Reviewerly/exHarmony huggingface dataset:
Time-interleaved quantum random number generation within a coherent classical communication channel

We propose and analyse a suitable setup for a time-interleaved vacuum-based quantum random number generator (QRNG) within a classical communication channel, which removes the need for a dedicated generation device. The experimental setup was characterized to assess the conditions where quantum ﬂuctuations are dominant, and support for generation rates up to 1.3 Gbps is observed. Finally, the random bit stream is subjected to the NIST randomness test suite, consistently passing all evaluations.
Anomaly Detection for Cyber-Physical Systems Using Transformers

Abstract Safety and reliability are two critical factors of modern Cyber-Physical Systems (CPS). However, the increasing structural and behavioral complexity of modern automation systems significantly increases the possibility of system errors and failures, which can easily lead to economic loss or even hazardous events. Anomaly Detection (AD) techniques provide a potential solution to this problem, and conventional methods, e.g., Autoregressive Integrated Moving Average model (ARIMA), are no longer the best choice for anomaly detection for modern complex CPS. Recently, Deep Learning (DL) and Machine Learning (ML) anomaly detection methods became more popular, and numerous practical applications have been presented in many industrial scenarios. Most of the modern DL-based anomaly detection methods use the prediction approach and LSTM architecture. The Transformer is a new neural network architecture that outperforms LSTM in natural language processing. In this paper, we show that the Transformer-based deep learning model, which has received much attention recently, can be applied to the anomaly detection of industrial automation systems. Specifically, we collect time-series data from a system of two industrial robots using a Simulink model. Then, we feed these data into our Transformer-based model and train it to be a time-series data predictor. The paper presents the experimental results that show the comparison of precision and speed of a Long-Short Time Memory (LSTM) predictor and our Transformer-based predictor.
Modeling Satellites Transactions as Space Digital Tokens (SDT): A Novel Blockchain-Based Approach

Blockchain technology can play a novel role in the space industry and satellite communications. This disruptive technology can be used for building decentralized and secure techniques for processing and manipulating satellite transactions using a new concept called digital space tokens (SDT). Tokenizing space transactions within a satellite swarm in the form of SDT will reflect plenty of various blockchain-based applications in the space mining industry, such as designing new blockchain protocols for tracking, managing, and securing all space transactions and communications in a transparent, verifiable, and immutable approach. In this paper, a novel blockchain-based model is proposed for modeling satellite transactions as Space Digital Tokens (SDT). Ethereum blockchain was used in the implementation of this model. Also, its performance was evaluated using transaction time, transaction latency, read latency, and Ethereum GAZ usage.
THE “PRECEPTS” OF ORPHEUS: “THE ARCHAIC” AS A CREATIVE PRINCIPLE OF SYMBOLIST ARTISTS

The article undertakes a multidimensional philosophical and aesthetic analysis of symbolist artists' perception of Antiquity through the prism of visual myth-making, archaic in its inner nature. The legendary time of the poetic covenants of Orpheus before the ancient Greek classics, as well as the period of Homer's archaicism, are seen as metaphorical coordinates to separate true historical Antiquity from those images in which the modern age has found its illusory Hellas. Using examples from Western European and Russian art, succinct generalisations and, for the first time, introducing documentary material, the author concludes that the question of the “archaic” or creatively primordial (partly chaotic) beginning of the Symbolist art is the question of their relationship not so much with history as with the poetry of the ghostly past, which the modernist artists sought to awaken, to revive, hoping to find in it the true life features of their imagined artistic ideals.
Utilizing Federalism, Science and Technology for Economically Viable United States of Africa

Colonialism brought about an unviable federal system of government where it is practiced in many African states, which impacts directly on socio-economic development of the continent. African states are a byproduct of the Industrial Revolution in Europe; thus, its political formations were fashioned according to the interest of the metropoles as well as the neo-colonial elites at the expense of the poor local indigenes. As a result of these, the continent is characterized by policy failures; authoritarianism; inappropriate state structures and other vices. Consequently, this paper intends to provide answers to: why has development challenges persistent in Africa when the continent prides itself with differently endowed resources and series of development strategies adopted; how can Africa be salvaged from this development quandary and policy failures; and what roles can Science, Technology and Innovation (STI) play, in this circumstance, to excuse the continent from these doldrums? The paper embraces qualitative and historical research methodology and adopts content analysis of secondary sources of data. This method involves the in-depth review of literature and scientific papers on the subject matter, and making judgemental inference therefrom. Evidence from the review indicates that real federation and sustainable economic development for global competitiveness can only be achieved through sincerity, trust and political dedication on the part of the leaders, people and respective states. It also requires all stakeholders’ readiness to redirect all the regions’ resources leveraging on effective channelization of indigenous knowledge (IK) towards the development of STI driven by effective policy formulation and implementation.
Comparative Analysis of Traditional and Optimization Algorithms for Feature Selection

