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Auflistung Künstliche Intelligenz 27(1) - März 2013 nach Titel
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- ZeitschriftenartikelA Brief Tutorial on How to Extract Information from User-Generated Content (UGC)(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Egger, Marc; Lang, AndréIn this brief tutorial, we provide an overview of investigating text-based user-generated content for information that is relevant in the corporate context. We structure the overall process along three stages: collection, analysis, and visualization. Corresponding to the stages we outline challenges and basic techniques to extract information of different levels of granularity.
- ZeitschriftenartikelCan Computers Learn from the Aesthetic Wisdom of the Crowd?(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Bauckhage, Christian; Kersting, KristianThe social media revolution has led to an abundance of image and video data on the Internet. Since this data is typically annotated, rated, or commented upon by large communities, it provides new opportunities and challenges for computer vision. Social networking and content sharing sites seem to hold the key to the integration of context and semantics into image analysis. In this paper, we explore the use of social media in this regard. We present empirical results obtained on a set of 127,593 images with 3,741,176 tag assignments that were harvested from Flickr, a photo sharing site. We report on how users tag and rate photos and present an approach towards automatically recognizing the aesthetic appeal of images using confidence-based classifiers to alleviate effects due to ambiguously labeled data. Our results indicate that user generated content allows for learning about aesthetic appeal. In particular, established low-level image features seem to enable the recognition of beauty. A reliable recognition of unseemliness, on the other hand, appears to require more elaborate high-level analysis.
- ZeitschriftenartikelCrowd-Powered Systems(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Bernstein, Michael S.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. We introduce computational techniques that decompose complex tasks into simpler, verifiable steps to improve quality, and optimize work to return results in seconds. Using these techniques, we prototype a set of interactive 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. These systems point to a future where social and crowd intelligence are central elements of interaction, software, and computation.
- ZeitschriftenartikelEditorial(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Klügl, F.
- ZeitschriftenartikelFrom Texts to Networks: Detecting and Managing the Impact of Methodological Choices for Extracting Network Data from Text Data(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Diesner, JanaThis thesis (Diesner in Technical Report CMU-ISR-12-101, 2012) addresses a series of methodological problems related to extracting information on socio-technical networks from natural language text data. Theories and models from the social sciences are leveraged and combined with computational approaches to (a) construct, analyze and compare network data and (b) combine text data and network data for analysis. This thesis entails various projects that serve three purposes: First, the impact of various common coding choices, including reference resolution and co-occurrence-based link formation, on network data and analysis results is empirically identified across multiple types of text data and domains. Second, different relation extraction methods are compared across various over-time, open-source, large-scale datasets with respect to the resulting network data and analysis results. This study offers a complement to traditional strategies for accuracy assessment. The relation extraction methods considered include network data construction based on (a) manually versus automatically built thesauri, (b) meta-data, and (c) collaboration with subject matter experts. Third, the concepts of grouping and roles from network analysis are integrated with text mining methods to enable the theoretically grounded, joint consideration of text data and network data for real-world applications.Overall, in this thesis, an interdisciplinary and computationally rigorous approach is used; thereby advancing the intersection of network analysis, natural language processing and computing. The contributions made with this work help people to utilize text data for network analysis, and to collect, manage and interpret rich network data at any scale. These steps are preconditions for asking substantive and graph-theoretic questions, testing hypotheses, and advancing theories about networks.
- ZeitschriftenartikelInterview with Bernado A. Huberman on Social Media and Collective Intelligence(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Metaxas, Panagiotis Takis
- ZeitschriftenartikelInterview with Jim Hendler on Social Media and Collective Intelligence(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Metaxas, Panagiotis Takis
- ZeitschriftenartikelJazzFlow—Analyzing “Group Flow” Among Jazz Musicians Through “Honest Signals”(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Gloor, Peter A.; Oster, Daniel; Fischbach, KaiIn this project we aim to analyze “honest signals” between Jazz musicians by using sociometric badges with the goal of identifying structural properties of self-organizing creative teams. In particular, we are interested in the pre-requisites for “flow,” the state of work where “time flies,” and workers are at their most-productive best. We extend the concept of individual “flow” as defined by Csikszentmihalyi (Flow: the psychology of optimal experience. Harper Row, New York, 1990) to the group level (Sawyer in Group creativity: music, theater, collaboration. Psychology Press, Oxford, 2003; Group genius: the creative power of collaboration. Basic Books, New York, 2007), trying to identify some of the conditions indicative of the group flow state. We speculate that a band of Jazz musicians is particularly well suited to study group flow, because they are an archetype of a self-organizing creative team, involved in highly creatively work while passing leadership of the tune for the solo part from one band member to the next.
- ZeitschriftenartikelLearning to Discover Political Activism in the Twitterverse(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013) Finn, Samantha; Mustafaraj, EniWhen analysing social media conversations, in search of the public opinion about an unfolding political event that is being discussed in real-time (e.g., presidential debates, major speeches, etc.), it is important to distinguish between two groups of participants: political activists and the general public. To address this problem, we propose a supervised machine-learning approach, which uses inexpensively acquired labeled data from mono-thematic Twitter accounts to learn a binary classifier for the labels “political activist” and “general public”. While the classifier has a 92 % accuracy on individual tweets, when applied to the last 200 tweets from accounts of a set of 1000 Twitter users, it classifies accounts with a 97 % accuracy. Our work demonstrates that machine learning algorithms can play a critical role in improving the quality of social media analytics and understanding, whose importance is increasing as social media adoption becomes widespread.
- ZeitschriftenartikelNews(KI - Künstliche Intelligenz: Vol. 27, No. 1, 2013)