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Object detection and location from remote sensing (RS) images is challenging, computationally expensive, and labor intense. Benefiting from research on convolutional neural networks (CNNs), the performance in this field has improved in the recent years. However, object detection methods based on CNNs require a large number of images with annotation information for training. For object location, these annotations must contain bounding boxes. Furthermore, objects in RS images are usually small and densely co-located, leading to a high cost of manual annotation. We tackle the problem of weakly supervised object detection under such conditions, aiming to learn detectors with only image-level annotations, i.e., without bounding box annotations. Based on the fact that the feature maps of a CNN are localizable, we hierarchically fuse the location information from the shallow feature map with the class activation map to obtain accurate object locations. In order to mitigate the loss of small or densely distributed objects, we introduce a divergent activation module and a similarity module into the network. The divergent activation module is used to improve the response strength of the low-response areas in the shallow feature map. Densely distributed objects in RS images, such as aircraft in an airport, often exhibit a certain similarity. The similarity module is used to improve the feature distribution of the shallow feature map and to suppress background noise. Comprehensive experiments on a public dataset and a self-assembled dataset (which we made publicly available) show the superior performance of our method compared to state-of-the-art object detectors.

Despite recent advances, guaranteeing the correctness of large-scale distributed applications without compromising performance remains a challenging problem. Network and node failures are inevitable and, for some applications, careful control over how they are handled is essential. Unfortunately, existing approaches either completely hide these failures behind an atomic state machine replication (SMR) interface, or expose all of the network-level details, sacrificing atomicity. We propose a novel, compositional, atomic distributed object (ADO) model for strongly consistent distributed systems that combines the best of both options. The object-oriented API abstracts over protocol-specific details and decouples high-level correctness reasoning from implementation choices. At the same time, it intentionally exposes an abstract view of certain key distributed failure cases, thus allowing for more fine-grained control over them than SMR-like models. We demonstrate that proving properties even of composite distributed systems can be straightforward with our Coq verification framework, Advert, thanks to the ADO model. We also show that a variety of common protocols including multi-Paxos and Chain Replication refine the ADO semantics, which allows one to freely choose among them for an application's implementation without modifying ADO-level correctness proofs.

Basic EPS objects, which service life has exceeded normative value, increasingly affect – every year to a greater extent – the efficiency of overall performance. This manifests itself in increase of a number of automatic emergency shutdowns, an amount and complexities of accident-hazardous defects. After the expiration of the standard service life, there is a special need for a quantitative assessment of reliability and safety of an object. It is recommended to organize the operation, maintenance and repair of these objects according to their technical condition, and since it determines the reliability and safety of the object, these properties should be taken into account more fully. The relevant recommendations in electric power systems are implemented at a qualitative level, intuitively, according to the operating experience. There are neither quantitative evaluations nor methodology for their performance. Therefore, a method and algorithm of quantitative assessment of integral indicators of reliability and safety of operation of thermal power units of thermal power plants as concentrated objects of continuous operation were previously analyzed by the authors. The present paper examines distributed objects of continuous operation, viz. overhead power transmission lines with a voltage of 110 kV and higher, whose service life exceeds the standard value. Attention is paid to the issues of quantitative assessment of the degree of aging for a set of overhead power transmission lines, classification of these lines to identify the most significant classes and methodology for assessing the difference in the degree of aging when classifying them according to specified types of signs (for example, the difference in the degree of aging of overhead power transmission lines of grid enterprises of electric power systems). It is shown that it unacceptable to use the estimates of the relative number of overhead power transmission lines, the service life of which exceeds the calculated one, for comparison since it causes a great risk of an erroneous decision. The methodology and algorithm of methodological support of the management of electric power systems and grid enterprises in the organization of operation, maintenance and repair have been developed.

The problems of control and management of geographically distributed objects are considered. The sensor networks operating on the ZigBee technology are considered. The characteristics of the 802.15.4 ZigBee standard are given. The advantages of this technology are shown when building networks that are not very critical to traffic delays. The elements of such a network are considered. The primary converters used in such networks and their energy characteristics are considered. The issues of reducing and compensating delays in control circuits are considered. It is shown that modern wireless sensor networks can be considered as distributed information measuring and information control systems.

GenoVault is a cloud-based repository for handling Next Generation Sequencing (NGS) data. It is developed using OpenStack based private cloud with various services like keystone for authentication, cinder for block storage, neutron for networking and nova for managing compute instances for the Cloud. GenoVault uses object-based storage, which enables data to be stored as objects instead of files or blocks for faster retrieval from different distributed object nodes. Along with a web-based interface JavaFX-based desktop client has also been developed to meet the requirements of large file uploads ( > 5 GB) that is usually seen in NGS datasets. Users can store as many as one million files in their respective object based storage areas and the metadata provided by the user during file uploads is used for querying the database. GenoVault repository is designed taking into account future needs and hence can scale both vertically and horizontally without any need for modification in the design. Users have an option to make the data shareable to the public or restrict the access as private. Data security is ensured as every container is a separate entity in object-based storage architecture also supported by secured file transfer protocol during data upload and download. The data is uploaded by the user in their individual containers that include raw read files (fastq), processed alignment files (bam, sam, bed) and output of variation detection (vcf). GenoVault architecture allows verification of the data in terms of integrity and authentication before making it available to collaborators as per user permissions. GenoVault is useful for maintaining the organization wide NGS data generated by experiments in various labs which is not yet published and submitted to public repositories like NCBI. GenoVault also provides support to share NGS data among the collaborating institutions. GenoVault can thus manage vast volumes of NGS data on any OpenStack-based private cloud.

