Reviewerly/exHarmony huggingface dataset:
Graph Convolutional Neural Networks for Learning Attribute Representations for Word Spotting

Graphs are an intuitive and natural way of representing handwriting. Due to their high representational power, they have shown high performances in different learning-free document analysis tasks. While machine learning is rather unexplored for graph representations, geometric deep learning offers a novel framework that allows for convolutional neural networks similar to the image domain. In this work, we show that the concept of attribute prediction can be adapted to the graph domain. We propose a graph neural network to map handwritten word graphs to a symbolic attribute space. This mapping allows to perform query-by-example word spotting as it was also tackled by other learning-free approaches in the graph domain. Furthermore, our model is capable of query-by-string, which is out of scope for other graph-based methods in the literature. We investigate two variants of graph convolutional layers and show that learning improves performances considerably on two popular graph-based word spotting benchmarks.
The document as a cognitive object

The problem of scholarly knowledge strata is one of the key subjects in modern philosophy of science and epistemology (theory of knowledge). The threelevel model is the most popular in the document sphere as a sociocultural domain; it comprises empirical, theoretical and philosophical scientific knowledge. The author describes collision and contradictions between theoretical documentology and the empirical disciplines of document studies and book studies. He appraises achievements of empirical knowledge in the documentosphere and compares theoretical and methodological ways of three types: informational, conventional and medialogical ones. The author is skeptical about the following statement: the concept of document is relative, conventional and conditional in its essence. The author examines “The Strategy for Librarianship in the Russian Federation for the Period up to the Year 2030”. He insists that the Strategy has to be supported by the scientific strategy aimed to promote each level of empirical, theoretical and philosophical knowledge. He is also concerned about lacking philosophical level in document studies. The wisdom of philosophy lies in its comprising both rational and irrational sides of objects being studied. With document rational and irrational attributes in view, he offers the philosophical definition of document: document is the statement of humanness within social environment and historical time. This definition comprises anthropological and national variations of homo sapiens and philosophical concept of humanness as the unity of the opposite universal principles – the material and ideal.
An Exploratory Analysis of the Perception of the Utility of Proven Practices of the Software Basic Profile of ISO/IEC 29110 by a Set of VSEs in Mexico

Around the world, Very Small Entities (VSEs) are very important to the economy because they have an important place contributing to the growing demand for software. This fact allows them to achieve a steady growth in the software market as well as a great responsibility regarding the production of quality software products and services. However, implementing a software process improvement culture in VSEs is not an easy task, and represented a real challenge to software engineering researchers. In this context, the WG24 of ISO/IEC has provided the ISO/IEC 29110 series of standards that are targeted to help VSEs to improve their software development process, their quality and their productivity. But how are the proven practices provided by the ISO/IEC 29110 perceived by VSEs? This paper presents an exploratory analysis of the perception of the practices by a sample of 12 VSEs of Mexico. This research is relevant, because most VSEs do not implement a practice if they do not perceive it as useful. The results of our research highlight the practices that are perceived as either low or high-value.
A New Hybrid Method for Caption and Scene Text Classification in Action Video Images

Achieving a better recognition rate for text in action video images is challenging due to multiple types of text with unpredictable actions in the background. In this paper, we propose a new method for the classification of caption (which is edited text) and scene text (text that is a part of the video) in video images. This work considers five action classes, namely, Yoga, Concert, Teleshopping, Craft, and Recipes, where it is expected that both types of text play a vital role in understanding the video content. The proposed method introduces a new fusion criterion based on Discrete Cosine Transform (DCT) and Fourier coefficients to obtain the reconstructed images for caption and scene text. The fusion criterion involves computing the variances for coefficients of corresponding pixels of DCT and Fourier images, and the same variances are considered as the respective weights. This step results in Reconstructed image-1. Inspired by the special property of Chebyshev-Harmonic-Fourier-Moments (CHFM) that has the ability to reconstruct a redundancy-free image, we explore CHFM for obtaining the Reconstructed image-2. The reconstructed images along with the input image are passed to a Deep Convolutional Neural Network (DCNN) for classification of caption/scene text. Experimental results on five action classes and a comparative study with the existing methods demonstrate that the proposed method is effective. In addition, the recognition results of the before and after the classification obtained from different methods show that the recognition performance improves significantly after classification, compared to before classification.
Interpretation of Learning-Based Automatic Source Code Vulnerability Detection Model Using LIME

