instruction:
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.
question:
We consider the problem of repeatedly solving a variant of the same dynamic programming problem in successive trials. An instance of the type of problems we consider is to find the optimal binary search tree. At the beginning of each trial, the learner probabilistically chooses a tree with the n keys at the internal nodes and the n+ 1 gaps between keys at the leaves. It is then told the frequencies of the keys and gaps and is charged by the average search cost for the chosen tree. The problem is online because the frequencies can change between trials. The goal is to develop algorithms with the property that their total average search cost (loss) in all trials is close to the total loss of the best tree chosen in hind sight for all trials. The challenge, of course, is that the algorithm has to deal with exponential number of trees. We develop a methodology for tackling such problems for a wide class of dynamic programming algorithms. Our framework allows us to extend online learning algorithms like Hedge [9] and Component Hedge [15] to a significantly wider class of combinatorial objects than was possible before.
answer:
Online Dynamic Programming


question:
This paper is motivated by the automation of neuropsychological tests involving discourse analysis in the retellings of narratives by patients with potential cognitive impairment. In this scenario the task of sentence boundary detection in speech transcripts is important as discourse analysis involves the application of Natural Language Processing tools, such as taggers and parsers, which depend on the sentence as a processing unit. Our aim in this paper is to verify which embedding induction method works best for the sentence boundary detection task, specifically whether it be those which were proposed to capture semantic, syntactic or morphological similarities.
answer:
Evaluating Word Embeddings for Sentence Boundary Detection in Speech Transcripts


question:
Collaborative security initiatives are increasingly often ad-<lb>vocated to improve timeliness and effectiveness of threat mit-<lb>igation. Among these, collaborative predictive blacklisting<lb>(CPB) aims to forecast attack sources based on alerts con-<lb>tributed by multiple organizations that might be targeted in<lb>similar ways. Alas, CPB proposals thus far have only fo-<lb>cused on improving hit counts, but overlooked the impact of<lb>collaboration on false positives and false negatives. More-<lb>over, sharing threat intelligence often prompts important pri-<lb>vacy, confidentiality, and liability issues. In this paper, we<lb>first provide a comprehensive measurement analysis of two<lb>state-of-the-art CPB systems: one that uses a trusted cen-<lb>tral party to collect alerts [Soldo et al., Infocom’10] and a<lb>peer-to-peer one relying on controlled data sharing [Freudi-<lb>ger et al., DIMVA’15], studying the impact of collaboration<lb>on both correct and incorrect predictions. Then, we present<lb>a novel privacy-friendly approach that significantly improves<lb>over previous work, achieving a better balance of true and<lb>false positive rates, while minimizing information disclosure.<lb>Finally, we present an extension that allows our system to<lb>scale to very large numbers of organizations.
answer:

Building and Measuring Privacy-Preserving Predictive Blacklists