Improving optimization using adaptive algorithms

Abstract

In the research projects and industrial projects severe optimization problems can be met, where the number of variables is high, there are a lot of constraints, and they are highly nonlinear and mostly discrete issues, where the running time can be calculated sometimes in weeks with the usual optimization methods on an average computer. In most cases in the logistics industry, the most robust constraint is the time. The optimizations are running on a typical office configuration, and the company accepts the suboptimal solution what the optimization method gives within the appropriate time limit. That is, why adaptivity is needed. The adaptivity of the optimization technique includes parameters of fine-tuning. On this way, the most sensitive setting can be found. In this article, some additional adaptive methods for logistic problems have been investigated to increase the effectivity, improve the solution in a strict time condition.

Identification of online harassment using ensemble fine-tuned pre-trained Bert

“…The most recent study by [5] has used simple BERT architecture without considering the importance of preprocessing steps like handling of Not a Number (NaN) values, stopword removal, PoS tagging, contractions, stemming and lemmatization, which suggests that probably their model was not trained on good data, which may have led to model over-fitting [21][22][23][24]. The researchers also did not consider the fine-tuning strategies [25][26], which supplement the model to achieve better results. In this study, all the steps mentioned above were performed and try to identify the abuse in the multilingual text as it is shown in Table 1.…”

Detecting security vulnerabilities with static analysis – A case study

“…Juliet uses the concept of a flow variant [15]. Each test case [16] is generated with one of three possible flow variants: Baseline, Control flow, and Data flow, which refer to the required analysis to reveal a flaw.…”