Reviewerly/exHarmony huggingface dataset:
Problems faced by EFL students in learning to write

&lt;p&gt;&lt;em&gt;&lt;span lang="EN-US"&gt;Writing in English is not an easy task for students. Many of the foreign language learners (EFL) find writing is a challenging activity and for some of them, writing is stressful. This paper is intended to describe the students’ writing problems when they are joining a writing course. The study uses a descriptive quantitative design. The participants were eighteen students from the English department of the Faculty of Letters, State University of Malang, and the rest were students who were interested in ESL from several universities. The instrument used in the study was a Likert Scale questionnaire, distributed by using a google form. Participants were asked to answer the questions listed in the questionnaire about problems in writing by choosing one of four options (strongly agree, agree, disagree, and strongly disagree) for the close-ended question, a short text answer for open-ended questions, and several choices regarding the types of writing errors. The finding shows that there are four main problems experienced by EFL students in learning to write in the Faculty of Letters, Universitas Negeri Malang, such as writer’s block (43.5%), difficulty in expressing ideas (17.4 %), feeling less confident (21.7%), and afraid of making mistakes (17.4 %). The finding also shows that most errors and mistakes made by the students in their writing are the content of the writing (the unity and coherence), the organization, and the grammar. The study implies that the internal factors play an important factor in the students’ success to accomplish the writing task. Thus, the writing instructors/lecturers need to give positive reinforcement to the students so that they have more confidence to write.&lt;/span&gt;&lt;/em&gt;&lt;/p&gt;
A Hierarchical Approach to Multi-Event Survival Analysis

In multi-event survival analysis, one aims to predict the probability of multiple different events occurring over some time horizon. One typically assumes that the timing of events is drawn from some distribution conditioned on an individual's covariates. However, during training, one does not have access to this distribution, and the natural variation in the observed event times makes the task of survival prediction challenging, on top of the potential interdependence among events. To address this issue, we introduce a novel approach for multi-event survival analysis that models the probability of event occurrence hierarchically at different time scales, using coarse predictions (e.g., monthly predictions) to iteratively guide predictions at finer and finer grained time scales (e.g., daily predictions). We evaluate the proposed approach across several publicly available datasets in terms of both intra-event, inter-individual (global) and intra-individual, inter-event (local) consistency. We show that the proposed method consistently outperforms well-accepted and commonly used approaches to multi-event survival analysis. When estimating survival curves for Alzheimer's disease and mortality, our approach achieves a C-index of 0.91 (95% CI 0.88-0.93) and a local consistency score of 0.97 (95% CI 0.94-0.98) compared to a C-index of 0.75 (95% CI 0.70-0.80) and a local consistency score of 0.94 (95% CI 0.91-0.97) when modeling each event separately. Overall, our approach improves the accuracy of survival predictions by iteratively reducing the original task to a set of nested, simpler subtasks.
Planning to Chronicle

An important class of applications entails a robot monitoring, scrutinizing, or recording the evolution of an uncertain time-extended process. This sort of situation leads to an interesting family of planning problems in which the robot is limited in what it sees and must, thus, choose what to pay attention to. The distinguishing characteristic of this setting is that the robot has influence over what it captures via its sensors, but exercises no causal authority over the evolving process. As such, the robot’s objective is to observe the underlying process and to produce a ‘chronicle’ of occurrent events, subject to a goal specification of the sorts of event sequences that may be of interest. This paper examines variants of such problems when the robot aims to collect sets of observations to meet a rich specification of their sequential structure. We study this class of problems by modeling a stochastic process via a variant of a hidden Markov model, and specify the event sequences of interest as a regular language, developing a vocabulary of ‘mutators’ that enable sophisticated requirements to be expressed. Under different suppositions about the information gleaned about the event model, we formulate and solve different planning problems. The core underlying idea is the construction of a product between the event model and a specification automaton. The paper reports and compares performance metrics by drawing on some small case studies analyzed in depth in simulation.
How Effective is Continuous Integration in Indicating Single-Statement Bugs?

