Reviewerly/exHarmony huggingface dataset:
Method for compressing DICOM images with bit-normalization and video CODECs

The constant increase in the volume of data generated by various medical modalities has generated discussions regarding the space needed for storage. Although the storage and network bandwidth costs are decreasing, medical data production grows faster, thus forcing an increase in spending. With the application of image compression and decompression techniques, such expectations and challenges overcoming can preserve all clinically relevant information. This research proposes and evaluates a method that combines an adaptive normalization for each DICOM slice and volume compression using a video CODEC. Similarity metrics results show that the best result achieved for these tests was the method combines the normalization function and the H264 using as parameters FPS 60, bitrate 120, with an image in PNG format, where SSIM and CC have the maximum value, PSNR has a high value and CR higher than competitors in the literature.
Noncontact button operation system using hand gestures

Recently, many noncontact sensors are intended for facilities that require hygiene to be considered. However, there is a problem in that noncontact sensors could malfunction unintentionally. In addition, there is a demerit in that noncontact sensor is not possible to perform complicated operations with one sensor because it would be limited to the simple operation of turning on when hold hand over it. In this paper, we examine the principle of a noncontact sensor that responds only to the action of waving in front of the sensor as an intentional action and does not malfunction. Additionally, since there are large and small hand waving methods, hand waving alone could be used for multiple signals. Experiments show that the proposed system is to realize not only the states of ON and OFF but also various operations with a single noncontact button.
Data Security for Connected Governments and Organisations

Digital ecosystems have grown rapidly over the years, and governments are investing in digital provision for their processes and services. Despite the advantages of distributed technologies, there are many security issues as well that result in breaches of data privacy with serious impact including legal and reputational implications. To deal with such threats, government agencies need to thoughtfully improve their security defences to protect data and systems by using automation and artificial intelligence (AI), as well as easing the data security measures including early warning of threats and detection. This study provides a comprehensive view of AI and automaton to highlight challenges and issues concerning data security and suggests steps to combat the issues. The authors demonstrate the role of AI-driven security tools and automation to mitigate the impact of data breaches to also propose recommendations for government agencies to enhance their data security protection.
Video Retrieval Based on CNN Feature and Scalar Quantization

In recent years, the video dissemination has become an important information medium with the development of the Internet and the rise of short video platforms, and infringements against long videos have followed, so an method of efficient and automated short video infringement detection is necessary. This paper proposes a method of video copyright detection based on CNN features and Scalar Quantizer, in which the deep convolutional neural network is used to obtain the decoded video frame’s feature vector, and then the Scalar Quantizer is used to search the feature vector based on the approximate nearest neighbor search, and finally the target video is determined by finding the shortest average Euclidean distance of the target video frames. This paper sets a distance threshold and a ratio threshold based on this method to form a new method, and then compares the recall and precision of two methods.
<i>instance</i>: Soma-based multi-user interaction design for the telematic sonic arts

The telematic work instance is a performance for viola and dance that digitally connects performers in Vancouver and Cape Town. The network interface enables a violist and a dancer to simultaneously play multi-user digital music-dance instruments over the internet with music and dance. The composition, design and performance interaction of instance draw from acoustic multi-user instrument paradigms and music-dance interactions in the African performing arts to explore the idiosyncrasies of the telematic performance space. The iterative design process implements soma-based research methods to inspire sonic compositional material with the body and to explore the performers’ embodied experience of sonic aesthetics during their interaction.
Improved Semantic-aware StyleGAN-based Real Face Editing Model

The quality of the face editing is greatly affected by the latent codes. As far as to the most popular StyleGAN series models, many researches have shown that the W space is better for reconstruction quality, while the W+ space is better for editing quality. Since editing real faces, instead of those fake faces generated by GAN model, involves both face reconstruction and face editing, so most of the previous approaches have to make a trade-off between reconstruction and editing quality. In this paper, we attribute this problem to the mismatch of the latent space in training and inference, i.e., training in W space but inference in W+ space, which leads to the semantic loss. To this end, we propose a semantic-aware StyleGAN-based real face editing model. In this model, training and inference are performed in the similar space by connecting mapping layers and synthesis layers directly. Besides, the non-linear mapping between pixel space and latent space, also leads to the semantic loss as no semantic relations are guaranteed. To solve this problem, we introduce a manifold regularization term to keep the manifold during StyleGAN training. We conduct extensive experiments of face reconstruction and face editing, and the results suggest that our method significantly outperforms the previous models.
Question Answering-based Socio-economic Indicator Extraction

