pwc-archive/papers-with-abstracts huggingface dataset:
Advancing Fake News Detection: Hybrid DeepLearning with FastText and Explainable AI
Authors: EHTESHAM HASHMI1, SULE YILDIRIM YAYILGAN1, MUHAMMAD MUDASSAR YAMIN1, SUBHAN ALI2, Mohamed Abomhara
Date: 2024-03-25 00:00:00
Proceeding: 29-03 2024 3
Tasks: Fake News Detection, Misinformation, Word Embeddings

The widespread propagation of misinformation on social media platforms poses a signiﬁcantconcern, prompting substantial endeavors within the research community to develop robust detectionsolutions. Individuals often place unwavering trust in social networks, often without discerning the originsand authenticity of the information disseminated through these platforms. Hence, the identiﬁcation of media-rich fake news necessitates an approach that adeptly leverages multimedia elements and effectively enhancesdetection accuracy. The ever-changing nature of cyberspace highlights the need for measures that mayeffectively resist the spread of media-rich fake news while protecting the integrity of information systems.This study introduces a robust approach for fake news detection, utilizing three publicly available datasets:WELFake, FakeNewsNet, and FakeNewsPrediction. We integrated FastText word embeddings with variousMachine Learning and Deep Learning methods, further reﬁning these algorithms with regularization andhyperparameter optimization to mitigate overﬁtting and promote model generalization. Notably, a hybridmodel combining Convolutional Neural Networks and Long Short-Term Memory, enriched with FastTextembeddings, surpassed other techniques in classiﬁcation performance across all datasets, registering accu-racy and F1-scores of 0.99, 0.97, and 0.99, respectively. Additionally, we utilized state-of-the-art transformer-based models such as BERT, XLNet, and RoBERTa, enhancing them through hyperparameter adjustments.These transformer models, surpassing traditional RNN-based frameworks, excel in managing syntacticnuances, thus aiding in semantic interpretation. In the concluding phase, explainable AI modeling wasemployed using Local Interpretable Model-Agnostic Explanations, and Latent Dirichlet Allocation to gaindeeper insights into the model’s decision-making process.
Near-Driven Autonomous Rover Navigation in Complex Environments: Extensions to Urban Search-and-Rescue and Industrial Inspection
Authors: Dhadkan Shrestha, Lincoln Bhattarai
Date: 2025-04-11 00:00:00
Tasks: Autonomous Navigation, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Transfer Learning

This paper explores the use of an extended neuroevolutionary approach, based on NeuroEvolution of Augmenting Topologies (NEAT), for autonomous robots in dynamic environments associated with hazardous tasks like firefighting, urban search-and-rescue (USAR), and industrial inspections. Building on previous research, it expands the simulation environment to larger and more complex settings, demonstrating NEAT's adaptability across different applications. By integrating recent advancements in NEAT and reinforcement learning, the study uses modern simulation frameworks for realism and hybrid algorithms for optimization. Experimental results show that NEAT-evolved controllers achieve success rates comparable to state-of-the-art deep reinforcement learning methods, with superior structural adaptability. The agents reached ~80% success in outdoor tests, surpassing baseline models. The paper also highlights the benefits of transfer learning among tasks and evaluates the effectiveness of NEAT in complex 3D navigation. Contributions include evaluating NEAT for diverse autonomous applications and discussing real-world deployment considerations, emphasizing the approach's potential as an alternative or complement to deep reinforcement learning in autonomous navigation tasks.
Breaking art: Synthesizing abstract expressionism through image rearrangement
Authors: Christopher Palazzolo, Oliver van Kaick, David Mould
Date: 2025-04-27 00:00:00
Proceeding: Computers and Graphics 2025 4

We present an algorithm that creates interesting abstract expressionist images from segments of an input image. The algorithm operates by first segmenting the input image at multiple scales, then redistributing the resulting segments across the image plane to obtain an aesthetic abstract output. Larger segments are placed using neighborhood-aware descriptors, and smaller segments are arranged in a Poisson disk distribution. In our thorough analysis, we show that our results score highly according to several relevant aesthetic metrics, and that our style is indeed abstract expressionism. The results are visually appealing, provided the exemplar has a somewhat diverse color pallette and some amount of structure.
Adaptive Weight Modified Riesz Mean Filter For High-Density Salt and Pepper Noise Removal
Authors: Md Jahidul Islam
Date: 2025-04-25 00:00:00
Tasks: Salt-And-Pepper Noise Removal, SSIM

