Reviewerly/exHarmony huggingface dataset:
Cybersecurity in the Context of Moroccan Energy Companies

The aim of this research is to assess the level of maturity of Moroccan companies in the energy sector in terms of cybersecurity and to identify the factors influencing their abilities to implement effective security measures to address the risks associated with cyber-attacks. To achieve this, we have adopted an exploratory approach. A questionnaire was sent to energy sector companies in Morocco to investigate their cybersecurity practices. This entails examining the current security measures in place, security policies and procedures, as well as employee training programs. Furthermore, we are exploring the human and financial factors that can influence the ability of energy companies to implement effective security measures against cyberattacks. The results clearly indicate that most of these companies demonstrate a high level of maturity in terms of cybersecurity. However, several significant factors influence their ability to implement effective security measures. Among these factors, the lack of qualified personnel, the high costs associated with acquiring and implementing security technologies, as well as expenses related to training and awareness, can be mentioned.
The outcome efficacy of the entity risk management requirements of the NIS 2 Directive

Abstract The NIS 2 Directive (2022/2555) of the European Union (EU) identifies the cybersecurity risk management requirements for essential and important entities in EU member states. The principal question we address is, how effective are the cybersecurity risk management measures of the NIS 2 Directive against cyberattacks on essential and important entities in EU member states? It was observed, through statutory interpretation and cyber kill chain model analysis, that the cybersecurity risk management measures of the NIS 2 Directive may be significantly limited in their effectiveness against cyberattacks on essential and important entities in EU member states. The limited effectiveness is primarily due to the narrow scope of the cybersecurity risk management measures, including the lack of specific measures focused on the reconnaissance phase of a cyberattack.
The Application of Cloud Technologies in the Educational Process of Higher Education Institutions

The article highlights aspects of the implementation of cloud technologies in the educational process of a higher education institution, which are significantly changing education nowadays, forming the latest approaches. They have been revolutionized the way students and lecturers interact with information and collaborate with each other. Cloud technologies allow users to access information and software from any Internet-connected device and have transformed traditional classrooms into interactive and collaborative learning spaces. A number of advantages in the use of cloud technologies by students have been considered, in particular: unimpeded access to educational materials; joint cooperation on group projects and tasks online; formation of teamwork and communication skills; cost savings; backup and data management capabilities, etc. The shortcomings are outlined as well. The positive aspects of cloud technologies for teaching staff have been presented. It allows sharing course materials with students, collaborating on lesson plans, assessing the level of educational achievements and monitoring student progress in real time, identifying weaknesses, and providing individual feedback to adjust own learning strategy, to better meet the needs of students.
Literature for Children and Youth as a Digital Text in the Modern Educational and Informational Environment

The article characterizes the Reading Development Strategy for the period up to 2032 «Reading as a Live Strategy», which has been approved by the Cabinet of Ministers of Ukraine, outlines its goals and objectives. Attention is paid to their solution in the context of digitalization of modern society. The need for readers (children and adults) to master digital technology skills has been actualized. The concept of digital text has been clarified. The potential of literature for children and youth as a digital text has been determined in the context of implementation of the reading development strategy, the possibilities of its use in modern educational and information environment have been revealed. The results of a sociological survey on the compliance of modern online content with the needs of secondary school students, their parents, and teachers have been described. The most popular digital resources in the educational environment, which present materials on literature for children and youth (books, information about writers, educational, methodological and scientific materials), have been characterized. The quality of the presented content has been determined. Mobile applications with Ukrainian and foreign artistic works in text and audio formats have been analysed. Perspective directions of work on promoting children’s reading and ensuring digital accessibility of Ukrainian fiction books have been identified.
Humans as path-finders for mobile robots using teach-by-showing navigation

Abstract One of the most important barriers towards a widespread use of mobile robots in unstructured, human populated and possibly a-priori unknown work environments is the ability to plan a safe path. In this paper, we propose to delegate this activity to a human operator that walks in front of the robot marking with her/his footsteps the path to be followed. The implementation of this approach requires a high degree of robustness in locating the specific person to be followed (the path-finder ). We propose a three phases approach to fulfil this goal: 1. Identification and tracking of the person in the image space, 2. Sensor fusion between camera data and laser sensors, 3. Point interpolation with continuous curvature paths. The approach is described in the paper and extensively validated with experimental results.
Using the Machine Learning Naive Bayes Algorithms for Sentiment Analysis on Online Product Reviews in the Air of Energy Optimization

