Reviewerly/exHarmony huggingface dataset:
Benchmarking Meta-heuristic Optimization

Solving an optimization task in any domain is a very challenging problem, especially when dealing with nonlinear problems and non-convex functions.Many meta-heuristic algorithms are very efficient when solving nonlinear functions.A meta-heuristic algorithm is a problem-independent technique that can be applied to a broad range of problems.In this experiment, some of the evolutionary algorithms will be tested, evaluated, and compared with each other.We will go through the Genetic Algorithm, Differential Evolution, Particle Swarm Optimization Algorithm, Grey Wolf Optimizer, and Simulated Annealing.They will be evaluated against the performance from many points of view like how the algorithm performs throughout generations and how the algorithm's result is close to the optimal result.Other points of evaluation are discussed in depth in later sections.
Nonclosedness of sets of neural networks in Sobolev spaces

We examine the closedness of sets of realized neural networks of a fixed architecture in Sobolev spaces. For an exactly m-times differentiable activation function ρ, we construct a sequence of neural networks (Φn)n∈N whose realizations converge in order-(m−1) Sobolev norm to a function that cannot be realized exactly by a neural network. Thus, sets of realized neural networks are not closed in order-(m−1) Sobolev spaces Wm−1,p for p∈[1,∞). We further show that these sets are not closed in Wm,p under slightly stronger conditions on the mth derivative of ρ. For a real analytic activation function, we show that sets of realized neural networks are not closed in Wk,p for any k∈N. The nonclosedness allows for approximation of non-network target functions with unbounded parameter growth. We partially characterize the rate of parameter growth for most activation functions by showing that a specific sequence of realized neural networks can approximate the activation function’s derivative with weights increasing inversely proportional to the Lp approximation error. Finally, we present experimental results showing that networks are capable of closely approximating non-network target functions with increasing parameters via training.
Quantum-classical reinforcement learning for decoding noisy classical parity information

Learning a hidden parity function from noisy data, known as learning parity with noise (LPN), is an example of intelligent behavior that aims to generalize a concept based on noisy examples. The solution to LPN immediately leads to decoding a random binary linear code in the presence of classification noise. This problem is thought to be intractable classically, but can be solved efficiently if a quantum oracle can be queried. However, in practice, a learner is more likely to receive data from classical oracles. In this work, we show that a naive application of the quantum LPN algorithm to classical data encoded in an equal superposition state requires an exponential sample complexity. We then propose a quantum-classical reinforcement learning algorithm to solve the LPN problem for data generated by a classical oracle and demonstrate a significant reduction in the sample complexity. Simulations with a hidden bit string of length up to 12 show that the quantum-classical reinforcement learning performs better than known classical algorithms when the sample complexity, run time, and robustness to classical noise are collectively considered. Our algorithm is robust to any noise in the quantum circuit that effectively appears as Pauli errors on the final state.
Dificultades y temores de las enfermeras que enfrentan la pandemia de COVID-19 en Brasil

Introduction: The high transmissibility and expansion of the new coronavirus (SARS-CoV-2) in the world constitute a high occupational risk for health professionals, especially nurses. Objective: To identify the difficulties and fears of nurses facing the COVID-19 pandemic in Brazil. Methods: This is an exploratory and qualitative study, carried out in March 2020, with nurses from various health institutions. The data collection was done through the WhatsApp application, with a questionnaire. Content analysis was used for data analysis. Results: The difficulties and fears presented were related to the daily risk of exposure to the virus, problems of access and use of personal protective equipment, doubts in the differential diagnosis as well as work overload and the increased demand for care with patients and relatives. Discussion: The vulnerability of the nurses in the study is worrying, since they lack support and care, they interfere with health and the quality of care. This sum of factors increases the fear, anxiety, insecurity and uncertainty facing the future.
The essence and main components of professional competence a teacher of educational robotics

The article is devoted to the theoretical substantiation of the essence, as well as the identification of the main components and methods of forming the professional competence of the teacher of educational robotics. As part of the study, it was found that the subject, methodological, technical, special, organizational and personal components are essential for the successful professional activity of an educational robotics teacher. The professional competence of the teacher of educational robotics should be formed and improved systematically in the framework of specialized areas of training and advanced training of teachers.
View-Invariant Action Recognition

