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MDNN:Predicting Student Engagement via Gaze Direction and Facial Expression in Collaborative Learning

computer Modeling in Engineering & sciences 2023年第7期136卷 381-401页

作者： Yi Chen Jin Zhou Qianting Gao Jing Gao Wei Zhang School of Computer Science Central China Normal UniversityWuhan430079China Computer Science School of ScienceRensselaer Polytechnic InstituteTroy12180USA National Engineering Laboratory of Educational Big Data Application Technology Central China Normal UniversityWuhan430079China

Prediction of students’engagement in aCollaborative Learning setting is essential to improve the quality of *** learning is a strategy of learning through groups or *** cooperative learning behavior occurs,each student in the group should participate in teaching *** showed that students who are actively involved in a class gain *** behavior and facial expression are important nonverbal indicators to reveal engagement in collaborative learning *** studies require the wearing of sensor devices or eye tracker devices,which have cost barriers and technical interference for daily teaching *** this paper,student engagement is automatically analyzed based on computer *** tackle the problem of engagement in collaborative learning using a multi-modal deep neural network(MDNN).We combined facial expression and gaze direction as two individual components of MDNN to predict engagement levels in collaborative learning *** multi-modal solution was evaluated in a real collaborative *** results show that the model can accurately predict students’performance in the collaborative learning environment.

关键词： Engagement facial expression deep network gaze

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Learning in sinusoidal spaces with physics-informed neural networks

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第3期5卷 985-1000页

作者： Wong, Jian Cheng Ooi, Chin Chun Gupta, Abhishek Ong, Yew Soon Institute of High Performance Computing Agency for Science Technology and Research Singapore138632 Singapore School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Singapore Institute of Manufacturing Technology Agency for Science Technology and Research Singapore138632 Singapore Agency for Science Technology and Research Singapore138632 Singapore Data Science and Artificial Intelligence Research Centre School of Computer Science and Engineering Nanyang Technological University Singapore138632 Singapore

A physics-informed neural network (PINN) uses physics-Augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensure its output is consistent with fundamental physics laws. However, it turns out to be difficult to train an accurate PINN model for many problems in practice. In this article, we present a novel perspective of the merits of learning in sinusoidal spaces with PINNs. By analyzing behavior at model initialization, we first show that a PINN of increasing expressiveness induces an initial bias around flat output functions. Notably, this initial solution can be very close to satisfying many physics PDEs, i.e., falling into a localminimum of the PINN loss that onlyminimizes PDE residuals, while still being far from the true solution that jointly minimizes PDE residuals and the initial and/or boundary conditions. It is difficult for gradient descent optimization to escape from such a local minimum trap, often causing the training to stall. We then prove that the sinusoidalmapping of inputs-in an architecture we label as sf-PINN-is effective to increase input gradient variability, thus avoiding being trapped in such deceptive local minimum. The level of variability can be effectively modulated to match high-frequency patterns in the problem at hand. A key facet of this article is the comprehensive empirical study that demonstrates the efficacy of learning in sinusoidal spaces with PINNs for a wide range of forward and inversemodeling problems spanning multiple physics domains. Impact Statement-Falling under the emerging field of physicsinformed machine learning, PINN models have tremendous potential as a unifying AI framework for assimilating physics theory and measurement data. However, they remain infeasible for broad science and engineering applications due to computational cost and training challenges, especially for more complex problems. Instead of focusing on empirical demonstration of appli

关键词： Gradient methods

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Graph Classification with GNNs: Optimisation, Representation & Inductive Bias 27

Graph Classification with GNNs: Optimisation, Representation...

