Related papers: TeleCrowd: A Crowdsourcing Approach to Create Info…
Crowd-powered conversational assistants have been shown to be more robust than automated systems, but do so at the cost of higher response latency and monetary costs. A promising direction is to combine the two approaches for high quality,…
Context: Highly dynamic and competitive crowdsourcing software development (CSD) marketplaces may experience task failure due to unforeseen reasons, such as increased competition over shared supplier resources, or uncertainty associated…
In crowdsourcing, a group of common people is asked to execute the tasks and in return will receive some incentives. In this article, one of the crowdsourcing scenarios with multiple heterogeneous tasks and multiple IoT devices (as task…
Here we present a massive longitudinal dataset of public Telegram content, comprising over 5.9 billion messages dating from 2015 to 2025, collected from 712 thousand channels and groups, enriched with metadata on forwards, reactions, and…
Audio captioning is a novel field of multi-modal translation and it is the task of creating a textual description of the content of an audio signal (e.g. "people talking in a big room"). The creation of a dataset for this task requires a…
Quality improvement methods are essential to gathering high-quality crowdsourced data, both for research and industry applications. A popular and broadly applicable method is task assignment that dynamically adjusts crowd workflow…
Crowdsourcing works by distributing many small tasks to large numbers of workers, yet the true potential of crowdsourcing lies in workers doing more than performing simple tasks---they can apply their experience and creativity to provide…
Microtask crowdsourcing has enabled dataset advances in social science and machine learning, but existing crowdsourcing schemes are too expensive to scale up with the expanding volume of data. To scale and widen the applicability of…
Conducting a manual evaluation is considered an essential part of summary evaluation methodology. Traditionally, the Pyramid protocol, which exhaustively compares system summaries to references, has been perceived as very reliable,…
Temporal relation extraction (TRE) aims to grasp the evolution of events or actions, and thus shape the workflow of associated tasks, so it holds promise in helping understand task requests initiated by requesters in crowdsourcing systems.…
With the rapid development of crowdsourcing platforms that aggregate the intelligence of Internet workers, crowdsourcing has been widely utilized to address problems that require human cognitive abilities. Considering great dynamics of…
Crowdsourcing has gained immense popularity in machine learning applications for obtaining large amounts of labeled data. Crowdsourcing is cheap and fast, but suffers from the problem of low-quality data. To address this fundamental…
In Internet of Things (IoT) driven smart-world systems, real-time crowd-sourced databases from multiple distributed servers can be aggregated to extract dynamic statistics from a larger population, thus providing more reliable knowledge for…
Crowdsourcing can collect many diverse ideas by prompting ideators individually, but this can generate redundant ideas. Prior methods reduce redundancy by presenting peers' ideas or peer-proposed prompts, but these require much human…
The growing use of supervised machine learning in research and industry has increased the need for labeled datasets. Crowdsourcing has emerged as a popular method to create data labels. However, working on large batches of tasks leads to…
Crowd-sourcing is a cheap and popular means of creating training and evaluation datasets for machine learning, however it poses the problem of `truth inference', as individual workers cannot be wholly trusted to provide reliable…
Crowdfunding has emerged as a widespread strategy for startups seeking financing, particularly through reward-based methods. However, understanding its economic impact at both micro and macro levels requires thorough analysis, often…
The main goal of this paper is to discuss how to integrate the possibilities of crowdsourcing platforms with systems supporting workflow to enable the engagement and interaction with business tasks of a wider group of people. Thus, this…
Imitation Learning has empowered recent advances in learning robotic manipulation tasks by addressing shortcomings of Reinforcement Learning such as exploration and reward specification. However, research in this area has been limited to…
Schema matching is a central challenge for data integration systems. Inspired by the popularity and the success of crowdsourcing platforms, we explore the use of crowdsourcing to reduce the uncertainty of schema matching. Since…