Reviewerly/exHarmony huggingface dataset:
An E-commerce Medicine Website Deployed no AWS ith Prescription Verificationw

E-commerce websites have made everyone’s lives simpler in the current date due to the luxury provided by them of getting almost anything delivered at the doorstep at any time required. There are also websites that provide the same advantage to order medicines whenever needed. The drawback of such websites is that the prescription is not verified on those. A prescription is the most important component when it comes to buying prescription based drugs. The proposed work aims to develop an e-commerce website that offers medicines and drugs with the feature of connecting the customers with their doctors using simple web development tools. Also, the doctors will be able to write the prescriptions via this website for their patients. The customers can use this prescription to order medicines for themselves safely or can use the same prescription to order medicines from their local chemist. A prescription parser will be created to check the prescriptions by using the python-docx, docx2pdf module of Python. These prescriptions will be signed and verified using the RSA algorithm. For this, the rsa module of python will be used. The encryption of the prescriptions will be done using the ‘SHA-512’ hash module. This website will be deployed on Amazon Web Services (AWS) due to their low cost in their services, flexibility, security and pay-as-you-go pricing. The services used for the same will be EC2, RDS, S3. Also, a round of Vulnerability Assessment and Penetration Testing (VAPT) will be performed on the website to remove the vulnerabilities and reduce the chances of Ethical Hacking.
Authors and Writing

Abstract This chapter introduces a range of approaches to the measurement and digital quantification of literary style: stylometry or digital stylistics. The chapter begins with a history of stylometric thinking, ranging from approximately 1851 through to contemporary multi-dimensional fingerprinting techniques, such as Burrows’s delta method. It then progresses to discuss close vs. large-scale literary reading and the problematic terminology of ‘distant reading’ (namely, that one can use computational techniques also to read closely, despite this also being a type of ‘distant’ reading). Further covered in this first chapter are analyses of genre and its lifecycles (Ted Underwood), approaches to poetry (Tanya E. Clement on Gertrude Stein), court cases that have turned on stylometry, the Mosteller and Wallace methods applied to the Federalist papers, stylistic analyses of Flann O’Brien and James Joyce (James O’Sullivan et al.), and authorship controversies (Don Foster’s infamous digital work on Shakespeare).
Cloud Computing-Based Smart Grid Power Big Data Analysis Technology

In data management, conventional data processing models cannot meet the demand for large amounts of data and complex data types. Therefore, a big data approach is proposed to analyze the collected data to mine the data support that is hidden in the big data and can be used to make strategic, tactical and operational decisions. This paper mainly introduces the function of Apache Spark in big data analysis from the grid data source, related data processing technology and application in the grid, with grid price as the application background, and proposes a data platform for unified processing and application.
Fully Convolutional Neural Network Structure and its Loss Function for Image Classification

The overall structure of a convolutional neural network classifier includes multiple convolutional layers + one or more linear layers. Due to the fully connected characteristics of linear layer networks, there are usually many parameters, which may easily lead to local optimization and over-fitting of the model. Therefore, its indispensability in convolutional neural networks classifier is questionable. At the same time, the excessive number of output features of the convolutional layer will also lead to the curse of dimensionality. Since the convolutional layers (except the activation functions) and the average pooling layers are essentially linear layers, based on the above considerations, we propose a fully convolutional neural network classifier architecture, which removes the linear layers and the corresponding activation functions from the conventional convolution neural network classifiers. By modifying the number of output channels of the last convolutional layer, the network can be trained directly through Softmax Loss. Furthermore, POD Loss is adopted instead of Softmax Loss to obtain better recognition performance. Experiments on multiple commonly used datasets and typical networks have proved that the network structure not only reduces the amount of parameters and calculation, but also obviously improves the recognition rate. In the meanwhile, the adoption of POD Loss further improves the classification accuracy and robustness of the model, making them play a better synergy. Code is available in https://github.com/TianYuZu/Fully-Convolutional-Network .
Huberization Image Restoration Model from Incomplete Multiplicative Noisy Data

In this paper, a preprocessing denoising scheme is added in the algorithm to solve a double regularization image restoration model while the image is polluted by high-level multiplicative noise. The model is established in frequency domain by incomplete sampling operator with wavelet sparse and total variation regularizing terms. Huber approximation is used to avoid the much smoothly. Augmented Lagrangians with multiplier method and Bregman iteration are used to solve the modified image restoration model. The fixed-point algorithm is applied to solve the minimization problem in the Bregman iteration. We use real MRI images to test the proposed algorithm in numerical experiments and compare it with the method based on alternative iteration scheme. The experimental results demonstrate that the proposed model and algorithm is very efficient and feasible for the image restoration from limited incomplete multiplicative noisy data.
Retrieval-Based Factorization Machines for CTR Prediction

