Digital Cosmetics

Leone, Massimo

doi:10.1515/css-2020-0030

Cited by 7 publications

(3 citation statements)

References 12 publications

(5 reference statements)

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“…Contemporary developments in digital representation technologies provide a broad spectrum of options for teachers and learners to operate within, but there are always limited to predefined boundaries (Leone, 2020). It gives them the experience of using a more comprehensive semiotic vocabulary to discover the meanings of science in their education process (Areljung et al, 2021), exchange and share these meanings.…”

Section: Discussionmentioning

confidence: 99%

Human, Technologies and Quality of Education, 2023

2023

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The aim of the study was to explore social measurements and COVID-19 -related behavioral choices from a network structure perspective. We tested network models by comparing network structures in 46 countries by cultural dimensions (Hofstede et al., 2010). The pre-registered hypothesis was that the network structure of variables predicting COVID-19-related behavioral choices will be robust across countries, regardless of the mean differences of these variables between countries characterized by high vs low culture dimension indices. Behavioural choices toward COVID-19 situation were: physical hygiene, policy support, and physical contact. In network analysis we used several social/psychological constructs: social belonging, trait optimism, collective narcissism, moral identity, self-esteem, national identification, trait self-control, morality as cooperation, conspiracy beliefs, political ideology, and narcissism. We analysed twelve conditions based on dichotomized culture dimensions (high vs. low Power distance, Individualism, Masculinity, Uncertainty avoidance, Long-term orientation, and Indulgence) across demographics (e.g., age, gender). We conducted network structure analysis using high-dimensional undirected graph estimation with glasso procedure splitting the data by culture dimensions in total with N = 40 795 individuals. The study findings indicated that the network models were fairly consistent across cultures with different scores on each of Hofstede's six cultural dimensions. This similarity reflects the previous studies in terms of the stability of associations between variables regardless of sex, age, and political beliefs.

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Section: Discussionmentioning

confidence: 99%

Human, Technologies and Quality of Education, 2023

2023

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“…Digitising museum resources is being studied extensively. [34][35][36] Cloud computing and digital museums are examined. Museums employ digital technologies to manage cultural treasures.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploratory Data Analysis for Textile Defect Detection

Thivakaran,

Priyanka,

Antony

et al. 2023

IJRITCC

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The capacity to recognize anomalies in real-world visual data is essential for many computer vision uses. New approaches and ideas in unsupervised defective garments identification require data for training and evaluation. Understanding the constraints of the currently employed approach of human inspection is crucial for improving clothing quality. Uses for digital image processing in the textile sector are suggested. This method proposes a novel quantitative measuring strategy by fusing digital image processing with the Lab view platform. As this study progresses, it becomes clear that the FLDA yields the best results, with 95% accuracy, while the Hoeffiding Tree yields the lowest results, with 60% accuracy. When compared to other models, the FLDA's precision of 0.96 is the best you'll find, while the Hoeffiding Tree's is the lowest at 0.62. The FLDA provides the best result, with a recall value of 0.95, while the Hoeffiding Tree shows the lowest result, with a recall value of 0.60. The FLDA yields the best results (0.90 kappa value), whereas the Hoeffiding Tree yields the worst (0.20 kappa value).The FLDA exhibits the best results, with an F-Measure value of 0.95, while the Hoeffiding Tree displays the lowest results, with an F-Measure value of 0.58. The FLDA provides the best results, with an MCC value of 0.91, while the Hoeffiding Tree displays the worst results, with an MCC value of 0.22. The FLDA yields the best results (0.98 ROC value), whereas the Decision Table produces the worst results (0.69 ROC value). The best prediction accuracy is shown by the FLDA, at 0.98 of the PRC value, while the worst is shown by the Decision Table, at 0.67. The MAE is lowest (0.07) for the FLDA and highest (0.39) for the Hoeffiding Tree. The MAE deviation of the Bayes Net is 0.19. The best result is shown by the FLDA, with an RMSE of 0.22, while the largest RMSE deviation is found in the Hoeffiding Tree, at 0.62. The RMSEdeviation for Bayes Net is 0.41. The finest RAE is shown by the FLDA, at 13.39%, while the largest RAE deviation is 78.28% for the Hoeffiding Tree. The Bayes Net explains 38.74% of the variation in RAE. The best result is shown by the FLDA, with an RRSE of 44.36%; the largest RRSE variation is shown by the Hoeffiding Tree, with 123.99%. When compared to other models, the IBK's preparation time of 0 seconds is by far the shortest. While the Bayes Net completes its task in 0.03 seconds, FLDA can take up to 0.17 seconds. The FLDA model is found to have superior performance in this study.

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“…In recent years, the sign-interpretation-object triangle is used to explain social network marketing communication in social network research (Mingers & Willcocks, 2014;Leone M, 2020). Using the sign-interpretation-object triangle, some studies use communication effectiveness to measure social network marketing effectiveness (Dong et al, 2014;Fjrtoft & Lai, 2020).…”

Section: The Sign-interpretation-object Triangle In Social Networkmentioning

confidence: 99%

Analyzing the Effectiveness of Interaction Between Brand Awareness and Social Network Advertising

2022

International Journal of Technology and Human Interaction

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Although social network advertising has become part of the online marketing strategy, there is still a lack of research related to the interaction between brand awareness and advertising contents in social network advertising. Using the semiotic theory to explain the interaction in social network advertising, this study introduced sign-interpretation-object triangle to analyze the interactions of advertising content and brand awareness as signs, users’ experience as interpretation, and social networks advertising effectiveness as object moderated by social facilitation. This study was made in an experiment followed by an online survey, which contains 303 participants in Mainland China. This study shows some major findings. There is an interaction effect on advertising content and low brand awareness in a social network. The moderating role of social facilitation is significant in advertising content with high brand awareness.

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Digital Cosmetics

Cited by 7 publications

References 12 publications

Human, Technologies and Quality of Education, 2023

Human, Technologies and Quality of Education, 2023

Exploratory Data Analysis for Textile Defect Detection

Analyzing the Effectiveness of Interaction Between Brand Awareness and Social Network Advertising

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