Reviewerly/exHarmony huggingface dataset:
YorAA: An Authorship Attribution of Yorùbá Texts

The process of establishing the most likely author of a collection of texts or documents whose authorship must be verified is known as authorship attribution. Several studies have been reported in the literature on the task, but rarely any reported work on Yorùbá language texts. In this paper, the development of an automatic Yorùbá written texts authorship attribution system (YorAA) is reported. The literary works of six Yorùbá authors were considered. Stylometry features were extracted from the texts using the BoW approach and lexical/syntactic word frequencies approach. The Support Vector Machine, Multilayer Perceptron and Random Forest algorithms were used for the classification analysis. The experimental results showed that the developed YorAA system achieved accuracy, recall, precision and F1 measures values of 95%, 83%, 84% and 84% respectively on the average, for all the six authors. The results demonstrate that with a database of written texts in Yorùbá language, that is enough to extract relevant stylometry ´ features of the author and appropriate methods and tools applied to such features; the authorship of the texts can be identified or verified.
Cloud Migration Fundamentals

Chapter 1 presented an in-depth introduction to cloud computing, including its essential characteristics, service delivery models, and deployment models. This chapter continues to build on those concepts and includes the following objectives:
SURVEY ON DIFFERENT DATA MINING ALGORITHM FOR PREDICTION

Data mining refers to the mining or extracting the useful information from a pool of data.Data mining algorithms are widely used to analyse in Education, Fraud Detection, Criminal Investigation and Bio Informatics.Data mining is a technique uniquely used for identifying unknown pattern and to convert the raw data into the unambiguous information and it is also defined as the finding the in depth data or the hidden information from the databases Huge algorithms has been proposed in the past for the data mining extraction.The various data mining techniques such as classification, clustering, association rule mining, decision tree and more on.The aim of this paper focuses on Different data mining algorithms that are used mostly for prediction and classification.
A State History Queue for Efficient Implementation of a Reinforcement Learning System

This paper presents a modification to the BOXES-ASE/ACE reinforcement learning algorithm to improve implementation efficiency. We introduce a state history queue (SHQ) to replace the decay variables associated with the states, decoupling the hardware complexity from the number of control states. We analyzed the effectiveness of the SHQ and constructed both a simulation and an actual hardware of a pole-cart balancer. Analysis shows this technique preserves performance, while decimating the required computation time and memory demand of tracking access to each state.
Electronics Diagnose for Jaundice Disease Integrated with Informative SMS Notification: Diagnose Jaundice Expert System (DIJAD)

Medical expert system is very important nowadays to detect any disease as early as we can. Electronics Diagnose For Jaundice Disease Integrated With Informative SMS Notification: Diagnose Jaundice Expert System (DIJAD) has been designed and developed to help early detection of jaundice by identifying the symptoms statistically after diagnosing the patient, based on the information available in its database. The system is conducted to detect the type of the jaundice faster based on the initial sign or symptom of the disease. It will provide the result of diagnosing jaundice and the nearest clinic via Short Message Service (SMS) to the patient. The interview were conducted to validate the functionality and effectiveness of the DIJAD, two method is used which is user test and system function test. The result indicated that 5 random user 2 doctors agree that the system is user-friendly and easy to use. Other than that, the result from system function test. It can be concluded that the system is fully functioning, and no error occurred. This system uses Forward Chaining method. Forward Chaining is method that is driven by the data where the tracking stars from the observation of the input information and the try to describe the conclusions. DIJAD developed using Hypertext Preprocessor (PHP) for the programming language in conjunction with MySQL as a database system. The outcome of DIJAD able to facilitate community in diagnose early jaundice disease that can save time and give positive impact in our effort to prevent jaundice to become worst.
A Brain-inspired Decision-making Model for Spatio-temporal Pattern Recognition

Decision-making is a fundamental task in both brain functions and AI applications. In this study, we develop a brain-inspired decision-making network for spatio-temporal pattern recognition. We analyze the mechanism and properties of the proposed model using synthetic data and demonstrate the potential application of the model in speech recognition.
THE INFLUENCE OF DIRECTED READING THINKING ACTIVITY (DRTA) TOWARDS STUDENTS’ READING ABILITY

