Reviewerly/exHarmony huggingface dataset:
The Role of Indicators of Ferrokinetics and Endogenous Erythropoietin in Anemia of Pregnant Women

To study the parameters of ferrokinetics in the dynamics of pregnancy, taking into account the severity of anemia For the period from 2017-2019. We examined 90 pregnant women with anemia, 50 of them were untreated, received in the maternity ward with a kind of activity, and 40 were treated in the department of pathology of ward with a kind of activity, and 40 were treated in the department of pathology of pregnant women. The control group consisted of 40 conditionally healthy pregnant women. Under our supervision and examination, there were 40 pregnant women with anemia, aged 17–35 (28 ± 0.1) years, who were registered in female consultations and were treated in the department of pathology of pregnant urban maternity complex No. 1. Bukhara.
The Factor Analysis of University English Examination Results Based on the Multilevel Model

The traditional factor analysis models, such as the generalized linear regression model and the gistic regression model have disadvantages of large standard error of analysis results. For this purpose, a multilevel factor... | Find, read and cite all the research you need on Tech Science Press
What is the Message About? Automatic Multi-label Classification of Open Source Repository Messages into Content Types

Users of Open Source Software (OSS) projects discuss a diverse range of topics online. The content of a post often corresponds to one or more context-sensitive content types, e.g. a suggestion for a solution, a request for further clarification or indication that a proposed solution did not work. The detection of content types can provide several benefits for software developers. For instance, content types can be used as indicators that summarise the content of the messages. These indicators can be exploited as part of a developer-centric knowledge mining platform allowing developers and project managers to create action alerts concerning new bugs found outside of a bug tracker or they can be combined with other metrics to assess the quality of an OSS project. We present a multi-label classifier, able to classify messages exchanged on communication means about OSS, and detailed evaluation results. We experimented with two state-of-the-art multi-label classification approaches HOMER (Hierarchy Of Multilabel classifiER) and RAkEL (RAndom k-labELsets) as these met the technical requirements of the CROSSMINER project. A manually-annotated threaded corpus of posts form newsgroups discussions, bug tracking systems and forums related to Eclipse projects was also used. The results are promising and indicate the potential to attract novel and deeper research for this task.
DIGITAL COMPETENCES OF HIGHER EDUCATION STUDENTS: CURRENT APPROACHES AND COLLABORATION POSSIBILITIES BY DEVELOPING AND IMPLEMENTING A CURRICULUM-INTEGRATED PROGRAM AND SELF-ASSESSMENT-TOOL

Information about the paper titled "DIGITAL COMPETENCES OF HIGHER EDUCATION STUDENTS: CURRENT APPROACHES AND COLLABORATION POSSIBILITIES BY DEVELOPING AND IMPLEMENTING A CURRICULUM-INTEGRATED PROGRAM AND SELF-ASSESSMENT-TOOL" at IATED Digital Library
Learning Object Permanence from Video

Object Permanence allows people to reason about the location of non-visible objects, by understanding that they continue to exist even when not perceived directly. Object Permanence is critical for building a model of the world, since objects in natural visual scenes dynamically occlude and contain each-other. Intensive studies in developmental psychology suggest that object permanence is a challenging task that is learned through extensive experience. Here we introduce the setup of learning Object Permanence from data. We explain why this learning problem should be dissected into four components, where objects are (1) visible, (2) occluded, (3) contained by another object and (4) carried by a containing object. The fourth subtask, where a target object is carried by a containing object, is particularly challenging because it requires a system to reason about a moving location of an invisible object. We then present a unified deep architecture that learns to predict object location under these four scenarios. We evaluate the architecture and system on a new dataset based on CATER, and find that it outperforms previous localization methods and various baselines.
Development of Linguistic Intelligence Instruments for Elementary Schools Student

This research method is a 4-D model that has stages which include: 1) defining, 2) design, 3) development and 4) dissemination. Respondents tested a limited instrument involving 21 fourth grade students in elementary school. The results of this study obtained an assessment from experts in the validation of linguistic intelligence instruments with an average of 3.5 with worthiness of 86.75 in the very feasible category. The next activity is the stage of developing instruments to assess linguistic intelligence and then a limited trial of 21 students with the results of linguistic intelligence obtained by students 1 (4.8%) female students who have very high linguistic intelligence, 7 (33.33%) students who linguistic intelligence with a high category, 11 (52.4%) students who have linguistic intelligence with a high enough category and 2 (9.5%) students who have linguistic intelligence with a low category. It can be concluded that the instrument for assessing linguistic intelligence in elementary school students can be used on a broad scale
Dynamic Algorithm Selection for Mobile Robots Motion Planning

