Reviewerly/exHarmony huggingface dataset:
Multimedia teaching system based on art interaction technology

With the development of the times, traditional classroom education has gradually failed to meet the needs of teaching. Now, with the application of computers in modern education, hybrid learning has developed rapidly in the field of education. How to promote the better development of hybrid learning has become a new research hot spot. Therefore, this paper aims at improving the MOOC platform, which is the largest application of hybrid learning. It integrates animation technology and multimedia technology, and designs a multimedia-teaching platform based on art interaction technology, which effectively improves the attraction of MOOC platform to learners. Firstly, this paper introduces multimedia technology, animation technology and interactive animation technology in detail, and applies them to MOOC platform. Secondly, according to the analysis of the research results of teaching platform requirements, the design principles and system framework of this paper are given. Finally, the information processing system of B/S architecture mode is built to make the improved platform have high response speed and data processing ability. In addition, this paper constructs a small-scale multimedia hybrid learning platform for testing, and finds that the multimedia teaching platform based on art interactive technology designed in this paper can well promote students' autonomous learning and improve the effect of students' learning.
Learning Position and Target Consistency for Memory-based Video Object  Segmentation

This paper studies the problem of semi-supervised video object segmentation(VOS). Multiple works have shown that memory-based approaches can be effective for video object segmentation. They are mostly based on pixel-level matching, both spatially and temporally. The main shortcoming of memory-based approaches is that they do not take into account the sequential order among frames and do not exploit object-level knowledge from the target. To address this limitation, we propose to Learn position and target Consistency framework for Memory-based video object segmentation, termed as LCM. It applies the memory mechanism to retrieve pixels globally, and meanwhile learns position consistency for more reliable segmentation. The learned location response promotes a better discrimination between target and distractors. Besides, LCM introduces an object-level relationship from the target to maintain target consistency, making LCM more robust to error drifting. Experiments show that our LCM achieves state-of-the-art performance on both DAVIS and Youtube-VOS benchmark. And we rank the 1st in the DAVIS 2020 challenge semi-supervised VOS task.
Explanatory models in neuroscience: Part 2 -- constraint-based  intelligibility

Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain. In the context of neural network models for neuroscience, concerns have been raised about model intelligibility, and how they relate (if at all) to what is found in the brain. We claim that what makes a system intelligible is an understanding of the dependencies between its behavior and the factors that are causally responsible for that behavior. In biological systems, many of these dependencies are naturally "top-down": ethological imperatives interact with evolutionary and developmental constraints under natural selection. We describe how the optimization techniques used to construct NN models capture some key aspects of these dependencies, and thus help explain why brain systems are as they are -- because when a challenging ecologically-relevant goal is shared by a NN and the brain, it places tight constraints on the possible mechanisms exhibited in both kinds of systems. By combining two familiar modes of explanation -- one based on bottom-up mechanism (whose relation to neural network models we address in a companion paper) and the other on top-down constraints, these models illuminate brain function.
Towards Evaluating and Training Verifiably Robust Neural Networks

Recent works have shown that interval bound propagation (IBP) can be used to train verifiably robust neural networks. Reseachers observe an intriguing phenomenon on these IBP trained networks: CROWN, a bounding method based on tight linear relaxation, often gives very loose bounds on these networks. We also observe that most neurons become dead during the IBP training process, which could hurt the representation capability of the network. In this paper, we study the relationship between IBP and CROWN, and prove that CROWN is always tighter than IBP when choosing appropriate bounding lines. We further propose a relaxed version of CROWN, linear bound propagation (LBP), that can be used to verify large networks to obtain lower verified errors than IBP. We also design a new activation function, parameterized ramp function (ParamRamp), which has more diversity of neuron status than ReLU. We conduct extensive experiments on MNIST, CIFAR-10 and Tiny-ImageNet with ParamRamp activation and achieve state-of-the-art verified robustness. Code and the appendix are available at https://github.com/ZhaoyangLyu/VerifiablyRobustNN.
Towards High Fidelity Face Relighting with Realistic Shadows

