Reviewerly/exHarmony huggingface dataset:
Sensing Control Parameters of Flute from Microphone Sound Based on Machine Learning from Robotic Performer

When learning to play a musical instrument, it is important to improve the quality of self-practice. Many systems have been developed to assist practice. Some practice assistance systems use special sensors (pressure, flow, and motion sensors) to acquire the control parameters of the musical instrument, and provide specific guidance. However, it is difficult to acquire the control parameters of wind instruments (e.g., saxophone or flute) such as flow and angle between the player and the musical instrument, since it is not possible to place sensors into the mouth. In this paper, we propose a sensorless control parameter estimation system based on the recorded sound of a wind instrument using only machine learning. In the machine learning framework, many training samples that have both sound and correct labels are required. Therefore, we generated training samples using a robotic performer. This has two advantages: (1) it is easy to obtain many training samples with exhaustive control parameters, and (2) we can use the correct labels as the given control parameters of the robot. In addition to the samples generated by the robot, some human performance data were also used for training to construct an estimation model that enhanced the feature differences between robot and human performance. Finally, a flute control parameter estimation system was developed, and its estimation accuracy for eight novice flute players was evaluated using the Spearman's rank correlation coefficient. The experimental results showed that the proposed system was able to estimate human control parameters with high accuracy.
Leukocytes identification using augmentation and transfer learning based convolution neural network

Most haematological diseases can be diagnosed using the morphological analysis of the microscopic blood image. The basic routine of the morphological analysis can be performed using the microscopic device which requires the skills and experiences of the haematologists. An inexperienced haematologist can lead to critical human errors. Therefore, this paper aims to propose an automated classification system used to classify different types of leukocytes based on the convolution neural network (CNN) algorithm. CNN has achieved robust performance in various fields especially in medical applications. A dataset of microscopic blood cells images of the conforming tags (basophil, eosinophil, erythroblast, lymphocyte, monocyte, neutrophil, and platelet) was used to train and test the proposed algorithm. The augmentation and deep transfer approaches were used to improve and enhance the performance of the CNN algorithm. The overall accuracy of the proposed classifier was 98% with Visual Geometry Group-19 (VGG-19). The obtained accuracy was higher than the state-of-art algorithms. To conclude that using the augmentation and deep transfer approaches with VGG-19 can obtain better classification results.
An Intelligent Ensemble Classification Method For Spam Diagnosis in Social Networks

In recent years, the destructive behavior of social networks spammers has seriously threatened the information security of ordinary users. To reduce this threat, many researchers have extracted the behavioral characteristics of spam and obtained good results based on machine learning algorithms to identify them. However, most of these studies use a single classification technique that often works differently for different spam data. In this paper, an intelligent ensemble classification method for social networks spam detection is introduced. The proposed heterogeneous ensemble learning framework is based on stack generalization and uses an evolutionary algorithm to improve the modeling process and reduce complexity. In particular, particle swarm optimization has been used as an evolutionary algorithm to optimize model parameters to reduce model complexity. These parameters include a subset of effective features and a subset of the most appropriate single classification techniques. The SPAM E-mail dataset used in this article contains the correct and effective features in spam prediction. Experimental results show that the proposed algorithm effectively improves the detection rate of spam and performs better than the methods used.
A New Model for Emotions Analysis in Social Network Text Using Ensemble Learning and Deep learning

Recently, emotion analysis has become widely used. Therefore, increasing the accuracy of existing methods has become a challenge for researchers. The proposed method in this paper is a hybrid model to improve the accuracy of emotion analysis; Which uses a combination of convolutional neural network and ensemble learning. In the proposed method, after receiving the dataset, the data is pre-processed and converted into process able samples. Then the new dataset is split into two categories of training and test. The proposed model is a structure for machine learning in the form of ensemble learning. It contains blocks consisting of a combination of convolutional networks and basic classification algorithms. In each convolutional network, the base classification algorithms replace the fully connected layer. Evaluate the proposed method, in IMDB, PL04 and SemEval dataset with accuracy, precision, recall and F1 criteria, shows that, on average, for all three datasets, the precision of polarity detection is 90%, the recall of polarity detection is 93%, the F1 of polarity detection is 91% and finally the accuracy of polarity detection is 92%.
Auction and Classification of Smart Contracts

