Reviewerly/exHarmony huggingface dataset:
Grammatical Error Correction in Low-Resource Scenarios

Grammatical error correction in English is a long studied problem with many existing systems and datasets.However, there has been only a limited research on error correction of other languages.In this paper, we present a new dataset AKCES-GEC on grammatical error correction for Czech.We then make experiments on Czech, German and Russian and show that when utilizing synthetic parallel corpus, Transformer neural machine translation model can reach new state-of-the-art results on these datasets.
Scaling Autoregressive Video Models

Due to the statistical complexity of video, the high degree of inherent stochasticity, and the sheer amount of data, generating natural video remains a challenging task. State-of-the-art video generation models attempt to address these issues by combining sometimes complex, often video-specific neural network architectures, latent variable models, adversarial training and a range of other methods. Despite their often high complexity, these approaches still fall short of generating high quality video continuations outside of narrow domains and often struggle with fidelity. In contrast, we show that conceptually simple, autoregressive video generation models based on a three-dimensional self-attention mechanism achieve highly competitive results across multiple metrics on popular benchmark datasets for which they produce continuations of high fidelity and realism. Furthermore, we find that our models are capable of producing diverse and surprisingly realistic continuations on a subset of videos from Kinetics, a large scale action recognition dataset comprised of YouTube videos exhibiting phenomena such as camera movement, complex object interactions and diverse human movement. To our knowledge, this is the first promising application of video-generation models to videos of this complexity.
Using Augmented Reality Technology to Improve the Quality of Transport Services

The problem of marking the area of the preferred route by means of mobile technology of augmented reality for public transport routes is considered. The analysis of modern theoretical and applied solutions of using the technology of augmented reality in mobile applications is carried out. The task is to reduce the time interval for the search for the route of public transport and the decision to choose a vehicle for an ignorant urban resident, in the conditions of a limited time resource. A theoretical method for marking the routes of public transport on the basis of the transport utility function is proposed. An algorithm is developed for marking the routes of public transport. The software implementation of the algorithm in mobile execution is performed in the Unity and Vuforia environment, using the Yandex API. The problem of finding information on the routes of passenger transport and the problem of improving the perception of transport information through its visual display on a mobile device are solved. A comparison is made between the speed of the developed technology and the existing solutions by the time criterion. Recommendations on the further use of the developed approaches are discussed.
Interpol review of digital evidence 2016 - 2019

This review paper covers the forensic-relevant literature in digital evidence from 2016 to 2019 as a part of the 19th Interpol International Forensic Science Managers Symposium. The review papers are also available at the Interpol website at: https://www.interpol.int/content/download/14458/file/Interpol Review Papers 2019.pdf
An Efficient Task Assignment Framework to Accelerate DPU-Based Convolutional Neural Network Inference on FPGAs

Field Programmable Gate Array (FPGA) has become an efficient accelerator for convolutional neural network (CNN) inference due to its high performance and flexibility. To further improve the performance of CNN inference on FPGAs, an Intellectual Property core (IP core) called Deep Learning Processor Unit (DPU) is released by Xilinx. Unlike previous FPGA-based hardware designs focusing on specific functions or CNNs, the DPU IP supports ample basic functions of deep learning, and the developers can take advantage of DPUs to accelerate CNN inference conveniently. In DPU-based CNN acceleration platform, an encapsulated scheduler plays a crucial role in task scheduling between heterogeneous ARM and multiple DPUs. However, the current scheduler is unsatisfactory because its low schedule efficiency. This paper thus presents a high performance task assignment framework built upon Xilinx hybrid CPU-FPGA MPSoC devices. We first evaluate the main causes of low schedule efficiency problem. Then, we explore the scheduler rules and improve shedule efficiency through purposeful observations and analysis. Finally, we integrate our optimizations, and propose an efficient task assignment framework to maximize performance on DPU-based CNN acceleration platform. Experimental results on Xilinx Zynq UltraScale+ MPSoC zcu104 show that our efficient task assignment framework significantly boosts schedule efficiency for small-scale CNNs (from 36% to 70%), medium-scale CNNs (from 65% to 95%), and large-scale CNNs (from 77% to 99%) compared with original schedule strategy.
Using Deep Learning in Infrared Images to Enable Human Gesture Recognition for Autonomous Vehicles

