Nature of Computation and Communication

Online learning has gained significant popularity, but maintaining learner focus remains a challenge, especially in financial enterprise training systems. The need for training has increased with banking and finance digitalization trends, yet high learning curves and prolonged sessions often lead to distractions. This research introduces an online learning tool that monitors and quantifies learner attention in real-time. Using the MobileNet Convolutional Neural Network, we detect seven core emotions, which, combined with attention scores, form a Concentration Index (CI). Learners are then categorized as “Highly-engaged,” “Normally Engaged,” or “Disengaged.” With 70% accuracy on training and 65% on testing, our engagement metrics provide actionable insights for educators and administrators, enhancing virtual learning and aiding in analytical problem-solving strategies.

This paper represents verification algorithms and register transfer level (RTL) specification as algebraic aspects proposed to validate the results of RTL synthesis. Major properties of this approach, the conception of an algebraic semantics-based model (ASM), to be interpreted as a Chu space, is viewed as an algebraic semantics foundation for the RTL formalization and the conception of algebraic semantics-based specification automata (\({{\text{ASA}}}_{{\text{SPEC}}}\)) are given for formal correctness of the results of RTL synthesis. Approaching formal verification is focused on functional equivalence examining to define if the algebraic RTL automata (\({{\text{ASA}}}_{{\text{RTL}}}\)) are equivalent to \({{\text{ASA}}}_{{\text{SPEC}}}\). To put it another way, the comparison is determined as an assessing that examines the synthesis algorithm is produced an effective RTL specification.

English is the most common language globally, and it is increasingly important. English has been compiled in most online documents, information, and contents. However, with a considerable vocabulary, learning English is difficult for many people to remember. Therefore, many modern technologies have been proposed to support English learning, such as English learning technology through word-matching games to help children become excited and easily approach English from an early age. In addition, translation tools can help users look up vocabularies, antonyms, synonyms, and examples. This study presents a method to support learning English via object detection in videos, images, or even live-stream videos in real-time using deep learning architectures such as You Look Only Once (YOLO) - one of the finest families of object detection models with state-of-the-art performances. The method to obtain an mAP is 55.6 with 17GFlops. The results are vocabulary, meaning, and making sentences with that. Our method has good accuracy in data of 2786 images belonging to 59 classes.

This paper focuses on addressing the challenge of maintaining information coherence and robustness within a multi-agent system (MAS) that aggregates information from distributed sources, some of which may be defective intentionally or unintentionally. We propose a self-organizational approach in this context, emphasizing a systemic perspective that considers structural coupling across two levels: direct information gathering and communication. Specifically, we integrate a trust mechanism with local behavioral rules and selective environmental pressures to facilitate the emergence of two co-evolving organizations: one at the social level and the other at the spatial level. The social organization mirrors the trust relationships developed among the agents, while the spatial organization represents the deployment of agents in the environment to encourage exploration. The local behavioral rules encompass three categories: deployment rules, communication rules, and retro-action rules governing communication and deployment. We conduct simulations to experiment with the combination of these behavioral rules, observing the emergence of organizational structures and roles within the system.

The term Smart City represents a strategic concept for a city or region that involves the use of modern technologies to influence the quality of life in the city. At the technological level, a wide range of IoT devices are used, which are interconnected through modern low-latency networks to enable the creation of intelligent applications with added value for their users. However, this relatively simple and noble idea represents a wide range of technologies and approaches, making the idea of ensuring Cyber Security in Smart Cities difficult. When implementing any technology in an organization, the processes, assets, and people that bring the technology to life, are crucial. The aim of this paper is to analyze the key capabilities, frameworks and standards that would facilitate and support the possibility of developing Smart Cities. The first part of the article introduces the issue of Cyber Security and Smart Cities. Subsequently, the key approaches for ensuring security in creating Smart Cities are analyzed. The final part presents the BPMN-SC data model based on business process model notation and key security standards while incorporating the specifics of Smart Cities.

