The evolution of LGBT labelling words

English Today ◽  
2019 ◽  
Vol 36 (4) ◽  
pp. 33-39
Author(s):  
Yaqian Shi ◽  
Lei Lei
Keyword(s):  
Literature Review ◽  
Data Driven ◽  
Word Embeddings ◽  
Computational Semantics ◽  
Promising Tool

Semantic shifts have been explored via a range of methods (Allan & Robinson 2012). Typically, semantic shifts were usually noted or described with methods such as a literature review or dictionary checking (e.g. Blank & Koch, 1999; Stockwell & Minkova, 2001; Williams, 1976), which are very labour-intensive and time-consuming methods. Other more recently developed methods involve sociolinguistic interviews (Robinson, 2012; Sandow & Robinson, 2018). However, with the development of large-sized corpora and computational semantics, diachronic semantic shifts have started to be captured in a data-driven way (Kutuzov et al., 2018). Recently, the word embeddings technique (Mikolov et al., 2013) has been proven to be a promising tool for the tracking of semantic shifts (e.g. Hamilton, Leskovec & Jurafsky, 2016a, 2016b; Kulkarni et al., 2015; Kutuzov et al., 2017). For example, Hamilton et al. (2016b) exemplified how to use the technique to capture the subjectification process of the word ‘actually’ during the 20th century.

scholarly journals Sustainable, Smart, and Sensing Technologies for Cyber-Physical Manufacturing Systems: A Systematic Literature Review

2021 ◽  
Vol 13 (10) ◽  
pp. 5495
Author(s):  
Mihai Andronie ◽  
George Lăzăroiu ◽  
Roxana Ștefănescu ◽  
Cristian Uță ◽  
Irina Dijmărescu
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Literature Review ◽  
Manufacturing Systems ◽  
Manufacturing System ◽  
Data Driven ◽  
Smart Manufacturing ◽  
Production Networks ◽  
Production Logistics ◽  
Time Production

With growing evidence of the operational performance of cyber-physical manufacturing systems, there is a pivotal need for comprehending sustainable, smart, and sensing technologies underpinning data-driven decision-making processes. In this research, previous findings were cumulated showing that cyber-physical production networks operate automatically and smoothly with artificial intelligence-based decision-making algorithms in a sustainable manner and contribute to the literature by indicating that sustainable Internet of Things-based manufacturing systems function in an automated, robust, and flexible manner. Throughout October 2020 and April 2021, a quantitative literature review of the Web of Science, Scopus, and ProQuest databases was performed, with search terms including “Internet of Things-based real-time production logistics”, “sustainable smart manufacturing”, “cyber-physical production system”, “industrial big data”, “sustainable organizational performance”, “cyber-physical smart manufacturing system”, and “sustainable Internet of Things-based manufacturing system”. As research published between 2018 and 2021 was inspected, and only 426 articles satisfied the eligibility criteria. By taking out controversial or ambiguous findings (insufficient/irrelevant data), outcomes unsubstantiated by replication, too general material, or studies with nearly identical titles, we selected 174 mainly empirical sources. Further developments should entail how cyber-physical production networks and Internet of Things-based real-time production logistics, by use of cognitive decision-making algorithms, enable the advancement of data-driven sustainable smart manufacturing.

scholarly journals Data-Driven Structural Health Monitoring and Damage Detection through Deep Learning: State-of-the-Art Review

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2778 ◽  
Author(s):  
Mohsen Azimi ◽  
Armin Eslamlou ◽  
Gokhan Pekcan
Keyword(s):  
Deep Learning ◽  
Structural Health Monitoring ◽  
Health Monitoring ◽  
High Speed ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Data Driven ◽  
Structural Health ◽  
Promising Tool ◽  
Significant Attention

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to recent technological advancements in sensors, as well as high-speed internet and cloud-based computation. Since the introduction of deep learning (DL) in civil engineering, particularly in SHM, this emerging and promising tool has attracted significant attention among researchers. The main goal of this paper is to review the latest publications in SHM using emerging DL-based methods and provide readers with an overall understanding of various SHM applications. After a brief introduction, an overview of various DL methods (e.g., deep neural networks, transfer learning, etc.) is presented. The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed. The review concludes with prospects and potential limitations of DL-based methods in SHM applications.

