Reviewerly/exHarmony huggingface dataset:
Oral English Development of EFL Learners from the Perspective of Complexity Dynamic Theory

With the development of economic globalization and cultural integration, the learning of a second language has become increasingly important for economic, political, and cultural exchanges in the world today. Complex dynamic system theory is an important research theory of foreign language learning methods for nonnative speakers. However, the research and application of the complex dynamic system theory in oral foreign language learning of English as a Foreign Language (EFL) learners are not prominent. The purpose of this paper is to explore the learning situation of oral English of EFL learners from the perspective of the complex dynamic system theory and provide some new learning methods and suggestions for EFL learners’ oral English learning based on the research of complex dynamic system theory. First, this article discusses and analyzes the current situation of oral English learning of EFL learners through literature research and a questionnaire survey. Then, based on the related research on the theory of complex dynamic systems, this article proposes countermeasures and learning method recommendations for the problems existing in the current oral English learning of EFL learners. After that, this paper sets up a controlled test of oral English learning through the learning method formulated by the complex dynamic system theory and verifies the learning effect of the oral English learning method proposed in this paper according to the mathematical theory research method of the complex dynamic system. Finally, this article discusses the reliability of the experimental research conclusions based on the error analysis and confidence level estimation of the experimental results. The research results show shows that there is 95% confidence that the second language learning method proposed in this paper under the perspective of the complex dynamic system theory has a positive effect on the oral English learning of EFL learners.
A Conceptual Research Model for the Partner Selection Problem

In the conditions of intensive development of scientific communities' mobility to ensure openness of innovations, it is essential to rationalize the choice of partners rational. The article considers a conceptual model of research on the problem of choosing partners for the implementation of research or educational projects. The main components of the conceptual model are to determine the criteria for selecting potential partners, applying a multi-objective optimization method, expert evaluation, building the benefits of partners or project executors, deciding on assigning partners project tasks that meet the relevant work package. The article defines the general requirements for the development of information technology to select potential partners for research and educational projects. Problems that need to be solved have also identified. It assumed that the introduction of this information technology will increase the efficiency of projects to obtain stable educational and scientific results in the medium and long term.
Classification and Analysis of Techniques and Tools for Data Visualization Teaching

Data Visualization addresses the use of graphics with the purpose to obtain or transmit the knowledge in a easier and faster way, this is it main, and in many cases unique purpose. Since their invention Data graphics has evolved and many techniques has been developed, and in the last decades, with the definition and evolution of the Data Science, Data Visualization has become to be used profusely, in that manner that, by one side, the Data Science Body of Knowledge, DS-BoK, define five knowledge area groups that should be taught when learning Data Science, in all of them Data Visualization is taken a main role for different reasons applying each knowledge area; and by other side all the Data Science development environments, open source or proprietary, include tools for performing Data Visualizations. This paper presents the results of a research carried out with the main objective of improving the teaching of data visualization using two ways: propose a new system to classify the large amount of different graphical techniques for presenting data that can be found in the literature; and analyze using different attributes quite all the most important different tools, open source and private, that are available to develop data graphics mainly form a data visualization teaching point of view.
Eclat_RPGrowth: Finding Rare Patterns Using Vertical Mining and Rare Pattern Tree

Frequent pattern mining is one of the key research areas in the Data Mining (DM) paradigm. There are many algorithms in the literature to identify the frequent itemsets whereas research on rare pattern mining is in the burgeoning stage. Rare items are the infrequent items, where few applications like medical diagnosis, telecommunications, and false alarm detection in industries demand for rare patterns and rare associations with frequent or infrequent items sets in the database. The algorithms that are used to identify frequent items can also be used to identify rare patterns. However, such algorithms suffer from RareItemProblem. Rare Pattern Mining algorithms that are based on Apriori and FP-Growth were designed but Eclat-based rare pattern mining algorithms have not been explored. This paper proposes an Eclat-RPGrowth, algorithm to find rare patterns and the support of itemset is calculated by using intersection of BitSets for corresponding k − 1 itemsets. Also, this research work proposes a variant of Eclat_RPGrowth as Eclat_PRPgrowth. Both the algorithms are outperformed in execution time, and with the number of rare items generated.
Toward an Augmented and Explainable Machine Learning Approach for Classification of Defective Nanomaterial Patches

