Reviewerly/exHarmony huggingface dataset:
Concertmaster’s and actor’s algorithm while working on the role-part in the tradition of Russian music theatre in the 20th century

The author of the article takes into the account his work as a stage director with the dramaturgy of the vocal material in the Russian music theatre, as well as the analysis of the rehearsal process of the famous theatrical figures on the music and text material of the opera. He reveals the specific features of the joint creative process of both the accompanist and the actor on the part-role in the process of creating an artistic images in an opera performance. It is analyzed the situation, common in the rehearsal process, when most practitioners in the field of performing arts focus on the final process of embodying the image of the character. Thus they miss the initial stage of preparing the actor for learning the music and poetic text. In the center of the article are systematic statements and recorded experience of working with the actors and accompanists of the chamber music theatre on the part-role of the famous practitioners of the Russian music scene L. A. Mogilevskaya and B.A. Pokrovsky. It reveals the connection of their directorial rehearsal methodology with the systematic works of F.I. Shalyapin and K.S. Stanislavsky. Developing N.I. Kuznetsov’s concept about the stages of careful studying of musical and textual opera material together with the accompanist, practical recommendations are given to novice singers, mature opera actors and teachers on the details of the interaction between the actor and the coach in modern realities. In many ways, they are based on the author’s own observations of the rehearsal process of the chief guest conductor Jan Latham-Koenig (Great Britain) at The Kolobov Novaya Opera Theatre of Moscow. The results obtained allow us to judge about the full involvement of world-famous conductors in the problems of the work of an accompanist and a professional vocalist. It is proposed to systematize the main stages of the opera actort’s work on the part-role. It is important to take into account the real theatrical experience and the requirements of the conductor in the perspective of the opportunities that open up before him in the process of cooperation with the accompanist at the opera stage.
Smart Contracts: Potential and Legal Status

Blockchain has been posed as a revolutionary technology. Its application is diametrically opposed to the centralized conventional mechanisms. However, it would appear that blockchain has only been able to have a significant impact on payment mechanisms and financial transactions. Blockchain technology went on to revolutionize the financial industry with the advent of Bitcoin in 20081. However, many within the legal and business fraternity believe that smart contracts will outsize the disruption caused by crypto-currency and believe that smart contracts will reinvent the way businesses and people alike enter into transactions2. The purpose of this paper is two pronged: first, to analyse the use of smart contracts to understand if its positives outweigh its negatives or viceversa and analyse how this technology can benefit India; and second, to analyse the legal recognition and enforceability of smart contracts in India while drawing from the regulatory experiences of other jurisdictions, with a focus on the experience of the United Kingdom.
Premonition of disaster: The traditions of Chekhov’s drama in the play by T. L. Shchepkina- Kupernik “Happy Woman”

Tatyana Lvovna Shchepkina-Kupernik is known as the author of theatrical memoirs and brilliant plays’ translations, but her dramaturgy heritage still lacks the research attention. The article analyzes one of the most striking plays by T.L. Shchepkina-Kupernik “The Happy Woman”. In 1911 it received the Griboyedov award for the best original dramatic play of that theatrical season, though nowadays it is undeservedly forgotten. The article disputes the prevailing idea of the play “The Happy Woman” as a work of exclusively gender themes, focused on the “women’s issue”. The analysis of the play’s poetics (features of the drama conflict, the nature of the dialogues, scenography, the specifics of the author’s remarks, the symbolism of sound images) allows us to conclude that Shchepkina-Kupernik consistently implemented the traditions of Chekhov’s dramaturgy. Certain symbolic overtones are created by the motif of “the lost life”, embodied in the fate of the main character, the emphasized weakness of male characters (focus on a career, superficiality, inability to influence the situation, unavailability). It deepens the content of the dramatic conflict and leads to the tragic outcome, just like in Chekhov’s plays, “not individual people are to blame, but the whole existing composition of life as a whole”. There is a bitter irony in the very title of the play, exposing the self-deception of the representatives of the world and the inability of those in power to maintain their dominant position. It is concluded that in the play “The Happy Woman” Shchepkina-Kupernik managed to identify the most dramatic collisions of the era. Written between two revolutions, the play was a reflection of the situation in the country on the eve of the impending catastrophe.
DESIGNING AND IMPLEMENTING THE LOGICAL SECURITY FRAMEWORK FOR ECOMMERCE BASED ON SERVICE ORIENTED ARCHITECTURE

