Near maximum distance separable (NMDS) codes have been widely used in various fields such as communication systems, data storage, and quantum codes due to their algebraic properties and excellent error-correcting capabilities. This paper focuses on Roth-Lempel codes and establishes necessary and sufficient conditions for them to be NMDS and further completely determine its weight distributions. Besides, we illustrate the linearly inequivalence of Roth-Lempel codes and NMDS codes of elliptic-curve type when their corresponding code lengths exceed 4(q+2√q+1)/5 -1. Finally we show that some special linear codes of elliptic-curve type are not linearly equivalent to the Roth-Lempel code C by Schur product.

For the solution to the problems of being difficult to gain mass class-labeled samples in the supervised learning process and of reducing the cost of data labeling, a fast optimization of support vectors based on convex hull vector is proposed with the learning mechanism of Support Vector Machine (SVM). By means of calculating the hull vectors of the sample set, label those chosen hull vectors that are largest possible to be support vectors, and also add the unlabeled samples with high confidence coefficient of classifiers to the training sample set. The very information beneficial to the learner in the unlabeled samples set will be exploited. Hence, the thick convex hull method and the modified weighted SVM are separately directed for the nonlinear separable problem and the unbalanced training sample set. Via experimental testing on the UCI data set, the results demonstrate that the algorithm harvests SVM classifiers of higher classification accuracy and better generalization performance with fewer labeled samples, so as to cut down the labeling cost of samples for SVM training and learning.

Recent research shows that, even for vulnerability reports archived by MITRE/NIST, they usually contain incomplete information about the software's vulnerable versions, making users of under-reported vulnerable versions at risk. In this work, we address this problem by introducing a fuzzing-based method. Technically, this approach first collects the crashing trace on the reference version of the software. Then, it utilizes the trace to guide the mutation of the PoC input so that the target version could follow the trace similar to the one observed on the reference version. Under the mutated input, we argue that the target version's execution could have a higher chance of triggering the bug and demonstrating the vulnerability's existence. We implement this idea as an automated tool, named VulScope. Using 30 real-world CVEs on 470 versions of software, VulScope is demonstrated to introduce no false positives and only 7.9% false negatives while migrating PoC from one version to another. Besides, we also compare our method with two representative fuzzing tools AFL and AFLGO. We find VulScope outperforms both of these existing techniques while taking the task of PoC migration. Finally, by using VulScope, we identify 330 versions of software that MITRE/NIST fails to report as vulnerable.