Volume 15 - 2023
Foundry Virtual Table Top as a Foundation for Digitizing Interpersonal Activities and the Development of a Metaverse
This text provides an overview of the Foundry Virtual Table Top system as the foundation for digital role-playing games. Considering that role-playing games have only recently become a widespread hobby, specific data and scientific papers about them are still relatively limited. The goal of this text is to fill this gap by exploring relevant literature in the fields of gamification, education, mental health, human-computer interaction, virtual table top programs, metaverse theory, artificial intelligence and blockchain. We hope that it can serve as a basis for further improvement of the analyzed solutions, primarily in the field of digital social games.
Analysis, Selection, and Development of Open Source ERP Solution for Integrated Business Process Management in a Food Company
This research paper explores the application of open-source ERP (Enterprise Resource Planning) systems in the context of small and medium-sized enterprises (SMEs), specifically focusing on the food industry. The reasons why SMEs increasingly opt for open-source ERP systems are analyzed, highlighting the low implementation costs and adaptability of these systems to the specific needs of businesses.
Through a case study of a micro-enterprise in producing alcoholic beverages and cold storage management, the paper thoroughly examines two potential open-source ERP solutions: ODOO and ERPNext. We have conducted a deep analysis and explored the advantages and challenges of each system.
After careful analysis, we have decided to implement the ODOO ERP system, recognizing its greater flexibility and support from an active user community and its competitive pricing advantage. The research implications emphasize the necessity of a thorough analysis of a company's needs and the correct implementation of an ERP system to achieve efficiency, control, and competitive advantage. The ODOO ERP system is also a cost-effective option that provides all the necessary functionalities for successful business operations.
This paper underscores that choosing an ERP system for micro-enterprises is a technical decision and a strategic step toward improving business and gaining a competitive edge. Thorough analysis and understanding of the company's specificities are crucial for long-term success in a dynamic business environment.
Challenges of Implementing Internet of Things in Biological Systems
The topic of this paper is the fundamentals of the Internet of Things architecture in biological systems, including architecture layers, sensors, wireless communication, and the cloud as a central data processing system. We describe a proposal for developing a plant hormone monitoring system based on the Internet of Things. The potential for continuous and remote monitoring of plant conditions opens up through the combination of appropriate technologies, protocols for wireless connection, and sensors for measuring hormone levels. Such a system should enable obtaining key information about the hormonal status of plants and timely response to plant needs.
Retrieval Augmented Large Language Models
In the previous period, there has been a great development of systems that use large language models. These systems have shown great potential in various scenarios, but have also demonstrated significant shortcomings, such as hallucinations. This paper will present a solution that uses an external database that is supposed to im- prove the performance of language models and solve existing problems.
Application of IOT technologies in dentistry
The Internet of Things (IoT) is a network of devices (called "things"), which contain sensors, software and other technologies that are connected to internet network for data exchange and interaction with other devices I to people (referred to as "users"). The term "things" includes multitudes of possible devices, such as smart home sensors and lighting, devices for health status monitoring, security system components and many others, where all devices are connected to the Internet. From smart city to
smart light, Internet of Things technology has become a part of everyday life and today it is no longer associated only with the field of entertainment and leisure, but is found application in many industries, primarily in the field of medicine and dentistry. Namely, in recent years, a number of solutions for improvement have been developed diagnostics, improving options for monitoring and patient care in these fields so that today new scientific fields are also defined: Internet of Things in Medicine (IoMT- Internet of Medical Things), as well as the Internet of Things in Dentistry (IoDT- Internet of Dental Things).
This master's thesis explores the applications, benefits and challenges associated with use technologies of the Internet of things in dentistry and accordingly are in the work presented and analyzed in detail different technologies of the Internet of Things which are currently used in this field. The paper presents in detail the hardware and software solutions that used in IoT dental devices, as well as communication methods and protocols used by IoT devices in dentistry. In addition, an analysis of various of machine learning algorithms currently being implemented, as well as opportunities for their further development. The paper also provides a comparative comparison of the mentioned algorithms for machine learning, as well as their analysis accuracy during implementation in practice.
