Tootfinder

This https://arxiv.org/abs/2505.11205 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

IssueCourier: Multi-Relational Heterogeneous Temporal Graph Neural Network for Open-Source Issue Assignment
Issue assignment plays a critical role in open-source software (OSS) maintenance, which involves recommending the most suitable developers to address the reported issues. Given the high volume of issue reports in large-scale projects, manually assigning issues is tedious and costly. Previous studies have proposed automated issue assignment approaches that primarily focus on modeling issue report textual information, developers' expertise, or interactions between issues and developers based on h…

This https://arxiv.org/abs/2504.09132 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Self-Supervised Autoencoder Network for Robust Heart Rate Extraction from Noisy Photoplethysmogram: Applying Blind Source Separation to Biosignal Analysis
Biosignals can be viewed as mixtures measuring particular physiological events, and blind source separation (BSS) aims to extract underlying source signals from mixtures. This paper proposes a self-supervised multi-encoder autoencoder (MEAE) to separate heartbeat-related source signals from photoplethysmogram (PPG), enhancing heart rate (HR) detection in noisy PPG data. The MEAE is trained on PPG signals from a large open polysomnography database without any pre-processing or data selection. Th…

This https://arxiv.org/abs/2403.18213 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Long-Term Open-Pit Mine Planning with Large Neighbourhood Search
We present a Large Neighbourhood Search based approach for solving complex long-term open-pit mine planning problems. An initial feasible solution, generated by a sliding windows heuristic, is improved through repeated solves of a restricted mixed-integer program. Each iteration leaves only a subset of the variables in the planning model free to take on new values. We form these subsets through the use of neighbourhood formation strategies that exploit model structure. We show that our approach…

This https://arxiv.org/abs/2505.19644 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attribution
A key research area in deepfake speech detection is source tracing - determining the origin of synthesised utterances. The approaches may involve identifying the acoustic model (AM), vocoder model (VM), or other generation-specific parameters. However, progress is limited by the lack of a dedicated, systematically curated dataset. To address this, we introduce STOPA, a systematically varied and metadata-rich dataset for deepfake speech source tracing, covering 8 AMs, 6 VMs, and diverse paramete…

This https://arxiv.org/abs/2505.08421 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAR_…

e-GPU: An Open-Source and Configurable RISC-V Graphic Processing Unit for TinyAI Applications
Graphics processing units (GPUs) excel at parallel processing, but remain largely unexplored in ultra-low-power edge devices (TinyAI) due to their power and area limitations, as well as the lack of suitable programming frameworks. To address these challenges, this work introduces embedded GPU (e-GPU), an open-source and configurable RISC-V GPU platform designed for TinyAI devices. Its extensive configurability enables area and power optimization, while a dedicated Tiny-OpenCL implementation pro…

This https://arxiv.org/abs/2505.19641 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

SynLogic: Synthesizing Verifiable Reasoning Data at Scale for Learning Logical Reasoning and Beyond
Recent advances such as OpenAI-o1 and DeepSeek R1 have demonstrated the potential of Reinforcement Learning (RL) to enhance reasoning abilities in Large Language Models (LLMs). While open-source replication efforts have primarily focused on mathematical and coding domains, methods and resources for developing general reasoning capabilities remain underexplored. This gap is partly due to the challenge of collecting diverse and verifiable reasoning data suitable for RL. We hypothesize that logica…

This https://arxiv.org/abs/2503.02817 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Open Source at a Crossroads: The Future of Licensing Driven by Monetization
The widespread adoption of open source libraries and frameworks can be attributed to their licensing. Open Source Software Licenses (OSS licenses) ensure that software can be sold or distributed as part of aggregate programs from various sources without requiring a royalty or fee. The quality of such code rivals that of commercial software, with open source libraries forming large parts of the supply chain for critical commercial systems in industry. Despite this, most open source projects rely…

This https://arxiv.org/abs/2502.11191 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Primus: A Pioneering Collection of Open-Source Datasets for Cybersecurity LLM Training
Large Language Models (LLMs) have shown remarkable advancements in specialized fields such as finance, law, and medicine. However, in cybersecurity, we have noticed a lack of open-source datasets, with a particular lack of high-quality cybersecurity pretraining corpora, even though much research indicates that LLMs acquire their knowledge during pretraining. To address this, we present a comprehensive suite of datasets covering all major training stages, including pretraining, instruction fine-…

This https://arxiv.org/abs/2506.02858 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSD_…

DGMO: Training-Free Audio Source Separation through Diffusion-Guided Mask Optimization
Language-queried Audio Source Separation (LASS) enables open-vocabulary sound separation via natural language queries. While existing methods rely on task-specific training, we explore whether pretrained diffusion models, originally designed for audio generation, can inherently perform separation without further training. In this study, we introduce a training-free framework leveraging generative priors for zero-shot LASS. Analyzing naïve adaptations, we identify key limitations arising from m…

OSS-UAgent: An Agent-based Usability Evaluation Framework for Open Source Software
Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai, Peng Cheng, Wenyuan Yu, Wenjie Zhang, Xuemin Lin, Jingren Zhou
https://arxiv.org/abs/2505.23239

OSS-UAgent: An Agent-based Usability Evaluation Framework for Open Source Software
Usability evaluation is critical to the impact and adoption of open source software (OSS), yet traditional methods relying on human evaluators suffer from high costs and limited scalability. To address these limitations, we introduce OSS-UAgent, an automated, configurable, and interactive agent-based usability evaluation framework specifically designed for open source software. Our framework employs intelligent agents powered by large language models (LLMs) to simulate developers performing pro…