Results 21 to 30 of about 667,722 (335)

Scaling Exponents of Time Series Data: A Machine Learning Approach

Entropy, 2023
In this study, we present a novel approach to estimating the Hurst exponent of time series data using a variety of machine learning algorithms. The Hurst exponent is a crucial parameter in characterizing long-range dependence in time series, and ...
Sebastian Raubitzek, Luiza Corpaci, Rebecca Hofer, Kevin Mallinger   +3 more
doaj   +1 more source

Listening to bluetooth beacons for epidemic risk mitigation

Scientific Reports, 2022
The ongoing COVID-19 pandemic let to efforts to develop and deploy digital contact tracing systems to expedite contact tracing and risk notification. Unfortunately, the success of these systems has been limited, partly owing to poor interoperability with
Gilles Barthe, Roberta De Viti, Peter Druschel, Deepak Garg, Manuel Gomez-Rodriguez, Pierfrancesco Ingo, Heiner Kremer, Matthew Lentz, Lars Lorch, Aastha Mehta, Bernhard Schölkopf   +10 more
doaj   +1 more source

Spillover and crossover effects of exposure to work‐related aggression and adversities: A dyadic diary study

Aggressive Behavior, Volume 49, Issue 1, Page 85-95, January 2023., 2023
Abstract The past two decades have produced extensive evidence on the manifold and severe outcomes for victims of aggression exposure in the workplace. However, due to the dominating individual‐centered approach, most findings miss a social network perspective.
Alexander Herrmann, Jürgen Glaser, Tobias Greitemeyer   +2 more
wiley   +1 more source

Privacy dynamics [PDF]

Proceedings of the 11th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, 2016
Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that, although OSN capabilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make ...
Calikli, Gul, Law, Mark, Bandara, Arosha K., Russo, Alesandra, Dickens, Luke, Price, Blaine A., Stuart, Avelie, Levine, Mark, Nuseibeh, Bashar   +8 more
openaire   +2 more sources

Image quality improvement in low‐dose chest CT with deep learning image reconstruction

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Objectives To investigate the clinical utility of deep learning image reconstruction (DLIR) for improving image quality in low‐dose chest CT in comparison with 40% adaptive statistical iterative reconstruction‐Veo (ASiR‐V40%) algorithm. Methods This retrospective study included 86 patients who underwent low‐dose CT for lung cancer screening ...
Qian Tian, Xinyu Li, Jianying Li, Yannan Cheng, Xinyi Niu, Shumeng Zhu, Wenting Xu, Jianxin Guo   +7 more
wiley   +1 more source

Privacy Profiles and Amplification by Subsampling

The Journal of Privacy and Confidentiality, 2020
Differential privacy provides a robust quantifiable methodology to measure and control the privacy leakage of data analysis algorithms. A fundamental insight is that by forcing algorithms to be randomized, their privacy leakage can be characterized by ...
Borja Balle, Gilles Barthe, Marco Gaboardi   +2 more
doaj   +1 more source

Application value of contrast‐enhanced ultrasound in preoperative localization of microwave ablation for primary hyperparathyroidism

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Background Ultrasonography (US) and 99mTechnetium‐sestamibi scintigraphy (99mTc‐MIBI) are currently first‐line imaging modalities to localize parathyroid adenomas with sensitivities of 80% and 84%, respectively. Therefore, finding other modalities to further improve the diagnostic accuracy for preoperative localization is critically needed ...
Fangyi Liu, Li Zang, Yunlin Li, Zhiwei Guan, Yang Liu, Xiaoling Yu, Zhiyu Han, Ping Liang   +7 more
wiley   +1 more source

An Incremental Self-Labeling Strategy for Semi-Supervised Deep Learning Based on Generative Adversarial Networks

IEEE Access, 2020
The recent success of deep neural networks is attributed in part to large-scale well-labeled training data. However, with the ever-increasing size of modern datasets, combined with the difficulty of obtaining label information, semi-supervised learning ...
Xiaotao Wei, Xiang Wei, Weiwei Xing, Siyang Lu, Wei Lu   +4 more
doaj   +1 more source

Software Engineering for Privacy in-the-Large [PDF]

2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, 2015
There will be an estimated 35 zettabytes (35$\times$10\textsuperscript{21}) of digital records worldwide by the year 2020. This effectively amounts to privacy management on an ultra-large-scale. In this briefing, we discuss the privacy challenges posed by such an ultra-large-scale ecosystem - we term this ``Privacy in the Large''.
Anthonysamy, Pauline, Rashid, Awais
openaire   +3 more sources

Digital radiography image quality evaluation using various phantoms and software

Journal of Applied Clinical Medical Physics, Volume 23, Issue 12, December 2022., 2022
Abstract Purpose To investigate the effect of the exposure parameters on image quality (IQ) metrics of phantom images, obtained automatically using software or from visual evaluation. Methods Three commercial phantoms and a homemade phantom constructed according to the instructions given in the IAEA Human Health Series No.
Ioannis A. Tsalafoutas, Shady AlKhazzam, Virginia Tsapaki, Huda AlNaemi, Mohammed Hassan Kharita   +4 more
wiley   +1 more source