Reviewerly/exHarmony huggingface dataset:
Research on Improving OCR Recognition Based on Bending Correction

With the emergence of smart phones, the traditional document image capture method has gradually evolved into the handheld camera shooting. After the shooting, a series of problems such as perspective distortion, uneven illumination and lens distortion etc. have brought many technical challenges to the recognition of document image in OCR. On page curl result in the text of the distortion, this paper proposes a proposed a document image to distort algorithm based on continuous skeleton image, first collected text image filtering, and morphological processing, it is concluded that the text of the minimum circumscribed rectangle to calculate the slope of the text, then further morphological operation to calculate the text used the baseline, combining three times bezier curve correction, complete text line finally uses tesseract's own library to the text of the corrected image recognition, get the data information of the text. Through experiments, the modified algorithm has a better correction effect on character distortion, and it takes time to correct a single frame image, which can improve the recognition rate of OCR and meet the needs of daily life.
Sensor-Based Human Activity Recognition: A Comparative Study of Machine Learning Techniques

Human activity recognition (HAR) is a wide field of study which identifies a person's specific movement or behavior based on sensor data. Recognition of human behavior is the origin of many technologies, such as those concerned with personal biometric signatures, sports training, digital computing, security, health and fitness tracking, ambient-assisted living and management. Studying recognition of human activity shows that researchers are mostly interested in human everyday activities. HAR models input is the reading of the raw sensor data, and output is the prediction of the movement activities of the user. The HAR framework is becoming an evolving discipline in intelligent computing applications in the field of pervasive computing. In our study, we applied several machine learning algorithm along with some preprocessing techniques to identify which algorithm performs better in dataset acquired from the WISDM laboratory, which is available in public domain. The experiment shows that the highest accuracy is achieved in phone accelerometer data using Principal Component Analysis (PCA) with Random Forest (RF) than any other algorithm and preprocessing techniques in terms of human activity recognition. This experiment will help perform more work on the basis of implementing classification and preprocessing techniques to identify human activities.
Diagnosis of Hodgkin's disease by identifying Reed-Sternberg cell nuclei in histopathological images of lymph nodes stained with Hematoxylin and Eosin

Hodgkin's disease is a cancer of the lymphatic system, which is part of the immune system. For an accurate diagnosis, a pathologist examines a slide of a sample of lymph node tissue stained with hematoxylin and eosin to find a tumoral cell called Reed-Sternberg cell. The diagnosis is subjective and prone to inter/intra-observer variations. Furthermore, it is a time-consuming task. Therefore, there is a necessity to provide an automatic system for better diagnosis and detection. In this paper, a method for identifying Reed-Sternberg cell nuclei in histopathological images of lymph nodes stained with (H&E) is presented. In the preprocessing stage, noise and annoying structures are removed. Then, we identify RS cell nuclei using three different segmentation algorithms based on morphological, color, and textural features. Using the Chan-Vese Active Contour model, we find the exact boundary of the RS cell nuclei in the histopathological image and distinguish them from other objects in the image with high accuracy. The proposed scheme is tested on an actual dataset containing 98 Reed-Sternberg cell images. The experiments' results show a high correlation between the results of the proposed algorithm and the ground-truth described by the pathologists. Moreover, a comparative study with other cell nuclei segmentation methods on histopathological images demonstrates the proposed method's efficiency. It gives the highest average accuracy rate (93.80 %) compared to recent approaches.
Research on Predictive Maintenance Technology of Stepping Motor Based on Load Value Analysis

Stepper motor control system includes driver, motor, rotating mechanism and mechanical load, which requires high precision control. Predictive maintenance of stepper motor control system is conducive to improving the stability of the system. In this paper, predictive maintenance analysis is carried out for the common problems of mechanical load increase and shaft misalignment. The predictive maintenance platform of stepper motor is established. Through predictive maintenance means, the running status of stepping motor is monitored, and the load value signal and position residual signal parameters of stepping motor are collected and analyzed to realize real-time perception of health status and trend prediction, and timely judge the problem of shaft misalignment and load increase of stepping motor, so as to formulate reasonable predictive maintenance The strategy can improve the stability and reliability of the whole system.
Oracle Bone Inscriptions Detection in Rubbings Based on Deep Learning

The rubbings of oracle bone inscriptions are an important carrier of oracle bone inscriptions, which are often used by ancient Chinese language expert. Oracle bone inscriptions detection in rubbings is a basic step for studying oracle bone inscriptions by using computer technology. Because of the remote times, some of the oracle bones have been worn seriously or broken, the content in the rubbings may be interfered by noise. How to effectively detect the oracle bone inscriptions in rubbings is the focus of oracle bone inscriptions research based on rubbings. In this paper we applied YOLOv4 model to the oracle bone inscriptions detection. The experimental results show that the YOLOv4 model can be effectively used to detect the characters in rubbings, but there are still some deficiencies in the ability to distinguish noise.
Robust Multiple Kernel Subspace Clustering Based on Low Rank Consensus Kernel Learning

