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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.

In the engineering applications, the distribution of objects is mostly random. Therefore, scattering analysis of randomly distributed objects has been one of the important problems in broadband electromagnetic calculation field. To resolve the problem, the Asymptotic Waveform Evaluation technique in conjunction with Monte Carlo Method is presented. First, the stochastic distribution is modeled by the Monte Carlo Method, and then the Asymptotic Waveform Evaluation technique using Padé approximation is utilized to achieve the Radar Cross Section at a wide frequency band. Numerical results show that the Asymptotic Waveform Evaluation technique can solve the random distributed object problems efficiently and accurately.

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.

With the advent of the era of big data, cloud computing, Internet of things, and other information industries continue to develop. There is an increasing amount of unstructured data such as pictures, audio, and video on the Internet. And the distributed object storage system has become the mainstream cloud storage solution. With the increasing number of distributed applications, data security in the distributed object storage system has become the focus. For the distributed object storage system, traditional defenses are means that fix discovered system vulnerabilities and backdoors by patching, or means to modify the corresponding structure and upgrade. However, these two kinds of means are hysteretic and hardly deal with unknown security threats. Based on mimic defense theory, this paper constructs the principle framework of the distributed object storage system and introduces the dynamic redundancy and heterogeneous function in the distributed object storage system architecture, which increases the attack cost, and greatly improves the security and availability of data.

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.

In spite of the diversity of solutions developed in the Internet of Things (IoT) domain, some features are shared by numerous IoT deployments and the data they process. These include incompleteness and latency in data transmission from multiple distributed objects. Among others, the systems tracking the location of vehicles are affected by these problems. The primary objective of this work is to address the way the latency in location data acquisition, referred to also as timeliness, can be dealt with. We propose a hybrid method combining machine learning models such as multilayer perceptrons trained in batch mode and online learning methods to perform short-term prediction of vehicle delay data. In this way, stream instances that have not arrived yet from the sensors can be temporarily replaced with predicted values. The method we propose successfully integrates stream mining methods developed for stationary and non-stationary conditions i.e. also the methods developed for concept drifting data streams. For all examined reference data sets and hybridised stream methods, the method reduced prediction error and addressed the risk of using static prediction models not matching or no longer matching the evolving process for which the prediction is performed.

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.

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.

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.

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.

Erasure coding is widely used in building reliable distributed object storage systems despite its high repair cost. Regenerating codes are a special class of erasure codes, which are proposed to minimize the amount of data needed for repair. In this paper, we assess how optimal repair can help to improve object storage systems, and we find that regenerating codes present unique challenges: regenerating codes repair at the granularity of chunks instead of bytes, and the choice of chunk size leads to the tension between streamed degraded read time and repair throughput. To address this dilemma, we propose Geometric Partitioning, which partitions each object into a series of chunks with their sizes in a geometric sequence to obtain the benefits of both large and small chunk sizes. Geometric Partitioning helps regenerating codes to achieve 1.85x recovery performance of RS code while keeping degraded read time low.

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.

Most general-purpose distributed storage systems are not designed with near data processing (NDP) in mind. They do not respect semantic data boundaries when writing data, for example splitting a record across servers. This reduces NDP effectiveness by requiring data collation before computation. While semantic data awareness and NDP functions can be retroactively added to existing distributed storage, it is often complex and difficult to accomplish in practice. We propose sharing storage system layout information with data writers so they can adjust data layouts to prevent data alignment issues regardless of the underlying architectures. By doing so, we can simplify NDP implementation by reducing the need for data reassembly, and reduce the need for complex storage system or application extensions. We demonstrate a hinting mechanism on both HDFS with computational block storage and an erasure coded MinIO deployment, reducing data movement by up to 99% when querying CSV data with NDP co-located with the stored data. This was accomplished purely with client side data alignment, no modifications to the server side write paths, and no inter-node collation of data.

The method of replacing the volume distributed object (moisture target) with two-dimensional four-point partially coherent matrix simulator's models is proposed in this paper. The synthesis of the starting multipoint model of the moisture target based on requirements to powers and wind velocities distributions over the object is explored. Expressions allowing one to replace the moisture target with the low-point partially coherent model based on the multipoint starting model are defined.