Socio-economic indicators are important statistical data used to monitor and evaluate the development of economy and society. They are of great value to policy makers of government, organizations and enterprises. Previous methods to measure socio-economic conditions are through data collecting or large-scale surveys by manpower. Over recent years, with the quick development of Internet, a huge amount of online text becomes available and contains clues of socio-economic indicators. As a result, rule-based methods and traditional machine learning methods are emerging to extract indicators more effectively but they require too much prior domain knowledge and feature engineering. Recently, deep learning approaches are dominating in Natural Language Processing (NLP) and formulating NLP tasks as machine reading comprehension (MRC) problems has been proven to be effective. Therefore, we propose a RoBERTa-based model that treats indicator extraction as a question answering (QA) task. By answering questions customized for indicator elements which are the components of the indicator, it transforms extracting indicator elements to identifying and extracting answer spans from the given contexts. Experiments show that it outperforms other deep learning baselines.
Interactive Data Visualization: Open Data of Pandemic Disease

A pandemic disease or pandemic disease is an infectious disease with a wide area of distribution, spread in several countries or even throughout the world. Several types of pandemic diseases that have existed include Severe Acute Respiratory Syndrome and Ebola. The development of the pandemic disease is one of the concerns of the world health organization. Open data (open data) on the spread of pandemic diseases is published to make it easier for related parties such as the government, health organizations to the public to obtain an overview of the problem of pandemic disease. However, published open data is still presented in numerical form so that the expected objectives are not achieved. In this research, data visualization will be carried out using open data on pandemic diseases. The research stages to be carried out were preceded by data analysis. The next stage is the clustering process, and the last stage is interactive data visualization. Literature studies were carried out during this research. The results in this study are interactive data visualization of pandemic disease open data, clustered by country and type of pandemic disease, cumulative number of cases, cumulative number of deaths and pandemic disease intersection in each country.
Video-Based Abnormal Behaviour Detection in Smart Surveillance Systems

Due to increasing demand for security, the instant detection of abnormal behavior in video surveillance systems becomes a critical issue in a smart surveillance system. The currently applied semiautomatic systems mainly depend on human intervention to detect the abnormal activities and suspicious human behaviours from video context. Due to these limitations, it has become an urgent need for intelligence systems to avoid the very slow response and reduce the human observer and interventions. In this paper, a method that can trace abnormalities of human behaviour from video is presented. Techniques related to bounding box measurements and descriptions for behaviour representation were used. Moreover, the performance evaluation of the proposed method is presented.
COMMUNICATION IN THE MODERN RUSSIAN SCIENCE

The article examines various types and tools of scientific communication: from traditional ones that have existed for several centuries, to completely new ones that have arisen in connection with the development of information technologies and the global Internet space. In order to show the differences between the new communication tools and those that existed earlier, the author gives a brief historical overview of the development of scientific communication since the emergence of Respublica Literaria in the 15th century until the emergence of networked scientific communities at the beginning of the XXI century. As a result of the study, it was found that the development of network scientific communities is a response to the crisis of traditional primary communication tools and provides the scientist with a new space for creativity and scientific research.
Research on the application of low-light enhancement methods in radiographic inspection pretreatment

In this paper, the application of the low-light enhancement method of radiographic inspection is proposed to realize more accurate X-ray nondestructive detection of workpiece defects. In order to solve the problem that the brightness of the original image is too dark in the process of ray image acquisition, gamma correction, histogram equalization, traditional illumination-based method, and deep learning-based method are selected to process the actual workpiece radiographs. Then the advantages and disadvantages of each algorithm are summarized by comparing and evaluating the results of each algorithm, which provides a basis for the selection of low-light image enhancement algorithms in the practical application of radiographic detection.
View Synthesis with Multi-scale Cost Aggregation and Confidence Prior

