Reviewerly/exHarmony huggingface dataset:
Performance Analysis and Classification using Naive bayes and Logistic Regression on Big Data

In the era of of fast growth of information technology create massive volume of datasets from social media, mobile the devices, internet of things and other sources. These generate large amounts of unstructured data; this data is known as Big Data. Big data handling is the effective control of the storage and processing of a very high volume of data. Data in organized and unstructured formats necessitate a distinct strategy to overall processing. In the field of data processing and analysis, a dataset may be a vast number of attributes that limit data usefulness and application; consequently, data set dimensions must be minimized. In the classification method, the Naive Bayes Classifier from the UCI repository is used. To explore the influence of feature selection methods, they are applied to various characteristics datasets to produce selected feature vectors, which are then categorized according to each dataset category. The design and implementation of this research are to implement the Naive Bayes Classification and Logistic Regression, we will utilize the well-known Iris Flower and Mushroom Dataset. Experimental results indicate the classification using the pairwise plot, and prediction values between naive bayes and logistic regression.
Towards Secure and Trustworthy Flash Loans: A Blockchain-Based Trust Management Approach

Flash loan has become one of the most creative tools in DeFi industry. The explosive development of flash loan is due to its circumvention to borrower default risk and platform liquidity risk. But it also greatly reduces the cost required by the attacker to execute an attack. Hackers can use flash loans to obtain a large amount of disposable funds in a very short period of time, and then exploit the vulnerabilities in smart contracts to carry out attacks and steal a large amount of wealth. Therefore, a trustworthy framework has become one of the security measures that flash loan platforms must take. This paper makes a comprehensive study on flash loan, especially flash loan attack. Firstly, we briefly introduce the basic concept, platforms and application scenarios of flash loan, summarize the existing flash loan attack events and classify them. After that, we compare the existing credit scoring agencies. Then, we introduce the RFM model in marketing and improve it by combining the on-chain and off-chain behaviors of users. Finally, we propose a trustworthy architecture combining the improved RFM model with the role-based trust assessment model and periodic node behavior trust evaluation model. The future development of DeFi and Flash Loan is also analyzed.
Know Your Customer (KYC) as a service for Business Process Management (BPM) using Cloud Computing

Business Process Management (BPM) is used in this article to explain the implementation of a know your customer process in the cloud servers.This assists in the identification of individuals working in banking and financial institutions, as well as the effective monitoring of unusual activity and transactions. Customer comprehensive research refers to the efforts that financial institutions and other regulated companies must undertake in order to identify their clients and get key information relevant to conducting business with them, as well as the banking rules that controls such operations.When applied to the organization, business process management (BPM) makes it possible to automate and manage end-to-end business operations by bringing all of the components of the process together into a more efficient whole, including people, resources, and tools. Numerous financial institutions can benefit from cloud computing by gaining access to a centralised service model that is made available through Software as a service(SaaS), Platform as a service(PaaS), and Infrastructure as a service(IaaS) services. Customer know-how was proved to be faster than human know-how during the execution of a plan of this complexity when compared to human know-how.
Persona Finetuning for Online Gaming Using Personalisation Techniques

Automatic persona generation has been shown to have specific measurable benefits for application creators and users. In most situations, personas are adequately descriptive and diversified to achieve user type accuracy and coverage. For specific market segments, such as online gaming, using personas may accurately describe existing user base but not changing habit and need that are introduced by the fluidity of the offerings and the delivery methods. Changes in the ways that applications are marketed, such as new payment methods, for example, subscription models, pay-to-play and pay-to-win, payment-driven-gamification, seriously affect user needs and result in direct impact on user acceptance. This work utilises structured user needs from online gaming players to augment personas using personalisation techniques. The personas are finetuned and de-diversified to result in concise personas, based on user needs that successfully convey information for creators and users alike.
National Travelling Salesman Problem for Indian Cities: a Case Study

National traveling salesman problem (NTSP) is the subset of the World traveling salesman problem. NTSP has solved for twenty-six countries not of the country India. In the national traveling salesman problem, the user has to travel all the cities without revisiting the visited city of the given country. In this paper, the NTSP is solved for the Indian cities using the genetic algorithm, artificial neural network self-organizing map (SOM), and Simulated Annealing (SA). The early results show that the self-organizing map is an efficient method than the genetic algorithm and simulated annealing. But if the input is noisy then the genetic algorithm and simulated annealing perform better than the self-organizing map.
FEATURES OF STAFF INCENTIVE SYSTEM AT UNIVERSITY: FOREIGN EXPERIENCE

