Reviewerly/exHarmony huggingface dataset:
Low-Light Shadow Imaging using Quantum-Noise Detection with a Camera

We experimentally demonstrate an imaging technique based on quantum noise modification after interaction with an opaque object. By using a homodyne-like detection scheme, we eliminate the detrimental effect of the camera's dark noise, making this approach particularly attractive for imaging scenarios that require weak illumination. Here, we reconstruct the image of an object illuminated with a squeezed vacuum using a total of 800 photons, utilizing less than one photon per frame on average.
Hardness Sampling for Self-Training Based Transductive Zero-Shot  Learning

Transductive zero-shot learning (T-ZSL) which could alleviate the domain shift problem in existing ZSL works, has received much attention recently. However, an open problem in T-ZSL: how to effectively make use of unseen-class samples for training, still remains. Addressing this problem, we first empirically analyze the roles of unseen-class samples with different degrees of hardness in the training process based on the uneven prediction phenomenon found in many ZSL methods, resulting in three observations. Then, we propose two hardness sampling approaches for selecting a subset of diverse and hard samples from a given unseen-class dataset according to these observations. The first one identifies the samples based on the class-level frequency of the model predictions while the second enhances the former by normalizing the class frequency via an approximate class prior estimated by an explored prior estimation algorithm. Finally, we design a new Self-Training framework with Hardness Sampling for T-ZSL, called STHS, where an arbitrary inductive ZSL method could be seamlessly embedded and it is iteratively trained with unseen-class samples selected by the hardness sampling approach. We introduce two typical ZSL methods into the STHS framework and extensive experiments demonstrate that the derived T-ZSL methods outperform many state-of-the-art methods on three public benchmarks. Besides, we note that the unseen-class dataset is separately used for training in some existing transductive generalized ZSL (T-GZSL) methods, which is not strict for a GZSL task. Hence, we suggest a more strict T-GZSL data setting and establish a competitive baseline on this setting by introducing the proposed STHS framework to T-GZSL.
Research on the Training Practice of Intelligent Financial Professionals in Colleges and Universities

&lt;p&gt;At present, the development of society as a whole in our country in the process of acceleration, the national education work in colleges and universities to give importance more and more high, the domestic university actively mobilize social forces, taking advantage of the industrial sector, the effect of increased intelligence work, all of these talents has played a powerful role in promoting.Therefore, under the background of intelligence, this paper also analyzes the specific work mode of financial professional personnel training in colleges and universities, hoping to optimize the overall work process by using new work development methods, so as to achieve the best effect of talent training and transport more high-quality financial professionals for our society.&lt;/p&gt;
A Study on EFL Instructional Design from the Perspective of Cognitive Load Theory

Cognitive load is commonly defined as the amount of mental effort that performing a specific task imposes on a learner’s cognitive system. For EFL learners, with their limited English proficiency and cultural immersion, always find it overwhelming to comprehend a content lesson delivered in English. Cognitive load theory draws on an understanding of human cognitive architecture to provide explanations for why certain designs of multimedia educational materials are effective and why some are not. This study evaluated the split-attention and redundancy principles in an Intensive Reading lesson for non-English majored students and their potential to decrease mental effort and increase learning.
Analysis of Basic English Teaching Strategies in Colleges and Universities from the Perspective of Applied Undergraduates

Application-oriented Undergraduate Colleges and universities pay more attention to the cultivation of students' knowledge application ability, so that students can flexibly use their knowledge to solve practical problems, so as to achieve the purpose of applying what they have learned, and lay a good foundation for students' future employment and development. Therefore, from the perspective of Applied Functional undergraduate, we should innovate the teaching mode in basic English Teaching in Colleges and universities, carry out teaching with new ideas, new ways and new ideas, cultivate students' knowledge application ability and promote the teaching reform in Colleges and universities. Especially in college basic English teaching, from the perspective of applied undergraduate, college basic English teaching should focus on cultivating students' English language communication and expression ability, pay attention to practical teaching, and lay the foundation for college students' future English knowledge learning. So how can we improve the efficiency of basic English Teaching in Colleges and universities from the perspective of application-oriented undergraduates? This is the main content of this paper.
The Second Classroom of “One Festival One Platform” Helps to Cultivate Applied Talents

This paper expounds that relying on the “Mathematics Culture Festival”, mathematics culture is integrated into education and teaching to help the growth of young teachers and the cultivation of applied talents, and relying on the “professional association” platform, integrate application innovation into skill training, broaden professional knowledge, improve professional skills, &#x0D; promote the construction of study style and improve the effectiveness of education. This paper summarizes the achievements, and &#x0D; provides a reference for the second classroom to help the cultivation of applied talents.
Research on the Influence Mechanism of Artificial Intelligence Technology on the Career of Animation Practitioners in the Future

