Reviewerly/exHarmony huggingface dataset:
A disease category feature database construction method of brain image based on deep convolutional neural network

Constructing a medical image feature database according to the category of disease can achieve a quick retrieval of images with similar pathological features. Therefore, this approach has important application values in the fields such as auxiliary diagnosis, teaching, research, and telemedicine.Based on the deep convolutional neural network, an image classifier applicable to brain disease was designed to distinguish between the image features of the different brain diseases with similar anatomical structures. Through the extraction and analysis of visual features, the images were labelled with the corresponding semantic features of a specific disease category, which can establish an association between the visual features of brain images and the semantic features of the category of disease which will permit to construct a disease category feature database of brain images.Based on the similarity measurement and the matching strategy of high-dimensional visual feature, a high-precision retrieval of brain image with semantics category was achieved, and the constructed disease category feature database of brain image was tested and evaluated through large numbers of pathological image retrieval experiments, the accuracy and the effectiveness of the proposed approach was verified.The disease category feature database of brain image constructed by the proposed approach achieved a quick and effective retrieval of images with similar pathological features, which is beneficial to the categorization and analysis of intractable brain diseases. This provides an effective application tool such as case-based image data management, evidence-based medicine and clinical decision support.
A Graph-Based Extension for the Set-Based Model Implementing Algorithms Based on Important Nodes

The purpose of this paper is the expansion of the set-based model, namely an information retrieval model, with the use of graphs. The indexing process implements a graphical representation, while the querying and document representation are based on the classical set-based model. The root of the set-based model corresponds to the use of term sets to complete the querying process based on the terms of the query. However, in the weighting process, this paper presents a wholly different approach elaborating on algorithms that may clearly benefit the process based on the k-core decomposition of each single graph. The main focus will finally be the estimation and presentation of the most important nodes belonging to each graph. These intend to be regarded as keywords presenting the evaluation of their major influence.
Remote Attestation for Intelligent Electronic Devices in Smart Grid Based on Trusted Level Measurement

To reduce the implementation complexity and excessive overhead caused by remote attestation, we propose a new remote attestation scheme based on trusted level measurement considering the limited system resources of the Intelligent electronic devices (IEDs) in smart grid. In this scheme, a lightweight trusted level measurement mechanism is designed, and a new dynamic weighted multi-classifier integration method based on Binary tree Support vector machine (BT-SVM) is proposed. The trusted level of configuration information and running state of an IED are measured through trusted third party. The measurement parameters and attestation period are dynamically adjusted according to different trusted levels of an IED, so that reducing computational overhead and communication delays in remote attestation process. Analysis and experiments show that the proposed scheme can enhance the security of IEDs in smart grid by occupying a small amount of computation and communication resources.
Marketplace affiliates potential analysis using cosine similarity and vision-based page segmentation

One success factor of an online affiliate is determined by the quality of the content source. Therefore, affiliate marketplaces need to do an objective assessment to retrieve content data that will be used to choose the right product in the appropriate product filter. Usually, the selection is not made using a good and measured system so that the selection of product content is only based on parts that are not in accordance with what is seen or subjective. However, if analyzed using a good and measurable system will produce an objective product content and can have a positive impact on users because the selection is based on factual data. The purpose of this research is to analyze the potential of the affiliate marketplace by combining cosine similarity with vision-based page segmentation. This is a new breakthrough made for optimization to get the best content in accordance with the required criteria. This work will produce a number of product recommendations that are appropriate for publication and then made use of for comparison that matches the required criteria. At the limited evaluation stage, the performance of the proposed model obtained satisfactory results, in which 5 queries tested were all as expected.
Texture based FACE recognition using GLCM and LBP schemes

Objectives: Automatic face recognition has been an important area of biometric authentication and verification system in various applications including crime detection, access control, video surveillance, tracking service and other related area.Methods/Statistical analysis: In this study, we present Grey Level Co-occurrence Matrix (GLCM) over Local Binary Patterns (LBP) named as GOL texture feature technique for face classification.The experiments have been conducted on AT & T Cambridge Laboratory face images also called (ORL-faces) and Georgia Tech (GT-faces) databases respectively.Findings: We performed comparative analysis of GLCM and LBP method separately and results showed that proposed GOL method outperformed in terms of average sensitivity, average specificity, and retrieval time.These findings show efficacy of our proposed system.
A NEW METHOD FOR SEARCHING A HAMILTON CYCLE ON A GRAPH

