Reviewerly/exHarmony huggingface dataset:
Implementation of Present Counter-Mode Algorithm as a Challenge Value Generator in Color Combo Access Control Prototype

In access control context, user identification usually consists of a username and password. A good password usually consists of a complex combination of characters which are long and difficult to remember. Easy-to-remember passwords are usually text-based passwords, but these passwords are vulnerable to shoulder surfing attack and guessing attack. To solve this problem, another authentication scheme can be implemented, such as color combo scheme. Color combo scheme is an authentication scheme that combines colors and numbers. In our color combo scheme, a challenge value generator is added by using a cryptographic algorithm. In this paper, Present Counter-Mode Algorithm is implemented as the challenge value generator on the color combo access control prototype. The counter mode of operation is used in Present algorithm so the algorithm functions as a stream cipher algorithm. Four aspects are analyzed in this paper i.e. generation time of the challenge value, memory, user authentication successful rate, and power consumption. Based on the measurement result, challenge value generation time is 9.4 ms, memory usage in the sketch is 8850 bytes (27%) and in global variables is 982 bytes (47%), user successful rate is 100%, and power consumption is 2.07 watts.
An algorithm for pseudocoloring images in FPGA

An FPGA circuit (Field Programmable Gate Array) is one of the most flexible programmable integrated circuits that allow the user to build his own hardware architecture optimized for a specific application. Implementation of algorithms in hardware is possible using these circuits, even having some advantages related to their execution speed. In this paper we aim to build in MATLAB a simple algorithm for pseudocoloring a monochrome image, and then to implement the same algorithm in FPGA. The idea used is to assign to the value of each pixel in the image other three values, corresponding to the 3 color matrices R, G and B, which form the color image. These values are assigned based on a conversion table. The two color images are then compared in terms of accuracy and execution speed of the algorithms.
“Radical Reorganization…”: M. I. Pyrohov as Trustee (1858–1861) and Measures in Improving Professionalism of Teachers of Kyiv Educational District

Reformation of the educational system of independent Ukraine encourages the study of concrete experience of implementing radical changes in the field of education. The purpose of the article is to study the innovations of the trustee of the Kyiv Educational District M. I. Pyrohov in teacher training in the state. The methodological basis of the study is the socio-cultural approach in combination with psychohistory. As a result of the study it is proved that the authoritative doctor and scientist M. I. Pyrohov in Kyiv Educational District continued the mission of “serving the truth and the Motherland”. Progressive pedagogy, sincere humanism and democracy in the educational process became the core of the trustee’s administrative activity. The personality of a teacher was in the focus of change. The trustee implemented measures to improve the professionalism of the teaching staff in the District: projects of the Pedagogical Seminary and a special Pedagogical Gymnasium were prepared; training of university students in didactics was improved and their practical classes were organized on the basis of the Kyiv gymnasiums; the concept of pedagogical courses at provincial gymnasiums for training of teachers of the county and parish schools was offered; competitive procedure for appointing teachers; educational and methodical meetings of pedagogical councils were initiated. The strategy of innovations was to increase the pedagogical authority and personal dignity of teachers.We believe that under Pyrohov as the trustee there was an irreversible turn towards true democratization of the management system, relations between the subjects of the educational process, modernization of didactic and pedagogical technologies in the Kyiv Educational District. The success of innovations was due to the high public authority of Pirogov, his exceptional professionalism and integrity as an administrator, a sincere desire for the progress of education in the state.
The Problem of Constructing the GMDH Neural Networks with Active Neurons

Characteristics of the existing neural networks of GMDH with active neurons are given and their main advantages and disadvantages are analyzed. An approach to increasing the efficiency of inductive construction of complex system models from statistical data based on the creation of a new class of GMDH neural networks with active neurons using methods of computational intelligence is proposed.
A Study of University Website Content Classification Using Machine Learning

Abstract A university website is a gateway to the institution’s information, products, and services. As websites grow into millions in numbers, it is essential to ensure that the content reflects the needs of its students, staff, and other academic institution as their primary users. This research investigates the development of a new framework that uses machine learning techniques based on webometrics and web usability to classify the web pages of academic websites automatically. The framework briefly introduced how it can help classify web content and eliminate unrelated content and reduce storage space. The findings can also be used to analyse other web-based data to give additional insights that may be beneficial for webometrics studies and identify university website’ characteristics.
Data Analysis and Application of Resource Search Algorithm in Basic Education Fairness

