Reviewerly/exHarmony huggingface dataset:
Intelligent Water Drops Algorithm Hand Calculation Using a Mathematical Function

The intelligent water droplets (IWD) approach is based on the dynamic of events and changes that take place in a river system. The IWD method is a solution-oriented methodology in which a group of individual’s moves in discrete stages from one node to the next as a complete population of solutions is generated. The velocity, soil are the features of natural water drops in the IWD algorithm are modified over a sequence of transitions relating to water drop movement. In this study, the IWD algorithm approach is used with a mutation-based local search to obtain the optimal values of numerical functions.
The Concept of an Adaptive Intelligent Tutoring System on Solving Problems for Students of Higher Educational Institutions

The purpose of this paper is to present the concept of a new adaptive intelligent tutoring system designed for joint work of students and teachers in the process of mastering the mathematical and physical disciplines content. The described intelligent system can be extended for other subject areas, including working with numerical and graphic information, formulas and symbols. A review of the available adaptive intelligent training platforms used mainly in foreign education systems is provided; their essential properties are highlighted; the movement of a trainee along an individual trajectory built by an adaptive intelligent training system is modeled; a structural and functional approach to building a system of basic tasks is applied. In this case the basis is considered as a set of diverse, multilevel interactions of elements (tasks), their interlacing and mutual influence. The application of task approach to designing an adaptive intellectual training system is substantiated. The description of the development phases and the structure of interaction between its elements is given. The formalization of the model is carried out and the scenarios of the system functioning are described. During formalization the methods of statistical data processing are used. Different variants of disclosing individual characteristics on the quantitative basis are systematized and the evaluation of the current position of the learner’s personality in the system is substantiated. The interpretation of personality positions for further action of an intelligent learning system is developed and methods of system adaptation to changing individual characteristics are described. The model of learners’ individual educational trajectories progression is formed. The ways out of the system from the situations of teacher’s help request and impossibility to automatically determine the further change of individual trajectory are described.
Effect of Questions Misspelling on Chatbot Performance: A Statistical Study

In recent years, the size of NLP models, the number of parameters they use, and their performances have gotten all the attention. The race for gigantism increases the score of the models at the expense of the environment and high energy consumption. However, the quality of the data with which they interact influences the reliability of these models. To our knowledge, this is the first paper to investigate the influence of misspelling on question-answering agent responses. It is an important aspect of chatbot seen as a whole and complex system with its dynamic relations and interactions. According to the results, the R-NET model performs well against misspelling.
Human Capital of a Smart University: Trajectories of Innovative Development

Nowadays there are many interesting approaches to the assessment of human capital and its impact for different areas of society. One of them is aimed at assessing the competencies that make up the structure of human capital. The main purpose of the article is to study the peculiarities of human capital managing in modern intellectual organizations. The directions of HR departments activity for internal structure of human capital and trajectories of its development have been revealed. The managing HR model in the context of modern trend “work with people” is offered. The methods of research are the methods of systemic, complex and competence approaches. The practical significance of the obtained results consists in the describing new tasks for HR departments at higher education systems to increase the efficiency of human capital activity.
Review of Interoperability Issues Influencing Acceptance and Adoption of Cloud Computing Technology by Consumers

This paper presents a review of cloud computing issues and challenges bedeviling cloud services and precluding acceptance by consumers, with a particular interest in interoperability challenges in the cloud space. The work summaries the essential compositions in the cloud computing technology phenomenon. Furthermore, a three-way general solution mechanism for the challenges and issues is presented, including the preventive solution mechanism; the detective solution mechanism; and the corrective solution mechanism. A list outlining five critical solution approaches to the interoperability challenge in the cloud is included. Finally, a desk search empirical survey using contents analysis methodology was employed to ascertain the common solution types advanced by researchers in overcoming interoperability issues. The survey identified six key solution types that researchers adopt to mitigate or eliminate the interoperability challenge: standardization, model-driven approaches, open APIs, open protocols, abstraction layers, and SOA-based solutions. This survey revealed that 40% of the initiatives work implemented the standardization solution type, which is the highest solution-type initiative implemented. At the same time, the individual model-driven approach has the highest number of authors’ percentage implementation, which is 70% but with only 16% initiatives implementation. The solutions presented herein are not exhaustive, and more research work in this direction is encouraged. Albeit, these proffered solutions are imperative, crucial, and pertinent to clear doubts in the minds of current and potential consumers of cloud services, thereby helping them accept and enjoy the full benefits accrued in the adoption of cloud computing phenomenon.
A Method for Detecting the Death State of Caged Broilers Based on Improved Yolov5

