Most of the previous approaches to lyrics-to-audio alignment used a pre-developed automatic speech recognition (ASR) system that innately suffered from several difficulties to adapt the speech model to individual singers. A significant aspect missing in previous works is the self-learnability of repetitive vowel patterns in the singing voice, where the vowel part used is more consistent than the consonant part. Based on this, our system first learns a discriminative subspace of vowel sequences, based on weighted symmetric non-negative matrix factorization (WS-NMF), by taking the self-similarity of a standard acoustic feature as an input. Then, we make use of canonical time warping (CTW), derived from a recent computer vision technique, to find an optimal spatiotemporal transformation between the text and the acoustic sequences. Experiments with Korean and English data sets showed that deploying this method after a pre-developed, unsupervised, singing source separation achieved more promising results than other state-of-the-art unsupervised approaches and an existing ASR-based system.

Sometimes things work out of the box. Such occasions present an opportunity to get better. Object detection using Haar feature-based cascade classifiers is more than ax decade and a half old. OpenCV framework provides a pre-built Haar and LBP based cascade classifiers for face and eye detection which are of reasonably good quality. However, I had never measured the accuracy of these face and eye detectors.

Competition Aims to Improve Early Detection of Lung Cancer: Low-dose computed tomography (CT) scans can reduce lung cancer deaths by 20 percent, as demonstrated in National Cancer Institute (NCI) sponsored screening trials. This reduction would save more lives each year than any cancer-screening test in history. However, there are significant challenges as low-dose CT scans have a high false-positive rate, creating patient anxiety and potentially leading to costly and unnecessary diagnostic work like invasive biopsies that put patients at risk for collapsed lungs and other complications. Reducing the false positive rate is a critical step in making these scans available to more patients. Participants Will Use Machine Learning and Artificial Intelligence to Scan Lung Images: Using a data set of anonymized high-resolution lung scans provided by the Cancer Imaging Program of the National Cancer Institute, Data Science Bowl participants will develop artificial intelligence algorithms that accurately determine when lesions in the lungs are cancerous, and thereby dramatically decrease the false positive rate of current low-dose CT technology.

Phytoplankton plays an important role in marine ecosystem. It is defined as a biological factor to assess marine quality. The identification of phytoplankton species has a high potential for monitoring environmental, climate changes and for evaluating water quality. However, phytoplankton species identification is not an easy task owing to their variability and ambiguity due to thousands of micro and pico-plankton species. Therefore, the aim of this paper is to build a framework for identifying phytoplankton species and to perform a comparison on different features types and classifiers. We propose a new features type extracted from raw signals of phytoplankton species. We then analyze the performance of various classifiers on the proposed features type as well as two other features types for finding the robust one. Through experiments, it is found that Random Forest using the proposed features gives the best classification results with average accuracy up to 98.24%.

We introduce a novel approach for predicting the progression of adolescent idiopathic scoliosis from 3D spine models reconstructed from biplanar X-ray images. Recent progress in machine learning have allowed to improve classification and prognosis rates, but lack a probabilistic framework to measure uncertainty in the data. We propose a discriminative probabilistic manifold embedding where locally linear mappings transform data points from high-dimensional space to corresponding low-dimensional coordinates. A discriminant adjacency matrix is constructed to maximize the separation between progressive and non-progressive groups of patients diagnosed with scoliosis, while minimizing the distance in latent variables belonging to the same class. To predict the evolution of deformation, a baseline reconstruction is projected onto the manifold, from which a spatiotemporal regression model is built from parallel transport curves inferred from neighboring exemplars. Rate of progression is modulated from the spine flexibility and curve magnitude of the 3D spine deformation. The method was tested on 745 reconstructions from 133 subjects using longitudinal 3D reconstructions of the spine, with results demonstrating the discriminatory framework can identify between progressive and non-progressive of scoliotic patients with a classification rate of 81% and prediction differences of 2.1$^{o}$ in main curve angulation, outperforming other manifold learning methods. Our method achieved a higher prediction accuracy and improved the modeling of spatiotemporal morphological changes in highly deformed spines compared to other learning methods.

Lung cancer is one of the most common types of cancer, with nearly 225,000 new cases of the disease expected in the U.S. in 2016. Using a data set of high-resolution scans of lungs provided by the National Cancer Institute, participants will develop artificial intelligence algorithms to accurately determine when lesions in the lungs are cancerous. This will dramatically reduce the false positive rate that prevents low-dose CT scans from being widely used for lung cancer detection. Competition results have the potential to advance our understanding of how all types of cancer develop and spread in the body. They'll also free radiologists to spend more time with patients.

In this paper, a genetic algorithm-based frequency-domain feature search (GAFDS) method is proposed for the electroencephalogram (EEG) analysis of epilepsy. In this method, frequency-domain features are first searched and then combined with nonlinear features. Subsequently, these features are selected and optimized to classify EEG signals. The extracted features are analyzed experimentally. The features extracted by GAFDS show remarkable independence, and they are superior to the nonlinear features in terms of the ratio of inter-class distance and intra-class distance. Moreover, the proposed feature search method can additionally search for features of instantaneous frequency in a signal after Hilbert transformation. The classification results achieved using these features are reasonable, thus, GAFDS exhibits good extensibility. Multiple classic classifiers (i.e., $k$-nearest neighbor, linear discriminant analysis, decision tree, AdaBoost, multilayer perceptron, and Na\"ive Bayes) achieve good results by using the features generated by GAFDS method and the optimized selection. Specifically, the accuracies for the two-classification and three-classification problems may reach up to 99% and 97%, respectively. Results of several cross-validation experiments illustrate that GAFDS is effective in feature extraction for EEG classification. Therefore, the proposed feature selection and optimization model can improve classification accuracy.

Imagine a building that tells you – before it happens – that the heating is about to fail. Some companies are using machine learning to do just that. Software firm CGnal, based in Milan, Italy, recently analysed a year's worth of data from the heating and ventilation units in an Italian hospital. Sensors are now commonly built into heating, ventilation and air conditioning units, and the team had records such as temperature, humidity and electricity use, relating to appliances in operating theatres and first aid rooms as well as corridors. They trained a machine learning algorithm on data from the first half of 2015, looking for differences in the readings of similar appliances.

Statistical characteristics of network traffic have attracted a significant amount of research for automated network intrusion detection, some of which looked at applications of natural statistical laws such as Zipf's law, Benford's law and the Pareto distribution. In this paper, we present the application of Benford's law to a new network flow metric "flow size difference", which have not been studied before by other researchers, to build an unsupervised flow-based intrusion detection system (IDS). The method was inspired by our observation on a large number of TCP flow datasets where normal flows tend to follow Benford's law closely but malicious flows tend to deviate significantly from it. The proposed IDS is unsupervised, so it can be easily deployed without any training. It has two simple operational parameters with a clear semantic meaning, allowing the IDS operator to set and adapt their values intuitively to adjust the overall performance of the IDS. We tested the proposed IDS on two (one closed and one public) datasets, and proved its efficiency in terms of AUC (area under the ROC curve). Our work showed the "flow size difference" has a great potential to improve the performance of any flow-based network IDSs.

Several of the preprocessing steps necessary for indexing as discussed in Chapter 2: detecting a document's encoding (ASCII, Unicode UTF-8 etc; page 2.1.1);