Reviewerly/exHarmony huggingface dataset:
Language Artificial Intelligences' Communicative Performance Quantified Through the Gricean Conversation Theory

This study pragmatically investigates an artificial intelligence (AI) speaker (AIS)'s verbal communicative performance based on real AI-human conversation data. Specifically, this study explores Grice's conversation theory, which enables the categorization of an AIS's mistaken utterances as violations of specific conversational maxims. Twenty native Korean-speaking participants recorded at least 50 conversations with Kakao Mini AISs, provided by Daum Kakao, Inc., in Korea. Each conversation, either for information sharing or as daily dialogue, was required to contain at least two turn-taking instances. A total of 1,026 recorded dialogues were decomposed into adjacency pairs based on turn-taking. The dialogues were arranged into 3,365 adjacency pairs, and each pair was then classified as a conversational success or failure based on whether the AIS answered the user's utterance appropriately. Language users' evaluations of the AIS's mistaken expressions were also quantified via an additional acceptability rating test with 1,024 adjacency pairs. The overall results indicate that Grice's "maxim of relation" is most frequently flouted by AISs and is considered to be the least natural to language users. These findings suggest that to improve AISs' natural communication capacity, more detailed AI algorithms that generate utterances relevant to either the partner's preceding utterance or a broader conversational context should be created. Although the verbal communicative capacities of the AIS we test are substantially overtaken by those of recent large language models, such as generative pretrained transformer, the pragmatic evaluation described in the current study will remain useful for more precise linguistic quantification of current/future language AI's communicative performance/competence.
U*: GA-based path planning algorithm for surface floating garbage cleaning robot

Marine debris is a serious global problem that is not limited to areas where humans live but also drifts around the world with wind and currents. More than 10 million tons of plastic waste flow into the ocean every year, posing a major threat to humanity. This study designs a path planning algorith m for surface garbage-cleaning robots called U*, which aims to improve the efficiency of salvaging marine debris and reduce labor and time costs. The U* algorithm consists of two procedures: exploration and path-planning. The exploration procedure searches for marine debris, while the path-planning procedure predicts the possible location of marine debris using the velocity and direction of ocean currents and finds the shortest path by using a genetic algorithm (GA) to collect the found marine debris. According to the experimental results, the U* method is more efficient in terms of reducing path length and time costs.
Developing learning and strategic competence in pre-service teachers’ professional oral English communication within reflection and differentiation

This article deals with developing learning and strategic competence in professional oral English communication of preservice teachers. It highlights the need for seeking new teaching approaches due to recent changes in university curriculum, i.e., the decrease in class hours and the increase in self-study hours. The research analyses the specifics of implementing modern approaches and proves their effectiveness. The paper introduces the definition of learning and strategic competence in professional oral communication of pre-service English teachers, which defines the speakers’ ability to learn consciously and manage their speaking activity in professional situations both effectively and autonomously. Moreover, this case study examines and explains the relevance of reflection and differentiation. It shows that reflective approach enables students to analyse and manage their learning process while mastering learning and communicative strategies, identify the most efficient ones, plan further actions in order to improve earlier developed skills. This paper stresses the necessity of mastering chosen reflection skills by implementing five sequential stages of the reflection mechanism. However, differentiation gives teachers the opportunity to individualise the learning process, varying teaching methods and tools, adapting exercises and activities to students’ cognitive learning styles, peculiarities and needs. Therefore, it suggests combining differentiation with individualization while applying their most relevant aspects. In terms of differentiation the investigation describes mechanisms of adaptation, regulation and improvement, and provides the evidence for importance of individual, pair and group work.
Justifying the selection of a neural network linguistic classifier

