A review of content-based video retrieval techniques for person identification

The rise of technology spurs the advancement in the surveillance field. Many commercial spaces reduced the patrol guard in favor of Closed-Circuit Television (CCTV) installation and even some countries already used surveillance drone which has greater mobility. In recent years, the CCTV Footage have also been used for crime investigation by law enforcement such as in Boston Bombing 2013 incident. However, this led us into producing huge unmanageable footage collection, the common issue of Big Data era. While there is more information to identify a potential suspect, the massive size of data needed to go over manually is a very laborious task. Therefore, some researchers proposed using Content-Based Video Retrieval (CBVR) method to enable to query a specific feature of an object or a human. Due to the limitations like visibility and quality of video footage, only certain features are selected for recognition based on Chicago Police Department guidelines. This paper presents the comprehensive reviews on CBVR techniques used for clothing, gender and ethnic recognition of the person of interest and how can it be applied in crime investigation. From the findings, the three recognition types can be combined to create a Content-Based Video Retrieval system for person identification

Antioxidant, biofilm inhibition and mutagenic activities of newly substituted fibrates

Purpose: A series of benzylidene-2-(4-bromophenoxy)-2-methyl propane hydrazides (1-10) were synthesized and assay them for their biofilm inhibition, antioxidant and mutagenic. Methods: All derivatives were prepared by condensation of various substituted benzaldehyde and acetophenones with 2-(4-bromorophenoxy)-2-methyl propane hydrazide, which was itself prepared by hydrazinolysis of ethyl-2-(4-bromophenoxy)-2-methyl propanoate and were characterized by FTIR, 1H NMR 13C NMR, mass spectrometry. They were screened for their in-vitro anti-oxidant, biofilm inhibition and mutagenicity by established methods. Results: Anti-oxidant results revealed that the electron donating group enhanced the scavenging ability of the compounds as seen in compounds 4b, 4h and 4i. In biofilm inhibition studies, all compounds were more active against Gram –ive bacterial strain when compared to gram +ive strain. The mutagenicity assay results indicate that the compound having chloro group substitution is mutagenic. Conclusion: The benzylidine compounds of 2-(4-bromophenoxy)-2-methyl hydrazide possessing electron donating substituents exhibit superior activities to the electron withdrawing group substituents

Multilingual Deep Bottle Neck Features: A Study on Language Selection and Training Techniques

Previous work has shown that training the neural networks for bottle neck feature extraction in a multilingual way can lead to improvements in word error rate and average term weighted value in a telephone key word search task. In this work we conduct a systematic study on a) which multilingual training strategy to employ, b) the effect of language selection and amount of multilingual training data used and c) how to find a suitable combination for languages. We conducted our experiment on the key word search task and the languages of the IARPA BABEL program. In a first step, we assessed the performance of a single language out of all available languages in combination with the target language. Based on these results, we then combined a multitude of languages. We also examined the influence of the amount of training data per language, as well as different techniques for combining the languages during network training. Our experiments show that data from arbitrary additional languages does not necessarily increase the performance of a system. But when combining a suitable set of languages, a significant gain in performance can be achieved

Identification and modeling of word fragments in spontaneous speech.

ABSTRACT This paper presents a novel approach to handling disfluencies, word fragments and self-interruption points in Cantonese conversational speech. We train a classifier that exploits lexical and acoustic information to automatically identify disfluencies during training of a speech recognition system on conversational speech, and then use this classifier to augment reference annotations used for acoustic model training. We experiment with approaches to modeling disfluencies in the pronunciation dictionary, and their effect on the polyphonic decision tree clustering. We achieve automatic detection of disfluencies with 88% accuracy, which leads to a reduction in character error rate of 1.9% absolute. While the high baseline error rates are due to the task we are currently working on, we demonstrate that this approach works well on the Switchboard corpus, for which the conversational nature of speech is also a major problem

CMULAB: An Open-Source Framework for Training and Deployment of Natural Language Processing Models

Effectively using Natural Language Processing (NLP) tools in under-resourced languages requires a thorough understanding of the language itself, familiarity with the latest models and training methodologies, and technical expertise to deploy these models. This could present a significant obstacle for language community members and linguists to use NLP tools. This paper introduces the CMU Linguistic Annotation Backend, an open-source framework that simplifies model deployment and continuous human-in-the-loop fine-tuning of NLP models. CMULAB enables users to leverage the power of multilingual models to quickly adapt and extend existing tools for speech recognition, OCR, translation, and syntactic analysis to new languages, even with limited training data. We describe various tools and APIs that are currently available and how developers can easily add new models/functionality to the framework. Code is available at https://github.com/neulab/cmulab along with a live demo at https://cmulab.devComment: Live demo at https://cmulab.de

