Reviewerly/exHarmony huggingface dataset:
A High-Speed NTT-Based Polynomial Multiplication Accelerator with Vector Extension of RISC-V for Saber Algorithm

Saber is a module-learning with rounding-based post-quantum cryptography (PQC) scheme for key encapsulation mechanism (KEM). It is characterized by the use of power-of-two moduli, which makes all modulus reductions free in hardware. However, such a decision prevents the direct implementation of the asymptotically fastest number theoretic transform (NTT) for the time-consuming polynomial multiplication in Saber. To efficiently multiply polynomials, researches have been done using a schoolbook or Toom-Cook or Karatsuba algorithm. Though these approaches result in decent operating speed at moderate area cost, they are disadvantageous when considering expanding the system to support multiple PQC protocols. To enable NTT for Saber, we choose an appropriate prime and use the sign-magnitude format for computation. A concise and efficient vectorized NTT algorithm has been proposed, based on which we design a configurable vector NTT unit to perform NTT and other arithmetic operations. The accelerator is dedicatedly pipelined to achieve high speed and is driven by custom vector instruction extension of RISC-V. We implement the proposed architecture with vector lanes of 32 and 16 on Xilinx UltraScale+ ZCU111. Results show that our design can achieve up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5\mathrm{x}$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$3\mathrm{x}$</tex> improvement in computation time and area-time-product (ATP) respectively for degree-256 polynomials multiplication, compared to the state-of-the-art Saber polynomial multiplier counterparts.
Mapping the Scientific Landscape of Metaverse Using VOSviewer and Bibliometrix

The concept of the Metaverse has gained increasing attention as advances in virtual and augmented reality (AR) technologies have enabled the creation of immersive and interactive virtual environments. However, most of these studies remain independent and only a few studies attempted to investigate their relationships. To analyze key trends in Metaverse research and conduct a thorough bibliometric analysis, we used VOSviewer and Bibliometrix, R-tool package on the Scopus database. Our co-occurrence analysis revealed that the hot topics are related to virtual reality, augmented reality, the Internet of Things, and blockchain, and there are potential areas for future research, such as privacy, security, and education in the Metaverse. In addition, our analysis identified the most active countries and institutions in the field, the top subject areas, as well as potential gaps in the literature that could be explored in future research. This study provides valuable insights into Metaverse research and can help guide future research in this field.
Medical X-Rays Categorization and Irregularity Recognition of Bone and Diagnosis of Bone disorders Based on Xception Model

This study adopted a hybrid classification method for determining bone kinds and identifying bone deformities. The proposed methodology perceives the shoulder bones, forearm, humerus, elbow, hand, finger, leg, wrist,and knee. For this study, the Xception pre-trained model is used. The single-view and multi-view techniques are the two techniques used during the testing phase. The improved images send to first stage, which classifies them into the one of nine categories: shoulder, humerus, forearm, elbow, wrist, hand, finger, leg,and knee. Following categorization, the bones are input to second stage, that determines whether bones are normal or pathological. The MURA dataset is used for the experiments. Furthermore, the SVM layer or the classifier replaces the final layer of the used model. The results show that the SVM layer is superior. The study suggests the detection of normal and abnormal bone xrays. Determining whether the bone is normal or abnormal. If abnormal, include symptoms, diagnosis, and home remedies.
Autoencoder-Based Anomaly Detection for Time Series Data in Complex Systems

In this paper, we present a new anomaly detection method for time-series data in complex systems such as power grid and cellular networks. The proposed anomaly detection method is developed following unsupervised learning, where an AutoEncoder based on Gated Recurrent Units (GRU-AE) is trained to reconstruct a time-series of interest, and anomalies are detected via detecting exceptionally large reconstruction errors. A multi-timestamp stacking method is adopted to reduce the number of time steps in the GRU-AE to facilitate the training of the model and a new training scheme with random shuffling is proposed to prevent overfitting. The proposed GRU-AE based detector is applied in multiple time scales to detect different types of anomalies. Numerical results obtained via time-series data from real cellular network demonstrate the performance of the proposed method.
Design of Matrix Filter Using Discrete Cosine Transform and Path Graph

In this paper, the design of matrix filter using discrete cosine transform (DCT) and path graph is presented. First, the DCT is used to design matrix filter without considering the prior information of transform basis of DCT. Then, the graph spectral theory is employed to show that the DCT is the graph Fourier transform (GFT) of path graph such that graph filter method can be used to design and implement the matrix filter. Next, the weighted least squares (WLS) method is applied to design the graph filters which can be used to implement the matrix filter with low computational complexity because graph shift matrix is very sparse. Finally, the design examples of matrix Butterworth filter and matrix Riesz fractional order differentiator (FOD) are illustrated to demonstrate the effectiveness of the proposed matrix filter design method.
An Energy Efficient Precision Scalable Computation Array for Neural Radiance Field Accelerator

Neural Radiance Field (NeRF), a recent advance in neural rendering, demonstrates impressive results for photo-realistic novel view synthesis. However, it faces challenges for deployment in practical rendering applications due to the large amount of multiply-accumulate (MAC) operations. For hardware accelerator design, precision-scalable MAC array, which can support computations with various precision can be used to optimize the power consumption of NeRF rendering accelerators. Recently, a variety of precision-scalable MAC arrays have been proposed to reduce the computational complexity of Convolutional Neural Networks (CNN). However, most of them require a lot of control logic to support different levels of precision. This paper proposes a precision-scalable MAC array with serial mode, which can support the multiplication with different precision of weight in multiple cycles with little overhead. Implementation results show that the energy efficiency of the proposed MAC array is about 14.54 TOPS/W and 4.83 TOPS/W for 4-bit and 8-bit computation modes, superior to other existing precision-scalable solutions.
Thermometer Code of Log Mel-Frequency Spectral Coefficient for BNN-based Keyword Spotting System

For keyword spotting (KWS) systems that usually work in mobile devices, a low-complexity design is essential for long stand-by time. Audio feature extraction and classifier modeling are the two main components of KWS systems. Log Mel-Frequency Spectral Coefficient (MFSC) is common for audio feature extraction due to its low complexity and good performance. Binary neural network (BNN) classifier, which owns binary weights and activations and performs convolution with XNOR, is applicable to low-complexity KWS applications. However, audio features are usually quantized with multiple-bit binary code to maintain high classification accuracy, which requires addition (ADD) operations in the first convolutional layer of the BNN model. Therefore, both XNOR and ADD units are needed in the BNN accelerator. To further reduce the complexity of KWS systems, we propose a new feature extraction method: Thermometer Codes of MFSC (MFSC-TC). Without LOG and DELTA operations, it is simpler than other MFSC-based methods. More importantly, convolution of all layers can be done by XNOR units due to the feature of thermometer code. The experiments with the Google Speech Commands dataset validate that the MFSC-TC-based BNN models outperform the models with more layers using other feature extraction methods.
A Power Effective DLA for PBs in Opto-Electrical Neural Network Architecture

Deep neural networks (DNN) have been widely used in many real-time artificial intelligent (AI) applications because of effective hardware accelerators. However, most present designs either suffer from high area cost or low hardware usage. This paper presents a design of a digital logic accelerator (DLA) for use in PBs (processing block) of an opto-electrical neural network (OENN). The proposed DLA uses processing elements that detects underflow and overflow. Besides, it also increased the processing time to resolve the timing problems. The details of the design together with post-layout simulations are presented in this paper. The DLA is implemented using a typical 40-nm CMOS process. It showed a performance result of 51.2 GOPS and the power consumption is 91.3 mW at 125 MHz.
PHILOSOPHICAL PRINCIPLES OF THE FORMATION OF A CULTURALLY COMPETENT PERSONALITY

The article is devoted to the conceptualization of philosophical principles of the formation of a culturally competent personality. It has been established that these principles are implemented in anthropological, existential, epistemological and axiological planes, which determines the sustainable development of personality. The anthropological context is associated with the understanding of man as a holistic developing system. A culturally competent personality is about a person in culture and for culture. The existential and epistemological contexts outline the place and significance of the Other in the personal formation of I, in shaping its life position in terms of cognitive and behavioral systems of interaction. The axiological context raises the question of the moral and value attitude of I to itself, the world and others like itself. Culture is defined as an ontological condition for the realization of a culturally competent personality. Emphasis is placed on the human-creating function of culture, its ability to regulate the space of all-unity. The competence component of a person’s personal development is realized in the fact that by learning the cultural codes of interpersonal interaction, constructing himself and the surrounding space, a person is aware of cultural differentiation and organizes his activities according to plurality of human needs, demands and objectives. The formation of a culturally competent personality takes into account the principle of dialogue. As shown in the article, the dialogical interaction between I and the Other is a factor in the integrity of a person's personal development. Dialogue encodes the space «between» I and the Other; fixes the ontological and epistemological openness of I, its ambivalence towards the Other.
Policies and strategies for the development of artificial intelligence in the countries of the world: quo vadis? (part 1)

The organizational and economic and legal aspects of the development and implementation of policies and strategies for the development of artificial intelligence (AI) in the leading countries of the world have been studied. All major economies (more than 60 countries) have developed national policies (strategies) for the development of AI. The following countries are considered advanced in the implementation of national AI strategies: USA, China, Canada, UK, Japan, UAE, France, Germany, South Korea, India and most countries of the European Union (EU). The structure of AI development strategies, priorities, funding models were considered, the main principles of the development and use of AI technologies, priority directions, goals and objectives of the use of AI were analyzed. The problems associated with the use of AI are highlighted: these are issues of data for processing AI, control over the use of AI, tracking AI decisions and responsibility for their adoption, control over confidentiality, ensuring the protection of personal data. Comparing the Ukrainian concept of AI development with the strategies of developed countries, we can conclude that it will not contribute to the effective development of AI, since investments in AI technologies differ hundreds of times, incentive tools and specific actions for the development of AI are not provided. The Institute of Artificial Intelligence Problems of the Ministry of Education and Science of Ukraine and the National Academy of Sciences of Ukraine have developed a project of the Strategy for the Development of Artificial Intelligence in Ukraine for 2022–2030. The Cabinet of Ministers of Ukraine needs to take measures to adopt the Strategy for the Development of Artificial Intelligence in Ukraine. It is concluded that there is a process of formation of two large spaces in the field of AI technologies in the international arena: the first unites the OECD countries with the unconditional financial, technological and value-normative dominance of the USA and the EU. The second is formed around China, in whose orbit countries fall, for which cooperation with the West is complicated due to a wide range of international conflicts (including Russia). Countries that are unable to resist the technological hegemony of China and the United States are faced with the dilemma of choosing between two large technological spaces.
Social and Humanitarian Support for the Professional Development of an Employee of the Internal Affairs Bodies

The object of research in the article is the process of professional development of an employee of the internal affairs bodies in the system of higher professional education. The subject of the study is the social and humanitarian support of the process of professional development of an employee of the internal affairs bodies. Social and humanitarian support is understood as the process of creating psychological and pedagogical conditions for optimizing the interaction of psychological and pedagogical support for self-actualization of the student's personality as a necessary condition for an in-depth understanding of modern problems of social and humanitarian knowledge and the existential orientation of the teacher's personality. The axiological component of this interaction is the creation of specific situations in the educational activities of the future employee of the internal affairs bodies, which requires him to psychologically move away from the state of learned helplessness, imitation of the understanding of the lack of subjectivity. The novelty of the research lies in the combination of problem-based learning and the student's psychological preparedness for action in extreme situations. This combination is achieved by combining modern psychological knowledge and practices with the social construction of the reality of the educational environment. The interaction of educational psychologists, professional psychologists, consultants and teachers of social and humanitarian disciplines is based on a departure from the existing reality that deforms the personality of the educational environment of higher education institutions of the power profile. The main method here is a philosophical dialogue between the student, the teacher-psychologist and the teacher of social and humanitarian disciplines. The main goal of this dialogue is the creation of groups-meetings aimed at creating a facilitative (helping) environment for self-actualization of the student's self, as a necessary psychological and pedagogical component of his professional maturity.
Subject-generated environments as the basis for the formation of a personal-developing digital educational environment

The article discusses the features of the formation of a personality-developing educational environment in the context of global digitalization. The concept of the digital educational environment (hereinafter referred to as the DSP) is revealed in the context of its personality–developing nature, the basic scheme of the functioning of the subject-generated environments in the digital educational environment is substantiated, the factors determining the personality-developing potential of the digital educational environment are highlighted, as well as the features of the teacher's activity on the formation of the developing environment by digital means in the refraction of traditions and innovations. The traditional understanding of the educational environment is undergoing a significant transformation due to digitalization. The study showed that the formation of a developing digital educational environment should be carried out with a mandatory emphasis on the subject-generated characteristics of the environment, resolving the contradiction between the multiplicity and openness of potentially developing resources, on the one hand, and the uncertainty, non-obviousness of the results of interaction of subjects, on the other hand.For the teacher, the digital educational environment acts as a discrete education, whereas for the student it is a single structured digital space (a single environment). The activity of a teacher on the formation of a developing environment by digital means, giving it developing functions are associated with pedagogical competence to fill the environment with appropriate situational and event content, contributing to the generation of environments in education by students themselves and filling them with authentic meaning. The specified competencies of the teacher depend on the completeness of the theoretical understanding of the peculiarities of the emergence of subject-generated environments in the digital educational environment. The scientific novelty lies in the fact that for the first time a personality-developing digital educational environment is considered through the concept of subject-generated environments, which brings the student's personality to the center of relevant educational projects. The development of the student's personality in the DSP is a multifactorial and open subject-dependent process with a variability of resources for self-change, the research attitude to which corresponds to the post-non-classical stage of the development of science.
Topological properties of multi-valued functions

In this paper, we generalize properties for single-valued functions in homotopy theory to multi-valued functions. We present new definitions for multi-valued functions such as locally m-pathwise connected, m-simply connected, m-covering space, and regular m-covering space. We apply some topological properties from algebraic topology to the multi-valued functions.
Explaining a Machine-Learning Lane Change Model With Maximum Entropy Shapley Values

Artificial intelligence (AI) techniques have been widely implemented in the domain of autonomous vehicles (AVs). However, existing AI techniques, such as deep learning and ensemble learning, have been criticized for their black-box nature. Explainable AI is an effective methodology to understand the black box and build public trust in AVs. In this article, a maximum entropy-based Shapley Additive exPlanation (SHAP) is proposed for explaining lane change (LC) decision. Specifically, we first build an LC decision model with high accuracy using eXtreme Gradient Boosting. Then, to explain the model, a modified SHAP method is proposed by introducing a maximum entropy base value. The core of this method is to determine the base value of the LC decision model using the maximum entropy principle, which provides an explanation more consistent with the human intuition. This is because it brings two properties: 1) maximum entropy has a clear physical meaning that quantifies a decision from chaos to certainty, and 2) the sum of the explanations is always isotropic and positive. Furthermore, we develop exhaustive statistical analysis and visualization to present intuitive explanations of the LC decision model. Based on the explanation results, we attribute the causes of predictions with wrong results to model defects or sample sparsity, which provides guidance to users for model optimization.
A Generalized Freeman Chain Code for Offline Arabic Character Recognition

This work goes through several stages: First, the Arabic character is preprocessed and thinned. And a preliminary classification of the character is done according to the number of its loops and the nature, number, and position of its complementary parts (Dots, Hamza, etc.). Then, the Freeman chain code of 8 directions is generated according to a new innovative algorithm that can represent the internal parts of the Arabic character if they exist and without redundant information. After that, a normalization process based on the generalization of the code of 8 directions to a code of 24 directions is done. Finally, and for comparison, the two algorithms, traditional and proposed, are implemented separately to recognize Arabic characters. After 1023 tests on a set of 300 printed characters, isolated and distributed over 10 fonts, the recognition rate obtained was always higher than that obtained with the traditional algorithm, from 90.7 to 100%. Also, the number of font combinations giving good results almost doubled, and the execution time when normalizing the generated code was less.
A Retrospective on OOADARE as an Automated Object-Based Approach for Requirements Engineering

In agile methods, the communication between customer and development team is ensured by requirements, most often presented in an unstructured textual format, which frequently involves redundancy or non-precision information. This leads, in practice, to poor system quality, especially if we use classical approaches such as scenario-based approach. Yet, OOADARE approach is introduced in this way, using semi-structured text models in the form of user stories and constraint story cards (CSC), to automate the object-oriented transformation of requirements into a class diagram. However, the approach failed to capture all the elements needed to construct a correct and complete class diagram. This paper, at that point, proposes templates in natural language, which are part of the same perspectives of CSCs proposed by OOADARE, namely semi-structured text, to fill these gaps and ensure the completeness of the class diagrams thus generated.