A Vietnamese Handwritten Text Recognition Pipeline for Tetanus Medical Records

Machine learning techniques are successful for optical character recognition tasks, especially in recognizing handwriting. However, recognizing Vietnamese handwriting is challenging with the presence of extra six distinctive tonal symbols and vowels. Such a challenge is amplified given the handwriting of health workers in an emergency care setting, where staff is under constant pressure to record the well-being of patients. In this study, we aim to digitize the handwriting of Vietnamese health workers. We develop a complete handwritten text recognition pipeline that receives scanned documents, detects, and enhances the handwriting text areas of interest, transcribes the images into computer text, and finally auto-corrects invalid words and terms to achieve high accuracy. From experiments with medical documents written by 30 doctors and nurses from the Tetanus Emergency Care unit at the Hospital for Tropical Diseases, we obtain promising results of 2% and 12% for Character Error Rate and Word Error Rate, respectively

Clinical features, antimicrobial susceptibility patterns and genomics of bacteria causing neonatal sepsis in a children's hospital in Vietnam: protocol for a prospective observational study.

INTRODUCTION: The clinical syndrome of neonatal sepsis, comprising signs of infection, septic shock and organ dysfunction in infants ≤4 weeks of age, is a frequent sequel to bloodstream infection and mandates urgent antimicrobial therapy. Bacterial characterisation and antimicrobial susceptibility testing is vital for ensuring appropriate therapy, as high rates of antimicrobial resistance (AMR), especially in low-income and middle-income countries, may adversely affect outcome. Ho Chi Minh City (HCMC) in Vietnam is a rapidly expanding city in Southeast Asia with a current population of almost 8 million. There are limited contemporary data on the causes of neonatal sepsis in Vietnam, and we hypothesise that the emergence of multidrug resistant bacteria is an increasing problem for the appropriate management of sepsis cases. In this study, we aim to investigate the major causes of neonatal sepsis and assess disease outcomes by clinical features, antimicrobial susceptibility profiles and genome composition. METHOD AND ANALYSIS: We will conduct a prospective observational study to characterise the clinical and microbiological features of neonatal sepsis in a major children's hospital in HCMC. All bacteria isolated from blood subjected to whole genome sequencing. We will compare clinical variables and outcomes between different bacterial species, genome composition and AMR gene content. AMR gene content will be assessed and stratified by species, years and contributing hospital departments. Genome sequences will be analysed to investigate phylogenetic relationships. ETHICS AND DISSEMINATION: The study will be conducted in accordance with the principles of the Declaration of Helsinki and the International Council on Harmonization Guidelines for Good Clinical Practice. Ethics approval has been provided by the Oxford Tropical Research Ethics Committee 35-16 and Vietnam Children's Hospital 1 Ethics Committee 73/GCN/BVND1. The findings will be disseminated at international conferences and peer-reviewed journals. TRIAL REGISTRATION NUMBER: ISRCTN69124914; Pre-results

大規模カテゴリカル混合データセットのためのクラスタリングアルゴリズムへの局所性鋭敏ハッシュの組み込み手法

Supervisor:Huynh Nam Van先端科学技術研究科博

Fast Search of Audio Fingerprint using K40 GPGPU

Nowadays, there are millions of audio and video contents uploaded to the Internet, so the searching speed and database organization are the problems for the audio management system. Audio fingerprint is the digital fingerprint that can help to identify the audio content. With the advantages of audio fingerprint, we can reduce the size of data to hundreds of times less than storing original audio raw data. And with audio fingerprint, we have a standard format that supports to compare or structuralize the database. In this thesis, we propose a new hierarchy searching system that can detect the meta information for fingerprint in real time by using the advantages of K-modes and Locality Sensitive Hashing (LSH). The K-modes is used as Level 1 in our method and works in CPU. K-modes supports in clustering the big database into sub-databases that can store to GPGPU devices. In searching step, K-modes is responsible for finding the nearest centroid of every query and send this query to suitable GPGPU device. LSH will handle the data structure of GPGPU devices' sub-database and respond for management the kernel that is compatible with parallel in single GPGPU. Our method can combine the advantages of both CPU and GPGPUs by putting together in the same computer system. With the power of multiple GPGPU devices, we can obtain the meta information for a query within 2 milliseconds for 10 million songs' database.Supervisor:井口　寧情報科学研究科修

Locality-Sensitive Hashing for Information Retrieval System on Multiple GPGPU Devices

It is challenging to build a real-time information retrieval system, especially for systems with high-dimensional big data. To structure big data, many hashing algorithms that map similar data items to the same bucket to advance the search have been proposed. Locality-Sensitive Hashing (LSH) is a common approach for reducing the number of dimensions of a data set, by using a family of hash functions and a hash table. The LSH hash table is an additional component that supports the indexing of hash values (keys) for the corresponding data/items. We previously proposed the Dynamic Locality-Sensitive Hashing (DLSH) algorithm with a dynamically structured hash table, optimized for storage in the main memory and General-Purpose computation on Graphics Processing Units (GPGPU) memory. This supports the handling of constantly updated data sets, such as songs, images, or text databases. The DLSH algorithm works effectively with data sets that are updated with high frequency and is compatible with parallel processing. However, the use of a single GPGPU device for processing big data is inadequate, due to the small memory capacity of GPGPU devices. When using multiple GPGPU devices for searching, we need an effective search algorithm to balance the jobs. In this paper, we propose an extension of DLSH for big data sets using multiple GPGPUs, in order to increase the capacity and performance of the information retrieval system. Different search strategies on multiple DLSH clusters are also proposed to adapt our parallelized system. With significant results in terms of performance and accuracy, we show that DLSH can be applied to real-life dynamic database systems

