Reviewerly/exHarmony huggingface dataset:
Predictive Filter Flow Network for Universal Demosaicking

Demosaicking is an image reconstruction process for restoring full-color images from color filter array (CFA) data. In recent years, many deep convolutional neural network (CNN)-based demosaicking methods have been reported, and state-of-the-art accuracy has been achieved. In this paper, we propose a novel demosaicking method using the predictive filter flow (PFF) network for various CFA patterns. The PFF is a model that predicts a spatial variant linear filter that transforms an input image into a target image. To incorporate the PFF into demosaicking, the proposed network synthesizes the filter flow corresponding to each channel by means of a network trained by integrating RGB channels. Our model, designed to apply demosaicking with the PFF to various CFA patterns, provides versatility and extensibility. Experimental results demonstrate that the proposed method provides better or competitive results compared with several state-of-the-art deep-CNN-based demosaicking algorithms.
Simulation study of Sobel operator and machine learning algorithm in hazard identification and detection in public places

With the development of computer and image processing technology, intelligent image recognition method has been widely used in the field of public security. However, due to the influence of angle, light, noise and other factors, the accuracy of image recognition has been seriously reduced. To solve these problems, an improved Sobel operator and region selection image recognition method are proposed in this paper. Gauss smoothing and median filtering are used to reduce image noise. Sobel operator is used to extract image edge, and then the contour is binarized. Expansion and corrosion treatment expands and refines each area. Finally, region preference algorithm is used to locate the location of ID card. In this paper, Python language is used to simulate the ID image in the actual scene, which proves the effectiveness of the algorithm and strong environmental adaptability. It can effectively reduce noise, improve the accuracy of identification and location of hazards in complex public places, and effectively acquire the ability of risk prevention and control analysis. This technology can be widely used in the field of public safety, such as image recognition, crowd detection and so on. It can effectively strengthen the ability of accident prevention and control and emergency disposal. It has a certain application prospect and practical value.
Cognitive Visualisation as a Way to Develop Learner Autonomy in Foreign Language Education

The research aims to determine the didactic potential of cognitive visualisation in students’ learner autonomy development in the course of foreign language learning. The paper provides specific task options aimed at students’ independent cognitive visualisation of foreign language material in order to enhance their learner autonomy skills. Scientific novelty of the research lies in justifying in a comprehensive theoretical manner the usefulness of cognitive visualisation means for the development of learners’ autonomous activity skills in foreign language education. As a result of the research, the effectiveness of using cognitive visualisation means in the development of foreign language learners’ autonomy has been proved.
Multivariate wavelet leaders Rényi dimension and multifractal formalism in mixed Besov spaces

In this paper, we first establish a general lower bound for the multivariate wavelet leaders Rényi dimension valid for any pair [Formula: see text] of functions on [Formula: see text] where [Formula: see text] belongs to the Besov space [Formula: see text] with [Formula: see text] and [Formula: see text] belongs to [Formula: see text] with [Formula: see text]. We then prove the optimality of this result for quasi all pairs [Formula: see text] in the Baire generic sense. Finally, we compute both iso-mixed and upper-multivariate Hölder spectra for all pairs [Formula: see text] in the same [Formula: see text]-set. This allows to prove (respectively, study) the Baire generic validity of the upper-multivariate (respectively, iso-multivariate) multifractal formalism based on wavelet leaders for such pairs.
Detecting Asian Values in Asian News via Machine Learning Text Classification

The study is aimed at developing supervised machine learning models to automatically detect Asian values of harmony and support in Asian English-language newspaper articles. Harmony has two classes (“harmony” vs. “conflict”), with “harmony” defined as a void of conflict. Support has two classes (“supportive” vs. “critical”), with “supportive” defined as supporting economic, political, and communal unity and strength at home. Nine algorithms, with their parameters tuned, were compared for performance. Results showed logistic regression is the top performer for both the “harmony” and “support” models, with 93.0% and 91.2% accuracy rates, respectively. Logistic regression models were then deployed through web pages to make classifications of unseen, unlabeled data. The testing of the deployed models demonstrated the utilities of the models developed.
Strengthening the Benefit of PPID-UM Website Development and Innovation through Leadership Intervention

