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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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Dipole Antenna Array Synthesis for the Improvement of the Performance Using FPA 16

Dipole Antenna Array Synthesis for the Improvement of the Pe...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Patidar, Hemant Singh, Prasanna Kumar De, Arnab Upadhyay, Prashant Yadav, Kuldeep Oriental University Department of Electrical & Electronics Engineering M. P. Indore India Niet Greater Department of Electronics and Communication Engineering Noida India School of Computing and Informatics Vignans Foundation for Science and Research Guntur India Vit Chennai India

ISBN: (纸本)9798331505264

In this paper, the authors present a strategy for the synthesis of linear array antennas for performance increase with minimum variation in active impedances by employing a meta-heuristic optimization process known as the flower pollination algorithm (FPA). This approach is utilized in conjunction with the development of a far-field pattern with a defined side lobe level (SLL), first null beam width (FNBW), and several single and one broad null placement. Taking the asymmetric excitation voltage amplitudes of the array element, the requisite outputs are obtained. The mutual coupling of parallel dipole antennas has been studied. In one example, thirty parallel dipole antennas are used to show the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Antenna lobes

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AI-Driven Learning Management Systems:Modern Developments, Challenges and Future Trends during theAge of ChatGPT

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Computers, Materials & Continua 2024年第8期80卷 3289-3314页

作者： Sameer Qazi Muhammad Bilal Kadri Muhammad Naveed Bilal AKhawaja Sohaib Zia Khan Muhammad Mansoor Alam Mazliham Mohd Su’ud Department of Computer Science Faculty of EngineeringScience and TechnologyIQRA UniversityKarachi75500Pakistan College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Engineering Department of Electrical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Engineering Department of Mechanical EngineeringIslamic University of MadinahP.O.BOX 170Madinah41411Saudi Arabia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Computing and Informatics(FCI)Multimedia University Cyberjaya63100Malaysia Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyNSW 2007Australia

COVID-19 pandemic restrictions limited all social activities to curtail the spread of the *** foremost and most prime sector among those affected were schools,colleges,and *** education system of entire nations had shifted to online education during this *** shortcomings of Learning Management Systems(LMSs)were detected to support education in an online mode that spawned the research in Artificial Intelligence(AI)based tools that are being developed by the research community to improve the effectiveness of *** paper presents a detailed survey of the different enhancements to LMSs,which are led by key advances in the area of AI to enhance the real-time and non-real-time user *** AI-based enhancements proposed to the LMSs start from the Application layer and Presentation layer in the form of flipped classroom models for the efficient learning environment and appropriately designed UI/UX for efficient utilization of LMS utilities and resources,including AI-based *** layer enhancements are also required,such as AI-based online proctoring and user authentication using *** extend to the Transport layer to support real-time and rate adaptive encrypted video transmission for user security/privacy and satisfactory working of *** also needs the support of the Networking layer for IP-based geolocation features,the Virtual Private Network(VPN)feature,and the support of Software-Defined Networks(SDN)for optimum Quality of Service(QoS).Finally,in addition to these,non-real-time user experience is enhanced by other AI-based enhancements such as Plagiarism detection algorithms and Data Analytics.

关键词： Learning management systems chatbots ChatGPT online education Internet of Things(IoT) artificial intelligence(AI) convolutional neural networks natural language processing

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Twister2 Cross-platform resource scheduler for big data

Twister2 Cross-platform resource scheduler for big data

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作者： Uyar, Ahmet Gunduz, Gurhan Kamburugamuve, Supun Wickramasinghe, Pulasthi Widanage, Chathura Govindarajan, Kannan Perera, Niranda Abeykoon, Vibhatha Akkas, Selahattin Fox, Geoffrey School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Department of Computer Engineering Faculty of Engineering Mugla Sitki Kocman University Mugla Turkey

Twister2 is an open-source big data hosting environment designed to process both batch and streaming data at scale. Twister2 runs jobs in both high-performance computing (HPC) and big data clusters. It provides a cross-platform resource scheduler to run jobs in diverse environments. Twister2 is designed with a layered architecture to support various clusters and big data problems. In this paper, we present the cross-platform resource scheduler of Twister2. We identify required services and explain implementation details. We present job startup delays for single jobs and multiple concurrent jobs in Kubernetes and OpenMPI clusters. We compare job startup delays for Twister2 and Spark at a Kubernetes cluster. In addition, we compare the performance of terasort algorithm on Kubernetes and bare metal clusters at AWS cloud. © 2021 John Wiley & Sons, Ltd.

