Reviewerly/exHarmony huggingface dataset:
Restore Globally, Refine Locally: A Mask-Guided Scheme to Accelerate Super-Resolution Networks

Single image super-resolution (SR) has been boosted by deep convolutional neural networks with growing model complexity and computational costs. To deploy existing SR networks onto edge devices, it is necessary to accelerate them for large image (4K) processing. The different areas in an image often require different SR intensities by networks with different complexity. Motivated by this, in this paper, we propose a Mask Guided Acceleration (MGA) scheme to reduce the computational costs of existing SR networks while maintaining their SR capability. In our MGA scheme, we first decompose a given SR network into a Base-Net and a Refine-Net. The Base-Net is to extract a coarse feature and obtain a coarse SR image. To locate the under-SR areas in the coarse SR image, we then propose a Mask Prediction (MP) module to generate an error mask from the coarse feature. According to the error mask, we select K feature patches from the coarse feature and refine them (instead of the whole feature) by Refine-Net to output the final SR image. Experiments on seven benchmarks demonstrate that our MGA scheme reduces the FLOPs of five popular SR networks by 10%–48% with comparable or even better SR performance. The code is available at https://github.com/huxiaotaostasy/MGA-scheme .
Reconstructing Medical Images Using Generative Adversarial Networks: A Study

Generative adversarial networks (GANs) have been studied and utilized as an alternative to solve medical imaging problems. Major medical imaging applications include image reconstruction, denoising, segmentation, data augmentation, anomaly detection, and synthesis using image-to-image translation (I2I) techniques. There have been many notable improvements for GANs recently, and therefore, a review of notable advances when applying GANs for medical image reconstruction has been conducted for this paper. The aim of this paper is to introduce key I2I ideas and algorithms that work for medical image reconstruction applications. This study presents a review of various GAN architectures and loss functions used for medical image reconstruction that has not been done before.
Flow Graph to Video Grounding for Weakly-Supervised Multi-step Localization

In this work, we consider the problem of weakly-supervised multi-step localization in instructional videos. An established approach to this problem is to rely on a given list of steps. However, in reality, there is often more than one way to execute a procedure successfully, by following the set of steps in slightly varying orders. Thus, for successful localization in a given video, recent works require the actual order of procedure steps in the video, to be provided by human annotators at both training and test times. Instead, here, we only rely on generic procedural text that is not tied to a specific video. We represent the various ways to complete the procedure by transforming the list of instructions into a procedure flow graph which captures the partial order of steps. Using the flow graphs reduces both training and test time annotation requirements. To this end, we introduce the new problem of flow graph to video grounding. In this setup, we seek the optimal step ordering consistent with the procedure flow graph and a given video. To solve this problem, we propose a new algorithm - Graph2Vid - that infers the actual ordering of steps in the video and simultaneously localizes them. To show the advantage of our proposed formulation, we extend the CrossTask dataset with procedure flow graph information. Our experiments show that Graph2Vid is both more efficient than the baselines and yields strong step localization results, without the need for step order annotation.
A Dual-Path Multihead Feature Enhancement Detector for Oriented Object Detection in Remote Sensing Images

Oriented object detection in remote sensing images (RSI) has received more and more attention due to its broader applicability in natural scenes relative to horizontal bounding boxes. The complex scenes and multi-scale targets in remote sensing images make it often difficult for existing studies to extract key features of the targets effectively. At the same time, due to the problem of feature inconsistency in different layers, the direct fusion of these features is likely to cause feature conflicts, resulting in degradation of detection accuracy. To solve these problems, the dual-path multi-head feature enhancement detector (DP-MHFE Det), which contains two novel architectures, is proposed in this letter. The dual-path rotation feature aggregation module (DP-RFAM) improves the feature extraction capability of the network for rotating objects through dual-path structure and deformable convolution (DCN). To use these features effectively, the multi-head multi-level feature fusion enhancement network (MMFFENet) is proposed to guide the feature layers to learn and retain the key features they need autonomously, and then enhance their features according to the characteristics of different subtasks. Experiments conducted on two remote sensing datasets, DOTA and HRSC2016, show that DP-MHFE Det is faster than almost all detection methods compared to the state-of-the-art methods while showing strong competitiveness in accuracy.
Weakly-Supervised Semantic Feature Refinement Network for MMW Concealed Object Detection

