Reviewerly/exHarmony huggingface dataset:
Group training and team coaching as practices of personnel leadership development: A comparative analysis

The modern world is in a stream of constant technological changes, which require society to have the appropriate knowledge, skills, and abilities that will allow one to effectively perform one’s professional duties, improve leadership qualities, and reveal hidden potential. Today, training sessions and coaching sessions are popular methods of influencing results, development of an individual or staff. The purpose of the research work is to study the effectiveness of the application of group training and team coaching, the analysis of their systemic differences. To achieve the goal, the following methods were used: the method of analysis and synthesis, the hermeneutic method, the comparative method, questionnaires, the conversation method, and the generalization method. The results of the research revealed the essence and elements of group training and team coaching, revealed the advantages of the group form of work, analyzed the characteristics of the professional competence of trainers, investigated the role of the trainer and coach as a central figure in the formation of trust and interaction in the team, analyzed the types, techniques, methods of conducting group trainings and team coaching classes, the methodology of their creation, the role of the use of “tactical empathy”, models, psychotechnologies and techniques used in training classes to improve leadership qualities is determined, a valuable approach in leadership is revealed, an assessment of the criteria of effectiveness and expediency is carried out on the basis of the conducted questionnaire regarding the need for the use of coaching and training classes. A clear separation of the concepts of coaching and training is important for the practical application of these techniques for the effective development of leadership qualities among the participants of these events. The obtained results will be useful both for personal development specialists and for their clients
Using GPT-3 as a Text Data Augmentator for a Complex Text Detector

In this work, we explore the problem of complex text detection. This problem is a frequent challenge when implementing text simplification pipelines. Identifying complex text segments can trigger text simplification models, making a better resource usage as state of the art Large Language Models are expensive to use. We focus in Spanish, as it is an under-represented language, given the lack of simple/complex paired datasets. We use a novel paired dataset in Spanish of financial educational texts to train and test our methods. To improve the performance of the classifier, we propose the usage of text simplifications generated with GPT-3 (data augmenter) to alleviate the need to label a large number of text segments as simple or complex. We use the BERT pre-trained model on Spanish data known as Spanish BERT (BETO) and explore the effect of augmenting target data in the model performance.
Dynamic Threshold Fine-Tuning in Anomaly Severity Classification for Enhanced Solar Power Optimization

This study explores an innovative approach to anomaly severity classification within the realm of solar power optimization. Leveraging established machine learning algorithms—including Isolation Forest (IF), Local Outlier Factor (LOF), and Principal Component Analysis (PCA)—we introduce a novel framework marked by dynamic threshold fine-tuning. This adaptive paradigm aims to refine the accuracy of anomaly classification under varying environmental conditions, addressing factors such as dust storms and equipment irregularities. The research builds upon datasets derived from Errachidia, Morocco. Results underscore the effectiveness of dynamically adjusting severity thresholds in optimizing anomaly classification and subsequently improving the overall efficiency of solar power generation. The study not only reaffirms the robustness of the initial framework but also emphasizes the practical significance of fine-tuning anomaly severity classification for real-world applications in solar energy management. By providing a more nuanced perspective on anomaly detection, this research advances our understanding of the intricate precision required for optimal solar power generation efficiency. The findings contribute valuable insights into the broader field of machine learning applications in renewable energy, offering a pathway for the refinement of existing frameworks for enhanced sustainability and operational effectiveness.
Web Application Security through Comprehensive Vulnerability Assessment

In current information era, every small-scale industry, MNC's, Schools, and Colleges utilise Web Applications to promote their organizations and provide services to society. Web Applications have become a simple and vital medium for communicating with society. This leads to an increase in website utilization. Threats and cyber-attacks are increasing according to the rate of growth of Web Applications. Any organization must ensure the privacy and security of its users' data in order to maintain the company's and users' integrity. Web application vulnerabilities represent major security risks, making it critical to conduct extensive assessments and penetration testing to discover and remediate such flaws. The paper provides a complete overview of web application vulnerability assessment and penetration testing, emphasizing the need of proactive security measures in protecting sensitive data and preserving application integrity. This study aims to identify and categorize web application vulnerabilities (OWASPs) that may compromise the security of the application and result in user data breaches. The primary focus of this project is the security and privacy of user data. Integrating security practices into the software development life cycle through investigating real-world case studies and industry standards. Ultimately, our research adds to the growing body of knowledge in web application security, assisting organizations in creating resilient defenses against cyber threats.
Classification of Stakeholders of an Information System using Fuzzy based Adjusted Cosine Similarity Measure during Requirements Elicitation Process

