Reviewerly/exHarmony huggingface dataset:
Multi-task Learning for Multi-turn Dialogue Generation with Topic Drift Modeling

Multi-turn dialogue generation aims to generate natural and fluent responses that should be consistent with multiple consecutive utterances history. It is a more challenging task compared to its single-turn counterpart since it requires the model to capture the topic drift along with the multi-turn dialogue history. In this paper, we propose a multi-turn dialogue generation model which incorporates topic drift aware information into a hierarchical encoder-decoder framework to generate coherent responses. This model first utilizes a Convolutional Neural Network (CNN) based topic model to obtain the topic representation of each utterance. Then a topic drift model is employed to encode the sequential topics of multi-turn dialogue history to infer the topic of response. During the response generation, a specially designed topic drift aware generator is proposed to dynamically balance the impact of the inferred topic of response and local word structure. Fur-thermore, we employ multi-task learning to optimize the topic drift model and dialogue generation simultaneously. Extensive experimental results on two benchmark datasets (i.e. Cornell Movie Dialog Corpus and Ubuntu Dialogue Dataset) indicate that our proposed model can generate more coherent responses, and significantly outperform other dialogue generation models.
On one approach to recognizing fuzzy images of faces based on an ensemble

This article implements and analyzes face recognition algorithms based on fuzzy image representations using several main classifiers, including Decision Tree, Random Forest, and K-Nearest Neighbors. The main focus is on the extraction of informative features to correct recognition and classification of the human face. The algorithms under study recognize a person by their eyes if their face is covered with something. According to the research results, the accuracy of the KNN, Random Forest and Decision Tree algorithms is 95.5%, 96.1% and 92.3%, respectively. In order to improve the results, the ensemble learning method was implemented, which allows a combination of several classification results using a metaclassifier. The first level of ensembling uses the three basic models mentioned above to predict the output of the test datasets. In the second level of this process, a new result was generated, taking all the results of the underlying models as input. As a result of the ensembling, the accuracy was 96.8%, exceeding the accuracy of each algorithm. A comparative assessment of the developed ensemble algorithm quality with other ensemble algorithms based on the boosting approach is also made.
COVID’19 in India: Emotion of the Nation A novel Spatio-Temporal Unsupervised Sentiment Analysis

The coronavirus was originated in Wuhan City in China in 2019, and it led to something for which the world was not prepared. In the world where the extent of COVID’19 was ubiquitous, India was one of the countries that witnessed multiple phases of its spread. Moreover, since India has one of the largest populations, this made analyzing the sentiment of people during this time a task that held significance. COVID’19 brought a mix of emotions across the different periods in its first 18 months. During this time, social media was flooded with tweets and hashtags expressing both favorable and negative opinions about COVID’19, pandemic, lockdown, and vaccines.
AI solutions for drafting in Magic: the Gathering

Drafting in Magic the Gathering is a sub-game within a larger trading card game, where several players progressively build decks by picking cards from a common pool. Drafting poses an interesting problem for game and AI research due to its large search space, mechanical complexity, multi-player nature, and hidden information. Despite this, drafting remains understudied, in part due to a lack of high-quality, public datasets. To rectify this problem, we present a dataset of over 100,000 simulated, anonymized human drafts collected from Draftsim.com. We also propose four diverse strategies for drafting agents, including a primitive heuristic agent, an expert-tuned complex heuristic agent, a Naive Bayes agent, and a deep neural network agent. We benchmark their ability to emulate human drafting, and show that the deep neural network agent outperforms other agents, while the Naive Bayes and expert-tuned agents outperform simple heuristics. We analyze the accuracy of AI agents across the timeline of a draft, and describe unique strengths and weaknesses for each approach. This work helps to identify next steps in the creation of humanlike drafting agents, and can serve as a benchmark for the next generation of drafting bots.
Novice Teacher Induction in Russia: Management and Mentoring

The issues of a novice teacher’s induction to the profession and the future teachers’ professional training are relevant due to the social significance of the professionalism factor in the Russian education modernization process. Particular attention is paid by the Russian government and by local authorities to projects related to the effective adjustment of novice teachers in the professional pedagogical environment. The purpose of these projects is to ensure the professional growth of novice teachers. The dominant role in its implementation is played by the school itself, and by its teaching and managerial staff. The chapter covers the development of innovative organizational models of advanced training for novice teachers at the regional and municipal levels.
Cryptocurrencies, Evolution of Means of Payments and Validity of Monetary Principles