Based on the results of the monitoring and evaluation by the Central Information Commission, UM is a public body that won the &#x201C;informative&#x201D; category in 2020. From various aspects assessed, innovation is one of the main indicators set by KIP. This study aimed to describe PPID website development and innovation and its benefits for UM as public bodies and users of UM&#x0027;s information and documentation services. This research was approached quantitatively and qualitatively. Quantitatively, this research used the R&#x0026;D method to develop and innovate the PPID website to make it more effective and efficient. Meanwhile, a qualitative approach was used to uncover and describe the phenomenon of a leadership intervention on the PPID website benefits. The results showed: 1) the website development and innovation that focused on its performance, e-PPID, e-report, online statistics integration, COVID-19 services, and the Covid 19 Help Desk, impacted the website to run very effectively and efficiently; 2) the PPID website development and innovation increased the benefits of the PPID website to the community; 3) Leadership intervention is a critical factor for efforts to develop, innovate, and make maximum use of the website.
Methodology for the Evaluation of Machine Translation Quality

Along with the development and widespread dissemination of translation by artificial intelligence, it is becoming increasingly important to continuously evaluate and improve its quality and to use it as a tool for the modern translator. In our research, we compared five sentences translated from Armenian into Russian and English by Google Translator, Yandex Translator and two models of the translation system of the Armenian company Avromic to find out how effective these translation systems are when working in Armenian. It was necessary to find out how effective it would be to use them as a translation tool and in the learning process by further editing the translation.&#x0D; As there is currently no comprehensive and successful method of human metrics for machine translation, we have developed our own evaluation method and criteria by studying the world's most well-known methods of evaluation for automatic translation. We have used the post-editorial distance evaluation criterion as well. In the example of one sentence in the article, we have presented in detail the evaluation process according to the selected and developed criteria. At the end we have presented the results of the research and made appropriate conclusions.
VAN: Voting and Attention Based Network for Unsupervised Medical Image Registration

In this paper, a novel unsupervised network for medical image registration called VAN (Voting and Attention based Network) is proposed, in which the final deformation field is determined by the voting process between multiple registration branches. To reduce model parameters, multiple registration branches share one encoder. Besides, the attention mechanism is introduced, which further improves the network accuracy. We also adopt the method of single training and multiple-registrations to deal with the problem of the large deformation field. The experimental results show that the registration effect of our proposed network outperforms the baselines VoxelMorph and Symmetric Normalization (SyN) on three brain MRI image datasets.
Dynamic Runtime Feature Map Pruning

High bandwidth requirements in edge devices can be a bottleneck for many systems - especially for accelerating both training and inference of deep neural networks. In this paper, we analyze feature map sparsity for several popular convolutional neural networks. When considering run-time behavior, we find a good probability of dynamically disabling many feature maps. By evaluating the number of 0-valued activations within feature maps, we find many feature maps that can be dynamically pruned. This is particularly effective when a ReLU activation function is used. To take advantage of inactive feature maps, we present a novel method to dynamically prune feature maps at runtime reducing bandwidth by up to 11.5% without loss of accuracy for image classification. We further apply this method on Non-ReLU activation functions by allowing the output of the activation function to be within an epsilon of 0. Additionally, we also studied how video streaming applications could benefit from bandwidth reduction.
Efficient technique for real-time face detection

Currently, the top-performing face detectors use Convolution Neural Networks (CNN). These detectors have high performance but require large computational resources. Hence, it is a challenge to use these for practical applications with limited resources. To address this problem, this research paper proposes a single-stage face detector for practical applications with better accuracy and speed. The proposed face detector is a lightweight and fast CNN-based face detector. To achieve fast detection speed, by swiftly reducing the size of the input feature space through a carefully designed architecture using depth-wise convolution and Receptive Field Blocks (RBF). The high accuracy of the method is achieved by preserving the feature information from the early layers using CReLU (Concatenate Rectified Linear Units) activation in the CNN architecture. To detect faces of different pixel sizes, we used parallel RF blocks, which use multiple filters of different kernel sizes and dilation. The proposed method was tested on benchmark datasets such as WIDER FACE, AFW, and PASCAL faces, and performed well when compared to recent state-of-the-art real-time face detection algorithms.
Proposal to Improve Software Testing in Small and Medium Enterprises

Faced with their massive demand for software products, their quality has been increasingly questioned and, consequently, the quality of the processes by which they are produced. Different institutions have developed different models and standards for the continuous improvement of these development processes, mainly focused on the testing stage. Even in this proliferation of improvement models, there are none applicable to software developing SMEs in our region, industries that count with many opportunities but face challenges such as lack of resources, skills and experience in their pursuit to create quality software and survive in the market. This article presents a management proposal that allows to improve the testing process to obtain higher quality software products in SMEs in the region, based on a set of successful methods for software testing in large companies.
Video Sketches as the Means to Improve Students’ Creativity in Studying Foreign Languages

