Reviewerly/exHarmony huggingface dataset:
The Working Principle of Artificial Intelligence in Video Games

In this paper, we delve into the concept of artificial intelligence (AI) in video games and its working principles. Our research aims to explore the various ways in which AI is implemented in games to create non-playable characters (NPCs) that simulate intelligence and entertain the player. We also investigate the different approaches used by game developers in creating AI and how they strive to make NPCs adapt to different situations and change their behavior accordingly. Furthermore, we analyze the limitations of game AI in terms of creativity and problem-solving abilities, which are still far from achieving human-like intelligence. However, we argue that despite these limitations, AI in games is an essential aspect of game development as it adds depth, immersion, and replayability to a game. To achieve our research objectives, we conducted a comprehensive review of existing literature on the topic, including academic papers, industry reports, and online sources. We also examined various case studies of popular games that employ AI to enhance the gaming experience.
State Tracking and Evaluation Technology for Big Data Objects Based on a New Digital Watermarking Algorithm

Currently, enterprises generally face the following problems in managing big data: Multiple versions or replicas of the same data object cross-system interfaces, delayed data destruction with invalid data not being cleared in time, difficulty in tracking data with no support of data traceability technology, weak data security and insufficient risk analysis capabilities. To address these problems, this article proposes a new digital watermarking algorithm that is suitable for embedding digital watermarks in big data objects. Combined with flow data analysis technology, it can track and monitor the processes of generating, storing, transmitting, processing, and publishing big data objects, mark the version status of data objects, draw the distribution map of enterprise data status, we innovatively apply this recognition technology to calculate key indicators such as the health value of big data object references and the number of invalid data object references, and analyze data objects using predefined weaknesses and risk rules to discover data security issues and provide danger warnings for enterprises.
Blockchain-Based Payment Channel Network: Challenges and Recent Advances

Blockchains, introduced in 2008, have garnered significant attention from both academia and industry. Cryptocurrencies, such as Bitcoin, which rely on blockchains, have great commercial value. However, the transaction processing speed of blockchains falls short of the traditional financial system. To address this, payment channels have emerged as a promising research direction for improving blockchain system performance by reducing transaction acknowledgment delay and enhancing transaction throughput. Payment channel networks facilitate a large number of transactions to be executed off-chain, preserving asset security, reducing costs, and promoting blockchain scalability. Among payment channel networks, Lightning Network is deployed on Bitcoin and has demonstrated efficient performance and high economic benefits in actual operations, attracting significant attention and research. This paper systematically analyzes the research outcomes of payment channel networks, focusing on the potential attacks and development direction of Lightning Network. Existing research is refined into security attacks, privacy vulnerabilities, and challenging system assumptions while exploring and comparing recent solutions in Lightning Network. Finally, this paper emphasizes the potential of payment channel networks, identifies the challenges that need to be addressed, and advances the study of blockchain payment channel networks.
On The Irrelevance of Machine Learning Algorithms and the Importance of Relativity

Information explosion has brought us a wide range of data formats and machine learning keeps in constant evolution to develop mechanisms to extract knowledge from them. Modern models in the Deep Learning space have proven to be very successful in multiple applications, yet in the tabular space they fail to provide consistent competitive performance. However, in this work we claim model selection can become irrelevant as the key tends to lie in data processing. In this paper we introduce the concept of relativity in feature engineering, a powerful methodology to boost any classifier performance and we provide over 30 different configurations of models and feature engineering designs to prove we can bias any result to help an arbitrary model score best. Our results attribute 600% more value to feature engineering than model selection. In order to validate the effectiveness of our approach, we submitted our work to a live machine learning competition with outstanding results regardless of our model of choice.
Ontological Analysis of Cognitive Information Technologies Subject Area

The most important problem of automated ontology building is intellectualisation, and the related data integration and qualitative information retrieval. In the light of the issues of data science and advanced analytics, the task of automated ontology formation is quite relevant. A common criterion for the quality of an ontology is based on an assessment of the convenience and efficiency of working with it and the connection of cognitive processes with semantic ones for its construction and updating. The main difference of the analysis, the results of which are presented in this paper, is the cognitive information technologies of the subject area, cognitive and semantic analysis based on the theory of categories, mathematical logic and universal algebra, construction of an ontological dictionary, ontological constructions, which was carried out for the first time. The information interaction of the intellectual environment of the object with the subject is determined. For the formation of the concept in the intellectual environment due to the connected information space, the possibility and justification of its use for normalising the concept and its features within the framework of the problematic is determined.
Machine Learning Expert System for Recognizing Emotions in text “Umai Cloud Services”

