Reviewerly/exHarmony huggingface dataset:
IoT – Based Beard Detection System using Id-Card for Enhanced Security

In today's dynamic and technology-driven world, attendance management systems are undergoing transformation through the integration of Internet of Things and Facial hair detection and segmentation play an important role in forensic facial analysis. In this paper, we propose a fast, robust, fully automatic and self-training system for beard/moustache detection and segmentation in challenging facial images. In order to overcome the limitations of illumination, facial hair color and near-clear shaving, our facial hair detection self-learns a transformation vector to separate a hair class and a non-hair class from the testing image itself. A feature vector, consisting of Histogram of Gabor (HOG) and Histogram of Oriented Gradient of Gabor (HOGG) at different directions and frequencies, is proposed for both beard/moustache detection and segmentation in this paper. A feature-based segmentation is then proposed to segment the beard/moustache from a region on the face that is discovered to contain facial hair
Approaches to Independent Assessment of Qualifications at the University

In recent years, research has become relevant related to the introduction into practice of a new tool for legitimizing professional qualifications for Russia and increasing the effectiveness of processes for ensuring the country’s economy with qualified personnel. To solve this issue, organizations have been created designed to form an objective and transparent procedure for voluntary assessment of qualifications, which is guaranteed to confirm the qualification of a specialist with established requirements and standards – these are centers for assessing qualifications. Meanwhile, the prospects for the development of the integration of tools for independent assessment of qualifications into the educational process of Universities with the strengthening of the role of interaction The employer university is currently obvious. First, the successful completion of an independent qualification assessment increases the competitiveness of graduates in the labor market. Secondly, employers get the opportunity to form an order for training specialists and save on the costs associated with additional training of staff. Thirdly, for the University, an independent assessment of the qualification of a graduate is a source of revers communication necessary to improve the quality of educational programs and develop interaction with employers. The relevance of the research topic is caused by the choice of the optimal option for applying an independent assessment of qualifications in the education system ‒ combining the procedure for the final certification of students and a professional exam. The purpose of the study is to reveal the experience of implementing an independent assessment of qualifications at the University, to identify key problems and ways of further development. The authors believe that the introduction of the GIA-NOC procedure in educational activities will lead to the quality of educational results for students and graduates.
Conclusion

Abstract This chapter briefly summarizes the content of the book and describes practical concerns of visualizing time-oriented data in real-world data settings. Visual analytics is briefly outlined as a modern approach that combines visualization, interaction, and computational analysis more tightly to facilitate data analysis activities better. Finally, research opportunities for future work are discussed.
Guiding the Selection of Visualization Techniques

Abstract This chapter describes how researchers and practitioners can be guided in making informed decisions when selecting visualization techniques for time and time-oriented data. The TimeViz Browser is introduced and a general conceptual model for guided search for visualization techniques is outlined.
Introduction

Abstract Space and time are two outstanding dimensions because, in conjunction, they represent the four-dimensional space or simply the world we are living in. Basically, every piece of data we measure is related and often only meaningful within the context of space and time. Consider, for example, the price of a barrel of oil. The data value of $129 alone is not very useful. Only if assessed in the context of where (space) and when (time) is the oil price valid, and only then it is possible to meaningfully interpret the cost of $129.
Crafting Visualizations of Time-Oriented Data

Abstract Many different types of data are related to time. Meteorological data, financial data, census data, medical data, simulation data, news articles, photo collections, or project plans, to name only a few examples, all contain temporal information. In theory, because all these data are time-oriented, they should be representable with one and the same visualization approach. In practice, however, the data exhibit specific characteristics and hence each of the above examples requires a dedicated visualization.
Time and Time-Oriented Data

Abstract This chapter investigates in detail the characteristics of time and time-oriented data. Design aspects for modeling time and time-oriented data are introduced and discussed using examples. The chapter also sheds some light on data quality.
Historical Background

Abstract This chapter takes a look back in time and presents a selection of historical visual representations of time-oriented data. The representation of time in the arts is a second aspect covered in this chapter.
Involving the Human via Interaction

Abstract This chapter explains why involving the human is important when visually analyzing time and time-oriented data. Some general assertions will be made and basic interaction concepts introduced. The chapter also covers advanced interaction methods and goes beyond mouse and keyboard interaction.
Offensive Text Span Detection in Romanian Comments Using Large Language Models

