Reviewerly/exHarmony huggingface dataset:
Medição da informação científica na Web 2.0: explorando as possibilidades e limitações da plataforma Altmetric: exploring Altmetric's platafform possibilities and limitations

With the evolution of the Internet, new ways of collaboratively creating and sharing knowledge emerged, culminating in the Web 2.0 (Web social). With the evolution of the Internet, emerge new ways of creating and sharing knowledge collaboratively, culminating in the Web 2.0 (Web social). The products resulted from academic and scientific activities also began to circulate in the social media, inspiring the interest of researchers in the measurement and study of actions and interactions related to such activities, arising altmetrics. Since then, technologies, tools and platforms have been created to measure these phenomena, and Altmetric, from Digital Science, is currently the main platform for collecting altmetrics data. This research aims to explore the possibilities, limitations and structural characteristics of Altmetric as a source of data and altmetric indicators. The platform is analyzed in an exploratory and experimental way, based on data from SciELO’s collection and an interview with Altmetric's director of research relations, Stacy Konkiel. As a result, several possibilities, limitations and characteristics related to the conceptual foundations and the production of the platform indicators were presented and discussed, as well as the types of sources / data tracked; coverage range; calculation of the Altmetric score; popularity and media coverage; possibilities of studying correlations between citations and mentions.
What can be recommended to engineering teachers from the analysis of 16 European teaching and learning best practices

This work is based on outcomes of the project EXTEND –“Excellence in Engineering Education through Teacher Training and New Pedagogic Approaches in Russia and Tajikistan” that has been funded with support from the European Commission (Project Number: 586060-EPP-1-2017-1-RO-EPPKA2-CBHE-JP). This publication reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. The authors would like to thank to the members of the consortium and all that made possible to collect the data we used in this paper.This work has been supported by FCT –Fundacao para aCiencia e Tecnologia within the Project Scope UIDCEC003192019.
Formulation Of A Rational Option Pricing Model using Artificial Neural Networks

This paper inquires on the options pricing modeling using artificial neural networks to price Apple's European call options. The model is based on the premise that ANNs can be used as functional ap...
IMPROVING CLUSTER BASED FEATURE SELECTION USING MODIFIED MINIMUM SPANNING TREE

The issue of feature subset selection for data of high dimensionality in classifying opinions has been investigated in this paper. Feature selection schemes select the crucial feature subset producing similar or superior classification outcomes compared to the original feature set obtained. Despite their higher efficiencies, wrapper-based feature selection schemes have high overheads of computation. However, the issue is the problem is Non-deterministic Polynomial (NP) hard. This work suggests the clustering scheme based on MST optimized by the Group Search Optimization (GSO) for efficient selection of features. The Amazon camera review dataset is used for evaluating the suggested scheme. Experiments are conducted to evaluate the proposed method and compared with the MRMR feature selection, FCM clustering, and MST based clustering.
Similarity-Difference Relation Network for Few-Shot Learning

Few-shot learning aims to build a classification model by training a small amount of labeled sample data, which can be well adapted to new domains. The key point of few-shot learning is that a small amount of sample data cannot reflect the true data distribution. Training on a small amount of sample data will lead to over-fitting of the deep neural network model. The differences between different categories are ignored when using similarity measures for classification. This paper proposes a novel few-shot learning method based on similarity-difference relation network, which uses shallow wide residual network to extract the features of the training dataset and fuses them into a category prototype. Meanwhile, SDRN pays attention to the characterization of similarities and differences between positive and negative samples. This paper verifies the effectiveness of the similarity-difference relational network on the Mini-ImageNet and Tiered-ImageNet datasets. The experimental results show that the similarity-difference two-way relational network further improves image classification accuracy in the few-shot learning task.
Sequential estimation of quantiles from delayed observations

The problem of sequentially estimating quantiles is considered in the case when the observations become available at random times. Certain class of sequential estimation procedures which are composed of optimal stopping time and sequential minimum risk invariant estimator of a median is obtained under a invariant loss function and with the observation cost determined by a convex function of the moment of stopping and the number of observations up to this moment.
Towards Exemplar-based Polysemy

In this paper we criticize existing computational models of lexicon for assuming that for every word there is a fixed number of word sense that must be searched for the proper meaning of that word in a context. We reject this sense enumerative view and argue for a different model of lexicon in which the effects of context are not limited to selecting a word sense, and selected senses can be contextually modulated. We also explain how patterns of contextual effects could evolve in an exemplar-based fashion. A prototype implementation of this model is also discussed.
Pauli Error Estimation via Population Recovery.

