Reviewerly/exHarmony huggingface dataset:
Optimization of the Mixture Transition Distribution Model Using the March Package for R

Optimization of mixture models such as the mixture transition distribution (MTD) model is notoriously difficult because of the high complexity of their solution space. The best approach comprises combining features of two types of algorithms: an algorithm that can explore as completely as possible the whole solution space (e.g., an evolutionary algorithm), and another that can quickly identify an optimum starting from a set of initial conditions (for instance, an EM algorithm). The march package for the R environment is a library dedicated to the computation of Markovian models for categorical variables. It includes different algorithms that can manage the complexity of the MTD model, including an ad hoc hill-climbing procedure. In this article, we first discuss the problems related to the optimization of the MTD model, and then we show how march can be used to solve these problems; further, we provide different syntaxes for the computation of other models, including homogeneous Markov chains, hidden Markov models, and double chain Markov models.
MÖBIUS–FROBENIUS MAPS ON IRREDUCIBLE POLYNOMIALS

Abstract Let n be a positive integer and let $\mathbb{F} _{q^n}$ be the finite field with $q^n$ elements, where q is a prime power. We introduce a natural action of the projective semilinear group ${\mathrm{P}\Gamma\mathrm{L}} (2, q^n)={\mathrm{PGL}} (2, q^n)\rtimes {\mathrm{Gal}} ({\mathbb F_{q^n}} /\mathbb{F} _q)$ on the set of monic irreducible polynomials over the finite field $\mathbb{F} _{q^n}$ . Our main results provide information on the characterisation and number of fixed points.
Social Emotion Cause Extraction from Online Texts

Social emotion refers to the emotion evoked to the reader by a textual document. Compared to classical sentiment analysis conducted from the author's perspective, emotion analysis from the reader's perspective (i.e. social emotion mining) is an important task in web-based social media analytics, and particularly meaningful for security related applications. Mining the causes of social emotions, i.e. Social Emotion Cause Extraction (SECE), is a new challenging task in social emotion mining, which can help better explain the elicited social emotions in text. In this paper, we propose the SECE task for the first time, and construct a new SECE dataset to support our study. We develop the first computational method for this new task, and conduct experimental studies to evaluate the effectiveness of our proposed method based on the dataset we construct.
A research in the design of augmented reality gamified mobile applications for promoting traditional products implemented in ambient intelligence environment of small shops

In this study, challenges in the design of mobile applications in an ambient intelligent environment for promoting traditional products in small shops were analyzed. Fuzzy cognitive maps (FCM) were constructed from interviews with experts in presenting traditional products and from gamification literature. Simulations for investigating the causalities between the concepts of the FCMs followed and Business Process Model and Notation (BPMN) was used as a tool for quick modeling of an augmented reality mobile application. Afterwards scenarios based on different gamification user types were created and simulated. The case study was TalkingProducts research project which was focusing on the development of an augmented reality mobile application for promoting traditional local products, where product manufacturers and professionals of the creative industries sector cooperate in order to co-create smart interactive gamified experiences. The study of the FCM’s metrics and the results of the BPMN simulations showed that a better understanding of an application user’s behavior can be achieved which can lead to better design choices especially as regards to software requirements and functionality. The suggested methodological approach can offer valuable insights to the designing of interactive applications in ambient intelligence environments.
AdjointBackMap: Reconstructing effective decision hypersurfaces from CNN layers using adjoint operators

There are several methods in the exploration of Convolutional Neural Networks' (CNNs') inner workings. However, in general, finding the inverse of the function performed by CNNs as a whole is an ill-posed problem. In this paper, we propose a method based on adjoint operators to reconstruct, given an arbitrary unit in the CNN (except for the first convolutional layer), its effective hypersurface in the input space. Since the reconstructed hyperplane (each point on the hypersurface) resides in the input space, we can easily visualize it. Our results show that the reconstructed hyperplane, when multiplied by the original input image, would give nearly the exact output value of that unit. We find that the CNN unit's decision process is largely conditioned on the input, and the corresponding reconstructed hypersurfaces are highly sensitive to adversarial noise, thus providing insights on why CNNs are susceptible to adversarial attack.
Generation and Evaluation of a New Time-Dependent Dynamic S-Box Algorithm for AES Block Cipher Cryptosystems

