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Deep Reinforcement Learning with Time-Scale Invariant Memory 39

Deep Reinforcement Learning with Time-Scale Invariant Memory

引用

39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kabir, Md Rysul Mochizuki-Freeman, James Tiganj, Zoran Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

ISBN: (纸本)157735897X

The ability to estimate temporal relationships is critical for both animals and artificial agents. Cognitive science and neuroscience provide remarkable insights into behavioral and neural aspects of temporal credit assignment. In particular, scale invariance of learning dynamics, observed in behavior and supported by neural data, is one of the key principles that governs animal perception: proportional rescaling of temporal relationships does not alter the overall learning efficiency. Here we integrate a computational neuroscience model of scale invariant memory into deep reinforcement learning (RL) agents. We first provide a theoretical analysis and then demonstrate through experiments that such agents can learn robustly across a wide range of temporal scales, unlike agents built with commonly used recurrent memory architectures such as LSTM. This result illustrates that incorporating computational principles from neuroscience and cognitive science into deep neural networks can enhance adaptability to complex temporal dynamics, mirroring some of the core properties of human learning. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Deep reinforcement learning

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A VR Intervention to Develop Social Skills in Children with ASD: An Expert Evaluation 24

A VR Intervention to Develop Social Skills in Children with ...

引用

Proceedings of the 2024 ACM International Conference on Interactive Media Experiences

作者： Yujing Zhang Conor Keighrey Niall Murray Computer & Software Engineering Technological University of the Shannon Ireland Department of Engineering and Informatics Technological University of the Shannon Ireland Electronics & Informatics Technological University of the Shannon Ireland

ISBN: (纸本)9798400705038

Social interactions hold immense significance in the lives of individuals. Children with a diagnosis of autism spectrum disorder (ASD) may face difficulties when engaging in social situations. These challenges often manifest as neurodivergent behaviours and a reduced willingness to engage in social sharing. Sharing is an integral aspect of social interaction. A lack of awareness or a limited inclination to share can hinder the acquisition of vital social skills. In this paper, a virtual reality (VR) application inspired by the Social Story™ intervention is presented. The aim of the work is to help children with ASD understand social scenarios and gain sharing skills. The application can be used in two configurations: 2D screen and head-mounted display (HMD). Since the system is being designed for a vulnerable group, as part of the iterative application design process, we solicited opinions from six expert professionals who work with children with ASD. They tested, evaluated (questionnaires and physiological data capture) and provided open feedback on the system. The key recommendation from the expert assessors is that the HMD-based interventions are more suitable as an advanced teaching tool for showing skills and knowledge, whereas screen-based interventions could be used for daily practice in children with ASD. Experts evaluated the two configurations from three factors: immersion, enjoyment, and comprehension. In terms of physiological responses, there was a greater increase in electrodermal activity (EDA) responses for the HMD-based intervention compared to the 2D intervention. The significant potential of such a system was highlighted by the expert users.

关键词： VR intervention autism expert evaluation sharing social skills

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OnlineProver: Experience with a Visualisation Tool for Teaching Formal Proofs 13

OnlineProver: Experience with a Visualisation Tool for Teach...

引用

13th International Workshop on Theorem Proving Components for Educational Software, ThEdu 2024

作者： Perháč, Ján Novotný, Samuel Chodarev, Sergej Kristensen, Joachim Tilsted Tveito, Lars Shturmov, Oleks Thomsen, Michael Kirkedal Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia Department of Informatics University of Oslo Oslo Norway Department of Computer Science University of Copenhagen Copenhagen Denmark

OnlineProver is an interactive proof assistant tailored for the educational setting. Its main features include a user-friendly interface for editing and checking proofs. The user interface provides feedback directly within the derivation, based on error messages from a proof-checking web service. A basic philosophy of the tool is that it should aid the student while still ensuring that the students construct the proofs as if they were working on paper. We gathered feedback on the tool through a questionnaire, and we conducted an intervention to assess its effectiveness for students in a classroom setting, alongside an evaluation of technical aspects. The initial intervention showed that students were satisfied with using OnlineProver as part of their coursework, providing initial confirmation of the learning approach behind it. This gives clear directions for future developments, with the potential to find and evaluate how OnlineProver can improve the teaching of natural deduction. © J. Perháč et al.

关键词： Teaching

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Application of Parallel Signal Processing Using Reverse Blockchain Technology in Conditions of Network Instability 4

Application of Parallel Signal Processing Using Reverse Bloc...

