Application and practice of industrial big data by Aijin YUAN, Binnan YUE, Xin YAN, Jian HUANG

“…With the advent of the Internet of things and the era of Cyber Physical System,more data can be collected and analyzed,and industrial big data have become important driving force for industry innovation and transformation.Unlike traditional Internet big data,industrial big data are closely integrated with industrial logic.The difference between industrial big data and internet big data was compared in detail.Combined with the classical cases,the process of demand driven transformation of traditional manufacturing industry and the development path of industrial big data were expounded.The structure and working principles of industrial big data platform were introduced.At last,taking the excavator business as an example,it shows the application effect of the industrial big data platform,and that having a share in data and models will make data based application produce more value was put forward.…”
Get full text

Embedding With Preservation of Semantics of the Original Data by M. E. Vatkin, D. A. Vorobey, M. V. Yakovlev, M. G. Krivova

“…In the modern world, the data used to describe objects is often presented as sparse vectors with a large number of features. …”
Get full text

Data caching technologies in modern microprocessors by V. A. Egunov, V. A. Shabalovsky

“…The study presented in the paper is aimed at studying the methods for improving the efficiency of software in modern computing systems with a hierarchical memory structure. Method. The study is based on data caching technologies in microprocessors. …”
Get full text

Big data for imaging assessment in glaucoma by Douglas R. da Costa, Felipe A. Medeiros

“…This review evaluates the use of Big Data and AI in glaucoma research, providing an overview of most relevant topics and discussing various models for screening, diagnosis, monitoring disease progression, correlating structural and functional changes, assessing image quality, and exploring innovative technologies such as generative AI.…”
Get full text

Data Inconsistency Evaluation for Cyberphysical System by Hao Wang, Jianzhong Li, Hong Gao

“…To deal with the large dynamic data, we provide a compact structure based on B-tree for storing independent residual subgraph in order to update inconsistency efficiently. …”
Get full text

Efficient processing of raster and vector data. by Fernando Silva-Coira, José R Paramá, Susana Ladra, Juan R López, Gilberto Gutiérrez

“…More concretely, we present algorithms for solving a spatial join between a raster and a vector dataset imposing a restriction on the values of the cells of the raster; and an algorithm for retrieving K objects of a vector dataset that overlap cells of a raster dataset, such that the K objects are those overlapping the highest (or lowest) cell values among all objects. The raster data is stored using a compact data structure, which can directly manipulate compressed data without the need for prior decompression. …”
Get full text

The Logarithmic Derivative in Scientific Data Analysis by Ruediger Grunwald

“…The logarithmic derivative has been shown to be a useful tool for data analysis in applied sciences because of either simplifying mathematical procedures or enabling an improved understanding and visualization of structural relationships and dynamic processes. …”
Get full text

Challenges in Dental Statistics: Data and Modelling by Domenica Matranga, Paolo Castiglia, Giuliana Solinas

“…During the meeting, we dealt with very important topics of statistical methodology for the analysis of dental data, covering the analysis of hierarchically structured and over-dispersed data, the issue of calibration and reproducibility, as well as some problems related to survey methodology, such as the design and construction of unbiased statistical indicators and of well conducted clinical trials. …”
Get full text

Data Science in the Management of Healthcare Organizations by Pedro Faria, Victor Alves, José Neves, Henrique Vicente

“…This study emphasizes the role of Data Science, with the incorporation of Artificial Intelligence tools, in enabling data-driven and interconnected management strategies. …”
Get full text

An Exploration of Consistency Learning with Data Augmentation by Connor Shorten, Taghi M. Khoshgoftaar

“…Nearly all of these successes require very large manually annotated datasets. Data augmentation has enabled Supervised Learning with less labeled data, while avoiding the pitfalls of overfitting. …”
Get full text

Big-Data-Assisted Urban Governance: A Machine-Learning-Based Data Record Standard Scoring Method by Zicheng Zhang, Tianshu Zhang

“…However, existing methods for assessing data record standards focus predominantly on structured data, exhibiting notable inadequacies in handling the complexities inherent in unstructured or semi-structured textual hotline records. …”
Get full text

Data preparation in crowdsourcing for pedagogical purposes by Tanara Zingano Kuhn, Špela Arhar Holdt, Iztok Kosem, Carole Tiberius, Kristina Koppel, Rina Zviel-Girshin

