Reviewerly/exHarmony huggingface dataset:
ROLE OF ENTREPRENEURIAL SUCCESS IN ECONOMIC DEVELOPMENT OF A COUNTRY

The study tells that an entrepreneur is someone who furthers economic activities in a country by his decisions for conducting business and expansion. These decisions play a significant role in developing not only the industry he is in but also the country. His success is attributed by the increase in his net worth over the time period. The people with net worth of over billion dollars are termed as billionaires. Hence, new billionaires are someone who are successful entrepreneurs. An entrepreneur is someone who increases his own net worth along with offering new forms of products and services resulting in increase in employment and many other factors in the nation’s economy. Thereby, furthering different macroscopic aspects of the nation’s economy.
A Generative Model for the Creation of Large Synthetic Image Datasets Used for Distance Estimation

This paper presents a generative model for the creation of large synthetic image datasets. The model is implemented as a 3D scene, representing an urban environment, in Blender. The images are rendered using the Cycles rendering engine which allows for the creation of high-fidelity data. During the process of image rendering, the data was automatically labeled for the purpose of distance estimation. The process of data labeling was achieved by extracting object metadata from the 3D scene, allowing for the generative model to be reconfigured to generate datasets for other purposes such as classification, object detection, semantic segmentation, etc. The data acquired in the manner described previously was used for the end-to-end training of a convolutional neural network, designed to estimate distance from stereoscopic images. Evaluation of the neural network’s performance showed that the generative model presented in this paper is viable for the generation of large image datasets for the training of predictive models, eliminating the need for time-consuming manual data acquisition and labeling.
Analyze Performance of Embedded Systems with Machine Learning Algorithms

This article aims to analyze the performance of embedded systems using various machine learning algorithms on embedded systems. For this, data set were analyzed using Python programming language and related libraries. Using different machine learning algorithms, it has been useful in terms of giving us an idea about how these cards are sufficient for solving deep learning and artificial intelligence problems or how close they will be to the desired performances. Raspberry pi and jatson nano embedded system boards were used for performance measurement and they were compared with notebook performance.
An Hybrid Attention-Based System for the Prediction of Facial Attributes

Abstract Recent research on face analysis has demonstrated the richness of information embedded in feature vectors extracted from a deep convolutional neural network. Even though deep learning achieved a very high performance on several challenging visual tasks, such as determining the identity, age, gender and race, it still lacks a well grounded theory which allows to properly understand the processes taking place inside the network layers. Therefore, most of the underlying processes are unknown and not easy to control. On the other hand, the human visual system follows a well understood process in analyzing a scene or an object, such as a face. The direction of the eye gaze is repeatedly directed, through purposively planned saccadic movements, towards salient regions to capture several details. In this paper we propose to capitalize on the knowledge of the saccadic human visual processes to design a system to predict facial attributes embedding a biologically-inspired network architecture, the HMAX. The architecture is tailored to predict attributes with different textural information and conveying different semantic meaning, such as attributes related and unrelated to the subject’s identity. Salient points on the face are extracted from the outputs of the S2 layer of the HMAX architecture and fed to a local texture characterization module based on LBP (Local Binary Pattern). The resulting feature vector is used to perform a binary classification on a set of pre-defined visual attributes. The devised system allows to distill a very informative, yet robust, representation of the imaged faces, allowing to obtain high performance but with a much simpler architecture as compared to a deep convolutional neural network. Several experiments performed on publicly available, challenging, large datasets demonstrate the validity of the proposed approach.
Increasing the accuracy of the automatic control system of unmanned aircraft under conditions of non-stationary interference

An approach to solving the problem of improving the accuracy of an automatic control system for unmanned aerial vehicles (UAVs) in conditions of non-stationary interference is considered. Such interference is caused by random changes in the noise of the measuring sensors, which are installed in the UAV, when changing the yaw, roll and flight altitude of the UAV due to restructuring or transformation of the operating mode of the propulsion system of the UAV. Non-stationary noises do not allow obtaining the maximum effect of applying the theory of optimal filtration based on the classical Kalman filter. The authors proposed a method of stabilizing sensor noise by using a noise automatic gain control circuit installed in front of the Kalman filter. For its quantitative assessment, a team of authors developed a computer model and carried out dynamic modeling based on test signals, which showed the applicability of the classical Kalman filter in non-stationary conditions and a decrease in the filtering errors of the automatic control system.
Users’ Awareness of Augmented Reality Technology in Mobile Applications

