Autoethnography as a Decolonizing Methodology: Reflections on Masta’s What the Grandfathers Taught Me

As an Asian graduate student and a Native professor at a U.S. Midwestern Predominantly White Institution, we reflected upon Masta’s (2018) article, What the Grandfathers Taught Me: Lessons for an Indian Country Researcher, to examine the decolonizing aspects of autoethnography. Masta’s use of autoethnography to explore her experiences provides a deeply personal view into the phenomenon of living and researching Indigenous in an America that is inherently White in character, tradition, structure, and culture. The use of participatory and constructivist Indigenous autoethnography places the lived experience of an Indigenous woman at the center of the study, using the Indigenous lens to respect the cultural values, beliefs, and teachings of a community that remains largely overlooked in Eurocentric research. Such an appreciation and understanding led us to argue that autoethnography is a promising decolonizing methodology which has the potential to inform decolonization and social justice movements

Linear Query Approximation Algorithms for Non-monotone Submodular Maximization under Knapsack Constraint

This work, for the first time, introduces two constant factor approximation algorithms with linear query complexity for non-monotone submodular maximization over a ground set of size $n$ subject to a knapsack constraint, $\mathsf{DLA}$ and $\mathsf{RLA}$. $\mathsf{DLA}$ is a deterministic algorithm that provides an approximation factor of $6+\epsilon$ while $\mathsf{RLA}$ is a randomized algorithm with an approximation factor of $4+\epsilon$. Both run in $O(n \log(1/\epsilon)/\epsilon)$ query complexity. The key idea to obtain a constant approximation ratio with linear query lies in: (1) dividing the ground set into two appropriate subsets to find the near-optimal solution over these subsets with linear queries, and (2) combining a threshold greedy with properties of two disjoint sets or a random selection process to improve solution quality. In addition to the theoretical analysis, we have evaluated our proposed solutions with three applications: Revenue Maximization, Image Summarization, and Maximum Weighted Cut, showing that our algorithms not only return comparative results to state-of-the-art algorithms but also require significantly fewer queries

Design an Intelligent System to automatically Tutor the Method for Solving Problems

Nowadays, intelligent systems have been applied in many real-word domains. The Intelligent chatbot is an intelligent system, it can interact with the human to tutor how to work some activities. In this work, we design an architecture to build an intelligent chatbot, which can tutor to solve problems, and construct scripts for automatically tutoring. The knowledge base of the intelligent tutoring chatbot is designed by using the requirements of an Intelligent Problem Solver. It is the combination between the knowledge model of relations and operators, and the structures of hint questions and sample problems, which are practical cases. Based on the knowledge base and tutoring scripts, a tutoring engine is designed. The tutoring chatbot plays as an instructor for solving real-world problems. It simulates the working of the instructor to tutor the user for solving problems. By utilizing the knowledge base and reasoning, the architecture of the intelligent chatbot are emerging to apply in the real-world. It is used to build an intelligent chatbot to support the learning of high-school mathematics and a consultant system in public administration. The experimental results show the effectiveness of the proposed method in comparison with the existing systems

Slice-level Detection of Intracranial Hemorrhage on CT Using Deep Descriptors of Adjacent Slices

The rapid development in representation learning techniques such as deep neural networks and the availability of large-scale, well-annotated medical imaging datasets have to a rapid increase in the use of supervised machine learning in the 3D medical image analysis and diagnosis. In particular, deep convolutional neural networks (D-CNNs) have been key players and were adopted by the medical imaging community to assist clinicians and medical experts in disease diagnosis and treatment. However, training and inferencing deep neural networks such as D-CNN on high-resolution 3D volumes of Computed Tomography (CT) scans for diagnostic tasks pose formidable computational challenges. This challenge raises the need of developing deep learning-based approaches that are robust in learning representations in 2D images, instead 3D scans. In this work, we propose for the first time a new strategy to train \emph{slice-level} classifiers on CT scans based on the descriptors of the adjacent slices along the axis. In particular, each of which is extracted through a convolutional neural network (CNN). This method is applicable to CT datasets with per-slice labels such as the RSNA Intracranial Hemorrhage (ICH) dataset, which aims to predict the presence of ICH and classify it into 5 different sub-types. We obtain a single model in the top 4% best-performing solutions of the RSNA ICH challenge, where model ensembles are allowed. Experiments also show that the proposed method significantly outperforms the baseline model on CQ500. The proposed method is general and can be applied to other 3D medical diagnosis tasks such as MRI imaging. To encourage new advances in the field, we will make our codes and pre-trained model available upon acceptance of the paper.Comment: Accepted for presentation at the 22nd IEEE Statistical Signal Processing (SSP) worksho

The Vietnamese Version of the Brief Illness Perception Questionnaire and the Beliefs about Medicines Questionnaire:Translation and Cross-cultural Adaptation

