scholarly journals Towards multidomain and multilingual abusive language detection: a survey

2021 ◽  
Author(s):  
Endang Wahyu Pamungkas ◽  
Valerio Basile ◽  
Viviana Patti
Keyword(s):  
Online Communication ◽  
Research Direction ◽  
Open Problems ◽  
Cross Domain ◽  
Current State ◽  
Robust Model ◽  
The Cross ◽  
Cross Lingual ◽  
Future Work ◽  
Language Detection

AbstractAbusive language is an important issue in online communication across different platforms and languages. Having a robust model to detect abusive instances automatically is a prominent challenge. Several studies have been proposed to deal with this vital issue by modeling this task in the cross-domain and cross-lingual setting. This paper outlines and describes the current state of this research direction, providing an overview of previous studies, including the available datasets and approaches employed in both cross-domain and cross-lingual settings. This study also outlines several challenges and open problems of this area, providing insights and a useful roadmap for future work.

scholarly journals Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification (Extended Abstract)

2018 ◽  
Author(s):  
Alejandro Moreo Fernández ◽  
Andrea Esuli ◽  
Fabrizio Sebastiani
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Sentiment Classification ◽  
Training Data ◽  
Target Domain ◽  
Source Domain ◽  
Machine Learning Methods ◽  
Cross Domain ◽  
Current State ◽  
Cross Lingual

Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a “target” domain when the only available training data belongs to a different “source” domain. In this extended abstract, we briefly describe our new DA method called Distributional Correspondence Indexing (DCI) for sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. The experiments we have conducted show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification.

scholarly journals Distributional Correspondence Indexing for Cross-Lingual and Cross-Domain Sentiment Classification.

2016 ◽  
Vol 55 ◽  
pp. 131-163 ◽  
Author(s):  
Alejandro Moreo Fernández ◽  
Andrea Esuli ◽  
Fabrizio Sebastiani
Keyword(s):  
Domain Adaptation ◽  
State Of The Art ◽  
Computational Cost ◽  
Sentiment Classification ◽  
Training Data ◽  
Target Domain ◽  
Machine Learning Methods ◽  
Cross Domain ◽  
Current State ◽  
Cross Lingual

Domain Adaptation (DA) techniques aim at enabling machine learning methods learn effective classifiers for a "target'' domain when the only available training data belongs to a different "source'' domain. In this paper we present the Distributional Correspondence Indexing (DCI) method for domain adaptation in sentiment classification. DCI derives term representations in a vector space common to both domains where each dimension reflects its distributional correspondence to a pivot, i.e., to a highly predictive term that behaves similarly across domains. Term correspondence is quantified by means of a distributional correspondence function (DCF). We propose a number of efficient DCFs that are motivated by the distributional hypothesis, i.e., the hypothesis according to which terms with similar meaning tend to have similar distributions in text. Experiments show that DCI obtains better performance than current state-of-the-art techniques for cross-lingual and cross-domain sentiment classification. DCI also brings about a significantly reduced computational cost, and requires a smaller amount of human intervention. As a final contribution, we discuss a more challenging formulation of the domain adaptation problem, in which both the cross-domain and cross-lingual dimensions are tackled simultaneously.

scholarly journals A Review on Sliding Vane and Rolling Piston Compressors

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 125
Author(s):  
Kuan Thai Aw ◽  
Kim Tiow Ooi
Keyword(s):  
Piston Compressor ◽  
Research Direction ◽  
Rotary Compressor ◽  
Disruptive Technology ◽  
Design Evolution ◽  
Heating And Cooling ◽  
Current State ◽  
Technological Advances ◽  
Rolling Piston ◽  
Future Work

Rotary compressors have been employed in heating and cooling for more than a century and are ubiquitous in daily life but there has not been any comprehensive record of their development and technological advances. This review paper attempts to provide a comprehensive account of the advances in R&D and design evolution of these rotary compressors since their inception, namely the sliding vane compressor, rolling piston compressor, and their design variants in open literature. This is to showcase the current state-of-the-art for these compressors so that researchers can use it as a basis for future work. Based on authors’ insight, inter-disciplinary research combined with advancements in ‘disruptive’ technology such as artificial intelligence and advancements in additive manufacturing might be a promising research direction to bring about improvements in rotary compressor performance to meet mankind’s growing needs for cooling and heating applications.