Rapid evolution of information technology has contributed to the evolution of more sophisticated E- commerce system with the better transaction time and protection. The currently used E-commerce models lack in quality properties such as logical security because of their poor designing and to face the highly equipped and trained intruders. This editorial proposed a security framework for small and medium sized E-commerce, based on service oriented architecture and gives an analysis of the eminent security attacks which can be averted. The proposed security framework will be implemented and validated on an open source E-commerce, and the results achieved so far are also presented.
Performance Modeling of Microservice Platforms

Microservice architecture has transformed the way developers are building and deploying applications in the nowadays cloud computing centers. This new approach provides increased scalability, flexibility, manageability, and performance while reducing the complexity of the whole software development life cycle. The increase in cloud resource utilization also benefits microservice providers. Various microservice platforms have emerged to facilitate the DevOps of containerized services by enabling continuous integration and delivery. Microservice platforms deploy application containers on virtual or physical machines provided by public/private cloud infrastructures in a seamless manner. In this paper, we study and evaluate the provisioning performance of microservice platforms by incorporating the details of all layers (i.e., both micro and macro layers) in the modelling process. To this end, we first build a microservice platform on top of Amazon EC2 cloud and then leverage it to develop a comprehensive performance model to perform what-if analysis and capacity planning for microservice platforms at scale. In other words, the proposed performance model provides a systematic approach to measure the elasticity of the microservice platform by analyzing the provisioning performance at both the microservice platform and the back-end macroservice infrastructures.
Semantic and Influence aware k-Representative Queries over Social Streams

Massive volumes of data continuously generated on social platforms have become an important information source for users. A primary method to obtain fresh and valuable information from social streams is \emph{social search}. Although there have been extensive studies on social search, existing methods only focus on the \emph{relevance} of query results but ignore the \emph{representativeness}. In this paper, we propose a novel Semantic and Influence aware $k$-Representative ($k$-SIR) query for social streams based on topic modeling. Specifically, we consider that both user queries and elements are represented as vectors in the topic space. A $k$-SIR query retrieves a set of $k$ elements with the maximum \emph{representativeness} over the sliding window at query time w.r.t. the query vector. The representativeness of an element set comprises both semantic and influence scores computed by the topic model. Subsequently, we design two approximation algorithms, namely \textsc{Multi-Topic ThresholdStream} (MTTS) and \textsc{Multi-Topic ThresholdDescend} (MTTD), to process $k$-SIR queries in real-time. Both algorithms leverage the ranked lists maintained on each topic for $k$-SIR processing with theoretical guarantees. Extensive experiments on real-world datasets demonstrate the effectiveness of $k$-SIR query compared with existing methods as well as the efficiency and scalability of our proposed algorithms for $k$-SIR processing.
Overbooking Microservices in the Cloud

We consider the problem of scheduling serverless-computing instances such as Amazon Lambda functions, or scheduling microservices within (privately held) virtual machines (VMs). Instead of a quota per tenant/customer, we assume demand for Lambda functions is modulated by token-bucket mechanisms per tenant. Such quotas are due to, e.g., limited resources (as in a fog/edge-cloud context) or to prevent excessive unauthorized invocation of numerous instances by malware. Based on an upper bound on the stationary number of active "Lambda servers" considering the execution-time distribution of Lambda functions, we describe an approach that the cloud could use to overbook Lambda functions for improved utilization of IT resources. An earlier bound for a single service tier is extended to multiple service tiers. For the context of scheduling microservices in a private setting, the framework could be used to determine the required VM resources for a token-bucket constrained workload stream. Finally, we note that the looser Markov inequality may be useful in settings where the job service times are dependent.
PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Clouds

Point clouds obtained with 3D scanners or by image-based reconstruction techniques are often corrupted with significant amount of noise and outliers. Traditional methods for point cloud denoising largely rely on local surface fitting (e.g., jets or MLS surfaces), local or non-local averaging, or on statistical assumptions about the underlying noise model. In contrast, we develop a simple data-driven method for removing outliers and reducing noise in unordered point clouds. We base our approach on a deep learning architecture adapted from PCPNet, which was recently proposed for estimating local 3D shape properties in point clouds. Our method first classifies and discards outlier samples, and then estimates correction vectors that project noisy points onto the original clean surfaces. The approach is efficient and robust to varying amounts of noise and outliers, while being able to handle large densely-sampled point clouds. In our extensive evaluation, both on synthesic and real data, we show an increased robustness to strong noise levels compared to various state-of-the-art methods, enabling accurate surface reconstruction from extremely noisy real data obtained by range scans. Finally, the simplicity and universality of our approach makes it very easy to integrate in any existing geometry processing pipeline.
Variational quantum generators: Generative adversarial quantum machine learning for continuous distributions

