Jove
Visualize
Contact Us

Related Concept Videos

Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
Machines01:19

Machines

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
Machines: Problem Solving I01:22

Machines: Problem Solving I

A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
The toggle clamp system is a machine structure consisting of movable, pin-connected multi-force members that form a stabilized system to transmit forces. The...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Azidophosphonamido Rhodium(I) and Iridium(I) Complexes: Synthesis, Properties, and Linkage Isomerism.

Chemistry (Weinheim an der Bergstrasse, Germany)·2026
Same author

Organophosphinidene in a T-Shaped Environment.

Journal of the American Chemical Society·2026
Same author

Metal-Free Catalytic Cross-Coupling of Esters and Boranes.

Journal of the American Chemical Society·2025
Same author

Going to Extreme Lengths: Bicyclic Diborane(6) Anions and the Limits of Boron-Boron σ Bonding.

Journal of the American Chemical Society·2025
Same author

Rapid, iterative syntheses of unsymmetrical di- and triarylboranes from crystalline aryldifluoroboranes.

Chemical science·2023
Same author

Lewis Acid Behavior of MoF<sub>5</sub> and MoOF<sub>4</sub>: Syntheses and Characterization of MoF<sub>5</sub>(NCCH<sub>3</sub>), MoF<sub>5</sub>(NC<sub>5</sub>H<sub>5</sub>)<sub></sub>, and MoOF<sub>4</sub>(NC<sub>5</sub>H<sub>5</sub>)<sub></sub> (<i>n</i> = 1, 2).

Inorganic chemistry·2021
Same journal

The TaMYB55-TaSnRK1α1-TabZIP9 module confers heat stress tolerance in wheat.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Superstatistics approach to turbulent circulation fluctuations.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

A molecular timescale for evolution of cobamide biosynthesis.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Pierre Chambon, a pioneer of molecular biology and gene regulation in eukaryotes.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Granulosa cell glycogen fuels the avascular corpus luteum.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same journal

Synthetic essentiality of TRAIL/TNFSF10 in VHL-deficient renal cell carcinoma.

Proceedings of the National Academy of Sciences of the United States of America·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Game-powered machine learning.

Luke Barrington1, Douglas Turnbull, Gert Lanckriet

  • 1Electrical and Computer Engineering Department, University of California at San Diego, [corrected] La Jolla, CA 92093, USA. lukeinusa@gmail.com

Proceedings of the National Academy of Sciences of the United States of America
|March 31, 2012
PubMed
Summary
This summary is machine-generated.

Game-powered machine learning efficiently annotates music using online games and artificial intelligence. This approach makes vast multimedia content searchable through semantic keyword tagging.

Related Experiment Videos

Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Music Information Retrieval

Background:

  • Multimedia content search relies on accurate semantic keyword annotation.
  • Scalable and reliable methods are needed for large-scale multimedia data annotation.

Purpose of the Study:

  • To introduce and evaluate game-powered machine learning for multimedia content annotation, specifically music.
  • To combine human computation via online games with machine learning for efficient data labeling.

Main Methods:

  • Developed "Herd It," a social music annotation game leveraging the "wisdom of the crowds."
  • Trained a supervised machine learning system using crowd-sourced annotations.
  • Implemented active learning where the ML system directs game-based data collection for optimal model improvement.

Main Results:

  • The game-powered machine learning framework successfully collected reliable music annotations.
  • The trained system can automatically annotate music corpora far exceeding human annotation capacity.
  • Automatically annotated music is searchable via semantic relevance to text queries.

Conclusions:

  • Actively coupling annotation games with machine learning offers a robust and scalable solution for searchable multimedia data.
  • This integrated approach enhances the discoverability of large multimedia collections.
  • Game-powered machine learning significantly improves the efficiency and scale of content annotation.