OpenRaiser/Intern-Atlas huggingface dataset:
Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
Venue: arXiv cs.SE 2025
Year: 2025

Purpose: Quantum computing promises to transform problem-solving across various domains with rapid and practical solutions. Within Software Evolution and Maintenance, Quantum Machine Learning (QML) remains mostly an underexplored domain, particularly in addressing challenges such as detecting buggy software commits from code repositories. Methods: In this study, we investigate the practical application of Quantum Support Vector Classifiers (QSVC) for detecting buggy software commits across 14 open-source software projects with diverse dataset sizes encompassing 30,924 data instances. We compare the QML algorithm PQSVC (Pegasos QSVC) and QSVC against the classical Support Vector Classifier (SVC). Our technique addresses large datasets in QSVC algorithms by dividing them into smaller subsets. We propose and evaluate an aggregation method to combine predictions from these models to detect the entire test dataset. We also introduce an incremental testing methodology to overcome the difficulties of quantum feature mapping during the testing approach. Results: The study shows the effectiveness of QSVC and PQSVC in detecting buggy software commits. The aggregation technique successfully combines predictions from smaller data subsets, enhancing the overall detection accuracy for the entire test dataset. The incremental testing methodology effectively manages the challenges associated with quantum feature mapping during the testing process. Conclusion: We contribute to the advancement of QML algorithms in defect prediction, unveiling the potential for further research in this domain. The specific scenario of the Short-Term Activity Frame (STAF) highlights the early detection of buggy software commits during the initial developmental phases of software systems, particularly when dataset sizes remain insufficient to train machine learning models.
Exact computation of the color function for triangular element interfaces
Venue: arXiv cs.GR 2025
Year: 2025

The calculation of the volume enclosed by curved surfaces discretized into triangular elements, and a cube is of great importance in different domains, such as computer graphics and multiphase flow simulations. We propose a robust algorithm, the Front2VOF (F2V) algorithm, to address this problem. The F2V algorithm consists of two main steps. First, it identifies the polygons within the cube by segmenting the triangular elements on the surface, retaining only the portions inside the cube boundaries. Second, it computes the volume enclosed by these polygons in combination with the cube faces. To validate the algorithm's accuracy and robustness, we tested it using a range of synthetic configurations with known analytical solutions.
Towards resilient cities: A hybrid simulation framework for risk mitigation through data driven decision making
Venue: arXiv cs.MA 2025
Year: 2025

Providing a comprehensive view of the city operation and offering useful metrics for decision making is a well known challenge for urban risk analysis systems. Existing systems are, in many cases, generalizations of previous domain specific tools and or methodologies that may not cover all urban interdependencies and makes it difficult to have homogeneous indicators. In order to overcome this limitation while seeking for effective support to decision makers, this article introduces a novel hybrid simulation framework for risk mitigation. The framework is built on a proposed city concept that considers urban space as a Complex Adaptive System composed by interconnected Critical Infrastructures. In this concept, a Social System, which models daily patterns and social interactions of the citizens in the Urban Landscape, drives the CIs demand to configure the full city picture. The frameworks hybrid design integrates agent based and network based modeling by breaking down city agents into system dependent subagents, to enable both inter and intra system interaction simulation, respectively. A layered structure of indicators at different aggregation levels is also developed, to ensure that decisions are not only data driven but also explainable. Therefore, the proposed simulation framework can serve as a DSS tool that allows the quantitative analysis of the impact of threats at different levels. First, system level metrics can be used to get a broad view on the city resilience. Then, agent level metrics back those figures and provide better explainability. On implementation, the proposed framework enables component reusability (for eased coding), simulation federation (enabling the integration of existing system oriented simulators), discrete simulation in accelerated time (for rapid scenario simulation) and decision oriented visualization (for informed outputs).
Search engines in polarized media environment: Auditing political information curation on Google and Bing prior to 2024 US elections
Venue: arXiv cs.CY 2025
Year: 2025

Search engines play an important role in the context of modern elections. By curating information in response to user queries, search engines influence how individuals are informed about election-related developments and perceive the media environment in which elections take place. It has particular implications for (perceived) polarization, especially if search engines' curation results in a skewed treatment of information sources based on their political leaning. Until now, however, it is unclear whether such a partisan gap emerges through information curation on search engines and what user- and system-side factors affect it. To address this shortcoming, we audit the two largest Western search engines, Google and Bing, prior to the 2024 US presidential elections and examine how these search engines' organic search results and additional interface elements represent election-related information depending on the queries' slant, user location, and time when the search was conducted. Our findings indicate that both search engines tend to prioritize left-leaning media sources, with the exact scope of search results' ideological slant varying between Democrat- and Republican-focused queries. We also observe limited effects of location- and time-based factors on organic search results, whereas results for additional interface elements were more volatile over time and specific US states. Together, our observations highlight that search engines' information curation actively mirrors the partisan divides present in the US media environments and has the potential to contribute to (perceived) polarization within these environments.
Video Summarisation with Incident and Context Information using Generative AI
Venue: arXiv cs.CV 2025
Year: 2025