Machine learning enables the automation of the system to generate results without direct assistance from the environment once the machine is trained for all possible scenarios. This is achieved by a series of processes such as collecting relevant data in raw format, exploratory data analysis, selection and implementation of required models, evaluation of those models, and so forth. The initial stage of the entire pipeline involves the necessary task of feature selection. The feature selection process includes extracting more informative features from the pool of input attributes to enhance the predictions made by machine learning models. The proposed approach focuses on the traditional feature selection algorithms and bio-inspired modified Ant Colony Optimization (ACO) algorithm to remove redundant and irrelevant features. In addition, the proposed methodology provides a comparative analysis of their performances. The results show that the modified ACO computed fewer error percentages in the Linear Regression Model of the dataset. In contrast, the traditional methods used outperformed the modified ACO in the SVR model.
Interaction Effects of Internet and ODA on Economic Growth

Based on a panel of 135 countries from 1994 to 2015, this paper shows the interaction of ODA amounts and the internet user shares in the ODA recipient countries has a positive and statistically significant coefficient for both GDP per capita and its growth rates. The main results are robust even when I control for region-specific time trends or use instrumental variables. The results also do not change when I control for the interaction term between mobile phone user shares and ODA amounts, between landline phone user shares and ODA amounts, or between the corruption perceptions index and ODA amounts. These results suggest that the internet has a unique impact on the effectiveness of ODA, distinct from the impact of ICT or institution in general.
Background-Aware Domain Adaptation for Plant Counting

Deep learning-based object counting models have recently been considered preferable choices for plant counting. However, the performance of these data-driven methods would probably deteriorate when a discrepancy exists between the training and testing data. Such a discrepancy is also known as the domain gap. One way to mitigate the performance drop is to use unlabeled data sampled from the testing environment to correct the model behavior. This problem setting is also called unsupervised domain adaptation (UDA). Despite UDA has been a long-standing topic in machine learning society, UDA methods are less studied for plant counting. In this paper, we first evaluate some frequently-used UDA methods on the plant counting task, including feature-level and image-level methods. By analyzing the failure patterns of these methods, we propose a novel background-aware domain adaptation (BADA) module to address the drawbacks. We show that BADA can easily fit into object counting models to improve the cross-domain plant counting performance, especially on background areas. Benefiting from learning where to count, background counting errors are reduced. We also show that BADA can work with adversarial training strategies to further enhance the robustness of counting models against the domain gap. We evaluated our method on 7 different domain adaptation settings, including different camera views, cultivars, locations, and image acquisition devices. Results demonstrate that our method achieved the lowest Mean Absolute Error on 6 out of the 7 settings. The usefulness of BADA is also supported by controlled ablation studies and visualizations.
A Data Hiding Method Based on Partition Variable Block Size with Exclusive-or Operation on Binary Image

In this paper, we propose a high capacity data hiding method applying in binary images. Since a binary image has only two colors, black or white, it is hard to hide data imperceptible. The capacities and imperception are always in a trade-off problem. Before embedding we shuffle the secret data by a pseudo-random number generator to keep more secure. We divide the host image into several non-overlapping (2n+1) by (2n+1) sub-blocks in an M by N host image as many as possible, where n can equal 1, 2, 3 , …, or min(M,N). Then we partition each sub-block into four overlapping (n+1) by (n+1) sub-blocks. We skip the all blacks or all whites in each (2n+1) by (2n+1) sub-blocks. We consider all four (n+1) by (n+1) sub-blocks to check the XOR between the non overlapping parts and center pixel of the (2n+1) by (2n+1) sub-block, it embed n 2 bits in each (n+1) by (n+1) sub-block, totally are 4*n 2 . The entire host image can be embedded 4×n 2×M/(2n+1)×N/(2n+1) bits. The extraction way is simply to test the XOR between center pixel with their non-overlapping part of each sub-block. All embedding bits are collected and shuffled back to the original order. The adaptive means the partitioning sub-block may affect the capacities and imperception that we want to select. The experimental results show that the method provides the large embedding capacity and keeps imperceptible and reveal the host image lossless.
Parasite: Mitigating Physical Side-Channel Attacks Against Neural Networks