Click-through rate (CTR) prediction is a crucial task for personalized services such as online advertising and recommender system. Many methods including Factorization Machines (FM) and complex deep neural models have been proposed to predict CTR and achieve good results. However, they usually optimize the parameters through a global objective function such as minimizing logloss and mean square error for all training samples. Obviously they intend to capture global knowledge of user click behavior, but ignore local information. Therefore, we propose a novel approach of Retrieval-based Factorization Machines (RFM) for CTR prediction, which enhances FM by the neighbor-based local information. During online testing, we also leverage the K-Means clustering technique to partition the large training set to multiple small regions for efficient retrieval of neighbors. We evaluate our RFM model on three public datasets. The experimental results show that RFM performs better than existing models including FM and deep neural models, and is efficient because of the small number of model parameters.
Fusing Multi-attention and Side Information for Distantly-Supervised Relation Extraction

Distantly supervised relation extraction has been widely used to extract the relational facts between two entities from texts. And it has proven to be effective to scale relation extraction up to large-scale corpus. However, distant supervision inevitably suffers from wrong labeling problem and long-tail problem. To alleviate the two issues, we propose a model fusing Multi-Attention and Side Information (MASI) for distant supervision relation extraction. In this paper, important words are concerned by using a word-attention scheme, which regards importance of each word to entity pairs as different. The MASI uses a hierarchical attention scheme to select the valid instances on a specific hierarchical chain, which can balance the training data for long-tail relations. Meanwhile, it employs entity type side information for imposing soft constrains while predicting relations. The extensive experiments were completed on the New York Times (NYT) corpus. The experimental results on benchmark dataset achieve state-of-the-art AUC (0.458).
Target Recognition and Grab Location Based on Machine Vision and Deep Learning

The application of target recognition and sorting has greatly improved the level of industrial automation, liberated human hands, and effectively saved resources and improved efficiency. The purpose of this paper is to study target recognition and location based on machine vision and deep learning. Firstly, the development of machine vision is summarized, the algorithm of spatial structure feature extractor is proposed, and the overall architecture of fusion model is explained simply. After comparing the network model of this paper with that of SSD, the network positioning accuracy is analyzed. The experimental results show that when the threshold IoU is less than 0.5, the detection performance of faster R-CNN is better than that of SSD network, and when the threshold IoU is more than 0.5, the situation is the opposite. The experimental results show that the positioning accuracy of SSD network is higher than that of faster R-CNN, and the comprehensive detection performance gap between the two network models will reverse with the improvement of task requirements for positioning accuracy.
SBHPS: A High Performance Consensus Algorithm For Blockchain

Blockchain is one of the most promising new technologies with its characteristics of decentralization, traceability, and trustlessness. However, it has two problems: 1) the system throughput rate is low and cannot meet the requirements of large-scale scenarios. As the core technology of blockchain technology, the consensus algorithm has become an important factor affecting the performance of blockchain. 2) The ever-increasing volume of transaction data rapidly consumes system storage resources which restricts the development of blockchain technology.Therefore, this paper combines the hybrid consensus algorithm and blockchain sharding technology to design and implement a consensus algorithm that can greatly improve the performance of public blockchain. Firstly, this paper makes some improvements to PoA, and on this basis, it combines with SBFT consensus algorithm, and proposes a high performance scalable hybrid consensus algorithm based on sharding technology, named SBHPS. Experimental results show that the SBHPS consensus algorithm has higher throughput and lower transaction confirmation delay.
MODERN CONCEPTUALIZATION OF THE WAYS OF DEVELOPMENT OF MUSICAL THINKING OF THE STUDENT-MUSICIAN

The author of this investigation discusses the importance of musical-Choral Art in the formation of the student-musician. The main focus is on the analysis of the concept of «musical thinking». One of the definitions of this concept is given from the point of view of the internalization of music, as the mechanism of functioning of musical thinking. In this context this phenomenon is analyzed and characterized from various points of view. The author concludes that internalization is an indicator of the development of musical thinking of the student’s personality, and its development, as an integral phenomenon, opens up unlimited opportunities for the formation of creative abilities and the formation of the student’s spiritual culture.
Research on Obstacle Avoidance Path Planning of Manipulator Based on Improved RRT Algorithm

