Tootfinder

LRDDv2: Enhanced Long-Range Drone Detection Dataset with Range Information and Comprehensive Real-World Challenges
Amirreza Rouhi, Sneh Patel, Noah McCarthy, Siddiqa Khan, Hadi Khorsand, Kaleb Lefkowitz, David K. Han
https://arxiv.org/abs/2508.03331

LRDDv2: Enhanced Long-Range Drone Detection Dataset with Range Information and Comprehensive Real-World Challenges
The exponential growth in Unmanned Aerial Vehicles (UAVs) usage underscores the critical need of detecting them at extended distances to ensure safe operations, especially in densely populated areas. Despite the tremendous advances made in computer vision through deep learning, the detection of these small airborne objects remains a formidable challenge. While several datasets have been developed specifically for drone detection, the need for a more extensive and diverse collection of drone ima…

Revisiting Third-Party Library Detection: A Ground Truth Dataset and Its Implications Across Security Tasks
Jintao Gu, Haolang Lu, Guoshun Nan, Yihan Lin, Kun Wang, Yuchun Guo, Yigui Cao, Yang Liu
https://arxiv.org/abs/2509.04091

Revisiting Third-Party Library Detection: A Ground Truth Dataset and Its Implications Across Security Tasks
Accurate detection of third-party libraries (TPLs) is fundamental to Android security, supporting vulnerability tracking, malware detection, and supply chain auditing. Despite many proposed tools, their real-world effectiveness remains unclear.We present the first large-scale empirical study of ten state-of-the-art TPL detection techniques across over 6,000 apps, enabled by a new ground truth dataset with precise version-level annotations for both remote and local dependencies.Our evaluation ex…

GLOBAL CHANGE - GOV WebRip
A project emerging from a loose collaboration within Safeguarding Research and Culture.
With consultation from Peertech Industrial Concern Intl., Corporate Outreach, Waste-Metal Corrosives, & Archives Division.
https://sciop.net/datasets/globalchang

movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps

Towards the Datasets Used in Requirements Engineering of Mobile Apps: Preliminary Findings from a Systematic Mapping Study
Chong Wang, Haoning Wu, Peng Liang, Maya Daneva, Marten van Sinderen
https://arxiv.org/abs/2509.03541

Towards the Datasets Used in Requirements Engineering of Mobile Apps: Preliminary Findings from a Systematic Mapping Study
[Background] Research on requirements engineering (RE) for mobile apps employs datasets formed by app users, developers or vendors. However, little is known about the sources of these datasets in terms of platforms and the RE activities that were researched with the help of the respective datasets. [Aims] The goal of this paper is to investigate the state-of-the-art of the datasets of mobile apps used in existing RE research. [Method] We carried out a systematic mapping study by following the g…

NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation
Huhong Xian, Rui Liu, Berrak Sisman, Haizhou Li
https://arxiv.org/abs/2509.03829

NE-PADD: Leveraging Named Entity Knowledge for Robust Partial Audio Deepfake Detection via Attention Aggregation
Different from traditional sentence-level audio deepfake detection (ADD), partial audio deepfake detection (PADD) requires frame-level positioning of the location of fake speech. While some progress has been made in this area, leveraging semantic information from audio, especially named entities, remains an underexplored aspect. To this end, we propose NE-PADD, a novel method for Partial Audio Deepfake Detection (PADD) that leverages named entity knowledge through two parallel branches: Speech …

LMDG: Advancing Lateral Movement Detection Through High-Fidelity Dataset Generation
Anas Mabrouk, Mohamed Hatem, Mohammad Mamun, Sherif Saad
https://arxiv.org/abs/2508.02942 htt…

LMDG: Advancing Lateral Movement Detection Through High-Fidelity Dataset Generation
Lateral Movement (LM) attacks continue to pose a significant threat to enterprise security, enabling adversaries to stealthily compromise critical assets. However, the development and evaluation of LM detection systems are impeded by the absence of realistic, well-labeled datasets. To address this gap, we propose LMDG, a reproducible and extensible framework for generating high-fidelity LM datasets. LMDG automates benign activity generation, multi-stage attack execution, and comprehensive label…

Revisiting Simple Baselines for In-The-Wild Deepfake Detection
Orlando Castaneda, Kevin So-Tang, Kshitij Gurung
https://arxiv.org/abs/2509.04150 https://ar…

Revisiting Simple Baselines for In-The-Wild Deepfake Detection
The widespread adoption of synthetic media demands accessible deepfake detectors and realistic benchmarks. While most existing research evaluates deepfake detectors on highly controlled datasets, we focus on the recently released "in-the-wild" benchmark, Deepfake-Eval-2024. Initial reporting on Deepfake-Eval-2024 showed that three finetuned open-source models achieve accuracies between 61% and 69%, significantly lagging behind the leading commercial deepfake detector with 82% accuracy. Our work…

movielens_100k: MovieLens 100K (1998)
Three bipartite networks that make up the MovieLens 100K Dataset, a stable benchmark dataset of 100,000 ratings from 1000 users on 1700 movies. These data capture the tag-movie, user-movie, and user-tag networks. (Also available from MovieLens are 1M, 10M and 20M folksonomy datasets.).
This network has 24129 nodes and 95580 edges.
Tags: Informational, Folksonomy, Unweighted, Multigraph, Timestamps

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
Qiyu Chen, Zhen Qu, Wei Luo, Haiming Yao, Yunkang Cao, Yuxin Jiang, Yinan Duan, Huiyuan Luo, Chengkan Lv, Zhengtao Zhang
https://arxiv.org/abs/2508.03447

CoPS: Conditional Prompt Synthesis for Zero-Shot Anomaly Detection
Recently, large pre-trained vision-language models have shown remarkable performance in zero-shot anomaly detection (ZSAD). With fine-tuning on a single auxiliary dataset, the model enables cross-category anomaly detection on diverse datasets covering industrial defects and medical lesions. Compared to manually designed prompts, prompt learning eliminates the need for expert knowledge and trial-and-error. However, it still faces the following challenges: (i) static learnable tokens struggle to …