This article reflects on key concepts of historical thinking proposed by doctoral students and young researchers. Established concepts such as the social role of history, professional historian and (imagined) space are still important to the new generation of historians. At the same time, some new concepts are emerging, such as political exhumations, mass graves, motion, embodied historical research, ahistorical memory politics, websites as historical sources, critical heritage studies and heritagisation, treason, preposterous history – an idea taken from Mieke Bal, and “Supreme Peace” – a notion drawn from the Chinese philosophy of history. To interpret these concepts, I build word clouds as a way of creating knowledge involving non‑human factors (algorithms) while enabling speculative interpretations of the relations between words. The idea of a secure past comes to the fore and I therefore examine whether historical security and being secure in history could be considered important elements of interdisciplinary security studies.

The biggest software development companies conduct daily more than hundreds deployments which influence currently operating IT (Information Technology) systems. This is possible due to the availability of automatic mechanisms which are providing their functional testing and later applications deployment. Unfortunately, nowadays, there are no tools or even a set of good practices related to the problem on how to include IT security issues into the whole production and deployment processes. This paper describes how to deal with this problem in the large mobile telecommunication operator environment.

Defects affect the properties and behavior of the casting during its service life. Since the defects can occur due to different reasons, they

must be correctly identified and categorized, to enable applying the appropriate remedial measures. several different approaches for

categorizing casting defects have been proposed in technical literature. They mainly rely on physical description, location, and formation

of defects. There is a need for a systematic approach for classifying investment casting defects, considering appropriate attributes such as

their size, location, identification stage, inspection method, consistency, appearance of defects. A systematic approach for categorization of

investment casting defects considering multiple attributes: detection stage, size, shape, appearance, location, consistency and severity of

occurrence. Information about the relevant attributes of major defects encountered in investment casting process has been collected from

an industrial foundry. This has been implemented in a cloud-based system to make the system freely and widely accessible.

The article presents a method for 3D point cloud segmentation. The point cloud comes from a FARO LS scanner – the device creates a dense point cloud, where 3D points are organized in the 2D table. The input data set consists of millions of 3D points – it makes widely known RANSAC algorithms unusable. We add some modifi cations to use RANSAC for such big data sets.

The process of designing and creating an integrated distributed information system for storing digitized works of scientists of research institutes of the Almaty academic city is analyzed. The requirements for the storage of digital objects are defined; a comparative analysis of the open source software used for these purposes is carried out. The system fully provides the necessary computing resources for ongoing research and educational processes, simplifying the prospect of its further development, and allows to build an advanced IT infrastructure for managing intellectual capital, an electronic library that is intended to store all books and scientific works of the Kazakhstan Engineering Technological University and research institutes of the Almaty academic city.

A review of a new Polish translation of Aristophanes’ Clouds by Olga Śmiechowicz.

A response to the review of a new Polish translation of Aristophanes’ Clouds, which appeared in the previous issue of “Meander”.

This paper deals with a methodology for the implementation of cloud manufacturing (CM) architecture. CM is a current paradigm in which dynamically scalable and virtualized resources are provided to users as services over the Internet. CM is based on the concept of coud computing, which is essential in the Industry 4.0 trend. A CM architecture is employed to map users and providers of manufacturing resources. It reduces costs and development time during a product lifecycle. Some providers use different descriptions of their services, so we propose taking advantage of semantic web technologies such as ontologies to tackle this issue. Indeed, robust tools are proposed for mapping providers’ descriptions and user requests to find the most appropriate service. The ontology defines the stages of the product lifecycle as services. It also takes into account the features of coud computing (storage, computing capacity, etc.). The CM ontology will contribute to intelligent and automated service discovery. The proposed methodology is inspired by the ASDI framework (analysis–specification–design–implementation), which has already been used in the supply chain, healthcare and manufacturing domains. The aim of the new methodology is to propose an easy method of designing a library of components for a CM architecture. An example of the application of this methodology with a simulation model, based on the CloudSim software, is presented. The result can be used to help the industrial decision-makers who want to design CM architectures.

Confidential algorithm for the approximate graph vertex covering problem is presented in this article. It can preserve privacy of data at every stage of the computation, which is very important in context of cloud computing. Security of our solution is based on fully homomorphic encryption scheme. The time complexity and the security aspects of considered algorithm are described.