The subject matter of this article revolves around the exploration of neural network architectures to enhance the accuracy of text classification, particularly within the realm of natural language processing. The significance of text classification has grown notably in recent years due to its pivotal role in various applications like sentiment analysis, content filtering, and information categorization. Given the escalating demand for precision and efficiency in text classification methods, the evaluation and comparison of diverse neural network models become imperative to determine optimal strategies. The goal of this study is to address the challenges and opportunities inherent in text classification while shedding light on the comparative performance of two well-established neural network architectures: Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN). To achieve the goal, the following tasks were solved: a comprehensive analysis of these neural network models was performed, considering several key aspects. These aspects include classification accuracy, training and prediction time, model size, data distribution, and overall ease of use. By systematically assessing these attributes, this study aims to provide valuable information about the strengths and weaknesses of each model and enable researchers and practitioners to make informed decisions when selecting a neural network classifier for text classification tasks. The following methods used are a comprehensive analysis of neural network models, assessment of classification accuracy, training and prediction time, model size, and data distribution. The following results were obtained: The LSTM model demonstrated superior classification accuracy across all three training sample sizes when compared to CNN. This highlights LSTM's ability to effectively adapt to diverse data types and consistently maintain high accuracy, even with substantial data volumes. Furthermore, the study revealed that computing power significantly influences model performance, emphasizing the need to consider available resources when selecting a model. Conclusions. Based on the study's findings, the Long Short-Term Memory (LSTM) model emerged as the preferred choice for text data classification. Its adeptness in handling sequential data, recognizing long-term dependencies, and consistently delivering high accuracy positions it as a robust solution for text analysis across various domains. The decision is supported by the model's swift training and prediction speed and its compact size, making it a suitable candidate for practical implementation.
Enhancing aspect-based financial sentiment analysis through contrastive learning

The subject of research in the article explores the specialized application of Aspect-Based Financial Sentiment Analysis (ABFSA), focusing on the intricate and multifaceted emotional landscape of financial textual data. The study extends the current understanding of sentiment analysis by addressing its limitations and opportunities within a financial context. The purpose of the work is to advance the field of Aspect-Based Financial Sentiment Analysis by developing a more nuanced and effective methodology for analyzing sentiments in financial news. Additionally, the study aims to assess the efficacy of recent advancements in Natural Language Processing (NLP) and machine learning for enhancing ABFSA models. The article deals with the following tasks: Firstly, the study focuses on the rigorous pre-processing of the SEntFiN dataset to make it more amenable to advanced machine learning techniques, specifically contrastive learning methodologies. Secondly, it aims to architect a unified model that integrates state-of-the-art machine learning techniques, including DeBERTa v3, contrast learning, and LoRa fine-tuning. Lastly, the research critically evaluates the proposed model's performance metrics across the test dataset and compares them with existing methodologies. The following methods are used: Firstly, the study employs pre-processing techniques tailored for the SEntFiN dataset, which is explicitly designed for entity-sensitive sentiment analysis in financial news. Secondly, it utilizes advanced machine learning techniques such as DeBERTa v3 for language model pre-training, contrast learning for focusing on causal relationships, and LoRa for fine-tuning large language models. Lastly, performance evaluation methods are used to assess the efficacy of the proposed model, including comparisons with existing methodologies in the field.The following results were obtained: The study reveals that the proposed pre-processing framework successfully accommodates the variable number of entities present in financial news, thereby improving the granularity of sentiment classification. Furthermore, the integration of advanced NLP and machine learning techniques significantly enhances the accuracy and efficiency of ABFSA models. Conclusions: The paper concludes that specialized ABFSA methodologies, when augmented with advanced NLP techniques and a robust pre-processing framework, can offer a more nuanced and accurate representation of sentiment in financial narratives. The study lays the groundwork for future research in this nascent yet crucial interdisciplinary field, providing actionable insights for stakeholders ranging from investors to financial analysts.
Notation System for Comparing and Synthesis of Intelligent Key Phrase Extraction Methods for Ontological Models in Information Systems

The article is dedicated to the problem of extracting keywords from English texts. This task is essential for building ontological models of information retrieval systems, comparing different texts, and other similar tasks. A comparative analysis of well-known methods for extracting both individual words and their combinations has been conducted. Indeed, the most valuable keywords are multi-word ones, as several such words allow for a complete description of both the uniqueness of the text and its correspondence to broader thematic categories. A new notation system and graphical representation of the main stages of each keyword extraction method have been developed. Using this system, notation and graphic visualization of all the most popular methods have been carried out. The advantages and disadvantages of all considered methods have been analyzed. Using the developed notation, several new methods have been proposed, which possess the advantages of known methods but lack their disadvantages. The effectiveness of the known and proposed methods has been tested on a real example of texts from a monograph by one of the authors, which was published with the funding of a grant from the Swedish government’s SIDA on the topic of water resource management in a river basin. The analysis showed that the newly developed methods are more effective, proving the scientific and practical value of the proposed methods notation and constructing new methods based on it.
Immersive technologies in education : the collection of materials

The collection includes materials of presentations that were featured at the scientific and practical conference "Immersive Technologies in Education." The presentations delve into scientific and methodological aspects of the digitization of society and education, defining the essence and innovativeness of immersive technologies for educational development at all levels. They explore the aspects of using virtual and augmented reality in the educational practices of educators. Special attention is directed towards the modeling, designing, and utilization of educational environments through virtual and augmented reality technologies, along with an examination of the impact of virtual reality environments on the health, behavior, and cognitive activities of students. The presented materials can be utilized by scientists, educational and pedagogical professionals, graduate and doctoral students, as well as teachers in general education institutions.
The Link Between Return and Risk in the Case of Bitcoin

