A LIGHTWEIGHT MULTI-PERSON POSE ESTIMATION SCHEME BASED ON JETSON NANO

As the basic technology of human action recognition, pose estimation is attracting more and more researchers' attention, while edge application scenarios pose a higher challenge. This paper proposes a lightweight multi-person pose estimation scheme to meet the needs of real-time human action recognition on the edge end. This scheme uses AlphaPose to extract human skeleton nodes, and adds ResNet and Dense Upsampling Revolution to improve its accuracy. Meanwhile, we use YOLO to enhance AlphaPose’s support for multi-person pose estimation, and optimize the proposed model with TensorRT. In addition, this paper sets Jetson Nano as the Edge AI deployment device of the proposed model and successfully realizes the model migration to the edge end. The experimental results show that the speed of the optimized object detection model can reach 20 FPS, and the optimized multi-person pose estimation model can reach 10 FPS. With the image resolution of 320×240, the model’s accuracy is 73.2%, which can meet the real-time requirements. In short, our scheme can provide a basis for lightweight multi-person action recognition scheme on the edge end

Bacterial Quorum Sensing and Microbial Community Interactions

Many bacteria use a cell-cell communication system called quorum sensing to coordinate population density-dependent changes in behavior. Quorum sensing involves production of and response to diffusible or secreted signals, which can vary substantially across different types of bacteria. In many species, quorum sensing modulates virulence functions and is important for pathogenesis. Over the past half-century, there has been a significant accumulation of knowledge of the molecular mechanisms, signal structures, gene regulons, and behavioral responses associated with quorum-sensing systems in diverse bacteria. More recent studies have focused on understanding quorum sensing in the context of bacterial sociality. Studies of the role of quorum sensing in cooperative and competitive microbial interactions have revealed how quorum sensing coordinates interactions both within a species and between species. Such studies of quorum sensing as a social behavior have relied on the development of “synthetic ecological” models that use nonclonal bacterial populations. In this review, we discuss some of these models and recent advances in understanding how microbes might interact with one another using quorum sensing. The knowledge gained from these lines of investigation has the potential to guide studies of microbial sociality in natural settings and the design of new medicines and therapies to treat bacterial infections

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress. Integrating cognitive diagnosis with deep learning has shown promising results in personalized exercise recommendations. However, the black-box nature of the deep learning model hinders their interpretability. This makes it challenging for educators and students to understand the reasons behind the model's predictions for the next problem, and this limits their opportunity to take an active role in improving the learning process. To address this limitation, this article presents a novel personalized exercise question recommendation model based on knowledge tracing. The approach incorporates graph convolutional neural networks to model the student's abilities, thus enhancing the interpretability of the model. By employing Bidirectional gate recurrent unit (Bi-GRU), the model effectively traces fluctuations in students' abilities over time and predicts their responses to exercise questions. Experimental results demonstrate the effectiveness of this model, achieving an accuracy of 90.8% and 92.6% on ASSISTment 2009 and ASSISTment 2017 datasets, containing 4218 and 1709 student records, respectively. Moreover, the experiment was also conducted to validate the model's exercise difficulty setting. Results indicate an acceptable level of effectiveness in generating appropriate difficulty-level recommendations for individual students. The proposed model contributes to advancing personalized exercise recommendations by offering valuable insights that can lead to more efficient and effective student learning experiences

AI-Driven Analysis: Optimizing Tertiary Education Policy through Machine Learning Insights

Tertiary education is pivotal in equipping individuals with the necessary knowledge and skills for success, prompting global initiatives for equitable access to quality higher education. The Philippines' Universal Access to Quality Tertiary Education (UAQTE) Act exemplifies this commitment by providing free tertiary education to eligible Filipino students. This study evaluates the UAQTE program's implementation through the perspectives of student beneficiaries, employing a combined approach of qualitative analysis and machine learning techniques. The study utilizes supervised and unsupervised machine learning to analyze student responses, specifically multiclass text classification using BERT and topic modeling with BERTopic. The results reveal insights into students' experiences and perceptions of the UAQTE program. While BERT demonstrates effectiveness in certain categories, challenges such as overfitting and balancing sequence length versus model performance are identified. BERTopic highlights the importance of capturing two-word combinations for enhancing topic coherence. Key themes identified through both approaches include "Educational Opportunity," "Program Implementation," "Financial Support," and "Appreciation and Gratitude," emphasizing their significance within the UAQTE program. Alignment between machine learning analyses and domain experts' insights underscores the relevance and effectiveness of the methodologies employed. Recommendations for optimizing the UAQTE program include refining focus areas, strengthening support systems, incorporating two-word combinations in analysis, and fostering continuous monitoring and interdisciplinary collaboration. By leveraging insights from qualitative and machine learning analyses, administrators can make informed decisions to enhance program effectiveness and comprehensively address students' diverse needs

