93 research outputs found
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Implications for Alien Rescue's future enhancements through comparisons with university-developed educational games
Educational games are digital games designed specifically for education. Educational game developers need to take pedagogy into consideration when designing games for learning. Otherwise, students will fail to recognize the curricular value in the game. In 2017, Alien Rescue (AR), an online problem-based 3D immersive learning environment for sixth grade science, released its 6th iteration. The stability that this version of AR has provided is welcomed by schools that collaborate with the AR team. However, the AR team wants to offer more than just stability in the long run, but to also better address students’ learning in science. As a member of the AR team, I intend to review university-created educational games to gain insights so that we can make better enhancements for AR. Through comparing four university-developed educational games, the report summarizes four points that the AR team could consider in making improvements: (a) evaluating the necessity of switching to Unity3D based on future development needs; (b) continuing to prioritize web support; (c) extending the implementation of current game mechanisms with new features; (d) developing derivative versions based on Alien Rescue.Curriculum and Instructio
LO-Net: Deep Real-time Lidar Odometry
We present a novel deep convolutional network pipeline, LO-Net, for real-time
lidar odometry estimation. Unlike most existing lidar odometry (LO) estimations
that go through individually designed feature selection, feature matching, and
pose estimation pipeline, LO-Net can be trained in an end-to-end manner. With a
new mask-weighted geometric constraint loss, LO-Net can effectively learn
feature representation for LO estimation, and can implicitly exploit the
sequential dependencies and dynamics in the data. We also design a scan-to-map
module, which uses the geometric and semantic information learned in LO-Net, to
improve the estimation accuracy. Experiments on benchmark datasets demonstrate
that LO-Net outperforms existing learning based approaches and has similar
accuracy with the state-of-the-art geometry-based approach, LOAM
Point2Node: Correlation Learning of Dynamic-Node for Point Cloud Feature Modeling
Fully exploring correlation among points in point clouds is essential for
their feature modeling. This paper presents a novel end-to-end graph model,
named Point2Node, to represent a given point cloud. Point2Node can dynamically
explore correlation among all graph nodes from different levels, and adaptively
aggregate the learned features. Specifically, first, to fully explore the
spatial correlation among points for enhanced feature description, in a
high-dimensional node graph, we dynamically integrate the node's correlation
with self, local, and non-local nodes. Second, to more effectively integrate
learned features, we design a data-aware gate mechanism to self-adaptively
aggregate features at the channel level. Extensive experiments on various point
cloud benchmarks demonstrate that our method outperforms the state-of-the-art.Comment: AAAI2020(oral
Long-MIL: Scaling Long Contextual Multiple Instance Learning for Histopathology Whole Slide Image Analysis
Histopathology image analysis is the golden standard of clinical diagnosis
for Cancers. In doctors daily routine and computer-aided diagnosis, the Whole
Slide Image (WSI) of histopathology tissue is used for analysis. Because of the
extremely large scale of resolution, previous methods generally divide the WSI
into a large number of patches, then aggregate all patches within a WSI by
Multi-Instance Learning (MIL) to make the slide-level prediction when
developing computer-aided diagnosis tools. However, most previous WSI-MIL
models using global-attention without pairwise interaction and any positional
information, or self-attention with absolute position embedding can not well
handle shape varying large WSIs, e.g. testing WSIs after model deployment may
be larger than training WSIs, since the model development set is always limited
due to the difficulty of histopathology WSIs collection. To deal with the
problem, in this paper, we propose to amend position embedding for shape
varying long-contextual WSI by introducing Linear Bias into Attention, and
adapt it from 1-d long sequence into 2-d long-contextual WSI which helps model
extrapolate position embedding to unseen or under-fitted positions. We further
utilize Flash-Attention module to tackle the computational complexity of
Transformer, which also keep full self-attention performance compared to
previous attention approximation work. Our method, Long-contextual MIL
(Long-MIL) are evaluated on extensive experiments including 4 dataset including
WSI classification and survival prediction tasks to validate the superiority on
shape varying WSIs. The source code will be open-accessed soon
RF-Net: An End-to-End Image Matching Network based on Receptive Field
This paper proposes a new end-to-end trainable matching network based on
receptive field, RF-Net, to compute sparse correspondence between images.