Selecting the appropriate algorithms to construct a synthetic planner for mobile robots in a given environment is a very beneficial task. The planner of mobile robots is a highlyconfigurable system, which mainly includes obstacle avoidance and navigation algorithms. This paper presents a framework to dynamically combine the synthetic planner according to the robot surrounding environment and the robotic states. Furthermore, two feasible approaches are introduced to select, i.e., the blackbox approach and the rule-based approach.
COMPARATIVE EVALUATION OF PROFESSIONAL POTENTIAL OF FUTURE ENGINEERS IN MODERN FORMATS OF TECHNICAL EDUCATION

L. Boronina, A. BaliasovFederal State Autonomous Educational Institution of Higher Education "Ural Federal University" (RUSSIAN FEDERATION)
DEVELOPMENT OF A DECENTRALIZED VOTING APPLICATION USING BLOCKCHAIN TECHNOLOGY

To date, blockchain technologies have gained general interest as a new approach to creating secure storage and data processing systems. This approach can be applied in many industries and opens up new opportunities to increase transparency and improve the performance of distributed systems. When voting, it is important to have a guarantee that no one will be able to manipulate the data and that the information is accessible to all. The development of a decentralized voting application using blockchain technology is being considered.
Sampled Training and Node Inheritance for Fast Evolutionary Neural Architecture Search

The performance of a deep neural network is heavily dependent on its architecture and various neural architecture search strategies have been developed for automated network architecture design. Recently, evolutionary neural architecture search (ENAS) has received increasing attention due to the attractive global optimization capability of evolutionary algorithms. However, ENAS suffers from extremely high computation costs because a large number of performance evaluations is usually required in evolutionary optimization and training deep neural networks is itself computationally very intensive. To address this issue, this paper proposes a new evolutionary framework for fast ENAS based on directed acyclic graph, in which parents are randomly sampled and trained on each mini-batch of training data. In addition, a node inheritance strategy is adopted to generate offspring individuals and their fitness is directly evaluated without training. To enhance the feature processing capability of the evolved neural networks, we also encode a channel attention mechanism in the search space. We evaluate the proposed algorithm on the widely used datasets, in comparison with 26 state-of-the-art peer algorithms. Our experimental results show the proposed algorithm is not only computationally much more efficiently, but also highly competitive in learning performance.
The generic combinatorial algorithm for image matching with classes of projective transformations

Image matching is an important task arising in video compression, optical character recognition, medical imaging, watermarking and in many others fields. Given two digital images A and B, image matching determines a transformation f for A such that it most closely resembles B. In this paper, we introduce the first general discretization technique that works for the class of projective transformations as well as plenty of its subclasses such as affine transformations and several combinations of scaling, rotation and translation. Based on this, we provide a fully generic image matching algorithm for all these classes that runs in polynomial time.
Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression

In many real-world machine learning applications, unlabeled samples are easy to obtain, but it is expensive and/or time-consuming to label them. Active learning is a common approach for reducing this data labeling effort. It optimally selects the best few samples to label, so that a better machine learning model can be trained from the same number of labeled samples. This paper considers active learning for regression (ALR) problems. Three essential criteria -- informativeness, representativeness, and diversity -- have been proposed for ALR. However, very few approaches in the literature have considered all three of them simultaneously. We propose three new ALR approaches, with different strategies for integrating the three criteria. Extensive experiments on 12 datasets in various domains demonstrated their effectiveness.
Towards Backward-Compatible Representation Learning

We propose a way to learn visual features that are compatible with previously computed ones even when they have different dimensions and are learned via different neural network architectures and loss functions. Compatible means that, if such features are used to compare images, then "new" features can be compared directly to "old" features, so they can be used interchangeably. This enables visual search systems to bypass computing new features for all previously seen images when updating the embedding models, a process known as backfilling. Backward compatibility is critical to quickly deploy new embedding models that leverage ever-growing large-scale training datasets and improvements in deep learning architectures and training methods. We propose a framework to train embedding models, called backward-compatible training (BCT), as a first step towards backward compatible representation learning. In experiments on learning embeddings for face recognition, models trained with BCT successfully achieve backward compatibility without sacrificing accuracy, thus enabling backfill-free model updates of visual embeddings.
CryptoPIM: In-memory Acceleration for Lattice-based Cryptographic Hardware.