Cryptographic communication systems depend extensively on the Advanced Encryption Standard (AES) block cipher algorithm. The encryption strength in the AES algorithm is derived from the nonlinearity introduced by the application of s-boxes inside the encryption process. Today, most s-boxes are either static in which the s-box content does not change during the encryption process or dynamic which continuously changes depending on the encryption key. Two major problems exist in such systems that increase the risk of unauthorized decryption. First, the encryption of the same data yields the same cipher key repeatedly. Seconds, the key-dependent s-boxes require sharing the encryption key each time a new s-box is generated. In this paper, a dynamic s-box is introduced that constantly changes independent of the encryption key. The new s-box depends on the epoch timestamp that exists in every digital system as well as transmitted with digital satellite communication systems. The generation of the nonlinear s-box occurs inside the transmitter and receiver identically. The main strength of the proposed approach is that the ciphertext changes while keeping the encryption key constant ensuring different encryption results for the same data. This paper also investigates and analyzes the strength and quality of the new s-box using the avalanche effect method. The results showed that different ciphertext is produced after every encryption process. Moreover, the modified dynamic s-box reached a high avalanche effect exceeding the Strict Avalanche Criterion (SAC).
DRAMA

As the density of DRAM becomes larger, the refresh overhead becomes more significant. This becomes more problematic in the systems that require large DRAM capacity, such as the training of deep neural networks (DNNs). To solve this problem, we present DRAMA, a novel architecture which employs the approximate characteristic of DNNs. We make that non-critical bits are not refreshed while critical bits are normally refreshed. The refresh time of the critical bits are concealed by employing per-bank refreshes, significantly improving the training system performance. Furthermore, the potential racing hazard of bank-refresh technique is simply prevented by our novel command scheduler in DRAM controllers. Our experiments on various recent DNNs show that DRAMA can improve the training system performance by 10.4% and save 23.77% DRAM energy compared to the conventional architecture.
THE CORRUPTION AND HUMAN DEVELOPMENT TO THE ECONOMIC GROWTH OF OIC COUNTRIES

This paper aims to estimate the effect of corruption (represented by data of Corruption Perception Index) and human development (represented by data of Human Development Index) on the economic growth (represented by data of Gross Domestic Product) in 44 (forty-four) countries who join the Organization of Islamic Cooperation (OIC). this paper uses multiple linear regression with panel data of Corruption Perception Index from transparency.org, Human Development Index from United Nations Development Program (UNDP) and Gross Domestic Product from World Bank. The 10 years range of data (2009-2018) finds the Corruption Perception Index does not have a significant effect on the economic growth, but The Human Development Index has a positive significant effect on the economic growth or Gross Domestic Product. It can be seen that the commitment of OIC countries in human development is on the right path, since they are in line with their economic growth rates. When viewed together, the Corruption Perception Index and the Human Development Index have a simultaneous influence on the economic growth. So, this result is important to help the OIC as additional references to their Annual Summit or Conference to focus more on Human Development strategy to enhance the economic growth.
RETRACTED: Simulation of football based on PID controller and BP neural network

Wheel robot soccer speed control system using a ball object detection method and PID controller. A control system is based on the object detection system's behavior based on the robot position's orientation to the target position. PIDs are instruments, pressure, speed, and other operational factors used in control, temperature adjustment flow, and industrial control applications. The PID controller uses control loop feedback dynamics to control functional variables and is the most accurate and stable controller. The robot position is held by placing the ball vertically. When the robot's work is perpendicular to the ball, the robot moves with a certain speed controlled by the PIT controller based on the robot's distance and the ball. Standard conditions (standard ball) test results show that the robot can detect the ball material while in the vertical position, whether on the robot's right or left. In the random test that changes direction, the robot can move more dynamically as the ball's change in place.
Some aspects of using mixed reality technologies in the training of physics teachers

Abstract The introduction of modern technologies in the learning process at school is an important task, both for the education system as a whole and for an individual teacher. The ability to work with new teaching aids allows the teacher to interest students in their subject. One of these tools is virtual and mixed reality headsets. The emphasis is on mixed reality, although the two are often closely related. The article provides a brief overview of works devoted to the use of mixed reality in teaching physics, a study of the readiness of students of a pedagogical university to use this technology in their future professional activities is carried out. The questionnaire for expert assessment of mixed reality developments has been modified, and a survey of experts in this field has been conducted. The results of the research may be of interest to teachers of various specialties, senior students and graduate students in the field of physics teaching methods.
Feasibility of Kd-Trees in Gaussian Process Regression to Partition Test Points in High Resolution Input Space