The existing advanced automatic vulnerability detection methods based on source code are mainly learning-based, such as machine learning and deep learning. These models can capture the vulnerability pattern through learning, which is more automatic and intelligent. However, the outputs of many learning-based vulnerability detection models are unexplainable, even though they usually show high accuracy. It’s meaningful to verify the credibility of the models so that we can better understand and use them in practice. To alleviate the above issue, we use an interpretation method called LIME to explain the learning-based automatic vulnerability detection model. For one thing, the preprocessing methods are all interpretable, including symbolization and vector representation, where the Bag of words model is chosen for source code vector representation. For another, the vulnerability detection models we select are based on Logistic Regression and Bi-LSTM. The former is interpretable, which is used to verify the effectiveness of LIME in the field of source code vulnerability detection. The latter is unexplained that is interpreted by LIME to its credibility on source code vulnerability detection. The experimental results show that LIME can effectively explain the learning-based automatic vulnerability detection model. Moreover, we find that under the condition of local interpretation, the predictions of the model based on Bi-LSTM are credible.
An Improved Method of CRIMINISI Algorithm

Nowadays, the research on image inpainting technology is a research topic of great significance in computer vision and graphics. As for the missing part of the image, humans can only analyze the remaining areas based on their own visual senses and experience, and then fill in the remaining missing areas because they do not know the original appearance of the image. In general, image restoration is a technology based on human vision and experience. In 2003, Criminisi et al. proposed a patching algorithm based on sample blocks. The algorithm gives priority to pixels based on the confidence level and adopts the best matching module method. This article provides a method to improve its confidence, changing its factor multiplication to linear addition and adding an additional judgment factor, which improves the effect of the inpainting.
Digital Transformation and the Evolution of the Platform Economy

This chapter provides an overview of the evolution of the platform economy through the lens of digital transformation and transit from Industry 3.0 (I3.0) to Industry 4.0 (I4.0). The platform economy belongs to both I3.0 and I4.0 and goes through two cycles of digital transformation within them. In I3.0, the starting point of the platform economy is the digitization of social and commercial interactions over user-generated content. The resulting issues of trust and regulation of user interactions find solutions in new business models based on online reputation systems and algorithmic regulation. The specificity of I4.0 is the tendency to platform products, homes, factories, and cities through broad digitization of interactions between humans and things, and things and things. For the platform economy, the new cycle of digital transformation in the context of I4.0 means creating business models based on the ultimate customization of both the production and consumption of product-as-platforms and the rental of digital product models.
Cross-language article linking with deep neural network based paragraph encoding

Cross-language article linking (CLAL), the task of generating links between articles in different languages from different encyclopedias, is critical for facilitating sharing among online knowledge bases. Some previous CLAL research has been done on creating links among Wikipedia wikis, but much of this work depends heavily on simple language patterns and encyclopedia format or metadata. In this paper, we propose a new CLAL method based on deep learning paragraph embeddings to link English Wikipedia articles with articles in Baidu Baike, the most popular online encyclopedia in mainland China. To measure article similarity for link prediction, we employ several neural networks with attention mechanisms, such as CNN and LSTM, to train paragraph encoders that create vector representations of the articles' semantics based only on article text, rather than link structure, as input data. Using our "Deep CLAL" method, we compile a data set consisting of Baidu Baike entries and corresponding English Wikipedia entries. Our approach does not rely on linguistic or structural features and can be easily applied to other language pairs by using pre-trained word embeddings, regardless of whether the two languages are on the same encyclopedia platform.
Feature Maps Training for Substation Defect Detection

