1. What are the contributions in "Newnectar: collaborative active learning for knowledge-based probabilistic activity recognition†" ?
The increasing popularity of ambient assisted living solutions is claiming adaptive and scalable tools to monitor activities of daily living.. In this work, the authors address this problem by proposing a novel hybrid approach that couples collaborative active learning with probabilistic and knowledge-based reasoning.. The rationale of their approach is that a generic, and possibly incomplete, knowledge-based model of activities can be refined to target specific individuals and environments by collaboratively acquiring feedback from inhabitants.. Specifically, the authors propose a collaborative active learning method exploiting users ’ feedback to ( i ) refine correlations among sensor events and activity types that are initially extracted from a high-level ontology, and ( ii ) mine temporal patterns of sensor events that are frequently generated by the execution of specific activities.
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2. What have the authors stated for future works in "Newnectar: collaborative active learning for knowledge-based probabilistic activity recognition†" ?
As future work, the authors plan to improve it by devising an algorithm to continuously adjust correlations and temporal patterns as the updates are received.. The potentials of homomorphic encryption, Journal of Emerging Trends in Computing and Information Sciences 2 ( 10 ) ( 2011 ) 546–552. [ 43 ]
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