Reviewerly/exHarmony huggingface dataset:
Accurate Structured-Text Spotting for Arithmetical Exercise Correction

Correcting arithmetical exercise is a labor intensive and time consuming task for primary school teachers all the time. To reduce their burdens, we propose Arithmetical Exercise Checker (AEC), which is the first system that automatically evaluates all arithmetical expressions (AEs) on exercise images. The major challenge is that AE is formed by printed and handwritten texts with particular arithmetical patterns (e.g., multi-line, fraction). Despite being part of AE, handwritten texts usually lead to zigzag boundaries and tangled rows. What's worse, AE may be arithmetical incorrect, which makes the contextual information less valuable for recognition. To tackle these problems, we introduce integrated detection, recognition and evaluation branches by leveraging AE's intrinsic features, namely 1) boundary indistinctive, 2) locally relevant patterns and 3) globally irrelevant symbols. Experimental results demonstrate that AEC yields a 93.72% correction accuracy on 40 kinds of mainstream primary arithmetical exercises. So far, the online service of AEC processes 75, 000 arbitrary exercises on average per day, and already reduced the burden of over 1, 000, 000 users. AEC shows the benefits for implementing an vision-based system as a way to aid teachers in reducing reduplicative tasks.
A Generalized Framework for Edge-Preserving and Structure-Preserving Image Smoothing

Image smoothing is a fundamental procedure in applications of both computer vision and graphics. The required smoothing properties can be different or even contradictive among different tasks. Nevertheless, the inherent smoothing nature of one smoothing operator is usually fixed and thus cannot meet the various requirements of different applications. In this paper, a non-convex non-smooth optimization framework is proposed to achieve diverse smoothing natures where even contradictive smoothing behaviors can be achieved. To this end, we first introduce the truncated Huber penalty function which has seldom been used in image smoothing. A robust framework is then proposed. When combined with the strong flexibility of the truncated Huber penalty function, our framework is capable of a range of applications and can outperform the state-of-the-art approaches in several tasks. In addition, an efficient numerical solution is provided and its convergence is theoretically guaranteed even the optimization framework is non-convex and non-smooth. The effectiveness and superior performance of our approach are validated through comprehensive experimental results in a range of applications.
A 5 GHz and 7.5 V multi-amplitude modulator driving circuit for practical high-speed quantum key distribution

Quantum key distribution (QKD) offers the ability of information theoretic security key exchange. The secure key rate is an important indicator for the practical QKD systems, which determines what kinds of applications can be supported. One most effective way to enhance the secure key rate is to increase the system repetition frequency. Here, we report an implementation of a high-speed DC-coupled modulator driving circuit with a repetition rate of up to 5 GHz. The circuit outputs a multi-amplitude return-to-zero pattern pulse with a maximum amplitude of 7.5 V. The design adapts to the various electro-optic modulators widely employed in QKD systems. The minimum pulse width is measured as 75 ps, with the relative noise level less than 1.5% for all the output amplitudes under random modulation.
Identification of public procurement contracts with a high risk of non-performance based on neural networks

The article is devoted to the use of machine learning methods, neural networks in particular, to predict the results of the execution of public procurement contracts. In the framework of the study, a classification of public contracts in pipe industry was carried out in order to identify contracts with high risk of non-performance, a Python application had been developed for the solution of the problem, which implemented compositions of neural network models of the architecture proposed in this work. The results of mathematical models application are described and their accuracy is analyzed. It is noted that the developed software product can be used for solving the described classification problem using neural network models with proper selection of training data.
Stitcher: Feedback-driven Data Provider for Object Detection

Object detectors commonly vary quality according to scales, where the performance on small objects is the least satisfying. In this paper, we investigate this phenomenon and discover that: in the majority of training iterations, small objects contribute barely to the total loss, causing poor performance with imbalanced optimization. Inspired by this finding, we present Stitcher, a feedback-driven data provider, which aims to train object detectors in a balanced way. In Stitcher, images are resized into smaller components and then stitched into the same size to regular images. Stitched images contain inevitable smaller objects, which would be beneficial with our core idea, to exploit the loss statistics as feedback to guide next-iteration update. Experiments have been conducted on various detectors, backbones, training periods, datasets, and even on instance segmentation. Stitcher steadily improves performance by a large margin in all settings, especially for small objects, with nearly no additional computation in both training and testing stages.
Latent Regularized Generative Dual Adversarial Network For Abnormal Detection

