In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[Q]: One weakness of machine-learned NLP models is that they typically perform poorly on out-of-domain data. In this work, we study the task of identifying products being bought and sold in online cybercrime forums, which exhibits particularly challenging cross-domain effects. We formulate a task that represents a hybrid of slot-filling information extraction and named entity recognition and annotate data from four different forums. Each of these forums constitutes its own “fine-grained domain” in that the forums cover different market sectors with different properties, even though all forums are in the broad domain of cybercrime. We characterize these domain differences in the context of a learning-based system: supervised models see decreased accuracy when applied to new forums, and standard techniques for semi-supervised learning and domain adaptation have limited effectiveness on this data, which suggests the need to improve these techniques. We release a dataset of 1,938 annotated posts from across the four forums.1
[A]: Identifying Products in Online Cybercrime Marketplaces: A Dataset for Fine-grained Domain Adaptation


[Q]: Background: Lung cancer was known as primary cancers and the survival rate of cancer is about 15%. Early detection of lung cancer is the leading factor in survival rate. All symptoms (features) of lung cancer do not appear until the cancer spreads to other areas. It needs an accurate early detection of lung cancer, for increasing the survival rate. For accurate detection, it need characterizes efficient features and delete redundancy features among all features.Feature selection is the problem of selecting informative features among all features. Materialsand Methods: Lung cancer database consist of 32 patient records with 57 features. This database collected by Hong and Youngand indexed in the University of California Irvine repository. Experimental contents include the extracted from the clinical data and X-ray data, etc. The data described 3 types of pathological lung cancers and all features are taking an integer value 0-3. In our study, new method is proposed for identify efficient features of lung cancer. It is based on Hyper-Heuristic. Results:We obtained an accuracy of 80.63% using reduced 11 feature set.The proposed method compare to the accuracy of 5 machine learning feature selections.The accuracy of these 5 methods are 60.94, 57.81, 68.75, 60.94 and 68.75. Conclusions: The proposed method has better performance with the highest level of accuracy. Therefore, the proposed model is recommended for identifying an efficient symptom of Disease. These finding are very important in health research, particularly in allocation of medical resources for patients who predicted as high-risks
[A]: Hyper-Heuristic Algorithm for Finding Efficient Features in Diagnose of Lung Cancer Disease


[Q]: Concept Trees are a type of database that can organise arbitrary textual information using a very simple rule. Each tree tries to represent a single cohesive concept and the trees can link with each other for navigation and semantic purposes. The trees are therefore a type of semantic network and would benefit from having a consistent level of context for each of the nodes. The Concept Tree nodes have a mathematical basis allowing for a consistent build process. These would represent nouns or verbs in a text sentence, for example. New to the design can then be lists of descriptive elements for each of the nodes. The descriptors can also be weighted, but do not have to follow the strict counting rule of the tree nodes. With the new descriptive layers, a much richer type of knowledge can be achieved and still reasoned over automatically. The linking structure of the licas network is very relevant to building the concept trees now and forms the basis for their construction. The concept tree symbolic neural network relation is also extended further.
[A]:
Adding Context to Concept Trees