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10th International Conference on Electronics, Computers and Artificial Intelligence (ECAI)

作者： Dumitrescu, Stefan Daniel Avram, Andrei Marius Morogan, Luciana Toma, Stefan-Adrian Res Inst Artificial Intelligence Bucharest Romania Mil Tech Acad Fac Mil Elect & Informat Syst Bucharest Romania

ISBN: (纸本)9781538649015

The paper presents (1) a new library (api) that provides easy access to the Romanian WordNet and (2) an application based on the api to perform embedded vector retrofitting. The new api (further referred to as RoWN) is built in python and offers direct access to all the data provided in the Romanian WordNet. It implements all basic I/O and query operations. As RoWN is based on a directed graph structure powered by the versatile networkx library, users are provided with the groundwork to implement powerful graph algorithms. The second part of the paper presents an application built on RoWN: the process of vector retrofitting. "Retrofitting" refers to the semantic specialization of word vectors, a process of fine-tuning each vector under a specified constraint. We use the synonymy/antonymy relations extracted from RoWN to tune the word vectors under these semantic constraints, effectively specializing the vector space.

关键词： Romanian WordNet python api semantic space vector retrofitting

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Accelerate Innovations in Robust Circuit Design with python api to Circuit Simulation

Accelerate Innovations in Robust Circuit Design with Python ...

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IEEE International Conference on Solid-State and Integrated Circuit Technology

作者： Frank Lee Synopsys Inc Mountain View CA USA

Traditional robust circuit design method relies on conventional Monte Carlo Analysis. With the increase in instance counts of reused Intellectual Properties (IP's) in today's complex System-on-Chips (SoC's,) and the stringent quality requirements in automotive Integrated Circuits (IC's), the required sigma for the IP's have increased correspondingly. In some IP's, it is not uncommon these days to see 6-sigma design specifications. This requires billions of traditional Monte Carlo simulations which is impractical in most of the cases. Recent advances in advanced sampling and machine learning classification methods have created new breeds of techniques that can reduce the number of samples required significantly while achieving similar results with traditional Monte Carlo analysis. Many of these state-of-the-art techniques are available in python, which is one of the most popular programming languages used today. This motivates us to create a framework that can combine the flexibility of the python programming with industry-leading powerful circuit simulators to support high sigma design methods. The fusion of these techniques can accelerate the innovations in high quality designs for SoC's and automotive applications.

关键词： Accelerate Innovations Robust Circuit Design python api

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text2graphapi: A library to transform text documents into different graph representations

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SOFTWAREX 2024年 28卷

作者： Valdez-Valenzuela, Andric Gomez-Adorno, Helena Univ Nacl Autonoma Mexico Posgrad Ciencia & Ingn Computac Ciudad de Mexico Mexico UNAM Inst Invest Matemat Aplicadas & Sistemas Ciudad de Mexico 04510 Mexico

This paper introduces a new python api called text2graphapi. It is an easy-to-use library for transforming text documents into different graph representations, such as Word-Cooccurrence, Heterogeneous, and Integrated Syntactic Graphs. In addition, it contains a text pre-processing module that supports input text in different languages: English, Spanish, and French. These generated graph structures can be used to solve tasks in various areas, such as Authorship Analysis, Information Retrieval, and Topic Classification, to name a few.

关键词： python api Text graph representation Natural language processing Graph neural networks

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Estimation of LAI across phenological stages of wheat using google earth engine

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APPLIED GEOMATICS 2025年第1期17卷 117-128页

作者： Sur, Koyel Verma, V. K. Singh, Manpreet Al-Quraishi, Ayad M. Fadhil Arora, Parshottam Pateriya, Brijendra Punjab Remote Sensing Ctr Geospatial Resource Mapping & Applicat Grp Ludhiana 141004 Punjab India Punjab Agr Univ Dr JC Bakhshi Reg Res Stn Abohar 152116 Punjab India Tishk Int Univ Petr & Min Engn Dept Erbil Iraq Punjab Remote Sensing Ctr Ludhiana 141004 Punjab India

The Leaf Area Index (LAI) is a measure of photosynthesis and transpiration, and it has become the common currency for agro-climatic researchers. The non-destructive technique of LAI estimation using remote sensing has immense potential. The challenge lies in estimating LAI at the field scale for implementing research results for crop management using Google Earth Engine (GEE) integrated with python. Sentinel-2A datasets empowered by high spatial, spectral, and temporal resolution over an arid region of southwest Punjab, India were used to estimate LAI at field and district level. Wheat LAI was estimated for two consecutive years, 2016-2017 and 2017-2018. The comprehensive data analysis approach comprised of processing and estimation of LAI, designed for four significant phenological stages followed by validation with in situ field observed LAI collected from the experimental plots as well as with the Moderate Resolution Imaging Spectroradiometer (MODIS)'s LAI data products. The results showed a strong positive co-relationship between observed field LAI and Sentinel-2A estimated LAI as 0.64 and 0.47, with MODIS dataset as 0.24 and 0.19 for both years. Therefore, it can be concluded that field-level LAI can be estimated from Sentinal-2A satellite images with moderate accuracy by agricultural specialists and practitioners.

