Reviewerly/exHarmony huggingface dataset:
A Hybridization of Fuzzy Logic and Deterministic Learning Machine for Face Recognition

In this chapter, a new method for face recognition (FR) is proposed by integrating fuzzy logic in deterministic learning machine called fuzzy deterministic learning machine (FDLM). The main steps of the proposed approach are the fuzzification and classification step. The fuzzification step is done using π-membership function (MF) to map the grades of association of each input feature to each subject in a fuzzy matrix form and classification is done with the help of fast learning, parameter-free deterministic learning machine. To show the efficacy and superiority of the proposed approach, we have conducted a comparison analysis of the proposed approach with other methods available in the literature on Georgia Tech face database. Experimental results obtained reveal that the proposed approach shows a significant improvement in recognition performance for FR.
A Robust Embedding for Attributed Networks with Outliers

Network embedding, as a promising tool, aims to learn low-dimensional embeddings for nodes in a network. Most existing methods work well when the topological structure is closely correlated to node attributes. However, real-world networks often contain outliers that have abnormal attributes. These attributes are quite different from the properties of their neighboring nodes, and they are not consistent with network structure. Thus, outliers exert negative impacts on the learned embeddings. Several methods only obtain unsatisfied results as they don't consider the effects of outliers. Hence, how to eliminate outlier impacts is essential for network embedding. In this paper, we propose a novel method called REANO for learning a Roust Embedding for an Attributed Network with Outliers. An overview of REANO combines residual analysis with attributed network embedding. In detail, residual analysis smooths out the negative impacts from outliers. Meanwhile, network embedding aggregates node attributes with network structure by using deep neural networks. By developing a joint optimization framework, REANO effectively alleviates outlier effects on the learned embeddings, and improve the robustness of node embeddings. Experiments on real-world datasets and manually planted outliers show that REANO learns more robust embeddings of nodes than the state-of-the-art algorithms.
A New Combiner for Key Encapsulation Mechanisms

Key encapsulation mechanism (KEM) combiners, recently formalized by Giacon, Heuer, and Poettering (PKC'18), enable hedging against insecure KEMs or weak parameter choices by combining ingredient KEMs into a single KEM that remains secure assuming just one of the underlying ingredient KEMs is secure. This seems particularly relevant when considering quantum-resistant KEMs which are often based on arguably less well-understood hardness assumptions and parameter choices. We propose a new simple KEM combiner based on a one-time secure message authentication code (MAC) and two-time correlated input secure hash. Instantiating the correlated input secure hash with a t-wise independent hash for an appropriate value of t, yields a KEM combiner based on a strictly weaker additional primitive than the standard model construction of Giaon et al. and furthermore removes the need to do n full passes over the encapsulation, where n is the number of ingredient KEMs, which Giacon et al. highlight as a disadvantage of their scheme. However, unlike Giacon et al., our construction requires the public key of the combined KEM to include a hash key, and furthermore requires a MAC tag to be added to the encapsulation of the combined KEM.
Power integral bases in cubic and quartic extensions of real quadratic fields

Investigations of monogenity and power integral bases were recently extended from the absolute case (over Q) to the relative case (over algebraic number fields). Formerly, in the relative case we only succeeded to calculate generators of power integral bases when the ground field is an imaginary quadratic field. This is the first case when we consider monogenity in the more difficult case, in extensions of real quadratic fields. We give efficient algorithms for calculating generators of power integral bases in cubic and quartic extensions of real quadratic fields, more exactly in composites of cubic and quartic fields with real quadratic fields. In case the quartic field is totally complex, we present an especially simple algorithm. We illustrate our method with two detailed examples.
Using Evolutionary Algorithms for Hyperparameter Tuning and Network Reduction Techniques to Classify Core Porosity Classes Based on Petrographical Descriptions

Classifying the porosity of sedimentary information is an important field of study with applications to tasks such as oil reservoir characterisation. Classifying porosity into groups based on Petrographical characteristics has been attempted in the past using: expert systems, supervised clustering techniques and neural networks. In this paper, we expand upon the usage of neural networks for this classification task by applying Evolutionary Algorithms to determine optimal parameters. Despite recent advances in techniques to select hyperparameters it is still difficult to determine the optimal parameters for a given dataset. We further apply network reduction techniques to further improve classification accuracy. We produce results similar to the work done by Gedeon et al. [1] on this dataset.
Analysis and Investigation of Frame Invariance and Particle Behavior for Piecewise-Linear Particle Swarm Optimizer

