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MACs and signature algorithms are syntactically identical but a MAC implies a shared secret key. This specification defines a set of algorithms, their URIs, and requirements for implementation. Requirements are specified over implementation, not over requirements for signature use.

Prior study of data collection process has focused on tasks with limited scope and performed intrinsic data analysis, which may not be indicative of impact on trained model performance. In this paper, we present a study of crowdsourcing methods for a user intent classification task in one of our deployed dialogue systems. Our task requires classification over 47 possible user intents and contains many intent pairs with subtle differences.

We look at the types of errors that currently exist in a state-of-the-art Abstract Meaning Representation parser, and explore the problem of how to integrate world knowledge to reduce these errors. We look at three types of knowledge from WordNet hypernyms and super senses, Wikipedia entity links, and retraining a named entity recognizer to identify concepts in AMR. The retrained entity recognizer is not perfect and cannot recognize all concepts in AMR and we examine the limitations of the named entity features using a set of oracles. The oracles show how performance increases if it can recognize different subsets of AMR concepts. These results show improvement on multiple fine-grained metrics, including a 6% increase in named entity F-score, and provide insight into the potential of world knowledge for future work in Abstract Meaning Representation parsing. Most summarization research focuses on summarizing the entire given text, but in practice readers are often interested in only one aspect of the document or conversation.

Our approach leads to significant accuracy improvements in an example dialog task. This paper describes the task definition, provided datasets, baselines and evaluation set-up for each track. We also summarize the results of the submitted systems to highlight the overall trends of the state-of-the-art technologies for the tasks. The standard task-oriented dialogue pipeline uses intent classification and slot-filling to interpret user utterances. While this approach can handle a wide range of queries, it does not extract the information needed to handle more complex queries that contain relationships between slots.

We demonstrate one potential sensemaking interface based on concordance tables, showing that we are able to detect problematic outputs and distributional shifts in minutes, despite not knowing exactly what kind of problems to look for. Parsers are often the bottleneck for data acquisition, processing text too slowly to be widely applied. One way to improve the efficiency of parsers is to construct more confident statistical models. By introducing new features and using an automatically annotated corpus we are able to double parsing speed on Wikipedia and the Wall Street Journal, and gain accuracy slightly when parsing Section 00 of the Wall Street Journal. However, this remains an open classifcation challenge for both automatic and crowdsourcing approaches. Machine Learning approaches only work in narrow domains where labeled training data is available, and non-expert annotators tend to confate IEC with EML.

If the surface representation of the signed data can change between signing and verification, then some way to standardize the changeable aspect must be used before signing and verification. For example, even for simple ASCII text there are at least three widely used line ending sequences. The here function returns a node-set containing the attribute or processing instruction node or the parent element of the text node that directly bears the XPath expression. This expression results in an error if the containing XPath expression does not appear in the same XML document against which the XPath expression is being evaluated. An important scenario would be a document requiring two enveloped signatures. Each signature must omit itself from its own digest calculations, but it is also necessary to exclude the second signature element from the digest calculations of the first signature so that adding the second signature does not break the first signature.