Subspace clustering method based on kernel learning shows superior performance when dealing with nonlinear high dimensional data. However, the clustering performance of existing single kernel subspace clustering methods largely depends on the selected kernel function, and the low-rank structure of the data in the feature space after kernel mapping is not considered. In addition, the learned affinity matrix cannot maintain the block diagonal property, which may reduce the clustering performance. In this paper, we propose a robust multiple kernel subspace clustering based on low rank consensus kernel learning (MKLRSC) method for data clustering. Our model has three innovations: (1) The introduction of correntropy in the multiple kernel weighting strategy helps to learn the optimal consensus kernel. (2) In order to maintain the low-rank structure of input data, we impose a nuclear norm constraint on the optimal consensus kernel matrix. (3) Considering the block diagonal property of the affinity matrix, MKLRSC applies block diagonal constraint to the coefficient matrix. Compared with several state-of-the-art multiple kernel subspace clustering methods, experiments on three datasets confirm that MKLRSC achieves more competitive clustering results.
Stream-Based Active Learning with Multiple Kernels

Online multiple kernel learning (OMKL) has provided an attractive performance in nonlinear function learning tasks. Leveraging a random feature (RF) approximation, the major drawback of OMKL, known as the curse of dimensionality, has been recently alleviated. These advantages enable RF-based OMKL to be considered in practice. In this paper we introduce a new research problem, named stream-based active multiple kernel learning (AMKL), where a learner is allowed to label some selected data from an oracle according to a selection criterion. This is necessary in many real-world applications since acquiring a true label is costly or time-consuming. We theoretically prove that the proposed AMKL achieves an optimal sublinear regret O(√T) as in OMKL with little labeled data, implying that the proposed selection criterion indeed avoids unnecessary labelrequests.
A Multi-patch Deep Learning System for Text-Independent Writer Identification

Offline handwriting identification is widely used in many fields of modern society, such as judicial authentication, identity verification, ancient manuscripts, etc. Compared with traditional methods, methods based on deep learning have been proven to extract more distinctive handwriting features from large amounts of data and show better performance. For deep learning methods, data preprocessing and global feature coding determine its performance. To solve this problem, this paper proposes an offline writer identification method, which combines multi-patch data preprocessing and transfer learning. First, multi-patching and data enhancement techniques are used to process handwritten images. Subsequently, the pretrained residual network of the image dataset is used for local feature extraction, and finally, the mean feature method is used for global feature encoding. The proposed system is tested on ICDAR2013 and CVL standard datasets. Experimental results show that this novel method has a relatively stable and good recognition effect
NF-VGA: Incorporating Normalizing Flows into Graph Variational Autoencoder for Embedding Attribute Networks

Network embedding (NE), aiming to embed a network into a low dimensional latent representation while preserving the inherent structural properties of the network, has attracted considerable attention recently. Variational Autoencoder (VAE) has been widely studied for NE. Existing VAE based methods let the network follow a unimodal distribution, that is, they typically use some fixed distribution as the prior, e.g. Gaussian distribution. However, in reality networks often contain many complicated structural properties [5], [6] (such as the first/second order proximity, the motif or community structures, power-law, etc). The latent representation from unimodal and fixed distribution is not capable of describing such multi-modal characteristic of networks. To address this issue, we develop a new VAE method for NE, named Normalizing Flow Variational Graph Autoencoder (NF-VGA). We design a prior-generative module based on normalizing flows to generate flexible, multi-modal distribution as the prior of the latent representation. To make the generated prior better describe the coupling relationship between nodes, we further utilize network local structures to guide the prior generation. Extensive experiments on some real-world networks show a superior performance of the new approach over some state-of-the-art methods on some popular network embedding tasks.
Mappings on abstract cellular complex and their applications in image analysis

The notion of Abstract Cellular Complex, which was initiated by Kovalevsky, is a consistent topology for representing images through lower-dimensional cells in the combinatorial grid. This paper introduces the concept of subspace topology in Abstract Cellular Complex and also characterizes it using the lower-dimensional cells. Further, the paper initiates the modified Chain Code algorithm and implements it in the various images and the results thus obtained are compared with the other existing methods.
Motion Guided Feature-Augmented Network for Action Recognition