Big data-driven strategic orientation in international marketing

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shahriar Akter ◽  
Md Afnan Hossain ◽  
Qiang (Steven) Lu ◽  
S.M. Riad Shams
Keyword(s):  
Big Data ◽  
Literature Review ◽  
Systematic Literature Review ◽  
International Marketing ◽  
Strategic Orientation ◽  
Data Driven ◽  
Future Research ◽  
Content Type ◽  
Strategic Orientations ◽  
Marketing Activities

PurposeBig data is one of the most demanding topics in contemporary marketing research. Despite its importance, the big data-based strategic orientation in international marketing is yet to be formed conceptually. Thus, the purpose of this study is to systematically review and propose a holistic framework on big data-based strategic orientation for firms in international markets to attain a sustained firm performance.Design/methodology/approachThe study employed a systematic literature review to synthesize research rigorously. Initially, 2,242 articles were identified from the selective databases, and 45 papers were finally reported as most relevant to propose an integrative conceptual framework.FindingsThe findings of the systematic literature review revealed data-evolving, and data-driven strategic orientations are essential for performing international marketing activities that contain three primary orientations such as (1) international digital platform orientation, (2) international market orientation and (3) international innovation and entrepreneurial orientation. Eleven distinct sub-dimensions reflect these three primary orientations. These strategic orientations of international firms may lead to advanced analytics orientation to attain sustained firm performance by generating and capturing value from the marketplace.Research limitations/implicationsThe study minimizes the literature gap by forming knowledge on big data-based strategic orientation and framing a multidimensional framework for guiding managers in the context of strategic orientation for international business and international marketing activities. The current study was conducted by following only a systematic literature review exclusively in firms' overall big data-based strategic orientation concept in international marketing. Future research may extend the domain by introducing firms' category wise systematic literature review.Originality/valueThe study has proposed a holistic conceptual framework for big data-driven strategic orientation in international marketing literature through a systematic review for the first time. It has also illuminated a future research agenda that raises questions for the scholars to develop or extend theory in this area or other related disciplines.

scholarly journals LitRev: A data driven method for quick literature review from PubMed

2021 ◽  
Author(s):  
Gourab Das
Keyword(s):  
Literature Review ◽  
Mutual Information ◽  
Power Law ◽  
Data Driven ◽  
Power Law Distribution ◽  
Information Score ◽  
Data Driven Approach

LitRev is a novel robust data driven approach, devel-oped for quick literature review on a particular topic of interest. This method identifies common biological phrases that follow a power law distribution and important phrases which have the normalized point wise mutual information score greater than zero.

scholarly journals A Review on “Management by Exception” Surveillance for Well Management: To maximize Oil Production

2018 ◽  
Vol 7 (3.25) ◽  
pp. 90
Author(s):  
Azlinda Abdul Malik ◽  
Mohd Hilmi Hasan ◽  
Mazuin Jasamai
Keyword(s):  
Literature Review ◽  
Business Processes ◽  
Oil And Gas ◽  
Production Performance ◽  
Data Driven ◽  
Well Production ◽  
Monitoring Well ◽  
Oil And Gas Operations ◽  
Manual Interaction ◽  
Structured Literature Review

The business processes and decisions of oil and gas operations generate large amounts of data, which causes surveillance engineers to spend more time gathering, and analyzing them. To do this manually is inefficient. Hence, this study is proposed to leverage on data driven surveillance by adopting the principle of management by exception (MBE). The study aims to minimize the manual interaction between data and engineers; hence will focus on monitoring well production performance through pre-determined parameters with set of rules. The outcome of this study is a model that can identify any deviations from the pre-set rules and the model will alert user for deviations that occur. The model will also be able to predict on when the well be offline if the problem keeps on persisting without immediate action from user. The objective of this paper is to present a literature review on the prediction and management by exception for the above mentioned well management. The results presented in this paper will help in the development of the proposed prediction and management model. The literature review was conducted based on structured literature review methodology, and a comparative study among the collected works is analyzed and presented in this paper.  