In this research, the focus is on recognizing 28 emotions in a text using the Roberta model, which is a state-of-the-art pre-trained language model that has achieved outstanding results in various natural language processing tasks. The study explores the effectiveness of the Roberta model for emotion recognition and compares it with other approaches, such as CNNs and RNNs. In addition, the research investigates the problem of toxicity detection, which involves identifying and flagging potentially harmful or offensive language in a given text. Various techniques for toxicity detection are considered, including supervised learning and deep learning methods. The study also explores the process of extracting key phrases and words from a text using machine learning algorithms. This involves applying NLP techniques such as part-of-speech tagging, named entity recognition, and text summarization. All of these methods are implemented and tested using a cloud service provided by Umai Cloud Services, a Kazakh startup company that offers machine learning and artificial intelligence solutions. The results of the study demonstrate the effectiveness of the Roberta model for emotion recognition and show promising results for toxicity detection and text summarization.
Two-Layered Logics for Paraconsistent Probabilities

We discuss two-layered logics formalising reasoning with paraconsistent probabilities that combine the Łukasiewicz [0, 1]-valued logic with Baaz $$\triangle $$ operator and the Belnap–Dunn logic. The first logic (introduced in [7]) formalises a ‘two-valued’ approach where each event $$\phi $$ has independent positive and negative measures that stand for, respectively, the likelihoods of $$\phi $$ and $$\lnot \phi $$ . The second logic that we introduce here corresponds to ‘four-valued’ probabilities. There, $$\phi $$ is equipped with four measures standing for pure belief, pure disbelief, conflict and uncertainty of an agent in $$\phi $$ . We construct faithful embeddings of and into one another and axiomatise using a Hilbert-style calculus. We also establish the decidability of both logics and provide complexity evaluations for them using an expansion of the constraint tableaux calculus for .
Structural Completeness and Superintuitionistic Inquisitive Logics

In this paper, the notion of structural completeness is explored in the context of a generalized class of superintuitionistic logics involving also systems that are not closed under uniform substitution. We just require that each logic must be closed under D-substitutions assigning to atomic formulas only $$\vee $$ -free formulas. For these systems we introduce four different notions of structural completeness and study how they are related. We focus on superintuitionistic inquisitive logics that validate a schema called Split and have the disjunction property. In these logics disjunction can be interpreted in the sense of inquisitive semantics as a question forming operator. It is shown that a logic is structurally complete with respect to D-substitutions if and only if it includes the weakest superintuitionistic inquisitive logic. Various consequences of this result are explored. For example, it is shown that every superintuitionistic inquisitive logic can be characterized by a Kripke model built up from D-substitutions. Additionally, we resolve a conjecture concerning superintuitionistic inquisitive logics due to Miglioli et al..
Learning Context-Free Grammars from Positive Data and Membership Queries

I review some recent results on learning subclasses of the context-free grammars from positive data and membership queries. I motivate the relevant learning algorithms through comparison with a similar and arguably reasonable learning algorithm for regular languages.
An Axiom System for Basic Hybrid Logic with Propositional Quantifiers

We present an axiom system for basic hybrid logic extended with propositional quantifiers (a second-order extension of basic hybrid logic) and prove its (basic and pure) strong completeness with respect to general models.
A Principled Approach to Expectation Maximisation and Latent Dirichlet Allocation Using Jeffrey’s Update Rule

Expectation Maximisation (EM) and Latent Dirichlet Allocation (LDA) are two frequently used inference algorithms, for finding an appropriate mixture of latent variables, and for finding an allocation of topics for a collection of documents. A recent insight in probabilistic learning is that Jeffrey’s update rule gives a decrease of Kullback-Leibler divergence. Its logic is error correction. It is shown that this same rule and divergence decrease logic is at the heart of EM and LDA, ensuring that successive iterations are decreasingly wrong.
Relevant Reasoning and Implicit Beliefs

Combining relevant and classical modal logic is an approach to overcoming the logical omniscience problem and related issues that goes back at least to Levesque’s well known work in the 1980s. The present authors have recently introduced a variant of Levesque’s framework where explicit beliefs concerning conditional propositions can be formalized. However, our framework did not offer a formalization of implicit belief in addition to explicit belief. In this paper we provide such a formalization. Our main technical result is a modular completeness theorem.
Cyber Risk Insurance Framework Considerations

