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Smart Sampling: Self-Attention and Bootstrapping for Improved Ensembled Q-Learning 37

Smart Sampling: Self-Attention and Bootstrapping for Improve...

引用

37th International Florida Artificial Intelligence Research Society Conference, FLAIRS 2024

作者： Khan, Muhammad Junaid Ahmed, Syed Hammad Sukthankar, Gita Dept. of Computer Science University of Central Florida Orlando United States

We present a novel method aimed at enhancing the sample efficiency of ensemble Q learning. Our proposed approach integrates multi-head self-attention into the ensembled Q networks while bootstrapping the state-action pairs ingested by the ensemble. This not only results in performance improvements over the original REDQ (Chen et al. 2021) and its variant DroQ (Hiraoka et al. 2022), thereby enhancing Q predictions, but also effectively reduces both the average normalized bias and standard deviation of normalized bias within Q-function ensembles. Importantly, our method also performs well even in scenarios with a low update-to-data (UTD) ratio. Notably, the implementation of our proposed method is straightforward, requiring minimal modifications to the base model. Copyright © 2024 by the authors.

关键词： Reinforcement learning

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A Photovoltaic Interleaved Boost Integrated CL Resonant Converter for Magnetron Application

A Photovoltaic Interleaved Boost Integrated CL Resonant Conv...

引用

2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Molaei, Aref Kanathipan, Kajanan Lam, John York University Dept. of Electrical Engineering and Computer Science Toronto Canada

ISBN: (纸本)9798350376067

A new single-stage high-gain interleaved boost modular CL resonant converter is proposed in this paper for photovoltaic-powered magnetron application. The proposed converter is able to provide high voltage gain for the magnetron which is a device for microwave applications. Perturb and Observe (P&O) method was used to achieve Maximum Power Point Tracking (MPPT) in the integrated interleaved boost stage by controlling the duty-cycle of the two primary leg switches. The operational principles of the converter are briefly discussed in this paper. Results on a 1.7kW, 230V/3.8kV design are presented to verify the proposed converter performance. © 2024 IEEE.

关键词： Magnetrons

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Designing for Embodied Sense-making of Mathematics: Perspectives on Directed and Spontaneous Bodily Actions

Designing for Embodied Sense-making of Mathematics: Perspect...

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2024 ACM Designing Interactive Systems Conference, DIS 2024

作者： Chatain, Julia Gashaj, Venera Muttappillil, Bibin Sumner, Robert W. Kapur, Manu ETH Zurich Dept. of Computer Science Dept. of Humanities Social and Political Sciences Switzerland Loughborough University School of Science Centre for Early Mathematics Learning United Kingdom ETH Zurich Dept. of Computer Science Switzerland ETH Zurich Dept. of Humanities Social and Political Sciences Switzerland

ISBN: (纸本)9798400705830

While mathematics is conventionally viewed as an abstract discipline, contemporary perspectives on embodied cognition underscore the signifcance of integrating students’ bodily experiences into the learning process. However, the efcacy of embodied learning activities, as compared to traditional methods, remains under scrutiny. We argue that both directed and spontaneous bodily actions should be considered when designing embodied learning activities, and explore such bodily actions through two studies. A quantitative user study involving directed bodily actions in Virtual Reality and on tablet reveals vr’s support for math-anxious and body-aware learners, and distinct movement patterns related to varying mathematical abilities. A subsequent qualitative analysis identifes key characteristics of spontaneous bodily actions, namely coarseness, muscle tension, repetitions, anchors, perspective, and metaphors. Derived from both studies, we propose design recommendations, advocating for expanded embodied interaction design, consideration of embodied metaphors, coarse gesturing for deep features identifcation, supporting of sense-making anchors, and in-vr learning assessments. © 2024 Copyright held by the owner/author(s).

关键词： Virtual reality

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Impact of Novel Pre-Processing Techniques on Retinal Images 2

Impact of Novel Pre-Processing Techniques on Retinal Images

引用

2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Sahu, Deepti Kaur, Mandeep Sharda University Dept. of Computer Science & Engineering Greater Noida India

ISBN: (纸本)9798350371055

Glaucoma, a gradually developing eye condition that can damage the optic nerve, is one of the leading causes of irreversible blindness worldwide. The detection of glaucoma is required in its initial phases for the prevention of the disease. Several machine learning and deep learning models have been developed to detect the disease. These models require a preprocessed structured data set for better efficiency and accuracy of detection. The image datasets used for this purpose have been found with several issues of outliers and noise that limit the accuracy of the results. Hence, proper handling of the dataset in terms of pre-processing is vital. This paper presents a novel pre-processing technique for better glaucoma detection as it is necessary to remove outliers from the retinal images. The proposed 'EMF' model for pre-processing comprises Enhancement, Morphology, and Filtering of the images to get better results on the Retina. Drishti dataset has been used to evaluate the efficiency of the proposed technique. This dataset has several outliers that limit the accuracy, making it difficult to diagnose glaucoma. However, with the usage of the proposed technique, the dataset could be efficiently utilized for glaucoma detection. Compared to current state-of-the-art techniques, the findings from the proposed approach were shown to be better in terms of MSE, PSNR, and entropy. © 2024 IEEE.