Ensuring the quality of education and compliance with the market, society and the state, achieving the strategic goals of the country's development depends on highly professional staff of universities. Methods of stimulating personnel have a significant impact on the results of their activities. The analysis of the experience of foreign universities showing notable achievements in the fields of education and science is interesting. The purpose of the study is to identify the features of the university staff incentive system used in foreign universities. The article discusses the existing methods of stimulating employees of foreign universities. The frequency of using various tools that increase the labor motivation of university staff is determined. The key features of stimulating employees of foreign universities are revealed. As a result, a wide variety of incentive measures was identified, while methods of non-material incentives are of particular importance. Domestic higher education institutions can use the incentive tools used in foreign universities. Improved personnel policies in terms of staff incentives will lead to an increase in employee performance and the development of the university.
Vision Data Link

Vision Data Link (VDL) is proposed in this paper, which is a new type of Data Link (DL) for semantic interaction in the spectral domain. It carries out information interaction in the way of “vision + behavior”, and achieves machine consensus through machine mutual viewing. This paper first introduces the concept and architecture of VDL, then introduces the principle and method from three aspects: vision semantic acquisition, consensus formation model and behavior control model. Finally, it introduces a verification experiment of vision-lamp information self-feedback controlling. The experiment preliminarily shows that “vision + behavior” can carry out information interaction, and the basic principle of VDL is feasible. It provides an information interaction way in free radio model for the cooperative application of unmanned swarm.
Probabilistic Assessment of Narrowband vs Broadband Solar Irradiance Temporal Variability in Ottawa

Using a recently-created database for Ottawa, Canada, a 9-month longitudinal study of the solar irradiance, measured with a custom spectral pyranometer every 250ms, was conducted with a specific focus on comparing the narrowband and broadband response to the temporal variability. Deterministic diurnal and orbital dependencies of the spectral irradiance were removed by clear sky normalization, and the resulting clear sky index was forward differenced across time steps ranging from the sub-second to ~30 minutes. The stochastic behavior of this spectral clear sky index increment was assessed by kernel density estimates of the probability distributions for data from each of the nine narrow wavelength channels of the spectral pyranometer and the derived broadband global horizontal irradiance (GHI). Scaling analyses of their peak densities and their full widths at half maximum (FWHM) with increment time step revealed power law scaling with consistent stationary breaks at very-short and short times. Broadband scaling was consistent with some narrowband dependencies, but not with others, which may reflect the wavelength dependence of different sky conditions. The existence of three distinct scaling regimes, each of which coincides with an operationally significant time period (primary/secondary control, automatic generation control, and real time market dispatch), has implications for short term probabilistic forecasting.
Test Case Prioritization for Deep Neural Networks

A variety of neural network-based models are increasingly being applied to real life, including face recognition, autonomous driving, etc. However, neural networks cannot fully guarantee reliability due to their black-box nature. Currently, a large number of test cases are usually used to ensure the safety of neural networks in different scenarios, and yet this will lead to high data labeling costs. Aiming at the above problems, we design a prioritized system for test cases based on Gini impurity. The system will model a series of dependent operations into a directed acyclic graph. After obtaining the inference result of the neural network model, the system computes the measurement value according to the formula of Gini impurity and uses this as the basis for comparison when sorting the test cases. The test case prioritization system can effectively filter out test cases that the neural network cannot make clear decisions, and help users inspect whether the data feature engineering is complete. In addition, using high-priority data for retraining can further improve the generalization ability of the model. The system has been deployed into MoocTest artificial intelligence infrastructure platform. Through a questionnaire survey, the satisfaction of our user experience reaches 92.8%.
Impact of PhD students' research activity on CRIS systems

The Spanish regulatory framework governing university doctoral studies, establishes that, for all purposes, PhD students are researchers in training and, as such, all the activities carried out by these students during their training as doctors, must be recorded. These activities must be detailed in a formal "doctoral activity document" (DAD) that serves to evaluate the student. If these students are considered, to all intents and purposes, researchers (in training), their activities (all or some of them) should be part of the scientific production of the institution and, therefore, be included in their CRIS systems. Through the results of a survey conducted in 18 relevant Spanish universities, this presentation aims to analyse how the research activities carried out during the doctoral training period impact, or may impact, on CRIS systems.
Human-Computer Interaction Technology for Upper Limb Rehabilitation Based on Gesture Recognition Control

