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AI-based Adaptive Programming Education for Socially Disadvantaged Students: Bridging the Digital Divide

TechTrends 2025年 1-18页

作者： Katona, Jozsef Gyonyoru, Klara Ida Katonane Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary Institute of Electronics and Communication Systems Kandó Kálmán Faculty of Electrical Engineering Obuda University Budapest Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest University of Economics and Business Budapest Hungary Department of Organizational Development and Communication Science Institute of Social Sciences University of Dunaujvaros Dunaujvaros Hungary

In the context of the digital economy, programming proficiency is an essential competency that promotes upward socio-economic mobility and expands career opportunities. However, students from socially disadvantaged backgrounds often face significant barriers to acquiring these skills, such as limited access to technology and educational resources. This study explores the impact of AI-based adaptive programming education on socially disadvantaged students' learning outcomes and engagement levels. The 122 participants in the research were divided into an experimental group (EG) that received AI-driven adaptive instruction, and a control group (CG) taught through the traditional curriculum during the 13-week experimental period. In the research, the combined pre-test/post-test assessments and self-report engagement questionnaires were used to focus on behavioural, emotional, and cognitive engagement. The findings showed that the EG demonstrated higher levels in programming knowledge and full-scale engagement across behavioural, emotional, and cognitive dimensions compared to the CG. This difference was confirmed through ANOVA, while ANCOVA, which controlled for students' socio-economic factors, further confirmed that the AI system had a positive impact on students’ results regardless of their socio-economic background. Indeed, current research findings suggest that AI-based adaptive learning environments can hold a promising opportunity to narrow educational gaps, boost engagement, and contribute toward better learning outcomes for socially disadvantaged students. © The Author(s) 2025.

关键词： Adaptive learning AI-based education Disadvantaged students Programming education Student engagement

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Advancing Systematic Literature Reviews Methodology Through Topic Modeling 4th

Advancing Systematic Literature Reviews Methodology Through ...

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4th International Conference on Intelligent systems and Pattern Recognition, ISPR 2024

作者： Mekaoui, Salma Chaker, Ilham Zarghili, Arsalane Nikolov, Nikola S. Department of Computer Science and Information Systems University of Limerick LimerickV94 T9PX Ireland Laboratory of Intelligent Systems and Applications Faculty of Sciences and Technology University Sidi Mohamed Ben Abdellah Route Immouzer Fez Morocco

ISBN: (纸本)9783031821493

The benefits of conducting a systematic review (SR) within a research project are well recognised. Nonetheless, nowadays an SR demands significant time and effort as it typically requires manually sifting through a large corpus of papers. In this work, we propose an application that simplifies the SR process for researchers by performing topic modeling. Our approach involves more than just conducting topic modeling;it entails selecting the optimal method tailored to our system’s needs. We explore two widely recognized methods in topic modeling, namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF), alongside two prominent libraries, Scikit-learn and Gensim, which provide implementations for these methods. The evaluation of these methods and libraries is done with the help of the cosine similarity metric. Our findings indicate that, for our research context, LDA implemented in Scikit-learn stands out as the most effective method for topic modeling. Additionally, our study suggests that employing 10 topics, along with stemming and lemmatization, yields better results. Our findings culminate in the development of an application to facilitate SRs for researchers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Non-negative matrix factorization

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Real-Time Face and Facial Landmark Joint Detection Based on End-to-End Deep Network

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Wu, Qingtian Wang, Xiaoming Li, Nannan Fong, Simon Zhang, Liming Yang, Jinfeng Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen518055 China University of Macau Faculty of Sciences and Technology Department of Computer and Information Science China International Institute of Next Generation Internet Macao University of Science and Technology China

Facial landmark detection (FLD) is an important task in computer vision, involving the extraction of keypoints from facial images. Traditional methods typically employ a two-stage approach: first detecting faces, then predicting facial landmarks. However, computing deep features for accurate face and landmark detection is time intensive, and the features from each stage are not shared. This makes these methods suboptimal for real-time applications, especially on edge devices. In this article, we present a novel end-to-end deep network for joint face and FLD. Our approach builds upon the YOLO framework with minimal modifications, primarily involving the adjustment of multitarget labels for face detection and the addition of a separate head for landmark localization. Furthermore, we enhance the model using structural reparameterization, channel shuffling, and implicit modules. Experimental evaluations on the 300 W dataset demonstrate that our proposed method achieves high accuracy while maintaining real-time processing speeds, surpassing several state-of-the-art (SOTA) methods. Additional testing on challenging datasets such as Caltech Occluded Faces in the Wild (COFW) and AFLW2000-3D further highlights the robustness of our model in diverse conditions. Our model and source code will be made publicly available. © 2025 IEEE. All rights reserved.

