hung bui - Academia.edu (original) (raw)

Papers by hung bui

Journal of Artificial Intelligence Research

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, un... more In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence propertie...

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Journal of strength and conditioning research, Jan 7, 2018

Allisse, M, Bui, HT, Léger, L, Comtois, A-S, and Leone, M. Updating the skating multistage aerobi... more Allisse, M, Bui, HT, Léger, L, Comtois, A-S, and Leone, M. Updating the skating multistage aerobic test and correction for V[Combining Dot Above]O2max prediction using a new skating economy index in elite youth ice hockey players. J Strength Cond Res XX(X): 000-000, 2018-A number of field tests, including the skating multistage aerobic test (SMAT), have been developed to predict V[Combining Dot Above]O2max in ice hockey players. The SMAT, like most field tests, assumes that participants who reach a given stage have the same oxygen uptake, which is not usually true. Thus, the objectives of this research are to update the V[Combining Dot Above]O2 values during the SMAT using a portable breath-by-breath metabolic analyzer and to propose a simple index of skating economy to improve the prediction of oxygen uptake. Twenty-six elite hockey players (age 15.8 ± 1.3 years) participated in this study. The oxygen uptake was assessed using a portable metabolic analyzer (K4b) during an on-ice ma...

International Journal of Pattern Recognition and Artificial Intelligence

In this paper, we consider the problem of tracking an object and predicting the object's futu... more In this paper, we consider the problem of tracking an object and predicting the object's future trajectory in a wide-area environment, with complex spatial layout and the use of multiple sensors/cameras. To solve this problem, there is a need for representing the dynamic and noisy data in the tracking tasks, and dealing with them at different levels of detail. We employ the Hidden Markov Models (AHMM), an extension of the well-known Hidden Markov Model (HMM) and a special type of Dynamic Probabilistic Network (DPN), as our underlying representation framework. The AHMM allows us to explicitly encode the hierarchy of connected spatial locations, making it scalable to the size of the environment being modeled. We describe an application for tracking human movement in an office-like spatial layout where the AHMM is used to track and predict the evolution of object trajectories at different levels of detail.

AI Magazine

The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was ... more The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14‚Äì15, 2013 at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statist...

Journal of the Neurological Sciences

Ataxia refers to a group of neurological disorders characterized by a lack of coordination during... more Ataxia refers to a group of neurological disorders characterized by a lack of coordination during voluntary movements. One of the most commonly used tests to assess upper-limb coordination is the Archimedes spiral test. The purpose of this research is to present an innovative computer-based Archimedes spiral test that can accurately assess coordination. Forty nine individuals (age: 25.2±7.1years) were recruited including thirteen patients diagnosed with Autosomal Recessive Spastic Ataxia of Charlevoix/Saguenay (ARSACS). Participants were instructed to trace a spiral on the touch-screen with the tip of their index finger at a self-paced velocity by following an on-screen spiral template. Mean error and maximum error as well as frequency analysis were calculated to classify healthy and ARSACS participants. While mean and maximum errors provided good results, the highest classification success rate was obtained using frequency analysis, particularly between f=1.2Hz and f=1.7Hz. Interpretation of traditional paper-drawn Archimedes spirals is limited, and several computerized versions have been reported. Herein, we present a custom-made tool that allows discrimination of measures assessing ataxia in ARSACS. This utilizes a proposed frequency method that may have the potential to track the evolution of upper-limb incoordination in patients and therefore help clinicians and scientists to better monitor their patients.

Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)., 2003

This paper presents a method for finding and classifying objects within real-world scenes by usin... more This paper presents a method for finding and classifying objects within real-world scenes by using the activity of humans interacting with these objects to infer the object's identity. Objects are labelled using evidence accumulated over time and multiple instances of human interactions. This approach is inspired by the problems and opportunities that exist in recognition tasks for intelligent homes, namely cluttered, wide-angle views coupled with significant and repeated human activity within the scene. The advantages of such an approach include the ability to detect salient objects in a cluttered scene independent of the object's physical structure, adapt to changes in the scene and resolve conflicts in labels by weight of past evidence. This initial investigation seeks to label chairs and open floor spaces by recognising activities such as walking and sitting. Findings show that the approach can locate objects with a reasonably high degree of accuracy, with occlusions of the human actor being a significant aid in reducing over-labelling.

Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004

Segmentation of individual actions from a stream of human motion is an open problem in computer v... more Segmentation of individual actions from a stream of human motion is an open problem in computer vision. This paper approaches the problem of segmenting higher-level activities into their component sub-actions using Hidden Markov Models modified to handle missing data in the observation vector. By controlling the use of missing data, action labels can be inferred from the observation vector during inferencing, thus performing segmentation and classification simultaneously. The approach is able to segment both prominent and subtle actions, even when subtle actions are grouped together. The advantage of this method over sliding windows and Viterbi state sequence interrogation is that segmentation is performed as a trainable task, and the temporal relationship between actions is encoded in the model and used as evidence for action labelling.

2011 IEEE International Symposium of Circuits and Systems (ISCAS), 2011

This paper presents a novel hardware architecture for the Goertzel algorithm used in exon predict... more This paper presents a novel hardware architecture for the Goertzel algorithm used in exon prediction. It also describes the rapid-prototyping methodology for the implementation of this system. The approach used in this work relies on a top-down philosophy which starts from a MATLAB model and ends in a hardware implementation on FPGA. The design was implemented on a PCI FPGA board installed in the PC. The algorithm was implemented in C++ and with Novakod Studio using the parallel synchronous C (psC) language. With this development environment, the design and implementation time of the FPGA module was greatly reduced. The resulting program successfully identified coding regions in DNA sequences. I.

