Reviewerly/exHarmony huggingface dataset:
Do 5W+H Commute? Examples in Calculus and Communication of Science

Scientific communication relies on two main different structures, the IMRAD (Introduction Methods Results Analysis Discussion) and the 5W+H (Who Where Why What When How). The former is quite rigidly applied to all peer-to-peer communication, while the latter appears more loosely adaptable to dissemination and communication to the general public. We analyzed the evolution of the content of Calculus books widely adopted in Italian scientific academic courses over the past sixty years and the syllabus of a Communication of Science short course offered by the University of Bari in the last three years. We conclude that the 5Ws can be commuted to some extend with no harm to final result, while the same does not hold for the H that should always remain as the final question. Notwithstanding this flexibility, it is compulsory to answer to all 5Ws and H questions for effective communication.
Combining Augmented Reality and Fairy Tales to Teach Science to Primary School Students: Teachers’ Experience from the Fairy Tale Science Augmented (FAnTASIA) Project

The use of storytelling has long been covering a wide variety of subjects across different levels of education, from preschool and K12 to professional training. Regardless of the content being taught, storytelling methods are assumed to catch students’ attention and involve both their cognitive and affective skills. Educational literature has also collected evidence for effectiveness of the use of augmented reality (AR) technology in promoting greater academic achievement and engagement among students, com-pared with traditional and other digital media-related lessons. Based on existing research, aim of the Fairy Tale Science Augmented (FAnTASIA) project was to design a multi-lingual educational package to integrate storytelling and AR to support the teaching of science concepts and skills in children aged from 5 to 12 years. The present paper first provides a description of the FAnTASIA project and the developed educational package. Following, the paper presents the research design implemented to evaluate the effectiveness of the package in promoting children’s knowledge and cognitive skills and users’ (students and teachers) satisfaction with its use. In this regard, the final section describes teachers’ experience with the use of the educational kit from the results of a questionnaire specifically administered to this purpose.
Application of the Piecewise Linear Approximation Method in a Hardware Accelerators of a Neural Networks Based on a Reconfigurable Computing Environments

This paper considers the application of piecewise linear approximation in hardware accelerators of neural networks built according to the concept of reconfigurable computing environments (RCE). A method for providing an uneven approximation step is shown. The results of the application of the proposed approaches for the approximation of exponential and sigmoid functions actively used in machine learning algorithms are presented. The use of RCE allows the development of hardware algorithms for information processing, characterized by high performance characteristics due to the architectural properties of RCE.
Application of Convolutional Neural Networks for Image Detection and Recognition Based on a Self-written Generator

Object recognition is a branch of artificial vision and one of the pillars of machine vision. It consists in identifying the forms described in advance in a digital image and, in general, in a digital video stream. Although, as a rule, it is possible to perform recognition from video clips, the learning process is usually performed on images. In this paper, an algorithm for classifying and recognizing objects using convolutional neural networks is considered. The purpose of the work is to implement an algorithm for detecting and classifying various graphic objects fed from a webcam. The task is to first classify and recognize an object with high accuracy according to a given data set, and then demonstrate a way to generate images to increase the volume of the training data set by using a self-written generator. The classification and recognition algorithm used is invariant to transfer, shift and rotation. A significant novelty of this work is the creation of a self-written generator that allows using various types of augmentation (artificial increase in the volume of the training sample by modifying the training data) to form new groups of modified images each time.
On Effectiveness of the Adversarial Attacks on the Computer Systems of Biomedical Images Classification

The problems of vulnerability of the computer systems of biomedical images classification to adversarial attacks on are investigated. The aim of the work is to study the effectiveness of the impact of various models of adversarial attacks on biomedical images and the values of control parameters of algorithms for generating their attacking versions. The effectiveness of attacks prepared using the projected gradient descent algorithm (PGD), Deep Fool (DF) algorithm and Carlini-Wagner algorithm (CW) is investigated. Experimental studies were carried out on the example of solving typical problems of medical images classification using deep neural networks VGG16, EfficientNetB2, DenseNet121, Xception, ResNet50 as well as data containing chest X-rays images and brain MRI-scan images. Our findings in this work are as follows. Deep models were very susceptible to adversarial attacks, which led to decrease of the accuracy classification of the models for all datasets. Prior to the use of adversarial methods, we achieved a classification accuracy of 93.6% for brain MRI and 99.1% for chest X-rays. During the DF attack the accuracy of the VGG16 model showed a maximum absolute decrease of 49.8% for MRI-scans, and 57.3% for chest X-rays images. The gradient descent (PGD) algorithm with the same values of malicious image disturbances is less effective than the DF and the CW adversarial attacks. VGG16 deep model is more effective in accuracy classification on considered datasets and most vulnerable to adversarial attacks among other deep models. We hope that these results would be useful to design more robust and secure medical deep learning systems.
New Educational Scenarios in the Framework of Blended Learning