Requirements elicitation process is employed for the identification of stakeholders of an information system. A large number of stakeholders from different domains across the globe participate during the requirements elicitation process. Based on our review, we found that existing methods for the analysis of stakeholders do not support how to classify the stakeholders based on the similarity measures. These measures play an important role for the recommendation of an item/user in a recommendation system. Thus, the objective of this paper is to classify the stakeholders of an information system using fuzzy-based adjusted cosine similarity measure. We have identified the stakeholders of library information system and their opinions for different requirements are recorded. The identified stakeholders have been classified and analysed based on the following similarity measures under fuzzy environment, i.e., Cosine similarity measure, Euclidean distance, Pearson coefficient correlation etc., so that stakeholders having similar requirements can be recommended to strengthen the requirements elicitation process.
AI-based Integrated Approach for the Development of Intelligent Document Management System (IDMS)

In the digital age, organizations confront the challenge of managing diverse documents efficiently while ensuring security, accuracy, and accessibility. Conventional document management approaches often must catch up, leading to inefficiencies and increased costs. This paper introduces the Intelligent Document Management System (IDMS), which employs advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Optical Character Recognition (OCR) to enhance document workflows. This research extends the capabilities of IDMS to encompass the extraction and processing of data from three important document types: medical bills, Aadhar cards, and PAN cards. The research and development efforts done in this paper have concentrated on seamlessly integrating of these models into the IDMS framework, offering a comprehensive solution for extracting and processing data from various document types. In this paper, two approaches, namely Easy OCR and a hybrid approach of combining NLP (Regular Expression) and CV (OCR) have been applied and compared. The results revealed that the proposed hybrid approach (NLPCV) is better, with higher accuracy of 97%,71 %, and 78% for hospital invoices, Aadhar cards, and PAN cards, respectively.
Bi-ETC: A Bidirectional Encrypted Traffic Classification Model Based on BERT and BiLSTM

While packet encryption technology provides user privacy protection, it also brings challenges to traffic classification. Traditional traffic classification technologies based on packet fields or payload data content are becoming powerless in the face of encrypted traffic. Although traffic encryption technology can hide data content, it cannot change the inherent characteristics of application traffic. Therefore, how to classify encrypted traffic by mining traffic features has become a topic of widespread concern. An enormous amount of research shows that machine learning algorithms achieve good results in encrypted traffic classification. However, there is still room for improvement. For example, deep learning methods require a large amount of labeled training data, and it is difficult for pre-trained models to capture the associated features of long sequences of packet. This paper propose a Bidirectional encrypted traffic classification model based on BERT and BiLSTM(Bi-ETC), by inserting [CLS]Token at the connection of Token sequence between BERT and BiLSTM, strengthens BiLSTM's focus on packet-level features while maintaining BilSTM's capture of Token sequence context. Experimental results show that the Bi-ETC model is superior to all proposed classification methods on the ISCX VPN dataset, reaching 99.43%in F1 score and 99.7%in accuracy.
Vehicle Crowd Analysis via Transfer Learning

We propose a deep learning based approach to vehicle density estimation that adopts CSRNet, originally designed for person crowd analysis, to vehicle crowd analysis. The objective is to exploit the transfer learning to accurately estimate the vehicle density with an increased learning speed. Specifically, the CSRNet architecture pre-trained on the person domain is fine tuned on the vehicle domain by feature tranformation. This is achieved by end-to-end retraining the network to output the spatial distribution of vehicles in congested scenes. The approach is evaluated on Waymo and TRANCOS data sets while ShanghaiTech data set is used for pretraining. Performance reported by the metrics of MAE and RMSE, and PSNR on different test cases, demonstrate the transfer learning significantly improves vehicle density estimation accuracy, compared to the learning from stretch. In particular, the learning accuracy achieved on Waymo, with a small size training data, is validating the potential of the approach in enhancing vehicle crowd analysis for autonomous driving task.
Learning Second-order Covariation with GeoGebra and Augmented Reality