This paper introduces a novel filter, the Adaptive Weight Modified Riesz Mean Filter (AWMRmF), designed for the effective removal of high-density salt and pepper noise (SPN). AWMRmF integrates a pixel weight function and adaptivity condition inspired by the Different Adaptive Modified Riesz Mean Filter (DAMRmF). In my simulations, I evaluated the performance of AWMRmF against established filters such as Adaptive Frequency Median Filter (AFMF), Adaptive Weighted Mean Filter (AWMF), Adaptive Cesaro Mean Filter (ACmF), Adaptive Riesz Mean Filter (ARmF), and Improved Adaptive Weighted Mean Filter (IAWMF). The assessment was conducted on 26 typical test images, varying noise levels from 60% to 95%. The findings indicate that, in terms of both Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics, AWMRmF outperformed other state-of-the-art filters. Furthermore, AWMRmF demonstrated superior performance in mean PSNR and SSIM results as well.
Spectral Bias Correction in PINNs for Myocardial Image Registration of Pathological Data
Authors: Bastien C. Baluyot, Marta Varela, Chen Qin
Date: 2025-04-24 00:00:00
Tasks: Image Registration

Accurate myocardial image registration is essential for cardiac strain analysis and disease diagnosis. However, spectral bias in neural networks impedes modeling high-frequency deformations, producing inaccurate, biomechanically implausible results, particularly in pathological data. This paper addresses spectral bias in physics-informed neural networks (PINNs) by integrating Fourier Feature mappings and introducing modulation strategies into a PINN framework. Experiments on two distinct datasets demonstrate that the proposed methods enhance the PINN's ability to capture complex, high-frequency deformations in cardiomyopathies, achieving superior registration accuracy while maintaining biomechanical plausibility - thus providing a foundation for scalable cardiac image registration and generalization across multiple patients and pathologies.
Crowd Detection Using Very-Fine-Resolution Satellite Imagery
Authors: Tong Xiao, Qunming Wang, Ping Lu, Tenghai Huang, Xiaohua Tong, Peter M. Atkinson
Date: 2025-04-28 00:00:00

Accurate crowd detection (CD) is critical for public safety and historical pattern analysis, yet existing methods relying on ground and aerial imagery suffer from limited spatio-temporal coverage. The development of very-fine-resolution (VFR) satellite sensor imagery (e.g., ~0.3 m spatial resolution) provides unprecedented opportunities for large-scale crowd activity analysis, but it has never been considered for this task. To address this gap, we proposed CrowdSat-Net, a novel point-based convolutional neural network, which features two innovative components: Dual-Context Progressive Attention Network (DCPAN) to improve feature representation of individuals by aggregating scene context and local individual characteristics, and High-Frequency Guided Deformable Upsampler (HFGDU) that recovers high-frequency information during upsampling through frequency-domain guided deformable convolutions. To validate the effectiveness of CrowdSat-Net, we developed CrowdSat, the first VFR satellite imagery dataset designed specifically for CD tasks, comprising over 120k manually labeled individuals from multi-source satellite platforms (Beijing-3N, Jilin-1 Gaofen-04A and Google Earth) across China. In the experiments, CrowdSat-Net was compared with five state-of-the-art point-based CD methods (originally designed for ground or aerial imagery) using CrowdSat and achieved the largest F1-score of 66.12% and Precision of 73.23%, surpassing the second-best method by 1.71% and 2.42%, respectively. Moreover, extensive ablation experiments validated the importance of the DCPAN and HFGDU modules. Furthermore, cross-regional evaluation further demonstrated the spatial generalizability of CrowdSat-Net. This research advances CD capability by providing both a newly developed network architecture for CD and a pioneering benchmark dataset to facilitate future CD development.
Facial Geometric Feature Extraction for Dimensional Emotion Analysis Using Genetic Programming
Authors: Wenlong Fu, Qi Chen, Bing Xue, Mengjie Zhang
Date: 2025-04-17 00:00:00
Proceeding: Applications of Evolutionary Computation 2025 4
Tasks: Arousal Estimation, Continuous Affect Estimation, Emotion Recognition, Valence Estimation