Motion information is a crucial factor to identify human action recognition in videos. Existing state-of-the-art methods use traditional optical flow features representing the short-term motion information as the supplementary to appearance features, achieving performance improvements. However, conventional optical flow needs high computation cost and can't be optimized in an end-to-end fashion. To reduce the computation complexity and make the computation fully-differential, we utilize a representation flow layer to mimic the process of iterative flow optimization. Besides, to attach high importance to the most critical elements, motion information is used to locate motion salient objects. Toward this end, we propose a motion-augmented module to adaptively aggregate multiple optical flow features, deriving motion saliency maps that can alleviate the unreliable motion problem. Then, these motion saliency maps attend appearance features, yielding motion-augmented features. Further, joining the motion-augmented module and flow layer, we propose a Motion Guided Feature-Augmented Network to learn motion-enhanced features. Experiments on two challenging action recognition benchmarks verify that the motion-augmented module can locate salient objects that are relevant to actions, and the proposed network can achieve the competitive performance with state-of-the-art methods.
Development of Adaptive Fuzzy-Neuro Generalized Learning-Vector Quantization Using PI Membership Function (AFNGLVQ-PI)

In a real-world environment, there are several difficult obstacles to overcome in classification. Those obstacles are data overlapping and skewness of data distribution. Overlapping data occur when many data from different classes overlap with each other; this condition often occurs when there are many classes in a data set. On other hand, skewness of data distribution occurs when the data distribution is not a Gaussian (normal) distribution. To overcome these two problems, a new method called Adaptive Fuzzy-Neuro Generalized Learning Vector Quantization using PI membership function (AFNGLVQ-PI) is proposed in this study. AFNGLVQ-PI is derived from Fuzzy-Neuro Generalized Learning Vector Quantization using the PI membership function (FNGLVQ-PI). In FNGLVQ-PI, the updated values for minimum and maximum variables in the fuzzy membership function are set based on the mean of the updated values. Whereas, in the newly proposed AFNGLVQ-PI, updated values for minimum, maximum, and mean variables are derived based on the differential equations to approximate the data distribution better. In this study, the newly proposed AFNGLVQ-PI algorithm was tested and verified on twelve different data sets. Two of the data sets are synthetic data sets where we could compare the performance of the data sets in different overlapping conditions and levels of skewness. The rest of the data sets were chosen and used as a benchmark to compare the performance of the proposed algorithm. In the experiment, AFNGLVQ-PI took first place in 18 out of 29 experiments. Furthermore, AFNGLVQ-PI also achieved positive improvements for all data sets used in the experiments, which could not be achieved by the Learning Vector Quantization (LVQ), Generalized Learning Vector Quantization (GLVQ), and other commonly used algorithms, such as SVM, kNN, and MLP.
Counting rectangular objects on conveyors using machine vision

Counting the number of objects that are transported on a conveyor belt is frequently encountered in production facilities, airports or post offices. Although most of these tasks may usually be solved by using common photoelectric or inductive sensors, there are cases when objects have to be counted using more complex sensing systems based on machine vision. In this paper, an image-processing algorithm for segmenting, detecting and counting rectangular objects which are being transported on a conveyor belt is presented. The method is specifically designed to detect rectangular objects that can be partly occluded. The application is implemented using OpenCV/C++ library. Two different test scenarios are analyzed in the paper. Experimental results suggest that the proposed method has promising accuracy and it is applicable in real-world applications.
Integration of Agile Methods in Automotive Software Development Processes

Agile practices are widely used approaches in software development due to its flexible, timely delivery, implementation affluence, and iterative nature. Considering wide range of benefits, automotive industry is also moving from traditional approaches towards agile practices for the development of automotive software's. The key challenges in automotive software are safety, complexity, user involvement, flexibility and innovation; which are difficult to handle by implementing traditional approaches. In order to overcome these challenges, it is essential to adopt such methods which should be flexible, fast with change management and user participation and can accomplish the automotive requirements and standards. Therefore, to accelerate the development process in automotive software there is a need to integrate agile development processes. This paper gives insights to the implementation of agile methods and to understand the effect of combined usage of agile processes and V model in the development of automotive software. This paper reveals that from both research and industrial perspective, it is still not clear how to combine two approaches i.e. agile methods and V model. The findings of this paper show that combined use of agile processes and V model in the development of automotive software is promising and there is a need to develop a significant approach that combines agile methods and V model for automotive software development.
Petri Net Modeling and Analysis of the Drug Traceability System Based on Blockchain

