Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Shah, A.

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    Link budget for a terrestrial FSO link and performance of space time block codes over FSO channels
    (Institution of Engineering and Technology, 2019) Shah, A.; Moorthy, K.K.N.; Kallapur, P.R.; Shripathi Acharya, U.S.
    [No abstract available]
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    Impact of group norms in eliciting response in a goal driven virtual community
    (UHAMKA PRESS uhamkapress@yahoo.co.id, 2013) Jain, S.; Sinha, T.; Shah, A.; Sharma, C.; Rosé, C.
    With the proliferation of social media into our daily lives, online communities have become an important platform for collaborative learning and education. To connect users with varying knowledge levels and increase the net learning throughput, these communities often follow a question-answer based approach. Understanding what drives attention to help-seeking questions can reduce the amount of questions that go unnoticed or remain unanswered by the community. In this paper we discuss an important feature that affects the activity of the community, namely the community norms. We present a machine learning based trigger-driven feedback model that functions by (i) differentiating between help-seeking questions and follow-up posts - i.e. posts that are part of an ongoing discussion, and (ii) a dynamic intervention scheme to help improve question formulation. Our findings show that adhering to the community norms significantly increases the chance of eliciting a response.
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    Frame instance extraction and clustering for default knowledge building
    (CEUR-WS, 2017) Shah, A.; Basile, V.; Cabrio, E.; Kamath S․, S.S.
    Obtaining and representing common-sense knowledge, useful in a robotics scenario for planning and making inference about the robots' surroundings, is a challenging problem, because such knowledge is typically found in unstructured repositories such as text corpora or small handmade resources. The work described in this paper presents a methodology for automatically creating a default knowledge base about real-world objects for the robotics domain. The proposed method relies on clustering frame instances extracted from natural language text as a way of distilling default knowledge. We collect and parse a natural language corpus using the Web as a source, then perform an agglomerative clustering of frame instances according to an appropriately defined similarity measure, and finally extract prototypical frame instances from each cluster and publish them in LOD-complaint format to promote reuse and interoperability.