Luca Galli

Modeling CrowdSourcing Scenarios in Socially-Enabled Human Computation Applications

User models have been defined since the 1980s, mainly for the purpose of building context-based, user-adaptive applications. However, the advent of social networked media, serious games, and crowdsourcing/human computation platforms calls for a more pervasive notion of user model, capable of representing the multiple facets of social users and performers, including their social ties, interests, capabilities, activity history, and topical affinities. In this paper, we define a comprehensive model able to cater for all the aspects relevant for applications involving social networks and human computation; we capitalize on existing social user models and content description models, enhancing them with novel models for human computation and gaming activities representation. Finally, we report on our experiences in adopting the proposed model in the design and implementation of three socially enabled human computation platforms.

Fashion-focused creative commons social dataset

In this work, we present a fashion-focused Creative Commons dataset, which is designed to contain a mix of general images as well as a large component of images that are focused on fashion (i.e., relevant to particular clothing items or fashion accessories). The dataset contains 4810 images and related metadata. Furthermore, a ground truth on images tags is presented. Ground truth generation for large-scale datasets is a necessary but expensive task. Traditional expert based approaches have become an expensive and non-scalable solution. For this reason, we turn to crowdsourcing techniques in order to collect ground truth labels; in particular we make use of the commercial crowdsourcing platform, Amazon Mechanical Turk (AMT). Two different groups of annotators (i.e., trusted annotators known to the authors and crowdsourcing workers on AMT) participated in the ground truth creation. Annotation agreement between the two groups is analyzed. Applications of the dataset in different contexts are discussed. This dataset contributes to research areas such as crowdsourcing for multimedia, multimedia content analysis, and design of systems that can elicit fashion preferences from users.

A Draw-and-Guess Game to Segment Images

Human Computation is defined as the integration of human tasks and automated algorithms to achieve superior quality in complex tasks like multimedia content analysis. This paper discusses a scenario in which human computation is used to segment time stamped fashion images for mining trends based on visual features of garments (e.g., color and texture) and attributes of portrayed subjects (e.g., gender and age). State-of-the-art algorithms for body part detection and feature extraction can produce low quality results when parts of the body are occluded and when dealing with complex human poses. In such cases, these algorithms could benefit from the assistance of human agents. In order to jointly leverage the potential of crowds and image analysis algorithms, a game with a purpose (GWAP) is proposed, whereby players can help segment images for which specialized algorithms have failed, so as to improve the extraction of color and texture features of garments and their association with the features of the subject wearing them.

On the application of game mechanics in information retrieval

The exponential growth of digital generated content in the form of audio, video and complex data structures calls for novel methods and tools able to cope with the limitation of automated analysis techniques. Gamification, the process of using game design methodologies and game mechanics to enhance traditional applications, is a promising tool that can help to increase the active involvement of humans in the Information Retrieval processes. This work contributes to the emerging research field of Gamification in Information Retrieval by providing an overview on: 1) the fundamental elements of a game; 2) the major game mechanics that have been applied in traditional games and gamication techniques; and 3) an overview of the possible adoption of such techniques in a typical IR scenario. The goal is to lay a path for the adoption of these new tools in IR systems, focusing on their application to the traditional building blocks of the query and content analysis processes.

A cheating detection framework for Unreal Tournament III: A machine learning approach

Cheating reportedly affects most of the multi-player online games and might easily jeopardize the game experience by providing an unfair competitive advantage to one player over the others. Accordingly, several efforts have been made in the past years to find reliable and scalable approaches to solve this problem. Unfortunately, cheating behaviors are rather difficult to detect and existing approaches generally require human supervision. In this work we introduce a novel framework to automatically detect cheating behaviors in Unreal Tournament III by exploiting supervised learning techniques. Our framework consists of three main components: (i) an extended game-server responsible for collecting the game data; (ii) a processing backend in charge of preprocessing data and detecting the cheating behaviors; (iii) an analysis frontend. We validated our framework with an experimental analysis which involved three human players, three game maps and five different supervised learning techniques, i.e., decision trees, Naive Bayes, random forest, neural networks, support vector machines. The results show that all the supervised learning techniques are able to classify correctly almost 90% of the test examples.

Learning a context-aware weapon selection policy for Unreal Tournament III

Modern computer games are becoming increasingly complex and only experienced players can fully master the game controls. Accordingly, many commercial games now provide aids to simplify the player interaction. These aids are based on simple heuristics rules and cannot adapt neither to the current game situation nor to the player game style. In this paper, we suggest that supervised methods can be applied effectively to improve the quality of such game aids. In particular, we focus on the problem of developing an automatic weapon selection aid for Unreal Tournament III, a recent and very popular first person shooter (FPS). We propose a framework to (i) collect a dataset from game sessions, (ii) learn a policy to automatically select the weapon, and (iii) deploy the learned models in the game to replace the default weaponswitching aid provided in the game distribution. Our approach allows the development of weapon-switching policies that are aware of the current game context and can also imitate a particular game style.

Integrating real and digital games with data analytics for water consumption behavioral change: a demo

The demo showcases the SmartH2O platform1, a system for water demand management based on an original mix of data analytics and behavioral science. SmartH2O collects consumption data from the automatic meter infrastructure of a (water) utility and allows customers access them in a Web portal, where they can see information about their actual and forecasted consumption, compare with the neighborhood, and obtain personalized water saving tips and leak alerts. Engagement is reinforced through a unique mix of in-app gamification techniques, digital educational games and real board games, which provide a rich set of behavior change stimuli to all household members. Lab tests show good acceptance and engagement by pilot users, deployment to a large set of consumers is scheduled shortly.

Achievement Systems Explained

In the chapter of Achievement Systems Explained, Galli and Fratenali provide an insight on achievements, their purposes and the way in which they have evolved, and illustrate a taxonomy of possible achievements along with a set of guidelines to be followed when developing them. Finally, Galli and Fratenali introduce a model that can be used to describe all the existing systems in order to try to put the basis for an open platform capable of integrating existing gaming communities.

Matching Game Mechanics and Human Computation Tasks in Games with a Purpose

The massive amount of time that people spend in online gaming is being increasingly exploited by a particular kind of Serious Games, the Games with a Purpose (GWAP), used to solve complex problems as a byproduct of their gameplay. The design of the tasks and the choice of game mechanics able to solve them has been done so far without consolidated guidelines and with few considerations with respect to traditional game design princpiples. Without proper best practices to follow, the design of a GWAP may incur in increased development time and costs or even failures. This work attempts to solve these shortcomings for novel designers by providing: 1) a development process to follow when designing new GWAPs 2) the definition of the multimedia refinement tasks best suited to be solved with GWAPs and 3) the list of traditional game mechanics that best match these tasks.

Robust aggregation of GWAP tracks for local image annotation

The possibility of assigning labels to localized regions in an image enables flexible image retrieval paradigms. However, the process of automatically segmenting and tagging images is notoriously hard, due to the presence of occlusions, noise, challenging illumination conditions, background clutter, etc. For this reason, human computation has recently emerged as a viable alternative when computer vision algorithms fail to provide a satisfactory answer. For example, Games with a purpose (GWAP) represent a powerful crowdsourcing mechanism to collect implicit annotations from human players. In this paper we consider the problem of aggregating the gaming tracks collected by a GWAP we developed to solve challenging instances of image segmentation problems. In particular we consider the existence of malicious players, who might try to fool the rules of the game to achieve higher rewards. The proposed solution can automatically estimate the reliability of human players, thus identifying cheaters. This information is exploited to aggregate the gaming tracks, thus significantly improving the image segmentation result and the quality of local image annotations.