OBJECTIVE: To translate and cross-culturally adapt the Brief Illness Perception Questionnaire (BIPQ) and the Beliefs about Medicines Questionnaire (BMQ) into Vietnamese. METHODS: We followed the guideline by Beaton et al. (2000 & 2007). Stage I: two translators (informed and uninformed) translated the questionnaires. Stage II: the translations were synthesized. Stage III: back translation was performed by two translators fluent in both Vietnamese and English but naïve to the outcome measurement. Stage IV: seven experts reached consensus on the pre-final Vietnamese version (BIPQ-V and BMQ-V). Stage V: field test of the questionnaires on 16 twelve-year-old students and 31 Vietnamese patients. In addition, we determined the internal consistency and test-retest reliability of the questionnaires in 34 Vietnamese patients with acute coronary syndrome. RESULTS: All experts agreed that there was semantic, idiomatic, experiential, and conceptual equivalence between the original and pre-final Vietnamese versions of the BIPQ and BMQ. Cronbach's alpha coefficients of the internal consistency were acceptable for the BMQ-V Specific-Necessity (0.64), BMQ-V Specific-Concerns (0.62), and BMQ-V General-Harm (0.60), with the exception of BMQ-V General-Overuse (0.27). Intra-class correlation coefficients of the test-retest reliability was acceptable for the subscales of BMQ-V (range: 0.77-0.86), and BIPQ-V items (range: 0.62-0.85) with the exception of BIPQ-V 1 (0.44, 95% CI -014-0.72) and BIPQ-V 4 (0.57, 95% CI 0.22-0.81). CONCLUSIONS: The Vietnamese version of BIPQ and BMQ are reliable tools to assess illness perceptions and beliefs about medicines of patients with acute coronary syndrome. Psychometric properties of these questionnaires should be tested in different patient populations

HOLLOW GOLD NANOSTRUCTURES PREPARED BY GALVANIC REPLACEMENT REACTION: SYNTHESIS AND OPTICAL PROPERTIES

This work describes a facile synthesis of hollow Au nanostructures involved in the galvanic replacement reaction utilising Ag nanoparticles as the templates. The effect of reaction conditions, including PVP concentration and reaction time on the morphology of Ag templates was investigated. Using Ag nanocubic-shape templates, 50 nm hollow Au nanostructures were prepared. The result indicated that the wavelength of SPR peak of the hollow Au nanostructures was strongly affected by Au precursor content and could be tuned between 460 and 860 nm when altering the volume of the Au precursor solution from 0.5 to 3 ml. The ability of conversion of photo energy into heat of the hollow Au nanostructures was also exploited. The optical heating data of Au solution (165 µg/ml) with an 808 nm laser at a power of 1.8 W showed that the sample temperature reached to 55 oC after just 5 min irradiation. The successfulness in fabrication hollow Au nanostructures having SPR peak in NIR region, relative small size and high capacity of conversion of photo energy into heat make them become a novel and promising material for photo thermal and photo imaging applications

Petrographic Characteristics and Depositional Environment Evolution of Middle Miocene Sediments in the Thien Ung - Mang Cau Structure of Nam Con Son Basin

This paper introduces the petrographic characteristics and depositional environment of Middle Miocene rocks of the Thien Ung - Mang Cau structure in the central area of Nam Con Son Basin based on the results of analyzing thin sections and structural characteristics of core samples. Middle Miocene sedimentary rocks in the studied area can be divided into three groups: (1) Group of terrigenous rocks comprising greywacke sandstone, arkosic sandstone, lithic-quartz sandstone, greywacke-lithic sandstone, oligomictic siltstone, and bitumenous claystone; (2) Group of carbonate rocks comprising dolomitic limestone and bituminous limestone; (3) Mixed group comprising calcareous sandstone, calcarinate sandstone, arenaceous limestone, calcareous claystone, calcareous silty claystone, dolomitic limestone containing silt, and bitumen. The depositional environment is expressed through petrographic characteristics and structure of the sedimentary rocks in core samples. The greywacke and arkosic sandstones are of medium grain size, poor sorting and roundness, and siliceous cement characterizing the alluvial and estuarine fan environment expressed by massive structure of core samples. The mixed calcareous limestone, arenaceous dolomitic limestone, and calcareous and bituminous clayey siltstone in the core samples are of turbulent flow structure characterizing shallow bay environment with the action of bottom currents. The dolomitic limestones are of relatively homogeneous, of microgranular and fine-granular texture, precipitated in a weakly reducing, semi-closed, and relatively calm bay environment

How Digital Natives Learn and Thrive in the Digital Age: Evidence from an Emerging Economy

As a generation of ‘digital natives,’ secondary students who were born from 2002 to 2010 have various approaches to acquiring digital knowledge. Digital literacy and resilience are crucial for them to navigate the digital world as much as the real world; however, these remain under-researched subjects, especially in developing countries. In Vietnam, the education system has put considerable effort into teaching students these skills to promote quality education as part of the United Nations-defined Sustainable Development Goal 4 (SDG4). This issue has proven especially salient amid the COVID−19 pandemic lockdowns, which had obliged most schools to switch to online forms of teaching. This study, which utilizes a dataset of 1061 Vietnamese students taken from the United Nations Educational, Scientific, and Cultural Organization (UNESCO)’s “Digital Kids Asia Pacific (DKAP)” project, employs Bayesian statistics to explore the relationship between the students’ background and their digital abilities. Results show that economic status and parents’ level of education are positively correlated with digital literacy. Students from urban schools have only a slightly higher level of digital literacy than their rural counterparts, suggesting that school location may not be a defining explanatory element in the variation of digital literacy and resilience among Vietnamese students. Students’ digital literacy and, especially resilience, also have associations with their gender. Moreover, as students are digitally literate, they are more likely to be digitally resilient. Following SDG4, i.e., Quality Education, it is advisable for schools, and especially parents, to seriously invest in creating a safe, educational environment to enhance digital literacy among students

Multichannel Photon Counting Lidar Measurements Using USB-based Digital Storage Oscilloscope

We present a simple method of making multichannel photon counting measurements of weak lidar signal from large ranges, using commonly available USB-based digital storage oscilloscopes. The single photon pulses from compact photomultiplier tubes are amplified and stretched so that the pulses are large and broad enough to be sampled efficiently by the USB oscilloscopes. A software interface written in Labview is then used to count the number of photon pulses in each of the prescribed time bins to form the histogram of LIDAR signal. This method presents a flexible alternative to the modular multichannel scalers and facilitate the development of sensitive lidar systems