Reviewerly/exHarmony huggingface dataset:
Examining the Influence of Innovative Leadership on the Innovative Work Environment

The study intended to examine the effect of innovative leadership on the innovative work environment of the Divine Word College of Laoag. To carry out the study, the literature was reviewed and the study used the descriptive research design. The questionnaires were used to gather the data and the respondents were the employees of the Divine Word College of Laoag. The study found that the innovative leadership of administrators and the innovative work environment of the Divine Word College of Laoag is considered high. However, the correlation test found that there is no correlation between innovative leadership and an innovative work environment. Thus, the hypothesis is rejected. It just means that innovative leadership has nothing to do with the innovative work environment. The innovative work environment can be caused by other factors not included in the current study.
Developing Employees’ Trust in Management Through Ethical Leadership

The study aims to examine the effect of ethical leadership on employees’ trust in management/administrators. To support the theory of the study, literature was reviewed. The study used assessment and correlational research design. The population of the study are employees of the Divine Word College of Laoag and total enumeration was used. Data gathering instruments were validated questionnaires which were adopted from Yukl, et al. (2013) and Seok, et al. (2015). The results indicate that ethical leadership and employees' trust in management are considered high and the correlation coefficient suggests that there is a significant correlation between ethical leadership and employees' trust in management.
The Effect of Ethical Leadership on the Work Performance of Employees

The study aimed to determine the effect of ethical leadership on the individual work performance of employees. To deepen the concept of the study and establish theories of the study, literature was reviewed. The study used a descriptive assessment and correlational research design. The population of the study was all the employees of the Divine Word College of Laoag and the data were gathered through research questionnaires. To analyze the data, the weighted mean and Pearson r correlation was used. The results suggest that ethical leadership is significantly correlated with the task performance and counterproductive work behavior and no correlation with the contextual performance.
A Lightweight Neural Network Model of Feature Pyramid and Attention Mechanism for Traffic Object Detection

The application of object detection technology has been used in people's daily lives widely, and it can be used accurately in traffic scenes is also crucial. Based on the YOLOX object detection model, a lightweight neural network for traffic object detection is proposed. First, we propose S-PANet (Simple Path Aggregation Network) structure based on the YOLOX_S baseline model. The purpose of the S-PANet structure is to reduce the parameters number of models and the amount of floating-point computation in training. Second, we propose to add CBAM (Convolutional Block Attention Module) to the network based on the S-PANet. Adding CBAM attention mechanism can improve the detection accuracy of models, but has less computation added. It is a compensation of the simplification on feature pyramid. Final, we carry out the lightweight experiments on three kinds of models, including S, Tiny, and Nano. The proposed methods are verified on KITTI and PASCAL VOC datasets. Our S model can achieve 92.9% mAP@0.5 and 21.33FPS on KITTI dataset. The Tiny and Nano models can achieve 84.3%, 79.4% mAP@0.5 and 33.07, 41.67FPS respectively. Compared with the baseline models, the detection accuracies mAP@0.5 have less difference and the parameters number of the models are reduced.
Multifunctionality of discourse marker portanto in Braga’s speech

"Multifunctionality of discourse marker portanto in Braga’s Speech. This paper explores the multifunctionality and usage patterns of the discourse marker portanto in Braga’s speech. Our main goal is to propose a revised description of portanto, while acknowledging the validity of previous research on the topic. The investigation focused on the co-occurrence of functions associated with portanto, emphasizing its pivotal role in speech construction. The frequency distribution of portanto usage was found to be consistent with earlier studies, affirming its most prevalent application as a filler (marker). Our study also highlighted that an excessive use of discourse markers can create the illusion of coherence, rather than genuinely ensuring it, especially in contexts characterized by frequent pauses, verbal hesitations, and syntactic interruptions. Furthermore, this research examined the influence of discourse genre on portanto’s usage patterns. Notably, the two predominant uses observed in Braga’s speech were linked to specific aspects of sociolinguistic interviews, particularly the recollection of life stories and features of spontaneous speech. The findings enrich our understanding of this discourse marker's multifaceted role in facilitating coherent communication and its significance in various speech contexts. Keywords: Braga’s speech, colloquial register, discourse markers, European Portuguese, multifunctionality."
Dynamic overtaking on curves using enhanced artificial potential field method

