Crowd-powered systems

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 217-237).Crowd-powered systems combine computation with human intelligence, drawn from large groups of people connecting and coordinating online. These hybrid systems enable applications and experiences that neither crowds nor computation could support alone. Unfortunately, crowd work is error-prone and slow, making it difficult to incorporate crowds as first-order building blocks in software systems. I introduce computational techniques that decompose complex tasks into simpler, verifiable steps to improve quality, and optimize work to return results in seconds. These techniques develop crowdsourcing as a platform so that it is reliable and responsive enough to be used in interactive systems. This thesis develops these ideas through a series of crowd-powered systems. The first, Soylent, is a word processor that uses paid micro-contributions to aid writing tasks such as text shortening and proofreading. Using Soylent is like having access to an entire editorial staff as you write. The second system, Adrenaline, is a camera that uses crowds to help amateur photographers capture the exact right moment for a photo. It finds the best smile and catches subjects in mid-air jumps, all in realtime. Moving beyond generic knowledge and paid crowds, I introduce techniques to motivate a social network that has specific expertise, and techniques to data mine crowd activity traces in support of a large number of uncommon user goals. These systems point to a future where social and crowd intelligence are central elements of interaction, software, and computation.by Michael Scott Bernstein.Ph.D

Web page enhancement on desktop and mobile browsers

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2013."February 2013." Cataloged from PDF version of thesis.Includes bibliographical references (p. 154-165).The Web is a convenient platform to deliver information, but reading web pages is not as easy as it was in 1990s. This thesis focuses on investigating techniques to enhance web pages on desktop and mobile browsers for two specific populations: non-native English readers and mobile users. There are three issues addressed in this thesis: web page readability, web page skimmability and continuous reading support on mobile devices. On today's primarily English-language Web, non-native readers encounter some problems, even if they have some fluency in English. This thesis focuses on content presentation and proposes a new transformation method, Jenga Format, to enhance web page readability. A user study with 30 non-native users showed that Jenga transformation not only improved reading comprehension, but also made the web page reading easier. On the other hand, readability research has found that average reading times for non-native readers has remained the same or even worse. This thesis studies this issue and proposes Froggy GX (Generation neXt) to improve reading under time constraints. A user study with 20 non-native users showed that Froggy GX not only enhanced reading comprehension under time constraints, but also provided higher user satisfaction than reading unaided. When using the Web on mobile devices, the reading situation becomes challenging. Even worse, context switches, such as from walking to sitting, static standing, or hands-free situations like driving, happen in reading in on-the-go situations, but this scenario was not adequately addressed in previous studies. This thesis investigates this scenario and proposes a new mobile browser, Read4Me, to support continuous reading on a mobile device. A user study with 10 mobile users showed that auto-switching not only provided significantly fewer dangerous encounters than visual-reading, but also provided the best reading experience.by Chen-Hsiang Yu.Ph.D

Using VXML to construct a speech browser for a public-domain SpeechWeb

Despite the fact that interpreters for the voice-application markup language VXML have been available for around five years, there is very little evidence of the emergence of a public-domain SpeechWeb. This is in contrast to the huge growth of the conventional web only a few years after the introduction of HTML. One reason for this is that architectures for distributed speech applications are not conducive to public involvement in the creation and deployment of speech applications. In previous research, a new architecture for a public-domain SpeechWeb has been proposed. However, a non-proprietary speech browser is needed for this new architecture. In this thesis, it is shown that through a novel use of VXML, a viable public-domain SpeechWeb browser can be built as a single VXML page. This thesis is proven through the development and implementation of a single VXML page SpeechWeb browser. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .S8. Source: Masters Abstracts International, Volume: 45-01, page: 0366. Thesis (M.Sc.)--University of Windsor (Canada), 2006