This paper proposes an approach that demonstrates the development of single window document circulation systems at the early stage of their design, based on the use of formal methods in the specification of a system, the specification of metrics for its analysis and the estimation of metrics values. An example of a single window document circulation system is modelled formally within the framework of the distributed object-based stochastic hybrid systems (DOBSHS) model using the SHYMaude specification language. Several metrics are proposed to evaluate the system. These metrics are specified formally using the QuaTEx language. The system is analysed statistically using the MultiVeStA tool, which analyses a single window document circulation system, represented as a rewriting logic Maude specification obtained by the translation of the SHYMaude system specification. In the process of the statistical analysis, the number of employees required for the effective system functioning is determined. The resulting value is used as a starting value in an extended system, in which there is an officer number management maintaining the length of the application queue in the desired range. A statistical study of the extended system reveals a drawback that is eliminated by adjusting the system, showing how this approach can be used to study and refine systems of this type at the early stage of designing the system model itself.

Web service technology has emerged as a popular way for building distributed applications involving distributed databases. It is the next generation technology in the long journey from functions to objects to components to services. Today’s comparing SOAP as a wire protocol to the commonly used distributed object technologies and their wire protocols in use. SOAP makes use of openly available technologies that, when combined, specify a wire protocol. This protocol can be used to facilitate highly and ultra-distributed architecture. SOAP commonly uses the HTTP protocol to transport XML-encoded serialized method argument data from system to system. This serialized argument data is used on the remote end to execute the client’s method call on that system, rather than the client’s local system. This case study provides a more details comparison of the SOAP and Distributed objects.

One of the major issues in system integration is to deal with interoperability of legacy systems which use traditional System Integration Patterns (SIP). Information are unable to exchange effectively when the systems involved comes from developer that tended to not interoperate and this leads to the interoperability problem in heterogeneous system integration. To address the interoperability issues, interfacing processes need to be made more easily by defining components, processes, and interfaces that affect the system integration architecture at the initial design stage. This paper includes a basic concept on types of traditional SIP covering File-Based, Common Database, Remote Procedure Call (RPC), Distributed Objects, and Messaging. An overview of three Service Interface Design (SID) approaches for systems interoperability is discussed. The discussions on these approaches serve as a basis for the solution of interoperability of heterogeneous systems which use traditional SIP.

A key step in medical image-based diagnosis is image segmentation. A common use case for medical image segmentation is the identification of single structures of an elliptical shape. Most organs like the heart and kidneys fall into this category, as well as skin lesions, polyps, and other types of abnormalities. Neural networks have dramatically improved medical image segmentation results, but still require large amounts of training data and long training times to converge. In this paper, we propose a general way to improve neural network segmentation performance and data efficiency on medical imaging segmentation tasks where the goal is to segment a single roughly elliptically distributed object. We propose training a neural network on polar transformations of the original dataset, such that the polar origin for the transformation is the center point of the object. This results in a reduction of dimensionality as well as a separation of segmentation and localization tasks, allowing the network to more easily converge. Additionally, we propose two different approaches to obtaining an optimal polar origin: (1) estimation via a segmentation trained on non-polar images and (2) estimation via a model trained to predict the optimal origin. We evaluate our method on the tasks of liver, polyp, skin lesion, and epicardial adipose tissue segmentation. We show that our method produces state-of-the-art results for lesion, liver, and polyp segmentation and performs better than most common neural network architectures for biomedical image segmentation. Additionally, when used as a pre-processing step, our method generally improves data efficiency across datasets and neural network architectures.

Large scale distributed systems require to embrace the trade off between consistency and availability, accepting lower levels of consistency to guarantee higher availability. Existing programming languages are, however, agnostic to this compromise, resulting in consistency guarantees that are the same for the whole application and are implicitly adopted from the middleware or hardcoded in configuration files. In this paper, we propose to integrate availability in the design of an object-oriented language, allowing developers to specify different consistency and isolation constraints in the same application at the granularity of single objects. We investigate how availability levels interact with object structure and define a type system that preserves correct program behavior. Our evaluation shows that our solution performs efficiently and improves the design of distributed applications.

This article proposes an approach that allows us to study a mathematical model of a virus protection system at the stage of its design using statistical analysis of an executable model specification based on the formalism of Distributed Object Based Stochastic Hybrid Systems (DOBSHS). Important aspects of the model are its distributed and probabilistic nature. These aspects make the model more difficult to carry out attacks, but at the same time significantly complicate the understanding of its properties by the developer. In this example, we show how, using the specification of the system as a DOBSHS model, coupled with its statistical analysis, we can investigate its properties at an early stage of design and how, using this approach, we can detect «defects» of the model and correct them during the process of creating the model.

CephFS is a network filesystem built upon the Reliable Autonomic Distributed Object Store (RADOS). At CERN we have demonstrated its reliability and elasticity while operating several 100-to-1000TB clusters which provide NFS-like storage to infrastructure applications and services. At the same time, our lab developed EOS to offer high performance 100PB-scale storage for the LHC at extremely low costs while also supporting the complete set of security and functional APIs required by the particle-physics user community. This work seeks to evaluate the performance of CephFS on this cost-optimized hardware when it is combined with EOS to support the missing functionalities. To this end, we have setup a proof-of-concept Ceph Octopus cluster on high-density JBOD servers (840 TB each) with 100Gig-E networking. The system uses EOS to provide an overlayed namespace and protocol gateways for HTTP(S) and XROOTD, and uses CephFS as an erasure-coded object storage backend. The solution also enables operators to aggregate several CephFS instances and adds features, such as third-party-copy, SciTokens, and high-level user and quota management. Using simple benchmarks we measure the cost/performance tradeoffs of different erasure-coding layouts, as well as the network overheads of these coding schemes. We demonstrate some relevant limitations of the CephFS metadata server and offer improved tunings which can be generally applicable. To conclude, we reflect on the advantages and drawbacks related to this architecture, such as RADOS-level free space requirements and double-network penalties, and offer ideas for improvements in the future.

