Reviewerly/exHarmony huggingface dataset:
Research on Methods of Parsing and Classification of Internet Super Large-scale Texts

Abstract Web crawlers are an important part of modern search engines. With the development of the times, data has shown explosive growth, and mankind has entered a “big data era”. For example, Wikipedia, which carries knowledge achievements from all over the world, records real-time news that occurs every day and provides users with a good text search database[1]. Wikipedia updates data up to 50+GB every day. This project focuses on solving the problems of data acquisition and data analysis. At the same time, it downloads and parses the latest data of Wikipedia and analyzes XML files, and then uses SVM algorithm and Naive Bayes algorithm to classify articles, Train the model to download Wikipedia files efficiently and parse XML files.
Toward the development of an optical technique for identifying and imaging white blood cells in whole blood

Abstract An optical-disk-based system was developed to obtain images of blood cells for healthcare applications. The blood cells on the disk are scanned using a laser, and the intensity data of reflected or transmitted light from the sample are collected and reconstructed to form an image. Images of red and white blood cells were successfully obtained and both types of blood cells were visually distinguishable in the transmitted-light image. Our method is promising for finding a specific cell, among many other types, since the optical-disk system can scan a large area at high speed.
Analysis of t-distribution in Variable Step Size Firefly Algorithm in the Applications of Machine Learning

The firefly algorithm in the field of swarm intelligence is recognized as one of the widely demanded algorithm for machine learning applications. This algorithm is inspired by natural leaving pattern of firefly. This algorithm is modified by several researchers for getting better exploration of solution space for various applications. In all these modifications the variable step size firefly algorithm is gathering popularity in the field of machine learning because of its simplicity in modification. In this paper, this modified version is further enhanced by the addition of t-distribution function. This newly proposed version helps in the improvement of exploration along with the exploitation of the searched space to generate better solutions. Simulation of the novel projected version is done with standard benchmarking functions to prove the enhancement in the solution. The analysis of results proves the betterment of the solution in a variety of cases. This approach of modification can be utilized for applications in machine learning.
A Target Detection Method in SAR Images Based on Superpixel Segmentation

A synthetic aperture radar (SAR) target detection method based on the fusion of multiscale superpixel segmentations is proposed in this paper. SAR images are segmented between land and sea firstly by using superpixel technology in different scales. Secondly, image segmentation results together with the constant false alarm rate (CFAR) detection result are coalesced. Finally, target detection is realized by fusing different scale results. The effectiveness of the proposed algorithm is tested on Sentinel-1A data.
Comparative Analysis of Image Enhancement using Adaptive Threshold

In today's generation, as medical applications keep growing on an everyday basis, it is becoming increasingly necessary to develop different enhancement techniques to help gather precise information efficiently from biomedical images. They suffer from various degradations and some of the most common ones are poor quality due to additive noise, low contrast, etc. The basic motivation of this paper is to develop an image enhancement algorithm using adaptive thresholding in the frequency domain. Biomedical images are transformed into the frequency domain using DCT, DST and Hadamard Transform algorithms. DCT and DST algorithms are implemented for block sizes of 8x8, 16x16 and 32x32. While Hadamard Transform is implemented for a block size of 4x4. Setting a threshold that can completely remove all the noise is a time-intensive process. Thus, upon converting the image into the frequency domain, a statistical parameter, i.e standard deviation is calculated to fix up a threshold. For each value of the threshold, the results are compared using MSE and PSNR. The best MSE and PSNR values obtained were for DCT, i.e "around 120" and "around 27 dB" respectively.
Multilinguistic approach towards Information Retrieval System for Big Data

This paper delivers the manipulation of the lingual perspective and its approaches in the Information Retrieval System. Research work explored the linguistic aspects of Information Retrieval, namely Bilingual, Cross-lingual, and Multilingual IR With the advancement and rise of globalization, the importance of the concept of information in form of Big Data has become a priority. The modern age and socialism give rise to large amounts of data due to which the requirement of information in different languages becomes a primary requirement and thus Multilingual Information Retrieval System approach for Big Data came into existence. Though in use, this field of Computer Science is still under research and optimization.
Updating of the Content of Bachelor's Degree Students’ Education in Socio-Cultural Activities Based on Indicators of Socio-Cultural Inequality in the Inclusive Culture of Russian Society

