Reviewerly/exHarmony huggingface dataset:
Role of Wireless Communications in Digital Economy in the Present Context

Wireless communication has connected millions of people across the world to internet to enable them to reap benefits of digital economy in present scenario. Almost all the sectors of economy including healthcare, agriculture and transportation nowadays rely largely upon wireless communications. In the present context, the sudden outbreak of Covid-19 has caused governments to impose restrictions on various activities and employees have been directed to work from their homes to ensure safety. In such a situation, wireless communication has made it possible to connect with each other via the internet. Apart from this, Smart healthcare-system has been developed through utilisation of wireless communication systems. During this pandemic situation, delivery of healthcare services in remote areas has gained huge momentum and wireless communication plays a vital role in this regard. Aim of current critical assessment is to provide an in-depth analysis of the role played by wireless communication in establishment of a well-structured digital economy in present scenario. In literature-review, different applications of wireless technology via ICT in various sectors and aspects of life have been discussed through a conceptual and a theoretical framework. The Method section of current study has provided an in-depth insight into the ways required information have been collected in this study. As found in the results and discussion section, a positive relation exists between wireless-communication and establishment of digital economy in current situation. This study has concluded that there are certain challenges associated with wireless communication; however, it has significant influence in making processes smarter and faster as compared to earlier.
Concept and Variable Models of Digital Competence Formation in a Computer Science Teacher

The study aims to develop variable models of digital competence formation in a computer science teacher. The paper describes the features of teacher training in the conditions of education digitalisation; defines the structure of digital competence of a computer science teacher by a system of general-use, general-pedagogical and subject-pedagogical digital competencies; identifies variants for digital competence formation in a computer science teacher in a digital educational environment. Scientific novelty lies in developing a concept of digital competence formation in a computer science teacher and specifying the said concept in the form of appropriate variable models. The main results of the study are represented by the following variable models of digital competence formation in a computer science teacher: during traditional professional teacher training at the level of undergraduate and graduate education; in the system of additional professional education, in-house training; through self-education, professional and personal self-development of a teacher.
Research on the Effect of Pre-problems on the Learning Effect of Declarative Knowledge in VR Videos

In order to explore whether pre-embedding can better support learning in VR videos, this paper takes 30 college students as the research object, and conducts a single-factor randomized experiment of “pre-embedding problems and non-embedding problems in VR videos” The results show that pre-problems can effectively increase the time learners pay attention to the corresponding learning content, have a positive impact on learning retention and long-term memory, and increase the cognitive load in the learning process and have no significant impact on knowledge transfer. The research conclusions of this article can provide reference for the development of VR video teaching resources.
Research on Intelligent Sentiment Analysis and Theme Evolution of Personal Technology Blog

Accurately mining the emotional trends and theme evolution laws of social media and personal blogs will help understand blog dynamics from different angles. Simultaneously, it can predict the author&#x0027;s emotional tendency in real-time and sort out the research direction, which is of great significance to the perception of public opinion events. This paper proposes a sentiment analysis and topic evolution method for personal technology blogs. This method uses the emotional time series analysis method of fusion emotional dictionary to predict the emotional trend of bloggers. It explores blog creation by constructing a topic relationship map and topic evolution method based on Price&#x0027;s law. The research in this paper will provide direction for social media and technical blog text mining under the new situation. This method will help to understand blog dynamics from different angles, predict the author&#x0027;s emotional tendency in real-time, and sort out the research direction, which is very important for the perception of public opinion events. Moreover, provide corresponding suggestions for public opinion monitoring and topic analysis of social media.
DEVELOPMENT OF PROFESSIONALISM OF TEACHERS MEANS OF METHODICAL WORK

The article deals with the problem of analisis of some potentialities latent in school methodology servise and its possibility to increase tcachers' professional skills. The expediency in use of block-and-module approach, while choosing (selecting) contents and forms of methodological work organization, is proved.
Concept Drift Visualization Using Feature Importance on the Streaming Data

Currently, online processing of the data streams is a very active research topic. Streaming data are usually dynamic, where the underlying data distributions evolve during the time. Predictive data analytical tasks, such as classification, must be able to reflect such dynamics. This phenomenon is called a concept drift, and multiple adaptive classification methods have been proposed to handle drifting streams. To understand how the adaptive models work, it is necessary to use the techniques able to visualize how the model performs, as well as to provide explanations of the drift occurrence. In this paper, we present the visualization technique based on feature importance. In this case, we want to provide information about the continuous importance of the input features and use it to explain the possible drifts in the data. We used the commonly used ADWIN adaptive streaming classifier and evaluated the technique on the two real-world data streams with concept drift.
Introducing Agility into the Processes of Manufacturing Companies: A Method for Evaluating Success, Support and Applicability

