Reviewerly/exHarmony huggingface dataset:
Governance and the capital flight trap in Africa

The study examines the use of governance tools to fight capital flight by reducing the capital flight trap. Two overarching policy syndromes are addressed in the study. It first assesses whether governance is an effective deterrent to the capital flight trap in Africa, before examining what thresholds of government quality are required to fight the capital flight trap in the continent. The following findings are established. Evidence of a capital flight trap is apparent because past values of capital flight have a positive effect on future values of capital flight. The net effects from interactions of the capital flight trap with political stability, regulation quality, economic governance and corruption-control on capital flight are positive. The critical masses at which â€œvoice & accountabilityâ€ and regulation quality can complement the capital flight trap to reduce capital flight are respectively, 0.120 and 0.680, which correspond to the best performing countries. Policy implications are discussed.
Amazon SageMaker Autopilot: a white box AutoML solution at scale

AutoML systems provide a black-box solution to machine learning problems by selecting the right way of processing features, choosing an algorithm and tuning the hyperparameters of the entire pipeline. Although these systems perform well on many datasets, there is still a non-negligible number of datasets for which the one-shot solution produced by each particular system would provide sub-par performance. In this paper, we present Amazon SageMaker Autopilot: a fully managed system providing an automated ML solution that can be modified when needed. Given a tabular dataset and the target column name, Autopilot identifies the problem type, analyzes the data and produces a diverse set of complete ML pipelines including feature preprocessing and ML algorithms, which are tuned to generate a leaderboard of candidate models. In the scenario where the performance is not satisfactory, a data scientist is able to view and edit the proposed ML pipelines in order to infuse their expertise and business knowledge without having to revert to a fully manual solution. This paper describes the different components of Autopilot, emphasizing the infrastructure choices that allow scalability, high quality models, editable ML pipelines, consumption of artifacts of offline meta-learning, and a convenient integration with the entire SageMaker suite allowing these trained models to be used in a production setting.
Feature Selection for Handwritten Signature Recognition Using Neighborhood Component Analysis

Advance trends adopted in classification of images using deep learning networks have paved opportunities for handwritten signature identification and verification. Convolution neural network has demonstrated to be profoundly proficient in image classification, yet this methodology has a few weaknesses also. Notably, a huge number of images are required for training to achieve high level of a precision for classification. Transfer learning with pretrained deep convolution neural network model can be utilized to conquer these issues. To bring about further enhancements in terms of recognition rate, storage and computational time, feature selection procedure plays a key role by distinguishing the most significant features. In this work, we used a pretrained DCNN model, i.e., GoogLeNet for feature extraction using transfer learning and further used neighborhood component analysis to reduce the irrelevant features. Support vector machine is used for classification. Experiments have been conducted using three signature datasets, i.e., CEDAR, SigComp2011 and UTSig.
Intelligent Garbage Classification System Based on Open MV

In the 20th century, the United Kingdom first implemented waste sorting and recycling, placing sorting barrels on the streets and recycling available resources. This method has also become popular worldwide. With the proposal of “smart city”, people have higher and higher requirements on quality of life, and the intelligentization of sorting buckets has become an inevitable trend. In order to respond to the call for garbage classification, this article has designed a set of intelligent garbage classification system based on STM32F103 Single-chip microcomputer, image processing technology and sensor technology. This article gives a more detailed and systematic description of the software framework and hardware design of the entire project. The design mainly uses the SSD algorithm and the Open MV module to identify and classify the types of garbage. The system controls the rotation of the steering gear baffle and the barrel in the garbage bin through the STM32F103 single-chip microcomputer, thereby performing accurate garbage classification. The intelligent trash can designed in this paper can realize the intelligent identification of garbage types and automatically complete the classification processing of garbage. In the future of the popularization of artificial intelligence, the design has certain innovative value, practical value and scientific research value.
Practice-oriented Training of School Children in the Conditions of the Federal State Educational Standard

The article deals with one of the directions of educational practice — practice-oriented training. In the implementation of practice-oriented training in modern education can help institutions of additional education, because they have a high adaptation to changes in society, quickly respond to individual educational and other needs of children, and most importantly, unlike regulated school education, offer the freedom to choose programs, directions of training, education and development. New requirements for the organization of the educational process in the system of additional education was a prerequisite for the development of a model of formation of universal educational actions on the basis of Children’s Ecological and Biological Center of Zheleznogorsk, Krasnoyarsk Krai. When creating the model, we took into account that the educational activity of the Children’s Ecological and Biological Center is based on a system-activity approach, which will ensure: the formation of readiness for self-development and continuous education, design and construction of the social environment of students in the education system, active educational and cognitive activity of students, construction of the educational process taking into account the individual age, psychological and physiological characteristics of students. The proposed model can be implemented in all institutions of additional education of natural Sciences.
Decision support methods in optical research

