Reviewerly/exHarmony huggingface dataset:
When explainable AI meets IoT applications for supervised learning

Abstract This paper introduces a novel and complete framework for solving different Internet of Things (IoT) applications, which explores eXplainable AI (XAI), deep learning, and evolutionary computation. The IoT data coming from different sensors is first converted into an image database using the Gamian angular field. The images are trained using VGG16, where XAI technology and hyper-parameter optimization are introduced. Thus, analyzing the impact of the different input values in the output and understanding the different weights of a deep learning model used in the learning process helps us to increase interpretation of the overall process of IoT systems. Extensive testing was conducted to demonstrate the performance of our developed model on two separate IoT datasets. Results show the efficiency of the proposed approach compared to the baseline approaches in terms of both runtime and accuracy.
Active obstacle avoidance method of autonomous vehicle based on improved artificial potential field

Aiming at the local minimum point problem in an artificial potential field based on a safe distance model, this article proposes an algorithm for active obstacle avoidance path planning and tracking of autonomous vehicles using an improved artificial potential field. First, a possible road operating condition in which the artificial potential field based on the safety-distance model falls into a local minimum point is studied. Subsequently, an improved artificial potential field method is proposed by introducing the second virtual target attraction potential field, which successfully overcomes the local minimum point problem. Second, a model for autonomous vehicle active obstacle avoidance path planning and tracking based on the improved artificial potential field is established. Finally, MATLAB/CarSim co-simulations were performed under the road conditions of constant- and variable-velocity obstacle vehicles. The simulation results demonstrate that the improved artificial potential field method can effectively solve the local minimum point problem of the artificial potential field based on the safe distance model. Additionally, the safety and stability of autonomous vehicle active obstacle avoidance are improved.
A new scheme for the development of IMU-based activity recognition systems for telerehabilitation

Wearable human activity recognition systems (HAR) using inertial measurement units (IMU) play a key role in the development of smart rehabilitation systems. Training of a HAR system with patient data is costly, time-consuming, and difficult for the patients. This study proposes a new scheme for the optimal design of HARs with minimal involvement of the patients. It uses healthy subject data for optimal design for a set of activities used in the rehabilitation of PD1 patients. It maintains its performance for individual PD subjects using a single session data collection and an adaptation procedure. In the optimal design, several classifiers (i.e. NM, k-NN, MLP with RBF as a hidden layer, and multistage RBF SVM) were investigated. Features were signal-based in the time, frequency, and time-frequency domains. Double-stage feature extraction by PCA and fisher technique was used. The optimal design reached a recall of 95% on healthy subjects using only two sensors on the left thigh and forearm. Implementing the adaptation procedure on two PD subjects, the performance was maintained above 80%. Post analysis on the performance of the adapted HAR showed a slight drop in precision (above 87% to above 81%) for activities that was performed in sitting condition.
Survival prediction in triple negative breast cancer using multiple instance learning of histopathological images

Computational pathology is a rapidly expanding area for research due to the current global transformation of histopathology through the adoption of digital workflows. Survival prediction of breast cancer patients is an important task that currently depends on histopathology assessment of cancer morphological features, immunohistochemical biomarker expression and patient clinical findings. To facilitate the manual process of survival risk prediction, we developed a computational pathology framework for survival prediction using digitally scanned haematoxylin and eosin-stained tissue microarray images of clinically aggressive triple negative breast cancer. Our results show that the model can produce an average concordance index of 0.616. Our model predictions are analysed for independent prognostic significance in univariate analysis (hazard ratio = 3.12, 95% confidence interval [1.69,5.75], p < 0.005) and multivariate analysis using clinicopathological data (hazard ratio = 2.68, 95% confidence interval [1.44,4.99], p < 0.005). Through qualitative analysis of heatmaps generated from our model, an expert pathologist is able to associate tissue features highlighted in the attention heatmaps of high-risk predictions with morphological features associated with more aggressive behaviour such as low levels of tumour infiltrating lymphocytes, stroma rich tissues and high-grade invasive carcinoma, providing explainability of our method for triple negative breast cancer.
Dynamic Causal Collaborative Filtering