The realization of a novel human gesture recognition algorithm is essential to enable the effective collision avoidance of autonomous vehicles.Compared to visible spectrum cameras, the use of infrared imaging can enable more robust human gesture recognition in a complex environment.However, gesture recognition in infrared images has not been extensively investigated.In this work, we propose a model to detect human gestures, based on the improved YOLO-V3 network involving a saliency map as the second input channel to enhance the reuse of features and improve the network performance.Three DenseNet blocks are added before the residual components in the YOLO-V3 network to enhance the convolutional feature propagation.The saliency maps are obtained by multiscale superpixel segmentation, superpixel block clustering and cellular automata saliency detection.The obtained five scale saliency maps are fused using a Bayesian based fusion algorithm, and the final saliency image is generated.The infrared images composed of 4 main gesture classes are collected, each of which contains several approximated gestures in morphological terms.The training and testing datasets are generated, including original and augmented infrared images with a resolution of 640 × 480.The experimental results show that the proposed approach can enable real time human gesture detection for autonomous vehicles, with an average detection accuracy of 86.2%.
Multi-Modal Stacked Denoising Autoencoder for Handling Missing Data in Healthcare Big Data

Supply and demand increase in response to healthcare trends. Moreover, personal health records (PHRs) are being managed by individuals. Such records are collected using different avenues and vary considerably in terms of their type and scope depending on the particular circumstances. As a result, some data may be missing, which has a negative effect on the data analysis, and such data should, therefore, be replaced with appropriate values. In this study, a method for estimating missing data using a multi-modal autoencoder applied to the field of healthcare big data is proposed. The proposed method uses a stacked denoising autoencoder to estimate the missing data that occur during the data collection and processing stages. Autoencoders are neural networks that output value of x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">^</sup> similar to an input value of x. In the present study, data from the Korean National Health Nutrition Examination Survey (KNHNES), conducted by the Korea Centers for Disease Control and Prevention (KCDC), are used. As representative healthcare data from South Korea, they contain a large number of parameters identical to those used in the PHRs. Based on this, models can be generated to estimate missing data occurring in PHRs. Furthermore, PHRs involve a multi-modality that allows the data to be collected from multiple sources for a single object. Therefore, the stacked denoising autoencoder applied is configured under a multi-modal setting. Through pre-processing, a set of data without missing value in KNHNES is designed. In the data set based learning, a label is set as original data, and an autoencoder input is set as noised input that additionally has as many random zero numbers as noise factor. In this way, the autoencoder learns in the way of making the zero-based noise value similar to the original label value. When the amount of missing data in a dataset reaches approximately 25%, the accuracy of the proposed method using a multi-modal stacked denoising autoencoder is 0.9217, which is higher than that achieved by other ordinary methods. For a single-modal denoising autoencoder, the accuracy is 0.932, with a slight difference of approximately 0.01, which falls within the allowable limits in data analysis. In terms of computational performance, a single-modal autoencoder has 10,384 parameters, which is 5,594 more than those used in a multi-modal stacked autoencoder. These parameters affect the speed of the model. Both models exhibit a significant difference in the number of parameters but demonstrate a relatively small difference in accuracy, suggesting that the proposed multi-modal stacked denoising autoencoder is advantageous over a single-modal model when used on a personal device. Moreover, a multi-modal model can save additional time when processing large amounts of data in locations such as hospitals and institutions.
Multilayer optical thin film design with deep Q learning

Abstract Multilayer optical film plays a significant role in broad fields of optical application. Due to the nonlinear relationship between the dispersion characteristics of optical materials and the actual performance parameters of optical thin films, it is challenging to optimize optical thin film structure with the traditional models. In this paper, we present an implementation of Deep Q-learning, which suited for the most part for optical thin film. As a set of concrete demonstrations, we optimize solar absorber. The optimal program could optimal this solar absorber in 500 epoch (about 200 steps per-epoch) without any human intervention. Search results perform better than researchers’ manual searches.
Real-time 3D shape measurement with dual-frequency composite grating and motion-induced error reduction

Phase-shifting profilometry has been increasingly sought and applied in dynamic three-dimensional (3D) shape measurement. However, the object motion will lead to extra phase shift error and thus measurement error. In this paper, a real-time 3D shape measurement method based on dual-frequency composite phase-shifting grating and motion-induced error reduction is proposed for a complex scene containing dynamic and static objects. The proposed method detects the motion region of a complex scene through the phase relations of the dual-frequency composite grating and reduces the motion-induced error with the combination of the phase calculated by a phase-shifting algorithm and the phase extracted by Fourier fringe analysis. It can correctly reconstruct the 3D shape of a complex dynamic scene and ensure high measurement accuracy of its static object as well. With the aid of the phase-shifting image ordering approach, the dynamic 3D shape of complex scenes can be reconstructed and the motion-induced error can also be suppressed in real time. Experimental results well proved that the proposed method is effective and practical.
Deep time-delay reservoir computing: Dynamics and memory capacity

