bluuebunny/crossref_metadata_2025 huggingface dataset:
EXTRACTION OF TRAFFIC FEATURES IN SOFTWARE- DEFINED NETWORKS USING AN SDN CONTROLLER
Authors: Volkov S. , Kurochkin I.
Published: January 2022

Machine learning methods can be used to solve the problems of detecting and countering attacks onsoftware-defined networks. For such methods, it is necessary to prepare a large amount of initial datafor training. Mininet is used as a modeling environment for SDN. The main tasks of modeling asoftware-defined network are studying traffic within the network, as well as practicing variousscenarios of attacks on network elements. The SDN controller ONOS (Open Network OperatingSystem) is used as the network controller. Various network topologies are considered in the modeling.The possibility of analyzing information about traffic within the network using an SDN controller inreal time is investigated, as well as the possibility of collecting information in the form of a set offeatures. Modeling of software-defined networks under different initial conditions and for differentattack scenarios can be carried out on a distributed computing system. Since the computationalproblem to be solved can be divided according to the data into many autonomous tasks, it is possibleto use desktop grid system and voluntary distributed computing to speed up the process.
HIGH RESOLUTION IMAGE PROCESSING AND LAND COVER CLASSIFICATION FOR HYDRO- GEOMORPHOLOGICAL HIGH-RISK AREA MONITORING
Authors: Miniello G. , La Salandra M.
Published: January 2022

High-resolution image processing for land surface monitoring is fundamental to analyze the impact ofdifferent geomorphological processes on Earth surface for different climate change scenarios. In thiscontext, photogrammetry is one of the most reliable techniques to generate high-resolutiontopographic data, being key to territorial mapping and change detection analysis of landforms inhydro-geomorphological high-risk areas. An important issue arises as soon as the main goal is toconduct analyses over extended areas of the Earth surface (such as fluvial systems) in a short time,since the need to capture large datasets to develop detailed topographic models may limit thephotogrammetric process, due to the high demand of high-performance hardware. In order toinvestigate the best set up of computing resources for these very peculiar tasks, a study of theperformance of a photogrammetric workflow based on a FOSS (Free Open-Source Software) SfM(Structure from Motion) algorithm using different cluster configurations was conducted, leveragingthe computing power of ReCaS-Bari data center infrastructure, which hosts several services such asHTC, HPC, IaaS, PaaS. Exploiting the high-computing resources available at clusters and choosingspecific set up for the workflow steps, an important reduction of several hours in the processing timewas recorded, especially compared to classic photogrammetric programs processed on a singleworkstation with commercial softwares. The high quality of the image details can be used for landcover classification and preliminary change detection studies using Machine Learning techniques. Asubset of the datasets used for the workflow implementation has been considered to test theperformance of different Convolutional Neural Networks, using progressively more complex layersequences, data augmentation and callback functions for training the models. All the results are givenin terms of model accuracy and loss and performance evaluation.
DETECTION OF FERTILE SOILS BASED ON SATELLITE IMAGERY PROCESSING
Authors: Grishkin V. , Zhivulin E. , Khokhriakova A. , Karimov S.
Published: January 2022

The paper proposes a method for detecting fertile soils based on the processing of satellite images. Asa result of its application, a map of the location of fertile and infertile soils for a given region of theearth's surface is formed and the corresponding areas are calculated. The method for detecting fertilesoils is based on the fact that fertile soil includes areas covered with vegetation in the spring-summerperiod. Therefore, by measuring the spectral characteristics of these areas in the late autumn period,when there is no vegetation on them, it is possible to obtain objective parameters of fertile soils. Fordetection, a number of classifiers are being built that recognize two classes - fertile soil and sand,which is especially important when monitoring areas prone to desertification. The feature vector usedfor classification is a set of indices similar to the well-known NDVI index. This set of indices iscalculated for each pixel of the image by its values in different spectral channels. Classifiers areimplemented using CUDA parallel computing technology on a GPU. Based on the results of theexperimental study, a classifier is selected that has shown the best characteristics of the recognitionquality.
SERVICES OF COMPUTATIONAL NEUROBIOLOGY TASKS, BASED ON THE DISTRIBUTED MODULAR PLATFORM «DIGITAL LABORATORY» NRC «KURCHATOV INSTITUTE»
Authors: Enyagina I. , Polyakov A. , Kokovin D.
Published: January 2022

This paper describes program services for performing tasks of computational neurobiology, based onthe distributed modular platform "Digital Laboratory" NRC «Kurchatov Institute». These servicesexpanded the system "Neuroimaging" for storing, processing and analyzing experimental MRI / fMRIdata of the human brain. The main goal of creating these program services is the softwareimplementation of methods for calculating the functional connectivity of regions of the human brain atrest using fMRI data, and visualization of the results.
HPC WORKLOAD BALANCING ALGORITHM FOR CO- SCHEDULING ENVIRONMENTS
Authors: Kuchumov R. , Korkhov V.
Published: January 2022