This work introduces readers to the use of BL in educational institutions. A theoretical framework will be built, in which the latest developments of BL in higher education environments are reported, with an examination of the digital services that are made available to teachers and students during the pandemic. The main purpose is to present a critical reflection on the possibilities that BL has offered during the COVID-19 pandemic and what it will be able to offer in the post-pandemic age. The crucial aspects of digital culture in the contemporary world of education and training are highlighted, outlining an essential theoretical path that emphasizes the substantial ad-vantages and tools offered by technology. A definition of BL is provided below, based on the definitions given by authors who have received the most international credit for their work. The study is presented as an exploratory investigation of the blended model for higher education, and is articulated with various references to the most recent strategies implemented by educational institutions. The main reference is the aforementioned Horizon 2021 Report, published by EDUCAUSE. Based on the results, we can gauge the willingness of educational institutions to invest in the design of new educational paths that use a blended methodology, to provide concrete support to teachers to fill in any gaps with respect to their digital skills. The reflection is supported by concrete examples that demonstrate some good practices that were followed at international universities, and provides as an outcome, an updated list of best practices using which it is possible to stimulate a constructive debate, innovative study, and re-search projections for the global scientific community.
Face Emotion Expression Recognition Using DLIB Model and Convolutional Neural Network Approach for Supporting Online Learning

Many sectors have experienced the impact of the COVID-19 outbreak, without exception education. The method of process learning transformed from face-to-face meeting learning became online learning. Learners tried to adapt to this unexpected circumstance. In the online learning approach, the instructors only assumed the degree of learners’ understanding with their face emotion expressions spontaneously. Advancement technology enables the machine to learn data fast and accurately. Mostly, the position of the learner’s face in front of the camera when attending the online course, and the DLIB’s shape detector model map the landmark of the captured face. Deep learning is a subset domain of machine learning. Convolutional Neural Network (CNN) model as a deep learning approach has characteristics in the high computation and ease of implementation. The work proposed a face-emotion expression recognition model for supporting online learning. The combination ratio images dataset was 80% data training and 20% data testing, and the condition expression was determined with a deep learning approach. The experimental results showed that the recognition accuracy of the proposed model achieved 97% for dataset image input.
Angular Position Estimation for Human-Following and Robot Navigation

Mobile robot navigation and human following are two related areas under the field of robotics that have garnered a lot of interest over the years, due to their advantages in the real world, in various settings. Modern techniques depending exclusively on computer vision for environmental data input commonly rely on state-of-the-art object detection methods to obtain data about the location of detected objects in a scene. However, these detection frameworks do not directly provide the angular position of obstacles and human targets in images. In this research project, the Mask R-CNN instance segmentation framework detects static objects and humans in an environment. A chain of algorithms is then used to transform the image’s content and pixel information into a one-dimensional array that can be mapped to the robot’s field of view. The findings show that the result can aid a mobile robot in estimating the angular position of obstacles and a human in a scene, which is necessary for collision-free navigation and robot-human following in an unknown environment. The proposed method also shows adaptivity as it works outdoors and indoors under poor lighting conditions. It can be used as a standalone algorithm in robotics simulations with webcams and static images.
Q&amp;A Generation for Flashcards Within a Transformer-Based Framework

Flashcards are the main tool used in the Spaced Repetition memorization method, yet there are not always available for many topics due to the high effort required to create them. The combination of Transformer-based models with a Recommender System (RS) can enable a dynamic model to auto generate flashcards recommendation for learners and serious game players in order to improve their skills in learning programming. In previous work we introduced an Intelligent Serious Games (ISG) model that combined Deep Knowledge Tracing (DKT) with a Transformer-based Recommender. The ISG aimed at predicting the outcomes of the next missions in gameplay and enabling flashcard recommendations to complete the missions successfully. This research extends previous work by introducing a novel architecture and specifications for a Transformer-based recommender. We introduce a novel Transformer-based framework tailored to three different NLP tasks to dynamically generate flashcards in the form of supporting paragraphs, questions, and answers. We fine-tuned GPT-2, GPT-Neo, BART and T5 models on three new programming skills datasets, and evaluated them using standard metrics that target coherence and semantics. Our findings revealed that the framework is capable of generating coherent flashcards in a fully automated process using a single input string as prompt.
Research on Information Platform of University Performance Appraisal Based on Big Data Collection Based on Internet of Things Technology

