Reviewerly/exHarmony huggingface dataset:
Keyword Extraction Method Based on Graph Attention Network

At present, the accuracy of unsupervised learning in Chinese text keyword extraction algorithms is generally low. However, while supervised learning achieves improved accuracy, the efficiency of the algorithm is significantly reduced. In response to this series of problems, this paper proposes a semi supervised algorithm, TextGank, based on the graph attention network GAT algorithm and the TextRank algorithm. Firstly, noise is removed from the text through text preprocessing and word nodes that need to be used are obtained. Secondly, a word co-occurrence network graph is formed for each document and word vector information of the word nodes is formed. Finally, the word network graph and word vector are substituted into the algorithm for calculation. By sorting the results, a set of keywords is obtained. Compared with classic keyword extraction algorithms, the accuracy of the algorithm in this paper has been improved by 12.23%, The recall rate has increased by 10.58%, and the F1 value has increased by 13.39%.
Improved Grey Wolf Optimization Algorithm Based on Logarithmic Power Index Inertia Weight

Grey Wolf Optimization (GWO) algorithm is an efficient optimization technique, but in some cases, it usually encounters challenges such as, optimization accuracy, unable to balance between convergence speed and avoiding falling into local optimum. To address above problems, we prosed, an improved Grey Wolf Optimization algorithm based on logarithmic power index inertia weight (LGWO), which uses the characteristics of logarithmic power function to adjust the inertia weight non-linearly, so as to enhance the algorithm's optimization efficiency. LGWO was subjected to simulations across seven standard test functions, revealing its superior convergence speed and solution accuracy in comparison to various other swarm intelligence algorithms present in the literature.
Research on Navigation and Positioning of Intelligent Service Robot in High-speed Railway Station Based on ROS System

High-speed train stations are getting bigger as social railways develop further. The chance of missing the departure time increases as a result of the lengthier search times for destination platforms. This work seeks to study and construct an intelligent service robot for high-speed railway stations in order to overcome this problem. The robot focuses on map creation, indoor location, and map-based navigation. This study uses laser SLAM as the research object and applies the Gmapping mapping algorithm to indoor localization and map generation. By combining data from the IMU and odometer, it also makes use of expanded Kalman filtering localisation. The Dijkstra global path planning algorithm is researched and used as the navigation algorithm. The designed robot uses a differential driving mode and is built on the ROS system. For SLAM and navigational purposes, it integrates a number of sensors, including Lidar, IMU, and odometer. Voice technology from iFlytek is also used.
Application of Artificial Intelligence Technology in Teaching English Language to Engineering Bachelors

Neural networks function and interact in different areas. Artificial Intelligence (AI) technologies are applied in teaching foreign languages in the modern educational system. The purpose of the study was to identify the possibilities of using available artificial intelligence services in teaching the English language in the academic environment. The study used theoretical analysis of pedagogical literature and the questionnaire-diagnostic method. The System Usability Scale (SUS) and Technology Acceptance Model (TAM) were chosen as diagnostic tools for analyzing the characteristics of AI services (Scribble Diffusion, Metavoice, Pictory, ChatGPT). The study involved 96 undergraduate engineering bachelors from South Ural State University. The degree of students' satisfaction with the implementation of AI services was analyzed through the questionnaire. The presented results of the ascertaining experiment confirmed the feasibility and the effectiveness of integrating AI services in teaching the English language to engineering bachelors in the educational process.
Phishing Attacks Isolation via DNS Prevention of 64-Bit Time Synchronized Public Key

Phishing attack is very big problem in the field of internet security. To combat phishing and domain name system (DNS) spoofing, authors work implements a quick authentication system. In DNS spoofing, an attacker clones of IP address and inserts a fake DNS server, which redirects network traffic to the incorrect destination. In phishing, phisher (sales and industrial espionage) clones the legitimate website, which the user believes that is his/her original website. We can host a phishing website but can't relay links through popular hosting sites such as Google or Facebook. To route users to phony servers, phishers utilize DNS spoofing through fake DNS servers to trick legitimate users to click on phishing URLs. As a result, our suggested solution decouples DNS spoofing and phishing assaults with 64-bit time- synchronized public-key cryptography to prevent them.
Applying DOI Theory to Assess the Required Level of Explainability in Artificial Intelligence-empowered Medical Applications