The concealed object detection in millimeter-wave human body images is a challenging task due to the noise and dim-small objects. Exploiting the spatial dependencies to mine the difference between the object and the noise is vital for the discrimination of objects. However, most approaches ignore the context around the object. In this paper, a concealed object detection framework based on structural context is proposed to suppress noise interference and refine localizable semantic features. The framework consists of two subnetworks, structural region-based multi-scale weakly supervised feature refinement and local context-based concealed object detection. The multi-scale weakly supervised feature refinement is constructed to learn position-aware semantics of objects of various sizes while suppressing background noises in structural regions. Specifically, a multi-scale pooling method is proposed to better localize objects of different sizes, and an object-activated region enhancement module is designed to strengthen object semantic representations and suppress the background interference. Moreover, an adaptive local context aggregation module is designed to integrate the local context around the bounding box in the concealed object detection, which improves the discrimination of the model for the dim-small objects. Experimental results on the AMMW and the PMMW datasets demonstrate that the proposed approach improves detection performance with lower false alarm rates.
Research on LSTM-XGBoost Integrated Model of Photovoltaic Power Forecasting System

In view of the insufficient feature extraction that affects the accuracy of photovoltaic forecasting, a photovoltaic power generation power forecasting model is presented, which integrates the Long Short-Time Memory (LSTM) algorithm and the Extreme Gradient Boosting (XGBoost) algorithm. In this paper, the advantages and disadvantages of LSTM algorithm and XGBoost algorithm are analyzed, and the advantages of the two forecasting models are integrated to obtain a more accurate forecasting model, XGBoost-LSTM; and compare the model with the popular Gated Recurrent Unit (GRU) and Deep Belief network, DBN). The experimental results show that the PV power forecasting model based on XGBoost-LSTM integration has higher forecasting accuracy, which has guiding value for photovoltaic grid-connected and off-grid.
LanePainter: Lane Marks Enhancement via Generative Adversarial Network

For safety purposes, understanding the quality of the lane marks is essential for advanced driving technologies and pavement road maintenance. In practice, the performance of existing lane detection algorithms [1]–[3] struggles with lane marks in poor conditions, especially in rural areas where the lane marks are less maintained. However, enhancing the quality of lane marks is still not thoroughly studied. In this research, we collected a new public dataset to benchmark the performance of lane detection algorithms on various lane marks conditions from different rural areas in the U.S. Also, this paper proposed LanePainter, a Generative Adversarial Network (GAN) based model, which simultaneously enhances and assesses lane marks quality in street view images. To increase the detectability of lane marks, LanePainter enhances degraded lane marks of street view images directly on image pixels without affecting the high-quality lane marks. Our experiments demonstrated a substantial increase in the performance of current lane detection algorithms on low-quality lane marks after enhancing the images using our proposed LanePainter algorithm. Also, pavement maintenance can take benefit from our classification assessment method to quickly and accurately locate the regions with low-quality lane marks.
Plain Template Insertion: Korean-Prompt-Based Engineering for Few-Shot Learners

Prompt-based learning is a method used for language models to interpret natural language by remembering the prior knowledge acquired and the training objective. Recent prompt-based few-shot learners have achieved superior performance by alleviating the catastrophic forgetting that occurs in pretrained language models. Few-shot learning contributes towards solving the data scarcity problem, an enormous challenge in AI systems and a significant consideration in natural language processing research. In spite of the significance of few-shot learning, research on Korean language-based few-shot learning is insufficient, and whether the prompt-based approach is appropriate for the Korean language has not been thoroughly verified. As a step toward realizing a Korean-prompt-based few-shot learner, we attempt to apply prompt engineering to the Korean language understanding benchmark dataset and introduce plain template insertion to overcome data scarcity in a more practical few-shot setting. The contributions of this study are as follows: (1) presumably, this is the first study to apply prompt-based few-shot learning to Korean benchmark datasets. With 32 few-shot settings, it improves performance by +14.88, +29.04, and +1.81 in the natural language inference, semantic textual similarity, and topic classification tasks. (2) We present prompt engineering, which merely inserts a plain template and increases data efficiency without training example selection, augmentation, reformulation, and retrieval. (3) Our approach is robust to the Korean prompt’s contextual information and sentence structure and is applicable to both hard- and soft-prompt.
Statistical Scrutiny of the Prediction Capability of Different Time Series Machine Learning Models in Forecasting Bitcoin Prices

Cryptocurrencies (CTC) are decentralised digital currency. In the past decade, there has been a massive increase in its usage due to the advancement made in the field of blockchain. Bitcoin (BTC) is the first decentralised CTC which garnered a lot of attention from the media as well as the public due to its ability to sustain the momentum in the market. However, investing in BTC is not the first choice of the investor due to the market’s erratic behaviour, price volatility and lack of a model that could be used to predict its price. In this direction, the present study aims in developing a time-series forecasting model that can efficiently as well as effectively predict the price of Bitcoins. For this purpose three machine learning (ML) models namely Long Short Term Memory (LSTM), Autoregressive Integrated Moving Average method (ARIMA) and Seasonal Autoregressive Integrated Moving Average method (SARIMA) models have been employed which are statistically scrutinised on the basis of the performance metrics namely Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and coefficient of determination (R2). The computed value of RMSE, MAPE and $\mathrm{R}^{2}$ for the LSTM model is 1447.648, 3.059% and 0.9702 respectively, ARIMA model is 1288.5, 3.479% and 0.9566 respectively and the SARIMA model is 1802.31, 4.665% and 0.9505 respectively.
Study on the Path and Countermeasures of Blockchain Technology to Promote High-Quality Development of Digital Economy in Guangzhou