Over time, cryptocurrencies have experienced a widespread adoption, with bitcoin emerging as the most prominent example. In an increasingly uncertain world, the significance of possessing a stable store of value, traditionally fulfilled by gold, has escalated. Bitcoin has been often referred to as a digital equivalent of gold. Hence, this study primarily focuses on analyzing the price dynamics of this particular cryptocurrency. A comprehensive literature review will be employed to examine the regulatory obstacles encountered within the cryptocurrency market. Additionally, considering the contentious nature of this field, special attention will be devoted to the clash of perspectives surrounding this innovation. Subsequently, concentrating on the period 2016-2021, this paper will investigate the factors that define a risk-weighted investment, utilizing the Sharpe ratio and Sortino ratio. However, there has been significant volatility in the price of Bitcoin in 2020-2021, and our research fills a gap in the relationship between Bitcoin returns and risk in the post-2016 period. Overall, the analysis concludes that bitcoin exhibits highly turbulent investment characteristics. Despite its substantial price appreciation, the findings indicate that bitcoin displays significant volatility. Consequently, selecting this investment alternative entails considerable risks. Based on our results, there were years between 2016-2021 when bitcoin was a good investment, but in most cases its returns were associated with excessive volatility and risk. For this reason, it is not recommended for risk-averse rational investors.
Correction: Siddiqui, Z.A.; Park, U. A Drone Based Transmission Line Components Inspection System with Deep Learning Technique. Energies 2020, 13, 3348

The authors wish to make the following corrections to their paper [...]
Research

Abstracts of the 10th Annual Scientific Meeting of the Indonesian Heart Rhythm Society (InaHRS) 2023: Research
Knowledge management system’s implementation in private universities through SECI model

The purpose of this research is to analyze the implementation of the Knowledge Management System (KMS) at x University in Jakarta. The research model implemented is qualitative research. Research data were collected through observation, interviews, documentation and triangulation techniques. The technical analysis of the collected data uses data reduction with the SECI model from Nonaka and Takeuci (Socialization, Externalization, Combination and Internalization), data display and drawing conclusions. These data were tested to ensure the validity of the data by testing the credibility, transferability test and confirmability test. The research results prove that x University has implemented a Knowledge Management System using the SECI Model. These results are a new theory originating from the field (grounded theory).
A Smooth Oversampling Technique Using SLP

The class imbalance problem occurs when the number of samples in a given dataset is not balanced according to the class labels. In a binary classification problem of two classes, one class contains a minority of samples while the other has a majority. The commonly utilized SMOTE technique generates synthetic samples in the middle of a pair of original samples in the feature space to oversample the minority class. The midlines and randomness characteristics of this technique and its extensions capture the local characteristics of the minority class. Besides, these techniques do not significantly improve the classification problem’s performance. Accordingly, this paper proposes a new technique for oversampling using a single-layer perceptron that captures the general characteristics of the data with low resource consumption. The proposed technique uses the weights trained for the network as input to other networks to generate new samples within the min-max range. The results showed that the proposed technique overperformed the SMOTE techniques, as reported using several machine learning techniques, including decision tree, random forest, support vector machine, and K-nearest neighbors. The experiments are conducted using the highly imbalanced credit card transaction dataset, which contains 492 fraudulent transactions out of 284,807 total transactions. The random forest reported the best results, in which the proposed technique improves the recall and the f-measure while maintaining full precision, similar to SMOTE.
A network analysis-based framework to understand the representation dynamics of graph neural networks

Abstract In this paper, we propose a framework that uses the theory and techniques of (Social) Network Analysis to investigate the learned representations of a Graph Neural Network (GNN, for short). Our framework receives a graph as input and passes it to the GNN to be investigated, which returns suitable node embeddings. These are used to derive insights on the behavior of the GNN through the application of (Social) Network Analysis theory and techniques. The insights thus obtained are employed to define a new training loss function, which takes into account the differences between the graph received as input by the GNN and the one reconstructed from the node embeddings returned by it. This measure is finally used to improve the performance of the GNN. In addition to describe the framework in detail and compare it with related literature, we present an extensive experimental campaign that we conducted to validate the quality of the results obtained.
SUBSTANCE OF AMULETS AND CULTURAL DUTY