In this paper, according to the motion characteristics of human upper limbs, a two-limb universal upper limb rehabilitation robot with 7 degrees of freedom is designed to satisfy the use of left and right limbs, and in order to verify the rationality, the static analysis of key parts is carried out. Secondly, the non-contact human-computer interaction gesture recognition technology based on Kinect is studied. The hand tracking and positioning of the left and right hands are respectively realized by the Kinect skeleton acquisition technology, and real-time static gesture recognition based on SVM which can distinguish between left and right hands is implemented to judge the gesture recognition results and control the motion of the upper limb rehabilitation robot. Finally, a human-computer interaction experimental platform based on gesture recognition is built to test and realize a variety of rehabilitation modes for left and right limb training.
Visual Knowledge Tracing

Each year, thousands of people learn new visual categorization tasks – radiologists learn to recognize tumors, birdwatchers learn to distinguish similar species, and crowd workers learn how to annotate valuable data for applications like autonomous driving. As humans learn, their brain updates the visual features it extracts and attend to, which ultimately informs their final classification decisions. In this work, we propose a novel task of tracing the evolving classification behavior of human learners as they engage in challenging visual classification tasks. We propose models that jointly extract the visual features used by learners as well as predicting the classification functions they utilize. We collect three challenging new datasets from real human learners in order to evaluate the performance of different visual knowledge tracing methods. Our results show that our recurrent models are able to predict the classification behavior of human learners on three challenging medical image and species identification tasks.
THE RELATIONSHIP OF DIGITAL LITERACY TO STUDENTS' COGNITIVE ABILITY IN ECOLOGY COURSE

This correlation study aims to determine the relationship between digital literacy and students' cognitive abilities in ecology courses. The subjects of this study were students at the State University of Medan 2018 class of Biology Education who took ecology courses in the 2020/2021 academic year. There were 152 students, and samples were taken using the purposive sampling technique of 61 students. The data were processed with descriptive qualitative research, and the data collection techniques used in this research were a questionnaire. The results showed a relationship between digital literacy and students' cognitive abilities in ecology courses with r = 0.791, and digital literacy contributed (62.6%) to students' cognitive abilities in ecology courses. In conclusion, someone with good literacy will also have good cognitive abilities. The growth of technology for education will continue to develop increasingly over time so that digital literacy in the educational environment is beneficial for students in covering broader and deeper information and will help students to complete their assignments and find accurate information.
Firebase

Firebase is the name of Google’s cloud and (offshore) development platform. In this chapter we talk about cloud data storage and cloud-based messaging.
Polarized Extractive Summarization of Online Product Reviews

In the e-commerce domain, online customer reviews offer valuable information to manufacturers and potential buyers. Many fast-moving products receive hundreds or even thousands of online reviews. Therefore, summarizing multiple online reviews makes it easier for merchants and buyers to establish the usefulness of products and to prioritize the issues that are most important to them. Motivated by this, we designed an automated two-step pipeline for Polarized Extractive Summarization that uses Machine Learning and Deep Learning models. The first module of the pipeline is trained on a manually annotated Amazon cellphone dataset that contains multiple reviews of verified purchases on Amazon. Polarized Sentiments from these reviews are extracted by splitting the review(s) into sentences. The second module removes the duplicates and near-duplicates; the selected sentences are given to the graph-based model so as to rank these sentences based on the Link Analysis. The top-ranked sentences are chosen to generate positively and negatively polarized summaries. We evaluate our proposed models on manually annotated Amazon review dataset for sentence classification as well as summarization which yields better results on both modules. This research sets out to identify the sentiments in each review that contributes towards the summarization and removal of near duplicates. It has been shown from the experimental results that the generated summaries are more accurate and informative.
Penerapam Pembelajaran Snowball Throwig Untuk Meningkatkan Hasil Belajar Siswa Pada Materi Pranata Dan Penyimpangan Sosial Di Kelas VIII SMP Muhammadiyah 6 Tikep