引用

4th International Conference on Electrical, computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Khilenko, Volodymyr Zinchenko, Andrii Stepanov, Oleksii Kotuliak, Ivan Ries, Michal Institute of Computer Engineering and Applied Informatics Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia Institute of Transport Systems and Technologies National Academy of Sciences of Ukraine Dnipro Ukraine National University of Life and Environmental Sciences of Ukraine Kyiv Department of Information Technologies Ukraine Slovak University of Technology in Bratislava Faculty of Informatics and Information Technologies Bratislava Slovakia

ISBN: (纸本)9798350391183

The theses is in line with works aimed at studying the possibility of using mathematical algorithms for parallel processing of information in reverse blockchain technology. The paper examines the use of parallel signal processing algorithms to improve the speed and quality of control systems for autonomous dynamic objects using reverse blockchain technology. The use of reverse blockchain technology is due to the possibility of loss of control signals and, at the same time, the inadmissibility of loss of control in the event of changes in the conditions and parameters of the operation of the obj ect. The purpose of the work is to analyze the implementation of solutions for parallel information processing for various models of industrially produced equipment and to develop solutions to increase the efficiency of reverse blockchain technology when using parallel data processing. © 2024 IEEE.

关键词： blockchain dynamic objects and technological processes improving the quality of control systems instability of communication channels loss of information and control signals reverse blockchain technology

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Performance Evaluation of Multi-Contrast Dixon MRI and CT for Abdominal Fat and Muscle Segmentation Using a UNet CNN

Performance Evaluation of Multi-Contrast Dixon MRI and CT fo...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Heller, Andrew Liu, Andrew Chang, Lin-Ching Cohen, Gregg Jones, Elizabeth C. Hsu, Li-Yueh National Institutes of Health Clinical Center BethesdaMD United States Catholic University of America Department of Electrical Engineering and Computer Science WashingtonDC United States New Jersey Institute of Technology Department of Informatics NewarkNJ United States

ISBN: (纸本)9798350362480

We evaluate the performance of a deep learning framework for segmenting abdominal fat and muscle using multi-contrast Dixon magnetic resonance (MR) and computed tomography (CT) images. We aim to compare MR image segmentation by testing Dixon fat-only, water-only, and combination of both types of images and comparing the results with CT images. Nineteen subjects underwent abdominal CT and Dixon MR imaging on the same day. For each participant, three pairs of matched axial images from both CT and MR were selected at the intervertebral levels of L2-L3, L3-L4, and L4-L5 for analysis. References labels were generated through semi-automated segmentation of subcutaneous adipose tissue, visceral adipose tissue, and muscle areas. They were then used to train and evaluate a U-Net based Convolutional Neural Network (CNN) framework with a 3-fold cross-validation to compare the segmentation performance across CT, Dixon fat-only and water-only MR images. Combining the fat-only and water-only MR image inputs produced superior results in all labels. Our study demonstrates that CNN-based segmentation performance for abdominal fat and muscle improves with the inclusion of additional input channels, such as combining Dixon fat-only and water-only MR images. While CT results represent the gold standard in abdominal image segmentation, increasing the number of input image channels used in MR segmentation can approach, and even match, the results of CT. © 2024 IEEE.

关键词： Image segmentation

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Internet of Things Based System for Health Monitoring for Distribution Transformer 4

Internet of Things Based System for Health Monitoring for Di...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Kumar, B. Arun Duggal, Angel Swastik Naim, Arshi Hussain, Mohammad Rashid Ahmed, Mohammad Nadeem Dildar, Muhammad Shahid Lovely Professional University School of Electronics and Electrical Engineering Punjab Phagwara India University Campus King Khalid University AlSamer Department of Business Management Abha Saudi Arabia King Khalid University College of Business Department of Business Informatics Saudi Arabia King Khalid University College of Computer Science Department of Computer Science Saudi Arabia

ISBN: (纸本)9798350364729

Electricity distribution from substations to residences and businesses depends on distributed transformers. However, a number of things, including overload, overheating, and outside elements like moisture and dust, can harm them. For distributed transformers to remain operational and prevent damage, health monitoring is crucial. Using sensors to assess vital variables like voltage, current, temperature, and humidity is one approach to keep an eye on the condition of distributed transformers. To find any possible issues, the data from these sensors can be gathered and analysed. If an issue is found, it can be fixed before the transformer is harmed. A system employing Node MCU and Arduino was suggested in a study for the health monitoring of distributed transformers. An open-source platform called Node MCU makes use of the ESP8266 microcontroller. Interactive projects are developed on the open-source Arduino electronics platform. The suggested system gathers data from the sensors and transmits it to an Arduino using a Node MCU. The data is subsequently transmitted from the Arduino via the internet to a cloud server. The data can be stored on the cloud server, which can also analyse it to find any potential issues. An alert can be provided to the user if a problem is found. This technology offers a simple and affordable solution to keep track of the condition of distributed transformers. It can aid in avoiding damage and ensuring the ongoing functionality of these crucial devices. © 2024 IEEE.