The paper covers the application of video sketches as the way to provide out-of-class monitoring that could stimulate students’ creativity and motivation to study foreign languages at technical universities. The study introduces the data analysis based on the statistics survey conducted in academic groups of 1-course students of technical specialties of Peter the Great St. Petersburg Polytechnic University during the autumn term of 2020–2021 academic year. Two groups of students were randomly chosen as experimental and control groups. The students of the control group were to present their monologues in a traditional format, whereas students of an experimental group had an opportunity to prepare video sketches instead. The authors emphasize the benefits of recording video sketches compared to face-to-face format of delivering a monologue as well as the possible problems with the ways to overcome them. On the basis of the research and the results of the experiment the authors conclude that the application of video sketches can improve students’ speaking abilities, stimulate their creativity and overall make the process of teaching foreign languages at the university more efficient.
Graph Embedding in Vector Spaces Using Matching-Graphs

An evergrowing amount of readily available data and the increasing rate at which it can be acquired leads to fast developments in many fields of intelligent information processing. Often the underlying data is complex, making it difficult to represent it by vectorial data structures. This is where graphs offer a versatile alternative for formal representations. Actually, quite an amount of graph-based methods for pattern recognition and related fields have been proposed. A considerable part of these methods rely on graph matching. In our recent work we propose a novel encoding of specific graph matching information. The idea of this encoding is to formalize the stable cores of specific classes by means of graphs (called matching-graphs). In the present paper we propose to use these matching-graphs to create a vectorial representation of a given graph. The basic idea is to produce hundreds of matching-graphs first, and then represent each graph g as a binary vector that shows the occurrence of each matching-graph in g. In an experimental evaluation on three data sets we show that this graph embedding is able to improve the classification accuracy of two reference systems with statistical significance.
IDENTIFYING MODULATION FORMATS USING INTEGRATED CLUSTERING ALGORITHM

We propose an integrated-clustering-algorithm-base modulation format identification scheme. Experiments shows that it can achieves 100% identification accuracy when OSNR is lower than 7% FEC threshold and have a good tolerate to residual chromatic dispersion.
Seismic Logistics and Planning

The success of hydrocarbon explorationSeismic logistics and planninghydrocarbon exploration depends on the quality of the three-dimensional information of the subsurface. Seismic data play an important role in generating high quality subsurface models. The planning of onshore seismic surveys aims at acquiring the best possible data quality with the least effort. In flat open terrain without obstacles the planning of seismic surveysSeismic logistics and planningsurveys is a straight-forward task and the seismic acquisition is mostly optimized to fast acquisition. In complex terrain, however, significant attention is paid to mitigating the risks related to people, equipment and data quality in addition to fast acquisition with the goal to optimize all parameters for the most efficient operation. This part of the planning is where remote sensing dataSeismic logistics and planningremote sensing data prove to be most useful. Typically the following remote sensing data and derivativesSeismic logistics and planningderivatives are used for the task: The three aspects covered in this section, i.e. logistics, data qualitySeismic logistics and planningdata quality and near surface modelingSeismic logistics and planningsurface modeling, are closely linked. Therefore in each chapter, we will have a major focus on one aspects whilst mentioning aspects of the other two aspects as well.
Applying the Principle of Least Privilege to System Management Interrupt Handlers with the Intel SMI Transfer Monitor

Recent years have seen a growing concern over System Management Mode (SMM) and its broad access to platform resources. The SMI Transfer Monitor (STM) is Intel’s most powerful executing CPU context. The STM is a firmware-based hypervisor that applies the principle of least privilege to powerful System Management Interrupt (SMI) handlers that control runtime firmware. These handlers have traditionally had full access to memory as well as the register state of applications and kernel code even when their functionality did not require it. The STM has been been enabled for UEFI and, most recently, coreboot firmware, adding protection against runtime SMM-based attacks as well as establishing a firmware-based Trusted Execution Environment (TEE) capability. We provide a detailed overview of the STM architecture, evaluate its protections, and quantify its performance. Our results show the STM can protect against published SMM vulnerabilities with tolerable performance overheads.
Benefits of the Cloud for Calibration Management Software Systems

