This work investigates how tutoring discourse interacts with students'
proximal knowledge to explain and predict students' learning outcomes. Our work
is conducted in the context of high-dosage human tutoring where 9th-grade
students (N= 1080) attended small group tutorials and individually practiced
problems on an Intelligent Tutoring System (ITS). We analyzed whether tutors'
talk moves and students' performance on the ITS predicted scores on math
learning assessments. We trained Random Forest Classifiers (RFCs) to
distinguish high and low assessment scores based on tutor talk moves, student's
ITS performance metrics, and their combination. A decision tree was extracted
from each RFC to yield an interpretable model. We found AUCs of 0.63 for talk
moves, 0.66 for ITS, and 0.77 for their combination, suggesting interactivity
among the two feature sources. Specifically, the best decision tree emerged
from combining the tutor talk moves that encouraged rigorous thinking and
students' ITS mastery. In essence, tutor talk that encouraged mathematical
reasoning predicted achievement for students who demonstrated high mastery on
the ITS, whereas tutors' revoicing of students' mathematical ideas and
contributions was predictive for students with low ITS mastery. Implications
for practice are discussed.

本研究探讨了教学对话与学生的近距离知识如何相互作用，从而解释和预测学生的学习成果。研究结果表明，鼓励严谨思考的教学对话促进了学生在数学学习中的成就，而导师对学生的数学思想和贡献的重述对低程度的智能辅导系统掌握的学生起到了预测作用。

对齐教师话语以支持严格思维与学生内容掌握，预测数学成就

Aligning Tutor Discourse Supporting Rigorous Thinking with Tutee Content  Mastery for Predicting Math Achievement

Recently, there has been a paradigm shift in stereo matching with
learning-based methods achieving the best results on all popular benchmarks.
The success of these methods is due to the availability of training data with
ground truth; training learning-based systems on these datasets has allowed
them to surpass the accuracy of conventional approaches based on heuristics and
assumptions. Many of these assumptions, however, had been validated extensively
and hold for the majority of possible inputs. In this paper, we generate a
matching volume leveraging both data with ground truth and conventional wisdom.
We accomplish this by coalescing diverse evidence from a bidirectional matching
process via random forest classifiers. We show that the resulting matching
volume estimation method achieves similar accuracy to purely data-driven
alternatives on benchmarks and that it generalizes to unseen data much better.
In fact, the results we submitted to the KITTI and ETH3D benchmarks were
generated using a classifier trained on the Middlebury 2014 dataset.

本文介绍了一种利用基于学习的方法和传统智慧生成匹配体积，以提高立体匹配准确性的方法。作者使用随机森林分类器将来自双向匹配过程的不同证据融合在一起，证明了该方法的准确性与纯数据驱动替代方案相当，并且能够更好地应用于未见过的数据。

CBMV：一种用于视差估计的合并双向匹配体积

CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation

In this paper, we describe our algorithmic approach, which was used for
submissions in the fifth Emotion Recognition in the Wild (EmotiW 2017)
group-level emotion recognition sub-challenge. We extracted feature vectors of
detected faces using the Convolutional Neural Network trained for face
identification task, rather than traditional pre-training on emotion
recognition problems. In the final pipeline an ensemble of Random Forest
classifiers was learned to predict emotion score using available training set.
In case when the faces have not been detected, one member of our ensemble
extracts features from the whole image. During our experimental study, the
proposed approach showed the lowest error rate when compared to other explored
techniques. In particular, we achieved 75.4% accuracy on the validation data,
which is 20% higher than the handcrafted feature-based baseline. The source
code using Keras framework is publicly available.

本文描述了我们在第五届 Emotion Recognition in the Wild（EmotiW 2017）小组级情感识别子挑战赛中使用的算法方法，其中我们使用了卷积神经网络来提取检测到的脸部的特征向量，而不是传统的预训练情感识别问题。最终，我们的管道中学习了一个随机森林集成分类器来预测情感分数，该集成分类器在实验研究中显示出与其他探索技术相比最低的错误率。