In the unmanned breeding mode of broilers, the automatic identification of the death state of broilers in a complex cage environment can effectively improve the picking efficiency and reduce its negative impact. An improved Yolov5 object detection method is proposed in this study to rapidly and accurately detect the presence of dead broilers in caged structures. The Yolov5 model within the CSPDarknet53 framework is constructed, and the Yolov5 model is experimentally pruned to reduce the number of convolution kernels. The Ghost structure and the Involution structure are combined with the Yolov5 backbone network to effectively reduce the number of parameters and computation with a negligible loss of accuracy, making it easier to deploy applications. The results show that with the decrease of the accuracy of 0.1%, The number of parameters is reduced from 7.05M to 0.31M, and the amount of calculation is reduced from 16.3 to 0.3. It can be seen that the improved Yolov5 network is effective and suitable for high-precision real-time detection and recognition of the death state of broilers in a caged structure.
Study on Intelligent Tutoring System for Learner Assessment Modeling Based on Bayesian Network

The most crucial part of the educational system is the intelligent tutoring system. A computer system that intends to give learners quick and customised lessons or feedback, usually without the intervention of a professor, is known as an intelligent tutoring system. Artificial intelligence technology is employed in an intelligent tutoring system to provide a lot of help to learners in terms of acquiring skills and knowledge. Human professors are not required to contribute to the organisation in this process, and Bayesian Network has been employed to solve this problem. An intelligent tutoring system’s heart is the beginner learner model. Using a Bayesian network with high self-learning ability to build an intelligent tutoring system for the novice concept can considerably improve the level of comprehension of the intelligent tutoring system. The core philosophy of an intelligent tutoring system for the beginner concept will be the major focus. The rudiments of impact on the learners learning method are then studied at this level, starting with the perception of the beginner’s expertise in teaching, mutual with the state of learning, and the features of the beginner. Based on bayesian network, this work presented a study model for constructing an intelligent tutoring system for learner assessment. The tutoring system’s design model takes into account a client model and a learner model. The bayesian network was employed in an e-learning environment to assess the learner’s current level of knowledge so that the model may evolve and offer new knowledge to improve learner performance.
Fact Aware Multi-task Learning for Text Coherence Modeling

Coherence is an important aspect of text quality and is crucial for ensuring its readability. It is essential for outputs from text generation systems like summarization, question answering, machine translation, question generation, table-to-text, etc. An automated coherence scoring model is also helpful in essay scoring or providing writing feedback. A large body of previous work has leveraged entity-based methods, syntactic patterns, discourse relations, and traditional deep learning architectures for text coherence assessment. However, these approaches do not consider factual information present in the documents. The transitions of facts associated with entities across sentences could help capture the essence of textual coherence better. We hypothesize that coherence assessment is a cognitively complex task that requires deeper fact-aware models and can benefit from other related tasks. In this work, we propose a novel deep learning model that fuses document-level information with factual information to improve coherence modeling. We further enhance the model efficacy by training it simultaneously with Natural Language Inference task in multi-task learning setting, taking advantage of inductive transfer between the two tasks. Our experiments with popular benchmark datasets across multiple domains demonstrate that the proposed model achieves state-of-the-art results on a synthetic coherence evaluation task and two real-world tasks involving prediction of varying degrees of coherence.
“AskBot”: The AI Chatbot that Enhances the Learning Process

The application of artificial intelligence (AI) in the field of education has increased significantly over the last decade. In fact, several works have been carried out to implement intelligent systems to help learners in their learning processes. Educational chatbot is one of the AI solutions that help tutors to capitalize their knowledge and transfer it to learners. Its main feature is to interact effectively with human requests using natural language processing (NLP) tools, in order to understand the human intention and respond to it through text or voice. As a result, chatbot improves efficiency and productivity in the field of education. In this paper, we present our chatbot's architecture “AskBot” that supports students in their learning process by giving them appropriate answers to their questions. Our proposed architecture guarantees a continuous and autonomous update of AskBot’s knowledge base (KB). To demonstrate the feasibility of our approach, we implemented the kernel components of the proposed architecture.
Machine Learning-Based Human Activity Recognition Using Smartphones

