Hung Quoc Ngo

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Context-awareness is one of the fundamental requirements for achieving user-oriented ubiquity. In this paper, we present the design and approach to a middleware solution that expedites context-awareness in a ubiquitous computing environment. Context-Aware Middleware for Ubiquitous computing Systems (CAMUS) envisions a comprehensive middleware solution that(More)
Ubiquitous computing is viewed as a computing paradigm where minimal user intervention is necessitated emphasizing detection of environmental conditions and user behaviors in order to maximize user experience. Context-awareness plays vital role in achieving such user-centered ubiquity. In this paper, we describe the desired characteristics of a middleware(More)
In this paper we consider a human-swarm interaction scenario based on hand gestures. We study how the swarm can incrementally learn hand gestures through the interaction with a human instructor providing training gestures and correction feedback. The main contribution of the paper is a novel incremental machine learning approach that makes the robot swarm(More)
Activity recognition is becoming an important research area, and finding its way to many application domains ranging from daily life services to industrial zones. Sensing hardware and learning algorithms are two important components in activity recognition. For sensing devices, we prefer to use accelerometers due to low cost and low power requirement. For(More)
Artificial curiosity tries to maximize learning progress. We apply this concept to a physical system. Our Katana robot arm curiously plays with wooden blocks, using vision, reaching, and grasping. It is intrinsically motivated to explore its world. As a by-product, it learns how to place blocks stably, and how to stack blocks.
A reinforcement learning agent that autonomously explores its environment can utilize a curiosity drive to enable continual learning of skills, in the absence of any external rewards. We formulate curiosity-driven exploration, and eventual skill acquisition, as a selective sampling problem. Each environment setting provides the agent with a stream of(More)
We introduce a novel algorithm called Upper Confidence Weighted Learning (UCWL) for online multiclass learning from binary feedback. UCWL combines the Upper Confidence Bound (UCB) framework with the Soft Confidence Weighted (SCW) online learning scheme. UCWL achieves state of the art performance (especially on noisy and nonseparable data) with low(More)
We introduce a novel algorithm called <i>upper</i> <i>confidence</i>-<i>weighted</i> <i>learning</i> (UCWL) for online multiclass learning from binary feedback (e.g., feedback that indicates whether the prediction was right or wrong). UCWL combines the upper confidence bound (UCB) framework with the soft confidence-weighted (SCW) online learning scheme. In(More)
In-network aggregation is essential for correlated data gathering in wireless sensor networks which are resourceconstraint in terms of energy, computation and storage. In this paper, we consider the problem of building a minimum cost hierarchical architecture for correlated data gathering with innetwork aggregation, which is formulated as a min-sum(More)