Tootfinder

More than 3 million gallons of oil field wastewater, a.k.a. produced water, a.k.a. brine, spilled due to a pipeline leak in northwest #NorthDakota 's Williams County on Monday. It's one of the larger spills recorded in the state. #Bakken

3 million gallon oil field wastewater spill hits Northwest North Dakota
A Continental Resources pipeline leaked over 3 million gallons of produced water in Williams County on Monday,...

An Enhanced Privacy-preserving Federated Few-shot Learning Framework for Respiratory Disease Diagnosis
Ming Wang, Zhaoyang Duan, Dong Xue, Fangzhou Liu, Zhongheng Zhang
https://arxiv.org/abs/2507.08050 https://arxiv.org/pdf/2507.08050 https://arxiv.org/html/2507.08050
arXiv:2507.08050v1 Announce Type: new
Abstract: The labor-intensive nature of medical data annotation presents a significant challenge for respiratory disease diagnosis, resulting in a scarcity of high-quality labeled datasets in resource-constrained settings. Moreover, patient privacy concerns complicate the direct sharing of local medical data across institutions, and existing centralized data-driven approaches, which rely on amounts of available data, often compromise data privacy. This study proposes a federated few-shot learning framework with privacy-preserving mechanisms to address the issues of limited labeled data and privacy protection in diagnosing respiratory diseases. In particular, a meta-stochastic gradient descent algorithm is proposed to mitigate the overfitting problem that arises from insufficient data when employing traditional gradient descent methods for neural network training. Furthermore, to ensure data privacy against gradient leakage, differential privacy noise from a standard Gaussian distribution is integrated into the gradients during the training of private models with local data, thereby preventing the reconstruction of medical images. Given the impracticality of centralizing respiratory disease data dispersed across various medical institutions, a weighted average algorithm is employed to aggregate local diagnostic models from different clients, enhancing the adaptability of a model across diverse scenarios. Experimental results show that the proposed method yields compelling results with the implementation of differential privacy, while effectively diagnosing respiratory diseases using data from different structures, categories, and distributions.
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Breaking Resource Barriers in Speech Emotion Recognition via Data Distillation
Speech emotion recognition (SER) plays a crucial role in human-computer interaction. The emergence of edge devices in the Internet of Things (IoT) presents challenges in constructing intricate deep learning models due to constraints in memory and computational resources. Moreover, emotional speech data often contains private information, raising concerns about privacy leakage during the deployment of SER models. To address these challenges, we propose a data distillation framework to facilitate…

Securing AI Systems: A Guide to Known Attacks and Impacts
Naoto Kiribuchi, Kengo Zenitani, Takayuki Semitsu
https://arxiv.org/abs/2506.23296 https://

Securing AI Systems: A Guide to Known Attacks and Impacts
Embedded into information systems, artificial intelligence (AI) faces security threats that exploit AI-specific vulnerabilities. This paper provides an accessible overview of adversarial attacks unique to predictive and generative AI systems. We identify eleven major attack types and explicitly link attack techniques to their impacts -- including information leakage, system compromise, and resource exhaustion -- mapped to the confidentiality, integrity, and availability (CIA) security triad. We…

Thread and Memory-Safe Programming with CLASS
Lu\'is Caires (Instituto Superior T\'ecnico)
https://arxiv.org/abs/2505.20848 https://

Thread and Memory-Safe Programming with CLASS
CLASS is a proof-of-concept general purpose linear programming language, flexibly supporting realistic concurrent programming idioms, and featuring an expressive linear type system ensuring that programs (1) never misuse or leak stateful resources or memory, (2) never deadlock, and (3) always terminate. The design of CLASS and the strong static guarantees of its type system originates in its Linear Logic and proposition-as-types foundations. However, instead of focusing on its theoretical found…

Evaluating and Improving Large Language Models for Competitive Program Generation
Minnan Wei, Ziming Li, Xiang Chen, Menglin Zheng, Ziyan Qu, Cheng Yu, Siyu Chen, Xiaolin Ju
https://arxiv.org/abs/2506.22954

Evaluating and Improving Large Language Models for Competitive Program Generation
Context: Due to the demand for strong algorithmic reasoning, complex logic implementation, and strict adherence to input/output formats and resource constraints, competitive programming generation by large language models (LLMs) is considered the most challenging problem in current LLM-based code generation. However, previous studies often evaluate LLMs using simple prompts and benchmark datasets prone to data leakage. Moreover, prior work has limited consideration of the diversity in algorithm…

CodeGuard: A Generalized and Stealthy Backdoor Watermarking for Generative Code Models
Haoxuan Li, Jiale Zhang, Xiaobing Sun, Xiapu Luo
https://arxiv.org/abs/2506.20926

CodeGuard: A Generalized and Stealthy Backdoor Watermarking for Generative Code Models
Generative code models (GCMs) significantly enhance development efficiency through automated code generation and code summarization. However, building and training these models require computational resources and time, necessitating effective digital copyright protection to prevent unauthorized leaks and misuse. Backdoor watermarking, by embedding hidden identifiers, simplifies copyright verification by breaking the model's black-box nature. Current backdoor watermarking techniques face two mai…