Reviewerly/exHarmony huggingface dataset:
Sentiment Analysis Using the Vader Model for Assessing Company Services Based on Posts on Social Media

Abstract The provision of services by companies in specific domains requires continuous commitment and a constructive approach to meet customer requirements, however, it is often challenging to determine the level of customer satisfaction without their feedback. Therefore, this paper attempts to provide a solution to this problem by using comments from social networks and evaluating their sentiment using the VADER model. In order to accomplish the aim of our research, a lexicon has been built with more than 9500 adjectives and verbs from the Albanian language based on VADER which is just in the initial form and the sentiments are evaluated as positive, neutral, or negative. The lexicon was constructed for the Albanian language and two companies of the Republic of Kosovo were researched as case studies. Furthermore, the sentiment estimation, using the VADER model, in case of our datasets, we obtained a high accuracy, approximately between 89% and 95%. This level of accuracy has been primarily attributed to the application of all preprocessing steps within the dataset, which significantly enhances the model’s performance.
The effect of VR on learners' engagement and motivation in K12 English education

This paper examines how virtual reality (VR) technology can be used in K-12 classrooms, specifically focusing on its potential impact on learners' engagement and motivation, with a particular emphasis on English language learning. The paper begins by providing an introduction to VR technology and its relevance in education, especially in the COVID-19 pandemic context. It reviews existing literature on learners' engagement and motivation, emphasizing the importance of these factors in the learning process. The study explores the initial step of VR technology in English instruction, highlighting the challenges faced in traditional language classes and the opportunities that VR presents. A case study on English VR teaching in middle school is presented, followed by a detailed case design for a cutting-edge VR learning platform tailored for young primary school English learners. The case study for K-12 students and design showcase how VR technology can be utilized to create engaging and immersive learning environments that foster comprehensive language skills development. The paper concludes by acknowledging the potential of VR educational games and embodied cognition theory in enhancing language learning outcomes and encourages further research in this area.
Mechanism Behind Cyberbullying: Summary and Reflections

Cyberbullying involves the perpetration of personal assaults and has witnessed a significant increase in prevalence in the Internet era. This escalation may be attributed to several causes, including the ability to remain anonymous and the influence of group dynamics. This article discusses the origins and repercussions of cyberbullying, uncovering a multifaceted interaction between personal, collective, and digital elements. This study aims to provide insight into the role of social identity and group polarization in fostering online hatred by thoroughly examining existing literature. The bystander effect and diffusion of culpability were analyzed in this review, focusing on their potential to amplify cyberbullying inside the digital realm. Moreover, the anonymity provided by the Internet is widely seen as a significant enabler of unethical conduct. To mitigate the occurrence of cyberbullying, we advocate for the use of comprehensive and diverse methods. It is strongly encouraged for individuals to cultivate self-awareness, empathy, and digital literacy competencies. The transformation of group dynamics into inclusive and good online environments, with an emphasis on promoting bystander intervention, is crucial. The establishment of explicit standards and laws can facilitate the creation of a more secure online environment inside society.
The Design of Virtual Reality Learning Module in Neurolinguistics: Focus on Aphasia

The metaverse, which is essentially a virtual environment parallel to the actual world, is the most recent stage in the evolution of visual immersion technology and is quickly emerging as a testing ground for new social innovations. Since two-dimensional network technology struggles to meet students' demands for immersive learning environments and organic human-computer interactions, meta-universes will change how online education is taught to support students' individualized learning and overall development. In order to push the boundaries of virtual reality technology utilized in various disciplines, this paper will create a virtual reality classroom for neurolinguistics students to learn about aphasia. Different scenarios are built to help students experience communicating with aphasic patients, familiarize themselves with different types of aphasic symptoms and causes, and discuss the rehabilitation program for aphasic patients with peers. Students can complete the whole process of knowledge cognition-experience-construction in a richer and more vivid learning form.
Evaluating High School Students' Views on Online Learning with Meeting Apps during COVID-19

This descriptive qualitative research aims to explore students' experiences in online learning via meeting applications. The purposive sampling method was used to select 11 members of the "Ryca Study Group" as respondents. The interview results show that online learning through meeting applications has become the new norm during the Covid-19 pandemic. The majority of students feel able to understand some subjects well, but there are also technical challenges and a lack of direct interaction with teachers and classmates. Some students are happy with the flexibility of time and accessibility of online learning, while others feel less motivated and experience boredom. In improving online learning, students suggested more interesting material, use of on-cam features for better interaction, and more active teacher involvement. By combining these suggestions, schools can continue to improve the quality of online learning and provide a better learning experience for students.
Interaction Design and Experiment with Conversational Storytelling for Road Safety Culture