Human action recognition is an important problem in computer vision. It has a wide range of applications in surveillance, human-computer interaction, augmented reality, video indexing, and retrieval. The varying pattern of spatio-temporal appearance generated by human action is key for identifying the performed action. We have seen a lot of research exploring this dynamics of spatio-temporal appearance for learning a visual representation of human actions. However, most of the research in action recognition is focused on some common viewpoints, and these approaches do not perform well when there is a change in viewpoint. Human actions are performed in a 3-dimensional environment and are projected to a 2-dimensional space when captured as a video from a given viewpoint. Therefore, an action will have a different spatio-temporal appearance from different viewpoints. The research in view-invariant action recognition addresses this problem and focuses on recognizing human actions from unseen viewpoints.
Platform “Profosvita” as innovative educational and digital environment for specialists' professional development

The article substantiates organizational and methodological principles of the Educational platform “Profosvita” as an innovative educational and digital environment to ensure the continuous professional development of specialists in accordance with the requirements of the state policy in the field of education, of employers and key stakeholders, as well as to meet educational needs of an individual as educational services customer. The Educational platform “Profosvita” developers summarized and systematized theoretical assets and took into account the latest tendencies of platforms creation for distance and mixed training. The proposed structure of the educational-digital environment provides mastery for learners, customers of educational services of different directions and specialties of certain theoretical and practical aspects of educational courses, educational headings. The presentative and educational locations are described as the main elements of the Educational platform “Profosvita” structure, forms of participant participation, principles of work organization. The research results can be used in the modernization process of postgraduate education system, in particular in the activity of postgraduate education institutions of all levels of accreditation, district methodical services for organization of continuous professional development of specialists in the conditions of sustainable development of education and science.
Distributed Computing and/or Distributed Database Systems

Distributed computing involves many loosely coupled computing platforms, located in different places connected in a network by using LAN or WAN for achieving a common goal. The computing entities have autonomous behavior of computing different operations by using their own computing power and storage capacity. Distributed computing systems can run on homogenous and heterogeneous computing platforms, different hardware that is provided by several vendors and software may use a variety of standard based software elements. These single autonomous systems use independent fundamental software and various communication protocols. Distributed computing requires support from various programming languages and operating system levels. Communication between these independent systems conceptuale through complex message passing methods. Dedicated lightweight processes all have the capacity to communicate with each other via message passing. The nature of distributed systems is unpredictable because they use complex algorithms making it impossible to solve using a deterministic algorithm. Algorithms are different strong applied mathematical game theoretic complex algorithms for placing a single computing entity in the distributed system. The result of the complex placement of the computing entity leads to higher security. Each single entity contributes their job result only without knowing the other entities connected in the network computing pool. In the computing system’s hardware diagnosis of participating computing node is performed by using underlying software that running on many nodes. It is hard to predict the behavior of full or individual components so that distributed computing is invulnerable, non-deterministic and secure. The advancement of distributed computing enables new technology for invulnerability, reliability and performance. Blockchain is the current underlying technology, collaborating used for distributed ledgers. One of the popular applications of blockchain is cryptocurrency – Bitcoin. In Bitcoin each node can act as a distributed system. All the participating nodes share their transactions and each of them keeps its own personal ledger. The application of the blockchain extends to all areas like government, business, finance, etc.
Clifford algebras, algebraic spinors, quantum information and applications

We give an algebraic formulation based on Clifford algebras and algebraic spinors for quantum information. In this context, logic gates and concepts such as chirality, charge conjugation, parity and time reversal are introduced and explored in connection with states of qubits. Supersymmetry and M-superalgebra are also analyzed with our formalism. Specifically we use extensively the algebras [Formula: see text] and [Formula: see text] as well as tensor products of Clifford algebras.
Supporting Student Success

Hande, Karen DNP, RN, ANP-BC, CNE, FAANP; Williams, Ty DNP, RN, ACNP-BC, FNP-BC; McClure, Natasha DNP, PNP-PCAuthor Information
Tackling Cybercrimes in the 5G Era

The study examined the readiness and digital skills of the Nigerian Police in tackling cybercrimes in the 5G era. Consequently, four specific research objectives were identified, and three hypotheses were tested in the study. The descriptive correlational research design was adopted for the study and the population comprised all police officers from the rank of Assistant Superintendent of Police (ASP) and above in the Criminal Investigation Department in Edo State (including the Anti-Fraud Unit). The sample for the study was 35 police officers in the state. The instruments for data gathering was self-designed questionnaire, and the data collected were analyzed using both descriptive and inferential statistics. From the data analyzed, it was gathered that significant relationship existed among the readiness, digital skills of the Nigerian Police, and the tackling of cybercrimes in 5G era. The study concluded that the deployment of 5G technology will bring with it a new level of cyber threat, which will truly test the resolve of cyber security in and outside the country.
Abnormal Behavior Recognition Based on Spatio-temporal Context