In the future, artificial intelligence technology will replace a lot of repetitive work, and in the process of animation &#x0D; creation, many are repetitive work, such as character animation, light rendering, physical effect simulation and so on. The difference &#x0D; between artificial intelligence technology and true human is emotion and imagination, and animation is a way to express human &#x0D; emotion through different roles and stories. Only by constantly exploring how to express role emotion through a good story can let &#x0D; future animation practitioners meet the needs of future animation development.
Teaching Reform Thinking and Practice Based on Enterprises’ Demand for Accounting Skills

The basic idea of the new curriculum reform education is to promote the development of students and cultivate innovative talents in line with the needs of The Times. [1] Therefore, the teaching reform thinking and practice based on enterprises’ &#x0D; demand for accounting skills should truly reflect the goal of promoting students’ development, promoting the growth and development of the majority of students and meeting the social needs of the new era. But at present, there are some phenomena such &#x0D; as poor teaching quality and insufficient attention to professional skills teaching in some colleges and universities. Based on the &#x0D; demand of accounting talents in modern enterprises, this paper aims to cultivate professional talents with information skills, craftsman spirit and innovation ability.
Current Situation and Suggestions of Informatization Construction of College Student Management in the Era of Big Data

In the era of big data, with the acceleration of the development of information technology in China, the management &#x0D; of college students should also keep pace with the times and actively innovate. However, there are still some problems to be &#x0D; solved in the information management of colleges and universities at this stage. As a place with active ideas and well-developed &#x0D; network technology, colleges and universities should keep up with the pace of development of the times, promote the further &#x0D; improvement and improvement of student management under the background of big data, and realize the vigorous and rapid &#x0D; development of modern information technology under the guidance of informatization. Relevant educators also need to combine &#x0D; the development level of educational science and technology in the period of big data to choose a scientific and feasible route that &#x0D; is consistent with the practice of college student management in China, so as to have a positive impact on the further improvement &#x0D; and standardization of college student management.
Skillful Application of Layered Teaching Method in Senior High School English Reading

English reading, as an important part of high school English teaching, plays a great role in high school English teaching. &#x0D; Therefore, English teachers should pay attention to the cultivation of students’ reading ability. In the actual teaching, the method of &#x0D; teachers to cultivate students’ reading ability is relatively single, mainly through reading the text, which leads to the unsatisfactory &#x0D; reading effect. The reason for this situation is that teachers do not take into account the differences between students, and the one &#x0D; size fits all teaching method is difficult to improve students’ reading ability. In English reading, hierarchical teaching method is &#x0D; adopted, which is combined with students’ reading ability, so that each student’s reading ability can be improved. Therefore, this &#x0D; paper analyzes the application of layered teaching in senior high school English reading, hoping to provide reference for senior &#x0D; high school English reading reform and improve the level of English reading teaching.
Influencing Factors of Mobile Autonomous Learning and Mobile Self-Efficacy Among Law Atudents in Xi’an

Although abundant literature can be found on autonomous learning, there is limited literature available for law major &#x0D; undergraduates’ autonomous learning in Chinese context. The main purpose of this study is to examine factors influencing university law undergraduates’ autonomous learning in mobile learning environment in Xi’an, China, taking into consideration of mobile &#x0D; self-efficacy. The data collected from the survey is put into SPSS 23.0, which is used to calculate descriptive as well as inferential &#x0D; statistical measures. The results denote that the four independent variables, namely Motivation, Learning Strategy, Goal Orientation, Teacher Support all have positive impacts of Autonomous Learning. The mediating test of Mobile Self-efficacy proves that &#x0D; Mobile Self-efficacy acts as a mediator in the relationships between Learning Strategy and Autonomous Learning, Goal Orientation and Autonomous Learning, Teacher Support and Autonomous Learning.
Discussion on the Teaching of the Electronic Technology Foundation

The course Electronic Technology Foundation is a basic course of information subject, which has a very important &#x0D; position of courses. This paper expounds the nature, purpose, task and characteristics of the course, and various teaching methods &#x0D; about the course, so as to improve the teaching effectiveness.
Research on How to Apply Language Expression Skills in News Interview

At this stage, the development and application of information technology has brought the whole society into the new &#x0D; media era, which also makes the competition in the media industry more and more fierce. At the same time, the society also puts &#x0D; forward higher requirements for news reporters. In the specific news interview work, interviewers should combine the actual &#x0D; situation, apply appropriate interview skills, use the correct language expression, obtain more news information and ensure the &#x0D; quality of news interview content, so as to attract more audiences and promote the development of the media platform.
CNN Retrieval based Unsupervised Metric Learning for Near-Duplicated  Video Retrieval