The paper explores the role, evolution and ruling principles of the concept of “money” in the 21st Century. In this continuously evolving context, cryptocurrencies and Blockchain technology are widely considered the most relevant monetary innovations of the last decades. By means of a macro-founded logical-analytical approach combined with statistical evidence, the paper provides arguments: 1. dismissing the “innovation myth” behind cryptocurrencies because of de facto representing a comeback of the private issue of means of payments and, more problematically, seigniorage at its best; 2. confirming that crypto-tokens do not comply with basic, still ruling monetary principles; 3. suggesting that excess liquidity is already invested in crypto-markets (which are themselves “inflationary”, namely not backed by real value (i.e. GDP). The concrete risk is, once again in economic history, represented by facing a financial bubble.
Application of Decision Tree Algorithm in University Educational Administration

The decision tree algorithm plays a key role, but the existing algorithms have the problems of insufficient data analysis and low accuracy. Therefore, this paper puts forward the application of decision tree algorithm in university educational administration. In this paper, the university educational administration management mode and the important role. It is considered that the existing massive data in university educational administration management is difficult to effectively use through traditional human resources and data mining technology is one of the main solutions to this problem. This paper analyzes the problems of insufficient data mining and low accuracy in the existing educational administration management system, and optimizes and improves the information gain algorithm. According to the actual needs, combined with the decision tree algorithm, the information gain calculation method is simplified, so as to achieve the purpose of improving the accuracy. In the corresponding simulation test, the maximum error rate of the improved algorithm is 0.2%, which is greatly improved compared with the traditional algorithm.
Circuit-Free General-Purpose Multi-Party Computation via Co-Utile Unlinkable Outsourcing

Multiparty computation (MPC) consists in several parties engaging in joint computation in such a way that each party's input and output remain private to that party. Whereas MPC protocols for specific computations have existed since the 1980s, only recently general-purpose compilers have been developed to allow MPC on arbitrary functions. Yet, using today's MPC compilers requires substantial programming effort and skill on the user's side, among other things because nearly all compilers translate the code of the computation into a Boolean or arithmetic circuit. In particular, the circuit representation requires unrolling loops and recursive calls, which forces programmers to (often manually) define loop bounds and hardly use recursion. We present an approach allowing MPC on an arbitrary computation expressed as ordinary code with all functionalities that does not need to be translated into a circuit. Our notion of input and output privacy is predicated on unlinkability. Our method leverages co-utile computation outsourcing using anonymous channels via decentralized reputation, makes a minimalistic use of cryptography and does not require participants to be honest-but-curious: it works as long as participants are rational (self-interested), which may include rationally malicious peers (who become attackers if this is advantageous to them). We present example applications, including e-voting. Our empirical work shows that reputation captures well the behavior of peers and ensures that parties with high reputation obtain correct results.
Improvement of groundwater monitoring on the basis of modern info-communication and GIS technologies

The article describes the methodology for improving the monitoring system of groundwater on the basis of modern information and communication and GIS technologies, which allows: firstly, to significantly reduce the time for collecting and analyzing information for making operational management decisions and, secondly, to greatly increase the reliability, to eliminate the human factor, as well as: visualize in the form of digital data on the level, chemical composition and physical properties of groundwater; assess the state of flooded areas, as well as carry out a comprehensive assessment of these areas based on the use of modern hardware and software, integrate, analyze and provide information support for making managerial decisions to prevent negative consequences.
Machine Learning Methods for Content - Classification and Categorization

These days online gatherings and web-based media stages have furnished people with the necessary resources to advance their contemplations and put themselves out there free paying little heed to the kind of language used to communicate those thoughts, in certain examples these internet based remarks contain express language which might hurt the peruser. We likewise evaluate the class irregularity issues related with the dataset by utilizing inspecting procedures and misfortune. Models we applied yield high in general exactness with moderately minimal expense. To diminish the adverse consequence of poisonous remark in everyday life we have endeavored to plan a Toxic Language detector.
Automatically detecting player roles in Among Us

Player role identification in the online social deduction game Werewolf has been a common research topic in the past decade. Among Us, a fairly new online social deduction game, gained a lot of popularity recently. Given the popularity of the game, an opportunity arises to extract information on social deduction. This research focuses on extracting information from emergency meetings in Among Us and subsequently using this information to automatically detect player roles. First, a framework is presented that can be used to extract information from videos with gameplay of Among Us. This framework can extract the chat messages from the emergency meetings, detect imposters at the end of the game, and extract voting data. Secondly, the framework is used to process videos with 59 games of Among Us gameplay. The data produced by the framework will be normalized and transformed into numerical data using term frequency-inverse document frequency (tf-idf), Finally, two types of Support Vector Machines (SVM) and a Naive Bayes classifier are used to automatically detect player roles. We show that detecting player roles is a difficult yet learnable challenge.
Learning to Decompose and Restore Low-light Images with Wavelet Transform