In the field of obstacle avoidance path planning, the traditional Rapidly-Exploring Random Tree (RRT) algorithm has many problems, such as no direction and low efficiency. So it is often used to adjust the growth direction of random tree nodes by introducing a target bias strategy to decrease the search blindness. On this basis, the end movement distance and the variation range of each joint during the manipulator trajectory planning process have been focused on in this paper. Considering the requirements of the speed of the planning executable trajectory and the smoothness of the moving process, a cost function about the path length and the smooth change of the joints has been designed. Then, under the premise of the stability of the path planning results, an improved RRT algorithm on dynamical adjustment of the new nodes generation has been proposed to increase the planning efficiency obviously. Its feasibility and effectiveness have been verified fully by a series of simulation experiments based on MATLAB platform.
Text Similarity Based on Post-training BERT

Text similarity is an important taskin natural language processing. The pre-training BERT model which is get from through large-scale corpus traininghas achieved good results in various natural language processing tasks. However, domain knowledge is not introduced to the model. After the post-training through domain data, the bias of the model for the domain knowledge will be reduced, which improves performance in reading comprehension and emotional aspect extraction. In this paper, the domain knowledge is introduced through the post-training andthen text similarity is discussed.
Unitary fractional-order derivative operators for quantum computation

Along with recent progresses in quantum computation technologies, researchers have addressed practical computational supremacies of quantum computers. The research works in the quantum computation domain mainly focus on progressive quantum algorithms and circuits in order to cope with computationally expensive engineering problems. This study aims to introduce possible implications of fractional calculus in quantum computation practice. In this perspective, a unitary fractional-order derivative operator family, which can be implemented by using phase operators, is defined and their possible utilizations for modeling and controlling quantum circuits are discussed. Moreover, the study demonstrates that the fractional derivative order can be used for controlling Shannon entropy of measurement probability distribution of qubits. Operation modes of single-sided and double-sided quantum interference circuits are analyzed, and optimal design of quantum interference circuits to obtain target probability distributions is investigated by using a genetic algorithm. This groundwork is helpful to extend topics of fractional calculus to quantum fractional calculus.
Reproducing Kernel Hilbert Space, Representer Theorem

One of the key concepts in machine learning is the feature space, which is often referred to as the latent space. A feature space is usually a higher or lower-dimensional space than the original one where the input data lie (which is often referred to as the ambient space).
A Method for improving Data Quality of Large-scale Simulation

Data quality has always been a concern for both system developers and users, especially in recent years, the accuracy and reliability of data in the application of some large simulation systems are becoming more and more imporant. Based on the six-grade scale model, firstly this paper sets on an index hierarchy of data quality evaluation and its weights, in order to understand the comprehensive status of large-scale simulation system data quality, then based on pearl curve function establishes data fitting model, and from the curve which we got can be directly seen the relationship between funding strength and data quality. At last based on the fund allocation model of genetic algorithm, we can promot the continuous improvement of the data quality, and further improve credibility of the large-scale large simulation system.
Paraphrasing Tool Using the NooJ Platform

The dissemination of scientific research is gaining momentum and provides an opportunity for novice and established researchers to contribute to their field. Therefore, there is a particularly great and growing demand for paraphrasing tools, which can effectively and efficiently aid researchers in rewriting sentences to avoid plagiarism. Moreover, paraphrasing is useful in several NLP applications such as question-answering, summarization or machine translation. The aim of paraphrasing is to generate different expressions with the same meaning. Over the past decades, several paraphrase tools have appeared. Nevertheless, these tools give unsatisfied results. For example, the generated sentence maybe does not jibe well the specificities of the given language such as the sentence structure. Nowadays, this issue can be achieved with the advent of new NLP tools. The aim of this paper is to propose a method to paraphrase sentences in French. Our proposal is based on transducers and dictionaries. It consists in replacing some words of the sentence by synonyms or antonyms with negation, or in switching to the passive form. In addition, we have divided these words into two parts – one part related to words starting with a vowel and another part for words that do not start with a vowel – in order to eliminate the ambiguity of the apostrophe form case. The linguistic resources are implemented using the NooJ platform. Experimentations of our paraphrasing tool show interesting results.
Towards Developing Better Object Detectors for Real-World Use

