SOMEF: a metadata extraction framework from software documentation
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Presentation done at the council of software registries on March, 2021. SOMEF is a python package for automatically extracting over 25 metadata categories from a readme file. The output is then exported in JSON or in JSON-LD using the codemeta representation
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This session covers the web architecture, Git/GitHub and makes a real rails app that is deployed to Heroku at the end.
Thanks,
Henry
stackconf 2021 | Embracing change: Policy-as-code for Kubernetes with OPA and...
As more enterprises migrate to cloud native environments like Kubernetes the need for more scalable ways to define and enforce fine-grained policies increases: how can I limit the number of replicas of a pod for certain users? how can I ensure that all images come from trusted registries? Gatekeeper, a CNCF project, allows to define policy as Kubernetes objects, making it easier to adopt policy-as-code practices in Kubernetes environments and sharing reusable policy templates. In this talk we will demo Gatekeeper for Kubernetes environments. You will learn how to adopt policy-as-code techniques and how you can integrate Gatekeeper with your existing tools.
Django, What is it, Why is it cool?
This text presentation attempts to hit on the highlight features and structure of Django and its ecco-system. It is intended as an introduction for those who are curious about , what is it?
Enterprise guide to building a Data Mesh
Making Data Mesh simple, Open Source and available to all; without vendor lock-in, without complex tooling and to use an approach centered around ‘specifications’, existing tools and baking in a ‘domain’ model.
Whats new in_mlflow
In the last several months, MLflow has introduced significant platform enhancements that simplify machine learning lifecycle management. Expanded autologging capabilities, including a new integration with scikit-learn, have streamlined the instrumentation and experimentation process in MLflow Tracking. Additionally, schema management functionality has been incorporated into MLflow Models, enabling users to seamlessly inspect and control model inference APIs for batch and real-time scoring. In this session, we will explore these new features. We will share MLflow’s development roadmap, providing an overview of near-term advancements in the platform.
Managing and Versioning Machine Learning Models in Python
Practical machine learning is becoming messy, and while there are lots of algorithms, there is still a lot of infrastructure needed to manage and organize the models and datasets. Estimators and Django-Estimators are two python packages that can help version data sets and models, for deployment and effective workflow.
OSMC 2023 | What’s new with Grafana Labs’s Open Source Observability stack by...
Open source is at the heart of what we do at Grafana Labs and there is so much happening! The intent of this talk to update everyone on the latest development when it comes to Grafana, Pyroscope, Faro, Loki, Mimir, Tempo and more. Everyone has had at least heard about Grafana but maybe some of the other projects mentioned above are new to you? Welcome to this talk 😉 Beside the update what is new we will also quickly introduce them during this talk.
Automatic generation of hardware memory architectures for HPC
Many HPC applications are massively parallel and can benefit from the spatial parallelism offered by reconfigurable logic. While modern memory technologies can offer high bandwidth, designers must craft advanced communication and memory architectures for efficient data movement and on-chip storage. Addressing these challenges requires to combine compiler optimizations, high-level synthesis, and hardware design.
In this talk, I will present challenges, solutions, and trends for generating massively parallel accelerators on FPGA for high-performance computing. These architectures can provide performance comparable to software implementations on high-end processors, and much higher energy efficiency thanks to logic customization.
Introduction to Apache Hivemall v0.5.0
Talk at ApacheCon North America, 2018
https://apachecon.dukecon.org/acna/2018/#/scheduledEvent/0cbf85b79b554dee6
Content Strategy and Developer Engagement for DevPortals
Slides from Write the Docs Ottawa Meet Up at Shopify HQ in Canada, June 24, 2019
We’ll walk through 5 scenarios and concrete ways of reaching a developer community for frictionless and increased engagement.
Similar to SOMEF: a metadata extraction framework from software documentation (20)
Primers or Reminders? The Effects of Existing Review Comments on Code Review
Primers or Reminders? The Effects of Existing Review Comments on Code Review
DataFinder: A Python Application for Scientific Data Management
DataFinder: A Python Application for Scientific Data Management
Tutorial Workgroup - Model versioning and collaboration
Tutorial Workgroup - Model versioning and collaboration
stackconf 2021 | Embracing change: Policy-as-code for Kubernetes with OPA and...
stackconf 2021 | Embracing change: Policy-as-code for Kubernetes with OPA and...