Bayesian inference using Gaussian processes on large datasets have been studied extensively over the past few years. However, little attention has been given on how to apply these on a high resolution input space. By approximating the set of test points (where we want to make predictions, not the set of training points in the dataset) by a kd-tree, a multi-resolution data structure arises that allows for considerable gains in performance and memory usage without a significant loss of accuracy. In this paper, we study the feasibility and efficiency of constructing and using such a kd-tree in Gaussian process regression. We propose a cut-off rule that is easy to interpret and to tune. We show our findings on generated toy data in a 3D point cloud and a simulated 2D vibrometry example. This survey is beneficial for researchers that are working on a high resolution input space. The kd-tree approximation outperforms the naïve Gaussian process implementation in all experiments.
Fuzzy Ant Bee Colony For Security And Resource Optimization In Cloud Computing

Cloud computing (CC) systems prevail to be the widespread computational paradigms for offering immense scalable and elastic services. Computing resources in cloud environment should be scheduled to facilitate the providers to utilize the resources moreover the users could get low cost applications. The most prominent need in job scheduling is to ensure Quality of service (QoS) to the user. In the boundary of the third party the scheduling takes place hence it is a significant condition for assuring its security. The main objective of our work is to offer QoS i.e. cost, makespan, minimized migration of task with security enforcement moreover the proposed algorithm guarantees that the admitted requests are executed without violating service level agreement (SLA). These objectives are attained by the proposed Fuzzy Ant Bee Colony algorithm. The experimental outcome confirms that secured job scheduling objective with assured QoS is attained by the proposed algorithm.
Safe Storage of Biometric Data

The paper provides an overview of modern identification methods based on the physiological characteristics of a person, as well as the basic principles of their implementation. A method for reliable storage of user biometric data using algorithms based on a residual number system is proposed.
Acceptance of Ubiquitous Registration Information Systems

Acceptance of information systems is very obvious in the continuity of its use, especially for an organization when implementing information systems requires no short time and no small cost. This research aims to find out what factors influence the acceptance of the use of ubiquitous registration information systems in rural areas. The research model used is a technology acceptance model. The research method used three stages consisting of a literature review, operationalization, and reporting. The results that have been obtained from this study are the attitude towards use has a significant effect on the intention to be used, the perception of ease of use has a significant effect on attitude towards use, and the perception of ease of use has a significant effect on the perception of usefulness. The results of this study are expected to provide input to related parties, especially decision-makers in the development and application of an information system.
Hamiltonian and Q-Inspired Neural Network-Based Machine Learning

The goal of this study is to present a universal large-scale machine learning model based on spectral processing. By machine learning, we mean input-output mapping approximation generated by training sets. We treat tasks such as pattern recognition and classification as special problems in mapping approximation. The structures of the approximators are implemented using Hamiltonian neural network-based biorthogonal and orthogonal transformations. From a mathematical point of view, these structures can be seen as an implementation of non-expansive mappings. An interesting property of approximators is the reconstruction and recognition of incomplete or distorted patterns. The reconstruction property gives rise to a proposition of a superposition processor and reversible computations. Finally, the models of machine learning described here are adequate for processing data with real and complex values by defining Q-inspired neural networks.
TPM, a pattern for an architecture for trusted computing

Trusted Computing (TC) is a valuable technology to use when we need to process highly sensitive data and several variants of it are now available. We present here a pattern that describes the architecture of a standard for TC, the Trusted Platform Module (TPM), which is an architecture for which there are several possible implementations. Standards are effectively patterns because of the freedom to implement them in a variety of ways. The TPM architecture contains cryptographic functions to attest the use of legitimate hardware and software platforms and defines operations to perform related trusted operations. Some of its variants are intended to only store private keys in a secure way. Although promising, this technology often has not been properly used. This motivated us for defining a comprehensive methodology that facilitates the design and development of secure TC-equipped systems that can fully exploit the functionalities that this technology provides. This paper provides the first step of this methodology in the form of a security pattern. We have tried to provide a reasonable amount of information in our pattern description, aimed at the needs of application designers. This pattern is described in an abstract form independent of implementation details, but expressed with enough detail and precision for the use of designers.
An IoT Smart Environment in Support of Disease Diagnosis Decentralization