Neural Networks (NNs) are now the target of various side-channel attacks whose aim is to recover the model’s parameters and/or architecture. We focus our work on EM side-channel attacks for parameter extraction. We propose a novel approach to countering such side-channel attacks, based on the method introduced by Chabanne et al. in 2021, where parasitic convolutional models are dynamically applied to the input of the victim model. We validate this new idea in the side-channel field by simulation.
Introduction

Chapter Contents: Chapter 2 — Fundamentals of voice biometrics: classical and machine learning approaches Chapter 3 — Voice biometrics: attackers perspective Chapter 4 — Voice biometrics: privacy in paralinguistic and extralinguistic tasks for health applications Chapter 5 — Voice privacy in biometrics: speaker de-identification Chapter 6 — Performance evaluation of voice biometrics solutions Chapter 7 — Voice biometrics: how the technology is standardized Chapter 8 — Voice biometrics: perspective from the industry Chapter 9 — Joining forces of voice and facial biometrics: a case study in the scope of NIST SRE19 Chapter 10 — Voice biometrics: future trends and challenges ahead
Cognitive Computing for Decision-Making and Choice Modeling of the Interactive Relationship Between Local Higher Education Development and Regional Economics

Higher education plays an important role in the construction of a new growth engine of China’s economy under the new normal state. If innovation is likened to a new engine of economic growth in China, higher education is equivalent to energy and fuel provided by the engine energy for intellectual support and knowledge support. Based on Denison factor analysis, the panel data of 30 provinces in the east, middle and west of China from 1997 to 2014 was used to conduct the Cognitive computing for decision-making and choice about the impact of higher education level of human resources on China’s economic growth. The results show that the output elasticity of material capital gradually decreases from west to east, while the output effect of higher education increases gradually from west to east.
Fusion of sparse LIDAR and uncertainty-based 3D vision for accurate depth estimation with Bayesian Kriging

Accurate 3D perception is vital for the localization of mobile robots and autonomous vehicles. Sparse 3D LIDAR scanners and 3D vision sensors (stereo and RGB-D cameras) are commonly used environment perception sensors. The former could provide accurate but sparse-range data, while the latter is dense but full of uncertainty. Since the acquired depth map by each sensor alone could not accurately perceive complex surrounding environment, an innovative probabilistic fusion model based on Bayesian Kriging (BK) is proposed for dense high-precision depth map estimation, to fully utilize the complementary characteristics of the two distinct sensors. Combing the geometric piece-wise planar hypotheses of the scene, the noisy disparity map from the 3D vision sensor is segmented into multiple small planes. In each segmented plane, the BK-based fusion estimator is founded to predict depth for each pixel with sparse scattered LIDAR points. In particular, we focus on analyzing the spatial variation prior and correlation between these two types of data, based on regionalized variable theory. Experiments on various scenes of KITTI stereo datasets are carried out to demonstrate the effectiveness of our algorithm, and validate the superiority compared with existing fusion methods.
Ring structures of the Norilsk metallogenic district in neural network cluster analysis of geophysical data

An integrated geophysical and mathematical model of the Norilsk metallogenic region has been built according to the authors’ method of automatic express interpretation of areal geophysical data. It consists of fifteen levels of hierarchy. The model is based on the use of the mathematical apparatus of fuzzy logic — artificial neural networks without a teacher using the method of self-organizing Kohonen maps. Formation of cluster groups is justified. Clusters characterize to the greatest extent possible connections between multidimensional geophysical data. The presence of relationships between them is analyzed by identifying correlation dependences. Analysis of various geophysical transformants using self-organizing Kohonen maps is carried out. A number of input indicators-representatives of their groups are determined, on the basis of which a geophysical-mathematical model is built. A model in the form of a two-dimensional map of clusters using fuzzy logic tools. Terminological sets were formed for each group of clusters. The form of membership functions of previously unknown geological objects is given according to new interpreted data. The calculated clusters according to the results of the study reflect the northwestern fragment of the basaltic magmatism fields of the trap formation, within which two ring structures have been distinguished. In the western part of the study area, the model ring structure reflects the Bolgokhtokhsky stock of granodiorites. The second, the Pyasinskaya ring structure, previously unknown, indicates the presence of a dome-shaped object at a great depth — a possible source of root zones of intrusions of the Norilsk complex. The conducted experimental study confirmed the adequacy of the constructed model and the effectiveness of its use for the purpose of express analysis of geophysical data and decision-making in geological prospecting tasks.
The Difficulties and Optimization Mechanism of Big Data Application in SMEs