Quantum computers promise to solve hard mathematical problems such as integer factorization and discrete logarithms in polynomial time, making standardized public-key cryptosystems insecure. Lattice-Based Cryptography (LBC) is a promising post-quantum public key cryptographic protocol that could replace standardized public key cryptography, thanks to the inherent post-quantum resistant properties, efficiency, and versatility. A key mathematical tool in LBC is the Number Theoretic Transform (NTT), a common method to compute polynomial multiplication. It is the most compute-intensive routine and requires acceleration for practical deployment of LBC protocols. In this paper, we propose CryptoPIM, a high-throughput Processing In-Memory (PIM) accelerator for NTT-based polynomial multiplier with the support of polynomials with degrees up to 32k. Compared to the fastest FPGA implementation of an NTT-based multiplier, CryptoPIM achieves on average 31x throughput improvement with the same energy and only 28% performance reduction, thereby showing promise for practical deployment of LBC.
UNIVERSITY PROCESSES AND DIGITAL TRANSFORMATION

Digital technologies widely deployed and used to process separate agendas generate extensive data and evoke or allow digital transformation of business models as well as the processes at the universities related to all part of kniwledge triangel. The focus on digital technology is different from other potential driving forces of transformation, such as political, social, cultural or economic shifts. The technologies and business models underlying the digital transformation at University are not fixed. Currently, the most important element related to the digital business transformation is the following transformations:• new analytics tools and applications that use big data• platforms for sharing digital features such as different cloud solutions and marketplace applications,• social media tools and applications,• the internet of things, covering all connected devices and intelligent networks.The digitalization at University including processes is reflected in economic and learning, transfering processes by simply changing traditional models to platform-based models. The difference of platforms with the structures used so far lies in the very interaction between manufacturers and users. Present structures that are being replaced by platforms create value by performing ongoing process control, activity optimization, and managers are focused on sales growth. Platforms displace these traditional models and replace them by using customer data to create mechanisms to facilitate sales, easier functional structuring, product value enhancement, or services that are co-created by users themselves.Transformation is basically about change, and organizational change is the basis for digital business transformation. Organizational changes related to people, processes, strategies, structures and dynamics are the place where its most of the challenges and opportunities. Most of this value would be unblocked through business changes leading to acceleration of innovation, higher productivity, greater efficiency of processes and improved customer experience. Organizational change requires a clear recognition of the need to transform, an understanding of what needs to be changed, and a plan to make the changes required. University of Žilina applies this new tendencie and the paper discusses the mail of them.
Development of intelligent warehouse management system based on Internet of things technology

Abstract With the development of electric power enterprises, the types and quantity of storage management materials are also increasing. With the rapid increase of warehousing frequency, warehousing management is very complex and diversified. The traditional manual warehouse operation mode has been difficult to meet the rapid storage management, which will seriously affect the operation efficiency of enterprises. By improving the intelligent warehouse management system, we must use RFID application technology, visualization technology, etc., which will improve the efficiency of material management. By reducing the cost of material management, we will achieve the optimal management of warehouse materials. Firstly, this paper analyzes the development trend of storage management and RFID technology. Then, the whole idea of system design is established.
Research on Computer-aided Translation Technology on the Background of Information Age

Abstract The rapid development of science and technology has brought a strong impact on people’s traditional habits, among which there is no lack of subversive impact. Even in language communication, the progress of computer technology has been involved and achieved a place. Although computer-aided translation software cannot replace translators to complete translation work independently, it plays an important role in translation work. It not only greatly simplifies the work of translators, lightens the burden for translators, improves their work efficiency, but also helps translators to make faster progress in translation, which can be described as half the result. Through the discussion of the help of computer translation technology, computer aided translation software and computer translation software to translators in translation and learning, as well as the application of computer aided translation technology, the author studies the computer aided translation technology in the background of information age.
Semantic service composition model based on cloud computing

The user group encountered in cloud computing is large, and the amount of tasks and data to be processed is also large. How to schedule tasks effectively becomes an important problem that can be solved in cloud computing. A binary fitness genetic algorithm (DFGA) is proposed, which is aimed at the programming model of cloud computing. Not only can the algorithm find a plan result with a short overall task completion time, but the short-term performance of the plan result is relatively short. The algorithm was compared with the adaptive genetic algorithm (AGA) through simulation experiments in a short time. The experimental results show that the algorithm is superior to the adaptive genetic algorithm and is an effective task planning algorithm in the cloud computing environment. Genetic algorithm is a global heuristic algorithm used to solve optimization problems. It is adaptive, learning, and parallel. Genetic algorithms have great advantages, especially when dealing with a large number of tasks. Tasks are assigned to multiple processors for processing simultaneously. At the same time, genetic algorithms are also extensible, which can be easily combined with other algorithms to absorb the advantages of other algorithms to make up for their own shortcomings. At present, many authors of scientific research have used the advantages of genetic algorithms to apply them to task scheduling problems, and the scheduling results obtained are superior to traditional scheduling solutions. This paper studies the task scheduling algorithm based on improved genetic algorithm in cloud computing environment. This article uses CloudSim as the object, and through data analysis, compared with HEFT, the SLR of the HEFTD algorithm is shortened by 10.55%, 8.99%, 16.99%, 19.79%, and 9.89%, that is, HEFTD in different CCR. When the CCR is 1 and 2, the algorithm performance is greatly improved.
Why would a book need “pictures”?