Cyber insurance is a necessity in the context of the digital transformation of society. We live in a world where the chains of usual operation, in general, and those of development and research in informatics must face the associated risks. Prevention and recovery processes are complemented by mitigation processes, which suppose, among others, complex cyber insurances, which involve a priori analyses and evaluations, before concluding the insurance policy and post-factum, after the occurrence of the insured risk. The study seeks to substantiate the need to develop clear procedures, with levels and stages defined as much detailed as possible, formulating and assuming policies, supporting regulations, sectoral strategies to increase the level of maturity of consumers, individuals, institutions, and private legal entities, regarding protection against cyber threats. The study has identified the need for cooperation between insurers (and re-insurers alike) to provide appropriate types of coverage and exclusions, while splitting the risk among them. We also intend to introduce in the article the schematic presentation of a reporting system to the state authority of attacks and losses generated by cyber risks, including those directed against critical infrastructures. This type of reporting can ensure transparency for the insurers about the entities they intend to ensure. Nevertheless, given the sensitive information it entails, the implementation details should be carefully thought out.
Performance Investigation of SVM and Modified SVM Algorithms for Acute Health Diagnosis

Early diagnosis and accurate identification of acute diseases such as heart attacks are crucial for effective control and management. Automated techniques utilizing machine learning tools can retrieve valuable information from clinical and laboratory patient data, helping physicians to maximize accuracy in identifying acute diseases. In this study, we utilized SVM, a widely-used machine learning algorithm, and its modified versions, FFA-SVM and BAT-SVM, to predict heart disease using a dataset with 14 predictive parameters. Results showed that the modified SVM algorithms outperformed the standard SVM algorithm in classification accuracy, demonstrating the effectiveness of feature selection in improving classification performance. These findings significantly impact the development of more accurate and efficient diagnostic tools in the healthcare industry. This research is essential to achieve automated, effective, timely health diagnosis, and improving patient healthcare outcomes.
A Four-Valued Epistemic Logic for Metadata Modelling from Medical Articles on Pain Therapies

In this paper, a four-valued epistemic logic for metadata modelling from medical articles on pain therapies has been proposed. The advantage of using a four-valued epistemic logic is that it allows us to reason with partial, incomplete or even incoherent information. This is especially the case in the information sciences, which have to deal with an extremely varied quantity and quality of data. The application of logic to metadata extraction provides a powerful and rigorous tool capable of assessing the quality of the extracted information; that is, establishing the correctness of the extracted information.
The Role of Digital Leadership in Achieving Organizational Excellence in Jordan’s Banking Sector

The study examined the role of digital leadership in achieving organizational excellence in Jordan’s banking sector. A survey research design was adopted. The population of the study was all employees and customers of banks. The Jordanian Banking sector was purposively selected among other sectors in Jordan. The research drew from a theoretical population with no limits on size. because there is a shortage of available individual data on bank personnel and customers in Jordan. An estimated sample size of 163 was drawn from the research population using the Cochran (1977) method for determining sample size when the population is infinite (i.e., greater than 50,000). The research instrument was a structured questionnaire. The study employed descriptive (frequency counts, percentage and bar-chart) and inferential statistics (Structural Equation Modeling and Pearson Product Moment Correlation Coefficient) for data analysis. The results were presented in tables and discussed according to the research objectives and hypotheses. The study revealed a significant impact of digital leadership on achieving organizational excellence in Jordan’s banking sector. There was a significant relationship between digital leadership and customer satisfaction in Jordan’s banking sector (P < α). Increases customer satisfaction, drives data-based insights, enables future digital growth, increases operational efficiency, encourages an environment of employee excellence, limits human error, increases agility, encourages collaboration & improves communication, allows high-quality user experience and enables software monetization were the extent to which digital leadership has improved the performance of banks in Jordan. The study recommended that banks in Jordan organize more training and workshops for their employees on the effective use of digital devices in improving their job performance.
The Extent of Awareness of Cyber Security Among the Superior and Ordinary Students in the Faculty of Education in Al Ain University

This study aimed to identify the extent of awareness of cyber security among a sample of superior and ordinary students in the faculty of education in Al Ain University. The study sample consisted of (94) male and female students from the faculty of education, with (32) males and (62) females, and based on the student’s type, they were (14) superior students and (80) ordinary students. In order to achieve the study objectives, the researcher used the scale of awareness of cyber security as the study instrument. The results showed that the level of cyber security awareness among the talented and ordinary students in the faculty of education in Al Ain University was high for the overall score of the scale. The results revealed that there are no statistically significant differences in the degree of awareness of cyber security attributed to the variable of gender (male, female), while the results revealed that there are statistically significant differences in the level of awareness of cyber security attributed to the variable of the student’s type (superior, ordinary) in favor of the superior students.
Qualitative Analysis of West African Economic and Monetary Union Decades’ Economic Growth