This paper presents a new approach for detecting abnormal behaviors in complex surveillance scenes where anomalies are subtle and difficult to distinguish due to the intricate correlations among multiple objects’ behaviors. Specifically, a cascaded probabilistic topic model was put forward for learning the spatial context of local behavior and the temporal context of global behavior in two different stages. In the first stage of topic modeling, unlike the existing approaches using either optical flows or complete trajectories, spatio-temporal correlations between the trajectory fragments in video clips were modeled by the latent Dirichlet allocation (LDA) topic model based on Markov random fields to obtain the spatial context of local behavior in each video clip. The local behavior topic categories were then obtained by exploiting the spectral clustering algorithm. Based on the construction of a dictionary through the process of local behavior topic clustering, the second phase of the LDA topic model learns the correlations of global behaviors and temporal context. In particular, an abnormal behavior recognition method was developed based on the learned spatio-temporal context of behaviors. The specific identification method adopts a top-down strategy and consists of two stages: anomaly recognition of video clip and anomalous behavior recognition within each video clip. Evaluation was performed using the validity of spatio-temporal context learning for local behavior topics and abnormal behavior recognition. Furthermore, the performance of the proposed approach in abnormal behavior recognition improved effectively and significantly in complex surveillance scenes.
GT-GAN: A General Transductive Zero-Shot Learning Method Based on GAN

Most zero-shot learning (ZSL) methods based on generative adversarial networks (GANs) utilize random noise and semantic descriptions to synthesize image features of unseen classes, which alleviates the problem of training data imbalance between seen and unseen classes. However, these methods usually only learn the distributions of seen classes in the training stage, ignoring the unseen ones. Due to the different distributions of seen and unseen samples, i.e., image features, these methods cannot generate unseen features of sufficient quality, so the performances are also limited, especially for the generalized zero-shot learning (GZSL) setting. In this article, we propose a general transductive method based on GANs, called GT-GAN, which can improve the quality of generated unseen image features and therefore benefit the classification. A new loss function is introduced to make the relative positions between each unseen image and its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbors in the feature space as consistent as possible with their relative positions in the semantic space; this loss function may be easily applied in most existing GAN-based models. Experimental results on five benchmark datasets show a significant improvement in accuracy compared with that of original models, especially in the GZSL setting.
An Enhanced Corpus for Arabic Newspapers Comments

In this paper, we propose our enhanced approach to create a dedicated corpus for Algerian Arabic newspapers comments. The developed approach has to enhance an existing approach by the enrichment of the available corpus and the inclusion of the annotation step by following the Model Annotate Train Test Evaluate Revise (MATTER) approach. A corpus is created by collecting comments from web sites of three well know Algerian newspapers. Three classifiers, support vector machines, naïve Bayes, and k-nearest neighbors, were used for classification of comments into positive and negative classes. To identify the influence of the stemming in the obtained results, the classification was tested with and without stemming. Obtained results show that stemming does not enhance considerably the classification due to the nature of Algerian comments tied to Algerian Arabic Dialect. The promising results constitute a motivation for us to improve our approach especially in dealing with non Arabic sentences, especially Dialectal and French ones
A Mundane Cyborg

This chapter is concerned with how the development of telecommunication technologies in the city has affected the ways in which ordinary people practice their everyday lives as mundane cyborgs which are human-machine hybrids, and how the combination of human bodies and mobile phones has transfigured the landscapes of urban streets. It provides a brief consideration about the vulnerability of mundane cyborgs as bodies-with-mobiles which are activated and mediated through outside technological networks beyond the boundaries of human bodies. Smartphones can be seen as technological objects which afford the new practices of human bodies as mundane cyborgs in a mobile and networked environment. In relation to the use of smartphones, one of the interesting phenomena is that bodies without smartphones can be thought of as abnormal, deficient, or bizarre ones, although currently they are rarely seen so.
New role for circuit expansion for learning in neural networks