The deep time-delay reservoir computing concept utilizes unidirectionally connected systems with time-delays for supervised learning. We present how the dynamical properties of a deep Ikeda-based reservoir are related to its memory capacity (MC) and how that can be used for optimization. In particular, we analyze bifurcations of the corresponding autonomous system and compute conditional Lyapunov exponents, which measure generalized synchronization between the input and the layer dynamics. We show how the MC is related to the systems’ distance to bifurcations or magnitude of the conditional Lyapunov exponent. The interplay of different dynamical regimes leads to an adjustable distribution between the linear and nonlinear MC. Furthermore, numerical simulations show resonances between the clock cycle and delays of the layers in all degrees of MC. Contrary to MC losses in single-layer reservoirs, these resonances can boost separate degrees of MC and can be used, e.g., to design a system with maximum linear MC. Accordingly, we present two configurations that empower either high nonlinear MC or long time linear MC.
Fake Review Detection Based on Multiple Feature Fusion and Rolling Collaborative Training

Fake reviews may mislead consumers.A large number of fake reviews will even cause huge property losses and public opinion crises.Therefore, it is necessary to detect and filter fake reviews.However, most existing methods have lower accuracy in detecting fake reviews due to they just use single features and lack of labeled experimental data.To solve this problem, we propose a novelty method to detect fake reviews based on multiple feature fusion and rolling collaborative training.First, the method requires an initial index system with multiple features such as text features, sentiment features of reviews and behavior features of reviewers.Second, the method needs an initial training sample set.Thus, we designed related algorithms to extract all the features of a review.Then the classification of the review is labeled manually.Finally, the method uses the initial sample set to train 7 classifiers, and the most accurate classifier will be selected to classify new reviews.The novelty of the method lies in that the features and the classification labels of the new reviews will be added into the initial sample set as new samples.So the size of the sample set will increase automatically.The experimental results in the reviews of yelp shopping website show that the accuracy of the proposed method for detecting fake reviews is 84.45%, which is 3.5% higher than the baseline methods.And compared with the latest deep learning model, its baseline precision has increased by 5.3%.According to the Friedman test, the support vector machine (SVM) classifier and random forest (RF) classifier has been proven to be the best one by statistical means.It means our method which uses multiple features has higher accuracy than the baseline models.Meanwhile, it also resolves the problem of lacking labeled training samples in fake reviews detection.
ChartOCR: Data Extraction from Charts Images via a Deep Hybrid Framework

Chart images are commonly used for data visualization. Automatically reading the chart values is a key step for chart content understanding. Charts have a lot of variations in style (e.g. bar chart, line chart, pie chart and etc.), which makes pure rule-based data extraction methods difficult to handle. However, it is also improper to directly apply end- to-end deep learning solutions since these methods usually deal with specific types of charts. In this paper, we propose an unified method ChartOCR to extract data from various types of charts. We show that by combing deep framework and rule-based methods, we can achieve a satisfying generalization ability and obtain accurate and semantic-rich intermediate results. Our method extracts the key points that define the chart components. By adjusting the prior rules, the framework can be applied to different chart types. Experiments show that our method achieves state-of-the- art performance with fast processing speed on two public datasets. Besides, we also introduce and evaluate on a large dataset ExcelChart400K for training deep models on chart images. The code and the dataset are publicly available at https://github.com/soap117/DeepRule.
Filter Sketch for Network Pruning

We propose a novel network pruning approach by information preserving of pretrained network weights (filters). Network pruning with the information preserving is formulated as a matrix sketch problem, which is efficiently solved by the off-the-shelf frequent direction method. Our approach, referred to as FilterSketch, encodes the second-order information of pretrained weights, which enables the representation capacity of pruned networks to be recovered with a simple fine-tuning procedure. FilterSketch requires neither training from scratch nor data-driven iterative optimization, leading to a several-orders-of-magnitude reduction of time cost in the optimization of pruning. Experiments on CIFAR-10 show that FilterSketch reduces 63.3% of floating-point operations (FLOPs) and prunes 59.9% of network parameters with negligible accuracy cost for ResNet-110. On ILSVRC-2012, it reduces 45.5% of FLOPs and removes 43.0% of parameters with only 0.69% accuracy drop for ResNet-50. Our code and pruned models can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/lmbxmu/FilterSketch</uri> .
The Higher Education Sustainability before and during the COVID-19 Pandemic: A Spanish and Ecuadorian Case