The execution of smart contracts (SCs) relies on consensus algorithms that validate the miner who executes the contract and gets a fee to cover her expenditure. In this sense, miners are strategic agents who may focus on executing those contracts with the largest fee, to the detriment of other SCs’ execution times, which also harms the blockchain’s reputation. This paper analyzes the impact of miners’ competition on SCs’ execution times in a public blockchain. First, we explain that the Proof-of-Work mechanism casts similarities with a time auction, where the one who first adds blocks is the one who executes the contract and gets the fee. At equilibrium, costs negatively affect execution times, while the opposite holds concerning fees. However, this result does not capture the competition for other contracts; hence, we apply the Naïve Bayes method to classify SCs by considering a simulated database that comprises miners’ competition for several contracts. We observe that simultaneous competition generates patterns that differ from the ones expected by the auction solution. For example, miners’ valuation does not accelerate contracts’ execution, and high-cost smart contracts do not necessarily execute at last places.
Chatbot for Children Assistance

Abstract: Children of age 3 to 7 are unaware of language and they face trouble while learning new things so this chatbot application is designed for them to make their start towards learning easy and in interactive way. This application will help them to learn basic things required in daily life and also will entertain them. This application is helpful to enhance their skills. This is designed to provide an interactive learning medium which results in fast progress of child. A chatbot is artificial intelligence (AI) software that can simulate a conversation (or a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone. A chatbot is often described as one of the most advanced and promising expressions of interaction between humans and machines. Machine Learning and artificial intelligence are fast growing technologies and are used in any area to make human activities easy and fast. Chatbots are way more than simple conversational agents. They can be connected to various APIs which will for example enable them to deal with a wider range of children requests. Multifunctional chatbot assistance built using this technology will help children in day to day activity. During 19 pandemic some issues are raised as big concern one of them is children health and growth. Parents are unable to give their proper attention to their child due to work pressure, work from home. Chatbot assistance will help them out in daily activities and give guidelines which will beneficial to their health and growth. Chatbot will work as a their study partner.
How Leaders' Motivational Language Boost Innovative Work Behavior of Employee in Chinese Service Sectors: The Mediating Role of Psychological Empowerment.

The study drawing on motivational language theory (MLT), examines how leaders boost innovative work behavior (IWB) through motivational languages. The study also used psychological Empowerment as a mediating variable among leader motivational language (LML) and IWB. The data was collected from 291 employees at work services sectors (airlines, hospitals, and shopping malls) in two main phases. The study collected data with six sub-phases from China's services mentioned above sectors. By taking in survey full-time airline employees working in cargo transportation, ground staff, strategic planning, and front desk. The hospital employees respond mainly to technical workers in OPD, laboratory, and nursing staff. In the end, the study gets in the survey from the employee working in shopping malls, stock handling, and sales and planning department. For innovative work behavior, this study collects data from the shift in charge, supervisors, unit head, manager, and career development department from airlines, hospitals, and shopping malls. The study analyzed the data using SMART-PLS-SEM. The findings of the study indicate that there is a positive relationship between LML and IWB. This study also confirmed that psychological Empowerment mediates the relationship. The study found that leaders' communication during training improves employee productivity. The motivational language of leaders reduces the employee's stress level, and they feel free to give feedback and share their ideas with the supervisor at the workplace. The mutual trust among subordinates and leaders brings them together, making an innovative environment to perform at the workplace. The study theoretically contributes and confirms (MLT) as the best tool to motivate subordinates. Practically this study informed the studied sectors how they implement this model to boost employees' innovative work behavior. In the end, the study also discussed limitations with future directions.
Can Recurrent Neural Networks Validate Usage-Based Theories of Grammar Acquisition?

It has been shown that Recurrent Artificial Neural Networks automatically acquire some grammatical knowledge in the course of performing linguistic prediction tasks. The extent to which such networks can actually learn grammar is still an object of investigation. However, being mostly data-driven, they provide a natural testbed for usage-based theories of language acquisition. This mini-review gives an overview of the state of the field, focusing on the influence of the theoretical framework in the interpretation of results.
Reading from Screen Vs Reading from Paper: Does It Really Matter?