Managing and Versioning Machine Learning Models in Python
Managing and Versioning Machine Learning Models in Python
OSMC 2023 | What’s new with Grafana Labs’s Open Source Observability stack by...
OSMC 2023 | What’s new with Grafana Labs’s Open Source Observability stack by...
Automatic generation of hardware memory architectures for HPC
Automatic generation of hardware memory architectures for HPC
Content Strategy and Developer Engagement for DevPortals
Content Strategy and Developer Engagement for DevPortals
More from dgarijo
WDPlus: Leveraging Wikidata to Link and Extend Tabular Data
Today, data about any domain can be found on the web in data repositories, web APIs and many millions of spreadsheets and CSV files. Researchers and organizations make these data available in a myriad of formats, layouts, terminology and cleanliness that make it difficult to integrate together. As a result, researchers aiming to use data in their analyses face three main challenges. The first one is finding datasets related to a feature, variable or topic of interest. For example, climate scientists need to look for years of observational data from authoritative sources when estimating the climate of a region. The second challenge is completing a given dataset with existing knowledge: machine learning applications are data hungry and require as many data points and features as possible to improve their predictions, which often requires integrating data from different sources. The third challenge is sharing integrated results: once several datasets have been merged together, how to make them available to the rest of the community?
Towards Human-Guided Machine Learning - IUI 2019
Automated Machine Learning (AutoML) systems are emerging
that automatically search for possible solutions from a large space of possible kinds of models. Although fully automated machine learning is appropriate for many applications, users often have knowledge that supplements and constraints the available data and solutions. This paper proposes human-guided machine learning (HGML) as a hybrid approach where a user interacts with an AutoML system and tasks it to explore different problem settings that reflect the user’s knowledge about the data available. We present: 1) a task analysis of HGML that shows the tasks that a user would want to carry out, 2) a characterization of two scientific publications, one in neuroscience and one in political science, in terms of how the authors would search for solutions using an AutoML system, 3) requirements for HGML based on those characterizations, and 4) an assessment of existing AutoML systems in terms of those requirements.
Capturing Context in Scientific Experiments: Towards Computer-Driven Science
Scientists publish computational experiments in ways that do not facilitate reproducibility or reuse. Significant domain expertise, time and effort are required to understand scientific experiments and their research outputs. In order to improve this situation, mechanisms are needed to capture the exact details and the context of computational experiments. Only then, Intelligent Systems would be able help researchers understand, discover, link and reuse products of existing research.
In this presentation I will introduce my work and vision towards enabling scientists share, link, curate and reuse their computational experiments and results. In the first part of the talk, I will present my work for capturing and sharing the context of scientific experiments by using scientific workflows and machine readable representations. Thanks to this approach, experiment results are described in an unambiguous manner, have a clear trace of their creation process and include a pointer to the sources used for their generation. In the second part of the talk, I will describe examples on how the context of scientific experiments may be exploited to browse, explore and inspect research results. I will end the talk by presenting new ideas for improving and benefiting from the capture of context of scientific experiments and how to involve scientists in the process of curating and creating abstractions on available research metadata.
WIDOCO: A Wizard for Documenting Ontologies
WIDOCO is a WIzard for DOCumenting Ontologies that guides users through the documentation process of their vocabularies. Given an RDF vocabulary, WIDOCO detects missing vocabulary metadata and creates a documentation with diagrams, human readable descriptions of the ontology terms and a summary of
changes with respect to previous versions of the ontology. The documentation consists on a set of linked enriched HTML pages that can be further extended by end users. WIDOCO is open source and builds on well established Semantic Web tools. So far, it has been used to document more than one hundred ontologies in different domains.