Many sensory pathways in the brain include sparsely active populations of neurons downstream from the input stimuli. The biological purpose of this expanded structure is unclear, but it may be beneficial due to the increased expressive power of the network. In this work, we show that certain ways of expanding a neural network can improve its generalization performance even when the expanded structure is pruned after the learning period. To study this setting, we use a teacher-student framework where a perceptron teacher network generates labels corrupted with small amounts of noise. We then train a student network structurally matched to the teacher. In this scenario, the student can achieve optimal accuracy if given the teacher's synaptic weights. We find that sparse expansion of the input layer of a student perceptron network both increases its capacity and improves the generalization performance of the network when learning a noisy rule from a teacher perceptron when the expansion is pruned after learning. We find similar behavior when the expanded units are stochastic and uncorrelated with the input and analyze this network in the mean-field limit. By solving the mean-field equations, we show that the generalization error of the stochastic expanded student network continues to drop as the size of the network increases. This improvement in generalization performance occurs despite the increased complexity of the student network relative to the teacher it is trying to learn. We show that this effect is closely related to the addition of slack variables in artificial neural networks and suggest possible implications for artificial and biological neural networks.
A Marine Radar Dataset for Multiple Extended Target Tracking

The marine radar remains one of the most extensively used sensor for maritime surveillance. Owing to improvedtechnologies, it can nowadays be exploited to gain information about the extents of targets, since multiplemeasurements can be obtained from a single target. This paper introduces an open multitarget marine radarbased dataset subjected to a linear-time joint probabilistic data association (JPDA) filter for tracking extendedtargets using ellipsoidal approximations. A tailored version of the Multiplicative Error Model-Extended KalmanFilter* (MEM-EKF*) algorithm is used for estimating the orientations and kinematic properties of multipletargets recursively. Using the automatic identification system (AIS) information as ground truth, the positionalerrors are evaluated using the optimal sub pattern assignment (OSPA) metric and the performance of thealgorithm for orientation estimation is rationally discussed with respect to the ground truth and dataset scenario.The dataset proposed is intended for comparing different algorithms for the purpose of multiple extended targettracking (METT).
Survey of UAV Autonomous Landing Based on Vision Processing

At present, with the rapid development of UAV technology, UAV has begun to play an important role in the field of military and civilian applications, which has attracted more and more attention. In the context of automation, there are more and more attention on how UAV achieves autonomous landing. In this paper, it is to describe the research situation of autonomous landing of UAV based on visual processing at home and abroad first, and then introduce several technologies of image processing, target tracking, position estimation and autonomous control according to the process. Based on these three process components, the existing shortcomings are put forward. Finally combined with the actual situation, the research ideas are proposed.
Didactic And Heuristic Potential Of American Horror Cinema

The article is dedicated to the role of American horror films in the formation of the modern education institute. They make possible visualizing the significant meanings of culture by creating and visualizing a system of argumentation based on destructive artistic images. Emotional immaturity and information saturation of the educational process are the causes of the formation of the youth inferiority complex, which American horror film tries to compensate for the. The formalization of educational communication provokes emotional instability of the young generation, striving for maximum freedom while achieving a high position in the group hierarchy. First of all, school teachers are symbols of mature social communication, which is sharply denied by young people, which leads to the complete displacement of the experience of the older generation from the youth picture of the world. The American horror film visualizes this contradiction by introducing images of monsters into the film space, eliminating all representatives of the older generation. The genre of horror films reanimates archaic lower mythology and appeals to the sacred potential of cosmogonical myths and heroic images. Hollywood horror films directly correspond to the archaic myth-scenarios of the youth individuation, which indicates the fundamental nature of these archetypal and interrelated processes. Over time, they turn into stereotypes, assessments and behavior patterns. Hollywood creates and broadcasts ideological schemes that restore the archaic connection of man with the world, which gives American horror films a special didactic and heuristic value.
Explicit Construction of Optimal Witnesses for Input-Output Correlations Attainable by Quantum Channels

Given a quantum channel — that is, a completely positive trace-preserving linear map — as the only communication resource available between two parties, we consider the problem of characterizing the set of classical noisy channels that can be obtained from it by means of suitable classical-quantum encodings and quantum-classical decodings, respectively, on the sender’s and the receiver’s side. We consider various classes of linear witnesses and compute their optimum values in closed form for several classes of quantum channels. The witnesses that we consider here are formulated as communication games, in which Alice’s aim is to exploit a single use of a given quantum channel to help Bob guess some information she has received from an external referee.