Loud and Trendy: Crowdsourcing Impressions of Social Ambiance in Popular Indoor Urban Places

New research cutting across architecture, urban studies, and psychology is contextualizing the understanding of urban spaces according to the perceptions of their inhabitants. One fundamental construct that relates place and experience is ambiance, which is defined as "the mood or feeling associated with a particular place". We posit that the systematic study of ambiance dimensions in cities is a new domain for which multimedia research can make pivotal contributions. We present a study to examine how images collected from social media can be used for the crowdsourced characterization of indoor ambiance impressions in popular urban places. We design a crowdsourcing framework to understand suitability of social images as data source to convey place ambiance, to examine what type of images are most suitable to describe ambiance, and to assess how people perceive places socially from the perspective of ambiance along 13 dimensions. Our study is based on 50,000 Foursquare images collected from 300 popular places across six cities worldwide. The results show that reliable estimates of ambiance can be obtained for several of the dimensions. Furthermore, we found that most aggregate impressions of ambiance are similar across popular places in all studied cities. We conclude by presenting a multidisciplinary research agenda for future research in this domain

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to preserve privacy while still allowing for effective learning over graph-structured datasets. However, achieving an ideal balance between accuracy and privacy in GNNs remains challenging due to the intrinsic structural connectivity of graphs. In this paper, we propose a new differentially private GNN called ProGAP that uses a progressive training scheme to improve such accuracy-privacy trade-offs. Combined with the aggregation perturbation technique to ensure differential privacy, ProGAP splits a GNN into a sequence of overlapping submodels that are trained progressively, expanding from the first submodel to the complete model. Specifically, each submodel is trained over the privately aggregated node embeddings learned and cached by the previous submodels, leading to an increased expressive power compared to previous approaches while limiting the incurred privacy costs. We formally prove that ProGAP ensures edge-level and node-level privacy guarantees for both training and inference stages, and evaluate its performance on benchmark graph datasets. Experimental results demonstrate that ProGAP can achieve up to 5%-10% higher accuracy than existing state-of-the-art differentially private GNNs

Mining large-scale smartphone data for personality studies

In this paper, we investigate the relationship between automatically extracted behavioral characteristics derived from rich smartphone data and self-reported Big-Five personality traits (extraversion, agreeableness, conscientiousness, emotional stability and openness to experience). Our data stem from smartphones of 117 Nokia N95 smartphone users, collected over a continuous period of 17months in Switzerland. From the analysis, we show that several aggregated features obtained from smartphone usage data can be indicators of the Big-Five traits. Next, we describe a machine learning method to detect the personality trait of a user based on smartphone usage. Finally, we study the benefits of using gender-specific models for this task. Apart from a psychological viewpoint, this study facilitates further research on the automated classification and usage of personality traits for personalizing services on smartphone

Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity

Understanding the social context of eating is crucial for promoting healthy eating behaviors by providing timely interventions. Multimodal smartphone sensing data has the potential to provide valuable insights into eating behavior, particularly in mobile food diaries and mobile health applications. However, research on the social context of eating with smartphone sensor data is limited, despite extensive study in nutrition and behavioral science. Moreover, the impact of country differences on the social context of eating, as measured by multimodal phone sensor data and self-reports, remains under-explored. To address this research gap, we present a study using a smartphone sensing dataset from eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the UK). Our study focuses on a set of approximately 24K self-reports on eating events provided by 678 college students to investigate the country diversity that emerges from smartphone sensors during eating events for different social contexts (alone or with others). Our analysis revealed that while some smartphone usage features during eating events were similar across countries, others exhibited unique behaviors in each country. We further studied how user and country-specific factors impact social context inference by developing machine learning models with population-level (non-personalized) and hybrid (partially personalized) experimental setups. We showed that models based on the hybrid approach achieve AUC scores up to 0.75 with XGBoost models. These findings have implications for future research on mobile food diaries and mobile health sensing systems, emphasizing the importance of considering country differences in building and deploying machine learning models to minimize biases and improve generalization across different populations

“Invisibles” y despojados, pero portadores de una experiencia de clase: obreros chilenos en el noreste de Chubut, Patagonia Argentina

Observamos la radicación de obreros chilenos en el noreste de Chubut (Patagonia argentina) durante los años posteriores al golpe de estado de 1973. El desarrollo de la industrialización subsidiada por el estado argentino en el noreste de Chubut planteaba la necesidad de mayor cantidad de obreros que trabajasen en las fábricas que se instalaban. En esa “nueva” clase obrera se destacó la presencia de trabajadores chilenos, quienes cumplieron un rol clave en lo político, por la experiencia de organización que traían consigo. Estos trabajadores realizaron un exilio político no público. Partir hacia la Patagonia argentina fue una alternativa para las clases populares, a diferencia de los exilios europeos, más reservados a los sectores con otro nivel de ingresos u otras redes políticas. Al ser obreros su exilio quedó subsumido en la apariencia de migración económica. En este artículo presentamos parte de su experiencia, y demostramos que su presencia aportó elementos claves para el desarrollo de esta clase obrera en la provincia de Chubut.We observe the arrival of Chilean workers at the northeast of Chubut (Patagonia Argentina) during the years after the 1973 coup. h e industrialization development subsidized by the Argentine government in the northeast of Chubut raised the need of for more workers who would work in factories that were installed. In this “new” working class the presence of Chilean workers played a key role in their development because they brings a strong experience political and organizational. h ese Chilean workers held a non-public political exile. h eir departure for the Patagonia-Argentina was an alternative to the popular classes, unlike the European exile, more reserved to other sectors with betters income or other political connections. h is worker’s exile was subsumed in the appearance of economic migration. In this article we present part of their experience, and demonstrated that their was a key for understand the formation and the i ghts of this working class in the province of Chubut.Fil: Gatica, Mónica Graciela. Universidad Nacional de la Patagonia; ArgentinaFil: Perez Alvarez, Gonzalo Gabriel. Universidad Nacional de la Patagonia; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; Argentin

Signal Processing in the Workplace

According to the U.S. Bureau of Labor Statistics, during 2013 employed Americans "worked an average of 7.6 hours on the days they worked," and "83% did some or all of their work at their workplace" [1]. Understanding processes in the workplace has been the subject of disciplines like organizational psychology and management for decades. In particular, the study of nonverbal communication at work is fundamental as "face-to-face interaction with superiors, subordinates, and peers consumes much of our time and energy" [2] and a variety of phenomena including job stress, rapport, and leadership can be revealed by and perceived from the tone of voice, gaze, facial expressions, and body cues of coworkers and managers [2]. ©2015IEEE