PureWow is an American digital media company that publishes women's lifestyle content. Acquired by Gary Vaynerchuk in 2017 as part of Gallery Media Group, PureWow tailors lifestyle topics for Millennials and Generation X, including fashion, beauty, home decor, recipes, entertainment, travel, technology, literature, wellness and money. == History == PureWow was founded by Ryan Harwood in September 2010, along with Bob Pittman's Pilot Group and the women of wowOwow Joni Evans, Mary Wells Lawrence, Whoopi Goldberg, Liz Smith, Candice Bergen, and Lesley Stahl, among others. In January 2013, PureWow hired former Real Simple editor Mary Kate McGrath as its first editor-in-chief. In August 2014, PureWow was listed as no. 352 on Inc. Magazine's 2014 list of the top 500 fastest-growing privately owned companies. In May 2015, PureWow raised $2.5 million. In 2017, serial entrepreneur Gary Vaynerchuk and Miami Dolphins' owner Stephen Ross' venture firm, RSE Ventures, acquired PureWow to form Gallery Media Group as a creative agency and media firm. PureWow's CEO, Ryan Harwood serves as the chief executive of Gallery Media Group. == Editions == PureWow publishes national content as well as local content for New York City, Los Angeles, Chicago, San Francisco, Dallas, and the Hamptons. The company publishes content across fashion, beauty, homecare topics, technology, entertainment, books, wellness and finances. PureWow articles are distributed via its website PureWow.com, email, and over social media channels.
VOCEDplus
VOCEDplus is a free international research database about tertiary education, maintained and developed by staff at the c (NCVER) in Adelaide, South Australia. The focus of the database content is the relation of post-compulsory education and training to workforce needs, skills development, and social inclusion. == Structure == The content of the VOCEDplus database encompasses vocational education and training (VET), higher education, lifelong learning, informal learning, VET in schools, adult and community education, apprenticeships/traineeships, international education, providers of education and training, and workforce development. It is international in scope and contains over 84,000 English language records, many with links to full text documents. VOCEDplus contains extensive Australian materials and includes a wide range of international information, covering outcomes of tertiary education in the shape of published research, practice, policy, and statistics. Entries are included for the following types of publications: reports; annual reports; papers; discussion papers; occasional papers; working papers; books; book chapters; conference papers; conference proceedings; journals; journal articles; policy documents; published statistics; theses; podcasts; and teaching and training materials. Each database entry contains standard bibliographic information and an abstract. Many entries include full text access via the publisher's website or a digitised copy. == History == === 1989-1997 === In the early years VOCEDplus was known as VOCED. The original database was produced by a network of clearinghouses across Australia with the aim of sharing activities in the technical and further education (TAFE) sector. VOCED was produced in hardcopy and an electronic version was distributed on diskette. === 1997-2001 === 1997 - the first web version of VOCED was made available from the National Centre for Vocational Education Research (NCVER) organisational website 1998 - a major project to upgrade the database and expand its international coverage commenced 2001 - creation of VOCED's own website 2001 - VOCED endorsed as the UNESCO international database for technical and vocational education and training (TVET) research information === 2001-2009 === Many changes to the database and website occurred during this period with a focus on continuous improvement to meet the needs of users and utilise emerging technologies. 2006 - materials produced for two adult literacy and learning programs funded by the Australian Department of Education, Employment and Workplace Relations (DEEWR) - the Workplace English Language and Learning (WELL) Programme and the Adult Literacy National Project (ALNP) included in VOCED 2007 - the Australian clearinghouse network transferred most of the hardcopy collections to NCVER, to form a centralised repository of resources 2009 - materials produced by Reframing the Future (RTF) a vocational education and training workforce development initiative of the Australian, State and Territory Governments included in VOCED === 2009-2014 === A major rebuild of the database and website was undertaken during this period to take advantage of the potential of new technologies to provide improved services and incorporate Web 2.0 technologies (RSS feeds, and share and bookmark tools). 