In recent years, blockchain technology has been rapidly developed and widely applied, among which traceability is one of the most important application areas. In this paper, research is carried out around drug traceability (drug traceability refers to achieving the traceability of data from drugs' production to sales). Moreover, a drug traceability model based on blockchain technology is designed and validated. Specifically, the drug traceability system is designed at first, and then the Petri net model of drug traceability system is modeled. Finally, the incidence matrix and PIPE tool are applied to verify the system.
How does grazing incidence ultrasonic microscopy work? A study based on grain-scale numerical simulations

Grazing incidence ultrasonic microscopy (GIUM) is an experimental method for visualising the microstructures of polycrystals with local preferential orientations. It has previously been demonstrated on an austenitic stainless steel weld, exposing grains much smaller than the propagating wavelength, but the physical mechanism of the method has only been proposed as a hypothesis. In this paper, we use grain-scale finite element simulations based on the EBSD measurements to verify the principles behind GIUM images further and to assess how deep does the method penetrate the component under examination. The simulations indicate that while lateral contraction of grains contains microstructure signatures, the free surface effect is the crucial factor contributing to the generation of the images. Further, we show that only features up to the depth in the order of the average grain size in that direction can be visualised.
Detecting for Bronze Inscriptions

The bronze inscriptions from archaeology have important cultural and academic value. The first task of studying bronze inscriptions is marking. The work of marking bronze inscriptions with blurred and messy fonts can only be completed by senior experts, which requires significant time and energy. In this paper, an end-to-end object detector of bronze inscriptions based on deep learning is proposed, which can detect the locations of bronze inscriptions automatically and completely. We use Darknet-53 as the feature extractor. The experimental results show that the proposed model achieves mean average precision (mAP) of 65.0%, which greatly shortens the time for archaeologists to mark data manually.
A Comparative Study on Sentiment Analysis Approaches and Methods

With the rapid development of Internet and its usages, Sentiment analysis, the subsidiary of Natural Language processing has also grown. Through, it the implicit emotion in the text can be powerfully mined and this information can be used by organization or enterprises to take further decisions and the unpredictable growth of data indubitably brings more opportunities and challenges to sentiment analysis. Binary Classification Problem, Data sparsely problem, polarity shift, accuracy related issues are primary problems. Multiples algorithms which are based on many approaches used for sentiment analysis, still to get the sentiment features from the particular content of text is still difficult one. In this paper provides a deep imminent of different sentiment methodologies and approaches in a comprehensive way and provide a insight of future challenges in sentiment analysis.
OpinionScraper: A News Comments Extraction Tool for Opinion Mining

Information websites, known as On-line Press, contain a tremendous amount of data which is potentially promising. All this information is available in real-time. Due to the velocity in the information broadcasting and the volume of available data, the traditional data extraction methods appear unsuitable to extract the good information on the right pages. It is in this context that we propose a new data extraction. OpinionScraper is a tool for collecting, merging and categorizing journalistic data in order to store it in json format. This tool allows information to be extracted from Web pages in an optimal way. It also represents this information according to the model defined for opinion mining. The interest of implementing a scraping tool is to build an easily exploitable database from journalistic comments in order to respond to the algorithmic complexity of opinion mining. Our tool succeeds in efficiently extracting comments from 132 websites and classify them in the Json informat database.
Computational Acquisition of Meteorological Data for Applications in Electric Power Systems

Humans heavily rely on mother nature for hospitable living conditions, plentiful harvests, and energy generation, yet have no control. The best humans can do is plan and predict. Climatological statistics and forecasts provided by public weather services serve as traditional methods for obtaining meteorological information. However, through Numerical Weather Prediction models, one can simulate climate fluctuations with high spatial resolution over long periods. Uses for Numerical Weather Prediction models include analyzing the energy flux of smart homes, smart grid technology, impact on power transmission infrastructure, and energy production through wind and photovoltaic farms. The efficiency of these technologies is dependant on the surrounding weather phenomena. The optimization of these systems to the environment upon which they exist can both reduce wasted resources and the economic impact on consumers and organizations. This paper outlines the methods used for the acquisition of weather data through computer simulations at a spatial resolution of 1.2 km in 15-minute intervals with an accuracy of 2%.
Deep Learning Based Attack on Phase-Truncated Optical Encoding

We apply the learning based attack to study the vulnerability of phase-truncated optical encoding scheme. By using a number of ciphertext-plaintext pairs to train a designed learning model, an attacker can effectively analyze the vulnerability of optical encryption scheme based on phase truncation. The learning based attacks for phase-truncated optical encoding can retrieve unknown plaintexts from the given ciphertexts, which can avoid the retrieval of security keys and the design of complex phase retrieval algorithms. It is demonstrated that the learning based attack can provide a promising approach for vulnerability analysis of phase-truncated optical cryptosystems.