Reviewerly/exHarmony huggingface dataset:
Speech Master: Natural Language Processing and Deep Learning Approach for Automated Speech Evaluation

Every English speaker wishes to expertise his/her public speaking skills sharply. However, it is extremely difficult and requires a significant amount of practice and experience on an individual basis. This paper introduces a novel online tool “Speech Master” to practice and improve public English speech delivering skills in a professional manner. Using natural language processing, machine learning, and deep learning approaches, the proposed system analyzes the user's speech in terms of content, grammatical accuracy, grammatical richness, facial expressions, and flow. The accuracy was checked by comparing actual results taken from experts with the predicted results obtained from the tool. “Speech Master” achieves an average accuracy of more than 80% and produces a better overall result. This novel tool benefits English speakers all over the world by meeting the demand for a simple and easy-to-use solution for improving or practicing English speech delivery skills; enhancing oratory skills, boosting confidence, and delivering well-articulated speeches.
Modeling of the content of the physics course based on the percolation coefficient

Abstract This article deals with the problem of modeling the assessment of the level of integrity of the content of the physics course learned by students. The need for such modeling is due to the need to bring education courses to online format. The degree of integrity of learning is proposed to be established by modeling the percolation of intradisciplinary connections established in the course structure. The model uses the graph model of intradisciplinary connections (T. N Gnitetskaya) and the principles of percolation theory (P-theory). The article provides a few practical solutions for the use of P-theory. An algorithm has been developed that allows forming the topics of the physics course into the studied structure.
Unification of Imprecise Data

Inference systems are a well-defined technology derived from knowledge-based systems. Their main purpose is to model and manage knowledge as well as expert reasoning to insure a relevant decision making while getting close to human induction. Although handled knowledge are usually imperfect, they may be treated using a non classical logic as fuzzy logic or symbolic multi-valued logic. Nonetheless, it is required sometimes to consider both fuzzy and symbolic multi-valued knowledge within the same knowledge-based system. For that, we propose in this paper an approach that is able to standardize fuzzy and symbolic multi-valued knowledge. We intend to convert fuzzy knowledge into symbolic type by projecting them over the Y-axis of their membership functions. Consequently, it becomes feasible working under a symbolic multi-valued context. Our approach provides to the expert more flexibility in modeling their knowledge regardless of their type. A numerical study is provided to illustrate the potential application of the proposed methodology.
Towards Business Cost Mining

The aim of organizations is to achieve their business goals with reduced costs. Therefore, it is very important to accurately compute the cost of their business processes and to extend the business process models with cost perspective in order to support decision-makers in reducing these costs. Different approaches proposed an extension of business process models with cost perspective using event logs. However, these approaches do not consider the achievement of the business goals, while it is crucial to provide information about costs incurred by the business process execution that led (or not) to the achievement of the business goals. Such costs, named business costs, depend on the reliability of the business process. Thus, we consider to propose a business cost mining approach allowing the extension of business process models with business cost perspective using cost-annotated event logs. This approach includes business cost description and analysis in order to extract knowledge about factors influencing on business costs. This will further support decision-makers to reduce the costs of their business processes. In this paper, we propose an approach towards business cost mining allowing the extension of business process models with business cost perspective at the activity level.
Application of Artificial Intelligence Technology in College English Teaching

In order to give full play to the value of artificial intelligence technology in college English teaching, this paper will carry out related research, mainly discussing the basic concept of the technology, and put forward the basic framework of technology teaching application. Then, an intelligent system for college English teaching is designed around the basic framework, and finally, the application mode of the system is analyzed. By using this system, many problems in college English teaching can be solved with the help of artificial intelligence technology, and the teaching can be optimized to improve the quality and efficiency of teaching.
Security Issues of a Decentralized Blockchain-Based Messaging System

This paper presents a decentralized messaging system based on blockchain technology. This system allows their users to securely send and receive digital messages in the network. Since the messages stored in a conventional blockchain could be easily read by everyone in the network, under the proposed approach these messages are previously encrypted using public-key cryptography, while the sender and recipient remain anonymous. The proposed system incorporates a browser-based user interface that enable their users to interact seamlessly in a peer-to-peer fashion.
Not All Tasks Are Created Equal: Adaptive Resource Allocation for Heterogeneous Tasks in Dynamic Workflows

