2,520 research outputs found
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 perĂodo de permanĂŞncia 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
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