Existing face relighting methods often struggle with two problems: maintaining the local facial details of the subject and accurately removing and synthesizing shadows in the relit image, especially hard shadows. We propose a novel deep face relighting method that addresses both problems. Our method learns to predict the ratio (quotient) image between a source image and the target image with the desired lighting, allowing us to relight the image while maintaining the local facial details. During training, our model also learns to accurately modify shadows by using estimated shadow masks to emphasize on the high-contrast shadow borders. Furthermore, we introduce a method to use the shadow mask to estimate the ambient light intensity in an image, and are thus able to leverage multiple datasets during training with different global lighting intensities. With quantitative and qualitative evaluations on the Multi-PIE and FFHQ datasets, we demonstrate that our proposed method faithfully maintains the local facial details of the subject and can accurately handle hard shadows while achieving state-of-the-art face relighting performance.
HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural  Network for Code Documentation Generation in Jupyter Notebooks

Jupyter notebook allows data scientists to write machine learning code together with its documentation in cells. In this paper, we propose a new task of code documentation generation (CDG) for computational notebooks. In contrast to the previous CDG tasks which focus on generating documentation for single code snippets, in a computational notebook, one documentation in a markdown cell often corresponds to multiple code cells, and these code cells have an inherent structure. We proposed a new model (HAConvGNN) that uses a hierarchical attention mechanism to consider the relevant code cells and the relevant code tokens information when generating the documentation. Tested on a new corpus constructed from well-documented Kaggle notebooks, we show that our model outperforms other baseline models.
Harmonic Beltrami Signature: A Novel 2D Shape Representation for Object Classification

There has been a growing interest in shape analysis in recent years. We present a novel shape signature for 2D Jordan domains. The proposed signature is based on Sharon's conformal welding signature [E. Sharon and D. Mumford, Internat. J. Comput. Vis., 70 (2006), pp. 55--75], which is one of the main building blocks of our proposed shape signature. The conformal welding signature is a well-known shape signature used to represent 2D shapes. Nevertheless, it is not invariant under rotation. It is also sensitive to the choice of particular feature points and shape perturbations. Motivated by this, we propose in this paper an invariant shape signature under rigid transformations and scaling. The proposed signature does not require the delineation of feature points and is robust under shape perturbations. More specifically, the proposed signature is a Beltrami coefficient of the harmonic extension of the conformal welding. We show that there is a one-to-one correspondence between a quotient space of Beltrami coefficients and the space of 2D Jordan domains up to a translation, rotation, and scaling. With a suitable normalization, each equivalence class in the quotient space is associated with a unique representative named the Harmonic Beltrami Signature (HBS). As such, each shape is associated with a unique HBS. Conversely, the associated shape of an HBS can be reconstructed based on quasiconformal Teichmüller theories, which are uniquely determined up to a translation, rotation, and scaling. The HBS is thus an effective fingerprint to represent a 2D shape. The robustness of the HBS is studied both theoretically and experimentally. With the HBS, simple metrics, such as $L^2$, can measure geometric dissimilarity between shapes. Experiments have been carried out to classify shapes into different classes using HBS. Results show good classification performance, which demonstrates the efficacy of our proposed shape signature.
Roughness Index and Roughness Distance for Benchmarking Medical  Segmentation

Medical image segmentation is one of the most challenging tasks in medical image analysis and has been widely developed for many clinical applications. Most of the existing metrics have been first designed for natural images and then extended to medical images. While object surface plays an important role in medical segmentation and quantitative analysis i.e. analyze brain tumor surface, measure gray matter volume, most of the existing metrics are limited when it comes to analyzing the object surface, especially to tell about surface smoothness or roughness of a given volumetric object or to analyze the topological errors. In this paper, we first analysis both pros and cons of all existing medical image segmentation metrics, specially on volumetric data. We then propose an appropriate roughness index and roughness distance for medical image segmentation analysis and evaluation. Our proposed method addresses two kinds of segmentation errors, i.e. (i)topological errors on boundary/surface and (ii)irregularities on the boundary/surface. The contribution of this work is four-fold: (i) detect irregular spikes/holes on a surface, (ii) propose roughness index to measure surface roughness of a given object, (iii) propose a roughness distance to measure the distance of two boundaries/surfaces by utilizing the proposed roughness index and (iv) suggest an algorithm which helps to remove the irregular spikes/holes to smooth the surface. Our proposed roughness index and roughness distance are built upon the solid surface roughness parameter which has been successfully developed in the civil engineering.
Leveraging Unlabeled Data for Entity-Relation Extraction through  Probabilistic Constraint Satisfaction

