Related papers: Getting It All from the Crowd
Recently, counting the number of people for crowd scenes is a hot topic because of its widespread applications (e.g. video surveillance, public security). It is a difficult task in the wild: changeable environment, large-range number of…
Crowdsourcing, in which human intelligence and productivity is dynamically mobilized to tackle tasks too complex for automation alone to handle, has grown to be an important research topic and inspired new businesses (e.g., Uber, Airbnb).…
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…
We provide an overview of tools enabling users to utilize data from open sources for decision-making support in weakly-structured subject domains. Presently, it is impossible to replace expert data with data from open sources in the process…
Neither artificial intelligence designed to play Turing's imitation game, nor augmented intelligence built to maximize the human manipulation of information are tuned to accelerate innovation and improve humanity's collective advance…
Crowdsourcing allows to instantly recruit workers on the web to annotate image, web page, or document databases. However, worker unreliability prevents taking a workers responses at face value. Thus, responses from multiple workers are…
Clustering algorithms aim to organize data into groups or clusters based on the inherent patterns and similarities within the data. They play an important role in today's life, such as in marketing and e-commerce, healthcare, data…
Suppose a decision maker wants to predict weather tomorrow by eliciting and aggregating information from crowd. How can the decision maker incentivize the crowds to report their information truthfully? Many truthful peer prediction…
Distributed, parallel crowd workers can accomplish simple tasks through workflows, but teams of collaborating crowd workers are necessary for complex goals. Unfortunately, a fundamental condition for effective teams - familiarity with other…
Modern hardware heterogeneity brings efficiency and performance opportunities for analytical query processing. In the presence of continuous data volume and complexity growth, bridging the gap between recent hardware advancements and the…
Data sensing and gathering is an essential task for various information-driven services in smart cities. On the one hand, Internet of Things (IoT) sensors can be deployed at certain fixed locations to capture data reliably but suffer from…
Number of connected devices is steadily increasing and these devices continuously generate data streams. Real-time processing of data streams is arousing interest despite many challenges. Clustering is one of the most suitable methods for…
As the use of technology increases and data analysis becomes integral in many businesses, the ability to quickly access and interpret data has become more important than ever. Information retrieval technologies are being utilized by…
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…
Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect and upload sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive…
This paper is concerned with the problem of designing, from data, agents that are able to craft their behavior from a number of contributors in order to fulfill some agent-specific task. This is not necessarily known to the contributors.…
Popular Internet services in recent years have shown that remarkable things can be achieved by harnessing the power of the masses using crowd-sourcing systems. However, crowd-sourcing systems can also pose a real challenge to existing…
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigates the structure of mobile sensing schemes and introduces…
Data integration has been recently challenged by the need to handle large volumes of data, arriving at high velocity from a variety of sources, which demonstrate varying levels of veracity. This challenging setting, often referred to as big…
As the population of world is increasing, and even more concentrated in urban areas, ensuring public safety is becoming a taunting job for security personnel and crowd managers. Mass events like sports, festivals, concerts, political…