Particle swarm optimization (PSO) is a swarm intelligence algorithm and has good search performance and simplicity in implementation. Because of its properties, PSO has been applied to various optimization problems. However, the search performance of the classical PSO (CPSO) depends on reference frame of solution spaces for each objective function. CPSO is an invariant algorithm through translation and scale changes to reference frame of solution spaces but is a rotationally variant algorithm. As such, the search performance of CPSO is worse in solving rotated problems than in solving non-rotated problems. In the reference frame invariance, the search performance of an optimization algorithm is independent on rotation, translation, or scale changes to reference frame of solution spaces, which is a property of preferred optimization algorithms. In our previous study, piecewise-linear particle swarm optimizer (PPSO) has been proposed, which is effective in solving rotated problems. Because PPSO particles can move in solution spaces freely without depending on the coordinate systems, PPSO algorithm may have rotational invariance. However, theoretical analysis of reference frame invariance of PPSO has not been done. In addition, although behavior of each particle depends on PPSO parameters, good parameter conditions in solving various optimization problems have not been sufficiently clarified. In this paper, we analyze the reference frame invariance of PPSO theoretically, and investigated whether or not PPSO is invariant under reference frame alteration. We clarify that control parameters of PPSO which affect movement of each particle and performance of PPSO through numerical simulations.
Histological Images Segmentation by Convolutional Neural Network with Morphological Post-filtration

An algorithm for segmentation of cell nuclei in histological images is developed and studied. It is based on the implementation of U-Net neural network. As a means of additional processing, the use of morphological filtration is proposed. The training and testing of the algorithm was carried out on NVIDIA DGX-1 supercomputer using a histological image dataset with automatically generated markup. In the course of studies, the values of such metrics as simple match coefficient, Tversky index, and Sørensen coefficient are obtained. According to the results of calculating these metrics, a quality of histological image segmentation developed by the morphological filtering algorithm exceeds the similar performance for the algorithm without using morphological filtering, which allows us to recommend the morphological filter as a means of additional image processing at the output of the neural network algorithm in real medical practice.
Long-Range Decoder Skip Connections: Exploiting Multi-Context Information for Cardiac Image Segmentation

The heart is one of the most important organs in our body and many critical diseases are associated with its malfunctioning. To assess the risk for heart diseases, Magnetic Resonance Imaging (MRI) has become the golden standard imaging technique, as it provides to the clinicians stacks of images for analyzing the heart structures, such as the ventricles, and thus to make a diagnosis of the patient's health. The problem is that examination of these stacks, often based on the delineation of heart structures, is tedious and error prone due to inter-and intra-variability among manual delineations. For this reason, the investigation of fully automated methods to support heart segmentation is paramount. Most of the successful methods proposed to solve this problem are based on deep-learning solutions. Especially, encoder-decoder architectures, such as the U-Net [1], have demonstrated to be very effective architectures for medical image segmentation. In this paper, we propose to use long-range skip connections on the decoder-part to incorporate multi-context information onto the predicted segmentation masks and also to improve the generalization of the models. In addition, our method obtains smoother segmentations through the combination of feature maps from different stages onto the final prediction layer. We evaluate our approach in the ACDC [2] and LVSC [3] heart segmentation challenges. Experiments performed on both datasets demonstrate that our approach leads to an improvement on both the total Dice score and the Ejection Fraction Correlation, when combined with state-of-the-art encoder-decoder architectures.
A Weakness Relevance Evaluation Method Based on PageRank

In security incidents, vulnerability is always caused by several weaknesses. But in the vulnerability assessment, most of studies did not take weakness into account, resulting in inaccurate assessment of vulnerability. In the Common Weakness Enumeration(CWE), the weakness is hierarchical, and for the hierarchical correlation of weaknesses, a propagation algorithm of weakness importance is proposed in this paper. The algorithm calculates the severity scores of each weakness based on the page ranking algorithm PageRank, and compares it with the expert evaluation results. The experimental results show that the algorithm can effectively evaluate the weakness.
Research on acquisition and recognition of Naxi speaker's speech information

The human–machine dialogue system allows people and the machine to communicate basically. This technology is also attracting more and more attention. It is conducive to the realisation of resource sharing, inheritance, and protection of Dongba culture worldwide to analyse the Naxi speech recognition for the protection and inheritance needs of Naxi Dongba culture. The model was established for Naxi speaker recognition, and the method is studied with text-independent speaker speech voice recognition. The practical analysis method was used to compare the application effect of Gaussian mixture model (GMM) and hidden Markov model (HMM) in the processing of speaker recognition in Naxi speaker's pronunciation. It is very useful to provide a theoretical basis for the establishment of a Naxi language-based human–computer interaction system to find out the speaker's voice characteristics, personality differences, and different regions. This is a theoretically necessary analysis of ethnic minority languages and the protection of ethnic and folk cultures. It can also provide practical significance for carrying out ethnic cultural differences analysis.
WEB RESOURCE FOR STUDYING THE ARDUINO PLATFORM FOR SOFTWARE ENGINEERS IN HIGHER EDUCATION OF UKRAINE

