Archive for the ‘Congress’ Category

INTERACT 2027 * Call for Papers

INTERACT 2027 – 21st IFIP TC13 International Conference on Human-Computer Interaction August 23 – 27, 2027 Tallinn, Estonia https://interact2027.org/ *************************************************************************************** Call for Papers INTERACT 2027 is the 21st International Conference of Technical Committee 13 (Human-Computer Interaction) of IFIP (International Federation for Information Processing). INTERACT is a leading international conference welcoming papers and contributions on all […]

Webinar GT11 HCI

Cordial saludo Desdelel GT 11 de HCI y la Red HCI-Collab, invitamos al próximo Webinar HCI en Iberoamérica! 🗓️ 27 de agosto – 10:00 AM (COL)  🎙️ Con Paula Alexandra Silva (Universidad de Coimbra, Portugal). Tema: ✨ “Once a Gymnast, Always a Gymnast: Understanding Feedback in Rhythmic Gymnastics Training Through a Sports HCI Lens” 🔴 Transmisión en vivo por […]

TrustVis @ BMVC 2026 – Trustworthy Visual AI for Online and Public Safety

Dear colleagues, We are pleased to invite submissions to TrustVis: Trustworthy Visual AI for Online and Public Safety, a workshop at BMVC 2026, to be held on 26 November 2026 in Lancaster, UK. The workshop brings together researchers working on trustworthy visual and multimodal AI, including deepfake and synthetic-media forensics, multimodal media integrity, harmful-content understanding, […]

DEADLINE EXTENDED – AAAI 2026 Fall Symposium on “Unifying Representations for Robot Application Development” (UR-RAD)

**DEADLINE EXTENDED 1 WEEK** The new deadline for paper submissions is August 31, 2026. What:  AAAI 2026 Fall Symposium on Unifying Representations for Robot Application Development (UR-RAD) When: November 5-7, 2026 Where: Arlington, VA Web: ur-rad.github.io — Dear colleagues, We invite you to submit a paper and/or participate in the 4th AAAI Fall Symposium on […]

Courses How to build yolov8 dental object detection model

For anyone studying computer vision and medical image analysis, applying deep learning to dental radiography presents distinct challenges in feature extraction and anatomical localization. Dental radiographs often feature dense, overlapping structures, low contrast margins, and significant variation across imaging hardware. Manual landmark identification remains labor-intensive and susceptible to inter-observer variability. This tutorial utilizes YOLOv8 as […]

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