Second-order covariation is a form of mathematical reasoning that needs to be characterized both concerning students' cognitive processes and the design of tasks that promote it. This contribution aims to shed light on these two issues by comparing data related to the same learning experiment performed in two different learning environments: GeoGebra and augmented reality. The data analysis focuses on a characterization of the forms of second-order covariation emerging from students' learning process. Findings reveal how the two environments enable students to engage in second-order covariation: in the GeoGebra environment the students engaged in second-order covariation while using the digital tool; in the augmented reality environment, secondorder covariation fully emerged only after the students performed the experiment. Possible interpretations for this result are discussed.
Personalizing Content Moderation on Social Media: User Perspectives on Moderation Choices, Interface Design, and Labor

Social media platforms moderate content for each user by incorporating the outputs of both platform-wide content moderation systems and, in some cases, user-configured personal moderation preferences. However, it is unclear (1) how end users perceive the choices and affordances of different kinds of personal content moderation tools, and (2) how the introduction of personalization impacts user perceptions of platforms' content moderation responsibilities. This paper investigates end users' perspectives on personal content moderation tools by conducting an interview study with a diverse sample of 24 active social media users. We probe interviewees' preferences using simulated personal moderation interfaces, including word filters, sliders for toxicity levels, and boolean toxicity toggles. We also examine the labor involved for users in choosing moderation settings and present users' attitudes about the roles and responsibilities of social media platforms and other stakeholders toward moderation. We discuss how our findings can inform design solutions to improve transparency and controllability in personal content moderation tools.
Benchmarking regularisation methods for quantum process tomography on NISQ devices

Abstract Quantum process tomography (QPT) is a crucial tool for characterizing and validating quantum devices and quantum algorithms. However, the problem of finite sampling leads to an estimated process matrix which is non-positive semi-definite (non-PSD), which can yield a reconstructed quantum channel that is non-physical. To address this problem, various methods have been proposed to correct the issue of finite sampling in the estimation of the process matrix. In this work, we perform a comparison of regularisation methods that will be used to tackle the problem of finite sampling in QPT. For this comparison we simulate some common single qubit quantum channels. We use two metrics, the minimum eigenvalue of the Choi matrix and the fidelity, to compare the effectiveness of these methods. Our results show that the spectral transformations perform the best overall in dealing with finite sampling present in reconstructing the quantum channel in the NISQ era.
Verb database: Structure, clusters and options

The content and volume of language corpora provide an opportunity to obtain reliable information about the real use of a particular linguistic unit. Nowadays, there is a large number of corpora in different languages, their formation technologies are being improved. Nevertheless, some problems and limitations arise when using these resources in comparative studies. Corpora users need to work with annotated data submitted to tagging through annotation protocols. The article presents the structure and functionality of the supracorpora verb database (SVD) developed on the basis of a parallel Russian-French subcorpus of the Russian National Corpus (RNC) and reveals the difference in their potentials. The described database is a pilot version of the final software, which is currently under development and is being tested. It consists of several clusters focused on solving such linguistic tasks as studying the grammatical semantics specifics and the distribution of verb forms in Russian and French; identifying the polysemantic structure in the two languages, which in turn verifies the understanding of the linguistic worldview of the speakers of Russian and French. It has been found that the mechanism of functioning of SVD cluster formations allows us to study both individual characteristics of verbs and the semantics of verbal lexemes and collocations. The manual annotation enables users to identify the systematic asymmetry of verb forms and cases of contextual and low-frequency asymmetry. Thus, SVD can be used in language pedagogy, teaching and studying discursive grammar, as well as the analysis of translation models variability.
Reenvisioning Patient Education with Smart Hospital Patient Rooms

Smart hospital patient rooms incorporate various smart devices to allow digital control of the entertainment --- such as TV and soundbar --- and the environment --- including lights, blinds, and thermostat. This technology can benefit patients by providing a more accessible, engaging, and personalized approach to their care. Many patients arrive at a rehabilitation hospital because they suffered a life-changing event such as a spinal cord injury or stroke. It can be challenging for patients to learn to cope with the changed abilities that are the new norm in their lives. This study explores ways smart patient rooms can support rehabilitation education to prepare patients for life outside the hospital's care. We conducted 20 contextual inquiries and four interviews with rehabilitation educators as they performed education sessions with patients and informal caregivers. Using thematic analysis, our findings offer insights into how smart patient rooms could revolutionize patient education by fostering better engagement with educational content, reducing interruptions during sessions, providing more agile education content management, and customizing therapy elements for each patient's unique needs. Lastly, we discuss design opportunities for future smart patient room implementations for a better educational experience in any healthcare context.
Augmented reality for machine monitoring in industrial manufacturing: framework and application development