The research aimed to know the influence of using DRTA Strategy towards students’ reading ability at MTs Nurul Iman Pesawaran. The research was conducted at the eighth grade of MTs Nurul Iman  Pesawaran in academic year 2020/2021. The writer took two clasess in  the eighth grade as the samples. The population was 40 students’ in 2 clasess. The sample was taken by using cluster random sampling technique. 20 students’ were as experimental class and 20 students’ were as control class. The main technique in measuring students’ reading comprehension was multiple choice test that consisted of 40 test items. The score for each item was 2,5 for true and 0 for wrong. In analyzing the data, the writer used t-test formula. Based on the data analysis, The result showed that t-test was 2.9. From the distribution table at the significance level of 5% is known that t-table = t (1- ½ α) (df) = 2.00 and at the significance level of 1% is known that t-table = t (1- ½ α) (df) = 2.37. It means t-test > t-table as in (2.9> 2.00 > 2.37). Based on the calculation, Ha was accepted, it means that there was an influence of using DRTA (Directed Reading Thinking Activity) strategy towards the students’ reading ability at the eighth grade of MTs Nurul Iman Pesawaran in 2020/2021. Then the average score of students reading ability who learnt through DRTA (Direct Reading Thinking Activity) was higher than those who learnt reading throurgh conventional technique. It was 70.1 > 65.6. Based on the result above, it is clear that DRTA (Directed Reading Thinking Activity) can increase  students’ reading ability at the eighth grade of MTs Nurul Iman Pesawaran in 2020/2021.
A Robotic Vision Model via Xception and Light Gradient Boosting Machine

Image classification plays a significant role in robotic vision. This paper proposes an image classification model: Xception-LightGBM, which combines with Xception and Light Gradient Boosting Machine for hybrid image classification. The proposed algorithm produces the image feature extraction via Xception and classifies these feature vectors using Light Gradient Boosting Machine (LightGBM). The Xception-LightGBM model is compared with five representative image prediction models, such as VGG16, VGG19, InceptionV3, DenseNet121, and Xception. The experiments on six data sets demonstrate this proposed model leads to successful runs and provides optimal performances. It shows this model achieves the best results for all six evaluation metrics: accuracy, precision, recall, F1-Score, loss, and Jaccard. Furthermore, this proposed model acquires the highest accuracy on six image data sets, which has at least 1.1% in accuracy improved to the Xception architecture. It suggests this model may be preferable for robotic vision.
And Now for Something Completely Different: Advanced Python

After immersing ourselves deeper in the world of Java in the previous chapter, it's time to do the same with Python. We'll start off with file operations, moving on to multithreading and other more advanced topics later in this chapter. The purpose here is to give you a solid foundation on some of the deeper mechanics in Python for you to build on later according to your needs.
Using digital means in education: phobia, illusions, progress

Shown is, why the formula “it was the industrial revolutions that were the factors of main transformations of education” underlying the strategic project for using digital means in education in the former Soviet countries cannot be taken as an unconditional one. It is proved that the using digital means in education as a strategic project should, first of all, be aimed at teaching digital technologies in the framework of vocational education and at training the elite category of workers for the development of scientific and technological progress.
The Formation of Interest in the Students to the Teaching Profession

The article deals with the formation of students ' in the teaching profession. The author considers the concept of interest from the point of view of philosophical, sociological, psychological and pedagogical. Based on these provisions, an attempt is made to reveal the essence and concept of among students, to determine its relationship with cognitive and to draw a conclusion about subordination. The article traces the connection of with the concept of professional and pedagogical orientation of the In this regard, it is noted that can be considered as a complex personal education, which is part of the orientation of the individual. Having studied various approaches to the question of the problem of to the author, he revealed the structure of interest, which includes a number of interrelated interdependent components: intellectual, emotional-volitional and need-activity. The article focuses on the fact that it is necessary to include future teachers in active activities, as close as possible to ones, during which conditions are created that are favorable for the formation of a specific idea of the modern school, the functions of the teacher, the educational process, in addition, a positive emotional attitude to future teaching activities is strengthened
Review of Criminal Behavior Analysis based on Machine Learning Techniques

Crimes are increasing with a high frequency rates in this new era of world and hence it's a devastating issue that everyone has been experiencing. For finding a pattern that can be used for prediction is necessary. The extraction of new information is predicted using the existing datasets. Many approaches for analysis and prediction in data mining had been performed. But, many few efforts has made in the criminology field. Many few have taken efforts for comparing the information all these approaches produce. The police stations and other similar criminal justice agencies hold many large databases of information which can be used to predict or analyze the criminal movements and criminal activity involvement in the society. The criminals can also be predicted based on the crime data. The main aim of this work is to perform a survey on the machine learning techniques that has been applied towards criminal identification.
Experience with Using a Neural Network for Forecasting Time Series

Prediction of future values of time series is an important problem both in engineering and in science. Stochastic model building and forecasting is one of the techniques available for the analysis of discrete time series in the time-domain. Though these models have proven to be accurate for forecasting, they have several important limitations such as an a priori guess of model structure. In this paper, a feed forward neural network implementation is used to predict the future values of different time series by using past knowledge. The network is trained with statistical features such as standard deviation This hybrid neural network model is shown to be better in prediction than the traditional statistical models.
Analisa dan Implementasi Wireless Outdoor Connection Menggunakan Unifi AC Mesh Pada PT.Bandar Abadi