&lt;p style="text-align: justify;"&gt;The study analyses the long growth rate period contribution to human development in West African Economic and Monetary Union (WAEMU) from 1996 to 2019 introducing time and institutional indicators effects analyses. Time and institutional effects both greatly improve model's diagnostics statistics. Time effects reduce growth variable coefficient and its significance, but not institutional effects. Combined effects inhibit growth contribution to human development. It appears essential to increase the resiliency of growth and the efficiency of government institutions.&lt;/p&gt;
Research on sleep staging method based on multi-scale convolution and self-attention mechanism

Sleep is a very important physiological process for humans, and sleep staging is an important basis for evaluating sleep quality and diagnosing sleep-related diseases. Manual sleep staging is time-consuming and laborious, and the staging results are easily affected by subjective factors. Therefore, the research on automatic sleep staging models has extremely high research value and clinical application value. This paper proposes a sleep staging method based on multi-scale one-dimensional convolutional neural network and self-attention mechanism. The attention mechanism mines the channel correlation and temporal correlation of different channels respectively. This paper selects the sleep data of 153 normal subjects in the public dataset Sleep -EDF for model training and testing. The average staging accuracy rate was 84.5%, and the consistency test value was 0.781, which was better than the consistency test value of 0.75 for the inter-physician score. The experimental results show that the algorithm model in this paper has a high staging effect, can make full use of sleep data, and has important research significance for assisting clinicians in rapid sleep staging.
Attack Analysis on Two-party Signature and Threshold Signature Based on Dilithium

With the rapid development of post-quantum cryptography study, the Crystals-Dilithium digital signature algorithm based on the lattice has received much attention, and many extended studies have appeared on its basis. In this paper, we focus on the two-party signature protocol and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(t,\ n)$</tex> -threshold signature protocol based on Dilithium, and analyze their feasibility and security. After repeated experiments with the official rejection sampling code of the Aigis-sig scheme, it is shown that the two-party signature protocol basically cannot pass rejection sampling verification properly and is not feasible. At the same time, both protocols have a security risk that private key is divulged and internal members forge signatures. Through security analysis and experimental results, we found that there is close to 100% possibility that the internal members can crack the y value and private key information to complete the signature forgery. It will make the security of signatures unable to be guaranteed.
AI and IoT based Intrusion Detection System for Cybersecurity

A network's traffic can be monitored by an Intrusion Detection System (IDS), which then sends out warnings if any malicious behavior is detected. It's a program that checks if a website link leads to malicious content or if there was a policy violation on the network. As a case study for IDS, identifying phishing websites has been performed. One of the most effective tools for hackers to carry out a variety of security attacks is the widespread distribution of fake website Uniform Resource Locators (URLs) via messages, email, etc., with the goal of fooling unaware consumers into providing over sensitive information or worse. Traditional methods for detecting phishing websites, such as employing blacklists, are used to make predictions about phishing URLs. Traditional solutions based on blacklists are not effective at predicting many phishing websites, because so many new ones are created and released on the Internet every day. Three distinct Artificial Intelligence (AI) models-Random Forest (RF), Naive Bayes (NB), and XGBoost-are used to accurately detect dangerous URLs. Both the authentic and the fraudulent URLs were obtained from various sources. The AI model is trained with the retrieved relevant features. Following this, performance indicators are used to assess the quality of the AI models. According to the outcome of the testing data, the XGBoost model achieved a maximum accuracy of 96.98%, which is higher than the accuracy of the remaining models tested. Then the XGBoost model has been finalized and deployed as a website, anyone can immediately recognize the phishing URL.
DDoS Attack Detection in a Real Urban IoT Environment Using Federated Deep Learning

today, alongside the opportunities provided by Internet of Things (IoT), Distributed Denial of Service (DDoS) attacks are one of the most significant attacks that target the overall availability and reliability of the network. Many researches have been devoted to propose new machine learning-based detection mechanisms. However, centralized learning models require the traffic data and learning process to be concentrated on a specific device, which leads to more computational complexity and privacy concerns. Consequently, in this paper, detection and prediction of such attacks is modeled as a distributed cooperative learning scheme, which is conducted based on federated deep learning implemented in a real smart city environment. The results compared with traditional centralized deep learning models indicate high performance and accuracy, while maintaining confidentiality of traffic data. More precisely, in terms of common learning metrics, our proposed model is capable of gaining 0.953 and 0.0369 accuracy and loss rates, respectively.