In discrete mathematics, there are many problems that belong to the NP class of complexity. The solution of these problems has both theoretical and practical value. One of them is the problem of finding a Hamiltonian cycle on a graph. The aim of the work is to develop a new method and algorithm for solving this problem, which are preferable to the existing methods by time and quality of the solution. The paper analyzes the problem and existing methods of solving it, and identifies the disadvantages of these methods. It is shown that all known methods for solving this problem are based on the implementation of enumeration of solutions or on intuitive heuristics. The first of them are characterized by a non-polynomial amount of time for solving, and the second does not provide the optimal solution. The reason for this state is the inability to formulate conditions that determine the optimal solution of the problem. In this case, the only possible way to solve the problem is still to enumerate over the options, and to reduce the cost of finding a solution, you must resort to reducing the space for iterating over the options. The paper presents new principles for finding a solution and offers a new method for solving the problem. On the basis of the new method, a polynomial algorithm for solving the problem is developed. The search for a Hamiltonian cycle in a graph is reduced to the search for a closed path in a new graph of shortest paths. Dijkstra's algorithm is used to construct the shortest paths graph. The space of enumeration of solutions to the problem consists of solutions that are constructed from each vertex of the graph in the shortest paths graph. Testing of the developed program showed the efficiency of the developed method and algorithm for solving the problem. The proposed solution method significantly reduces the search space and allows us to find the optimal solution with polynomial complexity, both in full and incomplete graphs. The considered method is suitable for parallel implementation, which gives an additional gain in time and allows you to fully use the parallel capabilities of modern multi-core processors.
Transformation concept "Information technologies" in modern scientific discourse

The purpose of the study is to reveal the objective transformations that have occurred in information technology.Conceptualization of these transformations is also important.System, information, network and cognitive approaches allow to reveal the main aspects of the development of information technologies -technical and technological, software and hardware, network, communication, cognitive.The article considers the evolution of information technology.The relationship of information technology with its increasing role and importance of information in the life of man and society is analyzed.Information technologies are disclosed as technologies to create, store, record and process information.Technical and technological direction of information technology development is analyzed.The connection of the information technology concept transformation and development of the information society theory, in particular, the theory of post-industrial society, information society, network society, knowledge society and the digital world is revealed.The connection of the information technology concept transformation and the emergence of the media civilization and global information space is also revealed.The article shows that the development of information technology is an important factor in the development of the information society.Technology transition (the electronic computer as a machine) to the modern stage of development of digital technologies is characterized by the formation of a single technical and technological basis for the formation of the information society.The classification of information technology according to its application scope is proposed.Information and communication technologies characterize subject-to-subject interactions, among which pedagogical and political technologies are primarily distinguished.Information space is separate area of information technology application.Information processing technologies in the information space are the basis of modern democratic discourse.An important area of information technology is the information sphere.Information sphere is the sphere of information activity, information exchange and information relations, the regulator of which is the information law.Information security technologies ensure the protection of human rights, society and the state in obtaining objective information.Cybersecurity technologies protect critical infrastructure.The technologies of reality -virtual, cognitive, and augmented -prov ide th e a b ility of a p er son 's con sciou sn ess an d thinking to process and create new information.Identifying the social significance of the technical and technological development of information technologies becomes the basis of recommendations for the implementation of a strategies for the formation of the information society in Ukraine.
Students’ Attitude Towards Small Group Discussion In Reading Comprehension

In this globalization era, English becomes an important language that has to be mastered by all people around the world. There are four skills of English language; listening, speaking, reading, writing. All of the skills is important and integrated each other. Almost all activity in the classroom has relationship with reading. As stated in Sukirah Kustaryo (1998) “Reading is a process of making sense of written text through meaningful interpretation in relation to reader’s use of text and experimental/conceptual background for concept of written language, story structure, purpose and content of what is read”. Reading comprehension is a skill in reading. The reader cannot get information without comprehending the text. Desciptive research was applied in this research. The object of this study is 30 students from management informatics department who take English 1 class. Observation and surveys were used as data collection in this study. The result above showed that not all the students have a positive attiude towards discussion activity in reading comprehension. A small number of them, about 2.34%, have a negative attitude towards it. The intelligence affects their comprehension in reading a text. They comprehend the text easier than other.
Digital Competencies as Necessary Requirements for Successful Professional Teachers in Future

