This paper describes how information foraging is useful in the implementation of new algorithms to anticipate cyber attacks. The exploration of publicly available data has been used to predict events in the socio-political domain, but the adversarial and covert behavior of actors in cyber security creates additional challenges. This paper describes a framework for Information Foraging for Algorithm Discovery (IFAD) that addresses standard data-science issues of volume and variety, by balancing human intuition with automation, and thus taking initial steps toward supporting the increasing need for rapid analysis of, and tool development for, big data. Our results demonstrate that cognitive augmentation, and information foraging in particular, is useful in the development of tools to anticipate cyber attacks using publicly available data.

This Blue Sky presentation focuses on a major shift toward a notion of “ambient intelligence” that transcends general applications targeted at the general population. The focus is on highly personalized agents that accommodate individual differences and changes over time. This notion of Extended Ambient Intelligence (EAI) concerns adaptation to a person’s preferences and experiences, as well as changing capabilities, most notably in an environment where conversational engagement is central. An important step in moving this research forward is the accommodation of different degrees of cognitive capability (including speech processing) that may vary over time for a given user—whether through improvement or through deterioration. We suggest that the application of divergence detection to speech patterns may enable adaptation to a speaker’s increasing or decreasing level of speech impairment over time. Taking an adaptive approach toward technology development in this arena may be a first step toward empowering those with special needs so that they may live with a high quality of life. It also represents an important step toward a notion of ambient intelligence that is personalized beyond what can be achieved by mass-produced, one-size-fits-all software currently in use on mobile devices.

We present methods for reducing the worst-case and typical-case complexity of a context-free parsing pipeline via hard constraints derived from finite-state pre-processing. We perform O(n) predictions to determine if each word in the input sentence may begin or end a multi-word constituent in chart cells spanning two or more words, or allow single-word constituents in chart cells spanning the word itself. These pre-processing constraints prune the search space for any chart-based parsing algorithm and significantly decrease decoding time. In many cases cell population is reduced to zero, which we term chart cell “closing.” We present methods for closing a sufficient number of chart cells to ensure provably quadratic or even linear worst-case complexity of context-free inference. In addition, we apply high precision constraints to achieve large typical-case speedups and combine both high precision and worst-case bound constraints to achieve superior performance on both short and long strings. These bounds on processing are achieved without reducing the parsing accuracy, and in some cases accuracy improves. We demonstrate that our method generalizes across multiple grammars and is complementary to other pruning techniques by presenting empirical results for both exact and approximate inference using the exhaustive CKY algorithm, the Charniak parser, and the Berkeley parser. We also report results parsing Chinese, where we achieve the best reported results for an individual model on the commonly reported data set.

Spoken responses produced by subjects during neuropsychological exams can provide diagnostic markers beyond exam performance. In particular, characteristics of the spoken language itself can discriminate between subject groups. We present results on the utility of such markers in discriminating between healthy elderly subjects and subjects with mild cognitive impairment (MCI). Given the audio and transcript of a spoken narrative recall task, a range of markers are automatically derived. These markers include speech features such as pause frequency and duration, and many linguistic complexity measures. We examine measures calculated from manually annotated time alignments (of the transcript with the audio) and syntactic parse trees, as well as the same measures calculated from automatic (forced) time alignments and automatic parses. We show statistically significant differences between clinical subject groups for a number of measures. These differences are largely preserved with automation. We then present classification results, and demonstrate a statistically significant improvement in the area under the ROC curve (AUC) when using automatic spoken language derived features in addition to the neuropsychological test scores. Our results indicate that using multiple, complementary measures can aid in automatic detection of MCI.

Abstract This paper describes DialogueView, a tool for annotating dialogues with utterance boundaries, speech repairs, speech act tags, and hierarchical discourse blocks. The tool provides three views of a dialogue: WordView, which shows the transcribed words time-aligned with the audio signal; UtteranceView, which shows the dialogue line-by-line as if it were a script for a movie; and BlockView, which shows an outline of the dialogue. The different views provide different abstractions of what is occurring in the dialogue. Abstraction helps users focus on what is important for different annotation tasks. For example, for annotating speech repairs, utterance boundaries, and overlapping and abandoned utterances, the tool provides the exact timing information. For coding speech act tags and hierarchical discourse structure, a broader context is created by hiding such low-level details, which can still be accessed if needed. We find that the different abstractions allow users to annotate dialogues more quickly without sacrificing accuracy. The tool can be configured to meet the requirements of a variety of annotation schemes.

Mixed-initiative in dialogue does not mean that either speaker is free to take the lead in the conversation at any time. Rather, who can show initiative, and when, is restricted. In this paper, we define initiative and control as two levels of dialogue phenomena, and give further evidence for our theory of restricted initiative that control belongs to the initiator of a discourse segment. We show that the initiator and the non-initiator play different roles in terms of showing initiative.