Geometric features derived from single static images have the potential to be highly effective for facial emotion analysis, as shape, structure, and spatial relationships are key factors. However, these aspects are rarely explored in existing research. In this paper, we propose a novel approach that utilizes Genetic Programming (GP) to automatically extract geometric features for more effective emotional representation. The proposed GP system uses various evaluation strategies, evolving either a single feature per run or multiple features within a single run. These GP-evolved features capture critical angular and distance-based relationships between facial landmarks, which are then integrated with an existing deep learning model to enhance performance. The results show that the proposed method achieves improved performance in dimensional emotion analysis, providing a more comprehensive understanding of emotional expressions in static images. In addition, our approach is effective in improving the accuracy of emotion predictions, establishing a foundation for more precise facial emotion analysis using geometric information.
A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification
Authors: Junichiro Niimi
Date: 2025-04-26 00:00:00
Tasks: Sentiment Analysis, text-classification, Text Classification

With the advance of large language models (LLMs), LLMs have been utilized for the various tasks. However, the issues of variability and reproducibility of results from each trial of LLMs have been largely overlooked in existing literature while actual human annotation uses majority voting to resolve disagreements among annotators. Therefore, this study introduces the straightforward ensemble strategy to a sentiment analysis using LLMs. As the results, we demonstrate that the ensemble of multiple inference using medium-sized LLMs produces more robust and accurate results than using a large model with a single attempt with reducing RMSE by 18.6%.
MASF-YOLO: An Improved YOLOv11 Network for Small Object Detection on Drone View
Authors: Liugang Lu, Dabin He, Congxiang Liu, Zhixiang Deng
Date: 2025-04-25 00:00:00
Tasks: Novel Object Detection, object-detection, Object Detection, Small Object Detection

With the rapid advancement of Unmanned Aerial Vehicle (UAV) and computer vision technologies, object detection from UAV perspectives has emerged as a prominent research area. However, challenges for detection brought by the extremely small proportion of target pixels, significant scale variations of objects, and complex background information in UAV images have greatly limited the practical applications of UAV. To address these challenges, we propose a novel object detection network Multi-scale Context Aggregation and Scale-adaptive Fusion YOLO (MASF-YOLO), which is developed based on YOLOv11. Firstly, to tackle the difficulty of detecting small objects in UAV images, we design a Multi-scale Feature Aggregation Module (MFAM), which significantly improves the detection accuracy of small objects through parallel multi-scale convolutions and feature fusion. Secondly, to mitigate the interference of background noise, we propose an Improved Efficient Multi-scale Attention Module (IEMA), which enhances the focus on target regions through feature grouping, parallel sub-networks, and cross-spatial learning. Thirdly, we introduce a Dimension-Aware Selective Integration Module (DASI), which further enhances multi-scale feature fusion capabilities by adaptively weighting and fusing low-dimensional features and high-dimensional features. Finally, we conducted extensive performance evaluations of our proposed method on the VisDrone2019 dataset. Compared to YOLOv11-s, MASFYOLO-s achieves improvements of 4.6% in mAP@0.5 and 3.5% in mAP@0.5:0.95 on the VisDrone2019 validation set. Remarkably, MASF-YOLO-s outperforms YOLOv11-m while requiring only approximately 60% of its parameters and 65% of its computational cost. Furthermore, comparative experiments with state-of-the-art detectors confirm that MASF-YOLO-s maintains a clear competitive advantage in both detection accuracy and model efficiency.
Beyond Levels of Driving Automation: A Triadic Framework of Human-AI Collaboration in On-Road Mobility
Authors: Gaojian Huang, Yantong Jin, Wei-Hsiang Lo
Date: 2025-04-27 00:00:00

The goal of the current study is to introduce a triadic human-AI collaboration framework for the automated vehicle domain. Previous classifications (e.g., SAE Levels of Automation) focus on defining automation levels based on who controls the vehicle. However, it remains unclear how human users and AI should collaborate in real-time, especially in dynamic driving contexts, where roles can shift frequently. To fill the gap, this study proposes a triadic human-AI collaboration framework with three AI roles (i.e., Advisor, Co-Pilot, and Guardian) that dynamically adapt to human needs. Overall, the study lays a foundation for developing adaptive, role-based human-AI collaboration strategies in automated vehicles.
Human-Centered AI and Autonomy in Robotics: Insights from a Bibliometric Study
Authors: Simona Casini, Pietro Ducange, Francesco Marcelloni, Lorenzo Pollini
Date: 2025-04-28 00:00:00