Abstract Agile approaches are increasingly being used in appropriate use cases for the development of mechatronic systems.In the process of implementing agile elements in the development processes, the question of the success of the transformation often arises. In order to support the agile transition in measuring process improvement, a framework was developed that evaluates the success of the implementation process by means of interviews at 4 maturity levels in the transformation. The method was evaluated in 3 use cases. On this basis, continuous adaptations can be made to the implementation process.
AI Assistant in a Smart Cloud

We introduce our AI assistant that automatically handles such everyday matters of a corporate that require attention. This task can be a practical extension of proactive incident management. We present a secure way of storing ARMs (Attention Requiring Matters) in a blockchain. Thus, this collection cannot be tampered. As an example, we show how our AI assistant extends our computing power capacity in a smart cloud. Describing the corporate preliminaries of excellent ARM management we give operational and asset handling excellence criteria.
AR Image Presentation based on Gaze Information for Operator support in teleoperation

The authors have been tackling a new teleoperation system with a head-mounted display (HMD) and an omnidirectional camera to reduce the workload of an operator. Some experimental results verified that presentation of AR images based on sensor information could reduce the workload and prevent the deterioration of the task quality due to the transmission delay of images. However, the range of the field of view (FOV) of an HMD is narrower than that of a human. This difference might cause accidents by overlooking hazards that are outside the FOV of the HMD. To solve this problem, the authors propose a new method of AR image presentation based on gaze information not to overlook hazards. Some experimental results using a driving simulator verified the usefulness of the proposed method.
A Multi-view 3D Human Pose Estimation Algorithm Based On Positional Attention

With the development of CNNs, the human pose estimation research has made great progress, but there is still a problem: the relationships of the human each joint location are not well exploited in previous CNNs-based methods. Considering the order of global spatial information and human body location information, we propose a multi-view 3D human pose estimation algorithm based on position attention. In 2D detection stage, position coding is adopted to rebuild the image in the global space position relation. The attention mechanism can model the relationship between various channels and capture feature maps the dependencies between the horizontal and vertical direction, and the details are mined from the feature location relationship to generate high-quality feature maps. In the last stage of feature extraction, adjacent view features are used to enhance the spatial expression ability of feature images, so as to better solve occlusion and oblique view. Experiments on the Human3.6M data set show that when using Resnet-50 as the backbone network and 256&#x00D7;256 of the image size, the average joint error of our algorithm is reduced to 25.2mm, which reaching the competitive result.
The Design of Hardware Processing System Platform for Directional Sound Sources

The DSP pipeline serial structure restricts the performance of the system, while the FPGA parallel processing data high frequency sampling data to meet the design needs. Firstly, this paper introduces the FPGA processing chip and development process, designs the power amplifier circuit and does a detailed analysis. Secondly, the WM8731 audio processing chip and its workflow are introduced, and the design process of the directional sound source system is described. Then, the hardware platform and software program are integrated together to build the system test platform, and the generated test files are downloaded to the board, and the test data are recorded one by one at different angles within a certain distance after power-on. The test results are plotted on the directivity line graph, and the results show that the sound has strong directivity and high sound pressure intensity.
OACNNs: Orientation adaptive convolutional neural networks

Geometric invariant feature representation plays an indispensable role in the field of image processing and computer vision. Recently, convolution neural networks (CNNs) have witnessed a great research progress, however CNNs do not excel at dealing with geometrically transformed images. Existing methods enhancing the ability of CNNs learning invariant feature representation rely partly on data augmentation or have a relatively weak generalization ability. This paper proposes orientation adaptive kernels (OA kernels) and orientation adaptive max pooling (OA max pooling) that comprise a new topological structure, orientation adaptive neural networks (OACNNs). OA kernels output the orientation feature maps which encode the orientation information of images. OA max pooling max-pools the orientation feature maps by automatically rotating the pooling windows according to their orientation. OA kernels and OA max pooling together allow for the eight orientation response of images to be computed, and then the max orientation response is obtained, which is proved to be a robust rotation invariant feature representation. OACNNs are compared with state-of-the-art methods and consistently outperform them in various experiments. OACNNs demonstrate a better generalization ability, yielding a test error rate 3.14 on the rotated images but only trained on “up-right” images, which outperforms all state-of-the-art methods by a large margin.
A Low Distortion and Steganalysis-resistant Reversible Data Hiding for 2D Engineering Graphics

