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School Sustainable Education Personal Learning Assistant Using Artificial Neural Network 12th

School Sustainable Education Personal Learning Assistant Usi...

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12th international conference on Frontiers of intelligent Computing: theory and Applications, FICTA 2024

作者： Rasheeduddin, Sayyad Sravani Kumar, V. Praveen Kumar, T. Ramesh, L.V. Sivaiah, Borra Bhanu, Sasi CMR Engineering College Telangana Hyderabad India CMR Engineering College Telangana Hyderabad India Department of MBA CMR Technical Campus Telangana Hyderabad India Department of IT CMR Technical Campus Telangana Hyderabad India Department of CSE CMR College of Engineering and Technology Telangana Hyderabad India CMR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819601462

Computer-based systems for knowledge and computation are emerging as important sources of competitive advantage for various industry sectors. Global educational systems and learning procedures are therefore about to undergo a significant digital revolution. In the context of computer education in primary and secondary schools, this research aims to investigate the idea of an intelligent tutoring system (ITS) based on artificial neural networks (ANNs). A learning assistant is one of the elements of an ITS that can help students ask for help whenever they need it, from anywhere. the pilot chatbot prototype is created as part of this study to act as learning aid for Scratch subjects (Scratch is the graphical tool employed to teach schoolchildren fundamentals of programming). Students asked questions to the chatbot using an open-source ANN library and Slack, and the chatbot responded with the desired explanation. the chatbot’s ANN extraction and information retrieval ability was assessed through a two-phase testing procedure. the testing results demonstrated the reasonable accuracy of the ontology model for this kind of learning assistant and indicated that it may be developed further as cloud solutions in the future. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ability testing

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GSEA: Global Structure-Aware Graph Neural Networks for Entity Alignment 13th

GSEA: Global Structure-Aware Graph Neural Networks for Entit...

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13th international conference on Natural Language Processing and Chinese Computing

作者： Wang, Cunda Wang, Weihua Liang, Qiuyu Yu, Jie Gao, Guanglai Inner Mongolia Univ Coll Comp Sci Hohhot Peoples R China Natl & Local Joint Engn Res Ctr Intelligent Infor Hohhot Peoples R China Inner Mongolia Key Lab Multilingual Artificial In Hohhot Peoples R China Natl Univ Def Technol Coll Comp Sci Changsha Peoples R China

ISBN: (纸本)9789819794331;9789819794348

Entity alignment (EA) aims to identify the same real-world entities presented in different knowledge graphs (KGs), which is the most crucial step in integrating multi-source KGs. Existing entity alignment methods based on Graph Neural Networks mainly focus on aggregating neighborhood structure information within the original graph to generate entity embeddings. However, these methods fail to effectively leverage the global structural information of entities. In this paper, we propose a novel method based on Global Structure-Aware Graph Neural Networks to capture the global structure between entities. Specifically, our method learns global embeddings from the augmented graph reconstructed by Singular Value Decomposition. Moreover, we employ contrastive learning to maximize the consistency between global and local embeddings while mitigating the over-smoothing problem. Extensive experimental results on benchmark datasets, along with further analysis, demonstrate the superiority and effectiveness of our method. the source code is available at https://***/wonderCS1213/GSEA.

关键词： knowledge graph Entity alignment Graph neural networks Contrastive learning

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Regularized Multi-LLMs Collaboration for Enhanced Score-based Causal Discovery 25th

Regularized Multi-LLMs Collaboration for Enhanced Score-Bas...

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25th international conference on Web information systems engineering, WISE 2024

作者： Li, Xiaoxuan Liu, Yao Wang, Ruoyu Yao, Lina University of New South Wales Sydney Australia School of Computer Science and Engineering Northeastern University Shenyang110169 China School of Computing Macquarie University Sydney Australia CSIRO’s Data61 Sydney Australia

ISBN: (纸本)9789819605729

As the significance of understanding the cause-and-effect relationships among variables increases in the development of modern systems and algorithms, learning causality from observational data has become a preferred and efficient approach over conducting randomized control trials. However, purely observational data could be insufficient to reconstruct the true causal graph. Consequently, many researchers tried to utilise some form of prior knowledge to improve causal discovery process. In this context, the impressive capabilities of large language models (LLMs) have emerged as a promising alternative to the costly acquisition of prior expert knowledge. In this work, we further explore the potential of using LLMs to enhance causal discovery approaches, particularly focusing on score-based methods, and we propose a general framework to utilise the capacity of not only one but multiple LLMs to augment the discovery process. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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An intelligent Optimization Technique Of Automatic Speech Recognition For Smart Homes 13

An Intelligent Optimization Technique Of Automatic Speech Re...

