Reviewerly/exHarmony huggingface dataset:
Perception of entrepreneurship as alternative to facing the post-pandemic crisis

This article aims to identify the perception of a group of incubated projects about the importance of entrepreneurship as alternative to face the economic crisis due to the new coronavirus pandemic. Entrepreneurship is a transversal subject to many areas of research and has been the object of study within themes such as economic, technological and innovation. The crisis in several sectors caused by the pandemic of the new coronavirus, raised other research possibilities. With a qualitative approach and through the case study method, using a structured questionnaire, it sought to investigate a group of companies/projects incubated in a technology-based business incubator, in the municipality of Mossoró in the state of Rio Grande do Norte. With the results, it was possible to verify that incubated have a positive perception about the importance of entrepreneurship in facing the crisis, thus pointing out that it is an alternative for the post-pandemic crisis period.
Quad-Rotor Path Planning for Cluttered Environments Using Obstacle Clustering

This paper investigates the validity of the path planning algorithm that was developed in a previous study for cluttered environments with obstacle clustering. Using Successive Convexification (SCvx) and compound State-Triggered Constraints (STCs), the path planning of a small unmanned aerial system flying through obstacles is assessed. The obstacles with various radii are placed with uniform distribution along with the flight course to make cluttered environments. Hierarchical clustering is used for obstacle clustering, and minimum bounding circles for each cluster are obtained by convex optimization. Various configurations of the obstacles are used in the path planning computation, and the statistics of computation time and collisions with obstacles are discussed to assess the path planning. The results show that the path planning method with obstacle clustering is effective and reliable for cluttered environments since obstacle clustering can reduce collisions with obstacles and computation loads in dense obstacle regions. For future work, a method that can improve obstacle clustering is proposed.
Some Applications to Computational Science and Data Science

Linear systems of the form $$A\boldsymbol{x}=\boldsymbol{b}$$ arise in a rich variety of applications such as computational science and data science. For computational science, physical phenomena are often described as partial differential equations. For solving partial differential equations, the finite-difference methods and the finite element method are widely used, leading to a problem of solving linear systems. Large-scale electronic structure calculation for condensed matter physics and lattice quantum chromodynamics for particle physics require solving large-scale linear systems. For data science, regression and classification are fundamental tasks that also require solving linear systems. Minimizing or maximizing functions is one of the most important optimization problems, which requires solving linear systems when Newton’s method is used. We will see in this chapter how the linear systems arise in these applications by using simple and concrete examples.
Oriented Approach In Open Science

Theses examine a number of current, ongoing projects that conduct user research to create and improve research infrastructures in open access to the scientific space. The priority needs of scientists and specialists, which are the most important for open science now, have been determined.
Accessible Website for the educational inclusion of People with Sensory Disabilities

This work presents the problem of educational inclusion as a proposal to improve the development of an accessible web page, which considers the WCAG 2.1 guides. Likewise, the tools that allow the development of characteristics that support the inclusion of people with sensory disabilities are described, among them the NVDA screen reader, Augmented Reality through hololink, it also contains some adaptations such as allowing to change the font size. and the contrast between the background and the letter so that it adapts to other types of people with different types of sensory disabilities, finally an evaluation carried out through a checklist is shown.
Ontological Cybernetics as an Integrator of Organizational Systems in Hybrid Reality Environments

Our purpose in this paper is to clarify ontological approaches to the development of cybernetics based on epistemological and methodological propositions grounded in the concepts of scientific classical, non-classical, post-non-classical rationality – as proposed by V.S. Stepin (2005). We need to increase our under­standing of the subject-oriented approach to the analysis of cybernetic interac­tions: “subject – object” in first-order cybernetics, “subject – subject” in second-order cybernetics and “subject – metasubject” in the cybernetics of self-develop­ing reflexive-active environments, third-order cybernetics. The ideas of three or­ders of cybernetics, first, second and third have the potential of integrating a strong ontological approach to the social sciences. For this integration we pro­pose Beer’s Viable System Model (VSM), Espejo’s Viplan Methodology and Lepskiy’s self-developing reflexive-active environments. This ontological cyber­netics corresponds to the development of the philosophical and methodological foundations of cybernetics and systems towards subject-oriented approaches, grounded on Ashby’s Law of Requisite Variety. We expect them to contribute to achieving adequate responses to the challenges of the 21st century at all struc­tural levels. They should be focused on adequate performance to achieve desir­able outcomes for sustainability and viability on the grounds of socio-humanitar­ian innovative criteria, using today’s digital technologies, converging with the increasingly powerful ontologies of artificial intelligence. We are offering on­tologies for organizing hybrid reality environments. Our aim through this paper is creating new ontologies adequate to the effective use of digital technologies and artificial intelligence to improve the control and communication mechanisms of social systems.
Pengaruh Model Pembelajaran Quantum Teaching Learning terhadap Hasil Belajar Siswa Kelas V MI