Users running dynamic workflows in distributed systems usually have inadequate expertise to correctly size the allocation of resources (cores, memory, disk) to each task due to the difficulty in uncovering the obscure yet important correlation between tasks and their resource consumption. Thus, users typically pay little attention to this problem of allocation sizing and either simply apply an error-prone upper bound of resource allocation to all tasks, or delegate this responsibility to underlying distributed systems, resulting in substantial waste from allocated yet unused resources. In this paper, we will first show that tasks performing different work may have significantly different resource consumption. We will then show that exploiting the heterogeneity of tasks is a desirable way to reveal and predict the relationship between tasks and their resource consumption, reduce waste from resource misallocation, increase tasks&#x0027; consumption efficiency, and incentivize users&#x0027; cooperation. We have developed two info-aware allocation strategies capitalizing on this characteristic and will show their effectiveness through simulations on two modern applications with dynamic workflows and five synthetic datasets of resource consumption. Our results show that info-aware strategies can cut down up to 98.7&#x0025; of the total waste incurred by a best-effort strategy, and increase the efficiency in resource consumption of each task on average anywhere up to 93.9&#x0025;.
Automations in Chemostratigraphy: Toward Robust Chemical Data Analysis and Interpretation

Abstract Elemental chemostratigraphy has become an established stratigraphic correlation technique over the last 15 years. Geochemical data are generated from rock samples (e.g., ditch cuttings, cores or hand specimens) for up to c. 50 elements in the range Na-U in the periodic table using various analytical techniques. The data are commonly displayed and interpreted as ratios, indices and proxy values in profile form against depth. The large number of possible combinations between the determined elements (more than a thousand combinations), makes it a time-consuming effort to identify meaningful variations that resulted in correlative chemostratigraphic boundaries and zones between wells. The large number of combination means that 30-40% of the information is not used for the correlations that maybe crucial to understand the geological processes. Automation and artificial intelligence (AI) are envisaged as likely solutions to this challenge. Statistical and machine learning techniques are tested as a first step to automate and establish a workflow to define (chemo-) stratigraphic boundaries, and to identify geological formations. The workflow commences with a quality check of the input data and then with principle component analysis (PCA) as a multivariate statistical method. PCA is used to minimize the number of elements/ratios plotted in profile form, whilst simultaneously identifying multidimensional relationships between them. A statistical boundary picking method is then applied define chemostratigraphic zones, for which reliability is determined utilizing quartile analysis, which tests the overlap of chemical signals across these statistical boundaries. Machine learning via discriminant function analysis (DFA) has been developed to predict the placement of correlative boundaries between adjacent sections/wells. The proposed workflow has been tested on various geological formations and areas in Saudi Arabia. The chemostratigraphic correlations proposed using this workflow broadly correspond to those defined in the standard workflow by experienced chemostratigraphers, while interpretation times and subjectivity are reduced. While machine learning via DFA is currently further researched, early results of the workflow are very encouraging. A user-friendly software application with workflows and algorithms ultimately leading to automation of the processes is under development.
Research on the Calibration Method of Torque Sensor

Abstract Remote calibration has the property of real-time and remote location, and can be used to control the calibration method for the calibration instrument. It is an effective method to improve the calibration efficiency. Based on the torque sensor calibration method for research, because the remote calibration technology has complex structure, calibration are not allowed to wait for a characteristic, through the remote technology combined with a torque sensor automatic calibration technology, analyze the torque sensor remote calibration technology research, which not only can improve the traditional calibration technology cycle is long, low efficiency of faults. Remote calibration technology based on the basic methods and characteristics are studied, by using the method of torque meter calibration, according to the characteristics of torque sensor, choose corresponding calibration program and method, which can effectively improve the technical problems in the process of system design and analysis of torque sensor remote calibration method research, in addition, through the analysis of electromagnetic interference technology research, which can restrain the interference is proposed; Finally, the remote calibration technology of the torque sensor is effectively improved through the calibration experiment of the system debugging.
A software defect detection algorithm based on asymmetric classification evaluation