The Association between Race and Survival among Pediatric Patients with Neuroblastoma in the US between 1973 and 2015

Background: Conclusive information regarding the influence of race on survival among neuroblastoma patients is limited. Our objective is to investigate the association between race and cause-specific survival in pediatric patients diagnosed with neuroblastoma in the US between 1973 and 2015.Methods: This was a retrospective cohort study using the Surveillance, Epidemiology, and End Result (SEER) database. Patients aged 17 and younger of black, white, or Asian Pacific Islander (API) race diagnosed with neuroblastoma from 1973-2015 were included (n = 2,119). The outcome variable was time from diagnosis to death. Covariates included age, gender, ethnicity, stage, tumor site, and year of diagnosis. Cox proportional hazard models were used to calculate hazard ratios and 95% confidence intervals.Results: There were no statistically significant differences in the hazard of survival for blacks (HR 0.93; 95% confidence interval (CI) 0.74-1.16) or API (HR 1.02; 95% CI 0.76-1.37) compared with whites. However, patients diagnosed between 2000-2004 (HR 0.46; 95% CI 0.36-0.59) and 2005-2015 (HR 0.33; 95% CI 0.26-0.41) had decreased hazards of death when compared to patients treated during 1973 to 1999.Conclusions: No association between race and survival time was found. However, survival improved among all patients treated during 2000-2004 and 2005-2015 compared with those treated before the year 2000, leading to a narrowing of the racial disparity based on survival.Peer reviewe

An improved approach for medical image fusion using sparse representation and Siamese convolutional neural network

Multimodal image fusion is a contemporary branch of medical imaging that aims to increase the accuracy of clinical diagnosis of the disease stage development. The fusion of different image modalities can be a viable medical imaging approach. It combines the best features to produce a composite image with higher quality than its predecessors and can significantly improve medical diagnosis. Recently, sparse representation (SR) and Siamese Convolutional Neural Network (SCNN) methods have been introduced independently for image fusion. However, some of the results from these approaches have recorded defects, such as edge blur, less visibility, and blocking artifacts. To remedy these deficiencies, in this paper, a smart blending approach based on a combination of SR and SCNN is introduced for image fusion, which comprises three steps as follows. Firstly, entire source images are fed into the classical orthogonal matching pursuit (OMP), where the SR-fused image is obtained using the max-rule that aims to improve pixel localization. Secondly, a novel scheme of SCNN-based K-SVD dictionary learning is re-employed for each source image. The method has shown good non-linearity behavior, contributing to increasing the fused output's sparsity characteristics and demonstrating better extraction and transfer of image details to the output fused image. Lastly, the fusion rule step employs a linear combination between steps 1 and 2 to obtain the final fused image. The results depict that the proposed method is advantageous, compared to other previous methods, notably by suppressing the artifacts produced by the traditional SR and SCNN model

Wavelet-based aortic annulus sizing of echocardiography images

Aortic stenosis (AS) is a condition where the calcification deposit within the heart leaflets narrows the valve and restricts the blood from flowing through it. This disease is progressive over time where it may affect the mechanism of the heart valve. To alleviate this condition without resorting to surgery, which runs the risk of mortality, a new method of treatment has been introduced: Transcatheter Aortic Valve Implantation (TAVI), in which imagery acquired from real-time echocardiogram (Echo) are needed to determine the exact size of aortic annulus. However, Echo data often suffers from speckle noise and low pixel resolution, which may result in incorrect sizing of the annulus. Our study therefore aims to perform an automated detection and measurement of aortic annulus size from Echo imagery. Two stages of algorithm are presented – image denoising and object detection. For the removal of speckle noise, Wavelet thresholding technique is applied. It consists of three sequential steps; applying linear discrete wavelet transform, thresholding wavelet coefficients and performing linear inverse wavelet transform. For the next stage of analysis, several morphological operations are used to perform object detection as well as valve sizing. The results showed that the automated system is able to produce more accurate sizing based on ground truth