University of Texas at Arlington

About: University of Texas at Arlington is a education organization based out in Arlington, Texas, United States. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 11758 authors who have published 28598 publications receiving 801626 citations. The organization is also known as: UT Arlington & University of Texas-Arlington.

Practical Combinatorial Testing: Beyond Pairwise

Abstract: With new algorithms and tools, developers can apply high-strength combinatorial testing to detect elusive failures that occur only when multiple components interact. In pairwise testing, all possible pairs of parameter values are covered by at least one test, and good tools are available to generate arrays with the value pairs. In the past few years, advances in covering-array algorithms, integrated with model checking or other testing approaches, have made it practical to extend combinatorial testing beyond pairwise tests. The US National Institute of Standards and Technology (NIST) and the University of Texas, Arlington, are now distributing freely available methods and tools for constructing large t-way combination test sets (known as covering arrays), converting covering arrays into executable tests, and automatically generating test oracles using model checking (http://csrc.nist.gov/acts). In this review, we focus on real-world problems and empirical results from applying these methods and tools.

Dynamic-Hash-Table Based Public Auditing for Secure Cloud Storage

Abstract: Cloud storage is an increasingly popular application of cloud computing, which can provide on-demand outsourcing data services for both organizations and individuals. However, users may not fully trust the cloud service providers (CSPs) in that it is difficult to determine whether the CSPs meet their legal expectations for data security. Therefore, it is critical to develop efficient auditing techniques to strengthen data owners’ trust and confidence in cloud storage. In this paper, we present a novel public auditing scheme for secure cloud storage based on dynamic hash table (DHT), which is a new two-dimensional data structure located at a third parity auditor (TPA) to record the data property information for dynamic auditing. Differing from the existing works, the proposed scheme migrates the authorized information from the CSP to the TPA, and thereby significantly reduces the computational cost and communication overhead. Meanwhile, exploiting the structural advantages of the DHT, our scheme can also achieve higher updating efficiency than the state-of-the-art schemes. In addition, we extend our scheme to support privacy preservation by combining the homomorphic authenticator based on the public key with the random masking generated by the TPA, and achieve batch auditing by employing the aggregate BLS signature technique. We formally prove the security of the proposed scheme, and evaluate the auditing performance by detailed experiments and comparisons with the existing ones. The results demonstrate that the proposed scheme can effectively achieve secure auditing for cloud storage, and outperforms the previous schemes in computation complexity, storage costs and communication overhead.

Measurement of the inelastic proton-proton cross section at √s=13 TeV with the ATLAS detector at the LHC

Abstract: This Letter presents a measurement of the inelastic proton-proton cross section using 60 μb^{-1} of pp collisions at a center-of-mass energy sqrt[s] of 13 TeV with the ATLAS detector at the LHC. Inelastic interactions are selected using rings of plastic scintillators in the forward region (2.07 10^{-6}, where M_{X} is the larger invariant mass of the two hadronic systems separated by the largest rapidity gap in the event. In this ξ range the scintillators are highly efficient. For diffractive events this corresponds to cases where at least one proton dissociates to a system with M_{X}>13 GeV. The measured cross section is compared with a range of theoretical predictions. When extrapolated to the full phase space, a cross section of 78.1±2.9 mb is measured, consistent with the inelastic cross section increasing with center-of-mass energy.

Metamodeling development for vehicle frontal impact simulation

Abstract: Response surface methods or metamodels are commonly used to approximate large computationally expensive engineering systems. Five response surface methods are studied: Stepwise Regression, Moving Least Square, Kriging, Multiquadric, and Adaptive and Interactive Modeling System. A real-world frontal impact design problem is used as an example, which is a complex, highly nonlinear, transient, dynamic, large deformation finite element model. To study the accuracy of the metamodel, the optimal Latin Hypercube Sampling method is used to distribute the sampling points uniformly over the entire design space. The Root Mean Square Error (RMSE) is used as the error indicator. Convergence rate, widely used in the arena of the finite element method for evaluating new element’s performance, was exploited in this vehicle impact example.