关键词： Frequency domain Glaucoma Pre-processing Region of interest Retinal image Spatial domain

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Move Match: Live Dance Motion Monitoring and Feedback System

Move Match: Live Dance Motion Monitoring and Feedback System

引用

2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Saral, G. Baby Subasri, S. Ramya Shree, R. Bose, K. Regin Rajalakshmi Institute of Technology Dept. Artificial Intelligence & Data Science Chennai India Artificial Intelligence and Data Science Rajalakshmi Institute of Technology Chennai India Artificial Intelligence and Data Science Student Rajalakshmi Institute of Technology Chennai India Rajalakshmi Institute of Technology Dept. AI&DS & Cloud Computing Chennai India

ISBN: (纸本)9798331512965

Dance movement accuracy prediction is a critical component of modern dance training, yet traditional methods often lack objectivity due to the subjective nature of evaluations and the complexity of human movement. To address these challenges this paper introduces MoveMatch, which is a web-based platform built for real-time dance movement prediction. By leveraging deep learning techniques, the platform performs posture estimation in real-time and detects key points. The system compares the detected poses using Euclidean distance to calculate the alignment between corresponding key points which provides a quantitative measure of the user's accuracy. Based on this comparison, MoveMatch delivers real-time feedback, offering an accuracy score and personalized suggestions for improvement. This immediate feedback fosters a more efficient and engaging learning experience for dancers. Extensive testing of MoveMatch in various dance styles demonstrates its effectiveness in objectively assessing performance, enhancing both training quality and accessibility. The integration of pose estimation, deep learning and real-time comparison offers a novel, user-friendly solution for dancers seeking to improve their skills, transforming traditional dance education with AI-driven insights. © 2025 IEEE.

关键词： Deep learning

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Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance 15

Analyzing the Impact of AI Tools on Student Study Habits and...

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15th IEEE Annual Computing and Communication Workshop and Conference, CCWC 2025

作者： Ward, Ben Bhati, Deepshikha Neha, Fnu Guercio, Angela Kent State University Dept. of Computer Science KentOH United States

ISBN: (纸本)9798331507695

This study explores the effectiveness of AI tools in enhancing student learning, specifically in improving study habits, time management, and feedback mechanisms. The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis. Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA, suggesting positive academic outcomes. Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified, emphasizing the need for AI tools to complement conventional educational strategies rather than replace them. data were collected through a survey with a Likert scale and follow-up interviews, providing both quantitative and qualitative insights. The analysis involved descriptive statistics to summarize demographic data, AI usage patterns, and perceived effectiveness, as well as inferential statistics (T-tests, ANOVA) to examine the impact of demographic factors on AI adoption. Regression analysis identified predictors of AI adoption, and qualitative responses were thematically analyzed to understand students' perspectives on the future of AI in education. This mixed-methods approach provided a comprehensive view of AI's role in education and highlighted the importance of privacy, transparency, and continuous refinement of AI features to maximize their educational benefits. © 2025 IEEE.

关键词： Adaptive learning paths AI in education Personalized learning Real-time feedback Study habits

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Leveraging Deep Learning With Multi-Head Attention for Accurate Extraction of Medicine From Handwritten Prescriptions 6

Leveraging Deep Learning With Multi-Head Attention for Accur...

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6th International Conference on Robotics and Automation in Industry, ICRAI 2024

作者： Ali, Usman Ranmbail, Sahil Nadeem, Muhammad Ishfaq, Hamid Ramzan, Muhammad Umer Ali, Waqas GIFT University Dept. of Computer Science Gujranwala Pakistan Lahore Pakistan

ISBN: (纸本)9798350355703

Extracting medication names from handwritten doctor prescriptions is challenging due to the wide variability in handwriting styles and prescription formats. This paper presents a robust method for extracting medicine names using a combination of Mask R-CNN and Transformer-based Optical Character Recognition (TrOCR) with Multi-Head Attention and Positional Embeddings. A novel dataset, featuring diverse handwritten prescriptions from various regions of Pakistan, was utilized to fine-tune the model on different handwriting styles. The Mask R-CNN model segments the prescription images to focus on the medicinal sections, while the TrOCR model, enhanced by Multi-Head Attention and Positional Embeddings, transcribes the isolated text. The transcribed text is then matched against a pre-existing database for accurate identification. The proposed approach achieved a character error rate (CER) of 1.4% on standard benchmarks, highlighting its potential as a reliable and efficient tool for automating medicine name extraction. © 2024 IEEE.