This paper proposes a software defect detection algorithm based on asymmetric classification evaluation, and applies the algorithm to a complete system. The system includes software data input interface, controller and detection result output port. The controller is used to detect the received software module, obtain the original software measurement data set, and calculate the number of the original software measurement data set. According to the data preprocessing, the data is divided into training samples and test samples. The training sample data is classified into asymmetric model. Discriminated and structured dictionary is used to evaluate the performance of asymmetric classifier. The detection is transferred to the test samples, the model is used to detect the defects of the software detection module, the evaluation results are fed back to the tester to complete the detection; and then through the detection results, If the detection result is provided to the user. The algorithm proposed in this paper can enhance the representation ability of dictionary and has good discrimination performance. At the same time, it can effectively solve the error caused by the imbalance of data and accurately locate the location of software defects.
A Comparative Study of Sentiment Analysis Tools

COVID-19 outbreak compelled people to stay at home due to complete lockdown in all the working areas. Immense use of World Wide Web and social media to exchange and share opinions, generated enormous web data to be utilized in the research work of the Natural Language Processing (NLP) field. Being a dominant side of NLP, Sentiment Analysis uses numerous tools to classify human sentiments as Positive (1), Negative (-1) and Neutral (0) so as to reach various conclusions. This research work focused on sentiment analysis of four datasets, web scraped from four different sources namely: Twitter, Facebook, Economic Times Headlines and news articles keyed by stock market. Seven contemporary and tremendously used sentiment analysis tools: Stanford, SVC, TextBlob, Henry, Loughran-McDonald, Logistic Regression and VADER are considered here to process four scraped datasets individually and analyses result in two ways: Facebook scraped data generates maximum overall positive sentiment score as 38.17% and VADER tool performs best among seven tools. VADER calculates overall positive sentiment score as 56.63%
Parametrical Construction of MDS Based on Block Cyclic Shift

With the advent of the information age, using the same standard algorithm with fixed logic is difficult to meet the increasingly diversified cryptographic application requirements of cloud computing, Internet of things and 5g network environment. Dynamic variable cryptographic algorithm can improve the security and adaptability of cryptographic algorithm, which is the focus of cryptographic algorithm research and design at present. The key to the research and design of dynamic variable cryptographic algorithms is to find dynamic variable cryptographic components with good cryptographic performance. MDS (Maximum Distance Separable) is often used in the diffusion layer design of block ciphers because of its optimal diffusion characteristics. Traditional MDS transformation generally adopts MDS matrices in the finite field. While using them in dynamic variable cryptographic algorithms, it is difficult to realize parametric application because of their logical differences and implementation imbalance. Aiming at the lightweight MDS variable design requirements of dynamic variable cryptographic algorithm, this paper firstly proposes a MDS parametric construction method based on block cyclic shift and XOR operation. Compared with the traditional MDS construction in the finite field, we obtain a class of MDS component clusters with larger scale which is convenient for software and hardware implementation. The main design idea is to convert the multiplication operation in the finite field into cyclic shift operation, and introduce parameters to participate in the MDS operation in the way of direct XOR. Based on the MDS matrix in AES diffusion layer, two kinds of variable MDS diffusion layers are given by this method. The consistent structure based on cyclic shift and XOR operation is adopted, and each parameter corresponds to different MDS transformation, which can be used for the parametric design of diffusion layer of dynamic variable cryptographic algorithm.
Research and Design of an RFID Based Intelligent Position Device for Laboratory Instruments

Radio Frequency Identification is a kind of technology whose operating principle is non-touching data communication between reader and e-tag for the purpose of target-identifying. In the traditional manual management mode, laboratory managers undertake loads of identifying jobs during which some mistakes inevitably occur, leading to high management costs but low working efficiency which seriously affects regular work in the laboratory. Taking into account this problem, this thesis presented an RFID-based positioning device for fast locating laboratory instruments which mainly involves a specially designed UHF RFID e-tag and an UHF reader. The e-tag with the function of LEDs is installed on a given laboratory instrument adopting the passive approach, remotely identifying the instrument via RFID reader. Upon receiving search instruction, the corresponding LED emits light to indicate the desired instrument’s exact position so that laboratory workers can easily and quickly find it which significantly improves working efficiency and reduces management costs. Additionally, the e-tags’ chip possesses storage function with super memory capacity, which is of great convenience for technicians to input the checking information of the laboratory instruments into the e-tags’ chip via the reader as well as record these information via a handheld terminal. Thus, they will easily figure out the present state of the instruments without going through a large number of paper records. As a consequence, the RFID-based locating device significantly improves work efficiency and save labor costs involved in the checking process, and contributes to achieving completely automatic management of laboratory instrument. In particular, it is beneficial to take realizing paperless office one step further to a large degree when this invention allows technicians read out the information regarding the large quantities of laboratory instruments.
INVESTIGATION OF DOGECOIN PRICE MOVEMENTS: A GSADF ANALYSIS

