A game to crowdsource data for affective computing

This game submission describes BeFaced, a tile matching casual tablet game that enables massive crowdsourcing of facial expressions to advance affective computing. BeFaced uses state-of-theart facial expression tracking technology with dynamic difficulty adjustment to keep the player engaged and hence obtain a large and varied face dataset. FDG attendees will experience a novel affective game input interface and also investigate how the game design enables massive crowdsourcing in an extensible manner

An introduction to crowdsourcing for language and multimedia technology research

Language and multimedia technology research often relies on large manually constructed datasets for training or evaluation of algorithms and systems. Constructing these datasets is often expensive with significant challenges in terms of recruitment of personnel to carry out the work. Crowdsourcing methods using scalable pools of workers available on-demand offers a flexible means of rapid low-cost construction of many of these datasets to support existing research requirements and potentially promote new research initiatives that would otherwise not be possible

Crowd-powered positive psychological interventions

Recent advances in crowdsourcing have led to new forms of assistive technologies, commonly referred to as crowd-powered devices. To best serve the user, these technologies crowdsource human intelligence as needed, when automated methods alone are insufficient. In this paper, we provide an overview of how these systems work and how they can be used to enhance technological interventions for positive psychology. As a specific example, we describe previous work that crowdsources positive reappraisals, providing users timely and personalized suggestions for ways to reconstrue stressful thoughts and situations. We then describe how this approach could be extended for use with other positive psychological interventions. Finally, we outline future directions for crowd-powered positive psychological interventions

Platformer level design for player believability

Player believability is often defined as the ability of a game playing character to convince an observer that it is being controlled by a human. The agent's behavior is often assumed to be the main contributor to the character's believability. In this paper we reframe this core assumption and instead focus on the impact of the game environment and aspects of game design (such as level design) on the believability of the game character. To investigate the relationship between game content and believability we crowdsource rank-based annotations from subjects that view playthrough videos of various AI and human controlled agents in platformer levels of dissimilar characteristics. For this initial study we use a variant of the well-known Super Mario Bros game. We build support vector machine models of reported believability based on gameplay and level features which are extracted from the videos. The highest performing model predicts perceived player believability of a character with an accuracy of 73.31%, on average, and implies a direct relationship between level features and player believability.We would like to thank all participants of the crowdsourcing experiment. This work has been supported in part by the FP7 Marie Curie CIG project AutoGameDesign (630665).peer-reviewe

The platformer experience dataset

Player modeling and estimation of player experience have become very active research fields within affective computing, human computer interaction, and game artificial intelligence in recent years. For advancing our knowledge and understanding on player experience this paper introduces the Platformer Experience Dataset (PED) - the first open-access game experience corpus - that contains multiple modalities of user data of Super Mario Bros players. The open-access database aims to be used for player experience capture through context-based (i.e. game content), behavioral and visual recordings of platform game players. In addition, the database contains demographical data of the players and self-reported annotations of experience in two forms: ratings and ranks. PED opens up the way to desktop and console games that use video from webcameras and visual sensors and offer possibilities for holistic player experience modeling approaches that can, in turn, yield richer game personalization.peer-reviewe

Crowdsourcing Emotions in Music Domain

An important source of intelligence for music emotion recognition today comes from user-provided community tags about songs or artists. Recent crowdsourcing approaches such as harvesting social tags, design of collaborative games and web services or the use of Mechanical Turk, are becoming popular in the literature. They provide a cheap, quick and efficient method, contrary to professional labeling of songs which is expensive and does not scale for creating large datasets. In this paper we discuss the viability of various crowdsourcing instruments providing examples from research works. We also share our own experience, illustrating the steps we followed using tags collected from Last.fm for the creation of two music mood datasets which are rendered public. While processing affect tags of Last.fm, we observed that they tend to be biased towards positive emotions; the resulting dataset thus contain more positive songs than negative ones

SemAxis: A Lightweight Framework to Characterize Domain-Specific Word Semantics Beyond Sentiment

Because word semantics can substantially change across communities and contexts, capturing domain-specific word semantics is an important challenge. Here, we propose SEMAXIS, a simple yet powerful framework to characterize word semantics using many semantic axes in word- vector spaces beyond sentiment. We demonstrate that SEMAXIS can capture nuanced semantic representations in multiple online communities. We also show that, when the sentiment axis is examined, SEMAXIS outperforms the state-of-the-art approaches in building domain-specific sentiment lexicons.Comment: Accepted in ACL 2018 as a full pape

Initial perceptions of a casual game to crowdsource facial expressions in the wild

The performance of affective computing systems often depend on the quality of the image databases they are trained on. However, creating good quality training databases is a laborious activity. In this paper, we evaluate BeFaced, a tile matching casual tablet game that enables massive crowdsourcing of facial expressions for the purpose of advancing facial expression analysis. The core aspect of BeFaced is game quality, as increased enjoyment and engagement translates to an increased quantity of varied facial expressions obtained. Hence a pilot user study was performed on 18 university students whereby observational and interview data were obtained during playtests. We found that most users enjoyed the game and were intrigued by the novelty in interacting with the facial expression gameplay mechanic, but also uncovered problems with feedback provision and the dynamic difficulty adjustment mechanism. These findings hence provide invaluable insights for the other researchers/ practitioners working on similar crowdsourcing games with a purpose, as well as for the development of BeFaced