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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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Gendering Robots in Human-Robot Interaction: An Interdisciplinary Approach 25

Gendering Robots in Human-Robot Interaction: An Interdiscipl...

引用

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

作者： Neziha Akalin Maria Arnelid Katherine Harrison Dept. of Computer Science and Informatics School of Engineering Jönköping University Jönköping Sweden Dept. of Thematic Studies Unit of Gender Studies Linköping University Linköping Sweden

ISBN: (数字)9798350378931

ISBN: (纸本)9798350378948

This paper emphasizes the importance of interdisciplinary collaboration in exploring the concept of robot gender within Human-Robot Interaction (HRI). It draws on a case study of the authors' own collaboration, where interdisciplinary discussions on the nature of gender informed the design of a multimethod study. This approach allowed us to avoid assigning a binary gender to Pepper robot, while still encouraging participants to reflect on their own gendering practices during interactions with robots. Additionally, we invite readers to consider alternative ways of conceptualizing robot gender in HRI—specifically, as fluid and performative rather than binary, which relies on stereotypical cues. After describing how our discussions on gender influenced and reshaped the study design, we offer practical advice on fostering interdisciplinary collaborations. These suggestions focus on communication strategies, mindset, and the practical setup of collaborative studies. We hope that these recommendations will inspire other researchers to continue exploring new and interdisciplinary ways of approaching robot gender in HRI.

关键词： Fluids Buildings Human-robot interaction Collaboration Medical services Robots

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Coreference as an indicator of context scope in multimodal narrative

arXiv

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arXiv 2025年

作者： Ilinykh, Nikolai Lappin, Shalom Sayeed, Asad Loáiciga, Sharid Dept. of Philosophy Linguistics Theory of Science University of Gothenburg Sweden School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom Dept. of Informatics King’s College London United Kingdom

We demonstrate that large multimodal language models differ substantially from humans in the distribution of coreferential expressions in a visual storytelling task. We introduce a number of metrics to quantify the characteristics of coreferential patterns in both human- and machine-written texts. Humans distribute coreferential expressions in a way that maintains consistency across texts and images, interleaving references to different entities in a highly varied way. Machines are less able to track mixed references, despite achieving perceived improvements in generation quality. © 2025, CC BY-NC-SA.

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Experimental Analysis of a Self-Coherent M-QAM Receiver by Means of Recurrent Optical Spectrum Slicing and Direct Detection

arXiv

引用

arXiv 2025年

作者： Sozos, Kostas Da Ros, Francesco Yankov, Metodi P. Deligiannidis, Stavros Sarantoglou, George Mesaritakis, Charis Bogris, Adonis University of West Attica Dept. of Informatics and Computer Engineering. Aghiou Spiridonos Egaleo Athens12243 Greece University of West Attica Dept. of Biomedical Engineering Aghiou Spiridonos Egaleo Athens12243 Greece Dept. of Electrical and Photonics Engineering Technical University of Denmark Kgs. LyngbyDK-2800 Denmark

High order modulation formats constitute the most prominent way for increasing spectral efficiency in transmission systems. Coherent transceivers that support such higher order formats require heavy digital signal processing (DSP), which increases the power consumption of coherent pluggables, well above the intensity modulation and direct detection (IM/DD) counterparts. Self-coherent or phase retrieval methods have emerged as potential solutions, trying to combine the merits of coherent technology with the simplicity of direct detection. In this work, we experimentally demonstrate the reception of quadrature amplitude modulation (QAM) modulation formats based on direct detection aided by the recurrent optical spectrum slicing (ROSS) photonic accelerator, utilizing minimal DSP and low modulator driving voltages. We experimentally demonstrate 32 Gbaud QAM-4/16 for 25 km, 50 km and 75 km in the C-band aided by a linear digital equalization and the use of programmable photonics as recurrent optical spectrum slicers. We showcase successful detection with driving swings below Vπ/3 in contrast to the full swing required by conventional coherent transceivers. We further improve the system performance utilizing geometric constellation shaping. Finally, we explore the potential power consumption improvement for the next-generation 1.6T pluggables, showcasing over 40% reduction with respect to the most lightweight state of the art coherent solutions reported in literature. © 2025, CC BY.