Towards Automating Data Narratives
We propose a new area of research on automating data narratives. Data narratives are containers of information about computationally generated research findings. They have three major components: 1) A record of events, that describe a new result through a workflow and/or provenance of all the computations executed; 2) Persistent entries for key entities involved for data, software versions, and workflows; 3) A set of narrative accounts that are automatically generated human-consumable renderings of the record and entities and can be included in a paper. Different narrative accounts can be used for different audiences with different content and details, based on the level of interest or expertise of the reader. Data narratives can make science more transparent and reproducible, because they ensure that the text description of the computational experiment reflects with high fidelity what was actually done. Data narratives can be incorporated in papers, either in the methods section or as supplementary materials. We introduce DANA, a prototype that illustrates how to generate data narratives automatically, and describe the information it uses from the computational records. We also present a formative evaluation of our approach and discuss potential uses of automated data narratives.
Automated Hypothesis Testing with Large Scale Scientific Workflows
(Credit to Varun Ratnakar and Yolanda Gil).
The automation of important aspects of scientific data analysis would significantly accelerate the pace of science and innovation. Although important aspects of data analysis can be automated, the hypothesize-test-evaluate discovery cycle is largely carried out by hand by researchers. This introduces a significant human bottleneck, which is inefficient and can lead to erroneous and incomplete explorations. We introduce a novel approach to automate the hypothesize-test-evaluate discovery cycle with an intelligent system that a scientist can task to test hypotheses of interest in a data repository. Our approach captures three types of data analytics knowledge: 1) common data analytic methods represented as semantic workflows; 2) meta-analysis methods that aggregate those results, represented as meta-workflows; and 3) data analysis strategies that specify for a type of hypothesis what data and methods to use, represented as lines of inquiry. Given a hypothesis specified by a scientist, appropriate lines of inquiry are triggered, which lead to retrieving relevant datasets, running relevant workflows on that data, and finally running meta-workflows on workflow results. The scientist is then presented with a level of confidence on the initial hypothesis (or a revised hypothesis) based on the data and methods applied. We have implemented this approach in the DISK system, and applied it to multi-omics data analysis.
OntoSoft: A Distributed Semantic Registry for Scientific Software
Credit to Yolanda Gil.
OntoSoft is a distributed semantic registry for scientific software. This paper describes three major novel contributions of OntoSoft: 1) a software metadata registry designed for scientists, 2) a distributed approach to software registries that targets communities of interest, and 3) metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology along six dimensions that matter to scientists: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software. OntoSoft is a distributed registry where each site is owned and maintained by a community of interest, with a distributed semantic query capability that allows users to search across all sites. The registry has metadata crowdsourcing capabilities, supported through access control so that software authors can allow others to expand on specific metadata properties.
OEG tools for supporting Ontology Engineering
In this talk we do an overview of the suite of tools developed at the OEG for supporting ontology engineering. The tasks we support are ontology documentation, evaluation, diagraming and publication with permanent ids and content negotiation. All the tools are integrated in OnToology, which uses GitHub to publish the outcome produced for each ontology.
Software Metadata: Describing "dark software" in GeoSciences
Credit to Yolanda Gil.
In this talk I provide an overview of the current state of the art for software description in geosciences, along with our approach to facilitate this task in OntoSoft, a distributed semantic registry for scientific software. Three key aspects of OntoSoft are: a software metadata ontology designed for scientists, a distributed approach to software registries that targets communities of interest, and metadata crowdsourcing through access control. Software metadata is organized using the OntoSoft ontology, designed to support scientists to share, document, and reuse software, and organized along six dimensions: identify software, understand and assess software, execute software, get support for the software, do research with the software, and update the software.
Reproducibility Using Semantics: An Overview
Overview of the different approaches for addressing reproducibilities (using semantics) in laboratory protocols, workflow description and publication and workflow infrastructure. Furthermore, Research Objects are introduced as a means to capture the context and annotations of scientific experiments, together with the privacy and IPR concerns that may arise. This presentation was presented in Dagstuhl Seminar 16041: http://www.dagstuhl.de/16041
PhD Thesis: Mining abstractions in scientific workflows
Slides of the presentation for my PhD dissertation. I strongly recommend downloading the slides, as they have animations that are easier to see in power point. The abstract of the thesis is as follows: "Scientific workflows have been adopted in the last decade to represent the computational methods used in in silico scientific experiments and their associated research products. Scientific workflows have demonstrated to be useful for sharing and reproducing scientific experiments, allowing scientists to visualize, debug and save time when re-executing previous work. However, scientific workflows may be difficult to understand and reuse. The large amount of available workflows in repositories, together with their heterogeneity and lack of documentation and usage examples may become an obstacle for a scientist aiming to reuse the work from other scientists. Furthermore, given that it is often possible to implement a method using different algorithms or techniques, seemingly disparate workflows may be related at a higher level of abstraction, based on their common functionality. In this thesis we address the issue of reusability and abstraction by exploring how workflows relate to one another in a workflow repository, mining abstractions that may be helpful for workflow reuse. In order to do so, we propose a simple model for representing and relating workflows and their executions, we analyze the typical common abstractions that can be found in workflow repositories, we explore the current practices of users regarding workflow reuse and we describe a method for discovering useful abstractions for workflows based on existing graph mining techniques. Our results expose the common abstractions and practices of users in terms of workflow reuse, and show how our proposed abstractions have potential to become useful for users designing new workflows".