Electrospinning is a manufacturing technique used to produce nanofibers for engineering applications. This process depends on several control parameters (such as solution concentration, applied voltage, flow rate, tip-to-collector distance) whose variations during the experiments may lead to the formation of defective nanofibers (D-NF) along with non-defective nanofibers (ND-NF). D-NF present either with impurities or morphological defects that prevent their practical use in nanotechnology. In this context, here, a data augmentation based machine learning approach is proposed to classify Scanning Electron Microscope (SEM) images in two classes (i.e., D-NF vs. ND-NF). To this end, a custom Convolutional Neural Network (CNN) is developed to perform the binary classification task, achieving an accuracy rate up to 93.85%. Moreover, the explainability of the proposed CNN is also explored by means of an occlusion sensitivity analysis in order to investigate which area of the SEM image mostly contributes to the classification task. The achieved encouraging findings stimulate the use of the proposed framework in industrial applications.
DLB3M : A noble load balancing algorithm for cloud computing services

The idea of cloud computing is flourished on a basic principle of reusability of IT capabilities. It is completely a web-based technology, which enhances its usefulness as well as monitors PAYG model. Cloud computing follows the technology, services, applications that are analogous to those on the internet and shifts them into a self-service utility. Cloud computing synopsis the details of system implementation from developers and users. In cloud computing, load balancing is a ticklish topic. It is a procedure that utilizes several nodes and allocates dynamic workload among them. The original purpose of load balancing is the minimization of overall response time and best use of resources. This paper is largely concentrated on the DLB3M (Dynamic Load Balancing Max-Min and Max) algorithms. This reduces the overall response time compared to other load balancing algorithms. This paper describes briefly the DLB3M algorithm and why it is best suited in the cloud computing environment.
Effective selection of the optimal virtual reality (VR) lens interval of VR devices using the test patterns of the different shapes for the left and right eyes

Abstract Selection of the optimal virtual reality (VR) lens interval L I between two convex lenses of a VR device is important for the comfort of viewers. This study examined how the participants selected L I for two types of test patterns. Twenty‐one participants with interpupillary distances (IPDs) of 59–68 mm were selected. When the participants were asked to change the L I to match specific positions of the test pattern in which the shapes for the two eyes were different, the selected values of L I did not depend on the initial lens distances. When they were asked to change L I for their best viewing distances using the test pattern with the same shapes for two eyes, the selected values strongly depended on the initial lens conditions. This dependence can be attributed to the finite range of eye rotation inside which binocular fusion can occur. No noticeable dependence of the selected values on IPD was observed. Excessive eye rotation to enable binocular fusion can cause eye strain. In the former test pattern, binocular fusion does not occur, and each eye is aligned along its fusion‐free eye position with little eye strain. Hence, the former test pattern using the different shapes is preferable for controlling the lens interval.
Movement-based Music Making: An Aesthetics-based Evaluation

This proposed research investigates movement-based interaction techniques for designing new musical instruments. It provides an aesthetics-based evaluation of a case study, Bodyharp, a new digital musical instrument at the intersection of music and dance. My dissertation examines not only developing new musical instruments but also performance practices that can be shared across diverse skill sets and abilities. In this project, I investigate this approach by bringing both the musician's and the mover's experiences closer to each other. In this paper, the design of the instrument and its earlier iterations are provided as well as an ongoing user study. The preliminary results of the user study, the future directions of this dissertation project, and its anticipated contributions are discussed.
Sensitivity Analysis of Random Forest Hyperparameters

Hyperparameter tuning is the process of choosing an optimal set of parameters that govern the learning process of machine learning models. Most modelers spend a lot of time executing hyperparameter tuning on their model. Moreover, there might be no improvement in the performance of the model. This study, thus, performs the sensitivity analysis of the random forest's popular hyperparameters, namely n-estimator, max depth, min sample leaf, and min sample split, to determine the effect of changes in the hyperparameters on the accuracy and F1-score of the model. One-at-a-time (OAT) and Latin hypercube sampling (LHS) are used together with the analysis of variance (ANOVA). Four datasets with different characteristics are examined. The ANOVA results display both supporting and opposing outputs from the OAT and LHS experiments. The results reported in both experiments suggest that the accuracy and F1-score of the studied models using four distinct datasets are mainly sensitive to min sample leaf.
A new oversampling method in the string space

