modeling techniques
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2022 ◽  
Vol 54 (7) ◽  
pp. 1-35
Author(s):  
Uttam Chauhan ◽  
Apurva Shah

We are not able to deal with a mammoth text corpus without summarizing them into a relatively small subset. A computational tool is extremely needed to understand such a gigantic pool of text. Probabilistic Topic Modeling discovers and explains the enormous collection of documents by reducing them in a topical subspace. In this work, we study the background and advancement of topic modeling techniques. We first introduce the preliminaries of the topic modeling techniques and review its extensions and variations, such as topic modeling over various domains, hierarchical topic modeling, word embedded topic models, and topic models in multilingual perspectives. Besides, the research work for topic modeling in a distributed environment, topic visualization approaches also have been explored. We also covered the implementation and evaluation techniques for topic models in brief. Comparison matrices have been shown over the experimental results of the various categories of topic modeling. Diverse technical challenges and future directions have been discussed.


2022 ◽  
Vol 32 (1) ◽  
pp. 1-33
Author(s):  
Jinghui Zhong ◽  
Dongrui Li ◽  
Zhixing Huang ◽  
Chengyu Lu ◽  
Wentong Cai

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.


2022 ◽  
Vol 3 (1) ◽  
pp. 1-25
Author(s):  
Narjes Shojaati ◽  
Nathaniel D. Osgood

Opioids have been shown to temporarily reduce the severity of pain when prescribed for medical purposes. However, opioid analgesics can also lead to severe adverse physical and psychological effects or even death through misuse, abuse, short- or long-term addiction, and one-time or recurrent overdose. Dynamic computational models and simulations can offer great potential to interpret the complex interaction of the drivers of the opioid crisis and assess intervention strategies. This study surveys existing studies of dynamic computational models and simulations addressing the opioid crisis and provides an overview of the state-of-the-art of dynamic computational models and simulations of the opioid crisis. This review gives a detailed analysis of existing modeling techniques, model conceptualization and formulation, and the policy interventions they suggest. It also explores the data sources they used and the study population they represented. Based on this analysis, direction and opportunities for future dynamic computational models for addressing the opioid crisis are suggested.


Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 189
Author(s):  
Álvaro de Pablo ◽  
Oscar Araque ◽  
Carlos A. Iglesias

The analysis of the content of posts written on social media has established an important line of research in recent years. The study of these texts, as well as their relationship with each other and their dependence on the platform on which they are written, enables the behavior analysis of users and their opinions with respect to different domains. In this work, a hybrid machine learning-based system has been developed to classify texts using topic modeling techniques and different word-vector representations, as well as traditional text representations. The system has been trained with ride-hailing posts extracted from Reddit, showing promising performance. Then, the generated models have been tested with data extracted from other sources such as Twitter and Google Play, classifying these texts without retraining any models and thus performing Transfer Learning. The obtained results show that our proposed architecture is effective when performing Transfer Learning from data-rich domains and applying them to other sources.


2022 ◽  
pp. 000276422110660
Author(s):  
K. Hazel Kwon ◽  
Kirstin Pellizzaro ◽  
Chun Shao ◽  
Monica Chadha

The spread of misinformation through a variety of communication channels has amplified society’s challenge to manage the COVID-19 pandemic. While existing studies have examined how misinformation spreads, few studies have examined the role of psychological distance in people’s mental processing of a rumor and their propensity to accept self-transformed narratives of the message. Based on an open-ended survey data collected in the U.S. ( N = 621) during an early phase of the pandemic, the current study examines how psychological distance relates to the transformation and acceptance of conspiratorial narratives in the context of the COVID-19 pandemic. Two instances of misinformation are examined, both of which were widely heard at the time of data collection: the role of (a) Bill Gates and (b) government during the outbreak of the pandemic. This study uses topic modeling techniques to capture distinctive topical attributes that emerged from rumor narratives. In addition, statistical analyses estimate the psychological distance effects on the salience of topical attributes of a rumor story and an individual’s propensity to believe them. Findings reveal that psychological distance to the threats of COVID-19 influences how misinformation evolves through word-of-mouth, particularly in terms of who is responsible for the pandemic and why the world finds itself in the current situation. Psychological distance also explains why people accept the message to be true. Implications for misinformation and rumor psychology research, as well as avenues for future research, are discussed.


2022 ◽  
Keyword(s):  

Abstract Research Square has withdrawn this preprint due to extensive overlap with another posted article.


Doklady BGUIR ◽  
2022 ◽  
Vol 19 (8) ◽  
pp. 58-62
Author(s):  
O. G. Romanov ◽  
Ya. K. Shtykov ◽  
I. A. Timoshchenko

The  work  provides  the  description  of  theoretical  and  numerical  modeling  techniques of thermomechanical effects that take place in absorbing micro- and nanostructures of different materials under the action of pulsed laser radiation. A proposed technique of the numerical simulation is based on the solution of equations of motion of continuous media in the form of Lagrange for spatially inhomogeneous media. This model allows calculating fields of temperature, pressure, density, and velocity of the medium depending on the parameters of laser pulses and the characteristics of micro- and nanostructures.


2022 ◽  
Vol 61 (1) ◽  
pp. 20-39
Author(s):  
Jamal Asfahani

Aerial gamma-ray spectrometric technique is used herein to evaluate the radioactive heat production (HP) of Ar-Rassafeh Badyieh Area (Area-2), Syria. The nine already established lithological scored units of Area-2 have been separately characterized for the heat production HP parameter. The Concentration-number (C-N) model and the log-log plots associated with fractal technique are proposed and applied as a new approach to map the measured equivalent uranium (eU), the equivalent thorium (eTh), and potassium (K%) and the computed heat production (HP) of Area-2. The HP of Area-2 varies between a minimum of 0.06 and a maximum of 4.28 ?w/m3 with an average of 0.548 ?w/m3 and a standard deviation of 0.27?w/m3. The highest observed HP values are related to the phosphatic environments represented by two lithological scored units A and B.


2021 ◽  
Vol 10 (88) ◽  

With the rapid advances in visual perception and processing technologies, it has become easier to create 3D models (three dimensional visuals that have width height and depth data) of objects by processing 2D (two dimensional images that have width and height data like photography) images obtained from real life with the help of certain algorithms. These systems, which convert from two-dimensional painting to three-dimensional model format, now describe and translate most objects correctly. Like photogrametry and laser scanning, is used to quickly transfer large areas to 3D media, especially with coating materials. 3D images obtained by scanning 2D images show differences in terms of the obtained model quality and polygon density. This system, which serves to obtain very fast 3D models, is frequently used in computer games development, digital art and production / cinema studies, painting, sculpting, ceramic and photography to obtain a spesific result. In the research, image-based 3D model creation technologies were mentioned. The types of this technology and its usage purposes, methods and problems are the topics of this article Also problems faced while engaging the models accured from this methods to other platforms are included in the article. In this context, the aim of the study is to recognize the new scanning modeling processes and algorithms supported by artificial intelligence and to determine the usage areas of these modeling techniques in art. Keywords: Art, 3D Model, A.I., LIDAR, Photogrametry, Digital Art


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