“…The focus of this paper is on data preparation, given the crucial role that such a stage plays in any crowdsourcing project dealing with the creation of language learning resources. …”
Get full text

The Property Graph Data Format (PGDF) by Renzo Angles, Sebastian Ferrada, Ignacio Burgos

“…The simplicity of PGDF comes from its tabular-like structure, where each line in a PGDF file contains a single schema or data declaration. …”
Get full text

MANAGEMENT OF PERSONAL DATA PROCESSING AND PROTECTION by Elena COFAS

“…To address these worries, the European Union has implemented a comprehensive regulatory structure, widely recognized as the General Data Protection Regulation (GDPR). …”
Get full text

Technology for collecting and analyzing relational data by E. N. Fedorova, G. M. Ruzhnikov, R. K. Fedorov

“…System operation "Faramant" is based on a model document, which includes three components: description of the data structure; visualization; logic of form work. …”
Get full text

Selective Dynamical Imaging of Interferometric Data by Joseph Farah, Peter Galison, Kazunori Akiyama, Katherine L. Bouman, Geoffrey C. Bower, Andrew Chael, Antonio Fuentes, José L. Gómez, Mareki Honma, Michael D. Johnson, Yutaro Kofuji, Daniel P. Marrone, Kotaro Moriyama, Ramesh Narayan, Dominic W. Pesce, Paul Tiede, Maciek Wielgus, Guang-Yao Zhao, The Event Horizon Telescope Collaboration, Antxon Alberdi, Walter Alef, Juan Carlos Algaba, Richard Anantua, Keiichi Asada, Rebecca Azulay, Anne-Kathrin Baczko, David Ball, Mislav Baloković, John Barrett, Bradford A. Benson, Dan Bintley, Lindy Blackburn, Raymond Blundell, Wilfred Boland, Hope Boyce, Michael Bremer, Christiaan D. Brinkerink, Roger Brissenden, Silke Britzen, Avery E. Broderick, Dominique Broguiere, Thomas Bronzwaer, Sandra Bustamente, Do-Young Byun, John E. Carlstrom, Chi-kwan Chan, Koushik Chatterjee, Shami Chatterjee, Ming-Tang Chen, Yongjun Chen, Ilje Cho, Pierre Christian, John E. Conway, James M. Cordes, Thomas M. Crawford, Geoffrey B. Crew, Alejandro Cruz-Osorio, Yuzhu Cui, Jordy Davelaar, Mariafelicia De Laurentis, Roger Deane, Jessica Dempsey, Gregory Desvignes, Sheperd S. Doeleman, Ralph P. Eatough, Heino Falcke, Vincent L. Fish, Ed Fomalont, H. Alyson Ford, Raquel Fraga-Encinas, Per Friberg, Christian M. Fromm, Charles F. Gammie, Roberto Garc’a, Olivier Gentaz, Ciriaco Goddi, Roman Gold, Arturo I. Gómez-Ruiz, Minfeng Gu, Mark Gurwell, Kazuhiro Hada, Daryl Haggard, Michael H. Hecht, Ronald Hesper, Luis C. Ho, Paul Ho, Chih-Wei L. Huang, Lei Huang, David H. Hughes, Shiro Ikeda, Makoto Inoue, Sara Issaoun, David J. James, Buell T. Jannuzi, Michael Janssen, Britton Jeter, Wu Jiang, Alejandra Jimenez-Rosales, Svetlana Jorstad, Taehyun Jung, Mansour Karami, Ramesh Karuppusamy, Tomohisa Kawashima, Garrett K. Keating, Mark Kettenis, Dong-Jin Kim, Jae-Young Kim, Jongsoo Kim, Junhan Kim, Motoki Kino, Jun Yi Koay, Patrick M. Koch, Shoko Koyama, Carsten Kramer, Michael Kramer, Thomas P. Krichbaum, Cheng-Yu Kuo, Tod R. Lauer, Sang-Sung Lee, Aviad Levis, Yan-Rong Li, Zhiyuan Li, Rocco Lico, Greg Lindahl, Michael Lindqvist, Jun Liu, Kuo Liu, Elisabetta Liuzzo, Wen-Ping Lo, Andrei P. Lobanov, Laurent Loinard, Colin Lonsdale, Ru-Sen Lu, Nicholas R. MacDonald, Jirong Mao, Nicola Marchili, Sera Markoff, Alan P. Marscher, Iván Martí-Vidal, Satoki Matsushita, Lynn D. Matthews, Lia Medeiros, Karl M. Menten, Izumi Mizuno, Yosuke Mizuno, James M. Moran, Monika Moscibrodzka, Cornelia Müller, Alejandro Mus Mejas, Gibwa Musoke, Hiroshi Nagai, Neil M. Nagar, Masanori Nakamura, Gopal Narayanan, Iniyan Natarajan, Antonios Nathanail, Joey Neilsen, Roberto Neri, Chunchong Ni, Aristeidis Noutsos, Michael A. Nowak, Hiroki Okino, Héctor Olivares, Gisela N. Ortiz-León, Tomoaki Oyama, Feryal zel, Daniel C. M. Palumbo, Jongho Park, Nimesh Patel, Ue-Li Pen, Vincent Piétu, Richard Plambeck, Aleksandar PopStefanija, Oliver Porth, Felix M. Pötzl, Ben Prather, Jorge A. Preciado-López, Dimitrios Psaltis, Hung-Yi Pu, Venkatessh Ramakrishnan, Ramprasad Rao, Mark G. Rawlings, Alexander W. Raymond, Luciano Rezzolla, Bart Ripperda, Freek Roelofs, Alan Rogers, Eduardo Ros, Mel Rose, Arash Roshanineshat, Helge Rottmann, Alan L. Roy, Chet Ruszczyk, Kazi L. J. Rygl, Salvador Sánchez, David Sánchez-Arguelles, Mahito Sasada, Tuomas Savolainen, F. Peter Schloerb, Karl-Friedrich Schuster, Lijing Shao, Zhiqiang Shen, Des Small, Bong Won Sohn, Jason SooHoo, He Sun, Fumie Tazaki, Alexandra J. Tetarenko, Remo P. J. Tilanus, Michael Titus, Kenji Toma, Pablo Torne, Efthalia Traianou, Tyler Trent, Sascha Trippe, Ilse van Bemmel, Huib Jan van Langevelde, Daniel R. van Rossum, Jan Wagner, Derek Ward-Thompson, John Wardle, Jonathan Weintroub, Norbert Wex, Robert Wharton, Kaj Wiik, George N. Wong, Qingwen Wu, Doosoo Yoon, André Young, Ken Young, Ziri Younsi, Feng Yuan, Ye-Fei Yuan, J. Anton Zensus, Shan-Shan Zhao