Numbers are concepts whose content, structure, and organization are influenced by the material forms used to represent and manipulate them. Indeed, as argued here, it is the inclusion of multiple forms (distributed objects, fingers, single- and two-dimensional forms like pebbles and abaci, and written notations) that is the mechanism of numerical elaboration. Further, variety in employed forms explains at least part of the synchronic and diachronic variability that exists between and within cultural number systems. Material forms also impart characteristics like linearity that may persist in the form of knowledge and behaviors, ultimately yielding numerical concepts that are irreducible to and functionally independent of any particular form. Material devices used to represent and manipulate numbers also interact with language in ways that reinforce or contrast different aspects of numerical cognition. Not only does this interaction potentially explain some of the unique aspects of numerical language, it suggests that the two are complementary but ultimately distinct means of accessing numerical intuitions and insights. The potential inclusion of materiality in contemporary research in numerical cognition is advocated, both for its explanatory power, as well as its influence on psychological, behavioral, and linguistic aspects of numerical cognition.

A Data Processing Network (DPN) streams massive volumes of data collected and stored by the network to multiple processing units to compute desired results in a timely fashion. Due to ever-increasing traffic, distributed cache nodes can be deployed to store hot data and rapidly deliver them for consumption. However, prior work on caching policies has primarily focused on the potential gains in network performance, e.g., cache hit ratio and download latency, while neglecting the impact of cache on data processing and consumption. In this paper, we propose a novel framework, DeepChunk, which leverages deep Q-learning for chunk-based caching in wireless DPN. We show that cache policies must be optimized for both network performance during data delivery and processing efficiency during data consumption. Specifically, DeepChunk utilizes a model-free approach by jointly learning limited network, data streaming, and processing statistics at runtime and making cache update decisions under the guidance of deep Q-learning. It enables a joint optimization of multiple objectives including chunk hit ratio, processing stall time, and object download time while being self-adaptive under the time-varying workload and network conditions. We build a prototype implementation of DeepChunk with Ceph, a popular distributed object storage system. Based on real-world Wifi and 4G traces, our extensive experiments and evaluation demonstrate significant improvement, i.e., 52% increase in total reward and 68% decrease in processing stall time, over a number of baseline caching policies.

This article offers a mathematical model for distributed object­oriented stochastic hybrid systems (DOBSHS). DOBSHS are composite objects communicating with other objects through the exchange of messages through an asynchronous medium such as a network. An important component of the model is the probabilistic nature of the DOBSHS, in which the state of the system is described by stochastic differential equations with instantaneous probabilistic state changes when certain conditions are met. Also probabilistic is the nature of the messaging environment, in which the model of message delivery time is a random variable. Such problems are often encountered in practice in various areas and issues of formal modeling and verification of their properties are very important. The article presents a mathematical model of DOBSHS and proved that it has a Markov property.

The Large Hadron Collider (LHC) is about to enter its third run at unprecedented energies. The experiments at the LHC face computational challenges with enormous data volumes that need to be analysed by thousands of physics users. The ATLAS EventIndex project, currently running in production, builds a complete catalogue of particle collisions, or events, for the ATLAS experiment at the LHC. The distributed nature of the experiment data model is exploited by running jobs at over one hundred Grid data centers worldwide. Millions of files with petabytes of data are indexed, extracting a small quantity of metadata per event, that is conveyed with a data collection system in real time to a central Hadoop instance at CERN. After a successful first implementation based on a messaging system, some issues suggested performance bottlenecks for the challenging higher rates in next runs of the experiment. In this work we characterize the weaknesses of the previous messaging system, regarding complexity, scalability, performance and resource consumption. A new approach based on an object-based storage method was designed and implemented, taking into account the lessons learned and leveraging the ATLAS experience with this kind of systems. We present the experiment that we run during three months in the real production scenario worldwide, in order to evaluate the messaging and object store approaches. The results of the experiment show that the new object-based storage method can efficiently support large-scale data collection for big data environments like the next runs of the ATLAS experiment at the LHC.

Хомутова Тамара Николаевна, доктор филологических наук, доцент, зав. кафедрой лингвистики и перевода, Южно-Уральский государственный университет (Челябинск), khomutovatn@susu.ru. Денисенко Марина Георгиевна, преподаватель кафедры лингвистики и перевода, Южно-Уральский государственный университет (Челябинск),mamontovamg@susu.ru. Tamara N. Khomutova, Doctor of Philology, Assistant Professor Head of the Department of Linguistics and Translation Studies, South Ural State University, Chelyabinsk, khomutovatn@susu.ru. Marina G. Denisenko, University Teacher, Department of Linguistics and Translation Studies, South Ural State University, Chelyabinsk, mamontovamg@susu.ru. В статье рассматривается проблема туристического дискурса как объекта интегральной лингвистики. Основной целью работы является теоретическое обоснование применения интегрального подхода к исследованию туристического дискурса, определение понятия туристического дискурса как интегрального рассредоточенного объекта и создание его интегральной метамодели. Для решения поставленных задач используются логический, дескриптивный, сравнительно-сопоставительный и эвристический методы анализа, а также метод лингвистического моделирования. Наше исследование доказывает, что туристический дискурс представляет собой процесс сопряжения коммуникативных деятельностей представителей разных социумов, в ходе которого вербализуются фрагменты знания, языка, национальной культуры и социального пространства, связанные с определенным локусом, в их глобальном единстве и взаимообусловленности. Интегральная метамодель туристического дискурса представлена как рассредоточенный объект, единство четырех секторов: когнитивного, языкового, культурного и социального, единицы которых актуализуются в процессе коммуникации. Построение актуальных моделей различных типов туристического дискурса является перспективным направлением интегральных исследований. The paper focuses on the problem of tourism discourse as an object of integral linguistics. We aim to prove the effectiveness of the integral approach in tourism discourse studies, to give the definition of tourism discourse as an integral distributed object, and to build its integral metamodel. We use various research methods, including linguistic modeling. Our research proves that tourism discourse is a type of joint communication between members of different social groups, in the course of which fragments of knowledge, language, national culture and social space of a certain locus are verbalized. The integral metamodel of tourism discourse is presented in the form of a distributed object, a unity of four quadrants: the cognitive, language, cultural, and social ones, the units of which are actualized in the process of communication. Исследование выполнено при финансовой поддержке РФФИ и Челябинской области в рамках научного проекта № 20-412-740011. The research was funded by RFBR and Chelyabinsk Region, project number 20-412-740011