Rapidly evolutions and dependence on modern digital technologies require new skills and competences for doing work effectively. The integration of digital technologies in education have a huge perspective and considerable impact on the development of future teachers’ digital competences and skills at universities. This research examines future/current teachers’ self-assessment level of digital competence based on the research questionnaire. In order to conduct the research, a questionnaire was developed based on the digital competence model and framework given in the theoretical part. We further analyze the research findings and suggest ways to develop future teachers’ digital competence. Data indicate that current future/current teachers are motivated to use modern technology in the educational process, but they lack sufficient knowledge/level or skills to use digital technologies in the teaching process. Ultimately, the results expand our understanding of future/current teachers’ strengths and shortcomings in combining digital technologies with pedagogy at a higher level.
A Verified SAT Solver Framework including Optimization and Partial Valuations

Based on our formal framework for CDCL (conflict-driven clause learning) using the proof assistant Isabelle/HOL, we verify an extension of CDCL computing cost-minimal models called OCDCL. It is based on branch and bound and computes models of minimal cost with respect to total valuations. The verification starts by developing a framework for CDCL with branch and bound, called CDCLBnB, which is then instantiated to get OCDCL. We then apply our formalization to three different applications. Firstly, through the dual rail encoding, we reduce the search for cost-optimal models with respect to partial valuations to searching for total cost-optimal models, as derived by OCDCL. Secondly, we instantiate OCDCL to solve MAX-SAT, and, thirdly, CDCLBnB to compute a set of covering models. A large part of the original CDCL verification framework was reused without changes to reduce the complexity of the new formalization. To the best of our knowledge, this is the first rigorous formalization of CDCL with branch and bound and its application to an optimizing CDCL calculus, and the first solution that computes cost-optimal models with respect to partial valuations.
ATM Shield: Analysis of Multitier Security Issues of ATM in the Context of Bangladesh

Over the last decade, consumers have been largely dependent on and trust the Automatic Teller Machine (ATM) to conveniently meet their banking needs. However, despite the numerous advantages of ATM system, ATM fraud has recently become more widespread. In this paper, we provide an overview of the possible fraudulent activities that may be perpetrated against ATMs and investigates recommended approaches to prevent these types of frauds. In particular, we develop a prototype model for the utilization of three tier security equipped ATM to provide security solutions against must of the well-known breaches. In this research article, the tools and techniques of ATM fraud are contemplated. A secure three layer electronic transaction mechanism of ATM is developed to prevent ATM frauds. In this three layer authentication systems the users can improve ATM security against frauds and crimes.
Some data of wavelet analysis on the overall dynamics of the dead and recovered during infection COVID-19

Coronaviruses are one of the dangerous sources of human infection.Such a source can be fatal.At the same time, there are certain difficulties in the treatment of diseases that are associated with coronavirus.To solve such issues, it is important to obtain reliable statistics.Equally important is the analysis of the data that are available.Moreover, the necessary condition for the use of such data is the ability to explain the existing relationships between the various parameters that describe the dynamics of the pandemic.The disclosure of these issues is the subject of this study.
Research on Image Quality Evaluation Method of Depth Learning Model Based on Core Concept

Abstract In view of the fact that the existing image quality evaluation methods are generally artificial design features, it is difficult to automatically and effectively extract image features that conform to the human visual system. Inspired by human visual characteristics, a new full reference image quality evaluation method based on depth learning model based on core concepts is proposed. Firstly, depth learning algorithm is used to extract multi-layer features from reference images and distorted images respectively. Then, the local similarity of the feature map of the reference image and the distorted image in each layer is calculated as the local quality description of the corresponding depth. Finally, the local quality of all layers is synthesized to obtain the overall quality score of the image. On the basis of the pre-training model, the depth model network is fine-tuned by using the image visual evaluation data set to obtain a depth model for evaluation. The standard experiment shows that fine-tuning training of each pre-training model on the standard data set achieves good classification results. Experiments show that the designed depth learning model based on core concepts is superior to the existing full reference image quality evaluation methods, and its prediction results have good accuracy and consistency with subjective quality evaluation.
Wireless Sensor Network for m-Healthcare Monitoring of Human Being