Road safety culture among young people necessarily needs to be addressed with a multidisciplinary approach, since they are more reluctant than adults to engage and be openly evaluated in safety research. The present research, using a psychographic through psychometrics approach, combines concepts and methods from cognitive psychology and design to investigate behaviors, beliefs, and attitudes and to increase knowledge about the design and effects of adopting interactive technologies. In particular, we describe the processes, experiences, and results of the research and interaction design of a conversational storytelling mobile app for road safety, in which mobile computing was used to collect and give meaning to qualitative and quantitative data related to road safety decisions. The results of the analyses carried out on the data collected by the mobile app confirm the effectiveness of this interactional tool in capturing the complexity of the combination of contextual, personal, and behavioral factors related to compliance toward road safety.
How to Compliment a Human – Designing Affective and Well-being Promoting Conversational Things

With today’s technologies it seems easier than ever to augment everyday things with the ability to perceive their environment and to talk to users. Considering conversational user interfaces, tremendous progress has already been made in designing and evaluating task oriented conversational interfaces, such as voice assistants for ordering food, booking a flight etc. However, it is still very challenging to design smart things that can have with their users an informal conversation and emotional exchange, which requires the smart thing to master the usage of social everyday utterances, using irony and sarcasm, delivering good compliments, etc. In this paper, we focus on the experience design of compliments and the Complimenting Mirror design. The paper reports in detail on three phases of a human-centered design process including a Wizard of Oz study in the lab with 24 participants to explore and identify the effect of different compliment types on user experiences and a consequent field study with 105 users in an architecture museum with a fully functional installation of the Complimenting Mirror. In our analyses we argue why and how a “smart” mirror should compliment users and provide a theorization applicable for affective interaction design with things in more general. We focus on subjective user feedback including user concerns and prepositions of receiving compliments from a thing and on observations of real user behavior in the field i.e. transitions of bodily affective expressions comparing affective user states before, during, and after compliment delivery. Our research shows that compliment design matters significantly and using the right type of compliments in our final design in the field test, we succeed in achieving reactive expressions of positive emotions, “sincere” smiles and laughter, even from the seemingly sternest users. We conclude by providing an outlook of our contribution for the new age of large language models and prompt engineering.
Conditions of eefective social interaction of students of younger classes during distance learning

The development of social interaction skills is important in elementary school age, because the ability to cooperate, communicate, control and express one`s feelings, make compromises, oveercome difficulties, etc. is a guarantee for the further realization of the individual in life. Unfortunately, in modern realities, in connection with the martial law, younger-schoolchildren in most regions of Ukraine do not have the opportunity to be in daily "live" communication with peers and teachers, where social interaction skills are actively developed. The distance learning format with online lessons raises a number of pedagogical and psychological problems associated with a decrease in the effectiveness of interaction between teachers and students, students among themselves, which negatively affects the development of social interaction skills. In this context, new challenges arise for teachers (as well as far parents).
WAYS OF ORGANIZING THE EDUCATIONAL PROCESS OF ACADEMICALLY GIFTED STUDENTS IN HIGHER EDUCATION INSTITUTIONS OF CHINA

In the process of identifying capable and academically gifed students in Chinese higher education institutions and students in secondary comprehensive schools, it is possible to go in several directions: The ﬁrst of them is the diagnosis of the abilities of students whom we classify as academically gifted. The second direction in the research of scientists, related to ﬁnding the criteria of giftedness, is based on three component spheres of personality - cognitive, emotional-eﬀective and psychomotor.
Digital literacy among students in medical institutions of higher education in distance learning situations