International audience; This paper introduces a new communication abstraction, called Set-Constrained Delivery Broadcast (SCD-broadcast), whose aim is to provide its users with an appropriate abstraction level when they have to implement objects or distributed tasks in an asynchronous message-passing system prone to process crash failures. This abstraction allows each process to broadcast messages and deliver a sequence of sets of messages in such a way that, if a process delivers a set of messages including a message m and later delivers a set of messages including a message , no process delivers first a set of messages including and later a set of message including m.After having presented an algorithm implementing SCD-broadcast, the paper investigates its programming power and its computability limits. On the “power” side it presents SCD-broadcast-based algorithms, which are both simple and efficient, building objects (such as snapshot and conflict-free replicated data types), and distributed tasks. On the “computability limits” side it shows that SCD-broadcast and read/write registers are computationally equivalent.

We present the Robotics Intelligent Systems & Control (RISC) Lab multiagent testbed for reliable search and rescue and aerial transport in outdoor environments. The system consists of a team of three multirotor unmanned aerial vehicles (UAVs), which are capable of autonomously searching, picking up, and transporting randomly distributed objects in an outdoor field. The method involves vision-based object detection and localization, passive aerial grasping with our novel design, GPS-based UAV navigation, and safe release of the objects at the drop zone. Our cooperative strategy ensures safe spatial separation between UAVs at all times and we prevent any conflicts at the drop zone using communication-enabled consensus. All computation is performed onboard each UAV. We describe the complete software and hardware architecture for the system and demonstrate its reliable performance using comprehensive outdoor experiments and by comparing our results with some recent, similar works.

Instance segmentation in aerial images is of great significance for remote sensing applications, and it is inherently more challenging because of cluttered background, extremely dense and small objects, and objects with arbitrary orientations. Besides, current mainstream CNN-based methods often suffer from the trade-off between labeling cost and performance. To address these problems, we present a pipeline of hybrid supervision. In the pipeline, we design an ancillary segmentation model with the bounding box attention module and bounding box filter module. It is able to generate accurate pseudo pixel-wise labels from real-world aerial images for training any instance segmentation models. Specifically, bounding box attention module can effectively suppress the noise in cluttered background and improve the capability of segmenting small objects. Bounding box filter module works as a filter which removes the false positives caused by cluttered background and densely distributed objects. Our ancillary segmentation model can locate object pixel-wisely instead of relying on horizontal bounding box prediction, which has better adaptability to arbitrary oriented objects. Furthermore, oriented bounding box labels are utilized for handling arbitrary oriented objects. Experiments on iSAID dataset show that the proposed method can achieve comparable performance (32.1 AP) to fully supervised methods (33.9 AP), which is obviously higher than weakly supervised setting (26.5 AP), when using only 10% pixel-wise labels.

Directly benefiting from the deep learning methods, object detection has witnessed a great performance boost in recent years. However, drone-view object detection remains challenging for two main reasons: (1) Objects of tiny-scale with more blurs w.r.t. ground-view objects offer less valuable information towards accurate and robust detection; (2) The unevenly distributed objects make the detection inefficient, especially for regions occupied by crowded objects. Confronting such challenges, we propose an end-to-end global-local self-adaptive network (GLSAN) in this paper. The key components in our GLSAN include a global-local detection network (GLDN), a simple yet efficient self-adaptive region selecting algorithm (SARSA), and a local super-resolution network (LSRN). We integrate a global-local fusion strategy into a progressive scale-varying network to perform more precise detection, where the local fine detector can adaptively refine the target’s bounding boxes detected by the global coarse detector via cropping the original images for higher-resolution detection. The SARSA can dynamically crop the crowded regions in the input images, which is unsupervised and can be easily plugged into the networks. Additionally, we train the LSRN to enlarge the cropped images, providing more detailed information for finer-scale feature extraction, helping the detector distinguish foreground and background more easily. The SARSA and LSRN also contribute to data augmentation towards network training, which makes the detector more robust. Extensive experiments and comprehensive evaluations on the VisDrone2019-DET benchmark dataset and UAVDT dataset demonstrate the effectiveness and adaptivity of our method. Towards an industrial application, our network is also applied to a DroneBolts dataset with proven advantages. Our source codes have been available at https://github.com/dengsutao/glsan .

The distributed object decision (DOD) was applied to choose a single solution for problem among many complexes solutions. Most of DOD systems depend on traditional technique like small form factor optical (SFFO) method and scalable and oriented fast-based local features (SOFF) method. These two methods were statistically complex and depended to an initial value. In this paper proposed new optimal technical called gray wolf optimization (GWO) which is used to determine threshold of sensor decision rules from fusion center. The new algorithm gave better performance for fusion rule than numerical results. The results are providing to demonstrate of fusion system reduced of bayes risk by a high rate of 15%-20%. This algorithm also does not depend on the initial values and shows the degree of complexity is better than other algorithms.

Resumo A ideia de memória de Alfred Gell, presente em seu “Arte e Agência”, que atravessa a biografia da pessoa (entendida não apenas enquanto humana, mas também como o próprio objeto de arte) é posta em diálogo com estudos contemporâneos sobre memória cultural a fim de se debater o papel da tradução na perpetuação do que se quer lembrar. As ideias de media de memória de Aleida Assmann intermedeiam todo o trabalho, sendo a hipótese de que a tradução é um medium de memória o que traça um caminho entre todo o raciocínio aqui desenvolvido. A tradução é aqui estudada em contato direto com a vida, bios, como parte da construção biográfica da pessoa e da obra de arte, ou, especificamente, literária, a fim de recolocá-la nos debates sobre memória cultural. Abstract Alfred Gell’s idea of memory presented in his “Art and Agency”, which passes through the biography of the person (understood not only as human being, but also as the object of art itself), is put in dialogue with contemporary studies on cultural memory in order to debate the role of translation on the perpetuation of remembrance. Aleida Assmann’s media and memory concepts mediate all this work, bringing up the hypothesis that translation is a medium of memory. The translation is here studied in direct contact with life, bios, as part of the biographical construction of the person and the work of art, or, specifically, of literature, in order to reallocate it in the debates about cultural memory.

