301 research outputs found
Comparing Neural and Attractiveness-based Visual Features for Artwork Recommendation
Advances in image processing and computer vision in the latest years have
brought about the use of visual features in artwork recommendation. Recent
works have shown that visual features obtained from pre-trained deep neural
networks (DNNs) perform very well for recommending digital art. Other recent
works have shown that explicit visual features (EVF) based on attractiveness
can perform well in preference prediction tasks, but no previous work has
compared DNN features versus specific attractiveness-based visual features
(e.g. brightness, texture) in terms of recommendation performance. In this
work, we study and compare the performance of DNN and EVF features for the
purpose of physical artwork recommendation using transactional data from
UGallery, an online store of physical paintings. In addition, we perform an
exploratory analysis to understand if DNN embedded features have some relation
with certain EVF. Our results show that DNN features outperform EVF, that
certain EVF features are more suited for physical artwork recommendation and,
finally, we show evidence that certain neurons in the DNN might be partially
encoding visual features such as brightness, providing an opportunity for
explaining recommendations based on visual neural models.Comment: DLRS 2017 workshop, co-located at RecSys 201
The Expansion of Higher Education in Colombia : Bad Students or Bad Programs?
Rapid expansion in the demand for post-secondary education triggered an unprecedented boom of higher education programs in Colombia, possibly deteriorating quality. This paper uses rich administrative data matching school admission information, wages and detailed socio-economic characteristics of the young graduates, and standardized test scores pre- and post-tertiary education, to assess the heterogeneity in the value added generated by higher education programs. Our findings show that once we account for self-selection, the penalty of attending a recently created program that initially appeared to be large becomes much smaller, and close to zero
Segundo Seminario Miradas históricas y contemporáneas sobre la pobreza y la desigualdad en Uruguay y América Latina
Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release
The explosion of data collection has raised serious privacy concerns in users
due to the possibility that sharing data may also reveal sensitive information.
The main goal of a privacy-preserving mechanism is to prevent a malicious third
party from inferring sensitive information while keeping the shared data
useful. In this paper, we study this problem in the context of time series data
and smart meters (SMs) power consumption measurements in particular. Although
Mutual Information (MI) between private and released variables has been used as
a common information-theoretic privacy measure, it fails to capture the causal
time dependencies present in the power consumption time series data. To
overcome this limitation, we introduce the Directed Information (DI) as a more
meaningful measure of privacy in the considered setting and propose a novel
loss function. The optimization is then performed using an adversarial
framework where two Recurrent Neural Networks (RNNs), referred to as the
releaser and the adversary, are trained with opposite goals. Our empirical
studies on real-world data sets from SMs measurements in the worst-case
scenario where an attacker has access to all the training data set used by the
releaser, validate the proposed method and show the existing trade-offs between
privacy and utility.Comment: to appear in IEEESmartGridComm 2019. arXiv admin note: substantial
text overlap with arXiv:1906.0642
Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning
The rapid development and expansion of the Internet of Things (IoT) paradigm
has drastically increased the collection and exchange of data between sensors
and systems, a phenomenon that raises serious privacy concerns in some domains.
In particular, Smart Meters (SMs) share fine-grained electricity consumption of
households with utility providers that can potentially violate users' privacy
as sensitive information is leaked through the data. In order to enhance
privacy, the electricity consumers can exploit the availability of physical
resources such as a rechargeable battery (RB) to shape their power demand as
dictated by a Privacy-Cost Management Unit (PCMU). In this paper, we present a
novel method to learn the PCMU policy using Deep Reinforcement Learning (DRL).
We adopt the mutual information (MI) between the user's demand load and the
masked load seen by the power grid as a reliable and general privacy measure.
