相关论文: Learning to Hire Teams

Spatial crowdsourcing (SC) engages large worker pools for location-based tasks, attracting growing research interest. However, prior SC task allocation approaches exhibit limitations in computational efficiency, balanced matching, and…

Crowdsourcing system has emerged as an effective platform for labeling data with relatively low cost by using non-expert workers. Inferring correct labels from multiple noisy answers on data, however, has been a challenging problem, since…

Collaborative Mobile crowdsourcing (CMCS) allows entities, e.g., local authorities or individuals, to hire a team of workers from the crowd of connected people, to execute complex tasks. In this paper, we investigate two different CMCS…

This paper investigates a novel hybrid worker recruitment problem where the mobile crowd sensing and computing (MCSC) platform employs workers to serve MCSC tasks with diverse quality requirements and budget constraints, under uncertainties…

To successfully complete a complex project, be it a construction of an airport or of a backbone IT system, agents (companies or individuals) must form a team having required competences and resources. A team can be formed either by the…

We consider the problem of completing a set of $n$ tasks with a human-robot team using minimum effort. In many domains, teaching a robot to be fully autonomous can be counterproductive if there are finitely many tasks to be done. Rather,…

Data fusion has played an important role in data mining because high-quality data is required in a lot of applications. As on-line data may be out-of-date and errors in the data may propagate with copying and referring between sources, it…

Hierarchies of concepts are useful in many applications from navigation to organization of objects. Usually, a hierarchy is created in a centralized manner by employing a group of domain experts, a time-consuming and expensive process. The…

We consider a stochastic online problem where $n$ applicants arrive over time, one per time step. Upon arrival of each applicant their cost per time step is revealed, and we have to fix the duration of employment, starting immediately. This…

Traditional employment usually provides mechanisms for workers to improve their skills to access better opportunities. However, crowd work platforms like Amazon Mechanical Turk (AMT) generally do not support skill development (i.e.,…

Our work contributes to aiding requesters in deploying collaborative tasks in crowdsourcing. We initiate the study of recommending deployment strategies for collaborative tasks to requesters that are consistent with deployment parameters…

Linguistically diverse datasets are critical for training and evaluating robust machine learning systems, but data collection is a costly process that often requires experts. Crowdsourcing the process of paraphrase generation is an…

The unprecedented demand for large amount of data has catalyzed the trend of combining human insights with machine learning techniques, which facilitate the use of crowdsourcing to enlist label information both effectively and efficiently.…

Mobile Crowdsourcing (MC) is an effective way of engaging large groups of smart devices to perform tasks remotely while exploiting their built-in features. It has drawn great attention in the areas of smart cities and urban computing…

We present CrowdHub, a tool for running systematic evaluations of task designs on top of crowdsourcing platforms. The goal is to support the evaluation process, avoiding potential experimental biases that, according to our empirical…

Collaborative Mobile Crowdsourcing (CMCS) allows platforms to recruit worker teams to collaboratively execute complex sensing tasks. The efficiency of such collaborations could be influenced by trust relationships among workers. To obtain…

Crowdsourcing platforms are a powerful and convenient means for recruiting participants in online studies and collecting data from the crowd. As information work is being more and more automated by Machine Learning algorithms, creativity…

The success of software crowdsourcing depends on active and trustworthy pool of worker supply. The uncertainty of crowd workers' behaviors makes it challenging to predict workers' success and plan accordingly. In a competitive crowdsourcing…

Worker selection is a significant and challenging issue in crowdsourcing systems. Such selection is usually based on an assessment of the reputation of the individual workers participating in such systems. However, assessing the credibility…

Digital crowdsourcing (CS) is a modern approach to perform certain large projects using small contributions of a large crowd. In CS, a taskmaster typically breaks down the project into small batches of tasks and assigns them to so-called…