The goal of this research work is to reduce wait time of HPC (high performance computing) applications in schedulers queue by applying a co-scheduling strategy. This strategy allows the execution of more than one task with different non-overlapping requirements for computational resources simultaneously. Co-scheduling strategy reduces task queue wait time and improves utilization of cluster resources when compared to the scheduling strategies that do not allow for parallel task execution on the same machine. We have proposed a method for measuring application processing speed in its run-time, which can be used as a feedback for scheduling strategies. In this work, we have formalized the co-scheduling problem and proposed strategies for solving it. For some strategies we have shown analytically the upper bounds values of their competitive ratios. Besides that for the proposed scheduling strategies we ran numerical experiments using imitation models to show how they compare to the optimal strategy.
CONCEPT OF PEER-TO-PEER CACHING DATABASE FOR TRANSACTION HISTORY STORAGE AS AN ALTERNATIVE TO BLOCKCHAIN IN DIGITAL ECONOMY
Authors: Belov M. , Grishko S. , Cheremisina E. , Tokareva N.
Published: January 2022

This paper discusses the concept of a distributed horizontally scalable and cascadable peer-to-peercaching database, optimized for the digital economy needs and suitable for storing the history of alarge number of transactions of every citizen involved in business processes based on digitaltechnologies, starting from receiving public and social services in electronic form and ending withconsumption of electronic goods and services produced by e-business and e-commerce. We also offeran approach to organizing student teamwork for the development of the solution at the Dubna StateUniversity based on the use of our innovative data center project «Virtual Computer Lab»
DEEP LEARNING FOR AUTOMATIC RF-MODULATION CLASSIFICATION
Authors: Dima M. , Dima T.
Published: January 2022

Classical methods use statistical-moments to determine the type of modulation in question. Thisessentially correct approach for discerning amplitude modulation (AM) from frequency modulation(FM) fails for more demanding cases such as AM vs. AM-LSB (lower side-band rejection) - radiosignals being richer in information than statistical moments. Parameters with good discriminatingpower were selected in a data conditioning phase and binary deep-learning classifiers were trained forAM-LSB vs. AM-USB, FM vs. AM, AM vs. AM-LSB, etc. The parameters were formed asfeatures, from wave reconstruction primary parameters: rolling pedestal, amplitude, frequency andphase. Very encouraging results were obtained for AM-LSB vs. AM-USB with stochastic training,showing that this particularly difficult case (inaccessible with stochastic moments) is well solvablewith multi-layer perceptron (MLP) neuromorphic software.
JINR DISTRIBUTED INFORMATION AND COMPUTING ENVIRONMENT: PARTICIPANTS, FEATURES AND CHALLENGES
Authors: Balashov N. , Kuprikov I. , Kutovskiy N. , Makhalkin A. , Mazhitova Ye. , Pelevanyuk I. , Semenov R.
Published: January 2022

The JINR Distributed Information and Computing Environment (DICE) was created to combineresources for solving common scientific tasks, as well as to distribute peak loads across the resourcesof partner organizations from the JINR Member States. To monitor the hardware resources andservices of the growing DICE infrastructure, a system based on Prometheus and Thanos was designedand deployed. The collected metrics, including the geographical location of the JINR DICEparticipants, are visualized using Grafana. Software distribution is carried out with the help of theCERN Virtual Machines File System. All these topics, as well as challenges and possible overcomes,are covered in detail.
SOME ASPECTS OF THE WORKFLOW SCHEDULING IN THE COMPUTING CONTINUUM SYSTEMS
Authors: Kashansky V. , Prodan R. , Radchenko G.
Published: January 2022

Contemporary computing systems are commonly characterized in terms of data-intensive workflows, that are managed by utilizing large number of heterogeneous computing and storage elements interconnected through complex communication topologies. As the scale of the system grows and workloads become more heterogeneous in both inner structure and the arrival patterns, scheduling problem becomes exponentially harder, requiring problem-specifc heuristics. Despite several decades of the active research on it, one issue that still requires effort is to enable efficient workflows scheduling in such complex environments, while preserving robustness of the results. Moreover, recent research trend coined under term "computing continuum" prescribes convergence of the multiscale computational systems with complex spatio-temporal dynamics and diverse sets of the management policies. This paper contributes with the set of recommendations and brief analysis for the existing scheduling algorithms.
APPLYING NEW VIRTUALIZATION TECHNOLOGIES TO THE LEARNING PROCESS
Authors: Gasitashvili Z. , Modebadze Z. , Satseradze D.
Published: January 2022

The active adoption of virtualization methods in existing IT infrastructures and the transition to the useof GRID and cloud computing requires new personnel who own these technologies. The articleproposes an approach for creating a training ground using new methods of virtualization of computingresources. Using the proposed approach on the basis of existing computer technology, it is possible tocreate a failover cluster, inside which already configured virtual environments are launched in thecorresponding containers for various computing tasks, as well as for organizing the training process.
BENCHMARK OF GENERATIVE ADVERSARIAL NETWORKS FOR FAST HEP CALORIMETER SIMULATIONS
Authors: Rehm F. , Vallecorsa S. , Borras K. , Krücker D.
Published: January 2022