Human activity recognition (HAR) research using Artificial Intelligence (AI) is being leveraged in many areas such as healthcare monitoring, surveillance motion tracking, sports training, and assisted living. The proposed work has developed a machine learning algorithm to analyze the data, collected in series time signals, from smartphones sensors like accelerometer and 3D gyroscope. The objective of the work is also to decrease the vast dimensions of the datasets. The activities of human are classified and predicted using a fusion of ML supervised and active learning models like logistic regression, QDA, k-nearest neighbor, support vector machine, ANN–classifier, decision tree, Naïve Bayes. The algorithm compares model scores to attain maximum efficiency through dataset training and prediction. With the proposed algorithm, precision levels >90% were obtained for six human activity.
A comparison between “rod counting” and “box counting” methods in linear fractal dimensions measurements

In this work, the “rod counting” method is reviewed in comparison with the “box counting” method applied to linear measurements on irregular figures. Both methods are applied in measurements of fractal dimension of some knowing fractals. Results shows that the “rod counting” method is more accurate in linear measurements. Later the method is applied in a Mandelbrot fractal figure to measure its perimeter fractal dimension.
Adaptive Electronic Health Records Management and Secure Distribution Using Blockchain

In this world, people meet many challenges in the healthcare system. There is a circumstance where an individual has to maintain a comprehensive health report from time to time, and it feels not an easy job. A person may not be suffering from one type of disease; he/she may be undergoing multiple problems at once, and it is challenging to maintain all the reports. There is another circumstance where a sufferer visits multiple hospitals/consulting doctors; in a before-mentioned situation, it is tough for such patients to maintain them, and they may suggest the repeated process. In such a situation, an approach to maintaining Electronic Health Records is proposed in this paper, which uses Blockchain methodology to provide the most secure way to store and share patient reports whenever they need. The proposed system makes the task easy for patient’s to maintain multiple records in a single block, and the testing result leads a pathway to adopt in this current scenario.
SkipBERT: Efficient Inference with Shallow Layer Skipping

In this paper, we propose SkipBERT to accelerate BERT inference by skipping the computation of shallow layers.To achieve this, our approach encodes small text chunks into independent representations, which are then materialized to approximate the shallow representation of BERT.Since the use of such approximation is inexpensive compared with transformer calculations, we leverage it to replace the shallow layers of BERT to skip their runtime overhead.With off-the-shelf early exit mechanisms, we also skip redundant computation from the highest few layers to further improve inference efficiency.Results on GLUE show that our approach can reduce latency by 65% without sacrificing performance.By using only two-layer transformer calculations, we can still maintain 95% accuracy of BERT.
Human Activity Detection-Based Upon CNN with Pruning and Edge Detection

Human activity detection is basic requirement especially within smart environments like smart homes. The primary requirement of smart environment is energy conservation. To achieve this, first target is to detect the human activities accurately using neural network-based approach. This paper presents a unique combination of CNN with pruning and edge detection mechanism to accurately detect human activities. The entire process of human activity detection is portioned into set of phases. In the first phase, data acquisition is performed. The CNN with pruning and edge detection utilized KTH dataset derived from Kaggle. In the second phase, pre-processing to eliminate the noise from the image frames. In the third phase, edge detection and feature extraction were ensured. In the last phase, classification is performed. The result of the human activity detection mechanism is expressed in the form of classification accuracy. High classification accuracy of over 95% is observed.
Risk Controlling Toolkit at Smart University

Modern digital conditions bring the educational activities of higher educational institutions to a new level. There are new needs for methods and forms of education. It is important to timely prepare effective virtual environments, develop educational content that will improve the quality of educational services. Smart universities play a key role in such conditions. They combine a complex of activities: educational, research, innovation, financial and economic. Each type of activity is influenced by various external and internal factors. The impact of some factors is easy to predict, while others are quite difficult. In any case, most of these factors can cause certain risks. Smart universities are characterized by specific risks that arise due to the nature of their activities. Currently, there is a need for timely identification and controlling of risks, taking into account the specifics of the work of smart universities. This requires an extended study of this problem. The article highlights the main methods for diagnosing the risks of a smart university. The procedures for preventive and operational risk controlling of a smart university are presented, taking into account the main processes of its activities. The result of the study is the proposed algorithm for controlling the risks of a smart university. The results obtained were tested during risk controlling in the university's Smart division on the example of the department of Togliatti State University.
Design of RAMF for Impulsive Noise Cancelation from Chest X-Ray Image