This research was carried out because there were various problems that were found by researchers including, (1) learning models that were less varied, (2) students were less active in learning unable to voice everyday knowledge in learning, (3) when learning students did not pay attention to what being conveyed by the teacher, (4) students tend to only use memorizing methods without being accompanied by the ability to reason and associate them in everyday life. So that this has an impact on student learning outcomes that are not good. This study aims to see how influential the quantum teaching learning model is on student learning outcomes. This research is a quantitative research with an experimental quasy research design with data analysis steps using t the population used in this study are students of class V MI 25 Surabaya consisting of four classes and taken research samples class V D as an experimental class and class V B as a control class. The data obtained was taken from the test sheet and pre-test conducted by the researcher. from the results of data analysis it was found that learning using the quantum teaching model could improve student learning outcomes in science subjects.
ОСОБЛИВОСТІ ОРГАНІЗАЦІЇ ТА ПРОВЕДЕННЯ РУХЛИВИХ ІГОР З ДІТЬМИ ДОШКІЛЬНОГО ВІКУ

The method of organizing and conducting mobile games with children of younger preschool age is substantiated. Pedagogical conditions of a complex approach to their selection, preparation and pedagogical guidance in the process of their learning, consolida
Design and Security Analysis of Cryptosystems

The development of cryptography is closely related to the development of computers [...]
Identifying Diffuse Spatial Structures in High-energy Photon Lists

Abstract Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point sources that may be superposed. We have developed a novel nonparametric event list segmentation algorithm to divide up the field of view into distinct emission components. We use photon location data directly, without binning them into an image. We first construct a graph from the Voronoi tessellation of the observed photon locations and then grow segments using a new adaptation of seeded region growing that we call Seeded Region Growing on Graph , after which the overall method is named SRGonG . Starting with a set of seed locations, this results in an oversegmented data set, which SRGonG then coalesces using a greedy algorithm where adjacent segments are merged to minimize a model comparison statistic; we use the Bayesian Information Criterion. Using SRGonG we are able to identify point-like and diffuse extended sources in the data with equal facility. We validate SRGonG using simulations, demonstrating that it is capable of discerning irregularly shaped low-surface-brightness emission structures as well as point-like sources with strengths comparable to that seen in typical X-ray data. We demonstrate SRGonG ’s use on the Chandra data of the Antennae galaxies and show that it segments the complex structures appropriately.
An image correction algorithm and implementation for remote sensing camera

Real time control, diversified functions, system integration and miniaturization are an important development direction of video electronics system. Embedded design based on FPGA can manage system resources more reasonably and effectively, and use limited resources to achieve more complex functions. It will become an important design method to promote the development of video electronics technology. Based on the embedded MicroBlaze microprocessor in V FPGA chip, combined with the model application background and Xilinx embedded development platform, an eight channel gray image channel inconsistency correction algorithm is implemented. On board experiments show the feasibility of applying embedded system to video electronic design, and show its advantages in interface control, storage management, process control, floating-point operation, development efficiency and so on, and it can be applied to real-time image processing of satellite remote sensing camera
An End-to-End Scheme for Learning Over Compressed Data Transmitted Through a Noisy Channel

Within the emerging area of goal-oriented communications, this paper introduces a novel end-to-end transmission scheme dedicated to learning over a noisy channel, under the constraint that no prior training dataset is available. In this scheme, the transmitter makes use of powerful Spherical Harmonic Transform and Irregular Hexagonal Quadratic Amplitude Modulation techniques, while the receiver relies on a Complex-Valued Neural Network (CVNN) so as to realize the learning task onto the received noisy data. As a main feature of the proposed scheme, the transmitter is fixed and does not depend on the source statistics, while the receiver is trained from a first data transmission phase, thus providing an efficient transmission-versus-learning approach under the considered constraint. The proposed transmission scheme may be adapted to a variety of learning problems, and the paper specifically investigates clustering and classification, two very common learning tasks. In the last part of the paper, the source/channel coding rate of the proposed transmission scheme is evaluated theoretically and from numerical simulations. This analysis shows a clear advantage in terms of coding rate of our scheme compared to conventional coding approaches, when targeting the same learning performance level.
Video Super-Resolution Based on Inter-Frame Information Utilization for Intelligent Transportation