Highly precise simulations of elementary particles interaction and processes are fundamental toaccurately reproduce and interpret the experimental results in High Energy Physics (HEP) detectorsand to correctly reconstruct the particle flows. Today, detector simulations typically rely on MonteCarlo-based methods which are extremely demanding in terms of computing resources. The need forsimulated data at future experiments - like the ones that will run at the High Luminosity Large HadronCollider (HL-LHC) - are expected to increase by orders of magnitude, increasing drastically thecomputational challenge. This expectation motivates the research for alternative deep learning-basedsimulation strategies.In this research we speed-up HEP detector simulations for the specific case of calorimeters usingGenerative Adversarial Networks (GANs) with a huge factor of over 150 000x compared to thestandard Monte Carlo simulations. This could only be achieved by designing smart convolutional 2Dnetwork architectures for generating 3D images representing the detector volume. Detailed physicsevaluation shows an accuracy similar to the Monte Carlo simulation.Furthermore, we quantize the data format for the neural network architecture (float32) with the IntelLow Precision Optimization tool (LPOT) to a reduced precision (int8) data format. This results in anadditional 1.8x speed-up on modern Intel hardware while maintaining the physics accuracy. Theseexcellent results consolidate the beneficial use of GANs for future fast detector simulations
USING LABOR MARKET DATA FOR ANALYSIS AND EDUCATION
Authors: Filozova I. , Gavrilenko Yu. , Ilyina A. , Javadzade J. , Korenkov V. , Priakhina D. , Velieva T.
Published: January 2022

Education systems provide specialists of different levels and specializations for the labor market. However, in the modern dynamic world of artificial intelligence, pandemic, and remote work, the labor market is evolving dramatically from year to year. Universities and colleges must keep track of these changes to adapt educational programs and manage the number of student slots offered for different specializations. Detailed demand statistics from the labor market are a good data source for analysis to gauge current needs and predict future demand. Usually, there is no single source of data on all vacancies and CVs. Therefore, it is necessary to collect, preprocess, analyze and visualize existing fragmented data. In this work, we study different raw data sources, their strong and weak points. A set of basic metrics is proposed to be derived from the data for analysis. Several types of roles are defined as primary users of the aimed system, their features and standard use cases. A conceptual system design is proposed to fulfill the requirements of the labor market analysis task. The evolutionary prototypes of the services are presented.
MINIMIZING IMAGES OF DOCKER CONTAINER ROOT FILE SYSTEMS
Authors: Nikolaeva I. , Gankevich I.
Published: January 2022

Containers is a tool which is often used in development and testing applications because they are easyto-use and lightweight. The container is built based on an image, which is a template for the containerand a way to transmit it over the network. Images can be up to several gigabytes in size. Thus, if youneed to transfer the large application container over the network, or the available memory is scarce(for example, when developing IoT systems), it is necessary to minimize the size of the image.Container images are built on top of the Linux kernel. For interactive work with containers, theyinclude instructions and files that the application never works with inside the container. Theapplications that will be launched in the container and their number are known in advance, so you canuse Linux debugging tools to find out which files are used by applications in the container, and whichcan be excluded from it. This is how the Chainsaw application works. This paper presents the resultsof a study of several different Docker container images. For some application we obtained threefolddecrease of the image size; also, we found that the size of the reduced image is significantly affectedby the base image and the programming language in which the application that runs inside thecontainer is written.
DEVELOPING A TOOLKIT FOR TASK CHARACTERISTICS PREDICTION BASED ON ANALYSIS OF QUEUE’S HISTORY OF A SUPERCOMPUTER
Authors: Rezaei M. , Salnikov A. , Shiryaev A.
Published: January 2022

Empirical studies have repeatedly shown that in High-Performance Computing HPC users’ resource estimations lack accuracy. Therefore, resource underestimation may remove the job at any step ofcomputing and subsequently allocated resources will be wasted. Moreover, resource overestimationalso will waste resources. In this work, to effectively utilize the overall HPC system, we proposed anew approach to predict the required resources such as; number of required CPUs, time slots etc. fornewly submitted job. The study focused on predictive analytics tasks including regression andclassification. A supervised machine learning system, comprising several models, was trained basedon the collection of statistical data including per-job and per-user features collected from the referencequeue systems. Results indicated that adding more features to the dataset improves the predictionaccuracy. The possibility of designing a plugin to apply our machine learning system in practicalapplications was studied. A dynamically connected SLURM SPANK plugin was created that adds the“--predict-time” option and takes control on srun and sbatch commands while they are executed. Itwas found that the plugin enables practical use of our proposed machine learning system.
DEVELOPMENT OF A TOOL FOR INTERACTIVE DETAILING OF AREAS OF OBJECTS FOR THE STRENGTH MODELLING SYSTEM
Authors: Budlov E. , Iakushkin O. , Sedova O. , Gogotov A.
Published: January 2022