Enhancing data visualization on the shop floor provides support for dealing with the increasing complexity of production and the need for progressing towards emerging goals like energy efficiency. It enables personnel to make informed decisions based on real-time data displayed on user-friendly interfaces. Augmented reality (AR) technology provides a promising solution to this problem by allowing for the visualization of data in a more immersive and interactive way. The aim of this study is to present a framework to visualize live and historic data about energy consumption in AR, using Power BI and Unity, and discuss the applications’ capabilities. The study demonstrated that both Power BI and Unity can effectively visualize near-real-time machine data with the aid of appropriate data pipelines. While both applications have their respective strengths and limitations, they can support informed decision-making and proactive measures to improve energy utilization. Additional research is needed to examine the correlation between energy consumption and production dynamics, as well as to assess the user-friendliness of the data presentation for effective decision-making support.
Spatial-Temporal Masked Autoencoder for Multi-Device Wearable Human Activity Recognition

The widespread adoption of wearable devices has led to a surge in the development of multi-device wearable human activity recognition (WHAR) systems. Nevertheless, the performance of traditional supervised learning-based methods to WHAR is limited by the challenge of collecting ample annotated wearable data. To overcome this limitation, self-supervised learning (SSL) has emerged as a promising solution by first training a competent feature extractor on a substantial quantity of unlabeled data, followed by refining a minimal classifier with a small amount of labeled data. Despite the promise of SSL in WHAR, the majority of studies have not considered missing device scenarios in multi-device WHAR. To bridge this gap, we propose a multi-device SSL WHAR method termed Spatial-Temporal Masked Autoencoder (STMAE). STMAE captures discriminative activity representations by utilizing the asymmetrical encoder-decoder structure and two-stage spatial-temporal masking strategy, which can exploit the spatial-temporal correlations in multi-device data to improve the performance of SSL WHAR, especially on missing device scenarios. Experiments on four real-world datasets demonstrate the efficacy of STMAE in various practical scenarios.
Reflected Reality

We propose Reflected Reality: a new dimension for augmented reality that expands the augmented physical space into mirror reflections. By synchronously tracking the physical space in front of the mirror and the reflection behind it using an AR headset and an optional smart mirror component, reflected reality enables novel AR interactions that allow users to use their physical and reflected bodies to find and interact with virtual objects. We propose a design space for AR interaction with mirror reflections, and instantiate it using a prototype system featuring a HoloLens 2 and a smart mirror. We explore the design space along the following dimensions: the user's perspective of input, the spatial frame of reference, and the direction of the mirror space relative to the physical space. Using our prototype, we visualise a use case scenario that traverses the design space to demonstrate its interaction affordances in a practical context. To understand how users perceive the intuitiveness and ease of reflected reality interaction, we conducted an exploratory and a formal user evaluation studies to characterise user performance of AR interaction tasks in reflected reality. We discuss the unique interaction affordances that reflected reality offers, and outline possibilities of its future applications.
An Architecture for Adaptive Machine Learning Models using Adversarial and Transfer Learning

Machine Learning and especially deep neural networks are more and more used in industrial applications, because they offer new possibilities to gain knowledge from the available data. The algorithms therefore should work during the lifetime of the industrial system, to which the algorithms are applied for improving the overall performance of the system. However, over the lifetime of an industrial system or application, data sources are likely to change. A sensor may be damaged and delivers faulty values or a new sensor is added to the system. A classical deep neural network will deliver incorrect outputs in case of a faulty sensor or cannot use the additional information coming from a new sensor. In both cases, a retraining with the new input space would be necessary, which requires effort for data collection and training of the network. To address these challenges, this paper proposes an architecture that combines separate approaches, namely the Fast Gradient Sign Method and Transfer Learning. To evaluate the combined architecture, it is applied to a real-world task of failure detection in the Air Pressure System (APS) of an automobile.
Statistical Methods in Computer Science: A Comparative Review of Inductive and Deductive Approaches

Combination issues lie at the core of computer science and modern discrete mathematics. Artificial intelligence lays the foundation for induction and deduction techniques. Induction in computer science involves establishing statements based on specific to general observations, while deduction operates in the opposite direction. In this paper, we analyze induction and deduction using three criteria: the difference in space and time complexity, inconsistencies within induction and deduction, and their application areas within the computer science domain. Induction enhances the power of observation and analysis, whereas deduction plays a crucial role in the logical realm of computer science. Deduction is primarily employed for quantitative analysis, including statistical analysis.