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Con2Mix (Contrastive Double Mixup) is a new semi-supervised learning methodology that innovates a triplet mixup data augmentation approach for finding code vulnerabilities in imbalanced, tabular security data sets. Tabular data sets in cybersecurity domains are widely known to pose challenges for machine learning because of their heavily imbalanced data (e.g., a small number of labeled attack samples buried in a sea of mostly benign, unlabeled data). Semi-supervised learning leverages a small subset of labeled data and a large subset of unlabeled data to train a learning model. While semi-supervised methods have been well studied in image and language domains, in security domains they remain underutilized, especially on tabular security data sets which pose especially difficult contextual information loss and balance challenges for machine learning. Experiments applying Con2Mix to collected security data sets show promise for addressing these challenges, achieving state-of-the-art performance on two evaluated data sets compared with other methods. [ABSTRACT FROM AUTHOR]

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage. [ABSTRACT FROM AUTHOR]

Background. Some special problems of neonates are related to the adverse effects of delivery such as birth trauma with high mortality and morbidity. The aim of this study was to determine the incidence of birth trauma and related risk factors in a referral hospital in Rafsanjan, a city in the south of Iran. Materials and methods. In this cross-sectional study, we reviewed medical records of all 3340 live neonates that were born between March 2004 and March 2005 at Nicknafs maternity center. Special questionnaires of study were completed and data was analyzed. Results. Among all neonates, 0.8% of them had birth trauma. The most common types of trauma were cephalohematoma 37.03%, massive hematoma 37.03%, and clavicular fracture 11.2%. The incidence of brachial plexus injury and clavicular fracture was higher in macrosomic neonates, whereas cephalhematoma, skin injury and massive hematoma were more prevalent in microsomic neonates. Neonatal birth trauma was more frequent in vaginal delivery than cesarean section (1.3% vs 0.1%, p = 0.0001). However, the incidence of birh trauma in instrumental delivery and normal vaginal delivery were similar (2.8% vs 1.3%, p = 0.303). Conclusion. The incidence of birth trauma in this region is 0.8% and birthweight and normal vaginal delivery are strong predictors for birth injuries. [ABSTRACT FROM AUTHOR]