Publicación de datos y métodos científicos en investigación
¿Cuáles son los retos a la hora de publicar datos en investigación? En esta presentación se abordan las partes asociadas a la publicación de un experiment científico en un ambiente académico, así como las propuestas que estamos siguiendo en el Grupo de Ingeniería Ontológica de la Universidad Politécnica de Madrid
EDBT 2015: Summer School Overview
This presentation shows an overview of the main concepts introduced in the EDBT2015 Summer School, which took place in Palamos. For each area, we summarize the main issues and current approaches. We also describe the challenges and main activities that were undertaken in the summer school
Similarity in Wikipedia Articles (EDBT Summer School)
Presentation done for the EDBT Summer school, challenge nº4. How to define similarity between two given Wikipedia Articles?
Semantic web 101: Benefits for geologists
Short introduction to the main concepts of the Semantic Web (RDF, Linked Data) for geologists. Identification of the main challenges.
Is preserving data enough? Towards the preservation of scientific methods
In recent years there have been many efforts towards the preservation of data belonging to scientific research. Institutions like the Virtual Observatory and journals like PLOS ONE, Geoscience Data Journal, Ecological Archives accept datasets that support or were produced in scientific publications. Other efforts like Figshare allow citing data from unpublished research and research in progress, allowing acknowledging authors and improving the shareability of their work. At the same time, many of the challenges associated to the preservation and sharing of data has been a topic of discussion in international initiatives like the Research Data Alliance, which through its working and interest groups aims at identifying requirements and proposing reference solutions to improve such tasks like data citation and provision of correct e-infrastructure for repositories.
However, data per se is often not relevant without proper description metadata, its provenance and the software used for its creation. In fact, scientists are starting to be more concerned about the preservation of the software and methods used to deliver a particular scientific result. Reproducibility and inspectability are crucial for enabling the interpretation and the reusability of a given dataset. In "in vitro" and "in vivo" sciences, protocols exist to capture the methods necessary to reproduce an experiment. In computational sciences this is achieved with scientific workflows, which capture the method (i.e., steps and data dependencies) used to obtain a specific result. In this short talk we will introduce the set of checklists we have developed for the proper conservation of scientific workflows, encapsulated as Research Objects, by adapting existing standards for data preservation.
Towards Workflow Ecosystems Through Semantic and Standard Representations
Workflows are increasingly used to manage and share scientific computations and methods. Workflow tools can be used to design, validate, execute and visualize scientific workflows and their execution results. Other tools manage workflow libraries or mine their contents. There has been a lot of recent work on workflow system integration as well as common workflow interlinguas, but the interoperability among workflow systems remains a challenge. Ideally, these tools would form a workflow ecosystem such that it should be possible to create a workflow with a tool, execute it with another, visualize it with another, and use yet another tool to mine a repository of such workflows or their executions. In this paper, we describe our approach to create a workflow ecosystem through the use of standard models for provenance (OPM and W3C PROV) and extensions (P-PLAN and OPMW) to represent workflows. The ecosystem integrates different workflow tools with diverse functions (workflow generation, execution, browsing, mining, and visualization) created by a variety of research groups. This is, to our knowledge, the first time that such a variety of workflow systems and functions are integrated
Workflow Reuse in Practice: A Study of Neuroimaging Pipeline Users
eScience 2014, Guarujá (Brasil). Abstract: Workflow reuse is a major benefit of workflow systems and shared workflow repositories, but there are barely any studies that quantify the degree of reuse of workflows or the practical barriers that may stand in the way of successful reuse. In our own work, we hypothesize that defining workflow fragments improves reuse, since end-to-end workflows may be very specific and only partially reusable by others. This paper reports on a study of the current use of workflows and workflow fragments in labs that use the LONI Pipeline, a popular workflow system used mainly for neuroimaging research that enables users to define and reuse workflow fragments. We present an overview of the benefits of workflows and workflow fragments reported by users in informal discussions. We also report on a survey of researchers in a lab that has the LONI Pipeline installed, asking them about their experiences with reuse of workflow fragments and the actual benefits they perceive. This leads to quantifiable indicators of the reuse of workflows and workflow fragments in practice. Finally, we discuss barriers to further adoption of workflow fragments and workflow reuse that motivate further work.
