Networked haptic virtual environments (NHVEs) are increasingly being used in medical simulation, aircraft maintenance training, and other similar fields. In this paper we present the implementation of a network emulator that can create realistic Internet-like characteristics in a laboratory setting for networked haptics. We compare the quality of this delay emulator to actual measurements taken...

"It was a Saturday night in the month of February, 1953. The moon was shining bright in a cloudless sky and cold breeze was blowing. A camp fire was burning in the lawn of the S. N. Mullick Health Centre in the village of Singur. Sixty students from all over India, who had come for training in rural public health work were sitting round the camp fire after dinner. Some were in a joyful state, c...

DRDC Valcartier is actively working on the assessment of the extent of energetic materials contamination on Canadian DND training ranges. This work is carried out with the objective of creating sustainable training conditions for the Canadian Forces. As part of this effort, research is being undertaken to study, in laboratory conditions, the leaching of explosives from cracked or opened munitio...

There is almost always a cost associated with acquiring training data. We consider the sit­ uation where the learner, with a fixed budget, may 'purchase' data during training. In par­ ticular, we examine the case where observ­ ing the value of a feature of a training exam­ ple has an associated cost, and the total cost of all feature values acquired during train­ ing must remain less than this ...

This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training with new images? (3) learned features elucidate classification results, and how do they vary among different models? To achieve these aims, f...

Since resources for data acquisition are seldom infinite, both learners and classifiers must act intelligently under hard budgets. In this paper, we consider problems in which feature values are unknown to both the learner and classifier, but can be acquired at a cost. Our goal is a learner that spends its fixed learning budget bL acquiring training data, to produce the most accurate “active cl...

In this paper, the Ssair (Semi-Supervised Active Image Retrieval) approach, which attempts to exploit unlabeled data to improve the performance of content-based image retrieval (Cbir), is proposed. This approach combines the merits of semi-supervised learning and active learning. In detail, in each round of relevance feedback, two simple learners are trained from the labeled data, i.e. images f...

Early sport specialization (ESS) refers to intense year round training in a specific sport with the exclusion of other sports at a young age. This approach to training is heavily debated and there are claims both in support and against ESS. ESS is considered to be more common in the modern day youth athlete and could be a source of overuse injuries and burnout. This case describes a 16 year old...

We have recently been studying the case where have a training set T generated from an underlying distribution and our goal is to find some good hypothesis, with respect to the true underlying distribution, using the training set T . We now examine how to use online learning algorithms (which work on individual, arbitrary sequences) in a stochastic setting. Let us consider the training set T as ...

We present an active learning framework for image segmentation with user interaction. Our system uses a sparse Gaussian Process classifier (GPC) trained on manually labeled image pixels (user scribbles) and refined in every active learning round. As a special feature, our method uses a very efficient online update rule to compute the class predictions in every round. The final segmentation of t...