关键词： Face recognition

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EmoSyn: Adaptive Emotion Framework for Sentiment Analysis and Internet of Medical Things

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Awan, Kamran Ahmad Alqahtani, Abdullah M. Cengiz, Korhan Alshammari, Alya Alrashdi, Ibrahim The University of Haripur Faculty of Information Technology and Numerical Sciences Department of Information Technology Khyber Pakhtunkhwa Pakistan Jazan University Department of Electrical and Electronic Engineering Collage of Engineering and Computer Science Saudi Arabia Prince Mohammad Bin Fahd University Department of Electrical Engineering Al Khobar31952 Saudi Arabia Princess Nourah bint Abdulrahman University Department of Applied Linguistics College of Languages Riyadh11671 Saudi Arabia Jouf University Department of Computer Science College of Computer and Information Sciences Al-Jouf Sakaka73211 Saudi Arabia

Sentiment analysis, or more specifically, the integration of IoMT into healthcare systems, requires frameworks that must adapt at runtime with high precision. Most existing methods have several limitations of either latency or accuracy issues, and therefore perform less effectively in dynamic scenarios. This study aims to address these challenges by proposing EmoSyn, a novel framework that incorporates Emotion Wave Modulation (EWM), Neuro-Cognitive Language Dynamics (NCLD), and the Sentient IoMT Interaction Protocol (SIP). EWM generates dynamic emotional waveforms using high-dimensional feature vectors and kernelized mappings, while NCLD employs synthetic neural mappings and adaptive linguistic modeling to capture semantic transitions. SIP facilitates real-time IoMT recalibration through bidirectional sentiment-driven feedback. Implemented using mathematical frameworks and a custom Emotion-Aware Predictive Synthesis Algorithm (EAPSA), EmoSyn ensures precise sentiment interpretation and efficient IoMT interactions. The framework was evaluated using MOSEI and MIMIC-III datasets in a Python-based simulation environment. The results showed EmoSyn achieving 91% precision for MOSEI and 86% for MIMIC-III, with average latencies of 29 ms and 27 ms. © 2013 IEEE.

关键词： Semantics

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A QoS-aware adaptive cross-layer cyber-attack detection algorithm for vehicular networks

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Vehicular Communications 2025年 54卷

作者： Kaur, Avneet Saiyed, Makhduma F. Al-Anbagi, Irfan Shamim Hossain, M. Faculty of Engineering and Applied Science University of Regina Regina SK Canada Department of Software Engineering College of Computer and Information Sciences King Saud University Riyadh 12372 Saudi Arabia

Securing data transmission and detecting cyber-attacks in consumer Vehicular Ad hoc Networks (VANETs) pose significant challenges due to the highly dynamic topology of the network and frequent mobility of nodes. These characteristics enable attackers to exploit vulnerabilities and evade detection, making real-time attack detection complex while maintaining the Quality of Service (QoS). In this paper, we propose an Adaptive Cross-layer Cyber-attack Detection (ACCD) algorithm that dynamically detects and isolates malicious nodes while optimizing traffic routing based on application-specific requirements. The proposed algorithm uses a cross-layer traffic-aware approach to classify data flows into security-critical and delay-sensitive applications, ensuring optimal routing through the selection of Ant Colony Optimization (ACO) and Ad hoc On-Demand Multipath Distance Vector (AOMDV) protocols. The integration of pre-route authentication (PRA) enhances malicious node isolation, reducing the impact of selective forwarding and blocking attacks. Simulation results show that ACCD achieves lower end-to-end delay and higher Packet Delivery Ratio (PDR) while effectively balancing network security and performance. © 2025

关键词： ACO AOMDV Cross layer Cyber-attacks End-to-end delay Intelligent transportation system Reliability VANETs

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Fine-grained Feature and Template Reconstruction for TIR Object Tracking

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IEEE Transactions on Circuits and systems for Video Technology 2025年

