Tootfinder

Secrecy Offloading Analysis of UAV-assisted NOMA-MEC Incorporating WPT in IoT Networks
Gia-Huy Nguyen, Anh-Nhat Nguyen, Minh-Sang Nguyen, Khai Nguyen, Tung-Son Ngo, Ngoc-Anh Bui, Phuong-Chi Le, Manh-Duc Hoang
https://arxiv.org/abs/2507.08352

Secrecy Offloading Analysis of UAV-assisted NOMA-MEC Incorporating WPT in IoT Networks
This article studies the efficiency of secrecy data offloading for an unmanned aerial vehicle (UAV)-assisted nonorthogonal multiple access (NOMA)-integrated mobile-edge computing (MEC) incorporating wireless power transfer (WPT) within an Internet of Things (IoT) network. Specifically, this study assumes an UAV to function in dual roles: as a mobile computation platform and as an aerial power-supply station, offering substantial advantages for resource-constrained edge devices (EDs) in mitigati…

Improving the Developer Experience with a Low-Code Process Modelling Language
Henrique Henriques, Hugo Louren\c{c}o, Vasco Amaral, Miguel Goul\~ao
https://arxiv.org/abs/2508.06299

Improving the Developer Experience with a Low-Code Process Modelling Language
Context: The OutSystems Platform is a development environment composed of several DSLs, used to specify, quickly build, and validate web and mobile applications. The DSLs allow users to model different perspectives such as interfaces and data models, define custom business logic and construct process models. Problem: The DSL for process modelling (Business Process Technology (BPT)), has a low adoption rate and is perceived as having usability problems hampering its adoption. This is problematic…

Generation of Indoor Open Street Maps for Robot Navigation from CAD Files
Jiajie Zhang, Shenrui Wu, Xu Ma, S\"oren Schwertfeger
https://arxiv.org/abs/2507.00552

Generation of Indoor Open Street Maps for Robot Navigation from CAD Files
The deployment of autonomous mobile robots is predicated on the availability of environmental maps, yet conventional generation via SLAM (Simultaneous Localization and Mapping) suffers from significant limitations in time, labor, and robustness, particularly in dynamic, large-scale indoor environments where map obsolescence can lead to critical localization failures. To address these challenges, this paper presents a complete and automated system for converting architectural Computer-Aided Desi…

A Machine Learning empowered search for Sub-Minute Optical Transient Events with the Deeper, Wider, Faster programme
Simon R. Goode, Sara A. Webb, Jeff Cooke, Jielai Zhang, James Freeburn, Amy Lien, Mohsen Shamohammadi, Alexandra Rosenthal, Laura N. Driessen, Christopher Fluke, Ashish Mahabal, Anais M\"oller, Dougal Dobie, Adam Batten, Natasha Van Bemmel
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A Machine Learning empowered search for Sub-Minute Optical Transient Events with the Deeper, Wider, Faster programme
Optical transient surveys continue to generate increasingly large datasets, prompting the introduction of machine-learning algorithms to search for quality transient candidates efficiently. Existing machine-learning infrastructure can be leveraged in novel ways to search these datasets for new classes of transients. We present a machine-learning accelerated search pipeline for the Deeper, Wider, Faster (DWF) programme designed to identify high-quality astrophysical transient candidates that con…

IoT-based Noise Monitoring using Mobile Nodes for Smart Cities
Bhima Sankar Manthina (International Institute of Information Technology-Hyderabad), Shreyash Gujar (International Institute of Information Technology-Hyderabad), Sachin Chaudhari (International Institute of Information Technology-Hyderabad), Kavita Vemuri1 (International Institute of Information Technology-Hyderabad), Shivam Chhirolya (Prezent.AI, India)

IoT-based Noise Monitoring using Mobile Nodes for Smart Cities
Urban noise pollution poses a significant threat to public health, yet existing monitoring infrastructures offer limited spatial coverage and adaptability. This paper presents a scalable, low-cost, IoT-based, real-time environmental noise monitoring solution using mobile nodes (sensor nodes on a moving vehicle). The system utilizes a low-cost sound sensor integrated with GPS-enabled modules to collect geotagged noise data at one-second intervals. The sound nodes are calibrated against a referen…

3D Software Synthesis Guided by Constraint-Expressive Intermediate Representation
Shuqing Li, Anson Y. Lam, Yun Peng, Wenxuan Wang, Michael R. Lyu
https://arxiv.org/abs/2507.18625

3D Software Synthesis Guided by Constraint-Expressive Intermediate Representation
Graphical user interface (UI) software has undergone a fundamental transformation from traditional two-dimensional (2D) desktop/web/mobile interfaces to spatial three-dimensional (3D) environments. While existing work has made remarkable success in automated 2D software generation, such as HTML/CSS and mobile app interface code synthesis, the generation of 3D software still remains under-explored. Current methods for 3D software generation usually generate the 3D environments as a whole and can…

Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol
Christos Nikou, Theodoros Giannakopoulos
https://arxiv.org/abs/2507.06070

Contrastive and Transfer Learning for Effective Audio Fingerprinting through a Real-World Evaluation Protocol
Recent advances in song identification leverage deep neural networks to learn compact audio fingerprints directly from raw waveforms. While these methods perform well under controlled conditions, their accuracy drops significantly in real-world scenarios where the audio is captured via mobile devices in noisy environments. In this paper, we introduce a novel evaluation protocol designed to better reflect such real-world conditions. We generate three recordings of the same audio, each with incre…

Sovereign AI for 6G: Towards the Future of AI-Native Networks
Swarna Bindu Chetty, David Grace, Simon Saunders, Paul Harris, Eirini Eleni Tsiropoulou, Tony Quek, Hamed Ahmadi
https://arxiv.org/abs/2509.06700

Sovereign AI for 6G: Towards the Future of AI-Native Networks
The advent of Generative Artificial Intelligence (GenAI), Large Language Models (LLMs), and Large Telecom Models (LTM) significantly reshapes mobile networks, especially as the telecom industry transitions from 5G's cloud-centric to AI-native 6G architectures. This transition unlocks unprecedented capabilities in real-time automation, semantic networking, and autonomous service orchestration. However, it introduces critical risks related to data sovereignty, security, explainability, and regula…

Can Internal Software Metrics Predict App Popularity at Launch? Yeas! and Nays!
Md Nahidul Islam Opu, Fatima Islam Mouri, Rick Kazman, Yuanfang Cai, Shaiful Chowdhury
https://arxiv.org/abs/2507.02110

Can Internal Software Metrics Predict App Popularity at Launch? Yeas! and Nays!
Predicting mobile app popularity before release can provide developers with a strategic advantage in a competitive marketplace, yet it remains a challenging problem. This study explores whether internal software metrics, measurable from source code before deployment, can predict an app's popularity, defined by user ratings (calculated from user reviews) and DownloadsPerYear (yearly downloads). Using a dataset of 446 open-source Android apps from F-Droid, we extract a wide array of features, inc…

Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels
Deemah H. Tashman, Soumaya Cherkaoui, Walaa Hamouda
https://arxiv.org/abs/2507.06981

Optimizing Cognitive Networks: Reinforcement Learning Meets Energy Harvesting Over Cascaded Channels
This paper presents a reinforcement learning (RL) based approach to improve the physical layer security (PLS) of an underlay cognitive radio network (CRN) over cascaded channels. These channels are utilized in highly mobile networks such as cognitive vehicular networks (CVN). In addition, an eavesdropper aims to intercept the communications between secondary users (SUs). The SU receiver has full-duplex and energy harvesting capabilities to generate jamming signals to confound the eavesdropper a…