OpenRaiser/Intern-Atlas huggingface dataset:
Fast and Adaptive Task Management in MEC: A Deep Learning Approach Using Pointer Networks
Venue: arXiv cs.NI 2025
Year: 2025

Task offloading and scheduling in Mobile Edge Computing (MEC) are vital for meeting the low-latency demands of modern IoT and dynamic task scheduling scenarios. MEC reduces the processing burden on resource-constrained devices by enabling task execution at nearby edge servers. However, efficient task scheduling remains a challenge in dynamic, time-sensitive environments. Conventional methods -- such as heuristic algorithms and mixed-integer programming -- suffer from high computational overhead, limiting their real-time applicability. Existing deep learning (DL) approaches offer faster inference but often lack scalability and adaptability to dynamic workloads. To address these issues, we propose a Pointer Network-based architecture for task scheduling in dynamic edge computing scenarios. Our model is trained on a generated synthetic dataset using genetic algorithms to determine the optimal task ordering. Experimental results show that our model achieves lower drop ratios and waiting times than baseline methods, and a soft sequence accuracy of up to 89.2%. Our model consistently achieves inference times under 2 seconds across all evaluated task counts, whereas the integer and binary programming approaches require approximately up to 18 seconds and 90 seconds, respectively. It also shows strong generalization across varying scenarios, and adaptability to real-time changes, offering a scalable and efficient solution for edge-based task management.
Reliable Task Offloading in MEC through Transmission Diversity and Jamming-Aware Scheduling
Venue: arXiv cs.NI 2025
Year: 2025

Mobile Edge Computing (MEC) enables low-latency applications by bringing computation closer to the user, but dynamic task arrivals and communication threats like jamming complicate reliable task offloading and resource allocation. In this paper, we formulate a dynamic MEC framework considering the transmission diversity that jointly addresses task scheduling and resource block (RB) assignment in the presence of jamming. First, we define and evaluate key network metrics-including dropped task ratio and bandwidth utilization-while maintaining service continuity by accounting for the existing commitments of the edge server to previously offloaded tasks. Then, we propose a jamming-aware offloading and RB allocation framework that leverages transmission diversity and optimal scheduling across distributed gNBs. The proposed solution is compared to a similar scenario without transmission diversity and two baseline strategies of first-come-first-served (FCFS) and shortest task first (STF). The proposed algorithm effectively mitigates the impact of jamming while enhancing resource utilization and minimizing task drop rates, making it highly suitable for mission-critical MEC applications. At signal-to-jamming-and-noise ratio (SJNR) of 4 dB, the proposed method achieves a $0.26$ task drop rate, outperforming the scenario without transmission diversity with a task drop rate of 0.50 and STF and FCFS strategies with 0.52 and 0.63 task drop rates, respectively.
Impute With Confidence: A Framework for Uncertainty Aware Multivariate Time Series Imputation
Venue: arXiv cs.LG 2025
Year: 2025

Time series data with missing values is common across many domains. Healthcare presents special challenges due to prolonged periods of sensor disconnection. In such cases, having a confidence measure for imputed values is critical. Most existing methods either overlook model uncertainty or lack mechanisms to estimate it. To address this gap, we introduce a general framework that quantifies and leverages uncertainty for selective imputation. By focusing on values the model is most confident in, highly unreliable imputations are avoided. Our experiments on multiple EHR datasets, covering diverse types of missingness, demonstrate that selectively imputing less-uncertain values not only reduces imputation errors but also improves downstream tasks. Specifically, we show performance gains in a 24-hour mortality prediction task, underscoring the practical benefit of incorporating uncertainty into time series imputation.
Automated Multi-Class Crop Pathology Classification via Convolutional Neural Networks: A Deep Learning Approach for Real-Time Precision Agriculture
Venue: arXiv cs.CV 2025
Year: 2025

Crop diseases present a significant barrier to agricultural productivity and global food security, especially in large-scale farming where early identification is often delayed or inaccurate. This research introduces a Convolutional Neural Network (CNN)-based image classification system designed to automate the detection and classification of eight common crop diseases using leaf imagery. The methodology involves a complete deep learning pipeline: image acquisition from a large, labeled dataset, preprocessing via resizing, normalization, and augmentation, and model training using TensorFlow with Keras' Sequential API. The CNN architecture comprises three convolutional layers with increasing filter sizes and ReLU activations, followed by max pooling, flattening, and fully connected layers, concluding with a softmax output for multi-class classification. The system achieves high training accuracy (~90%) and demonstrates reliable performance on unseen data, although a validation accuracy of ~60% suggests minor overfitting. Notably, the model integrates a treatment recommendation module, providing actionable guidance by mapping each detected disease to suitable pesticide or fungicide interventions. Furthermore, the solution is deployed on an open-source, mobile-compatible platform, enabling real-time image-based diagnostics for farmers in remote areas. This research contributes a scalable and accessible tool to the field of precision agriculture, reducing reliance on manual inspection and promoting sustainable disease management practices. By merging deep learning with practical agronomic support, this work underscores the potential of CNNs to transform crop health monitoring and enhance food production resilience on a global scale.
GreenCrossingAI: A Camera Trap/Computer Vision Pipeline for Environmental Science Research Groups
Venue: arXiv cs.CV 2025
Year: 2025

