Abstract

We examine the utility of implicit behavioral cues in the form of EEG brain signals and eye movements for gender recognition (GR) and emotion recognition (ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded (no mask) as well as partially occluded (eye and mouth masked) emotive faces. Obtained experimental results reveal that (a) reliable GR and ER is achievable with EEG and eye features, (b) differential cognitive processing especially for negative emotions is observed for males and females and (c) some of these cognitive differences manifest under partial face occlusion, as typified by the eye and mouth mask conditions.

Abstract

We examine the utility of implicit user behavioral signals cap- tured using low-cost, off-the-shelf devices for anonymous gender and emotion recognition. A user study designed to examine male and female sensitivity to facial emotions confirms that females rec- ognize (especially negative) emotions quicker and more accurately than men, mirroring prior findings. Implicit viewer responses in the form of EEG brain signals and eye movements are then ex- amined for existence of (a) emotion and gender-specific patterns from event-related potentials (ERPs) and fixation distributions and (b) emotion and gender discriminability. Experiments reveal that (i) Gender and emotion-specific differences are observable from ERPs, (ii) multiple similarities exist between explicit responses gathered from users and their implicit behavioral signals, and (iii) Signifi- cantly above-chance (≈70%) gender recognition is achievable on comparing emotion-specific EEG responses– gender differences are encoded best for anger and disgust. Also, fairly modest valence (positive vs negative emotion) recognition is achieved with EEG and eye-based features.

Abstract

In the states of Andhra Pradesh and Telangana in India, the number of engineering institutions grew from under 10 to over 500 from 1978 to 2008. This resulted in a severe shortage of good quality teachers and therefore poor quality of graduating students. In India, the employability of engineering graduates is 25%. Considering the scale and wickedness of the problem, the lead author and her team worked on designing innovative and radical solutions to this problem during the last eight years. We restrict our research focus to those colleges who responded to our call for partnership in Andhra Pradesh and Telangana and to the center, EnhanceEdu, we created at IIIT (International Institute of Information Technology) at Hyderabad. We present the EnhanceEdu design story using a new pragmatic approach called Design Story Research (DeStoRe) which has its basis in Design Science Research. An overarching design story frames past, present and future work of an entity, seeing possibilities for radical innovation

Abstract

Unsupervised representation learning for tweets is an important research field which helps in solving several business applications such as sentiment analysis, hashtag prediction, paraphrase detection and microblog ranking. A good tweet representation learning model must handle the idiosyncratic nature of tweets which poses several challenges such as short length, informal words, unusual grammar and misspellings. However, there is a lack of prior work which surveys the representation learning models with a focus on tweets. In this work, we organize the models based on its objective function which aids the understanding of the literature. We also provide interesting future directions, which we believe are fruitful in advancing this field by building high-quality tweet representation learning models.

Abstract

Web is indeed prolific with useful content. However, some segments of the user population – for example, students of less rigorous colleges from rural India – may find some of the relevant material on the web complex, foreign and incomprehensible. So, while good content exists, not all are able to benefit from it. The intent of our research effort is to be able to make existing web content be more accessible to wider range of end-users who may have different sociocultural and cognitive abilities. We propose to overcome this gap at a Presentation Semantics level by developing a framework for renarration through Semantic Style Sheets. Using this framework, we facilitate the renarration of existing educational content to be used by thus far overlooked community of users.

Abstract

Owing to the tremendous increase in the volume and variety of user generated content, train–once–apply–forever models are insufficient for supervised learning tasks. Œe need is to develop algorithms that can adapt across domains by leveraging labeled data from source domain(s) and efficiently perform the task in the unlabeled target domain. Towards this, we present a novel two-stage neural network learning algorithm for domain adaptation which learns a multi-part hidden layer where individual parts contribute dif- ferently to the tasks in source and target domains. Œe multiple parts of the representation (i.e. hidden layer) are learned while being cognizant of what characteristics to transfer across domains and what to preserve within domains for enhanced performance. Œe first stage embroils around learning a two-part representation i.e. source specific and common representations in a manner such that the former do not detract the ability of the later to represent the target domain. In the second stage, the generalized common representation is further iteratively extended with discriminating target specific characteristics to adapt to the target domain. We em- pirically demonstrate that the learned representations, in different arrangements, outperform existing domain adaptation algorithms in the source classification as well as the cross-domain classification tasks o

Abstract

HTTPS error warnings are supposed to alert browser users to network attacks. Unfortunately, a wide range of non-attack circumstances trigger hundreds of millions of spurious browser warnings per month. Spurious warnings frustrate users, hinder the widespread adoption of HTTPS, and undermine trust in browser warnings. We investigate the root causes of HTTPS error warnings in the field, with the goal of resolving benign errors. We study a sample of over 300 million errors that Google Chrome users encountered in the course of normal browsing. After manually reviewing more than 2,000 error reports, we developed automated rules to classify the top causes of HTTPS error warnings. We are able to automatically diagnose the root causes of two-thirds of error reports. To our surprise, we find that more than half of errors are caused by client-side or network issues instead of server misconfigurations. Based on these findings, we implemented more actionable warnings and other browser changes to address clientside error causes. We further propose solutions for other classes of root causes.

Abstract

A dominating set of a small size is useful in several settings including wireless networks, document summarization, secure system design, and the like. In this paper, we start by studying three distributed algorithms that produce a small sized dominating sets in a few rounds. We interpret these algorithms in the natural shared memory setting and experiment with these algorithms on a multi-core CPU. Based on the observations from these experimental results, we propose variations to the three algorithms and also show how the proposed variations offer interesting trade-offs with respect to the size of the dominating set produced and the time taken.

Abstract

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[9] 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.

Abstract

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.