Abstract Augmented reality (AR) is becoming an increasingly popular technology, used in many fields. Particular prospects for its development are inherent in the dissemination of AR-based applications on widely used mobile devices. To guide the emerging research in this field, the level of knowledge of AR technology among potential consumers needs to be identified. The aim of this study was to determine the level of awareness of AR in mobile applications among their potential users, attempting to determine whether it is possible to define the profile of a user characterized by a greater level of knowledge of AR. Statistical analysis of the results of original research carried out with the CAWI in the spring of 2020 showed that the knowledge of augmented reality technology among potential consumers is at a relatively low level. Moreover, this study found no characteristics correlated with this knowledge, in terms of either demographics, or interest in and knowledge of modern technologies in general.
An Exploration of Interactivity and Tangibles in Blended Play Environments

In this paper, we present a study exploring digital augmentation as an integral part of spatial experience in children’s play with physical objects. We introduce a blended play environment, a combination of digital and physical media, for enhancing children’s physical activity and play through interaction with tangibles. This play environment called Monnom, a novel digitally-enhanced physical environment, offers body-object interaction where the body frames the scene, controls and improvises the play. The prototype has been assessed in studies with 67 children (4–12 years old) in two different settings, one at a museum and the other at a school. Based on an analysis of existing designs, we highlight different play actions that children may employ, and delineate various resources for meaning making. Digital technologies for play are mostly structured, rule-bound and goal-directed virtual playgrounds. Our study expands these and suggests a set of qualities to think about interaction design for children’s play and future research.
A Substitution-Box Structure Based on Crowd Supply Infinite Noise TRNG

Practical applications in cryptology science have a very wide spectrum. One of the important primitives based on many of these applications is the substitution-box structures. Since substitution-box structures are the main component targeted by many attacks, developing new substitution-box structures resistant to new attack is a hot research topic. Random selection-based designs have become increasingly popular recently, as they are more resistant to side channel attacks than mathematically based designs. In this study, a random selection-based substitution-box structure is proposed. The proposed substitution-box is designed based on a true random number generator hardware. Analysis results showed that the proposed substitution-box has better performance criteria than many designs in the literature. These successful results indicate that the outputs of study can be used successfully in many practical applications that will be designed in the future.
Characterization of quantum entanglement via a hypercube of Segre embeddings

A particularly simple description of separability of quantum states arises naturally in the setting of complex algebraic geometry, via the Segre embedding. This is a map describing how to take products of projective Hilbert spaces. In this paper, we show that for pure states of n particles, the corresponding Segre embedding may be described by means of a directed hypercube of dimension $$(n-1)$$ , where all edges are bipartite-type Segre maps. Moreover, we describe the image of the original Segre map via the intersections of images of the $$(n-1)$$ edges whose target is the last vertex of the hypercube. This purely algebraic result is then transferred to physics. For each of the last edges of the Segre hypercube, we introduce an observable which measures geometric separability and is related to the trace of the squared reduced density matrix. As a consequence, the hypercube approach allows to measure entanglement, naturally relating bipartitions with q-partitions for any $$q\ge 1$$ . We test our observables against well-known states, showing that these provide well-behaved and fine measures of entanglement.
Personal authentication and recognition of aerial input Hiragana using deep neural network

We use Leap Motion and a deep neural network to perform personal authentication and character recognition of all hiragana characters entered in the air. We use Leap Motion to detect the index finger and store its trajectory as time series data. The input data was pre-processed to unify the data length by linear interpolation. For identification, the accuracy of Long Short Term Memory (LSTM) was compared with Support Vector Machine (SVM). As a result, SVM and LSTM achieved 97.25% and 98.18% F-measure in character recognition, respectively. In personal authentication, SVM has an accuracy of 92.45%, False Acceptance Rate (FAR) was 0.73%, and False Rejection Rate (FRR) was 41.59%. On the other hand, LSTM had an accuracy of 96.13%, FAR of 1.73% and FRR of 14.55%. Overall, the LSTM performed better than the SVM.
Nodes detection in CDN with enhanced naive bayes tree

Abstract In recent years, Content Distribution Network (CDN) plays a critical and central part of Internet infrastructure. There are more and more studies on CDN. Nodes detection in CDN, as a key technology among them, has become a hot research topic. Current methods mainly focus on collecting one or some CDN vendors nodes by manually constructing features set. However, because CDN nodes detection is not restricted by specific CDN vendors, these methods are not applicable. In this paper, we proposed a novel machine learning algorithm, i.e. enhanced Naive Bayes Tree to identify CDN IP addresses. This algorithm makes full use of the advantages of Decision Tree and Naive Bayes algorithm, and further improves the performance through enhanced part. We build this classifier based on analysis of the characteristics of DNS resolutions, HTTP logs and WHOIS lookup. Experimental results show that our approach outperforms in terms of accuracy and amount of packages. Moreover, we separately tested the enhanced part and it performs well. We believe that this method could be applied to CDN IP addresses detection.
Hide Secret Message in Image Transform Conformal Mapping Based Image 2DHWT