Self-localization enables a system to navigate and interact with its environment. In this study, we propose a novel sparse semantic self-localization approach for robust and efficient indoor localization. &ldquo, Sparse semantic&rdquo, refers to the detection of sparsely distributed objects such as doors and windows. We use sparse semantic information to self-localize on a human-readable 2D annotated map in the sensor model. Thus, compared to previous works using point clouds or other dense and large data structures, our work uses a small amount of sparse semantic information, which efficiently reduces uncertainty in real-time localization. Unlike complex 3D constructions, the annotated map required by our method can be easily prepared by marking the approximate centers of the annotated objects on a 2D map. Our approach is robust to the partial obstruction of views and geometrical errors on the map. The localization is performed using low-cost lightweight sensors, an inertial measurement unit and a spherical camera. We conducted experiments to show the feasibility and robustness of our approach.

For the circulated programming frameworks evolvement, Object Oriented (OO) approach is utilized by architects with originators in the point of reference period which results in Distributed Object Oriented (DOO) frameworks. The main aspect of DOO frameworks remains as the equipped scattering of programming classes among different hubs. The essential plan of DOO applications has no top-class circulation, henceforth rebuilding must be finished. The DOO programming rebuilding is done by means of a proposed versatile strategy called Neural Network (NN), to strengthen the exhibition further. At first, Class Dependency Graph (CDG) is developed, in which the hubs speak to the classes, and furthermore the associations between the hubs speak to the conditions between the classes. Presently, the components of articles, strategies, factors, lines, and import connected with the classes in the CDG are extricated and given as contributions to the NN for the preparation procedure. Presently, bunching of the prepared highlights is finished by which the OO framework is sectioned into subsystems with low coupling utilizing Class Dependency Based Clustering (CDBC) strategy. Presently, the grouped classes are amassed into bunch diagrams utilizing K‑Medoid bunching method lastly, the mapping is finished with the made parcels to the fixed accessible machines utilizing Recursive K Means grouping in the focused on circulated design. Reenactment results uncovered that the proposed work yields upgraded results in a useful manner contrasted with the current systems.

Introduction. In the branch structure of management of Russia an important place is occupied by geographically distributed objects (branches) of large industrial enterprises of the fuel and energy complex. The considered enterprises, in accordance with the criteria approved by the legislation, belong to the category of hazardous production facilities. An important task is to determine the cause of the danger. Methods of research. To detect hazards in any automated control systems used methods by which information is collected on the parametric values of the functioning of production facilities. To conduct research on the detection of hazards, a number of approaches are used: based to determine the parameters (invariants) of the models of controlled objects; to solve the problems of modeling (forecasting); to use analytical redundancy. There is a modelfree method of hazard detection in automated control systems, which is based on the representation of only the data of control signals and measurements of the parameters of the functioning of dynamic objects. It is based on the algebraic condition of solvability of the problem of identification of a mathematical model of dynamic object functioning. Problem statement. It’s required on the basis of the measurement results the input signals coming into the automated control system, to develop a parametric value for the critical zone of occurrence of hazards with the aim of displaying information on the display of the workstation operator. Problem solution. It is offered to represent models of object, in serviceable and faulty States, in the form of matrices that will allow solving problems of identification of the closed objects for any input signals, irrespective of availability of information on parameters of control system. The speed and accuracy of detection of the fact and time of danger (system failure) are determined by the sampling frequency of the signals and coincide with the time interval between the two consecutive measurements. Conclusion. The advantage of the proposed approach is its independence from the parameters of the controlled object model. The use of the proposed approach for detection makes it possible to transfer the security management system of the enterprise to a new qualitative level due to constant traceability of the process of functioning of production facilities, increasing the speed and reliability of detection of the fact and time of danger.

Structural, cost and functional characteristics of distributed systems are largely determined by the topology of their subsystems and elements. In its turn the topology of subsystems and elements determines the topology of communication links, providing the exchange of resources and information between elements and subsystems. It is common for many modern technical, organizational and technical, and socio-economic systems. The article analyzes the factors, which determine the efficiency of the network of Main Center of the Special Monitoring objects as a territorially distributed system, formalizes its structural description and designation purpose. It formulates the problem of optimizing the network of Main Center of the Special Monitoring objects that includes complexes of tasks, which deal with the choice of structure, topology, technology of functioning, parameters of elements and connections, comprehensive assessment and choice of options at various stages of the network life cycle. The article determines the expediency of developing a methodology for optimizing the network of the objects of Main Center of the Special Monitoring on the ideas of aggregation-decomposition and block-hierarchical approaches. It suggests the three-level scheme for decomposition of the network optimization problem; that scheme includes the sets of tasks for the network design, evolution planning, adaptation and reengineering. In the article the composition and scheme of interconnections with input and output data of the tasks of network system design are determined. It is concluded that practical application of the obtained results for solution of the problem of optimizing the network of Main Center of the Special Monitoring objects will reduce the time expenditures inevitable in solution of the tasks of the network design and its development planning; it will also reduce the expenses for its modernization or reengineering, through the joint solution of tasks it will improve the quality of solutions and enhance on that basis the network's functional characteristics. The methodology of systemologic analysis of the problem dealing with optimization of geographically distributed objects can generally be used in the design of any information systems, distributed service systems and large-scale monitoring.

