realistic crowd simulation and has been applied to a range of applications, such as anomaly detection and game design. In the past decades, a number of data-driven crowd modeling techniques have been proposed, providing many options for people to generate virtual crowd simulation. This article provides a comprehensive survey of these state-of-the-art data-driven modeling techniques. We first describe the commonly used datasets for crowd modeling. Then, we categorize and discuss the state-of-the-art data-driven crowd modeling methods. After that, data-driven crowd model validation techniques are discussed. Finally, six promising future research topics of data-driven crowd modeling are discussed.

Data-driven modeling decadal-to-centennial ENSO variability and its response to external forcing

2021 ◽  
Author(s):  
Aleksei Seleznev ◽  
Dmitry Mukhin ◽  
Andrey Gavrilov ◽  
Alexander Feigin
Keyword(s):  
Co2 Concentration ◽  
Data Driven ◽  
Time Interval ◽  
Solar Forcing ◽  
Russian Science ◽  
Dynamical Mechanism ◽  
Enso Variability ◽  
The Past ◽  
Sst Variability ◽  
Data Driven Modeling

<p>We investigate the decadal-to-centennial ENSO variability based on nonlinear data-driven stochastic modeling. We construct data-driven model of yearly Niño-3.4 indices reconstructed from paleoclimate proxies based on three different sea-surface temperature (SST) databases at the time interval from 1150 to 1995 [1]. The data-driven model is forced by the solar activity and CO2 concentration signals. We find the persistent antiphasing relationship between the solar forcing and Niño-3.4 SST on the bicentennial time scale. The dynamical mechanism of such a response is discussed.</p><p>The work was supported by the Russian Science Foundation (Grant No. 20-62-46056)</p><p>1. Emile-Geay, J., Cobb, K. M., Mann, M. E., & Wittenberg, A. T. (2013). Estimating Central Equatorial Pacific SST Variability over the Past Millennium. Part II: Reconstructions and Implications, Journal of Climate, 26(7), 2329-2352.</p>

State of the Art and Key Design Challenges of Telesurgical Robotics

2019 ◽  
pp. 1100-1111
Author(s):  
Sajid Nisar ◽  
Osman Hasan
Keyword(s):  
State Of The Art ◽  
Robotic Systems ◽  
Future Research ◽  
Research Directions ◽  
Remote Locations ◽  
Comprehensive Survey ◽  
Future Research Directions ◽  
Surgical System ◽  
Surgical Operations ◽  
Design Challenges

Telesurgical robotic systems allow surgeons to perform surgical operations from remote locations with enhanced comfort and dexterity. Introduction of robotic technology has revolutionized operation theaters but its multidisciplinary nature and high associated costs pose significant challenges. This chapter provides a comprehensive survey of the current progress in the field of surgical robotics with a detailed discussion on various state-of-the-art telesurgical robotic systems. The key design approaches and challenges are identified, and their solutions are recommended. A set of parameters that can be used to assess usefulness of a telesurgical robot are discussed. Finally, guidelines for selection of a suitable surgical system and the future research directions are described.

Economic Sanctions in Flux: Enduring Challenges, New Policies, and Defining the Future Research Agenda

2020 ◽  
Vol 21 (4) ◽  
pp. 438-477
Author(s):  
Bryan R Early ◽  
Menevis Cilizoglu
Keyword(s):  
Research Agenda ◽  
State Of The Art ◽  
Economic Sanctions ◽  
Future Research ◽  
Factors Affecting ◽  
The Past ◽  
Future Research Agenda ◽  
Policy Tool ◽  
Wide Range ◽  
New Policies

Abstract Policymakers employ economic sanctions to deal with a wide range of international challenges, making them an indispensable foreign policy tool. While scholarship on sanctions has tended to focus on the factors affecting their success, newer research programs have emerged that explore the reasons for why sanctions are threatened and initiated, the ways they are designed and enforced, and their consequences. This scholarship has yielded a wealth of new insights into how economic sanctions work, but most of those insights are based on sanctions observations from the 20th Century. The ways that policymakers employ sanctions have fundamentally changed over the past two decades, though, raising concerns about whether historically derived insights are still relevant to contemporary sanctions policies. In this forum, the contributors discuss the scholarly and policy-relevant insights of existing research on sanctions and then explore what gaps remain in our knowledge and new trends in sanctions policymaking. This forum will inform readers on the state of the art in sanctions research and propose avenues for future research.