As artificial intelligence-empowered applications flourish and machine learning methodologies are commonly used to process large chunks of data and make decisions, the need for explainable artificial intelligence is becoming ever more pressing. In this work, we present results from a survey we conducted on the level of adoption of artificial intelligence-empowered applications by physicians. Next, we employ the diffusion of Innovation theory to determine what kind of adopters the doctors are and thus outline an implementation strategy and the level and depth of required explainability.
ChatGPT in Artificial Intelligence-Empowered E-Learning for Cultural Heritage: The case of Lyrics and Poems

This paper explores the integration of ChatGPT, a state-of-the-art language model, in the domain of cultural heritage Artificial Intelligence-based e-learning, with a specific focus on the analysis and interpretation of lyrics and poems. Cultural heritage preservation and education have increasingly turned to Artificial Intelligence (AI) technologies to enhance learning experiences and promote the understanding and appreciation of diverse artistic expressions. We have conducted an initial empirical study on how ChatGPT can assist learners in analysing and interpreting poetic and lyrics works, offering insights into the historical, cultural, and literary significance embedded within the verses. Our findings highlight ChatGPT's significant potential but also major weaknesses with respect to poetry and lyrics. ChatGPT was found particularly good in the field of interpreting poetic content. Additionally, ChatGPT showed a very good performance in analogy identification, demonstrating a broad knowledge base encompassing various form of poetry, from Ancient Greek works to ecclesiastic hymns and modern compositions. However, opportunities for enhancement are evident in translation and historical context provision. Finally, ChatGPT's weakest aspect was its factual accuracy, occasionally producing errors without indicating uncertainty in its responses. Consequently, while ChatGPT displays substantial promise, its application in e-learning settings is not mature due to observed inaccuracies. Furthermore, we introduced MUSILYAN, a specialised software tool designed for musicological and lyrical analysis, complementing ChatGPT's capabilities. While Chat-GPT excels in natural language understanding and generation, MUSILYAN offers structured semantic content management and thematic organisation of lyrics. The integration of ChatGPT and MUSILYAN holds promise for enhancing the exploration and understanding of poetry and lyrics.
FedRH: Federated Learning Based Remote Healthcare

More and more people are developing chronic illnesses, which require regular visits to the hospital for treatment and monitoring. Due to the advancements in wearable computing technology, there is a growing interest in remote health monitoring worldwide. The Internet of Medical Things (IoMT) devices allow users to access their health data. Recent developments in smart healthcare have shown that machine learning model training using vast amounts of healthcare data has been remarkably successful. However, remote healthcare is facing a significant challenge as user data is stored in isolated silos, thus making data aggregation impractical while ensuring data privacy and security. To overcome this challenge, this paper presents FedRH, a federated learning framework for remote healthcare. FedRH solves the issue of data isolation through a cloud-edge-based computing architecture and FL, providing personalized healthcare without sacrificing privacy and security. Experiments show that FedRH achieves a greater accuracy of 5.6% compared to conventional approaches. FedRH is a flexible and versatile tool that can be applied to various healthcare applications, making it an ideal choice for many use cases.
Sentiment Analysis with the Use of Transformers and BERT

Recognizing the emotional content of natural language sentences can improve the way humans communicate with a computer system by enabling them to recognize and imitate emotional expressions. The field of emotion recognition occupies an important place in the applications of artificial intelligence. The rapid increase in the popularity of social media has created the need to study and document their use. In this paper, both classical classification algorithms and various neural network architectures were tested. In the context of this paper, we designed and developed deep learning methods and BERT-based implementations for recognizing emotional content in user-generated data. Extensive experiments were conducted using these models on a variety of textual data and all the designed methods were evaluated.
Interoperability of Information Sources for Identification With Privacy Preservation and Early Fraud Detection

The present work aims at the development of smart tools and techniques for the interoperability of information sources and the identification, timely detection, and suppression of forms of fraud with the assurance of data protection. The main goal is the research of smart techniques that will allow direct access to properly encoded and anonymized personal data. The innovation is in the ability to perform comparisons and actions that will allow all the necessary controls to address and prevent fraud, identifying the people that are involved in it, but not revealing personal information for other people recorded in files and registers. This is a novel approach to upgrade the operational action of public bodies and authorities and to give the possibility of dealing with fraud, and applying control to business groups and SMEs. In this work, the classification of users in terms of software use cases, the architecture of the approach and an integrated system of interoperability of information sources process is proposed. All technological developments were taken into account and the appropriate technological choices were made so that the implementing team is able to proceed with the implementation of the functional parts of the system software. Initial results are reported for a privacy-preserving process while cross-referencing files and information.
A Study on Human Perception of Document Paragraph Layout