To reduce the distortion resulting from the large number of crossing quantization cells and resist steganalysis, a reversible data hiding scheme for 2D engineering graphics is put forward based on reversible dual-direction quantization index modulation (RDQIM). The quantization cell index of the host data is first computed, and its distances to the embedding cells in both the left and the right directions are calculated. After that, the data hiding is performed by modifying the data to the nearest embedding cell. To guarantee the reversibility, each quantization cell is further subdivided into three sub-cells, and the source quantization interval of the host data is marked by the index of the located sub-cell. The data extraction is accomplished by calculating the index of the quantization cell where the stego data is in. Meanwhile, the lossless recovery of the stego data is realized by combining the index of the located sub-cell and the relative distance within the sub-cell. Besides, different embedding strategies are adopted for different types of entities to achieve steganalysis-resistant ability. Experimental results and analysis show that the proposed scheme can strike a good balance among imperceptibility, semi-fragility, and steganalysis-resistant ability. Moreover, under the same conditions, the average imperceptibility and the average capacity are, respectively, improved by at least 7.487% and 41.045% compared with the existing methods.
Books Tracking and Review Application

The majority of libraries today still utilise traditional methods to distribute books to its consumers. Librarians gather information on the customer and the book while one person is speaking with the librarian, the others must wait until he or she has finished the process this makes it difficult for other people to obtain the books. So our objective is to simplify this manual entry procedure so that individuals may advance to the essential steps without having to wait for others to finish them. People profit from our paper in various ways, such as &#x201C;Pre- Booking&#x201D; the necessary books before arriving at the library, so that the book is ready to take up when you arrive, and another feature may be &#x201C;Transfer of Book&#x201D; to understand this. What this feature does is assume person1 has a book, now person2 needs the same book that person1 has, before person2 can withdraw the book person1 must return the book for that both of them must get to the library, now to save time what we propose is if both person1 and person2 meet and exchange the books with each other to approve this, notification regarding the transfer will be sent to both their applications they must confirm then the transfer will start. Other features include &#x201C;review of the books&#x201D; that individuals borrowed, which includes the reason they borrowed the book as well as some other information.
Robust 3D brain segmentation in magnetic resonance image with weighted feature fusion

Accurate tissue segmentation in brain magnetic resonance image is of paramount importance for brain reconstruction and analysis. Supervoxels analysis enables relatively robust segmentation of brain tissues, by assigning the supervoxels to tissue clusters according to the inherent information of multiple features captured from the supervoxels. In this popular framework, difficulties are encountered in clustering imposed by the insufficient selection, deficient utilisation of features, and rough supervoxel boundaries due to the influence of noise. To address these aforementioned challenges, the authors propose a weighted feature fusion clustering segmentation method for dividing supervoxels into three tissues on the basis of a robust feature fused similarity matrix. To comprehensively capture the similarities of features among the supervoxels, we construct three complementary similarity matrices on the basis of multiple features from three aspects, that is, appearance, shape, and space location. Next, the authors propose a weighted similarity network fusion method to discriminatively fuse the three similarity matrices into one single matrix. This novel fusion process cannot only extract the common and complementary information of features, but also automatically adjust the weight of similarities according to its reliability. Finally, we achieve initial segmentation results by performing spectral clustering on the fused similarity matrix. To optimise the segmented boundaries affected by noise in the initial segmentation results, the authors construct a voxel-wise diffusion energy function by considering the intensity information of neighbouring voxels. Experiments on two public brain magnetic resonance image datasets demonstrate the robustness of the proposed approach.
Financial risk evaluation method of blockchain digital currency based on CART algorithm

In order to overcome the problems existing in traditional financial risk evaluation methods, such as high generalization error and fitting degree with actual value, this paper designs a financial risk evaluation method of blockchain digital currency based on CART algorithm. After screening and ranking the financial risk indicators of blockchain digital currency, the financial risk level of blockchain digital currency is determined by combining CART algorithm. Finally, by calculating the covariance matrix of the financial risk decision matrix, the positive ideal solution and the negative ideal solution of the financial risk are found, and then the final evaluation result is obtained by combining the progress of the financial risk. Experimental results show that the minimum generalization error of this method is only 0.021, the fitting degree of the obtained results and the actual risk can reach 98.0%, and the maximum risk accuracy can reach 0.978.
Istiqamah App: A Mobile Application for Sunnah and Hadith reminder using Flutter framework

