In response to the COVID-19 pandemic, traditional physical classrooms have transitioned to online environments, necessitating effective strategies to ensure sustained student engagement. A significant challenge in online teaching is the absence of real-time feedback from teachers on students learning progress. This paper introduces a novel approach employing deep learning techniques based on facial expressions to assess students engagement levels during online learning sessions. Human emotions cannot be adequately conveyed by a student using only the basic emotions, including anger, disgust, fear, joy, sadness, surprise, and neutrality. To address this challenge, proposed a generation of four complex emotions such as confusion, satisfaction, disappointment, and frustration by combining the basic emotions. These complex emotions are often experienced simultaneously by students during the learning session. To depict these emotions dynamically,utilized a continuous stream of image frames instead of discrete images. The proposed work utilized a Convolutional Neural Network (CNN) model to categorize the fundamental emotional states of learners accurately. The proposed CNN model demonstrates strong performance, achieving a 95% accuracy in precise categorization of learner emotions.
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Model architecture refinement is a challenging task in deep learning research fields such as remote photoplethysmography (rPPG). One architectural consideration, the depth of the model, can have significant consequences on the resulting performance. In rPPG models that are overprovisioned with more layers than necessary, redundancies exist, the removal of which can result in faster training and reduced computational load at inference time. With too few layers the models may exhibit sub-optimal error rates. We apply Centered Kernel Alignment (CKA) to an array of rPPG architectures of differing depths, demonstrating that shallower models do not learn the same representations as deeper models, and that after a certain depth, redundant layers are added without significantly increased functionality. An empirical study confirms these findings and shows how this method could be used to refine rPPG architectures.
Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Reinforcement Learning (CoSHRL) mechanism that combines an upper level constrained search agent (which computes a reward maximizing policy from a given start to a far away goal state while satisfying cost constraints) with a low-level goal conditioned RL agent (which estimates cost and reward values to move between nearby states). A major advantage of CoSHRL is that it can handle constraints on the cost value distribution (e.g., on Conditional Value at Risk, CVaR) and can adjust to flexible constraint thresholds without retraining. We perform extensive experiments with different types of safety constraints to demonstrate the utility of our approach over leading approaches in constrained and hierarchical RL.
We address the challenge of ensuring differential privacy (DP) guarantees in training deep retrieval systems. Training these systems often involves the use of contrastive-style losses, which are typically non-per-example decomposable, making them difficult to directly DP-train with since common techniques require per-example gradient. To address this issue, we propose an approach that prioritizes ensuring query privacy prior to training a deep retrieval system. Our method employs DP language models (LMs) to generate private synthetic queries representative of the original data. These synthetic queries can be used in downstream retrieval system training without compromising privacy. Our approach demonstrates a significant enhancement in retrieval quality compared to direct DP-training, all while maintaining query-level privacy guarantees. This work highlights the potential of harnessing LMs to overcome limitations in standard DP-training methods.
Past research into robotic planning with temporal logic specifications, notably Linear Temporal Logic (LTL), was largely based on singular formulas for individual or groups of robots. But with increasing task complexity, LTL formulas unavoidably grow lengthy, complicating interpretation and specification generation, and straining the computational capacities of the planners. In order to maximize the potential of LTL specifications, we capitalized on the intrinsic structure of tasks and introduced a hierarchical structure to LTL specifications, and designed an algorithm to ascertain whether they are satisfied given an input sequence. Second, we employ a search-based approach to synthesize plans for a multi-robot system, accomplishing simultaneous task allocation and planning. The search space is approximated by loosely interconnected sub-spaces, with each sub-space corresponding to one LTL specification. The search is predominantly confined to a single sub-space, transitioning to another sub-space under certain conditions, determined by the decomposition of automatons. Moreover, multiple heuristics are formulated to expedite the search significantly. A theoretical analysis concerning completeness and optimality is conducted under mild assumptions. When compared with existing methods on service tasks, our method outperforms in terms of execution times with comparable solution quality. Finally, scalability is evaluated by testing a group of 30 robots and achieving reasonable runtimes.
Corn Yield Prediction Model with Deep Neural Networks for Smallholder Farmer Decision Support System
Given the nonlinearity of the interaction between weather and soil variables, a novel deep neural network regressor (DNNR) was carefully designed with considerations to the depth, number of neurons of the hidden layers, and the hyperparameters with their optimizations. Additionally, a new metric, the average of absolute root squared error (ARSE) was proposed to address the shortcomings of root mean square error (RMSE) and mean absolute error (MAE) while combining their strengths. Using the ARSE metric, the random forest regressor (RFR) and the extreme gradient boosting regressor (XGBR), were compared with DNNR. The RFR and XGBR achieved yield errors of 0.0000294 t/ha, and 0.000792 t/ha, respectively, compared to the DNNR(s) which achieved 0.0146 t/ha and 0.0209 t/ha, respectively. All errors were impressively small. However, with changes to the explanatory variables to ensure generalizability to unforeseen data, DNNR(s) performed best. The unforeseen data, different from unseen data, is coined to represent sudden and unexplainable change to weather and soil variables due to climate change. Further analysis reveals that a strong interaction does exist between weather and soil variables. Using precipitation and silt, which are strong-negatively and strong-positively correlated with yield, respectively, yield was observed to increase when precipitation was reduced and silt increased, and vice-versa.
