Computers, Tablets, and Smart Phones: The Truth About Web-based Surveys

Merle, Patrick; Gearhart, Sherice; Craig, Clay; VanDyke, Matthew S.; Brooks, Mary; Rahimi, Mehrnaz

doi:10.29115/sp-2015-0028

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…The type of device used to complete data collection may be associated with quality. For example, participants spend more time completing surveys on smartphones (Revilla, Toninelli, and Ochoa 2016), and younger panelists utilize mobile devices for participation (Merle et al 2015). Although limited panel/MTurk comparisons have investigated devices used for survey completion, more should be known about differences.…”

Section: Data Comparisonmentioning

confidence: 99%

Collecting Online Survey Data: A Comparison of Data Quality among a Commercial Panel & MTurk

Zhang¹,

Gearhart²

2020

Surv Pract

Self Cite

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Scholars seeking online data collection have sought the assistance of professional panels, which are commercial entities that recruit prospective research participants. Although not intended for recruiting participants, Amazon's Mechanical Turk (MTurk) service is a crowdsourcing platform that is being utilized for a lower cost. This study compares data collected from MTurk (N = 760) and a professional panel (N = 1,110) to assess aspects of data quality and respondent features. Results show that MTurk may produce better data quality based on completion rate and success in passing manipulation checks. Participants also differ in terms of educational attainment, age, and preferences of certain devices used for participation. Overall, results provide insight that researchers collecting online data should take into consideration before choosing a data collection platform. Researchers collecting online data increasingly utilize professional panels (Antoun et al. 2016). Opt-in panels allow researchers to cost-effectively recruit respondents compared to offline recruitment (Antoun et al. 2016). Amazon's Mechanical Turk (MTurk) crowdsourcing platform provides an alternate avenue for participant recruitment in exchange for payment (Hitlin 2016). This study compares data collected from MTurk and a professional panel to assess data quality, respondent features, and devices used for responding. Results showcase practical implications for acquiring valid data when recruiting online. Literature Review Data Collection Online participant recruitment has been adopted for their convenience, efficiency, and low cost (Paolacci, Chandler, and Ipeirotis 2010). Two popular means of online recruitment include professional panel companies and crowdsourcing websites, such as MTurk.

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Section: Data Comparisonmentioning

confidence: 99%

Collecting Online Survey Data: A Comparison of Data Quality among a Commercial Panel & MTurk

Zhang¹,

Gearhart²

2020

Surv Pract

Self Cite

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“…It encompasses a vast set of practices, ranging from guidelines to applications designed to protect data. With the development of digital systems including the Internet of Things (IoT) [1], Internet of Vehicles (IoV) [2], Internet of Battlefield Things (IoBT) [3], and Cyber-Physical Systems (CPS) [4], as well as technologies such as smartphones, tablets [5], autonomous vehicles, digital twins [6], cryptocurrencies [7], and smart homes [8], the scope of cyber security is expanding accordingly. The more these technologies integrate into daily life, they also widen the potential landscape for cyber threats.…”

Section: Introductionmentioning

confidence: 99%

A Deep Marginal-Contrastive Defense against Adversarial Attacks on 1D Models

Hassanin¹,

Moustafa²,

Tahtalı³

2020

2020 IEEE Symposium Series on Computational Intelligence (SSCI)

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The recent progression of Large Language Models (LLMs) has witnessed great success in the fields of data-cetric applications. LLMs trained on massive textual datasets showed ability to encode not only context but also ability to provide powerful comprehension to downstream tasks. Interestingly, Generative Pre-trained Transformers utilised this ability to bring AI a step closer to human being replacement in at least datacentric applications. Such power can be leveraged to identify anomalies of cyber threats, enhance incident response, and automate routine security operations. We provide an overview for the recent activities of LLMs in cyber defense sections, as well as categorization for the cyber defense sections such as threat intelligence, vulnerability assessment, network security, privacy preserving, awareness and training, automation, and ethical guidelines. Fundamental concepts of the progression of LLMs from Transformers, Pre-trained Transformers, and GPT is presented. Next, the recent works of each section is surveyed with the related strengths and weaknesses. A special section about the challenges and directions of LLMs in cyber security is provided. Finally, possible future research directions for benefiting from LLMs in cyber security is discussed.

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