作者： Liao, Donghai Shu, Xiu Li, Zhihui Liu, Qiao Yuan, Di Chang, Xiaojun He, Zhenyu Xidian University Guangzhou Institute of Technology Guangzhou510555 China Guangzhou University School of Computer Science and Cyber Engineering Guangzhou510006 China University of Science and Technology of China School of Information Science and Technology Hefei230026 China Chongqing Normal University National Center for Applied Mathematics Chongqing401331 China University of Technology Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology Sydney2007 Australia Harbin Institute of Technology School of Computer Science and Technology Shenzhen518055 China

Thermal infrared (TIR) object tracking is a significant subject within the field of computer vision. Currently, TIR object tracking faces challenges such as insufficient representation of object texture information and underutilization of temporal information, which severely affects the tracking accuracy of TIR tracking methods. To address these issues, we propose a TIR object tracking method (called: FFTR) based on fine-grained feature and template reconstruction. Specifically, aiming at the fine-grained information of the TIR object, we employ a frequency channel attention mechanism that transforms TIR images into the frequency domain using discrete cosine transform features. By capturing the fine-grained feature of TIR images from the frequency domain, we enhance the model’s ability to comprehend these images. To better leverage temporal information, we utilize a template region reconstruction method. This method reconstructs the template from the previous frame based on the search area of the current frame, which is then incorporated into the attention computation for the subsequent frame, thereby improving the tracking capability of TIR objects. Extensive quantitative and qualitative experiments show that our method achieves competitive tracking performance on the TIR benchmarks. © 1991-2012 IEEE.

关键词： Object tracking

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Enhancing Marine Litter Management in the Gulf of Aqaba Through AI 9th

Enhancing Marine Litter Management in the Gulf of Aqaba Thro...

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9th International Conference on information, Communication and Computing Technology, ICICCT 2024

作者： Wahsha, Mohammad Wahsheh, Heider Al-Najjar, Tariq Marine Science Station The University of Jordan Aqaba Branch Aqaba Jordan Department of Information Systems College of Computer Science and Information Technology King Faisal University Al-Ahsa Al Hofuf31982 Saudi Arabia Faculty of Basic and Marine Sciences The University of Jordan Aqaba Branch Aqaba Jordan

ISBN: (纸本)9783031724824

This paper investigates the critical issue of marine litter in the Gulf of Aqaba, a key marine ecosystem in the Red Sea known for its rich biodiversity and economic significance to tourism and fishing industries. It emphasizes the vulnerability of this semi-enclosed body of water to various types of marine litter, including plastic waste and discarded fishing gear, posing significant threats to marine life through ingestion and entanglement. To address the limitations of traditional monitoring methods, the study proposes the innovative use of Artificial Intelligence (AI) technologies, such as machine learning and computer vision, to enhance the detection, quantification, and categorization of marine debris. The paper aims to develop an advanced prototype for underwater image analysis using AI, designed to significantly contribute to environmental preservation efforts by providing efficient, real-time assessments of marine litter in the Gulf of Aqaba, demonstrating the potential of AI to transform marine conservation strategies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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Weak and Extremely Low Frequency Magnetic Fields Alter the Growth Rates of HT-1080 Fibrosarcoma

Weak and Extremely Low Frequency Magnetic Fields Alter the G...

引用

US National Committee of URSI National Radio science Meeting (USNC-URSI NRSM)

作者： Nhat Dang Jason Keller Marek Bajtos Hakki Gurhan Frank Barnes Department of Electrical Computer and Energy Engineering College of Engineering and Applied Science University of Colorado Boulder Boulder Colorado USA Department of Electromagnetics and Biomedical Engineering Faculty of Electrical Engineering and Information Technology University of Zilina Zilina Slovakia

ISBN: (数字)9781946815200

ISBN: (纸本)9798331513979

This study presents experimental results showing that weak, extremely low frequency electromagnetic (EM) fields influence the growth rates of HT-1080 fibrosarcoma cells in vitro and offers a theoretical explanation for these effects. The results demonstrate that weak magnetic fields both increase and decrease the growth rates of fibrosarcoma cells by altering the mitochondrial superoxide $(\mathrm{O}^{2-})$ and calcium ions $(\text{Ca}^{2+})$ concentrations in living cells. Over a 4-day exposure period, fibrosarcoma growth rates were modulated in the presence of low-level alternating current (AC) magnetic fields at a $10 \mu \mathrm{T}$ amplitude at 13.5 Hz to 30 Hz frequency, superimposed on a $45 \mu \mathrm{T}$ static background field. Effects due to changes in amplitude from 10 to $30 \mu \mathrm{T}$ at selected frequencies are also investigated with the same superimposed static field.