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13th international conference on Research in ComputIng at Feminine, RIF 2024

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Electrical and Computer Engineering Department College of Engineering Effat University Jeddah Saudi Arabia Computer Science Department Faculty of Computer Science Nahda University Beni Suef Egypt

the creation of new approaches to the design and configuration of smart buildings relies heavily on AI tools and Machine Learning (ML) algorithms, particularly optimization techniques. the widespread use of electronic devices has sparked a strong desire to incorporate the Internet of things (IoT) into houses, leading to the development of smart homes. As networked gadgets proliferate rapidly, this phenomenon is characterized by rapid proliferation. In smart buildings, smart cities, smart grids, and smart homes, interconnected electronic devices are becoming more popular. the objective of this paper is to enhance the functionality of home automation systems through the performance of speech recognition using the Bat-Salp Swarm Optimization (BSSO). this paper investigates the notion of (BSSO), a data analysis methodology that facilitates the automated construction of analytical models. the implementation of BSSO provides an enhancement to the feature selection process in speech recognition, providing an approximation solution that improves the accuracy of system decisions. the use of the BSSO technique improves the precision of the voice recognition system and also incorporates an Artificial Neural Network (ANN) for the classification part. the findings substantiated the efficacy of the employed methodology. © 2024 Copyright for this paper by its authors.

关键词： Bat Optimization Algorithm Salp Optimization Algorithm Smart homes Speech recognition

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th international conference on Computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. this paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. the complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. the proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. the deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning Image processing

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Development of EEG-based Optimal Biomarkers for Decoding Fatigue during Motor Imagery Tasks 13

Development of EEG-Based Optimal Biomarkers for Decoding Fat...

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13th international Winter conference on Brain-Computer Interface, BCI 2025

作者： Lee, Jun-Seok Lee, Seung-Won Kim, Sang-Rok Hwang, Han-Jeong Dept. Electronics and Information Engineering Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of

ISBN: (纸本)9798331521929

this study aimed to identify electroencephalography (EEG)-based neurophysiological biomarkers for monitoring mental fatigue during motor imagery (MI) tasks. We used EEG data recorded from 29 healthy adults during MI tasks. For the preprocessing of the EEG data, we sequentially performed down-sampling into 200 Hz, independent component analysis (ICA), band-pass filtering between 0.5 and 50 Hz, and common average reference (CAR). the preprocessed EEG data were analyzed using a fast Fourier transform (FFT) to calculate spectral power in the δ (0.5–3 Hz), θ (4–7 Hz), α (8–13 Hz), and β (13–30 Hz) frequency bands. Subsequently, seventeen fatigue-related metrics were calculated using individual spectral powers and their combinations. these fatigue-related metrics were averaged for the frontal lobe (F3 and F4), central lobe (FCC3h and FCC4h), parietal lobe (P3 and P4), and occipital lobe (POO1 and POO2). the statistical significance of the average fatigue metrics was evaluated through Pearson correlation analysis with the sequence of trials. As a result, we observed strong positive correlations (r > 0.9, p © 2025 IEEE.

关键词： Fast Fourier transforms

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A Preliminary Work on Tenyidie Stemming Corpus Creation and Deep Learning Applications 4th

A Preliminary Work on Tenyidie Stemming Corpus Creation and...

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4th international conference on Emerging Trends and Technologies on intelligent systems, ETTIS 2024

作者： Angami, Teisovi Tuithung, themrichon Department of Computer Science and Engineering National Institute of Technology Nagaland Dimapur India Department of Information Technology School of Engineering and Technology Nagaland University Lumami India

ISBN: (纸本)9789819757022

Tenyidie language belonging to the Tibeto-Burman family is one of the major languages of Nagaland in northeastern India. It is a low-resource, tonal, SOV, and a high agglutinative language. Stemming is an important Natural Language Processing (NLP) task wherein the stem or the root word is identified and extracted. To the best of the authors knowledge, there has been no work reported on corpus creation and stemming using deep learning for the Tenyidie language. In this paper, we build an annotated corpus of 5400 manually stemmed words in Tenyidie to apply deep learning to the stemming task. We applied state-of-the-art deep learning models, such as the Encoder-decoder models, and compared our results with the rule-based approach, which uses both affix stripping and root word matching methods. Our experimental result shows that the deep learning models outperform the rule-based approach. We achieved the highest accuracy of 85.55% with Encoder-decoder (BLSTM) with attention. We also provide an analysis of the overstemmed and understemmed words in our test dataset. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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13th international conference on knowledge Management and information systems, KMIS 2021 as part of IC3K 2021 - Proceedings of the 13th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management

13th International Conference on Knowledge Management and In...

