Facial Expression Recognition Based on SVM in E-learning

AbstractFacial expression is one of the most powerful channels of nonverbal communication which contains plenty of affective information. Recognition of facial expression and sending them back to the teacher is potentially helpful in E-learning. In this paper, we differentiate between person-relevant and person-irrelevant situations. Our goal is to extract powerful features used for facial expression recognition system in real-time and person-irrelevant situation. Previous work suggests that both facial shape features and appearance features could be used to recognize facial expressions. The first type is shape features calculated from positions on a face. The second type is a set of multi-scale and multi-orientation Gabor wavelet coefficients. The classifier is based on Support Vector Machines (SVM) and our expriments cover both person-relevant and person-irrelevant situations. The result shows that in person-irrelevant situation, using facial shape features outperforms using Gabor wavelet and it is faster. Furthermore, the radial basis function of SVM is more suitable for person-associated situation and the linear function describes person-irrelevant problems better

Exploratory Research on an Affective e-Learning Model

Abstract. This paper explores how emotion evolves during the learning process with the longer term aim of developing learning systems that are able to recognize and respond appropriately to emotions exhibited by learners. We undertook this research by designing and building an experimental prototype of an emotion aware learning system conducting experiments and studying the relationship between emotion and learning. We report on our initial results which not only indicate there is a usable relationship between affect and learning, but by using the emotion states in Russell's affective model, we have been able to make some significant progress towards experimental validation of Kort's learning spiral model, which has not been empirically validated to-date

Triterpenoid glycoside from Astragalus adsurgens Pall

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Exploring the causal relationships between rheumatoid arthritis and oral phenotypes: a genetic correlation and Mendelian randomization study

BackgroundRheumatoid arthritis (RA) frequently presents with oral manifestations, including gingival inflammation, loose teeth, and mouth ulcers; however, the causal connections between these conditions remain unclear. This study aims to explore the genetic correlations and causal relationships between RA and prevalent oral phenotypes.MethodsUsing summary data from genome-wide association studies of European populations, a cross-trait linkage disequilibrium score regression was conducted to estimate the genetic correlations between RA and six oral phenotypes. Subsequently, a two-sample Mendelian randomization (MR) approach was employed to assess the causal relationships, corroborated by various sensitivity analyses. Heterogeneity was addressed through the RadialMR method, while potential covariates were corrected using the multivariable MR approach.ResultsA significant negative genetic correlation was detected between RA and denture usage (rg = −0.192, p = 4.88 × 10−8). Meanwhile, a heterogenous causal relationship between RA and mouth ulcers was observed (OR = 1.027 [1.005–1.05], p = 0.016, Pheterogeneity = 4.69 × 10−8), which remained robust across sensitivity analyses. After excluding outlier variants, the results demonstrated robustly consistent (OR = 1.021 [1.008–1.035], p = 1.99 × 10−3, Pheterogeneity = 0.044). However, upon adjusting for covariates such as smoking, alcohol consumption, body mass index, and obesity, the significance diminished, revealing no evidence to support independent genetic associations.ConclusionGenetically predicted RA increases the risk of mouth ulcers, and a negative genetic correlation is identified between RA and denture use. The observed heterogeneity suggests that shared immunological mechanisms and environmental factors may play significant roles. These findings highlight the importance of targeted dental management strategies for RA patients. Further clinical guidelines are required to improve oral health among vulnerable RA patients

Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devices

Distributed Artificial Intelligence (AI) model training over mobile edge networks encounters significant challenges due to the data and resource heterogeneity of edge devices. The former hampers the convergence rate of the global model, while the latter diminishes the devices' resource utilization efficiency. In this paper, we propose a generative AI-empowered federated learning to address these challenges by leveraging the idea of FIlling the MIssing (FIMI) portion of local data. Specifically, FIMI can be considered as a resource-aware data augmentation method that effectively mitigates the data heterogeneity while ensuring efficient FL training. We first quantify the relationship between the training data amount and the learning performance. We then study the FIMI optimization problem with the objective of minimizing the device-side overall energy consumption subject to required learning performance constraints. The decomposition-based analysis and the cross-entropy searching method are leveraged to derive the solution, where each device is assigned suitable AI-synthesized data and resource utilization policy. Experiment results demonstrate that FIMI can save up to 50% of the device-side energy to achieve the target global test accuracy in comparison with the existing methods. Meanwhile, FIMI can significantly enhance the converged global accuracy under the non-independently-and-identically distribution (non-IID) data.Comment: 13 pages, 5 figures. Submitted to IEEE for possible publicatio

The ER UDPase ENTPD5 Promotes Protein N-Glycosylation, the Warburg Effect, and Proliferation in the PTEN Pathway

SummaryPI3K and PTEN lipid phosphatase control the level of cellular phosphatidylinositol (3,4,5)-trisphosphate, an activator of AKT kinases that promotes cell growth and survival. Mutations activating AKT are commonly observed in human cancers. We report here that ENTPD5, an endoplasmic reticulum (ER) enzyme, is upregulated in cell lines and primary human tumor samples with active AKT. ENTPD5 hydrolyzes UDP to UMP to promote protein N-glycosylation and folding in ER. Knockdown of ENTPD5 in PTEN null cells causes ER stress and loss of growth factor receptors. ENTPD5, together with cytidine monophosphate kinase-1 and adenylate kinase-1, constitute an ATP hydrolysis cycle that converts ATP to AMP, resulting in a compensatory increase in aerobic glycolysis known as the Warburg effect. The growth of PTEN null cells is inhibited both in vitro and in mouse xenograft tumor models. ENTPD5 is therefore an integral part of the PI3K/PTEN regulatory loop and a potential target for anticancer therapy