At present, there are still some bottlenecks in the development of digital industrialization and industrial digitalization of blockchain in Guangzhou, which are mainly manifested as follows: firstly, it is not easy to land the digital business model of blockchain industry; secondly, the digital industrialization of blockchain is limited by the existing infrastructure and technology level.In order to further grasp the status of Guangzhou blockchain in the international and domestic arena, we compare and analyze the development status of the top five blockchain cities, including Guangzhou, Beijing, Shenzhen, Shanghai and Hangzhou, from four perspectives: industrial policy, development scale, talent cultivation and application scenarios; and compare and analyze the typical examples of Guangzhou in China, Hamburg in Germany, Seoul in Korea, New York in the United States, and Melbourne in Australia from three aspects: governmental decision, talent cultivation and education, and application scenarios.The comparison and analysis of typical examples are from Guangzhou, Hamburg, Seoul, New York and Melbourne, in order to obtain the experience and lessons for the future development of blockchain in Guangzhou, on this basis, the development opportunities of blockchain in Guangzhou are carefully analyzed.The following favorable conditions for the development of blockchain in Guangzhou during the 14th Five-Year Plan period are considered: firstly, the support of national strategic policy provides a broad prospect for the development of blockchain in Guangzhou; secondly, the good business environment and strong strength provide quality soil for the How to cite this paper:
Integrated Physics-Based Nowcasting Model for Precipitation Radar Image Sequence

This paper proposes an image-based prediction method that can physically predict near-future spatio-temporal image changes, i.e., nowcasting, using fluid-like image sequences from precipitation image sequences. Since motion estimation is one of the most influential factors for very short, i.e., 30 mins or 1 hr, to short term, i.e., 3 hrs or longer, mix of local and global motion estimation methods, i.e., multiple optical flow models, has been proposed to apply. A physically motivated advection equation is used to predict the next time image sequence from the past time image sequence, where previous image degradation issue of losing heavy rainfall regions has been improved by the Cubic Interpolation Profile. Navier-Stokes equation also conducts to provide a time-varying and physical future motion field. A proposed switching prediction model integrates different prediction models in order to spatio-temporally adapt different radar image property during a few hours or longer durations. Using heavy rainfall events, i.e., squall lines and typhoons, it has been proven that the proposed nowcasting method has outperformed the world best nowcasting method, i.e., Rainymotion of open source, in terms of accuracy of precipitation amount.
An efficient method for Blind Image Restoration using GAN

The purpose of this project is to convert blind (or blurred) images to well-defined images using various stages of image processing such as preprocessing, image enhancement, segmentation and image restoration. The previous method involved Convolutional Neural Network (CNN) which was unable to identify noisy data appropriately using statistical methods. The main idea behind the project is to give a correct estimation of the image from a degraded version. In the proposed system incorporating Generative Adversarial Networks (GANs) to enhance the image thus, leading to a well-defined image for further study and development. There are some instances where the blind face (or blurred images) cannot be enhanced with human help so face image restoration is proposed to enhance such images in order to get clear images.
Stereoscopic Universal Perturbations across Different Architectures and Datasets

We study the effect of adversarial perturbations of images on deep stereo matching networks for the disparity estimation task. We present a method to craft a single set of perturbations that, when added to any stereo image pair in a dataset, can fool a stereo network to significantly alter the perceived scene geometry. Our perturbation images are “universal” in that they not only corrupt estimates of the network on the dataset they are optimized for, but also generalize to different architectures trained on different datasets. We evaluate our approach on multiple benchmark datasets where our perturbations can increase the D1-error (akin to fooling rate) of state-of-the-art stereo networks from 1% to as much as 87%. We investigate the effect of perturbations on the estimated scene geometry and identify object classes that are most vulnerable. Our analysis on the activations of registered points between left and right images led us to find architectural components that can increase robustness against adversaries. By simply designing networks with such components, one can reduce the effect of adversaries by up to 60.5%, which rivals the robustness of networks finetuned with costly adversarial data augmentation. Our design principle also improves their robustness against common image corruptions by an average of 70%.
Official Document Identification and Data Extraction using Templates and OCR