The percentage of seniors in the global population is constantly growing and solutions in the field of fall detection and early detection of neuro-degenerative pathologies have a crucial role in order to increase life expectancy and quality of life. This study aims to extend fall detection and effective recognition of early signs of diseases to new smart environments, conceiving the decentralization of diagnostic monitoring in everyday life activities in a more pervasive paradigm. Inspiring to research outcomes, in this work an architecture is designed to detect falls in crowded indoor environments during events/exhibitions, for favoring a timely and effective intervention. It also foresees a continue monitoring of the gait for seniors during the visit, thus extracting key features which are stored on a dedicated database. The proposed solution allows third party researchers to perform analysis on the obtained gait datasets, through the adoption of advanced data-mining techniques for the detection of early signs of neuro-degenerative diseases and other pathologies. The architecture designed here aims to provide a step forward concerning the extension of smart monitoring environments for the detection of falls and early signs of pathologies in everyday life, in a more pervasive and decentralized paradigm.
A New Defect Detection Method for Improving Text Detection and Recognition Performances in Natural Scene Images

This paper presents a new idea for improving text detection and recognition performances by detecting defects in the text detection results. Despite the rapid development of powerful deep learning based models for scene text detection and recognition in the wild, in complex situations (logos or decorated components connected with text), existing methods do not yield satisfactory results. In this paper, we propose to use post-processing method to improve the text detection and recognition performance. The proposed method extracts features, namely phase congruency, entropy and compactness for the text detection results. To strengthen discriminative power for feature extraction, we explore the combination of SVM classifier and Gaussian distribution of text components to determine proper weight, which represents true text component. The weights are multiplied with the features to detect defect components though clustering. The bounding boxes are redrawn, which results proper bounding box without defects components. Experimental results show that the proposed defect detection reports satisfactory results. To validate the effectiveness of defect detection, we conduct experiments on benchmark datasets of MSRA-TD-500 and SVT for detection and recognition before and after defect detection. The result shows that the performance of text detection and recognition improves significantly after defect detection.
Innovations of internet resources under digitalization of the educational process

The author of this article analyzes Internet resources that are designed to improve the online education efficiency in the context of spreading of the coronavirus infection. The article considers the problems of using new technical and information tools of the global information and educational environment to improve the quality of traditional and e-learning. The emphasis is placed on defining educational content on the Internet that develops the cognitive and professional interests of students of technical and humanitarian major subjects. The article specifies the audience of virtual space users who has participated in the experiment for four years, summarizes comparisons of quantitative indicators of the content of cognitive resources, and presents the results and conclusions of the study. The main research methods were the analysis of scientific literature, generalization of features of educational content on the Internet, questionnaire polls, testing, and performance of tasks in the modern information space.
Analysis of Customer Satisfaction in Go-Food Services: Customer Relationship Management

This research was conducted in the relation of popular use among Indonesian towards the scheme of food delivery services through online application by motorcycle driver or commonly called Go-Food. The survey was conducted through quantitative methods in the form of online questionnaires involves 56 student respondents. Actually, this study aims to examine several factors that influence decision making in terms of satisfaction in utilizing the service of Go-Food. Thus, this study uses the six variables according to the Technology Acceptance Model (TAM), which are subjective norms, perceived use usability, ease of use perspective, usage attitude, behavior to use, and system usage in the application Gojek. Overall, this study presents two variables has been found to be reliable, which is meant consistent based on customer satisfaction attributes.
$k$-Variance: A Clustered Notion of Variance

We introduce $k$-variance, a generalization of variance built on the machinery of random bipartite matchings. $k$-variance measures the expected cost of matching two sets of $k$ samples from a distribution to each other, capturing local rather than global information about a measure as $k$ increases; it is easily approximated stochastically using sampling and linear programming. In addition to defining $k$-variance and proving its basic properties, we provide in-depth analysis of this quantity in several key cases, including one-dimensional measures, clustered measures, and measures concentrated on low-dimensional subsets of ${\mathbb R}^n$. We conclude with experiments and open problems motivated by this new way to summarize distributional shape.
Modified Method of Ant Colonies Application in Search for Rational Assignment of Employees to Tasks

The approach to determining the rational appointment of employees for the tasks of an innovative project is considered. It is proposed to apply a modification of the ant colony method to solve the assignment problem for cases when the task completion time for an employee is determined by a fuzzy set, taking into account the loss of time for employee interaction. To modify the ant colony method, recommendations on the choice of parameters are proposed: the number of agents in the group, the evaporation coefficient, the parameters of the elite and rank algorithm. The problem of “looping” the ant colony algorithm due to the allocation of one path by all agents is considered. To solve the problem, it is proposed to reset the solution graph and return to its initial state. Improving the algorithm when resetting the decision graph is proposed to introduce weights from the best solutions.