The development of technological progress makes increased demands on the strength properties of structural elements of buildings and structures, machines and mechanisms, and a decrease in their material consumption. This leads to the need for effective use of existing and creation of new methods of solid mechanics and the training of new highly qualified specialists. Additionally, having control over the objects` sizes will allow potential users (designers, engineers etc.) to consider the desired properties in the optimization problem. Otherwise, it would be both quite difficult and inefficient to utilize. This article describes a system for CAD modelling. Its implementation is based on open-source software, such as Python programming language, Jupyterlab and FEniCS library. The system also supports topological optimization and allows its user to integrate the desired numerical methods, which is currently not supported in the existing solutions. The execution time of solving the example problem was measured and presented together with the result. Special attention was paid to the comparison of the described solution with the currently existing ones.
IMPLEMENTATION OF THE CONDITION DATABASE FOR THE EXPERIMENTS OF THE NICA COMPLEX
Authors: Gertsenberger K. , Chebotov A. , Klimai P. , Alexandrov I. , Alexandrov E. , Filozova I. , Moshkin A.
Published: January 2022

The processing and analysis of experimental and simulated data are an integral part of all modern high-energy physics experiments. These tasks are of particular importance in the experiments of the NICA project at the Joint Institute for Nuclear Research due to the high interaction rate and large particle multiplicity of ion collision events, therefore, the task of automating the considered processes for the NICA complex has particular relevance. The paper describes a new information system based on the Condition Database, as well as related services to automate storing, managing, searching and using various parameters and operation modes of the on-going and future NICA experiments for experimental and simulated data processing. The implemented system provides the necessary information for event processing and physics analysis tasks, and organises the transparent, unifiedaccess to and management of the required parameter data in scientific research. The scheme and purposes of the Condition Database, its attributes, key aspects of the development are described. In addition, a common deployment system of the developed database and related services for the NICA experiments is noted.
IMPLEMENTING THE GRAPH MODEL OF THE SPREAD OF A PANDEMIC ON GPUS
Authors: Sudakov V. , Yashin N.
Published: January 2022

Modeling the spread of viruses is an urgent task in modern conditions. In the created model, contactsbetween people are represented in the form of the Watz and Strogatz graph. We studied graphs withtens of thousands of vertices with a simulation period of six months. The paper proposes methods foraccelerating computations on graph models using graphics processors. In the considered problem,there were two resource-intensive computational tasks: generating an adjacency matrix of a graph thatsimulates the presence of contacts between people and traversing this graph in order to simulateinfection. The calculations were carried out in sequential mode and with acceleration on GPUs. Themodeling system software is implemented using the Cuda, CuPy, PyTorch libraries. The calculationswere carried out using the Tesla T4 graphics accelerator. Compared to computations without usinggraphics accelerators, their application gave a 7-fold increase in speed.
THE TECHNOLOGY AND TOOLS FOR THE BUILDING OF INFORMATION EXCHANGE PACKAGE BASED ON SEMANTIC DOMAIN MODEL
Authors: Akatkin Yu. , Bich M. , Yasinovskaya E. , Shilin A.
Published: January 2022

This paper presents the technology developed by the authors with the aim to improve the semantic interoperability in heterogeneous environment, where the systems use web-services orchestrated by an object-oriented exchange bus for cross-agency interaction and information sharing. The suggested solution, including the set of tools, allows to map the models of interacting information systems with aunified data model (domain otology) on the semantic level during the development of an information exchange package.
DIRECT COMPUTATIONAL EXPERIMENT IN STORM HYDRODYNAMICS OF MARINE OBJECTS
Authors: Degtyarev A. , Gankevich I. , Khramushin V.
Published: January 2022

The paper presents and discusses a new computer toolkit for assessing the seaworthiness of a ship instormy sailing conditions, intended for testing new design solutions for promising ocean-going shipsand ships of unlimited ocean navigation, as well as organizing full-function simulators. The presentedtoolkit can be used by captains to select effective and safe sailing modes, as well as to train personnel.A modern computational experiment allows direct modeling of intense sea waves, which fullyrecreates the hydromechanics of external influences on the ship, and ensures its full positioningrelative to the crests of storm waves. Such experiments require the use of high-performance computingtechnology based on GPGPU, as well as distributed data preparation and processing. Parameters ofthree structures of real sea waves, including extremely high heights, are determined as the initialconditions for modeling. The ship also possesses real dynamic characteristics, and she is capable to goby arbitrary courses in relation to waves. The possibility of dynamically changing the parameters ofthe ship during the experiment is provided. It is required to assess the motion state in various cases ofship loading, including emergency. The most important feature of the software is a full-fledged threedimensional visualization of all storm waves, as well as the spatial position and trajectory of the shipand its parameters. A special experimental environment for engineering surveys for projected ships iscreated using the graphical tools of OpеnGL. This computer toolkit is considered within theframework of the concept of a virtual testbed.
ACCOUNTING AND MONITORING INFRASTRUCTURE FOR DISTRIBUTED COMPUTING IN THE ATLAS EXPERIMENT
Authors: Alekseev A. , Barberis D. , Beermann T.
Published: January 2022

The ATLAS experiment uses various tools to monitor and analyze the metadata of the main distributed computing applications. One of the tools is fully based on the Unified Monitoring Infrastructure provided by the CERN-IT MonIT group. The Unified Monitoring Infrastructure uses modern and efficient open-source solutions such as Kafka, InfluxDB, ElasticSearch, Kibana and Grafana to collect, store and visualize metadata produced by data and workflow management systems. This software stack is adapted for the ATLAS experiment and allows the development of dedicated monitoring and accounting dashboards in Grafana visualization environment. The current state of the monitoring infrastructure and overview of core monitoring and accounting dashboards in the ATLAS are presented in this contribution.
IPV6 DUAL-STACK DEPLOYMENT FOR THE DISTRIBUTED COMPUTING CENTER
Authors: Kotliar A. , Kotliar V.
Published: January 2022