PT. Bandar Abadi is a company engaged in shipping and ship repair with 1000 employees working there. Currently, the company still does not have and is experiencing problems in providing interconnection services that are on employee devices or CCTV installed in the outside environment. This of course disrupts employee performance due to difficulties in data transmission and CCTV control due to the network that does not cover the area. The methodology applied in this research is the NDLC (Network Development Life Cycle) method where this method functions as an infrastructure development and design that allows monitoring of the network. The results of this study are in the form of applications that can cover a large area by utilizing the Unifi AC Mesh device so that with the implementation, it can solve the problem of wireless services that exist at PT. Bandar Abadi
Visual Hull Refinement with Hierarchical Clustering Deformation for 3-D Reconstruction

This paper presents a hierarchical clustering deformation approach to improve the visual hull based 3-D model re-construction technique. The proposed technique takes a reference 3-D computer model and multi-view image captures of the target as inputs. A model deformation process is then carried out under the constraints imposed by the silhouettes of acquired images and the camera geometry. Therefore, it is able to approximate the true 3-D models with limited images. To facilitate this constrained 3-D modeling, a clustering method is proposed to divide the 3-D model into several sub-groups for individual matching and deformation. In the experiments, the real images of several test objects are used for our performance evaluation. The results have demonstrated the feasibility of the proposed hierarchical deformation technique for 3-D model reconstruction.
Investigating the use of Adaptive Neuro-Fuzzy Inference System in Software Development Effort Estimation

Modelling of software development effort estimation models have been a hot research topic over the last three decades. Numerous models were proposed in these decades to predict the effort. The key challenges for future software development is providing accurate software estimations. The failure to acknowledge the accuracy of effort estimation can cause inaccurate estimation, customer disappointment, and consequently poor software development or project failure. This research presents a new computational technique namely adaptive neuro-fuzzy inference system (ANFIS) for modeling the software effort estimation. The proposed model was developed utilizing COCOMO dataset. The mean magnitude relative-error (MMRE) and the coefficient of correlation (R) are applied as assessment indices. Results show that the accuracy of the proposed model is quite satisfactory in comparison to actual values. Moreover, a comparison study was conducted with other approach, and it was concluded that ANFIS produced better results in comparison with the APF, SLIM, and COCOMO methods. It is recommended that ANFIS to be used as a predictive model for software development effort estimation.
A Scalable Data Processing Model for Big Data Analysis of Enterprise Technology Innovation

In many areas such as enterprise technology innovation, the volume of data to be analyzed grows rapidly. In order to analyze and use these huge data resources, we must rely on effective data analysis technology. However, the traditional data processing technology has encountered certain obstacles in scalability. A Scalable Data Processing Model (SDPM) was proposed in this study to solve problems concerning innovative services of enterprises. The model performed formal analysis and description of SDPM, and through this model, it implemented the clustering analysis and incremental computing algorithm of enterprise technology innovation application data. Research results demonstrate the potential of the proposed model can effectively analyze enterprise technology innovation data, and have similar performance in incremental computing for different sizes of data sets. The query response time is less than 1 second on average. The research results of this study show that the SDPM can adapt to the service needs of enterprise technology innovation data and improve the efficiency of data processing. This proposed model provided technical and theoretical support for big data processing.
On the Information Entropy of Ray-Chaotic Indoor Environments

Indoor scenario has emerged as one of the most congested, contested, and competitive wireless environments. As the emergence of the Internet of Everything (IoE) and Fourth Industrial Revolution (Industry 4.0) infrastructures, the importance and relevance will continue to grow in the near future. Indoor wireless is qualitatively different than outdoor wireless. In the high-frequency regime, as the wavelength is much shorter than the typical size of indoor scatterers, the wave scattering process may exhibit chaotic ray dynamics. The reflections from irregular walls and scatterers superimpose and lead to speckle like, random wave fields. Due to the rich scattering phenomena, the signals relayed to individual receivers have high probability of being uncorrelated (or distinguishable). Thereby, the network capacity may proportionally increase with respect to a number of antennas or bandlimited parallel channels.
The new frontier of platform policy

Platform policies aimed at the misbehaviour of users that occurs off of the platform, especially offline abuse, are a relatively new and understudied phenomenon that may represent a new frontier of platform policy. Policies of this nature may be necessary to create healthy online communities, but they raise unique problems in comparison to on-platform content moderation that exacerbate existing concerns about the accountability and transparency of platforms. This article provides the background and context for the development of such policies through the case study of Twitch.tv. It then discusses three unique challenges raised by the creation and enforcement of policies aimed at off-platform abuse. These are 1) the need for investigation and verifying evidence, 2) the difficulty of balancing the privacy of all parties with a fair process, and 3) the increased potential adverse impacts of error. It argues that current policies are opaque and offer few guarantees to either complainants or the targets of complaints. Further steps, especially greater transparency and broader consultation, must be taken to ensure accountability to users and the public when such policies are implemented.It concludes by discussing the possibility that similar policies will be adopted by other platforms and calls for greater public and academic discussion of where, when, and how such policies should be implemented.
Artificial Intelligence in the IoT World (Applied IoT)