关键词： Frequency modulation Calcium Ions Magnetic fields In vitro Electromagnetics

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arXiv

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arXiv 2025年

作者： Fidêncio, Aline Xavier Grün, Felix Klaes, Christian Iossifidis, Ioannis Robotics and BCI Laboratory Institute of Computer Science Ruhr West University of Applied Sciences Mülheim an der Ruhr Germany Faculty of Electrical Engineering and Information Technology Ruhr University Bochum Bochum Germany KlaesLab Department of Neurosurgery University Hospital Knappschaftskrankenhaus Ruhr University Bochum Bochum Germany Faculty of Computer Science Ruhr University Bochum Bochum Germany

Brain-computer interfaces (BCIs) are developed for individuals with motor disabilities to offer alternative methods of communication, such as controlling and interacting with external devices. These systems are designed to improve quality of life, particularly for those with limited mobility. BCIs that rely on non-invasive recordings, such as electroencephalography (EEG), have been developed for a wide range of scenarios due to their practicality and safety. However, BCI performance is often limited by non-stationarities in the EEG data, which can arise from changes in the subject’s mental state or device characteristics, such as electrode impedance. These challenges motivate ongoing research into the development of adaptive BCIs capable of handling such variations. Over the past years, the interest in using the so-called error-related potentials (ErrPs) to improve BCI performance has increased. This is because ErrPs represent the neural response to both self-made and external errors and can be measured using non-invasive techniques. These signals have been combined with different BCI paradigms and used in different works to improve BCI performance via error correction or adaptation. In this work we introduce and explore a new approach for creating an adaptive ErrP-based BCI by evaluating the use of reinforcement learning (RL). This study demonstrates the feasibility of a RL-driven adaptive brain-computer interface (BCI) framework that integrates error-related potentials (ErrPs) and motor imagery. By employing two RL agents to dynamically adapt to EEG non-stationarities, we validate the framework on both a publicly available motor imagery dataset and using a novel experimental protocol involving a fast-paced game designed to enhance user engagement. Results highlight the framework’s potential: RL agents successfully learned control policies from user interactions, achieving robust performance across datasets. However, a critical finding emerged from the game-based p

关键词： Reinforcement learning

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Image Classification and Segmentation for Estimating Calories of Food

Image Classification and Segmentation for Estimating Calorie...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Udomkan Chanthanuwatkun Anuchit Jitpattanakul Narit Hnoohom Department of Computer Engineering Image Information and Intelligence Laboratory Faculty of Engineering Mahidol University Nakhon Pathom Thailand Department of Mathematics Intelligent and Nonlinear Dynamic Innovations Research Center Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Accurate estimation of calorie content in meals is essential for promoting healthy dietary practices and supporting healthcare efforts. However, the lack of nutritional knowledge poses challenges for individuals in estimating the calorie content of foods. This study aims to develop a deep learning-based model for estimating the calorie content of real foods through image classification and segmentation techniques. Image classification was employed to identify various one-dish Thai menus, while image segmentation categorized food components into fats, carbohydrates, and proteins. A new dataset, consisting of 15 one-dish Thai menus, was created using a combination of publicly available datasets (THFOOD-50 and FoodyDudy) and self-collected images. Nutritional analysis was based on the Thai nutritional table from the Bureau of Nutrition, Department of Health, Ministry of Public Health. We compared the performance of YOLOv5x, YOLOv8x, and YOLOv9e on the ThaiFood dataset using identical hyperparameters. The results showed that YOLOv8x achieved the highest mAP@0.5 of 0.883, with an accuracy of 0.829 and a recall of 0.902. YOLOv5x closely followed with a mAP@0.5 of 0.881, an accuracy of 0.822, and a recall of 0.945. YOLOv9e demonstrated competitive performance, achieving a mAP@0.5 of 0.846, an accuracy of 0.775, and a recall of 0.91. These findings highlight YOLOv8x as the most suitable model for Thai food classification and detection tasks under the evaluated conditions.

关键词： Proteins Image segmentation Accuracy Estimation Fats Public healthcare Image classification

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