cs papers | Gist.Science

Optimized Dual Temporal Gated Multi-Graph Convolution Network for Land Use and Land Cover Classification Incorporating Temporal Feature Tracking and High Resolution Satellite Imagery Analysis

Despite a title suggesting a focus on land use classification via satellite imagery, the paper actually proposes a secure and energy-efficient routing and clustering framework for edge-assisted Wireless Sensor Networks (WSN) that integrates a Spatial Bayesian Neural Network, Forward Private Verifiable Dynamic Searchable Symmetric Encryption, a Humboldt Squid Optimization Algorithm for cluster head selection, and a Twin Actor Twin Delayed Deep Deterministic policy gradient for duty cycling to significantly improve energy efficiency, network lifetime, and packet delivery ratio.

Kamalakkannan D, Parul Awasthi, Shaman Bhat, Monali Shetty2026-08-24

💻 computer science

Cost-Aware Evaluation of Time-Series Foundation Models for Urban Air-Quality and Temperature Forecasting

This study demonstrates that for urban air-quality and temperature forecasting in resource-constrained cities, zero-shot foundation models often match or outperform specialized and transfer-learning approaches when evaluated under realistic data and compute constraints, challenging the assumption that heavy foundation models are unsuitable for such deployments.

Md Muhtasim Munif Fahim, Md. Rezaul Karim2026-08-24

💻 computer science

Blind Smart Navigator Optimizer (BSNO): A Bio-Inspired Hybrid Metaheuristic Algorithm for Expensive Black-Box Optimization

This paper introduces the Blind Smart Navigator Optimizer (BSNO), a novel bio-inspired hybrid metaheuristic algorithm that mimics the sequential navigation strategies of visually impaired individuals through a unified perception–decision–action paradigm to efficiently solve expensive black-box optimization problems by balancing global exploration and local exploitation while minimizing computational costs.

Majid Darehmiraki2026-08-24

💻 computer science

Objective Mismatch Limits Densest-Subgraph Detection of Money-Laundering Typologies

This paper demonstrates that the effectiveness of densest-subgraph detection in identifying money laundering is fundamentally limited by objective mismatch, as density-based methods structurally fail to detect flow-based typologies like mixing pools and long cycles while flow-based detectors outperform them in those specific scenarios.

Arturo Alejandro Arvizu Velazquez2026-08-24

💻 computer science

TCGSplat: Temporal Confidence Guided 3D Gaussian Splatting for RGB-D SLAM

TCGSplat is a robust 3D Gaussian Splatting-based SLAM system that enhances tracking, mapping, and loop closure by maintaining and rendering time-evolving confidence scores for each Gaussian primitive to filter out unreliable observations.

Xingchen Guo, Zhi Liu, Dandan Huang, Xingzhao Wang, Huiji Wang, Huilin Jiang2026-08-24

💻 computer science

Adaptive Lagrangian Attention for Constrained Multimodal Multi-objective Optimization

This paper proposes an adaptive attention-driven Lagrangian relaxation evolutionary algorithm (AALR-CMMOEA) that employs a dual-population co-evolution framework, dynamic constraint pressure adjustment, and an adaptive resource allocation strategy to effectively solve constrained multimodal multi-objective optimization problems by balancing feasibility, diversity, and convergence.

Shaobo Deng, Wenbin Xiao, Xinyu Hu, Yuhang Liu, Xiumei Tian, Yong Qin, Min Hu, Min Li, Sujie Guan, Hua Rao2026-08-24

💻 computer science

A Systematic Map Review of European Cybersecurity Skills Frameworks using Hierarchical Knowledge Graphs

This paper presents a systematic map review of European cybersecurity skills frameworks, utilizing hierarchical knowledge graphs to synthesize evidence from 94 sources into structured limitation and enhancement pillars, thereby addressing the current fragmentation and providing a unified evidence base for ecosystem harmonization.

Gaetano Perrone, Simon Pietro Romano2026-08-24

💻 computer science

ODConv-GFPN-LSPCD: A Multi-Module Enhanced YOLOv11n for Marine Organism Detection

This paper proposes ODConv-GFPN-LSPCD, a multi-module enhanced YOLOv11n architecture that integrates Omni-dimensional Dynamic Convolution, a Giraffe Feature Pyramid Network, and a Lightweight Shared Convolutional Detection Head to achieve superior accuracy and real-time efficiency for detecting marine organisms in challenging underwater environments.

Shuzhi Zheng, Shuqiang Gao, Ruhui Zuo, Ruijie Xie, Yilin Hu, Yeqi Guo2026-08-24

💻 computer science

Data leakage inflates reported accuracy of deep learning for kidney stone detection on CT: a leakage-free, calibrated and uncertainty-aware reassessment across three centers

This study demonstrates that image-level data splitting in deep learning models for kidney stone detection on CT artificially inflates reported accuracy by approximately nine percentage points due to data leakage, and advocates for patient-wise, calibrated, and uncertainty-aware evaluation protocols to ensure reliable clinical performance.

Okoi Obeten, Abas Aliu, Omowumi Aliu, Ahmed Jimoh, Zibril Aliyu, Christiana Ezeanya2026-08-24

💻 computer science

Smooth multi-function evolutionary algorithm

This paper proposes a novel evolutionary algorithm for symbolic regression that utilizes a universal function template with smooth, single-variable parametrized translations between function pairs to reduce the function set and ensure derivative continuity, demonstrating its effectiveness on standard benchmarks.

Peter Michalicek, Tomas Brandejsky2026-08-24

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