The understanding of the convoluted evolution of infant brain networks during the first postnatal year is pivotal for identifying the dynamics of early brain connectivity development. Existing deep learning solutions suffer from three major limitations. First, they cannot generalize to multi-trajectory prediction tasks, where each graph trajectory corresponds to a particular imaging modality or connectivity type (e.g., T1-w MRI). Second, existing models require extensive training datasets to achieve satisfactory performance which are often challenging to obtain. Third, they do not efficiently utilize incomplete time series data. To address these limitations, we introduce FedGmTE-Net++, a federated graph-based multi-trajectory evolution network. Using the power of federation, we aggregate local learnings among diverse hospitals with limited datasets. As a result, we enhance the performance of each hospital's local generative model, while preserving data privacy. The three key innovations of FedGmTE-Net++ are: (i) presenting the first federated learning framework specifically designed for brain multi-trajectory evolution prediction in a data-scarce environment, (ii) incorporating an auxiliary regularizer in the local objective function to exploit all the longitudinal brain connectivity within the evolution trajectory and maximize data utilization, (iii) introducing a two-step imputation process, comprising a preliminary KNN-based precompletion followed by an imputation refinement step that employs regressors to improve similarity scores and refine imputations. Our comprehensive experimental results showed the outperformance of FedGmTE-Net++ in brain multi-trajectory prediction from a single baseline graph in comparison with benchmark methods.
Multimodal Large Language Models (MLLMs) are experiencing rapid growth, yielding a plethora of noteworthy contributions in recent months. The prevailing trend involves adopting data-driven methodologies, wherein diverse instruction-following datasets are collected. However, a prevailing challenge persists in these approaches, specifically in relation to the limited visual perception ability, as CLIP-like encoders employed for extracting visual information from inputs. Though these encoders are pre-trained on billions of image-text pairs, they still grapple with the information loss dilemma, given that textual captions only partially capture the contents depicted in images. To address this limitation, this paper proposes to improve the visual perception ability of MLLMs through a mixture-of-experts knowledge enhancement mechanism. Specifically, we introduce a novel method that incorporates multi-task encoders and visual tools into the existing MLLMs training and inference pipeline, aiming to provide a more comprehensive and accurate summarization of visual inputs. Extensive experiments have evaluated its effectiveness of advancing MLLMs, showcasing improved visual perception achieved through the integration of visual experts.
Physics-Informed Neural Networks for High-Frequency and Multi-Scale Problems using Transfer Learning
Physics-informed neural network (PINN) is a data-driven solver for partial and ordinary differential equations(ODEs/PDEs). It provides a unified framework to address both forward and inverse problems. However, the complexity of the objective function often leads to training failures. This issue is particularly prominent when solving high-frequency and multi-scale problems. We proposed using transfer learning to boost the robustness and convergence of training PINN, starting training from low-frequency problems and gradually approaching high-frequency problems. Through two case studies, we discovered that transfer learning can effectively train PINN to approximate solutions from low-frequency problems to high-frequency problems without increasing network parameters. Furthermore, it requires fewer data points and less training time. We elaborately described our training strategy, including optimizer selection, and suggested guidelines for using transfer learning to train neural networks for solving more complex problems.
This research paper addresses the critical challenge of diabetic retinopathy (DR), a severe complication of diabetes leading to potential blindness. The proposed methodology leverages transfer learning with convolutional neural networks (CNNs) for automatic DR detection using a single fundus photograph, demonstrating high effectiveness with a quadratic weighted kappa score of 0.92546 in the APTOS 2019 Blindness Detection Competition. The paper reviews existing literature on DR detection, spanning classical computer vision methods to deep learning approaches, particularly focusing on CNNs. It identifies gaps in the research, emphasizing the lack of exploration in integrating pretrained large language models with segmented image inputs for generating recommendations and understanding dynamic interactions within a web application context.Objectives include developing a comprehensive DR detection methodology, exploring model integration, evaluating performance through competition ranking, contributing significantly to DR detection methodologies, and identifying research gaps.The methodology involves data preprocessing, data augmentation, and the use of a U-Net neural network architecture for segmentation. The U-Net model efficiently segments retinal structures, including blood vessels, hard and soft exudates, haemorrhages, microaneurysms, and the optical disc. High evaluation scores in Jaccard, F1, recall, precision, and accuracy underscore the model's potential for enhancing diagnostic capabilities in retinal pathology assessment.The outcomes of this research hold promise for improving patient outcomes through timely diagnosis and intervention in the fight against diabetic retinopathy, marking a significant contribution to the field of medical image analysis.
Transformer is an emerging neural network model with attention mechanism. It has been adopted to various tasks and achieved a favorable accuracy compared to CNNs and RNNs. While the attention mechanism is recognized as a general-purpose component, many of the Transformer models require a significant number of parameters compared to the CNN-based ones. To mitigate the computational complexity, recently, a hybrid approach has been proposed, which uses ResNet as a backbone architecture and replaces a part of its convolution layers with an MHSA (Multi-Head Self-Attention) mechanism. In this paper, we significantly reduce the parameter size of such models by using Neural ODE (Ordinary Differential Equation) as a backbone architecture instead of ResNet. The proposed hybrid model reduces the parameter size by 94.6% compared to the CNN-based ones without degrading the accuracy. We then deploy the proposed model on a modest-sized FPGA device for edge computing. To further reduce FPGA resource utilization, we quantize the model following QAT (Quantization Aware Training) scheme instead of PTQ (Post Training Quantization) to suppress the accuracy loss. As a result, an extremely lightweight Transformer-based model can be implemented on resource-limited FPGAs. The weights of the feature extraction network are stored on-chip to minimize the memory transfer overhead, allowing faster inference. By eliminating the overhead of memory transfers, inference can be executed seamlessly, leading to accelerated inference. The proposed FPGA implementation achieves 12.8x speedup and 9.21x energy efficiency compared to ARM Cortex-A53 CPU.
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