We propose a hybrid quantum-classical approach to model continuous classical probability distributions using a variational quantum circuit. The architecture of the variational circuit consists of two parts: a quantum circuit employed to encode a classical random variable into a quantum state, called the quantum encoder, and a variational circuit whose parameters are optimized to mimic a target probability distribution. Samples are generated by measuring the expectation values of a set of operators chosen at the beginning of the calculation. Our quantum generator can be complemented with a classical function, such as a neural network, as part of the classical post-processing. We demonstrate the application of the quantum variational generator using a generative adversarial learning approach, where the quantum generator is trained via its interaction with a discriminator model that compares the generated samples with those coming from the real data distribution. We show that our quantum generator is able to learn target probability distributions using either a classical neural network or a variational quantum circuit as the discriminator. Our implementation takes advantage of automatic differentiation tools to perform the optimization of the variational circuits employed. The framework presented here for the design and implementation of variational quantum generators can serve as a blueprint for designing hybrid quantum-classical architectures for other machine learning tasks on near-term quantum devices.
A Fusion Method of 3D Object Detection Graph Neural Network Based on Local and Global Data Augmentation

LiDAR-based 3D object detection is an important task for autonomous driving because it provides the location information of objects on the road. However, the existing methods perform poorly in the detection of distant objects since they suffer from sparse and incomplete point clouds. To overcome the problems caused by multi-scale and occupancy, we propose a novel graph-based framework, i.e. named a fusion method of 3D object detection graph neural network based on local and global data augmentation (LGDA-GNN), for accurate 3D object detection from the point clouds. Firstly, we summarize seven characteristics of point cloud data, and point out a new way to improve accuracy of this task. Secondly, to select effective vertices of graph neural network and to enhance local features and the proportion of point cloud at medium and long distances, we propose methods including self-adaptive multi-scale voxel downsampling and multi-step graph construction. Thirdly, to improve the accuracy of difficult category, a fusion method of self-attention module and visibility property is presented for global feature reinforcement. Finally, we integrate our refinement modules into a graph-based pipeline, and extensive experiments on the KITTI benchmark show that we achieve a great performance on the difficult car object for the bird eye view detection and 3D object detection task.
Maximum-Relevance and Maximum-Complementarity Feature Selection with Random Forest

In feature selection for high-dimensional data, in order to select the minimum number of features that can well explain the target, it requires finding the relevant features to the predictive target, as well as removing the redundant information by discovering the feature interactions. Existing approaches usually measure the feature interactions using relevance between features without considering their joint dependencies on the target. In this paper, we propose a new feature selection criterion, Maximum-Relevance and Maximum-Complementarity (MRMC). Besides the relevance with the target, MRMC takes into consideration the complementary information of a candidate feature to a selected feature or feature subset when predicting the target. We then present an efficient approach to calculate the information complementarity between features with random forests. Finally we implement MRMC Feature Selection using sequential forward search (SFS). Experimental results on 18 data sets show that SFS-MRMC achieved the best overall performances compared with other information-theoretical feature selection methods and RF-RFE.
The Effectiveness of Virtual Events During Covid-19 Pandemics

Events suffer from severe impacts of COVID-19 restrictions on mass gatherings. The industry shifts to digital environment to sustain. However, virtual events’ limitation is multi-dimensional interaction resulting in consumer experience and satisfaction. Therefore, the effectiveness of virtual events is questioned. Key themes of COVID-19 impacts on the event industry and virtual event environment are review. Then, a conceptual model on event experience is applied as the theoretical framework of this research. This research is guided by the constructivism approach. Using qualitative methods and a semi-structured questionnaire, six interviews were conducted to exploit narratives on virtual event interaction and attendee experience. Data are analyzed according to three key themes of pre-, during-, and post-event experience. The incompletion of five senses in virtual event design restrains interpersonal interaction which negatively affect attendee experience. Some practices are recommended to increase virtual event’s effectiveness, including virtual avatars, three-dimensional projection, and hybrid events. Experience on virtual events is negative due to low degree of interpersonal interaction. Various application of virtual reality technology is suggested to improve effectiveness. Despite some limitations, this research has expanded the literature of virtual events.
A technique for designing nano-scale circuits using a fuzzy logic and nature-inspired fish swarm optimization algorithm

