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Adaptive AI-Based Personalized Learning for Accelerated Vocabulary and Syntax Mastery in Young English Learners

International Journal of Advanced computer science and Applications 2025年第4期16卷 678-687页

作者： Aravind, Angalakuduru Durairaj, M. Chitkara, Preeti Baker El-Ebiary, Yousef A. Muniyandy, Elangovan Ushasree, Linginedi Ammar, Mohamed Ben Kodad Telangana 508206 India Dept.of English Panimalar Engineering College Poonamallee Chennai600123 India Head PR & International Relations Department of Applied Sciences KIET Group of Institutions Delhi-NCR Ghaziabad India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences-Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh Guntur Dist. 522302 India Center for Scientific Research and Entrepreneurship Northern Border University Arar73213 Saudi Arabia

Language acquisition is an integral part of early schooling, but young English language learners struggle to learn vocabulary and syntax since they are not provided with specialized instruction. Conventional teaching may vary according to different learning speeds and it leads to unbalanced levels of proficiency among students and possibly leading to disengagement among slow learners. The present computer-assisted learning aids provide practice interactively but without real-time adaptation and personalized feedback, limiting their capacity to address learners’ unique problems. To overcome these constraints, this study suggests an Artificial Intelligence based personalized learning system that supports vocabulary and syntax learning via adaptive learning models, NLP-based chatbots and gamified interactive lessons. The system dynamically adapts content according to students’ most recent performance in real time to enable a personalized learning experience, which results in efficient Learning. The research has experimental study design, and two groups are considered, an AI-supported learning group and a traditional learning group. Pre-test and post-test design measures the effects of the system on vocabulary recall and syntax correctness. Other learner engagement rates like survey results and qualitative feedback inform learner experience and learning efficacy. Initial results indicate that learners working with the Artificial Intelligence powered learning system gained 25percent in recalling vocabulary and 30percent in syntax accuracy over the control group. Further, learner engagement rates are elevated because of real-time feedback and gamification components. These results emphasize the promise of AI-based personalized learning to boost language acquisition and lay the basis for further effective innovations in adaptive education technologies. © (2025), (science and Information Organization). All rights reserved.

关键词： Students

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Near-Optimal Quantum Algorithm for Minimizing the Maximal Loss

arXiv

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arXiv 2024年

作者： Wang, Hao Zhang, Chenyi Li, Tongyang School of EECS Peking University China Computer Science Department Stanford University United States Center on Frontiers of Computing Studies School of Computer Science Peking University China

The problem of minimizing the maximum of N convex, Lipschitz functions plays significant roles in optimization and machine learning. It has a series of results, with the most recent one requiring O(N∊−2/3 + ∊−8/3) queries to a first-order oracle to compute an ǫ-suboptimal point. On the other hand, quantum algorithms for optimization are rapidly advancing with speedups shown on many important optimization problems. In this paper, we conduct a systematic study for quantum algorithms and lower bounds for minimizing the maximum of N convex, Lipschitz functions. On one hand, we develop quantum algorithms with an improved complexity bound of Õ(√N∊−5/3 + ∊−8/3).1 On the other hand, we prove that quantum algorithms must take Ω˜(√N∊−2/3) queries to a first order quantum oracle, showing that our dependence on N is optimal up to poly-logarithmic factors. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum theory

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SciMRC: Multi-perspective Scientific Machine Reading Comprehension 30

SciMRC: Multi-perspective Scientific Machine Reading Compreh...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Zhang, Xiao Zheng, Heqi Nie, Yuxiang Huang, Heyan Mao, Xian-Ling School of Computer Science and Technology Beijing Institute of Technology China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China Southeast Academy of Information Technology Beijing Institute of Technology China State Grid Smart Grid Research Institute Co. Ltd. China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

