39,225 research outputs found
Differentially private partitioned variational inference
Learning a privacy-preserving model from sensitive data which are distributed
across multiple devices is an increasingly important problem. The problem is
often formulated in the federated learning context, with the aim of learning a
single global model while keeping the data distributed. Moreover, Bayesian
learning is a popular approach for modelling, since it naturally supports
reliable uncertainty estimates. However, Bayesian learning is generally
intractable even with centralised non-private data and so approximation
techniques such as variational inference are a necessity. Variational inference
has recently been extended to the non-private federated learning setting via
the partitioned variational inference algorithm. For privacy protection, the
current gold standard is called differential privacy. Differential privacy
guarantees privacy in a strong, mathematically clearly defined sense.
In this paper, we present differentially private partitioned variational
inference, the first general framework for learning a variational approximation
to a Bayesian posterior distribution in the federated learning setting while
minimising the number of communication rounds and providing differential
privacy guarantees for data subjects.
We propose three alternative implementations in the general framework, one
based on perturbing local optimisation runs done by individual parties, and two
based on perturbing updates to the global model (one using a version of
federated averaging, the second one adding virtual parties to the protocol),
and compare their properties both theoretically and empirically.Comment: Published in TMLR 04/2023: https://openreview.net/forum?id=55Bcghgic
Advances on Concept Drift Detection in Regression Tasks using Social Networks Theory
Mining data streams is one of the main studies in machine learning area due
to its application in many knowledge areas. One of the major challenges on
mining data streams is concept drift, which requires the learner to discard the
current concept and adapt to a new one. Ensemble-based drift detection
algorithms have been used successfully to the classification task but usually
maintain a fixed size ensemble of learners running the risk of needlessly
spending processing time and memory. In this paper we present improvements to
the Scale-free Network Regressor (SFNR), a dynamic ensemble-based method for
regression that employs social networks theory. In order to detect concept
drifts SFNR uses the Adaptive Window (ADWIN) algorithm. Results show
improvements in accuracy, especially in concept drift situations and better
performance compared to other state-of-the-art algorithms in both real and
synthetic data
A Protocol for Cast-as-Intended Verifiability with a Second Device
Numerous institutions, such as companies, universities, or non-governmental
organizations, employ Internet voting for remote elections. Since the main
purpose of an election is to determine the voters' will, it is fundamentally
important to ensure that the final election result correctly reflects the
voters' votes. To this end, modern secure Internet voting schemes aim for what
is called end-to-end verifiability. This fundamental security property ensures
that the correctness of the final result can be verified, even if some of the
computers or parties involved are malfunctioning or corrupted.
A standard component in this approach is so called cast-as-intended
verifiability which enables individual voters to verify that the ballots cast
on their behalf contain their intended choices. Numerous approaches for
cast-as-intended verifiability have been proposed in the literature, some of
which have also been employed in real-life Internet elections.
One of the well established approaches for cast-as-intended verifiability is
to employ a second device which can be used by voters to audit their submitted
ballots. This approach offers several advantages - including support for
flexible ballot/election types and intuitive user experience - and it has been
used in real-life elections, for instance in Estonia.
In this work, we improve the existing solutions for cast-as-intended
verifiability based on the use of a second device. We propose a solution which,
while preserving the advantageous practical properties sketched above, provides
tighter security guarantees. Our method does not increase the risk of
vote-selling when compared to the underlying voting protocol being augmented
and, to achieve this, it requires only comparatively weak trust assumptions. It
can be combined with various voting protocols, including commitment-based
systems offering everlasting privacy
Learning Robust Visual-Semantic Embedding for Generalizable Person Re-identification
Generalizable person re-identification (Re-ID) is a very hot research topic
in machine learning and computer vision, which plays a significant role in
realistic scenarios due to its various applications in public security and
video surveillance. However, previous methods mainly focus on the visual
representation learning, while neglect to explore the potential of semantic
features during training, which easily leads to poor generalization capability
when adapted to the new domain. In this paper, we propose a Multi-Modal
Equivalent Transformer called MMET for more robust visual-semantic embedding
learning on visual, textual and visual-textual tasks respectively. To further
enhance the robust feature learning in the context of transformer, a dynamic
masking mechanism called Masked Multimodal Modeling strategy (MMM) is
introduced to mask both the image patches and the text tokens, which can
jointly works on multimodal or unimodal data and significantly boost the
performance of generalizable person Re-ID. Extensive experiments on benchmark
datasets demonstrate the competitive performance of our method over previous
approaches. We hope this method could advance the research towards
visual-semantic representation learning. Our source code is also publicly
available at https://github.com/JeremyXSC/MMET
The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions
The Metaverse offers a second world beyond reality, where boundaries are
non-existent, and possibilities are endless through engagement and immersive
experiences using the virtual reality (VR) technology. Many disciplines can
benefit from the advancement of the Metaverse when accurately developed,
including the fields of technology, gaming, education, art, and culture.