Denoising of medical images is an important pre-processing step for analysis, diagnosis, and treatment of various diseases. Images are normally affected by impulse noise when being transmitted through communication channels or because of noisy sensors. The most common noise that occurs in electronic communication is an impulse noise, specifically a salt-and-pepper noise. The median filter is typically used to reduce the presence of such noise. However, it works well for images with low-noise density. So, in order to get a better image restoration, we can use another image restoration technique which is adaptive median filtering which works very well for any density of noise. The adaptive median filter is frequently used in image processing to improve or restore data by eliminating undesirable noise without severely affecting the image’s structures. This method works in a two-step process. We tested the images containing noise levels ranging from 10 to 50% and calculated the PSNR value.
Educational Ecosystem and Government Relations Based on the Company’s Intellectual Capital

The purpose of the work is a systematic studies of the ecosystem “Learning Ecosystem and Government Relations”, the intellectual capital of the ecosystem. The key methods for system assessment have been used and they include analysis, synthesis, identification, aggregation methods, as well as subject-oriented methods of statistics, mathematical modeling and others. The main results of the article: 1) a system analysis of intellectual capital taking into account the evolution of the ecosystem has been conducted; 2) indicators of type D. Tobin, Social Mining have been adapted; 3) the estimations for quality of intellectual capital at companies have been proposed. The analysis will make it possible to develop effective mechanisms for managing “educational ecosystems,” and improving the capitalization of competencies.
Detecting SQL Injection Vulnerabilities Using Artificial Bee Colony and Ant Colony Optimization

Due to the complexity and interconnection of many systems, nowadays is hardly to affirm that a system is secure. Thus, any tool, process or awareness that can improve security is very helpful. The Open Web Application Security listed “Injection” as the third most security risk for web applications in 2021. There are many automated tools to assist professionals, in order to identify this vulnerability. However, keeping these tools up to date has proven to be a challenge. Thus, there has been some interest in applying Artificial Intelligence (AI) in this field. In this paper, we propose an approach to detect SQL injection vulnerabilities in the source code, using swarm-based algorithms, Artificial Bee Colony (ABC) and Ant Colony Optimization (ACO). To test this approach empirically we used web applications purposefully vulnerable as Bricks, bWAPP, and Twitterlike. Simulation results verify the effectiveness of the ABC and ACO when compared with a static analysis tool.
Analysis of the Success Factors of the Quality of E-learning in the Medical School in a Neutrosophic Environment

The COVID-19 pandemic has affected the presence of students in the classroom, especially in medical schools where the practice is essential to gain skills. Virtual teaching or e-learning has emerged as the answer to that problem in this environment. Despite its many advantages, this process has faced different problems that have been overcome thanks to success factors. Considering the need to search for quality in medical schools in Ecuador, a study was carried out on those applied factors that have contributed to guaranteeing the quality of the teaching-learning process. That is the main objective of this research, supported by the bibliographic search and Plithogeny. It can be concluded that the highest level of impact has been made possible by the combination of the correct use of Information and Communication Technologies with adequate teacher training.
Transformer-Based Multilingual G2P Converter for E-Learning System

Phonetic transcription is an approach to represent speech sounds to specific symbols. The most common alphabet we used is the International Phonetic Alphabet (IPA), and the characters in the IPA are phonetic symbols. To support the phonetic transcription process in the phonetic exams of our linguistic E-learning system, we designed a machine translation tool that aims to translate English words to their phonetic formats. This progress can also be expressed as grapheme to phoneme (G2P). The Transformer model has been utilized to develop this G2P module. Also, to improve the functionality of the E-learning system, we trained multiple language models and generated a multilingual G2P translator. Moreover, we evaluated our G2P system by word error rate (WER) and phoneme error rate (PER) with edit distance.
Multi-view Vision Transformer for Driver Action Recognition

Distracted driving causes many accidents every year, most of which can be avoided with automatic recognition. As a result, vision-based driver action recognition is receiving increasing research attention. In a limited in-vehicle space, actions can be very ambiguous from an individual view. Therefore exploring efficient multi-view action recognition architecture is meaningful. This study aims to detect the distraction of drivers while identifying the cause. A novel driver action recognition architecture named multi-view vision transformer (MVVT) is proposed, which combines classical convolutional neural networks (CNNs) with vision transformer. Self-attention mechanism is utilized to dynamically aggregate temporal information and fuse features of different views jointly. Experiments demonstrate that MVVT can effectively recognize drivers’ behaviors with multi-view input. A promising result of 84.9% accuracy is achieved on a large public driver action dataset.