2009 - scope expanded to more fully encompass the higher education sector 2011 - launch of VOCEDplus with the name change representing the enhanced features and extended focus 2012 - a major retrospective digitisation project commenced and by the end of the 2012-2013 financial year a total of 9,328 publications (593,534 pages/microfiche frames) had been digitised, ensuring these publications are available electronically for free === 2014-2019 === A number of significant curated content products were released during this period. 2015 - release of a refreshed look to adopt the new NCVER branding plus a number of search enhancements (Guided search, Expert search, and Glossary search) were added 2015 - first in the series of 'Focus on...' pages released 2016 - launch of the 'Pod Network', a convenient and efficient platform that allows instant access to research and a multitude of resources on a range of subjects 2017 - completion of the 'Pod Network', consisting of 20 Pods (on broad subjects including Apprenticeships and traineeships, Foundation skills, Teaching and learning, Career development, and Students) and 74 Podlets (on narrow topics including Online learning, Social media, VET in schools, STEM skills, and Adult literacy) 2018 - launch of the 'Timeline of Australian VET Policy Initiatives' and the 'VET Knowledge Bank' which contains a suite of products capturing Australia's diverse, complex and ever-changing VET system 2019 - after an internal review, a refreshed, streamlined version of the 'Pod Network' was released, consisting of 13 Pods and 20 Podlets 2019 - launch of the 'VET Practitioner Resource' which contains a range of information to support VET practitioners in their work and is organised into three sections: (1) Teaching, training and assessment: standards, guidance, research and good practice resources to inform daily work; (2) Practitioners as researchers: information for undertaking practitioner-led research; and (3) The VET workforce: information about VET teachers and trainers, and the professional development needs of the VET workforce 2019 - VOCEDplus celebrated 30 years of providing information to the tertiary education sector and the homepage was refreshed to make it more modern and easier to use === 2020- === VOCEDplus continued to be accessible throughout the COVID-19 pandemic. 2020-2021 - the VET Knowledge Bank added a dedicated page, 'COVID-19 announcements', that showcases the measures introduced by the Australian, state and territory governments to mitigate the impact of the pandemic and promote economic recovery 2020-2024 - published research about the effects of the pandemic on education and training, providers, students, labour markets, employment and employees was collected and made permanently available in the database 2024 - VOCEDplus celebrated 35 years of providing information to the tertiary education sector. The homepage was refreshed and a number of enhancements and new features were implemented including a new My Profile feature, improvements to My Selection, accessible search history and saved searches, enhanced search functionality, and improved navigation.
List of Ada software and tools
This is a list of software and programming tools for the Ada programming language, including IDEs, compilers, libraries, verification and debugging tools, numerical and scientific computing libraries, and related projects. == Compilers == GNAT — GCC Ada compiler and toolchain, maintained by AdaCore AdaCore GNAT Pro — commercial Ada compiler with advanced tooling for high-integrity and real-time systems Green Hills compiler for Ada — Ada compiler for embedded and safety-critical systems ObjectAda — Ada development environment for safety-critical and embedded systems == Integrated development environments (IDEs) and editors == GNAT Studio — IDE developed by AdaCore Emacs — supports Ada editing with Ada mode and syntax checking Eclipse — supports Ada through GNATbench plugin Visual Studio Code — Ada support via Ada Language Server extensions == Libraries and frameworks == See also: Ada Libraries on Wikibooks Ada.Calendar — date and time library Ada Web Services (AWS) — support for RESTful and SOAP web services Ada.Text_IO — standard library for text input/output Florist (POSIX Ada binding) – open-source implementation of the POSIX Ada bindings GNAT – Ada compiler part of GCC, which also provides an extensive runtime and library package hierarchy. GtkAda – Ada bindings for the GTK+ graphical user interface toolkit Matreshka – multipurpose Ada framework supporting Unicode, XML, JSON, and more. XML/Ada – XML and Unicode processing library == Real-time and embedded systems == Ada tasking — built-in concurrency support with tasks, protected objects, and rendezvous. Ada.Real_Time — real-time clocks, delays, and scheduling. ARINC 653 Ada profiles — for