Continuous Integration (CI) is the process of automatically compiling, building, and testing code changes in the hope of catching bugs as they are introduced into the code base. With bug fixing being a core and increasingly costly task in software development, the community has adopted CI to mitigate this issue and improve the quality of their software products. Bug fixing is a core task in software development and becomes increasingly costly over time. However, little is known about how effective CI is at detecting simple, single-statement bugs.In this paper, we analyze the effectiveness of CI in 14 popular open source Java-based projects to warn about 318 single-statement bugs (SStuBs). We analyze the build status at the commits that introduce SStuBs and before the SStuBs were fixed. We then investigate how often CI indicates the presence of these bugs, through test failure. Our results show that only 2% of the commits that introduced SStuBs have builds with failed tests and 7.5% of builds before the fix reported test failures. Upon close manual inspection, we found that none of the failed builds actually captured SStuBs, indicating that CI is not the right medium to capture the SStuBs we studied. Our results suggest that developers should not rely on CI to catch SStuBs or increase their CI pipeline coverage to detect single-statement bugs.
A DQN-based Approach to Finding Precise Evidences for Fact Verification

Computing precise evidences, namely minimal sets of sentences that support or refute a given claim, rather than larger evidences is crucial in fact verification (FV), since larger evidences may contain conflicting pieces some of which support the claim while the other refute, thereby misleading FV.Despite being important, precise evidences are rarely studied by existing methods for FV.It is challenging to find precise evidences due to a large search space with lots of local optimums.Inspired by the strong exploration ability of the deep Q-learning network (DQN), we propose a DQN-based approach to retrieval of precise evidences.In addition, to tackle the label bias on Q-values computed by DQN, we design a postprocessing strategy which seeks best thresholds for determining the true labels of computed evidences.Experimental results confirm the effectiveness of DQN in computing precise evidences and demonstrate improvements in achieving accurate claim verification.
Deep learning-enabled resolution-enhancement in mini- and regular microscopy for biomedical imaging

Artificial intelligence algorithms that aid mini-microscope imaging are attractive for numerous applications. In this paper, we optimize artificial intelligence techniques to provide clear, and natural biomedical imaging. We demonstrate that a deep learning-enabled super-resolution method can significantly enhance the spatial resolution of mini-microscopy and regular-microscopy. This data-driven approach trains a generative adversarial network to transform low-resolution images into super-resolved ones. Mini-microscopic images and regular-microscopic images acquired with different optical microscopes under various magnifications are collected as our experimental benchmark datasets. The only input to this generative-adversarial-network-based method are images from the datasets down-sampled by the Bicubic interpolation. We use independent test sets to evaluate this deep learning approach with other deep learning-based algorithms through qualitative and quantitative comparisons. To clearly present the improvements achieved by this generative-adversarial-network-based method, we zoom into the local features to explore and highlight the qualitative differences. We also employ the peak signal-to-noise ratio and the structural similarity, to quantitatively compare alternative super-resolution methods. The quantitative results illustrate that super-resolution images obtained from our approach with interpolation parameter α = 0.25 more closely match those of the original high-resolution images than to those obtained by any of the alternative state-of-the-art method. These results are significant for fields that use microscopy tools, such as biomedical imaging of engineered living systems. We also utilize this generative adversarial network-based algorithm to optimize the resolution of biomedical specimen images and then generate three-dimensional reconstruction, so as to enhance the ability of three-dimensional imaging throughout the entire volumes for spatial-temporal analyses of specimen structures.
Constrained Risk-Averse Markov Decision Processes

We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. We begin by formulating the problem in a Lagrangian framework. Under the assumption that the risk objectives and constraints can be represented by a Markov risk transition mapping, we propose an optimization-based method to synthesize Markovian policies that lower-bound the constrained risk-averse problem. We demonstrate that the formulated optimization problems are in the form of difference convex programs (DCPs) and can be solved by the disciplined convex-concave programming (DCCP) framework. We show that these results generalize linear programs for constrained MDPs with total discounted expected costs and constraints. Finally, we illustrate the effectiveness of the proposed method with numerical experiments on a rover navigation problem involving conditional-value-at-risk (CVaR) and entropic-value-at-risk (EVaR) coherent risk measures.
Ensemble Learning Based on Policy Optimization Neural Networks for Capability Assessment