The study of the theory of neighborhood systems is promising in the training of students of technical specialties, since they allow you to model and control complex distributed objects and processes that are found in industry, while being simple and accurate. For modeling distributed processes, it is advisable to use fuzzy-neighborhood systems, and in particular systems with dynamic coefficients of neighborhood fuzziness. In this work, the problem of stabilizing the nominal mode due to control parameters and due to dynamic coefficients of neighborhood fuzziness for initial linear and bilinear neighborhood models is considered.

Semantic segmentation can distinguish objects in remote sensing images at the pixel level. However, traditional semantic segmentation algorithms are more and more difficult to meet people's needs. With the rapid development of deep learning, especially its application in remote sensing images has greatly improved the parsing ability and efficiency. But, the complexity and diversity of remote sensing image content make the accuracy of semantic segmentation still need to be improved. Thus, a semantic segmentation method that combines the characteristics of EfficientNet and UNet++ is proposed in this paper. The method can make the segmentation boundary clearer and improve the segmentation effect of densely distributed objects. The results show that the proposed method achieves good performance in the Vaihingen dataset.

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 exponentially growing demand for storage puts a huge stress on traditional distributed storage systems. Historically, I/Ops (Inputs/Outputs per second) of hard drives have been the main limitation of storage systems. With the rapid deployment of solid state drives (SSDs) and the expected evolutions of their capacities, price and performance, we claim that CPU and network capacities will become bottlenecks in the future. In this context, we introduce 6Stor, an innovative, software-defined distributed storage system fully integrated with the networking layer. This storage system departs from traditional approaches in two manners: it leverages IPv6 new capabilities to increase the efficiency of its data plane—notably by using directly UDP and TCP rather than HTTP—and thus its performance; and it circumvents scalability limitations of other distributed systems by using a fully distributed metadata layer of indirection to offer flexibility. In this paper, we introduce and describe in details the architecture of 6Stor, with an emphasis on dynamic scalability and robustness to failure. We also present a testbed that we use to evaluate our novel approach by using Ceph—another well known distributed storage system—as baseline. Results obtained on an extensive testbed are presented and some initial conclusions are drawn.

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.

A new approach is described that allows replacing traditional monitoring with cognitive monitoring. The basic provisions of cognitive monitoring are exposed and its peculiar features identified. A set of nested hierarchical relatively finite operation automatons is proposed for the formal description of cognitive monitoring models. The general cognitive monitoring algorithm is exposed. Quantitative cognitive monitoring efficiency estimates are provided by two key indices, namely the number of simultaneously observed objects and fullness of synthesized object models.

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 article presents the results of research in the field of forming a unified geoinformation environment. The concept of the geo-information environment is a development of the concept of the cyber environment of virtual enterprises, which is formed from agents of three types: individuals, legal entities and man-made objects, by introducing geo-information into it. The geoinformation cyber environment provides tools that are best suited to automate the management of spatially distributed objects.

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.

Their hierarchical organization and metadata management limit the traditional file storage systems’ scalability. The industry widely uses Distributed Object Storage Systems because they keep the advantages of traditional file storage systems, e.g., data sharing, and alleviate the scalability issue through benefiting from the flat namespaces and integrating the meta-data in the object. However, evaluating and comparing distributed object storage systems remains a significant challenge. The existing DOSS benchmarks provide simple read/write operations without considering the complex workload characteristics in request arrival patterns and request size distribution. This paper presents an Object Storage Benchmark suite, named OStoreBench, which characterizes critical paths of three real-world application scenarios, including online service, big data analysis, and file backup. We evaluate three state-of-the-practice object storage systems using OStoreBench, including Ceph, Openstack Swift, and Haystack. The benchmark suite is publicly available from https://github.com/EVERYGO111/OStoreBench.

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.

An algorithmic mapping of storage locations brings high storage efficiency to the storage system, but the loss of efficient scheduling makes systems prone to crashing at low usage. This paper uses the Ceph storage system as a research sample to analyze these issues and proposes a grading mapping adaptive storage resource collaborative optimization mechanism. This approach grading both the storage device and the storage content, and introduced random factors and influence factors as two-factors to quantify the grading mapping relationship between the two of them. This relation coordinates the storage systems’ performance and reliability. In addition, a collaborative storage algorithm is proposed to realize balanced storage efficiency and control data migration. The experimental results show that in comparison with the inherent mechanism in the traditional Ceph system, the proposed cooperative storage adaptation mechanism for data balancing has increased the average system usage by 17% and reduces data migration by 50% compared to the traditional research approach.

