A Case Study for Massive Text Mining: K Nearest Neighbor Algorithm on PubMed data

poster abstractUS National Library of Medicine (NLM) has a huge collections of millions of books, journals, and other publications relating to medical domain. NLM creates the database called MEDLINE to store and link the citations to the publications. This database allows the researchers and students to access and find medical articles easily. The public can search on MEDLINE using a database called PubMed. When the new PubMed documents become available online, the curators have to manually decide the labels for them. The process is tedious and time-consuming because there are more than 27,149 descriptor (MeSH terms). Although the curators are already using a system called MTI for MeSH terms suggestion, the performance needs to be improved. This research explores the usage of text classification to annotate new PubMed document automatically, efficiently, and with reasonable accuracy. The data is gathered from BioASQ Contest, which contains 4 millions of abstracts. The research process includes preprocess the data, reduce the feature space, classify and evaluate the result. We focus on the K nearest neighbor algorithm in this case study

Cash holding, state ownership and firm value: The case of Vietnam

Using a sample of 650 listed firms on the Vietnamese stock exchange over the period 2008-2015, we examine the effect of cash holding level on firm value. The results find out the cash holding has an impact on firm value in an inverted U-shaped form. Furthermore, this study investigates whether the state ownership influences firm value. We point out that there is a statistically insignificant positive relationship between state ownership and firm value unless the state ownership’s advantages are utilized. The findings have implications of cash management in state-owned firms. © 2016, Econjournals. All rights reserved

COMPLEX SENTENCES USED IN ENGLISH-MAJORED STUDENTS’ ESSAY WRITING: PEDAGOGICAL IMPLICATIONS

The research investigates the use of complex sentences in students’ essay writing, exploring pedagogical implications for teachers to enhance students’ writing proficiency. 212 essays were collected from English-majored students in the last semester for essay learning at Can Tho University, but only 162 essays were qualified for analysis, equivalent to the number of essays in the corpus by Truong & Do (2021). Their complex sentence structures were analyzed and compared with the corpus to identify similarities and differences in the use of complex sentence structures. The findings of this research suggest that there are clear patterns in the types and frequency of complex sentences used by students, which may have implications for the design of the curriculum as well as the teaching and learning of writing essays. Implications for classroom practice and future research directions are discussed.  Article visualizations

GPU-OSDDA: A Bit-Vector GPU-based Deadlock Detection Algorithm for Single-Unit Resource Systems

This article presents a GPU-based single-unit deadlock detection methodology and its algorithm, GPU-OSDDA. Our GPU-based design utilizes parallel hardware of GPU to perform computations and thus is able to overcome the major limitation of prior hardware-based approaches by having the capability of handling thousands of processes and resources, whilst achieving real-world run-times. By utilizing a bit-vector technique for storing algorithm ma- trices and designing novel, efficient algorithmic methods, we not only reduce memory usage dramatically but also achieve two orders of magnitude speedup over CPU equivalents. Additionally, GPU-OSDDA acts as an interactive service to the CPU, because all of the aforementioned computations and matrix management techniques take place on the GPU, requiring minimal interaction with the CPU. GPU-OSDDA is implemented on three GPU cards: Tesla C2050, Tesla K20c, and Titan X. Our design shows overall speedups of 6-595X over CPU equivalents

Progress in the microscopic description of nucleon-nucleus elastic scattering at low-energy

In this brief report, we make a short review of progress in developing the microscopic optical potential in recent years. In particular, we present our current studies and plans on building the microscopic optical potential based on the so-called nuclear structure models at low energies.    &nbsp

Real-time Human Detection in Fire Scenarios using Infrared and Thermal Imaging Fusion

Fire is considered one of the most serious threats to human lives which results in a high probability of fatalities. Those severe consequences stem from the heavy smoke emitted from a fire that mostly restricts the visibility of escaping victims and rescuing squad. In such hazardous circumstances, the use of a vision-based human detection system is able to improve the ability to save more lives. To this end, a thermal and infrared imaging fusion strategy based on multiple cameras for human detection in low-visibility scenarios caused by smoke is proposed in this paper. By processing with multiple cameras, vital information can be gathered to generate more useful features for human detection. Firstly, the cameras are calibrated using a Light Heating Chessboard. Afterward, the features extracted from the input images are merged prior to being passed through a lightweight deep neural network to perform the human detection task. The experiments conducted on an NVIDIA Jetson Nano computer demonstrated that the proposed method can process with reasonable speed and can achieve favorable performance with a [email protected] of 95%.Comment: 5 pages, 6 figures, 2 table

Virtual Learning Project

This project aims to utilize the power of virtual learning and social networking to help reduce high school drop-out rate and increase college entrance quota. In the pre-study, two sample MCAS exams, along with online review lectures, were given to high school students to investigate the relevance of virtual learning to high school education. The students\u27 points of views toward college education and their network of academic support were inquired in a separate survey. The result provided valuable information to design Tootor.org with exclusive features to help high school students improve their academic performance, build meaningful connections with college students, and eventually consider college as their next natural stop

Does foreign ownership impact accounting conservatism adoption in Vietnam?

This study investigates the effects of foreign ownership on accounting conservatism adoption in Vietnam. Although foreign ownership is found to have a positive relationship with accounting conservatism in Korea (An, 2015), there is still no general agreement on it. In this regard, the purpose of this study is to shed more light on the association between foreign ownership and accounting conservatism. Using data from Vietnamese firms listed on stock exchanges, the study finds that in contrast to the findings of An, foreign ownership is negatively associated with accounting conservatism. This result supports the transient hypothesis of foreign ownership, indicating that foreign investors with the low level of ownership do not have significant incentives to oversee managers, thus not influencing financial reporting quality. © 2017 Prague Development Center.Internal Grant Agency of FaME; TBU - The Relationship between Concentration Ownership and Financial Reporting Quality [IGA/FaME/2017/004

Towards Training the Extended Voltage Manifold Computer (EVMC) using Particle Swarm Optimization

poster abstractExtended Analog Computers (EAC) have been explored as a substrate for unconventional computing techniques since the early 1990s. A particular strength of the technique is the near instantaneous speed it solves computational problems. However, application of the EAC and specific EAC classes, as the Extended Voltage Manifold Computer (EVMC), to real-world problems await the development of methods to program EACs. A property of the EVMC is that each output voltage can be described by a class of radial basis functions (RBF). Linking multiple EVMCs, a neural network called a radial basis function network (RBFN) can be implemented. The specific aim of this work is to develop the means to train EVMCs and networks of EVMC based RBFNs. The strategy employed in the present work is to develop a method using EVMCs implemented as finite element method (FEM) simulations to define the error state-space and error gradient of the untrained EVMC manifold. Once defined the EVMC simulation can be recursively configured to reduce the error in a Hebbian sense. Furthermore, particle swarm optimization (PSO) is being explored to improve the speed of convergence. FEM simulations were constructed using COMSOL Multiphysics to model EVMC manifolds in different states. In parallel, a particle swarm optimizer was altered to demonstrate training of simple RBF manifolds. Examination of FEM simulations verified the kernel function as hyperbolic and radially based. These preliminary findings indicated that the EVMC can be accurately modeled and manipulated using COMSOL, and PSO can be used once the error manifold is defined. From this we can take the possibility of improving the speed of training the EVMC via PSO. The next step to verify this possibility is to combine the COMSOL and Python codes to confirm the EVMC can be trained