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- Yao, Mingguang, et al.
(författare)
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Pressure-induced transformation and superhard phase in fullerenes : the effect of solvent intercalation
- 2013
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Ingår i: Applied Physics Letters. - : AIP Publishing. - 0003-6951 .- 1077-3118. ; 103:7, s. 071913-
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Tidskriftsartikel (refereegranskat)abstract
- We studied the behavior of solvated and desolvated C-60 crystals under pressure by in situ Raman spectroscopy. The pressure-induced bonding change and structural transformation of C60s are similar in the two samples, both undergoing deformation and amorphization. Nevertheless, the high pressure phases of solvated C-60 can indent diamond anvils while that of desolvated C(60)s cannot. Further experiments suggest that the solvents in the solvated C-60 act as both spacers and bridges by forming covalent bonds with neighbors in 3D network at high pressure, and thus, a fraction of fullerenes may preserve the periodic arrangement in spite of their amorphization.
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| 3. |
- Yu, Xiao, et al.
(författare)
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Improving Ranking-Oriented Defect Prediction Using a Cost-Sensitive Ranking SVM
- 2020
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Ingår i: IEEE Transactions on Reliability. - : Institute of Electrical and Electronics Engineers Inc.. - 0018-9529 .- 1558-1721. ; 69:1, s. 139-153
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Tidskriftsartikel (refereegranskat)abstract
- Context: Ranking-oriented defect prediction (RODP) ranks software modules to allocate limited testing resources to each module according to the predicted number of defects. Most RODP methods overlook that ranking a module with more defects incorrectly makes it difficult to successfully find all of the defects in the module due to fewer testing resources being allocated to the module, which results in much higher costs than incorrectly ranking the modules with fewer defects, and the numbers of defects in software modules are highly imbalanced in defective software datasets. Cost-sensitive learning is an effective technique in handling the cost issue and data imbalance problem for software defect prediction. However, the effectiveness of cost-sensitive learning has not been investigated in RODP models. Aims: In this article, we propose a cost-sensitive ranking support vector machine (SVM) (CSRankSVM) algorithm to improve the performance of RODP models. Method: CSRankSVM modifies the loss function of the ranking SVM algorithm by adding two penalty parameters to address both the cost issue and the data imbalance problem. Additionally, the loss function of the CSRankSVM is optimized using a genetic algorithm. Results: The experimental results for 11 project datasets with 41 releases show that CSRankSVM achieves 1.12%-15.68% higher average fault percentile average (FPA) values than the five existing RODP methods (i.e., decision tree regression, linear regression, Bayesian ridge regression, ranking SVM, and learning-to-rank (LTR)) and 1.08%-15.74% higher average FPA values than the four data imbalance learning methods (i.e., random undersampling and a synthetic minority oversampling technique; two data resampling methods; RankBoost, an ensemble learning method; IRSVM, a CSRankSVM method for information retrieval). Conclusion: CSRankSVM is capable of handling the cost issue and data imbalance problem in RODP methods and achieves better performance. Therefore, CSRankSVM is recommended as an effective method for RODP. © 1963-2012 IEEE.
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