avionics real-time applications OpenMP Ada bindings — parallel programming for multi-core embedded systems Ravenscar profile — subset of Ada tasking for real-time and deterministic execution == Numerical and scientific computing == Ada.Numerics — libraries for numerical methods, linear algebra, and mathematical functions. SPARK math libraries — formal-methods-compliant numerical routines == Verification, debugging, and analysis == GNATprove — formal verification and static analysis tool for Ada and SPARK GNATstack — runtime stack analysis and checking GNATcoverage — code coverage measurement for Ada projects AdaControl — style checking and metrics for Ada == Testing frameworks == AUnit — unit testing framework for Ada GNATtest — automated testing framework for Ada == Documentation and code generation == GNATdoc — generates HTML documentation from Ada source code
Tinybop
Tinybop is a Brooklyn based publisher of apps for children. == History == Tinybop is a Brooklyn-based children's media company established in 2011 by Raul Gutierrez. App titles are released in two series: the Explorer's Library - a series of science apps and Digital Toys - series of open-ended construction apps. == Published apps == Explorer's Library Titles: The Human Body – An anatomy app for children. Released 2013. The company's first app was illustrated by Kelli Anderson and has been downloaded millions of times. Selected for the American Library Association's Notable Children's Media List in 2022. Named Apple App Store's Best of 2013. Winner of the Digital Ehon Yuichi Kimura Prize for Children's Digital Media. Plants – An app about biomes around the world. Homes – An app about houses around with world. Illustrated by Tuesday Bassen. Winner of the Parents Gold Choice Award for children's apps. Simple Machines – A children's physics app about simple machines. The Earth – An app for children about the geologic Earth illustrated by Sarah Jacoby. Weather – A children's weather app. Skyscrapers – A children's app about building tall buildings. Space – An interactive solar system. Mammals – A children's app about mammals illustrated by Wenjia Tang. Winner of the Digital Ehon Award for Children's Educational media. Coral Reef – An app about marine ecosystems. Winner of an Excellence in Early Learning Digital Media Honor from the American Library Association. State of Matter – An app covering solids, liquids, and gases. Winner of Excellence in Early Learning Digital Media Honor from the American Library Association. Light and Color – An app about light and color. Selected for The American Library Association's Notable Children's Media List 2023. Winner of the 2022 Yoichi Sakakihara Prize for Children's Media. Digital Toys Titles: The Robot Factory – A robot building app for children illustrated by Owen Davey. Apple named The Robot Factory as iPad App of the Year in 2015. The Everything Machine – A visual coding app for children. The Everything Machine was named Apple's Best of 2015. Monsters – A monster creation app illustrated by Tianhua Mao. The Infinite Arcade – An arcade game building app. Me: A Kids Diary – A digital journal for children. Selected for The American Library Association's Notable Children's Media List 2020. The Creature Garden – An app that allows children to create fantastical animals illustrated by Natasha Durley. Selected for The American Library Association's Notable Children's Media List 2021. Things that Go Bump – A multiplayer game set in an enchanted Japanese house, released on Apple Arcade in 2018.
Plumbr
Plumbr was an Estonian software product company founded in late 2011 that developed performance monitoring software. The Plumbr product was built on top of a proprietary algorithm that automatically detected the root causes of performance issues by interpreting application performance data. In October 2020, Plumbr was acquired by Splunk. == Products == Plumbr monitored customers' JVM applications for memory leaks, garbage collection pauses and locked threads. Plumbr problem detection algorithms were based on analysis of performance data of thousands of applications. Plumbr consisted of an agent and a portal. Plumbr Agent was attached to application runtime and sent memory usage and garbage collection information to Plumbr Portal. On Plumbr Portal one could see information such as heap and permgen memory usage, garbage collection pauses' and lock contention duration. Clients that were not able to send data to third parties could order a self-hosted portal and have a full solution in-house. In case of performance incidents Plumbr provided its users with information on problem severity and problem's root cause location in source code or runtime configuration, and listed the steps needed to take to remediate the problem. Clients included NASA, NATO, Dell, HBO, Experian, EMC Corporation.