This study investigated whether reading comprehension would differ when the texts are studied and tested on screen or on paper. Participants were 69 college students who were attending a college in midwestern United States. Participants read two expository texts each, under comparable paper and screen conditions and answered comprehension questions. Test forms and the order of the conditions were counterbalanced. The correlations between reading outcomes and reader characteristics were examined. Participants also completed a survey on their views on the two media (paper or screen). The results did not indicate a significant difference on students’ reading comprehension as a function of medium and reader characteristics. However, students reported preferring paper-based reading for complex material.
Security and Efficiency of Linear Feedback Shift Registers in GF(2n) Using n-Bit Grouped Operations

Many stream ciphers employ linear feedback shift registers (LFSRs) to generate pseudorandom sequences. Many recent LFSRs are defined in GF(2n) to take advantage of the n-bit processors, instead of using the classic binary field. In this way, the bit generation rate increases at the expense of a higher complexity in computations. For this reason, only certain primitive polynomials in GF(2n) are used as feedback polynomials in real ciphers. In this article, we present an efficient implementation of the LFSRs defined in GF(2n). The efficiency is achieved by using equivalent binary LFSRs in combination with binary n-bit grouped operations, n being the processor word’s length. This improvement affects the general considerations about the security of cryptographic systems that uses LFSR. The model also allows the development of a faster method to test the primitiveness of polynomials in GF(2n).
Intention to adopt Industry 4.0 technologies among small and medium enterprises in the Malaysian dairy manufacturing industry

Small and medium enterprises (SMEs) are one of the key economic growth engines which play vital roles in Malaysia’s economy. In the manufacturing sector, SMEs are considered the backbone of industrial development. Presently, the dairy manufacturing industry is one with high potential. Technology is one of the enablers that can assist companies to improve their performances. Presently, SMEs are still hesitant to embrace and adopt new technology. The fundamental objective of this study was to identify the determinants of the intention to adopt Industry 4.0 technologies among SMEs in Malaysian. Data were collected from 114 managers of Small and Medium Enterprises (SMEs) and was analysed using SPSS. The findings from multiple regression revealed that the intention of SMEs to adopt Industry 4.0 Technologies was positively influenced by perceived usefulness (PU), perceived ease of use (PEU), subjective norm (SN), perceived behaviour control (PBC) and attitude (Att). Attitude had partial mediation effects on both relationships between PU and intention. It also has relationships between PEU and intention in adopting Industry 4.0 Technologies. The paper concludes and supports the assumptions of two theories - Theory of Planned Behaviour (TPB) and Technology Acceptance Model (TAM). This study contributes to enhancing managers and higher authority of SMEs in deciding the future course of their manufacturing firms and policymakers in mapping out plans for Malaysian SMEs in the dairy manufacturing industry.
The Visual Movement Analysis of Physical Education Teaching considering the Generalized Hough Transform Model

In order to promote the effect of college physical education reform, this paper combines the generalized Hough transform model to analyze the visual movement of physical education teaching. The idea proposed in this paper uses the position information of the edge image itself and the direction information of the curve segment to directly eliminate the impossible targets, which fundamentally alleviates the problem of invalid sampling and accumulation. Moreover, this paper greatly constrains the parameter space based on the results of each segment of the curve, which greatly reduces the search burden of high-dimensional parameters, and combines the improved algorithm to construct a sports teaching video action analysis system. The experimental research shows that the visual movement analysis system of physical education teaching considering the generalized Hough transform model proposed in this paper can effectively analyze the sports teaching actions and improve the efficiency of physical education.
SOCIOCULTURAL DIMENSION OF INFODEMIC UNDER CONDITIONS OF GLOBAL INSTABILITY

The socio-cultural essence of infodemic, as a factor of social instability in the global processes of our time, has been determined. It has been proved that the spread of mass culture and mass consciousness served as the basis for the emergence of infodemic. The influence of stereotypes of public consciousness on the process of spreading misinformation has been revealed. It has been substantiated that infodemic can be an integral part of information and hybrid wars. Negative consequences of public life informatization and computerization, in the context of the critical thinking development, the emergence of "clip consciousness" and gadget addiction have been analysed.
Improving the Mean-Field Fluid Model of Processor Sharing Queueing Networks for Dynamic Performance Models in Cloud Computing

Resource management in cloud computing is a difficult problem, as one needs to balance between adequate service to clients and cost minimization in a dynamic environment of interconnected components. To make correct decisions in such an environment, good performance models are necessary. A common modeling methodology is to use networks of queues, but as these are prohibitively expensive to evaluate for many applications, approximation methods for key metrics are frequently employed. One such method-that provides both transient solutions and short, scalable computation times-is the fluid model, which approximates the dynamics of the mean queue lengths using a system of ordinary differential equations. However, finding a fluid model that can adequately approximate an arbitrary queueing network is in general difficult. In the paper, we extend the state of the art with the following three contributions. First, we show that for any mixed multiclass queueing network of processor sharing and delay queues with phase-type service time distributions, such a fluid model can be found via the meanfield approximation. Furthermore, we propose an improved model based on smoothing of the processor share function that improves the performance of certain systems. Finally, using the smoothed mean-field model, we introduce an accurate closed-form approximation of the response time CDF over any subset of classes and queues.
FACE RECOGNITION BASED ATTENDANCE MONITORING SYSTEM

