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.
Example: We propose a local coherence model based on a convolutional neural network that operates over the entity grid representation of a text. The model captures long range entity transitions along with entity-specific features without loosing generalization, thanks to the power of distributed representation. We present a pairwise ranking method to train the model in an end-to-end fashion on a task and learn task-specific high level features. Our evaluation on three different coherence assessment tasks demonstrates that our model achieves state of the art results outperforming existing models by a good margin.
Example solution: A Neural Local Coherence Model
Example explanation: This statement "A Neural Local Coherence Model" is taken from the subtext "convolutional neural network" and its explanation in the passage. This is a positive example because the title belongs to the mentions in the passage

Problem: Machine Learning (ML) has found it particularly useful in malware detection. However, as the malware evolves very fast, the stability of the feature extracted from malware serves as a critical issue in malware detection. The recent success of deep learning in image recognition, natural language processing, and machine translation indicates a potential solution for stabilizing the malware detection effectiveness. In this research, we haven't extract selected any features (e.g., the control-flow of op-code, classes, methods of functions and the timing they are invoked etc.) from Android apps. We develop our own method for translating Android apps into rgb color code and transform them to a fixed-sized encoded image. After that, the encoded image is fed to convolutional neural network (CNN) for automatic feature extraction and learning, reducing the expert's intervention. Deep learning usually involves a large number of parameters that cannot be learned from only a small dataset. In this way, we currently have collected 1500k Android apps samples, have run our system over these 800k malware samples (benign and malicious samples are roughly equal-sized), and also through our back-end (60 million monthly active users and 10k new malware samples per day), we can effectively detect the malware. We believe that our methodology and the corresponding use of deep learning malware classification can overcome the weakness, and computational cost of the common static/dynamic analysis process or machine learning-based of Android malware detection approach.
Solution: R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections