Reviewerly/exHarmony huggingface dataset:
Green Aware Based VM-Placement in Cloud Computing Environment Using Extended Multiple Linear Regression Model

In recent years, because of the increase in the huge volume of data and increase in data analytics in various research areas like health care, image processing etc., it is highly needed to provide required resources for processing the information. Cloud computing process an approach for delivering required resources by improving the utilization of data-center resources which results in increasing the energy costs. In order to overcome this new energy-efficient algorithms are introduced, that decreases the overall energy consumption of computation and storage. To reduce the energy-efficiency in cloud data centers, server consolidation technique is used, which plays a major road block. To address this issue, this project proposes a Prediction based Thermal Aware Server Consolidation (PTASC) model, a consolidation method, which takes numeric and local architecture into consideration along with Service Level Agreement. PTASC, consolidates servers (VM Migration) using a statistical learning method.
Modelling of global solar irradiance on sloped surfaces in climatic conditions of Kraków

Abstract The paper presents calculations of global solar irradiance on inclined surfaces of any orientation in the hourly time step. For computational purposes there were used the data from typical meteorological years (TMY) available in a form of text files on the website of the Ministry of Infrastructure and Development. Hourly solar global horizontal irradiance from measurements from the file for Kraków was used as input for five anisotropic models (Hay, Muneer, Reindl, Perez and Perez according to the new PN-EN ISO 52010-1 standard). Direct normal and diffuse horizontal and then global irradiances were calculated. To illustrate the effects of using different models, for the exemplary residential building, monthly solar heat gains and heating demand was determined according to the monthly method of PN-EN ISO 13790. In comparison to the solar data from the TMY, an average decrease in the value of solar gains amounted 37%, what resulted in an increase in the calculated heat demand of the building by 10%. This is very important since this change takes place without any modernisation works.
Compression's Methods based on Frequency and Time Frequency Transforms applied on DNA's images

Nowadays, the image's compression presents major interest for storage and communication. This paper deals with DNA's particular images and compression based on frequency and time frequency Transform methods. Three compression methods realized by such Transforms have been studied. The first is based on the Discrete Cosine Transform (DCT), the second used the “Wavelet Transform” and the third is realized by the Curvelet Transform. We have investigated all the methods and adopted them on DNA's images (Chromosome I of C. Elegans). Then we evaluated their performances by calculating the Compression Rate (CR), the Peak Signal to Noise Ratio (PSNR) and also the Structural Similarity (SSIM) Index. We obtained promising results, especially with the Curvelet Transform that was used to improve our approach. CR, PSNR, SSIM reached 93.63, 97.0767 and 1, respectively.
Interactive Supervision with t-SNE

Knowledge capture from human experts in domain-specific settings can benefit from incisive use of machine intelligence to reduce expended time and effort. Such a capability can be of significant value to deep learning, given its demand for large labeled data. We propose an ML-based system for interactive labeling of image datasets to speed up class attribution performed by domain experts. The tool visualizes feature spaces and makes it directly editable through online integration of applied labels. We propose realistic annotation emulation to evaluate the system design of interactive active learning, based on our improved semi-supervised extension of t-SNE dimensionality reduction. We contribute globally normalized attractions, semi-supervised repulsion, smoothed label integration, and parameter optimization in our improved t-SNE. Our active learning tool can significantly increase labeling efficiency compared to uncertainty sampling, and we show that less than 100 labeling actions are typically sufficient for good classification on a variety of specialized image datasets. Our contribution is unique given that it needs to perform dimensionality reduction, feature space visualization and editing, interactive label propagation, low-complexity active learning, human perceptual modeling, annotation emulation and unsupervised feature extraction for specialized datasets in a production-quality implementation.
A Critical Approach to Accounting in the Digital Era: Assessment of the Impacts of Industry 4.0 on Financials

The Information Age has led to the emergence of Industry 4.0 with rapidly developing technological devices making the data collection process much easier. Industry 4.0, which is also called the Second Machine Age, is expected to shorten the process, reduce margin of error and costs with the inclusion of self-learning robots in the production process. This shift will definitely have an impact on financial statements which depict the performance of a business organization. In addition, the items in a financial statement will undoubtedly change because of the use of virtual money, just-in-time production, and the decreasing role of humans in the production process being replaced by robots. In consequence, the financial analysis ratios will be modified in accordance with the changes in financial statements. This chapter focuses on the effects of Industry 4.0 on financial statements and financial statement analysis from a theoretical perspective. The possible effects of developing information technologies on financial statements and ratio analysis will be discussed.
Virtual Adversarial Training for Semi-supervised Verification Tasks

The goal in verification tasks is to determine the similarity of two samples or verifies if they belong to the same category or not. In this paper, we propose a semi-supervised embedding technique for verification tasks using deep neural networks. The proposed model exploits the unlabeled data by making the model robust to the perturbation of the input with virtual adversarial training. It increases the generalization of the embedding function and prevents overfitting which are crucial in verification tasks. The proposed algorithm, named VerVAT, is evaluated on several verification tasks and compared with state-of-the-art algorithms. Experiments show the effectiveness of VerVAT especially in cases where limited labeled data is available.
A Novel Construction of Constrained Verifiable Random Functions

Constrained verifiable random functions (VRFs) were introduced by Fuchsbauer. In a constrained VRF, one can drive a constrained key <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>s</mml:mi><mml:msub><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math> from the master secret key <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>s</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:math>, where S is a subset of the domain. Using the constrained key <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>s</mml:mi><mml:msub><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>, one can compute function values at points which are not in the set S . The security of constrained VRFs requires that the VRFs’ output should be indistinguishable from a random value in the range. They showed how to construct constrained VRFs for the bit-fixing class and the circuit constrained class based on multilinear maps. Their construction can only achieve selective security where an attacker must declare which point he will attack at the beginning of experiment. In this work, we propose a novel construction for constrained verifiable random function from bilinear maps and prove that it satisfies a new security definition which is stronger than the selective security. We call it semiadaptive security where the attacker is allowed to make the evaluation queries before it outputs the challenge point. It can immediately get that if a scheme satisfied semiadaptive security, and it must satisfy selective security.
An Efficient Training Strategy for a Temporal Difference Learning Based Tic-Tac-Toe Automatic Player

Temporal Difference (TD) learning is a well-known technique used to train automatic players by self-play, in board games in which the number of possible states is relatively small. TD learning has been widely used due to its simplicity, but there are important issues that need to be addressed. Training the AI agent against a random player is not effective, as several millions of games are needed until the automatic player starts to play intelligently. On the other hand, training it against a perfect player is not an acceptable option due to exploratory concerns. In this paper we present an efficient training strategy for a TD-based automatic game player, which proves to outperform other techniques, needing only roughly two hundred thousand games of training to behave like a perfect player. We present the results obtained by simulation for the classic Tic-Tac-Toe game.
Advanced Bayesian Network Models with Fuzzy Extension

Fuzzy Bayesian networks (FBNs) are the variant of standard/classical Bayesian networks (BNs), which have intrinsic capability of handling ambiguity due to lack of expert knowledge and eventually reduces the epistemic uncertainty when used as computational models. Of late, FBNs have gained substantial research interest to be applied for time series prediction in both non-spatial and spatial domains . This chapter discusses a number of fuzzy BN models that have recently been proposed in literature. The central attention is paid on how the discrete Bayesian analysis in the previously discussed enhanced BN models can be further improved through incorporated fuzziness so as to make them more realistic for dealing with various contexts of spatial time series prediction. A comparative study, at the end of the chapter, demonstrates superiority of the fuzzified enhanced BN models, compared to those having no incorporated fuzziness.
Robot Operating System Based Path Planning

As autonomous cars are catapulted into prominence, path planning has taken the center stage. In this study, We implement the Tangent-bug approach to path planning. We have discretised the Tangent-bug algorithm into a set of independent operations. Transition from one iteration to the next is governed by the value of the range sensor and the convergent criterion. The tangent detection and tangent selection algorithms are tested by implementing on a 2D differential drive robot in the player stage simulator. Contrary to existing solutions, this paper proposes implementation on standard robotics middle-ware ROS (Robot Operating System) allowing effortless deployment on various robotics platforms. Features developed in this module are decentralised, amenable to scaling across multiple robotics platforms with minimal configuration.
Numerical Solution of Non-Linear Diffusion Equation in Image Blurring Using Two-Point EGSOR Iterative Method

Abstract The non-linear diffusion equation is known to be a significant application in solving image processing issues. The equations provided the image filtering techniques that blurring the image without degrade the edge information which is also one of crucial study in computer vision. Nonetheless, an intense amount of computations is needed in filtering the image as the sizes that keep getting bigger. Along these lines, this paper constructs an analysis to speed up the required computation in solving the developed linear system with the faster iterative method, i.e. two-point Explicit Group Successive Over-Relaxation or known as 2-EGSOR. For the performances comparison, the standard Gauss-Seidel (GS), Successive Over-Relaxation (SOR) and 2-EGSOR iterative method will be set to produce almost the similar quality image of Jacobi iterative method measured by using percentage error of the overall pixels difference. Subsequently, it is discovered the 2-EGSOR offers faster approach to blur the image compared the others iterative methods with the least iterations and computational time.
PARIESA: Practice and Research in Enactive Sonic Art

This research presents an overview of the extended applications of enactive and embodied music cognition within creative music and sonic art practice. Digital and haptic technologies, improvisation, and performance have been used to explore a range of interdisciplinary collaborations from pedagogy to health and wellbeing, as well as music perception.
Transformation process of modern education: to teach something another way?

The question of the need for modernization processes in modern education are raises in the article. The relevance of the research of educational problems is not controversial, because the political, economic, cultural, intellectual potential of the state, the welfare of society, development of all humanity depends on the state and possibilities of its development are noted . The purpose of the article is to find a new way of thinking, vision, ideas, etc. In order to ensure the success of education, its entrepreneurship in the context of the interdisciplinary nature of the pedagogical study scenario. The emphasis is on transformational processes of education development. It is generalized that education can never exist separately from society, from its needs, in addition, it is largely responsible for both the achievement of humanity and for humanitarian catastrophes and global problems. For the social and economic growth of the country, the level of education and culture of the population is important. Education of the XXI century is an intellectual potential, it is an education with a cultural-developing, developing dominant, a factor in the perfection of a person; this is the education of a responsible person who is capable of self-education and self-development, able to think critically, develop various information, use acquired knowledge and skills for creative solving. the problems, aspires to change for the better their life and life of their country.&#x0D; The art of education, which is the most difficult of arts, is firmly rooted in society, must be truly high art. Therefore, this problem is not only philosophical or pedagogical, but also global. Issues of ethical basis, emotional intelligence, upbringing man of culture should take the top priority in educational labyrinths. But before modern education, there are many more complex tasks, the solution of which would contribute to a significant increase in the formation of a high-grade personality with high moral and spiritual qualities.
Emotion recognition method of Tibetan micro-blog text based on sentiment dictionary

Abstract With the development of network technology, social media represented by micro-blog has become an important platform for personal emotional expression. Automatic analysis of micro-blog texts and identification of their emotions is important for understanding people’s emotions. In this paper, the emotion of Tibetan micro-blog text is analyzed automatically for the first time, the emotion in text expression is recognized, the analysis method based on Tibetan sentiment dictionary and rules is put forward, and the language characteristics of emotion expression in Tibetan micro-blog are discussed. The experimental results show that the accuracy of emotion recognition based on Tibetan sentiment dictionary reaches 78.6%, which provides a basis for establishing a high-precision Tibetan textual sentiment classification system.
New Entropic Inequalities for Qudit (Spin j = 9/2)

We consider information characteristics of a single qudit (spin j = 9/2), such as the von Neumann entropy, and define the von Neumann mutual information for this system, which does not contain any subsystems. In addition, we consider the von Neumann information of a qudit toy model as a function of real parameters. The inequalities obtained describe the quantum hidden correlations in the single-qudit system.
An Universal Perturbation Generator for Black-Box Attacks Against Object Detectors

With the continuous development of deep neural networks (DNNs), it has become the main means of solving problems in the field of computer vision. However, recent research has shown that deep neural networks are vulnerable to well-designed adversarial examples. In this paper, we used a deep neural network to generate adversarial examples to attack black-box object detectors. We trained a generation network to produce universal perturbations, achieving a cross-task attack against black-box object detectors. We demonstrated the feasibility of task-generalizable attacks. Our attack generated efficient universal perturbations on classifiers then attack object detectors. We proved the effectiveness of our attack on two representative object detectors: Faster R-CNN based on proposal and regression-based YOLOv3.
Application of Firefly Algorithm for Face Recognition

Face Recognition is steadily making its way into commercial products. As such, the accuracy of Face Recognition systems is becoming extremely crucial. In the Firefly Algorithm, the brightness of fireflies is used to measure attraction between a pair of unisex fireflies. The firefly with higher brightness attracts the less bright firefly. The objective function is defined in proportion to the brightness, to define a maximization problem. This chapter aims to present the promising application of the Firefly Algorithm for Face Recognition. The Firefly Algorithm is used in a hyperdimensional feature space to select features that maximize the recognition accuracy. This chapter delineates how the Firefly Algorithm is a suitable algorithm for selection of the features in a Face Recognition model. The Firefly Algorithm is then applied to this feature space to identify and select the best features. Fireflies are arbitrarily placed on various focal points of the image under consideration. The advantage of this approach is its fast convergence in selecting the best features. The gamma parameter (\(\gamma \)) controls the movement of fireflies in this feature space and can be tuned for gaining an improvement in the performance of the Face Recognition model. This chapter aims to evaluate the performance and viability of using the Firefly Algorithm for Face Recognition.
Automated support for spacecraft onboard equipment design on the basis of a heterogeneous model