In this digital era, face recognition system plays a significant role in nearly each sector. Face recognition is one in all the principally used bioscience. It will used for security, authentication, identification, and has more blessings. Despite of getting low accuracy when put next to iris recognition and fingerprint recognition, it's being wide used because of its contactless and non- invasive method. what is more, face recognition system also can be used for attending marking in colleges, colleges, offices, etc.This method aims to make a category attending system that uses the construct of face recognition as existing manual attending system is time consuming and cumbersome to take care of. And there could also be possibilities of proxy attending. Thus, the requirement for this method will increase.This method consists of 4 phases- info creation, face detection, face recognition, attending change. info is formed bythe pictures of thestudents in school. Face detection and recognition is performed exploitation Haar-Cascade classifier and native Binary Pattern bar chart algorithmic rule severally. Faces square measure detected and recognized from live streaming video of the room.attending are going to be mail-clad to the various college at the top of the session. keywords—Automatic Attendance, Face Detection, Face Recognition, Image Enhancement, Enrollment,
Research on Evaluation Method of Physical Education Teaching Quality in Colleges and Universities Based on Decision Tree Algorithm

Ensuring and improving quality in higher education is a never-ending theme in university construction and development. College teaching quality includes the quality of physical education instruction. Physical education’s unique focus necessitates the use of teaching quality evaluation tools. This paper develops a decision tree algorithm-based evaluation index system for college physical education teaching quality. The evaluation index and weight of the system for evaluating the quality of physical education teaching are determined using the expert consultation method and analytic hierarchy process. To put it another way, while discussing data mining theory in general, the paper focuses on decision tree algorithm flow and structure. To develop a decision tree model for the established PE teaching quality evaluation system, the modified C4.5 algorithm was applied to classroom teaching evaluation data from 125 teachers, and the model’s correctness and applicability were verified. When it is all said and done, it highlights key decision-making attributes that have an impact on how well physical education teachers are evaluated, and it offers some sound advice based on these findings.
Fine-Tuning BERT Based Approach for Multi-Class Sentiment Analysis on Twitter Emotion Data

Tweets are difficult to classify due to their simplicity and frequent use of non-standard orthodoxy or slang words. Although several studies have identified highly accurate sentiment data classifications, most have not been tested on Twitter data. Previous research on sentiment interpretation focused on binary or ternary sentiments in monolingual texts. However, emotions emerge in bilingual and multilingual texts. The emotions expressed in today's social media, including microblogs, are different. We use a dataset that combines everyday dialogue, easy and emotional stimulation to carry out the algorithm to create a balanced dataset with five labels: joy, sad, anger, fear, and neutral. This entails the preparation of datasets and conventional machine learning models. We categorized tweets using the Bidirectional Encoder Representations from Transformers (BERT) language model but are pre-trained in plain text instead of tweets using BERT Transfer Learning (TensorFlow Keras). In this paper we use the HuggingFace’s transformers library to fine-tune pretrained BERT model for a classification task which is termed as modified (M-BERT). Our modified (M-BERT) model is an average F1-score of 97.63% in all of our taxonomy, which leaves more space for change, is our modified (M-BERT) model. We show that the dual use of an F1-score as a combination of M-BERT and Machine Learning methods increases classification accuracy by 24.92%. as related to baseline BERT model.
A Study of Profanity Effect in Sentiment Analysis on Natural Language Processing Using ANN

The development of wireless communication technology and mobile devices has brought about the advent of an era of sharing text data that overflows on social media and the web. In particular, social media has become a major source of storing people’s sentiments in the form of opinions and views on specific issues in the form of unstructured information. Therefore, the importance of emotion analysis is increasing, especially with machine learning for both personal life and companies’ management environments. At this time, data reliability is an essential component for data classification. The accuracy of sentiment classification can be heavily determined according to the reliability of data, in which case noise data may also influence this classification. Although there is stopword that does not have meaning in such noise data, data that does not fit the purpose of analysis can also be referred to as noise data. This paper aims to provide an analysis of the impact of profanity data on deep learning-based sentiment classification. For this purpose, we used movie review data on the Web and simulated the changes in performance before and after the removal of the profanity data. The accuracy of the model trained with the data and the model trained with the data before removal were compared to determine whether the profanity is noise data that lowers the accuracy in sentiment analysis. The simulation results show that the accuracy dropped by about 2% when judging profanity as noise data in the sentiment classification for review data.