In today's age of big data, websites are collecting an increasingly wide variety of information about their users. The texts of websites' privacy policies, which serve as legal agreements between service providers and users, are often long and difficult to understand. Automated analysis of those texts has the potential to help users better understand the implications of agreeing to such policies. In this work, we present a technique that combines machine learning and crowdsourcing to semi-automatically extract key aspects of website privacy policies that is scalable, fast, and cost-effective.

Games With a Purpose can enable an intelligent agent to persistently and pervasively sense the real world by using game players as reconfigurable sensors. We propose a technique whereby an intelligent agent incentivizes players to collect data by translating data collection tasks into a series of quests played on a mobile device. In this paper, we define the concept of Proactive Sensing and provide a framework for Game-Based Proactive Sensing that can adapt games and narrative that optimizes for data collection and long-term player engagement.

Programming by Demonstration (PbD) can allow end-users to teach robots new actions simply by demonstrating them. However, learning generalizable actions requires a large number of demonstrations that is unreasonable to expect from end-users. In this paper, we explore the idea of using crowdsourcing to collect action demonstrations from the crowd. We propose a PbD framework in which the end-user provides an initial seed demonstration, and then the robot searches for scenarios in which the action will not work and requests the crowd to fix the action for these scenarios. We use instance-based learning with a simple yet powerful action representation that allows an intuitive visualization of the action. Crowd workers directly interact with these visualizations to fix them. We demonstrate the utility of our approach with a user study involving local crowd workers (N=31) and analyze the collected data and the impact of alternative design parameters so as to inform a real-world deployment of our system.

Crowdsourcing platforms provide attractive solutions for collecting speech synthesis data for visually impaired person. However, quality control problems remain because of low-quality volunteer workers. In this paper, we propose the design of a crowdsourcing system that allows us to devise quality control methods. We introduce four worker selection methods; preprocessing filtering, real-time filtering, post-processing filtering, and guess-processing filtering. These methods include a novel approach that utilizes a collaborative filtering technique in addition to a basic approach involving initial training or use of gold-standard data. These quality control methods improved the quality of collected speech synthesis data. Moreover, we have already collected 140,000 Japanese words from 500 million web data for speech synthesis data.

In this talk, I will describe efforts being undertaken in a collaboration between human rights advocates and Social media and mobile phones with good cameras and computer scientists at Carnegie Mellon University to Internet access are dramatically changing the nature of develop tools, methods and algorithms that will make it human rights documentation, reporting and advocacy. Key to this process, and like YouTube, Live Leak, Vimeo, and Facebook every apropos of this session, is the development of mechanisms week. In Syria, more than 650,000 videos have been to enable "the crowd" (i.e., those individuals around the uploaded to social media sites since the conflict started world who care about human rights and have relevant three years ago. This trove of interest dies down or moves on to new issues or places. In presenting this relevant in the long-term, what is irrelevant to the project, I hope to get feedback from other participants in situation or repetitive, and what is patently false or the workshop on how to achieve this goal, particularly by misleading.

One way of teaching grammar, namely morphology and syntax, is to visualize sentences as diagrams capturing relationships between words. Similarly, such relationships are captured in a more complex way in treebanks serving as key building stones in modern natural language processing. However, building them is very time consuming, thus we have been seeking for an alternative cheaper and faster way, like crowdsourcing. The purpose of our work is to explore possibility to get sentence diagrams produced by students and teachers. In our pilot study, the object language is Czech, where sentence diagrams are part of elementary school curriculum.

Increasingly social networks are being used for citizen science, where members of the public contribute knowledge to scientific endeavours. Tasks can be presented and solved using human computation, termed groupsourcing, with users benefiting from community tuition and experts gaining knowledge from the crowd. This paper gives details of a prototype that utilises groupsourcing to solve image classification tasks, to support social learning and to facilitate knowledge discovery in the domain of marine biology.

Further, the raw data collected For a company to survive, it is important to develop new by these games are usually in the form of a word or products and services appealing to consumers. Thus, the a phrase, rather than lengthy sentences typical in ordinary company must comprehend the preferences of target consumers, questionnaires, and game logs are also available as supplemental for example, by capturing how they perceive related data. Thus, it will be easier to convert the raw products currently available in the market.

We propose an online scheduling algorithm for posting crowdsourcing tasks which maximizes a novel metric called task viewership. This metric is computed using stochastic model based on coverage process and it measures the likelihood that a task is viewed by multiple crowd workers, which is correlated to the likelihood that it will be selected and completed.

Recommending media objects to users typically requires users to rate existing media objects so as to understand their preferences. The number of ratings required to produce good suggestions can be reduced through collaborative filtering. Collaborative filtering is more difficult when prior users have not rated the same set of media objects as the current user or each other. In this work, we describe an approach to applying prior user data in a way that does not require users to rate the same media objects and that does not require imputation (estimation) of prior user ratings of objects they have not rated. This approach is applied to the problem of finding good equalizer settings for music audio and is shown to greatly reduce the number of ratings the current user must make to find a good equalization setting.