Automation is the application of machines to tasks once performed by human beings. Although the term mechanization is often used to refer to the simple replacement of human labor by machines, automation generally implies the integration of machines into a self-governing system. Automation has revolutionized those areas in which it has been introduced, and there is scarcely an aspect of modern life that has been unaffected by it.
Security in Cyber Crime

Abstract: The crime that involves and uses computer devices and Internet, is known as cybercrime. Cybercrime can be committed against an individual or a group; it can also be committed against government and private organizations. It may be intended to harm someone’s reputation, physical harm, or even mental harm. Due to gradually increase of the internet users and netizens, abusage of technology is broadening gradually which tends to cyber-crimes. Cybercrime causes loss of billions of USD every year. Cyber Security, a mechanism by which computer information and the equipments are protected from unauthorized and illegal access. This paper illustrates and focuses on cybercrime, how society suffered, types of threats, and cyber security. Keywords: Cybercrime, Types, Protection, Cyber security, Hackers, Impact.
Sequential Extraction of some Heavy Metals in Tropical Agricultural Soils from Ogun state, Nigeria

Speciation of metals in soil samples is an integral factor in appraising their potential environmental impacts. Sequential extraction method was used to determine speciation of heavy metals in tropical Agricultural soils.  Soil samples were randomly collected from arable and oil plantation farms in Ago-Iwoye farm settlement in Ogun state, Nigeria. The heavy metals in the soil samples were sequentially extracted by Tessier and Silveira methods and their concentrations were determined using atomic absorption spectrophotometer. Physicochemical parameters of the soils were also assessed. Results showed that levels of heavy metals extracted in Fe and Mn oxide fractions(mg/kg) with Tessier method were; Mn (<14.4), Fe(<387), Pb(<2.37), Zn(<52.4) and Ni(<6.96) while for Silveira; Mn (<27.7), Fe(<1091), Pb (10.9), Zn(<85.7) and Ni (<9.36). Some of the physicochemical obtained properties were; pH (<6.23), organic carbon (<22.4 g/kg), CEC (<6.28 cmol/kg) and Total Fe (<1812 mg/kg). These revealed that higher concentrations of metals were extracted in the three steps (Fe and Mn oxide fractions) extraction method of Silveira compared to single step extraction of Tessier. Physicochemical and mineralogical composition revealed that the soil was rich in Fe and carbonate minerals and these play vital role in metal speciation. Conclusively, this study has shown that Silveira sequential extraction method was more appropriate for the sequential extraction of heavy metals in the study soil as a typical tropical soil and has also provided more knowledge and use of appropriate sequential extraction of metals in soil management and pollution prevention.Key words: Sequential extraction, tropical soil, toxic metals, Fe-oxide mineral, soil properties, Mineralogical composition
SENIOR HIGH SCHOOL ENGLISH TEACHERS’ PUNCTUALITY, ATTENDANCE AND STUDENTS’ ACADEMIC PERFORMANCE

This study sought to determine the Senior High School English teachers’ teaching practices in terms of punctuality, attendance and its relationship to students’ academic performance. Specifically, it determined the Senior High School English Teachers’ level of punctuality and attendance; the Senior High School Students’ level of academic performance in English; and the significant relationships of the teachers’ level of punctuality and attendance and their Senior High students’ level of performance in English. The study used the descriptive-correlational survey method. The respondents of the study were the twenty four (24) teachers of the twelve (12) senior high schools of Maguindanao, Philippines. There were two (2) Senior High English teachers that were chosen randomly from each senior high schools of Maguindanao. It has a total of twenty-four (24) Senior High English Teachers. In every teacher, there are 10 senior high school students chosen randomly with a total of two hundred forty (240) senior high school students. The immediate supervisors of the teachers from the twelve (12) secondary schools answered the research problem number 1, while the final grades in English of the two hundred forty (240) senior high students were taken from their English teachers to answer the research problem number 2. The respondents of this study were chosen randomly using lottery. Pearson’s Product - Moment Correlation Coefficient was used to determine the significant relationship of the level of punctuality and attendance of the English teachers to the students’ level of academic performance. Based on the findings of the study, it is concluded that the Senior High School English Teachers’ level of teaching practices is influenced by their punctuality and attendance. It further concluded that teachers’ punctuality and attendance contributed to students’ level of academic performance in English.&#x0D; &#x0D; Keywords: Senior High School English Teachers, Punctuality, Attendance, Students’ Academic Performance