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Sigal Sina

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

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AAAI Conference 2015 Conference Paper

A Hybrid Approach of Classifier and Clustering for Solving the Missing Node Problem

Sigal Sina
Avi Rosenfeld
Sarit Kraus
Navot Akiva

An important area of social network research is identifying missing information which is not explicitly represented in the network or is not visible to all. In this paper, we propose a novel Hybrid Approach of Classifier and Clustering, which we refer to as HACC, to solve the missing node identification problem in social networks. HACC utilizes a classifier as a preprocessing step in order to integrate all known information into one similarity measure and then uses a clustering algorithm to identify missing nodes. Specifically, we used the information on the network structure, attributes about known users (nodes) and pictorial information to evaluate HACC and found that it performs significantly better than other missing node algorithms. We also argue that HACC is a general approach and domain independent and can be easily applied to other domains. We support this claim by evaluating HACC on a second authorship identification domain as well.

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AAAI Conference 2014 Conference Paper

Generating Content for Scenario-Based Serious-Games Using CrowdSourcing

Sigal Sina
Avi Rosenfeld
Sarit Kraus

Scenario-based serious-games have become an important tool for teaching new skills and capabilities. An important factor in the development of such systems is reducing the time and cost overheads in manually creating content for these scenarios. To address this challenge, we present Scenario- Gen, an automatic method for generating content about everyday activities through combining computer science techniques with the crowd. ScenarioGen uses the crowd in three different ways: to capture a database of scenarios of everyday activities, to generate a database of likely replacements for specific events within that scenario, and to evaluate the resulting scenarios. We evaluated ScenarioGen in 6 different content domains and found that it was consistently rated as coherent and consistent as the originally captured content. We also compared ScenarioGen’s content to that created by traditional planning techniques. We found that both methods were equally effective in generating coherent and consistent scenarios, yet ScenarioGen’s content was found to be more varied and easier to create.

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