Evaluating Content-centric vs User-centric Ad Affect Recognition

Despite the fact that advertisements (ads) often include strongly emotional content, very little work has been devoted to affect recognition (AR) from ads. This work explicitly compares content-centric and user-centric ad AR methodologies, and evaluates the impact of enhanced AR on computational advertising via a user study. Specifically, we (1) compile an affective ad dataset capable of evoking coherent emotions across users; (2) explore the efficacy of content-centric convolutional neural network (CNN) features for encoding emotions, and show that CNN features outperform low-level emotion descriptors; (3) examine user-centered ad AR by analyzing Electroencephalogram (EEG) responses acquired from eleven viewers, and find that EEG signals encode emotional information better than content descriptors; (4) investigate the relationship between objective AR and subjective viewer experience while watching an ad-embedded online video stream based on a study involving 12 users. To our knowledge, this is the first work to (a) expressly compare user vs content-centered AR for ads, and (b) study the relationship between modeling of ad emotions and its impact on a real-life advertising application.Comment: Accepted at the ACM International Conference on Multimodal Interation (ICMI) 201

Aesthetics Assessment of Images Containing Faces

Recent research has widely explored the problem of aesthetics assessment of images with generic content. However, few approaches have been specifically designed to predict the aesthetic quality of images containing human faces, which make up a massive portion of photos in the web. This paper introduces a method for aesthetic quality assessment of images with faces. We exploit three different Convolutional Neural Networks to encode information regarding perceptual quality, global image aesthetics, and facial attributes; then, a model is trained to combine these features to explicitly predict the aesthetics of images containing faces. Experimental results show that our approach outperforms existing methods for both binary, i.e. low/high, and continuous aesthetic score prediction on four different databases in the state-of-the-art.Comment: Accepted by ICIP 201

"You Tube and I Find" - personalizing multimedia content access

Recent growth in broadband access and proliferation of small personal devices that capture images and videos has led to explosive growth of multimedia content available everywhereVfrom personal disks to the Web. While digital media capture and upload has become nearly universal with newer device technology, there is still a need for better tools and technologies to search large collections of multimedia data and to find and deliver the right content to a user according to her current needs and preferences. A renewed focus on the subjective dimension in the multimedia lifecycle, fromcreation, distribution, to delivery and consumption, is required to address this need beyond what is feasible today. Integration of the subjective aspects of the media itselfVits affective, perceptual, and physiological potential (both intended and achieved), together with those of the users themselves will allow for personalizing the content access, beyond today’s facility. This integration, transforming the traditional multimedia information retrieval (MIR) indexes to more effectively answer specific user needs, will allow a richer degree of personalization predicated on user intention and mode of interaction, relationship to the producer, content of the media, and their history and lifestyle. In this paper, we identify the challenges in achieving this integration, current approaches to interpreting content creation processes, to user modelling and profiling, and to personalized content selection, and we detail future directions. The structure of the paper is as follows: In Section I, we introduce the problem and present some definitions. In Section II, we present a review of the aspects of personalized content and current approaches for the same. Section III discusses the problem of obtaining metadata that is required for personalized media creation and present eMediate as a case study of an integrated media capture environment. Section IV presents the MAGIC system as a case study of capturing effective descriptive data and putting users first in distributed learning delivery. The aspects of modelling the user are presented as a case study in using user’s personality as a way to personalize summaries in Section V. Finally, Section VI concludes the paper with a discussion on the emerging challenges and the open problems

Proceedings of the 3rd Workshop on Social Information Retrieval for Technology-Enhanced Learning

Learning and teaching resource are available on the Web - both in terms of digital learning content and people resources (e.g. other learners, experts, tutors). They can be used to facilitate teaching and learning tasks. The remaining challenge is to develop, deploy and evaluate Social information retrieval (SIR) methods, techniques and systems that provide learners and teachers with guidance in potentially overwhelming variety of choices. The aim of the SIRTEL’09 workshop is to look onward beyond recent achievements to discuss specific topics, emerging research issues, new trends and endeavors in SIR for TEL. The workshop will bring together researchers and practitioners to present, and more importantly, to discuss the current status of research in SIR and TEL and its implications for science and teaching

The Mature Adults Cohort of the Malawi Longitudinal Study of Families and Health (MLSFH-MAC)