Abstract The objective of this paper is steganography of data in images, after transform this image in any manner. Because in present to increases of social media, and internet and web networks. Therefore, it can need data security during transmit between sender and receiver. It will be proposed system for more security consisted of many phases. The first is compression image in two dimension Haar wavelet transform 2D-HWT to reduce size of image, in next is converts image to conformal mapping, and final hide secret message in image conformal mapping. The outcomes were obtained from a set of tests PSNR, MSE, Correlation Coefficient and entropy. The suggested system has excellent and efficiency, robustness, transparency, high security, and good capacity.
A Study of Effect of Architectural Design on Quality of Service of a Live Streaming Application with Multiple Endpoints over LTE Network

The number of streaming service providers has been increasing dramatically every year. Hence, users may prefer to publish their stream to multiple service endpoints simultaneously to increase visibility. However, most service providers prefer to monopolize their services. Hence, a study of a suitable architectural design of a streaming service that supports multiple streaming endpoints has not gained lots of attention. In this study, the effect of adopting different architectural design on developing a live streaming service over LTE network which can supports multiple streaming endpoints are investigated. Two major designs are selected which are a selective forwarding unit based architecture, and a non-selective forwarding unit based architecture. The results suggest that a selective forwarding unit architecture has an advantage over a non-selective forwarding unit based architecture on keeping overall average streaming end-to-end delay to be minimum., while a fluctuation in an end-to-end delay occurs in a non-selective forwarding unit based architecture in our experiment testbed. The results, discussions, and suggestions on future studies are given at the end of this study.
Systematic Translation of Formalizations of Type Theory from Intrinsic to Extrinsic Style

Type theories can be formalized using the intrinsically (hard) or the extrinsically (soft) typed style. In large libraries of type theoretical features, often both styles are present, which can lead to code duplication and integration issues. We define an operator that systematically translates a hard-typed into the corresponding soft-typed formulation. Even though this translation is known in principle, a number of subtleties make it more difficult than naively expected. Importantly, our translation preserves modularity, i.e., it maps structured sets of hard-typed features to correspondingly structured soft-typed ones. We implement our operator in the MMT system and apply it to a library of type-theoretical features.
Characterizing slope regions

Abstract This paper provides a theoretical characterization of monotonically connected image surface regions, called slope regions. The characterization is given by several topological properties described in terms of critical points relative to the region. We formally prove the necessary and sufficient conditions that a region needs to satisfy to be a slope region. We also provide a prototype of slope regions which is general and contains, as particular cases, the prototypes studied and published in previous conference papers.
Adapting the E-course for Teacher Training in the Teacher Professional Development System

In modern educational system reform in Russia, there is a deepening contradiction between the required and real level of professional competence of teachers. One way to overcome this contradiction is the systematic professional development of the teaching staff implemented with the help of distance learning technologies (DLT). One of the means to ensure high quality of training is course evaluation by trainees. It allows for motivation and a high level of training, to understand the directions of the course modification. At the same time, teachers themselves are professional experts in training, and their assessment of the e-course may be more objective than that of the trainees of other professions. Additional vocational education is primarily characterized by a high degree of applied orientation to modern learning technologies. Such are the realities that changes, especially in digital technology in education, are changing rapidly. And it is so fast that, in fact, one person cannot be aware of the full range of directions in which they (the changes) are taking place. This problem can be solved using the DLT through systematic modification of the e-course. The object of the study in this paper is the process of teachers’ training in the system of professional development of pedagogical staff using distance learning technologies. The subject of the study: the mechanism of optimization of elearning distance learning courses used in the system of professional development of pedagogical staff. Research purpose: to identify mechanisms to optimize the effectiveness of e-courses to increase trainees’ motivation, correlation of theory content with practice with the help of trainees themselves under conditions of synergetic approach [1]. Research objectives: a) to identify parameters for improving the effectiveness of e-courses; b) to develop a toolkit for improving the effectiveness of e-courses; c) to develop recommendations for improving the effectiveness of e-courses. The method used in the study is the analysis of pedagogical literature, Google-form-based questionnaire, synthesis, and generalization. The novelty of the study is related to the creation of conditions for modifying e-courses for teachers’ professional development through the examination of the course by the teachers themselves. This article is written within the framework of the Russian Foundation for Basic Research project 19-29-14176 “Building a Fundamental Model of Digital Transformation of the General Education System Using a Digital Platform for Results-Based Education”.
Trapdoor DDH Groups from Pairings and Isogenies

