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Oliphant, Andries Walter

doi:10.1163/18757421-90000186

Cited by 14 publications

(18 citation statements)

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“…Python packages integrated within the text-package include:PyTorch (Paszke et al, 2019), transformers (Wolf et al, 2019), nltk (Bird et al, 2009), and numpy (Oliphant, 2006). …”

Section: Discussionmentioning

confidence: 99%

“…PyTorch (Paszke et al, 2019), transformers (Wolf et al, 2019), nltk (Bird et al, 2009), and numpy (Oliphant, 2006).…”

Section: Discussionmentioning

confidence: 99%

“…The text-package can be downloaded and installed from CRAN or Github. The text-package uses an R-package called reticulate (Ushey et al, 2020) as an interface to Python (Van Rossum & Drake, 1995) and the Python packages torch (Paszke et al, 2019), transformers (Wolf et al, 2019), nltk (Bird, Klein, & Loper, 2009), and numpy (Oliphant, 2006). It is important to get versions of these libraries to work together.…”

Section: Installation Of Textmentioning

confidence: 99%

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The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers.

Kjell¹,

Giorgi²,

Schwartz³

2023

Psychological Methods

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The language that individuals use for expressing themselves contains rich psychological information. Recent significant advances in Natural Language Processing (NLP) and Deep Learning (DL), namely transformers, have resulted in large performance gains in tasks related to understanding natural language. However, these state-of-the-art methods have not yet been made easily accessible for psychology researchers, nor designed to be optimal for human-level analyses. This tutorial introduces text (https://r-text.org/), a new R-package for analyzing and visualizing human language using transformers, the latest techniques from NLP and DL. The text-package is both a modular solution for accessing state-of-the-art language models and an end-to-end solution catered for human-level analyses. Hence, text provides user-friendly functions tailored to test hypotheses in social sciences for both relatively small and large data sets. The tutorial describes methods for analyzing text, providing functions with reliable defaults that can be used off-the-shelf as well as providing a framework for the advanced users to build on for novel pipelines. The reader learns about three core methods: (1) textEmbed(): to transform text to modern transformer-based word embeddings; (2) textTrain() and textPredict(): to train predictive models with embeddings as input, and use the models to predict from; (3) textSimilarity() and textDistance(): to compute semantic similarity/distance scores between texts. The reader also learns about two extended methods: (1) textProjection()/textProjectionPlot() and (2) textCentrality()/ textCentralityPlot(): to examine and visualize text within the embedding space.

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“…Python packages integrated within the text-package include:PyTorch (Paszke et al, 2019), transformers (Wolf et al, 2019), nltk (Bird et al, 2009), and numpy (Oliphant, 2006). …”

Section: Discussionmentioning

confidence: 99%

“…PyTorch (Paszke et al, 2019), transformers (Wolf et al, 2019), nltk (Bird et al, 2009), and numpy (Oliphant, 2006).…”

Section: Discussionmentioning

confidence: 99%

Section: Installation Of Textmentioning

confidence: 99%

See 1 more Smart Citation

The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers.

Kjell¹,

Giorgi²,

Schwartz³

2023

Psychological Methods

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“…The ML models were extracted from Scikit Learn library, while the NN models were imported from Tensorflow (Keras). Multiple other libraries were used to manipulate the data and to present the results, such as pandas, 42 numpy, 43 and matplotlib. 44 The cleaned dataset after removing outliers included 130,634 samples.…”

Section: Methodsmentioning

confidence: 99%

Machine learning‐based anti‐jamming technique at the physical layer

Chehimi

Awad

Al‐Husseini³

et al. 2023

Concurrency and Computation

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The reliance on wireless services to exchange critical data is associated with various threats and attacks, which must be mitigated to ensure integrity and security of those wireless services. Posing a serious challenge to wireless systems, jamming is among these attacks. In order to mitigate jamming, directive antennas are used to minimize the signals that are received from the jammer, while maximizing the received legitimate signal from the authorized transmitter. In this paper, we propose a machine learning‐based anti‐jamming framework to provide a spatially dynamic and instantaneous anti‐jamming performance that is achieved at the physical layer. The proposed framework incorporates a dataset that can be deployed in the hardware of a receiver with a massive Multiple‐Inputs Multiple‐Outputs–(MIMO) antenna. Our extensive performance evaluation results demonstrate the effective performance of the proposed framework in preserving integrity of a massive–MIMO communication system despite the presence of a hostile jammer. Particularly, due to the tabular nature of the generated dataset, tree‐based random forest models achieved the best performance with a signal‐to‐interference‐plus‐noise ratio accuracy of 92%$$ 92\% $$ and fast anti‐jamming response in around 1.66 s under sever jamming conditions.

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“…The videos are read using the OpenCV library (2015) and loaded into memory frame by frame as NumPy (Oliphant, 2006) arrays. Since consecutive frames are likely to contain identical information, the script compares the information in each frame to the information in the subsequent frame and records the absolute difference between the two.…”

Section: Digital Trace Data In the Social Sciencesmentioning

confidence: 99%

A Novel iOS Data Donation Approach: Automatic Processing, Compliance, and Reactivity in a Longitudinal Study

Baumgartner

Sumter

Petkevič

et al. 2022

Social Science Computer Review

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For social scientists who are interested in understanding smartphone-usage patterns, as well as the causes and effects of smartphone use, it is crucial to get accurate estimates of smartphone and app usage. For this reason, we present a novel data donation approach based on screen video recordings of information provided by the iOS Battery Section. In this paper, we describe the Python script that can be used to process the data automatically. Moreover, we tested accuracy of the script, feasibility of the data collection method, as well as compliance and reactivity of the approach among a sample of 93 University students who participated in a 7-day mobile diary study. The findings of the study show that the approach works well, is very feasible and that the script resulted in accurate smartphone use data. Moreover, findings show that compliance rates were rather high, and no substantial signs of reactivity to the method occurred (i.e., participants did not change their smartphone behavior substantially while participating in this study). We therefore hope that the presented approach will encourage future researchers to use this or similar approaches to gain accurate smartphone use data in an ethical way.

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COSAW and Publishing for All

Cited by 14 publications

References 0 publications

The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers.

The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers.

Machine learning‐based anti‐jamming technique at the physical layer

A Novel iOS Data Donation Approach: Automatic Processing, Compliance, and Reactivity in a Longitudinal Study

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