Improving Texture Categorization with Biologically Inspired Filtering

Within the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms. However, preprocessing techniques have received much less attention despite their important potential for improving the overall classification performance. We address this question by proposing a novel, simple, yet very powerful biologically-inspired filtering (BF) which simulates the performance of human retina. In the proposed approach, given a texture image, after applying a DoG filter to detect the "edges", we first split the filtered image into two "maps" alongside the sides of its edges. The feature extraction step is then carried out on the two "maps" instead of the input image. Our algorithm has several advantages such as simplicity, robustness to illumination and noise, and discriminative power. Experimental results on three large texture databases show that with an extremely low computational cost, the proposed method improves significantly the performance of many texture classification systems, notably in noisy environments. The source codes of the proposed algorithm can be downloaded from https://sites.google.com/site/nsonvu/code.Comment: 11 page

Knowledge Creation And Green Entrepreneurship: A Study Of Two Vietnamese Green Firms

This paper aims to advance the understanding and practice of knowledge-based management in Vietnam by studying two Vietnamese agricultural companies. It provides illustrative examples of how knowledge-based management, pursuing a vision that fosters creativity and innovation by employees, could ultimately fulfil the profitability objective of the business and at the same time add value to the community’s quality of life. Using the SECI model as the parameter for analysis, we found that knowledge creation processes were affected by a combination of leadership, teamwork and Ba, corporate culture, and human resource management. Our conclusion emphasises the need for future research to further examine the practice of knowledge-based management in cross-industry segments in Vietnam and in other countries with similar conditions

OMG U got flu? Analysis of shared health messages for bio-surveillance

Background: Micro-blogging services such as Twitter offer the potential to crowdsource epidemics in real-time. However, Twitter posts ('tweets') are often ambiguous and reactive to media trends. In order to ground user messages in epidemic response we focused on tracking reports of self-protective behaviour such as avoiding public gatherings or increased sanitation as the basis for further risk analysis. Results: We created guidelines for tagging self protective behaviour based on Jones and Salath\'e (2009)'s behaviour response survey. Applying the guidelines to a corpus of 5283 Twitter messages related to influenza like illness showed a high level of inter-annotator agreement (kappa 0.86). We employed supervised learning using unigrams, bigrams and regular expressions as features with two supervised classifiers (SVM and Naive Bayes) to classify tweets into 4 self-reported protective behaviour categories plus a self-reported diagnosis. In addition to classification performance we report moderately strong Spearman's Rho correlation by comparing classifier output against WHO/NREVSS laboratory data for A(H1N1) in the USA during the 2009-2010 influenza season. Conclusions: The study adds to evidence supporting a high degree of correlation between pre-diagnostic social media signals and diagnostic influenza case data, pointing the way towards low cost sensor networks. We believe that the signals we have modelled may be applicable to a wide range of diseases

Monte Carlo Simulation of Correction Factors for Neutron Activation Foils

In the conventional neutron activation analysis, the elemental concentrations are normally determined from the comparison ratios between the measured specific activities of the sample and the standard reference material. An advantage in the comparison ratio method is that the systematic error due to neutron self-shielding and multi-scattering effects is canceled out, and the correction factors can be ignored but the preparation of reference standards to match the same conditions with those of various samples is the main difficulty. In the modern trend of neutron activation analysis, the K0-standardization method has been developed and applied in almost all the NAA laboratories. An important research work in the procedure under this method is the characteristic information regarding the neutron source, such as thermal and epithermal neutron fluxes, and epithermal spectrum shape-factor. These neutron spectrum parameters are experimentally determined by using the activation foils, in which the corrections for all neutron effects cause systematic errors should be taken into account. Using the MCNP5 code, a well-known Monte Carlo simulation program, the results of correction factors of thermal, epithermal and resonance neutron self-shielding factors for Au, Co, Mn, W activation foils are presented in this chapter

AN INVESTIGATION INTO ASSESSMENT CRITERIA OF THE CONTENT CONTEXT AND FORMS OF EXTRACURRICULAR SPORTS ACTIVITIES FOR STUDENTS AT SECONDARY SCHOOLS IN TAY NINH PROVINCE, VIETNAM

After using the conventional scientific research methods, observing the steps of document synthesis, interviewing, and checking the reliability of the questionnaire through Cronbach's Alpha, the paper has come up with five quantitative assessment criteria and eight qualitative assessment questions of the content and forms of extracurricular sports activities for students at secondary schools in Tay Ninh province. This research has contributed to renewing the content and forms of extracurricular sports activities for students at secondary school.  Article visualizations

Black-box modeling of nonlinear system using evolutionary neural NARX model

Nonlinear systems with uncertainty and disturbance are very difficult to model using mathematic approach. Therefore, a black-box modeling approach without any prior knowledge is necessary. There are some modeling approaches have been used to develop a black box model such as fuzzy logic, neural network, and evolution algorithms. In this paper, an evolutionary neural network by combining a neural network and a modified differential evolution algorithm is applied to model a nonlinear system. The feasibility and effectiveness of the proposed modeling are tested on a piezoelectric actuator SISO system and an experimental quadruple tank MIMO system

11-2007 Newsletter

Minnesota State University, Mankato, Library Services Newsletter for November 2007

Does BLEU Score Work for Code Migration?

Statistical machine translation (SMT) is a fast-growing sub-field of computational linguistics. Until now, the most popular automatic metric to measure the quality of SMT is BiLingual Evaluation Understudy (BLEU) score. Lately, SMT along with the BLEU metric has been applied to a Software Engineering task named code migration. (In)Validating the use of BLEU score could advance the research and development of SMT-based code migration tools. Unfortunately, there is no study to approve or disapprove the use of BLEU score for source code. In this paper, we conducted an empirical study on BLEU score to (in)validate its suitability for the code migration task due to its inability to reflect the semantics of source code. In our work, we use human judgment as the ground truth to measure the semantic correctness of the migrated code. Our empirical study demonstrates that BLEU does not reflect translation quality due to its weak correlation with the semantic correctness of translated code. We provided counter-examples to show that BLEU is ineffective in comparing the translation quality between SMT-based models. Due to BLEU's ineffectiveness for code migration task, we propose an alternative metric RUBY, which considers lexical, syntactical, and semantic representations of source code. We verified that RUBY achieves a higher correlation coefficient with the semantic correctness of migrated code, 0.775 in comparison with 0.583 of BLEU score. We also confirmed the effectiveness of RUBY in reflecting the changes in translation quality of SMT-based translation models. With its advantages, RUBY can be used to evaluate SMT-based code migration models.Comment: 12 pages, 5 figures, ICPC '19 Proceedings of the 27th International Conference on Program Comprehensio