We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Such knowledge takes the form of an ontology defining relations and their permissible arguments. Previous approaches set out to integrate such knowledge in their learning approaches either through self-training, or through approximations that lose the precise meaning of the logical expressions. By contrast, our approach employs semantic loss which captures the precise meaning of a logical sentence through maintaining a probability distribution over all possible states, and guiding the model to solutions which minimize any constraint violations. With a focus on low-data regimes, we show that semantic loss outperforms the baselines by a wide margin.
Skeleton Merger: an Unsupervised Aligned Keypoint Detector

Detecting aligned 3D keypoints is essential under many scenarios such as object tracking, shape retrieval and robotics. However, it is generally hard to prepare a high-quality dataset for all types of objects due to the ambiguity of keypoint itself. Meanwhile, current unsupervised detectors are unable to generate aligned keypoints with good coverage. In this paper, we propose an unsupervised aligned keypoint detector, Skeleton Merger, which utilizes skeletons to reconstruct objects. It is based on an Autoencoder architecture. The encoder proposes keypoints and predicts activation strengths of edges between keypoints. The decoder performs uniform sampling on the skeleton and refines it into small point clouds with pointwise offsets. Then the activation strengths are applied and the sub-clouds are merged. Composite Chamfer Distance (CCD) is proposed as a distance between the input point cloud and the reconstruction composed of sub-clouds masked by activation strengths. We demonstrate that Skeleton Merger is capable of detecting semantically-rich salient keypoints with good alignment, and shows comparable performance to supervised methods on the KeypointNet dataset. It is also shown that the detector is robust to noise and subsampling. Our code is available at https://github.com/eliphatfs/SkeletonMerger.
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings

Cross-lingual word embeddings (CLWE) have been proven useful in many cross-lingual tasks. However, most existing approaches to learn CLWE including the ones with contextual embeddings are sense agnostic. In this work, we propose a novel framework to align contextual embeddings at the sense level by leveraging cross-lingual signal from bilingual dictionaries only. We operationalize our framework by first proposing a novel sense-aware cross entropy loss to model word senses explicitly. The monolingual ELMo and BERT models pretrained with our sense-aware cross entropy loss demonstrate significant performance improvement for word sense disambiguation tasks. We then propose a sense alignment objective on top of the sense-aware cross entropy loss for cross-lingual model pretraining, and pretrain cross-lingual models for several language pairs (English to German/Spanish/Japanese/Chinese). Compared with the best baseline results, our cross-lingual models achieve 0.52%, 2.09% and 1.29% average performance improvements on zero-shot cross-lingual NER, sentiment classification and XNLI tasks, respectively.
On complementing end-to-end human behavior predictors with planning

High capacity end-to-end approaches for human motion (behavior) prediction have the ability to represent subtle nuances in human behavior, but struggle with robustness to out of distribution inputs and tail events. Planning-based prediction, on the other hand, can reliably output decent-but-not-great predictions: it is much more stable in the face of distribution shift (as we verify in this work), but it has high inductive bias, missing important aspects that drive human decisions, and ignoring cognitive biases that make human behavior suboptimal. In this work, we analyze one family of approaches that strive to get the best of both worlds: use the end-to-end predictor on common cases, but do not rely on it for tail events / out-of-distribution inputs -- switch to the planning-based predictor there. We contribute an analysis of different approaches for detecting when to make this switch, using an autonomous driving domain. We find that promising approaches based on ensembling or generative modeling of the training distribution might not be reliable, but that there very simple methods which can perform surprisingly well -- including training a classifier to pick up on tell-tale issues in predicted trajectories.
GAN Vocoder: Multi-Resolution Discriminator Is All You Need

Several of the latest GAN-based vocoders show remarkable achievements, outperforming autoregressive and flow-based competitors in both qualitative and quantitative measures while synthesizing orders of magnitude faster. In this work, we hypothesize that the common factor underlying their success is the multi-resolution discriminating framework, not the minute details in architecture, loss function, or training strategy. We experimentally test the hypothesis by evaluating six different generators paired with one shared multi-resolution discriminating framework. For all evaluative measures with respect to text-to-speech syntheses and for all perceptual metrics, their performances are not distinguishable from one another, which supports our hypothesis.
Zero-sum semi-Markov games with state-action-dependent discount factors