Decision support methods are developed for the problems in applied optics in which it is necessary to make decisions about image quality and its compliance with the requirements that are determined by its further use. The choice of the calibrated theoretical image on which correctness of carrying out of settlement work is checked is discussed. An algorithm according to which the obtained numerical image is improved is given. The paper identifies the disadvantages of machine vision systems, such as defects in system elements, system debugging, noise, unsuccessful experimental scheme that reduce the quality of the experimental image. To establish the relationship between the causes and consequences of defects, a table with the causes of the defects formation in the diffraction pattern according to the scheme of Ishikawa is builded. The quality and ways to improve the experimental image are established using a Pareto diagram. Improvement occurs iteratively and allows to improve gradually the image quality to the maximum possible value in the experimental scheme. Two stages of decision-making are introduced - the first determines the perfection of the experimental image, and the second its acceptability as the main result of the work according to experts’ opinion. The completeness of the alternatives in the analysis is given by the recommendations for further action, and the magnitude of the error due to a combination of two methods: Pareto diagram and experts opinion does not exceed 5 %.
BED: A Block-Level Deduplication-Based Container Deployment Framework

Container technology has gained great popularity in cloud environment since containers provide near-native performance and are lighter and less expensive than traditional virtual machines. However, starting up a non-local container, whose image is unavailable locally, is time-consuming. The reason is that pulling an image from a remote registry requires a long time. In this paper, we experimentally find that a lot of redundant data exists among image layers when we pull images from the registry. This redundant data causes additional pull time and makes the startup of a non-local container slower. To minimize the amount of pulled data while providing intact images, we propose a container deployment framework, BED, based on block-level deduplication. To be specific, BED stores an image layer as numerous data blocks and a fingerprint list which is generated based on these data blocks. When BED needs to pull an image, it pulls the fingerprint lists of the image, deduplicates these fingerprint lists, and pulls the data blocks non-existing locally from the registry. Based on the local and pulled blocks, BED reconstructs the image layers for a container. Experiments show that compared with original Docker, BED reduces the time of pulling images by 35% on average and saves about 48% data transmission in the network.
Blockchain Technologies in Enterprise Modeling and Enterprise Information Systems

This paper serves as an editorial to the corresponding special issue on Blockchain Technologies in Enterprise Modeling and Enterprise Information Systems.
Understanding Bird's-Eye View Semantic HD-Maps Using an Onboard Monocular Camera.

Autonomous navigation requires scene understanding of the action-space to move or anticipate events. For planner agents moving on the ground plane, such as autonomous vehicles, this translates to scene understanding in the bird's-eye view. However, the onboard cameras of autonomous cars are customarily mounted horizontally for a better view of the surrounding. In this work, we study scene understanding in the form of online estimation of semantic bird's-eye-view HD-maps using the video input from a single onboard camera. We study three key aspects of this task, image-level understanding, BEV level understanding, and the aggregation of temporal information. Based on these three pillars we propose a novel architecture that combines these three aspects. In our extensive experiments, we demonstrate that the considered aspects are complementary to each other for HD-map understanding. Furthermore, the proposed architecture significantly surpasses the current state-of-the-art.
GDPR Compliance: Proposed Guidelines for Cloud-Based Health Organizations

In this paper, we investigate the implications of the General Data Protection Regulation (GDPR) on the design of a Cloud-based Health System. Keeping secure healthcare information and protecting patients’ privacy is a major responsibility of all healthcare providers. On May 25th 2018, when the GDPR has become mandatory within the European Union, this responsibility has been increased. Failure to comply with GDPR can result in huge fines. For this reason, it is of vital importance any health care organization to explore ways for achieving protection of the data subjects and ensuring GDPR compliance.
Automated Calculation of Substation Auxiliaries in CAD “ZRU CAD”

When designing the substation, special attention should be paid to the development of the system of own needs, since the reliability of the equipment depends on the correct operation of their own needs. The main task when designing your own needs is the correct determination of the load, which depends on the number and power of transformers, type of substation, type of operating current, type of switches and so on. In order to speed up the process of calculating the load in the work, it is proposed to use a database containing information about all the loads of their own needs, as well as electrical devices necessary for the design of the system. In addition, the database contains data on operational current systems. In this paper, it is proposed to carry out computer-aided design of the substation's own needs system based on algorithms implemented in the original CAD switchgear software. This will accelerate the design process and eliminate errors and inaccuracies, as well as save the design engineer from routine work. The work is carried out with the financial support of a grant from the President of the Russian Federation for young scientists - candidates of sciences MK-939.2019.8.
Analisa Potensi Air Danau Karo Pulau Obi Halmahera