As important data carriers, the drastically increasing number of multimedia videos often brings many duplicate and near-duplicate videos in the top results of search. Near-duplicate video retrieval (NDVR) can cluster and filter out the redundant contents. In this paper, the proposed NDVR approach extracts the frame-level video representation based on convolutional neural network (CNN) features from fully-connected layer and aggregated intermediate convolutional layers. Unsupervised metric learning is used for similarity measurement and feature matching. An efficient re-ranking algorithm combined with k-nearest neighborhood fuses the retrieval results from two levels of features and further improves the retrieval performance. Extensive experiments on the widely used CC\_WEB\_VIDEO dataset shows that the proposed approach exhibits superior performance over the state-of-the-art.
Simplifying Software Defect Prediction (via the "early bird" Heuristic)

Before researchers rush to reason across all available data or try complex methods, perhaps it is prudent to first check for simpler alternatives. Specifically, if the historical data has the most information in some small region, then perhaps a model learned from that region would suffice for the rest of the project. To support this claim, we offer a case study with 240 GitHub projects, where we find that the information in those projects "clumped" towards the earliest parts of the project. A defect prediction model learned from just the first 150 commits works as well, or better than state-of-the-art alternatives. Using just this early life cycle data, we can build models very quickly, very early in the software project life cycle. Moreover, using this method, we have shown that a simple model (with just two features) generalizes to hundreds of software projects. Based on this experience, we doubt that prior work on generalizing software engineering defect prediction models may have needlessly complicated an inherently simple process. Further, prior work that focused on later-life cycle data needs to be revisited since their conclusions were drawn from relatively uninformative regions. Replication note: all our data and scripts are online at https://github.com/snaraya7/simplifying-software-analytics
Computational Morphology with Neural Network Approaches

Neural network approaches have been applied to computational morphology with great success, improving the performance of most tasks by a large margin and providing new perspectives for modeling. This paper starts with a brief introduction to computational morphology, followed by a review of recent work on computational morphology with neural network approaches, to provide an overview of the area. In the end, we will analyze the advantages and problems of neural network approaches to computational morphology, and point out some directions to be explored by future research and study.
Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds  with Optimal Transport and Random Walk

Due to the scarcity of annotated scene flow data, self-supervised scene flow learning in point clouds has attracted increasing attention. In the self-supervised manner, establishing correspondences between two point clouds to approximate scene flow is an effective approach. Previous methods often obtain correspondences by applying point-wise matching that only takes the distance on 3D point coordinates into account, introducing two critical issues: (1) it overlooks other discriminative measures, such as color and surface normal, which often bring fruitful clues for accurate matching; and (2) it often generates sub-par performance, as the matching is operated in an unconstrained situation, where multiple points can be ended up with the same corresponding point. To address the issues, we formulate this matching task as an optimal transport problem. The output optimal assignment matrix can be utilized to guide the generation of pseudo ground truth. In this optimal transport, we design the transport cost by considering multiple descriptors and encourage one-to-one matching by mass equality constraints. Also, constructing a graph on the points, a random walk module is introduced to encourage the local consistency of the pseudo labels. Comprehensive experiments on FlyingThings3D and KITTI show that our method achieves state-of-the-art performance among self-supervised learning methods. Our self-supervised method even performs on par with some supervised learning approaches, although we do not need any ground truth flow for training.
Contrastive Learning of Image Representations with Cross-Video  Cycle-Consistency

Recent works have advanced the performance of self-supervised representation learning by a large margin. The core among these methods is intra-image invariance learning. Two different transformations of one image instance are considered as a positive sample pair, where various tasks are designed to learn invariant representations by comparing the pair. Analogically, for video data, representations of frames from the same video are trained to be closer than frames from other videos, i.e. intra-video invariance. However, cross-video relation has barely been explored for visual representation learning. Unlike intra-video invariance, ground-truth labels of cross-video relation is usually unavailable without human labors. In this paper, we propose a novel contrastive learning method which explores the cross-video relation by using cycle-consistency for general image representation learning. This allows to collect positive sample pairs across different video instances, which we hypothesize will lead to higher-level semantics. We validate our method by transferring our image representation to multiple downstream tasks including visual object tracking, image classification, and action recognition. We show significant improvement over state-of-the-art contrastive learning methods. Project page is available at https://happywu.github.io/cycle_contrast_video.
TransferI2I: Transfer Learning for Image-to-Image Translation from Small  Datasets