Computing center of the Institute for High Energy Physics in Protvino provides computing and storageresources for various HEP experiments (Atlas, CMS, Alice, LHCb) and currently operates more than150 working nodes with around 3000 cores and provides near 2.5 PB of disk space. All theseresources are connected through two 10 GB/s links to LHCONE and other research networks. IHEPcomputing center has IPv4 address space limited to one C-sized network and all computing nodesworking behind the NAT that has some drawbacks for production use. To optimize routing, switchingand to get higher network throughput for data transfer the IPv6 dual-stack was deployed on thecomputing farm. This work shows the full cycle of the real IPv6 dual-stack deployment from zero toproduction.
GRAMMAR PARSER-BASED SOLUTION FOR THE DESCRIPTION OF THE COMPUTATIONAL GRAPH FOR THE GNA FRAMEWORK
Authors: Tsegelnik N. , Gonchar M. , Treskov K.
Published: January 2022

The data flow paradigm has established itself as a powerful approach to machine learning. In fact, it isalso very powerful for computational physics, although it is not used as much in the field. One of thecomplications is that physical models are much less homogeneous compared to ML, which makestheir description a complicated task. In this paper we present a syntax analyzer for the GNAframework. The framework is designed to build mathematical models as lazy evaluated directedacyclic graphs. The syntax analyzer introduces a way for a concise description and configuration ofthe models using math-like syntax, providing scalability and branching. The goal of the project is todevelop a technique and a software to facilitate a generic analysis and input data descriptioncompatible with multiple backends, e.g. GNA.
COMPLETE DECENTRALIZATION OF DISTRIBUTED DATA STORAGES BASED ON BLOCKCHAIN TECHNOLOGY
Authors: Demichev A. , Kryukov A.
Published: January 2022

The work presents a solution for completely decentralized data management systems in geographically distributed environments with administratively unrelated or loosely related user groups and in conditions of partial or complete lack of trust between them. The solution is based on the integration of blockchain technology, smart contracts and provenance metadata driven data management. Architecture, operation principles and algorithms developed provides fault-tolerant, safe and reliable management of provenance metadata, control of operations with data files, as well as resource access management in collaborative distributed computing systems. The latter refer to distributed systems formed by combining into a single pool of computer resources of various organizations (institutions) to work together in the framework of some project.
RISK MODEL OF APPLICATION OF LIFTING METHODS
Authors: Bogdanov A. , Degtyarev A. , Shchegoleva N. , Khvatov V. , Zaynalov N. , Kiyamov J. , Dik A. , Faradzhov A.
Published: January 2022

The article discusses the main provisions (methods, risk models, calculation algorithms, etc.) of theissue of organizing the protection of personal data (PD), based on the application of anonymizationprocedure. The authors reveal the relevance of the studied problem based on the tendency of thegeneral growth of informatization and the further development of the Big Data technology. Thiscircumstance leads to the need to use the so-called risk approach based on calculating the risk of PD asa probabilistic assessment of the amount of possible damage that the owner of the data resource mayincur as a result of a successfully carried out information attack. For this purpose, the article describesan algorithm for calculating the risk of PD and proposes a risk model of the depersonalizationprocedure, which considers confidentiality problems arising both as a result of unauthorized accessand as a consequence of planned data processing. To describe the risk model of the anonymizationprocedure, the types of attacks on the confidentiality of personal data, anonymization metrics andequivalence classes are analyzed, as well as the attacker's profiles and data distribution scenarios.Thus, the choice of a risk model for the depersonalization procedure was justified, and calculations forthe generated synthetic set of PDs were presented. As a conclusion, it should be noted that the modelof anonymization risk assessment proposed and tested on synthetic data makes it possible to abandonthe concept of guaranteed anonymized data, introducing certain boundaries for working with risks andbuilding a continuous process for assessing PD threats, taking into account the constantly growingvolume of stored and processed information.
USING DISTRIBUTED CLOUDS FOR SCIENTIFIC COMPUTING
Authors: Kutovskiy N. , Pelevanyuk I. , Zaborov D.
Published: January 2022

Nowadays, cloud resources are the most flexible tool to provide access to infrastructures forestablishing services and applications. However, it is also a valuable resource for scientific computing.At the Joint Institute for Nuclear Research, the computing cloud was integrated with the DIRACsystem. It allowed for the submission of scientific computing jobs directly to the cloud. Thanks to theexperience, the cloud resources of several organizations from the JINR Member States were integratedin the same way. It increased the total amount of cloud resources accessible in a uniform way throughDIRAC, in the scope of the so-called Distributed Information and Computing Environment (DICE).Folding@Home tasks related to the SARS-CoV-2 virus were submitted to all available cloudresources. In addition to useful scientific results, such experience was also helpful in obtaininginformation about the performance, limitations, strengths, and weaknesses of the combined system.Based on the gained experience, the DICE infrastructure was tuned to successfully perform real userjobs related to Monte-Carlo simulation for the Baikal-GVD experiment.
QUANTITATIVE AND QUALITATIVE CHANGES IN THE JINR CLOUD INFRASTRUCTURE
Authors: Balashov N. , Kuprikov I. , Kutovskiy N. , Makhalkin A. , Mazhitova Ye. , Semenov R.
Published: January 2022