Deep visual models are fast surpassing human-level performance for various vision tasks, including object detection, increasing their use in day-to-day life applications. It is often the case that standard models that perform well when evaluated on the validation dataset—usually collected from the same source as the training dataset, often perform poorly on data different from that of the training data. Recent works also prove that adversarial examples can easily fool deep learning models and are primarily opaque. To address the issue of making object detectors more compatible for real-world use, we propose some steps to make them more reliable and robust for deployment. Proposed methods include the explanation method and data augmentation techniques. Data augmentation improves the performance and outcomes of machine learning models by generalizing them and explanation methods for getting new insights into black-box detectors. Such understanding can also help improve resistance to a wide range of adversarial attacks.
Web-Base Information System of Rice Milling Places In Malind District

Rice is a strategic food commodity that can affect the lives of all Indonesian citizens and is given top priority from the government to achieve national food security. Malind district is an agricultural area with quite extensive rice plants covering an area of 551.24 km 2 with eight villages, and the number of rices milling places as many as fourteen places. The milling places play a very real role in advancing the economy nationally. Farmers in Malind District have problems finding and obtaining information from mills when grinding their unhulled rice. Website contains information that can be accessed easily and quickly using an address or link from an information system.
Pop vs rock: A comparison study of managing sessions in the recording studio and the influences of genre

This study examined the recording session management approaches of two music producers, Phil Harding and Greg Haver, to ascertain if and how their different approaches impact the outcomes of music production projects. Semi-structured interviews were conducted with each producer to gain insight into their approaches generally, and also, as a point of comparison, their work on the educational Gus Dudgeon Foundation/Joint Audio Media Education Support (GDF/JAMES) summer recording sessions specifically. Thematic analyses of the interviews revealed that the producers’ respective approaches are influenced by the genres in which each predominantly works. Harding, a pop producer, is very systematic . Haver, who is better known for his work in rock, uses an organic approach. Consequently, there were some clear differences in their decision making and organizational strategies at the GDF/JAMES sessions. The producers used different criteria to choose a project/artist. Each used distinctive approaches in pre-production (i.e. work on song arrangements and other technical musical decisions), production (recording) and mixing. Still, both produced pop recordings that largely conform to pop norms. Both recordings have similar pop arrangements and meet commercial standards. Findings provide new understanding of session management techniques that can benefit both music production education and practitioners.
Localising Diffusion Sources from Samples Taken Along Unknown Parametric Trajectories

In a recent paper we showed that it is possible to localise diffusion sources observed with a mobile sensor whilst simultaneously estimating the piecewise linear trajectory of the sensor. Here we address the case in which the sensor moves along an arbitrary unknown parametric trajectory and we show that by solving a linear system of equations, we can retrieve the inner products between the parameters of the trajectory. From these inner products we then retrieve the curve parameters up to an orthogonal transformation, which allows us to also perfectly estimate the amplitudes of the sources and find their locations up to an orthogonal transformation.
A Location-Enhanced and Multiscale-Friendly Crowd Detecting Approach for Tram

The automatic statistics of passengers distribution and movement in some public transportation scenes from online cameras video is significant to public security, passenger flow control, and guidance. However, it is hard to get the crowd counting results accurately in carriages or platforms due to the narrow space, complex background, severe perspective distortion, and scale variation. A new crowd count method called coordinate deformable net (CDNet) is proposed in this article. This method is comprised of four sections: the front end, coordinate attention module (CAM), deformable scale-aware module (DSM), and back end. The front end is a preliminary feature extractor with the structure of VGG-16 but does not contain the full connection layer. The back end is used for density map prediction with the construction of six stacked dilated convolution layers. The new add-in CAM module ensures that the model has more capabilities of resisting complex background noise and focusing on areas of interest that are crucial for generating high-quality density maps. In the new DSM module, multicolumn deformable convolution can extract multiscale features effectively and fuse them according to attention weights. The DSM module can improve the scale variation problem significantly to adapt different scale passengers’ detection and enhance the accuracy of head positioning. The experimental results demonstrate that the accuracy of CDNet is superior to other classical models. In addition, to protect personal privacy, the faces in our Tram dataset are blurred.
Prototype of a web and mobile application for inventory management of a parts store using QR code

Micro-companies have been a sector with a great disadvantage in recent months due to the global health problem; hence, the interest in offering a technological tool that allows them to improve customer service. The objective of this article is to present a project, which it was developed for an automotive parts store in Oaxaca city. The idea of introducing technologies such as the use of web applications integrating the QR code to facilitate inventory management will allow these micro-companies to offer an improved service. The contribution of this project is that it can be adapted to different types of micro-companies that require technological tools that support them in their survival in an increasingly competitive market. The prototype is developed with the agile Extreme Programming (XP) methodology, and the tools used are Laravel and Boostrap frameworks for the web application and for the mobile application: Android Studio and Material Design.