The idea of using the Cloud of Things is becoming more critical for e-government, as it is considered to be a useful mechanism of facilitating the government’s work. The most important benefit of using the Cloud of Things concept is the increased productivity that the e-governments would achieve; which eventually would lead to significant cost savings; which in turn would have a highly anticipated future impact on egovernments. E-government’s diversity goals face many challenges; trust is one of the major challenges that it is facing when deploying the Cloud of Things. In this study, a new trust framework is proposed which supports trust with the Internet of Things devices interconnected to the cloud; to support the services that are provided by e-government to be delivered in a trusted manner. The proposed framework has been applied to a use case study to ensure its trustworthiness in a real mission. The results show that the proposed trust framework is useful to ensure achieving a trusted environment for the Cloud of Things for it to continue providing and gathering the data needed for the services that are offered by users through E-government.

This paper proposes an advanced routing method in the purpose of increasing IoT routing device’s power-efficiency, which allows to centralize routing tables computing as well as to push loading, related to routing tables computation, towards the Cloud environment at all. We introduced a phased solution for the formulated task. Generally, next steps were performed: stated requirements for the system with Cloud routing, proposed possible solution, and developed the whole system’s structure. For a proper study of the efficiency, the experiment was conducted using the developed system’s prototype for real-life cases, each represents own cluster size (several topologies by each size), used sizes are: 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27 and 29. Expectable results for this research – decrease the time of cluster’s reaction on topology changes (delay, needed to renew routing tables), which improves system’s adaptivity.

The rapid development of the global economy has led to an increasing demand for resources. The disparity between the supply and demand of resources continues to be prominent and shows a situation of short supply. Resource investment projects with large amounts and long construction periods face many risks due to various unpredictable factors. Cultural, legal, economic and other environments vary between different countries. Therefore, comprehensive risk identification, understanding, evaluation, and analysis are important prerequisites for the success of mineral investment. In this paper, the risk of mineral resources investment in host countries is identified. A risk evaluation index system is established to objectively evaluate the risk environment of the host country. The risk evaluation index system includes four first-level indexes: political and legal risk, social and cultural risk, economic and financial risk, and natural risk. The subjective weight was determined by sending questionnaires to experts and scholars in the industry and conducting data processing. The entropy method was used to determine the objective weight. Finally, the subjective weight and the objective weight were combined to obtain a group of scientific and accurate combined weights. The matter-element theory was introduced into the cloud model and a risk assessment model based on the cloud matter-element theory was constructed with comprehensive consideration of the fuzziness and randomness of risks. Eight countries with relatively rich mineral resources were taken as cases to verify the model application. The research results provide a theoretical basis and decision-making methods for mineral enterprise investment.

The paper aims at the higher reactive power management complexity caused by the access of distributed power, and the problem such as large data exchange capacity, low accuracy of reactive power distribution, a slow convergence rate, and so on, may appear when the controlled objects are large. This paper proposes a reactive power and voltage control management strategy based on virtual reactance cloud control. The coupling between active power and reactive power in the system is effectively eliminated through the virtual reactance. At the same time, huge amounts of data are treated to parallel processing by using the cloud computing model parallel distributed processing, realize the uncertainty transformation between qualitative concept and quantitative value. The power distribution matrix is formed according to graph theory, and the accurate allocation of reactive power is realized by applying the cloud control model. Finally, the validity and rationality of this method are verified by testing a practical node system through simulation.

Recently, Google Earth Engine (GEE) provides a new way to effectively classify land cover utilizing available in-built classifiers. However, there have a few studies on the applications of the GEE so far. Therefore, the goal of this study is to explore the capacity of the GEE platform in terms of land cover classification in Dien Bien Province of Vietnam. Land cover classification in the year of 2003 and 2010 were performed using multiple-temporal Landsat images. Two algorithms – GMO Max Entropy and Classification and Regression Tree (CART) integrated into the Google Earth Engine (GEE) plat-form – were applied for this classification. The results indicated that the CART algorithm performed better in terms of mapping land use. The overall accuracy of this algorithm in the year of 2003 and 2010 were 80.0% and 81.6%, respective-ly. Significant changes between 2003 and 2010 were found as an increase in barren land and a reduction in forest land. This is likely due to the slash-and-burn agricultural practice of ethnic minorities in the province. Barren land seems to occur more at locations near water sources, reflecting the local people’s unsuitable farming practice. This study may provide use-ful information in land cover change in Dien Bien Province, as well as analysis mechanisms of this change, supporting en-vironmental and natural resource management for the local authorities.

