Tootfinder

Listen to 8 hours of sound dedicated to Palestine - and act -
#DoingGood #EndGenocide
https://

Listen to 8 hours of sound dedicated to Palestine - and act - CDM Create Digital Music
Music and sound are not action -- at least, not normally. But sound can recenter you, rewire your brain, and let you catch your breath before that action. So I find myself returning to this eight-hour broadcast by London's Sister Midnight FM, which includes Palestinian artists who have long been featured on CDM, including Muqata'a, bint mbareh, and Dakn.

The music industry is developing tools to detect AI-generated music across the music pipeline, with a focus on licensing and control rather than takedowns (Jack Buehrer/The Verge)
https://www.theverge.com/ai-artificial-int

The music industry is building the tech to hunt down AI songs
With no way to stop the onslaught of AI music, the industry is taking a different approach: figuring out how to make money off of it.

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Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
Md Min-Ha-Zul Abedin, Tazqia Mehrub
https://arxiv.org/abs/2507.16952

Evaluating Ensemble and Deep Learning Models for Static Malware Detection with Dimensionality Reduction Using the EMBER Dataset
This study investigates the effectiveness of several machine learning algorithms for static malware detection using the EMBER dataset, which contains feature representations of Portable Executable (PE) files. We evaluate eight classification models: LightGBM, XGBoost, CatBoost, Random Forest, Extra Trees, HistGradientBoosting, k-Nearest Neighbors (KNN), and TabNet, under three preprocessing settings: original feature space, Principal Component Analysis (PCA), and Linear Discriminant Analysis (L…

Dataset Distillation as Data Compression: A Rate-Utility Perspective
Youneng Bao, Yiping Liu, Zhuo Chen, Yongsheng Liang, Mu Li, Kede Ma
https://arxiv.org/abs/2507.17221

Dataset Distillation as Data Compression: A Rate-Utility Perspective
Driven by the ``scale-is-everything'' paradigm, modern machine learning increasingly demands ever-larger datasets and models, yielding prohibitive computational and storage requirements. Dataset distillation mitigates this by compressing an original dataset into a small set of synthetic samples, while preserving its full utility. Yet, existing methods either maximize performance under fixed storage budgets or pursue suitable synthetic data representations for redundancy removal, without jointly…

Perspective-Invariant 3D Object Detection
Ao Liang, Lingdong Kong, Dongyue Lu, Youquan Liu, Jian Fang, Huaici Zhao, Wei Tsang Ooi
https://arxiv.org/abs/2507.17665 https://

Perspective-Invariant 3D Object Detection
With the rise of robotics, LiDAR-based 3D object detection has garnered significant attention in both academia and industry. However, existing datasets and methods predominantly focus on vehicle-mounted platforms, leaving other autonomous platforms underexplored. To bridge this gap, we introduce Pi3DET, the first benchmark featuring LiDAR data and 3D bounding box annotations collected from multiple platforms: vehicle, quadruped, and drone, thereby facilitating research in 3D object detection fo…

Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries
Pengfei Cai, Yan Song, Qing Gu, Nan Jiang, Haoyu Song, Ian McLoughlin
https://arxiv.org/abs/2507.16343

Detect Any Sound: Open-Vocabulary Sound Event Detection with Multi-Modal Queries
Most existing sound event detection~(SED) algorithms operate under a closed-set assumption, restricting their detection capabilities to predefined classes. While recent efforts have explored language-driven zero-shot SED by exploiting audio-language models, their performance is still far from satisfactory due to the lack of fine-grained alignment and cross-modal feature fusion. In this work, we propose the Detect Any Sound Model (DASM), a query-based framework for open-vocabulary SED guided by …

The music industry is developing tools to detect AI-generated music across the music pipeline, with a focus on licensing and control rather than takedowns (Jack Buehrer/The Verge)
https://www.theverge.com/ai-artificial-int

The music industry is building the tech to hunt down AI songs
With no way to stop the onslaught of AI music, the industry is taking a different approach: figuring out how to make money off of it.

Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
Ioannis Panopoulos, Maria-Lamprini A. Bartsioka, Sokratis Nikolaidis, Stylianos I. Venieris, Dimitra I. Kaklamani, Iakovos S. Venieris
https://arxiv.org/abs/2506.18114

Dynamic Temporal Positional Encodings for Early Intrusion Detection in IoT
The rapid expansion of the Internet of Things (IoT) has introduced significant security challenges, necessitating efficient and adaptive Intrusion Detection Systems (IDS). Traditional IDS models often overlook the temporal characteristics of network traffic, limiting their effectiveness in early threat detection. We propose a Transformer-based Early Intrusion Detection System (EIDS) that incorporates dynamic temporal positional encodings to enhance detection accuracy while maintaining computati…

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
Jiachi Chen, Yiming Shen, Jiashuo Zhang, Zihao Li, John Grundy, Zhenzhe Shao, Yanlin Wang, Jiashui Wang, Ting Chen, Zibin Zheng
https://arxiv.org/abs/2506.18795

FORGE: An LLM-driven Framework for Large-Scale Smart Contract Vulnerability Dataset Construction
High-quality smart contract vulnerability datasets are critical for evaluating security tools and advancing smart contract security research. Two major limitations of current manual dataset construction are (1) labor-intensive and error-prone annotation processes limiting the scale, quality, and evolution of the dataset, and (2) absence of standardized classification rules results in inconsistent vulnerability categories and labeling results across different datasets. To address these limitatio…