Causal graph, as an effective and powerful tool for causal modeling, is usually assumed as a Directed Acyclic Graph (DAG). However, recommender systems usually involve feedback loops, defined as the cyclic process of recommending items, incorporating user feedback in model updates, and repeating the procedure. As a result, it is important to incorporate loops into the causal graphs to accurately model the dynamic and iterative data generation process for recommender systems. However, feedback loops are not always beneficial since over time they may encourage more and more narrowed content exposure, which if left unattended, may results in echo chambers. As a result, it is important to understand when the recommendations will lead to echo chambers and how to mitigate echo chambers without hurting the recommendation performance. In this paper, we design a causal graph with loops to describe the dynamic process of recommendation. We then take Markov process to analyze the mathematical properties of echo chamber such as the conditions that lead to echo chambers. Inspired by the theoretical analysis, we propose a Dynamic Causal Collaborative Filtering ($\partial$CCF) model, which estimates users' post-intervention preference on items based on back-door adjustment and mitigates echo chamber with counterfactual reasoning. Multiple experiments are conducted on real-world datasets and results show that our framework can mitigate echo chambers better than other state-of-the-art frameworks while achieving comparable recommendation performance with the base recommendation models.
DAVE: Deep Learning-Based Asymmetric Virtual Environment for Immersive Experiential Metaverse Content

In this study, we design an interface optimized for the platform by adopting deep learning in an asymmetric virtual environment where virtual reality (VR) and augmented reality (AR) users participate together. We also propose a novel experience environment called deep learning-based asymmetric virtual environment (DAVE) for immersive experiential metaverse content. First, VR users use their real hands to intuitively interact with the virtual environment and objects. A gesture interface is designed based on deep learning to directly link gestures to actions. AR users interact with virtual scenes, objects, and VR users via a touch-based input method in a mobile platform environment. A text interface is designed using deep learning to directly link handwritten text to actions. This study aims to propose a novel asymmetric virtual environment via an intuitive, easy, and fast interactive interface design as well as to create metaverse content for an experience environment and a survey experiment. This survey experiment is conducted with users to statistically analyze and investigate user interface satisfaction, user experience, and user presence in the experience environment.
Investigating the Co-Volatility Spillover Effects between Cryptocurrencies and Currencies at Different Natures of Risk Events

This paper examines and confirms the varying volatility of the relationship between cryptocurrency and currency markets at different time periods, such as when the market encountered multiple risk events including the US–China trade war, COVID-19, and the Russian–Ukraine war. We employ the Diagonal BEKK model and find that the co-volatility spillover effects between the returns of cryptocurrencies and currencies, with the exception of Tether and the U.S. dollar index, evolved significantly. Furthermore, the co-volatility spillover effects between cryptocurrencies and EUR have the largest effects and fluctuations. Large-cap cryptocurrencies (Bitcoin and Ethereum) have greater co-volatility spillover effects between them and currencies. Regarding the ability of cryptocurrencies to act as safe-haven for currencies, we observe that Bitcoin, Ethereum, and Tether served as safe-havens during the US–China trade war, and Bitcoin was a safe-haven during COVID-19. During the 2022 Russian–Ukraine war, Bitcoin and Tether were safe-havens. Interestingly, our findings point out that Bitcoin provides a more consistent safe-haven function for currency markets. Overall, by including multiple global risk events and a comprehensive dataset, the results support our conjecture (and earlier studies) indicating that the capabilities of cryptocurrency are time-varying and related to market status and risk events with different natures.
Digital teaching competence of university teachers: A comparative study at two European universities

This study analysed the level of digital competence of 910 university teachers at one Spanish and one Polish university, using a self-perception questionnaire based on the DigCompEdu framework. For this purpose, a quantitative methodology was used with the aim of finding out the situation at both institutions through focusing on gender, professional category, and areas of knowledge of the participants. The results showed that the university teachers generally perceived themselves as having an intermediate level of digital competence. In this sense, it was observed that the elements selected for analysis (university, gender, and professional category) can have direct implications for the results and that there are significant differences depending on the group to which they belong. Implications for practice or policy: Digital teaching competence need to be improved in the university context. Gender influences the self-perceived level of digital teaching competence and this needs to be considered in the university practices and policies. University teachers at the levels of junior lecturer and teaching assistant show better digital teaching competence and this needs to be considered in the university practices and policies.
The stability of different aggregation techniques in ensemble feature selection

