Reviewerly/exHarmony huggingface dataset:
Emotional Analysis of Russian Texts Using Emojis in Social Networks

Sentiment analysis is a key task in natural language processing and has a wide range of real-world applications. Traditional methods classify “plain texts” as “positive” and “negative”. We propose a method that is significantly different from traditional approaches. In addition to “plain text”, we have analyzed ways to express emotions in a message and a variety of emotional indicators, emoticons and emojis. The proposed model with emotional indicators to predict text polarity improves the prediction accuracy of sentiment classes by 6% compared to traditional models. The model uses an original data set marked up according to an extended list of Plutchik emotion classes. Therefore, the model predicts 8 independent sentiment classes: “joy”, “sad”, “distaste”, “fear”, “anger”, “surprise”, “attention” and “trust”. The novelty of the research also lies in using the Russian language data set. The model considers national linguistic, semantic and semiotic features of social environment.
User Profiling based on Tweeter Data using WordNet and News Paper Archive

In this paper, a method has been proposed for user profiling based on tweeter data. The sentiments of the tweets are retrieved programmatically with the help of WordNet and News Paper Archive. In this experiment, the English WordNet 2.1 has been used as an online semantic dictionary and machine readable version of the "Times of India" news paper has been used to generate a news paper archive. The algorithm is tested on a data set of 1000 tweets from four different categories which are initially tagged by their innate senses for validation of the derived result.First of all, the data set is evaluated with the help of newspaper archive by using lexical overlap and the accuracy in sense retrieval task is 48.7%. The reason behind this scenario is the varieties of representations of a single statement in natural language which creates a mare similarity between the lexical entities of the statements. To overcome this problem, the contexts of the tweets are expanded with the help of WordNet by considering the synonyms of every meaningful word of the tweets and after that the senses of these tweets are evaluated. As the contexts of the statements are expanded in this approach, semantic relatedness between the statements is resolved in an efficient way which leads the system towards a better performance.
Synthetic Target Domain Supervision for Open Retrieval QA

Neural passage retrieval is a new and promising approach in open retrieval question answering. In this work, we stress-test the Dense Passage Retriever (DPR)---a state-of-the-art (SOTA) open domain neural retrieval model---on closed and specialized target domains such as COVID-19, and find that it lags behind standard BM25 in this important real-world setting. To make DPR more robust under domain shift, we explore its fine-tuning with synthetic training examples, which we generate from unlabeled target domain text using a text-to-text generator. In our experiments, this noisy but fully automated target domain supervision gives DPR a sizable advantage over BM25 in out-of-domain settings, making it a more viable model in practice. Finally, an ensemble of BM25 and our improved DPR model yields the best results, further pushing the SOTA for open retrieval QA on multiple out-of-domain test sets.
Research on Feature Extraction and Matching Algorithm in Visual SLAM

Based on the application of visual SLAM, an improved ORB feature extraction and matching algorithm is proposed to overcome the shortcomings of the original ORB algorithm. In order to improve the stability of the scale, a multi-scale spatial pyramid is established to detect and extract the FAST feature points of each grid in different scales, so as to improve the stability of the scale; the quadtree method is used to divide the feature points to make the distribution more uniform; after the coarse matching, the PRASAC method is used to eliminate the wrong matching points, which improves the matching accuracy. Experimental results show that the algorithm can effectively improve the matching accuracy and efficiency, and meet the requirements of robustness and real-time in visual SLAM.
Sequence subgroups generated by lifting

We introduce and investigate some properties of lifted polynomials over finite fields. When f(t) is a lifted polynomial of degree 2 we give an exact count of the number of f-sequences that represent an f-subgroup, improving a previous result.
PCSAFace: Face Recognition with Parallel Channel and Spatial Attention

Recently, face recognition task has achieved a tremendous strides. It's widely accepted that the key to discriminate different identities is to enlarging the distance between different identities and decreasing the distance between different faces of same identity. Most researches focus on decreasing the intra-class distance and achieve a nice result. There is a trend that attention module is introduced into computer vision task. In this paper, we propose the "angular regularization" that focuses on enlarging the inter-class distance and introduce an novelty parallel channel and spatial attention block to build a new face recognition dedicated backbone network. The method, we call it PCSAFace, enlarges distance between different identities by directly penalizing the angle between cluster center of one identity and cluster centers of other identities. PCSAFace has a well defined geometric interpretation and is easy to do more in-depth research in face tasks. The method has no less good performance than previous works.
Breast Cancer Classification Using Ensemble Hard Voting with Random Under-Sampling