Navigating curved roads can be a formidable challenge for both autonomous vehicles and human drivers. In this paper, we introduce an advanced artificial potential field method aimed at tackling this issue head-on. Our approach centers on the creation of lane potential fields, which are calculated based on lane characteristics such as radius and width. These fields serve as virtual guides, ensuring that the vehicle remains safely within its designated lane throughout the journey. To further enhance safety and efficiency during overtaking maneuvers on curved roads, we introduce the concept of virtual target nodes into the planning process. These nodes act as strategic waypoints that help optimize overtaking trajectories and decision-making. By incorporating them into our method, we facilitate smoother and more secure overtaking, even in the presence of dynamic obstacles. Our paper presents compelling experimental results that underscore the effectiveness and safety of our improved artificial potential field method. Through a series of tests involving both static and dynamic obstacles, we demonstrate its prowess in dynamic overtaking scenarios on curved roads. This method, characterized by its adaptability and efficiency, proves to be a promising algorithm for addressing the intricate challenges posed by curved road navigation.
AGING AND MASS MARKETING FRAUD: EVIDENCE ON REPEAT VICTIMIZATION USING PERPETRATOR DATA

Abstract Fraud complaint data indicates that adults in their 70s and 80s experience significantly greater fraud losses than middle aged and young adults, however older adults are less likely to self-report victimization overall. One explanation for older adults’ relatively higher financial losses may be that they experience a greater incidence of repeat victimization. In this study, we examine the relationship between age and repeat mass marketing fraud using a novel dataset of nearly one million US mail fraud victims. The data come from four mail fraud enterprises that were investigated by the US Postal Inspection Service and that ran overlapping scams between 2000 and 2018. The perpetrators collected information on victims (name, address, age) and how often they paid money in response to scam solicitations. Datasets were merged, cleaned, and deduplicated. We use a Prentice-Williams-Peterson adaptation of a Cox proportional hazards model to estimate the effect of age on the likelihood of experiencing a subsequent victimization, controlling for the number of prior victimizations. Compared to victims in their 50s, victims aged 70-79 and 80-89 had a 9% greater risk of experiencing another victimization, whereas victims in their 20s had a 24% lower risk of experiencing another victimization. Logistic regression analyses indicate that adults in their 70s and 80s are significantly more likely than those younger than 50 to experience victimization by multiple types of mail scams. This is the first study to use perpetrator data, rather than self-report, to show that older adults face a greater risk of repeat fraud victimization.
Diploma Research Approbation in Internet Resources Integration Into Teaching English

Aim of the present research is to share the experience in organizing diploma paper approbation procedure as an important part of professional training of future English teachers in Akhmet Baitursynov Regional University, Pedagogical Institute after Umyrzak Sultangazin (Kazakhstan). Objectives of the research include brief description of the professional training process in the mentioned above higher educational institution with the focus on future English teachers training; presentation of the diploma paper main idea and contents; sharing the experience of approbation organization process. Main methods used in the present research include scientific analysis and synthesis, description, discussion, lesson modelling, observation, questionnaire. Results of the research include the future English teacher professional competence development and practical experience of applying the Internet resources to English teaching that led to the enhancement of educational process and growth of basic foreign language skills of the approbation participants. Conclusions that were made on the basis of the research show how organization of approbation contributed to the professional skill development of the future English teacher as well as how the integration of Internet resources influenced the educational process with both positive and negative effects it produced. Materials of the research are original and were obtained during the professional training of a future English teacher who graduated in June 2023 from the above-mentioned university and after successful passing of the National Teachers Professional Testing took a teaching job in Kazakhstan. Further research is intended to implement the findings of the present stage in the English teachers pre-service professional training with subsequent presentation of the obtained results in professional journals and conferences.
Research on Early Warning of Social Stability Based on Two-Dimensional Particle Swarm Algorithm

The concept of “thinking in peace” is a way to measure and warn about social stability, which has been highly valued by visionary rulers and politicians. In this paper, we first established a system of indicators affecting social stability, and the Pearson correlation coefficients indicate that they have strong correlation. Based on this, a machine learning model was established to obtain a supervised learning model for both, Bayesian optimization of SVM vector machines, supervised learning using self-referencing Gaussian medium SVM vector machines, prediction confidence histograms obtained by cross-validation methods, and prediction data brought into the model, the relative error between the true stability coefficient and the regression stability coefficient is small, with an accuracy of 91.8% and RMSE <0.4, with strong rationality and scientific validity. Finally, taking the Cuban color revolution as an example, the two-dimensional particle swarm algorithm was applied to analyze the changes of relevant index data before and after the outbreak of the color revolution, and it was concluded that the social stability should show a gradual decrease, and then the main reasons for the failure of the color revolution were analyzed.
Blockchain Implementation in Smart Grids