The necessity of updating of the content of education for a bachelor's degree in socio-cultural activities, taking into account the requirements of the federal state educational standard of higher education in the direction of preparation 51.03.03 "Socio-cultural activity" is substantiated. The concepts of the quality of life, cultural inclusion and indicators of socio-cultural inequality of people with disabilities included in the socially unprotected group of the population are considered. The article analyzes the statistical data presented by the Federal State Statistics Service concerning the costs of leisure services and the needs of people with disabilities in comparison with the healthy working-age population. The necessity of updating the content of professional training of bachelors of socio-cultural activity on the basis of identifying the needs of people with disabilities in various forms of cultural and leisure activities is proved. The question is raised about the relevance of further research of indicators of socio-cultural inequality in the cultural inclusion of people belonging to a socially vulnerable group of the population.
Design and Implementation of XML Schema Based Information System

The collection, editing and processing of all kinds of equipment data is the core of all kinds of weapon equipment integrated support information system. Traditional information processing systems are less scalable, stable, and reliable. This paper proposes an XML Schema based information processing system, which stores the result into XML format which is produced by processing the collected data. Firstly, the system is designed with loosely coupled design mode, which is divided into model transformation module, configuration file generation module, data editing module and data validation module. Secondly, the functions of each module of the system are defined in detail. Finally, each functional module is implemented to obtain an XML Schema information system. The verification results of the system show that the system can process various types of equipment data, and is available, reliable and stable.
Night-Time Vehicle Distance Estimation Using Camera Geometry and Deep Learning

Vehicle distance estimation has been considered as one of the important topics in traffic video processing. There have been many algorithms proposed to deal with this problem. However, these algorithms all have their own drawbacks such as long processing duration, huge data requirements, extremely computational consumption, and low accuracy response. In this circumstance, in this paper, a novel algorithm based on a combination of deep learning and camera geometry is proposed for vehicle distance estimation. The proposed algorithm demonstrates fast processing duration which is contributed by the deep learning base technique. Moreover, thanks to the simplicity of camera geometry, the proposed algorithm requires minimal data to perform the estimation. By combining deep learning and camera geometry, the proposed algorithm can provide high estimation accuracy while keeping the model simple and fast. The performance of the proposed algorithm is verified through experiments results.
Estimation of Body Occlusion Using Deep Learning for Advanced Intelligent Video Surveillance System

This paper presents the development of a new method for the estimation and resolution of body occlusion using deep learning for an advanced intelligent video surveillance system. A generative adversarial network is used to estimate and reconstruct an image of a hidden part of the human body. Furthermore, an alternative learning approach using 3DCG that was developed in our previous study is adopted to create a large dataset for deep learning. Experimental results indicate that the proposed method performs well in the estimation of hidden parts of the human body using images of actual people.
Multimodal-aware weakly supervised metric learning with self-weighting triplet loss

In recent years, we have witnessed a surge of interests in learning distance metrics from various data mining tasks. Most existing methods aim to pull all the similar samples closer while push the dissimilar ones as far as possible. However, when some classes of the dataset exhibit multimodal distribution, these goals conflict and thus can hardly be concurrently satisfied. Additionally, to ensure a valid metric, many methods require a repeated eigenvalue decomposition process, which is time-consuming and numerically unstable. Therefore, how to effectively learn an appropriate distance metric from weakly supervised data remains an open but challenging problem. To address this issue, in this paper, we propose a novel weakly supervised metric learning algorithm, named MultimoDal aware weakly supervised Metric Learning (MDaML). MDaML partitions training data into several clusters and allocates the local cluster center and weight for each sample. Then, combining it with the weighted triplet loss can further enhance the local separability, which encourages local dissimilar samples to keep a large distance from local similar samples. Meanwhile, MDaML casts the metric learning problem into an unconstrained optimization on SPD manifold, which can be efficiently solved by Riemannian Conjugate Gradient Descent (RCGD). Extensive experiments conducted on 13 datasets validate the superiority of the proposed method.
Fault tolerant SINS/GPS/SRS Navigation System with a Mahalanobis distance-based FKF

In order to improve the autonomy and accuracy of navigation, SINS /GPS/ SRS integrated navigation system is established. The combination of SRS and GPS navigation system can help to correct the error accumulation of SINS. On the premise of ensuring the accuracy and autonomy of the navigation system, the fault tolerance of the navigation system needs to be improved. Therefore, the fault diagnosis algorithm based on Mahalanobis distance is added to the SINS /GPS/SRS integrated navigation system. The simulation results show that the navigation system can get better performance.
An Extended Scheme for Shape Matching with Local Descriptors