Camera traps have long been used by wildlife researchers to monitor and study animal behavior, population dynamics, habitat use, and species diversity in a non-invasive and efficient manner. While data collection from the field has increased with new tools and capabilities, methods to develop, process, and manage the data, especially the adoption of ML/AI tools, remain challenging. These challenges include the sheer volume of data generated, the need for accurate labeling and annotation, variability in environmental conditions affecting data quality, and the integration of ML/AI tools into existing workflows that often require domain-specific customization and computational resources. This paper provides a guide to a low-resource pipeline to process camera trap data on-premise, incorporating ML/AI capabilities tailored for small research groups with limited resources and computational expertise. By focusing on practical solutions, the pipeline offers accessible approaches for data transmission, inference, and evaluation, enabling researchers to discover meaningful insights from their ever-increasing camera trap datasets.
Enhancing NeuroEvolution-Based Game Testing: A Branch Coverage Approach for Scratch Programs
Venue: arXiv cs.SE 2025
Year: 2025

Automated test generation for game-like programs presents unique challenges due to their non-deterministic behavior and complex control structures. The NEATEST framework has been used for automated testing in Scratch games, employing neuroevolution-based test generation optimized for statement coverage. However, statement coverage alone is often insufficient for fault detection, as it does not guarantee execution of all logical branches. This paper introduces a branch coverage-based fitness function to enhance test effectiveness in automated game testing. We extend NEATEST by integrating a branch fitness function that prioritizes control-dependent branches, guiding the neuroevolution process to maximize branch exploration. To evaluate the effectiveness of this approach, empirical experiments were conducted on 25 Scratch games, comparing Neatest with Statement Coverage (NSC) against Neatest with Branch Coverage (NBC). A mutation analysis was also performed to assess the fault detection capabilities of both techniques. The results demonstrate that NBC achieves higher branch coverage than NSC in 13 out of 25 games, particularly in programs with complex conditional structures. Moreover, NBC achieves a lower false positive rate in mutation testing, making it a more reliable approach for identifying faulty behavior in game programs. These findings confirm that branch coverage-based test generation improves test coverage and fault detection in Scratch programs.
Joint Access Point Activation and Power Allocation for Cell-Free Massive MIMO Aided ISAC Systems
Venue: arXiv cs.IT 2025
Year: 2025

Cell-free massive multiple-input multiple-output (MIMO)-aided integrated sensing and communication (ISAC) systems are investigated where distributed access points jointly serve users and sensing targets. We demonstrate that only a subset of access points (APs) has to be activated for both tasks, while deactivating redundant APs is essential for power savings. This motivates joint active AP selection and power control for optimizing energy efficiency. The resultant problem is a mixed-integer nonlinear program (MINLP). To address this, we propose a model-based Branch-and-Bound approach as a strong baseline to guide a semi-supervised heterogeneous graph neural network (HetGNN) for selecting the best active APs and the power allocation. Comprehensive numerical results demonstrate that the proposed HetGNN reduces power consumption by 20-25\% and runs nearly 10,000 times faster than model-based benchmarks.
GeoWarp: An automatically differentiable and GPU-accelerated implicit MPM framework for geomechanics based on NVIDIA Warp
Venue: arXiv cs.CE 2025
Year: 2025

The material point method (MPM), a hybrid Lagrangian-Eulerian particle method, is increasingly used to simulate large-deformation and history-dependent behavior of geomaterials. While explicit time integration dominates current MPM implementations due to its algorithmic simplicity, such schemes are unsuitable for quasi-static and long-term processes typical in geomechanics. Implicit MPM formulations are free of these limitations but remain less adopted, largely due to the difficulty of computing the Jacobian matrix required for Newton-type solvers, especially when consistent tangent operators should be derived for complex constitutive models. In this paper, we introduce GeoWarp -- an implicit MPM framework for geomechanics built on NVIDIA Warp -- that exploits GPU parallelism and reverse-mode automatic differentiation to compute Jacobians without manual derivation. To enhance efficiency, we develop a sparse Jacobian construction algorithm that leverages the localized particle-grid interactions intrinsic to MPM. The framework is verified through forward and inverse examples in large-deformation elastoplasticity and coupled poromechanics. Results demonstrate that GeoWarp provides a robust, scalable, and extensible platform for differentiable implicit MPM simulation in computational geomechanics.
Toward Developing Machine-Learning-Aided Tools for the Thermomechanical Monitoring of Nuclear Reactor Components
Venue: arXiv cs.LG 2025
Year: 2025