Digital educational technologies are an innovative way of organizing the educational process, based on the use of electronic and information systems, which ensure visibility of the formation of digital skills among students of higher education institutions. The article analyzes the use of digital technologies by students of medical institutions of higher education when studying natural sciences in the conditions of distance learning, using the example of the educational component «Medical and biological physics; Medical information technologies». The analysis and synthesis of search results in information systems was carried out in order to define research concepts, methods of expert evaluation, observation, modeling, and forecasting. The principles of the «digitalization of Ukraine» policy were concidered, the components of the concept of «digital information literacy» were defined, and the effectiveness of the use of digital programs and devices in the educational process in medical institutions of higher education was investigated. The survey method was used for the research. A structured questionnaire in the form of a Google form was developed to collect information. The role of digital learning technologies in the educational space and their application in the educational process was determined. It was found that the number of e-books in the respondent’s home reduced the probability of weak skills, but had no relationship with strong skills. Frequency of using digital skills at university reduced the likelihood of weak skills, but there was no association with strong skills. This suggests that the use of digital technologies in the university can protect against low productivity, but does not guarantee high qualifications. Students’ general cognitive abilities are positively related to their digital self-efficacy accuracy. Students with more experience in the informational Internet space demonstrate more accurate digital self-efficacy. It is shown that digital technologies in the educational space, due to their visibility, accessibility and orientation to the individual abilities of students, are a necessary condition for increasing the efficiency and effectiveness of the educational process. The use of digital technologies by students of medical institutions of higher education on the example of studying the educational component «Medical and biological physics; Medical information technologies» increase students’ motivation to study, contribute to the development of digital practical skills in future specialists.
Optimized Centroid-Based Clustering of Dense Nearly-square Point Clouds by the Hexagonal Pattern

Abstract An approach to optimize centroid-based clustering of flat objects is suggested, which is practically important for efficiently solving metric facility location problems. In such problems, the task is to find the best warehouse locations to optimally service a given set of consumers. An example is assigning mobiles to base stations of a wireless communication network. We suggest a hexagonal-pattern-based approach to partition flat nodes into clusters quicker than the k -means algorithm and its modifications do. First, a hexagonal cell lattice is applied to nodes to approximately determine centroids of the clusters. Then the centroids are used as initial centroids to start the k -means algorithm. The suggested method is efficient for centroid-based clustering of dense nearly-square point clouds of 0.1 million points and greater by using no fewer than 6 lattice cells along an axis. Compared to k -means, our method is at least 10 % faster and it is about 0.01 to 0.07 % more accurate in regular Euclidean distances. In squared Euclidean distances, the accuracy gain is 0.14 to 0.21 %. Applying a hexagonal cell lattice determines an upper bound of the clustering quality gap.
Testing, Debugging, and Repairing Individual Discrimination in Machine Learning Models

Trustworthy AI is crucial for the broad adoption of AI systems. An important question is, therefore, how to ensure this trustworthiness. The absence of algorithmic bias is a crucial attribute for an AI system to be considered trustworthy. In this book chapter, we address various problems related to the detection and mitigation of algorithmic bias in machine learning models, specifically individual discrimination. A model shows individual discrimination if two instances, predominantly differing in protected attributes like race, gender, or age, produce different decision outcomes. In a black-box setting, detecting individual discrimination requires extensive testing. We present a methodology that enables the automatic generation of test inputs with a high likelihood of identifying individual discrimination. Our approach unites the power of two widely recognized techniques, symbolic execution and local explainability, to generate test cases effectively. We further address the problem of localizing individual discrimination failures required for effective model debugging. We introduce a notion called Region of Individual Discrimination or RID, which is an interpretable region in the feature space, described by simple predicates on features, aiming to contain all the discriminatory instances. This region essentially captures the positive correlation between discriminatory instances and features. Finally, we describe a model repair algorithm that aims to repair individual discrimination in the model by effectively creating retraining data based on RID. We empirically show that our approaches are effective for assuring individual fairness of machine learning models.
Interpretability of Deep Neural Models

The rise of deep neural networks in machine learning has been remarkable, leading to their deployment in algorithmic decision-making. However, this has raised questions about the explainability and interpretability of these models, given their growing importance in society. To address this, the field of interpretability in machine learning has been developed, with the goal of creating frameworks that can explain the decisions of a machine learning model in a way that is comprehensible to humans. This could be essential in building trust in the system, as well as debugging models for potential errors and meeting legal requirements (e.g., GDPR). Even though the success of deep neural network is attributed to its ability to capture higher level feature interactions, most of existing frameworks still focus on highlighting important individual features (e.g., words in text or pixels in images). Hence, to further improve interpretability, we propose to quantify the importance of feature interactions in addition to individual features. In this work, we introduce integrated directional gradients (IDG), a game-theory inspired method for assigning importance scores to higher level feature interactions. Our experiments with DNN-based text classifiers on the task of sentiment classification demonstrate that IDG is able to effectively capture the importance of feature interactions.
Advancements in enhancing cyber-physical system security: Practical deep learning solutions for network traffic classification and integration with security technologies

