CzSL: Learning from citizen science, experts, and unlabelled data in astronomical image classification

Citizen science is gaining popularity as a valuable tool for labelling large collections of astronomical images by the general public. This is often achieved at the cost of poorer quality classifications made by amateur participants, which are usually verified by employing smaller data sets labelled by professional astronomers. Despite its success, citizen science alone will not be able to handle the classification of current and upcoming surv e ys. To alleviate this issue, citizen science projects have been coupled with machine learning techniques in pursuit of a more robust automated classification. Ho we v er, e xisting approaches have neglected the fact that, apart from the data labelled by amateurs, (limited) expert knowledge of the problem is also available along with vast amounts of unlabelled data that have not yet been exploited within a unified learning framework. This paper presents an innov ati ve learning methodology for citizen science capable of taking advantage of expert- and amateur-labelled data, featuring a transfer of labels between experts and amateurs. The proposed approach first learns from unlabelled data with a convolutional auto-encoder and then exploits amateur and expert labels via the pre-training and fine-tuning of a convolutional neural network, respectively. We focus on the classification of galaxy images from the Galaxy Zoo project, from which we test binary, multiclass, and imbalanced classification scenarios. The results demonstrate that our solution is able to impro v e classification performance compared to a set of baseline approaches, deploying a promising methodology for learning from different confidence levels in data labelling.Center of Excellence Severo Ochoa’ award to the Instituto de Astrof ´ısica de Andaluc ´ıa (grant no. SEV-2017-0709)A-TIC-434-UGR20 and PID2020-119478GB-I00NVIDIA Corporatio

Galaxy Image Classification Based on Citizen Science Data: A Comparative Study

Many research fields are now faced with huge volumes of data automatically generated by specialised equipment. Astronomy is a discipline that deals with large collections of images difficult to handle by experts alone. As a consequence, astronomers have been relying on the power of the crowds, as a form of citizen science, for the classification of galaxy images by amateur people. However, the new generation of telescopes that will produce images at a higher rate highlights the limitations of this approach, and the use of machine learning methods for automatic classification is considered essential. The goal of this paper is to shed light on the automated classification of galaxy images exploring two distinct machine learning strategies. First, following the classical approach consisting of feature extraction together with a classifier, we compare the state-of-the-art feature extractor for this problem, the WND-CHARM, with our proposal based on autoencoders for feature extraction on galaxy images. We then compare these results with an end-to-end classification using convolutional neural networks. To better leverage the available citizen science data, we also investigate a pre-training scheme that exploits both amateur-and expert-labelled data. Our experiments reveal that autoencoders greatly speed up feature extraction in comparison with WND-CHARM and both classification strategies, either using convolutional neural networks or feature extraction, reach comparable accuracy. The use of pre-training in convolutional neural networks, however, has allowed us to provide even better results

Elasticities of Passenger Transport Demand on US Intercity Routes: Impact on Public Policies for Sustainability

Passenger transport is a key sector of the economy, and its sustainability depends on achieving the greatest possible efficiency, avoiding problems of congestion or underuse of infrastructures, and reducing the sector’s environmental impact. Knowing the elasticities of demand is critical to achieving these objectives, estimating the intensity of transport demand, and predicting the effect of different policies on reducing greenhouse gas emissions. This research proposes a relatively simple model for estimating and predicting the elasticity of demand for different modes of transport at the route level. This model could be used by companies and public management to obtain a vision of the different analysed routes and the pressure of their demand, as well as a relative perspective of each of them. Such a model is used to estimate the price and income demand elasticities of passenger transport modes in domestic routes in the United States (2003–2019), where there is competition between road, rail, and air transport. Series of passenger numbers, fares, and budget shares are reconstructed from the available data. A Rotterdam demand model (RDM) is estimated using a seemingly unrelated regression method (SUR). The estimated income elasticities imply that demand for road transport increases somewhat more proportionally than the increase in income, somewhat less than proportionally for air transport, and with very low proportionality for rail transport. This indicates the need to target investment and service improvement efforts, as well as technological solutions, according to this difference in demand pressures. Finally, the demand response of the three modes of transport to price increases is inelastic, and there is little or no pass-through from one mode to another. This implies that fiscal or carbon pricing actions could have a very limited impact and high social costs. Again, strategies based on investments in technological progress, infrastructure development, and normative interventions could be more effective

DISTRIBUCIÓN DE LA AOD EN SERVICIOS SOCIALES BÁSICOS: ¿SE DIRIGE A LOS MÁS NECESITADOS?

El objetivo de la erradicación de la pobreza perseguido por los Objetivos de Desarrollo del Milenio pasa inexorablemente por la cobertura de las Necesidades Sociales Básicas en los países en desarrollo. Este artículo analiza si la asignación de la ayuda en Servicios Sociales Básicos (SSB) se dirige a los países con peores coberturas, a través de curvas de concentración, el índice Suits y el índice Kappa ponderado. El análisis muestra que el mapa geográfico de la ayuda varía considerablemente dependiendo de la necesidad estudiada (Educación, Salud, etc.) y del grupo considerado como receptor de la ayuda. Este estudio demuestra que aún muchos donantes no distribuyen la ayuda conforme a los acuerdos firmados por la comunidad internacional.