Today, people provide information through different channels. The information channels used can affect the decision-making mechanism due to asymmetric information or different tendencies. Especially in recent years, people use social media to reach information quickly. Therefore, notifications made on social media reveal economic results. Cryptocurrencies are digital currencies intended to be used as currency. Unlike their traditional financial rivals, cryptocurrencies are not backed by a central bank or authority.&#x0D; The success of cryptocurrencies depends on its infrastructure, the block chain. Especially in recent years, the popularity of cryptocurrencies has increased. After the popularization of cryptocurrencies, digital currencies are discussed more especially in the media. In addition to the positive features, negative features are also included in the media. There are concerns about the misuse of cryptocurrencies. It is mentioned that cryptocurrencies provide financing for criminal organizations and are used in money laundering. In addition to these, it is reported that cryptocurrencies are used for tax evasion. &#x0D; The lack of intrinsic value of cryptocurrencies puts investors in trouble in terms of investment and price determination. Cryptocurrencies, which are digital currencies, have many digital price determinants such as social media. Two different objectives were determined in this study. The first is the detection of the presence of bubbles in Dodgecoin prices. The second is the examination of the relationship between bubbles and tweeter notifications. In the study, Dodgecoin prices between May 2020 and May 2021 are examined with the GSADF test. From May 2020 until May 2021, 10 different price bubbles are observed. Some bubbles can be associated with tweets by Elon Musk. However, the biggest bubble observed, the April 2021 price bubble, is due to a different reason.
Critical Success Factor on E-Learning: Case Study Ruangguru Indonesia

E-Learning has become a new trend in recent years. One of the e-learning trends among school students in Indonesia is Ruangguru. Ruangguru has an animation learning videos and has a different e-learning model. This article aims to identify the factors that influence the success of Ruangguru using the Critical Success Factor (CSF) method. This study distributed questionnaires to 100 respondents, questionnaires made based on the CSF method. The CSF factor used has 6 factors and 36 sub-factors, the CSF factor consists of the characteristics of students, instructor characteristics, quality content, information technology quality, educational institution support and system support. The results of this study stated that CSF Factors together have a significant influence on the Success of Ruangguru&#x2019;s especially Information Technology Quality and System Support. From the results obtained produce some recommendations for the success of Ruangguru.
Total Optical Layer Information Security

In this work we show how some limits of quantum optical security that confine its use to the exchange of keys can be overcome by using other physical information protection techniques that, not pushing the limits of quantum physics, allow to better exploit the transmission capacity of the optical channel. These new techniques can therefore be used for symmetric cryptography carried out at the physical layer and alongside the QKD to achieve complete information protection at the optical layer.
Performance enrichment through parameter tuning of random forest classification for imbalanced data applications

One of the foremost application domains in today’s real-life scenario is unequal data distribution within datasets, related classifiers, and its Performance Enrichment Techniques (PET). Random Forest Classification (RFC) is one of the most efficient techniques that can function speedily over binary or multiclass imbalanced characteristics datasets. With its built-in ensemble capacity, building a generalized model on any Binary Imbalanced Datasets (BID) and Multiclass Imbalanced Datasets (MID) gets much easier. Related work carried out here implies that the attention of researchers is inclined towards ID applications and related machine learning techniques. RFC gives improvised performance due to Ensemble Approach (EA). EA generates several classifiers and segregates the results as PET. The performance of the single classifier is lower than the performance of the set of multiple classifiers. The associated study in this research paper provides insight into three PET, which are the count of folds in cross-validation, variable count of trees and the feature selection method through Regression Analysis (RA) and it’s tuning with RFC Classifier. Time and Cost required for classification is saved with the help of RA. Extensive experimental analysis carried out with a predictive modeler Salford System with proposed PET and tested on 32 benchmark datasets, which justifies the enhanced performance of RFC.