In syntactic and structural pattern recognition, data represented as strings appear in several supervised classification applications. In some situations, data collections show imbalanced class distributions, which typically results in the classifier biasing its performance to the class representing the majority of objects. To solve this problem, some oversampling methods have been proposed for data represented as strings. However, this type of method has been little studied in the literature. Therefore, in this paper, we present an oversampling method for working in string space that balances the minority class and gets better classification results than state-of-the-art oversampling methods, especially for highly imbalanced problems. Furthermore, according to our experiments, the proposed method is much faster than those reported in the literature.
Emotion Cause Extraction by Combining Intra-clause Sentiment-enhanced Attention and Inter-clause Consistency Interaction

Emotion cause extraction aims at identifying causes of the given emotion expression in a document. Many approaches have been proposed to solve this task, including rule-based approaches, traditional machine learning methods and deep neural networks. However, these approaches focus more on text content and suffer from the problem of the information deficiency issue. In this work, we exploit external sentiment knowledge, intra-clause syntactic dependency and inter-clause consistency to alleviate the problem. We design a sentiment-enhanced attention mechanism to fuse sentiment knowledge and syntactic dependency, and we obtain consistency information through the interaction between clauses on the same side of emotion expression. We finally achieve the best performance among 10 compared methods.
Great Paintings in Fully Immersive Virtual Reality

Large collections of paintings have been digitized by museums (e.g. The Metropolitan Museum of Art, New-York, USA) and companies such as Google. These resources can be provided to a wide audience through virtual reality as an instructional means to fully convey the size and the magnificence of these paintings. Accessing these resources in an immersive virtual environment can be beneficial to all the students who live far away from museums. In addition, making these resources available to a wide audience answers a current need related to the closure of many museums, because of the Covid-19 pandemic. This paper provides a description of a fully immersive virtual reality museum where 1000 paintings can be accessed in two modes: individually (one painting per room), or in galleries (multiple paintings in a room) using procedural generation. More importantly, the proposed application provides a means for museums and art instructors to insert their own collections of paintings. Such an approach aims at improving the transition of high resolution images of paintings into art galleries in virtual reality. The application has been deployed on the Steam platform, is available for free, and has been evaluated positively by users, suggesting a high interest for such an application.
Tracking Ground Targets with Road Constraints Using a JMS-GM-PHD Filter

The probability hypothesis density filter with linear Gaussian jump Markov system multi-target models is an attractive approach to tracking multiple maneuvering targets in the presence of data association uncertainty, clutter, noise, and detection uncertainty. However, these models are not precise enough to describe moving targets on road networks in ground target tracking scenario. In this paper, the road map information is integrated into the jump Markov system Gaussian mixture probability hypothesis density (JMS-GM-PHD) filter, and a road-constraint JMS-GM-PHD filter for ground target tracking is proposed. In addition, we then derive the recursive equation of the proposed filter. Simulation results show that the proposed road-constrained JMS-GM-PHD filter is effective in tracking ground moving targets.
An Intragroup and Intergroup Multiple Secret Images’ Sharing Scheme with Each Participant Holding One Shadow Image

In some particular situations, participants need to recover different secrets both within a group (i.e., intragroup) and between two groups (i.e., intergroup). However, most of the existing multilevel secret sharing (MLSS) and multigroup secret sharing (MGSS) schemes mainly focus on how to protect a secret between one or more groups. In this paper, we propose a polynomial-based scheme to share multiple secret images both within a group and between groups. The random elements’ utilization model of integer linear programming is used to find polynomial coefficients that meet certain conditions so that each participant holds only one shadow image and some of them can recover secrets of both intergroup and intragroup. In addition, our scheme based on polynomials has the advantage of low computational complexity. Theoretical analysis and experiments show that the proposed scheme is feasible and effective.
Adaptive Control of Nonlinear Systems Represented by Extreme Learning Machine (ELM) and the Fuzzy Logic Control (FLC)

Extreme learning machine (ELM) has been used in many fields due to its flexibility to approximate highly nonlinear functions. This is because the hidden node parameters are randomly generated and the outputs weights are computed analytically. The idea is based on producing high accuracy function approximation that can follow the system path in the appropriate time. One of the best approaches that work in this area for a long time is the Fuzzy Logic Control (FLC) but the problem of the FLC is that it needs a high volume of data to develop a fuzzy system and the approximation accuracy is related to the number of membership functions. In this paper, we present an adaptive control single-input-single-output (SISO) nonlinear system using ELM and FLC and analyze the results obtained in both cases. Our focus is to decrease the overall MSE produced and the control energy needed.
On the impact of tangled program graph marking schemes under the atari reinforcement learning benchmark