Data annotation
Data annotation is the process of labeling or tagging relevant metadata within a dataset to enable machines to interpret the data accurately. The dataset can take various forms, including images, audio files, video footage, or text. == Applications == Data is a fundamental component in the development of artificial intelligence (AI). Training AI models, particularly in computer vision and natural language processing, requires large volumes of annotated data. Proper annotation ensures that machine learning algorithms can recognize patterns and make accurate predictions. Common types of data annotation include classification, bounding boxes, semantic segmentation, and keypoint annotation. Data annotation is used in AI-driven fields, including healthcare, autonomous vehicles, retail, security, and entertainment. By accurately labeling data, machine learning models can perform complex tasks such as object detection, sentiment analysis, and speech recognition with greater precision. This growing demand has led to the emergence of specialized sectors and platforms dedicated to AI training and human-in-the-loop workflows, which often utilize Reinforcement Learning from Human Feedback (RLHF) to refine model behavior. == In computer vision == === Image classification === Image classification, also known as image categorization, involves assigning predefined labels to images. Machine learning algorithms trained on classified images can later recognize objects and differentiate between categories. For instance, an AI model trained to recognize furniture styles can distinguish between Georgian and Rococo armchairs. === Semantic segmentation === Semantic segmentation assigns each pixel in an image to a specific class, such as trees, vehicles, humans, or buildings. This type of annotation enables machine learning models to differentiate objects by grouping similar pixels, allowing for a detailed understanding of an image. === Bounding boxes === Bounding box annotation involves drawing rectangular boxes around objects in an image. This technique is commonly used in autonomous driving, security surveillance, and retail analytics to detect and classify objects such as pedestrians, vehicles, and products on store shelves. === 3D cuboids === 3D cuboid annotation enhances traditional bounding boxes by adding depth, enabling models to predict an object's spatial orientation, movement, and size. This method is particularly useful for autonomous vehicles and robotics, where understanding object dimensions and depth is critical. === Polygonal annotation === For objects with irregular shapes, such as curved or multi-sided items, polygonal annotation provides more precise labeling than bounding boxes. This technique is often used in applications that require detailed object recognition, such as medical imaging or aerial mapping. === Keypoint annotation === Keypoint annotation marks specific points on an object, such as facial landmarks or body joints, to enable tracking and motion analysis. This method is widely used in facial recognition, emotion detection, sports analytics, and augmented reality applications.
Apache Drill
Apache Drill is an open-source software framework that supports data-intensive distributed applications for interactive analysis of large-scale datasets. Built chiefly by contributions from developers from MapR, Drill is inspired by Google's Dremel system. Drill is an Apache top-level project. Drill supports a variety of NoSQL databases and file systems, including Alluxio, HBase, MongoDB, MapR-DB, HDFS, MapR-FS, Amazon S3, Azure Blob Storage, Google Cloud Storage, Swift, NAS and local files. A single query can join data from multiple datastores. Drill's datastore-aware optimizer automatically restructures a query plan to leverage the datastore's internal processing capabilities. In addition, Drill supports data locality, if Drill and the datastore are on the same nodes. Tom Shiran is the founder of the Apache Drill Project. It was designated an Apache Software Foundation top-level project in December 2016. == Features == One explicitly stated design goal is that Drill is able to scale to 10,000 servers or more and to be able to process petabytes of data and trillions of records in seconds. Schema-free JSON document model similar to MongoDB and Elasticsearch, without requiring a formal schema to be declared Industry-standard APIs: ANSI SQL, ODBC/JDBC, RESTful APIs Extremely user and developer friendly Pluggable architecture enables connectivity to multiple datastores Version 1.9 added dynamic user-defined functions Version 1.11 added cryptographic-related functions and PCAP file format support == Back-end support == Drill is primarily focused on non-relational datastores, including Apache Hadoop text files, NoSQL, and cloud storage. A notable feature also includes in situ querying of local JSON and Apache Parquet files. Some additional datastores that it supports include: All Hadoop distributions (HDFS API 2.3+), including Apache Hadoop, MapR, CDH and Amazon EMR NoSQL: MongoDB, Apache HBase, Apache Cassandra Online Analytical Processing: Apache Kudu, Apache Druid, OpenTSDB Cloud storage: Amazon S3, Google Cloud Storage, Azure Blob Storage, Swift, IBM Cloud Object Storage Diverse data formats, including Apache Avro, Apache Parquet and JSON RDBMs storage plugins (Using JDBC to connect to MySQL, PostgreSQL, and others) A new datastore can be added by developing a storage plugin. Drill's "schema-free" JSON data model enables it to query non-relational datastores in-situ . == Front-end support == Drill itself can be queried via JDBC, ODBC, or REST through a variety of methods and languages including Python and Java. The default install includes a web interface allowing end-users to execute ANSI SQL directly and export data tables as CSV files without any programming. The dashboard library, Apache Superset, is particularly well suited for visualization of data queried with Drill.