In this paper, we are going to develop an m-health care monitoring for a human being using a wireless sensor network.By using certain Wearable sensors, we detect abnormal and unforeseen situations by monitoring physiological parameters along with other symptoms.Our fundamental target is to actualize a checking framework which screens the Heartbeat, Temperature, and Humidity of the patient.This work presents a simple to-utilize framework for the quick checking of the Heartbeat.The gadgets are utilized as models.The simple sign is given to the Analog-to-Digital Converter (ADC) of Arduino.There, simple voltages are being changed over to advanced, and that computerized qualities will be put away in the EEPROM of Arduino.The qualities put away in EEPROM will be sent to the Wi-Fi module, which refreshes the cloud server like clockwork.From that server, anybody can get to that information through things speak site and see in android/ios application
Hybrid particle swarm optimization with spiral-shaped mechanism for solving high-dimension problems

Particle swarm optimization (PSO) is a well-known metaheuristic, whose performance for solving global optimization problems has been thoroughly explored. It has been established that without proper manipulation of the inertia weight parameter, the search for a global optima may fail. In order to handle this problem, we investigate the experimental performance of a PSO-based metaheuristic known as HPSO-SSM, which uses a logistic map sequence to control the inertia weight to enhance the diversity in the search process, and a spiral-shaped mechanism as a local search operator, as well as two dynamic correction factors to the position formula. Thus, we present an application of this variant for solving high-dimensional optimization problems, and evaluate its effectiveness against 24 benchmark functions. A comparison between both methods showed that the proposed variant can escape from local optima, and demonstrates faster convergence for almost every evaluated function.
Artistic-figurative thinking as one of the aspects of creative abilities formation in a future fine arts teacher

И. Н. Полынская, А
Neural Network based Whole Heart Segmentation from 3D CT images

The most recent research is showing the importance and suitability of neural networks for medical image processing tasks. Nonetheless, their efficiency in segmentation tasks is greatly dependent on the amount of available training data. To overcome issues of using small datasets, various data augmentation techniques have been developed. In this paper, an approach for the whole heart segmentation based on the convolutional neural network, specifically on the 3D U-Net architecture, is presented. Also, we propose the incorporation of the principal component analysis as an additional data augmentation technique. The network is trained end-to-end, i.e., no pre-trained network is required. Evaluation of the proposed approach is performed on CT images from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge dataset, delivering in a three-fold cross-validation an average dice coefficient overlap of 88.2% for the whole heart, i.e. all heart substructures. Final segmentation results show a high accuracy with the ground truth, indicating that the proposed approach is competitive to the state-of-the-art. Additionally, experiments on the influence of different learning rates are provided as well, showing the optimal learning rate of 0.005 to give the best segmentation results.
METHOD OF RISK MANAGEMENT OF STAKEHOLDERS OF WIND POWER PROJECTS

Effective management of wind power projects, which are now challenged by a dynamic turbulent environment, requires a continuous integrating activity. The goal of the latter is to maximize the return on integration of all stakeholders in such projects and balancing their opportunities and threats. Thus, the article covers latest research in and approaches to stakeholder management in project. The methods and mechanisms of stakeholder management of wind power projects are analyzed, the weaknesses of stakeholders are detected, major issues and factors influencing their management are identified as well. The study also reveals the loss-inducing features of the stakeholders of wind power projects. This model consists in the fact that in order to ensure effective stakeholder management in wind power projects, it is necessary to balance their risks: increase the opportunities for positive impact of participants and reduce the opportunity of threats, while maintaining the stable state of the system, within the three categories that correspond to the "magic" triangle of project management goals: duration, cost, quality that is possible by combining techniques of opportunity management (chance management) and threats (risk management). All stakeholders in wind power projects can be divided into three groups: threatening, harmonized and chance identified. There has been developed a method of risk management of stakeholders of wind power projects. It takes into account the possible states of the system of interaction of opportunities and threats of stakeholders in the balance of their risk balances, improves managerial decision-making by reducing high and medium-sized threats for threatening stakeholders.