关键词： Distribution transformers

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Enhancing Robotic Arm Activity Recognition with Vision Transformers and Wavelet-Transformed Channel State Information 35

Enhancing Robotic Arm Activity Recognition with Vision Trans...

引用

35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Zandi, Rojin Behzad, Kian Motamedi, Elaheh Salehinejad, Hojjat Siami, Milad Northeastern University Department of Electrical and Computer Engineering BostonMA United States Mayo Clinic Kern Center for the Science of Healthcare Delivery RochesterMN United States Mayo Clinic Department of Artificial Intelligence and Informatics RochesterMN United States

ISBN: (纸本)9798350362244

Vision-based methods are commonly used in robotic arm activity recognition. These approaches typically rely on line-of-sight (LoS) and raise privacy concerns, particularly in smart home applications. Passive Wi-Fi sensing represents a new paradigm for recognizing human and robotic arm activities, utilizing channel state information (CSI) measurements to identify activities in indoor environments. In this paper, a novel machine learning approach based on discrete wavelet transform and vision transformers for robotic arm activity recognition from CSI measurements in indoor settings is proposed. This method outperforms convolutional neural network (CNN) and long short-term memory (LSTM) models in robotic arm activity recognition, particularly when LoS is obstructed by barriers, without relying on external or internal sensors or visual aids. Experiments are conducted using four different data collection scenarios and four different robotic arm activities. Performance results demonstrate that wavelet transform can significantly enhance the accuracy of visual transformer networks in robotic arms activity recognition. © 2024 IEEE.

关键词： Robotic arms

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Empowering Real-Time Insights Through LLM, LangChain, and SAP HANA Integration 6th

Empowering Real-Time Insights Through LLM, LangChain, and SA...

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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Easin Arafat, Md. Asuah, Georgina Saha, Sourav Orosz, Tamas Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh

ISBN: (纸本)9789819734412

Real-time integration within the SAP HANA (High-Performance Analytics Appliance) environment through open-source components signifies a substantial financial investment in information technology (IT) projects. The prevalence of budget overruns, delays in implementation, and imprecise forecasts remain significant factors contributing to project failures. This has led many companies to adopt the usage of real-time insights in planning and decision-making facilities. By keeping the fundamental components of SAP, businesses can upgrade their information systems without having to completely reconfigure them, which proves to be cost-effective. This study initiates an exploration of the intricate integration of large language model (LLM), LangChain, and SAP HANA, underlining the intrinsic value of real-time insights and their multi-faceted applications. The study's main objective is to provide individuals with a road map for utilizing these essential components, customized to their enterprise's specific phase of development. This study highlights how important this integration is and provides a strong push in the direction of operational effectiveness and long-term expansion. The study equally outlines the challenges and considerations of such components in operational effectiveness and long-term expansion. The integrated framework offers the following key benefits, linguistic precision, seamless language-technology integration, and robust analytics infrastructure. As the integrated framework develops, it becomes a revolutionary instrument that changes how real-time insights and decision-making are conducted across a variety of industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Budget control

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Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

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2nd International Conference Computational and Characterization Techniques in engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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AdaptVR: An Adaptive Learning VR System for Dental Students Training

AdaptVR: An Adaptive Learning VR System for Dental Students ...

引用

International Conference on computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Artificial Intelligence Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The paper proposes “AdaptVR” a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challenges in dental education. The system aims to provide personalized learning experiences, facilitating skill development and comprehensive assessments for students. The proposed system was tested by 17 dentistry students and 5 experts. AdaptVR framework is done for operative dentistry which is removing decay especially Class I and Class II of classification of caries. Results from the experiment revealed that 77.3% of participants exceed their expectations, 59.1% saw a positive effect on their dental skills, 90.9% reported higher levels of engagement, and 65% indicated that vibration and force feedback provided students with a genuine sensation. AdpatVR's experimental findings show an average accuracy of 50.85% for Class I, 51.7% for Class II, and 82.09% for the Class II box technique. The error percentage averages 49% for Class I, 48% for Class II, and 18% for Class II box technique.

关键词： Training Vibrations Drilling Solid modeling Adaptive learning Adaptation models Force feedback Virtual reality Teeth Dentistry

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