关键词： Quadrature amplitude modulation

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A Study on Neuro-Symbolic Artificial Intelligence: Healthcare Perspectives

arXiv

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arXiv 2025年

作者： Hossain, Delower Chen, Jake Y. Dept. of Computer Science The University of Alabama Birmingham United States Dept. of Genetic Biomedical Engineering Informatics and Data Science Heersink School of Medicine The University of Alabama Birmingham United States Systems Pharmacology AI Research Center The University of Alabama Birmingham United States

Over the last few decades, Artificial Intelligence (AI) scientists have been conducting investigations to attain human-level performance by a machine in accomplishing a cognitive task. Within machine learning, the ultimate aspiration is to attain Artificial General Intelligence (AGI) through a machine. This pursuit has led to the exploration of two distinct AI paradigms. Symbolic AI, also known as classical or GOFAI (Good Old-Fashioned AI) and Connectionist (Sub-symbolic) AI, represented by Neural Systems, are two mutually exclusive paradigms. Symbolic AI excels in reasoning, explainability, and knowledge representation but faces challenges in processing complex real-world data with noise. Conversely, deep learning (Black-Box systems) research breakthroughs in neural networks are notable, yet they lack reasoning and interpretability. Neuro-symbolic AI (NeSy), an emerging area of AI research, attempts to bridge this gap by integrating logical reasoning into neural networks, enabling them to learn and reason with symbolic representations. While a long path, this strategy has made significant progress towards achieving common sense reasoning by systems. This article conducts an extensive review of over 977 studies from prominent scientific databases (DBLP, ACL, IEEExplore, Scopus, PubMed, ICML, ICLR), thoroughly examining the multifaceted capabilities of Neuro-Symbolic AI, with a particular focus on its healthcare applications, particularly in drug discovery, and Protein engineering research. The survey addresses vital themes, including reasoning, explainability, integration strategies, 41 healthcare-related use cases, benchmarking, datasets, current approach limitations from both healthcare and broader perspectives, and proposed novel approaches for future experiments. Moreover, it identifies critical challenges in applying Neuro-Symbolic AI to medicine and represents a comprehensive exploration of its transformative potential in the biomedical field. © 2025, CC BY-NC

关键词： Knowledge representation

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State of play and future directions in industrial computer vision AI standards

arXiv

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arXiv 2025年

作者： Stefanidou, Artemis Radoglou-Grammatikis, Panagiotis Argyriou, Vasileios Sarigiannidis, Panagiotis Varlamis, Iraklis Papadopoulos, Georgios Th. Dept. of Informatics and Telematics Harokopio University of Athens Athens Greece K3Y Ltd Sofia Bulgaria Dept. of Networks and Digital Media Kingston University Kingston upon Thames United Kingdom Dept. of Electrical and Computer Engineering University of Western Macedonia Kozani Greece

The recent tremendous advancements in the areas of Artificial Intelligence (AI) and Deep Learning (DL) have also resulted into corresponding remarkable progress in the field of computer Vision (CV), showcasing robust technological solutions in a wide range of application sectors of high industrial interest (e.g., healthcare, autonomous driving, automation, etc.). Despite the outstanding performance of CV systems in specific domains, their development and exploitation at industrial-scale necessitates, among other, the addressing of requirements related to the reliability, transparency, trustworthiness, security, safety, and robustness of the developed AI models. The latter raises the imperative need for the development of efficient, comprehensive and widely-adopted industrial standards. In this context, this study investigates the current state of play regarding the development of industrial computer vision AI standards, emphasizing on critical aspects, like model interpretability, data quality, and regulatory compliance. In particular, a systematic analysis of launched and currently developing CV standards, proposed by the main international standardization bodies (e.g. ISO/IEC, IEEE, DIN, etc.) is performed. The latter is complemented by a comprehensive discussion on the current challenges and future directions observed in this regularization endeavor. © 2025, CC BY.

关键词： Deep learning

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Healthcare Industry 5.0: Pareto Optimal IoT-Based Health Monitoring With Federated Learning

引用

IEEE Internet of Things Journal 2025年

作者： Diamantoulaki, Ioanna Tegos, Sotiris A. Bouzinis, Pavlos S. Sarigiannidis, Panagiotis Chatzakis, Christos Petousis, Stamatios Maglaveras, Nicos Karagiannidis, George K. Aristotle University of Thessaloniki Department of Electrical and Computer Engineering Thessaloniki54124 Greece Kila Kozani MetaMind Innovation P.C. Kozani50100 Greece University of Western Macedonia Department of Informatics and Telecommunications Engineering Kozani50100 Greece Aristotle University of Thessaloniki AUTH Campus Medical School 2nd Dept. of Obstetrics and Gynaecology Thessaloniki54636 Greece

With the recent expansion of patient data availability and storage capabilities, healthcare entities tend to store an increasing amount of medical data locally. In addition, ongoing advances in the era of healthcare industry 5.0 and Artificial Intelligence of Things can lead to more efficient use of medical data, resulting in better health monitoring at lower costs. However, due to strict privacy restrictions related to the sensitive nature of medical data, it is often only used locally and ultimately underutilized. To this end, federated learning (FL) offers a promising solution for the efficient use of medical data, facilitating the development of reliable and robust healthcare tools. This can be achieved thanks to its decentralized nature, which allows participating entities to collaborate and thus develop and train a centralized shared model without requiring data sharing. Taking this into account, we present a Pareto-front optimization framework for FL-based health monitoring that is able to mitigate false negative predictions for the required level of false positives. By applying the proposed framework to four different medical applications, it is shown that the risk of misdiagnosis is significantly reduced, providing an additional tool for medical professionals. © 2014 IEEE.