The article proposes and investigates the concept of proactive monitoring of critical events at the distributed objects of engineering communications of the city and in the urban road environment. The purpose of monitoring is to determine, assess and predict the dynamics of the risks of critical events, depending on changes in indicators and factors correlating with them. The main characteristics of distributed monitoring objects, the classification and analysis of critical events, the reasons for their occurrence and possible influencing factors are given. For predictive analysis and assessment of risks of critical events, collection, consolidation and analysis of big sensor and social data is carried out, reflecting the dynamics of changes in the indicators of monitored objects and external factors of influence. Big data includes indicators of monitored objects, information about events and possible causes of their occurrence, factors influencing the risks of their development. Cyber-physical (sensor) data is unloaded from spatially distributed photo and video recording complexes, video surveillance cameras, weather stations, measuring instruments and sensors of objects and pipelines of engineering networks. Cyber-social (social) data is collected from open sources of information on the Internet and mobile communications of the civilian population. The monitoring system is using a multi-agent approach, which involves the use of software agents directly on distributed data collection sources and robots for collecting data from open sources on the Internet, brokers for consolidation and protection of transmitted data, a component of a distributed information warehouse.

So far, we have discussed many IPC techniques in macOS. The latest is XPC which we are going to discuss in this chapter. Before XPC, a common way to use IPC, and provide services between processes, was through Sockets, Mach messages, or distributed objects.

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 .

Due to the influence of the shooting angle of view and the flight height, the images taken by UAV often have complex backgrounds and contain a large number of small and unevenly distributed objects. In order to solve the problem that it is difficult to accurately locate and recognize small objects in UAV images under complex backgrounds, this paper proposes an multi-scale feature fusion algorithm D-A-FS SSD (Dilated-Attention-Feature Fusion SSD) based on the combination of dilated convolution and attention mechanism. In the process of feature extraction, the receptive field of the feature is expanded through the dilated convolution, which improves the network's feature expression of object distribution and scale difference information. And a attention network is used in our method to effectively suppresse the background information. In the multi-scale detection stage, our method fuses the low-level feature map responsible for detecting small objects with the high-level feature map which have much higher semantic information to improve the recognition accuracy of small objects. Experimental results show that our method effectively improves the accuracy of UAV image object detection.

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.

Fault-tolerant distributed applications require communication abstractions with provable guarantees on message deliveries. For example, Set-Constrained Delivery Broadcast (SCD-broadcast) is a communication abstraction for broadcasting messages in a manner that, if a process delivers a set of messages that includes m and later delivers a set of messages that includes m , no process delivers first a set of messages that includes m′ and later a set of messages that includes m.Imbs et al. proposed this communication abstraction and its first implementation. They have demonstrated that SCD-broadcast has the computational power of read/write registers and allows for an easy building of distributed objects such as snapshot objects and consistent counters. Imbs et al. focused on fault-tolerant implementations for asynchronous message-passing systems that are prone to process crashes. This paper aims to design an even more robust SCD-broadcast communication abstraction, namely a self-stabilizing SCD-broadcast. In addition to process and communication failures, self-stabilizing algorithms can recover after the occurrence of arbitrary transient faults; these faults represent any violation of the assumptions according to which the system was designed to operate (as long as the algorithm code stays intact).This work proposes the first self-stabilizing SCD-broadcast algorithm for asynchronous message-passing systems that are prone to process crash failures. The proposed self-stabilizing SCD-broadcast algorithm has an $\mathcal{O}(1)$ stabilization time (in terms of asynchronous cycles). The communication costs of our algorithm are similar to the ones of the non-self-stabilizing state-of-the-art. The main differences are that our proposal considers repeated gossiping of $\mathcal{O}(1)$ bits messages and deals with bounded space (which is a prerequisite for self-stabilization). We advance the state-of-the-art also by two new self-stabilizing applications: an atomic construction of snapshot objects and sequentially consistent counters.