With the rapid development of the Internet, various information resources have expanded dramatically. How to retrieve effective information resources at a high speed in a large-scale network environment has become a severe challenge facing the Internet. In recent years, P2P, as a new type of network application mode, has become more and more popular due to its scalability and high fault tolerance. As the core technology of P2P applications, the resource search mechanism can find resources that meet user requirements at the fastest speed in a distributed dynamic environment such as P2P. The equity of basic education in the dual structure of urban and rural areas is the focus of Chinese society, and its data analysis is crucial in the research process, and the resource search algorithm can efficiently search for the resources needed in this article. Based on this, this article aims to study the resource search algorithm in P2P and its application in the basic education fairness of urban and rural dual structure. After analyzing the characteristics of the P2P network, the classic resource search algorithm and the impact of basic education injustice, use the resource search algorithm. The flooding algorithm obtains and analyzes the relevant data related to the equity of basic education in the dual structure of urban and rural areas that this article needs, and puts forward suggestions on the equity of basic education on this basis. The research results show that since 2013, the qualification rate of full-time teachers in urban and rural primary and secondary schools in Province G has reached more than 99%; in addition, the proportion of teachers with a college degree or above in urban primary schools and a bachelor degree or above in junior high schools in 2016 has increased by 12 compared to 2010. 21 and 25.02 percentage points, while the proportion of teachers in rural elementary and junior high schools increased by 19.23 and 28.72 percentage points respectively.
The Method to Simulate of Seismic Wave Prompting of SDOF System

the paper introduces the method of solve the Second-Order Linear Nonhomogeneous Differential Equations. Design three Model based on that method to simulation of seismic wave prompting of SDOF system by Simulink and made a simple Time sequence analysis.
THE ATTITUDE OF STUDENTS TO INDIVIDUAL WORK IN THE MODERN EDUCATIONAL PROCESS

Показано значение самостоятельной работы для обновления знаний, выделена структура категории «отношение», установлены предпочтения студентов вуза в сфере самостоятельной работы, определен индекс отношения студентов к самостоятельной работе. The importance of independent work for updating knowledge is shown, the structure of the category "attitude" is highlighted, the preferences of university students in the field of independent work are established, the index of students' attitude to independent work is determined.
Design and Implementation of a Blockchain-Based International Trade Stable Digital Currency Issuance System

With the development of Internet technology, digital currency is playing an increasingly important role in the field of international trade. The emergence of digital currency makes it play an increasingly important role in the fields of electronic payment and financial services. Blockchain technology enables paperless transactions and data sharing. At present, blockchain technology has become one of the research hotspots in the international financial field. Due to the vigorous development of international trade, the demand for digital currencies is also increasing. To this end, this article designs a digital currency issuance system based on blockchain technology for the stability of international trade. This article mainly uses the data analysis method to investigate and analyze the current situation of international trade, then uses the data collection method to understand the blockchain in depth, uses the experimental method to design and test the digital currency issuance system, and finally interviews the sampled users for questionnaires survey. Experimental research results show that 86.3% of people believe that the test content of the system is relatively comprehensive and has a certain significance for the stable issuance of digital currency.
An evolving framework for fault diagnosis of dynamic systems

<h2>Abstract</h2> Currently, many applications work with data streams, such as financial market analysis, detection of attacks on computer network systems, fraud detection, detection and fault identification, social media analysis, e-commerce analysis, and marketing campaign assistance. In this context, approaches based on evolving systems have shown to be quite adequate for analyzing and processing this kind of data. AutoCloud is an evolving algorithm specially designed for dynamic clustering of data stream with online learning. Here, the AutoCloud is applied to fault detection and diagnosis of dynamic systems using online and unsupervised learning.
Expert-guided Policy Optimization by Latent Space Planning with Attention

Planning in learned dynamics models has proven to have great potential in improving sample efficiency. However, learning the latent representation that embeds to model dynamics from high-dimensional observation is still challenging. We here propose a model-based policy gradient method, that quickly learns an optimal policy directly from pixel frames. First, the dynamics model is learned with a SENet architecture, that explicitly incorporates attention and gating mechanism to differentiate features for the latent representation. Second, the policy in the early stage is guided with successful experience to extract the intention of the experts thus to speed up the convergence. Finally, we use model rollout to decrease the value estimation bias and multi-step policy gradients to update the policy. Our approach outperforms state-of-the-art algorithms on multiple benchmark tasks in sampling efficiency and convergence performance.
A visualization Method about the Dataset of Energy Disaggregation for exploratory Research