Shape matching with local descriptors is an underlying scheme in shape analysis. We can visually confirm the matching results and also assess them for shape classification. Generally, shape matching is implemented by determining the correspondence between shapes that are represented by their respective sets of sampled points. Some matching methods have already been proposed; the main difference between them lies in their choice of matching cost function. This function measures the dissimilarity between the local distribution of sampled points around a focusing point of one shape and the local distribution of sampled points around a referring point of another shape. A local descriptor is used to describe the distribution of sampled points around the point of the shape. In this paper, we propose an extended scheme for shape matching that can compensate for errors in existing local descriptors. It is convenient for local descriptors to adopt our scheme because it does not require the local descriptors to be modified. The main idea of our scheme is to consider the correspondence of neighboring sampled points to a focusing point when determining the correspondence of the focusing point. This is useful because it increases the chance of finding a suitable correspondence. However, considering the correspondence of neighboring points causes a problem regarding computational feasibility, because there is a substantial increase in the number of possible correspondences that need to be considered in shape matching. We solve this problem using a branch-and-bound algorithm, for efficient approximation. Using several shape datasets, we demonstrate that our scheme yields a more suitable matching than the conventional scheme that does not consider the correspondence of neighboring sampled points, even though our scheme requires only a small increase in execution time.
Generation of Pseudo Random Sequence Using Modified Newton Raphson Method

Randomness brings unpredictability and applicable to a number fields like science, statistic, gaming, gambling etc. True random number generator generates a random sequence based on unpredictable physical means. On contrary pseudo random sequence is generated by some mathematical equation (algorithm) using some initial parameters known as seed. Pseudo random sequence looks like random but can be retrieved by the seed feeding in the algorithm. Pseudo random sequence has wide applications in cryptography, simulation etc. Chaotic map is a preferable choice to the researcher community for pseudo random sequence generation due to its underlying properties like deterministic, long term unpredictability, high sensitivity to initial condition etc. Several non-chaotic techniques are also taking their ways for pseudo random sequence generation. In this current communication a non-chaotic method based on modified Newton Raphson technique is proposed to generate a pseudo random sequence. Several test results including randomness testing using NIST suite provide the proposed technique a strong base in the field of pseudo random sequence generation.
Research on Data Traffic Model of Internet of Things Based on Statistical Analysis

In view of the sudden transmission of nodes in the Internet of things, this paper adopts the method of establishing data traffic model based on statistical analysis. On the basis of statistical analysis, the traffic is analyzed in the time domain, that is, the data traffic model is studied on different time scales. Then the traffic is analyzed in the spatial domain. It can be seen that the fitting effects of different time scales and different spatial domain are different. In addition, according to the energy consumption model of Internet of things nodes, it shows that the data traffic and energy consumption have a positive correlation.
Database application based on quick response method in Subulussalam Aceh

Abstract Customers are a very important part of the sustainability of a company. The PT. PLN (Persero) is a state electricity company in Indonesia. It has many customers in the Subulussalam district. This study aims to design a customer data management system for PT. PLN (Persero) using the QR code scanner. The method used in this system uses a use case diagram and waterfall model. The database design uses the Entity Relationship Diagram (ERD) model. The system implementation uses PHP as the system interface and MySQL as its database. By scanning the QR code on the customer's meter so the officer can find out the customer's identity number and data. The system test results show that field officers can access customer data in realtime so that the information obtained becomes faster and more accurate.
Matrix Factorization for Video Recommendation Based on Instantaneous User Interest

As the main field of natural language processing, the recommendation algorithm based on matrix factorization is not only widely studied in academia, but also widely used in industry. Most existing methods only pay attention to the influence of the model on the recommendation result, but ignore the effect of the basic user interest in a certain moment. Thus, in this paper we propose a matrix factorization rating prediction model for video recommendation that combined with the user's instantaneous interest. The model integrates the click-through rate (CTR) prediction network and the rating prediction network mainly to achieve the cross-feature effect. And the two models are combined by the user module and the item module, respectively. First, in the user module of the click-through rate prediction network, we use MLP to replace the random initialization of user features in the traditional matrix factorization network. Then, we use a double convolutional layer to replace the convolution-pooling layer to avoid losing the position information in TextCNN. And by combining the Transformer network that extracts long text features on the basis of the click rate prediction network, we can obtain rating prediction network. Finally, the user's click-through rate prediction results are weighted into the rating prediction network to enhance the interpretability of the model and improve the performance of the entire network by preset confidence factor. The experimental results prove that our model is superior to several compared methods on standard datasets.
Feature Selection Optimization for Identification of Citation Sentences in Scientific Journals