Data-Driven Modeling Techniques to Estimate Dispersion Relations of Structural Components

2018 ◽  
Author(s):  
Vijaya V. N. Sriram Malladi ◽  
Mohammad I. Albakri ◽  
Pablo A. Tarazaga ◽  
Serkan Gugercin
Keyword(s):  
Frequency Response ◽  
Dispersion Relations ◽  
Data Driven ◽  
Response Functions ◽  
Structural Components ◽  
Frequency Range ◽  
Frequency Response Functions ◽  
Material Inhomogeneity ◽  
Modeling Techniques ◽  
Data Driven Modeling

Dispersion relations describe the frequency-dependent nature of elastic waves propagating in structures. Experimental determination of dispersion relations of structural components, such as the floor of a building, can be a tedious task, due to material inhomogeneity, complex boundary conditions, and the physical dimensions of the structure under test. In this work, data-driven modeling techniques are utilized to reconstruct dispersion relations over a predetermined frequency range. The feasibility of this approach is demonstrated on a one-dimensional beam where an exact solution of the dispersion relations is attainable. Frequency response functions of the beam are obtained numerically over the frequency range of 0–50kHz. Data-driven dynamical model, constructed by the vector fitting approach, is then deployed to develop a state-space model based on the simulated frequency response functions at 16 locations along the beam. This model is then utilized to construct dispersion relations of the structure through a series of numerical simulations. The techniques discussed in this paper are especially beneficial to such scenarios where it is neither possible to find analytical solutions to wave equations, nor it is feasible to measure dispersion curves experimentally. In the present work, actual experimental data is left for future work, but the complete framework is presented here.

Intellectual Foundations of Current Research in Family Business: An Identification and Review of 25 Influential Articles

2009 ◽  
Vol 23 (1) ◽  
pp. 9-26 ◽  
Author(s):  
James J. Chrisman ◽  
Franz W. Kellermanns ◽  
Kam C. Chan ◽  
Kartono Liano
Keyword(s):  
Citation Analysis ◽  
Family Business ◽  
State Of The Art ◽  
Family Businesses ◽  
The State ◽  
Current Understanding ◽  
Future Research ◽  
The Past ◽  
Intellectual Foundations

This article identifies 25 articles that have been particularly influential in shaping the state of the art of research on family businesses. These works were identified based on a citation analysis of family business articles published over the past 6 years in the four journals that publish most of the research. The authors summarize those influential studies and discuss their most important contributions to scholars’ current understanding of family business. By identifying common themes among those studies, the authors are able to provide directions for future research in the field.

scholarly journals Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation

2018 ◽  
Vol 61 ◽  
pp. 65-170 ◽  
Author(s):  
Albert Gatt ◽  
Emiel Krahmer
Keyword(s):  
Natural Language ◽  
State Of The Art ◽  
Natural Language Generation ◽  
Data Driven ◽  
Research Topics ◽  
Language Generation ◽  
The Past ◽  
Current State ◽  
Linguistic Input ◽  
New Applications

This paper surveys the current state of the art in Natural Language Generation (NLG), defined as the task of generating text or speech from non-linguistic input. A survey of NLG is timely in view of the changes that the field has undergone over the past two decades, especially in relation to new (usually data-driven) methods, as well as new applications of NLG technology. This survey therefore aims to (a) give an up-to-date synthesis of research on the core tasks in NLG and the architectures adopted in which such tasks are organised; (b) highlight a number of recent research topics that have arisen partly as a result of growing synergies between NLG and other areas of artificial intelligence; (c) draw attention to the challenges in NLG evaluation, relating them to similar challenges faced in other areas of NLP, with an emphasis on different evaluation methods and the relationships between them.

scholarly journals Resilience Assessment Frameworks of Critical Infrastructures: State-of-the-Art Review

Buildings ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 464
Author(s):  
Dan Guo ◽  
Ming Shan ◽  
Emmanuel Kingsford Owusu
Keyword(s):  
Literature Review ◽  
Natural Disasters ◽  
State Of The Art ◽  
Future Research ◽  
Critical Infrastructures ◽  
The Past ◽  
Comprehensive Literature Review ◽  
The Common ◽  
Key Indicators ◽  
Resilience Assessment

During the past two decades, critical infrastructures (CIs) faced a growing number of challenges worldwide due to natural disasters and other disruptive events. To respond to and handle these disasters and disruptive events, the concept of resilience was introduced to CIs. Particularly, many institutions and scholars developed various types of frameworks to assess and enhance CI resilience. The purpose of this paper is to review the resilience assessment frameworks of the CIs proposed by quality papers published in the past decade, determine and analyze the common dimensions and the key indicators of resilience assessment frameworks of CIs, and propose possible opportunities for future research. To achieve these goals, a comprehensive literature review was conducted, which identified 24 resilience assessment frameworks from 24 quality papers. This paper contributes to the current body of resilience research by identifying the common dimensions and the key indicators of the resilience assessment frameworks proposed for CIs. In addition, this paper is beneficial to the practice, because it provides a comprehensive view of the resilience assessment frameworks of CIs from the perspective of implementation, and the indicators are pragmatic and actionable in practice.

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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