Intelligent transportation infrastructure is essential to intelligent transportation system (ITS). With the continuous development of Internet of Things (IoT) technology, remote monitoring has become a critical part of ITS. However, due to the limitations of network transmission, production cost, and other factors, some video monitoring can obtain only low-resolution (LR) video. LR video features are seriously lost, thus affecting the performance of ITS. In this paper, based on the research of existing super-resolution algorithms, we focus on improving the reconstruction quality of video frame sequences by aiming at the insufficient utilization of inter-frame information and low reconstruction quality of existing video super-resolution algorithms. This paper proposes a video super-resolution algorithm based on inter-frame information utilization, which can effectively improve the performance of ITS. First, a novel U-shaped feature extractor is designed to fully extract the feature expression of video frame sequences. Second, a deformable inter-frame alignment module based on residual learning is constructed to make the inter-frame alignment more accurate and thus promote the mutual utilization of inter-frame information. Finally, an up and down sampling residual block is proposed to extract features that better match the upsampling reconstruction requirements. The experimental results show that the method has better reconstruction quality for monitoring video and is advanced and applicable compared to mainstream video oversampling methods.
Alpha Capture Systems: Past, Present, and Future Directions

Alpha Capture Systems (ACS) describe a set of commercial and private computer programs that track investment recommendations and their subsequent performance to create an alpha-generating portfolio. This article presents an overview of the evolution of ACS from the first sell-side to the more recent buy-side systems, from capturing brokers’ ideas to analyzing the underlying positions of mutual funds and even hedge funds. We discuss the key features of the most recent ACS and flag their expected weaknesses. We suggest various improvements we expect to see in future ACS that, based on our experience, should enhance their performance significantly.
ELAA: An Ensemble-Learning-Based Adversarial Attack Targeting Image-Classification Model

The research on image-classification-adversarial attacks is crucial in the realm of artificial intelligence (AI) security. Most of the image-classification-adversarial attack methods are for white-box settings, demanding target model gradients and network architectures, which is less practical when facing real-world cases. However, black-box adversarial attacks immune to the above limitations and reinforcement learning (RL) seem to be a feasible solution to explore an optimized evasion policy. Unfortunately, existing RL-based works perform worse than expected in the attack success rate. In light of these challenges, we propose an ensemble-learning-based adversarial attack (ELAA) targeting image-classification models which aggregate and optimize multiple reinforcement learning (RL) base learners, which further reveals the vulnerabilities of learning-based image-classification models. Experimental results show that the attack success rate for the ensemble model is about 35% higher than for a single model. The attack success rate of ELAA is 15% higher than those of the baseline methods.
Financial Development, Economic Development and Poverty: Is there a Threshold Effect?

The purpose of this article is to propose an empirical study that allows the determination of the threshold effects of financial development and economic development to reduce poverty.Our study, based on the PTR model developed by Hansen (1999), will be carried out on a sample of 49 countries, over the period 2004-2017.The main results show that the effect of financial development on poverty is a function of levels of economic development and financial development.The results conclude that threshold levels associated with financial development and economic development are necessary to have a significant reduction in poverty.Thus, economic policies aiming to promote financial development are necessary in poverty reduction strategies; theses economic policies must be sufficient to place the economy above the thresholds and successfully fight poverty.
Nanoscale Waveguide Beam Splitter in Quantum Technologies

Studying beam splitters as sources of entangled photons is a relevant topic in modern physics. In most cases, in quantum optics the theories of waveguide beam splitters of large and small sizes have no difference. In this work, it is shown that a nanosized waveguide beam splitter has a significant difference from a similar device, but on a larger scale. The output wave function of the beam splitter’s ports is analyzed in the arbitrary case of photons entering the first and second ports. The results obtained are sensitive to the size of the waveguide beam splitter, the coupling parameter of two waveguides, the monochromatic photons case of a linear beam splitter and the non-monochromaticity grade for photons entering both input ports of the beam splitter. The results are very important for various quantum technologies (e.g. integrated circuits, optical quantum computers) which use this type of beam splitter and quantum metrology.
Raman microscopic measurement of materials used on banknotes