The paper considers methods for modeling fluctuations of the phase front of an electromagnetic wave reflected from a radar object (reflections) using models that emit statistically related signals. Previously, such models were considered only in relation to objects distributed over a single angular coordinate. A two-dimensional geometric model, emitting statistically related signals, was proposed for replacement of reflections from radar objects distributed over two angular coordinates. The emitters of the model are located at the vertices of the square. Expressions are obtained for determining the characteristics of signals supplied to the emitters of such model, at which the model provides the required parameters of the probability distribution density of the angular noise of the distributed object. Within the framework of the model under study, it is proved that it is possible to control the parameters of the probability density distribution of angular noise along two coordinate axes independently. The equivalence of the four-point partially coherent model and the five-point incoherent in terms of providing the probability characteristics of the angular noise is shown, and the limits of this equivalence are also determined. The proven equivalence of models allows developing an apparatus for the synthesis of spectral-correlation characteristics.

Different types of global information network services, used in the modern distributed software systems are described. Several approaches to service description and to service interaction are shown. Description of wide-spread and inculcated approaches to creation of distributed object interaction is given in the wide practice of the systems with spectrum of system and functional services. Web-service international standards stack (SOAP protocol, WSDL language, UDDI interface and service, XML, BPEL and BPMN notations, CORBA object broker and services, JEE application server) is briefly described. Models of web services, service-oriented approach (SOA), service-component architectures (SCA), and Windows Communication Foundation (WCF) service support application systems are considered for presentation of the business systems by the services ready to the decision of business-tasks. Features that support interoperability and multilanguage interaction are underlined. Principles of distributed software design (service composition, component engineering, interoperability, variance) that are of special care in SOA are listed. Some examples of presenting information using standard notations are given. An additional example of calculator service with functions of the data processing in ITK service-environment (WCF-based system) and the concept of web service implementation are presented.

In this paper, efforts are made to find the fuzzy optimal solutions for electric tomography imaging (ETI) process. Most of the conventional ETI methods depend on an optimization process with a deterministic objective function and constraint conditions. The effective application of these methods requires the availability of measured data. However, in practical ETI possess, the assessable data are uncertain, imprecise, incomplete, and characterized by fuzziness. In this paper, the fuzzy characteristics hidden in the ETI process are extracted, and a new model of fuzzy optimization is built up, whose optimal solution is obtained by using fuzzy linear programming (FLP). The FLP model can provide a robust methodology to boost the ETI spatial resolution. Thus it creates a new and promising research direction for the optimization of the ETI process. Experimental results prove that compared with other existing ETI algorithms, the proposed method is more effective in reconstructing complex and discretely distributed objects.

This paper presents a novel distributed object segmentation framework that allows one to extract potentially large coherent objects from digital images. The proposed approach requires minimum user supervision and permits to segment the objects accurately. It works in three steps starting with the user input in form of few mouse clicks on the target object. First, based on user input, the statistical characteristics of the target distributed object are modeled with Gaussian mixture model. This model serves as the primary segmentation of the object. In the second step, the segmentation result is refined by performing connected component analysis to reduce false positives. In the final step the resulting segmentation map is dilated to select the neighboring pixels that are potentially incorrectly classified; this allows us to recast the segmentation as a graph partitioning problem that can be solved using the well-known graph cut technique. Extensive experiments have been carried out on heterogeneous images to test the accuracy of the proposed method for the segmentation of various types of distributed objects. Examples of application of proposed technique in remote sensing to segment roads and rivers from aerial images are also presented. The visual and objective evaluation and comparison with the existing techniques show that the proposed tool can deliver optimal performance when applied to tough object segmentation tasks.

This paper introduces new techniques for ultra-wideband (UWB) joint spatial coding in order to establish a secure line-of-sight wireless communication link, performing precision localization of point objects, and generating high-resolution images from complex scenes. For the proposed technique, symbol generation and coding are jointly performed by multiple widely spaced transmitters. This method enables the transmission of directionally modulated signals that can only be decoded at a desired angle, resulting in a secure communication link. It is also shown that joint spatial coding by multiple transmitters that are synchronized with each other enables the precise localization of multiple point objects and allows high-resolution 3-D imaging of distributed objects. The technique is demonstrated experimentally in the frequency range 3-10 GHz by generating and detecting UWB pulses., IEEE Transactions on Microwave Theory and Techniques, 65 (7), ISSN:0018-9480, ISSN:1557-9670

The proposed mathematical model of specialized services for the prediction of arbitrary indicators of company activity is presented as a part of the micro-service architecture of the information system of an enterprise and provides dynamic replacement or addition of the prediction models without changing the overall algorithm of service operation. This model assigns a formal basis for the intra-component interaction of the service and makes it possible to change, add, and delete prediction services without the need for resetting the information environment of a company. The prediction model was proposed as a part of the prediction service of the company IS, based on neural network with the embedded model of moving average. This model allowed improvement of quality of predictive assessment in the case of existence of a trend in comparison with the classical neural network model due to the embedded model of moving average. The algorithm was developed for training a neural network forecasting model with the embedded moving average model, based on the inverse error spread, which allows us to tune the model to the examined time series. We considered practical aspects of using a specialized prediction service, a client application to this service, which clearly demonstrates its functionality under the mode of checking appropriateness of using a certain prediction model on a specific type of preset data.

The Social Internet of Things (SIoT) is a new paradigm that integrates social network concepts with the Internet of Things (IoT). It boosts the discovery, selection and composition of services and information provided by distributed objects. In SIoT, searching for services is based on the utilization of the social structure resulted from the formed relationships. However, current approaches lack modelling and effective analysis of SIoT. In this work, we address this problem and specifically focus on modelling the SIoT’s evolvement. As the growing number of IoT objects with heterogeneous attributes join the social network, there is an urgent need for identifying the mechanisms by which SIoT structures evolve. We model the SIoT over time and address the suitability of traditional analytical procedures to predict future relationships (links) in the dynamic and heterogeneous SIoT. Specifically, we propose a framework, namely SIoTPredict, which includes three stages: i) collection of raw movement data of IoT devices, ii) generating temporal sequence networks of the SIoT, and iii) predicting relationships among IoT devices which are likely to occur. We have conducted extensive experimental studies to evaluate the proposed framework using real SIoT datasets and the results show the better performance of our framework.

