Abstract
With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific collaboration from a multidisciplinary point of view. This paper aims at contributing to this end by exploiting a set of factors affecting attitudes towards the adoption of human-AI interaction into scientific work settings. In particular, we are interested in identifying the determinants of trust and acceptability when considering the combination of hybrid human-AI approaches for improving research practices. This includes the way as researchers assume human-centered artificial intelligence (AI) and crowdsourcing as valid mechanisms for aiding their tasks. Through the lens of a unified theory of acceptance and use of technology (UTAUT) combined with an extended technology acceptance model (TAM), we pursue insights on the perceived usefulness, potential blockers, and adoption drivers that may be representative of the intention to use hybrid intelligence systems as a way of unveiling unknown patterns from large amounts of data and thus enabling novel scientific discoveries.

Abstract

Abstract
The usage of non-traditional data in credit scoring, from microfinance institutions, is very useful when trying to address the problem, very common in emerging markets, of the lack of a verifiable customers' credit history. In this context, this paper relies on data acquired from smartphones in a loan classification problem. We conduct a set of experiments concerning feature selection, strategies to deal with imbalanced datasets and algorithm choice, to define a baseline model. This model is, then, compared to others adding network features to the original ones. For that comparison, we generate a network that links a given user to its phone book contacts which are users of a given mobile application, taking into account the ethics and privacy concerns involved, and use some feature extraction techniques, such as the introduction of centrality measures and the definition of node embeddings, in order to capture certain aspects of the network's topology. Several node embedding algorithms are tested, but only Node2Vec proves to be significantly better than the baseline model, applying Friedman's post hoc tests. This node embedding algorithm outperforms all the other, representing a relative improvement, in comparison with the baseline model, of 5.74% on the mean accuracy, 7.13% on the area under the Receiver Operating Characteristic curve and 30.83% on the Kolmogorov-Smirnov statistic scores. This method, therefore, proves to be very promising when trying to discriminate between "good" and "bad" customers, in credit scoring classification problems.

Abstract

Abstract
Quality automobile inspection is one of the critical application areas to achieve better quality at low cost and can be obtained with the advance computer vision technology. Whether for the quality inspection or the automatic assembly of automobile parts, automatic recognition of automobile parts plays an important role. In this article, vehicle parts are classified using deep neural network architecture designed based on ConvNet. The public dataset available in CompCars [1] were used to train and test a VGG16 deep learning architecture with a fully connected output layer of 8 neurons. The dataset has 20,439 RGB images of eight interior and exterior car parts taken from the front view. The dataset was first separated for training and testing purpose, and again training dataset was divided into training and validation purpose. The average accuracy of 93.75% and highest accuracy of 97.2% of individual parts recognition were obtained. The classification of car parts contributes to various applications, including car manufacturing, model verification, car inspection system, among others. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Abstract
Wikipedia is an online, free, multi-language, and collaborative encyclopedia, currently one of the most significant information sources on the web. The open nature of Wikipedia contributions raises concerns about the quality of its information. Previous studies have addressed this issue using manual evaluations and proposing generic measures for quality assessment. In this work, we focus on the quality of health-related content. For this purpose, we use general and health-specific features from Wikipedia articles to propose health-specific metrics. We evaluate these metrics using a set of Wikipedia articles previously assessed by WikiProject Medicine. We conclude that it is possible to combine generic and specific metrics to determine health-related content's information quality. These metrics are computed automatically and can be used by curators to identify quality issues. Along with the explored features, these metrics can also be used in approaches that automatically classify the quality of Wikipedia health-related articles.