Audio fingerprint hierarchy searching strategies on GPGPU massively parallel computer

Audio fingerprint was developed for representing the audio based on the content of waveform. With the audio fingerprint database, we can easily manage the song/music with high reliability and flexibility. However, with the well-developed Internet of today, the audio data have become bigger and bigger which make the management of audio/music data more difficult. There are two problems that we need to solve when the audio fingerprint database turn into bigdata: the size of the database needs to be sufficient for storing 10 millions of audio fingerprint and the strategies for searching the nearest song in acceptable time for thousands of queries at once [Nguyen Mau, T., & Inoguchi, Y. (2016). Audio fingerprint hierarchy searching on massively parallel with multi-gpgpus using K-modes and lsh. Eighth international conference on knowledge and systems engineering (KSE) (pp. 49–54). IEEE]. In this research, we propose the methods for storing the audio fingerprint using multiple GPGPU and nearest song searching strategies based on these databases. We also showed that our methods have the significant result for deploying the real system in the future

Simultaneous Determination of Zn(II), Cd(II), Pb(II), and Cu(II) Using Differential Pulse Anodic Stripping Voltammetry at a Bismuth Film-Modified Electrode

The simultaneous electrochemical determination of Zn(II), Cd(II), Pb(II), and Cu(II) in the aqueous solution has been developed on the basis of the bismuth film glassy carbon electrode (GCE) using differential pulse anodic stripping voltammetry (DP-ASV). The bismuth film electrode (BiFE) was prepared by adding 500 ppb bismuth(III) directly to the sample solution and simultaneously depositing bismuth and the metal analytes on GCE. The optimal operational parameters, namely, accumulation potential (–1.6 V), accumulation time (110 s), pulsed amplitude (0.07 V), and scan rate (0.021 V·s−1), were found using a Box–Behnken design. Under the optimum conditions, a linear relationship exists between the current and the concentration of Zn(II), Cd(II), Pb(II), and Cu(II) in the range between 5.0 ppb and 110.0 ppb with the detection limits of 1.07 for Zn(II), 0.93 ppb for Cd(II), 0.65 ppb for Pb(II), and 0.94 ppb for Cu(II) calculated on the basis of a signal-to-noise ratio equal to 3 (S/N = 3). The interference experiments show that Co(II), Ni(II), and Fe(III) have a little influence on the DP-ASV signals of Zn(II), Cd(II), Pb(II), and Cu(II). In addition, a high reproducibility was indicated from small relative standard deviations (1.03%, 1.74%, 1.32%, and 4.74%) for 25 repeated measurements of 15 ppb copper, lead, cadmium, and zinc solutions. BiFE was successfully applied to determine Zn(II), Cd(II), Pb(II), and Cu(II) in river samples, and the results are in a good agreement with those determined with graphite furnace atomic absorption spectrometry (GF-AAS)

Simultaneous Voltammetric Determination of Ascorbic Acid, Paracetamol, and Caffeine Using Electrochemically Reduced Graphene-Oxide-Modified Electrode

In the present paper, graphene oxide was directly electrodeposited by means of cyclic voltammetric techniques on the glassy-carbon electrode (GCE) to obtain a reduced graphene-oxide-modified electrode (ErGO/GCE). Cyclic voltammetry (CV) and differential pulse anodic stripping voltammetry (DP-ASV) had been utilized to study the electrochemical behavior of ErGO/GCE toward ascorbic acid (AA), paracetamol (PA), and caffeine (CA). Differential pulse voltammetry results show that AA, PA, and CA could be detected selectively and sensitively on ErGO/GCE with peak-to-peak separation of 312 mV and 756 mV for AA–PA and PA–CA, respectively. The factors affecting the voltammetric signals such as pH, scan rate, and interferents were addressed. The results reveal that the ErGO/GCE-modified electrode exhibits excellent electrochemical activity in the oxidation of PA, CA, and AA. The detection limits are 0.36 μM, 0.25 μM, and 0.23 μM for AA, PA and CA, respectively, suggesting that the ErGO/GCE can be utilized with high sensitivity and selectivity for the simultaneous determination of these compounds. Finally, the proposed method was successfully used to determine AA, PA, and CA in pharmaceutical preparations

ZIF-67/g-C3N4-Modified Electrode for Simultaneous Voltammetric Determination of Uric Acid and Acetaminophen with Cetyltrimethylammonium Bromide as Discriminating Agent

In the present paper, the ZIF-67/g-C3N4 composite was synthesized and utilized as a modifier for a glassy carbon electrode for the simultaneous voltammetric determination of uric acid (URA) and acetaminophen (ACE) with cetyltrimethylammonium bromide (CTAB) as a discriminating agent. The composite was characterized using X-ray diffraction, scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and nitrogen adsorption/desorption isotherms. The obtained ZIF-67/g-C3N4 composite exhibits good textural properties (specific surface area: 75 m2·g−1) and is stable in water with a pH range of 3 to 10. The ZIF-67/g-C3N4-modified electrode combined with CTAB as a discriminating agent possesses excellent catalytic electrochemistry towards URA and ACE with well-defined electrochemical responses. The electrochemical kinetics study is also addressed. The linear relation of the oxidation peak current of URA and ACE and the concentration ranging from 0.2 μM to 6.5 μM provide a detection limit of 0.052 μM for URA and 0.053 μM for ACE. The proposed method is well-suited to simultaneously analyze URA and ACE in human urine with comparable results with HPLC