The purpose of this study was to explore how consumers perceive two of the leading smartphone brands, Samsung and iPhone, using a corpus of tweets. Our approach involved sifting through the tweets to remove any irrelevant content, followed by a sentiment analysis to gain an overall perspective of how each brand was viewed. Our analysis demonstrated that Samsung received a higher proportion of tweets with negative sentiment as compared to iPhone. Moreover, the most common terms in tweets referring to Samsung reflected negative emotions like “concern,” “issue,” and “trouble,” while tweets about iPhone expressed positive emotions such as “like,” “great,” and “best.” These findings have significant implications for marketing research and offer valuable insights for businesses on how they can utilize social media to enhance their brand reputation and image.
Chinese text classification by combining Chinese-BERTology-wwm and GCN

Text classification is an important and classic application in natural language processing (NLP). Recent studies have shown that graph neural networks (GNNs) are effective in tasks with rich structural relationships and serve as effective transductive learning approaches. Text representation learning methods based on large-scale pretraining can learn implicit but rich semantic information from text. However, few studies have comprehensively utilized the contextual semantic and structural information for Chinese text classification. Moreover, the existing GNN methods for text classification did not consider the applicability of their graph construction methods to long or short texts. In this work, we propose Chinese-BERTology-wwm-GCN, a framework that combines Chinese bidirectional encoder representations from transformers (BERT) series models with whole word masking (Chinese-BERTology-wwm) and the graph convolutional network (GCN) for Chinese text classification. When building text graph, we use documents and words as nodes to construct a heterogeneous graph for the entire corpus. Specifically, we use the term frequency-inverse document frequency (TF-IDF) to construct the word-document edge weights. For long text corpora, we propose an improved pointwise mutual information (PMI*) measure for words according to their word co-occurrence distances to represent the weights of word-word edges. For short text corpora, the co-occurrence information between words is often limited. Therefore, we utilize cosine similarity to represent the word-word edge weights. During the training stage, we effectively combine the cross-entropy and hinge losses and use them to jointly train Chinese-BERTology-wwm and GCN. Experiments show that our proposed framework significantly outperforms the baselines on three Chinese benchmark datasets and achieves good performance even with few labeled training sets.
Connecting Reality and Virtuality

This chapter presents three examples that demonstrate the effective integration of Reality and Virtuality, resulting in captivating applications that significantly enhance and improve the user experience through their seamless combination. The primary objective of the first case study is to explore and examine various methods of fostering connections among individuals through the utilization of Augmented Reality technology for collaborative art experiences. The second case study focuses on proposing a connection between humans and plants through Virtual Reality to enhance mental and physical well-being, as well as overall quality of life. The third case study focuses on leveraging Augmented Reality technology for maintenance operations.
Intelligent high-type control based on evolutionary multi-objective optimization

In this paper, we formulate high-type intelligent control as a multi-objective problem and apply evolutionary algorithms to search for optimal solutions. Specifically, we consider the metrics of the system in both the frequency domain and the time domain. Integrated time and absolute error is used as a performance metric in the time domain, while bandwidth is used as a measure in the frequency domain. Simultaneously, the amplitude margin and phase margin are used as constraints to ensure the stability of the high-type control system. Then, we adopt evolutionary algorithms to solve the formulated multi-objective problem. Unlike most of the existing approaches, we formulate intelligent high type control as a multi-objective optimization problem based on our knowledge about the control system. Furthermore, evolutionary algorithms are adopted to search for optimal solutions to real-world controlling systems. Extensive experiments are conducted to evaluate the effectiveness of our proposed approach. Compared to the Z-N method and the extending symmetrical optimum criterion, our proposed method achieves an improvement in bandwidth of more than 126.6%, while reducing the overshoot by more than 56.8% and the settling time by more than 48.4% for all controlled objects used in the experiments. At the same time, the tracking errors of the ramp and parabolic signals are significantly reduced, which means this method effectively improves the system performance.
Applications of Multisensory Experiences in eXtended Reality

This chapter presents three examples demonstrating the advantages of multisensory experiences in eXtended Reality applications. The first case study involves a training application designed to familiarize users with laboratory machinery, incorporating the senses of sight, hearing, and smell. The second case study concerns the utilization of multisensory experiences in Augmented Reality to facilitate language learning and enhance the exploration of a new city. The third one concerns a multisensory application including visual components, sounds, and odors for learning music.
Augmented Reality Applications that Aid in Promoting Sustainability