The high demand for JINR cloud resources facilitated its sufficient growth. It triggered changes thatneeded to be made to overcome encountered problems and keep QoS for users: the main part ofcomputational resources was reorganized as pre-deployed worker nodes of the HTCondor-basedcomputing element to decrease the load on OpenNebula services during mass jobs submission; newSSD-based ceph pool for RBD VM disks with strong disk I/O requirements; dedicated ceph-basedstorage for the NOvA experiment; reorganized from scratch “Infrastructure-as-a-Code” approachbased on a role and profile model implemented with the help of foreman and puppet; migration to aprometheus-based software stack for resource monitoring and accounting, and some other changes.
DEVELOPMENT OF THE EVENT METADATA SYSTEM FOR THE NICA EXPERIMENTS
Authors: Alexandrov E. , Alexandrov I. , Degtyarev A.G. , Filozova I. , Gertsenberger K. , Klimai P. , Yakovlev A.
Published: January 2022

Particle collision experiments are known to generate a substantial amount of data that must be storedand then analyzed. Typically, only a small subset of all collected events is relevant when performing aparticular physics analysis task. Although it is possible to obtain the required subset of event recordsdirectly, by iterating through the whole volume of the collected data, the process is very time andresource consuming. Instead, a more convenient approach widely used in modern particle collisionexperiments is to have an event metadata or indexing system that stores summary properties of allevents and allows for the fast search for and retrieval of only necessary events based on given criteria.Such a system, called the Event Metadata System, has been developed for the current fixed-target andfuture collider experiments of the NICA project. The design of the system, its components, userinterfaces and the REST API service, its integration with existing experiment systems and software, aswell as associated challenges, are presented.
MULTI-INSTANCE LEARNING FOR RHETORIC STRUCTURE PARSING
Authors: Volkov S. , Devyatkin D. , Shvets A.
Published: January 2022

It would be helpful to consider various topic-independent features: syntax, semantics, and discourserelations between text fragments to accurately detect texts containing elements of hatred or enmity.Unfortunately, methods for identifying discourse relations in the texts of social networks are poorlydeveloped. The paper considers the task of classification of discourse relations between two parts ofthe text. The RST Discourse Treebank dataset (LDC2002T07) is used to assess the performance of themethods. Since the size of this dataset is too small for training large language models, the work uses amodel-pre fitting approach. Model pre-fitting is performed on a Reddit user comment dataset. Textsfrom this dataset are labeled automatically. Since automatic labeling is less accurate than manualmarking, we use the multiple-instance learning (MIL) method to train models. A distinctive feature ofmodern language models is the large number of parameters. Using several models at different levels ofsuch a text analyzer requires a lot of resources. Therefore, for the analyzer to work, it is necessary touse high-performance or distributed computing. The use of desktop grid systems can attract andcombine computing resources to solve this type of problem.
ANALYTICAL PLATFORM FOR SOCIO-ECONOMIC STUDIES
Authors: Belov S.D. , Ilina A.V. , Javadzade J.N. , Kadochnikov I.S. , Korenkov V.V. , Pelevanyuk I.S. , Tarabrin V.A. , Zrelov P.V. , Semenov R.N.
Published: January 2022

Started in natural sciences, the high demand for analyzing a vast amount of complex data reached suchresearch areas as economics and social sciences. Big Data methods and technologies provide newefficient tools for research. In this paper, we discuss the main principles and architecture of the digitalanalytical platform aimed to support socio-economic applications. Integrating specific open-sourcesolutions, the platform intended to cover full-cycle data analysis and machine learning experiments,from data gathering to visualization. One of the system's primary goals is to deliver the advantage ofthe cloud and distributed computing and GPU accelerators with Big Data analysis techniques. Theauthors present the approach of building the platform from low-level services such as storage, virtualinfrastructure, pass-through authentication, up to data flows processing, analysis experiments, andresults representation.
FRACTAL THERMODYNAMICS, BIG DATA AND ITS 3D VISUALIZATION
Authors: Paramonova E. , Kudinov A. , Mikheev S. , Tsvetkov V. , Tsvetkov I.
Published: January 2022

The article proposes to use fractal thermodynamics to analyze big data with fractal structure. The need for big data analysis arises in many areas of science and technology when creating databases in economics, medicine, geophysics, astronomy, particle physics and many others. This task is greatly simplified if big data has structural patterns. In this talk, we considered the case when big data forming a set A with a high degree of accuracy are multifractals. The analysis of the fractal structure of the set A was carried out on the basis of the fractal thermodynamics model. Its main methods and approaches are described. As a specific example of the set A, we consider the quantum phase spaces of the instantaneous heart rate Sq, built on the basis of big data on the RR-intervals of daily Holter monitoring of patients from the Tver Regional Clinical Hospital. It is shown that the sets Sq are fractals with a high degree of accuracy and, therefore, the method of fractal thermodynamics is quite applicable for studying their structure.
DISTRIBUTED FAULT-TOLERANT COMPUTING WITH SBN-PYTHON ON A REAL COMPANY CASE
Authors: Tereshchenko D. , Gankevich I.
Published: January 2022