The development of autonomous robotic systems offers significant potential for performing complex tasks with precision and consistency. Recent advances in Artificial Intelligence (AI) have enabled more capable intelligent automation systems, addressing increasingly complex challenges. However, this progress raises questions about human roles in such systems. Human-Centered AI (HCAI) aims to balance human control and automation, ensuring performance enhancement while maintaining creativity, mastery, and responsibility. For real-world applications, autonomous robots must balance task performance with reliability, safety, and trustworthiness. Integrating HCAI principles enhances human-robot collaboration and ensures responsible operation. This paper presents a bibliometric analysis of intelligent autonomous robotic systems, utilizing SciMAT and VOSViewer to examine data from the Scopus database. The findings highlight academic trends, emerging topics, and AI's role in self-adaptive robotic behaviour, with an emphasis on HCAI architecture. These insights are then projected onto the IBM MAPE-K architecture, with the goal of identifying how these research results map into actual robotic autonomous systems development efforts for real-world scenarios.
Backward bifurcation and sensitivity analysis for bacterial meningitis transmission dynamics with a nonlinear recovery rate
Authors: Joshua Kiddy K. Asamoaha, B, Farai Nyabadzac, Zhen Jina, ∗, Ebenezer Bonyahd, Muhammad Altaf Khane, f, Michael Y. Li g, Tasawar Hayat
Date: 2020-08-21 00:00:00
Proceeding: Chaos, Solitons and Fractals 2020 8
Tasks: Sensitivity

Bacterial meningitis is one of the major causes of death in sub-Saharan Africa and a medical emergency around the world. To increase the number of recoveries and/or reduce the number of sequelae,
and deaths need effective antibiotics: and also the ability to identify influential state variables, parameters and threshold properties. Therefore, we propose a mathematical model for bacterial meningitis to
include nonlinear recovery rate. The studies show instances for forward and backward bifurcation. We
use Latin hypercube sampling to test for influential parameters in the basic reproduction number, R0.
We use sensitivity heat map and parameter sensitivity spectrum to test for the group sensitivity of all
the state variables and parameters. The sensitivity heat map shows that the most sensitive state variable
to all parameters in the model during none seasonal transmission is the recovery class followed by the
susceptible class; and that the most sensitive state variable during seasonal transmission is the susceptible class followed by the carrier-class. Meanwhile, we use optimal control theory to study the impact of
inoculation in an endemic setting with limited number of antibiotics and hospital beds. The work provides a potential framework for the control of the disease spread in limited-resource settings. Potential
extensions of the model are likewise examined.
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination
Authors: Dixiao Wei, Peng Yi, Jinlong Lei, Yiguang Hong, Yuchuan Du
Date: 2025-04-28 00:00:00
Tasks: Code Generation, Hallucination, Language Modeling, Language Modelling, Large Language Model, Reinforcement Learning (RL)

Reinforcement Learning (RL) has demonstrated excellent decision-making potential in platoon coordination problems. However, due to the variability of coordination goals, the complexity of the decision problem, and the time-consumption of trial-and-error in manual design, finding a well performance reward function to guide RL training to solve complex platoon coordination problems remains challenging. In this paper, we formally define the Platoon Coordination Reward Design Problem (PCRDP), extending the RL-based cooperative platoon coordination problem to incorporate automated reward function generation. To address PCRDP, we propose a Large Language Model (LLM)-based Platoon coordination Reward Design (PCRD) framework, which systematically automates reward function discovery through LLM-driven initialization and iterative optimization. In this method, LLM first initializes reward functions based on environment code and task requirements with an Analysis and Initial Reward (AIR) module, and then iteratively optimizes them based on training feedback with an evolutionary module. The AIR module guides LLM to deepen their understanding of code and tasks through a chain of thought, effectively mitigating hallucination risks in code generation. The evolutionary module fine-tunes and reconstructs the reward function, achieving a balance between exploration diversity and convergence stability for training. To validate our approach, we establish six challenging coordination scenarios with varying complexity levels within the Yangtze River Delta transportation network simulation. Comparative experimental results demonstrate that RL agents utilizing PCRD-generated reward functions consistently outperform human-engineered reward functions, achieving an average of 10\% higher performance metrics in all scenarios.
BQSched: A Non-intrusive Scheduler for Batch Concurrent Queries via Reinforcement Learning
Authors: Chenhao Xu, Chunyu Chen, Jinglin Peng, Jiannan Wang, Jun Gao
Date: 2025-04-27 00:00:00
Tasks: Reinforcement Learning (RL), Scheduling