ISBN: (纸本)9782493814104

Scientific Machine Reading Comprehension (SMRC) aims to facilitate the understanding of scientific texts through human-machine interactions. While existing dataset has significantly contributed to this field, it predominantly focus on single-perspective question-answer pairs, thereby overlooking the inherent variation in comprehension levels among different readers. To address this limitation, we introduce a novel multi-perspective scientific machine reading comprehension dataset, SciMRC, which incorporates perspectives from beginners, students, and experts. Our dataset comprises 741 scientific papers and 6,057 question-answer pairs, with 3,306, 1,800, and 951 pairs corresponding to beginners, students, and experts respectively. Extensive experiments conducted on SciMRC using pre-trained models underscore the importance of considering diverse perspectives in SMRC and highlight the challenging nature of our scientific machine comprehension tasks. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Natural language processing systems

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How Can AI be Distributed in the computing Continuum? Introducing the Neural Pub/Sub Paradigm

arXiv

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arXiv 2023年

作者： Lovén, Lauri Morabito, Roberto Kumar, Abhishek Pirttikangas, Susanna Riekki, Jukka Tarkoma, Sasu The Center for Ubiquitous Computing University of Oulu Finland The Department of Computer Science University of Helsinki Finland

This paper proposes the neural publish/subscribe paradigm, novel approach orchestrating AI workflows in large-scale distributed AI systems in the computing continuum. Traditional centralized broker methodologies are increasingly struggling with managing the data surge resulting from the proliferation of 5G systems, connected devices, and ultra-reliable applications. Moreover, the advent of AI-powered applications, tures, particularly necessitates those leveraging a new approach advanced to orchestrate neural network and architec- schedule AI processes within the computing continuum. In response, the neural pub/sub paradigm aims to overcome these limitations by efficiently managing training, fine-tuning and inference workflows, improving distributed computation, facilitating dynamic resource allocation, and enhancing system resilience across the computing continuum. We explore this new paradigm through various design patterns, use cases, and discuss open research questions for further exploration. Copyright © 2023, The Authors. All rights reserved.

关键词： Edge computing

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Cross-domain depression detection via harvesting social media

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Physical Review Letters 2021年 1611-1617页

作者： Shen, Tiancheng Jia, Jia Shen, Guangyao Feng, Fuli He, Xiangnan Luan, Huanbo Tang, Jie Tiropanis, Thanassis Chua, Tat-Seng Hall, Wendy Department of Computer Science and Technology Tsinghua University Beijing100084 China Key Laboratory of Pervasive Computing Ministry of Education Beijing National Research Center for Information Science and Technology China School of Computing National University of Singapore Singapore Electronics and Computer Science University of Southampton United Kingdom

Depression detection is a significant issue for human well-being. In previous studies, online detection has proven effective in Twitter, enabling proactive care for depressed users. Owing to cultural differences, replicating the method to other social media platforms, such as Chinese Weibo, however, might lead to poor performance because of insufficient available labeled (self-reported depression) data for model training. In this paper, we study an interesting but challenging problem of enhancing detection in a certain target domain (e.g. Weibo) with ample Twitter data as the source domain. We first systematically analyze the depressionrelated feature patterns across domains and summarize two major detection challenges, namely isomerism and *** further propose a crossdomain Deep Neural Network model with Feature Adaptive Transformation & Combination strategy (DNN-FATC) that transfers the relevant information across heterogeneous domains. Experiments demonstrate improved performance compared to existing heterogeneous transfer methods or training directly in the target domain (over 3.4% improvement in F1), indicating the potential of our model to enable depression detection via social media for more countries with different cultural settings. © 2018 American Physical Society. All rights reserved.