&lt;abstract&gt;&lt;p&gt;Traditional network analysis frequently relied on manual examination or predefined patterns for the detection of system intrusions. As soon as there was increase in the evolution of the internet and the sophistication of cyber threats, the ability for the identification of attacks promptly became more challenging. Network traffic classification is a multi-faceted process that involves preparation of datasets by handling missing and redundant values. Machine learning (ML) models have been employed to classify network traffic effectively. In this article, we introduce a hybrid Deep learning (DL) model which is designed for enhancing the accuracy of network traffic classification (NTC) within the domain of cyber-physical systems (CPS). Our novel model capitalizes on the synergies among CPS, network traffic classification (NTC), and DL techniques. The model is implemented and evaluated in Python, focusing on its performance in CPS-driven network security. We assessed the model's effectiveness using key metrics such as accuracy, precision, recall, and F1-score, highlighting its robustness in CPS-driven security. By integrating sophisticated hybrid DL algorithms, this research contributes to the resilience of network traffic classification in the dynamic CPS environment.&lt;/p&gt;&lt;/abstract&gt;
IG-YOLOv5-based underwater biological recognition and detection for marine protection

Abstract Underwater biological detection is of great significance to marine protection. However, the traditional target detection techniques have some challenges, such as insufficient feature extraction for small targets and low feature utilization rate. To address these challenges, an underwater biological detection model IG-YOLOv5 based on the idea of feature reuse is proposed. An Improved-Ghost module with feature reuse is designed. The module adds batch normalization operations to the identity mapping branch using the Add operation with feature fusion and the Sigmoid Linear Unit activation function with smoother zeros. The proposed model uses the Improved-Ghost module to reconstruct the CSPDarknet structure of YOLOv5, so as to realize the lightweight and accuracy improvement of the model. In addition, in order to solve the problem of target size and shape change in underwater environment, the optimized loss function is Wise-IoU v3, which is used to evaluate the accuracy and robustness of detection results. The results show that the IG-YOLOv5 model performs well in the 2021URPPC data set, with 0.5 mAP reaching 74.2, 4.3% higher than that of YOLOv5 model, and 2.7 less floating-point operations. In a word, IG-YOLOv5 model has high accuracy and robustness in underwater target detection, and Wise-IoU index can evaluate the quality of target detection results more accurately, which is suitable for underwater robots, underwater monitoring, and other fields and has a practical application value.
The Future of Education. Application Peter Chew Triangle Diagram In Marine Engineering

The objective of Peter Chew Triangle Diagram is to clearly illustrate the topic solution of triangle and provide a complete design for the knowledge of AI age. Peter Chew's triangle diagram will suggest a better single rule that allows us to solve any problem of topic solution of triangle problems simple, directly, more easily and more accurately. There are two important rules for the topic solution of triangle , vicelike the sine rule and the cosine rule. The sine rule generally is used to find a non-included angle when we're given two sides and a non-included angle or the opposite side angle given when are given two angles and one side. The cosine rule generally is used to find the angle when we're given three sides or to find the third side when are given two sides and the included angle. Peter Chew Method can let us to find the third side simple when given two sides and a non-included angle . Peter Chew Rule allow us to find a non included angle simple, directly when given 2 sides and an included angle. Applying Peter Chew's Triangle Diagram to Marine Engineering problems allows us to solve Marine Engineering problems simply and easily. This can effectively allow Marine Engineering students to easily learn Marine Engineering, especially when similar covid-19 issues arise in the future.
ABOUT ONE APPROACH TO THE CONSTRUCTION OF SELF-ADAPTIVE ALGORITHMS BASED ON DISTRIBUTION MIXTURES

This article presents a novel approach for constructing self-optimizing algorithms designed to estimate parameters (hyperparameters) in complex systems, with a broader application to classical genetic and evolutionary algorithms. The central theme of this paper revolves around the exploration of multimodality in the objective function and advocates the effectiveness of employing distribution mixtures as opposed to single-peaked distributions in traditional scenarios. A significant focus of this research involves addressing the challenge of determining the dimensionality of the mixture and developing algorithms for both augmenting and reducing it. The methods employed for manipulating the mixture’s dimensionality are inspired by cluster analysis techniques, specifically those utilized in the CURE and BIRCH big data clustering algorithms. Furthermore, this work delves into a detailed examination of a self-adaptive algorithm grounded in a mixture of distributions, illustrated by the CMA-ES algorithm. It is evident that the proposed approach outlined in this paper exhibits versatility, making it applicable not only to the CMA-ES algorithm but also to various optimization algorithms involved in tasks such as classification or regression recovery.