关键词： Handwritten doctor prescriptions Mask R-CNN Multi-Head Attention Positional Embeddings TrOCR

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data Sharing-Aware Online Algorithms for Task Allocation in Edge Computing Systems 8

Data Sharing-Aware Online Algorithms for Task Allocation in ...

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8th IEEE International Conference on Edge Computing and Communications, EDGE 2024

作者： Rabinia, Sanaz Grosu, Daniel Wayne State University Dept. Computer Science Detroit United States

ISBN: (纸本)9798350368499

Many of the tasks offloaded to edge devices perform computation to analyze sensing data. Transferring this data from end-user devices to edge servers leads to increased latency and congestion in the edge network. Since many of the offloaded tasks may require processing the same data items, the task allocation algorithms can exploit this to reduce the traffic in the networks and the number of edge servers needed to execute the tasks. Therefore, in this paper we design online algorithms for task allocation in edge computing systems that take into account the sharing of data among the tasks offloaded to the same server. We perform an extensive performance analysis by comparing our proposed algorithms with several sharing-oblivious baseline algorithms. The results show that our algorithms are able to reduce the amount of data transferred in the network by 30.2% to 92.8% and the number of utilized servers by 1% to 82.8% compared to the sharing-oblivious baseline algorithms. © 2024 IEEE.

关键词： data assimilation

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Enhanced Building Prediction for Smart City Surveillance 1

Enhanced Building Prediction for Smart City Surveillance

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Natarajan, Yuvaraj Wadhwa, Gitanjali Saravanan, Lekshmipriya Natarajan, Mathivathani KPR Institute of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore641407 India KPR Institute of Engineering and Technology Dept. of Artificial Intelligence and Data Science Coimbatore641407 India

ISBN: (纸本)9798350348057

Nearly three-quarters of the total power used in the world is consumed by the built environment sector. Therefore, looking for viable methods to reduce building energy needs and decrease negative environmental repercussions is vital. By incorporating intelligence into applications, everyone in the current situation has become intelligent, which lessens the load of constant interruption or human control. By using Machine to Machine interactions, intelligence enables the ability to connect more real-time parameters and make moral judgments in perilous circumstances. The current study examined the framework, approach, and performance of currently available AI-based techniques. According to the research, choosing the best machine and deep learning model for a given task can be difficult. Among the primary areas of study in relation to the vast amounts of data collected is the viability in the practice of the used algorithms, both standard machine learning and the more recent branch known as deep learning. Present a quantitative analysis contrasting the effectiveness of conventional deep learning and machine learning strategies using the direction of human movement in detecting the applications based on the analogue pyroelectric infrared sensor (PIR) outputs. Here, Random Forest is used as the machine learning method because of its great prediction accuracy. Over deeper datasets, deeper CNNs outperform shallow models as a deep learning method. The study demonstrates that traditional Machine Learning performs with detecting values within the first few seconds, improving real-time detection. Additionally, it might be used on more smart buildings without upgrades. © 2023 IEEE.

关键词： Intelligent buildings

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Gaze Detection and Text Extraction from Images using CNN with DBSCAN-based Filter Design 11

Gaze Detection and Text Extraction from Images using CNN wit...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Jose, Jithin Ramanujan, Ajeesh College Of Engineering Trivandrum Dept. Of Computer Science And Engineering Trivandrum India

ISBN: (纸本)9798350379136

Gaze detection and text extraction are pivotal technologies in the domain of human-computer interaction and computer vision, enabling applications such as assistive technologies, user interface optimization, and automated content analysis. This paper presents a novel method that identifies the user's gaze direction and extracts the necessary portion of text, which constitutes the user's region of interest, using a Convolutional Neural Network (CNN) integrated with a spatial attention mechanism. A Density-Based Spatial Clustering of Applications with Noise (DBSCAN)-based filter is incorporated into the system to remove textual noise from the extracted image, thereby improving the model's accuracy and reducing computation time. The proposed method demonstrates reduced dependency on the CNN compared to previous approaches, resulting in enhanced real-time operation capability and lower latency. Computational optimization and real-time performance were thoroughly evaluated, showing that our system not only achieves high accuracy in gaze detection and text extraction but also maintains efficient processing speeds suitable for dynamic applications. This advancement offers significant improvements for applications in assistive technology, educational tools, and interactive media, where precise and efficient gaze detection and text extraction are critical. © 2024 IEEE.

关键词： Convolutional neural networks

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