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This paper presents an algorithm for reading digital video clocks reliably and quickly. Reading digital clocks from videos is difficult due to the challenges such as color variety, font diversity, noise, and low resolution. The proposed algorithm overcomes these challenges by using the novel methods derived from the domain knowledge. This algorithm first localizes the digits of a digital video clock and then recognizes the digits representing the time of digital video clock. It is a robust three-step algorithm. The first step is an efficient procedure that directly identifies the region of the second digit at a very low computational cost, which replaces the traditional tedious image processing procedure of identifying the second digit region. The success of the first step mainly leverages on the novel second-pixel periodicity method. Using the acquired second digit region as input, the second step is a clock digit localization procedure. It first acquires the colors of the digits of the digital video clock and performs the color conversion. Then it localizes the remaining clock digits. Finally, the last step is a clock digit recognition procedure. It first employs an enhanced digit-sequence recognition method to robustly recognize the digits on the second; it then adopts a deep learning procedure to recognize the remaining digits. The proposed algorithm is tested on a prepared benchmark of 1000 videos that is publicly available and the experimental results show that it can read digital video clocks with a 100% accuracy at a low computational cost.

In recent years, automatic map matching has received great technical progress. However, when it comes to vague matching situations such as improper vocabulary use, there still lack reliable solutions. To handle the current gap, this paper proposes a novel automatic map matching method based on the hybrid computing framework of hidden Markov model (HMM) and conditional random field. First, the data filtering is completed by performing second-order transformation towards automatic matching conditions of HMM. Then, the data classification is completed using automatic data classification based on the conditional random field. After that, a hybrid computing framework with spatial elements and layer selection is built to generate map matching results. Finally, some simulation experiments are conducted for evaluation. For one thing, the trend of matching accuracy changes under specified conditions is basically the same as that of nonspecified conditions. The maximum difference in matching calculation values is about 3 times. However, once the vocabulary continues to increase, the difference in matching results between the two narrows to 10–20%. For the other thing, the matching accuracy of a specified state is higher than that of sending a specified state. While nonspecified fuzzy matching accuracy is about 3 times higher and nonspecified precision matching accuracy is about 50% higher.

Optical coherence tomography (OCT) with high resolution was found lucrative for coronary heart disease diagnosis and treatment such as evaluating the degree of vascular stenosis and guiding stent implantation. Lumen contour is an essential information for assessing the shape and stenosis of blood vessels. The automatic and accurate extraction of lumen contour can significantly reduce the burden of doctors and improve the efficiency of diagnosis and treatment of coronary heart disease. The existing contour segmentation methods have poor performance in extracting the contour of the image with a missing blood vessel wall. Therefore, this paper proposes a two-stage contour extraction method based on deep learning. First, the method uses the basic contour extraction network to complete the feature image extraction and coarse contour segmentation, then inputs the feature image into the attentional class feature (ACF) module, and uses the coarse segmentation results to guide the generation of the new feature map for the refined contour segmentation. Finally, the fully connected conditional random field (CRF) module is used for post-processing yielding a more refined and smooth segmentation boundary. The experiments show that compared with the other methods, the lumen contour segmentation method proposed in this paper can adequately resolve the problem of partial vessel wall absence in OCT images and improve the accuracy of lumen contour extraction.

In this era, news is not only generated continuously with high speed but also growing in its amount by different web sources like talent hunt, news agencies, and so on. To predict the exact class of news depending on its topic, GepH (Grouped entity predictor for Hindi) is proposed using entity extraction and grouping. Entity extraction is popular for English corpus. Hindi is a national language due to its resource scarceness not being explored so much by researchers. More than 1,270 news are processed to apply entity extraction, clustering, and classification using the vector space model for Hindi (VSMH), Synset vector space model for Hindi (SVSMH), and grouped entity document matrix for Hindi (GEDMH). Synset-based dimension reduction techniques are used to get improved accuracy. Evaluation of HAC using three matrices shows the best performance of GEDMH for varied datasets. Thus labelled corpus obtained after applying HAC (Hierarchical agglomerative clustering) to GEDMH is used as a training dataset and predictions are done using random forest and Naïve Bayes. The Naïve Bayes classifier implemented using the proposed GEDMH performs the best. GepH shows 0.8 purity, 0.4 entropy, and 0.3 as error rate for 1,273 Hindi news.