Semi-Markov model is one of the most general models for stochastic dynamic systems. This paper deals with a two-person zero-sum game for semi-Markov processes. We focus on the expected discounted criterion with state-action-dependent discount factors. The state and action spaces are both Polish spaces, and the reward rate function is ω-bounded. We first construct a fairly general model of semi-Markov games under a given semi-Markov kernel and a pair of strategies. Next, based on the standard regularity condition and the continuity-compactness condition for semi-Markov games, we derive a “drift condition” on the semi-Markov kernel and suppose that the discount factors have a positive lower bound, under which the existence of the value function and an optimal pair of stationary strategies of our semi-Markov game are proved by using a general Shapley equation. Moreover, in the scenario of finite state and action spaces, a value iteration-type algorithm for approximating the value function and an optimal pair of stationary strategies is developed. The convergence and the error bound of the algorithm are also proved. Finally, we conduct numerical examples to demonstrate the main results.
Developing Cognitive Competence in Students of Non-linguistic Specialties in the Course of Working with Social Media Neologisms

The paper concentrates on developing of a cognitive competence in students of non-linguistic specialties during foreign language classes. The article considers the concept of cognitive competence as a key component in the composition of foreign language communicative competence and analyzes its role in the activity-based approach to learning, in which much attention is paid to the autonomy of students. The paper addresses the issue of increasing the hours allocated for independent work of students at non-linguistic departments, which implies a more research-based nature of learning. The relevance of the article consists in finding ways to organize and motivate students’ independent cognitive and practical activities aimed at mastering the material. The article also touches upon the issue related to the fact that students of non-linguistic specialties have very little time scheduled for the word-building of a foreign language. The paper considers the possibility of forming cognitive competence, as well as deepening knowledge about word-forming models of the English language in the process of independent cognitive activity of students associated with the search for the meanings of social media neologisms and identifying their word-building ways. Neologisms are studied as a phenomenon, practicable ways of forming these language units, similar to the word-building models of standard words, are given. The vocabulary of social networks is viewed as a language layer relevant for the modern student. Educational potential of the Internet resources for students to work independently is studied.
The Professional Portfolio and the Attitude of the Pedagogical Specialists for Its Development

A modern innovative technology that stimulates pedagogical specialists to build their own professional and personal image is the portfolio. The attitude to activity - mental state of readiness for activity, the result of the implementation of various dispositions to a particular form of response in pedagogical situations. A link between the substructures of the activity - motivational, target and operational. Its features are: integrity and unity; focus, stability and variability; individual character. The main functions are: stimulating, regulating variable, selective, reflective, corrective, transforming, etc.
Soft Skills as a Component of Foreign Language Training of Engineering Students

The present period of higher education development is characterized by the need to improve the professional training of engineering students, as well as the demand for the formation of soft skills that contribute to successful professional activity and effective interpersonal and intercultural interaction. However, employers often point to an unsatisfactory level of analytical, interpersonal and communication skills development of technical universities’ graduates. The analysis of engineering students’ professional training process indicates the absence of established practices for the formation of metasubject skills. The relevance of this study is due to the need to organize a systematic and purposeful work on the formation of soft skills among undergraduate students of technical profiles. According to the authors, the discipline “Foreign languages” has significant potential for personal self-development and students’ soft skills improvement. The aim of this study is a theoretical substantiation and experimental confirmation of the effective use of productive linguodidactic technologies for the soft skills formation. The methodological basis of the research is the integration of competence, productive and communicative approaches, which ensure the formation of foreign language communicative competence of students at the level necessary for solving social and intercultural problems in the context of international scientific, educational and professional cooperation. The results of the experimental study indicate the effectiveness of these approaches and the implemented pedagogical technologies for the soft skills development.
Basic Values Hierarchy of Students in Technical and Linguistic Field of Study

The purpose of the presented research is to comparatively analyze the hierarchy and content of basic values of undergraduate students studying in technical areas of training and students of humanitarian areas of training (linguistics), based on statistical analysis of a ranking experiment result. The working hypothesis of the study is that the hierarchy of personal values is significantly influenced not only by age and gender characteristics, but also by the professional activity of the individual. When ranking, the subject was presented with 35 stimuli words denoting basic values. The respondents had to arrange them in descending order of positive evaluativeness (1 – the highest grade, 35 – the lowest one). When establishing the incentive list, we relied on the proposed lists of basic values (Questionnaire by Schwartz, Ryabov / Kurbangaleeva) and identified recurring values in them. Based on the results obtained, a comparative analysis of the basic values hierarchy of technical university students and students of humanitarian universities is carried out using the nonparametric Mann-Whitney criterion. All measurements are carried out by methods of computer analysis using jamovi software. The results of the analysis make it possible to refute the hypothesis that statistically significant differences are traced in the basic values hierarchy of students in technical and humanitarian areas of training.