关键词： Big data

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Spatio-Temporal Road Traffic Prediction using Real-time Regional Knowledge

arXiv

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arXiv 2024年

作者： Han, Sumin An, Jisun Lee, Dongman School of Computing Korea Advanced Institute of Science and Technology Korea Republic of Luddy School of Informatics Computing and Engineering Indiana University Bloomington United States

For traffic prediction in transportation services such as carsharing and ride-hailing, mid-term road traffic prediction (within a few hours) is considered essential. However, the existing road-level traffic prediction has mainly studied how significantly micro traffic events propagate to the adjacent roads in terms of short-term prediction. On the other hand, recent attempts have been made to incorporate regional knowledge such as POIs, road characteristics, and real-time social events to help traffic prediction. However, these studies lack in understandings of different modalities of road-level and region-level spatio-temporal correlations and how to combine such knowledge. This paper proposes a novel method that embeds real-time region-level knowledge using POIs, satellite images, and real-time LTE access traces via a regional spatio-temporal module that consists of dynamic convolution and temporal attention, and conducts bipartite spatial transform attention to convert into road-level knowledge. Then the model ingests this embedded knowledge into a roadlevel attention-based prediction model. Experimental results on real-world road traffic prediction show that our model outperforms the baselines. © 2024, CC BY.

关键词： Prediction models

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Designing Quantum-Resilient Blockchain Frameworks: Enhancing Transactional Security with Quantum Algorithms in Decentralized Ledgers

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 618-628页

作者： Kale, Meenal R. Baker El-Ebiary, Yousef A. Sathiya, L. Burugari, Vijay Kumar Yulduz, Erkiniy Muniyandy, Elangovan Alanazi, Rakan Department of Humanities Yeshwantrao Chavan College of Engineering Hingna Nagpur India Faculty of Informatics and Computing UniSZA University Malaysia Department of CSE Panimalar Engineering College Chennai India Dept of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Applied Science Research Center Applied Science Private University Amman Jordan Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia

Quantum computing is progressing at a fast rate and there is a real threat that classical cryptographic methods can be compromised and therefore impact the security of blockchain networks. All of the ways used to secure blockchain like Rivest–Shamir–Adleman (RSA), Elliptic Curve Cryptography (ECC) and Secure Hash Algorithm 256-bit (SHA256) are the characteristic of the traditional cryptographic techniques vulnerable to attack by quantum algorithms: Shor’s and Grover’s algorithms: can efficiently break asymmetric encryption and speed up brute force attacks. Because of this vulnerability, there exists a need to develop an advance quantum resilient blockchain framework to protect the decentralized ledgers from the future threats of the quantum. This research proposes Post-Quantum Cryptography (PQC), Quantum Key Distribution (QKD) and Quantum Random Number Generation (QRNG) as a formidable architectural integration, to fortify security of blockchain. Classical encryption is replaced with PQC, QKD with secure key exchange by detecting eavesdropping, and QRNG with improving cryptographic randomness to remove the predictable key vulnerability. Only with a small loss of transaction efficiency, we increase transaction encryption accuracy, key exchange security, and resistance to quantum attacks. In this quantum enhanced blockchain design, the idea is to preserve the decentralization, transparency and security and at the same time overcome the future quantum threat. By going through rigorous analysis and comparative evaluation, we demonstrate that the approach saves blockchain networks from the emerging quantum risks to make sure that the decentralized finance, smart contracts and cross chain transactions. © (2025), (Science and Information Organization). All rights reserved.

关键词： Quantum cryptography

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Empowering the adoption of blockchain-based cryptocurrency as a payment method: A user-friendly educational approach

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Alrehaili, Ahmed White, Martin Beloff, Natalia University of Sussex School of Engineering and Informatics Informatics Brighton United Kingdom Islamic University of Madinah College of Computing and Information Systems Computer Science Department Medina Saudi Arabia

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of engineering and Technology.

关键词： blockchain platforms blockchains cryptocurrencies models and analysis

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"I Am There But...": Remote Students’ Experiences of Inclusion through Telepresence Robots

"I Am There But...": Remote Students’ Experiences of Inclus...