In this chapter, three examples are given of how Augmented Reality (AR) technologies can be used to promote sustainability. The initial case study involves an application that illustrates how the environment affects biochemical processes. The second case study features sustainable handmade toys that educate children on electronic circuits. The third one supports children in understanding the entire lifespan of a plant and learning its scientific microscopic processes.
Towards Recommender Systems Integrating Contextual Information from Multiple Domains through Tensor Factorization

Traditionally, single-domain recommender systems (SDRS) can suggest suitable products for users to alleviate information overload. Nonetheless, cross-domain recommender systems (CDRS) have enhanced SDRS by accomplishing specific objectives, such as improving precision and diversity and solving cold-start and sparsity issues. Rather than considering each domain separately, CDRS uses information gathered from a particular domain (e.g., music) to enhance recommendations for another domain (e.g., films). Context-aware Recommender System (CARS) focuses on optimizing the quality of suggestions, which are more appropriate for users depending on their context. Integrating these techniques is helpful for many cases where knowledge from several sources can be used to enhance recommendations and where relevant contextual information is considered. This work describes the main challenges and solutions of the state-of-the-art in Cross-Domain Context-Aware Recommender Systems (CD-CARS), taking into account the abundance of data on different domains and the systematic adoption of contextual data. CD-CARS have shown efficient methods to tackle realistic recommendation scenarios, preserving the benefits of CDRS (regarding cold-start and sparsity issues) and CARS (assuming accuracy). Therefore, CD-CARS may direct future research to recommender systems that use contextual information from multiple domains in a systematic way.
Developing a Content-based Recommender System for Author Specialization using Topic Modelling and Ranking Framework

In the era of information overloading, enormous scholarly data poses a challenge in identifying potential authors for productive outcomes. Researchers collaborate with fellow profiles to improve the eminence of Research and their academic profiles. This chapter proposes a content-based recommender system to generate author recommendations for collaborations that extracts the relevant keywords from the titles of research papers using MapReduce. To specify author specialization, the proposed technique comprises the feature extraction from the entire document using Latent Dirichlet Allocation (LDA), followed by an influence model which generates recommendations for the target authors. A ranking algorithm, such as TOPSIS is implemented to get Top-N recommendations based on multiple criteria. In this chapter, we investigated how the MapReduce framework is helpful in obtaining improved computational time for large-scale scholarly data and scalability. Experimental results on DBLP articles prove the relevance of ranking methods as an efficient and scalable platform for computing content-based recommendations&lt;br&gt;
Psychological Science Meets Wearable Cognitive Assistance

A wearable cognitive assistant (WCA) is a computer-based application that guides a user through a task with input from wearable devices with the aid of computational resources in nearby locations (cloudlets). Psychological science informs development of WCAs and is encountering new issues for research. We discuss three relevant research areas: response time, action segmentation, and task comprehension.
Stephanie Hare

Abstract Author, broadcaster and researcher Stephanie Hare's presentation on technology ethics was one of the most spoken about events at last year's BIALL conference, a thought-provoking talk that left many wanting more. With this in mind, Mike and Jas Breslin sat down with Stephanie to talk about the fascinating and very readable book it was based on, her thoughts on why technology ethics is especially important to information professionals, and whether she believed AI will ever do law librarians out of a job.
Corrigendum: Coupled cluster theory on modern heterogeneous supercomputers

[This corrects the article DOI: 10.3389/fchem.2023.1154526.].
Multi-supervised bidirectional fusion network for road-surface condition recognition

Rapid developments in automatic driving technology have given rise to new experiences for passengers. Safety is a main priority in automatic driving. A strong familiarity with road-surface conditions during the day and night is essential to ensuring driving safety. Existing models used for recognizing road-surface conditions lack the required robustness and generalization abilities. Most studies only validated the performance of these models on daylight images. To address this problem, we propose a novel multi-supervised bidirectional fusion network (MBFN) model to detect weather-induced road-surface conditions on the path of automatic vehicles at both daytime and nighttime. We employed ConvNeXt to extract the basic features, which were further processed using a new bidirectional fusion module to create a fused feature. Then, the basic and fused features were concatenated to generate a refined feature with greater discriminative and generalization abilities. Finally, we designed a multi-supervised loss function to train the MBFN model based on the extracted features. Experiments were conducted using two public datasets. The results clearly demonstrated that the MBFN model could classify diverse road-surface conditions, such as dry, wet, and snowy conditions, with a satisfactory accuracy and outperform state-of-the-art baseline models. Notably, the proposed model has multiple variants that could also achieve competitive performances under different road conditions. The code for the MBFN model is shared at https://zenodo.org/badge/latestdoi/607014079.
Analyzing the impact of human errors on interactive service robotic scenarios via formal verification

