Over half of cancer patients experience long-term pain management challenges.

Over half of cancer patients experience long-term pain management challenges.

The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the transferability of the knowledge gained by these models in one domain to other related domains, and the closeness of natural languages to high-level programming languages, such as C/C++, this work studies how to leverage (large) transformer-based language models in detecting software vulnerabilities and how good are these models for vulnerability detection tasks. In this regard, firstly, a systematic (cohesive) framework that details source code translation, model preparation, and inference is presented. Then, an empirical analysis is performed with software vulnerability datasets with C/C++ source codes having multiple vulnerabilities corresponding to the library function call, pointer usage, array usage, and arithmetic expression. Our empirical results demonstrate the good performance of the language models in vulnerability detection. Moreover, these language models have better performance metrics, such as F1-score, than the contemporary models, namely bidirectional long short-term memory and bidirectional gated recurrent unit. Experimenting with the language models is always challenging due to the requirement of computing resources, platforms, libraries, and dependencies. Thus, this paper also analyses the popular platforms to efficiently fine-tune these models and present recommendations while choosing the platforms.

Many contextualized word representations are now learned by intricate neural network models, such as masked neural language models (MNLMs) which are made up of huge neural network structures and trained to restore the masked text. Such representations demonstrate superhuman performance in some reading comprehension (RC) tasks which extract a proper answer in the context given a question. However, identifying the detailed knowledge trained in MNLMs is challenging owing to numerous and intermingled model parameters. This paper provides new insights and empirical analyses on commonsense knowledge included in pretrained MNLMs. First, we use a diagnostic test that evaluates whether commonsense knowledge is properly trained in MNLMs. We observe that a large proportion of commonsense knowledge is not appropriately trained in MNLMs and MNLMs do not often understand the semantic meaning of relations accurately. In addition, we find that the MNLM-based RC models are still vulnerable to semantic variations that require commonsense knowledge. Finally, we discover the fundamental reason why some knowledge is not trained. We further suggest that utilizing an external commonsense knowledge repository can be an effective solution. We exemplify the possibility to overcome the limitations of the MNLM-based RC models by enriching text with the required knowledge from an external commonsense knowledge repository in controlled experiments.

Data science, also known as data-driven decision, is an interdisciplinery field about scientific methods, process and systems to extract knowledge from data in various forms, and take descision based on this knowledge. A data scientist should not only be evaluated only on his/her knowledge on mahine learning, but he/she should also have good expertise on statistics. I will try to start from very basics of data science and then slowly move to expert level. Supervised machine learning requires training labeled data. Unsupervised machine learning doesn't required labeled data.