As the proportion of renewable energy sources in the energy system rises, new market approaches are needed to pricing and distribute the unsteady and dispersed generation. Markets for locally generated renewable energy that are decentralized and accessible to consumers and prosumers create a virtuous cycle of supply and demand. We provide a complete model of a local energy market serving 100 houses, including an explanation of how the market works and a simulation of how it might function. In order to highlight the decentralized character of regional energy markets, our approach is predicated on a distributed ledger system, sometimes known as a private blockchain. As a consequence, we give end-users and prosumers of energy a decentralized marketplace where they may trade locally produced energy directly with one another. We also present a preliminary economic assessment of the market mechanism and an agenda for further research into the technological assessment of blockchain technology as the principal information and communication technology for the local energy market.
Study of Machine Learning for Hand Gesture Recognition

Gestural input is starting to get more attention in today's technological landscape. The primary objective of this research is to create mathematical algorithms for HCI that can recognize human movements. The limited forms of human- computer interaction that now exist include the keyboard, mouse, touch screens, and a few more. While all of these gadgets have the potential to make computers more adaptable, they are limited in several ways. Among these methods is gesture detection, which may be used to create more intuitive interfaces for the average person. Hand and facial movements are common places to begin gesturing, although gestures may originate from wherever the body is moved or altered. Users may control the devices with just their gestures, eliminating the need for physical contact. Motions like page turning and vertical/horizontal scrolling need specific hand movements that are the focus of this research.
A Comparative Analysis of Deep Learning-Based Object Detectors for Embedded Systems

The paper presents a comparative analysis of popular deep learning-based object detectors, focusing on evaluating their real-time performance on embedded platforms. The objective was to identify the optimal algorithm that can efficiently operate within limited computational resources and memory constraints of embedded systems while minimizing energy consumption. We tested a set of popular detectors, such as Faster R-CNN, YOLO, and SSD MobileNet and evaluated their performance on the testing platform with CUDA cores. A faster variant of bounding box suppression, blazing fast Non-Maximum Suppression (NMS), was employed to enhance efficiency. Additionally, the generalized intersection over union (GIoU) metric was adopted as the optimal testing method, allowing for more accurate object localization and detection. Results demonstrate that the SSD MobileNet-V2 and Tiny YOLO detectors exhibit promising real-time performance on embedded platforms, although their accuracy is diminished compared to larger models.
Crypto Mining Data: Nature and Inferences

Using raw blockchain data from Ethereum from 2019 through 2023, the authors examine the current effective state of crypto and, specifically, the data validation process, known as mining. Focusing on the proof-of-work (POW) versus proof-of-stake (POS) models within different periods of evolution within Ethereum, the authors estimate different mining models’ respective impacts. They document that the recent switch in the Ethereum mining from POW to POS led to a more democratic mining process, resulting in stronger decentralization. The authors also find that a small minority of miners control the majority of mining revenues in both POW and POS systems, which is consistent with a recent study. However, the authors show that the revenues are directly proportional to the miners’ mining efforts and not due to centralization. They also show that the switch to POS has also drastically reduced the mining revenues for many miners, forcing several of the previously prominent miners out of business. Examining the mining data, the authors look for and do not find any overt evidence of manipulation by miners. The research benefits crypto ecosystem participants as well as regulators and traditional financial institutions seeking to understand crypto promises.
Deep Learning for MIMO Communications

Recently, deep learning (DL) is becoming a key feature of next-generation multiple-input multiple-output (MIMO) transceiver design with learning and inference capabilities embedded in the network, which achieves greatly enhanced system performance. Popular topics include end-to-end (E2E) learning for transceiver design, deep reinforcement learning (DRL) for communications, and model-driven deep unfolding techniques. In particular, E2E learning treats the communication system design as an E2E data reconstruction task that seeks to jointly optimize transceiver components, so that encoding and decoding are fostered by the learned weights of deep neural networks (DNN). E2E learning can be employed to solve various problems in MIMO communications, such as channel state information (CSI) feedback, beamforming, signal detection, and channel estimation. Moreover, DRL has been widely applied to solve high-dimensional non-convex optimization problems in designing the transceiver. However, these DNNs generally suffer from an inability to be interpreted or generalized, and they often lack performance guarantees. To overcome such drawbacks, substantial researches have proposed to unfold the iterations of an iterative optimization algorithm into a layer-wise structure analogous to a DNN. Inspired by the great potential of these DL methods, it is important to investigate AI-empowered transceivers for future MIMO systems.
The Continuous Wavelet Transform in Sobolev Spaces Over Locally Compact Abelian Group and Its Approximation Properties