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2025 CHI Conference on Human Factors in computing Systems, CHI EA 2025

作者： Elmimouni, Houda Sabanovic, Selma Department of Computer Science University of Manitoba WinnipegMB Canada University of Siegen Siegen Germany Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

ISBN: (纸本)9798400713958

The use of telepresence robots in educational settings offers a promising solution for students who are unable to attend classes in person due to illness, disability, or other barriers. However, the extent to which these technologies facilitate the perceived inclusion of remote students remains underexplored. This study investigates the experiences of remote students using telepresence robots to attend university classes, focusing on factors that mediate their sense of inclusion. Through an inductive analysis of interviews and observations from 22 remote students, we identify technological and socio-pedagogical factors that influence inclusion. Our findings reveal that while telepresence robots enhance physical presence and agency compared to traditional video conferencing tools, challenges such as technological limitations, social norms, and pedagogical practices can hinder full integration. We offer recommendations for educators and institutions to better support remote students and suggest design improvements for telepresence technologies to foster inclusivity in classroom settings. © 2025 Copyright held by the owner/author(s).

关键词： Teaching

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Low Complexity Deep Learning Based Coordinated Beamforming for mmWave Massive MIMO Vehicular Networks

Low Complexity Deep Learning Based Coordinated Beamforming f...

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2024 Panhellenic Conference on Electronics and Telecommunications, PACET 2024

作者： Rekkas, Vasileios P. Sotiroudis, Sotirios P. Sarigiannids, Panagiotis Karagiannidis, George K. Christodoulou, Christos G. Goudos, Sotirios K. University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani Greece Aristotle University of Thessaloniki School of Electrical and Computer Engineering Thessaloniki Greece The University of New Mexico Department of Engineering and Computing United States Aristotle University of Thessaloniki ELEDIA@AUTH School of Physics Thessaloniki Greece Bharath University Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350318845

Enabling high mobility in millimeter-wave (mmWave) systems is crucial for next-generation wireless communication systems. Challenges arise in applications such as vehicular communications and wireless virtual/augmented reality. MmWave systems grapple with concerns related to narrow beams, signal vulnerability to obstructions that affect coverage, and the necessity for frequent handovers. Additionally, identifying optimal beamforming vectors in large antenna array mmWave systems involves substantial training overhead, impacting efficiency. This paper introduces a novel solution to address challenges in enabling high mobility in mmWave systems. The approach combines deep learning (DL) and coordinated beamforming to support applications such as vehicular communications and wireless virtual/augmented reality. The solution involves multiple coordinating base stations (BSs) serving a mobile user, utilizing a single pilot training sequence. A DL model predicts beamforming vectors based on received signals, providing reliable coverage, low latency, and minimal training overhead for highly mobile mmWave applications. Simulations demonstrate its effectiveness in high-mobility scenarios, approaching the performance of an optimal solution, a genie-aided solution that knows the optimal beamforming vectors without any training overhead. © 2024 IEEE.

关键词： Millimeter waves

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Product Recommendation Using Collaborative Filtering and K-Means Clustering 5

Product Recommendation Using Collaborative Filtering and K-M...

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5th IEEE International Conference on computing, Power, and Communication Technologies, IC2PCT 2024

作者： Kumar, M. Ranjith Vishnu, S. Roshen, G. Kumar, D. Niranjan Revathi, P. Baster, David Ricardo Ledo Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Mathematics Chennai India Amrita Vishwa Vidyapeetham Dept. of Computer Science & Engineering Amrita School of Computing Chennai India C. Abdul Hakeem College of Engineering and Technology Dept. of Computer Science & Engineering Vellore Melvisharam India University of Holguin Dept. of Informatics and Mathematics Cuba

ISBN: (纸本)9798350383522

The growth of e-commerce has altered how consumers shop, providing a digital space where convenience, vast product offerings, and competitive pricing converge. In today's world, e-commerce websites are transitioning from traditional search-driven methods to customized and intuitive approaches via product suggestions. Product recommendation systems are vital in e-commerce, from bringing new business to retaining existing ones. Our three-part recommendation system is designed so that new users have a great and engaging experience as the Product Popularity -Based System shows them carefully chosen products that are in demand. Collaborative Filtering Recommendations are highly personalized recommendations given to people who have already made their first purchases based on their prior actions and preferences. The K-Means Clustering-Based Recommendation System uses textual clustering analysis to deliver contextually relevant recommendations. We use a variety of evaluation metrics, such as click-through rates, user engagement, and the Silhouette Score, to assess the effectiveness and accuracy of our recommendation systems. Our findings show significant increases in user engagement, conversion rates, and relevant recommendations. Our findings demonstrate the transformative power of well-designed recommendation systems, which improve user experiences and retention and provide invaluable solutions for businesses entering the e-commerce space. This paper provides an in-depth examination of the multifaceted landscape of e-commerce recommendations, shedding light on their far-reaching implications for customer acquisition and retention in this dynamic digital era. © 2024 IEEE.

关键词： clustering collaborative filtering E-commerce systems K-means algorithm recommender system

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