Automatic Distractor Generation for Multiple Choice Questions in Standard Tests

To assess the knowledge proficiency of a learner, multiple choice question is an efficient and widespread form in standard tests. However, the composition of the multiple choice question, especially the construction of distractors is quite challenging. The distractors are required to both incorrect and plausible enough to confuse the learners who did not master the knowledge. Currently, the distractors are generated by domain experts which are both expensive and time-consuming. This urges the emergence of automatic distractor generation, which can benefit various standard tests in a wide range of domains. In this paper, we propose a question and answer guided distractor generation (EDGE) framework to automate distractor generation. EDGE consists of three major modules: (1) the Reforming Question Module and the Reforming Passage Module apply gate layers to guarantee the inherent incorrectness of the generated distractors; (2) the Distractor Generator Module applies attention mechanism to control the level of plausibility. Experimental results on a large-scale public dataset demonstrate that our model significantly outperforms existing models and achieves a new state-of-the-art.Comment: accepted by COLING202

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field

Despite the great success in 2D editing using user-friendly tools, such as Photoshop, semantic strokes, or even text prompts, similar capabilities in 3D areas are still limited, either relying on 3D modeling skills or allowing editing within only a few categories. In this paper, we present a novel semantic-driven NeRF editing approach, which enables users to edit a neural radiance field with a single image, and faithfully delivers edited novel views with high fidelity and multi-view consistency. To achieve this goal, we propose a prior-guided editing field to encode fine-grained geometric and texture editing in 3D space, and develop a series of techniques to aid the editing process, including cyclic constraints with a proxy mesh to facilitate geometric supervision, a color compositing mechanism to stabilize semantic-driven texture editing, and a feature-cluster-based regularization to preserve the irrelevant content unchanged. Extensive experiments and editing examples on both real-world and synthetic data demonstrate that our method achieves photo-realistic 3D editing using only a single edited image, pushing the bound of semantic-driven editing in 3D real-world scenes. Our project webpage: https://zju3dv.github.io/sine/.Comment: Accepted to CVPR 2023. Project Page: https://zju3dv.github.io/sine

Cartesian Impedance Control on Five-Finger Dexterous Robot Hand DLR-HIT II with Flexible Joint

This paper presents an impedance controller for five-finger dexterous robot hand DLR-HIT II, which is derived in Cartesian space. By considering flexibility in finger joints and strong mechanical couplings in differential gear-box, modeling and control of the robot hand are described in this paper. The model-based friction estimation and velocity observer are carried out with an extended Kalman filter, which is implemented with parameters estimated by Least Squares Method. The designed estimator demonstrates good prediction performance, as shown in the experimental results. Stability analysis of the proposed impedance controller is carried out and described in this paper. Impedance control experiments are conducted with the five-finger dexterous robot hand DLR-HIT II in Cartesian coordinates system to help study the effectiveness of the proposed controller with friction compensation and hardware architecture

Experimental Study on Impedance Control for the Five-Finger Dexterous Robot Hand DLR-HIT II

This paper presents experimental results on the five-finger dexterous robot hand DLR-HIT II, with Cartesian impedance control based on joint torque and nonlinearity compensation for elastic dexterous robot joints. To improve the performence of the impedance controller, system parameter estimations with extended kalman filter and gravity compensation have been investigated on the robot hand. Experimental results show that, for the harmonic drive robot hand with joint toruqe feedback, accurate position tracking and stable torque/force response can be achieved with cartesian and joint impedance controller. In addition, a FPGA-based control architecture with flexible communication is proposed to perform the designed impedance controller

Study on Bohai sea ice based on MODIS data

Based on MODIS L1B data, we identify information of sea ice area, peripheral line and density, and also use Landsat data with higher resolution to verify the results obtained from MODIS data. Using pseudo color processing technology is more intuitive and more clear to show the various information of the Bohai sea. The whole process of sea ice information extraction is described in detail from MODIS data downloads to image processing and analysis. The selected data basically cover the entire process from the initial ice age, the peak ice age to the final ice age. The ideal results are obtained, and it also shows that the MODIS satellite data is feasible to monitor sea ice, which provides a reference for the establishment of monitoring sea ice model

Study on Bohai sea ice based on MODIS data

Based on MODIS L1B data, we identify information of sea ice area, peripheral line and density, and also use Landsat data with higher resolution to verify the results obtained from MODIS data. Using pseudo color processing technology is more intuitive and more clear to show the various information of the Bohai sea. The whole process of sea ice information extraction is described in detail from MODIS data downloads to image processing and analysis. The selected data basically cover the entire process from the initial ice age, the peak ice age to the final ice age. The ideal results are obtained, and it also shows that the MODIS satellite data is feasible to monitor sea ice, which provides a reference for the establishment of monitoring sea ice model