&lt;span&gt;&lt;em&gt;The existence of Lake Karo is needed to meet the water needs of nickel mining activities which are located &lt;/em&gt;&lt;span&gt;&lt;em&gt;approximately 5 km to the North with a water storage area of 9.88 km2 and a watershed of 65.84 Km2. The &lt;/em&gt;&lt;span&gt;&lt;em&gt;analysis was carried out based on climate data, topography, watershed, geological maps, geohydrology, lake &lt;/em&gt;&lt;span&gt;&lt;em&gt;maps, river discharge, groundwater potential, land use, environmental documents. The method for estimating &lt;/em&gt;&lt;span&gt;&lt;em&gt;river discharge is developed by Mock (1973), this method requires climate data and soil characteristics, &lt;/em&gt;&lt;span&gt;&lt;em&gt;while in the calculation besides direct surface runoff there is also underground flow. The results of the &lt;/em&gt;&lt;span&gt;&lt;em&gt;simulation monthly discharge during the period 2014-2018, the mainstay discharge simulation of the Karo &lt;/em&gt;&lt;span&gt;&lt;em&gt;Lake watershed area with a probability of 95% is 0.505 m3 / second. The monitoring results show the &lt;/em&gt;&lt;span&gt;&lt;em&gt;symptoms of lake water level decline in recent years due to increased Lake Karo water intake. Therefore it is&lt;/em&gt;&lt;span&gt;&lt;em&gt;necessary to look for other alternative sources to overcome them.&lt;/em&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;/span&gt;&lt;br /&gt;&lt;br class="Apple-interchange-newline" /&gt;&lt;/span&gt;
Digitalization of higher education in the context of information inequality

Abstract The article examines the issues of information inequality in the context of digitalization of education in the higher education system of the Russian Federation. The difference between the concepts of “digital divide” and “digital inequality” is made, the specifics of these terms in relation to the digital educational environment are revealed, and the concept of “new digital divide” is given. As digital technologies are becoming more accessible today, the technological aspect of the digital divide in education will become less significant over time. The presence of digital inequality in the activities of Russian universities in the digital educational process is considered in a regional context, the leaders and outsiders of this process among the constituent entities of the Russian Federation are highlighted, problem areas in the context of digital educational activities are identified, and key directions of the national strategy for the digital transformation of education are proposed.
CNN and RNN

In this chapter, we will introduce the typical deep neural networks from the viewpoint of CNN family, especially region-based CNN, SSD, and YOLO. Meanwhile, from the viewpoint of time series analysis, we depict the RNN family, namely, LSTM, GRU, FRU, etc. In a nutshell, we hope to introduce deep learning from spatial and temporal aspects, deeply explore the knowledge of this state-of-the-art technology.
Scission: Performance-driven and Context-aware Cloud-Edge Distribution of Deep Neural Networks.

Partitioning and distributing deep neural networks (DNNs) across end-devices, edge resources and the cloud has a potential twofold advantage: preserving privacy of the input data, and reducing the ingress bandwidth demand beyond the edge. However, for a given DNN, identifying the optimal partition configuration for distributing the DNN that maximizes performance is a significant challenge. This is because the combination of potential target hardware resources that maximizes performance and the sequence of layers of the DNN that should be distributed across the target resources needs to be determined, while accounting for user-defined objectives/constraints for partitioning. This paper presents Scission, a tool for automated benchmarking of DNNs on a given set of target device, edge and cloud resources for determining optimal partitions that maximize DNN performance. The decision-making approach is context-aware by capitalizing on hardware capabilities of the target resources, their locality, the characteristics of DNN layers, and the network condition. Experimental studies are carried out on 18 DNNs. The decisions made by Scission cannot be manually made by a human given the complexity and the number of dimensions affecting the search space. The benchmarking overheads of Scission allow for responding to operational changes periodically rather than in real-time. Scission is available for public download at this https URL.
KNOWLEDGE-ORIENTED INFORMATION TECHNOLOGY TO VARIABILITY MANAGEMENT AT THE DOMAIN ANALYSIS STAGE IN SOFTWARE DEVELOPMENT