Accounting choice for measuring investment properties. Data mining techniques contribution to determine decision patterns

International Accounting Standard 40 (IAS 40 - Investment properties) offers an ideal setting for research on accounting choice as it represents a paradigmatic case choosing between the fair value and the historical cost as the measurement criteria. In this paper, we take the opportunity of this standard to provide additional evidence in a multinational and multi-context on the determinants that explain the accounting choice. Furthermore, in this paper, we introduce and compare the use of artificial neural networks and decision trees in order to assess the predictive capability of these methodologies, compared to other techniques commonly used to solve classification problems in this area such as the logistic regression. The classification results indicate that both neural networks and decision trees can be an interesting alternative to classical statistical methods such as the logistic regression. In particular, both methods outperformed the logistic regression in terms of predictive ability, although no significant differences were found between both

Una propuesta de optimización multicriterio para la asignación de la ayuda oficial al desarrollo: combinando los intereses de los donantes y de los receptores

It is well known that donors pursue different objectives (altruistic objectives or those based on recipient, and donor interests) in granting their aid. This study proposes an innovative tool that enables a combination of both types of objectives, allowing each donor (bilateral or multilateral) to better understand, optimise and target the distribution of its ODA according to the interests of both parties to the transaction (donor and recipient). This tool uses concentration curves and Suits’ indices to determine an optimal distribution of aid through the development of a constrained optimisation program that encompasses all of its purposes. Furthermore, used at the aggregate level, this tool could facilitate donor coordination to achieve international development goals.Es bien sabido que los donantes persiguen diferentes objetivos al conceder su ayuda oficial al desarrollo (objetivos altruistas, basados en los intereses de los receptores, pero también basados en los suyos propios). Este estudio propone una herramienta que posibilita combinar ambos tipos de objetivos, permitiendo a cada donante (bilateral o multilateral) comprender, optimizar y orientar mejor la distribución de su AOD en función de los intereses de ambas partes de la transacción (donante y receptor). Dicha herramienta utiliza curvas de concentración e índices de Suits para determinar una distribución óptima de la ayuda mediante el desarrollo de un programa de optimización restringido que incluya todos los objetivos perseguidos. Además, utilizada a nivel agregado, esta herramienta podría facilitar la coordinación de los donantes para alcanzar los objetivos internacionales de desarrollo

Potential Impact of Industry 4.0 in Sustainable Food Supply Chain Environment

Integration of suitable supply chain system with the industry 4.0 in the face of the evolving sustainability consciousness is of paramount importance to engineering and manufacturing industry at large; this is gradually becoming an irresistible option to manage production effectively and with high efficiency in engineering and manufacturing sector. Industry 4.0 also referred to as “smart factory” enables to address issues such as food safety, security, control, perishability, competitive pressure, demand predictions etc. within the food manufacturing aspects. The paper examines the challenges and opportunities towards the advancement of technology and that of industry 4.0 implications towards sustainability and more closely on sustainable food supply chain environments

A multi-criteria optimization proposal for aid allocation: combining donor and recipient interests

It is well known that donors pursue different objectives (altruistic objectives or those based on recipient, and donor interests) in granting their aid. This study proposes an innovative tool that enables a combination of both types of objectives, allowing each donor (bilateral or multilateral) to better understand, optimise and target the distribution of its ODA according to the interests of both parties to the transaction (donor and recipient). This tool uses concentration curves and Suits’ indices to determine an optimal distribution of aid through the development of a constrained optimisation program that encompasses all of its purposes. Furthermore, used at the aggregate level, this tool could facilitate donor coordination to achieve international development goals

Evaluating the role of gamification and flow in e-consumers: millennials versus generation X

This research has three main objectives. First, it examines influence of gamification on the behavioral intention to use an e-commerce platform. Second, it analyzes the role of the flow state given its importance in terms of behavior in online environments. Finally, the study aims to detect and analyze differences between Millennials and Generation X.Es la versión enviada del artículo. Se puede consultar la versión final en https://doi.org/10.1108/K-07-2018-035

Analysing the relationship between immigrant status and the severity of offending behaviour in terms of individual and contextual factors

Background: Social inclusion is a context for both risk and protective factors of migrant youth delinquency. This study aims to shed light on the issue by comparing delinquency amongst native, first-generation, and second-generation immigrant youths in Portugal, a country located in the south of Europe, an area where research in this field is still scarce. Methods: The research is based on the International Self-Reported Delinquency (ISRD-3) dataset, which includes information on over 4,000 adolescents, who self-reported on their socio-demographic status, leisure activities, school and neighbourhood environment, family bonds, and self-control. Results: Nested Logistic Regression analyses showed that a young first-generation immigrant is twice as likely to commit a crime, with or without violence, as a young native born in Portugal. However, no differences were found regarding the prevalence of delinquency amongst second-generation immigrants and natives, which is likely due to the integration and cultural assimilation of the immigrant over time. Regarding the analysed risk factors, it was found that both structural and individual factors, identified by the theories of control, stress, as well as situational action theory, have a direct effect on the commission of juvenile crimes (both non-violent and violent). Moreover, this effect is significant in adolescents living in Portugal in general, both immigrants and natives. The most influential variable for both types of delinquent behaviour, with and without violence, is peer delinquency, followed by low morality and self-control. Conclusion: These findings have relevant policy implications and are useful for evidence-based interventions aimed at promoting migrant adolescent well-being and targeting host countries’ performance.This work was supported by the Research Centre in Political Science (UID/CPO/00758/2013), University of Minho, supported by the FCT (Foundation for Science and Technology), and the Portuguese Ministry of Education and Science through national funds, and by the Research Centre on Child Studies (CIEC) financially supported by Portuguese national funds through the FCT (Foundation for Science and Technology) within the framework of the CIEC projects under the references UIDB/00317/2020 and UIDP/00317/2020