Analyzing Workers Performance in Online Mapping Tasks Across Web, Mobile, and Virtual Reality Platforms

In online crowd mapping, crowd workers recruited through crowdsourcing marketplaces collect geographic data. Compared to traditional mapping methods, where workers physically explore the area, the benefit of using online crowd mapping is the potential to be cost-effective and time-efficient. Previous studies have focused on mapping urban objects...

Detecting, classifying, and mapping retail storefronts using street-level imagery

Up-to-date listings of retail stores and related building functions are challenging and costly to maintain. We introduce a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. Specifically, we present a deep...

VirtualCrowd: A Simulation Platform for Microtask Crowdsourcing Campaigns

This demo presents VirtualCrowd, a simulation platform for crowdsourcing campaigns. The platform allows the design, configuration, step-by-step execution, and analysis of customized tasks, worker profiles, and crowdsourcing strategies. The platform will be demonstrated through a crowd-mapping example in two cities, which will highlight the...

Conversational crowdsourcing

The trend of remote work leads to the prosperity of crowdsourcing marketplaces. In crowdsourcing marketplaces, online workers can select their preferable tasks and then complete them to get paid, while requesters design and publish tasks to acquire their desirable data. The standard user interface of the crowdsourcing task is the web page,...

Training Data Augmentation for Detecting Adverse Drug Reactions in User-Generated Content

Social media provides a timely yet challenging data source for adverse drug reaction (ADR) detection. Existing dictionary-based, semi-supervised learning approaches are intrinsically limited by the coverage and maintainability of laymen health vocabularies. In this paper, we introduce a data augmentation approach that leverages variational...

Crowd-Mapping Urban Objects from Street-Level Imagery

Knowledge about the organization of the main physical elements (e.g. streets) and objects (e.g. trees) that structure cities is important in the maintenance of city infrastructure and the planning of future urban interventions. In this paper, a novel approach to crowd-mapping urban objects is proposed. Our method capitalizes on strategies for...

SmartPub: A Platform for Long-Tail Entity Extraction from Scientific Publications

This demo presents SmartPub, a novel web-based platform that supports the exploration and visualization of shallow meta-data (e.g., author list, keywords) and deep meta-data--long tail named entities which are rare, and often relevant only in specific knowledge domain--from scientific publications. The platform collects documents from different...

TSE-NER: An Iterative Approach for Long-Tail Entity Extraction in Scientific Publications

Named Entity Recognition and Typing (NER/NET) is a challenging task, especially with long-tail entities such as the ones found in scientific publications. These entities (e.g. “WebKB”, “StatSnowball”) are rare, often relevant only in specific knowledge domains, yet important for retrieval and exploration purposes. State-of-the-art NER approaches...

Concept Focus: Semantic Meta-Data For Describing MOOC Content

MOOCs promised to herald a new age of open education.<br/>However, efficient access to MOOC content is still hard, thus unneces-<br/>sarily complicating many use cases like efficient re-use of material, or<br/>tailored access for life-long learning scenarios. One of the reasons for this<br/>lack of accessibility is the shortage of meaningful...

Asking the right question in collaborative O&A systems

Collaborative Question Answering (cQA) platforms are a very popular repository of crowd-generated knowledge. By formulating questions, users express needs that other members of the cQA community try to collaboratively satisfy. Poorly formulated questions are less likely to receive useful responses, thus hindering the overall knowledge generation...