The Google Play Store recently introduced a data safety section in order to give users accessible insights into apps’ data collection practices. We analyzed the labels of 43,927 of the most popular apps. Almost one third of the apps with a label claims not to collect any data. But we also saw popular apps, including apps meant for children, admitting to collecting and sharing highly sensitive data like the user’s sexual orientation or health information for tracking and advertising purposes. To verify the declarations, we recorded the network traffic of 500 apps, finding more than one quarter of them transmitting tracking data not declared in their data safety label.

This data set contains a dump of our database, including the top chart data and data safety labels from September 07, 2022, and the recorded network traffic.

The analysis is available at our blog: https://www.datarequests.org/blog/android-data-safety-labels-analysis/ The source code for the analysis is available on GitHub: https://github.com/datenanfragen/android-data-safety-label-analysis

Narratives posted on the internet by patients contain a vast amount of information about various concerns. This study aimed to extract multiple concerns from interviews with breast cancer patients using the natural language processing (NLP) model bidirectional encoder representations from transformers (BERT). A total of 508 interview transcriptions of breast cancer patients written in Japanese were labeled with five types of concern labels: "treatment," "physical," "psychological," "work/financial," and "family/friends." The labeled texts were used to create a multi-label classifier by fine-tuning a pre-trained BERT model. Prior to fine-tuning, we also created several classifiers with domain adaptation using (1) breast cancer patients’ blog articles and (2) breast cancer patients’ interview transcriptions. The performance of the classifiers was evaluated in terms of precision through 5-fold cross-validation. The multi-label classifiers with only fine-tuning had precision values of over 0.80 for "physical" and "work/financial" out of the five concerns. On the other hand, precision for "treatment" was low at approximately 0.25. However, for the classifiers using domain adaptation, the precision of this label took a range of 0.40–0.51, with some cases improving by more than 0.2. This study showed combining domain adaptation with a multi-label classifier on target data made it possible to efficiently extract multiple concerns from interviews.

Microsoft Common Objects in Context (COCO) Dataset

The Common Objects in Context (COCO) dataset is a widely recognized collection designed to spur object detection, segmentation, and captioning research. Created by Microsoft, COCO provides annotations, including object categories, keypoints, and more. The model it a valuable asset for machine learning practitioners and researchers. Today, many model architectures are benchmarked against COCO, which has enabled a standard system by which architectures can be compared.

While COCO is often touted to comprise over 300k images, it's pivotal to understand that this number includes diverse formats like keypoints, among others. Specifically, the labeled dataset for object detection stands at 123,272 images.

The full object detection labeled dataset is made available here, ensuring researchers have access to the most comprehensive data for their experiments. With that said, COCO has not released their test set annotations, meaning the test data doesn't come with labels. Thus, this data is not included in the dataset.

The Roboflow team has worked extensively with COCO. Here are a few links that may be helpful as you get started working with this dataset:

• An introduction to the COCO dataset
• Weird images in COCO, and what that tells us about the utility and limits of COCO

Discover the top import markets for paper label globally, based on data from the IndexBox market intelligence platform. Explore key statistics and market insights.

Important Note: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) dark labels data hosted by Esri. This layer presents dark colored 3D “billboarded” labels designed for use over lighter toned base layers. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available.You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.

PigDetect: AI-Powered Pig Skin Disease Detection & Monitoring PigDetect is an innovative platform designed to assist farmers in the early detection and management of pig skin diseases. By utilizing cutting-edge image processing and machine learning algorithms, the system allows farmers to capture images of their pigs and instantly receive a diagnosis. PigDetect helps farmers make informed decisions about the health of their livestock, reducing potential outbreaks and increasing farm productivity. ** Key Features:**

• [ ] Real-Time

  Skin Disease Detection: Upload or capture images of pigs, and PigDetect will analyze them for signs of common skin diseases such as mange, ringworm, and erysipelas.

• [ ] Geolocation

  • [ ] Tracking:

  PigDetect maps the location of reported cases, helping farmers track disease spread and take preventive measures.

• [ ] Notifications:

  The system sends real-time notifications to farmers when a skin disease outbreak is detected within a certain radius of their farm. Admin Controls: Farm administrators can manage blog posts, monitor disease data, and interact with PigDetect's community of users.

• [ ] PigDetect

  provides farmers with a powerful tool to improve the health and welfare of their pigs, ultimately boosting productivity and preventing potential losses.