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: The practicality of a video surveillance system is adversely limited by the amount of queries that can be placed on human resources and their vigilance in response. To transcend this limitation, a major effort under way is to include software that (fully or at least semi) automatically mines video footage, reducing the burden imposed to the system. Herein, we propose a semi-supervised incremental learning framework for evolving visual streams in order to develop a robust and flexible track classification system. Our proposed method learns from consecutive batches by updating an ensemble in each time. It tries to strike a balance between performance of the system and amount of data which needs to be labelled. As no restriction is considered, the system can address many practical problems in an evolving multi-camera scenario, such as concept drift, class evolution and various length of video streams which have not been addressed before. Experiments were performed on synthetic as well as real-world visual data in non-stationary environments, showing high accuracy with fairly little human collaboration.

A: Active Mining of Parallel Video Streams
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Q: Eliminating the negative effect of highly non-stationary environmental noise is a long-standing research topic for speech recognition but remains an important challenge nowadays. To address this issue, traditional unsupervised signal processing methods seem to have touched the ceiling. However, data-driven based supervised approaches, particularly the ones designed with deep learning, have recently emerged as potential alternatives. In this light, we are going to comprehensively summarise the recently developed and most representative deep learning approaches to deal with the raised problem in this article, with the aim of providing guidelines for those who are going deeply into the field of environmentally robust speech recognition. To better introduce these approaches, we categorise them into singleand multi-channel techniques, each of which is specifically described at the front-end, the back-end, and the joint framework of speech recognition systems. In the meanwhile, we describe the pros and cons of these approaches as well as the relationships among them, which can probably benefit future research.

A: Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
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Q: Clinicians are expected to have up-to-date and broad knowledge of disease treatment options for a patient. Online<lb>health knowledge resources contain a wealth of information. However, because of the time investment needed to<lb>disseminate and rank pertinent information, there is a need to summarize the information in a more concise format.<lb>Our aim of the study is to provide clinicians with a concise overview of popular treatments for a given disease using<lb>information automatically computed from Medline abstracts. We analyzed the treatments of two disorders – Atrial<lb>Fibrillation and Congestive Heart Failure. We calculated the precision, recall, and f-scores of our two ranking<lb>methods to measure the accuracy of the results. For Atrial Fibrillation disorder, maximum f-score for the New<lb>Treatments weighing method is 0.611, which occurs at 60 treatments. For Congestive Heart Failure disorder,<lb>maximum f-score for the New Treatments weighing method is 0.503, which occurs at 80 treatments.

A:
Automatically extracting, ranking and visually summarizing the treatments for a disease
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