Motivated by estimation of quantum noise models, we study the problem of learning a Pauli channel, or more generally the Pauli error rates of an arbitrary channel. By employing a novel reduction to the Population Recovery problem, we give an extremely simple algorithm that learns the Pauli error rates of an n-qubit channel to precision e in 𝓁_∞ using just O(1/e²) log(n/e) applications of the channel. This is optimal up to the logarithmic factors. Our algorithm uses only unentangled state preparation and measurements, and the post-measurement classical runtime is just an O(1/e) factor larger than the measurement data size. It is also impervious to a limited model of measurement noise where heralded measurement failures occur independently with probability ≤ 1/4.We then consider the case where the noise channel is close to the identity, meaning that the no-error outcome occurs with probability 1-η. In the regime of small η we extend our algorithm to achieve multiplicative precision 1 ± e (i.e., additive precision eη) using just O(1/(e²η)) log(n/e) applications of the channel.
Scalable Graph Networks for Particle Simulations

Learning system dynamics directly from observations is a promising direction in machine learning due to its potential to significantly enhance our ability to understand physical systems. However, the dynamics of many real-world systems are challenging to learn due to the presence of nonlinear potentials and a number of interactions that scales quadratically with the number of particles N, as in the case of the N-body problem. In this work we introduce an approach that transforms a fully-connected interaction graph into a hierarchical one which reduces the number of edges to O(N). This results in a linear time and space complexity while the pre-computation of the hierarchical graph requires O(N log (N)) time and O(N) space. Using our approach, we are able to train models on much larger particle counts, even on a single GPU. We evaluate how the phase space position accuracy and energy conservation depend on the number of simulated particles. Our approach retains high accuracy and efficiency even on large-scale gravitational N-body simulations which are impossible to run on a single machine if a fully-connected graph is used. Similar results are also observed when simulating Coulomb interactions. Furthermore, we make several important observations regarding the performance of this new hierarchical model, including: i) its accuracy tends to improve with the number of particles in the simulation and ii) its generalisation to unseen particle counts is also much better than for models that use all O(N^2) interactions.
A Scheme for Checking Similarity and Its Application in Outsourced Image Processing

While the Internet provides convenience to people, security issues for data still exist. Images require huge storage space and cloud computing can help users to solve this problem, but the data on the cloud may be leaked. This paper proposes an image data processing scheme based on the Paillier encryption algorithm. To ensure the security of image, we use the Paillier algorithm to process the image data before uploading it to the cloud platform. Then, the cloud server applies the SIFT algorithm to extract and save the image features. A scheme for checking similarity is also proposed to ensure the security of the image in the cloud. We also discuss its application in outsourced image processing.
„Attacks on linking“ – Online-Gruppen in Pandemiezeiten

“Attacks on Linking” – Online Groups in Times of Pandemic Online group analysis is not the same as offline group analysis and requires special consideration. The following case report of an online group analysis invites to reflect on possible changes in the practice and theory of group analysis caused by the new setting.
Multilayer Self-Organizing Impulse Neural Network For Handwritten Digit Recognition

This paper proposes a multi-layer self-organizing impulse neural network. By adding an improved lateral inhibition mechanism and neuron model, the novel network can extract more information from the pictures, thus improving the expressive ability and elevating the recognition accuracy of the classification model. Besides, we replace the topology of the excitation layer and the inhibition layer with a recurrent connection structure. Several experiments have been carried out based on the proposed neural network and proved that the network can well complete the task of handwritten digit recognition. By tuning the network parameters, the recognition accuracy can reach 92.8%, which is at a high level in the same type of network. Besides, we also verified the robust performance of the network by randomly reducing the number of synapses and the number of neurons. It turns out that the network can still achieve high recognition accuracy after randomly discarding some neurons or synapses.
Qué es una categoría ontológica

In this article, I examine the concept of a natural class and propose a definition of in terms of that concept. Let's say a class is if its membership comprises a significant proportion of the things which are there. Let's say a class is if it is not a proper subclass of any natural class. Then a natural class is a primary ontological category if and only if (a) there are large natural classes, and (b) it is a high class. (Secondary, tertiary, etc., ontological categories are defined by an extension of this definition). I defend the definition, consider various ways in which it might be modified, and apply it to the problem of constructing a taxonomy of ontologies.
Enhanced Security Framework for Enabling Facial Recognition in Autonomous Shuttles Public Transportation During COVID-19

