Abstract--State-of-the-art upper limb myoelectric prostheses often use pattern recognition (PR) control systems that translate electromyography (EMG) signals into desired movements. As prosthesis movement complexity increases, users often struggle to produce sufficiently distinct EMG patterns for reliable classification. Existing training typically involves heuristic, trial-and-error user adjustments to static decoder boundaries. Goal: We introduce the Reviewer, a 3D visual interface projecting EMG signals directly into the decoder's classification space, providing intuitive, real-time insight into PR algorithm behavior. This structured feedback reduces cognitive load and fosters mutual, data-driven adaptation between user-generated EMG patterns and decoder boundaries. Methods: A 10-session study with 12 able-bodied participants compared PR performance after motor-based training and updating using the Reviewer versus conventional virtual arm visualization. Performance was assessed using a Fitts law task that involved the aperture of the cursor and the control of orientation. Results: Participants trained with the Reviewer achieved higher completion rates, reduced overshoot, and improved path efficiency and throughput compared to the standard visualization group. Significance: The Reviewer The Reviewer introduces decoder-informed motor training, facilitating immediate and consistent PR-based myoelectric control improvements. By iteratively refining control through real-time feedback, this approach reduces reliance on trial-and-error recalibration, enabling a more adaptive, self-correcting training framework. Conclusion: The 3D visual feedback significantly improves PR control in novice operators through structured training, enabling feedback-driven adaptation and reducing reliance on extensive heuristic adjustments.

After the success of last year's session on Machine Learning (ML) and the fruitful discussion that followed, it became apparent that there is plenty of interest in the application of these methods in archaeology. This interest might be partly ascribed to advances made in Deep Learning-in particular Convolution Neural Networks-across various disciplines. Applications using these methods now show high performance and in some cases exceed humans on challenging tasks ranging from computer vision to natural language processing. In digital archaeology we have seen and foresee applications of these techniques including automated object detection in remote sensing data, artefact image classification, use-wear analysis, text mining, paleography, predictive modelling, 3D shape analysis and recognition, and typology development. This session aims to: 1) offer a space for comparing methods, algorithms, code, APIs and workflows; 2) discuss the problems related to their application and; 3) offer insights into best practices including sources of error and validation methods.

By early July, each paper had been assigned to one supervisor SPC member and one PC member. The algorithm recorded the justifications for each assignment in terms of the specific bid and keyword match. When completed, the reviews were and Its Benefits to Society." The tutorial program was Hyderabad, India, January 6-12, 2007. At the chaired by Cynthia Braezeal. More The theme of the conference was "AI Figure 2 shows the distribution of their course work.