With the development of adversarial attack in deep learning, it is critical for abnormal detector to not only discover the out-of-distribution samples but also provide defence against the adversarial attacker. Since few previous universal detector is known to work well on both tasks, we consider against both scenarios by constructing a robust and effective technique, where one sample could be regarded as the abnormal sample if it exhibits a higher image reconstruction error. Due to the training instability issues existed in previous generative adversarial networks (GANs) based methods, in this paper we propose a dual auxiliary autoencoder to make a tradeoff between the capability of generator and discriminator, leading to a more stable training process and high-quality image reconstruction. Moreover, to generate discriminative and robust latent representations, the mutual information estimator regarded as latent regularizer is adopted to extract the most unique information of target class. Overall, our generative dual adversarial network simultaneously optimizes the image reconstruction space and latent space to improve the performance. Experiments show that our model has the clear superiority over cutting edge semi-supervised abnormal detectors and achieves the state-of-the-art results on the datasets.
Low-Resource Generation of Multi-hop Reasoning Questions

This paper focuses on generating multi-hop reasoning questions from the raw text in a low resource circumstance.Such questions have to be syntactically valid and need to logically correlate with the answers by deducing over multiple relations on several sentences in the text.Specifically, we first build a multi-hop generation model and guide it to satisfy the logical rationality by the reasoning chain extracted from a given text.Since the labeled data is limited and insufficient for training, we propose to learn the model with the help of a large scale of unlabeled data that is much easier to obtain.Such data contains rich expressive forms of the questions with structural patterns on syntax and semantics.These patterns can be estimated by the neural hidden semi-Markov model using latent variables.With latent patterns as a prior, we can regularize the generation model and produce the optimal results.Experimental results on the HotpotQA data set demonstrate the effectiveness of our model.Moreover, we apply the generated results to the task of machine reading comprehension and achieve significant performance improvements.
Accurate profile measurement method for industrial stereo-vision systems

Purpose Profile measurement with boundary information plays a vital role in the detection of quality in the assembly of aviation parts. The purpose of this paper is to improve the evaluation accuracy of the aerodynamic shapes of airplanes, the profiles of large-sized parts need to be measured accurately. Design/methodology/approach In this paper, an accurate profile measurement method based on boundary reference points is proposed for the industrial stereo-vision system. Based on the boundary-reference points, the authors established a priori constraint for extracting the boundary of the measured part. Combining with the image features of background and the measured part, an image-edge compensation model is established to extract the boundary of the measured part. The critical point of a laser stripe on the edge of the measured part is extracted corresponding to the boundary constraint. Finally, as per the principle of binocular vision, the profile of the measured part is reconstructed. Finding Laboratory experiments validate the measurement accuracy of the proposed method which is 0.33 mm. In the analysis of results between the measured data and the theoretical model, the measuring accuracy of the proposed method was found to be significantly higher than that of the other traditional methods. Practical implication An aviation part was measured in the part-assembly shop by the proposed method, which verified the feasibility and effectiveness of this method. The research can realize the measurement of smooth surface boundary which can solve existing profile reconstruction problems for aviation parts. Originality/value According to the two-dimensional contour constraint, critical points of the laser strip sequence at the edge of measured part are extracted and the accurate profile reconstruction with the boundary is realized.
Interactive free-viewpoint video generation

Free-viewpoint video (FVV) is processed video content in which viewers can freely select the viewing position and angle. FVV delivers an improved visual experience and can also help synthesize special effects and virtual reality content. In this paper, a complete FVV system is proposed to interactively control the viewpoints of video relay programs through multimedia terminals such as computers and tablets. The hardware of the FVV generation system is a set of synchronously controlled cameras, and the software generates videos in novel viewpoints from the captured video using view interpolation. The interactive interface is designed to visualize the generated video in novel viewpoints and enable the viewpoint to be changed interactively. Experiments show that our system can synthesize plausible videos in intermediate viewpoints with a view range of up to 180°.
Cloudiness Information Services for Solar Energy Management in West Africa

In West Africa (WA), interest in solar energy development has risen in recent years with many planned and ongoing projects currently in the region. However, a major drawback to this development in the region is the intense cloud cover that reduces the incoming solar radiation when present and causes fluctuations in solar power production. Therefore, understanding the occurrence of clouds and their link to the surface solar radiation in the region is important for making plans to manage future solar energy production. In this study, we use the state-of-the-art European Centre for Medium-range Weather Forecasts ReAnalysis (ERA5) dataset to examine the occurrence and persistence of cloudy and clear-sky conditions in the region. Then, we investigate the effects of cloud cover on the quantity and variability of the incoming solar radiation. The cloud shortwave radiation attenuation (CRASW↓) is used to quantify the amount of incoming solar radiation that is lost due to clouds. The results showed that the attenuation of incoming solar radiation is stronger in all months over the southern part of WA near the Guinea Coast. Across the whole region, the maximum attenuation occurs in August, with a mean CRASW↓ of about 55% over southern WA and between 20% and 35% in the Sahelian region. Southern WA is characterized by a higher occurrence of persistent cloudy conditions, while the Sahel region and northern WA are associated with frequent clear-sky conditions. Nonetheless, continuous periods with extremely low surface solar radiation were found to be few over the whole region. The analysis also showed that the surface solar radiation received from November to April only varies marginally from one year to the other. However, there is a higher uncertainty during the core of the monsoon season (June to October) with regard to the quantity of incoming solar radiation. The results obtained show the need for robust management plans to ensure the long-term success of solar energy projects in the region.
Ease.ml/snoopy in action