The influence of genetic variations on diseases or cellular processes is the main focus of many investigations, and results of biomedical studies are often only accessible through scientific publications. Automatic extraction of this information requires recognition of the gene names and the accompanying allelic variant information. In a previous work, the OSIRIS system for the detection of allelic variation in text based on a query expansion approach was communicated. Challenges associated with this system are the relatively low recall for variation mentions and gene name recognition. To tackle this challenge, we integrate the ProMiner system developed for the recognition and normalization of gene and protein names with a conditional random field (CRF)-based recognition of variation terms in biomedical text. Following the newly developed normalization of variation entities, we can link textual entities to Single Nucleotide Polymorphism database (dbSNP) entries. The performance of this novel approach is evaluated, and improved results in comparison to state-of-the-art systems are reported.

This paper reports on the development of a Named Entity Recognition (NER) system in Bengali by combining the outputs of the three classifiers, namely Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). A part of the Bengali news corpus developed from the web-archive of a leading Bengali newspaper has been manually annotated with the four major named entity (NE) tags, namely Person name, Location name, Organization name and Miscellaneous name. We have also used the annotated corpus of the IJCNLP-08 NER Shared Task for South and South East Asian Languages (NERSSEAL). An appropriate tag conversion routine has been developed in order to convert the fine-grained NE tagged NERSSEAL corpus to the form, tagged with the coarse-grained NE tagset of four tags. The system makes use of the different contextual information of the words along with the variety of orthographic word-level features that are helpful in predicting the four NE classes. In this work, we have considered language independent features as well as the language dependent features extracted from the various language specific resources. Lexical context patterns, which are generated from an unlabeled corpus of 10 million wordforms using an active learning technique, have been used for developing a baseline NER system as well as the features of the classifiers in order to improve their performance. A number of post-processing techniques have been used in order to improve the performance of the classifiers. Finally, the classifiers are combined together into a multiengine NER system using three weighted voting techniques. The system has been trained and tested with the datasets of 272K wordforms and 35K wordforms, respectively. Experimental results show the effectiveness of the proposed approach with the overall average Recall, Precision and F-Score values of 93.81%, 92.18% and 92.98%, respectively. The proposed system also outperforms the three other existing Bengali NER systems. The language independent versions of the ME, CRF and SVM based NER systems have been evaluated for the four other popular Indian languages, namely Hindi, Telugu, Oriya and Urdu, with the datasets obtained from the NERSSEAL shared task data. The SVM based system yielded the best performance with the F-Score values of 76.35%, 72.65%, 69.34% and 65.66% for Hindi, Telugu, Oriya and Urdu, respectively.

In silico prediction of unknown drug-target interactions (DTIs) has become a popular tool for drug repositioning and drug development. A key challenge in DTI prediction lies in integrating multiple types of data for accurate DTI prediction. Although recent studies have demonstrated that genomic, chemical and pharmacological data can provide reliable information for DTI prediction, it remains unclear whether functional information on proteins can also contribute to this task. Little work has been developed to combine such information with other data to identify new interactions between drugs and targets. In this paper, we introduce functional data into DTI prediction and construct biological space for targets using the functional similarity measure. We present a probabilistic graphical model, called conditional random field (CRF), to systematically integrate genomic, chemical, functional and pharmacological data plus the topology of DTI networks into a unified framework to predict missing DTIs. Tests on two benchmark datasets show that our method can achieve excellent prediction performance with the area under the precision-recall curve (AUPR) up to 94.9. These results demonstrate that our CRF model can successfully exploit heterogeneous data to capture the latent correlations of DTIs, and thus will be practically useful for drug repositioning. Supplementary Material is available at http://iiis.tsinghua.edu.cn/~compbio/papers/psb2014/psb2014_sm.pdf.

Named entity recognition (NER) on complex organizations (ORG), locations (LOC) and person names (PER) is a difficult issue in natural language processing. We propose a novel 6 tags method which combines boundary and Part of Speech (6-tag-boundary-PoS) to improve the recognition rate for NER based on conditional random field. Large-scale open tests on real corpus indicate that the 6-tag-boundary-PoS feature can effectively shorten the training time, and the F1 value, precision (P), recall (R) of ORG and LOC are 98.25%, 97.82%, 98.68% and 91.81%, 89.76%, 93.97%, respectively.