Capability assessment plays a crucial role in the demonstration and construction of equipment. To improve the accuracy and stability of capability assessment, we study the neural network learning algorithms in the field of capability assessment and index sensitivity. Aiming at the problem of overfitting and parameter optimization in neural network learning, the paper proposes an improved machine learning algorithm—the Ensemble Learning Based on Policy Optimization Neural Networks (ELPONN) with the policy optimization and ensemble learning. This algorithm presents an optimized neural network learning algorithm through different strategies evolution, and builds an ensemble learning model of multi-intelligent algorithms to assess the capability and analyze the sensitivity of the indexes. Through the assessment of capabilities, the algorithm effectively avoids parameter optimization from entering the minimum point in performance to improve the accuracy of equipment capability assessment, which is significantly better than previous neural network assessment methods. The experimental results show that the mean relative error is 4.10%, which is better than BP, GABP, and early stopping. The ELPONN algorithm has better accuracy and stability performance, and meets the requirements of capability assessment.
RGB-D Sensors as Marker-Less MOCAP Systems: A Comparison Between Microsoft Kinect V2 and the New Microsoft Kinect Azure

Marker-less motion capture (MOCAP) systems based on consumer technology simplify the analysis of movements in several research fields such as industry, healthcare and sports. Even if the marker-less MOCAP systems have performances with precision and accuracy lower than the marker-based MOCAP solutions, their low cost and ease of use make them the most suitable tools for full-body movements analysis. The most interesting category is relative to the use of RGB-D devices. This research work aims to compare the performances of the last two generations of Kinect devices as marker-less MOCAP systems: Microsoft Kinect v2 and Azure devices. To conduct the tests, a list of specific movements is acquired and evaluated. This work measures the improvements of the Azure in tracking human body movements. The gathered results are presented and discussed by evaluating performances and limitations of both marker-less MOCAP systems. Conclusions and future developments are shown and discussed.
Super-resolution method using generative adversarial network for Gaofen wide-field-view images

Accurate information on the spatial distribution of crops is of great significance for scientific research and production practices. Such accurate information can be extracted from high-spatial-resolution optical remote sensing images. However, acquiring these images with a wide coverage is difficult. We established a model named multispectral super-resolution generative adversarial network (MS_SRGAN) for generating high-resolution 4-m images using Gaofen 1 wide-field-view (WFV) 16-m images. The MS_SRGAN model contains a generator and a discriminator. The generator network is composed of feature extraction units and feature fusion units with a symmetric structure, and the attention mechanism is introduced to constrain the spectral value of the feature map during feature extraction. The generator loss introduces feature loss to describe the feature difference of the image. This is realized using pre-trained discriminator parameters and a partial discriminator network. In addition to realizing feature loss, the discriminator network, which is a simple convolutional neural network, also realizes adversarial loss. Adversarial loss can provide some fake high frequency details to the generator to get a more sharpened image. In the Gaofen 1 WFV image test, the performance of MS_SRGAN was compared with that of Bicubic, EDSR, SRGAN, and ESRGAN. The results show that the spectral angle mapper (3.387) and structural similarity index measure (0.998) of MS_SRGAN are higher than those of the other models. In addition, the image obtained by MS_SRGAN is more realistic; its texture details and color distribution are closer to the reference image to a greater extent.
ADaMaP: Automatic Alignment of Relational Data Sources Using Mapping Patterns

We propose a method for automatically extracting semantics from data sources. The availability of multiple data sources on the one hand and the lack of proper semantic documentation of such data sources on the other hand call for new strategies in integrating data sources by extracting semantics from the data source itself rather than from its documentation. In this work we focus on relational databases, observing they are created from semantically-rich designs such as ER diagrams, which are often not conveyed together with the database itself. While the relational model may be semantically-poor with respect to ontological models, the original semantically-rich design of the application domain leaves recognizable footprints that can be converted into ontology mapping patterns. In this work, we offer an algorithm to automatically detect and map a relational schema to ontology mapping patterns and offer an empirical evaluation using two benchmark datasets.
Application of biometric systems in face identification technologies

