58,723 research outputs found
Hybrid Algorithms Based on Integer Programming for the Search of Prioritized Test Data in Software Product Lines
In Software Product Lines (SPLs) it is not possible, in general, to test all products of the family. The number of products denoted by a SPL is very high due to the combinatorial explosion of features. For this reason, some coverage criteria have been proposed which try to test at least all feature interactions without the necessity to test all products, e.g., all pairs of features (pairwise coverage). In addition, it is desirable to first test products composed by a set of priority features. This problem is known as the Prioritized Pairwise Test Data Generation Problem. In this work we propose two hybrid algorithms using Integer Programming (IP) to generate a prioritized test suite. The first one is based on an integer linear formulation and the second one is based on a integer quadratic (nonlinear) formulation. We compare these techniques with two state-of-the-art algorithms, the Parallel Prioritized Genetic Solver (PPGS) and a greedy algorithm called prioritized-ICPL. Our study reveals that our hybrid nonlinear approach is clearly the best in both, solution quality and computation time. Moreover, the nonlinear variant (the fastest one) is 27 and 42 times faster than PPGS in the two groups of instances analyzed in this work.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. Partially funded by the Spanish Ministry of Economy and Competitiveness and FEDER under contract TIN2014-57341-R, the University of Málaga, Andalucía Tech and the Spanish Network TIN2015-71841-REDT (SEBASENet)
End-to-End Photo-Sketch Generation via Fully Convolutional Representation Learning
Sketch-based face recognition is an interesting task in vision and multimedia
research, yet it is quite challenging due to the great difference between face
photos and sketches. In this paper, we propose a novel approach for
photo-sketch generation, aiming to automatically transform face photos into
detail-preserving personal sketches. Unlike the traditional models synthesizing
sketches based on a dictionary of exemplars, we develop a fully convolutional
network to learn the end-to-end photo-sketch mapping. Our approach takes whole
face photos as inputs and directly generates the corresponding sketch images
with efficient inference and learning, in which the architecture are stacked by
only convolutional kernels of very small sizes. To well capture the person
identity during the photo-sketch transformation, we define our optimization
objective in the form of joint generative-discriminative minimization. In
particular, a discriminative regularization term is incorporated into the
photo-sketch generation, enhancing the discriminability of the generated person
sketches against other individuals. Extensive experiments on several standard
benchmarks suggest that our approach outperforms other state-of-the-art methods
in both photo-sketch generation and face sketch verification.Comment: 8 pages, 6 figures. Proceeding in ACM International Conference on
Multimedia Retrieval (ICMR), 201
Zero-Shot Hashing via Transferring Supervised Knowledge
Hashing has shown its efficiency and effectiveness in facilitating
large-scale multimedia applications. Supervised knowledge e.g. semantic labels
or pair-wise relationship) associated to data is capable of significantly
improving the quality of hash codes and hash functions. However, confronted
with the rapid growth of newly-emerging concepts and multimedia data on the
Web, existing supervised hashing approaches may easily suffer from the scarcity
and validity of supervised information due to the expensive cost of manual
labelling. In this paper, we propose a novel hashing scheme, termed
\emph{zero-shot hashing} (ZSH), which compresses images of "unseen" categories
to binary codes with hash functions learned from limited training data of
"seen" categories. Specifically, we project independent data labels i.e.
0/1-form label vectors) into semantic embedding space, where semantic
relationships among all the labels can be precisely characterized and thus seen
supervised knowledge can be transferred to unseen classes. Moreover, in order
to cope with the semantic shift problem, we rotate the embedded space to more
suitably align the embedded semantics with the low-level visual feature space,
thereby alleviating the influence of semantic gap. In the meantime, to exert
positive effects on learning high-quality hash functions, we further propose to
preserve local structural property and discrete nature in binary codes.
Besides, we develop an efficient alternating algorithm to solve the ZSH model.
Extensive experiments conducted on various real-life datasets show the superior
zero-shot image retrieval performance of ZSH as compared to several
state-of-the-art hashing methods.Comment: 11 page
Constraint-Based Heuristic On-line Test Generation from Non-deterministic I/O EFSMs
We are investigating on-line model-based test generation from
non-deterministic output-observable Input/Output Extended Finite State Machine
(I/O EFSM) models of Systems Under Test (SUTs). We propose a novel
constraint-based heuristic approach (Heuristic Reactive Planning Tester (xRPT))
for on-line conformance testing non-deterministic SUTs. An indicative feature
of xRPT is the capability of making reasonable decisions for achieving the test
goals in the on-line testing process by using the results of off-line bounded
static reachability analysis based on the SUT model and test goal
specification. We present xRPT in detail and make performance comparison with
other existing search strategies and approaches on examples with varying
complexity.Comment: In Proceedings MBT 2012, arXiv:1202.582
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