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

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