The proliferation of video content production has led to vast amounts of data, posing substantial challenges in terms of analysis efficiency and resource utilization. Addressing this issue calls for the development of robust video analysis tools. This paper proposes a novel approach leveraging Generative Artificial Intelligence (GenAI) to facilitate streamlined video analysis. Our tool aims to deliver tailored textual summaries of user-defined queries, offering a focused insight amidst extensive video datasets. Unlike conventional frameworks that offer generic summaries or limited action recognition, our method harnesses the power of GenAI to distil relevant information, enhancing analysis precision and efficiency. Employing YOLO-V8 for object detection and Gemini for comprehensive video and text analysis, our solution achieves heightened contextual accuracy. By combining YOLO with Gemini, our approach furnishes textual summaries extracted from extensive CCTV footage, enabling users to swiftly navigate and verify pertinent events without the need for exhaustive manual review. The quantitative evaluation revealed a similarity of 72.8%, while the qualitative assessment rated an accuracy of 85%, demonstrating the capability of the proposed method.
Teaching Simulation as a Research Method in Empirical Software Engineering
Venue: arXiv cs.SE 2025
Year: 2025

The chapter supports educators and postgraduate students in understanding the role of simulation in software engineering research based on the authors' experience. This way, it includes a background positioning simulation-based studies in software engineering research, the proposition of learning objectives for teaching simulation as a research method, and presents our experience when teaching simulation concepts and practice. For educators, it further provides learning objectives when teaching simulation, considering the current state of the art in software engineering research and the necessary guidance and recommended learning activities to achieve these objectives. For students, it drives the learning path for those interested in learning this method but had no opportunity to engage in an entire course on simulation in the context of empirical research.
Intelligent Gradient Boosting Algorithms for Estimating Strength of Modified Subgrade Soil
Venue: arXiv cs.LG 2025
Year: 2025

The performance of pavement under loading depends on the strength of the subgrade. However, experimental estimation of properties of pavement strengths such as California bearing ratio (CBR), unconfined compressive strength (UCS) and resistance value (R) are often tedious, time-consuming and costly, thereby inspiring a growing interest in machine learning based tools which are simple, cheap and fast alternatives. Thus, the potential application of two boosting techniques; categorical boosting (CatBoost) and extreme gradient boosting (XGBoost) and support vector regression (SVR), is similarly explored in this study for estimation of properties of subgrade soil modified with hydrated lime activated rice husk ash (HARSH). Using 121 experimental data samples of varying proportions of HARSH, plastic limit, liquid limit, plasticity index, clay activity, optimum moisture content, and maximum dry density as input for CBR, UCS and R estimation, four evaluation metrics namely coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) are used to evaluate the models' performance. The results indicate that XGBoost outperformed CatBoost and SVR in estimating these properties, yielding R2 of 0.9994, 0.9995 and 0.9999 in estimating the CBR, UCS and R respectively. Also, SVR outperformed CatBoost in estimating the CBR and R with R2 of 0.9997 respectively. On the other hand, CatBoost outperformed SVR in estimating the UCS with R2 of 0.9994. Feature sensitivity analysis shows that the three machine learning techniques are unanimous that increasing HARSH proportion lead to values of the estimated properties respectively. A comparison with previous results also shows superiority of XGBoost in estimating subgrade properties.
Advancing Retrieval-Augmented Generation for Persian: Development of Language Models, Comprehensive Benchmarks, and Best Practices for Optimization
Venue: arXiv cs.CL 2025
Year: 2025