The paper presents an analytical review of the application of biometric recognition systems in relation to facial image identification technologies. The classification of biometric systems is presented. The trends of technological progress in the field of biometrics and facial recognition capabilities are considered. It is determined that in 2020 there is a trend of transition from the use of biometric recognition technologies in traditional state security systems to the sphere of commercial and user applications. The process of «linking» encryption keys and passwords with the biometric parameters of the data subject is described. It is proposed that a biometric feature and a biometrics parameter mean a certain value that has a physical meaning that characterizes the subject itself. The possibility of using circular neighborhood and bilinear interpolation of pixel intensity values in biometrics is also presented. This will make it possible to build a local binary template. In order to solve the problem of identification of persons, it is advisable to investigate the essence of biometric systems in the technologies of identification of persons, their types, identifying the shortcomings of each of them, on the basis of which to present the directions of elimination and search for the most reliable technologies. The essence of the use of biometric systems in the technologies of identification of persons is, for example, that the user can provide the bank or other counterparty with evidence that it is he who wants to use the services on his accounts. At the same time, the demand has increased for contactless biometric solutions. These technologies are implemented in order to conduct additional biometric verification of users. This allows to minimize possible fraud or violation of the internal rules of the service, for example, the transfer of accounts of some registered users to others.
Single-Image HDR Reconstruction with Task-specific Network based on Channel Adaptive RDN

We describe our solution for the NTIRE–2021 High Dynamic Range Challenge with Single Frame Track where we achieved the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> place in terms of muPSNR and the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> place in terms of PSNR. Aiming at this challenge we introduce the Task-specific Network based on Channel Adaptive RDN(TCRDN) that achieves good performance on the similarity with the ground truth. The network is divided into three subnets: Image Reconstruction(IR), Detail Restoration(DR) and Local Contrast Enhancement(LCE). Each subnet processes its own task, and results are fused to produce the HDR output. We carefully design these subnets so that they are properly trained for their intended purpose: detail restoration in the IR subnet and contrast enhancement in the LCE subnet. The Channel Adaptive RDN is a novel network working as the subnet backbone that combines the classic Residual Dense Network(RDN) and the Gate Channel Transformation layer. The L1 loss is used for training the network and the final model can balance the trade–off between PSNR and muPSNR for high performance in the competition’s task.
BiAdam: Fast Adaptive Bilevel Optimization Methods

Bilevel optimization recently has attracted increased interest in machine learning due to its many applications such as hyper-parameter optimization and meta learning. Although many bilevel methods recently have been proposed, these methods do not consider using adaptive learning rates. It is well known that adaptive learning rates can accelerate optimization algorithms. To fill this gap, in the paper, we propose a novel fast adaptive bilevel framework to solve stochastic bilevel optimization problems that the outer problem is possibly nonconvex and the inner problem is strongly convex. Our framework uses unified adaptive matrices including many types of adaptive learning rates, and can flexibly use the momentum and variance reduced techniques. In particular, we provide a useful convergence analysis framework for the bilevel optimization. Specifically, we propose a fast single-loop adaptive bilevel optimization (BiAdam) algorithm, which achieves a sample complexity of $\tilde{O}(\epsilon^{-4})$ for finding an $\epsilon$-stationary solution. Meanwhile, we propose an accelerated version of BiAdam algorithm (VR-BiAdam), which reaches the best known sample complexity of $\tilde{O}(\epsilon^{-3})$. To the best of our knowledge, we first study the adaptive bilevel optimization methods with adaptive learning rates. Experimental results on data hyper-cleaning and hyper-representation learning tasks demonstrate the efficiency of our algorithms.
Is Human Scoring the Best Criteria for Summary Evaluation?

A summary quality measure is judged by how well it correlates with quality scores produced by human annotators.A higher correlation with human scores is considered to be a decisive indicator of a better measure.In this work we present observations that cast doubt on this view.We also show a possibility of an alternative indicator for selecting the best measure from a family of measures, a criterion that does not rely on human scores.
Soft skills development in personnel training

The relevance and demand of graduates on a labor market, ability to adapt quickly to employer`s requirements or to change specialty in a short period of time are one of the main aspects that are becoming more and more important and necessary. It is connected with the growing amount of information and the increase of uncertainty level. The development of soft skills (together with professional skills) of students helps to improve flexibility, prompt reaction, and speed of decision-making as well as sensitivity, system solution. So, as a result there is a stable personality and a professional employee. The article presents the research on soft skills development of undergraduate students from Samara State Agrarian University. The obtained results have shown the relevant ways, approaches and methods for development of soft skills. The recommendations for soft skills development for students have been also analyzed. It has been proved that additional education, especially connected with the foreign languages, contributes to the development of the following soft skills: multilingualism and multiculturalism, the ability to work with people, the ability to work under unstable conditions, creative thinking and flexibility of behavior.