Istiqamah App is a mobile application that utilizes the feature of notification to keep reminding users to perform sunnah activities daily. Besides, it also comes together with hadith and Quran quotations related to each sunnah activity. Five categories of notifications related to sunnah activities such eating Habbatus Sauda and honey, performing Tahajjud prayer, performing Sadaqah, maintaining water intake based on Body Mass Index, and taking medicines. Each time a user receives the notification, the Istiqamah App will display hadith or Quran quotations related to the sunnah activities. Users can choose one from the three actions available: 1) Skip the reminder; 2) Perform the sunnah and mark it as taken or complete; or 3) Remind them again five minutes later. The application also enables users to keep track of their progress. The project used the Rapid Application Development methodology to develop the mobile application. The mobile application is developed using Flutter framework, Dart programming language, and Firebase database.
Infrared and low-light-level image registration based on neighborhood difference chain code

The registration of the infrared (IR) image and the low-light-level (LLL) image remains a challenging problem due to poor dispersion of feature points, low correlation of structure and texture information. In this paper, we propose a method based on neighbourhood difference chain code to address the challenge. First we extracted the feature points of the images with the binary eight or sixteen-neighborhood information. And then construct the descriptor of the feature point by neighborhood difference chain code. At last we use the Euclidean distance to match the feature points. We adopt TNO and INO data sets to verify our method, and by comparing with four objective evaluation parameters obtained by other three methods. The result demonstrated that the proposed algorithm performs competitively, compared to the state-of-arts such as Harris, SIFT and SURF, in terms of accuracy of registration and speed.
Blended Learning in the IT Educational Programme: an Example of the Programming Technologies Course

This paper researches the blended learning technology usage for realization of the "Programming Technologies" course included in the IT programme under study in Ural Federal University. Today, blended learning has become not only prospective technology but the real necessity. The question now is not whether to blend or not; it is how to design an effective blend. In this paper, technologies, information platforms used for learning, and concepts underlying the course are under discussion. One of the concepts – cross-cutting technology usage – is discussed in detail. The results of the course accomplished by the final test are presented at the end of the paper. This course implementation in 2020 was a pilot one, and, according to the results, this experiment was recognized as a successful one in terms of the learning effect. Usage of different channels, technologies and teaching methods in combination has proved the effectiveness of blended learning, while the results obtained in each activity (in terms of marks) have balanced each other.
Forensic Investigation of Remnant Data on USB Storage Devices Sold in New Zealand

The digital forensic tools used by law enforcement agencies for forensic investigations are mostly proprietary and commercially expensive; although open-source tools are used, the investigations conducted with such tools are not verified by reputable organisations, and hence, users are reluctant to practice such tools. To address this issue, we experimentally evaluate three open-source forensic tools based on various requirements recommended by the National Institute of Standards and Technology (NIST) framework for forensic investigation. The experimental setup consists of a forensic workstation, write-blocker, and purchased USB hard drives investigated via digital forensic imaging tools, i.e., DC3DD, DCFLDD, and Guymager. We create various test cases, which distribute USB hard drives in different groups and investigate the functional and optional requirements of NIST along with recovering and analysing remnant data. We evaluate these forensic tools by analysing the log information, following, anonymously (to ensure that data were not disclosed or misused during or after the investigations) collecting, examining, and classifying the remnant data restored from the USB hard drives. We observe that the percentage of hardware resources usage and the processing time of each tool are remarkably different, e.g., Guymager was the fastest tool and met all the functional requirements in each test case, but it utilised more CPU and memory resources than DC3DD, DCFLDD. We note that 88.23% of the USB hard drives contained sensitive personal or business information (e.g., personal photos, bank transactions, and contracts). Subsequently, the remnant data analysis shows that consumers in New Zealand are unaware of personal data security and the associated vulnerabilities of data leakages.
Bipolar Intuitionistic Fuzzy Competition Graphs

Abstract The present study aims to introduce a bipolar intuitionistic fuzzy competition graph which is the generalization of competition graphs. In a bipolar intuitionistic fuzzy digraph, the targets are valuable as it depends on the competition power of the competitors. Also, the number of competitors may vary sometimes. Based on these clues, this study presents two generalizations of bipolar intuitionistic fuzzy competition graphs namely bipolar intuitionistic fuzzy k -competition graph and bipolar intuitionistic fuzzy p -competition graph. The concept of the directed bipolar intuitionistic fuzzy graph is coined in this study. Moreover, some results on the bipolar intuitionistic fuzzy graph are derived.