The advent of online platforms and services has revolutionized communication, enabling users to share opinions and ideas seamlessly. However, this convenience has also brought about a surge in offensive and harmful language across various communication mediums. In response, social platforms have turned to automated methods to identify offensive content. A critical research question emerges when investigating the role of specific text spans within comments in conveying offensive characteristics. This paper conducted a comprehensive investigation into detecting offensive text spans in Romanian language comments using Transformer encoders and Large Language Models (LLMs). We introduced an extensive dataset of 4800 Romanian comments annotated with offensive text spans. Moreover, we explored the impact of varying model sizes, architectures, and training data volumes on the performance of offensive text span detection, providing valuable insights for determining the optimal configuration. The results argue for the effectiveness of BERT pre-trained models for this span-detection task, showcasing their superior performance. We further investigated the impact of different sample-retrieval strategies for few-shot learning using LLMs based on vector text representations. The analysis highlights important insights and trade-offs in leveraging LLMs for offensive-language-detection tasks.
PrOuD: Probabilistic Outlier Detection Solution for Time-Series Analysis of Real-World Photovoltaic Inverters

Anomaly detection methods applied to time series are mostly viewed as black boxes that solely provide a deterministic answer for the detected target. Without a convincing explanation, domain experts can hardly trust the detection results and must conduct further time-series diagnoses in real-world applications. To overcome this challenge, we mathematically analyzed the sources of anomalies and novelties in multivariate time series as well as their relationships from the perspective of Gaussian-distributed non-stationary noise. Furthermore, we proposed mathematical methods to generate artificial time series and synthetic anomalies, with the goal of solving the problem of it being difficult to train and evaluate models for real-world applications due to the lack of sufficient data. In addition, we designed Probabilistic Outlier Detection (PrOuD), which is a general solution to provide interpretable detection results to assist domain experts with time-series analysis. PrOuD can convert the predictive uncertainty of a time-series value from a trained model into the estimated uncertainty of the detected outlier through Monte Carlo estimation. The experimental results obtained on both artificial time series and real-world photovoltaic inverter data demonstrated that the proposed solution can detect emerging anomalies accurately and quickly. The implemented PrOuD demo case shows its potential to make the detection results of existing detection methods more convincing so that domain experts can more efficiently complete their tasks, such as time-series diagnosis and anomalous pattern clustering.
DETERMINATION OF THE RELATIONSHIP BETWEEN RESEARCH, RESEARCH ACTIVITY AND RESEARCH INTEREST

The socioeconomic development of a state is determined by the level of education and skills of a younger generation. Formation of students� necessary professional competencies is the main task of a higher education system, which can be achieved by integrating two types of activities in learning process as scientific and educational. Currently, the importance of students� research activity is increasing, it is becoming an integral element of the entire educational process in a higher education, an important component in training of specialists. Students� research activities will not be successful without an interest in research [1]. In this paper, an individual theoretical analysis of the concepts of research, research activity, and research interest were found and identified the structural logical relationship between them as well as received the results of students survey. The results of student survey showed that students actively participate in scientific research. The research works at the university were carried out by 26.4% of students, 45.3% of them did not want to take part in research works. In addition, 28.3% of students wanted to be involved in research work of the university. In the given article, the relationship between the concepts of research, research activity, and research interest were determined. Research interest is an increase in the interest of an applicant, as a result of vigorous activity, an increase in his perseverance in achieving the goal and to ascertain the truth.
THE USE OF ARTIFICIAL INTELLIGENCE IN RECRUITMENT AND SELECTION PROCESSES. EVIDENCE FROM CORPORATE RECRUITERS

Globalization and the evolution of technology have put a lot of pressure on organizations to reshape and update themselves, and there is an upward trend in terms of organizations adapting to technology, especially the use of Artificial Intelligence (AI) in business. Currently, a recruitment process is largely carried out by recruiters who review CVs, online profiles and other sources to find suitable candidates for vacancies. Because employees have limited skills, keeping up with all the necessary tasks is not an easy job and usually time-consuming for each recruiter. In Romania, AI represents an opportunity that will certainly help the country's economic development. Although Romania does not have an official plan to use this technology, its implementation in the public sector has been put on the agenda of a newly established institution - the Authority for the Digitization of Romania. This paper presents the relationship between AI and recruitment and selection processes. Moreover, a view of recruiters from a multinational company in Romania will be presented, describing their different perspectives on AI adoption, future and risk associated with AI in recruitment. This paper aims to investigate how AI is implemented in the traditional recruitment process and what would be the implications of having AI in recruiting many candidates.
DEMO Models Based Automatic Smart Contract Generation: A Case in Logistics Using Hyperledger