Breast cancers emerged as one of the fatal diseases among women. Particular efforts are being taken continuously around the globe to help this cause. Taking advantage of Machine Learning (ML) technology is a measure to lessen the death toll of this disease. This paper has emphasized breast cancer classification to expedite the diagnostic process by identifying whether the tumor is benign or malignant. Moreover, in this process, we have combined the ensemble learning-based Hard Voting (HV) technique with random under-sampling using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset. This dataset provides several features upon performing Fine Needle Aspirate (FNA) test to each affected individual, and five features have been chosen to accomplish the classification task. These features have been further scaled using RobustScaler, and the classes (benign or malignant) were balanced using random under-sampling to achieve an accurate outcome. Four distinguished ML classifiers, e.g., Decision Tree Classifier (DTC), k-Nearest Neighbor (KNN), Random Forest (RF), and Support Vector Machine (SVM), have been applied to form an HV meta-classifier. The HV meta-classifier has obtained 99.42% test accuracy in classifying breast cancer incidents. This result has been further verified by considering other distinct parameters.
HCGM-Net: A Deep Unfolding Network for Financial Index Tracking

Tracking the performance of a financial index by selecting a subset of assets composing the index is a problem that raises several difficulties due to the large size of the stock market. Typically, optimisation algorithms with high complexity are employed to address such problems. In this paper, we focus on sparse index tracking and employ a Frank-Wolfe-based algorithm which we translate into a deep neural network, a strategy known as deep unfolding. Numerical experiments show that the learned model outperforms the iterative algorithm, leading to high accuracy at a low computational cost. To the best of our knowledge, this is the first deep unfolding design proposed for financial data processing.
Design and Implementation of Driver Recommendation Algorithm in Enterprise Transportation

Cargo transportation plays an important role in the manufacturing industry. It is a bridge between the producer and the customer, and the driver is an indispensable part of the goods transportation. Excellent drivers can deliver goods to customers quickly and safely, thus gaining a good reputation for the company and improving its efficiency. In actual production, companies often use manual allocation when arranging the distribution tasks of orders. There will be human subjective factors in it, resulting in unreasonable allocation results. For example, orders from high-quality customers may be allocated to poorer quality customers. The driver will lose customers and cause losses to the company. This paper proposes a driver recommendation algorithm based on machine learning, and proposes an implementation method. By collecting various data generated by the driver when delivering the order, the driver behavior characteristic model is constructed. When a new order is generated, the driver behavior model can be used to match the order with a suitable driver without manual intervention, which improves delivery efficiency and customer satisfaction.
Learning Instance Motion Segmentation With Geometric Embedding

Most existing deep learning-based motion segmentation methods treat motion segmentation as a binary segmentation problem, which is generally not the real case in dynamic scenes. In addition, the object and camera motion are often mixed, making the motion segmentation problem difficult. This paper proposes a joint learning method which fuses semantic features and motion clues using CNNs with deformable convolution and a motion embedding module, to address multi-object motion segmentation problem. The deformable convolution module serves to fusion color and motion information. And the motion embedding module learns to distinguish objects' motion status with inspiration from geometric modeling methods. We perform extensive quantitative and qualitative experiments on benchmark datasets. Especially, we label over 9000 images of KITTI visual odometry dataset to help training the deformable module. Our method achieves superior performance in comparison to the current state-of-the-art in terms of speed and accuracy.
Content of Foreign-Language Education at Higher Education Institution as Means for Fostering Personal Intellectual Culture in Future Teachers

The study aims to provide theoretical justification of informational-semantic, verbal-substantive, motivational-regulatory, creative-activity, cognitive and value-ethical content of foreign-language education at higher education institutions for fostering personal intellectual culture in undergraduate pedagogical students. The article presents development of content of foreign-language education as a means for comprehensive and integrated fostering of intellectual culture in future teachers. Scientific novelty of the research lies in identifying and substantiating content features of foreign-language education at higher education institutions in accordance with fundamental characteristics and functional manifestations of personal intellectual culture subsystems of future teachers as a benchmark for education intellectualisation at higher education institutions. As a result of the study, the researcher has justified the main requirements for selecting content of foreign-language education at higher education institutions to ensure practical implementation of the target component concerning fostering of personal intellectual culture in undergraduate pedagogical students by means of foreign-language education.
Automatic Correction Service System for English Composition based on Big Data