Machine Learning Based Predictive Action on Categorical Non-Sequential Data

2020 ◽  
Vol 13 (5) ◽  
pp. 1020-1030
Author(s):  
Pradeep S. ◽  
Jagadish S. Kallimani
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Categorical Data ◽  
Numerical Data ◽  
Processing Technique ◽  
Machine Learning Algorithms ◽  
Sequential Data ◽  
Industry Standard ◽  
Robust Model ◽  
Future Work

Background: With the advent of data analysis and machine learning, there is a growing impetus of analyzing and generating models on historic data. The data comes in numerous forms and shapes with an abundance of challenges. The most sorted form of data for analysis is the numerical data. With the plethora of algorithms and tools it is quite manageable to deal with such data. Another form of data is of categorical nature, which is subdivided into, ordinal (order wise) and nominal (number wise). This data can be broadly classified as Sequential and Non-Sequential. Sequential data analysis is easier to preprocess using algorithms. Objective: The challenge of applying machine learning algorithms on categorical data of nonsequential nature is dealt in this paper. Methods: Upon implementing several data analysis algorithms on such data, we end up getting a biased result, which makes it impossible to generate a reliable predictive model. In this paper, we will address this problem by walking through a handful of techniques which during our research helped us in dealing with a large categorical data of non-sequential nature. In subsequent sections, we will discuss the possible implementable solutions and shortfalls of these techniques. Results: The methods are applied to sample datasets available in public domain and the results with respect to accuracy of classification are satisfactory. Conclusion: The best pre-processing technique we observed in our research is one hot encoding, which facilitates breaking down the categorical features into binary and feeding it into an Algorithm to predict the outcome. The example that we took is not abstract but it is a real – time production services dataset, which had many complex variations of categorical features. Our Future work includes creating a robust model on such data and deploying it into industry standard applications.

New Frontiers in Private Fiduciary Law

2019 ◽  
pp. 890-908
Author(s):  
Paul B. Miller
Keyword(s):  
Legal Theory ◽  
Corporate Law ◽  
Health Law ◽  
Legal Ethics ◽  
Behavioral Economic ◽  
Legal Studies ◽  
Current State ◽  
Sociological Inquiry ◽  
Future Work ◽  
Scholarly Inquiry

This chapter charts new frontiers of scholarly inquiry in fiduciary law. The chapter first orients the reader by taking stock of the current state of play in fiduciary scholarship. It then identifies a range of important questions that should inspire future work in the field. More specifically, it identifies pressing questions of legal theory (conceptual and normative analysis), economic and empirical legal studies (including classical and behavioral economic analysis), and historical and sociological inquiry. The chapter also raises questions of interest to private law theorists and scholars interested in exploring the significance of fiduciary principles within various subfields, from trust and corporate law to health law and legal ethics.

scholarly journals Towards the cross-domain interoperability of IoT platforms

2016 ◽  
Author(s):  
Sergios Soursos ◽  
Ivana Podnar Zarko ◽  
Patrick Zwickl ◽  
Ivan Gojmerac ◽  
Giuseppe Bianchi ◽  
...  
Keyword(s):  
Cross Domain ◽  
Iot Platforms ◽  
The Cross

scholarly journals Review on the Current State of Developing of Advanced Creep Damage Constitutive Equations for High Chromium Alloy

2012 ◽  
Vol 510 ◽  
pp. 776-780 ◽  
Author(s):  
Li Li An ◽  
Qiang Xu ◽  
Dong Lai Xu ◽  
Zhong Yu Lu
Keyword(s):  
Constitutive Equations ◽  
Creep Damage ◽  
Chromium Alloy ◽  
High Chromium ◽  
Current State ◽  
Specific Material ◽  
Future Work

This paper presents a review of developing of creep damage constitutive equations for high chromium alloy (such as P91 alloy). Firstly, it briefly introduces the background of creep damage for P91 materials. Then, it summarizes the typical creep damage constitutive equations developed and applied for P91 alloy, and the main deficiencies of KRH (Kachanov-Robatnov-Hayhurst) type and Xus type constitutive equations. Finally it suggests the directions for future work. This paper contributes to the knowledge for the developing creep damage constitutive equations for the specific material.

Applying the FEW nexus concept at the local scale

2021 ◽  
Author(s):  
Henry P. Huntington ◽  
Jennifer Schmidt ◽  
Philip A. Loring ◽  
Erin Whitney ◽  
Srijan Aggarwal ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Local Scale ◽  
Theory And Practice ◽  
Food Energy ◽  
Cross Domain ◽  
Rural Alaska ◽  
Domain Interactions ◽  
The Cross

The food-energy-water (FEW) nexus describes interactions among domains that yield gains or tradeoffs when analyzed together rather than independently. In a project about renewable energy in rural Alaska communities, we applied this concept to examine the implications for sustainability and resilience. The FEW nexus provided a useful framework for identifying the cross-domain benefits of renewable energy, including gains in FEW security. However, other factors such as transportation and governance also play a major role in determining FEW security outcomes in rural Alaska. Here we show the implications of our findings for theory and practice. The precise configurations of and relationships among FEW nexus components vary by place and time, and the range of factors involved further complicates the ability to develop a functional, systematic FEW model. Instead, we suggest how the FEW nexus may be applied conceptually to identify and understand cross-domain interactions that contribute to long-term sustainability and resilience.

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