Reviewerly/exHarmony huggingface dataset:
Analysis of Risk Factors of Neurobiological Pipeline Care and Investigation of Preventive Measures

During clinical care, most neurosurgical patients are critically ill. They have sudden onset of illness that should be treated on time with proper care. The patients require continuous hospitalization for proper treatment. The recovery of patients may be relatively slow and takes some time. Patients and Methods. To explore where the risks of pipeline care lie and the preventive measures. (1) In this paper, 100 neurosurgical patients were treated in our hospital from September 2018 to March 2020. They were firstly selected and divided into two groups. Group A was implemented with routine pipeline care and group B was implemented with the intervention developed by the pipeline team. (2) The design and SMOTE assume that, during the generation of a new synthetic sample of minority classes, the immediate neighbors of the minority class instances were also all minority classes, regardless of their true distribution characteristics, to analyze risk factors during care and summarize preventive measures. Results. The experimental results showed that the total efficiency of nursing care was higher in group B as compared to group A, <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>P</mi> <mo>&lt;</mo> <mn>0.05</mn> </math> ; also, the number of pipeline accidents was lower in group B. Conclusion It is important to be meticulous and thoughtful in pipeline care and to comprehensively analyze the possible risk events and then propose preventive measures so that risk events can be reduced.
Trends in Daily Temperature and Precipitation Extremes over Dire-Dawa, 1980-2018

Extreme climate events have profound impacts on economies and livelihoods of many regions of the world.In Ethiopia, for example the major floods such as in Dire-dawa and Omo basin of June 2006 illustrates the risks to ecosystems, human health and welfare, and infrastructure from short duration weather extremes.There is a paucity of information on trends in climate extremes in the country.However, a joint World Meteorological Commission for Climatology/World Climate Research Programme (WCPRP) project on climate Variability and Predictability (WMO CCl/CLIVAR) Expert Team (ET) on Climate Change Detection, Monitoring and Indices have defined 27 core climate indices mainly focusing on extreme events which can be derived through the use of RClim-Dex Software.In this study, therefore, the RClim-Dex software has been used to derive climate extreme indices for Dire-Dawa station in the Eastern Ethiopia region based on climate data for the period 1980 to 2018.The objective was to examine changes in climate extremes over the city for water provision and urban planning such as flood management practice.These indices have shown insignificant decreasing trends in annual rainfall, Consecutive maximum 5 days precipitation and R95p, R95p% precipitation.A Significant warming pattern was evident in both the maximum and minimum temperature indices.In general the results of this study are in a general agreement with what we have been observed with people's perception about extreme weather events in the last 39 years.
Information Theory and Symbolic Analysis: Theory and Applications

Symbolic analysis has been developed and used successfully in very diverse fields [...].
Self-Learning Pipeline for Low-Energy Resource-Constrained Devices

The trend of bringing machine learning (ML) to the Internet of Things (IoT) field devices is becoming ever more relevant, also reducing the overall energy need of the applications. ML models are usually trained in the cloud and then deployed on edge devices. Most IoT devices generate large amounts of unlabeled data, which are expensive and challenging to annotate. This paper introduces the self-learning autonomous edge learning and inferencing pipeline (AEP), deployable in a resource-constrained embedded system, which can be used for unsupervised local training and classification. AEP uses two complementary approaches: pseudo-label generation with a confidence measure using k-means clustering and periodic training of one of the supported classifiers, namely decision tree (DT) and k-nearest neighbor (k-NN), exploiting the pseudo-labels. We tested the proposed system on two IoT datasets. The AEP, running on the STM NUCLEO-H743ZI2 microcontroller, achieves comparable accuracy levels as same-type models trained on actual labels. The paper makes an in-depth performance analysis of the system, particularly addressing the limited memory footprint of embedded devices and the need to support remote training robustness.
Characterizing Quantum Instruments: From Nondemolition Measurements to Quantum Error Correction

A complete toolbox for the tomographic characterization of generic instruments is experimentally applied to detect qubit loss and leakage, offering in-depth information about the failure modes that degrade quantum error correction.
Optional Pólya trees: Posterior rates and uncertainty quantification

We consider statistical inference in the density estimation model using a tree–based Bayesian approach, with Optional Pólya trees as prior distribution. We derive near-optimal convergence rates for corresponding posterior distributions with respect to the supremum norm. For broad classes of Hölder–smooth densities, we show that the method automatically adapts to the unknown Hölder regularity parameter. We consider the question of uncertainty quantification by providing mathematical guarantees for credible sets from the obtained posterior distributions, leading to near–optimal uncertainty quantification for the density function, as well as related functionals such as the cumulative distribution function. The results are illustrated through a brief simulation study.
PhraseAttn: Dynamic Slot Capsule Networks for phrase representation in Neural Machine Translation