The National Institute of Standards and Technology (NIST) was directed by the federal government to define cloud computing to assist federal agencies in implementing cloud architectures. In 2011, NIST published NIST SP 800-145 “The NIST Definition of Cloud Computing” and defined cloud computing as: “Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction. This cloud model is composed of five essential characteristics, three service models, and four deployment models. [1]” Cloud computing is a relatively new IT concept and as such, there is much to learn about the benefits that this business model can bring to organizations. Cloud computing will transform how you do business. The objective of this paper is to explain the benefits of using a cloud architecture for calibration management software systems. The following benefits and topics will be discussed: Work From Anywhere, Always On, Reduced IT Costs, Scalability, Automatic Updates, Reduce Coordination Costs, Improved Quality Control, Disaster Recovery, Environmental Sustainability, Increased Competitiveness, Stronger Security, and Compliance Considerations [2].
The Professional Identity: Concept and Significance for Improving the Efficiency of Education of Law Students

The article deals with the problem of forming the professional identity of law students. Domestic and foreign approaches to the definition of the concept of "professional identity" are analyzed, the authors’ definition of professional identity is proposed. It is justified that an important condition for the formation of the professional identity of law students is their inclusion in real professional activities, interaction with representatives of various legal professions and persons interested in obtaining legal assistance. This organization of the educational process allows you to study the peculiarities of activity in the performance of duties in various legal specialties, which contributes to the formation of professional identity. The main stages of the development of professional identity fall on the period of study at the university, while practical training of students plays an important role, as a form of organizing educational process when mastering of the educational program happens in the invironment where students perform certain types of work related to future professional work and aimed at the formation, consolidation, development of practical skills and competencies in the profile of the corresponding educational program. Based on the analysis of normative legal acts, an option is proposed to implement practice-based training of students, which allows you to study the content of various legal specialties. It is noted that such development of the educational process will allow students to choose preferred options for legal work, which will ultimately contribute to successful professional growth. It was established that strengthening the practical-oriented part of the educational process in universities will eliminate the existing systemic gap between the level of qualifications of students during study and the degree of effectiveness of their subsequent professional identity as an important condition for the correct choice of the professional sphere.
Phishing Website Detection Using Random Forest and Support Vector Machine: A Comparison

As the Internet usage is growing rapidly, people are changing their choice from traditional shopping to electronic commerce. However, instead of robbery at banks and stores, criminals now locate their victims with some tricks in cyber world by applying the anonymous internet framework. Hackers are using new tactics, including phishing, to mislead the victims by using fake websites to gather sensitive information, including account ids, usernames, and password. Understanding whether a website is legitimate or phishing is an incredibly difficult problem because of its phishing attack structure, which primarily targets the vulnerabilities of web users. This work was proposed to solve these issues by using machine learning technology to detect the phishing websites. The system analyzed the HTML code structure that include in the hyperlink of the websites. Two machine learning techniques, namely Random Forest and Support Vector Machine are tested to identify the best machine learning algorithm in detecting phishing website. The performance metric for each algorithm was used as measured. The result average of accuracy for Random Forest is 99.98 percent and Support Vector Machine is 84.73 percent. This study aims to detect phishing websites hyperlink and produces the best algorithm which is Random Forest that achieved the highest accuracy to be used for the system.
Quantum Fashion as a New Technology in Costume Design

The purpose of this article is an attempt to introduce such new concepts as “quantum fashion”, “clothing-cultural artifact” into the modern theory and practice of costume. These concepts are considered against the background of historically replacing fundamental scientific paradigms or “pictures of the world”, as a result of which the value attitudes of people change, which cannot but be reflected in fashionable clothes. The theoretical basis of the article is the concept of the scientific picture of the world and its main historical forms introduced by Academician V.S. Stepin, as well as the principle of complementarity, which was simultaneously introduced by N. Bohr and R. Jacobson in the field of humanitarian knowledge. In their work, the authors use a combination of historical and logical research methods. The connection between the change in scientific pictures of the world and the evolution of fashionable processes is traced. The article provides a definition of the concept of quantum fashion and substantiates its applicability in social sciences and humanities. When applied to design, quantum fashion acts as a set of a wide variety of clothing styles, determined not by social factors, but by the creative interaction of a clothing designer with a consumer. Based on the creation of the Astromod brand (by Elena Makhinya), a number of empirical studies have been carried out, which have shown that quantum fashion is based on the synthesis of knowledge about the inner world of a person, his character, motivation and desires. As a practical result, a collection of clothes as an artifact was created, which is an additional source of energy for a person in the modern world.