Nevertheless, developing the Metaverse environment to its full potential is an
ambiguous task that needs proper guidance and directions. Existing surveys on
the Metaverse focus only on a specific aspect and discipline of the Metaverse
and lack a holistic view of the entire process. To this end, a more holistic,
multi-disciplinary, in-depth, and academic and industry-oriented review is
required to provide a thorough study of the Metaverse development pipeline. To
address these issues, we present in this survey a novel multi-layered pipeline
ecosystem composed of (1) the Metaverse computing, networking, communications
and hardware infrastructure, (2) environment digitization, and (3) user
interactions. For every layer, we discuss the components that detail the steps
of its development. Also, for each of these components, we examine the impact
of a set of enabling technologies and empowering domains (e.g., Artificial
Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on
its advancement. In addition, we explain the importance of these technologies
to support decentralization, interoperability, user experiences, interactions,
and monetization. Our presented study highlights the existing challenges for
each component, followed by research directions and potential solutions. To the
best of our knowledge, this survey is the most comprehensive and allows users,
scholars, and entrepreneurs to get an in-depth understanding of the Metaverse
ecosystem to find their opportunities and potentials for contribution
Economia colaborativa
A importância de se proceder à análise dos principais desafios jurÃdicos que a economia colaborativa coloca – pelas implicações que as mudanças de paradigma dos modelos de negócios e dos sujeitos envolvidos suscitam − é indiscutÃvel, correspondendo à necessidade de se fomentar a segurança jurÃdica destas práticas, potenciadoras de crescimento económico e bem-estar social.
O Centro de Investigação em Justiça e Governação (JusGov) constituiu uma equipa multidisciplinar que, além de juristas, integra investigadores de outras áreas, como a economia e a gestão, dos vários grupos do JusGov – embora com especial participação dos investigadores que integram o grupo E-TEC (Estado, Empresa e Tecnologia) – e de outras prestigiadas instituições nacionais e internacionais, para desenvolver um projeto neste domÃnio, com o objetivo de identificar os problemas jurÃdicos que a economia colaborativa suscita e avaliar se já existem soluções para aqueles, refletindo igualmente sobre a conveniência de serem introduzidas alterações ou se será mesmo necessário criar nova regulamentação.
O resultado desta investigação é apresentado nesta obra, com o que se pretende fomentar a continuação do debate sobre este tema.Esta obra é financiada por fundos nacionais através da FCT — Fundação para a Ciência e a Tecnologia, I.P., no âmbito do Financiamento UID/05749/202
Bounding Box Annotation with Visible Status
Training deep-learning-based vision systems requires the manual annotation of
a significant amount of data to optimize several parameters of the deep
convolutional neural networks. Such manual annotation is highly time-consuming
and labor-intensive. To reduce this burden, a previous study presented a fully
automated annotation approach that does not require any manual intervention.