We discuss an extension of classical combinatorics theory to the case of spatially distributed objects., Comment: 9 pages

In high-throughput, distributed systems, such as large-scale banking infrastructure, synchronization between actors becomes a bottle-neck in high-contention scenarios. This results in delays for users, and reduces opportunities for scaling such systems. This paper proposes Static Local Coordination Avoidance, which analyzes application invariants at compile time to detect whether messages are independent, so that synchronization at run time is avoided, and parallelism is increased. Analysis shows that in industry scenarios up to 60% of operations are independent. Initial performance evaluation shows that, in comparison to a standard 2-phase commit baseline, throughput is increased, and latency is reduced. As a result, scalability bottlenecks in high-contention scenarios in distributed actor systems are reduced for independent messages.

The object of research is the power supply system of railway transport, which is part of a unified system and belongs to the class of geographically distributed electrical facilities. The main component of the system are electric networks, which represent a key technological chain of the transport process, provide it with energy, make up a significant part of the costs, connect geographically distributed objects and therefore represent one of the technological foundations for the integration of transport control. This significantly affects the efficiency of all links of the railway transport, which is especially important from the point of view of the corporate interests of the industry.In the course of the study, a systematic approach, system analysis methods, approaches to optimal production management with the support of the technological process of current repair and maintenance of electricity distribution systems were used. As well as ways of organizing a single information space of primary information that reflects the state of the power supply system. An information model of the control system for the processes of ongoing maintenance and repair is obtained, which will improve the efficiency of the control system already at the initial stages of their design. This is due to the fact that the proposed model has a number of features of the functioning and organization of repair work. In particular, the planning of the work of power distribution systems, operation, taking into account the optimization of repair work, management decisions, the interaction of the structural units of the power supply service based on set of problems Zjk.Thanks to this, it is possible to obtain information about individual processes of organizing the planning of the current maintenance and repair of the distribution system of power supply of structural units of the service. Compared with similar ones, this provides such advantages as the ability to register, transmit and process information in the power supply control mode. As well as the ability to implement periodic and occasional control and assessment of the reliability of the system, software and hardware and information, quick system analysis of various situations, the ability to form the most effective management decisions., Объектом исследования является система электроснабжения железнодорожного транспорта, которая является частью объединенной системы и относится к классу территориально-распределенных электрических объектов. Основной составляющей системы являются электрические сети, которые представляют собой ключевую технологическую цепочку транспортного процесса, которые обеспечивают его энергией, составляет весомую часть расходов, связывает территориально-распределенные объекты и поэтому представляет собой одну из технологических основ интеграции управления транспортным процессом. Это существенно влияет на эффективность работы всех звеньев железнодорожного транспорта, особенно важное с точки зрения корпоративных интересов отрасли.В ходе исследования использовались системный подход, методы системного анализа, подходы оптимального управления производством по обеспечению технологического процесса текущему ремонту и содержанию распределительных систем электроснабжения. А также способы организации единого информационного пространства первичной информации, отражающей состояние системы электроснабжения. Получена информационная модель системы управления процессами текущего содержания и ремонта, которая позволит повысить эффективность системы управления уже на начальных стадиях их проектирования. Это связано с тем, что предложенная модель имеет ряд особенностей функционирования и организации ремонтных работ. В частности, планирование работ распределительных систем электроснабжения, функционирования с учетом оптимизации ремонтных работ, управленческих решений, взаимодействия структурных подразделений службы электроснабжения, которые строятся на множестве задач Zjk.Благодаря этому обеспечивается возможность получения информации об отдельных процессах организации планирования текущего содержания и ремонта распределительной системы электроснабжения структурных подразделений службы. По сравнению с аналогичными это обеспечивает такие преимущества, как возможность регистрации, передачи и обработки информации в режиме управления электроснабжения. А также возможность реализовывать периодический и эпизодический контроль и оценку надежности системы, программно-аппаратных средств и информации, быстрого проведения системного анализа различных ситуаций, возможность формирования наиболее эффективных управленческих решений., Об’єктом дослідження є система електропостачання залізничного транспорту, яка є частиною об’єднаної системи і відноситься до класу територіально-розподілених електричних об'єктів. Основною складовою системи є електричні мережі, які являють собою ключову технологічну ланку транспортного процесу, яка забезпечує його енергією, складає вагому частину витрат, зв’язує територіально-розподілені об’єкти і тому являє собою одну з технологічних основ інтеграції управління транспортним процесом. Це суттєво впливає на ефективність роботи всіх ланок залізничного транспорту, що особливо вагоме з точки зору корпоративних інтересів галузі. В ході дослідження використовувалися системний підхід, методи системного аналізу, підходи оптимального управління виробництвом із забезпеченням технологічного процесу поточного ремонту та утримання розподільних систем електропостачання. А також способи організації єдиного інформаційного простору первинної інформації, що відображає стан системи електропостачання. Отримано інформаційну модель системи управління процесами поточного утримання та ремонту, яка дозволить підвищити результативність системи управління вже на початкових стадіях їх проектування. Це пов’язано з тим, що запропонована модель має ряд особливостей функціонування та організації ремонтних робіт. Зокрема, планування робіт розподільних систем електропостачання, функціонування з врахуванням оптимізації ремонтних робіт, управлінських рішень, взаємодії структурних підрозділів служби електропостачання, які будуються на множині задач Zjk.Завдяки цьому забезпечується можливість отримання інформації про окремі процеси організації планування поточного утримання та ремонту розподільної системи електропостачання структурних підрозділів служби. У порівнянні з аналогічними це забезпечує такі переваги, як можливість реєстрації, передачі та обробки інформації в режимі управління електропостачання. А також можливість реалізовувати періодичний і епізодичний контроль і оцінку надійності системи, програмно-апаратних засобів та інформації, швидкого проведення системного аналізу різних ситуацій, можливість формування найбільш ефективних управлінських рішень.