The successful verification of the authenticity of banknotes, or the detection of counterfeits, is based on the combination of an effective measuring system and computer technology. In this paper, a modern diagnostic method Raman microscopy together with an exploratory tool for processing and interpreting multi-dimensional datasets, and cluster analysis, are used to study structural differences between paper and orange ink used on genuine banknotes. Six currencies of European countries were selected for this study: Croatian kuna, Czech crown, euro, Great British Pound, Polish zloty, and Swiss franc. Raman microscopy offers non-destructive, rapid analysis without any sample preparation and pre-processing, with the potential to recognize structural modification of substances, together with the advantages of optical microscopy. Cotton fibres, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{TiO}_{2}$</tex> in two different modifications, and other additives are successfully identified within the spectra. The patterns found in mathematically processed Raman data, especially between the paper, indicate both, the similarities and differences across the various banknotes, even of the same currency. The results serve as a base of the spectral database for authenticity control of banknotes and classification of the materials used.
Evaluation of Deep Learning Context-Sensitive Visualization Models

The introduction of Transformer neural networks has changed the landscape of Natural Language Processing (NLP) during the recent years. These models are very complex, and therefore hard to debug and explain. In this context, visual explanation became an attractive approach. The visualization of the path that leads to certain outputs of a model is at the core of visual explanation, as this illuminates the features or parts of the model that may need to be changed to achieve the desired results. In particular, one goal of a NLP visual explanation is to highlight the most significant parts of the text that have the greatest impact on the model output. Several visual explanation methods for NLP models were recently proposed. A major challenge is how to compare the performances of such methods since we cannot simply use the usual classification accuracy measures to evaluate the quality of visualizations. We need good metrics and rigorous criteria to measure how useful the extracted knowledge is for explaining the models. In addition, we want to visualize the differences between the knowledge extracted by different models, in order to be able to rank them. In this paper, we investigate how to evaluate explanations/visualizations resulted from machine learning models for text classification. The goal is not to improve the accuracy of a particular NLP classifier, but to assess the quality of the visualizations that explain its decisions. We describe several methods for evaluating the quality of NLP visualizations, including both automated techniques based on quantifiable measures and subjective techniques based on human judgements.
Visualizing Temporal Data using Time-dependent Non-decreasing Monotone Functions

Ahstract- The occurrence of seasonal natural phenomena depends on the conditions leading to it and not directly on the progression of time, meaning its context varies across time and space. Examples of this include comparing plant growth, insect development or wildfire risk during the same time period at different locations or in different time periods at the same location. However, visualizing and comparing such phenomena usually implies plotting it across the time axis as it's perceived as temporal data. Since it's not directly dependent of time, identifying patters of recurrence using this technique is inefficient. Because of this, we proposed transforming (when needed) the dependent function to a non-decreasing monotone one, in order to preserve the monotonic property of time progression. Then we used the resulting function as a time axis replacement to achieve an equal ground of comparison between the different contexts in which the phenomenon occurs. We applied this technique to real data from seasonal natural phenomena, such as plant and insect growth, to compare its progression in different temporal and spatial contexts. Since the dependent function of the phenomenon was scientifically known, we were able to directly use the technique to infer its seasonality patterns. Furthermore, we applied the technique to real data from the coronavirus worldwide pandemic by hypothesizing its dependent function and analysing if it was able to reduce the existing temporal misalignment between different contexts, like years and countries. The results achieved were positive, although not as remarkable as when the dependent function was known.
A smartphone application for irrigation scheduling of crops in northwestern India

Irrigation management helps determine when and how much water must be applied to irrigate crops.A mobile application (app) has been developed for irrigation scheduling selected crops for the semi-arid and arid regions of Rajasthan, India.These regions experience extreme temperatures in both the summer and winter months, and erratic rainfall conditions.The app uses the water balance approach to compute the most favourable irrigation cycle for a range of crops for selected locations in the state.It also utilizes meteorological, crop and soil data to derive a daily irrigation schedule for the selected month and location.