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Collecting, monitoring, and analyzing data automatically by well instrumented systems is frequently motivated by human decision-making. However, the same need occurs when system software decisions are to be justified. Compiler optimization or storage management requires several decisions which result in more or less resource consumption, be it energy, memory, or runtime. A magnitude of system data...
MEDLINE®, the flagship database of the U.S. National Library of Medicine, is a critical source of information for biomedical research and clinical medicine. The automated extraction of bibliographic data, such as article titles, author names, abstracts, and references, is essential to the affordable creation of this citation database. References, typically appearing at the end of journal articles,...
We propose an automatic method of extracting bibliographies for academic articles scanned with OCR markup. The method uses conditional random fields (CRF) for labeling serially OCR-ed text lines on an article's title page as appropriate names for bibliographic elements. Although we achieved excellent extraction accuracies for some Japanese academic journals, we needed a substantial amount of training...
Music can be viewed as a sequence of sound events. However, most of current approaches to genre classification either ignore temporal information or only capture local structures within the music under analysis. In this paper, we propose the use of a song tokenization method (which transforms the music into a sequence of units) in conjunction with a data mining technique for investigating the long-term...
In this paper, we present our experiments on the selection of basic phonetic units for the Vietnamese large vocabulary continuous speech recognition (LVCSR). Two acoustic models were compared. The first model has just used vowels or monophthongs as phonemes while the second one, which was proposed in this paper, has explored the use of diphthongs and triphthongs as phonemes as well. The two models...
This paper presents the chunker for Tamil using Machine learning techniques. Chunking is the task of identifying and segmenting the text into syntactically correlated word groups. The chunking is done by the machine learning techniques, where the linguistical knowledge is automatically extracted from the annotated corpus. We have developed our own tagset for annotating the corpus, which is used for...
We present CCRFs (cascaded conditional random fields): a cascaded approach to scale conditional random fields (CRFs) for Chinese POS tagging (labeling). General CRFs worked well on POS tagging, but met difficulty when dealing with a large training dataset and tag set because of high computation cost for training. CCRFs organize all tags in a hierarchy and run CRFs on each node of the hierarchy. In...
Human action recognition is a significant task in automatic understanding systems for video surveillance. Probabilistic Latent Semantic Analysis (PLSA) model has been used to learn and recognize human actions in videos. Specifically, PLSA employs the expectation maximization (EM) algorithm for parameter estimation during the training. The EM algorithm is an iterative estimation scheme that is guaranteed...
Event prediction in event stream is an important problem in temporal data mining. However, existing event prediction algorithms are based on string prediction in which a character represents an event or an event type, do not take into account event sequence semantic and can not predict for infrequent event sequences. In this paper, an event prediction algorithm based on event sequence semantic called...
The nondestructive optical coherence tomography measurement can show the shell-nucleus features, quantify the nacre thickness and thus has the potential to identify or grade the pearls [1]. However, the automated thickness measurement of nacreous layer based on OCT has not been reported. In this article, an automated approach is first time proposed to measure the thickness of nacreous layer using...
Transcription of music is the process of generating a symbolic representation such as a score sheet or a MIDI file from an audio recording of a piece of music. A statistical machine learning approach for detecting note onsets in polyphonic piano music is presented. An area from the spectrogram of the sound is concatenated into one feature vector. A cascade of boosted classifiers is used for dimensionality...
In this paper, we provide detailed insight on properties of errors generated by a stochastic morphosyntactic tagger assigning multext-East morphosyntactic descriptions to Croatian texts. Tagging the Croatia Weekly newspaper corpus by the CroTag tagger in stochastic mode revealed that approximately 85 percent of all tagging errors occur on nouns, adjectives, pronouns and verbs. Moreover, approximately...
In this paper, we analyze complex gaze tracking data in a collaborative task and apply machine learning models to automatically predict skill-level differences between participants. Specifically, we present findings that address the two primary challenges for this prediction task: (1) extracting meaningful features from the gaze information, and (2) casting the prediction task as a machine learning...
This paper proposes a 3D view-invariant human action recognition method based on Hidden Markov Models. The natures of the actions, as well as the characteristics of the actors and different performance styles have been successfully recognized. The results have been compared to nearest neighbor and similarity search based recognition for further evaluation. Also the research addresses the problem of...
This paper introduces a discriminative training for language models (LMs) by leveraging phoneme similarities estimated from an acoustic model. To train an LM discriminatively, we needed the correct word sequences and the recognized results that automatic speech recognition (ASR) produced by processing the utterances of those correct word sequences. But, sufficient utterances are not always available...
This paper proposes an interpolating extension to hidden Markov models (HMMs), which allows more accurate modeling of natural sounds sources. The model is able to produce observations from distributions which are interpolated between discrete HMM states. The model uses Gaussian mixture state emission densities, and the interpolation is implemented by introducing interpolating states in which the mixture...
This paper presents a novel method for learning classes of temporal sequences using a bag-of-features approach. We define a temporal sequence as a bag of temporal features and show how this representation can be used for the recognition and segmentation of temporal events. A codebook of temporal descriptors, representing the local temporal texture, is automatically constructed from a set of sample...
In this paper, a rough set based data mining technique is extended and applied to learn behaviour patterns of moving objects in videos. An intelligent image analysis system is proposed to discover knowledge from video collections. The system is applied to synthetic image sequences containing motions of a predator and prey behaving according to a set of rules. The results show that the system is able...
Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. Recently, some SVM-based risk assessment systems have been presented in the world. They estimate the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. However, how to improve the performance of the...
This paper establishes a speaker-independent pronunciation recognition and assessment system with 673 words for mandarin Chinese under the background of a Chinese learning system framework. The recognition part is based on HTK using HMM (Hidden Markov Models) and improved in the aspect of acoustic model. Making use of the recognition results and the log-likelihood obtained from the Viterbi coding,...
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