CebuaNER:A New Baseline Cebuano Named Entity Recognition Model

Despite being one of the most linguistically diverse groups of countries, computational linguistics and language processing research in Southeast Asia has struggled to match the level of countries from the Global North. Thus, initiatives such as open-sourcing corpora and the development of baseline models for basic language processing tasks are important stepping stones to encourage the growth of research efforts in the field. To answer this call, we introduce CebuaNER, a new baseline model for named entity recognition (NER) in the Cebuano language. Cebuano is the second most-used native language in the Philippines, with over 20 million speakers. To build the model, we collected and annotated over 4,000 news articles, the largest of any work in the language, retrieved from online local Cebuano platforms to train algorithms such as Conditional Random Field and Bidirectional LSTM. Our findings show promising results as a new baseline model, achieving over 70% performance on precision, recall, and F1 across all entity tags, as well as potential efficacy in a crosslingual setup with Tagalog

We're in this Together: Sensation of the Host Cell Environment by Endosymbiotic Bacteria

Bacteria inhabit diverse environments, including the inside of eukaryotic cells. While a bacterial invader may initially act as a parasite or pathogen, a subsequent mutualistic relationship can emerge in which the endosymbiotic bacteria and their host share metabolites. While the environment of the host cell provides improved stability when compared to an extracellular environment, the endosymbiont population must still cope with changing conditions, including variable nutrient concentrations, the host cell cycle, host developmental programs, and host genetic variation. Furthermore, the eukaryotic host can deploy mechanisms actively preventing a bacterial return to a pathogenic state. Many endosymbionts are likely to use two-component systems (TCSs) to sense their surroundings, and expanded genomic studies of endosymbionts should reveal how TCSs may promote bacterial integration with a host cell. We suggest that studying TCS maintenance or loss may be informative about the evolutionary pathway taken toward endosymbiosis, or even toward endosymbiont-to-organelle conversion.Peer reviewe

Improving The Levels of Reading Skills Of Grade 1 Learners Through Story-telling Via Phone Calling

The study aimed to assess the effectiveness of storytelling via phone calling strategy to improve the levels of reading skills of Grade 1 pupils at San Miguel Elementary School, Schools Division of Zambales during the School Year 2022-2023. Further delimitation included the following: (1) levels of reading skills based on pre-test and post-test results of reading inventory; and (2) significant difference in the reading levels of the learners in pre-test and post-test result of reading inventory. This research undertaking used the One-Group Pre-Test and Post-Test Experimental Design where a single group was measured or observed before and after the intervention program. After careful evaluation of parental consent and the willingness of the students to undergo intervention program, the researcher purposively selected twenty-eight students under “frustration reading level”. The study used a two-tailed Wilcoxon Signed Ranks Test with an alpha level of 0.05. It means that there is a significant difference between learners’ reading levels in pre-reading and post-reading inventory indicating a positive effect of the intervention used to the learners in the “frustration reading level”. The marked increase of the performance of the Grade 1 pupils in the pre-test and post-test can be concluded that the intervention used came to play as factor. With that, storytelling via phone calling is effective and responsive as one of the strategies to use in reading. One of the recommendations is Storytelling via phone calling reading strategy could be used by other teachers also to improve the levels of reading skills of their learners as well, especially amidst pandemic where there is no face-to-face interaction between students and teachers

Enhanced personalized learning exercise question recommendation model based on knowledge tracing

Personalized exercise question recommendation is a crucial aspect of smart education used to customize educational exercises and questions to individual students' distinct abilities and learning progress. Integrating cognitive diagnosis with deep learning has shown promising results in personalized exercise recommendations. However, the black-box nature of the deep learning model hinders their interpretability. This makes it challenging for educators and students to understand the reasons behind the model's predictions for the next problem, and this limits their opportunity to take an active role in improving the learning process. To address this limitation, this article presents a novel personalized exercise question recommendation model based on knowledge tracing. The approach incorporates graph convolutional neural networks to model the student's abilities, thus enhancing the interpretability of the model. By employing Bidirectional gate recurrent unit (Bi-GRU), the model effectively traces fluctuations in students' abilities over time and predicts their responses to exercise questions. Experimental results demonstrate the effectiveness of this model, achieving an accuracy of 90.8% and 92.6% on ASSISTment 2009 and ASSISTment 2017 datasets, containing 4218 and 1709 student records, respectively. Moreover, the experiment was also conducted to validate the model's exercise difficulty setting. Results indicate an acceptable level of effectiveness in generating appropriate difficulty-level recommendations for individual students. The proposed model contributes to advancing personalized exercise recommendations by offering valuable insights that can lead to more efficient and effective student learning experiences