This paper examines the specific obstacles of constructing Retrieval-Augmented Generation(RAG) systems in low-resource languages, with a focus on Persian's complicated morphology and versatile syntax. The research aims to improve retrieval and generation accuracy by introducing Persian-specific models, namely MatinaRoberta(a masked language model) and MatinaSRoberta(a fine-tuned Sentence-BERT), along with a comprehensive benchmarking framework. Three datasets-general knowledge(PQuad), scientifically specialized texts, and organizational reports, were used to assess these models after they were trained on a varied corpus of 73.11 billion Persian tokens. The methodology involved extensive pretraining, fine-tuning with tailored loss functions, and systematic evaluations using both traditional metrics and the Retrieval-Augmented Generation Assessment framework. The results show that MatinaSRoberta outperformed previous embeddings, achieving superior contextual relevance and retrieval accuracy across datasets. Temperature tweaking, chunk size modifications, and document summary indexing were explored to enhance RAG setups. Larger models like Llama-3.1 (70B) consistently demonstrated the highest generation accuracy, while smaller models faced challenges with domain-specific and formal contexts. The findings underscore the potential for developing RAG systems in Persian through customized embeddings and retrieval-generation settings and highlight the enhancement of NLP applications such as search engines and legal document analysis in low-resource languages.
Back Home: A Computer Vision Solution to Seashell Identification for Ecological Restoration
Venue: arXiv cs.CV 2025
Year: 2025

Illegal souvenir collection strips an estimated five tonnes of seashells from Costa Rica's beaches each year. Yet, once these specimens are seized, their coastal origin -- Pacific or Caribbean -- cannot be verified easily due to the lack of information, preventing their return when confiscated by local authorities. To solve this issue, we introduce BackHome19K, the first large-scale image corpus (19,058 photographs, 516 species) annotated with coast-level labels, and propose a lightweight pipeline that infers provenance in real time on a mobile-grade CPU. A trained anomaly filter pre-screens uploads, increasing robustness to user-generated noise. On a held-out test set, the classifier attains 86.3% balanced accuracy, while the filter rejects 93% of 180 out-of-domain objects with zero false negatives. Deployed as a web application, the system has already processed 70,000 shells for wildlife officers in under three seconds per image, enabling confiscated specimens to be safely repatriated to their native ecosystems. The dataset is available at https://huggingface.co/datasets/FIFCO/BackHome19K
Balancing Exploration and Cybersickness: Investigating Curiosity-Driven Behavior in Virtual Environments
Venue: arXiv cs.HC 2025
Year: 2025

During virtual navigation, users exhibit varied interaction and navigation behaviors influenced by several factors. Existing theories and models have been developed to explain and predict these diverse patterns. While users often experience uncomfortable sensations, such as cybersickness, during virtual reality (VR) use, they do not always make optimal decisions to mitigate these effects. Although methods like reinforcement learning have been used to model decision-making processes, they typically rely on random selection to simulate actions, failing to capture the complexities of real navigation behavior. In this study, we propose curiosity as a key factor driving irrational decision-making, suggesting that users continuously balance exploration and cybersickness according to the free energy principle during virtual navigation. Our findings show that VR users generally adopt conservative strategies when navigating, with most participants displaying negative curiosity across trials. However, curiosity levels tend to rise when the virtual environment changes, illustrating the dynamic interplay between exploration and discomfort. This study provides a quantitative approach to decoding curiosity-driven behavior during virtual navigation, offering insights into how users balance exploration and the avoidance of cybersickness. Future research will further refine this model by incorporating additional psychological and environmental factors to improve the accuracy of navigation pattern predictions.
SpecTf: Transformers Enable Data-Driven Imaging Spectroscopy Cloud Detection
Venue: arXiv cs.LG 2025
Year: 2025

Current and upcoming generations of visible-shortwave infrared (VSWIR) imaging spectrometers promise unprecedented capacity to quantify Earth System processes across the globe. However, reliable cloud screening remains a fundamental challenge for these instruments, where traditional spatial and temporal approaches are limited by cloud variability and limited temporal coverage. The Spectroscopic Transformer (SpecTf) addresses these challenges with a spectroscopy-specific deep learning architecture that performs cloud detection using only spectral information (no spatial or temporal data are required). By treating spectral measurements as sequences rather than image channels, SpecTf learns fundamental physical relationships without relying on spatial context. Our experiments demonstrate that SpecTf significantly outperforms the current baseline approach implemented for the EMIT instrument, and performs comparably with other machine learning methods with orders of magnitude fewer learned parameters. Critically, we demonstrate SpecTf's inherent interpretability through its attention mechanism, revealing physically meaningful spectral features the model has learned. Finally, we present SpecTf's potential for cross-instrument generalization by applying it to a different instrument on a different platform without modifications, opening the door to instrument agnostic data driven algorithms for future imaging spectroscopy tasks.
Plug-and-Play DISep: Separating Dense Instances for Scene-to-Pixel Weakly-Supervised Change Detection in High-Resolution Remote Sensing Images
Venue: arXiv cs.CV 2025
Year: 2025