We identify a list of characteristics of a text document that we use as the basis for a study on human perception of what the structure of a document should look like. Our study reveals that the native language dimension has a significant impact on the human perception of some such characteristics, including the average number of paragraphs starting with a verb, as well as other characteristics describing average paragraph proportions. The study results also show what the means values are, for the different document characteristics, and their distribution across the native language dimension, therefore providing some idea of what a normalised structure of a document should look like. The results of the study have a direct application in a new method for embedding secret messages in text documents that has been recently proposed by the authors and which uses manipulations in the paragraph layout of a document.
Promotion of food and touristic sustainable development in a lake environment using AR

This paper presents an augmented reality mobile application that promotes sustainable food and touristic development in a complex lake environment, by affecting the socioeconomic relations of local inhabitants, food producers and visitors. The application is based on previous research showing that appropriate fishing methods in the lake could have a positive impact on the quality of fishing population, on the income of fishermen and the broader development of the surrounding area and its population. After an introduction, related work defines what constitutes sustainable development in a complex lake environment. Findings of related work and previous research led to the design of the application and the implementation of a working prototype in Figma. The prototype has been evaluated by expert users and primary results are positive. Evaluator remarks will be considered towards a complete implementation of the application.
Towards Adoption of Green Blockchain with Emphasis on Blockchain Type, Consensus Protocols, Data Sharding and Smart Contracts

Blockchain and related Distributed Ledger Technologies (DLT) are anticipated to transform a the world of web from a centralised document-sharing platform to a comprehensive decentralised platform that facilitates the exchange of digital currency and supports autonomous management of digital assets. The central server is susceptible to attacks, distrust and collusion. If the web can assure reliable, safe, and responsible updates among independent participants without the need for a centralized server, the perception of a decentralised web can be re-instantiated. One of the essential technologies required to restore the openness of the internet while maintaining its security is distributed ledger technology (DLT). DLTs may now totally handle business and legal transactions online, creating a more trustworthy and accountable environment. Blockchain technology marks a major breakthrough by removing the need for a centralised trusted authority in a widely distributed network. Instead, a consensus must be reached among several sources of trust, based on an algorithm, that this transaction may be believed to be legitimate. The consensus algorithms in blockchain technology offer an immutable and permanent record of a transaction that is immutable, trustworthy and secured. Consensus algorithms are however energy consuming because of their computation heavy nature. This has been a biggest inhibition towards blockchain adoption. The energy needs for committing a blockchain transaction is also governed by whether it is a public/permissioned blockchain, its consensus algorithm, onchain-offchain data and the code complexity of smart contracts. The paper presents a state-of art evaluation of the current blockchain platforms and cryptos and evaluate them with the energy consumptions. Additionally, it also proposes a framework architecture of a green blockchain application. A green blockchain refers to the implementation of environmentally sustainable algorithms, tools and platforms in blockchain technology, aiming to reduce energy consumption, carbon emissions, and promote eco-friendly operations with energy-efficient algorithms and strategies.
Detection and Tracking Various Objects in Video Images

One of the most fundamental tasks and goals of computer vision is the tracking of moving objects in video images. As one might expect of such a common goal there are multiple computer vision techniques that allow for the tracking of moving objects in computer vision, however each has its own positives and consequences. This paper will explore several previously used techniques in motion tracking, as well as proposing and researching its own, showing the results of each in turn, and the positives and negatives of each method.
Classification of Acute Lymphoblastic Leukemia Using MobileNet and EfficientNetB3

Computer vision and deep learning are widely used in health care. Acute Lymphoblastic Leukaemia (ALL) is a severe form of malignancy that affects the body's white blood cells. An early and precise diagnosis of ALL is critical for successful therapy and improved patient outcomes. In this project, the effectiveness of deep learning techniques including MobileNet and EfficientNetB3 are explored in conjunction with data augmentation for the classification of Acute Lymphoblastic Leukemia (ALL) using C-NMC Leukemia Classification Dataset. The approach of this project involves generating augmented data using various techniques such as flipping, rotating, and zooming to remove data imbalance in a dataset. After that split the data into training and testing data and then fine-tune the MobileNet and EfficientNetB3 models on the trained data, and evaluate the performance of both models using testing data based on recall, accuracy, F1score, precision, and Area Under Curve.
Hybrid Reduced-Reference Video Quality Assessment of Streaming Services Over Reliable Transport