In machine learning studies, we default to an equal probability distribution for each category, which means that if we look at the whole dataset, the number of occurrences of each category in any given period of time is similar, or at least at the same quantitative level. As one of the classification problems, the research of energy disaggregation related to the problem of class imbalance in the dataset is still vacant. In this paper, we try to explore the unbalanced situation of categories in large datasets of the energy disaggregation research, and on the basis of removing the restriction of the number of input variables in the application of Karnaugh Map, we put forward a visualization scheme, which can enable researchers to effectively interpret the equilibrium situation of various categories in the datasets, to choose the appropriate datasets or optimize datasets according to the requirements.
A Comparison between Deep Belief Network and LSTM in Chaotic Time Series Forecasting

The application of deep neural networks in forecasting time series data is increasingly popular, aiming to improve prediction accuracy in this problem. However, as for chaotic time series, a special kind of time series data generated from the deterministic dynamics of a nonlinear system, there are very few research works applying deep neural networks to forecast this kind of time series data. So far, Deep Belief Network (DBN) and Long Short Term Memory (LSTM) are two kinds of deep neural networks used to extract meaningful features from the chaotic time series before forecasting. This article aims to compare the prediction performance of the LSTM model with that of the DBN model on chaotic time series data. Experimental results on six synthetic and real-world datasets in this study show that LSTM brings out better prediction accuracy than DBN in terms of three evaluation criteria.
Personality Enhancement for Speaker-dependent Voice Activity Detection

Recently, a various speaker-dependent Voice Activity Detections (VAD) have been proposed which detect target speaker's speeches in noisy environment. Speaker-dependent VAD is similar to knowledge distillation in which it learns distribution of each speaker from speaker embedding model trained with lots of speakers. That is, the key idea is to sufficiently learn speaker embedding vector distribution for enhancing personality. In this paper, we proposed new strategies to enhance personality of speaker-dependent VAD. To make better personal characteristics of speakers, we considered several factors based on model size, language, and gender. Our experiments show that the model strategies achieves significant performance improvement on Average Precision(AP) of 0.959, 0.935, compared to 0.735, 0.530 of baseline model for each language evaluation set.
Infinite Series as Sums of Triangular Areas

SummaryA method is developed for exhausting the area under certain curves using triangles generated by an infinite binary tree. The associated series depend on the choice of binary tree, and examples are given to illustrate how this method can be used to evaluate certain series.
On the Status Quo and Application of Online Virtual Art Exhibition Technologies

Online virtual art exhibition technology diversifies the forms of art exhibitions and provides an immersive experience of public cultural services to the general public, facilitating the popularization of art exhibitions at a national level. This paper expounds on the basic forms of presentation and course of development of online virtual art exhibitions from two perspectives of the exhibition based on physical display and exhibition composed of pure digital information. Through website browsing, this paper investigates the current situation of online virtual art exhibitions in China and discovers that only a few provincial art museum websites have online virtual art exhibitions. By prospecting the future development direction of online virtual art exhibition, the paper deems that the R&D in open virtual art exhibition technology is an irresistible trend of the future, only through which virtual art exhibition services at a national level may be popularized structurally.
Book review

This conversation analytic study describes the interactional uses of ciò [ʧɔ], a pragmatic particle that is used both in the regional Italian spoken in Veneto (a region of northeastern Italy) and in Trevigiano (Trevixàn [trevi’zaŋ]), an Italo-Romance variety widely used in the Treviso area. Preliminary results show that ciò (derived from the imperative form of the verb ciór/tòr ([ʧor]/[tɔr] ‘to take’) is mostly used as a preface in responsive position and is frequently preceded by the particle eh. In our data, the (eh) ciò-prefaced turns are designed as general, objective, obvious contributions through which participants provide a wider perspective on the topic while treating the preceding talk as defective or not dealing with relevant aspects of the subject under discussion. This study contributes to the documentation of linguistic forms that are used by speakers of an Italo-Romance variety as conversational resources in talk-in-interaction.
Petrophysics applied in mineralizations, hydrothermal alterations and lithology mapping: A case study from the Zn–Pb (Cu–Ag) epithermal deposit of Santa Maria, Brazil