To ensure the safe and reliable operation of substation, automatic detection of substation defects is the key of smart grid. Lightweight model is used in substation defect detection because of its small amount of parameters and calculation. However, the traditional lightweight model has the problem of low accuracy, which often leads to low recognition success rate. In this paper, a method of feature maps training is proposed. Because the feature map of complex network provides high-level knowledge, this training method takes the feature map of complex network as another training target. The actual defect image is taken as the experimental data set. The experimental results show that the accuracy of the network based on the feature maps training is higher than that of the traditional lightweight network, and it can effectively identify the substation defects.
AstronomicAL: an interactive dashboard for visualisation, integration and classification of data with Active Learning

AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the field's immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless
BCAC: Batch Classifier based on Agglomerative Clustering for traffic classification in a backbone network

Backbone network is the core part of the Internet. Due to the high transmission speed of traffic in the backbone network, Quality of Service (QoS) monitoring of services in the backbone network becomes a highly important and challenging issue. Traffic classification is the basis of QoS monitoring. The existing traffic classification is based on full traffic, which is impractical in high-speed backbone network traffic. This paper presents a method to classify the sampled traffic and gives an example of its application in QoS monitoring. Specifically, we design the Multiple Counter Sketch (MC Sketch) to quickly extract features from the sampled data stream in a backbone, propose the Batch Classifier based on Agglomerative Clustering (BCAC) for unsupervised clustering of traffic, and combine with the supervised machine learning method to train the labeled data in the clustering results to get the classification model. The experimental results of sampled traffic collected on a 10Gbps link show that even when the sampling ratio is 1:1024, the accuracy of our classification model reaches 96.3%. When different block sizes are set, the average clustering time of BCAC is only about one-third of the traditional agglomerative classifier. Moreover, we give an example of applying our traffic classification method to monitor the QoS, and the results show that our method can efficiently and accurately monitor the QoS dynamics of backbone network traffic.
Improved Partitioning Graph Embedding Framework for Small Cluster

Graph embedding is a crucial method to produce node features that can be used for various machine learning tasks. Because of the large number of embedded parameters in large graphs, a single machine cannot load the entire graph into GPUs at once, so a partitioning strategy is required. However, there are some problems with partitioning strategies. Firstly, partitioning introduces data I/O and processing overhead, which increases training time, especially on the cluster with a small number of sites. Secondly, partitioning can affect the performance of the model. For multi-relation graphs, this effect is often negative. To address these problems, we propose the training pipeline and random partitions recombination methods. The training pipeline can reduce the time overhead by masking data loading time to GPU computation, and partitions recombination can effectively improve multi-relation model performance. We conducted experiments on multi-relation graphs and social networks, and the results show that both methods are effective.
Prediction of distributed optical fiber monitoring data based on GRU-BP

In order to solve the problem that it is difficult to monitor the frequency shift value of the undistributed optical fiber monitoring points in the coal mining distributed optical fiber monitoring, the paper is based on the physical simulation experiment of the distributed optical fiber two-dimensional simulation mining overburden deformation, using the excavation distance and optical fiber monitoring Point height, elastic modulus, tensile strength, compressive strength and other experimental data construct the characteristic attributes of model samples, and propose a distributed optical fiber monitoring frequency shift value prediction algorithm based on GRU-BP. This method is used to predict the frequency shift of unknown monitoring points. It has high accuracy in terms of value, which provides an idea for predicting the frequency shift value at the monitoring point of the unburied fiber. At the same time, the traditional neural network model is established on the premise that the training sample and the test sample are unchanged, and the prediction results of the GRU-BP combined model are compared and analyzed. The experimental results show that: using the GRU-BP algorithm, the prediction effects of R2, MAE, and RMSE can reach 0.91, 0.45, and 0.73, respectively, and their effects are better than traditional neural network models.
Temporal Networks Based Industry Identification for Bitcoin Users

