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Одной из актуальных проблем современной теории управления организационными системами является разработка эффективных алгоритмических процедур управления территориально связанными организационными системами, в которых эффективность функционирования территориально распределенных объектов основной организационной системы зависит от степени влияния результатов их деятельности на объекты организационной системы, связанной с основной. В рамках данного исследования предложены проблемно-ориентированные процедуры анализа и управления территориально связанными системами отраслевого кластера на основе пространственно-временной информации, включающие: картографическую визуализацию результатов ГИС-ориентированного мониторингового оценивания функционирования объектов основной и связанной систем; экспертный анализ интенсивности результативного взаимодействия объектов на основе ГИС-ориентированной пространственно-временной информации с учетом вариантов перспективного планирования объемных показателей; выбор варианта, наилучшим образом обеспечивающего взаимодействие основной и связанной с ней системы внутри отраслевого кластера; распределение объемных показателей варианта между объектами внутри отраслевого кластера. Представлены результаты применения предложенной методики в практике управления результативным взаимодействием территориально связанных систем, входящих в кластер гражданской авиации, где в качестве основной системы выступает отраслевая система высшего и профессионального образования, а в качестве территориально связанной системы – система аэропортов Российской Федерации. Полученные результаты подтвердили эффективность предложенного подхода, что позволяет рекомендовать его к использованию в практике управления территориальными связанными организационными системами различных отраслевых кластеров., One of the urgent problems of modern management theory in organizational systems is the development of effective algorithmic procedures for managing geographically related organizational systems in which the effectiveness of geographically distributed objects operation of the main organizational system depends on the extent to which the results of their activities influence the objects of the organizational system associated with the main one. As part of this study, the authors propose problem oriented procedures for the analysis and management of geographically connected systems of an industry cluster based on spatial and temporal information including cartographic visualization of the results of a GIS-based monitoring assessment of the operation of the objects within the main and associated systems and an expert analysis of object effective interaction intensity based on GIS-oriented spatial and temporal information with consideration to long-term planning of volumetric indicators. Selection of the option that best ensures the interaction of the main and related systems within the industry cluster as well as distribution of volume indicators of a variant between objects within an industry cluster are also suggested. The article presents the results of employing the proposed methodology in the practice of managing the effective interaction of geographically related systems included in the civil aviation cluster where the branch system of higher and vocational education is the main and the geographically connected system is the system of airports of the Russian Federation. The findings confirmed the effectiveness of the proposed approach which makes it possible to recommend it for the use in the practice of managing territorial connected organizational systems of various industry clusters., МОДЕЛИРОВАНИЕ, ОПТИМИЗАЦИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ, Выпуск 3 (38) 2022, Pages 29-30

No-reference (NR) Video Quality Assessment (VQA) methods often use bitrate as one of the most important parameters. This usually does not take into account that the quality of the video sequence at a given bitrate depends not only on the encoder used but also on the complexity of the video content as well as its resolution. In this paper, we present a novel low complexity Content- Aware Bitrate-Based (CABB) VQA metric that takes into account the spatial resolution, bitrate, spatial and temporal information of video sequences and can be used to evaluate video quality in video streaming. The metric was developed based on the Structural Similarity Index Measure (SSIM) computed for 11 training sequences with different complexity and for 5 spatial resolutions. The metric itself does not use SSIM scoring to assess video quality but estimates the SSIM of a test video sequence. The accuracy of the proposed metric was tested on three video sequences that were not used for the metric modeling, and the Pearson Linear Correlation Coefficient (PLCC) values were calculated for the actual SSIM values and the values obtained by CABB are above 0.9. PLCC values for CABB results for 85 test video sequences and corresponding subjective Difference Mean Opinion Score values were also calculated. The PLCC results showed that the proposed metric achieved a higher correlation with the subjective quality scores compared to the published bitrate-based metric.

