Smartphone picture organization: a hierarchical approach

We live in a society where the large majority of the population has a camera-equipped smartphone. In addition, hard drives and cloud storage are getting cheaper and cheaper, leading to a tremendous growth in stored personal photos. Unlike photo collections captured by a digital camera, which typically are pre-processed by the user who organizes them into event-related folders, smartphone pictures are automatically stored in the cloud. As a consequence, photo collections captured by a smartphone are highly unstructured and because smartphones are ubiquitous, they present a larger variability compared to pictures captured by a digital camera. To solve the need of organizing large smartphone photo collections automatically, we propose here a new methodology for hierarchical photo organization into topics and topic-related categories. Our approach successfully estimates latent topics in the pictures by applying probabilistic Latent Semantic Analysis, and automatically assigns a name to each topic by relying on a lexical database. Topic-related categories are then estimated by using a set of topic-specific Convolutional Neuronal Networks. To validate our approach, we ensemble and make public a large dataset of more than 8,000 smartphone pictures from 40 persons. Experimental results demonstrate major user satisfaction with respect to state of the art solutions in terms of organization.Peer ReviewedPreprin

Selecting a multi-label classification method for an interactive system

International audienceInteractive classification-based systems engage users to coach learning algorithms to take into account their own individual preferences. However most of the recent interactive systems limit the users to a single-label classification, which may be not expressive enough in some organization tasks such as film classification, where a multi-label scheme is required. The objective of this paper is to compare the behaviors of 12 multi-label classification methods in an interactive framework where "good" predictions must be produced in a very short time from a very small set of multi-label training examples. Experimentations highlight important performance differences for 4 complementary evaluation measures (Log-Loss, Ranking-Loss, Learning and Prediction Times). The best results are obtained for Multi-label k Nearest Neighbours (ML-kNN), Ensemble of Classifier Chains (ECC) and Ensemble of Binary Relevance (EBR)

Enriching the fan experience in a smart stadium using internet of things technologies

Rapid urbanization has brought about an influx of people to cities, tipping the scale between urban and rural living. Population predictions estimate that 64% of the global population will reside in cities by 2050. To meet the growing resource needs, improve management, reduce complexities, and eliminate unnecessary costs while enhancing the quality of life of citizens, cities are increasingly exploring open innovation frameworks and smart city initiatives that target priority areas including transportation, sustainability, and security. The size and heterogeneity of urban centers impede progress of technological innovations for smart cities. We propose a Smart Stadium as a living laboratory to balance both size and heterogeneity so that smart city solutions and Internet of Things (IoT) technologies may be deployed and tested within an environment small enough to practically trial but large and diverse enough to evaluate scalability and efficacy. The Smart Stadium for Smart Living initiative brings together multiple institutions and partners including Arizona State University (ASU), Dublin City University (DCU), Intel Corporation, and Gaelic Athletic Association (GAA), to turn ASU's Sun Devil Stadium and Ireland's Croke Park Stadium into twinned smart stadia to investigate IoT and smart city technologies and applications

Understanding Older Vehicle Users: An Interpretative Approach

Future adaptations in vehicle design should be linked in some parts to the age-related changes often faced by the older users. The aim of this research is to investigate the multiple age-related changes of Chinese older vehicle users in order to assist designers to better understand current and future older users’ needs. Although qualitative interpretative approaches have rarely been applied in the field of traffic gerontology research, they are widely used in current design research to explore persons’ lived experiences, behaviours and emotions. Therefore, this study employed qualitative research methods consisting of observation, interview, travel logbook and co-discovery to explore older vehicle users’ travel needs. The interpretative analysis confirmed that multiple methods such as interview, travel logbook, and co-discovery are useful to gain a holistic understanding of older drivers’ travel needs. However, the one journey driving observation cannot provide valuable categories to explore older users’ multiple travel needs due to daily living context absence in the one trip experiment. It is clear that the useful methods for determining research for older users will depend on the product. The findings demonstrate that Chinese future older generations are more concerned about their age-related differences from social and cultural perspective rather than physiological perspective. Social and cultural context play important role to shape older vehicle users’ future travel needs. From design a point of view, understanding the social activity and cultural context surrounds older vehicle users should make it possible to predict older drivers’ needs related to vehicle property. Keywords: Older Vehicle Users; Cultural Context; Social Context; Vehicle Design; Qualitative Research</p