scholarly journals Lexicons on Demand: Neural Word Embeddings for Large-Scale Text Analysis

2017 ◽  
Author(s):  
Ethan Fast ◽  
Binbin Chen ◽  
Michael S. Bernstein
Keyword(s):  
Text Analysis ◽  
Large Scale ◽  
Data Driven ◽  
Word Embeddings ◽  
Human Language ◽  
Lexical Categories ◽  
On Demand ◽  
Small Set ◽  
Highly Correlated ◽  
The Web

Human language is colored by a broad range of topics, but existing text analysis tools only focus on a small number of them. We present Empath, a tool that can generate and validate new lexical categories on demand from a small set of seed terms (like "bleed" and "punch" to generate the category violence). Empath draws connotations between words and phrases by learning a neural embedding across billions of words on the web. Given a small set of seed words that characterize a category, Empath uses its neural embedding to discover new related terms, then validates the category with a crowd-powered filter. Empath also analyzes text across 200 built-in, pre-validated categories we have generated such as neglect, government, and social media. We show that Empath's data-driven, human validated categories are highly correlated (r=0.906) with similar categories in LIWC.

scholarly journals THESIGNIFICANCE OF PHARMACOGENETIC TESTING FOR BETTER ANAESTHETIC OUTCOME AND LESSSURGICALSTRESS. Literature Review

2020 ◽  
Vol 10 (4) ◽  
pp. 112-117
Author(s):  
Kristina Tatzhikova ◽  
Bela Kantemirova ◽  
Aleksei Zhidovinov ◽  
Irakliy Kitiashvili
Keyword(s):  
Drug Interaction ◽  
Genetic Polymorphism ◽  
Literature Review ◽  
Adverse Drug Reactions ◽  
Genetic Screening ◽  
Web Of Science ◽  
Depth Of Anesthesia ◽  
Drug Reactions ◽  
Promising Tool ◽  
Stress Protection

The review is devoted to the problem of optimizing the anesthetic manual based on pharmacogenetic data in order to achieve an adequate depth of anesthesia and stress protection and reduce the number of adverse drug reactions. We analyzed the data of Pub Med and Web of Science databases to investigate the influence of genetic polymorphism on the body's response to the main groups of drugs used for anesthesia, and changes in the effects of drug interaction. Specifically, we have reported that the use of preoperative genetic screening for a set of markers (polymorphic alleles of a number of cytochromes) is a promising tool in the anesthesiologist's practice.

scholarly journals Investigation of factors contributing to accumulation of sediments in stormwater management ponds

2021 ◽  
Author(s):  
Sidra Yasir Siddiqui
Keyword(s):  
Literature Review ◽  
Stormwater Management ◽  
Sediment Accumulation ◽  
Relevant Information ◽  
Data Driven ◽  
Accumulation Rates ◽  
Additional Information ◽  
Modelling Approach

The purpose of this study was to investigate factors contributing to sediment accumulation rates in Stormwater Management ponds. For the purpose of this study almost fifty municipalities in Ontario were contacted and in collaboration with five of those municipalities this study was conducted. A questionnaire was developed and sent to municipalities through email and followup with in-person meetings were conducted. After collecting data and analyzing various characteristics of sediment accumulation rates in SWM ponds, a database was developed to systematically record the relevant information. Additional information on pond properties and drainage areas was sought through a questionnaire and meeting with stormwater managers, and supplemented with historic information. Data collected and used in the study was anonymized in all resulting publications. The calculated accumulated rates from the provided data were compared against the values extracted from the literature review. The developed approach will serve in the development of data-driven modelling approach in SWM ponds.

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