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27th European Conference on Artificial Intelligence, ECAI 2024

作者： Kumar, P. Krishna Ramaswamy, Harish G. Department of Computer Science & Engineering IIT Madras India Department of Data Science & Artificial Intelligence IIT Madras India

ISBN: (纸本)9781643685489

Theoretical studies on the representation power of GNNs have been centered around understanding the equivalence of GNNs, using WL-Tests for detecting graph isomorphism. In this paper, we argue that such equivalence ignores the accompanying optimization issues and does not provide a holistic view of the GNN learning process. We illustrate these gaps between representation and optimization with examples and experiments. We also explore the existence of an implicit inductive bias (e.g. fully connected networks prefer to learn low frequency functions in their input space) in GNNs, in the context of graph classification tasks. We further prove theoretically that the message-passing layers in the graph, have a tendency to search for either discriminative subgraphs, or a collection of discriminative nodes dispersed across the graph, depending on the different global pooling layers used. We empirically verify this bias through experiments over real-world and synthetic datasets. Finally, we show how our work can help in incorporating domain knowledge via attention based architectures, and can evince their capability to discriminate coherent subgraphs. © 2024 The Authors.

关键词： Contrastive Learning

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Polyp Detection Using U-Net Neural Network Based Algorithm 2

Polyp Detection Using U-Net Neural Network Based Algorithm

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Bipin Kumar Singh, Devansh Shukla, Archita Dayananda Sagar University Department of Computer Science & Engineering Bengaluru India Abes Institute of Technology Department of Computer Science & Engineering-Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Polyp is an earlier stage of cancer development in gastro-intestinal tract. Despite the fact that numerous techniques for automatic segmentation and detection of polyps have been developed, it still remains an open problem. Examining color images captured by the board camera in endoscopy is contemplated the most reliable method for detectingdeveloping polyps. To automate this process, in this paper we present a modified U-Net Neural Network based algorithm. This algorithm is a binary classifier which labels the pixels as part of the polyp or not. This model can analyze the video of GItract frame by frame and produce the collection of frames with possibility of presence of polyp thus reducing the time required to process the GI-tract. Detecting, localizing, and segmenting polyps using Kvasir-SEG, an open-access dataset of colonoscopy images, we benchmark various modern day approaches in this work, assessing bothmethod speed and accuracy. This is attributed to outstanding performance of image classification compared to preceding techniques. The automatically identifying polyps serves to aid gastroenterologists during colonoscopies. While existing literature contains publications addressing the challenge of polyp detection, many of these systems remain confined toresearch settings and lack implementation for clinical use. Hence, we present the inaugural fully open-source automated polyp detection system, which not only outperforms the best- performing system documented in the literature but is also prepared for immediate clinical application. © 2024 IEEE.

关键词： Colonoscopy ColonSegNet Colorectal cancer Kvasir-SEG Medical image segmentation Polyp Detection U-net

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Diabetic Retinopathy Detection using Deep Learning Model ResNet15 2

Diabetic Retinopathy Detection using Deep Learning Model Res...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Rai, Bipin Kumar Ojha, Harsh Srivastava, Ishani Dayananda Sagar University Department of Computer Science & Engineering Bengaluru India Abes Institute of Technology Department of Computer Science & Engineering- Data Science U.P. Ghaziabad India

ISBN: (纸本)9798350371055

Diabetic Retinopathy stands out as one of the common retinal diseases which has a significant threat to vision and can lead to blindness. Several problems occurred due to DR can be stopped by controlling blood glucose and timely treatment, but the manual assessment of retinal images is often a lengthy process and can prevail human errors. This paper attempts in the direction of finding an automated way to classify the set of retinal images. The study starts with the collection of retinal images and pre- processing them, followed by training of a CNN model, Utilizing Deep Learning methodologies, CNN classifies the degree of diabetic retinopathy (DR) into four categories: 0 (no DR) to 4 (proliferative DR). We evaluate our model's performance using a diverse dataset. We use Convolutional Neural Networks (CNNs) strength to DR detection, which involves three major difficulties: Classification, Segmentation and Detection. We work towards developing a GUI based system to store and maintain a series of predictions with the patient's id and name. Twilio API have been used to make SMS connectivity to patients possible in case they are not accessible. Many research activities in the previous years have many limitations and disadvantages like models used other than Resnet have low accuracy, not capturing intricate features and patterns in images, Vanishing Gradient Descent and low performance for image classification. Resnet models that are previously used in DR detection with Resnet152, we can conclude that it has the lowest Training loss, lowest Test loss and it has the lowest loss Difference. We have worked on a RESNET-152 pre-trained deep learning model, which consists of thousands of layers and allows for the capturing of intricate features, high performance of image classification, and high accuracy, in order to overcome the issues and limitations of earlier developed models. © 2024 IEEE.