Recent breakthroughs in quantum processing software and hardware have shown substantial development toward operational results. We anticipate quantum computing to have significant applications in some domains ranging from machine learning and optimization to drug development in the future. Quantum computing and designing have gotten a lot of interest from investigators. Physical design is one of the two main stages in designing nano-scale circuits that receive a list of circuit nodes as input and produce the final arrangement in a particular technology. However, it has encountered several design issues and execution limits in the long term. Moreover, designing nano-scale circuits is an NP-hard problem in terms of cost, delay, and energy consumption. This paper has provided a new method for solving the optimal design of nano-scale circuits using a fuzzy-based Fish Swarm Algorithm (FSA). The FSA represents fish, hunting, and fish behavior to get the best results. It has a quick integration speed, a powerful global search capacity, and a significant shortcoming. In our proposed method, the fuzzy observer is responsible for detecting the system's state within the defined area and maintaining the state within it. The proposed method has been evaluated against some benchmarks, and cost, delay, energy, and area characteristics have been reduced. Simulation outcomes demonstrated the suggested method's correct performance in terms of "energy," "cost", "area", and "delay" where the suggested technique outperforms others (GA, PSO, FSA and ACO).
NOSQL DATABASE LOGIC DESIGN METHODS FOR MONGODB AND NEO4J

Modern developers of gaming mobile and Internet applications almost do not imagine themselves without the use of NoSQL databases, if they pursue the goal of creating scalable databases with high-performance and wide functionality. When designing a database for any NoSQL system, the developer needs a clear understanding of the logic of such databases and the capabilities of the tools offered by the corresponding DBMS. However, unfortunately, they do not have unified methods of logical design of such models, as in relational databases. Thus, there is a problem of developing effective methods for the logical design of such databases that would provide the necessary performance when implementing the business logic of the corresponding applications. The subject of the research is approaches to the logical design of NoSQL document and graph databases. The goal of the work is to propose unified logical modeling methods for MongoDB and Neo4j NoSQL systems based on an experimental study of their performance. The following tasks are solved in the work: analysis of current approaches to the logical design of document and graph databases\, the development of logical design methods for them; planning and experimental study of the performance of the proposed methods on the example of models developed with their help. The following methods are used: database design methods, database performance evaluation methods, development methods are based on MongoDB 5.0.5, Neo4j 4.4.3 DBMS, Visual Studio 2022 development environment. The following results are obtained: unified logical design methods for MongoDB and Neo4j NoSQL systems are proposed; on their basis, the corresponding logical models have been developed; experimental measurements of the number of resources required working with the developed models; recommendations on the proposed methods are formed. Conclusions: The proposed modeling methods for MongoDB have their own aspects of their effective use for different types of applications. The strengths and weaknesses of both methods were identified, but a mixed method based on a combination of modeling through normalization and denormalization was recommended. Even though Neo4j lost out to MongoDB in terms of consumed resources in most experiments, both DBMS's' demonstrate good productivity, taking into account the orientation to different tasks.
Design of Intelligent Interactive System for Economic Management Computer Simulation Laboratory based on Python Structured Module

Design of the intelligent interactive system for economic management computer simulation laboratory based on Python structured module is presented in the paper. The development language of the intelligent information operation and maintenance management platform chooses Python. The main reason for choosing the Python language is that it is simple, fast and flexible, commonly known as “glue language”. The application of the Python scripting language to web crawler design has its unique advantages. Scraping is an automatic web crawler framework developed and packaged based on Python language, through which directional data crawling can be performed. Based on these technique innovation, this paper gives the novel understanding of the intelligent interactive system for economic management computer simulation laboratory based on Python structured module. The simulation has proven the general performance.
Identification and Intelligent Analysis of Family Tragedies in Modern Dramas based on Information Extraction from Social Video Filters

In modern dramas, there are many excellent drama works with family as the prototype and background. They focus on discussing the presentation of family conflicts in individual families in the big era and the changes in the relationship between family members. This article is based on the extraction of social video filter information. In modern tragedies, the spatial thinking of dialogue drama scenes is extended, and the identification and intelligent analysis of spatial dimensions and dramatic conflict settings are explored from four aspects: regional space, political space, psychological space and room. This paper analyzes the feature extraction of social video information from the dimensions of social demand, communication subject, and communication content, and proposes to create an excellent video viewing environment to meet the needs of the audience and promote social video to achieve better communication effects.