Tangled program graphs (TPG) support emergent modularity by first identifying subsets of programs that can usefully coexist (a team/ graph node) and then identifying the circumstance under which to reference other teams (arc adaptation). Variation operators manipulate the content of teams and arcs. This introduces cycles into the TPG structures. Previously, this effect was eradicated at run time by marking nodes while evaluating TPG individuals. In this work, a new marking heuristic is introduced, that of arc (learner) marking. This means that nodes can be revisited, but not the same arcs. We investigate the impact of this through 18 titles from the Arcade Learning Environment. The performance and complexity of the policies appear to be similar, but with specific tasks (game titles) resulting in preferences for one scheme over another.
Research on the Application of Medical Teaching Based on XR and VR

With the continuous development of visualization technology and interactive technology, the application of augmented reality in medical education is increasingly extensive. Through the use of computers, wearables, etc., augmented reality can provide learners with three-dimensional virtual environment and human-computer interaction opportunities to help learners acquire real and interactive experience. Immersive visual, auditory, and tactile stimuli make augmented reality an ideal learning tool for medical education. In this paper, the application scenarios and quality assurance of augmented reality medical education are combed, and it is found that augmented reality can be used for knowledge and case presentation, operative skills practice and evaluation feedback, and XR system quality is influenced by technology, resources and instructional design, which helps medical educators make the most of the advantages of augmented reality technology to carry out teaching so as to improve the acceptance of educational application of augmented reality technology.
An AI-Enabled Framework for Real-Time Generation of News Articles Based on Big EO Data for Disaster Reporting

In the field of journalism, the collection and processing of information from different heterogeneous sources are difficult and time-consuming processes. In the context of the theory of journalism 3.0, where multimedia data can be extracted from different sources on the web, the possibility of creating a tool for the exploitation of Earth observation (EO) data, especially images by professionals belonging to the field of journalism, is explored. With the production of massive volumes of EO image data, the problem of their exploitation and dissemination to the public, specifically, by professionals in the media industry, arises. In particular, the exploitation of satellite image data from existing tools is difficult for professionals who are not familiar with image processing. In this scope, this article presents a new innovative platform that automates some of the journalistic practices. This platform includes several mechanisms allowing users to early detect and receive information about breaking news in real-time, retrieve EO Sentinel-2 images upon request for a certain event, and automatically generate a personalized article according to the writing style of the author. Through this platform, the journalists or editors can also make any modifications to the generated article before publishing. This platform is an added-value tool not only for journalists and the media industry but also for freelancers and article writers who use information extracted from EO data in their articles.
Blockchain and Clinical Data Economics

Clinical research evolved side-by-side with technology, leading to exponential data generation contributing to social and economic development. Nevertheless, data storage, integrity, and privacy concerns have emerged, raising trust issues regarding data sharing. This chapter will demonstrate how blockchain technology (BT) can address these problems and help optimizing processes, minimize costs, and monetize data. It will explain why these models are not fully explored and how cryptocurrencies are advantageous compared to traditional currency. Worldwide examples of companies developing network infrastructures that rely on private players will be provided, and European cases, where consortium models that count with different partners to build health blockchain infrastructures are being developed, will be discussed. Considering the business models to be addressed under the European Union (EU) jurisdiction, a hypothetical BT-based healthcare model with potential application in the EU scenario will also be highlighted.
Information Flow Control to Secure Data in the Cloud

Data security remains a major concern for organizations considering the use of cloud services to store their confidential, business-critical data. In this paper, we investigate how information flow control can be used in the cloud to enhance the confidence of enterprises, so they can safely and securely adopt cloud solutions for their data storage needs. We discuss how different techniques can be used with the CloudMonitor tool to guarantee the protection of data in the cloud. We then give an overview of how centralized and decentralized information flow control systems operate, and the comparative advantages and disadvantages of each approach. Our analysis suggests that CloudMonitor can achieve better data security with the use of decentralized information flow control. We then discuss different decentralized information flow tracking tools applied to monitoring data in the cloud. CloudMonitor enables the consumers and the providers of cloud services to agree on acceptable security policies as well as their implementation, to ensure secure data storage in the cloud.