关键词： Deep Learning Diabetic Retinopathy (DR) Fundus images Ophthalmologist Proliferative DR Resnet152

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A Comprehensive Survey on Deep Learning for Enhanced Node Position Prediction in Vehicular Ad-Hoc Networks 4

A Comprehensive Survey on Deep Learning for Enhanced Node Po...

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4th International Conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2024

作者： Nayomi, B.Deena Divya Jyothsna, V. Department of Computer Science and Engineering Andhra Pradesh Tirupati India Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798331529635

This research study presents a thorough examination of the application of deep learning techniques in enhancing node position prediction within Vehicular Ad-Hoc Networks (VANETs). As VANETs become increasingly integral to the development of intelligent transportation systems, accurate and efficient node position prediction emerges as a critical component for ensuring optimal network functionality, safety, and traffic management. This paper explores the performance and adaptability of multiple deep learning models in the dynamic and complicated VANET environment, such as Graph Neural Networks (GNNs), Recurrent Neural Networks (RNNs), and Convolutional Neural Networks (CNNs). We provide a detailed analysis of the existing literature, highlighting the strengths and limitations of different deep learning approaches in handling the high mobility, variable density, and recurrently changing topology of vehicular networks. Furthermore, the survey discusses the integration of contextual information, such as environmental factors and road conditions, into the prediction models, enhancing their accuracy and reliability. We also explore the implications of advanced methodologies like Transfer Learning and Federated Learning in this domain. In addition to providing insights into future research paths, the paper finishes with an assessment of the difficulties encountered in the practical application of these models, including data privacy and computing limits. The goal of this thorough investigation is to provide a roadmap for future developments in node location prediction in VANETs, making it an invaluable tool for scholars and professionals working in the subject of intelligent transportation systems. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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Large Language Models for Financial Aid in Financial Time-series Forecasting

Large Language Models for Financial Aid in Financial Time-se...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Islam, Md Khairul Karmacharya, Ayush Sue, Timothy Fox, Judy University of Virginia Computer Science Department United States University of Virginia School of Data Science Charlottesville United States

ISBN: (纸本)9798350362480

Considering the difficulty of financial time series forecasting in financial aid, much of the current research focuses on leveraging big data analytics in financial services. One modern approach is to utilize "predictive analysis", analogous to forecasting financial trends. However, many of these time series data in Financial Aid (FA) pose unique challenges due to limited historical datasets and high dimensional financial information, which hinder the development of effective predictive models that balance accuracy with efficient runtime and memory usage. Pre-trained foundation models are employed to address these challenging tasks. We use state-of-the-art time series models including pre-trained LLMs (GPT-2 as backbone), transformers, and linear models to demonstrate their ability to outperform traditional approaches, even with minimal ("few-shot") or no fine-tuning ("zero-shot"). Our benchmark study, which includes financial aid with seven other time series tasks, shows the potential of using LLMs for scarce financial datasets. © 2024 IEEE.