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13th international conference on knowledge Management and information systems, KMIS 2021 as part of 13th international Joint conference on knowledge Discovery, knowledge engineering and knowledge Management, IC3K 2021

ISBN: (纸本)9789897585333

the proceedings contain 80 papers. the topics discussed include: a case study and qualitative analysis of simple cross-lingual opinion mining;event log abstraction in client-server applications;improved session-based recommender system by context awareness in e-commerce domain;FormalStyler: GPT based model for formal style transfer based on formality and meaning preservation;towards a rule-based visualization recommendation system;product embedding for large-scale disaggregated sales data;an evaluation of hubness reduction methods for entity alignment with knowledge graph embeddings;matching entities from multiple sources with hierarchical agglomerative clustering;hierarchical control of swarms during evacuation;modelling and detection of driver’s fatigue using ontology;participatory design in knowledge management: an opportunity to small and medium business;eliciting design requirements for a knowledge management system in cultural and historical heritage;and a qualitative study on the categorization and prioritization of digital competencies and attitudes for managers and employees.

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SPOC "Gender in Digital Education": a Means to promote Egalitarian e-Pedagogy and Sustainable Development 13

SPOC "Gender in Digital Education": a Means to promote Egali...

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13th international conference on Research in ComputIng at Feminine, RIF 2024

作者： Cheniti-Belcadhi, Lilia El Khayat, Ghada Benelhadj, Mohamed El Hadi Sousse University ISITCom PRINCE Research Lab Tunisia Alexandria University Faculty of Business Computers and Information Systems Egypt Tamanrasset University Algeria

It is important to adopt innovative educational approaches to respond to new problems and challenges in the world, to consider diversity and to understand its imperative for problem-solving. It therefore becomes important to focus on education based on the seventeen SDGs allowing better use of human resources for better economic and social conditions. SDG 5 aims to achieve gender equality and empower all girls and women. Gender equality is indeed interconnected with the other objectives;everyone's actions will affect the achievement of SDG5. the Open2Sustain project (Open Education for Euro- Mediterranean Sustainable Development), funded by the Agence Universitaire de la Francophonie (AUF), is strongly inspired by SDG4, "ensuring equal access to quality education for all and promoting lifelong learning opportunities". the project is action research that brings together university partners from Euro-Mediterranean countries to create a synergy of North-South cooperation to support the co-construction of sustainable development knowledge, to integrate SDGs into academic courses. In addition, this project aims to prepare the creation of a network of SDG facilitators in the region, through the development of a SPOC on the SDGs, the sharing of innovative open scenarios and educational resources relating to the SDGs. A pilot experiment was carried out and it will be detailed in this paper. Resources and scenarios contributed by professors will be reused by other professors. Subsequently, the analysis of traces makes it possible to recommend scenarios to future users. Using open educational resources and artificial intelligence for scenario recommendation purposes will encourage sharing and make it possible to contextualize and enrich the SPOC content, over time. Participants to be involved in the development and scripting of the content for possible sharing and dissemination based on intelligent recommendations, to the teaching communities. © 2024 Copyright for this paper

关键词： Sustainable development goals

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Fractional-order Adaptive Sliding Mode Projective Synchronization of Different Hyperjerk systems

Fractional-order Adaptive Sliding Mode Projective Synchroniz...

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international conference on intelligent Control and information Processing (ICICIP)

作者： Baojie Zhang Changchao Liu Zhiyuan Zhu Yuming Feng Longzhou Cao College of Computer Science and Engineering Chongqing Three Gorges University Chongqing China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

this paper presents an adaptive fractional order sliding mode projective synchronization scheme for different hyperjerk systems with uncertainty and disturbances. A fractional order sliding mode controller with three fractional terms is proposed for the projective synchronization of different hyperjerk systems. the uncertainty and disturbance is considered with adaptive scheme. Numerical simulation verifies the effectiveness and feasibility of the control method.

关键词： Uncertainty Adaptive systems information processing Numerical simulation Control systems Synchronization intelligent control

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