关键词： Neural network models

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A Provably Secure and Lightweight Access Control Protocol for EI-Based Vehicle to Grid Environment

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IEEE Internet of Things Journal 2023年第18期10卷 16650-16657页

作者： Shamshad, Salman Mahmood, Khalid Shamshad, Usman Hussain, Ibrar Hussain, Shafiq Das, Ashok Kumar The University of Lahore Department of Software Engineering Lahore54590 Pakistan National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Douliu64002 Taiwan Riphah International University Riphah School of Computing and Innovation Lahore54000 Pakistan University of Sahiwal Department of Computer Science Sahiwal57000 Pakistan International Institute of Information Technology Hyderabad Center for Security Theory and Algorithmic Research Hyderabad500032 India Old Dominion University Virginia Modeling Analysis and Simulation Center SuffolkVA23435 United States

The energy Internet (EI) presents a novel paradigm for renewable energy distribution that utilizes communication and computing technologies to revolutionize the conventional intelligent transportation systems (ITSs) and power grid into a structure that provides assistance to open innovation. The EI offers sustainable and bidirectional transmission for the improvement and analysis of energy convention among electric vehicles (EVs) and service providers. To ensure efficient, reliable, and secure operation, EI must be safe from security attacks. Therefore, efficient and secure key negotiation is a significant issue for the EI-based Vehicle-to-Grid (V2G) architecture. Thus, to ensure the system's security, we have devised a robust scheme to facilitate a secure key agreement between the entities involved for the secure and efficient renewable energy distribution for the EI-based energy vehicles in V2G. The devised scheme's robustness is solicited through the widely accepted formal random or real (RoR) model. In addition, the informal security analysis is conducted on the devised scheme, which is evident that the designed scheme achieves all the required security features of EI-based ITS. Moreover, the performance evaluation results endorse that the designed scheme achieves the desired security and minimizes the communication, computation, and energy overhead by 29.33%, 27.94%, and 28.08% in comparison to the existing competitive schemes. © 2014 IEEE.

关键词： Vehicle-to-grid

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A Systematic Review in Educational Settings: Numerous Challenges to the Adoption of Augmented Reality 18

A Systematic Review in Educational Settings: Numerous Challe...

引用

18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kusuma Zamahsari, Gamal Nur Amalia, Mufidah Rifah, Lailatul Permana, Frihandhika Romadhon, Sahrul Prihatini, Arti Digital Language Learning Center Bina Nusantara University Faculty of Humanities CommunicationScience Department Jakarta Indonesia Digital Language Learning Center Visual Communication Design Bina Nusantara University Faculty of Humanities Jakarta Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta Indonesia Institute of Islamic Religion Madura Indonesian Language Education Study Program Pamekasan Indonesia Indonesian Language Education Faculty of Teacher Training and Education University of Muhammadiyah Malang Malang Indonesia

ISBN: (纸本)9798350331011

The integration of augmented reality has shown to be a successful implementation within the fields of education. The resultant positive impact is highly beneficial and introduces a novel dimension to the realm of education. Nevertheless, the advancement of technology and the evolving demands of education present inherent challenges that must be confronted. This study's main goal is to provide a thorough examination of the difficulties faced when implementing augmented reality (AR) technology in educational settings. This study involves an extensive review of literature focusing on the integration of augmented reality (AR) into educational contexts. Nevertheless, the availability of literature source databases remains constrained. Another researcher has asserted that this study aims to address the limitations previously discussed. The objective of this study is to adequately rectify the limitations. The bibliometric review was carried out utilizing analytical techniques offered by Scopus, while the viewing of bibliometric data was performed employing the Vos viewer application. The findings indicate that there exist multiple factors that stakeholders ought to consider while employing augmented reality within educational contexts. The field of augmented reality (AR) encompasses four separate challenges, namely technological complexities, user-related considerations, AR content creation, and the need for curriculum adjustments to meet the learning demands associated with AR integration. © 2024 IEEE.

关键词： Augmented reality

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A Generalized Network Generation Approach for Agent-Based Models

A Generalized Network Generation Approach for Agent-Based Mo...

