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Schema Markup Guide for Dummies:2018| An MRS Guide
This is a pretty good specimen of JSONLD-without-RDF.
jsonld  metadata  web  searchengine  howto  rdf 
9 hours ago by rybesh
MAT: Metadata Anonymisation Toolkit
MAT is a toolbox composed of a GUI application, a CLI application and a library, to anonymize/remove metadata.
anonymity  metadata  privacy  python  tool  opensource 
yesterday by raphman
Twitter
RT : Experts and : Please review DDI 3.3. It is your metadata standard! Deadline is extended…
Metadata  from twitter
6 days ago by aarontay
US Students Turn Grief Into Tech Startup After France Attack - The New York Times
Banerjee and several classmates have since turned their grief into a startup called Archer that builds digital tools to help journalists, investigators and human rights workers tackle terrorism, sanctions evasion, corruption and other global violence.
ee  osint  berkeley  human_rights  metadata 
7 days ago by osi_info_program
Open Semantic Search
Apache Solr (open source enterprise-search) based free software & open source research tools for faceted search, exploratory search, tagging & annotation, text mining, OCR & datavisualization
Apache Solr (open source enterprise-search) based free software & open source research tools for faceted search, exploratory search, tagging & annotation, text mining, OCR & datavisualization
apache  seo  semantic  metadata  meta  rdf  search  data 
9 days ago by michaelfox
Identifying People by Metadata - Schneier on Security
Interesting research: "You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information," by Beatrice Perez, Mirco Musolesi, and Gianluca Stringhini.
Abstract: Metadata are associated to most of the information we produce in our daily interactions and communication in the digital world. Yet, surprisingly, metadata are often still categorized as non-sensitive. Indeed, in the past, researchers and practitioners have mainly focused on the problem of the identification of a user from the content of a message.
privacy  metadata  ID  security 
10 days ago by rgl7194
DACS (Describing Archives: A Content Standard) Statement of Principles
What is archival description and what should it do?
User-centered archival description
Identifying aggregations of records
Provenance
Original order and arrangement as archival context
archives  GLAM  collections  metadata 
13 days ago by miaridge
Necsus | How machines see the world: Understanding image annotation
big companies like Amazon (Amazon Mechanical Turk) can hire a large number of digital workers, who manually annotate images presented to them. Working from home at their computers, these digital annotators describe, pigeonhole, mark, segment, and frame images. For example, when a strawberry is shown on the screen, they will label it ‘strawberry’ (object classification). All tagged images are then organised into semantic areas based on their labelling, and later collected in databases used to train machines and algorithms. But what does ‘annotation’ mean? To annotate means to define areas in an image and assign them a value. The information, or metadata, can be for instance a series of keywords that attribute a semantic value to the chosen portion of the image. To create a machine vision system able to automatically find a cat and define its location in a picture, for example, a large collection of manually annotated images is required. The tasks digital workers are assigned reflect ones that will subsequently be performed by machines and algorithms. These tasks include:

Object classification (Fig. 1): determining whether an object is present or absent in the image (Is there a cat in the image? Are human beings present in the image?).

Object detection (Fig. 2): identifying a particular object and its arrangement in space (Where is the dog located?). In this case, the worker is asked to draw a bounding box around a single object.

Scene classification (Fig. 3): classifying a given environment. Questions such as Is the building a museum or a hospital? are presented to the annotator, who has to assign the corresponding label.

Image segmentation or pixel-level image segmentation (Fig. 4): determining which object a pixel in the image belongs to. The worker is asked to outline single objects’ profiles and annotate every area separately.

Attribute recognition (Fig. 5): defining the visual properties or qualities of objects – how an object looks and not just where is it located. The worker is asked to choose adjectives that describe the object (Is the scene ‘cold’ or ‘hot’?)....

is it possible to reduce an image, a visual experience, to a mere group of words? Is it possible to translate visual information into language?...

In some cases, the images presented to the annotator do not match her knowledge, and therefore create an obstacle and force the worker to find a solution. The use of synonyms can also be problematic....

some crowdsourcing platforms establish a list of terms for which models will be trained, called attribute vocabulary...

Two additional cases are particularly problematic for annotators: describing an object that is partially hidden by other elements in the image, and objects reflected by surfaces such as mirrors or present in transparent containers
machine_vision  classification  metadata  annotation 
13 days ago by shannon_mattern
Twitter
RT : I read: AI and Museum Collections.
Faced with a Museum colle…
MachineLearning  MuseTech  metadata  from twitter
14 days ago by miaridge

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