ABSTRACT The epithermal Zn–Pb (Cu–Ag) deposit of Santa Maria represents a distal magmatic‐hydrothermal system, whose mineralizations are controlled by fault systems located in the sedimentary units of the upper Camaquã Basin, above the tectonic units of the Sul‐Riograndense Shield. The hydrothermal alteration zones contain illite, chlorite and pyrite, besides galena, sphalerite, chalcopyrite and bornite. To improve the knowledge of this mineral system, this work investigated the petrophysical footprints of samples representing the predominant lithology, altered rocks and hydrothermal mineralization. The core samples of the predominant lithology, altered rocks and hydrothermal deposit mineralizations were used to determine the following petrophysical properties, density, magnetic susceptibility, primary wave velocity, resistivity, conductivity and chargeability. Moreover, the quantitative evaluation of minerals by scanning electron microscopy coupled with an automated image analysis system allowed us to map lithological and alteration processes. The results indicate density as the most effective physical property to map lithology, hydrothermal alteration and the Zn–Pb (Cu–Au) mineralization. Furthermore, all studied physical properties have moderate effectiveness in the alteration zones of known geological and geophysical anomalies in the Santa Maria deposit. Chargeability could be used, especially when sulphides are disseminated, but additional geological factors complicate its interpretation. The mineralogical and petrophysical diversity of the Santa Maria deposit provided vital data for geological–geophysical interpretations while allowing the creation of a key exploration plan to investigate the Zn–Pb (Cu–Au) mineralization. Finally, petrophysics should be used in prospection to help understand complex geological processes, their overlapping subpopulations and to accelerate mineral research while reducing the use of technical and financial resources and expenditure on ineffective geophysical methodologies.
The Film Script Generation Analysis Based on the Fiction Book Text Using Machine Learning

The paper focuses on building a model for predicting the film&#x0027;s success based on machine learning techniques. A relatively high level of forecasting accuracy has been revealed. However, the efforts to improve the predictive power models have been limited to modifying the algorithms rather than finding significant features, which could be crucial to the film&#x0027;s success. With the help of system design tools, the most critical functionality of the system has been described. The advantages of competitors have also been analysed, and features of our system have been allocated. As a result, this system can be improved according to user needs.
Provisioning with Fine-grained Affinity for Container-enabled Cloud-edge System

Containerization effectively decouples the applications and facilitates those user-defined dependencies for extreme elasticity via existing container management frameworks. However, the variance occurs among container-enabled hosts at edges, since their copies of images and libraries, and available resources are quite different. Then, even for the same container, the costs of retrieving the dependency also vary over hosts, since existing mechanism forces each host to retrieve the missing parts from the central repository only upon its own status. Although previous researches have optimized the management of dependencies, the deployment mainly focuses on the entire image. Instead, upon the fine-grained difference on dependencies, we propose to minimize the overall latency of both retrieving the missing parts and the executions under constrained resources. We propose an integer linear program and then design a randomized algorithm to select suitable hosts to deploy the containers. The probability calculated in algorithm shows the preference and the affinity of hosts, and is derived from the status of fine-grained dependencies. Via rigorous proof, the result of our algorithm is concentrated on its optimum with high probability. Extensive simulations confirm that our algorithm outperforms the alternatives up to 44% regarding the average completion time of containers.
Analysis of Hardware Prefetchers Suitable for CNN Applications

Employing CPUs for convolutional neural networks (CNNs) is of interest to many users due to its availability. However, to make CPUs more competitive, it is necessary to bridge the performance gap between CPUs and other accelerators such as GPUs and TPUs. Date prefetching is the promising optimization technique because CNN operations tend to have predictable data access patterns. This paper examines and analyzes the usefulness of various hardware prefetchers in CNN applications.
Applications and implications of whole-slide imaging in breast pathology

Technological advances in whole slide imaging (WSI) technology and artificial intelligence (AI) applications in recent years have resulted in increasing adoption of this paradigm shift technology. This brings with it many advantages, new challenges, and potential adaptations to the microscopic assessment of specimens that pathologists need to be aware of. This article describes the applications and implications of WSI within the context of the reporting of breast pathology specimens. Challenging diagnostic entities in digital breast pathology are presented and the key areas in which AI could be useful in breast pathology are highlighted.
An Improved Conditional Generative Adversarial Network for Translating Depth Image from Color Image and Accurate Hand Gesture Recognition

Pix2Pix is a common method in the image-to-image translation task. In the field of gesture recognition, previous studies employed Pix2Pix for translating color images to depth images to improve the accuracy of the original color images. However, they mainly focused on improving the quality of the generated images, ignoring the goal of classifying gestures. In this study, we propose a discriminative Pix2Pix for translating depth images from color images. Our motivation is to generate more understandable images for neural networks instead of those for humans. We introduce a new discriminator called Feature-level Discriminator (FLD). The original Pix2Pix discriminator can be considered a Image-level Discriminator (ILD). FLD distinguishes the extracted feature map of an image by a specified convolutional neural network (CNN), whereas ILD focuses more on images. We evaluate our approach on the OUHAND dataset, indicating that FLD can significantly improve the accuracy of the generated image and color image using a two-stream framework.