Random testing (RT) is a fundamental software testing technique. Motivated by the rationale that neighbouring test cases tend to cause similar execution behaviours, adaptive random testing (ART) was proposed as an enh...
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ISBN:
(纸本)9780769533124
Random testing (RT) is a fundamental software testing technique. Motivated by the rationale that neighbouring test cases tend to cause similar execution behaviours, adaptive random testing (ART) was proposed as an enhancement of RT, which enforces random test cases evenly spread over the input domain. ART has always been compared with RT from the perspective of the failure-detection capability. Previous studies have shown that ART can use fewer test cases to detect the first software failure than RT. In this paper, we aim to compare ART and RT from the perspective of program-based coverage. Our experimental results show that given the same number of test cases, ART normally has a higher percentage of coverage than RT. In conclusion, ART outperforms RT not only in terms of the failure-detection capability, but also in terms of the thoroughness of program-based coverage. Therefore, ART delivers a higher confidence of the software under test than RT even when no failure has been revealed.
In this paper, we propose an age estimation system on the AIBO. AIBO is an entertainment robot produced by SONY co., Ltd.. AIBO has many sensors to get information around itself and moves according to its instinct. Th...
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In this paper, we propose an age estimation system on the AIBO. AIBO is an entertainment robot produced by SONY co., Ltd.. AIBO has many sensors to get information around itself and moves according to its instinct. This autonomous action is considerably natural. However, it is inadequate to communicate with people. If AIBO can estimate the human age from a face image, it becomes more excellent entertainment robot. Then, we propose the age estimation method on the AIBO by using face image. In this paper, the apparent age feature is extracted by the fast Fourier transform (FFT), and it is selected by the GA. Moreover, the age is estimated by the 1-dimensional SOM. In order to show the effectiveness of the proposed method, we show the simulation examples.
Emerging 64 bitOSpsilas supply a huge amount of memory address space that is essential for new applications using very large data. It is expected that the memory in connected nodes can be used to store swapped pages e...
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Emerging 64 bitOSpsilas supply a huge amount of memory address space that is essential for new applications using very large data. It is expected that the memory in connected nodes can be used to store swapped pages efficiently, especially in a dedicated cluster which has a high-speed network such as 10 GbE and Infiniband. In this paper, we propose the distributed large memory system (DLM), which provides very large virtual memory by using remote memory distributed over the nodes in a cluster. The performance of DLM programs using remote memory is compared to ordinary programs using local memory. The results of STREAM, NPB and Himeno benchmarks show that the DLM achieves better performance than other remote paging schemes using a block swap device to access remote memory. In addition to performance, DLM offers the advantages of easy availability and high portability, because it is a user-level software without the need for special hardware. To obtain high performance, the DLM can tune its parameters independently from kernel swap parameters. We also found that DLMpsilas independence of kernel swapping provides more stable behavior.
The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. In this study, we develop a Web-based bioinformatics software ...
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The rapid growth of the biomedical literature and genomic information presents a major challenge for determining the functional relationships among genes. In this study, we develop a Web-based bioinformatics software environment called FAUN or feature annotation using nonnegative matrix factorization (NMF) to facilitate both the discovery and classification of functional relationships among genes. Both the computational complexity and parameterization of NMF for processing gene sets are discussed. We tested FAUN on three manually constructed gene document collections, and then used it to analyze several microarray-derived gene sets obtained from studies of the developing cerebellum in normal and mutant mice. FAUN provides utilities for collaborative knowledge discovery and identification of new gene relationships from text streams and repositories (e.g., MEDLINE). It is particularly useful for the validation and analysis of gene associations suggested by microarray experimentation.
This paper presents an approach to synthesis of stylistically and syntactically controlled generator of the microprocessor test *** dependence of test length of the confidence level and the probabilities repartition o...
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This paper presents an approach to synthesis of stylistically and syntactically controlled generator of the microprocessor test *** dependence of test length of the confidence level and the probabilities repartition of coverage metric is *** procedure of synthesis of a test program generator is developed,represented by a stochastic grammar for a known *** performed test experiments clearly demonstrate the advantages of the proposed syntactic approach in comparison with other competitive methods of generation stimuli for verification of microprocessors
A software based hybrid test vector compression technique for testing system-on-chip integrated circuits using an embedded processor core was previously discussed by the authors. In this approach, a software program i...
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A software based hybrid test vector compression technique for testing system-on-chip integrated circuits using an embedded processor core was previously discussed by the authors. In this approach, a software program is loaded into the on-chip processor memory along with the compressed test data sets. To minimize on-chip storage besides testing time, the test data volume is first reduced by compaction in a hybrid manner before downloading into the processor. The proposed method utilizes a set of adaptive coding techniques for realizing lossless compression. The compaction program need not be loaded into the embedded processor, as only the decompression of test data is required for the automatic test equipment. The developed scheme necessitates minimal hardware overhead, while the on-chip embedded processor can be reused for normal operation on completion of testing. As an extension of this prior work, this paper reports further results on studies of the problem based on the use of Limpel-Ziv-Walsh coding besides Burrows-Wheeler transformation and demonstrates the feasibility of the suggested methodology with simulation results on ISCAS 85 combinational and ISCAS 89 full-scan sequential benchmark circuits.
In today's transnational admission environment, evaluating applicant qualifications is becoming increasingly challenging. While standardized tests can be helpful, studies have shown that they are rather noisy pred...
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ISBN:
(纸本)9781424410835
In today's transnational admission environment, evaluating applicant qualifications is becoming increasingly challenging. While standardized tests can be helpful, studies have shown that they are rather noisy predictors of performance. Predicting educational outcome is a viable alternative in such heterogeneous environments. Performance prediction models can be built by applying data mining techniques to enrollment data. In this paper we present an approach to using Bayesian networks to predict graduating cumulative Grade Point Average based on applicant background at the time of admission. While such prediction models can be helpful, their recommendations may not be followed by departmental faculty members making admission decisions if they are presented as black boxes. We thus present a novel approach to deriving a case-based retrieval mechanism from the Bayesian network prediction model in such a way that the similarity measure used by the case-based system is consistent with the predictive model. The case-based component retrieves the past student most similar to the applicant being evaluated. The Bayesian network model is evaluated using stratified ten-fold cross validation.
A new multisignature scheme using re-encryption technique based on the RSA algorithm is suggested what enhance version of Okamoto scheme. The suggested scheme results bit expansion in block length of the multisignatur...
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In recent artificial intelligence research, one of the fundamental problems is to handle uncertainty in symbolic data. To handle such uncertainty, one promising way is to construct a probabilistic model that represent...
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In recent artificial intelligence research, one of the fundamental problems is to handle uncertainty in symbolic data. To handle such uncertainty, one promising way is to construct a probabilistic model that represents the data well, and then to make probabilistic inferences based on the model. Furthermore, as the data get more complicated than traditional data matrices, like sequence data or relational databases, the importance of the expressivity of the modeling language has been increased. From such a background, there have been proposed plenty of formalisms that attempt to integrate first-order logic and probability. In this paper, we present a probabilistic logic programming system called PRISM, which is expected as an efficient tool for probabilistic modeling which features declarative semantics, high expressivity originated from logic programs, and built-in fast routines for various probabilistic inferences. We describe PRISM as an implemented tool with program examples, its functionalities, and the result of a benchmark evaluation of computing performance.
Identity authentication and key agreement schemes play significant role in ad hoc networks. In this study, a new identity authentication scheme relies on the threshold group signature is introduced. Compared with the ...
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