With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data. Remote sensing data clear showing the characteristics of Big Data such as hyper spectral, high spatial resolution, and high time resolution, thus, resulting in a significant increase in the volume, variety, velocity and veracity of data.This paper proposes a feature supporting, salable, and efficient data cube for timeseries analysis application, and used the spatial feature data and remote sensing data for comparative study of the water cover and vegetation change. In this system, the feature data cube building and distributed executor engine are critical in supporting large spatiotemporal RS data analysis with spatial features. The feature translation ensures that the geographic object can be combined with satellite data to build a feature data cube for analysis. Constructing a distributed executed engine based on dask ensures the efficient analysis of large-scale RS data. This work could provide a convenient and efficient multidimensional data services for many remote sens-ing applications.

The unpredictable and huge data generation nowadays by smart devices from IoT and mobile Crowd Sensing applications like (Sensors, smartphones, Wi-Fi routers) need processing power and storage. Cloud provides these capabilities to serve organizations and customers, but when using cloud appear some limitations, the most important of these limitations are Resource Allocation and Task Scheduling. The resource allocation process is a mechanism that ensures allocation virtual machine when there are multiple applications that require various resources such as CPU and I/O memory. Whereas scheduling is the process of determining the sequence in which these tasks come and depart the resources in order to maximize efficiency. In this paper we tried to highlight the most relevant difficulties that cloud computing is now facing. We presented a comprehensive review of resource allocation and scheduling techniques to overcome these limitations. Finally, the previous techniques and strategies for allocation and scheduling have been compared in a table with their drawbacks.

Workflow Scheduling is the major problem in Cloud Computing consists of a set of interdependent tasks which is used to solve the various scientific and healthcare issues. In this research work, the cloud based workflow scheduling between different tasks in medical imaging datasets using Machine Learning (ML) and Deep Learning (DL) methods (hybrid classification approach) is proposed for healthcare applications. The main objective of this research work is to develop a system which is used for both workflow computing and scheduling in order to minimize the makespan, execution cost and to segment the cancer region in the classified abnormal images. The workflow computing is performed using different Machine Learning classifiers and the workflow scheduling is carried out using Deep Learning algorithm. The conventional AlexNet Convolutional Neural Networks (CNN) architecture is modified and used for workflow scheduling between different tasks in order to improve the accuracy level. The AlexNet architecture is analyzed and tested on different cloud services Amazon Elastic Compute Cloud- EC2 and Amazon Lightsail with respect to Makespan (MS) and Execution Cost (EC).

The problem of performing software tests using Testing-as-a-Service cloud environment is considered and formulated as an~online cluster scheduling on parallel machines with total flowtime criterion. A mathematical model is proposed. Several properties of the problem, including solution feasibility and connection to the classic scheduling on parallel machines are discussed. A family of algorithms based on a new priority rule called the Smallest Remaining Load (SRL) is proposed. We prove that algorithms from that family are not competitive relative to each other. Computer experiment using real-life data indicated that the SRL algorithm using the longest job sub-strategy is the best in performance. This algorithm is then compared with the Simulated Annealing metaheuristic. Results indicate that the metaheuristic rarely outperforms the SRL algorithm, obtaining worse results most of the time, which is counter-intuitive for a metaheuristic. Finally, we test the accuracy of prediction of processing times of jobs. The results indicate high (91.4%) accuracy for predicting processing times of test cases and even higher (98.7%) for prediction of remaining load of test suites. Results also show that schedules obtained through prediction are stable (coefficient of variation is 0.2‒3.7%) and do not affect most of the algorithms (around 1% difference in flowtime), proving the considered problem is semi-clairvoyant. For the Largest Remaining Load rule, the predicted values tend to perform better than the actual values. The use of predicted values affects the SRL algorithm the most (up to 15% flowtime increase), but it still outperforms other algorithms.

