Kee-Eung Kim

Recognition of Grip-Patterns by Using Capacitive Touch Sensors

A novel and intuitive way of accessing applications of mobile devices is presented. The key idea is to use grip-pattern, which is naturally produced when a user tries to use the mobile device, as a clue to determine an application to be launched. To this end, a capacitive touch sensor system is carefully designed and installed underneath the housing of the mobile device to capture the information of the users grip-pattern. The captured data is then recognized by a minimum distance classifier and a naive Bayes classifier. The recognition test is performed to validate the feasibility of the proposed user interface system

Solving factored MDPs using non-homogeneous partitions

We present an algorithm for aggregating states in solving large MDPs (represented as factored MDPs) using search by successive refinement in the space of non-homogeneous partitions. Homogeneity is defined in terms of stochastic bisimulation and reward equivalence within blocks of a partition. Since homogeneous partitions that define equivalent reduced-state-space MDPs can have a large number of blocks, we relax the requirement of homogeneity. The algorithm constructs approximate aggregate MDPs from non-homogeneous partitions, solves the aggregate MDPs exactly, and then uses the resulting value functions as part of a heuristic in refining the current best nonhomogeneous partition. We outline the theory motivating the use of this heuristic and present empirical results. In addition to investigating more exhaustive local search methods we explore the use of techniques derived from research on discretizing continuous state spaces. Finally, we compare the results from our algorithms which search in the space of non-homogeneous partitions with exact and approximate algorithms which represent homogeneous and approximately homogeneous partitions as decision trees or algebraic decision diagrams.

Point-based value iteration for constrained POMDPs

Constrained partially observable Markov decision processes (CPOMDPs) extend the standard POMDPs by allowing the specification of constraints on some aspects of the policy in addition to the optimality objective for the value function. CPOMDPs have many practical advantages over standard POMDPs since they naturally model problems involving limited resource or multiple objectives. In this paper, we show that the optimal policies in CPOMDPs can be randomized, and present exact and approximate dynamic programming methods for computing randomized optimal policies. While the exact method requires solving a minimax quadratically constrained program (QCP) in each dynamic programming update, the approximate method utilizes the point-based value update with a linear program (LP). We show that the randomized policies are significantly better than the deterministic ones. We also demonstrate that the approximate point-based method is scalable to solve large problems.

Robust Performance Evaluation of POMDP-Based Dialogue Systems

Partially observable Markov decision processes (POMDPs) have received significant interest in research on spoken dialogue systems, due to among many benefits its ability to naturally model the dialogue strategy selection problem under unreliable automated speech recognition. However, the POMDP approaches are essentially model-based, and as a result, the dialogue strategy computed from POMDP is still subject to the correctness of the model. In this paper, we extend some of the previous MDP user models to POMDPs, and evaluate the effects of user models on the dialogue strategy computed from POMDPs. We experimentally show that the strategies computed from POMDPs perform better than those from MDPs, and the strategies computed from poor user models fail severely when tested on different user models. This paper further investigates the evaluation methods for dialogue strategies, and proposes a method based on the bias-variance analysis for reliably estimating the dialogue performance.

Variable bandwidth allocation scheme for energy efficient wireless sensor network

Increasing the lifetime of wireless sensors is essential for the proliferation of wireless sensor networks in various environments. In this paper, the relationship between bandwidth and energy consumption is exploited to increase the lifetime of the sensors. A variable bandwidth allocation scheme that uses time-frequency slot assignment is proposed to reduce the energy consumption of a collaborative sensor network which has large spatial variation in node density and event rates. To assign the time-frequency slots to the sensor network, a novel algorithm is presented, which results in significant energy savings over the conventional constant bandwidth allocation scheme.

A POMDP Approach to Optimizing P300 Speller BCI Paradigm

To achieve high performance in brain-computer interfaces (BCIs) using P300, most of the work has been focused on feature extraction and classification algorithms. Although significant progress has been made in such signal processing methods in the lower layer, the issues in the higher layer, specifically determining the stimulus schedule in order to identify the target reliably and efficiently, remain relatively unexplored. In this paper, we propose a systematic approach to compute an optimal stimulus schedule in P300 BCIs. Our approach adopts the partially observable Markov decision process, which is a model for planning in partially observable stochastic environments. We show that the thus obtained stimulus schedule achieves a significant performance improvement in terms of the success rate, bit rate, and practical bit rate through human subject experiments.

Optimizing Generative Dialog State Tracker via Cascading Gradient Descent

For robust spoken dialog management, various dialog state tracking methods have been proposed. Although discriminative models are gaining popularity due to their superior performance, generative models based on the Partially Observable Markov Decision Process model still remain attractive since they provide an integrated framework for dialog state tracking and dialog policy optimization. Although a straightforward way to fit a generative model is to independently train the component probability models, we present a gradient descent algorithm that simultaneously train all the component models. We show that the resulting tracker performs competitively with other top-performing trackers that participated in DSTC2.

Hierarchical Bayesian Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) is the problem of inferring the underlying reward function from the experts behavior data. The difficulty in IRL mainly arises in choosing the best reward function since there are typically an infinite number of reward functions that yield the given behavior data as optimal. Another difficulty comes from the noisy behavior data due to sub-optimal experts. We propose a hierarchical Bayesian framework, which subsumes most of the previous IRL algorithms as well as models the sub-optimality of the experts behavior. Using a number of experiments on a synthetic problem, we demonstrate the effectiveness of our approach including the robustness of our hierarchical Bayesian framework to the sub-optimal expert behavior data. Using a real dataset from taxi GPS traces, we additionally show that our approach predicts the driving behavior with a high accuracy.

Solving Factored MDPs with Large Action Space Using Algebraic Decision Diagrams

We describe an algorithm for solving MDPs with large state and action spaces, represented as factored MDPs with factored action spaces. Classical algorithms for solving MDPs are not effective since they require enumerating all the states and actions. As such, model minimization techniques have been proposed, and specifically, we extend the previous work on model minimization algorithm for MDPs with factored state and action spaces. Using algebraic decision diagrams, we compactly represent blocks of states and actions that can be regarded equivalent. We describe the model minimization algorithm that uses algebraic decision diagrams, and show that this new algorithm can handle MDPs with millions of states and actions.

Multi-view Automatic Lip-Reading Using Neural Network

It is well known that automatic lip-reading (ALR), also known as visual speech recognition (VSR), enhances the performance of speech recognition in a noisy environment and also has applications itself. However, ALR is a challenging task due to various lip shapes and ambiguity of visemes (the basic unit of visual speech information). In this paper, we tackle ALR as a classification task using end-to-end neural network based on convolutional neural network and long short-term memory architecture. We conduct single, cross, and multi-view experiments in speaker independent setting with various network configuration to integrate the multi-view data. We achieve 77.9%, 83.8%, and 78.6% classification accuracies in average on single, cross, and multi-view respectively. This result is better than the best score (76%) of preliminary single-view results given by ACCV 2016 workshop on multi-view lip-reading/audio-visual challenges. It also shows that additional view information helps to improve the performance of ALR with neural network architecture.