Building end-to-end trainable matching framework is desirable and challenging.
The very recent approach, LF-Net, successfully embeds the entire feature
extraction pipeline into a jointly trainable pipeline, and produces the
state-of-the-art matching results. This paper introduces two modifications to
the structure of LF-Net. First, we propose to construct receptive feature maps,
which lead to more effective keypoint detection. Second, we introduce a general
loss function term, neighbor mask, to facilitate training patch selection. This
results in improved stability in descriptor training. We trained RF-Net on the
open dataset HPatches, and compared it with other methods on multiple benchmark
datasets. Experiments show that RF-Net outperforms existing state-of-the-art
methods.Comment: 9 pages, 6 figure
Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference
For middle-school math students, interactive question-answering (QA) with
tutors is an effective way to learn. The flexibility and emergent capabilities
of generative large language models (LLMs) has led to a surge of interest in
automating portions of the tutoring process - including interactive QA to
support conceptual discussion of mathematical concepts. However, LLM responses
to math questions can be incorrect or mismatched to the educational context -
such as being misaligned with a school's curriculum. One potential solution is
retrieval-augmented generation (RAG), which involves incorporating a vetted
external knowledge source in the LLM prompt to increase response quality. In
this paper, we designed prompts that retrieve and use content from a
high-quality open-source math textbook to generate responses to real student
questions. We evaluate the efficacy of this RAG system for middle-school
algebra and geometry QA by administering a multi-condition survey, finding that
humans prefer responses generated using RAG, but not when responses are too
grounded in the textbook content. We argue that while RAG is able to improve
response quality, designers of math QA systems must consider trade-offs between
generating responses preferred by students and responses closely matched to
specific educational resources.Comment: 6 pages, presented at NeurIPS'23 Workshop on Generative AI for
Education (GAIED
Semantic Labeling of Mobile LiDAR Point Clouds via Active Learning and Higher Order MRF
【Abstract】Using mobile Light Detection and Ranging point clouds to accomplish road scene labeling tasks shows promise for a variety of applications. Most existing methods for semantic labeling of point clouds require a huge number of fully supervised point cloud scenes, where each point needs to be manually annotated with a specific category. Manually annotating each point in point cloud scenes is labor intensive and hinders practical usage of those methods. To alleviate such a huge burden of manual annotation, in this paper, we introduce an active learning method that avoids annotating the whole point cloud scenes by iteratively annotating a small portion of unlabeled supervoxels and creating a minimal manually annotated training set. In order to avoid the biased sampling existing in traditional active learning methods, a neighbor-consistency prior is exploited to select the potentially misclassified samples into the training set to improve the accuracy of the statistical model. Furthermore, lots of methods only consider short-range contextual information to conduct semantic labeling tasks, but ignore the long-range contexts among local variables. In this paper, we use a higher order Markov random field model to take into account more contexts for refining the labeling results, despite of lacking fully supervised scenes. Evaluations on three data sets show that our proposed framework achieves a high accuracy in labeling point clouds although only a small portion of labels is provided. Moreover, comparative experiments demonstrate that our proposed framework is superior to traditional sampling methods and exhibits comparable performance to those fully supervised models.10.13039/501100001809-National Natural Science Foundation of China; Collaborative Innovation Center of Haixi Government Affairs Big Data Sharin
Pairwise registration of TLS point clouds by deep multi-scale local features
Abstract(#br)Because of the mechanism of TLS system, noise, outliers, various occlusions, varying cloud densities, etc. inevitably exist in the collection of TLS point clouds. To achieve automatic TLS point cloud registration, many methods, based on the hand-crafted features of keypoints, have been proposed. Despite significant progress, the current methods still face great challenges in accomplishing TLS point cloud registration. In this paper, we propose a multi-scale neural network to learn local shape descriptors for establishing correspondences between pairwise TLS point clouds. To train our model, data augmentation, developed on pairwise semi-synthetic 3D local patches, is to extend our network to be robust to rotation transformation. Then, based on varying local neighborhoods, multi-scale subnetworks are constructed and fused to learn robust local features. Experimental results demonstrate that our proposed method successfully registers two TLS point clouds and outperforms state-of-the-art methods. Besides, our learned descriptors are invariant to translation and tolerant to changes in rotation
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