Autonomous Vehicles (AVs) can potentially reduce the accident risk while a human is driving. They can also improve the public transportation by connecting city centers with main mass transit systems. The development of technologies that can provide a sense of security to the passenger when the driver is missing remains a challenging task. Moreover, such technologies are forced to adopt to the new reality formed by the COVID-19 pandemic, as it has created significant restrictions to passenger mobility through public transportation. In this work, an image-based approach, supported by novel AI algorithms, is proposed as a service to increase autonomy of non-fully autonomous people such as kids, grandparents and disabled people. The proposed real-time service, can identify family members via facial characteristics and efficiently ignore face masks, while providing notifications for their condition to their supervisor relatives. The envisioned AI-supported security framework, apart from enhancing the trust to autonomous mobility, could be advantageous in other applications also related to domestic security and defense.
Characterizing the Relationship Between Unitary Quantum Walks and Non-Homogeneous Random Walks

Quantum walks on graphs are ubiquitous in quantum computing finding a myriad of applications. Likewise, random walks on graphs are a fundamental building block for a large number of algorithms with diverse applications. While the relationship between quantum and random walks has been recently discussed in specific scenarios, this work establishes a formal equivalence between the two processes on arbitrary finite graphs and general conditions for shift and coin operators. It requires empowering random walks with time heterogeneity, where the transition probability of the walker is non-uniform and time dependent. The equivalence is obtained by equating the probability of measuring the quantum walk on a given node of the graph and the probability that the random walk is at that same node, for all nodes and time steps. The first result establishes procedure for a stochastic matrix sequence to induce a random walk that yields the exact same vertex probability distribution sequence of any given quantum walk, including the scenario with multiple interfering walkers. The second result establishes a similar procedure in the opposite direction. Given any random walk, a time-dependent quantum walk with the exact same vertex probability distribution is constructed. Interestingly, the matrices constructed by the first procedure allows for a different simulation approach for quantum walks where node samples respect neighbor locality and convergence is guaranteed by the law of large numbers, enabling efficient (polynomial-time) sampling of quantum graph trajectories. Furthermore, the complexity of constructing this sequence of matrices is discussed in the general case.
Generating Adversarial Disturbances for Controller Verification

We consider the problem of generating maximally adversarial disturbances for a given controller assuming only blackbox access to it. We propose an online learning approach to this problem that adaptively generates disturbances based on control inputs chosen by the controller. The goal of the disturbance generator is to minimize regret versus a benchmark disturbance-generating policy class, i.e., to maximize the cost incurred by the controller as well as possible compared to the best possible disturbance generator in hindsight (chosen from a benchmark policy class). In the setting where the dynamics are linear and the costs are quadratic, we formulate our problem as an online trust region (OTR) problem with memory and present a new online learning algorithm (MOTR) for this problem. We prove that this method competes with the best disturbance generator in hindsight (chosen from a rich class of benchmark policies that includes linear-dynamical disturbance generating policies). We demonstrate our approach on two simulated examples: (i) synthetically generated linear systems, and (ii) generating wind disturbances for the popular PX4 controller in the AirSim simulator. On these examples, we demonstrate that our approach outperforms several baseline approaches, including $H_{\infty}$ disturbance generation and gradient-based methods.
Endless groundhog day": Sociological analysis of educational practices implemented by university students in a situation of forced isolation

The article examines learning modes during the forced transition to distance learning in the spring of 2020. During this period, many young people experienced a breakdown of the set-up of their everyday life: Some were closed in a confined living space, someone had to return to their parental family, someone was isolated in a university dormitory. Globally, the quarantine has created a situation when usual structures of everyday life and learning activities were broken. The authors are interested in how students' everyday life changed in the spring of 2020. Distance learning in the context of self-isolation is associated with a change in forms of social inclusion within the educational process, as well as with restructuring of the locality and temporality of learning practices, leisure, free time, communication, and the search for new ways of self-management (maintaining concentration and involvement in the educational process, fighting procrastination and trying to make your everyday life comfortable). The authors utilize main concepts of the sociology of everyday life (practice theory), focusing the micro-level of everyday communications. The empirical basis of the research is semi-structured interviews with students of a university in St. Petersburg (N = 35). The data analysis made it possible to understand how young people adjust to learning in isolation, what practices they develop and how they experience ruptures in the habitual fabric of everyday life. Students were forced to restructure the space and time by zoning, arranging virtual classrooms, creating a new daily routine, involving in distant localnesses, (a)synchronizing the time of study and extracurricular practices. The authors describe the modes of inclusion in educational communications camera on and camera off, thereby contributing to the discussion issues related to the sociological understanding of blurring the boundaries between private and public in contemporary society. © 2021 Russian Public Opinion Research Center, VCIOM. All rights reserved.
Cryptographic Scheme for Group Passwords Distribution in Steganographic Systems