We demonstrate ease.ml/snoopy, a data analytics system that performs feasibility analysis for machine learning (ML) applications before they are developed. Given a performance target of an ML application (e.g., accuracy above 0.95), ease.ml/snoopy provides a decisive answer to ML developers regarding whether the target is achievable or not. We formulate the feasibility analysis problem as an instance of Bayes error estimation. That is, for a data (distribution) on which the ML application should be performed, ease.ml/snoopy provides an estimate of the Bayes error - the minimum error rate that can be achieved by any classifier. It is well-known that estimating the Bayes error is a notoriously hard task. In ease.ml/snoopy we explore and employ estimators based on the combination of (1) nearest neighbor (NN) classifiers and (2) pre-trained feature transformations. To the best of our knowledge, this is the first work on Bayes error estimation that combines (1) and (2). In today's cost-driven business world, feasibility of an ML project is an ideal piece of information for ML application developers - ease.ml/snoopy plays the role of a reliable " consultant. "
Data-Driven Approach to Multiple-Source Domain Adaptation.

A key problem in domain adaptation is determining what to transfer across different domains. We propose a data-driven method to represent these changes across multiple source domains and perform unsupervised domain adaptation. We assume that the joint distributions follow a specific generating process and have a small number of identifiable changing parameters, and develop a data-driven method to identify the changing parameters by learning low-dimensional representations of the changing class-conditional distributions across multiple source domains. The learned low-dimensional representations enable us to reconstruct the target-domain joint distribution from unlabeled target-domain data, and further enable predicting the labels in the target domain. We demonstrate the efficacy of this method by conducting experiments on synthetic and real datasets.
What Makes Agile Test Artifacts Useful?

Background: The artifacts used in Agile software testing and the reasons why these artifacts are used are fairly well-understood. However, empirical research on how Agile test artifacts are eventually designed in practice and which quality factors make them useful for software testing remains sparse. Aims: Our objective is two-fold. First, we identify current challenges in using test artifacts to understand why certain quality factors are considered good or bad. Second, we build an Activity-Based Artifact Quality Model that describes what Agile test artifacts should look like. Method: We conduct an industrial survey with 18 practitioners from 12 companies operating in seven different domains. Results: Our analysis reveals nine challenges and 16 factors describing the quality of six test artifacts from the perspective of Agile testers. Interestingly, we observed mostly challenges regarding language and traceability, which are well-known to occur in non-Agile projects. Conclusions: Although Agile software testing is becoming the norm, we still have little confidence about general do's and don'ts going beyond conventional wisdom. This study is the first to distill a list of quality factors deemed important to what can be considered as useful test artifacts.
Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization

Epoch gradient descent method (a.k.a. Epoch-GD) proposed by Hazan and Kale (2011) was deemed a breakthrough for stochastic strongly convex minimization, which achieves the optimal convergence rate of $O(1/T)$ with $T$ iterative updates for the {\it objective gap}. However, its extension to solving stochastic min-max problems with strong convexity and strong concavity still remains open, and it is still unclear whether a fast rate of $O(1/T)$ for the {\it duality gap} is achievable for stochastic min-max optimization under strong convexity and strong concavity. Although some recent studies have proposed stochastic algorithms with fast convergence rates for min-max problems, they require additional assumptions about the problem, e.g., smoothness, bi-linear structure, etc. In this paper, we bridge this gap by providing a sharp analysis of epoch-wise stochastic gradient descent ascent method (referred to as Epoch-GDA) for solving strongly convex strongly concave (SCSC) min-max problems, without imposing any additional assumption about smoothness or the function's structure. To the best of our knowledge, our result is the first one that shows Epoch-GDA can achieve the optimal rate of $O(1/T)$ for the duality gap of general SCSC min-max problems. We emphasize that such generalization of Epoch-GD for strongly convex minimization problems to Epoch-GDA for SCSC min-max problems is non-trivial and requires novel technical analysis. Moreover, we notice that the key lemma can also be used for proving the convergence of Epoch-GDA for weakly-convex strongly-concave min-max problems, leading to a nearly optimal complexity without resorting to smoothness or other structural conditions.
Domain Adaptation as a Problem of Inference on Graphical Models