The proposed method associates a visual marker with an object and captures it
in the same image. However, because the previous method relied on moving the
object within the capturing range using a fixed-point camera, the collected
image dataset was limited in terms of capturing viewpoints. To overcome this
limitation, this study presents a mobile application-based free-viewpoint
image-capturing method. With the proposed application, users can collect
multi-view image datasets automatically that are annotated with bounding boxes
by moving the camera. However, capturing images through human involvement is
laborious and monotonous. Therefore, we propose gamified application features
to track the progress of the collection status. Our experiments demonstrated
that using the gamified mobile application for bounding box annotation, with
visible collection progress status, can motivate users to collect multi-view
object image datasets with less mental workload and time pressure in an
enjoyable manner, leading to increased engagement.Comment: 10 pages, 16 figure
Learning Over All Contracting and Lipschitz Closed-Loops for Partially-Observed Nonlinear Systems
This paper presents a policy parameterization for learning-based control on
nonlinear, partially-observed dynamical systems. The parameterization is based
on a nonlinear version of the Youla parameterization and the recently proposed
Recurrent Equilibrium Network (REN) class of models. We prove that the
resulting Youla-REN parameterization automatically satisfies stability
(contraction) and user-tunable robustness (Lipschitz) conditions on the
closed-loop system. This means it can be used for safe learning-based control
with no additional constraints or projections required to enforce stability or
robustness. We test the new policy class in simulation on two reinforcement
learning tasks: 1) magnetic suspension, and 2) inverting a rotary-arm pendulum.
We find that the Youla-REN performs similarly to existing learning-based and
optimal control methods while also ensuring stability and exhibiting improved
robustness to adversarial disturbances
Technical Dimensions of Programming Systems
Programming requires much more than just writing code in a programming language. It is usually done in the context of a stateful environment, by interacting with a system through a graphical user interface. Yet, this wide space of possibilities lacks a common structure for navigation. Work on programming systems fails to form a coherent body of research, making it hard to improve on past work and advance the state of the art.
In computer science, much has been said and done to allow comparison of programming languages, yet no similar theory exists for programming systems; we believe that programming systems deserve a theory too.
We present a framework of technical dimensions which capture the underlying characteristics of programming systems and provide a means for conceptualizing and comparing them.
We identify technical dimensions by examining past influential programming systems and reviewing their design principles, technical capabilities, and styles of user interaction. Technical dimensions capture characteristics that may be studied, compared and advanced independently. This makes it possible to talk about programming systems in a way that can be shared and constructively debated rather than relying solely on personal impressions.
Our framework is derived using a qualitative analysis of past programming systems. We outline two concrete ways of using our framework. First, we show how it can analyze a recently developed novel programming system. Then, we use it to identify an interesting unexplored point in the design space of programming systems.
Much research effort focuses on building programming systems that are easier to use, accessible to non-experts, moldable and/or powerful, but such efforts are disconnected. They are informal, guided by the personal vision of their authors and thus are only evaluable and comparable on the basis of individual experience using them. By providing foundations for more systematic research, we can help programming systems researchers to stand, at last, on the shoulders of giants
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The Epidemiology and Genetic Architecture of Vitamin D Deficiency in African Children
Vitamin D deficiency is a common public health problem worldwide. However, little is known about the epidemiology of vitamin D deficiency in Africa. In this thesis, I aimed to determine: 1) the prevalence of and risk factors associated with vitamin D deficiency in studies conducted in Africa; 2) the prevalence and predictors of vitamin D deficiency in African children; 3) the association between vitamin D and iron deficiency in African children; and 4) genetic variants that influence vitamin D status in Africans.
In a systematic review and meta-analyses of previous vitamin D studies in Africa, the average prevalence of low vitamin D status was 18.5%, 34.2% and 59.5% using cut-offs of 25-hydroxyvitamin D (25(OH)D) levels of <30 nmol/L, <50 nmol/L and <75 nmol/L, respectively. Populations at risk of vitamin D deficiency included newborns, women, and people living in high latitudes or urban areas.
In an epidemiological study of young children living in Africa, the prevalence of low vitamin D status was 0.6%, 7.8% and 44.5% using cut-offs of 25(OH)D levels of GC2 variant of the group-specific component (GC) gene, which encodes vitamin D binding protein.
Vitamin D deficiency was also associated with 80% higher odds of iron deficiency in these children. Adjusted regression models revealed that vitamin D deficiency was associated with higher ferritin and hepcidin levels suggesting lower iron status, and reduced sTfR and transferrin levels and increased TSAT and serum iron levels suggesting improved iron status.
Genome-wide association study (GWAS) in Africans revealed genetic variants that influence vitamin D status in vitamin D metabolism genes: DHCR7/NADSYN1, CYP2R1 and GC. However, the majority of SNPs from previous European GWASs did not replicate in the current GWAS.
Findings from this thesis indicate that vitamin D deficiency is prevalent in many African populations and should be considered in public health strategies in Africa
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