This article presents a practical research project aimed at developing a method for automatically generating smart contracts from business models. The project has as a context the logistics in- dustry and uses Hyperledger Fabric as the blockchain (BC) platform. The main contributions are a mapping from DEMO (Design and Engineering Methodology for Organizations) language to Hyperledger Chaincode using GO language, as well as an evolution of DEMO’s Action Model Grammar, that enable specification of elements necessary for automatic SC generation. The proposed approach extends the DEMO methodology so that it includes an SC concern, enabling the generation of reusable action rule specifications and other elements necessary for SC genera- tion. Our research contributes to combining the strengths of the DEMO methodology and smart contracts. The design and implementation considerations of this approach are discussed in de- tail, and the results can be applied in future business cases requiring enterprise interoperability supported by distributed ledger technology.
Exploring the Impact of Google Discover on Users and Publishers: A Data-Driven Study

The paper is dedicated to the analysis of Google Discover recommendation algorithms. The study is conducted on two online stores operating in Poland, for which the data from Google Search Console are available for 16 months. Google Discover is explored from the perspective of web users and web content publishers based on the total number of impressions and clicks and the click-through rate of the two websites. The results allow us to understand that although the users’ activity increases the website’s performance in Google Discover, its algorithms consider many more factors (like the popularity of other websites with similar content, users' location, and frequency of content updates) and may remove the website from Discover despite a relatively high number of clicks. Additionally, the literature review conducted in the paper revealed a gap in scientific research dedicated to the phenomena of this content recommendation system.
Linking Rare and Popular Tags in CQA Sites

Community Question Answer (CQA) sites are popular means for sharing knowledge in the form of questions and answers. These sites rely on tags for many purposes, such as content organi- zation, question routing, content searching, etc. Each CQA site has thousands of tags, making it challenging for the users to manually annotate their question posts with the appropriate tags. Understanding the semantic relationships between tags could aid in the tagging process, thereby properly routing the questions to the experts. Although it is relatively easier to mine the se- mantic relationships amongst the frequently used popular tags, it is difficult to do so for the less commonly used rare tags due to a lack of information about them. Most often, the rare tags are specific concepts subsumed by popular tags. For the questions to be routed to the right experts, they must be annotated with a proper mix of both popular and rare tags. In this paper, we pro- pose a novel approach to mine the semantic relationships between the rare and the popular tags. In addition, we show that the methods that are proposed to mine semantic relationships between popular tags cannot be used for rare tags. Specifically, we identify the top-k popular tags that are semantically related to a given rare tag, which is done using a set of semantic and topological features. Extensive evaluations on CQA datasets show the superiority of our proposed method over state-of-the-art methods.
A structural tonk

Lay Summary When logicians work with multiple-conclusion systems, they use a metalinguistic comma ‘,’ to aggregate premisses and/or conclusions. In this note, I present an analogy between this comma and Prior’s infamous connective tonk. The analogy reveals that these expressions have much in common. I argue that, indeed, the comma can be seen as a structural incarnation of tonk. The upshot is that, whatever story one has to tell about tonk, there are good reasons to tell a similar story about the comma in typical multiple-conclusion systems, and vice versa.
Real-time inspection and fault detection for large photovoltaic arrays based on drones and deep learning algorithms

Abstract In recent years, the installation of renewable energy generation systems based on photovoltaic (PV) panels has experienced massive increments and PV parks with thousands of panels are now becoming commonplace. Yet, there are some challenges, like inspection and fault detection. Lately, these operations have been approached using drones. This project adds the use of deep learning, more specifically proposes the convolutional neural network (CNN) algorithm, the YOLOv5 model and Real-Time Messaging Protocol (RTMP) protocol to achieve real-time detection of PV panels failures. The YOLOv5 model was trained by sets sorted into 9 different categories including fault and abnormal objects’ coverage. This multi-class classification system was investigated by a variety of evaluation indexes to show effectiveness and accuracy. The system was also examined with its different fault classes. The performance results demonstrate that the mean average precision could reach up to 98% with a good training set, confirming the feasibility of proposed approaches.
Approaches for improvement of reliability of the Prony’s method computation