The article deals with the analysis of the functions of book illustration in a historical retrospect – from Antiquity to the present. The purposes and tasks of including images of various types in the book, specificity of visual representation of its contents at the main stages of forming and development of book business is considered. The specificity of visualization of the text content of the book depending on its genre, addressee, and cultural status in different historical periods is traced. Industry specialization, professional approaches, creative strategies of illustrators are described.
MCFlow: Monte Carlo Flow Models for Data Imputation

We consider the topic of data imputation, a foundational task in machine learning that addresses issues with missing data. To that end, we propose MCFlow, a deep framework for imputation that leverages normalizing flow generative models and Monte Carlo sampling. We address the causality dilemma that arises when training models with incomplete data by introducing an iterative learning scheme which alternately updates the density estimate and the values of the missing entries in the training data. We provide extensive empirical validation of the effectiveness of the proposed method on standard multivariate and image datasets, and benchmark its performance against state-of-the-art alternatives. We demonstrate that MCFlow is superior to competing methods in terms of the quality of the imputed data, as well as with regards to its ability to preserve the semantic structure of the data.
UNIVERSITY – BUSINESS COLLABORATION IN TEACHING THERMAL MANAGEMENT OF ELECTRONIC EQUIPMENT

N. Kafadarova, D. Stoyanova, S. Stoyanova-Petrova, N. Mileva, V. MengovPlovdiv University Paisii Hilendarski (BULGARIA)
The design a scenario of multimedia learning model based on synchronization between English lesson and ablution lesson

Abstract Multimedia-based learning becomes a trend in schools that want their students to be able to absorb subject matter faster. Multimedia systems that are intended for learning must be designed to have a scenario that is aligned with the content of the lesson. In this research a scenario of multimedia-based learning model will be made using Data Flow Diagrams. The scenario model starts with Data Flow Level 0 which contains 3 processes, namely the learning process, the training process, and the quiz process. From the Data Flow Diagram Level 0, then the decompose process is carried out for each process to produce Data Flow Diagram Level 1. At level 1 Data Flow Diagrams for Learning Processes, 4 processes are generated, namely the process of Selecting Ablution Sequences, the Process of Show Ablution, Process Display of Physical Response, and Process of Display of Spell Speech. In the training process, 6 processes will be generated in the Level 1 Data Flow Diagram. In the quiz process, 4 processes will be generated in the Data Flow Diagram Level 1.
DIGITAL SEARCHLIGHT – A STUDY ON DIGITAL SKILLS BEING SOUGHT AMONGST LANGUAGE TEACHERS

T. Talmo1, M. Fominykh1, A. Giordano2, M. Soule3 1Norwegian University of Science and Technology (NTNU) (NORWAY) 2Pixel (ITALY) 3Cyprus University of Technology (CYPRUS)
Hubungan Antara Kadar Laju Endap Darah (LED) Dengan Kadar C-Reaktiv Protein (CRP) Pada Penderita Tuberkulosis (TBC) Di Wilayah Kerja Puskesmas Alas Barat

Tuberculosis is a disease caused by rod-shaped bacteria (basil) with another name Mycrobacterium tuberculosis. Mycrobacterium tuberculosis enters the body then causes inflammation, inflammation and bacterial invasion which then induces liver cells to synthesize acute phase C-reactive protein protein (CRP). CRP will increase sharply after inflammation, as well as the formation of aggregates derived from the acute phase protein hormone which causes an increase in the Erythrocyte sedimentation rate (ESR)). The purpose of this study was to determine the relationship Erytrocyte sedimentation rate (ESR) and levels of C-Reactiv protein (CRP) in tuberculosis (TBC) patients. This research is an analytical descriptive study using cross sectional. Data collectors are done using the Non Random Accidental Sampling technique. The number of respondents in this study were 11 people with tuberculosis. Based on the results of this study it is known that TBC patients with smear negative as much as 7 people (63.6%) and TB patients with positive smear as much as 4 people (36.4%), both in TB patients with negative smear and positive smear both have levels of ESR not normal with an average ESR level in positive smear of 101.3 mm / hour and the average ESR level in smear negative is 20.8 mm / hour. All TBC patients with smear positive CRP levels (not normal) with an average mean value of CRP of 36 mg / L or an average CRP level in TBC smear negative patients of 0.9 mg / L. Based on the Pearson test results between levels of erythrocyte sedimentation rate (ESR) with C-Reactive protein (CRP) in tuberculosis (TBC) patients obtained a value of p &lt;0.01 (0.000 &lt;0.01). This shows a strong relationship between LED and CRP levels in TB patients
COMPARATIVE ANALYSIS OF THE PRACTICE OF APPLYING THE PRINCIPLES OF PROJECT-BASED LEARNING IN RUSSIA

L. Daineko, Y. Davy, V. Larionova, N. Karavaeva, I. YurasovaUral Federal University (RUSSIAN FEDERATION)