In the era of digital economy, big data has become a strategic resource, and big data application capabilities have also become a strategic capability, which can effectively assist SMEs in precision marketing, enhancing corporate competitiveness, and achieving quality and efficiency improvements.However, the application of big data for SMEs is facing a series of difficulties such as insufficient understanding and lack of big data thinking, not enough investment and incomplete big data infrastructure, irregular management and low quality of basic big data, incomplete introduction and lack of big data talents.For this reason, it is necessary to raise awareness and establish big data thinking, increase investment and improve big data infrastructure, standardize management and improve the quality of basic big data, and pay equal attention to talent introduction and education, and strengthen the construction of big data talents.
In-and-Out: a data augmentation technique for computer vision tasks

This study focuses on the over-fitting problem in the training process of the deep convolutional neural network model and the problem of poor robustness when the model is applied in an occlusion environment. We propose a unique data augmentation method, In-and-Out. First, the information variance is enhanced through dynamic local operation while maintaining the overall geometric structure of the training image; compared with the global data augmentation method, our method effectively alleviates the overfitting problem of model training and significantly improves the generalization ability of the model. Then through the dynamic information removal operation, the image is hidden according to the dynamic patch generated by multiple parameters. Compared with other information removal methods, our method can better simulate the real-world occlusion environment, thus improving the robustness of the model in various occlusion scenes. This method is simple and easy to implement and can be integrated with most CNN-based computer vision tasks. Our extensive experiments show that our method surpasses previous methods on the Canadian Institute for Advanced Research dataset for image classification, the PASCAL Visual Object Classes dataset for object detection, and the Cityscapes dataset for semantic segmentation. In addition, our robustness experiments show that our method has good robustness to occlusion in various scenes.
METHODOLOGICAL SUPPORT OF TEACHERS OF PRESCHOOL EDUCATIONAL ORGANIZATIONS IN MODERN CONDITIONS

The article considers the development of human resources capacity in the management of activities of a preschool educational organization. Based on the analysis of the current situation of the educational organizations, the need for a qualitative change in the system of methodological work with the teaching staff, aimed at filling the deficit of professional knowledge and actions, is determined. Various forms of methodological support of teachers are analyzed, their essential characteristics are highlighted, the main purpose of support is defined – making decisions and performing actions that help a supported teacher to develop and progress professionally. The methodological model of teachers’ support of preschool educational institutions assumes a procedural and accompanying form, i. e. organizing the activities of the entire staff of the preschool institution with attention to each participant in the process and the nature of relations with other teachers.
The Matching Game: Experimenting with Librarian Mediated Patron-Driven Acquisition

Are you thinking of piloting Patron-Driven Acquisition in your library? In this session, we share our experiences with PDA. We will lead a discussion on the implementation decisions for selection and workflow and the differences and similarities between patron-selected vs. librarian-selected titles; and usage of electronic vs. print titles.
International Activity Intensification as a Means of Development of an Innovatively Active University

The article is aimed at analyzing various approaches to understanding the phenomenon of "internationalization of higher education institutions," and identifying tools for effective international activities of innovatively active higher education institutions (hereinafter referred to as IA HEIs) in the context of globalization, transformation and digitalization of society. Having studied substantive aspects and development of the "internationalization of higher education" concept, the authors found out that currently internationalization at the national, sectoral and institutional levels is defined as the process of implementing international, intercultural or global dimension in the purpose and/or function of and support for higher education. At the same time, the development of internalization processes in higher education takes place in the context of large-scale globalization challenges, thus requiring support for enhancing the competitiveness of national education systems in the global market of educational services. It is determined that higher education institutions are the main driving force in developing structures and strategies of international cooperation and play a systemic and strategic role in ensuring and developing internationalization processes. This role requires active development of modern forms, methods, technologies and innovations in higher education, for example, by increasing the effectiveness of the international activity of HEIs. Having analyzed numerous scientific information sources and modern approaches to understanding the content of the "international activity of a higher education institution" concept, as well as to considering its intrinsic features and key approaches to its implementation, the authors define international activity of higher education institutions as a process of organizing international cooperation of a university with foreign institutional, national and international entities, which has a systemic character and is aimed at effectively implementing its mission, improving its image and competitiveness in the national and international markets of educational services, ensuring institutional development and implementation of foreign policy objectives. The key components of the international activity of HEIs are proved to be interrelated. The authors consider the intrinsic features of the international activity of an innovatively active university, and define its purpose and the main tasks of its realization. A set of international activity types characteristic for an innovatively active university is developed. The authors suggest that these activity types be considered in terms of their educational, scientific and organizational components. The formation of a system of indicators for evaluating and monitoring the effectiveness of international activities of a university in the world educational and scientific space can become a prospect for further research.