This paper presents a learning-based novel view synthesis (NVS) approach from wide-baseline image pairs. Inspired by prior work, we first predict a depth probability volume which represents the scene structure as a set of depth probability layers (DPLs) within a reference view frustum. To reduce geometric uncertainty in ambiguous regions between input images, a multi-scale cost aggregation network is proposed to generate the DPLs for both input views without supervision. Furthermore, to mitigate the depth discretizaiton artifacts in distant views, we calculate the disparity map of the target view by passing the warped DPLs onto the target view to a CNN-based fusion network. Finally the predicted view could be obtained by incorporating the disparity map, warped input images and the confidence prior together. The proposed method improves the performance on challenging scenarios such as texture-less or non-textured regions, occlusion boundaries, non-Lambertian surfaces, and distant viewpoints. Experimental results show that our method achieves state-of-the-art view interpolation and extrapolation results on RealEstate10K mini dataset.
Elements of number theory as a mathematical basis for solving problems for processing integer numbers information

The article describes the methodology for preparing schoolchildren to solve problems for processing integer numbers information, where the use of a brute force algorithm is not rational. The statements of number theory necessary to solve the problems of fnding numbers with a given number of divisors of a certain form are formulated; their evidence is given. A set of exercises is presented that promotes the independent "discovery" of the listed statements by schoolchildren and the formation of a skill to identify the structure of a number with given properties. At the end of the article, algorithms and programs are presented to solve problem No. 25, presented in the training control and measuring materials of the Unifed State Exam in informatics in the 2020/2021 academic year. The exercise sets described in the article can be useful to both informatics teachers and schoolchildren preparing for the Unifed State Exam in informatics.
Reading Skill: A Key to the Development of Language Acquisition

Every human being more or less wants to express something to somebody for which one needs a language. Some expressions of poets, singers, artists, writers, social workers, leaders, politicians and philosophers are placed in printed materials - newspaper, periodicals, journal, magazines and above all books by means of language for future generations to come. Moreover, the same thing in same language can be expressed by different people in different ways in various places. Thus the scope of language gets enlarged day by day coming contact with other languages as the river gets widened running continually over years accompanied by other small drains, rivulets etc. Needless to say, unfathomable is the depth of language that cannot be measured in a single day or effort. The vast arena of language may be cultivated by the habit of constant reading which involves both speaking &amp; listening skills. By reading one move in the world of language. One cannot acquire the use of language unless and until one is exposed to it. It is the style of language that helps the speaker to attract and win everybody’s heart. This paper examines how language skills are cultured by reading activity. It also explores possible ways of how reading skill, firstly; enriches the readers with profound vocabulary, secondly; enables them to use language with clarity, accuracy and fluency, and last but not the least; makes the speakers well communicative, interactive, and economic in effective use of language. Lack of language creates a communication gap and this small gap invites a massive misunderstanding which very often spoils unity, integrity, brotherhood among the users, and after all makes human beings dumb and dull.
Knowledge Visualization for Research Design

This chapter presents a case of information and communication technology use in doctoral research processes. In particular, it presents the use of the Idea Puzzle software as a knowledge visualization tool for research design at the University of Auckland. The chapter begins with a review of previous contributions on knowledge visualization and research design. It then presents the Idea Puzzle software and its application at the University of Auckland. In addition, the chapter discusses the results of a large-scale survey conducted on the Idea Puzzle software in 71 higher education institutions as well as its first usability testing at the University of Auckland. The chapter concludes that the Idea Puzzle software stimulates visual integrative thinking for coherent research design in the light of Philosophy of Science.
MOVING OBJECT DETECTION FROM VIDEO CAPTURED BY MOVING SURVEILLANCE CAMERA

This paper presents an effective method for the detection of multiple moving objects from a video sequence captured by a moving surveillance camera. Moving object detection from a moving camera is difficult since camera motion and object motion are mixed. In the proposed method, we created a panoramic picture from a moving camera. After that, with each frame captured from this camera, we used the template matching method to found its place in the panoramic picture. Finally, using the image differencing method, we found out moving objects. Experimental results have shown that the proposed method had good performance with more than 80% of true detection rate on average.
Infrastructure Cyber-Attack Awareness Training