Abstract To mitigate the curse of dimensionality in high-dimensional datasets, feature selection has become a crucial step in most data mining applications. However, no feature selection method consistently delivers the best performance across different domains. For this reason and in order to improve the stability of the feature selection process, ensemble feature selection frameworks have become increasingly popular. While many have examined the construction of ensemble techniques under various considerations, little work has been done to shed light on the influence of the aggregation process on the stability of the ensemble feature selection. In contribution to this field, this work aims to explore the impact of some selected aggregation strategies on the ensemble’s stability and accuracy. Using twelve classification real datasets from various domains, the stability and accuracy of five different aggregation techniques were examined under four standard filter feature selection methods. The experimental analysis revealed significant differences in both the stability and accuracy behavior of the ensemble under different aggregations, especially between score-based and rank-based aggregation strategies. Moreover, it was observed that the simpler score-based strategies based on the Arithmetic Mean or L2-norm aggregation appear to be efficient and compelling in most cases. Given the data structure or associated application domain, this work’s findings can guide the construction of feature selection ensembles using the most efficient and suitable aggregation rules.
Digital Image Steganalysis: Current Methodologies and Future Challenges

With the growing use of the internet and social media, data security has become a major issue.Thus, researchers are focusing on data security techniques such as steganography and steganalysis.Steganography is the approach of concealing the existence of secret messages in digital media for secure transmission.Steganalysis techniques aim to detect the existence of concealed messages and extract them.Digital image steganography and steganalysis techniques are classified into the spatial and transform domains.In this paper, we provide a detailed survey of the state-of-the-art works that have been performed in two-dimensional and three-dimensional image steganalysis.We present the most popular datasets and explain some steganographic methods for embedding hidden data.Steganalysis is a very difficult task due to the lack of information about the characteristics of the cover media that can be exploited to detect hidden messages.Therefore, we review studies performed on image steganalysis in the spatial and transform domains using classical machine learning and deep learning approaches.Additionally, we present open challenges and discuss some directions for future research.
8th Workshop on Cloud Technologies in Education: Report

This is an introductory text to a collection of selected papers from the 8th Workshop on Cloud Technologies in Education (CTE 2020) which was held in Kryvyi Rih, Ukraine, on the December 18, 2020. It consists of short introduction, papers’ review and some observations about the event and its future.
Face Detection &amp; Recognition from Images &amp; Videos Based on CNN &amp; Raspberry Pi

The amount of multimedia content is growing exponentially and a major portion of multimedia content uses images and video. Researchers in the computer vision community are exploring the possible directions to enhance the system accuracy and reliability, and these are the main requirements for robot vision-based systems. Due to the change of facial expressions and the wearing of masks or sunglasses, many face recognition systems fail or the accuracy in recognizing the face decreases in these scenarios. In this work, we contribute a real time surveillance framework using Raspberry Pi and CNN (Convolutional Neural Network) for facial recognition. We have provided a labeled dataset to the system. First, the system is trained upon the labeled dataset to extract different features of the face and landmark face detection and then it compares the query image with the dataset on the basis of features and landmark face detection. Finally, it compares faces and votes between them and gives a result that is based on voting. The classification accuracy of the system based on the CNN model is compared with a mid-level feature extractor that is Histogram of Oriented Gradient (HOG) and the state-of-the-art face detection and recognition methods. Moreover, the accuracy in recognizing the faces in the cases of wearing a mask or sunglasses or in live videos is also evaluated. The highest accuracy achieved for the VMU, face recognition, and 14 celebrity datasets is 98%, 98.24%, 89.39%, and 95.71%, respectively. Experimental results on standard image benchmarks demonstrate the effectiveness of the proposed research in accurate face recognition compared to the state-of-the-art face detection and recognition methods.
A Novel Credible Carbon Footprint Traceability System for Low Carbon Economy Using Blockchain Technology

To achieve the goal of carbon neutrality, many countries have established regional carbon emission trading markets and tried to build a low-carbon economic system. At present, the implementation of carbon emission trading and low-carbon economic systems faces many challenges such as manipulation, corruption, opacity, lack of trust, and lack of data tracking means. The application of blockchain technology can perfectly solve the above problems. However, the data recorded on a blockchain are often multi-type and heterogeneous, and users at different levels such as regulators, enterprises, and consumers have different requirements for data types and granularity. This requires a quick and trustworthy method for monitoring the carbon footprint of enterprises and products. In this paper, the carbon footprint traceability of enterprises and products is taken as an application scenario, and the distributed traceability concept of "traceability off the chain and verification on the chain" is adopted. By reconstructing the pointer of the file structure of the distributed storage, an interactive traceability structure supporting type filtering is constructed, which enables fast retrieval and locating of carbon emission data in the mixed data on the chain. The experimental results show that using the interactive traceability structure that supports type filtering for traceability not only releases the computing power of full nodes but also greatly improves the traceability efficiency of the long-span transaction chain. The proposed carbon footprint traceability system can rapidly trace and track data on an enterprise's and a product's carbon footprint, as well as meet the needs of users at all levels for traceability. It also offers more advantages when handling large amounts of data requests.
Cloud resources use for students’ project activities

