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Vu Ha

dblp:49/3499 · DBLP profile ↗
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5ranked-venue papers
2as first author
0since 2021 · last 2012
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 2 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 1 first-authorSystems, architecture and hardware · 1Software engineering, systems software and programming languages · 1 · 1 first-authorDatabases, data management, data science and information retrieval · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Learning theory · 50% Trustworthy machine learning · 50%
Software engineering, system software, and programming languages
1 paper
Program verification · 100%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory
generalization bounds
0.112005
Generalization Bounds for Weighted Binary Classification with Applications to Statistical Verification · IJCAI 2005
Program verification › theorem proving
inductive theorem proving
0.012004
Feature-Based Decomposition of Inductive Proofs Applied to Real-Time Avionics Software: An Experience Report · ICSE 2004
Program verification
theorem proving
0.012004
Feature-Based Decomposition of Inductive Proofs Applied to Real-Time Avionics Software: An Experience Report · ICSE 2004
Embedded and real-time systems
real-time operating systems
0.012004
Feature-Based Decomposition of Inductive Proofs Applied to Real-Time Avionics Software: An Experience Report · ICSE 2004

Methods — techniques the papers use, named apart from their topics

model checking · 0.1PVS theorem prover · 0.1weighted binary classification · 0.1statistical learning theory · 0.1
YearPublicationVenuePosition
2012 NerdHerder: designing for physical actions in an augmented reality puzzle game
abstract
Physical interfaces have been widely adopted in digital games. In our project, we research one such type of physical interface---handheld augmented reality interfaces, and aim to create an engaging and novel play experience. Designing such games requires us to combine knowledge from both Human-computer interaction and game design domains. In this paper, we present a few research questions that we raised or encountered during the process of designing and developing the handheld AR game NerdHerder. We also present our current solutions and answers to these questions. We are going to conduct user studies to understand how handheld AR games are played in the wild. We will release the game and let mobile device users play it in their natural environments. Through the process of designing and iterating NerdHerder, we call for more research that explores how to adopt HCI principles into game design.
Sam Mendenhall, Vu Ha, Yan Xu 0011, Paul A. Tillery IV, Joshua Cohen 0001, John Sharp, Blair MacIntyre
FDG2
2005 Generalization Bounds for Weighted Binary Classification with Applications to Statistical Verification
Vu Ha, Tariq Samad
IJCAI1
2004 Feature-Based Decomposition of Inductive Proofs Applied to Real-Time Avionics Software: An Experience Report
abstract
The hardware and software in modern aircraft control systems are good candidates for verification using formal methods: they are complex, safety-critical, and challenge the capabilities of test-based verification strategies. We have previously reported on our use of model checking to verify the time partitioning property of the Deos/spl trade/ real-time operating system for embedded avionics. The size and complexity of this system have limited us to analyzing only one configuration at a time. To overcome this limit and generalize our analysis to arbitrary configurations we have turned to theorem proving. This paper describes our use of the PVS theorem prover to analyze the Deos scheduler. In addition to our inductive proof of the time partitioning invariant, we present a feature-based technique for modeling state-transition systems and formulating inductive invariants. This technique facilitates an incremental approach to theorem proving that scales well to models of increasing complexity, and has the potential to be applicable to a wide range of problems.
Vu Ha, Murali Rangarajan, Darren D. Cofer, Harald Ruess, Bruno Dutertre
ICSE1
2004 Statistical Verification of Two Non-linear Real-time UAV Controllers
abstract
We present a versatile statistical verification methodology and we illustrate different uses of this methodology on two examples of nonlinear real-time UAV controllers. The first example applies our statistical methodology to the verification of a computation time property for a software implementation of a high-performance controller as a function of controller state variable values. The second example illustrates our statistical verification methodology applied to finding verifiably safe flight envelopes for a class of maneuvers, again as a function of controller state variable values. We compare our approach to verification with other statistical techniques used for estimating execution times and controller performance. We close with candidate topics for future work.
Pam Binns, Michael Elgersma, Subhabrata Ganguli, Vu Ha, Tariq Samad
IEEE Real-Time and Embedded Technology and Applications Symposium4
2004 High-confidence control: Ensuring reliability in high-performance real-time systems
abstract
Technology transfer is an especially difficult proposition for real-time control. To facilitate it, we need to complement the “high-performance” orientation of control research with an emphasis on establishing “high confidence” in real-time implementation. Two particular problems are discussed and recent research directed at their solutions is presented. First, the use of anytime algorithms requires dynamic resource management technology that generally is not available today in real-time systems. Second, complex algorithms have unpredictable computational characteristics that, nevertheless, need to be modeled; statistical verification is suggested as a possible approach. In both cases, a synthesis of control engineering and computer science is required if effective solutions are to be devised. Simulation-based demonstrations with uninhabited aerial vehicles (UAVs) serve to illustrate the research efforts. © 2004 Wiley Periodicals, Inc.
Tariq Samad, Darren D. Cofer, Vu Ha, Pam Binns
Int. J. Intell. Syst.3