The purpose of this study is to provide insight as to how infrastructure countermeasures awareness training will impact individuals dealing with a nationwide catastrophic cyber-attack. Can this awareness training lessen the psychological effect of an attack? This study showed no value for this type of training. Reading about such an attack, the subjects had lower technical optimism and cyber self-efficacy. Reading about infrastructure countermeasures, before or after reading about a cyber-attack, did not improve or maintain the subjects' optimism and self-efficacy. A possible explanation is that emotional arousal may override or block rational thinking. Another explanation may be that a nationwide attack is towards the infrastructure and not the personal computer. Here the individual lacks any control. Future research needs to look at personal preparation and response training to see if it will help the psychological effects of a catastrophic cyber-attack.
Building a Model for Observing the Educational Practice of Mathematics Teachers

This study relies on a theoretical perspective to provide a model that can be used to observe the educational practice of mathematics teachers. To this end, various existing observation instruments are studied, which, in the case of mathematics, aim to observe the teaching practice employed in the classroom, without considering the before and after of the implementation, which is what characterizes the professional task of teaching. Indicators that emerge from the teaching practice are proposed, together with the teacher’s knowledge and reflection constructs, based on three phases: for, in, and on the educational practice. As a result, understanding the educational practice of mathematics teachers would allow various educational stakeholders (teachers, administrators, instructors of teachers, and others) to focus their attention on what elements develop so as to improve how students are taught, and consequently learn, mathematics.
Metro tunnel section test technology based on 350 degree laser scanner

Nowadays, the scale of cities continues to expand and rail transit is developing day by day. It is more and more important to ensure the efficient and safe operation of rail transit. As an important factor affecting the safe operation of subway lines, the profile of the tunnel section is constantly updated with the development of the line. Efficient, automated, and intelligent tunnel section detection new technology has become a research trend and hot spot. This paper analyzes the current situation of tunnel section detection technology in China and abroad. Introduced a metro tunnel section test technology based on 350 degree laser scanner. The system has the characteristics of digitalization, miniaturization, modularization, high integration and real-time. Apply the product to an existing subway line, analyze and process the obtained data, and obtain specific information about the intrusion limit hazard on the line, so as to verify the feasibility of the system.
Challenges in Agile IT Project: A Case Study of Banking Company

The development of industry 4.0 has influenced the financial sector, as well as the banking industry. This issue urges banks to transform digitally by digitizing their business processes and developing a breakthrough digital product. To accelerate the delivery of a product, a banking company in Indonesia adopting agile methodologies for software development, but there is still a delay on the IT projects. The objective of this research is to identify the challenges in agile IT projects, rank them based on the most significant to the least significant challenges using Analytical Hierarchical Process (AHP) and give recommendations. There are four criteria of challenges, namely team, process, organizational, and communication. The four criteria are decomposed into eleven challenges. The biggest challenge faced by the company is from the communication category, which is the lack of customer or stakeholder commitment in an agile project by 19,55%. This research contributes to academics, practitioners, this company, and other banking companies to list the challenges in agile projects and determine what challenges should be the main focus of the organization.
Impact of threats from the use of modern information technologies on university students’ learning outcomes. Part II

The second part of the publication discusses the results of practical research on the impact of threats to the use of modern information technologies and independent Internet-based acquisition of knowledge on the results of students’ learning outcomes. It presents the course of empirical research with the methods and tools used. The summary presents the conclusions that arose during and after the research. Attention was drawn to the broad spectrum, which so far has not been covered by civilian students of the hierarchical university. The negative impact of threats on unaware users of modern information technologies is highlighted and described.
Development of an Efficient Path Planning Algorithm for Indoor Navigation

In the present work, a path planning generation method is proposed, it is based on the learning and query phases of the original probabilistic roadmap (PRM) method, but a more efficient approach is presented, also an additional simplification and optimization phases are added to minimize the length of the resulting trajectory. The proposed method produces safe paths for navigation in a known static map without increasing execution time. The proposed method is compared to the existing implementation of the PRM method, available in Matlab. Finally a simple kinematic controller is presented to show realistic simulations of the trajectory realization, for this test real maps are used, which are obtained by means of the SLAM algorithm.
Estimation of the Conditional Probability Using a Stochastic Gradient Process

The use of conditional probabilities has gained in popularity in various fields such as medicine, finance, and imaging processing. This has occurred especially with the availability of large datasets that allow us to extract the full potential of the available estimation algorithms. Nevertheless, such a large volume of data is often accompanied by a significant need for computational capacity as well as a consequent compilation time. In this article, we propose a low-cost estimation method: we first demonstrate analytically the convergence of our method to the desired probability and then we perform a simulation to support our point.