When having a deal with a secret communication channel, hidden data are transmitted to a group of users. Sometimes it is necessary to send message only to a given subgroup of participants via a hidden channel. The purpose of this work is to build a cryptographic protocol that is able to provide the exchange of group passwords in the conditions of hiding the fact of secret messages transmission. A centralized password management system is used. Passwords dispatching for user groups are carried out through distribution of data necessary for solving system of equations. Roots of such equation system are used by users for calculation of the group password. The proposed scheme is based on the linear algebra theory and the cryptography. The third-party is not able to solve this system of equations, which is proved by the Kronecker-Capelli theorem. It is assumed that the protocol can be actively used in steganographic systems, where there is one source of messages and many receivers.
EPPR: blockchain for educational record sharing and recommendation using the Ethereum platform

There has been a great deal of discussion of the challenges on privacy of Educational Professional Personal Record (EPPR). Therefore, it is required to reassess the current models, in which various parties generate, exchange and observe a huge amount of personal data with regard to EPPR. Ethereum blockchain has shown that trusted, auditable transactions are detectible using a decentralised network of nodes. In this paper, we propose a novel decentralised framework to manage EPPR using Ethereum blockchain. The framework provides the owner of the EPPR a comprehensive immutable log and accessibility to their educational records across the educational record editors and consumers. Furthermore, it provides a recommender engine to endorse skills and competencies to the education record owners and similar candidates for educational records editors and consumers. The aim of the proposed framework is to enable educational stakeholders to participate in the network as blockchain miners rewarded by pseudonymised data.
Artificial Intelligence in the B2C Service Sector – A Literature Review

Artificial intelligence applications are transforming many service sectors. Research on artificial intelligence in business-to-consumer services suggests approaches for innovative kinds of customer services, enhancement of human service provision, and new customer experiences based on artificial intelligence applications. The chapter provides an overview of scholarly research on the scientific understanding of artificial intelligence in services, investigated artificial intelligence algorithms and technologies in concrete service contexts, and a summary of the study findings as well as suggestions for future research in this dynamically expanding field.
A do-it-yourself computer vision based robotic ball throw trainer

We demonstrate a self-training system for sports involving throwing a ball. We design a do-it-yourself (DIY) machinery that can be assembled using off-the-shelf items and integrates computer vision to visually track the ball throw accuracy. In this work, we demonstrate a system that can identify if the ball went through the hoop and approximately in which of the hoop's inner region. We envision that this preliminary design sets the foundation for a complete DIY sports IoT system that involves a hoola hoop, RaspberryPi, PiCamera and a LED strip, along with advanced ball placement and dynamics tracking.
Deep neural network joint multi-scale attention for remote sensing image colorization

Image colorization is a technology to transform gray-scale image into natural color image, which is always a challenging task in image processing. In this paper, we design a deep neural network model including multi-scale convolutional down-sampling and attention mechanism, which can effectively solve the problem of coloring gray remote sensing image. First, we input the grayscale remote sensing image into the feature extractor of our model for feature extraction and multi-scale down-sampling. Second, the feature map extracted by the feature extractor is input into the squeeze-andexcitation attention mechanism module to enhance the extraction of key information. Finally, the feature map output by the feature extractor and squeeze-and-excitation module is input into the feature reconstructor for feature reconstruction and color restoration, and then color remote sensing images are obtained. Unlike the conventional methods, our method is an end-to-end model, and without any human interaction in the coloring process. The experimental results show that the color remote sensing image generated by our method is superior to the existing methods in both subjective and objective metrics.
GoGlow Marker Pen Technology Enterpreneurship (ENT600) Technology Blueprint Report / Wan Mohd Zawir Wan Mokhtar

Our business is focusing in the production of glowing ink of marker pen. The main function of our marker pen product is the ink can be seen in the dark. Besides, the marker pen has two-sided features which allow consumers to write with the fine tip or broad tip of writing. The marker pen is well-designed using a low chemical material ink which is safe for consumers to use it as well safe to environment compared to the product of marker pen that currently available in the market which use a harmful chemical material for the ink of marker pen ad characterized by a very strong smell. Other than that, current marker pen does not provide the ink which can be seen in the dark. It would lead the users’ learning and teaching session being limited. For some view, although currently there is a glowing marker pen in the dark, it does not available in various colours of ink. Thus, this glowing marker pen can help to solve the common problems regarding current marker pen used in the market as it can fulfilled consumers’ need.