The research was conducted with the aim of improving the learning outcomes of eighth grade students of SMP Muhammadiyah 6 Tikep in social studies subjects through the application of the Snowball Throwing learning model. The type of research used is classroom action research, which is a study that addresses problems in the classroom which consists of four stages, namely Planning, Action, Observation, and Reflection. This research was conducted at SMP Muhammadiyah 6 Tikep. The subjects of this study were students of class VIII SMP Muhammadiyah 6 Tikep, totaling 20 students. The instruments used are tests and observation sheets. The data analysis used is using the percentage technique. The results obtained indicate that the application of the Snowball Throwing learning model can improve student learning outcomes, this can be seen where in the first cycle it is still low, namely from 20 students only 6 students are categorized as complete with a percentage of 30%, but in the second cycle it increases from 20 students. only 2 students who did not complete and 18 students who completed with a percentage of 90%. The Snowball Throwing learning model can motivate students to collaborate
Relevancias de la educación virtual en la lectura y escritura. Una revisión sistémica

The present and future education demands new knowledge and methodologies to ensure that our children learn to read and write properly, even more so during the state of the pandemic, virtual education takes on a relevant place since it is the only way to connect with the students, but at the same time its effectiveness could be uncertain. The article is based on the systemic review that aims to analyze during the years 2017 to 2022, the relevant aspects during the learning of reading and writing in virtual education, using the prism statement as a methodology, the criteria of inclusion and exclusion, using as database: Scopus, Scielo and academic Google and EBSCO. The review answers the questions: Which countries have carried out research regarding virtual education and literacy? How many investigations have been carried out per year on the subject? What relevant aspects are considered in virtual education to teach reading and writing?; Having as a result of the analysis that the most carried out investigations are in the countries of Spain and the United States, evidencing in 2021 a considerable increase in investigations on the subject and that the responsibility of parents in this new learning modality is the backbone so that children can consolidate literacy, the potential value of the virtual educational program and the correct use of ICT in this type of study.
A new method of multi-robot cooperative exploration

This paper aims at the task of the map fusion and environmental exploration method. The multi-robot collaborative map creation is based on the local map by each individual robot, and the nodes in the local map are matched to obtain the association between multiple maps. Multi-robot task allocation is also a process of maximising the benefits of resource utilisation. How to allocate resources reasonably so that each robot can quickly complete the corresponding tasks in coordination, and how to solve the problem of the heavy tasks for individual robots or few tasks for individual robots, multitask allocation based on market law can be a solution. The new topological scene map in this paper is used to represent the environment and carry out robot exploration. It can be seen that multi-robot cooperative exploration is more efficient efficiency.
A Study on the Analysis of Visual User Experience in HMD-Based Virtual Environments

In the HMD-based virtual environment, there is a need for an experience tracking method and tool that can check whether the creator has induced the user’s experience as intended. This paper intends to propose a method to analyze the user’s experience by separating the visual information exposed to the user and the visual information that the user noticed, based on the information processing theory. In addition, by visualizing the log data collected during the user’s visual information processing process, it is intended to support qualitative and quantitative analysis. We conducted an experiment to search for a specific object in a virtual environment using the proposed analysis method. Objects with a high or low degree of attention compared to the degree of exposure were analyzed through the experiment. Additionally, the effect of the degree of exposure and attention through the removal, addition, and arrangement of objects was objectively analyzed on the change in information acquisition difficulty.
An Explainable Deep Learning Model for Vision-based Human Fall Detection System

Human Fall is one of the major life-risking problems among elderly people. Accidental falls among elder persons may lead to many irreversible disabilities or even deaths. Fall detection has become a critical research problem in the Healthcare domain, which needs more reliable and efficient solutions to intelligently classify fall activities. With the development of the Internet of Things, such as wearable sensors, ambient sensors, and cameras, monitoring elderly people continuously has become feasible. The proposed work ensures Fall detection through Deep Learning classification using a Convolutional Neural Network with three hidden layers. The performance of the Deep Learning model is evaluated with the Fall Detection Dataset and the classification results have achieved an accuracy of 96.5%. However, the intelligent classification methods need to be trustworthy to be able to be accepted by healthcare professionals. Since the Deep Learning models are ’black-box’, the process behind the classification is not known and accuracy alone will not be sufficient for evaluating the performance of the model. Hence, in this paper, we also propose an Explainable AI model, called LIME to interpret the classification of fall activity. the results of LIME show the feature responsible for prediction by marking the boundaries of the input image.