Tootfinder

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software
Pragyan K C, Rambod Ghandiparsi, Thomas Herron, John Heaps, Mitra Bokaei Hosseini
https://arxiv.org/abs/2507.13555

Demystifying Feature Requests: Leveraging LLMs to Refine Feature Requests in Open-Source Software
The growing popularity and widespread use of software applications (apps) across various domains have driven rapid industry growth. Along with this growth, fast-paced market changes have led to constantly evolving software requirements. Such requirements are often grounded in feature requests and enhancement suggestions, typically provided by users in natural language (NL). However, these requests often suffer from defects such as ambiguity and incompleteness, making them challenging to interpr…

Quasi-1D Electronic Metadevices with Enhanced Electrical Properties
Abdallah Abushawish, Ziwen Huang, Mohammad Samizadeh Nikoo
https://arxiv.org/abs/2508.09656 https://

Quasi-1D Electronic Metadevices with Enhanced Electrical Properties
Electronic metadevices leveraging sub-wavelength metallic features have shown great potential for high-frequency switching. Theoretical analysis based on a one-dimensional (1D) model indicates that reducing the size of subwavelength features can enhance electrical properties, such as contact resistance and switching cutoff frequency. Here, we report higher-than-expected contact resistance in electronic metadevices when the subwavelength feature size is aggressively downscaled. We attribute this…

QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference
Xiangchen Li, Saeid Ghafouri, Bo Ji, Hans Vandierendonck, Deepu John, Dimitrios S. Nikolopoulos
https://arxiv.org/abs/2506.23934

QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference
As machine learning inferences increasingly move to edge devices, adapting to diverse computational capabilities, hardware, and memory constraints becomes more critical. Instead of relying on a pre-trained model fixed for all future inference queries across diverse edge devices, we argue that planning an inference pattern with a request-specific model tailored to the device's computational capacity, accuracy requirements, and time constraints is more cost-efficient and robust to diverse scenari…

Cloud Detection using Night Sky Background Light at the Pierre Auger Observatory
Fedor Tairli, for the Pierre Auger Collaboration
https://arxiv.org/abs/2509.06343 https://

Cloud Detection using Night Sky Background Light at the Pierre Auger Observatory
Rejection of cloud-contaminated data is a complex and important process at the Pierre Auger Observatory, one which combines information from several sources, including IR cameras, lidars, and satellite imaging. With the deteriorating quality of the IR cameras and challenges in using other sources, we propose a new method. We use continuous detector monitoring measurements to build a large database of night sky background fluxes for each pixel across 27 telescopes. Using this database, we genera…

DINOv3 with Test-Time Training for Medical Image Registration
Shansong Wang, Mojtaba Safari, Mingzhe Hu, Qiang Li, Chih-Wei Chang, Richard LJ Qiu, Xiaofeng Yang
https://arxiv.org/abs/2508.14809

DINOv3 with Test-Time Training for Medical Image Registration
Prior medical image registration approaches, particularly learning-based methods, often require large amounts of training data, which constrains clinical adoption. To overcome this limitation, we propose a training-free pipeline that relies on a frozen DINOv3 encoder and test-time optimization of the deformation field in feature space. Across two representative benchmarks, the method is accurate and yields regular deformations. On Abdomen MR-CT, it attained the best mean Dice score (DSC) of 0.7…

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
Ahmed Sabbah, Radi Jarrar, Samer Zein, David Mohaisen
https://arxiv.org/abs/2507.22231 https…

Understanding Concept Drift with Deprecated Permissions in Android Malware Detection
Permission analysis is a widely used method for Android malware detection. It involves examining the permissions requested by an application to access sensitive data or perform potentially malicious actions. In recent years, various machine learning (ML) algorithms have been applied to Android malware detection using permission-based features and feature selection techniques, often achieving high accuracy. However, these studies have largely overlooked important factors such as protection level…

A long time ago, when I've used the #Opera browser (the real one), I've discovered mouse #gestures. Even though I've only used the basic ones like back / forward / reload / close tab, I've found them brilliant. To this day, I can't comprehend why not only modern browsers rejected this feature, but also why it didn't become widespread across desktop GUIs.
But then, today the peak application #UX is a website packed in a vulnerable low quality embedded browser. And designers are blindly trying to copy bad mobile designs into desktop PC applications, with no consideration how ill fit they are.
#UI #GUI