In recent years, with the continuous advancement of the information construction process, the construction of a college information platform based on the smart campus has attracted people’s attention. At the same time, the rapid expansion of the demand for university resources also brings people’s vision to the performance of higher education. Big data collection based on Internet of Things technology is widely adopted by high-tech enterprises and applied to information platforms. Besides, it also provides new ideas for colleges and universities education development and talent training. This paper puts forward the idea of using big data collection based on Internet of Things technology to build an information platform for university performance assessment. Therefore, encourage teachers to increase their enthusiasm for scientific research, drive the continuous growth of university employees, and provide technical support for the realization of a more scientific and effective performance management mode. To promote the sustainable development of college education and teachers.
Estimating a Polyhedron Method Informativeness in the Problem of Checking the Automaton by the Statistical Properties of the Input and Output Sequences

The problem of verification of the Moore finite state machine by the statistical properties of the input and output sequences is considered. It is assumed that the initial state of the automaton is unknown. To verify the automaton, the polyhedra method is used. The results of computational experiments for four classes of non-autonomous binary shift registers are presented.
Boosting Adversarial Training in Adversarial Machine Learning

This study aims to investigate Fast Adversarial Training (AT) methods to strengthen models’ resilience against aggressive attacks. A software that can train the model on the database in a reasonable length of time is referred to as “fast training”. The phrase “adversarial training” describes a code that can survive purposefully altered data that cannot be identified by a human but can have detrimental effects on a Deep Neural Network (DNN) model. Current Fast AT frequently uses a random sample agnostic initialization, which increases efficiency but limits future improvements to robustness. Using a sample-dependent adversarial initialization, Fast AT is enhanced in this study. Experiments on four benchmark datasets show that the proposed technique performs better than state-of-the-art Fast AT methods and is robustly comparable to complex multi-step AT methods. It’s a prevalent misconception that networks can’t be accurate and robust at the same time, and that improving reliability requires sacrificing precision. Due to its non-smooth character, the commonly used ReLU activation function greatly worsens adversarial training. The second section of this study describes smooth adversarial training (SAT), a method for enhancing adversarial training that substitutes smooth approximations for ReLU. SAT makes advantage of smooth activation functions to find harder adversarial scenarios and compute better gradient updates during adversarial training. In the next part we present the result of combining two presented studies and we witness promising results.
Neuro-Fuzzy Model Based on Multidimensional Membership Function

Currently, systems based on multidimensional membership functions are being actively researched and developed. Most algorithms for determining the parameters of fuzzy membership functions are developed on the basis of one-dimensional membership functions. The fuzzy rules generated by these algorithms often overlap and cannot act as independent rules. The overlap of fuzzy rules in fuzzy systems does not allow one to evaluate the reliability of individual fuzzy rules and at the same time creates limitations in extracting knowledge from fuzzy systems. In this article, a neuro-fuzzy neural system will be built based on a multidimensional Gaussian membership function with the ability to describe the relationship of interaction between input variables, and at the same time, the generated fuzzy rules are capable of independent operation.
Visual Question Answering for Response Synthesis Based on Spatial Actions

The paper considers the automatic analysis problem of a user’s natural language query from an image. The mechanism synthesizes a logically correct non-binary response. Synthesis is carried out on the basis of combining the results of convolutional and recurrent networks and projection on a set of valid answers. A three-dimensional data set has been developed to search for an answer in a complex environment using a robotic arm. Similar systems examples and their comparison are given. The experiments results showed that our method is able to achieve indicators comparable with known models.
Concept of Quality in Online Environments: The Actual Role of Teaching and Learning Centers

The last two years have been characterized by important and rapid changes in teaching models and strategies, given the repercussions from the Covid-19 pandemic on world societies. In such a scenario, according to the research needs that have emerged, EDUCAUSE has developed a specific survey relating to the new challenges of higher education. Last year, its research team drafted and presented the Horizon report, with the subtitle, Teaching and Learning Edition. Among its various topics, quality online learning is analyzed, since one of the emerging challenges that higher education finds itself facing is involving all stakeholders of academic institutions in the quality assurance processes. The present study therefore develops an exploratory survey for the identification and comparison of good practices and development models of Teaching and Learning Centers (TLCs), with reference to online learning environments and digital skills of the teacher. It has in fact been noted that, among the projects recently triggered with respect to quality online learning, many of these have concerned the setting up of TLCs online. Consequently, in the second part of the article, some reference models about TLCs will be examined, developing a critical description of them, highlighting the strategies, tools and services involved.
Generalized Net Model of the Prostate Cancer Early Stages of Development