Cloud-based computational environments can offer elastic and flexible services to wide audiences. Małopolska Educational Cloud was originally developed to support the day-to-day collaboration of geographically scattered schools with universities which organized online classes, led by university teachers, as an amendment to face-to-face teaching. Due to the centralized management and ubiquitous access, both the set of services provided by MEC and their usage patterns can be adjusted rapidly. In this paper we show how – during the COVID-19 pandemic – the flexibility of Małopolska Educational Cloud was leveraged to speed up the transition from in-class to remote teaching, both in the classes and schools which were already involved in the MEC project, and newly added ones. We also discuss the actions that were required to support the smooth transition and draw conclusions for the future.

The research was aimed at analysing the factors that affect the accuracy of merging point clouds when scanning over longer distances. Research takes into account the limited possibilities of target placement occurring while scanning opposite benches of quarries or open-pit mines, embankments from opposite banks of rivers etc. In all these cases, there is an obstacle/void between the scanner and measured object that prevents the optimal location of targets and enlarging scanning distances. The accuracy factors for cloud merging are: the placement of targets relative to the scanner and measured object, the target type and instrument range. Tests demonstrated that for scanning of objects with lower accuracy requirements, over long distances, it is optimal to choose flat targets for registration. For objects with higher accuracy requirements, scanned from shorter distances, it is worth selecting spherical targets. Targets and scanned object should be on the same side of the void.

Terrestrial laser scanner (TLS) is a new class of survey instruments to capture spatial data developed rapidly. A perfect facility in the oil industry does not exist. As facilities age, oil and gas companies often need to revamp their plants to make sure the facilities still meet their specifications. Due to the complexity of an oil plant site, there are difficulties in revamping, having all dimensions and geometric properties, getting through narrow spaces between pipes and having the description label of each object within a facility site. So it is needed to develop an accurate observations technique to overcome these difficulties. TLS could be an unconventional solution as it accurately measures the coordinates identifying the position of each object within the oil plant and provide highly detailed 3D models. This paper investigates creating 3D model for Ras Gharib oil plant in Egypt and determining the geometric properties of oil plant equipment (tank, vessels, pipes . . . etc.) using TLS observations and modeling by CADWORX program. The modeling involves an analysis of several scans of the oil plant. All the processes to convert the observed points cloud into a 3D model are described. The geometric properties for tanks, vessels and pipes (radius, center coordinates, height and consequently oil volume) are also calculated and presented. The results provide a significant improvement in observing and modeling of an oil plant and prove that the TLS is the most effective choice for generating a representative 3D model required for oil plant revamping.

The focus of research works on cavitation has changed since the 1960s; the behaviour of a single bubble is no more the area of interest for most scientists. Its place was taken by the cavitating flow considered as a whole. Many numerical models of cavitating flows came into being within the space of the last fifty years. They can be divided into two groups: multifluid and homogeneous (i.e., single-fluid) models. The group of homogenous models contains two subgroups: models based on transport equation and pressure based models. Several works tried to order particular approaches and presented short reviews of selected studies. However, these classifications are too rough to be treated as sufficiently accurate. The aim of this paper is to present the development paths of numerical investigations of cavitating flows with the use of homogeneous approach in order of publication year and with relatively detailed description. Each of the presented model is accompanied by examples of the application area. This review focuses not only on the list of the most significant existing models to predict sheet and cloud cavitation, but also on presenting their advantages and disadvantages. Moreover, it shows the reasons which inspired present authors to look for new ways of more accurate numerical predictions and dimensions of cavitation. The article includes also the division of source terms of presented models based on the transport equation with the use of standardized symbols.

In the paper, a noise map service designated for the user interested in environmental noise is presented. Noise prediction algorithm and source model, developed for creating acoustic maps, are working in the cloud computing environment. In the study, issues related to the noise modelling of sound propagation in urban spaces are discussed with a particular focus on traffic noise. Examples of results obtained through a web application created for that purpose are shown. In addition, these are compared to results obtained from the commercial software simulations based on two road noise prediction models. Moreover, the computing performance of the developed application is investigated and analyzed. In the paper, a flowchart simulating the operation of the noise web-based service is presented showing that the created application is easy to use even for people with little experience in computer technology.