The process of studying in higher education institutions is transformed from the system of knowledge transfer to the system of their independent acquisition by students, the search for solutions to problems through research activities, inherent models of studying the Master’s degree level. The development of future software engineers’professional competencies in the contextof a modern university has the following contradictions: insufficient consideration ofdemadsof the specialization according to which training ofmasters in specialties 121 "Computer Sciences" and 122 "Software Engineering" is conducted, in thecontent of academicdisciplines; implementationofonly a general development function in the process of teaching these disciplines is carried out, while improving the skills of designing and developing software products in the training of future IT specialists remains a secondary task; the motivation for the research and creative activity of students undergoing training within the framework of the Master's program is not clearly identified. The article is devoted to the design of an educational Web resource for the study of the platform for Arduino's amateur constructing as a computer tool for the professional competence formation of 121 "Computer Sciences", 122 "Software Engineering" specialties students. The modeling of this web-resource is focused on systematization of knowledge and development of practical design skills in the Arduino hardwarecomputing platform in the study of "Quality Management of Electronic Educational Resources" and "Information Technology Management" Master’s courses. In the process of course design, students determine the purpose and objectives of learning through electronic educational resources, analyze existing analogues of Arduino study sites, identify major requirements groups, design information architecture, present a chart of precedents for roles specific to an educational website. The application of this topic into the educational process of the Kherson State University is aimed at the development of professional competences for future programmers through the active introduction of new educational technologies, the use of opportunities for the information space, and the partnership between teachers and students.
Distributed Multi-View Subspace Clustering via Auto-Weighted Spectral Embedding

This work examines a distributed multi-view clustering problem, where the data associated with different views is stored across multiple edge devices. A sparse subspace clustering method is adopted using auto-weighted spectral embedding to ensure that the clustering solution is consistent among local edge devices. A master-slave architecture is adopted where clustering is first performed separately at the edge devices based on their local single-view datasets but are coordinated by a spectral regularizer computed at the central node. The optimization is performed using an alternating optimization approach, where the local self-representation and the global cluster indicator matrices are optimized in turn until convergence. The weighting of the regularizer is updated in each iteration of the process and adapts automatically to the fit of the spectral embedding at different locations. The proof of convergence is provided, followed by experimental results on two public datasets, namely, Extended Yale-B and IXMAS, which demonstrate the effectiveness of the proposed method.
GMSD-based Perceptually Motivated Non-local Means Filter for Image Denoising

Due to increasing proliferation of multimedia signals, specifically, image, video and their applications in our daily life, it is indispensable to have methods that can efficiently predict and correct visual quality of images with high measures of accuracy. Therefore, in this work a state-of-the-art (STOA) image quality assessment (IQA) metric, gradient magnitude similarity deviation (GMSD) has been incorporated in a STOA least-square-based non-local means (NLM) filtering framework for image denoising. The denoising process works by estimating and weighting neighbouring patches similar to the patch being denoised in terms of Euclidean distance (ED) and GMSD coefficient. The overall process is broken down into two steps; initially, local noise estimates for the underlying noisy patch are approximated and removed, then the refined patch is fed to the weighting process as the final step. Further, the proposed methodology also helps in mitigating the patch jittering blur effect (PJBE) and over smoothing of denoised images as observed with conventional NLM algorithm. Experimental evaluations based on visual-quality assessment and least-square-based metrics have shown that the proposed algorithm yields better denoised image estimates than the conventional NLM algorithm. Moreover, experiments conducted on a subjective database, i.e. CSIQ, have shown higher performance in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and GMSD coefficients. The resultant denoised images were in high correlation with the subjective judgements compared to the ones obtained with conventional NLM algorithm.
Graph Schema Storage in SQL Object-Relational Database and NoSQL Document-Oriented Database: A Comparative Study

Over the past years, the main objective of database management systems was to store data and give users the possibility to manipulate those data. SQL databases were sufficient in the first time, but with the evolution of computer science technologies, other factors start to appear; such as the volume of the stored data, the velocity of the transactions between the user and data, the possibility to store heterogeneous data (variety) and many other Big Data challenges. This paper presents the major differences between the SQL and NoSQL databases in term of variety, velocity and ease of programming. For SQL, we used the Oracle object-relational database and for NoSQL, we used MongoDB document-oriented database. The comparative study was applied to data modeled by graphs, where we consider a set of graphs and for each graph, we measure the time needed to insert the graph in the database, the size of the graph in the database and other factors. Measurements presents in this process are generally automatic and supported by a set of developed algorithms.
Applying GPS Time-Differenced Carrier Phase velocity estimation for Calibration of LDV/INS Integrated Navigation Systems in land vehicles