In recent years, technological advances in various fields of human activity have enabled the development of smart city applications that can help improve life in modern cities. In order to validate that the functional requirements of the applications are met and, above all, to ensure security and resilience against vulnerabilities and constantly evolving cyber threats, it is imperative that these applications are adequately tested before being put into operation. The aim of this paper is to present an analysis of the use of fuzz testing to test the stability, correctness and security of applications and information systems that are applicable in the smart city domain. The paper presents an analysis of the possibilities of using different types of fuzz testing and maps the different fuzz testing tools that are applicable in implementation projects in different areas of smart cities. Furthermore, a testing method for the use of fuzz testing is proposed and presented. This method is then validated using a set of proposed tests and outputs for a selected project.

In recent years, the concept and implementation of smart cities has become an important topic at the level of countries, regions and cities. However, it is currently not clearly defined what all areas are part of a smart city, or what functional domains a smart city addresses. The aim of this paper is to analyse the current state of smart city concepts in the world and then to identify the functional domains that define the areas belonging to a smart city. This analysis represents an input to propose a further model of functional domains for Central Europe. The relevance of the model is verified by comparing the identified functional domains with existing regional and national smart city strategies in the Czech Republic.

The application of Artificial Intelligence (AI) and ontologies to the COVID-19 pandemic has been an active area of research and development. AI techniques such as machine learning, computer vision, and natural language processing have been used to analyze vast amounts of data generated by the pandemic, such as medical records, scientific literature, and social media posts. Ontologies, on the other hand, provide a structured representation of knowledge, which can be used to standardize data and facilitate data integration, enabling more efficient and effective data analysis.

This paper studies on linguistic topological sapces which are generate from Hedge algebra. We also indicate homotopy classes of homotopic functions on this spaces as well as its equivalence relations.

In this paper, we introduce two algorithms for reasoning with words on fuzzy dynamic system. The systems that use linguistic variables which are variables whose values may be expressed in terms of a specific natural or artificial language, for example \(\mathbb {L}=\) {very less true; less true; true; more true; very true; very very true ...}. In language of hedge algebra (\(\mathbb{H}\mathbb{A}\)), \(\mathbb {L}\) set which is generated from \(\mathbb{H}\mathbb{A}\) is the POSET (partial order set). The algorithms are Static reasoning and Dynamic reasoning. The former traverses the branch of the fuzzy graph whereas the later transform according to the equation of state and create a space of states of the system. Algorithms performed on linguistic variables and applied labeling techniques. And finally, the application of the algorithm on the mobile network model is also investigated.

This research employs the design of experimental (DoE) to examine how various parameters impact the economic feasibility and overall satisfaction of enterprises operating within eco-industrial parks (EIPs). A full factorial design is constructed, using economic feasibility and overall satisfaction as response variables, and experimental data is generated by simulating diverse scenarios. Each iteration of the experiment utilizes a single-leader multi-follower (SLMF) game optimization model, focusing on designing water exchange networks within EIPs. The investigation encompasses several parameters in a case study involving ten follower enterprises aiming to minimize their annual operational costs. Concurrently, the EIP authority assumes the leader role with the objective of reducing the collective freshwater consumption of the EIP. Furthermore, this study employs binary logistic and multi-linear regressions to establish causal relationships. These relationships link input parameters with economic feasibility and overall satisfaction of operating businesses within EIPs. Ultimately, the reliability of the DoE methodology is showcased, offering valuable insights into enterprise parameters, EIP design, economic feasibility, and overall satisfaction.

Stock market prediction holds significant importance in the world of finance, captivating the attention of both investors and financial researchers. The integration of artificial intelligence and advancements in computational power has led to substantial improvements in predicting stock prices, surpassing the effectiveness of traditional programmed prediction methods. In this paper, we explore three distinct and innovative methods for stock price prediction: Long Short-Term Memory (LSTM), LSTM combined with Simple Moving Average (LSTM-SMA), and LSTM combined with Exponential Moving Average (LSTM-EMA). Our analysis is conducted using a comprehensive historical dataset of Apple’s stock prices, and the performance of each model is rigorously evaluated using critical metrics, including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2 score. Additionally, the training time for each model is taken into account. The results show that all three models LSTM, LSTM-SMA, and LSTM-EMA give good prediction results for Apple’s stock price, in which the LSTM model gives the best prediction results for the 21-day cluster. However, in terms of computational time, the LSTM-SMA model and LSTM-EMA model are more efficient than the LSTM model. These findings highlight the potential of integrating advanced techniques to achieve more accurate and efficient stock price predictions.