Word representation plays a vital role in most Natural Language Processing systems, especially for Neural Machine Translation. It tends to capture semantic and similarity between individual words well, but struggle to represent the meaning of phrases or multi-word expressions. In this paper, we investigate a method to generate and use phrase information in a translation model. To generate phrase representations, a Primary Phrase Capsule network is first employed, then iteratively enhancing with a Slot Attention mechanism. Experiments on the IWSLT English to Vietnamese, French, and German datasets show that our proposed method consistently outperforms the baseline Transformer, and attains competitive results over the scaled Transformer with two times lower parameters.
The domestic high school professional education system transformation: constraining factors and the assessment ambiguity

The social transformations that took place under the influence of globalization and neoliberalization at the turn of the 20th – 21st centuries led to the need for deep structural and functional changes in the system of domestic higher professional education. Despite the fact that over the past twenty years in domestic practice a lot of directive documents have been developed and a number of serious management decisions have been adopted that have a direct impact on the development of the Russian education system at all levels, the domestic higher school in many respects cannot reach a level that makes it possible to make a serious competition to the participants of the world scientific and educational space. A retrospective analysis of the main conceptual documents that determine the development of the domestic education system, carried out in the work, showed that reforms of the domestic higher education system often do not reach their logical conclusion, since they are predominantly declarative in nature, far from always supported by a legislative framework, which leads to inconsistency in practical actions on their implementation. The article reveals that the incompleteness of the reforms is largely due to the existing gaps in cooperation between the institutions of education, science and industrial production, which all conceptually significant documents declare, but in practice, close mutually beneficial cooperation between these actors does not sufficiently manifest itself. The author indicates that such interaction can be carried out through the development of university systems, which, being integrated into the economy of the regions and acting as a driver of innovative development, can contribute to increasing the competitiveness of Russian universities in the international scientific and educational space.
Various Approaches to Institutional Theory in Increasing the Effectiveness and Efficiency of Organizational Innovation

This paper tries to find the application of institutional theory to innovation that can be applied in various types of organizations. Several studies have analyzed the institutional theory on innovation theory, including impact, how to analyze, and understanding the relationship between the two theories. Or in other words, starting from experimental learning, rules, cognition, and local practices in a social environment that become formal rules and regulations. That is, the contribution to institutional theory in analyzing institutions and organizational legitimacy is very important for innovation theory. In understanding and analyzing the institutional environment, some organizational theorists make several classifications based on several categories. Institutions, as explained by institutional theory, are an important element in understanding innovation theory. As result, the more rapid changes in technology and increasing business competition indicate an increasingly dynamic business environment.
Adaptive Constraint Handling in Optimization of Complex Structures by Using Machine Learning

Abstract Optimization of complex systems requires robust and computationally efficient global search algorithms. Constraints make this a very difficult task, significantly slowing down an algorithm, and can even prevent finding the true Pareto front. This study continues the development of a recently proposed repair approach that exploits infeasible designs to increase computational efficiency of a prominent genetic algorithm, and to find a wider spread of the Pareto front. This paper proposes adaptive and automatized discovery of sensitivity of constraints to variables, i.e. the link, which needed direct designer’s input in the previous version of the repair approach. This is achieved by using machine learning in the form of artificial neural networks (ANN). A surrogate model is afterwards utilized in optimization based on ANN. The proposed approach is used for the recently proposed constraint handling implemented into NSGA-II optimization algorithm. The proposed framework is compared with two other constraint handling methods. The performance is analyzed on a structural optimization of a 178 m long chemical tanker which needs to fulfil class society’s criteria for strength. The results show that the proposed framework is competitive in terms of convergence and spread of the front. This is achieved while discovering the link automatically using ANN, without an input from a user. In addition, computational time is reduced by 60%.
Artificial Intelligence as Futuristic Approach for Narrative Gaming