Distributed computing today is in demand in batch data processing tasks, but current solutionsthat allow to use them in Python either are too specific, or do not guarantee full fault tolerance. As apart of the final qualification work, a high-level Python interface (hereinafter SBN-Python) wasdeveloped for the new C++ framework called Subordination, in which the last problem was solved.The interface was implemented as an extension of the Python interpreter to achieve low-levelcompatibility and adaptation of all functionality. The purpose of this work was to test the possibility ofusing the new interface on a real case of LLC «Gazpromneft – Digital Solution» and to demonstratethe its core principles. To achieve this goal the current solution was analysed, a new architecture usingSBN-Python was thought out and implemented, and eventually, the resulting solution was deployed atthe company's facilities. As a result of the work, it turned out that using SBN-Python on a real casealso scales performance with the number of nodes in the cluster, gives the ability to process variousscenarios of node failure in a limited time, as well as some architectural advantages in organizingcalculations. In the future, it is planned to expand the boundaries of the new interface, implementingthe possibility of building distributed web services on its basis.
THE DEVELOPMENT OF A NEW CONDITIONS DATABASE PROTOTYPE FOR ATLAS RUN3 WITHIN THE CREST PROJECT
Authors: Alexandrov E. , Formica A. , Mineev M. , Roe S.
Published: January 2022

The CREST project for a new conditions database prototype for Run3 (intended to be used for production in Run4) is focused on improvement of Athena based access, metadata management and, in particular, global tag management. The project addresses evolution of the data storage design and conditions data access optimization, enhancing the caching capabilities of the system in the context of physics data processing inside the ATLAS distributed computing infrastructure. The CREST architecture is designed as a client server model, with the storage backend implemented in a relational database. The data access was realized with a pure REST API with JSON support. The new C++ client access library provides an HTTP query interface. A tool to convert the existing conditions data (stored in Oracle and accessible via the COOL API) into the new CREST system using a custom JSON format has also been implemented. Preliminary data migration has been done to allow testing data retrieval from Athena and the process of validation of the server and client functionalities is in progress.
NEW HARDWARE TESTING METHODOLOGY AT IHEP DATA CENTER
Authors: Ezhova V. , Kotliar V.
Published: January 2022

The modern computing center is not only about a production capacity, it is also about a stable work.Stability means not only software, the reliability of the software component but also assemblycomponents. It is important to ensure this before the production. One of the ways is testingcomponents for performance, reliability, assembly defects. In this work we will present themethodology for identifying problem points, the specific tests and some results of their work based onIHEP data center
MISSING MASS METHOD FOR SHORT-LIVED PARTICLE RECONSTRUCTION IN THE CBM AND STAR EXPERIMENTS
Authors: Fisyak Y. , Ivanov V. , Ke H. , Kisel I. , Kisel P. , Kozlov G. , Margetis S. , Tang A. , Vassiliev I.
Published: January 2022

The search for short-lived particles is an important part of physics research in experiments withrelativistic heavy ions. To find, select and study the properties of such short-lived particles in real timein the CBM experiment (FAIR/GSI, Germany), we have developed a package of algorithms, KFParticle Finder, which contains a search for more than 150 decay channels. The decays of short-livedcharged particles, when one of the daughter particles is neutral and cannot be registered in the detectorsystem, are also of great physics interest. To find and study such decays, we have extended the KFParticle Finder package by implementing the missing mass method, which is based on theconservation of energy and momentum laws.The method was studied in detail on simulated data of the CBM experiment, showing high efficiencywith a large signal-to-background ratio, as well as high significance. As part of the FAIR Phase-0program, the KF Particle Finder package of algorithms has been adapted for online and offlineprocessing in the STAR experiment (BNL, USA).Here we describe and discuss in detail the missing mass method for finding and analyzing short-livedparticles. The features of the application of the method to both simulated data in the CBM experimentand in the STAR experiment as part of real-time express data processing, as well as the results of thereal-time reconstruction of short-lived particle decays in the BES-II environment of the STARexperiment, are given.
PARTICIPATION OF RUSSIAN INSTITUTES IN THE PROCESSING AND STORAGE OF ALICE DATA
Authors: Kiryanov A. , Zarochentsev A.
Published: January 2022

This report presents the results of the work of Russian institutes in the processing of ALICE experiment data during the last 3 years of the operation of the Large Hadron Collider (LHC) including the end of the LHC Run2 and the 1st year of the COVID-19 pandemic. The main problems and tasks facing both ALICE Grid Computing and its Russian segment before the LHC Run3 including the problems of support and modernization of existing resources are considered. Also, plans for the preparation for the operation of the LHC in the HL (High Luminosity) mode are presented.
OPENMP COMPUTING OF A REFERENCE SOLUTION FOR COUPLED LORENZ SYSTEM ON [0,400]
Authors: Hristov G. , Hristova R. , Dimova S. , Armyanov P. , Shegunov N. , Puzynin I. , Puzynina T. , Sharipov Z. , Tukhliev Z.
Published: January 2022