关键词： Decentralized finance

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Optimal score estimation via empirical Bayes smoothing 37

Optimal score estimation via empirical Bayes smoothing

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37th Annual Conference on Learning Theory, COLT 2024

作者： Wibisono, Andre Wu, Yihong Yang, Kaylee Yingxi Department of Computer Science Yale University United States Department of Statistics and Data Science Yale University United States

We study the problem of estimating the score function of an unknown probability distribution ρ∗ from n independent and identically distributed observations in d dimensions. Assuming that ρ∗ is subgaussian and has a Lipschitz-continuous score function s∗, we establish the optimal rate of Θ˜(n-2/d+4) for this estimation problem under the loss function ŝ - s∗2L2(ρ∗) that is commonly used in the score matching literature, highlighting the curse of dimensionality where sample complexity for accurate score estimation grows exponentially with the dimension d. Leveraging key insights in empirical Bayes theory as well as a new convergence rate of smoothed empirical distribution in Hellinger distance, we show that a regularized score estimator based on a Gaussian kernel attains this rate, shown optimal by a matching minimax lower bound. We also discuss extensions to estimating β-Hölder continuous scores with β ≤ 1, as well as the implication of our theory on the sample complexity of score-based generative models. © 2024 A. Wibisono, Y. Wu & K.Y. Yang.

关键词： Probability distributions

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Leveraging user-item interactions and hotel characteristics for hotel recommendations in Indonesia with graph neural networks 9th

Leveraging user-item interactions and hotel characteristics ...

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9th International Conference on computer science and Computational Intelligence, ICCSCI 2024

作者： Panarto, Francis Alexander Phanghegar, Tyrone Matthew Gunardi, Vincent Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Travelers usually check information about the destination before they decide to go. However, sometimes the information is too much to handle and causes confusion. A filtering mechanism is needed to help them make their choices. The solution is to implement a recommendation system;some common techniques that are used for this are Collaborative Filtering and content-based filtering, but both techniques have their flaws. Therefore, we propose a solution to combine those two methods through Graph Neural Network architectures to help fix their flaws. The proposed method will be used for recommending hotels in Indonesia using the HotelRec dataset as the base dataset and hotel features acquired from the TripAdvisor API. We use several GNN architectures, such as GCN, LightGCN, GraphSAGE, and GAT, to implement our approach. Then, we compare it to several baseline models, such as SVD and NeuMF, which are common baselines in recommendation systems. The performance of these models is calculated based on three metrics: HitRate@K, NDCG@K, and Recall@K. Based on the result of this experiment, we found that incorporating hotel features into the learning process of the model, accompanied by user-item interactions, boosted the model's performance by at least 20% in all metrics, especially in datasets with a smaller number of interactions. © 2024 The Authors.

关键词： Collaborative filtering

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Efficient Algorithms for Maximizing Group Influence in Social Networks

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Tsinghua science and Technology 2022年第5期27卷 832-842页

作者： Peihuang Huang Longkun Guo Yuting Zhong College of Mathematics and Data Science Minjiang UniversityFuzhou 350108China College of Mathematics and Computer Science Fuzhou UniversityFuzhou 350116China

In social network applications,individual opinion is often influenced by groups,and most decisions usually reflect the majority’s *** imposes the group influence maximization(GIM) problem that selects k initial nodes,where each node belongs to multiple groups for a given social network and each group has a weight,to maximize the weight of the eventually activated *** GIM problem is apparently NP-hard,given the NP-hardness of the influence maximization(IM) problem that does not consider *** on activating groups rather than individuals,this paper proposes the complementary maximum coverage(CMC) algorithm,which greedily and iteratively removes the node with the approximate least group influence until at most k nodes *** the evaluation of the current group influence against each node is only approximate,it nevertheless ensures the success of activating an approximate maximum number of ***,we also propose the improved reverse influence sampling(IRIS) algorithm through fine-tuning of the renowned reverse influence sampling algorithm for ***,we carry out experiments to evaluate CMC and IRIS,demonstrating that they both outperform the baseline algorithms respective of their average number of activated groups under the independent cascade(IC)model.

关键词： complementary maximum coverage(CMC) improved reverse influence sampling(IRIS) group influence maximization(GIM) independent cascade(IC)model

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