Information and communication technologies (ICTs) are key to create sustainable higher education institutions (HEIs). Most researchers focused on the students’ perspective, especially during the online teaching caused by COVID-19; however, university teachers are often forgotten, having their opinion missing. This study’s objective was to determine the factors that contribute to the inclusion of ICTs. The research based on a comparative study through an online qualitative survey focused on the inclusion and use of ICTs in two HEIs and two different moments (pre- and post-lockdowns). There were differences regarding country and working experience (p &lt; 0.001), being linked to the ICTs use, evaluation of obstacles, and the role given to ICTs (p &lt; 0.05). The COVID-19 caused modifications of the teachers’ perspectives, including an improvement of the opinion of older teachers regarding the essentialness of ICTs in the teaching process (p &lt; 0.001) and worsening their perception about their ICTs skill (p &lt; 0.05). Additionally, an initial model focused only on the university teachers and their use of ICTs has been proposed. In conclusion, the less experienced university teachers used more ICTs, identified more greatly the problematic factors, and considered more important the ICTs, with the perception of all teachers modified by COVID-19.
How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches?

Unsupervised domain adaptation (UDA) aims to train a target classifier with labeled samples from the source domain and unlabeled samples from the target domain. Classical UDA learning bounds show that target risk is upper bounded by three terms: source risk, distribution discrepancy, and combined risk. Based on the assumption that the combined risk is a small fixed value, methods based on this bound train a target classifier by only minimizing estimators of the source risk and the distribution discrepancy. However, the combined risk may increase when minimizing both estimators, which makes the target risk uncontrollable. Hence the target classifier cannot achieve ideal performance if we fail to control the combined risk. To control the combined risk, the key challenge takes root in the unavailability of the labeled samples in the target domain. To address this key challenge, we propose a method named E-MixNet. E-MixNet employs enhanced mixup, a generic vicinal distribution, on the labeled source samples and pseudo-labeled target samples to calculate a proxy of the combined risk. Experiments show that the proxy can effectively curb the increase of the combined risk when minimizing the source risk and distribution discrepancy. Furthermore, we show that if the proxy of the combined risk is added into loss functions of four representative UDA methods, their performance is also improved.
TWC-Net: A SAR Ship Detection Using Two-Way Convolution and Multiscale Feature Mapping

Synthetic aperture radar (SAR) is an active earth observation system with a certain surface penetration capability and can be employed to observations all-day and all-weather. Ship detection using SAR is of great significance to maritime safety and port management. With the wide application of in-depth learning in ordinary images and good results, an increasing number of detection algorithms began entering the field of remote sensing images. SAR image has the characteristics of small targets, high noise, and sparse targets. Two-stage detection methods, such as faster regions with convolution neural network (Faster RCNN), have good results when applied to ship target detection based on the SAR graph, but their efficiency is low and their structure requires many computing resources, so they are not suitable for real-time detection. One-stage target detection methods, such as single shot multibox detector (SSD), make up for the shortage of the two-stage algorithm in speed but lack effective use of information from different layers, so it is not as good as the two-stage algorithm in small target detection. We propose the two-way convolution network (TWC-Net) based on a two-way convolution structure and use multiscale feature mapping to process SAR images. The two-way convolution module can effectively extract the feature from SAR images, and the multiscale mapping module can effectively process shallow and deep feature information. TWC-Net can avoid the loss of small target information during the feature extraction, while guaranteeing good perception of a large target by the deep feature map. We tested the performance of our proposed method using a common SAR ship dataset SSDD. The experimental results show that our proposed method has a higher recall rate and precision, and the F-Measure is 93.32%. It has smaller parameters and memory consumption than other methods and is superior to other methods.
Coverage path planning for maritime search and rescue using reinforcement learning

In maritime search and rescue (SAR), the planning of the search path will directly affect the efficiency of searching for people overboard in the search area. However, traditional SAR decision-making schemes often adopt a fixed search path planning mode, but the limits are poor flexibility, low efficiency, and insufficient intelligence. This paper plans a search path with the shortest time-consuming and priority coverage of high-probability areas, considering complete coverage of maritime SAR areas and avoiding maritime obstacles. Firstly, a maritime SAR environment model is built using marine environmental field data and electronic charts. Secondly, an autonomous coverage path planning model for maritime SAR is proposed based on reinforcement learning, in which a reward function with multiple constraints is designed to guide the navigation action of the vessel agent. In the iterative training process of the path planning model, the random action selection probability is dynamically adjusted by the nonlinear action selection policy to ensure the stable convergence of the model. Finally, the experimental verification is conducted in different small-scale maritime SAR simulation scenarios. The results indicate that the search path can cover the high-probability areas preferentially with lower repeated coverage and shorter path length compared with other path planning algorithms.