Proactive maintenance strategies, such as Predictive Maintenance (PdM), play an important role in the operation of Nuclear Power Plants (NPPs), particularly due to their capacity to reduce offline time by preventing unexpected shutdowns caused by component failures. In this work, we explore the use of a Convolutional Neural Network (CNN) architecture combined with a computational thermomechanical model to calculate the temperature, stress, and strain of a Pressurized Water Reactor (PWR) fuel rod during operation. This estimation relies on a limited number of temperature measurements from the cladding's outer surface. This methodology can potentially aid in developing PdM tools for nuclear reactors by enabling real-time monitoring of such systems. The training, validation, and testing datasets were generated through coupled simulations involving BISON, a finite element-based nuclear fuel performance code, and the MOOSE Thermal-Hydraulics Module (MOOSE-THM). We conducted eleven simulations, varying the peak linear heat generation rates. Of these, eight were used for training, two for validation, and one for testing. The CNN was trained for over 1,000 epochs without signs of overfitting, achieving highly accurate temperature distribution predictions. These were then used in a thermomechanical model to determine the stress and strain distribution within the fuel rod.
Unmanned Aerial Vehicle (UAV) Data-Driven Modeling Software with Integrated 9-Axis IMUGPS Sensor Fusion and Data Filtering Algorithm
Venue: arXiv cs.RO 2025
Year: 2025

Unmanned Aerial Vehicles (UAV) have emerged as versatile platforms, driving the demand for accurate modeling to support developmental testing. This paper proposes data-driven modeling software for UAV. Emphasizes the utilization of cost-effective sensors to obtain orientation and location data subsequently processed through the application of data filtering algorithms and sensor fusion techniques to improve the data quality to make a precise model visualization on the software. UAV's orientation is obtained using processed Inertial Measurement Unit (IMU) data and represented using Quaternion Representation to avoid the gimbal lock problem. The UAV's location is determined by combining data from the Global Positioning System (GPS), which provides stable geographic coordinates but slower data update frequency, and the accelerometer, which has higher data update frequency but integrating it to get position data is unstable due to its accumulative error. By combining data from these two sensors, the software is able to calculate and continuously update the UAV's real-time position during its flight operations. The result shows that the software effectively renders UAV orientation and position with high degree of accuracy and fluidity
Towards Agentic RAG with Deep Reasoning: A Survey of RAG-Reasoning Systems in LLMs
Venue: arXiv cs.CL 2025
Year: 2025

Retrieval-Augmented Generation (RAG) lifts the factuality of Large Language Models (LLMs) by injecting external knowledge, yet it falls short on problems that demand multi-step inference; conversely, purely reasoning-oriented approaches often hallucinate or mis-ground facts. This survey synthesizes both strands under a unified reasoning-retrieval perspective. We first map how advanced reasoning optimizes each stage of RAG (Reasoning-Enhanced RAG). Then, we show how retrieved knowledge of different type supply missing premises and expand context for complex inference (RAG-Enhanced Reasoning). Finally, we spotlight emerging Synergized RAG-Reasoning frameworks, where (agentic) LLMs iteratively interleave search and reasoning to achieve state-of-the-art performance across knowledge-intensive benchmarks. We categorize methods, datasets, and open challenges, and outline research avenues toward deeper RAG-Reasoning systems that are more effective, multimodally-adaptive, trustworthy, and human-centric. The collection is available at https://github.com/DavidZWZ/Awesome-RAG-Reasoning.
Online Micro-gesture Recognition Using Data Augmentation and Spatial-Temporal Attention
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we introduce the latest solution developed by our team, HFUT-VUT, for the Micro-gesture Online Recognition track of the IJCAI 2025 MiGA Challenge. The Micro-gesture Online Recognition task is a highly challenging problem that aims to locate the temporal positions and recognize the categories of multiple micro-gesture instances in untrimmed videos. Compared to traditional temporal action detection, this task places greater emphasis on distinguishing between micro-gesture categories and precisely identifying the start and end times of each instance. Moreover, micro-gestures are typically spontaneous human actions, with greater differences than those found in other human actions. To address these challenges, we propose hand-crafted data augmentation and spatial-temporal attention to enhance the model's ability to classify and localize micro-gestures more accurately. Our solution achieved an F1 score of 38.03, outperforming the previous state-of-the-art by 37.9%. As a result, our method ranked first in the Micro-gesture Online Recognition track.
EV-STLLM: Electric vehicle charging forecasting based on spatio-temporal large language models with multi-frequency and multi-scale information fusion
Venue: arXiv cs.CE 2025
Year: 2025

