Design and Analysis of Project-driven Flipping Classroom Teaching Cases

"Web Design and Production" is a strong practical computer science foundation course. The concept, ideas and techniques of web front-end development have an important impact on the follow-up courses. The paper compares and analyzes the reform in current teaching methods of the course, and proposes a project-driven flipping classroom teaching method, which rationally decomposes and reorganizes the curriculum knowledge system, and divides the curriculum content into several modules. Meanwhile, each module is driven by a project, mixing problem-based teaching methods, task-driven methods and flipping classroom teaching methods. The paper clarifies pre-class, in-class, and after-school tasks. Knowing the project tasks before class, understanding the knowledge and skills needed for the design project, using the micro-curriculum resources to learn and practice knowledge autonomously; detecting the learning effect of knowledge in the class, solving the problems in the self-learning, and apply the learned knowledge to the actual project development by the way of group collaboration in order to promote internalization and application of knowledge, when encountering new problems, teachers not only explain new knowledge to help students continue to implement the project, but also promptly recorded the completion of the project of the group collaboration; After class, teachers summarize questions, build a knowledge system, and guide students to complete extended design of project. In this way, students\u27 practical application ability, project development ability, self-learning ability and creative ability can be improved. This article also provides specific instructional design cases based on the  "Web Page Layout and Beautification" module and provides specific teaching design cases

The Research on the Effects of Abusive Supervision on Counter-Productive Work Behavior: The Moderating Effects of Emotional Intelligence

This empirical research built the theoretical model through the integration of existing literature, and then we explored the impact of abusive supervision on employees’ counter-productive work behavior and tested the moderating effects of emotional intelligence. After the statistical analysis of 181 valid data by using correlation analysis and hierarchical regression method, we drew conclusions that: (a) abusive supervision could have a significant positive correlation with employees’ counter-productive work behavior; (b) emotional intelligence could play a regulatory role on employees’ counter-productive work behavior. On the basis of the conclusion of the study, we proposed some management controls to the organizations those could be involved in the facts of abusive supervision, which would help to relieve the contradictions of labor and create a healthy workplace atmosphere.

Semantics-Aligned Representation Learning for Person Re-identification

Person re-identification (reID) aims to match person images to retrieve the ones with the same identity. This is a challenging task, as the images to be matched are generally semantically misaligned due to the diversity of human poses and capture viewpoints, incompleteness of the visible bodies (due to occlusion), etc. In this paper, we propose a framework that drives the reID network to learn semantics-aligned feature representation through delicate supervision designs. Specifically, we build a Semantics Aligning Network (SAN) which consists of a base network as encoder (SA-Enc) for re-ID, and a decoder (SA-Dec) for reconstructing/regressing the densely semantics aligned full texture image. We jointly train the SAN under the supervisions of person re-identification and aligned texture generation. Moreover, at the decoder, besides the reconstruction loss, we add Triplet ReID constraints over the feature maps as the perceptual losses. The decoder is discarded in the inference and thus our scheme is computationally efficient. Ablation studies demonstrate the effectiveness of our design. We achieve the state-of-the-art performances on the benchmark datasets CUHK03, Market1501, MSMT17, and the partial person reID dataset Partial REID. Code for our proposed method is available at: https://github.com/microsoft/Semantics-Aligned-Representation-Learning-for-Person-Re-identification.Comment: Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20), code has been release

D-type Minimal Conformal Matter: Quantum Curves, Elliptic Garnier Systems, and the 5d Descendants

We study the quantization of the 6d Seiberg-Witten curve for D-type minimal conformal matter theories compactified on a two-torus. The quantized 6d curve turns out to be a difference equation established via introducing codimension two and four surface defects. We show that, in the Nekrasov-Shatashvili limit, the 6d partition function with insertions of codimension two and four defects serve as the eigenfunction and eigenvalues of the difference equation, respectively. We further identify the quantum curve of D-type minimal conformal matters with an elliptic Garnier system recently studied in the integrability community. At last, as a concrete consequence of our elliptic quantum curve, we study its RG flows to obtain various quantum curves of 5d ${\rm Sp}(N)+N_f \mathsf{F},N_f\leq 2N+5$ theories.Comment: 36+6 page

Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective

In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image restoration from the causality perspective, to improve the generalization ability of DNNs for unknown degradations. Our method, termed Distortion Invariant representation Learning (DIL), treats each distortion type and degree as one specific confounder, and learns the distortion-invariant representation by eliminating the harmful confounding effect of each degradation. We derive our DIL with the back-door criterion in causality by modeling the interventions of different distortions from the optimization perspective. Particularly, we introduce counterfactual distortion augmentation to simulate the virtual distortion types and degrees as the confounders. Then, we instantiate the intervention of each distortion with a virtual model updating based on corresponding distorted images, and eliminate them from the meta-learning perspective. Extensive experiments demonstrate the effectiveness of our DIL on the generalization capability for unseen distortion types and degrees. Our code will be available at https://github.com/lixinustc/Causal-IR-DIL.Comment: Accepted by CVPR202