Towards a phonetic conspectus of preaspiration

Preaspiration, i.e. [hC], is a rare feature of stop production in the world’s languages that has been recently found to occur in Sienese Italian. We present a qualitative acoustic-phonetic description of voiceless geminate stops /p: t: k:/ with preaspiration that occurred in a corpus of spontaneous Sienese Italian speech (6 speakers). We outline the different fine-grained realizations of preaspiration and discuss the findings in the context of our general knowledge of the phenomenon across languages

A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation

Coherent Integration of Databases by Abductive Logic Programming

We introduce an abductive method for a coherent integration of independent data-sources. The idea is to compute a list of data-facts that should be inserted to the amalgamated database or retracted from it in order to restore its consistency. This method is implemented by an abductive solver, called Asystem, that applies SLDNFA-resolution on a meta-theory that relates different, possibly contradicting, input databases. We also give a pure model-theoretic analysis of the possible ways to `recover' consistent data from an inconsistent database in terms of those models of the database that exhibit as minimal inconsistent information as reasonably possible. This allows us to characterize the `recovered databases' in terms of the `preferred' (i.e., most consistent) models of the theory. The outcome is an abductive-based application that is sound and complete with respect to a corresponding model-based, preferential semantics, and -- to the best of our knowledge -- is more expressive (thus more general) than any other implementation of coherent integration of databases

Dataglove Measurement of Joint Angles in Sign Language Handshapes

In sign language research, we understand little about articulatory factors involved in shaping phonemic boundaries or the amount (and articulatory nature) of acceptable phonetic variation between handshapes. To date, there exists no comprehensive analysis of handshape based on the quantitative measurement of joint angles during sign production. The purpose of our work is to develop a methodology for collecting and visualizing quantitative handshape data in an attempt to better understand how handshapes are produced at a phonetic level. In this pursuit, we seek to quantify the flexion and abduction angles of the finger joints using a commercial data glove (CyberGlove; Immersion Inc.). We present calibration procedures used to convert raw glove signals into joint angles. We then implement those procedures and evaluate their ability to accurately predict joint angle. Finally, we provide examples of how our recording techniques might inform current research questions

An Argumentation-Based Reasoner to Assist Digital Investigation and Attribution of Cyber-Attacks

We expect an increase in the frequency and severity of cyber-attacks that comes along with the need for efficient security countermeasures. The process of attributing a cyber-attack helps to construct efficient and targeted mitigating and preventive security measures. In this work, we propose an argumentation-based reasoner (ABR) as a proof-of-concept tool that can help a forensics analyst during the analysis of forensic evidence and the attribution process. Given the evidence collected from a cyber-attack, our reasoner can assist the analyst during the investigation process, by helping him/her to analyze the evidence and identify who performed the attack. Furthermore, it suggests to the analyst where to focus further analyses by giving hints of the missing evidence or new investigation paths to follow. ABR is the first automatic reasoner that can combine both technical and social evidence in the analysis of a cyber-attack, and that can also cope with incomplete and conflicting information. To illustrate how ABR can assist in the analysis and attribution of cyber-attacks we have used examples of cyber-attacks and their analyses as reported in publicly available reports and online literature. We do not mean to either agree or disagree with the analyses presented therein or reach attribution conclusions