Abstract Parametric spectral analysis methods such as the Prony’s method can estimate the frequencies and amplitudes of a signal, conforming to their model, with great precision. At the same time, the addition of noise to the signal can lead to a complete model breakdown which leads to erroneous parameter values. This is especially true for the impulse noise. The article explores several possible algorithms which can be applied to the Prony’s method in order to refine the results and make them more noise resistant. Such algorithms include signal segmentation methods where the results of each segment processing influence the final estimate as well as the conceptually related method of point skipping. An approach based on the use of non-Euclidean norms as a measure of the linear algebraic equation system’s solution quality is developed and tested. Initially, the methods are applied to model digital signals, comprised of harmonic components with varying complex frequencies and amplitudes. Additive white Gaussian and impulse noise is added to the model signals. The results are then applied to the noisy results of a real-life synthetic aperture synthesis experiment obtained in the intermediate zone of radiation.
Method for planning the way of UGV using a modification of dynamic bi-directional RRT algorithm.

The path planning problem of unmanned autonomous ground vehicles has always been an acute problem in the field of autonomous ground robotic systems research. From the point of view of the complexity of the tasks during the conduct of combat operations in the urbanized space of the densely built-up city with a constantly changing landscape, the tasks entrusted to the UGV are constantly becoming more difficult, and the scenarios of the use of the UGV demonstrate a diversified trend in the development of new management and control systems. The rapidity of conducting modern military operations in an urbanized space appears as a complex and multifaceted task. Considering the complexity of the UGV movement process, at the current stage of the development of robotic systems, the general trend is to abandon remote control of robotic complexes with the transition to automatic modes, which requires the development and implementation of algorithms for automatic interaction and movement of military mobile robotic systems. To solve the problems of the Rapidly-Exploring Random Tree *Fixed Nodes algorithm regarding its low speed for obtaining track junctions and the impossibility of using it in a dynamic environment when planning a UGV path. To solve the problem of accelerating the acquisition of collision-free paths in real time in two-dimensional space, it is proposed to apply a modified dynamic bidirectional RRT* algorithm with reference nodes. The algorithm is a modification of Rapidly-Exploring Random Tree *Fixed Nodes using a bidirectional greedy search method to speed up and solve the problem of the unidirectional Rapidly-Exploring Random Tree algorithm regarding its slow search speed, as well as the difficulties of decision making in a narrow environment caused by blind random sampling. In the case of dynamic disturbance movement, taking advantage that reference nodes do not require much calculation in planning, in the process of iterative path optimization, the proposed algorithm updates the map information in real time and repairs the damaged output path to complete the dynamic path planning.
Mathematical Formulation of Learning and Its Computational Complexity for Transformers’ Layers

Transformers are the cornerstone of natural language processing and other much more complicated sequential modelling tasks. The training of these models, however, requires an enormous number of computations, with substantial economic and environmental impacts. An accurate estimation of the computational complexity of training would allow us to be aware in advance about the associated latency and energy consumption. Furthermore, with the advent of forward learning workloads, an estimation of the computational complexity of such neural network topologies is required in order to reliably compare backpropagation with these advanced learning procedures. This work describes a mathematical approach, independent from the deployment on a specific target, for estimating the complexity of training a transformer model. Hence, the equations used during backpropagation and forward learning algorithms are derived for each layer and their complexity is expressed in the form of MACCs and FLOPs. By adding all of these together accordingly to their embodiment into a complete topology and the learning rule taken into account, the total complexity of the desired transformer workload can be estimated.
QoE Estimation of WebRTC-based Audio-visual Conversations from Facial and Speech Features

The utilization of user’s facial- and speech-related features for the estimation of the Quality of Experience (QoE) of multimedia services is still underinvestigated despite its potential. Currently, only the use of either facial or speech features individually has been proposed and relevant limited experiments have been performed. To advance in this respect, in this study, we focused on WebRTC-based videoconferencing where it is often possible to capture both the facial expressions and vocal speech characteristics of the users (if they agree on). Firstly, we performed thorough statistical analysis to identify the most significant facial- and speech-related features for QoE estimation, which we extracted from the participants’ audio-video data collected during a subjective assessment. Secondly, we trained individual QoE estimation machine learning-based models on the separated facial and speech datasets. Finally, we employed data fusion techniques to combine the facial and speech datasets into a single dataset to enhance the QoE estimation performance due to the integrated knowledge provided by the fusion of facial and speech features. The obtained results demonstrate that the data fusion technique based on the Improved Centered Kernel Alignment (ICKA) allows for reaching a mean QoE estimation accuracy of 0.93, whereas the values of 0.78 and 0.86 are reached when using only facial or speech features, respectively.