Remote sensing satellites have been broadly applied to sea ice monitoring. The substantial increase in satellite imagery provides a large amount of data support for deep learning methods in the sea ice classification field. However, there is a lack of public remote sensing datasets to facilitate sea ice classification with spatial and temporal information and to benchmark the deep learning methods. In this paper, we provide a labeled large sea ice dataset derived from time-series sentinel-1 SAR images, dubbed SI-STSAR-7, and a validated dataset construction method for sea ice classification research. The SI-STSAR-7 dataset includes seven different sea ice types corresponding to different sea ice development stages in Hudson Bay during winter, and its samples are time sequences of SAR image patches in order to embody the differences of backscattering intensity and textures between different sea ice types, as well as the change of sea ice with time. We construct the dataset by first performing noise reduction and mitigation of incidence angle dependence on SAR images, and then producing data samples and labeling them based on our proposed sample-producing principles and the weekly regional ice charts provided by Canadian Ice Service. Three baseline classification methods are developed on SI-STSAR-7 to establish benchmarks, which are evaluated with accuracy and kappa coefficient. The sample-producing principles are verified through experiments. Based on the experimental results, sea ice classification can be implemented well on SI-STSAR-7.

MPEG Dynamic Adaptive Streaming over Hypertext Transfer Protocol (MPEG DASH) is adaptive streaming standard developed to unify previously developed adaptive streaming technologies. Over the years various MPEG DASH based adaptive streaming algorithms have been developed, mainly hybrid algorithms that take into account the buffer occupancy, available bandwidth and other parameters like video segment size. Adaptive streaming algorithms are used to determine the next video representation in order to ensure the best possible quality of experience in varying network conditions. They must balance well between the initial video delay and the number and depth of representation switching, as well as avoid video stalling events. This paper compares three different algorithms: Rate Adaptation Algorithm (LIU), Segment-Aware Rate Adaptation (SARA) and Content-Aware Rate Adaptation (CARA) in five network test cases with three video sequences. Obtained results show that on average LIU has the most representation switching events regardless of the spatial (SI) and temporal information (TI) of video sequence and it has the lowest achieved bitrate values. Although CARA and SARA achieve similar results, due to the fact that SARA has four phases of deciding on the representation of the next segment, it has the least number of switching events and a larger value of switching depth. CARA achieves highest average values of achieved bitrate in cases when video sequences Elephants Dream and Big Buck Bunny with lower SI and TI are used.

Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.

Nested arrays have recently attracted considerable attention in the field of direction of arrival (DOA) estimation owing to the hole-free property of their virtual arrays. However, such virtual arrays are confined to difference coarrays as only spatial information of the received signals is exploited. By exploiting the spatial and temporal information jointly, four kinds of novel nested arrays based on the sum-difference coarray (SDCA) concept are proposed. To increase the degrees of freedom (DOFs) of SDCA, a modified translational nested array (MTNA) is introduced first. Then, by analyzing the relationship among sensors in MTNA, we give the specific positions of redundant sensors and remove them later. Finally, we derive the closed-form expressions for the proposed arrays as well as their SDCAs. Meanwhile, different index sets corresponding to the proposed arrays are also designed for their use in obtaining the desirable SDCAs. Moreover, the properties regarding DOFs of SDCAs and physical apertures for the proposed arrays are analyzed, which prove that both the DOFs and physical apertures are improved. Simulation results are provided to verify the superiority of the proposed arrays.

International audience; Natural hazard analysis often makes use of alphanumeric data sets whose content is mainly expressed using natural language expressions that describe where and when events such as eruptions, avalanches, floods , etc. took place. However, a lot of expressions used in natural language are vague and imprecise and especially those which are used to locate places (around, near to, north of, etc.) and dates (between 1710 and 1711, at the beginning of the century, etc.). Some works propose approaches to define a spatial expansion representing the localization of any vague spatial information. Yet, these approaches do not allow to define any spatial or temporal localization that takes into account their physical environment: topographical or meteorological elements of the studied ground, nature and the characteristics of the observed phenomenon, etc. We propose a methodology that allows to improve the localization and the cartographic representation of imperfect spatial and temporal information. This methodology relies on a specific extension of Spatial ML and TimeML markup languages and an ontology for the integration, quantification and representation of imperfect spatio-temporal information expressed in natural language in order to locate natural hazard phenomena. In this paper, we present the elements of the STELLA (Spatial - TEmporal LocaLizAtion) methodology and framework .