Abstract With the rapid development of science and technology, big data is gradually being applied to the field of English writing teaching. On this basis, an automatic correction system is developed to automatically evaluate and feedback students’ compositions. This has become the future of feedback research and development Directions and trends. Writing is a cyclical creation process. The correction and feedback of composition is very important in writing teaching. However, in traditional teaching, due to the heavy workload of teachers, it is difficult to give feedback sentence by sentence, and there may also be inefficient feedback. In view of this, it is necessary to conduct an in-depth discussion on English composition based on automatic correction system, and on this basis, explore the automatic correction service system for English composition based on big data, so as to provide inspiration and reference for the evaluation of English writing teaching. The study found that the automatic correction service system came into being, which made up for the shortcomings of composition feedback in traditional teaching. Moreover, the automatic correction service system for English composition based on big data has many advantages compared with teacher correction, and it can accurately provide Students point out errors in writing, which can reduce teachers’ workload and improve teaching quality. In addition, this article also summarizes the shortcomings of the automatic correction service system and puts forward suggestions in order to better apply the automatic correction service system to English writing teaching.
Research on The Design of Power Enterprise Central Data Platform Framework Based on Big Data

Abstract With the rapid development of computer technology and Internet, the traditional data mining methods and technologies in power industry will face great difficulties, and it is difficult to carry out accurate data processing and analysis. How to mine valuable data from a large number of original data has become a research difficulty. Aiming at this problem, this paper establishes the framework design of power enterprise central data platform based on big data. In order to further improve the actual performance of the scheme, the defects of existing algorithms are analyzed by IM_Apriori improves the calculation method, simplifies the calculation steps, reduces the calculation times, and provides technical support for enterprise data analysis. Through the analysis of the test results, when the data peak reaches 100 m, the execution time is reduced by 25s, which is obviously superior to the traditional scheme. The test results show that the design scheme in this paper has a high comprehensive performance, compared with the traditional central data platform framework, the performance has been greatly improved. Through the analysis, the research in this paper has achieved ideal results, and has made a contribution to the research on the framework design of the central data platform of power enterprises.
Research and Construction of Embedded Software Evaluation Technology System

Due to the particularity of embedded software, the evaluation technology system of embedded software is constructed, aiming to provide reference for embedded software evaluation project and make contribution to ensure the quality of embedded software products. The system includes: test target, test standard library, traditional software test technology library, embedded software test technology library, software quality characteristic reference model, embedded software test environment, embedded software test process and test document library. The system is supported by the quality model and takes the standard library as the evaluation basis. The technology library is the means to improve the evaluation efficiency. The embedded software evaluation technology is deeply integrated with the industry standards to realize the comprehensive platform management and operation innovation.
The Augmented Reality Sandbox Makes Optimization Visible

Explaining concepts of optimization or even relevant algorithms to outsiders, like pupils or contacts from the industry, is a difficult task. Based on the idea of an augmented reality sandbox explaining geoscience processes we developed an exhibition piece to visually demonstrate fundamental ideas of numerical algorithms in the field of optimization and optimal control. In this paper we give an overview of our motivation to build the sandbox and how we apply it in our outreach projects. We discuss, how elementary problems from optimization and optimal control (as finding the best route) can be posed by modeling a landscape from sand. The sandbox features an interface to try out own basic algorithms and allows a comparison to the included state-of-the-art NLP solver WORHP. Finally, we present some further ideas to use the sandbox, which might be included in the future.
Analysis of transaction patterns at drug store with Apriori Algorithm

Abstract Data mining is a method for finding hidden data from big data which has been continuously applied in various fields such as marketing, education, bioinformatics and so on. Drug store is one of the business sectors that might take the advantage of the data mining. In the drug store, there is a sales transaction data which contains a big number of data. However, there is a limited number of analysis based on this sales transaction data. There are several information that can be obtained from this big data; one of them is the combination of items that consumers often buy. Apriori Algorithm is a data mining method that has been widely used in order to determine the combinations of frequently purchased products. By using the Apriori Algorithm in the sales transaction data of the drug store.
Generalized Extension in the Geometry of Goncharov Motivic and Grassmannian Conﬁguration Chain Complexes

