Affect Recognition in Ads with Application to Computational Advertising

Advertisements (ads) often include strongly emotional content to leave a lasting impression on the viewer. This work (i) compiles an affective ad dataset capable of evoking coherent emotions across users, as determined from the affective opinions of five experts and 14 annotators; (ii) explores the efficacy of convolutional neural network (CNN) features for encoding emotions, and observes that CNN features outperform low-level audio-visual emotion descriptors upon extensive experimentation; and (iii) demonstrates how enhanced affect prediction facilitates computational advertising, and leads to better viewing experience while watching an online video stream embedded with ads based on a study involving 17 users. We model ad emotions based on subjective human opinions as well as objective multimodal features, and show how effectively modeling ad emotions can positively impact a real-life application.Comment: Accepted at the ACM International Conference on Multimedia (ACM MM) 201

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

Influence of FRP width-to-spacing ratio on bond performance of externally bonded FRP systems on one way concrete slabs

Debonding of externally bonded fiber reinforced polymer (FRP) composite materials used for repair of reinforced concrete elements is commonly observed and is often the critical limit state for such systems. The FRP geometry, as quantified by the ratio of FRP width-to-substrate width, bf/b, (or FRP width-to-FRP spacing, bf/s, for slabs) is expected to affect the ultimate bond performance. Factors accounting for this effect are included in many design guides. An experimental program using concrete slab specimens having identical reinforcement ratios, strengthened with CFRP strips having different bf/s ratios is reported. The focus of the study is the strain in the CFRP and its eventual debonding. Thinner (lower bf/s) CFRP strip are observed to have greater strains at a given load level and to have a higher strain at debonding. The effect of the transverse strain gradient in the CFRP - the CFRP "edge effect" - is also investigated