Nowadays anyone possesses at least one personal document, whether it is an identity card or a driving license. In many of our daily activities, working with documents or the necessity to present them in different contexts became something normal. Given the current situation, we are all going through, having to deal with the current pandemic situation, we have adapted to some extent to new ways of communicating, working, and resolving things, in general, using Internet tools and platforms. In this context, a small part of the fight we have with the bureaucracy has been won, many of the documents that once could only be brought only in physical locations can now be scanned and sent by email or attached to a form on the website. Hoping that the pandemic has also taught us good things, we want to believe that these ways of communication will be kept in place even after this issue is over, to avoid unnecessary waste of time and congestion. The focus of this project is developing an application that can create textual information data from a simple image provided by the user that will enable the possibility of sharing digital versions of documents, making our lives so much better.
PoGaIN: Poisson-Gaussian Image Noise Modeling From Paired Samples

Image noise can often be accurately fitted to a Poisson-Gaussian distribution. However, estimating the distribution parameters from a noisy image only is a challenging task. Here, we study the case when paired noisy and noise-free samples are accessible. No method is currently available to exploit the noise-free information, which may help to achieve more accurate estimations. To fill this gap, we derive a novel, cumulant-based, approach for Poisson-Gaussian noise modeling from paired image samples. We show its improved performance over different baselines, with special emphasis on MSE, effect of outliers, image dependence, and bias. We additionally derive the log-likelihood function for further insights and discuss real-world applicability.
Explainable Deep Learning Approach for Multilabel Classification of Antimicrobial Resistance With Missing Labels

Predicting Antimicrobial Resistance (AMR) from genomic sequence data has become a significant component of overcoming the AMR challenge, especially given its potential for facilitating more rapid diagnostics and personalised antibiotic treatments. With the recent advances in sequencing technologies and computing power, deep learning models for genomic sequence data have been widely adopted to predict AMR more reliably and error-free. There are many different types of AMR; therefore, any practical AMR prediction system must be able to identify multiple AMRs present in a genomic sequence. Unfortunately, most genomic sequence datasets do not have all the labels marked, thereby making a deep learning modelling approach challenging owing to its reliance on labels for reliability and accuracy. This paper addresses this issue by presenting an effective deep learning solution, Mask-Loss 1D convolution neural network (ML-ConvNet), for AMR prediction on datasets with many missing labels. The core component of ML- ConvNet utilises a masked loss function that overcomes the effect of missing labels in predicting AMR. The proposed ML-ConvNet is demonstrated to outperform state-of-the-art methods in the literature by 10.5%, according to the F1 score. The proposed model’s performance is evaluated using different degrees of the missing label and is found to outperform the conventional approach by 76% in the F1 score when 86.68% of labels are missing. Furthermore, the ML-ConvNet was established with an explainable artificial intelligence (XAI) pipeline, thereby making it ideally suited for hospital and healthcare settings, where model interpretability is an essential requirement.
String Attractors and Infinite Words

The notion of string attractor has been introduced by Kempa and Prezza (STOC 2018) in the context of Data Compression and it represents a set of positions of a finite word in which all of its factors can be “attracted”. The smallest size $$\gamma ^*$$ of a string attractor for a finite word is a lower bound for several repetitiveness measures associated with the most common compression schemes, including BWT-based and LZ-based compressors. The combinatorial properties of the measure $$\gamma ^*$$ have been studied in [Mantaci et al., TCS 2021]. Very recently, a complexity measure, called string attractor profile function, has been introduced for infinite words, by evaluating $$\gamma ^*$$ on each prefix. Such a measure has been studied for automatic sequences and linearly recurrent infinite words in [Schaeffer and Shallit, arXiv 2021]. In this paper, we study the relationship between such a complexity measure and other well-known combinatorial notions related to repetitiveness in the context of infinite words, such as the factor complexity and the recurrence. Furthermore, we introduce new string attractor-based complexity measures, in which the structure and the distribution of positions in a string attractor of the prefixes of infinite words are considered. We show that such measures provide a finer classification of some infinite families of words.
Quadruplet Convolution Neural Network With C3 Loss (C3-QCNN) for Signal Classification

In this letter, a new Quadruplet Convolution Neural Network (QCNN) is proposed to cooperatively and contrastively learn deep features of raw radio signals for accurate Automatic Modulation Classification (AMC), when very few labeled samples are available. Four convolutional channels with shared weights are constructed to deal with the In-phase (I) path and Quadrature (Q) path of signals. Moreover, a new C3-loss function that contains the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cooperative</i> loss, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">contrastive</i> loss and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">classification</i> loss, is designed for QCNN to extract discriminative and concise features of signals. Some experiments are taken on the public dataset generated with GNU Radio, which has eleven types of modulation signals with different signal-to-noise ratios. The experimental results show that C3-QCNN achieves high classification accuracy and outperform its counterparts.