Interface adaptation allows code written for one interface to be used with a software component with another interface. When multiple adapters are chained together to make certain adaptations possible, we need a way to analyze how well the adaptation is done in case there are more than one chains that can be used. We introduce an approach to precisely analyzing the loss in an interface adapter chain using a simple form of abstract interpretation.
@cite_6 describes an ad hoc scheme for analyzing the loss in interface adapter chains. The scheme is based on boolean matrixes which specify the methods required in a source interface to implement a method in a target interface. A mapping product is defined on these matrixes which computes the loss incurred when interface adapters are chained. The mathematical model they use is not rigorously constructed, however. They also only consider the adaptation of methods as a whole and do not handle the case where methods could handle certain arguments but not others.
@cite_6
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In this paper, we consider boolean network tomography to identify link failures in a network. In boolean network tomography, the relationship between end-to-end measurements and link states are represented with a system of boolean equations, and failure links are identified by solving the equations. In order to establish measurement paths efficiently, we propose an adaptive boolean network tomography scheme, where measurement paths are established sequentially according to a candidate set of failure links. Here, to derive the candidate set, we extend CBP (Combinatorial Basis Pursuit), a representative decoding algorithm in Combinatorial Group Testing, and utilize its property that it can identify failure links without false negative errors. We evaluate the performance of the proposed scheme in terms of the number of measurement paths and compare it with a non-adaptive boolean network tomography scheme. Furthermore, we propose mobility-assisted boolean network tomography, which can improve the ambiguity problem in boolean network tomography. We propose a parallel-data-free voice-conversion (VC) method that can learn a mapping from source to target speech without relying on parallel data. The proposed method is general purpose, high quality, and parallel-data free and works without any extra data, modules, or alignment procedure. It also avoids over-smoothing, which occurs in many conventional statistical model-based VC methods. Our method, called CycleGAN-VC, uses a cycle-consistent adversarial network (CycleGAN) with gated convolutional neural networks (CNNs) and an identity-mapping loss. A CycleGAN learns forward and inverse mappings simultaneously using adversarial and cycle-consistency losses. This makes it possible to find an optimal pseudo pair from unpaired data. Furthermore, the adversarial loss contributes to reducing over-smoothing of the converted feature sequence. We configure a CycleGAN with gated CNNs and train it with an identity-mapping loss. This allows the mapping function to capture sequential and hierarchical structures while preserving linguistic information. We evaluated our method on a parallel-data-free VC task. An objective evaluation showed that the converted feature sequence was near natural in terms of global variance and modulation spectra. A subjective evaluation showed that the quality of the converted speech was comparable to that obtained with a Gaussian mixture model-based method under advantageous conditions with parallel and twice the amount of data. A key feature of ubiquitous computing is service continuity which allows a user to transparently continue his task regardless of his movement. For service continuity, the underlying system needs to not only discover a service satisfying a user's request, but also provide an interface differences resolution scheme if the interface of the service found is not the same as that of the service requested. For resolving interface mismatches, one of solutions is to use an interface adapter. The most serious problem in the interface adapter-based approach is the overhead of adapter generation. There are many research efforts about adapter generation load reduction and this paper focuses on an adapter chaining scheme to reduce the number of necessary adapters among different service interfaces. We propose a construction-time adaptation loss evaluation scheme and an adapter chain construction algorithm, which finds an adapter chain with minimal adaptation loss. Despite providing similar functionality, multiple network services may require the use of different interfaces to access the functionality, and this problem will only become worse with the widespread deployment of ubiquitous computing environments. One way around this problem is to use interface adapters that adapt one interface into another. Chaining these adapters allows flexible interface adaptation with fewer adapters, but the loss incurred because of imperfect interface adaptation must be considered. This study outlines a matrix-based mathematical basis for analysing the chaining of lossy interface adapters. The authors also show that the problem of finding an optimal interface adapter chain is NP-complete with a reduction from 3SAT.