The Sobolev space over locally compact abelian group Hs(G) is defined and we extend the continuous wavelet transform to Sobolev space Hs(G) for arbitrary real s. This generalisation of the wavelet transform naturally leads to a unitary operator between these spaces. Further, the asymptotic behaviour of the transforms of the L2 function for small scaling parameters is examined. In special cases, the wavelet transform converges to a generalized derivative of its argument. We also discuss the consequences for the discrete wavelet transform arising from this property. Numerical examples illustrate the main result.
INTRODUCING STUDENTS OF TECHNICAL UNIVERSITIES TO SCIENTIFIC AND TECHNOLOGICAL VALUES: PROBLEMS, CONDITIONS AND PROSPECTS

The purpose of the article is to identify the problems of introducing students of technical universities to the values of science, scientific and technological development and education in modern conditions; determine the psychological and pedagogical conditions under which a value attitude towards science, technological progress and education is formed; propose a number of activities through which future engineers can put into practice their interest in scientific and technical creativity.&#x0D; The methodological basis axiological approach as one of the key approaches in the formation of values. It is based on the understanding of value as the basis of human existence and attitude to the world.&#x0D; Results. The psychological and pedagogical conditions under which a value-based attitude towards science is formed are identified; activities are proposed with the help of which future engineers can put into practice their interest in scientific and technical creativity.&#x0D; Scope of application of the results. The results obtained can be applied in the educational process of higher education.
Development of Verification Model for Intelligence of Multiagent Systems

The development of distributed information systems essentially relies on the complex application of Web services technologies, cloud, “green”, GRID, multi-agent and many others. In many of these technologies, intellectualization is increasingly manifested, which is clearly inherent, for example, in semantic and intelligent Web services, multi-agent systems. Intelligence, often defined as a set of properties of knowledge and mechanisms for their formation, requires its own ways of formalization and implementation, which is also reflected in the problems of analysis and synthesis of intelligent systems, including the problems of their identification and verification. This paper consider the fuzzy and component development of a model for verifying the intelligence of multi-agent systems based on the previously proposed model for verifying the intelligence of multi-agent systems, which, in turn, is based on behavioral experiments for extended Petri nets. The features of the proposed model for verifying intelligence are the presentation of fuzzy production models of all components of intelligence for parallel, associative-evolutionary search for known identifying primitives and recognition of fragments of system behavior based on them, the construction of new identifying primitives as part of a three-level model of new knowledge, as well as a fuzzy inference mechanism, as the basis for obtaining new knowledge, and for identifying and verifying the behavioral properties of the system. The intelligence verification model makes it possible to reduce the computational complexity of the behavioral verification of multi-agent systems and increase its flexibility.
Validation Study of Digital Literacy Assessment Scale for Elementary School Teachers

Objectives This study was conducted to validate digital literacy assessment scale for elementary teachers, based on previous research, by constructing sub-domains and collecting items, and then verifying the validity and reli-ability of the scale.&#x0D; Methods A survey was conducted on 325 elementary school teachers from K, P, and A Cities for the research. To create the test questions, preliminary questions were developed by taking into account previous studies. Additionally, for data analysis purposes, the software packages SPSS 18 and Jamovi 2.3.21 were utilized.&#x0D; Results The results are as follows. First, content validity verification, exploratory factor analysis, and confirmatory factor analysis were conducted. Second, for reliability verification, item internal consistency was examined, re-sulting in the extraction of four sub-factors (Attitude, Skills, Execution, and Ethics) and 29 items.&#x0D; Conclusions By developing a measurement tool for digital literacy targeting elementary school teachers and ver-ifying its validity and reliability, this study can provide implications for enhancing elementary school teachers' digi-tal literacy competence by providing fundamental data.
HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction

Phishing techniques are the most frequently used threat by attackers to deceive Internet users and obtain sensitive victim information, such as login credentials and credit card numbers. So, it is important for users to know the legitimate website to avoid the traps of fake websites. However, it is difficult for lay users to distinguish legitimate websites, considering that phishing techniques are always developing from time to time. Therefore, a legitimate website detection system is an easy way for users to avoid phishing websites. To address this problem, we present a hybrid deep learning model by combining a convolution neural network and long short-term memory (HCNN-LSTM). A one-dimensional CNN with a LSTM network shared estimation of all sublayers, then implements the proposed model in the benchmark dataset for phishing prediction, which consists of 11430 URLs with 87 attributes extracted of which 56 parameters are selected from URL structure and syntax. The HCNN-LSTM model was successful in binary classification with accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, 95.00%, successively outperforming the CNN and LSTM. Thus, the results show that our proposed model is a competitive new model for the legitimate web prediction tasks.