This paper is concerned with data-driven unsupervised domain adaptation, where it is unknown in advance how the joint distribution changes across domains, i.e., what factors or modules of the data distribution remain invariant or change across domains. To develop an automated way of domain adaptation with multiple source domains, we propose to use a graphical model as a compact way to encode the change property of the joint distribution, which can be learned from data, and then view domain adaptation as a problem of Bayesian inference on the graphical models. Such a graphical model distinguishes between constant and varied modules of the distribution and specifies the properties of the changes across domains, which serves as prior knowledge of the changing modules for the purpose of deriving the posterior of the target variable $Y$ in the target domain. This provides an end-to-end framework of domain adaptation, in which additional knowledge about how the joint distribution changes, if available, can be directly incorporated to improve the graphical representation. We discuss how causality-based domain adaptation can be put under this umbrella. Experimental results on both synthetic and real data demonstrate the efficacy of the proposed framework for domain adaptation. The code is available at this https URL .
On the Role of Sparsity and DAG Constraints for Learning Linear DAGs

Learning graphical structures based on Directed Acyclic Graphs (DAGs) is a challenging problem, partly owing to the large search space of possible graphs. A recent line of work formulates the structure learning problem as a continuous constrained optimization task using the least squares objective and an algebraic characterization of DAGs. However, the formulation requires a hard DAG constraint and may lead to optimization difficulties. In this paper, we study the asymptotic role of the sparsity and DAG constraints for learning DAG models in the linear Gaussian and non-Gaussian cases, and investigate their usefulness in the finite sample regime. Based on the theoretical results, we formulate a likelihood-based score function, and show that one only has to apply soft sparsity and DAG constraints to learn a DAG equivalent to the ground truth DAG. This leads to an unconstrained optimization problem that is much easier to solve. Using gradient-based optimization and GPU acceleration, our procedure can easily handle thousands of nodes while retaining a high accuracy. Extensive experiments validate the effectiveness of our proposed method and show that the DAG-penalized likelihood objective is indeed favorable over the least squares one with the hard DAG constraint.
On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them

We analyze the influence of adversarial training on the loss landscape of machine learning models. To this end, we first provide analytical studies of the properties of adversarial loss functions under different adversarial budgets. We then demonstrate that the adversarial loss landscape is less favorable to optimization, due to increased curvature and more scattered gradients. Our conclusions are validated by numerical analyses, which show that training under large adversarial budgets impede the escape from suboptimal random initialization, cause non-vanishing gradients and make the models’ minima found sharper. Based on these observations, we show that a periodic adversarial scheduling (PAS) strategy can effectively overcome these challenges, yielding better results than vanilla adversarial training while being much less sensitive to the choice of learning rate.
Examining the Use Self-perceived by University Teachers about ICT Resources: Measurement and Comparative Analysis in a One-way ANOVA Design

The growing rise of information and communication technologies (ICT) in all areas of society demands that university professors have an adequate level of digital literacy, so that they can contribute effectively to the training of their students and respond to the demands of the job. The objective of this research is to know and compare the use by university teachers of different ICT resources, in their teaching, evaluation, and research (UTIC-EEI model, its acronym in Spanish), depending on the area of knowledge to which they belong (science and engineering-architecture, health sciences, art-humanities, and social-legal Sciences), in order to be able to take measures to effectively address the digital shortcomings of teachers. An ex post facto study is carried out, with a quantitative methodology utilising a survey technique, with a sample of 867 Spanish university teachers, with a descriptive and inferential analysis via ANOVA for multiple comparisons. The results showed a medium-high use by teachers of ICT resources in four areas, with there being a superior use in the teaching and research dimensions compared to the evaluation dimension in each area of knowledge. These data underline the need to continue training teachers to make excellent instrumental use of specific ICT resources in each area of knowledge.
A Causal View on Robustness of Neural Networks

We present a causal view on the robustness of neural networks against input manipulations, which applies not only to traditional classification tasks but also to general measurement data. Based on this view, we design a deep causal manipulation augmented model (deep CAMA) which explicitly models the manipulations of data as a cause to the observed effect variables. We further develop data augmentation and test-time fine-tuning methods to improve deep CAMA’s robustness. When compared with discriminative deep neural networks, our proposed model shows superior robustness against unseen manipulations. As a by-product, our model achieves disentangled representation which separates the representation of manipulations from those of other latent causes.