Image-to-image (I2I) translation has matured in recent years and is able to generate high-quality realistic images. However, despite current success, it still faces important challenges when applied to small domains. Existing methods use transfer learning for I2I translation, but they still require the learning of millions of parameters from scratch. This drawback severely limits its application on small domains. In this paper, we propose a new transfer learning for I2I translation (TransferI2I). We decouple our learning process into the image generation step and the I2I translation step. In the first step we propose two novel techniques: source-target initialization and self-initialization of the adaptor layer. The former finetunes the pretrained generative model (e.g., StyleGAN) on source and target data. The latter allows to initialize all non-pretrained network parameters without the need of any data. These techniques provide a better initialization for the I2I translation step. In addition, we introduce an auxiliary GAN that further facilitates the training of deep I2I systems even from small datasets. In extensive experiments on three datasets, (Animal faces, Birds, and Foods), we show that we outperform existing methods and that mFID improves on several datasets with over 25 points.
A Langevinized Ensemble Kalman Filter for Large-Scale Static and Dynamic  Learning

The Ensemble Kalman Filter (EnKF) has achieved great successes in data assimilation in atmospheric and oceanic sciences, but its failure in convergence to the right filtering distribution precludes its use for uncertainty quantification. We reformulate the EnKF under the framework of Langevin dynamics, which leads to a new particle filtering algorithm, the so-called Langevinized EnKF. The Langevinized EnKF inherits the forecast-analysis procedure from the EnKF and the use of mini-batch data from the stochastic gradient Langevin-type algorithms, which make it scalable with respect to both the dimension and sample size. We prove that the Langevinized EnKF converges to the right filtering distribution in Wasserstein distance under the big data scenario that the dynamic system consists of a large number of stages and has a large number of samples observed at each stage. We reformulate the Bayesian inverse problem as a dynamic state estimation problem based on the techniques of subsampling and Langevin diffusion process. We illustrate the performance of the Langevinized EnKF using a variety of examples, including the Lorenz-96 model, high-dimensional variable selection, Bayesian deep learning, and Long Short Term Memory (LSTM) network learning with dynamic data.
A Bit More Bayesian: Domain-Invariant Learning with Uncertainty

Domain generalization is challenging due to the domain shift and the uncertainty caused by the inaccessibility of target domain data. In this paper, we address both challenges with a probabilistic framework based on variational Bayesian inference, by incorporating uncertainty into neural network weights. We couple domain invariance in a probabilistic formula with the variational Bayesian inference. This enables us to explore domain-invariant learning in a principled way. Specifically, we derive domain-invariant representations and classifiers, which are jointly established in a two-layer Bayesian neural network. We empirically demonstrate the effectiveness of our proposal on four widely used cross-domain visual recognition benchmarks. Ablation studies validate the synergistic benefits of our Bayesian treatment when jointly learning domain-invariant representations and classifiers for domain generalization. Further, our method consistently delivers state-of-the-art mean accuracy on all benchmarks.
Provable Guarantees against Data Poisoning Using Self-Expansion and  Compatibility

A recent line of work has shown that deep networks are highly susceptible to backdoor data poisoning attacks. Specifically, by injecting a small amount of malicious data into the training distribution, an adversary gains the ability to control the model's behavior during inference. In this work, we propose an iterative training procedure for removing poisoned data from the training set. Our approach consists of two steps. We first train an ensemble of weak learners to automatically discover distinct subpopulations in the training set. We then leverage a boosting framework to recover the clean data. Empirically, our method successfully defends against several state-of-the-art backdoor attacks, including both clean and dirty label attacks. We also present results from an independent third-party evaluation including a recent \textit{adaptive} poisoning adversary. The results indicate our approach is competitive with existing defenses against backdoor attacks on deep neural networks, and significantly outperforms the state-of-the-art in several scenarios.
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise  Rollouts

This paper investigates the model-based methods in multi-agent reinforcement learning (MARL). We specify the dynamics sample complexity and the opponent sample complexity in MARL, and conduct a theoretic analysis of return discrepancy upper bound. To reduce the upper bound with the intention of low sample complexity during the whole learning process, we propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy Optimization (AORPO). In AORPO, each agent builds its multi-agent environment model, consisting of a dynamics model and multiple opponent models, and trains its policy with the adaptive opponent-wise rollout. We further prove the theoretic convergence of AORPO under reasonable assumptions. Empirical experiments on competitive and cooperative tasks demonstrate that AORPO can achieve improved sample efficiency with comparable asymptotic performance over the compared MARL methods.