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 of the long-term goals of artificial intelligence is to build an agent that can communicate intelligently with human in natural language. Most existing work on natural language learning relies heavily on training over a pre-collected dataset with annotated labels, leading to an agent that essentially captures the statistics of the fixed external training data. As the training data is essentially a static snapshot representation of the knowledge from the annotator, the agent trained this way is limited in adaptiveness and generalization of its behavior. Moreover, this is very different from the language learning process of humans, where language is acquired during communication by taking speaking action and learning from the consequences of speaking action in an interactive manner. This paper presents an interactive setting for grounded natural language learning, where an agent learns natural language by interacting with a teacher and learning from feedback, thus learning and improving language skills while taking part in the conversation. To achieve this goal, we propose a model which incorporates both imitation and reinforcement by leveraging jointly sentence and reward feedbacks from the teacher. Experiments are conducted to validate the effectiveness of the proposed approach.
[A]: Listen, Interact and Talk: Learning to Speak via Interaction

[Q]: The use of microblogging platforms such as Twitter during crises has become widespread. More importantly, information disseminated by affected people contains useful information like reports of missing and found people, requests for urgent needs etc. For rapid crisis response, humanitarian organizations look for situational awareness information to understand and assess the severity of the crisis. In this paper, we present a novel framework (i) to generate abstractive summaries useful for situational awareness, and (ii) to capture sub-topics and present a short informative summary for each of these topics. A summary is generated using a two stage framework that first extracts a set of important tweets from the whole set of information through an Integer-linear programming (ILP) based optimization technique and then follows a word graph and concept event based abstractive summarization technique to produce the final summary. High accuracies obtained for all the tasks show the effectiveness of the proposed framework.
[A]: Summarizing Situational and Topical Information During Crises

[Q]: 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.
[A]:
R2-D2: ColoR-inspired Convolutional NeuRal Network (CNN)-based AndroiD Malware Detections