Get full text

Data, dialogue, and design: patient and public involvement and engagement for natural language processing with real-world cancer data by Wuraola Oyewusi, Eliana M. Vasquez Osorio, Eliana M. Vasquez Osorio, Goran Nenadic, Issy MacGregor, Gareth Price, Gareth Price

“…IntroductionThis study describes the process and outcomes of a Patient and Public Involvement and Engagement (PPIE) event designed to incorporate patient perspectives into the application of Natural Language Processing (NLP) for analyzing unstructured free-text cancer medical notes. The analysis of routinely collected data aims to provide evidence to support clinical decision making in patient groups that are often under-represented in conventional clinical trials, highlighting the critical role of PPIE in responsibly implementing AI within healthcare. …”
Get full text

Generative adversarial networks for creating realistic training data for machine learning-based segmentation of FIB tomography data by Trushal Sardhara, Christian J Cyron, Martin Ritter, Roland C Aydin

“…Accurate 3D reconstruction of the structure of nanomaterials is essential for studying their physical properties. …”
Get full text

Multi‐data classification detection in smart grid under false data injection attack based on Inception network by H. Pan, H. Yang, C. N. Na, J. Y. Jin

“…A multi‐data detection model based on the Inception network is established, and the overall structure of the network and the individual Inception modules are given. …”
Get full text

Sensitivity analysis for time-to-event data accounting for intra-individual variability in time-varying covariates with missing data by Madiha Liaqat, Luciana Chiapella, Pradeep Mishra, Walid Emam, Yusra Tashkandy, Adelajda Matuka

“…Compared to other sensitivity analysis techniques, DA under MI explicitly adjusts imputed values using delta shifts, providing a structured approach to handling NMAR data. Unlike traditional methods that rely on pattern-mixture or selection models without direct imputation, DA generates multiple datasets with controlled sensitivity adjustments, ensuring a better variability assessment. …”
Get full text