Artificial intelligence (AI) has moved in practically in every sector of human life. In the 21st century, the gaming industry has attained new heights. With the contribution of virtual and augmented reality, the gaming sector is enticing more gamers due to the immersive nature. Game logic design necessitates thoughtful planning and a comprehensive blueprint to being prepared, trailed by the game dynamics. But at one point, gamers will leave the existing game and move on to the next one just because of stereotype nature and a typical logic. However, with the immersion of AI (machine learning), the game gets trained by the input pattern, or in another words, pattern of players. With this, it builds the new logic which could offer an entirely new experience to the player on his return to the game next time. This study focuses primarily on the three-dimensional (3D) narrative games, and how AI could enhance the gaming experience for the players with interesting narratives, which will be updated based on the previous play-pattern of the player. Various techniques are discussed in this chapter to support the study along with some of the major futuristic approaches such as speech recognition, gesture recognition, facial expression recognition, and physical interaction with the player through the gaming console.
Distance technologies in music education: history, trends and prospects

The article devoted to the disclosure of historical facts in distance learning of musical art. Analyzes the methodological and technological problems that teachers faced at different stages of Internet technologies and their implementation in process of teaching music. Revealed the effectiveness of distance learning future musicians. Described the main limitations of online training and ways to avoid them. A conclusion was about the current trends and prospects for development of distance music education.
Legal functional literacy: the concept and levels

The purpose of the article is to determine the essence of such a socio-legal phenomenon as functional legal literacy, as well as to identify and characterize its levels. The main methods on which the research has been based are the dialectical method of cognition, which allows studying socio-legal phenomena in their development and interaction, as well as a systematic approach that makes it possible to consider legal functional literacy as a set of interrelated elements. The authors conclude that legal functional literacy is a universal socio-legal phenomenon peculiar to any society. However, it always has specific content, depending on historical and geopolitical conditions, belonging to a certain legal family and other circumstances. The article substantiates the position that legal functional literacy is a structural component of functional literacy, which is a complex personal and group education, which is based on a system of theoretical knowledge and practical skills focused on the effective implementation of legal relations in society.
Design of an Efficient Trustful-Lightweight Cloud Service Provisioning Model using Service Optimizer

INTRODUCTION:The establishment of trusted cloud services pretends to provide high impactful service with better satisfaction to the web-users and cloud service providers.Moreover, various existing trust-aware algorithms use diverse QoS measurements and related attributes, leading to the complex selection of cloud services.OBJECTIVES: Thus, this research intends to propose a Trustful-Lightweight Cloud Service Provisioning algorithm (TL-CSP) using Service Optimizer (SO).METHODS: Initially, the QoS metrics are determined by evaluating attributes based on a ranking method based on the users' requests.It is performed with the computation of weighted coefficients of received requests from the users.The service optimization is performed using a global optimizer to assist the cloud users in selecting the service with better satisfaction.RESULTS: The proposed TL-CSP accuracy is validated and compared with the existing cloud service provisioning algorithm to measure the proposed model's efficiency.CONCLUSION: The simulation is carried out in a MATLAB environment.The proposed TL-CSP intends to shows a better trade-off in contrast to prevailing approaches.
Image descattering with synthetic polarization imaging and untrained network

Water scattering is a significant limiting factor for underwater imaging quality. It changes the transportation direction of the original light path, causes the attenuation of light intensity, and so on. In this work, we use a synthetic polarizing camera to capture the images with different polarization states and reduce the impact of water scattering in one step with the underwater light propagation model and the Stokes vector. In addition, an untrained deep network is designed to complete the image descattering processing. Compared with the methods based on deep learning or physical model prior, it is more efficient. This technology is suitable for use in portable underwater imaging optical systems for real-time imaging and detecting particulate matter such as microplastics and microbial particles. It also broadens the application of underwater polarization imaging.
ECSA Special Issue

Over 500 delegates took part in the third international ECSA conference in September 2020. Across 30 sessions, as well as keynote talks, e-poster presentations and more informal settings, they discussed and debated a diverse range of subjects related to citizen science. This special edition of ‘JCOM’ brings together some of the central themes that were under the spotlight at ECSA 2020. Since ECSA 2020 has been one of the first examples of a conference that moved completely online, and it has been considered a big success, we also include the Conference Report, as supplementary material with this editorial.
Self-Organization as a Factor of Academic Engagement of University Students

The article considers the phenomenon of self-organization and academic engagement of university students. The study aims at identifying the nature of interrelation of selforganization as the highest level of self-regulation, academic engagement and a number of specific regulation processes and personality attributes. The study revealed that students with a high level of self-organization show a more developed engagement into an activity, metacognitive engagement, self-efficacy and systemic reflection; academic self-motivation proves to dominate. Students’ engagement with educational and training activities has positive correlations with self-regulation, meta-cognitive involvement, self-efficacy, systemic reflection and quasi-reflection. Forward and backward linkages between engagement and particular components of academic engagement have been observed.
A Study on Energy Consumption Prediction from Building Energy Management System Data with Missing Values Using SSIM and VLSW Algorithms