Automatic Evaluation of Information Provider Reliablity and Expertise

Q&A social media have gained a lot of attention during the recent years. People rely on these sites to obtain information due to a number of advantages they offer as compared to conventional sources of knowledge (e.g., asynchronous and convenient access). However, for the same question one may find highly contradicting answers, causing an ambiguity with respect to the correct information. This can be attributed to the presence of unreliable and/or non-expert users. These two attributes (reliability and expertise) significantly affect the quality of the answer/information provided. We present a novel approach for estimating these user's characteristics relying on human cognitive traits. In brief, we propose each user to monitor the activity of his peers (on the basis of responses to questions asked by him) and observe their compliance with predefined cognitive models. These observations lead to local assessments that can be further fused to obtain a reliability and expertise consensus for every other user in the social network (SN). For the aggregation part we use subjective logic. To the best of our knowledge this is the first study of this kind in the context of Q&A SNs. Our proposed approach is highly distributed; each user can individually estimate the expertise and the reliability of his peers using his direct interactions with them and our framework. The online SN (OSN), which can be considered as a distributed database, performs continuous data aggregation for users expertise and reliability assesment in order to reach a consensus. In our evaluations, we first emulate a Q&A SN to examine various performance aspects of our algorithm (e.g., convergence time, responsiveness etc.). Our evaluations indicate that it can accurately assess the reliability and the expertise of a user with a small number of samples and can successfully react to the latter's behavior change, provided that the cognitive traits hold in practice. Furthermore, the use of the consensus operator for the aggregation of multiple opinions on a specific user, reduces the uncertainty with regards to the final assessment. However, as real data obtained from Yahoo! Answers imply, the pairwise interactions between specific users are limited. Hence, we consider the aggregate set of questions as posted from the system itself and we assess the expertise and realibility of users based on their response behavior. We observe, that users have different behaviors depending on the level at which we are observing them. In particular, while their activity is focused on a few general categories, yielding them reliable, their microscopic (within general category) activity is highly scattered

An Advanced eLearning Environment Developed for Engineering Learners

Monitoring and evaluating engineering learners through computer-based laboratory exercises is a difficult task, especially under classroom conditions. A complete diagnosis requires the capability to assess both the competence of the learner to use the scientific software and the understanding of the theoretical principles. This monitoring and evaluation needs to be continuous, unobtrusive and personalized in order to be effective. This study presents the results of the pilot application of an eLearning environment developed specifically with engineering learners in mind. As its name suggests, the Learner Diagnosis, Assistance, and Evaluation System based on Artificial Intelligence (StuDiAsE) is an Open Learning Environment that can perform unattended diagnostic, evaluation and feedback tasks based on both quantitative and qualitative parameters. The base architecture of the system, the user interface and its effect on the performance of postgraduate engineering learners are being presented

Interactive interface design: Graphic Design Archive prototype 2.0

None provided

TDRSS operations control analysis study

The use of an operational Tracking and Data Relay Satellite System (TDRSS) and the remaining ground stations for the STDN (GSTDN) was investigated. The operational aspects of TDRSS concepts, GSTDN as a 14-site network, and GSTDN as a 7 site-network were compared and operations control concepts for the configurations developed. Man/machine interface, scheduling system, and hardware/software tradeoff analyses were among the factors considered in the analysis

Information fusion for automated question answering

Until recently, research efforts in automated Question Answering (QA) have mainly focused on getting a good understanding of questions to retrieve correct answers. This includes deep parsing, lookups in ontologies, question typing and machine learning of answer patterns appropriate to question forms. In contrast, I have focused on the analysis of the relationships between answer candidates as provided in open domain QA on multiple documents. I argue that such candidates have intrinsic properties, partly regardless of the question, and those properties can be exploited to provide better quality and more user-oriented answers in QA.Information fusion refers to the technique of merging pieces of information from different sources. In QA over free text, it is motivated by the frequency with which different answer candidates are found in different locations, leading to a multiplicity of answers. The reason for such multiplicity is, in part, the massive amount of data used for answering, and also its unstructured and heterogeneous content: Besides am¬ biguities in user questions leading to heterogeneity in extractions, systems have to deal with redundancy, granularity and possible contradictory information. Hence the need for answer candidate comparison. While frequency has proved to be a significant char¬ acteristic of a correct answer, I evaluate the value of other relationships characterizing answer variability and redundancy.Partially inspired by recent developments in multi-document summarization, I re¬ define the concept of "answer" within an engineering approach to QA based on the Model-View-Controller (MVC) pattern of user interface design. An "answer model" is a directed graph in which nodes correspond to entities projected from extractions and edges convey relationships between such nodes. The graph represents the fusion of information contained in the set of extractions. Different views of the answer model can be produced, capturing the fact that the same answer can be expressed and pre¬ sented in various ways: picture, video, sound, written or spoken language, or a formal data structure. Within this framework, an answer is a structured object contained in the model and retrieved by a strategy to build a particular view depending on the end user (or taskj's requirements.I describe shallow techniques to compare entities and enrich the model by discovering four broad categories of relationships between entities in the model: equivalence, inclusion, aggregation and alternative. Quantitatively, answer candidate modeling im¬ proves answer extraction accuracy. It also proves to be more robust to incorrect answer candidates than traditional techniques. Qualitatively, models provide meta-information encoded by relationships that allow shallow reasoning to help organize and generate the final output