cs papers | Gist.Science

Knowledge Evolution and AI Paradigm Shifts in Pedestrian Detection: A Bibliometric and Main Path Analysis

This study integrates bibliometric analysis with an AI paradigm perspective to map the structural evolution of pedestrian detection research, revealing that the field's transition from handcrafted features to deep learning and transformers occurs through gradual knowledge integration rather than abrupt shifts, thereby offering a novel theoretical framework for understanding AI development.

Vo Thanh Kiet, Rene Jaros, Boris Pustejovsky, Jakub Stefansky, Ondrej Svoboda, Minh Ly Duc, Petr Bilik, Radek Martinek2026-06-29

💻 computer science

An ESCO-Based Skill-Gap Detection Framework for SMEs: A Design Science Prototype of an Intelligent Learning Management System

This study presents a Design Science prototype of an Intelligent Learning Management System that leverages the ESCO ontology and NLP to detect workforce skill gaps in SMEs and generate targeted reskilling recommendations, thereby addressing the human capital–technology complementarity constraints critical to Industry 4.0 and 5.0 transitions.

Angelo Leogrande, Mauro di Molfetta, Nicola Magaletti, Valeria Notarnicola, Maria Giovanna Trotta2026-06-29

💻 computer science

A Metaheuristic Solution for the Capacitated Vehicle Routing Problem: Empirical Evaluation of Flexible Capacity Constraints via Ant Colony Optimisation

This study demonstrates that an Ant Colony Optimisation approach incorporating flexible capacity constraints significantly outperforms a geographic clustering baseline in a real-world Colombian inbound logistics network, achieving substantial reductions in fleet size, travel distance, and operating costs while maximizing vehicle utilization.

Joseph Javier Sánchez Acuña2026-06-29

💻 computer science

Demographic Metadata as Construct-Irrelevant Noise in DistilBERT-Based Automated Essay Scoring

This study demonstrates that naively concatenating demographic metadata with text inputs in a DistilBERT-based Automated Essay Scoring model significantly degrades predictive accuracy, increases training loss, and exacerbates scoring bias compared to a text-only baseline.

Ch'ng Teik Peng2026-06-29

💻 computer science

StainSolver: Accelerating Diffusion-Controlled Neural Style Transfer with DPM-Solver++ and Quality-Driven Stain Normalization

This paper introduces StainSolver, a method that accelerates the StainFuser diffusion model for histopathology stain normalization by replacing its PNDM scheduler with the efficient DPM-Solver++ and evaluating performance with a novel Stain Normalization Quality Score (SNQS), achieving significant speedups and improved quality without retraining.

Amir Mollazadeh, Tapio Seppänen, Md Ziaul Hoque2026-06-29

💻 computer science

FlashSpec: Adaptive Speculative Decoding with Online Bandit Draft Selection and Triton-Optimised Verification

FlashSpec is an open-source, adaptive speculative decoding engine that achieves exact target-model distribution preservation through a novel O(1) vocabulary-size Triton GPU kernel for on-device verification and an online bandit-based mechanism for dynamic draft-model selection, significantly accelerating LLM inference while eliminating CPU bottlenecks and manual tuning.

Min Htet Myet2026-06-29

💻 computer science

Fake Review Detection on E-Commerce Platforms Using a Hybrid Anomaly Detection Approach: Combining Autoencoder and Isolation Forest

This paper proposes a hybrid unsupervised anomaly detection framework combining Autoencoders and Isolation Forests to effectively identify fake e-commerce reviews using TF-IDF and BERT features, offering a scalable and cost-effective alternative to supervised methods by eliminating the need for labeled training data.

Steven Laychi, Samuel Paul Arthur Karuntu, Daniel Hamonangan Sihombing, Rhio Sutoyo, Gabriel Asael Tarigan2026-06-29

💻 computer science

Enhancing Long-term Dual-task-based Prediction of Future Cognitive Decline

This paper proposes a novel deep learning framework that integrates long-term dual-task motor and cognitive data with block-wise resampling, SMOTE augmentation, and a Transformer model to accurately predict two-year cognitive decline with 80% precision, outperforming traditional MRI and biomarker-based methods.

Zhewen Liu, Shuqiong Wu, Ákos Godó, Yasushi Yagi2026-06-29

💻 computer science

Trusted and Explainable Collective Learning for Conflictive Multi-View Decision-Making

This paper proposes Trusted and Explainable Collective Learning (TECL), a novel method that actively leverages conflicts in multi-view data to assess individual consistency, prioritize views, and generate reliable, interpretable collective decisions, rather than simply eliminating such conflicts.

Nengjun Zhu, Zhiyu Zhang, Shenghui Lan, Jian Cao, Siji Zhu2026-06-29

💻 computer science

Data Augmentation for Clinical Multilingual Information Extraction

This paper proposes a computationally efficient, word-embedding-based data augmentation strategy using neighbor word replacement that significantly improves Named Entity Recognition and offers modest gains for Entity Linking across five languages, thereby addressing the challenge of limited annotated clinical text in multilingual health informatics.

Rodrigo Gonçalves, Andre Lamurias2026-06-29✓ Author reviewed ⓘ

← Previous Next →