Abstract

Abstract
Visualizing and examining the intellectual landscape and evolution of scientific communities to support collaboration is crucial for multiple research purposes. In some cases, measuring similarities and matching patterns between research publication document sets can help to identify people with similar interests for building research collaboration networks and university-industry linkages. The premise of this work is assessing feasibility for resolving ambiguous cases in similarity detection to determine authorship with natural language processing (NLP) techniques so that crowdsourcing is applied only in instances that require human judgment. Using an NLP-crowdsourcing convergence strategy, we can reduce the costs of microtask crowdsourcing while saving time and maintaining disambiguation accuracy over large datasets. This article contributes a next-gen crowd-artificial intelligence framework that used an ensemble of term frequency-inverse document frequency and bidirectional encoder representation from transformers to obtain similarity rankings for pairs of scientific documents. A sequence of content-based similarity tasks was created using a crowd-powered interface for solving disambiguation problems. Our experimental results suggest that an adaptive NLP-crowdsourcing hybrid framework has advantages for inter-researcher similarity detection tasks where fully automatic algorithms provide unsatisfactory results, with the goal of helping researchers discover potential collaborators using data-driven approaches.

Abstract
Paint bases are the essence of the color palette, allowing for the creation of a wide range of tones by combining them in different proportions. In this paper, an Artificial Neural Network is developed incorporating a pre-trained Decoder to predict the proportion of each paint base in an ink mixture in order to achieve the desired color. Color coordinates in the CIELAB space and the final finish are considered as input parameters. The proposed model is compared with commonly used models such as Linear Regression, Random Forest and Artificial Neural Network. It is important to note that the Artificial Neural Network was implemented with the same architecture as the proposed model but without incorporating the pre-trained Decoder. Experimental results demonstrate that the Artificial Neural Network with a pre-trained Decoder consistently outperforms the other models in predicting the proportions of paint bases for color tuning. This model exhibits lower Mean Absolute Error and Root Mean Square Error values across multiple objectives, indicating its superior accuracy in capturing the complexities of color relationships. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract

Abstract

Abstract
To strengthen theoretical and practical understanding of consumers' perceptions of luxury brands, previous literature has scrutinized the financial, functional, individual, and social dimensions of the luxury value construct. However, few authors have focused on linking the antecedent dimensions of luxury value to further attitudinal outcomes, besides purchase intention. Also, the few studies considering both dimensions focused on age or culture as moderator dimensions between such constructs. The gap identified in the literature constitutes the originality of the present study. As a result, the main goal of the present work is to measure the impact of luxury value perceptions in customer-based outcomes, as well as the possible moderator effect of Artificial Intelligence and Emotional Intelligence in the relationship between Owner Based Luxury Value and customer-based outcomes. Therefore, a quantitative methodological approach will be employed, through the development of a questionnaire.