关键词： LAI Sentinel-2A GEE python api Punjab

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WALGREEN: WEB BASED PLATFORM FOR SOIL ORGANIC CARBON INFERENCE APPLICATIONS

WALGREEN: WEB BASED PLATFORM FOR SOIL ORGANIC CARBON INFEREN...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Manuel Aroca, Jose Diez Pastor, Jose Francisco Latorre-Carmona, Pedro Canepa Oneto, Antonio Carlos Rad, Juan Camps-Valls, Gustau Elvira, Victor Garcia-Osorio, Cesar Univ Burgos Dept Ingn Informat Avda Cantabria S-N Burgos 09006 Spain Univ Burgos Grp Invest Compostaje C Villadiego S-N Burgos 09001 Spain Univ Valencia Image Proc Lab Parc Cient Valencia 46980 Spain Univ Edinburgh Sch Math Edinburgh EH9 3FD Midlothian Scotland

ISBN: (纸本)9798350360332;9798350360325

Remote sensing data management and its use for classification and inference purposes is at the forefront of research tasks nowadays. There are, however, some inherent drawbacks and difficulties when dealing with, and understanding how satellite information is provided (particularly when referring to multiband/multispectral satellite platforms) and how different and disparate datasets related to soil content can be used and merged with this imagery. We present WALGREEN. The aim of this tool is to provide a secure environment to handle the whole process to use polygons or, geographical coordinates in tiff/geotiff images, have real-time access to images, save and get soil organic carbon real measurements, and generate datasets for machine learning training and inferential methods. We also aim to providing a framework to preprocess soil organic carbon information from different but accepted sources, like the Land Use/Cover Area frame statistical Survey database, so that even without real measurements, researchers may be able to start training different machine learning methodologies.

关键词： Soil organic carbon machine learning datasets Sentinel integration Tiff and Geo Tiff Spring boot Framework Java Persistance api python api Flask

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Software to Assist Visually Impaired People During the Craps Game Using Machine Learning on python Platform 2nd

Software to Assist Visually Impaired People During the Craps...

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2nd International Conference on Smart Technologies, Systems and Applications (SmartTech-IC)

作者： Hernandez Diaz, Nicolas Penaloza, Yersica C. Yuliana Rios, Y. Magre Colorado, Luz A. Univ Tecnol Bolivar Parque Ind & Tecnol Carlos Velez Pombo Cartagena De Indias Colombia Univ Pamplona Km 1 Via Bucaramanga Pamplona Colombia

ISBN: (纸本)9783030991708;9783030991692

Pattern recognition is a prominent area of research in computer vision, where different methods have been proposed in the last 50 years. This work presents the development of a python api to identify the result of two six-sided dice used in the game called "Craps" as a no-controlled environment to help visually impaired people. The software is structured in four stages. The first one is capturing images through a device with a digital camera connected to the web via IP address. The second stage corresponds to the captured image processing;it is necessary to establish a standard image size and resize and equalize the digitized image. The third stage seeks to segment the object of study by artificial vision techniques to identify the result of the dice after being thrown. Finally, the fourth stage is to interpret the result and play it through a speaker. The expected possible result is a system that integrates the four stages mentioned above through an intuitive and accessible low-cost python api, mainly aimed at visually impaired people.

关键词： Craps game Visually impaired people Non controlled environment python api Artificial vision techniques Image processing

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Optimization design of cement mixing columns supported height embankment using Plaxis remote scripting and Gene-expression programming technique

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ADVANCES IN ENGINEERING SOFTWARE 2024年 193卷

作者： Pham, Van-Ngoc Univ Danang Univ Sci & Technol Fac Rd & Bridge Engn Danang 550000 Vietnam

This paper is going to present the advanced feature in Plaxis using remote scripting with python wrapper. python allows engineers to create Plaxis models, calculate construction phases, and plot the results automatically. The settlement estimation of a high embankment, supported by cement mixing columns (CMC), is presented as a case study. Approximately 500 models were created and analyzed within a few hours with different embankment heights, soft soil thickness, CMC spacing, and CMC diameter. This original database was used to develop a regression model using a Gene-expression programming (GEP) algorithm. The proposed GEP-based model with high accuracy could be applied to optimize the CMC design. In detail, the coefficient of correlation (R-value) of all phases is high and fluctuates from 0.967 to 0.976, while the mean absolute error of the model is lower than 0.009 m. The parametric study indicates that increasing the height of the fill embankment, the thickness of the peat layer, or the spacing between CMCs causes high settlement, while increasing the CMC diameter could significantly reduce the settlement of the embankment. The research results demonstrate that using Plaxis remote scripting and the GEP technique could help engineers run the numerical analysis much faster, leading to a more in-depth analysis to create better decision-making.