A generalized net model of the prostate cancer early stages of development is described. The relationship of the prostate with the human body respiratory, blood and circulatory, and endocrine systems are represented.
Intelligent Evaluation of English Language Teaching Effect with Fuzzy Inference Algorithm

The intelligent evaluation of English language teaching effect is the only way to improve the training quality of normal education in the era of intelligent education. The explosion of data and the updating iteration of machine learning algorithm make it possible to evaluate, classify and visualize the data of English teachers’ teaching ability on a large scale. The intelligent evaluation of English teaching effect based on fuzzy inference algorithm is an intelligent system to evaluate the effectiveness of new methods or procedures. It will be used to measure the effectiveness of different methods and schemes to find out which one is more effective. The system can also be used to evaluate the effectiveness of individual teachers, courses, schools or institutions. Based on fuzzy logic technology, it can be applied to many fields such as education, medicine, engineering and so on.
Teaching Quality Evaluation in Universities Based on Apriori Algorithm

The purpose of this paper is to establish a framework for teaching evaluation in colleges and universities by using decision trees and related rules, to study the relationship among educational factors, educational chain factors and educational effects, and to provide a decision framework for teaching evaluation. The study will take into full account many aspects that affect the quality of teaching, which is the combination of subjective factors (such as status, academic experience, age, gender, teaching methods and resources) and objective factors (such as type of course, course series, number of students, total hours per week), and make a comprehensive analysis of the relevant factors.
Generalized Net Model of the Vegetative (Autonomic) Innervation of Gastrointestinal Tract

The gastrointestinal tract (GIT) is a basic part of the human body. It interacts with many organs and systems and has a complex autonomic innervation. Generalized net model of GIT innervation is described and the relations with central nervous system are shown. Some applications of the model are discussed.
Research and Application of Chinese Literature Automatic Abstract Extraction Based on Textrank Algorithm in Characteristic Resources Construction

The purpose of this study is to study the application of automatic abstract extraction of Chinese literature based on text ranking algorithm in feature resource construction. The objective of this project is to provide a system for automatically extracting general features from Chinese literary works as an effective method for constructing feature resources. A large amount of data has been collected from the Internet, including novels, short stories, essays and other texts. Based on these data, we developed a new method to automatically extract general features using text ranking algorithm and machine learning technology. Research paper on Automatic Abstract extraction based on text ranking algorithm in feature resource construction. It is an important part of Chinese Literature Automatic Abstract extraction system, which can be used for automatic extraction and classification of Chinese Literature Feature resources. This paper mainly introduces the characteristics of the new method and presents some application examples.
Multicriteria Analysis of Oncology Data During the Covid Pandemic

In the article a data related to the treatment of Covid-19 patients for the period 28.10.2020–23.3.2022 were investigated. Some of the patients are with oncological diseases. The studied patients were admitted to a hospital in the city of Burgas, specialized in the treatment of cancer. The data contains information about the name of the disease according to the International Statistical Classification of Diseases and Health Problems (ICD), period of the patient’s hospitalization, number of patients, medical staff by profile, etc. The method of InterCriteria Analysis was applied to study the dependencies between the parameters describing the patients and the medical staff. A self-organized neural network was developed for clustering the information related to the type of cancer.
Research on an Intelligent Logistics Blockchain Consensus Algorithm

Aiming at the increasingly urgent problems to be solved in the traceability of big data and goods such as logistics, capital flow, information flow and business flow of logistics enterprises, this paper provides blockchain intelligent logistics model and blockchain consensus algorithm for the non repudiation and capital security of supply chain authentication and anti-counterfeiting technology of logistics enterprises. This paper mainly analyzes the technical problems and transaction modes encountered in the process of traditional logistics transactions, and puts forward the basic idea of using cloud computing and blockchain technology to overcome their shortcomings. According to the technical characteristics and characteristics of cloud computing and blockchain, combined with their respective advantages, this paper introduces the technical advantages of intelligent logistics blockchain. The definition and concept of intelligent logistics blockchain are proposed, and the calculation model based on the consistency algorithm of intelligent logistics blockchain is given.