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Crowdsourcing has emerged as an important data collection paradigm in participatory and human-centric sensing applications. While many crowdsourcing studies focus on sensing and recovering the status of the physical world, this paper investigates the problem of profiling the crowd sensors (i.e., humans). In particular, we study the problem of accurately inferring the home locations of people from...
Users' home location is important information for many advanced information services in big data applications (e.g., localized recommendation, target ads of local business and urban planning). In this paper, we study the problem of accurately inferring the home locations of people from the noisy and sparse data they voluntarily share on online social media. Previous studies have developed supervised...
The localness inference problem is to identify whether a person is a local resident in a city or not and the likelihood of a venue to attract local people. This information is critical for many applications such as targeted ads of local business, urban planning, localized news and travel recommendations. While there are prior work on geo-locating people in a city using supervised learning approaches,...
This paper presents an unsupervised approach toaccurately discover interesting places in a city from location-basedsocial sensing applications, a new sensing applicationparadigm that collects observations of physical world fromLocation-based Social Networks (LBSN). While there are alarge amount of prior works on personalized Point of Interests(POI) recommendation systems, they used supervised learningapproaches...
Social sensing has emerged as a new application paradigm for smart cities where a crowd of social sources (humans or devices on their behalf) collectively contribute a large amount of observations about the physical world. This paper focuses on an interesting place finding problem in social sensing where the goal is to accurately identify the interesting places in a city where people may have strong...
In this paper, we propose an algorithm that detects overlapping communities in networks (graphs) based on a simple node behavior model. The key idea in the proposed algorithm is to find communities in an agglomerative manner such that every detected community S has the following property: For each node i ∈ S, we have (i) the fraction of nodes in S \ {i} that are neighbors of node i is greater than...
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