A Review of Cyberbullying and Cybervictimization: Taxonomy, Challenges, Limitations, and Recommendations for Future Researchers

Cyberbullying and cybervictimization is vital and recent issue that still require further investigations. It is classified as a global problem, serious risk and danger that challenging researchers in term of assessment, nature and measuring that demand massive effort to limit and reduce its prevalence and consequences. The aim of the study is to underline challenges, limitations, and recommendations that related to cyberbullying and cybervictimization that need to be explored and investigated in greater depth in future researches. The current study intends to analyse the twenty- seven previous studies and build taxonomy that highlights gabs and shortage of the phenomena into four main sections: research, programs, methodology and theories and model. Based on researcher analysis there are four main sections that present the challenges and limitations, whereas eleven sub-sections and eight divisions give more details about these challenges and limitations. Discussion: The result of the review present that more factors need to integrate and re-investigate whereas theories need to link, and methodology aspects require to develop. In addition, new aspects and open areas that demand investigate in future. This review and taxonomy provide a unique opportunity to develop and create additional research fields. Received: 8 March 2021 / Accepted: 20 April 2021 / Published: 17 May 2021

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A Multi-RNN Research Topic Prediction Model Based on Spatial Attention and Semantic Consistency-Based Scientific Influence Modeling

Computational Intelligence and Neuroscience ◽

10.1155/2021/1766743 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Mingying Xu ◽

Junping Du ◽

Zeli Guan ◽

Zhe Xue ◽

Feifei Kou ◽

...

Keyword(s):

Prediction Model ◽

Computer Science ◽

Spatial Attention ◽

Research Field ◽

Research Topic ◽

Research Topics ◽

Semantic Consistency ◽

Model Based ◽

Research Fields ◽

Model Research

Computer science discipline includes many research fields, which mutually influence and promote each other’s development. This poses two great challenges of predicting the research topics of each research field. One is how to model fine-grained topic representation of a research field. The other is how to model research topic of different fields and keep the semantic consistency of research topics when learning the scientific influence context from other related fields. Unfortunately, the existing research topic prediction approaches cannot handle these two challenges. To solve these problems, we employ multiple different Recurrent Neural Network chains which model research topics of different fields and propose a research topic prediction model based on spatial attention and semantic consistency-based scientific influence modeling. Spatial attention is employed in field topic representation which can selectively extract the attributes from the field topics to distinguish the importance of field topic attributes. Semantic consistency-based scientific influence modeling maps research topics of different fields to a unified semantic space to obtain the scientific influence context of other related fields. Extensive experiment results on five related research fields in the computer science (CS) discipline show that the proposed model is superior to the most advanced methods and achieves good topic prediction performance.

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Trends in social inequality in the economic and political contexts

SHS Web of Conferences ◽

10.1051/shsconf/20185502003 ◽

2018 ◽

Vol 55 ◽

pp. 02003

Author(s):

Natalia Zausaeva

Keyword(s):

Social Inequality ◽

Contemporary World ◽

Global Problem ◽

Research Fields

The problem of inequality has been known in society since time immemorial and has not lost its significance to this day. Moreover, in the contemporary world, it acquires the features of a global problem, attracting the attention of representatives of various sciences, primarily economists and political scientists. In this article, certain approaches of the economists and political scientists to the analysis of the issues of inequality and its consequences are considered. The author concludes that there is the convergence of their research fields.

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STRUCTURAL E-PERMITS: AN OPENBIM, MODEL-BASED PROCEDURE FOR PERMIT APPLICATIONS PERTAINING TO STRUCTURAL ENGINEERING

Journal of Civil Engineering and Management ◽

10.3846/jcem.2021.15784 ◽

2021 ◽

Vol 27 (8) ◽

pp. 651-670

Author(s):

Vittoria Ciotta ◽

Angelo Ciccone ◽

Domenico Asprone ◽

Gaetano Manfredi ◽

Edoardo Cosenza

Keyword(s):

Structural Engineering ◽

Swot Analysis ◽

Structural Information ◽

Additional Research ◽

Automatic Extraction ◽

Proof Of Concept ◽

Information Requirements ◽

Model Based ◽

Engineering Information ◽

Preliminary Research

This paper investigates the creation and use of integrated IFC models to modernise traditional processes for applications to building authorities for structural engineering approvals and permits. First, we provide a brief overview of e-permit systems in the AEC sector, with the focus on solutions that implement openBIM standards like IFC, MVD, and IDM. Second, we conduct a study on the information requirements of Italy’s seismic-authorisation processes relating specifically to the field of structural engineering. Third, we describe preliminary research on defining the structural engineering information that needs to be incorporated in the IFC format for e-permitting scopes. Fourth, we present our early work on the development of an MVD that would enable the automatic extraction of integrated IFC models. Fifth, we illustrate the reference workflow of the Str.E.Pe. project and propose a preliminary proof-of-concept that makes use of an IFC model, which has been integrated with structural information to support the activities of the building authority in Avellino. The officers there have developed a SWOT analysis using IFC models to assist them in assessing the compliance of structural projects with seismic requirements. Finally, the paper sets out additional research we intend to undertake and our conclusions.

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The P2P Model Based on the Similarity of Active Peers

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.278-280.2044 ◽

2013 ◽

Vol 278-280 ◽

pp. 2044-2047

Author(s):

Hua Mei Du ◽

Yong Sheng Huang ◽

Geng Lu Zhang

Keyword(s):

Hot Spots ◽

Peer Groups ◽

Peer To Peer ◽

P2p Network ◽

Network Characteristics ◽

Model Based ◽

Research Fields ◽

Proposed Model ◽

Efficiency And Effectiveness ◽

Network Technologies

In application of network technologies, the peer to peer technologies is becoming one of the hot spots in network research fields. In order to enhance the efficiency and effectiveness of the P2P network, a lot of architecture schemes for organizing the peers and the files in the P2P network are put forward which are mainly concentrated on the network characteristics of the peers and the file transmission. A P2P model based on the similarity of active peers is put forward. In the proposed model, the peers’ interests to the files are utilized to describe the similarities of the peers. By the similarities among the peers, the peer groups are clustered. Utilizing the representative clustering group, the interconnections among active peers are built according to the representative group selected by Hamming network.

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Model-Based Research Programs

Conceptus ◽

10.1515/cpt-2014-0009 ◽

2014 ◽

Vol 41 (99-100) ◽

Cited By ~ 1

Author(s):

Stephan Kornmesser

Keyword(s):

Model Based ◽

Research Programs

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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