Abstract The laser Doppler velocimeter (LDV) is integrated with inertial navigation systems (INS) to form an integrated navigation system to acquire the land vehicle’s orientation, velocity, and position. As an integrated navigation system, the calibration items, including scale factor error of the LDV and the installation angle error between the INS and the LDV, should be well calibrated; otherwise the accuracy of navigation performance will be seriously affected. The paper represents a method about applying time-differenced carrier phase observations to deliver accurate reference velocity for calibration instead of differenced GPS (DGPS) or precise point position (PPP). The differential carrier phase between successive GPS epochs eliminates the effort of ambiguity and it is able to achieve millimetre-level relative velocity accuracy within minutes. In order to make up for the insufficient of calibration time, a iteration method is implemented for fast convergence. The navigation experiment shows that the calibrated LDV/INS navigation system can reach 34.7m/25.5km in horizon and 4.8m/25.5km in vertical.
Graphing Within-Subjects Effects

Most graphs in psychology articles fail to show distributional information other than the mean.Although many articles have suggested solutions to this problem for between-subjects designs, there has been relatively little discussion on how to show distributional information for within-subjects designs.Graphs of within-subjects data should be constructed so that betweensubjects variation does not appear as uncontrolled error.This article presents a variety of methods for graphing data from within-subjects designs including jittered dot plots of difference scores, sumdifference plots, box plots of difference scores, and plots of trend components.These graph types show descriptive distributional information while controlling for between-subjects variation.
Transfer learning for a Vietnamese dialogue system

Training data plays an essential role in the neural network-based dialogue system. Although most of the recent works researching about dialogue system base on a deep neural network show a significant improvement to traditional methods, they required many labeled data for both training and evaluating. In this paper, we present the result of using transfer learning in a Vietnamese dialogue system for the classification task, and we further show benefits of transfer learning on named entity recognition task by experiment results on public VLSP dataset and meets the performance of training from scratch with only 10% of training data.
Time Frame Detection Based on Online News Documents Using Vector Space Model

This study aims at finding an approach to identify news case duration or time frame as well as to identify the ways to find a time frame through the news document. News document used in this research refers to the previous research done by Rahim in 2018. The research uses three popular news website of Indonesia in 2018 which are tribunnews.com, detik.com and liputan6.com. However, in this research, the news website also uses as an object of analysis. The result conducted that the process to find a news time frame should define the cosine similarity minimum (threshold) between news and keyword (query). The researcher also finds that the workflow to determine the time frame uses news document based on the vector space model and combined with measurement technique if-idf and cosine similarity.
The Size and Taxation of Cryptocurrency: An Assessment for Emerging Economies

Cryptocurrency started to be used in many countries as soon as its system became widespread. Countries have begun to create their own cryptocurrencies as well. Along with this increase in the dimensions of cryptocurrencies across the world, some regulatory needs have arisen. Many countries have been regulating their legal and economic infrastructures to cover cryptocurrencies, and many others have been on a quest to do so. While developed countries have been taking certain steps concerning cryptocurrencies, developing countries do not have a well-established theoretical framework for cryptocurrencies. Furthermore, inquiry and debate are still ongoing regarding the legal dimensions of these circulating currencies as well as how they are to be handled and taxed within the economy. This chapter discussed the dimensions of cryptocurrencies in developing countries and shows the debates on taxation of these currencies. Since countries’ tax systems and taxable incomes may differ, the income category under which cryptocurrencies and income arising from such currencies will be treated and how they will be taxed are still being debated. In this regard, our aim was to determine the current situation in certain developing countries including Turkey and to put forward some policy recommendations regarding taxation.
Breast Cancer Detection Based on Medio-Lateral ObliqueView and Cranio-Caudal View Mammograms: An Overview

Breast cancer is a leading cause of death among women. At the early stage, no obvious symptoms were identified in breast cancer patients. Accurate detection of breast cancer at the earliest stage is very much essential to reduce mortality. Mammography has been used as a gold standard for over 40 years in diagnosing breast diseases. Interpretation of suspicious regions in screening mammograms is a subjective measure which depends on the image quality and the radiologist's experience. Computer Aided Detection (CAD) systems are developed as an alternative to assist radiologist and clinicians in reliable and accurate diagnosis. Cranio-Caudal (CC) view and Medio-Lateral Oblique (MLO) view are commonly used for breast cancer detection and diagnosis.