Obtaining a long term reference trajectory on the chaotic attractor for the coupled Lorenz system is adifficult task, due to the sensitive dependence on the initial conditions. Using the standard doubleprecision floating point arithmetic, we cannot obtain a reference solution longer than 2.5 time units.Combining OpenMP parallel technology together with GMP library (GNU multiple precision library),we parallelize the Taylor series algorithm for the coupled Lorenz system and obtain a referencesolution in the rather long time interval - [0,400]. We performed two large computations, each usingone CPU-node (32 cores), based on two Intel® Haswell processors. First computation was with 2158decimal digits of precision and 2480-th order method, and second computation was for verification -with 2254 decimal digits of precision and 2580-th order method. The needed time for the second(larger) computation was 6.3 days. The parallel speedup when using these 32 cores is 23.1 withparallel efficiency 72.1 %.
IT SOLUTIONS FOR JINR TASKS ON THE “GOVORUN” SUPERCOMPUTER
Authors: Podgainy D. , Belaykov D. , Nechaevsky A. , Streltsova O. , Vorontsov A. , Zuev M.
Published: January 2022

The “Govorun” supercomputer is a heterogeneous computing system that contains computingarchitectures of different types, including graphics accelerators. The given architecture of thesupercomputer allows users to choose optimal computing facilities for their tasks.To enhance the efficiency of solving user tasks, as well as to expand the efficiency of utilizing bothcomputing resources and data processing and storage resources, a number of special IT solutions havebeen implemented on the “Govorun” supercomputer. A hierarchical hyperconverged data processingand storage system with a software-defined architecture is referred to the first type of IT solutions. Theimplementation of this system is caused by the fact that modern supercomputers are used not only astraditional computing environments for carrying out massively parallel calculations, but also assystems for Big Data analysis and artificial intelligence tasks that arise in different scientific andapplied tasks. The second type of IT solutions lies in resource orchestration, which means thatcomputational elements (CPU cores and graphics accelerators) and data storage elements (SSDs) formindependent computing and data storage fields. Due to it, the user can allocate for his task the requirednumber and type of compute nodes (including the required number of graphics accelerators), as wellas the required volume and type of data storage systems.
COMPUTING ENVIRONMENT FOR THE SUPER-CHARM- TAU FACTORY DETECTOR PROJECT
Authors: Belozyorova M. , Maksimov D. , Razuvaev G. , Sukharev A. , Vorobyev V. , Zhadan A. , Zhadan D.
Published: January 2022

The project of the Super Charm-Tau (SCT) factory – a high-luminosity electron-positron collider forstudying charmed hadrons and tau lepton – is proposed by Budker INP. The project implies singlecollision point equipped with a universal particle detector. The Aurora software framework has beendeveloped for the SCT detector. It is based on trusted and widely used in high energy physics softwarepackages, such as Gaudi, Geant4, and ROOT. At the same time, new ideas and developments areemployed, in particular the Aurora project benefits a lot from the turnkey software for future colliders(Key4HEP) initiative. This paper describes the first release of the Aurora framework, summarizes itscore technologies, structure and roadmap for the near future. From the hardware point of view theBudker INP general computing facility (BINP/GCF) providing the required computational and storageresources is described.
SIMULATION OF DATA PROCESSING FOR THE BM@N EXPERIMENT OF THE NICA COMPLEX
Authors: Priakhina D. , Korenkov V. , Gertsenberger K. , Trofimov V.
Published: January 2022

The paper considers the application of a software complex for data processing simulation incomputing systems. The BM@N experiment data storage and processing system of the NICA complexis used as a simulated infrastructure. Simulation is performed in order to obtain recommendations onthe organization of experimental data processing during a session with available allocated resources.The paper presents the results of simulating the processing of data that will be received during theBM@N experiment session, with several scenarios for distributing job flows across existingprocessing centers. In addition, some recommendations for organizing experimental data processingare proposed. The status of the work and future plans for the development of the software complex areformulated.
HIGH WYCOMBE’S FURNITURE INDUSTRY: FROM THE BEGINNING TO THE ‘80S
Authors: Ioras Florin , Sawyer Gervais
Published: January 2022

UDK 674.23(420)

High Wycombe, a city located in the south-west of England, was the flagship of the furniture industry for the whole of the British Kingdom during the 19th century. Today, with the introduction of new materials and trends that would make furniture desirable, as well as the cost of cheap labor in Southeast Asia and Eastern Europe, this industrial sector has become a thing of the past. Today, only a few companies located in this city represent this sector.

This paper documents the evolution of the High Wycombe furniture industry over time and reveals what caused the decline in the chair manufacturing sector. It also presents the impact of industrialization on the development of the furniture industry, historically traces the rise and fall of this sector at the local level, and shows what status this industry had in the seventies.