Related papers: Mephisto: A Framework for Portable, Reproducible, …
New techniques leveraging IT-mediated crowds such as Crowdsensing, Situated Crowdsourcing, Spatial Crowdsourcing, and Wearables Crowdsourcing have now materially emerged. These techniques, here termed next generation Crowdsourcing, serve to…
Crowdsensing is a promising sensing paradigm for smart city applications (e.g., traffic and environment monitoring) with the prevalence of smart mobile devices and advanced network infrastructure. Meanwhile, as tasks are performed by…
Compound AI systems, orchestrating multiple AI components and external APIs, are increasingly vital but face challenges in managing complexity, handling ambiguity, and enabling effective development workflows. Existing frameworks often…
This work introduces a companion reproducible paper with the aim of allowing the exact replication of the methods, experiments, and results discussed in a previous work [5]. In that parent paper, we proposed many and varied techniques for…
Motivation: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base construction and protein structure determination all benefit from human input. In…
Co-designing efficient machine learning based systems across the whole hardware/software stack to trade off speed, accuracy, energy and costs is becoming extremely complex and time consuming. Researchers often struggle to evaluate and…
As the computational requirements for machine learning systems and the size and complexity of machine learning frameworks increases, essential framework innovation has become challenging. While computational needs have driven recent…
For decades, the crowdsourcing has gained much attention from both academia and industry, which outsources a number of tasks to human workers. Existing works considered improving the task accuracy through voting or learning methods, they…
Over the last several years, the computation landscape for conducting data analytics has completely changed. While in the past, a lot of the activities have been undertaken in isolation by companies, and research institutions, today's…
Recent studies have shown that the labels collected from crowdworkers can be discriminatory with respect to sensitive attributes such as gender and race. This raises questions about the suitability of using crowdsourced data for further…
Federated learning (FL) has found numerous applications in healthcare, finance, and IoT scenarios. Many existing FL frameworks offer a range of benchmarks to evaluate the performance of FL under realistic conditions. However, the process of…
What is the state of the literature in respect to Crowdsourcing for policy making? This work attempts to answer this question by collecting, categorizing, and situating the extant research investigating Crowdsourcing for policy, within the…
Representation learning approaches require a massive amount of discriminative training data, which is unavailable in many scenarios, such as healthcare, smart city, education, etc. In practice, people refer to crowdsourcing to get annotated…
Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several…
Smart devices with built-in sensors, computational capabilities, and network connectivity have become increasingly pervasive. The crowds of smart devices offer opportunities to collectively sense and perform computing tasks in an…
Optimising deep learning inference across edge devices and optimisation targets such as inference time, memory footprint and power consumption is a key challenge due to the ubiquity of neural networks. Today, production deep learning…
In recent years, imitation learning from large-scale human demonstrations has emerged as a promising paradigm for training robot policies. However, the burden of collecting large quantities of human demonstrations is significant in terms of…
Mobile Crowd Computing (MCdC) leverages the idle computational capacity of consumer smartphones to enable distributed task processing at scale; however, widespread real-world adoption remains constrained by the absence of developer-oriented…
Significant effort has been made to understand user motivation and to elicit user participation in crowdsourcing systems. However, incentive engineering, i.e., designing incentives that can purposefully motivate users, is still an open…
Numerical algorithms and computational tools are instrumental in navigating and addressing complex simulation and data processing tasks. The exponential growth of metadata and parameter-driven simulations has led to an increasing demand for…