A Channel Theoretic Approach to Conditional Reasoning

Institute for Communicating and Collaborative SystemsChannel Theory is a recently developed mathematical model of information flow, based on ideas emanating from situation theory.Channel theory addreses a number of important properties of information flow, such as context-dependence, modularity of information, and the possibility of error.This thesis is concerned with the use of channel theory as a formal framework for various constructs relating to conditional sentences. In particular,the main concern is to obtain logics for reasoning about conditionals,generics and default properties within the channel theoretic framework

SpeakerLDA: Discovering Topics in Transcribed Multi-Speaker Audio Contents

ABSTRACT Topic models such as Latent Dirichlet Allocation (LDA

Thinking head: Towards human centred robotics

Thinking Head project is a multidisciplinary approach to building intelligent agents for human machine interaction. The Thinking Head Framework evolved out of the Thinking Head Project and it facilitates loose coupling between various components and forms the central nerve system in a multimodal perception-action system. The paper presents the overall architecture, components and the attention system. The paper then concludes with a preliminary behavioral experiment that studies the intelligibility of the audiovisual speech output produced by the Embodied Conversational Agent (ECA) that is part of the system. These results provide the baseline for future evaluations of the system as the project progresses through multiple evaluate and refine cycles

Correction to: Navigate: A study protocol for a randomised controlled trial of an online treatment decision aid for men with low-risk prostate cancer and their partners

© 2021, The Author(s). Following publication of the original article [1], we were notified of a typo in the spelling of the 10th author name, originally spelt as “Cavdon” instead of “Cavedon” (i.e., missing “e”). The original article has been corrected

Boolean versus ranked querying for biomedical systematic reviews

Background: The process of constructing a systematic review, a document that compiles the published evidence pertaining to a specified medical topic, is intensely time-consuming, often taking a team of researchers over a year, with the identification of relevant published research comprising a substantial portion of the effort. The standard paradigm for this information-seeking task is to use Boolean search; however, this leaves the user(s) the requirement of examining every returned result. Further, our experience is that effective Boolean queries for this specific task are extremely difficult to formulate and typically require multiple iterations of refinement before being finalized. Methods: We explore the effectiveness of using ranked retrieval as compared to Boolean querying for the purpose of constructing a systematic review. We conduct a series of experiments involving ranked retrieval, using queries defined methodologically, in an effort to understand the practicalities of incorporating ranked retrieval into the systematic search task. Results: Our results show that ranked retrieval by itself is not viable for this search task requiring high recall. However, we describe a refinement of the standard Boolean search process and show that ranking within a Boolean result set can improve the overall search performance by providing early indication of the quality of the results, thereby speeding up the iterative query-refinement process. Conclusions: Outcomes of experiments suggest that an interactive query-development process using a hybrid ranked and Boolean retrieval system has the potential for significant time-savings over the current search process in the systematic reviewing