BEMS provide energy forecasting capabilities for effective energy management. Energy consumption prediction requires complete time series data, including missing data sets that may lead to biased results. However, it is a common problem that BEMS data is not measured due to power failure, communication barrier, maintenance and other reasons. Therefore, a solution is required for missing values. The existing methods will affect the results of the prediction model in the case of continuous missing interval or small amount of data. Therefore, this study uses VLSW algorithm which can generate fully learned data with only a small amount of data and SSIM model based on it to study the scheme to improve the prediction accuracy of building energy data set including missing values, and compares the difference with existing models. The results show that the SSIM model has better prediction accuracy than the existing LSTM technology. The concentration range of ASHRAE error level was satisfactory.
A spiral-like method to place in the space (and interact with) too many values

Modern information systems have to support the user in managing, understanding and interacting with, more and more data. Visualization could help users comprehend information more easily and reach conclusions in relative shorter time. However, the bigger the data is, the harder the problem of visualizing it becomes. In this paper we focus on the problem of placing a set of values in the 2D (or 3D) space. We present a novel family of algorithms that produces spiral-like layouts where the biggest values are placed in the centre of the spiral and the smaller ones in the peripheral area, while respecting the relative sizes. The derived layout is suitable not only for the visualization of medium-sized collections of values, but also for collections of values whose sizes follow power-law distribution because it makes evident the bigger values (and their relative size) and it does not leave empty spaces in the peripheral area which is occupied by the majority of the values which are small. Therefore, the produced drawings are both informative and compact. The algorithm has linear time complexity (assuming the values are sorted), very limited main memory requirements, and produces drawings of bounded space, making it appropriate for interactive visualizations, and visual interfaces in general. We showcase the application of the algorithms in various domains and interactive interfaces.
Yuly Aykhenvald’s “Natural Symbolism” (on the Question of the Aesthetic Method)

The paper considers the methodological principles of Yu.I. Aykhenvald’s literary criticism. Attention is drawn to the relationship between Aykhenvald’s declared “immanent method” and “principled impressionism” and his own “philosophy of life”. It is proved that Aykhenvald’s version of “impressionism” is much closer to the theory of symbolism than it seemed to contemporaries and researchers. The analysis takes into account both the aesthetic “sympathies” (Apollon Grigoryev, Vladimir Solovyov, Oscar Wilde, Remy de Gourmont, Émile Faguet) and “antipathies” (Vissarion Belinsky, Vasily Rozanov), that influenced Aykhenvald’s interpretation of the heritage of literary critics, philosophers, and writers of the 19th century — Pushkin, Tolstoy, Chekhov.
1. Introduction to the Internet of things

The Internet of things (IoT) is one of the most developing and hot topics in engineering and technology, which has massive growth and attracts many researchers, technologists, scientists, entrepreneurs, and start-ups. In IoT, the word thing refers to devices that have inbuilt sensors capable of gathering and transmitting the information in a network automatically without the need of a human being. It can also make decisions as technology is embedded into it, helping it to interact with the internal and external state of surroundings. IoT can connect multiple sensors or devices in a network, helping in communicating and transmitting data and messages with each other using the Internet. It integrated the physical world with the Internet and allows devices to be controlled remotely, creating many opportunities for a direct establishment of a link to the devices. It has entered into many fields, creating a lot of revenue, better lifestyles, ease of doing things, growth in the economy, generating employment, and also improved efficiency and accuracy. It created technologies like smart cities, smart homes, smart grids, and smart transportation, thereby saving everyone’s resources, time, and money. The technology consists of an umbrella of many technologies combined in it. Sensors, microcontrollers, operating systems, networking, security, data mining, machine learning, network protocols, and data communications are needed to support the IoT for successful implementation. Already, the revenue generated by this technology has crossed $40 billion and is increasing. IoT helps maintain the privacy of the people, trust, and security, and indeed much research is still going on to improve these fields in the technology. Many companies adopting IoT have shown more significant progress in terms of generating revenue, innovation, and saving human time. But the technology has to focus more on the secure transmission of data and to develop the standard protocols as there is an urgent need since the network is of heterogeneous objects and technology is continuously evolving.