Cohort purpose: The Mature Adults Cohort of the Malawi Longitudinal Study of Families and Health (MLSFH-MAC) contributes to global aging studies by providing a rare opportunity to study the processes of individual and population aging, the public health and social challenges associated with aging and the coincident shifts in disease burdens, in a low-income, high HIV prevalence, sub-Saharan African (SSA) context. Design and Measures: The MLSFH-MAC is a population-based cohort study of mature adults aged 45 years and older living in rural communities in three districts in Malawi (Mchinji, Balaka and Rumphi). Initial enrollment at baseline is 1,266 individuals in 2012. MLSFH-MAC follow-ups were in 2013, 2017, and 2018. Survey instruments cover aging-related topics such as cognitive and mental health, NCDs and related health literacy, subjective survival expectations, measured biomarkers including HIV, grip strength, hypertension, fasting glucose, BMI, a broad range of individual- and household-level social and economic information, a 2018 qualitative survey of mature adults and community officials, 2019 surveys of village heads, health care facilities and health care providers in the MLSFH-MAC study areas. Unique features: MLSFH-MAC is a data resource that covers 20 years of the life course of cohort members and provides a wealth of information unprecedented for aging studies in a low-income SSA context that broadly represents the socioeconomic environment of millions of individuals in south-eastern Africa. Among these are the longitudinal population-based data on depression and anxiety using clinically-validated instruments. MLSFH-MAC is also vanguard in measuring longitudinal changes in cognitive health among older individuals in SSA. Complemented by contextual and qualitative information, the extensive MLSFH-MAC data facilitate a life-course perspective on aging that reflects the dynamic and distinct settings in which people reach older ages in SSA LICs. Across many domains, MLSFH-MAC also allows for comparative research with global aging studies through harmonized measures and instruments. Collaboration and data access: Public-use version of the 2012 (baseline) MLSFH-MAC data can be requested at http://www.malawi.pop.upenn.edu. Sharing of additional MLSFH-MAC data is currently possible as part of collaborative research projects (if not overlapping with ongoing research projects, and subject to a Data Use Agreement)

Hybrid Spaces: Users\u27 Perceptions of Digitally Mediated Public Space

Public space has served as a central component to human settlement since the ancient Greeks, and a forum for unmediated discussion, communication, and debate (Hénaff & Strong, 2001; Mitchell 2003). During the industrialization of cities, public space continued to transform in form and typology, and has served society in various ways (Carr, Rivlin, Francis, & Stone, 1992). Public space is also a physical environment where interaction with digital environment occurs. Today, in the City of New York there are 503 privately owned public spaces (POPS) (Kayden, 2000); often these spaces provide varying levels of wireless access as public and/or private provisions. The role of new media and technology in physical space has captured the attention of many researchers in recent years (e.g. Manovich, 2001; Forlano, 2009; de Souza e Silva & Frith, 2012). As technology develops and becomes increasingly mobile and integrated within daily life, there is a need for researchers to also understand how this impacts the physical environment (Townsend, 2004; Forlano, 2009). Concurrently, recent literature suggests that urban public space, especially POPS, are increasingly regulated and controlled (Benton-Short, 2002; Miller, 2007; Németh & Schmidt, 2007, 2011); whereas new media technology continues to promote unmediated exchange and interaction (Manovich, 2001). Additionally, scholars have asserted that Internet access, because of its location within public space and the electronic connectivity it offers may have the ability to increase the overall use of public spaces (Hampton, Livio, & Sessions Goulet, 2010). Unfortunately, it is unclear how access to digital space within public space can affect public perception on the nature of these spaces. Forlano (2009) suggests that wireless networks can reconfigure people, places, and information in physical space. However, beyond the analysis of usage patterns there is little empirical research on how wireless technologies in public space can affect human behavior, interactions with the network, and human perceptions of these spaces and networks. Additionally, there is little research that examines the difference between device users and non-users within these environments. This study examines the role of Wi-Fi networks in five public spaces in Lower Manhattan, New York. A mixed methods approach pairs on-site observation with a survey that examines users\u27 perceptions of these spaces. Ultimately, this study contributes to a larger body of literature that discusses the \u27publicness\u27 of public space by including the role of new media and users\u27 behaviors in its current assessment. Findings demonstrate how access to digital media affects users\u27 perceptions of public space