With the proliferation of electric vehicles (EVs), accurate charging demand and station occupancy forecasting are critical for optimizing urban energy and the profit of EVs aggregator. Existing approaches in this field usually struggle to capture the complex spatio-temporal dependencies in EV charging behaviors, and their limited model parameters hinder their ability to learn complex data distribution representations from large datasets. To this end, we propose a novel EV spatio-temporal large language model (EV-STLLM) for accurate prediction. Our proposed framework is divided into two modules. In the data processing module, we utilize variational mode decomposition (VMD) for data denoising, and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) for data multi-frequency decomposition. Fuzzy information granulation (FIG) for extracting multi-scale information. Additionally, ReliefF is used for feature selection to mitigate redundancy. In the forecasting module, the EV-STLLM is used to directly achieve EV charging and occupancy forecasting. Firstly, we fully capture the intrinsic spatio-temporal characteristics of the data by integrating adjacency matrices derived from the regional stations network and spatio-temporal-frequency embedding information. Then, the partially frozen graph attention (PFGA) module is utilized to maintain the sequential feature modeling capabilities of the pre-trained large model while incorporating EV domain knowledge. Extensive experiments using real-world data from Shenzhen, China, demonstrate that our proposed framework can achieve superior accuracy and robustness compared to the state-of-the-art benchmarks.
It Only Gets Worse: Revisiting DL-Based Vulnerability Detectors from a Practical Perspective
Venue: arXiv cs.SE 2025
Year: 2025

With the growing threat of software vulnerabilities, deep learning (DL)-based detectors have gained popularity for vulnerability detection. However, doubts remain regarding their consistency within declared CWE ranges, real-world effectiveness, and applicability across scenarios. These issues may lead to unreliable detection, high false positives/negatives, and poor adaptability to emerging vulnerabilities. A comprehensive analysis is needed to uncover critical factors affecting detection and guide improvements in model design and deployment. In this paper, we present VulTegra, a novel evaluation framework that conducts a multidimensional comparison of scratch-trained and pre-trained-based DL models for vulnerability detection. VulTegra reveals that state-of-the-art (SOTA) detectors still suffer from low consistency, limited real-world capabilities, and scalability challenges. Contrary to common belief, pre-trained models are not consistently better than scratch-trained models but exhibit distinct strengths in specific contexts.Importantly, our study exposes the limitations of relying solely on CWE-based classification and identifies key factors that significantly affect model performance. Experimental results show that adjusting just one such factor consistently improves recall across all seven evaluated detectors, with six also achieving better F1 scores. Our findings provide deeper insights into model behavior and emphasize the need to consider both vulnerability types and inherent code features for effective detection.
Holistix: A Dataset for Holistic Wellness Dimensions Analysis in Mental Health Narratives
Venue: arXiv cs.LG 2025
Year: 2025

We introduce a dataset for classifying wellness dimensions in social media user posts, covering six key aspects: physical, emotional, social, intellectual, spiritual, and vocational. The dataset is designed to capture these dimensions in user-generated content, with a comprehensive annotation framework developed under the guidance of domain experts. This framework allows for the classification of text spans into the appropriate wellness categories. We evaluate both traditional machine learning models and advanced transformer-based models for this multi-class classification task, with performance assessed using precision, recall, and F1-score, averaged over 10-fold cross-validation. Post-hoc explanations are applied to ensure the transparency and interpretability of model decisions. The proposed dataset contributes to region-specific wellness assessments in social media and paves the way for personalized well-being evaluations and early intervention strategies in mental health. We adhere to ethical considerations for constructing and releasing our experiments and dataset publicly on Github.
Memory-Augmented SAM2 for Training-Free Surgical Video Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Surgical video segmentation is a critical task in computer-assisted surgery, essential for enhancing surgical quality and patient outcomes. Recently, the Segment Anything Model 2 (SAM2) framework has demonstrated remarkable advancements in both image and video segmentation. However, the inherent limitations of SAM2's greedy selection memory design are amplified by the unique properties of surgical videos-rapid instrument movement, frequent occlusion, and complex instrument-tissue interaction-resulting in diminished performance in the segmentation of complex, long videos. To address these challenges, we introduce Memory Augmented (MA)-SAM2, a training-free video object segmentation strategy, featuring novel context-aware and occlusion-resilient memory models. MA-SAM2 exhibits strong robustness against occlusions and interactions arising from complex instrument movements while maintaining accuracy in segmenting objects throughout videos. Employing a multi-target, single-loop, one-prompt inference further enhances the efficiency of the tracking process in multi-instrument videos. Without introducing any additional parameters or requiring further training, MA-SAM2 achieved performance improvements of 4.36% and 6.1% over SAM2 on the EndoVis2017 and EndoVis2018 datasets, respectively, demonstrating its potential for practical surgical applications.