关键词： Federated learning

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Hierarchical Context Transformer for Multi-level Semantic Scene Understanding

arXiv

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arXiv 2025年

作者： Hao, Luoying Hu, Yan Yue, Yang Wu, Li Fu, Huazhu Duan, Jinming Liu, Jiang Research Institute of Trustworthy Autonomous Systems Dept. of Computer Science and Engineering Southern University of Science and Technology China School of Computer Science University of Birmingham United Kingdom Division of Informatics Imaging and Data Sciences School of Health Sciences University of Manchester United Kingdom MGI Tech Co. Ltd. China Singapore

A comprehensive and explicit understanding of surgical scenes plays a vital role in developing context-aware computer-assisted systems in the operating theatre. However, few works provide systematical analysis to enable hierarchical surgical scene understanding. In this work, we propose to represent the tasks set [phase recognition → step recognition → action and instrument detection] as multi-level semantic scene understanding (MSSU). For this target, we propose a novel hierarchical context transformer (HCT) network and thoroughly explore the relations across the different level tasks. Specifically, a hierarchical relation aggregation module (HRAM) is designed to concurrently relate entries inside multi-level interaction information and then augment task-specific features. To further boost the representation learning of the different tasks, inter-task contrastive learning (ICL) is presented to guide the model to learn task-wise features via absorbing complementary information from other tasks. Furthermore, considering the computational costs of the transformer, we propose HCT+ to integrate the spatial and temporal adapter to access competitive performance on substantially fewer tunable parameters. Extensive experiments on our cataract dataset and a publicly available endoscopic PSI-AVA dataset demonstrate the outstanding performance of our method, consistently exceeding the state-of-the-art methods by a large margin. The code is available at https://***/Aurorahao/HCT. Copyright © 2025, The Authors. All rights reserved.

关键词： Transplantation (surgical)

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Endo-FASt3r: Endoscopic Foundation model Adaptation for Structure from motion

arXiv

引用

arXiv 2025年

作者： Zeinoddin, Mona Sheikh Islam, Mobarakol Tandogdu, Zafer Shaw, Greg Clarkson, Mathew J. Mazomenos, Evangelos Stoyanov, Danail UCL Hawkes Institute University College London United Kingdom Institute of Health Informatics University College London United Kingdom Dept of Urology University College London Hospitals United Kingdom Dept of Medical Physics & Biomedical Engineering University College London United Kingdom Dept of Computer Science University College London United Kingdom

Accurate depth and camera pose estimation is essential for achieving high-quality 3D visualisations in robotic-assisted surgery. Despite recent advancements in foundation model adaptation to monocular depth estimation of endoscopic scenes via self-supervised learning (SSL), no prior work has explored their use for pose estimation. These methods rely on low rank-based adaptation approaches, which constrain model updates to a low-rank space. We propose Endo-FASt3r, the first monocular SSL depth and pose estimation framework that uses foundation models for both tasks. We extend the Reloc3r relative pose estimation foundation model by designing Reloc3rX, introducing modifications necessary for convergence in SSL. We also present DoMoRA, a novel adaptation technique that enables higher-rank updates and faster convergence. Experiments on the SCARED dataset show that Endo-FASt3r achieves a substantial 10% improvement in pose estimation and a 2% improvement in depth estimation over prior work. Similar performance gains on the Hamlyn and StereoMIS datasets reinforce the generalisability of EndoFASt3r across different datasets. Our code is available at: Endo-FASt3r. Copyright © 2025, The Authors. All rights reserved.

关键词： Endoscopy

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From metadata application profiles to semantic profiling: Ontology refinement and profiling to strengthen inference-based queries on the semantic web

引用

International Journal of Metadata, Semantics and Ontologies 2007年第4期2卷 268-280页

作者： Koutsomitropoulos, Dimitrios Paloukis, George Papatheodorou, Theodore S. Computer Engineering and Informatics Dept. School of Engineering University of Patras Building B 26500 Patras-Rio Greece

Ontologies on the Semantic Web form a basis for representing human-conceivable knowledge in a machine-understandable manner. Ontology development for a specific knowledge domain is, however, a difficult task, because the representation produced has to be adequately detailed and broad enough at the same time. The CIDOC-CRM is such an ontology, pertaining to cultural heritage, which we align to the Semantic Web environment: first, transforming it to OWL and then profiling it not in the usual flat metadata sense, but by refining and extending its conceptual structures, taking advantage of OWL semantics. This kind of profiling maintains applicability of the model, while enabling more expressive reasoning tasks. To this end, we construct a mechanism for acquiring implied and web-distributed information that is used to conduct and present a series of experimental inferences on the CRM-profiled form. © 2007, Inderscience Publishers.

关键词： Semantic Web

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