Deep learning from crowds

Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the state-of-the-art in various domains. However, as the size of supervised artificial neural networks grows, typically so does the need for larger labeled datasets. Recently, crowdsourcing has established itself as an efficient and cost-effective solution for labeling large sets of data in a scalable manner, but it often requires aggregating labels from multiple noisy contributors with different levels of expertise. In this paper, we address the problem of learning deep neural networks from crowds. We begin by describing an EM algorithm for jointly learning the parameters of the network and the reliabilities of the annotators. Then, a novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels of multiple annotators, using only backpropagation. We empirically show that the proposed approach is able to internally capture the reliability and biases of different annotators and achieve new state-of-the-art results for various crowdsourced datasets across different settings, namely classification, regression and sequence labeling.Comment: 10 pages, The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI), 201

Heteroscedastic Gaussian processes for uncertainty modeling in large-scale crowdsourced traffic data

Accurately modeling traffic speeds is a fundamental part of efficient intelligent transportation systems. Nowadays, with the widespread deployment of GPS-enabled devices, it has become possible to crowdsource the collection of speed information to road users (e.g. through mobile applications or dedicated in-vehicle devices). Despite its rather wide spatial coverage, crowdsourced speed data also brings very important challenges, such as the highly variable measurement noise in the data due to a variety of driving behaviors and sample sizes. When not properly accounted for, this noise can severely compromise any application that relies on accurate traffic data. In this article, we propose the use of heteroscedastic Gaussian processes (HGP) to model the time-varying uncertainty in large-scale crowdsourced traffic data. Furthermore, we develop a HGP conditioned on sample size and traffic regime (SRC-HGP), which makes use of sample size information (probe vehicles per minute) as well as previous observed speeds, in order to more accurately model the uncertainty in observed speeds. Using 6 months of crowdsourced traffic data from Copenhagen, we empirically show that the proposed heteroscedastic models produce significantly better predictive distributions when compared to current state-of-the-art methods for both speed imputation and short-term forecasting tasks.Comment: 22 pages, Transportation Research Part C: Emerging Technologies (Elsevier

Towards Robust Deep Reinforcement Learning for Traffic Signal Control: Demand Surges, Incidents and Sensor Failures

Reinforcement learning (RL) constitutes a promising solution for alleviating the problem of traffic congestion. In particular, deep RL algorithms have been shown to produce adaptive traffic signal controllers that outperform conventional systems. However, in order to be reliable in highly dynamic urban areas, such controllers need to be robust with the respect to a series of exogenous sources of uncertainty. In this paper, we develop an open-source callback-based framework for promoting the flexible evaluation of different deep RL configurations under a traffic simulation environment. With this framework, we investigate how deep RL-based adaptive traffic controllers perform under different scenarios, namely under demand surges caused by special events, capacity reductions from incidents and sensor failures. We extract several key insights for the development of robust deep RL algorithms for traffic control and propose concrete designs to mitigate the impact of the considered exogenous uncertainties.Comment: 8 page

Multi-Output Gaussian Processes for Crowdsourced Traffic Data Imputation

Traffic speed data imputation is a fundamental challenge for data-driven transport analysis. In recent years, with the ubiquity of GPS-enabled devices and the widespread use of crowdsourcing alternatives for the collection of traffic data, transportation professionals increasingly look to such user-generated data for many analysis, planning, and decision support applications. However, due to the mechanics of the data collection process, crowdsourced traffic data such as probe-vehicle data is highly prone to missing observations, making accurate imputation crucial for the success of any application that makes use of that type of data. In this article, we propose the use of multi-output Gaussian processes (GPs) to model the complex spatial and temporal patterns in crowdsourced traffic data. While the Bayesian nonparametric formalism of GPs allows us to model observation uncertainty, the multi-output extension based on convolution processes effectively enables us to capture complex spatial dependencies between nearby road segments. Using 6 months of crowdsourced traffic speed data or "probe vehicle data" for several locations in Copenhagen, the proposed approach is empirically shown to significantly outperform popular state-of-the-art imputation methods.Comment: 10 pages, IEEE Transactions on Intelligent Transportation Systems, 201

Promoting Physical Exercise Participation: The Role of Interpersonal Behaviors for Practical Implications

he number of people engaging in physical exercise has been decreasing every year. These behaviors are known to be related with non-communicable chronic diseases and to drastically increase premature morbidity and mortality. Since “the lack of motivation” has been pointed out as one of the main reasons for not engaging in physical exercise, several theoretical and empirical studies have been conducted aimed at understanding what influences behavior regulation. According to literature, gym exercisers who perceive exercise instructors as supportive are more likely to maintain physical exercise participation over the long-run. Supporting autonomy, competence, and relatedness should be carefully considered when interacting with health club clients as a way to promote more autonomous motivation. Overall, it seems that exercise instructors should foster a supportive environment for gym exercisers, in order to encourage exercise as a habitual behavior.info:eu-repo/semantics/publishedVersio

Relatório de estágio na Câmara Municipal da Ribeira Grande

Este trabalho surge como resultado do estágio desenvolvido na Câmara Municipal da Ribeira Grande, ilha de S. Miguel, o qual pretendeu fazer a ponte entre a prática realizada em contexto académico e a prática profissional. Ilustra assim, as diferentes propostas de requalificação do espaço urbano, os pareceres e opiniões dadas a alguns projetos já existentes e as soluções encontradas para situações e problemas que foram surgindo, ao longo do meu período de permanência na referida Câmara; ABSTRACT: Internship Report on Ribeira Grande City Council In this report I will present all the work that I have done during my internship on Ribeira Grande city council in S. Miguel Island. Here I will present the conection with the academical skills and the professional practice. This report will also include the proposals of different requalifications for the urban space and the given opinions for some other existent projects. I am also going to enumerate the solutions for some difficulties found during my internship in the city council

The impact of Internet addiction on the life satisfaction of a remote worker

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Information Systems and Technologies ManagementWhile the use and implementation of Information Technology (IT) have been generally viewed as beneficial in the digital era, it is important to note that this is not always the case. While technology has enabled individuals to achieve unprecedented feats, it has also been the source of several societal challenges. Technology has increasingly become part of people's daily lives, particularly in the workplace. Thanks to advancements in digital technology and communication tools, employees now have the flexibility to work from any location and at any time. While remote work provides many benefits, there are also several downsides for employees. These may include limited opportunities for in-person communication and interaction, loneliness and isolation, the need to acquire necessary digital skills, and the challenge of separating work and home life. Some individuals may feel socially withdrawn when they are unable to satisfy their need for face-to-face communication and social connection. As a result, they may seek to fulfill this need through the Internet. Therefore, this study aims to address the question "How does Internet addiction and social isolation impact the remote work performance and life satisfaction of an individual?". The purpose of this paper is to determine and analyze whether an individual's level of Internet addiction and social isolation influences their remote work performance as well as their life satisfaction. This model was empirically validated using structural equation modelling (SEM)/partial least squares (PLS) in the context of Internet addiction in remote work professionals, with the development of a questionnaire. From this survey, a total of 172 responses from remote workers were taken into consideration to empirically test the model. It was discovered that social isolation and remote work performance directly affect the life satisfaction of a worker and Internet addiction indirectly influences it. Age was also taken into consideration and considered impactful, due to its impact in social isolation and Internet addiction