Trapdoor DDH groups are an appealing cryptographic primitive introduced by Dent–Galbraith (ANTS 2006), where DDH instances are hard to solve unless provided with additional information (i.e., a trapdoor). In this paper, we introduce a new trapdoor DDH group construction using pairings and isogenies of supersingular elliptic curves, and present two instantiations of it. The construction solves all shortcomings of previous constructions as identified by Seurin (RSA 2013). We also present partial attacks on a previous construction due to Dent–Galbraith, and we provide a formal security definition of the related notion of “trapdoor pairings”.
The Development and Implementation of Competency-Based ITS for Learners with Learning Disability

An Intelligent Tutoring System provides customized and immediate feedback or instruction to the learner, without any intervention from a physical educator. Intelligent tutoring systems tend to solve the problem faced by learners by giving learning material or instruction, but it does not enforce learners to complete the learning content or achieve mastery in a particular skill or competency. For focusing on learner’s skill development and one skill at a time, competency-based learning is used. Competency-based learning allows a learner to access another competency only after he/she achieves mastery in the first one. This paper discusses implementing competency-based learning in an intelligent tutoring system for learning disabled learners.
M-Learning Autonomous Model for Learning Mathematics with MatiMath Application in Students of a Confessional

Teaching-learning in mobile virtual environments is having a greater presence and impact on students and teachers, giving them more tools to learn, so an appropriate model for learning mathematics through mobile devices is sought. The study aims to propose an application based on the m-learning requirements that provides support to students and teachers in the area of mathematics fulfilling criteria such as technological requirements, pedagogical principles and UX elements. Which was carried out through 4 phases design, development, implementation and evaluation, where it was obtained as a result through the T test for independent samples that the students who used the application reached an average of 14,1 in contrast to 8.46 of the students who did not use the application so it is concluded that the MatiMath application had a positive influence and provided support in the learning process in the area of mathematics of university students.
Robust Camera Motion Estimation for Point-of-View Video Stabilization

Point-of-View videos recorded by Augmented Reality Glasses contain jitters because they are acquired under users’ actions in varying environments. Applying video stabilization on such videos is difficult due to weakness of conventional keypoint-based motion estimation to environmental conditions. They are prone to fail to track in low-textured or dark environments. To overcome this limitation, we propose a neural network-based motion estimation method for video stabilization. Our network predicts frame-to-frame motion in high accuracy by focusing on global camera motion, while ignoring local motion caused by moving objects. Motion prediction takes only up to 10 ms so that we achieve real-time stabilization on modern smartphones hardware. We demonstrate our method outperforms keypoint-based motion estimation and the quality of estimated motion is good enough for video stabilization. Our network is trainable without ground truth and easily scalable to large datasets.
Identidades usurpadas, nombres cancelados en Amanda, Olinda, Azzurra e le altre de Christiana de Caldas Brito.

espanolLos relatos contenidos en Amanda, Olinda, Azzurra e le altre de Christiana de Caldas Brito giran en torno al tema de las identidades en transito, explorando los multiples territorios (geograficos, afectivos, simbolicos) que las protagonistas, incorporan a su construccion identitaria individual. Dando voz a estas mujeres, la mayoria brasilenas inmigrantes en Italia que cuentan sus experiencias en primera persona, la narracion se plantea desde el punto de vista de las subalternas, que promueven una vision critica de la identidad colectiva. Nuestro analisis parte de las premisas de la Comunicacion Intercultural, que nos permiten colocar las protagonistas inmigrantes de Caldas Brito en una dimension interactiva, interpersonal y dialogica, en la que entra en juego la relacion de auto y hetero identificaciones, tanto en espacios fisicos como simbolicos. EnglishThe stories contained in Amanda, Olinda, Azzurra e le altre of Christiana de Caldas Brito revolve around identities in transit, exploring the multiple territories (geographical, affective, symbolic) that the protagonists incorporate into their individual identity construction. The aim is to give voice to these women, a majority of Brazilian immigrants in Italy who tell their experiences in first person. The narrative is presented from the point of view of the subordinates, who promote a critical vision of collective identity. Our analysis starts from the premises of Intercultural Communication, which allow us to place the immigrant protagonists of Caldas Brito in an interactive, interpersonal and dialogic dimension, in which the relationship of self and hetero identifications comes into play, both in physical and symbolic spaces.