Since distributed software systems are ubiquitous, their correct functioning is crucially important. Static verification is possible in principle, but requires high expertise and effort which is not feasible in many eco-systems. Runtime verification can serve as a lean alternative, where monitoring mechanisms are automatically generated from property specifications, to check compliance at runtime. This paper contributes a practical solution for powerful and flexible runtime verification of distributed, object-oriented applications, via a combination of the runtime verification tool Larva and the active object framework ProActive. Even if Larva supports in itself only the generation of local, sequential monitors, we empower Larva for distributed monitoring by connecting monitors with active objects, turning them into active, communicating monitors. We discuss how this allows for a variety of monitoring architectures. Further, we show how property specifications, and thereby the generated monitors, provide a model that splits the blame between the local object and its environment. While Larva itself focuses on monitoring of control-oriented properties, we use the Larva front-end StaRVOOrS to also capture data-oriented (pre/post) properties in the distributed monitoring. We demonstrate this approach to distributed runtime verification with a case study, a distributed key/value store., Comment: In Proceedings VORTEX 2018, arXiv:1908.09302

Meteorological data are important information data in China, and have made tremendous contributions to disaster prevention and disaster reduction in China. However, the current meteorological system has a long-term dependence on foreign hardware and software platforms, showing obvious security problems. It is an urgent task to complete the domestication and performance improvement of the meteorological system. At the same time, as the black box architecture of flash memory devices greatly hinders the co-optimization of software and hardware, open-channel flash memory has begun to attract attention as a new type of flash memory architecture. In this article, we have adopted new excellent domestic products including Phytium chips, Starblaze open-channel flash memory devices, and Kylin operating systems as components of a new domestic software and hardware platform. The object storage system of meteorological data has been implemented in a real environment by Ceph. We provides a built solution and optimizes the parameters. Based on the comparison with the commercial platform storage system, the problems and challenges faced by the meteorological data distributed object storage system in autonomy are discussed. Finally, this article looks forward to the distributed storage of meteorological data under domestic platforms.

This paper is on the construction and use of a shared memory abstraction on top of an asynchronous message-passing system in which up to t processes may commit Byzantine failures. This abstraction consists of arrays of n single-writer/multi-reader atomic registers, where n is the number of processes. These registers enable Byzantine tolerance by recording the whole history of values written to each one of them. A distributed algorithm building such a shared memory abstraction is first presented. This algorithm assumes t n / 3 , which is shown to be a necessary and sufficient condition for such a construction. Hence, the algorithm is resilient-optimal. Then the paper presents distributed objects built on top of this read/write shared memory abstraction, which cope with Byzantine processes. As illustrated by these objects, the proposed shared memory abstraction is motivated by the fact that, for a lot of problems, algorithms are simpler to design and prove correct in a shared memory system than in a message-passing system.

Class fragmentation and allocation is an important technique to improve the performance of a distributed object oriented database system. The class fragmentation is to split a class into smaller pieces in distributed databases aims to reduce the access to unnecessary data, the allocation is to locate fragmented classes into the sites in the connected network properly to reduce the cost of data transmission. Classes in object databases include attributes describing the characteristics of the object, methods describing the behavior, and relationships with objects with other classes, including relations inheritance. With such characteristics, class fragmentation and allocation in the distributed object oriented database system is more complex than fragmentation technique and design of relational databases. Fragmentation techniques applied in the design of distributed object-oriented database today often do not use cost between the sites, fragments are allocated to the site after getting a fragmentation method of data objects. This paper proposes an algorithm of fragmentation and allocation simultaneously, including the cost of data communication between the sites used for fragmentation to reduce communication costs when processing and querying distributed data.

Over the last two decades, ROOT TTree has been used for storing over one exabyte of High-Energy Physics (HEP) events. The TTree columnar on-disk layout has been proved to be ideal for analyses of HEP data that typically require access to many events, but only a subset of the information stored for each of them. Future colliders, and particularly HL-LHC, will bring an increase of at least one order of magnitude in the volume of generated data. Therefore, the use of modern storage hardware, such as low-latency high-bandwidth NVMe devices and distributed object stores, becomes more important. However, TTree was not designed to optimally exploit modern hardware and may become a bottleneck for data retrieval. The ROOT RNTuple I/O system aims at overcoming TTree's limitations and at providing improved efficiency for modern storage systems. In this paper, we extend RNTuple with a backend that uses Intel DAOS as the underlying storage, demonstrating that the RNTuple architecture can accommodate high-performance object stores. From the user perspective, data can be accessed with minimal changes to the code, that is by replacing a filesystem path by a DAOS URI. Our performance evaluation shows that the new backend can be used for realistic analyses, while outperforming the compatibility solution provided by the DAOS project., Accepted for Proceedings of 25th International Conference on Computing in High-Energy and Nuclear Physics

In large scale information fusion networks the statistical dependencies between nodes are rarely known. Failing to account for this detail in the design of distributed estimation schemes may lead to statistical inconsistencies and may even cause some estimators to diverge at various nodes within the network. Among the approaches that have been proposed to address this problem, covariance intersection and its generalization are perhaps the most well-known. Owing to its relationship with the statistical divergence known as Chernoff information the latter approach is referred to here as Chernoff fusion. Using Chernoff fusion requires tuneup for the fusion parameters, where in some applications, the Chernoff fusion may cause overhead. This work is concerned with the practical application of Chernoff fusion for distributed estimation. We provide a technique for fast particle filters fusion based on a linear variant of Chernoff fusion as well as a recursive tuning technique of the underlying mixing weights. The viability of the proposed schemes are demonstrated in such applications as distributed object tracking, cooperative robots localization, and image classification with ensembles of convolutional neural networks.