cs papers | Gist.Science

FusionAttNet Framework for Hierarchical Attention Driven Sentinel 1 and Sentinel 2 Fusion for Semi Arid Land Cover Classification in Far North Cameroon

This study introduces FusionAttNet, a novel deep learning framework that integrates Sentinel-1 SAR and Sentinel-2 optical data through a modality-aware hierarchical attention mechanism to achieve 96.75% accuracy in classifying semi-arid land cover in Far North Cameroon, significantly outperforming traditional fusion methods by effectively addressing spectral homogeneity, cloud cover, and seasonal variability.

Pountianus Berinyuy Wirba, Mvogo Ngono, Noumsi Woguia Auguste Vigny, Emile Tatinyuy Verdzekov, ELE Pierre2026-06-28

💻 computer science

Training Versus Hiring in Security Operations Centers: Agent-Based and Reinforcement Learning Simulations of Training Duration Effects on CyberTeam Performance

This study utilizes agent-based modeling and reinforcement learning to demonstrate that training duration in Security Operations Centers yields nonlinear performance gains, with optimal investment occurring between 500 and 1,000 episodes and highly trained teams achieving efficiency advantages equivalent to recruiting 20–25 additional personnel.

Max N. Yaw, Ferdinand Kpieleh, Aos Mulahuwaish, Basheer Qolomany, Jacques Bou Abdo2026-06-27

💻 computer science

Quantifying the Security-Performance Trade-off in Constrained IoT: A Multi-Vector MQTT Attack Framework with Hybrid Defense-in-Depth Evaluation

This paper presents a reproducible experimental framework demonstrating that chaining physical HID intrusions with MQTT network attacks exposes critical security-performance trade-offs in constrained IoT devices, revealing that while TLS offers strong confidentiality, it incurs prohibitive latency and memory costs on legacy hardware, thereby motivating a novel Hybrid Defense-in-Depth architecture that prioritizes Access Control Lists for efficient, scalable protection.

Thant Zin Moe, Julia Juremi, Manimegalai Rajenderan2026-06-27

💻 computer science

Computer-generated content and the AI/ML retraction record, 2018– 2026: a characterization study

This characterization study of 13,502 AI/ML-related retraction records from 2018 to 2026 reveals that computer-generated content is the largest single inferred cause of retractions, accounting for 34.8% of cases and predominantly affecting papers published between 2021 and 2023.

Anton Sokolov2026-06-27

💻 computer science

Secure and Intelligent IoT: A Dynamical-Systems View of Threat Modelling and Machine-Learning Defence

This paper proposes a unified state-space framework for securing IoT networks by integrating differential equations and machine learning to model threat propagation dynamics, detect anomalies, and optimize defensive control strategies, demonstrated through a simulated SEIRS malware case study.

Mussa Justin Muhindo2026-06-27

💻 computer science

Enhancing Cognitive Diagnosis with Group-Aware Graph Augmentation and Self-Supervised Alignment

This paper proposes the Group-Aware Cognitive Diagnosis (GACD) framework, which addresses data sparsity in student response data by grouping students with similar patterns, decomposing interactions into correct and incorrect subgraphs for independent learning, and employing self-supervised alignment to enhance the accuracy of cognitive state assessment.

Xiaomeng Zhang, Ruxiang Liu, Piao Shi, Qun Ren2026-06-26

💻 computer science

HERO-SNN: A Homeostatic Eligibility-based Reward- Optimised Spiking Neural Network for Spatiotemporal Brain Representation: An EEG Case Study

This paper proposes HERO-SNN, a homeostatic and reward-optimized spiking neural network framework that integrates task-specific feedback with local spike-based learning to enhance the classification accuracy and interpretability of spatiotemporal EEG data, outperforming both standard STDP and state-of-the-art deep learning models in a Havening-based touch protocol study.

Maryam Doborjeh, Zohreh Doborjeh, Nikola Kasabov, Alexander Sumich, Nadja Heym, Tony Burgess2026-06-26

💻 computer science

AutoML-XAI convolutional neural network for hypnotic susceptibility classification from segmented multichannel EEG

This paper proposes an interpretable AutoML-driven CNN framework that classifies hypnotic susceptibility from segmented multichannel EEG data, achieving perfect accuracy in its best fold and identifying alpha and gamma frequency bands as the most critical features through XAI techniques.

Ali Walid Daher2026-06-26

💻 computer science

FDCNet: Frequency-Aware and Dynamic Curve Network for Adversarially Robust Infrared and Visible Image Fusion

This paper proposes FDCNet, a novel adversarially robust infrared and visible image fusion framework that integrates a Frequency-Aware Defense Module and a Spatio-Frequency Dynamic Adversarial Loss to suppress perturbations, while employing an Extrema-guided Dynamic Tone Curve Module to balance defense effectiveness with visual quality restoration.

Pengcheng Gao, Shengyue Huang2026-06-26

💻 computer science

DeepNeutroBiLSTM: A Hybrid Neutrosophic and CNN-BiLSTM Framework with Sobel-Based Indeterminacy for Arabic Handwritten Character Recognition

This paper proposes DeepNeutroBiLSTM, a novel hybrid framework integrating neutrosophic uncertainty modeling with Sobel-based indeterminacy, CNNs, and BiLSTMs to achieve state-of-the-art accuracy and robustness in Arabic handwritten character recognition on the AHCD and HIJJA datasets.

Othmane Farhaoui, Mohamed Rida Fethi, Imad Zeroual, Ahmad El Allaoui2026-06-26

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