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2021 Winter Simulation Conference, WSC 2021

作者： Hesz, Kristina Himmelspach, Jan Reinhardt, Oliver Uhrmacher, Adelinde M. NORDAKADEMIE Department of Computer Science Elmshorn25337 Germany Institute for Visual and Analytic Computing University of Rostock Rostock18059 Germany

ISBN: (纸本)9781665433112

For their initialization, many agent-based models require a population which corresponds in its essential characteristics to the examined real population. It should reflect the real distribution of attributes of interest, e.g., age, gender, or income, as well as the social network between the agents. Since a disaggregated data set with all required information is rarely available, a synthetic population must be created. Methods that assign realistic attribute values to agents are well studied in the literature. In contrast, the generation of plausible social networks has been less extensively researched, but several comprehensive adhoc models have been developed. The focus of this work is to introduce a reusable, generalized approach for the generation of synthetic social networks. Symbolic regression is used to automatically train generation rules based on a network sample, instead of having to define rules a priori. Manually specified constraints are taken into account to avoid implausible relationships. © 2021 IEEE.

关键词： Autonomous agents

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GenRec: Unifying Video Generation and Recognition with Diffusion Models

arXiv

引用

arXiv 2024年

作者： Weng, Zejia Yang, Xitong Xing, Zhen Wu, Zuxuan Jiang, Yu-Gang Shanghai Key Lab of Intell. Info. Processing School of CS Fudan University China Shanghai Collaborative Innovation Center of Intelligent Visual Computing China Department of Computer Science University of Maryland United States

Video diffusion models are able to generate high-quality videos by learning strong spatial-temporal priors on large-scale datasets. In this paper, we aim to investigate whether such priors derived from a generative process are suitable for video recognition, and eventually joint optimization of generation and recognition. Building upon Stable Video Diffusion, we introduce GenRec, the first unified framework trained with a random-frame conditioning process so as to learn generalized spatial-temporal representations. The resulting framework can naturally supports generation and recognition, and more importantly is robust even when visual inputs contain limited information. Extensive experiments demonstrate the efficacy of GenRec for both recognition and generation. In particular, GenRec achieves competitive recognition performance, offering 75.8% and 87.2% accuracy on SSV2 and K400, respectively. GenRec also performs the best on class-conditioned image-to-video generation, achieving 46.5 and 49.3 FVD scores on SSV2 and EK-100 datasets. Furthermore, GenRec demonstrates extraordinary robustness in scenarios that only limited frames can be observed. Code will be available at https://***/wengzejia1/GenRec. Copyright © 2024, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Optimizing Distributed Denial of Service (DDoS) Attack Detection Techniques on Software Defined Network (SDN) Using Feature Selection 4

Optimizing Distributed Denial of Service (DDoS) Attack Detec...

引用

4th International Conference on Innovations in computer science, ICONICS 2024

作者： Haq, Inamul Khan, Salman A. Mohammad, Nazeeruddin Zaman, Ali College of Computing and Info. Sciences Karachi Institute of Economics and Technology Karachi Pakistan Cyber Security Center Prince Mohammad Bin Fahd University Al-Khobar Saudi Arabia Shaheed Zulfiqar Ali Bhutto University Computer Science Department Karachi Pakistan

ISBN: (纸本)9798331504342

Distributed denial of service (DDoS) attacks refer to a category of cyber attacks in which an attacker attempts to overwhelm a resource, such as a server, destination, or bandwidth through various means. This is usually done by either flooding the traffic or sending a heavy crunching job to exhaust the CPU or heavy amounts of data to pileup the storage. By the rise of virtualization and cloud technologies, networks have also evolved from hardware to a piece of software which virtualizes the operation of a network equipment. This virtualization is termed as a Software Defined Network (SDN). Due to the sheer impact of cyberattacks on the performance of networks, the study of DDoS attacks on SDN is vital in the present era. Contemporary approaches such as machine learning and deep learning have proven to be effective in attack detection of DDoS in SDNs. In this paper, a preliminary analysis is carried out to study the impact of feature selection to increase the throughput and response time of Machine learning techniques in detection of DDoS attacks on SDN. The results indicate that feature selection, when applied to machine learning algorithms, is an effective approach for DDoS attack detection in SDNs. © 2024 IEEE.

关键词： Cyber attacks

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