Abstract Developing robotic applications with human–robot interaction for the service sector raises a plethora of challenges. In these settings, human behavior is essentially unconstrained as they can stray from the plan in numerous ways, constituting a critical source of uncertainty for the outcome of the robotic mission. Application designers require accessible and reliable frameworks to address this issue at an early development stage. We present a model-driven framework for developing interactive service robotic scenarios, allowing designers to model the interactive scenario, estimate its outcome, deploy the application, and smoothly reconfigure it. This article extends the framework compared to previous works by introducing an analysis of the impact of human errors on the mission’s outcome. The core of the framework is a formal model of the agents at play—the humans and the robots—and the robotic mission under analysis, which is subject to statistical model checking to estimate the mission’s outcome. The formal model incorporates a formalization of different human erroneous behaviors’ phenotypes, whose likelihood can be tuned while configuring the scenario. Through scenarios inspired by the healthcare setting, the evaluation highlights how different configurations of erroneous behavior impact the verification results and guide the designer toward the mission design that best suits their needs.
Machine Learning Approaches for Text Mining and Spam E-mail Filtering: Industry 4.0 Perspective

&amp;nbsp;The revolution of Industry 4.0 will leave an impact on the domain of everyone's lives directly or indirectly. Several new complex applications will be developed in the days to come that are complicated to predict in the current scenario. With the help of machine learning approaches and intelligent IoT devices, people will be relieved from extra overheads of redundant work currently being performed. Industry 4.0 has become a significant catalyst for innovation and development in various industrial sectors like production processes and quality improvement with greater flexibility. This chapter applied different machine learning algorithms for spam detection and classifying emails into legitimate and spam. Seven classification models: Decision Trees, Random Forest, Artificial Neural Network, Gradient Boosting Machines, AdaBoost, Naive Bayes, and Support Vector Machines are applied. Three benchmark spam datasets are extracted from standard repositories to conduct the experiments. The chapter also presents a quantitative performance analysis. The results from rigorous experiments reveal that ensemble methods, Gradient Boosting and AdaBoost, outperformed other methods with an overall accuracy of 98.70% and 98.18%, respectively. The ensembled models are effective on a large-sized dataset embedded with more extensive features. The performance of non-ensemble methods, ANN and Naïve Bayes, was instrumental on large datasets as a viable alternative, with an overall accuracy of 98.38% and 97.63% on test data.&lt;br&gt;
An Introduction to Various Parameters of the Point of Interest

Point-of-Interest (POI) recommendation helps to find new places for users to visit, along with the popularity of locations. Recommendation of POI is the most important in location-based social networks (LBSNs). This paper discusses different parameters that significantly impact the POI recommendation process and make the prediction much more accurate. A comprehensive review of a few research works and the methodologies employed for POI recommendation have been presented. POI recommendation techniques have been classified based on many, such as the interest of the tourist in particular POI, popularity of the POI, weather conditions, etc. A summary of related research work is presented for each category, along with their respective drawbacks. Finally, the possible directions toward future work in this area are included, along with the conclusion.&lt;br&gt;
An Overview of Deep Learning-Based Recommendation Systems and Evaluation Metrics

The ever-increasing information on the internet and the rapid development of online movies, songs, and stores have enhanced the demands of customers to obtain the required information within the least time. A Recommendation System (RS) is designed to help customers with personalized information and interests to avoid overloading issues in entertainment and social media. Though traditional methods have made noteworthy developments, RS encounters challenges such as data limitations and cold starts. The present study aims to review the developments in the field of deep learning-based RS, thereby providing the required information for researchers. In addition, several applicable domains of employing deep learning-based RS have been analyzed. The review has been organized into RS type, deep learning approaches, deep learning-based recommendation systems in various applications, and evaluation metrics.&lt;br&gt;