Landmark based recognition of unvoiced word-initial stops is investigated. The relative effectiveness of acoustic-phonetic attributes versus more global spectral shape features is experimentally evaluated for four-way place classification of unvoiced, unaspirated stops. Various feature sets derived from the burst and vocalic transition regions of word initial consonants are compared via GMM bas...

Generic generation and manipulation of text is challenging and has limited success compared to recent deep generative modeling in visual domain. This paper aims at generating plausible text sentences, whose attributes are controlled by learning disentangled latent representations with designated semantics. We propose a new neural generative model which combines variational auto-encoders (VAEs) ...

The goal of our ARKTOS project is to build an intelligent knowledge-based system to classify satellite sea ice images. It involves acquiring knowledge from sea ice experts, quantifying such knowledge as computational entities, and ultimately building an intelligent classifier. In this paper we describe a two-stage knowledge engineering approach that facilitates explicit knowledge transfer, conv...

BACKGROUND Macroeconomic growth in China enables significant progress in health care and public health. It faces difficult choices regarding access, quality and affordability, while dealing with the increasing burden of chronic diseases. Policymakers are pressured to make complex decisions while implementing health strategies. This study shows how this process could be structured and reports th...

OBJECTIVE We developed the Medication Extraction and Normalization (MedXN) system to extract comprehensive medication information and normalize it to the most appropriate RxNorm concept unique identifier (RxCUI) as specifically as possible. METHODS Medication descriptions in clinical notes were decomposed into medication name and attributes, which were separately extracted using RxNorm dictio...

Several recent studies have demonstrated that people show large behavioural uniqueness. This has serious privacy implications as most individuals become increasingly re-identifiable in large datasets or can be tracked, while they are browsing the web, using only a couple of their attributes, called as their fingerprints. Often, the success of these attacks depends on explicit constraints on the...

Functional relationships between objects, called “attributes”, are of considerable importance in knowledge representation languages, including Description Logics (DLs). A study of the literature indicates that papers have made, often implicitly, different assumptions about the nature of attributes: whether they are always required to have a value, or whether they can be partial functions. The w...

Classical Cost-Benefit Analysis (CBA) is a preferred method for the evaluation of large-scale infrastructure investment projects in the private and government sector. This paper proposes the use of a method for tradespace exploration as an iterative to classical CBA to help overcome some of its shortcomings. Multi-Attribute Tradespace Exploration (MATE), a method for system decision making and ...

Location-Based services with social networks improve users’ experience and enrich people’s social live. However, location information is often inadequate due to privacy and security concerns. We seek to infer users’ ‘Current City’ on Facebook for coarse location based applications. We first extract users’ multiple explicit and implicit location attributes, and analyze correlations of these attr...

Contextual learning starts from the premise that learning cannot take place in a vacuum, but should somehow be connected with real world attributes to make sense to learners. Today, digital media tend to bring about new dimensions of context: internet connections and mobile devices enable learners to overcome restrictions of time and location, and neglect the physical boundaries and limitations...