Frag Flow: Automated Fragment Detection in Scientific Workflows
eScience 2014, Guarujá (Brasil). Abstract—Scientific workflows provide the means to define, execute and reproduce computational experiments. However, reusing existing workflows still poses challenges for workflow designers. Workflows are often too large and too specific to reuse in their entirety, so reuse is more likely to happen for fragments of workflows. These fragments may be identified manually by users as sub-workflows, or detected automatically. In this paper we present the FragFlow approach, which detects workflow fragments automatically by analyzing existing workflow corpora with graph mining algorithms. FragFlow detects the most common workflow fragments, links them to the original workflows and visualizes them. We evaluate our approach by comparing FragFlow results against user-defined sub-workflows from three different corpora of the LONI Pipeline system. Based on this evaluation, we discuss how automated workflow fragment detection could facilitate workflow reuse
More from dgarijo (20)
WDPlus: Leveraging Wikidata to Link and Extend Tabular Data
WDPlus: Leveraging Wikidata to Link and Extend Tabular Data
Capturing Context in Scientific Experiments: Towards Computer-Driven Science
Capturing Context in Scientific Experiments: Towards Computer-Driven Science
Automated Hypothesis Testing with Large Scale Scientific Workflows
Automated Hypothesis Testing with Large Scale Scientific Workflows
OntoSoft: A Distributed Semantic Registry for Scientific Software
OntoSoft: A Distributed Semantic Registry for Scientific Software
Software Metadata: Describing "dark software" in GeoSciences
Software Metadata: Describing "dark software" in GeoSciences
PhD Thesis: Mining abstractions in scientific workflows
PhD Thesis: Mining abstractions in scientific workflows
Publicación de datos y métodos científicos en investigación
Publicación de datos y métodos científicos en investigación
Similarity in Wikipedia Articles (EDBT Summer School)
Similarity in Wikipedia Articles (EDBT Summer School)
Is preserving data enough? Towards the preservation of scientific methods
Is preserving data enough? Towards the preservation of scientific methods
Creating abstractions from scientific workflows: PhD symposium 2015
Creating abstractions from scientific workflows: PhD symposium 2015
Towards Workflow Ecosystems Through Semantic and Standard Representations
Towards Workflow Ecosystems Through Semantic and Standard Representations
Workflow Reuse in Practice: A Study of Neuroimaging Pipeline Users
Workflow Reuse in Practice: A Study of Neuroimaging Pipeline Users
Frag Flow: Automated Fragment Detection in Scientific Workflows
Frag Flow: Automated Fragment Detection in Scientific Workflows
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Water billing management system project report.pdf
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
The Role of Electrical and Electronics Engineers in IOT Technology.pdf
The Role of Electrical and Electronics Engineers in IOT Technology.pdfNettur Technical Training Foundation
The Internet of Things (IoT) is a revolutionary concept that connects everyday objects and devices to the internet, enabling them to communicate, collect, and exchange data. Imagine a world where your refrigerator notifies you when you’re running low on groceries, or streetlights adjust their brightness based on traffic patterns – that’s the power of IoT. In essence, IoT transforms ordinary objects into smart, interconnected devices, creating a network of endless possibilities.
Here is a blog on the role of electrical and electronics engineers in IOT. Let's dig in!!!!