As multimedia programs are increasingly being consumed using OTT services, quality monitoring has become an important issue. Furthermore, there is a trade-off between video quality and bandwidth consumption in OTT streaming services. In such streaming services, it is essential to provide satisfactory QoE without over-using network resources. In this paper, we propose a hybrid reduced-reference video quality metric for streaming services. The hybrid metric uses some codec-parameters along with decoded video signals. The proposed method was tested with UHD video sequences and also independently tested using new test datasets. The proposed method was also evaluated for long video signals with adaptive streaming services. The proposed method showed good performance compared to traditional full reference video quality methods.
A Survey on Shared Perceptions, Help Seeking Patterns and Awareness of Cyber Bullying Among Bangladeshi Citizens

Cyber Bullying is one of the major social problems now-a-days which is called “Digital Crime”. Prevention of this problem has become the need of the hour. The main goal of this study is to find out the condition of Cyber Bullying cases among different genders, what do they actually do when they face it and what are their thinking against this major crime. The data set is collected from different professionals such as students, teachers, housewives, doctors etc via google form. The data set was dividend into 897 rows with 11 columns. The libraries that were used to analyze the data set are Pandas, Matplotlib, Seaborn etc. The data set has both responses of victim and non-victim people. The summary of this study says that women face cyber bullying more than men and the major opinion of the victims is, there are no efficient laws against this issue. People used to ask for help from their family and friends rather than police or institutional authorities. The major approaches to combat this cyber crime are to provide personal counseling and assistance regarding cyber bullying as well as open training on how to use personal information wisely on the internet.
Interpretable Machine Learning for Undeclared Work Prediction

Machine learning models have vastly proved their contribution to decision-making in many aspects of daily life. Yet, their acceptance is confined, especially in public authorities, due to the lack of trust and reliability in their results, often credited to the shortage of “black box” models' explainability and interpretability. The present study illustrates an application of interpretable machine learning to predict undeclared work and other labour law infringements, using real-life data of past inspections and employment declarations obtained from a Labour Inspectorate. Undeclared work is a severe socioeconomic issue causing significant losses in taxes and social security contributions and undermining competitiveness and people and state welfare, thus rendering its detection and tackling a critical goal for the enforcement authorities in charge. We employ Lazy Associative Classification and encompass data sampling methods to deal with class overlapping and imbalance problems, achieving increased prediction performance, tripling the inspection yields with regard to undeclared work detection, and, in parallel, transparency, offering precise explanations for each prediction, as well as general insights in the prevalence and feature patterns of undeclared work. This research also discusses the multiple benefits of embodying this machine learning approach in an inspection recommendation system for responsible enforcement authorities, with the most significant being simplicity, flexibility, broadened adaptability to the users' diverse and ever-changing needs, and enhanced acceptance and confidence of the users in the system inspection suggestions.
Optimization of University Operations By The Means Of Fuzzy Cognitive Maps

The digital transformation of HEIs after the boost it received in the Pandemic period due to the imposing problems that had to be solved, now offers a variety of new possibilities for the improvement of the existing methods of online teaching but for the overall operation of the Institutions as well. The nature of software tools, with their logistic accuracy and accounting capabilities, along with their flexibility and their continuous technological improvement offers options for the monitoring of various indexes of success, in both quantitative and qualitative metrics. Such metrics could involve both objective and subjective indexes. Examples of objective indexes could be the volume of participation; the subsequent dropout rate; the extent of students' involvement; the various levels of success in their texts. Subjective measures could include Teachers', students, or administrative staff's satisfaction. These could be evaluated in various levels of abstraction, starting from general, on university level and being successively detailed to individual Schools, Departments, Classes, all the way to lessons and classes, even specific tests or parts of the teaching material. The automated nature of digital operation makes it feasible to perform continuous monitoring of the trends of such satisfaction indexes, by the use of online questionnaires, offering valuable insight to all parties involved. In the administrative field and day-to-day operations, such measures could include the number of transactions; time length of operations. These measurements taken into forms of curves and time series can then be cross-correlated with various factors and useful conclusions can be drawn. This data can further be elaborated with various advanced tools, such as cognitive tree maps, fuzzy networks, or/and genetic algorithms, to exploit and define the way that the behavior of a complex system, such as a HEI, is affected by the various conditions changing in time. In the proposal, the layout of such a tool will be outlined, with the various factors and the formation of an optimization system that can define optimal values for these factors. The optimized values can then be treated as benchmarks by the HEIs' policy makers.