Since ancient times, each culture had an object called an “amulet” as a spiritual anchor to protect people from negative energy, evil, and injury. Humans were born in the world, and they need to confront many things for living their lives and survival. These things occurred with fear, pain, suffering, and others. Hence, amulets were created to strengthen mental power for getting ready to fight against what humans were unable to overcome. Humane belief in amulets has been passed down from generation to generation until today, the era of technological advancement and goodness. Those prosperities are a tool that humans can use to eliminate problems and feelings reasonably, such as small Buddha images, amulets, and symbols in different forms. Thus, this article aims to reveal knowledge or some theoretical opinions to support the legitimacy of amulet art containing history, art, culture, and phenomena by proof and references from research studies that present the importance of objects or amulet art in terms of how they have an effect or produce any factor on humans and what is the main purpose of amulet art that can be proven by scientific theories (scientific tools) from utilization and mental development and restoration rather than credulity.
AN EMPIRICAL STUDY ON FAKE REVIEW DETECTION

In recent years, the Internet has opened up opportunities for manufacturers and retailers to advertise and sell their products online. Online shopping is becoming a habit of consumers. Although there are many benefits of buying and selling online, such as easy product selection and comparison from many different sellers before deciding which one to buy, reading comments before buying a product is a habit of customers. It helps them learn from the experiences of former buyers. However, buying based on product reviews is risky, especially fake reviews. These reviews affect the buyers’ purchase decisions. Detecting fake reviews is a critical problem. This study proposed a machine learning-based framework for detecting fake reviews by extracting features from text and deployed six machine-learning models for classification tasks. Experimental results showed that the SVC is a reliable machine-learning algorithm for classifying truthful reviews and fake reviews using the TF-IDF feature extraction technique.
IDENTIFICATION OF TECHNICAL CHARACTERISTICS OF MULTIFUNCTIONAL LAPTOP TABLE PRODUCTS BY PAYING ATTENTION TO CUSTOMER VOICES

Products circulating in the market either in the form of goods or services to facilitate the daily life of consumers. A good product is a product that meets the needs and desires of consumers so that it can function optimally. Multifunctional laptop desk products are widely used by students or students to make it easier to do tasks such as writing, typing with a laptop, and so on. A multifunctional laptop table has several functions, namely placing the laptop or doing tasks without using a laptop, and also has several features such as a laptop cooling fan which is useful for keeping the laptop's temperature normal when the laptop is used. The voices of consumers include consumers who want multifunctional laptop desk products to be made in rectangular shape, products made of MDF wood, the size of the product is medium with a size of 60cm x 40cm x 30cm, products are made of strong materials and multifunctional laptop table products that will be made have additional features Cooler, Usb Port, and Drawer.
Utilizing the Clustering Techniques using Distance-Based Similarity Measures of SVNS in Medical Diagnosis

The clustering techniques, combined with distance-based similarity measures of Single Valued Neutrosophic Sets (SVNS) are studied and applied in medical diagnosis. The study starts with reviewing SVNS' theoretical foundations, emphasising its ability to capture and handle ambiguous data. This study focuses on integrating distance-based similarity measurements to improve the clustering process, which has seen limited implementation thus far. The set of data includes three patients with five symptoms and three diagnoses. To deal with the data in medical diagnosis, each patient is diagnosed with a disease based on distance-based similarity measures. The disease with the highest similarity measure value indicates the recognized disease for that patient. Then, the diseases are clustered into different categories depend on the values of confidence level. The obtained results show that the suggested method enhances the precision of medical diagnosis significantly, especially in cases with ambiguity and uncertainty.
Sistem Deteksi Kebakaran Menggunakan Esp32 Dan Arduino

,. Fire disasters are disasters that occur more often than other disasters such as floods, landslides, earthquakes or tsunamis. This disaster can happen at any time and no one knows for sure when this disaster will come. The impact of a fire disaster is not only material but can result in the loss of human life. Factors that cause fire disasters often occur due to human negligence and fires often occur in houses where the occupants are abandoned. The research carried out this time focuses on creating a fire detection system using ESP32 and Arduino. The system uses three sensors, namely a temperature sensor, a gas sensor and a fire sensor. Temperature sensors are useful for monitoring room temperature conditions, fire sensors are useful for detecting the presence of fire in a fire disaster and gas sensors are useful for detecting the presence of smoke that appears as a result of a fire disaster. This system uses Arduino Uno and ESP32 microcontrollers. The results of the fire detection system are expected to reduce the occurrence of fire accidents and also losses caused by fire accidents