For more such content visit: https://nttftrg.com/Understanding Inductive Bias in Machine Learning
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Cosmetic shop management system project report.pdf
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
reduced in a condensing heat exchanger by acid condensation. reduced in a condensing heat exchanger by acid condensation.
Condensation of vapors in flue gas is a complicated
phenomenon since heat and mass transfer of water vapor and
various acids simultaneously occur in the presence of noncondensable
gases such as nitrogen and oxygen. Design of a
condenser depends on the knowledge and understanding of the
heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
mass and energy balances for the system were derived to
predict variables such as flue gas exit temperature, cooling
water outlet temperature, mole fraction and condensation rates
of water and sulfuric acid vapors. The equations were solved
using an iterative solution technique with calculations of heat
and mass transfer coefficients and physical properties.
Building Electrical System Design & Installation
Guide for Building Electrical System Design & Installation
Immunizing Image Classifiers Against Localized Adversary Attacks
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.pdf
AKS UNIVERSITY Satna Final Year Project By OM Hardaha.
Thank me later.
samsarthak31@gmail.com
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留信网认证的作用:
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2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
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Hierarchical Digital Twin of a Naval Power System
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Final project report on grocery store management system..pdf
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
SFU毕业证原版定制【微信：176555708】【西蒙菲莎大学毕业证成绩单-学位证】【微信：176555708】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
◆◆◆◆◆ — — — — — — — — 【留学教育】留学归国服务中心 — — — — — -◆◆◆◆◆
【主营项目】
一.毕业证【微信：176555708】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【微信：176555708】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
学历顾问：微信：176555708
Heap Sort (SS).ppt FOR ENGINEERING GRADUATES, BCA, MCA, MTECH, BSC STUDENTS
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERS
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
Investor-Presentation-Q1FY2024 investor presentation document.pptx
this is the investor presemtaiton document for qurrter 1 2024
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SOMEF: a metadata extraction framework from software documentation
- 1. SOMEF: A Metadata Extraction Framework from Software Documentation Daniel Garijo @dgarijov Ontology Engineering Group, Universidad Politécnica de Madrid
- 2. The Importance of Software Metadata - An increasing amount of software is available online - Metadata is critical for: - Findability - Reusability - Understanding > 100 M repositories > 28 M repositories
- 3. Current Pipeline for Software Metadata Curation Icons from https://icons8.com/ Develop software & documentation Create entry in registry Curation and Validation
- 4. SOMEF: Automating Software Metadata Extraction SOMEF ● Readme Analysis ○ Supervised classification ○ Regular expressions ○ Header analysis ● File exploration ○ Notebooks ○ Dockerfiles ○ Documentation ● GitHub API Repository Extraction Results (Metadata) https://github.com/KnowledgeCaptureAndDiscovery/somef/
- 5. Recognizing Metadata Categories ● Name (GA) ● Full title (RE) ● Description (SC, HA) ● Citation (SC, RE, HA) ● Installation instructions (SC, HA) ● Invocation (SC) ● Usage examples (HA) ● Documentation (HA, FE) ● Requirements (HA) ● Contributors (HA) ● FAQ (HA) ● Support (HA) ● License (GA, HA) ● Stars (GA) ● Contact (HA) ● Download URL (HA, GA) ● DOI (RE) ● DockerFile (FE) ● Notebooks (FE) ● Executable notebooks (Binder) (RE) ● Owner: (RE) ● Keywords (GA) ● Source code (GA) ● Releases (GA) ● Changelog (GA) ● Issue tracker (GA) ● Programming languages (GA) ● Acknowledgements (HA) Method used: ● Supervised Classification (SC) ● Header Analysis and Synset comparison (HA) ● File Exploration (FE) ● Regular Expressions (RE) ● GitHub API (GA)
- 7. Beginning to capture software in context SOMEF helps automatically extract software metadata. Benefits: ● Let authors know what metadata is missing ● Help making software findable ● Capture context of software (setup, configuration, examples) But there is much more to explore: ● Better comparison ● Input/output preparation ● Using different software together Problems? Suggestions? New features? https://github.com/KnowledgeCaptureAndDiscovery/somef/issues