Existing Weakly-Supervised Change Detection (WSCD) methods often encounter the problem of "instance lumping" under scene-level supervision, particularly in scenarios with a dense distribution of changed instances (i.e., changed objects). In these scenarios, unchanged pixels between changed instances are also mistakenly identified as changed, causing multiple changes to be mistakenly viewed as one. In practical applications, this issue prevents the accurate quantification of the number of changes. To address this issue, we propose a Dense Instance Separation (DISep) method as a plug-and-play solution, refining pixel features from a unified instance perspective under scene-level supervision. Specifically, our DISep comprises a three-step iterative training process: 1) Instance Localization: We locate instance candidate regions for changed pixels using high-pass class activation maps. 2) Instance Retrieval: We identify and group these changed pixels into different instance IDs through connectivity searching. Then, based on the assigned instance IDs, we extract corresponding pixel-level features on a per-instance basis. 3) Instance Separation: We introduce a separation loss to enforce intra-instance pixel consistency in the embedding space, thereby ensuring separable instance feature representations. The proposed DISep adds only minimal training cost and no inference cost. It can be seamlessly integrated to enhance existing WSCD methods. We achieve state-of-the-art performance by enhancing {three Transformer-based and four ConvNet-based methods} on the LEVIR-CD, WHU-CD, DSIFN-CD, SYSU-CD, and CDD datasets. Additionally, our DISep can be used to improve fully-supervised change detection methods. Code is available at https://github.com/zhenghuizhao/Plug-and-Play-DISep-for-Change-Detection.
Emergence of Painting Ability via Recognition-Driven Evolution
Venue: arXiv cs.CV 2025
Year: 2025

From Paleolithic cave paintings to Impressionism, human painting has evolved to depict increasingly complex and detailed scenes, conveying more nuanced messages. This paper attempts to emerge this artistic capability by simulating the evolutionary pressures that enhance visual communication efficiency. Specifically, we present a model with a stroke branch and a palette branch that together simulate human-like painting. The palette branch learns a limited colour palette, while the stroke branch parameterises each stroke using Bézier curves to render an image, subsequently evaluated by a high-level recognition module. We quantify the efficiency of visual communication by measuring the recognition accuracy achieved with machine vision. The model then optimises the control points and colour choices for each stroke to maximise recognition accuracy with minimal strokes and colours. Experimental results show that our model achieves superior performance in high-level recognition tasks, delivering artistic expression and aesthetic appeal, especially in abstract sketches. Additionally, our approach shows promise as an efficient bit-level image compression technique, outperforming traditional methods.
SensorQA: A Question Answering Benchmark for Daily-Life Monitoring
Venue: arXiv cs.CL 2025
Year: 2025

With the rapid growth in sensor data, effectively interpreting and interfacing with these data in a human-understandable way has become crucial. While existing research primarily focuses on learning classification models, fewer studies have explored how end users can actively extract useful insights from sensor data, often hindered by the lack of a proper dataset. To address this gap, we introduce SensorQA, the first human-created question-answering (QA) dataset for long-term time-series sensor data for daily life monitoring. SensorQA is created by human workers and includes 5.6K diverse and practical queries that reflect genuine human interests, paired with accurate answers derived from sensor data. We further establish benchmarks for state-of-the-art AI models on this dataset and evaluate their performance on typical edge devices. Our results reveal a gap between current models and optimal QA performance and efficiency, highlighting the need for new contributions. The dataset and code are available at: https://github.com/benjamin-reichman/SensorQA.
GiNet: Integrating Sequential and Context-Aware Learning for Battery Capacity Prediction
Venue: arXiv cs.LG 2025
Year: 2025

The surging demand for batteries requires advanced battery management systems, where battery capacity modelling is a key functionality. In this paper, we aim to achieve accurate battery capacity prediction by learning from historical measurements of battery dynamics. We propose GiNet, a gated recurrent units enhanced Informer network, for predicting battery's capacity. The novelty and competitiveness of GiNet lies in its capability of capturing sequential and contextual information from raw battery data and reflecting the battery's complex behaviors with both temporal dynamics and long-term dependencies. We conducted an experimental study based on a publicly available dataset to showcase GiNet's strength of gaining a holistic understanding of battery behavior and predicting battery capacity accurately. GiNet achieves 0.11 mean absolute error for predicting the battery capacity in a sequence of future time slots without knowing the historical battery capacity. It also outperforms the latest algorithms significantly with 27% error reduction on average compared to Informer. The promising results highlight the importance of customized and optimized integration of algorithm and battery knowledge and shed light on other industry applications as well.