Abstract This article presents methods and software tools for modeling and analysis of space system onboard equipment operation. The authors suggest the design of a heterogeneous model uniting knowledge bases and virtual devices simulating work of data reception and transmission equipment. Model design considers tasks appearing during preparation of satellite production at a company-manufacturer of the space industry. The model can be used at the stages of designing and testing of the onboard equipment. For design support, the model allows assessment of parameters of the reception-and-transmission path and of destabilizing factor influence on communication line quality. For ground test support, the model simulates functions of the onboard equipment during command-and-software control of a spacecraft, providing reception and transmission methods for a wide range of commands and methods of generation of telemetry frame values. The comparison of precedents obtained during simulation tests with the results of the finished equipment testing, allows determination of special features of the onboard system operation that can stay unnoticed if other methods of test preparation and analysis are applied.
Improving Turkish Telephone Speech Recognition with Data Augmentation and Out of Domain Data

In this paper, we investigate the effects of data augmentation and adding out of domain data on Turkish spontaneous speech recognition. We apply different acoustic model training techniques including Gaussian Mixture Models, Deep Neural Network and Time Delay Neural Network to Babel Turkish spontaneous telephone speech data. We find that Time Delay Neural Network with iVectors based acoustic model performs the best result. We demonstrate the effect of data augmentation by adding speed and volume perturbation applied data in training. We investigate the effect of increasing acoustic model training data by including two call center data. We increase training data by adding about 100 hours of modified out of domain broadcast data. We also examine the effect of neural network based language modeling techniques like Recurrent Neural Network language models.
Attention Based Speech Model for Japanese Recognization

The Deep Neural Networks have been used for the Automatic Speech Recognition recently, and they have achieved great improvement in accuracy. Especially, CNN (Convolutional Neural Networks) are used on Acoustic feature extraction, which not only improves the accuracy of speech recognition, but also the parallel efficiency. Attention mechanism has shown very good performance in sequence to sequence patterns. Based on Attention mechanism with CNN and LSTM (Long Short-Term Memory) speech recognition model, this paper takes the 10,000 Japanese sentences as examples for training. Without any the language model, the pronunciation accuracy of Japanese fifty-tone diagrams reaches 89%.
The art of fieldworking together

This forum highlights conversations at the intersection of design methods and social studies of technology. By highlighting a diversity of perspectives on design interventions and programs, we aim to forge new connections between HCI design and communication, science and technology studies, and media studies scholarship. --- Daniela Rosner, Editor
Development of Building Micro Geodata for Earthquake Damage Estimation

Large-scale earthquakes are predicted to occur in Japan in the near future. Quantitative evaluation and comparison of micro-scale damage risk and initial response capability throughout the country is expected to support disaster prevention planning to mitigate damage during large-scale earthquakes. We developed an estimation method of fire resistance performance, structure, building age, and resident information of about 60 million buildings across Japan by disaggregating various census data into building polygon data. By comparing the results obtained by this method with true values, we found that the reliability of this method was high. Finally, by adding probabilistic seismic motions predicted to occur in Japan to these data, we could estimate damage risk corresponding to earthquake motion.
Discriminative common feature subspace learning for age‐invariant face recognition

Considering human ageing has a big impact on cross-age face recognition, and the effect of ageing on face recognition in non-ideal images has not been well addressed yet. In this study, the authors propose a discriminative common feature subspace learning method to deal with the problem. Specifically, they consider the samples of the same individual with big age gaps have different distributions in the original space, and employ the maximum mean discrepancy as the distance measure to compute the distances between the sample means of the different distributions. Then the distance measure is integrated into Fisher criterion to learn a discriminative common feature subspace. The aim is to map the images with different ages to the common subspace, and to construct new feature representation which is robust to age variations and discriminative to different subjects. To evaluate the performance of the proposed method on cross-age face recognition, the authors construct extensive experiments on CACD and FG-Net databases. Experimental results show that the proposed method outperforms other subspace based methods and state-of-art cross-age face recognition methods.