Scientific journal publication is an easy way to disseminate findings or research. But in doing the writing study, researchers often forget the source of the basics of the theory and the results of previous studies that are used as a reference. In some cases, these actions can be considered as a form of plagiarism, they can also damage the integrity of an academy. Therefore, citation writing is very important in research. In this study, researchers tried to detect citation and non-citation sentences contained in the CL-SciSumm dataset in 2018 using classifiers such as SVM. Not only that, classification is also helped by using feature selection to reduce unneeded dimensions. Feature selection used includes TF-IDF, IDF, WordNet and Word2Vet Similarity. In doing classification, it shows that the use of TF-IDF and SVM methods is the best combination of other features, this is evidenced by the high accuracy value that reaches 90,23%, recall reaches 92,76%, and f-measure reaches 88,13%.
Global trajectory planning for Autonomous Valet Parking with segmentation, rewind and relaxation

A significant amount of time and fuel energy can be wasted in the process of parking with so many vehicles in this world, while autonomous valet parking is capable of liberating drivers for parking with high efficiency. In recent years, various proposals for autonomous valet parking emerged. However, this problem still cannot be promptly handled with reliable trajectories for vehicles to follow. Therefore, this paper proposed a novel global trajectory planning method which generates reliable trajectories with given start configuration and vacant parking space. To be detailed, global path obtained by an improved A* algorithm, which is a rough solution, provides waypoints for the trajectory planning as the reference. Moreover, this planning of trajectory is transformed into an optimal control problem for the solving of trajectory. Considering the probability of failure in the solving process, segmented planning is utilized to reduce the size of constraints, as well as rewind and relaxation applied to improve the convergence. The performance of this global trajectory planning algorithm is evaluated through the simulation in a designed parking lot scenario.
Internet of things and other emerging technologies in digital pathology

The rapid growth of the Internet of things (IoT) and cloud computing in the last decade have brought healthcare services to the next level. In the era of digitization of pathology, an enormous amount of data is generated, and newer technologies are being developed for automated data processing that are accessible via the cloud at a low cost. The integration of emerging technologies such as artificial intelligence with IoT and cloud computing is reshaping the entire health paradigm by providing a rapid low-cost diagnosis. In the present scenario, with the scarcity of specialized experts, besides their availability at remote locations and the physical movement restrictions of patients, IoT-cloud technology can be of great help in providing e-diagnostic solutions. These innovations will help in cutting the barriers to affordable and effective healthcare access.
Creating intelligent content with lightweight DITA, by Evia, C. (2019). New York, NY: Routledge.

With growing attention to "intelligent content" and "dynamic delivery" in the advent of connected technologies (i.e., Internet of Things, artificial intelligence agents), component content management and structured authoring skills are becoming increasingly required of technical communicators today. To produce reusable intelligent content, technical communicators need a systematic workflow and common authoring standard. Our experience in industry and in educating technical communicators has led us to seek out resources for understanding existing standards and practicing them with technical communication students. As such, both authors have used the Darwin Information Typing Architecture (DITA) markup standard and experienced what may be a perplexing process in content creation and management. Carlos Evia's book, Creating Intelligent Content with Lightweight DITA , caught our attention as the title suggests an accessible way into learning and applying what has become a widely adopted standard for structured authoring. Understanding that Lightweight DITA (LwDITA) does not aim to replace existing DITA standards, we approach this review not with an intention to examine its viability, but rather a focus on the rhetorical work in structured content authoring and its continuous evolution.
A New Asymmetric Fully Homomorphic Encryption Scheme for Cloud Banking Data

Most banks in our time still use the common traditional systems of high cost and relatively slow, we are now in the era of speed and technology, and these systems do not keep pace with our current age, so saving cost and time will be considered a fantastic thing for banks. The way to that is to implement cloud computing strategies with Considering data security and protection when it comes to using the cloud. The best solution to protect data security on the cloud is fully homomorphic encryption systems. The time it takes to encrypt and decrypt data is one of the main barriers it faces. Our current research provides a new algorithm for a publicly-keyed encryption system to keep bank data from tampering and theft when stored on the cloud computing platform, and our new system achieves fully Homomorphic Encryption, which allows mathematical operations to be performed on the encrypted text without the need for the original text. The security of the new system depends on the issue of analyzing huge integers, which reach 2048 bits, to their prime factors, which are considered almost impossible or unsolvable. A banking application has also been created that encrypts the data and then stores it on the cloud. The application allows the user to create accounts and deposits, transfer and withdraw funds, and everything related to banking matters.