The modern educational system proclaims learning aimed at acquiring practical skills and based on the activity approach. Educational research projects are the necessary component of curricula in physics, computer science, biology and chemistry. There is a problem of specialized equipment and facilities using for the implementation of such projects in distance learning. Therefore, the issue of cloud resources using for distance learning organization in robotics is relevant. The article presents a brief overview of the current state of projects development in Ukrainian schools and approaches used in foreign educational institutions in teaching robotics distantly. The article describes the stages of robotics projects development such as organizational, communicative, project work, summarizing. The peculiarities of the stages in distance learning and the possibilities of cloud technologies in robotics are also considered. The authors’ experience in projects developing in this environment for students and future teachers is described.
Management of online platform development and support process

The matter of building an optimal model of managing the online platform “Higher School Mathematics Teacher” is considered in this article. The existing researches on the management of online platform development and support process have been analyzed in this paper. The model developers described the process of building an online platform according to the Software Development Lifecycle rules. The researchers described an online course platform creation and functioning process that corresponds to a five-stage pedagogical ADDIE model (Analysis, Design, Development, Implementation, and Evaluation) of online course preparation. The research describes the roles and objectives of online platform team members. These results allowed the authors of the article to model the organizational structure of online platform management. The method of survey among the team members of the online platform “Higher School Mathematics Teacher” and its participants was used to prove the efficiency of the offered model. The results allowed us to confirm the actuality of the research in the management of educational platforms, the convenience of teamwork in online platform management, and successful model implementation.
The selection of cloud services for ER-diagrams construction in IT specialists databases teaching

One of the main aspects of studying databases in higher education institutions by future IT specialists is database design and software product development. This, in turn, is the most important problem of the developer’s interaction with the customer. To facilitate the process of database design, ER-diagrams are used, which are based on the concepts of “Entity” and “Relationship”. An ER diagram allows you to present a database in the form of visual graphical objects that define a specific subject area. The article considers the available cloud services for the construction of ER-diagrams for learning databases of future IT specialists and their selection the method expert evaluation. For this purpose, the criteria and indicators for the selection of cloud services for the construction of ER-diagrams of databases by future information technology specialists have been determined. As a result, it was found that the cloud services Dbdesigner.net and Lucidchart are the most convenient to learn. It is determined that for a teacher of a higher education institution the use of cloud services is an opportunity to use licensed software in education without additional costs.
Methodology of teaching cloud technologies to future computer science teachers

The article deals with the problem of training future computer science teachers for the use of cloud technologies. The authors analyzed courses from leading universities to study cloud technologies. On this basis the model of application and studying of cloud technologies in the process of training of future teachers of informatics was developed. The basic principles of this model are proposed: systematic, gradual, continuous. It contains target, content, operating and effective component. Therefore, the stages of using cloud computing technology were proposed: as a means of organizing learning activities, as an object of study, as a means of development. The article summarizes the experience of designing a cloud-based learning environment (CBLE). The model is based on such philosophical and pedagogical approaches as systemic, competent, activity, personality-oriented, synergistic. Hybrid cloud is the most appropriate model for this environment. It combines public and private cloud platforms. CBLE also requires the integration of cloud and traditional learning tools. The authors described the most appropriate teaching methods for cloud technologies such as classroom learning, interactive and e-learning, practical methods. The article contains many examples of how to apply the proposed methodology in a real learning process.
The effect of corporate social responsibility and board diversity on earnings management: Evidence from Jordanian listed firms

The aim linked with the present article is to investigate the impact of corporate social responsibilities and board diversity on the earning management of the Jordanian listed firms. The top twelve trading listed firms have been selected from the Jordan stock exchange and data has been extracted from 2011 to 2020. The robust standard error along with the generalized method of moment (GMM) has been used for the analysis purpose. The results indicated that corporate social responsibilities and board diversity have a positive association with earning management of the Jordanian firm. These findings are suitable for the regulators while formulating policies on the corporate social responsibilities and earning management of the firm. These findings have guided the policymakers that they should enhance their focus on the corporate social responsibilities that enhance the earning management of the company. This study is also helpful for the new researcher while investigating this area in the future.