Low-light images often suffer from low visibility and various noise. Most existing low-light image enhancement methods often amplify noise when enhancing low-light images, due to the neglect of separating valuable image information and noise. In this paper, we propose a novel wavelet-based attention network, where wavelet transform is integrated into attention learning for joint low-light enhancement and noise suppression. Particularly, the proposed wavelet-based attention network includes a Decomposition-Net, an Enhancement-Net and a Restoration-Net. In Decomposition-Net, to benefit denoising, wavelet transform layers are designed for separating noise and global content information into different frequency features. Furthermore, an attention-based strategy is introduced to progressively select suitable frequency features for accurately restoring illumination and reflectance according to Retinex theory. In addition, Enhancement-Net is introduced for further removing degradations in reflectance and adjusting illumination, while Restoration-Net employs conditional adversarial learning to adversarially improve the visual quality of final restored results based on enhanced illumination and reflectance. Extensive experiments on several public datasets demonstrate that the proposed method achieves more pleasing results than state-of-the-art methods.
The Financial Information Management System Mechanism of Domestic Top Three Hospitals Based on Digital ID Technology and Crawler Mining

Based on the digital ID technology and crawler mining algorithm, this paper studies the financial information management system and mechanism of the domestic top three hospitals. First, it analyzes the financial informationization of the top three hospitals, the top three hospitals, the connotation of financial information, performance management, performance management, and the process of performance management., The purpose of performance management. Then it mainly analyzes the concrete manifestation of financial information in the top three hospitals. Including two aspects: content manifestation and strategic examples. Then use examples to illustrate the direct impact of hospital informatization, especially financial informatization, on medical benefits. Finally, the research system index was constructed by focusing on the expert consultation method, and the efficiency was increased by 7.5%.
Research on Blockchain Consensus Model for Joint Action of Unmanned Platform

In joint action, unmanned platforms are increasingly being used. However, due to constraints such as the means of obtaining intelligence, the type of information source, and the computing power, different unmanned platforms have quite different assessments of the status. To solve this problem, this paper proposes a multi-source blockchain state consensus model for joint action of unmanned platforms. Firstly, the light nodes in the model collect the status information of the target, the full nodes evaluate the status of the target, and the light nodes verify the status result. Secondly, in view of the inconsistency of the assessment results of the target state by different unmanned platforms, a weighted voting method is proposed to achieve the consensus on the target state. Finally, through simulation experiments, the model is feasible in the unmanned platform's consensus on the target state. Experimental data from the aspects of accuracy, safety, and real-time performance show that the model is more superior than the traditional target state assessment.
Multimodal Bare-Bone Niching Differential Evolution in Feature Selection

Feature selection (FS) is to select relevant feature subsets from a given feature set to reduce the dimensions of data. In the classification task, feature selection can not only reduce the calculation cost but also improve the classification accuracy. In this paper, a multimodal bare-bone niching differential evolution algorithm (MBNDE) is proposed for FS in classification problems. MBNDE finds multiple feature combinations that can achieve the highest classification rate. Three different strategies have been applied to MBNDE in the niching process, and the corresponding algorithms are termed MBNDE-CC (crowding and cluster), MBNDE-CS (crowding and speciation), as well as MBNDE (index-based). A modified 3-NN classifier is designed to evaluate the classification rate of different feature combinations. The results of the proposed method and some existing multimodal and unimodal algorithms for FS are compared. The proposed algorithm shows promising performance.
Face Recognition Using Local Multiple Zig-Zag Structure Pattern

This paper presents a novel algorithm local multiple zig-zag structure pattern (LMZSP) for face recognition. The proposed method modifies the Weber’s ratio and obtains the local multiple pattern (LMP) images by exploring the relationship between center and neighboring pixels. In order to make these patterns more robust to noise and intensity variations, local multiple zig-zag structure pattern (LMZSP) has been proposed by applying the local zig-zag graph structure (LZGS) on the four LMP images. Further, the resulting high dimensional feature vector is converted to low dimensional feature using linear discriminant analysis (LDA) algorithm. The robustness of feature is tested and analyzed on faces from uncontrolled environment. The recognition results clearly proves the effectiveness of the proposed method as compared to the other state-of-the-art approaches.
Generalized Conics with the Sharp Corners

This paper analyses the properties of generalized conics that are defined by N focal points with weights. The generated shapes can be convex or concave. By varying the weights, it is possible to obtain up to N corners associated with the focal points. From the shape analysis perspective, the generalized conics extend the capabilities of the ellipse by adding the single extra parameter. In general, they enrich the potential of approaches to describe or represent the shape.
Multi-view Relevance Matching Model of Scientific Papers Based on Graph Convolutional Network and Attention Mechanism

