Supporting the efficient discovery and use of Web APIs is increasingly important as their use and popularity grows. Yet, a simple task like finding potentially interesting APIs and their related documentation turns out to be hard and time consuming even when using the best resources currently available on the Web. In this paper we describe our research towards an automated Web API documentation crawler and search engine. This paper presents two main contributions. First, we have devised and exploited crowdsourcing techniques to generate a curated dataset of Web APIs documentation. Second, thanks to this dataset, we have devised an engine able to automatically detect documentation pages. Our preliminary experiments have shown that we obtain an accuracy of 80% and a precision increase of 15 points over a keyword-based heuristic we have used as baseline.

Profanity detection is often thought to be an easy task. However, past work has shown that current, list-based systems are performing poorly. They fail to adapt to evolving profane slang, identify profane terms that have been disguised or only partially censored (e.g., @ss, f$#%) or intentionally or unintentionally misspelled (e.g., biatch, shiiiit). For these reasons, they are easy to circumvent and have very poor recall. Secondly, they are a one-size fits all solution – making assumptions that the definition, use and perceptions of profane or inappropriate holds across all contexts. In this article, we present work that attempts to move beyond list-based profanity detection systems by identifying the context in which profanity occurs. The proposed system uses a set of comments from a social news site labeled by Amazon Mechanical Turk workers for the presence of profanity. This system far surpasses the performance of list-based profanity detection techniques. The use of crowdsourcing in this task suggests an opportunity to build profanity detection systems tailored to sites and communities.

While in the past taxonomic and ontological knowledge was traditionally produced by small groups of co-located experts, today the production of such knowledge has a radically different shape and form. For example, potentially thousands of health professionals, scientists, and ontology experts will collaboratively construct, evaluate and maintain the most recent version of the International Classification of Diseases (ICD-11), a large ontology of diseases and causes of deaths managed by the World Health Organization. In this work, we present a novel web-based tool — iCAT Analytics — that allows to investigate systematically crowd-based processes in knowledge-production systems. To enable such investigation, the tool supports interactive exploration of pragmatic aspects of ontology engineering such as how a given ontology evolved and the nature of changes, discussions and interactions that took place during its production process. While iCAT Analytics was motivated by ICD-11, it could potentially be applied to any crowd-based ontology-engineering project. We give an introduction to the features of iCAT Analytics and present some insights specifically for ICD-11.

The first indication of a new outbreak is often in unstructured data (natural language) and reported openly in traditional or social media as a new `flu-like' or `malaria-like' illness weeks or months before the new pathogen is eventually isolated. We present a system for tracking these early signals globally, using natural language processing and crowdsourcing. By comparison, search-log-based approaches, while innovative and inexpensive, are often a trailing signal that follow open reports in plain language. Concentrating on discovering outbreak-related reports in big open data, we show how crowdsourced workers can create near-real-time training data for adaptive active-learning models, addressing the lack of broad coverage training data for tracking epidemics. This is well-suited to an outbreak information-flow context, where sudden bursts of information about new diseases/locations need to be manually processed quickly at short notice.

The usefulness of intelligent applications/services reasoning with linked data is dependent on the availability and correctness of this data. The crowd potentially has an important role to play in performing the non-trivial tasks of creating, validating, and maintaining the online linked data sets used by applications and services. Additional information captured within a provenance record can be used in these tasks and others, such as evaluating the performance of the crowd and its members. In this paper we describe two roles for the crowd in the web of linked data (creation and maintenance), and argue that incorporating provenance into these tasks is beneficial especially in scenarios when the population of available workers is small. We also identify several challenges for the use of provenance in this context and define a set of requirements for a provenance model to address these challenges.

Currently, the majority of matchers are able to establish simple correspondences between entities, but are not able to provide complex alignments. Furthermore, the resulting alignments do not contain additional information on how they were extracted and formed. Not only it becomes hard to debug the alignment results, but it is also difficult to justify correspondences. We propose a method to generate complex ontology alignments that captures the semantics of matching algorithms and human-oriented ontology alignment definition processes. Through these semantics, arguments that provide an abstraction over the specificities of the alignment process are generated and used by agents to share, negotiate and combine correspondences. After the negotiation process, the resulting arguments and their relations can be visualized by humans in order to debug and understand the given correspondences.

This paper introduces SIFU, a system that recruits in real time native speakers as online volunteer tutors to help answer questions from Chinese language learners in reading news articles. SIFU integrates the strengths of two effective online language learning methods: reading online news and communicating with online native speakers. SIFU recruits volunteers from an online social network rather than recruits workers from Amazon Mechanical Turk.Initial experiments showed that the proposed approach is able to effectively recruit online volunteer tutors, adequately answer the learners' questions, and efficiently obtain an answer for the learner. Our field deployment illustrates that SIFU is very useful in assisting Chinese learners in reading Chinese news articles and online volunteer tutors are willing to help Chinese learners when they are on social network service.

This paper focuses on care plans (i.e., rough schedules) in care houses and evaluates them from the viewpoint of a deep and stable sleep which contributes to provide comfortable and healthy life for aged persons. For this purpose, this paper investigates the care plans which are basically based on the current care plans but change a small part of a schedule as an aged person desires. Through the human subject experiments in the actual care house, the following implications have been revealed: (1) the proposed care plan decreases the time of the light sleep; and (2) the proposed care plan provides the deep sleep (i.e., 9 years younger sleep in our experiment).

Do quantified and origin) and molecular aspects of our bodies like DNA can tweeting, heavily monitored and selfreporting animals, converge. Consumer genomics websites, crowdsourcing of humans, environments and food create some new biodata but also social networking over genes, together uniformity, a dangerously homogenous, objectified and with services monitoring food flows and food authenticity standardized collective or these data offer some new can create new models of research in nutrigenomics and opportunity for interaction? Are we creating new symbiotic projects related to dieting, health and relations over these data that can lead to a new sense of lifestyle choices. How to connect various scales from community or we are witnessing some depersonalization molecules to institutions and what will be the function of and objectification? How to make meaning out of large these interactions and interfaces? How to create quantities of data and how to bring user experience to data meaningful interaction across scales and large datasets?

Brain structure exhibits systematic relationships with a variety of an individual’s cognitive abilities and such relationships can be captured by voxel-based morphometry (VBM) that computes regional gray matter volume based on anatomical MRIs. This method has been successfully used to reveal brain regions that are associated with individual differences in a broad range of contexts such as perceptual performance, attention control, face recognition, introspection and personality traits. Here, we show that such relationships with brain structure extend to complex social behaviors by presenting our recent VBM studies that examined the relationships between brain structure and diverse aspects of socio-cognitive behavioral traits. Specifically, we identified brain regions in which individual differences in gray matter volumes were associated with political orientation, moral sentiment, empathy and loneliness. These findings suggest that information derived from standard MRI scans could be used to extract information about an individual’s real-world and online social behavior. Unlike conventional functional neuroimaging research, our structural neuroimaging approach does not require a virtual environment that emulates social interactions and thus can directly link brain structure to real-world human behavior. As such, our approach based on individual differences in brain structure and behavior provides an important anchor point that integrates genetic and environmental factors determining diversity of human cognition and behavior.