The subject matter of this paper is a research of issues related to the variability management at the stage of domain analysis (DA) in the full life cycle (FLC) of software products line (SPL). The main goal of this research work is the elaboration of a new knowledge-based information technology to support a variability management in DA as a most complex and weak-formalized stage in FLC of SPL. In order to reach this goal the following tasks were formulated and resolved: to analyze the variability issues in FLC on the example of the modern agile-development approach - in the Scrum-methodology; to study how the methods of knowledge handling can be used to support some users and domain-experts activities within the DA phase with respect to software variability modeling; to make the motivated choice of the suitable CASE-tools to elaborate an appropriate IT solution to support the knowledge-oriented approach to DA; to present this IT-solution in a structured form, to consider some its implementation issues, and to discuss the first testing results. The methods used in this research are: domain-driven design approach to software development, repertory grids method and ontologies for expert’s knowledge handling, IDEF0 notation for specification of the proposed IT solution, feature-oriented domain analysis (FODA) for variability modeling. Conclusions: the results of this research shown that the special attention has to be paid to the DA in a FLC, especially with usage of knowledge-based methods. To perform this process in an effective way the repertory grids method is motivated chosen and analyzed. To support the usage of this method in DA the proposal is made to elaborate the special IT-solution using some already available CASE-tools. The essentials functionality features of two such systems: GridSuite and SOVA (Semantical and Ontological Variability Analysis) are considered, and basing on this result, the integrated IT-solution is elaborated and presented in form of the IDEF0 diagram. Finally, the main technological facets of these tools installation are studied and tested, and the test-case to show the possibility to generate the FODA-variability model for the “Smart-Home” application domain is provided.
A Simple and fast method to detect garbage dumping using pedestrian attribute

This paper presents a new method for the effective detection of real-life garbage dumping actions. Garbage dumping has become a serious problem with the continuous increase of city populations around the world. While monitoring surveillance camera footage is commonly used to catch garbage dumping, this approach is often unable to recognize real-life garbage dumping actions, resulting in many false alarms. Thus, We propose a quick and easy way to detect a variety of garbage dumping actions using pedestrian attribute information. The proposed method includes a pedestrian detector that detects pedestrians and their attribute, and behavior discriminator that identifies a garbage dumping action by comparing the pedestrian attribute pattern in each image frame with a dumping behavior pattern DB. The performance of the proposed method is confirmed by experiments using a garbage dumping action data set similar to real-life situations. When compared with previous studies, the proposed method can be faster as it is simple and only makes deep learning calls once. The proposed method is shown to be capable of identifying pedestrian behavior at a speed of 15 fps, indicative of potential application to real CCTV.
Breaking Microsoft Azure Information Protection Viewer Using Memory Dump

Memory dump attack methods enabling attackers to read memory of many current computer applications, even from the security or document protection domain. The weak point supporting these attacks is that the data in the memory are not protected or encrypted. Disk and file protection means little here, as the key component for the attack is the main memory. Rights Managements Services (RMS) is an information protection technology that helps safeguard digital information from unauthorized use. With the use of the rights management, content owners will be able to define who can open, modify, print, forward, or take other actions with the content.
Kaersutite lamprophyres of the Pokrovo-Kyriivo massif (the Azov area, Ukraine): petrographic features, mineral composition, geochemistry

The paper represents results of the petrographical, geochemical and mineralogical studies of a dyke of porphyry-like mafic rock from the Pokrovo-Kyriivo massif of the Azov area, the Ukrainian Shield. This dyke of fine-grained rock has unique mineral composition as the main mineral in the groundmass is kaersutite. According to mineralogical and petrographic features, it can be identified as kaersutite lamprophyre. The Devonian Pokrovo-Kyriivo massif is located at a junction of the Azov Domain of the Ukrainian Shield and the folded structure of Donbas. This massif is composed of subalkaline (pyroxenite, gabbro) and alkaline (malignites, nepheline syenite) rocks which are cut by numerous dykes varying in composition from ultramafic to intermediate. The dyke of finegrained kaersutite lamprophyre was discovered in the bottom hole of the drillhole 36, where it is intruded into mediumgrained gabbroids and pyroxenites. The groundmass of the rock is composed of kaersutite, titanomagnetite, albite, chlorite, titanite, pyrite, chalcopyrite, apatite, monazite. The phenocrysts are represented by chlorite and talc pseudomorphoses after a diamond-shaped mineral with shortened obtuse angles. According to electron microprobe analysis, kaersutite has low TiO2 content and intermediate mg# values of 0.54—0.67. According to the chemical composition, studied kaersutites differ from the same mineral in the microgabro dyke in the Pokrovo-Kyriivo massif, and from kaersutites in camptonites of the Khlibodarivka open pit in the Azov area. At the same time, it is similar to kaersutites from camptonites of the Novoukrainka dyke field of the Inhul terrane. Kaersutite lamprophyre also differ from dyke rocks of the Pokrovo-Kyriivo massif by the absence of clinopyroxene, mica, ilmenite and low content of TiO2 in titanomagnetite, which does not exceed 6 %. This is due to a low TiO2 content in the rocks (up to 3.5 %), relative to other dyke gabbro of the area. According to mineralogical-petrographic and chemical characteristics, kaersutite lamprophyres are unique rocks that have no direct counterparts in the Azov area or on the Ukrainian Shield in the whole.