With the development of Bitcoin, many thriving activities have developed into stable industries, such as Miner. Identifying and analyzing the transaction behaviors of users within these industries helps to understand the Bitcoin ecosystem from a macro perspective. Currently, industry identification mainly faces two issues. First, the anonymity of Bitcoin makes it difficult to identify the industry identifiers of users who participate in activities through different addresses. Second, since users usually engage in multiple industries at different periods, both the identification of their dynamically changing industry identifiers and the detection of their mostly engaged industry are challenging research tasks.In this paper, we propose an industry identification approach for Bitcoin users. First, we develop a fine-grained address clustering method to mine the relationship between addresses and their owners. Compared with existing methods, this method improves 0.18 in accuracy and 0.60 in recall. Based on temporal networks, we then train a multi-label classification model to identify the dynamically changing industry identifiers of users with an average accuracy of 0.92. With respect to multi-industry users, we further propose a major industry identifier detection method to identify the industry where users are mostly engaged. Applying this approach, we reproduce the major activity trajectories of users across the industries, which provides us with an opportunity to analyze the transaction behaviors of users within the industries.
HRRegionNet: Chinese Character Segmentation in Historical Documents with Regional Awareness

Human beings, as the only species capable of developing high levels of civilization, the transmission of knowledge from historical documents plays an indispensable role in this process. The amount of historical documents accumulated in the last centuries is not to be belittled, and the knowledge they contain is not to be underestimated. However, these historical documents are also facing difficulties in preservation due to various factors. The digitization process was mostly performed manually in the past, but the costs made the process very slow and challenging, so how to automate the digitization process has been the focus of much research previously. The digitization of Chinese historical documents can divide into two main stages: Chinese character segmentation and Chinese character recognition. This study will only focus on Chinese character segmentation in historical documents because only accurate segmentation results can achieve high accuracy in Chinese character recognition. In this research, we further improve the model based on our previously proposed Chinese character detection model, HRCenterNet, by adding a transposed convolution module to restore the output to a higher resolution and use multi-resolution aggregation combine features in different resolutions. In addition, we also propose a new objective function such that the model can more comprehensively consider the features needed to segment Chinese characters during the learning process. In the MTHv2 dataset, our model achieves an IoU score of 0.862 and reaches state-of-the-art. Our source code is available on https://github.com/Tverous/HRRegionNet.
Accelerated Zeroth-Order Algorithm for Stochastic Distributed Non-Convex Optimization

This paper investigates how to accelerate the convergence of distributed optimization algorithms on non-convex problems with only zeroth-order information available. We propose a zeroth-order (ZO) distributed primal-dual stochastic coordinate algorithm equipped with "powerball" method to accelerate the convergence. We establish the convergence result of the considered algorithm for general non-convex cost functions. We verify the proposed algorithm through the benchmark example of generating adversarial examples from black-box DNNs in order to compare with the existing state-of-the-art centralized and distributed ZO algorithms. The numerical results demonstrate a faster convergence rate for the proposed algorithm and match the theoretical analysis.
A Hybrid Fusion-Based Algorithm for Underwater Image Enhancement Using Fog Aware Density Evaluator and Mean Saturation

Underwater images are degraded mainly due to scattering and absorption effects but are key in oceanographic studies and research. Therefore, we need to develop methods that generate visually pleasing images and retain the original information. In this paper, we propose a method that chooses between Multiscale Fusion, Edge Preserving Decomposition-Based Haze Removal Algorithm or a combination of both. The algorithm that is to be used in an image is based on mean saturation value and fog density using Fog Aware Density Evaluator (FADE). The resulting image retains the natural color distribution, is dehazed and enhanced. The proposed algorithm doesn’t require prior hardware usage or prerequisite knowledge of the underwater environment. The proposed algorithm performs considerably well when compared to previous approaches against various image quality metrics such as UIQM, PCQI, PIQE, BRISQUE and Average Gradient.
Self-Imitation Learning for Robot Tasks with Sparse and Delayed Rewards

The application of reinforcement learning (RL) in robotic control is still limited in the environments with sparse and delayed rewards. In this paper, we propose a practical self-imitation learning method named Self-Imitation Learning with Constant Reward (SILCR). Instead of requiring hand-defined immediate rewards from environments, our method assigns the immediate rewards at each timestep with constant values according to their final episodic rewards. In this way, even if the dense rewards from environments are unavailable, every action taken by the agents would be guided properly. We demonstrate the effectiveness of our method in some challenging continuous robotics control tasks in MuJoCo simulation and the results show that our method significantly outperforms the alternative methods in tasks with sparse and delayed rewards. Even compared with alternatives with dense rewards available, our method achieves competitive performance. The ablation experiments also show the stability and reproducibility of our method.