关键词： Plaxis automation Embankment Settlement Optimization python api CMC

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A survey of thermal expansion coefficients for organic molecular crystals in the Cambridge Structural Database

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ACTA CRYSTALLOGRAPHICA SECTION B-STRUCTURAL SCIENCE CRYSTAL ENGINEERING AND MATERIALS 2021年第3期77卷 357-364页

作者： Bond, Andrew D. Univ Cambridge Yusuf Hamied Dept Chem Lensfield Rd Cambridge CB2 1EW England

Typical ranges of thermal expansion coefficients are established for organic molecular crystals in the Cambridge Structural Database. The CSD python api is used to extract 6201 crystal structures determined close to room temperature and at least one lower temperature down to 90 K. The data set is dominated by structure families with only two temperature points and is subject to various sources of error, including incorrect temperature reporting and missing flags for variable-pressure studies. For structure families comprising four or more temperature points in the range 90-300 K, a linear relationship between unit-cell volume and temperature is shown to be a reasonable approximation. For a selected subset of 210 structures showing an optimal linear fit, the volumetric expansion coefficient at 298 K has mean 173 p.p.m. K-1 and standard deviation 47 p.p.m. K-1. The full set of 6201 structures shows a similar distribution, which is fitted by a normal distribution with mean 161 p.p.m. K-1 and standard deviation 51 p.p.m. K-1, with excess population in the tails mainly comprising unreliable entries. The distribution of principal expansion coefficients, extracted under the assumption of a linear relationship between length and temperature, shows a positive skew and can be approximated by two half normal distributions centred on 33 p.p.m. K-1 with standard deviations 40 p.p.m. K-1 (lower side) and 56 p.p.m. K-1 (upper side). The distribution for the full structure set is comparable to that of the test subset, and the overall frequency of biaxial and uniaxial negative thermal expansion is estimated to be < 5% and similar to 30%, respectively. A measure of the expansion anisotropy shows a positively skewed distribution, similar to the principal expansion coefficients themselves, and ranges based on suggested half normal distributions are shown to highlight literature cases of exceptional thermal expansion.

关键词： thermal expansion molecular crystals Cambridge Structural Database python api

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Development of Interactive Data Visualization System in Three-Dimensional Immersive Space

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2023年第10期14卷 1027-1033页

作者： Al Masud, Shah Murtaza Rashid Adiba, Homaira Hossain, Tamzid Saha, Aloke Kumar Rahman, Rashik Univ Asia Pacific Comp Sci & Engn Dhaka Bangladesh

Today's data-driven environments require innova-tive tools and methods to analyze and present data. The growth of data across many domains and remarkable technological ad-vances have necessitated a shift from 2D data representations. The rapid growth in dataset scale, variety, and speed has revealed the limitations of conventional charts and graphs. Significant progress has been made in the domain of interactive, three-dimensional data visualizations as a means to address this challenge. The integration of Virtual Reality (VR) and Augmented Reality (AR) technologies enables users to achieve a heightened level of immersion in a simulated environment, where data is transformed into physical and interactive creatures. Recent research in the domain of immersive analytics has provided evidence that virtual reality (VR) and augmented reality (AR) technologies possess the capacity to provide succinct multiple layouts, facilitate collabora-tive data exploration, enable immersive multiview maps, establish spatial environments, enhance spatial memory, and enable inter-actions in three dimensions. The primary aim of this research is to design and implement a sophisticated data visualization system that integrates the development of a data pipeline within the Unity 3D framework, with the specific goal of aggregating data. The resulting system will enable the presentation of data from CSV files within a three-dimensional immersive environment. The prospective ramifications of this development have the capacity to yield good effects in diverse domains, including E-commerce analysis, financial services, engineering technology, medical ser-vices, data analysis, and interactive data display, among others. The proposed system presents a methodical framework for the development of a 3D data visualization system that integrates virtual reality (VR) technologies, Unity, and python, with the aim of redefining the process of data exploration within a VR environment. This paper examines th

关键词： Immersive space data visualization VR system python api unity 3D

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HistoCartography: A Toolkit for Graph Analytics in Digital Pathology

HistoCartography: A Toolkit for Graph Analytics in Digital P...

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MICCAI Workshop on Computational Pathology (COMPAY)

作者： Jaume, Guillaume Pati, Pushpak Anklin, Valentin Foncubierta, Antonio Gabrani, Maria IBM Res Zurich Switzerland Ecole Polytech Fed Lausanne Lausanne Switzerland ETH Zurich Switzerland

Advances in entity-graph analysis of histopathology images have brought in a new paradigm to describe tissue composition, and learn the tissue structure-to-function relationship. Entity-graphs offer flexible and scalable representations to characterize tissue organization, while allowing the incorporation of prior pathological knowledge to further support model explainability. However, their analysis requires prerequisites for image-to-graph translation and knowledge of state-of-the-art algorithms applied to graph-structured data, which can potentially hinder their adoption. In this work, we aim to alleviate these issues by developing HistoCartography, a standardized python api with necessary preprocessing, machine learning and explainability tools to facilitate graph-analytics in computational pathology. Further, we have benchmarked the computational time and performance on multiple datasets across different imaging types and histopathology tasks to highlight the applicability of the api for building computational pathology workflows. HistoCartography is available at https://***/histocartography/histocartography.

关键词： Graph Representation Learning Computational Pathology python api

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