Proceedings - International Conference on Image Processing, ICIP, 2004

In this paper we present a coherent approach using the hierarchical HMM with shared slructures to... more In this paper we present a coherent approach using the hierarchical HMM with shared slructures to extract the structural units that form the building blocks of an rducationltraining video. Rather than using hand-crafted approaches to define the structural units, we use the data from nine training videos to learn the parameters of the HHMM, alld thus naturally extract the hierarchy. We then study this hicrdrchy and examine the nature of the structure at different levels of abstraction. Since the observable is continuous, we also show how to extend the parameter learning in the HHMM to deal with continuous observations.

Lecture Notes in Computer Science, 1997

We demonstrate an intelligent personal assistant agent that has been developed to aid a busy know... more We demonstrate an intelligent personal assistant agent that has been developed to aid a busy knowledge worker in managing time com- mitments and performing tasks. The PExA agent draws on a di- verse set of AI technologies that are linked within the SPARK BDI agent framework. We focus on our agent's ability to provide as- sistance within the context of current user activities, based on its recognition of user workflows and their progress, and on its context- sensitive proactive suggestions. We have instrumented a common suite of desktop applications so that, endowed with a sophisticated workflow tracker, PExA has the ability to pervasively monitor the user's desktop activities. PExA follows and responds to the user's progress on shared tasks, and is highly user-centric in its support for user needs and its adaptivity to user working style and preferences.

Journal of Clinical Microbiology, 2003

We assessed the prospective value of PCR amplification of a repetitive sequence from Leishmania n... more We assessed the prospective value of PCR amplification of a repetitive sequence from Leishmania nuclear DNA and sequencing for the diagnosis and typing of Old World Leishmania infection in an area of nonendemicity. During this 42-month study, 29 of 168 consecutive samples were examined and classified as positive for Leishmania by direct examination and/or in vitro culture. This molecular approach showed excellent sensitivity (97%) and specificity (100%) compared to direct examination (86 and 100%, respectively) and in vitro culture (72 and 100%, respectively). Isoenzymatic and molecular typing allowed similar identification for 12 samples. Besides, PCR and subsequent sequencing of DNA products permitted the species identification of 14 samples for which parasite culture remained negative or did not allow isoenzymatic characterization, indicating the complementarity of parasitological and molecular tools.

Annals of Emergency Medicine, 1990

Learning and understanding the typical patterns in the daily activities and routines of people fr... more Learning and understanding the typical patterns in the daily activities and routines of people from low-level sensory data is an important problem in many application domains such as building smart environments, or providing intelligent assistance. Traditional approaches to this problem typically rely on supervised learning and generative models such as the hidden Markov models and its extensions. While activity data can be readily acquired from pervasive sensors, e.g. in smart environments, providing manual labels to support supervised training is often extremely expensive. In this paper, we propose a new approach based on semi-supervised training of partially hidden discriminative models such as the conditional random field (CRF) and the maximum entropy Markov model (MEMM). We show that these models allow us to incorporate both labeled and unlabeled data for learning, and at the same time, provide us with the flexibility and accuracy of the discriminative framework. Our experimental results in the video surveillance domain illustrate that these models can perform better than their generative counterpart, the partially hidden Markov model, even when a substantial amount of labels are unavailable.

We present a new general framework for online probabilistic plan recognition called the Abstract ... more We present a new general framework for online probabilistic plan recognition called the Abstract Hidden Markov Memory Model (AHM M). The new model is an extension of the existing Abstract Hidden Markov Model to allow the policy to have internal memory which can be updated in a Markov fashion. We show that the AHM M can represent a richer class of probabilistic plans, and at the same time derive an efficient algorithm for plan recognition in the AHM M based on the Rao-Blackwellised Particle Filter approximate inference method.

This work addresses the issue of Bayesian robustness in the multivariate normal model when the pr... more This work addresses the issue of Bayesian robustness in the multivariate normal model when the prior covariance matrix is not completely specified, but rather is described in terms of positive semi-definite bounds. This occurs in situations where, for example, the only prior information available is the bound on the diagonal of the covariance matrix derived from some physical constraints, and that the covariance matrix is positive semi-definite, but otherwise arbitrary. Under the conditional Gamma-minimax principle, previous work by DasGupta and Studden shows that an analytically exact solution is readily available for a special case where the bound difference is a scaled identity. The goal in this work is to consider this problem for general positive definite matrices. The contribution in this paper is a theoretical study of the geometry of the minimax problem. Extension of previous results to a more general case is shown and a practical algorithm that relies on semi-definite programming and the convexity of the minimax formulation is derived. Although the algorithm is numerically exact for up to the bivariate case, its exactness for other cases remains open. Numerical studies demonstrate the accuracy of the proposed algorithm and the robustness of the minimax solution relative to standard and recently proposed methods.

ABSTRACT We demonstrate an intelligent personal assistant agent that has been developed to aid a ... more ABSTRACT We demonstrate an intelligent personal assistant agent that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The PExA agent draws on a diverse set of AI technologies that are linked within the SPARK BDI agent framework. We focus on our agent's ability to provide assistance within the context of current user activities, based on its recognition of user workflows and their progress, and on its context-sensitive proactive suggestions. We have instrumented a common suite of desktop applications so that, endowed with a sophisticated workflow tracker, PExA has the ability to pervasively monitor the user's desktop activities. PExA follows and responds to the user's progress on shared tasks, and is highly user-centric in its support for user needs and its adaptivity to user working style and preferences.