Unlike previous studies, we model the whole temporal correlation in the data to
learn the MI in its general form and use a neural network to estimate the
MI-based reward signal to guide the PCMU learning process. This approach is
combined with a model-free DRL algorithm known as the Deep Double Q-Learning
(DDQL) method. The performance of the complete DDQL-MI algorithm is assessed
empirically using an actual SMs dataset and compared with simpler privacy
measures. Our results show significant improvements over state-of-the-art
privacy-aware demand shaping methods
On the Impact of Side Information on Smart Meter Privacy-Preserving Methods
Smart meters (SMs) can pose privacy threats for consumers, an issue that has
received significant attention in recent years. This paper studies the impact
of Side Information (SI) on the performance of distortion-based real-time
privacy-preserving algorithms for SMs. In particular, we consider a deep
adversarial learning framework, in which the desired releaser (a recurrent
neural network) is trained by fighting against an adversary network until
convergence. To define the loss functions, two different approaches are
considered: the Causal Adversarial Learning (CAL) and the Directed Information
(DI)-based learning. The main difference between these approaches is in how the
privacy term is measured during the training process. On the one hand, the
releaser in the CAL method, by getting supervision from the actual values of
the private variables and feedback from the adversary performance, tries to
minimize the adversary log-likelihood. On the other hand, the releaser in the
DI approach completely relies on the feedback received from the adversary and
is optimized to maximize its uncertainty. The performance of these two
algorithms is evaluated empirically using real-world SMs data, considering an
attacker with access to SI (e.g., the day of the week) that tries to infer the
occupancy status from the released SMs data. The results show that, although
they perform similarly when the attacker does not exploit the SI, in general,
the CAL method is less sensitive to the inclusion of SI. However, in both
cases, privacy levels are significantly affected, particularly when multiple
sources of SI are included
Nuevas narrativas audiovisuales: el Periodismo en Instagram TV
Trabajo Final para optar al grado académico de Licenciatura en Comunicación Social, Universidad Nacional de Córdoba
Calificación 10 (Diez)
Orientación AudiovisualEl presente Trabajo Final de Grado consiste en el desarrollo de una investigación
sobre las estrategias narrativas que fue incorporando el periodismo contemporáneo, en
función de innovaciones tecnológicas y nuevas formas de producción informativa. Se hace
una descripción del estilo de periodismo que predomina en las redes sociales.
Se observan y describen algunas estrategias puntuales que caracterizan al periodismo
en sus orígenes, y luego se explica el surgimiento de nuevas prácticas que modifican la
estructura narrativa del género informativo. La conceptualización de un nuevo estilo de
periodismo y la aplicación de un recurso poco habitual en este género como lo es la
animación, permiten hacer énfasis en la red social Instagram, en tanto que manifiesta
vertiginosas transformaciones y adaptaciones vinculadas a las contemporáneas formas de lo
periodístico. En relación con esta red, se hace una descripción de su funcionamiento, de las
lógicas predominantes que rigen su comunidad de usuarios, del tratamiento de la imagen en
sus publicaciones y del trabajo periodístico que desarrollan algunas cuentas de medios de
comunicación. La mirada se centrará en la nueva herramienta disponible en esta plataforma:
Instagram TV (IGTV). Luego de un desarrollo sobre prácticas periodísticas contemporáneas
en esta interfaz, se procede a la selección y análisis del corpus.
El corpus de análisis se compone por producciones audiovisuales realizadas por los
medios La Voz del Interior (periódico), la TV Pública (canal de TV) y Mundo TKM (portal
de noticias web). Se toman un total de 10 trabajos publicados en los canales de IGTV
correspondientes a cada medio, para realizar un análisis sobre las estrategias que conforman
las estructuras narrativas en estas distintas producciones. Luego se reflexiona al respecto,
mencionando puntos centrales en las dimensiones analíticas de cada medio, estrategias
recurrentes y lineamientos propios de una nueva configuración del periodismo en este tipo
de formato audiovisual.Fil: Messina, Juan Pablo. Universidad Nacional de Córdoba. Facultad de Ciencias de la Comunicación; Argentina
- …