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prosodic focus, using a paradigm based on digit strings, in which the same material and discourse contexts can be used in different languages. We found a striking difference between languages like English and Mandarin Chinese, where prosodic focus is clearly marked in production and accurately recognized in perception, and languages like Korean, where prosodic focus is neither clearly marked in production...
Recent studies have demonstrated the potential of unsupervised feature learning for sound classification. In this paper we further explore the application of the spherical k-means algorithm for feature learning from audio signals, here in the domain of urban sound classification. Spherical k-means is a relatively simple technique that has recently been shown to be competitive with other more complex...
Methods for detection of overlapping sound events in audio involve matrix factorization approaches, often assigning separated components to event classes. We present a method that bypasses the supervised construction of class models. The method learns the components as a non-negative dictionary in a coupled matrix factorization problem, where the spectral representation and the class activity annotation...
Contexts and aspects have been distinguished as the significant factors in fabricating recommender systems. Most recommender systems aim at utilizing either non-contextual preferences or contextual preferences distinctly, while very few endeavors have been made to identify the significance of both. Hence an attempt has been made to study the influence of both, users' context dependent and context...
This paper describes a localization method for an IR-UWB (Impulse Radio Ultra Wideband) two-way ranging system developed for precise time-of-arrival measurements. The ranging system provides a time resolution of 275 ps, which allows precise indoor distance estimation with the accuracy of 4 cm. In this work, a two-way ranging algorithm has been extended into a localization algorithm without the need...
In this paper, we will examine the problem of historical storage and diagnosis of massive numbers of simultaneous streams. Such streams are common in very large sensor systems which collect many data streams simultaneously. For example, in a typical monitoring application, we may desire to determine specific abnormalities at sensor nodes or diagnose local regions of abnormal behavior. In other applications,...
Aiming at the problem of Chinese thesaurus construction, we propose a method of using HMM to extract new terms from academic literature to expand automatically entry-words for Chinese thesaurus. This method converts the new terms extraction problem to a sequence labelling problem. It uses HMM fully integrated lexical information and syntactic information of new terms, as well as local context information,...
With the proliferation of sensors, specifically those capable of providing positional estimates, it is now possible to build large sensor networks for the purpose of tracking objects within indoor environments. A key component of building such indoor tracking systems, is the ability to properly select a subset of these available sensors for the purpose of tracking. Often sensors do not afford the...
With the availability of various publicly available and personal sensors, recording and profiling of activities of daily living (ADL) is becoming a reality. The sensors are omnipresent — in smartphones, smartwatches, and smartglasses and even in the environment around us in the form of peer smartphones or even infrastructure sensors such as bluetooth low energy beacons. However, there are various...
In this work we address the problem of semantic segmentation of urban remote sensing images into land cover maps. We propose to tackle this task by learning the geographic context of classes and use it to favor or discourage certain spatial configuration of label assignments. For this reason, we learn from training data two spatial priors enforcing different key aspects of the geographical space:...
The growing affordability of smart phones and mobile devices has only added to this trend by encouraging prolonged durations of inactivity. In this paper, we present a middleware, called the Pervasive Middleware for Activity Recognition (PEMAR) that aims to increase the level of physical activity by creating a middleware for active games on mobile devices. For the PEMAR application, we present a human...
The rise of always-listening sensors integrated in energy-scarce devices such as watches and remote-controls increases the need for intelligent scalable interfaces. Contemporary sensor interfaces digitize raw sensor data to extract information with energy-intensive computations, such as FFT, which is inefficient if the end goal is to only extract selective information for classification tasks, e.g...
Karnatic Music (KM) is distinct because of the prevalence of gamaka — embellishments to musical notes in the form of frequency traversals. Another important aspect of KM is that the performance style is mostly extempore. Hence, Music Information Retrieval (MIR) tasks in the context of KM are highly challenging. This paper deals with the task of Audio Segmentation and its application to MIR challenges...
The single greatest opportunity to improve health and reduce premature death lies in personal behavior. While technology-based behavior intervention has been around for many years, the emerging smartphone and wearable sensing technology brings great promise to push health behavior change further by inferring and predicting real-time behavior occurrence and its context. In this paper, we envision how...
Feature selection has two important roles in the neuroimaging based classification. It possesses increased classification accuracy by eliminating the irrelevant features and identifying the best features for the discrimination of classes. Many approaches implemented for the feature selection in the context of neuroimaging. The development of feature selection methods is an active area of research...
Wireless indoor localization is crucial in ubiquitous computing environments. Although accurate and efficient indoor localization can be provided in dense wireless networks, most existing algorithms fail to locate a mobile user in sparse deployment networks. In order to address this issue, this paper presents a new fingerprinting localization algorithm based on cost function where received signal...
Social learning enables learners to interact with one another, which is a critical way to foster creativity. Although online social networking technologies have been widely adopted, few empirical studies have been conducted to illustrate the effect of online social interactions on creativity in the context of learning. In this research-in-progress, we attempt to fill this gap by investigating how...
This work represents an initial attempt to formalize a practical facilitation technique for collaborative sorting tasks that are used to help achieve convergence outcomes. The "Sort-your-own (SYO) Method" is a simple approach to a collaborative sorting task that (in practice) promises to be an efficient way to complete the idea organization phase of a session more quickly, and with improved...
Asynchronous or event-driven programming is now being used to develop a wide range of systems, including mobile and Web 2.0 applications, Internet-of-Things, and even distributed servers. We observe that these programs perform poorly on conventional processor architectures that are heavily optimized for the characteristics of synchronous programs. Execution characteristics of asynchronous programs...
User profiling is a technique aimed at capturing and exploiting significant characteristics of the users towards the provision of personalized services within adaptive systems such as Recommender Systems (RS). In the context of Technology enhanced Learning (TeL), from a teachers' perspective, the unique ICT competence characteristics of individuals have not been considered when providing Learning...
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