Deep learning has been widely used in text matching tasks. However, the existing deep learning models are mainly designed for short texts matching and cannot be directly applied to the search of scientific papers. The main reason is that the differences between long and short texts in scientific paper search have not been fully considered, and the structural information of the text will be lost when the length difference is large. In order to solve the above long-short scientific text matching problem, we propose a multi-view relevance matching model (MVRM) of scientific papers based on graph convolutional network and attention mechanism. First, we use scientific papers abstract to construct interactive graph to retain the structural information in the long text. Each vertex denotes the keyword in the abstract and the edge weight denotes similarity between the keywords. Second, we propose a matching network for interactive graph based on the graph convolution networks. Multiple keywords in the search term form multiple views, and each keyword under each view interacts with the interactive graph. Then the interaction feature vectors from multiple views are generated through graph convolution network. Finally, attention mechanism is used for fusion, and the final matching result is output through the multilayer perceptron. Experiments on several representative scientific paper search datasets demonstrate that our model achieves better performance.
ДУХОВНО-ІНТЕЛЕКТУАЛЬНА СКЛАДОВА ТВОРЧОГО САМОВИРАЖЕННЯ МАЙБУТНІХ МУЗИЧНОПЕДАГОГІЧНИХ ПРАЦІВНИКІВ

The article states that the creative self-expression of future music and pedagogical workers is carried out under the influence of spiritual and intellectual qualities of the individual. Such qualities include the development of the spiritual world of man, the discovery of the ability of intellectual thinking. Emphasis is placed on professional self-improvement of spiritual and intellectual qualities of the teacher, his popularization of the best examples of popular music.
Data Hiding Based on Redundant Space of WeChat Mini Program Codes

With the widespread use of mini program codes, research on data hiding based on mini program codes is necessary for protecting the copyright and security of the code. However, in the existing researches, the secret payload is low, which limits the practical application. In this paper, a data hiding scheme based on the redundant space of mini program codes is proposed, which embeds secret information into the uncoded redundant space. The redundant space includes adding extended modules outside each ray (also called rays extension) and adding fake rays (also called rays forgery). The proposed scheme has the advantages of large embedded capacity, good imperceptibility, and strong applicability. For the version v, and the number of modules expanded on the ray $$n_e$$ , the maximum embedding capacity is $$112 (4 - v/18) + 13 (4 - v/18) +56 n_e $$ . Theoretical analysis and experiments show the feasibility and effectiveness of the proposed scheme.
Taming the knight's tour: Minimizing turns and crossings

We introduce two new metrics of “simplicity” for knight's tours: the number of turns and the number of crossings. We give a novel algorithm that produces tours with 9.25n+O(1) turns and 12n+O(1) crossings on an n×n board, and we show lower bounds of (6−ϵ)n and 4n−O(1) on the respective problems of minimizing these metrics. Hence, our algorithm achieves approximation ratios of 9.25/6+o(1) and 3+o(1). Our algorithm takes linear time and is fully parallelizable, i.e., the tour can be computed in O(n2/p) time using p processors in the CREW PRAM model. We generalize our techniques to rectangular boards, high-dimensional boards, symmetric tours, odd boards with a missing corner, and tours for (1,4)-leapers. In doing so, we show that these extensions also admit a constant approximation ratio on the minimum number of turns, and on the number of crossings in most cases.
The Success of Virtual Reality (VR) in the Gaming Industry

With the development of technology, VR games conform to the development of technology and occupy an increasingly important position in the video game industry. Therefore, this paper introduces the success of the new “Beat Saber” VR game and how the new design ideas and technology are successfully applied in VR games. Finally, it is concluded that the increasingly mature design ideas and the ever-changing technological development have made VR games a huge success and quickly gained a place in the video game industry. In the future, it will bring more opportunities to the entire game industry.
Nearest Neighbor Synthesis of CNOT Circuit Based on Matrix Transformation

In order to obtain a CNOT circuit with better performance under the nearest neighbor (NN) constraint, the Boolean matrix representation of the CNOT circuit and its change in the NN synthesis process are analyzed. The matrix flipped by the center and the transpose matrix of the Boolean matrix can also be used for the NN synthesis, and the NN circuit with the least cost is chosen as the final result. In order to further reduce the runtime of the method and improve the efficiency of solving large-scale problems, the parallel implementation algorithm is put forward. Tested on different circuit lengths on five architectures, the results show that under the same parameters and environment, the number of NN CNOT gates is less than that of the existing method, and the maximum optimization rate is 29.75%. The average speedup of parallel execution is 3.02 with quad-core processor.