The purpose of this work is to propose generalized extensions of morphisms in the geometry of Grassmannian configuration and Goncharov motivic chain complexes. This work has two major divisions: in its first part the geometry of these complexes will be extended for weight n = 6, secondly, the generalization of this extension for any weight N will be presented. The generalized commutative diagram will also be exhibited
Learning Based Age Estimation Using Joint Loss and Facial Landmarks

Age recognition is an important technology in computer vision, surveillance systems, and commerce. It can be applied in many scenarios, including age restrictions in specific places, drinking restrictions, and reminders for underage or elderly drivers in traffic applications. In this study, we proposed an accurate age recognition algorithm, which applies the techniques of landmark-based alignment, the attention model, and the expected value method in the deep learning architecture. The algorithm consists of three stages. The first stage is data preprocessing, including face detection, cropping, face alignment (to normalize the position and angle of each face), and contrast adjustment. The second stage is a feature extraction model. It is based on the Residual Attention Model with the attention mechanism. Moreover, the domain filter, the joint loss, and the facial landmark extraction are also adopted. The third stage is the classification model. Its input is the l024-dimensional features extracted in the 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> stage and the input image. Then, the expected value method is applied to calculate the explicit age. Simulations show that the proposed algorithm outperforms other age estimation methods and can estimate the age accurately.
Personalised attraction recommendation for enhancing topic diversity and accuracy

Attraction recommendation plays an important role in tourism, such as solving information overload problems and recommending proper attractions to users. Currently, most recommendation methods are dedicated to improving the accuracy of recommendations. However, recommendation methods only focusing on accuracy tend to recommend popular items that are often purchased by users, which results in a lack of diversity and low visibility of non-popular items. Hence, many studies have suggested the importance of recommendation diversity and proposed improved methods, but there is room for improvement. First, the definition of diversity for different items requires consideration for domain characteristics. Second, the existing algorithms for improving diversity sacrifice the accuracy of recommendations. Therefore, the article utilises the topic ‘features of attractions’ to define the calculation method of recommendation diversity. We developed a two-stage optimisation model to enhance recommendation diversity while maintaining the accuracy of recommendations. In the first stage, an optimisation model considering topic diversity is proposed to increase recommendation diversity and generate candidate attractions. In the second stage, we propose a minimisation misclassification cost optimisation model to balance recommendation diversity and accuracy. To assess the performance of the proposed method, experiments are conducted with real-world travel data. The results indicate that the proposed two-stage optimisation model can significantly improve the diversity and accuracy of recommendations.
Detection of DLBCL regions in H&amp;E stained whole slide pathology images using Bayesian U-Net

We proposed a method that detects DLBCL (Diffuse Large B-Cell Lymphoma) regions from a H&amp;E stained whole slide pathology image by measuring the size of each nucleus. It is known that DLBCL cells would have about 2 to 3 times larger nuclei than typical lymphocytes. One can hence detect DLBCL regions by detecting every cell nucleus in a given H&amp;E stained pathology image and describing the spatial distribution of the large nuclei. For the detection of cell nuclei, we employ a U-Net and a Bayesian U-Net. We describe the details of the proposed method and report the experimental results, which demonstrate the proposed method works well in the DLBCL regions.
Experimental Evaluation of Homomorphic Comparison Methods

The use of cloud technologies for processing confidential data requires a solution to the data security problem. One of the mechanisms to solve it is homomorphic encryption. However, homomorphic encryption only allows the arithmetic operations of addition and multiplication to be performed over encrypted numbers. Consequently, when implementing algorithms for matrix algebra, artificial neural networks, and deep learning, it becomes necessary to implement the comparison operation in a homomorphic cipher. In this paper, we study methods for comparison operations in a homomorphic cipher. One of the methods is to use the subtraction of numbers and determine the sign of a number. Two families of polynomials and their composition are used to approximate the sign function. We provide estimates of the accuracy of the sign function approximation obtained as a result of modeling and show that they are 1.98 times better than the state-of-the-art theoretical estimate for polynomials fn(x). Also, we show that the theoretical estimate of gn(x) is not applicable for n ≤ 4.
On the Content of Information Systems Ontologies

Abstract Despite the fact that information systems ontologies [ISOs] support the mutual understanding between human beings and software applications, human beings and software applications do not understand ISOs' contents in the same way. The same applies to ontological integration. This paper attempts to account for such discrepancies by emphasizing that while human being can have access to entities represented in ISOs, software applications cannot.