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Automatic syllable stress detection is useful in assessing and diagnosing the quality of the pronunciation of second language (L2) learners in an automated way. Typically, the syllable stress depends on three prominence measures - intensity level, duration, pitch - around the sound unit with the highest sonority in the respective syllable. Stress detection is often formulated as a binary classification...
Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise...
We present ABC-MRT16—a new algorithm for objective estimation of speech intelligibility following the Modified Rhyme Test (MRT) paradigm. ABC-MRT16 is simple, effective and robust. When compared to subjective MRT data from 367 diverse conditions that include coding, noise, frame erasures, and much more, ABC-MRT16 (containing just one optimized parameter) yields a very high Pearson correlation (above...
We address the problem of determining the number of signals correlated between two high-dimensional data sets with small sample support. In this setting, conventional techniques based on canonical correlation analysis (CCA) cannot be directly applied since the canonical correlations are significantly overestimated when computed from few samples. To overcome this problem, a principal component analysis...
There are several analysis models and corresponding temporal analysis techniques for checking whether applications executed on multiprocessor systems meet their real-time constraints. However, currently there does not exist an exact end-to-end latency analysis technique for Homogeneous Synchronous Dataflow with auto-concurrency (HSDFa) models that takes the correlation between the firing durations...
The literature review shows a trend to arbitrary number of multi-field packet classification is evolved from standard 5-tuple matching to support new applications like OpenFlow switch which processes upto 15 fields [1]. However, arbitrary number of multi-field packet classification becomes a great challenge regarding to performance, memory requirement, and update cost. In this paper, a high performance...
Long Short-Term Memory (LSTM) based Recurrent Neural Networks (RNNs) are promising for cognitive intelligence applications like speech recognition, image caption and nature language processing, etc. However, the cascade dependent structure in RNN with huge amount of power inefficient operations like multiplication, memory accessing and nonlinear transformation, could not guarantee high computing speed...
We studied hemodynamics characteristics at the heart level for a lot of 15 obese patients. We investigated echocardiographic and multigated radionuclide parameters correlating with the body mass index (BMI) or left ventricular ejection fraction (LVEF). Furthermore, the linear relationship obtained by plotting end-systolic volume (ESV) against end-diastolic volume (EDV) can be more useful than the...
The efficiency of communication system is the key factor for the operational capability of naval ship. Through analyzing the evaluation index of naval fleet's communication system, a satisfactory-degree based on efficiency evaluation model is founded by applying the grey relational analysis. The steps of the model include gathering index data based on index tree, constituting the reference sequence...
The working principle of intelligent English translating search engine is discussed in this paper. Then the method of Meta search that is fused with acquired results of each search is analyzed in detail. In the research of fusion results we adopt the optimized scheme which integrates weight and quality method to extract the retrieval results of component search engine, and to perform purification...
The random forest algorithm is a new classification and prediction model algorithm. So far, there is not much research on the problem of unbalanced data for random forest classification, ditto, no direct and effective method. On the basis of feature selection algorithm based on correlation measure, the integration feature selection method was helpful to increase the selection probability of classification...
The result of Chinese housing market continues to prosper or not is related to the development of China, and further it also has an impact on the world finance. Thus forecasting the house price index is very important and challenging. In this paper we propose an unsupervised learnable neuron model (DNM) by including the nonlinear interactions between excitation and inhibition on dendrites. We use...
Neurons forms interconnected networks to process information. In brain such networks are implemented for the purpose of computation or decision making. Neuron cultures on Multi Electrode Array gives advantage to study the network topology of network of neurons as it facilitates recording from population of neurons. Spike train recorded from the dish is used for analysis of the information content...
Timber Purchasing Managers' Index (PMI) has been perceived as the barometer of timber industry evolution. The weight scheme is at the core of constructing the index. In this study, an in-depth analysis on the weight scheme of Timber PMI is conducted. The empirical results reveal that the current weight does not affect the leading function of Timber PMI, but it is not the optimal weighting method....
The association rules mining process enables the end users to analyze, understand, and use the extracted knowledge in an intelligent system or to support the decision-making processes. To find valuable association rules from a large number of redundant rules, this paper proposes a deeper mining process, multi-mode and high value association rules mining (MH-ARM). This method takes into account the...
Through the analysis of the reliability and validity for historical data of the students' evaluation of teaching, it is found that teacher-centered evaluation index system is the main reason for the decrease of the reliability and validity of the evaluation results. Therefore, a new evaluation index system with the students as the subject is constructed on the basis of extensive solicit their opinions,...
The stability matters in clinical prediction models because it makes the model to be interpretable and generalizable. It is paramount for high dimensional data, which employ sparse models with feature selection ability. We propose a new method to stabilize sparse support vector machines using intrinsic graph structure of the electronic medical records. The graph structure is exploited using the Jaccard...
Myoelectric pattern recognition (MPR) can be used for intuitive control of virtual and robotic effectors in clinical applications such as prosthetic limbs and the treatment of phantom limb pain. The conventional approach is to feed classifiers with descriptive electromyographic (EMG) features that represent the aimed movements. The complexity and consequently classification accuracy of MPR is highly...
Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing...
In this paper we present a self-avoiding walk-jump (SAWJ) algorithm for finding a maximum degree node on a large assortative graph. We offer two contributions: i) we use the theory of absorbing Markov chains to effectively approximate the required search time as a function of the number of nodes, the edge density, and the assortativity, and ii) we measure the performance of our algorithm against competing...
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