Most large enterprises build predefined data pipelines and execute them periodically to process operational data using SQL queries for various tasks. A key issue in minimizing the overall makespan of these pipelines is the efficient scheduling of concurrent queries within the pipelines. Existing tools mainly rely on simple heuristic rules due to the difficulty of expressing the complex features and mutual influences of queries. The latest reinforcement learning (RL) based methods have the potential to capture these patterns from feedback, but it is non-trivial to apply them directly due to the large scheduling space, high sampling cost, and poor sample utilization. Motivated by these challenges, we propose BQSched, a non-intrusive Scheduler for Batch concurrent Queries via reinforcement learning. Specifically, BQSched designs an attention-based state representation to capture the complex query patterns, and proposes IQ-PPO, an auxiliary task-enhanced proximal policy optimization (PPO) algorithm, to fully exploit the rich signals of Individual Query completion in logs. Based on the RL framework above, BQSched further introduces three optimization strategies, including adaptive masking to prune the action space, scheduling gain-based query clustering to deal with large query sets, and an incremental simulator to reduce sampling cost. To our knowledge, BQSched is the first non-intrusive batch query scheduler via RL. Extensive experiments show that BQSched can significantly improve the efficiency and stability of batch query scheduling, while also achieving remarkable scalability and adaptability in both data and queries. For example, across all DBMSs and scales tested, BQSched reduces the overall makespan of batch queries on TPC-DS benchmark by an average of 34% and 13%, compared with the commonly used heuristic strategy and the adapted RL-based scheduler, respectively.
Explainable Deep-Learning Based Potentially Hazardous Asteroids Classification Using Graph Neural Networks
Authors: Baimam Boukar Jean Jacques
Date: 2025-04-25 00:00:00
Tasks: Autonomous Navigation, Feature Importance, Graph Neural Network

Classifying potentially hazardous asteroids (PHAs) is crucial for planetary defense and deep space navigation, yet traditional methods often overlook the dynamical relationships among asteroids. We introduce a Graph Neural Network (GNN) approach that models asteroids as nodes with orbital and physical features, connected by edges representing their similarities, using a NASA dataset of 958,524 records. Despite an extreme class imbalance with only 0.22% of the dataset with the hazardous label, our model achieves an overall accuracy of 99% and an AUC of 0.99, with a recall of 78% and an F1-score of 37% for hazardous asteroids after applying the Synthetic Minority Oversampling Technique. Feature importance analysis highlights albedo, perihelion distance, and semi-major axis as main predictors. This framework supports planetary defense missions and confirms AI's potential in enabling autonomous navigation for future missions such as NASA's NEO Surveyor and ESA's Ramses, offering an interpretable and scalable solution for asteroid hazard assessment.
Score-Debiased Kernel Density Estimation
Authors: Elliot L. Epstein, Rajat Dwaraknath, Thanawat Sornwanee, John Winnicki, Jerry Weihong Liu
Date: 2025-04-27 00:00:00
Tasks: Density Estimation

We propose a novel method for density estimation that leverages an estimated score function to debias kernel density estimation (SD-KDE). In our approach, each data point is adjusted by taking a single step along the score function with a specific choice of step size, followed by standard KDE with a modified bandwidth. The step size and modified bandwidth are chosen to remove the leading order bias in the KDE. Our experiments on synthetic tasks in 1D, 2D and on MNIST, demonstrate that our proposed SD-KDE method significantly reduces the mean integrated squared error compared to the standard Silverman KDE, even with noisy estimates in the score function. These results underscore the potential of integrating score-based corrections into nonparametric density estimation.