Analysis and Real-Time Implementation of Video Analytics Solutions
The field of video analytics has been rapidly advancing over the past two decades. However, despite all efforts, practical surveillance systems in use today are still not capable of autonomously analyzing complex events within the camera's field of view. This is a significant drawback because video recordings from millions of surveillance cameras worldwide are not being analyzed in real-time and, therefore, cannot aid in accident prevention, crime detection, or counterterrorism efforts. Currently, these recordings, at best, are archived to facilitate forensic analysis after an event.
As the importance of video surveillance systems continues to grow, especially in security applications, video analytics will play a crucial role in the future development of these systems, presenting both challenges and opportunities for technological innovation. The primary challenge lies in developing models and algorithms for analyzing high-frequency scenes.
This work consists of two parts. The first part is theoretical, providing an overview of the concept of real-time video analytics. Technologies used in video analytics are analyzed, and the structure of these systems is explained. Applications relying on these techniques, as well as algorithms used in artificial intelligence, are described.
The second part of the work is practical, focusing on the implementation of a real video analytics system that includes recording and processing video signals in real-time using Dahua equipment and artificial intelligence. This system simulates the operation of video analytics in real working conditions and demonstrates how artificial intelligence is applied in such systems. The results of measurements performed are graphically presented and extensively analyzed in the paper. Furthermore, the paper explores possibilities for further practical implementation of this solution.
Multimodal prediction for tabular data with text fields based on transformers
The topic of this paper is the use of machine and deep learning methods and models in solving the prediction task on structured tabular data that includes text fields. The purpose of this paper is to improve the results of methods that have proven to be the best in working with tabular data (ensembles of decision / regression trees), by including methods that have proven to be the best in working with sequences and text (transformer models of deep learning based on the attention mechanism). Also, several classical machine learning and text processing methods will be used for referencing and comparison.
Financial derivatives pricing using quantum neural networks: state-of-the-art
This paper presents the state-of-the-art in financial derivatives pricing using quantum artificial neural networks. Through the presentation of the available literature, it was shown that this type of application is only in its infancy and that there are still many open questions. As an illustration, the use of quantum artificial neural network to solve the option pricing problem, with given values of underlying asset and strike price, is shown. Furthermore, it is shown that Greeks, such as delta and gamma, which are important measures in risk management, can be computed analytically with this neural network.
Building a Gigabit Passive Optical Network (GPON) – A Case Study
The subject of this paper is the presentation of the entire process of planning, construction, implementation, and maintenance of a current Gigabit Passive Optical Network (GPON), and everything that it entails in a case study of a moderately developed municipality of Šamac, primarily aimed at improving the quality and stability of the service provided by this network to the user and simplifying the maintenance of the constructed network, as well as analyzing the cost-effectiveness of introducing this optical network (GPON).
Analysis of Lightweight Cryptography Methods
The latest reports and Internet statistics predict that the number of devices connected to IP networks will be more than three times larger than the global population by the end of 2022. The increase in the number of devices is closely related to advancements in industry and the development of mobile communication systems, as well as the enormous increase of mobile users, followed by the development of 5G networks and Internet of Things (IoT). However, with technological advancements in IoT, devices that do not have enough resources for execution of complex algorythms but still require certain level of security, become more common. Lightweight Cryptography is a technology that aims to provide secure communication to such devices, taking into account their limited power, processing, and memory resources. Lightweight cryptography by definition is a cryptographic algorithm or protocol designed for use in restricted environments, which extends the use of cryptography to devices with limited resources (including RFID tags, sensors, contactless smart cards, medical, and similar devices). International standardization and guidelines for further development in this field are currently underway.
The aim of this master’s thesis is twofold. On the one hand, the thesis presents theoretical concepts, algorithms, and protocols related to the implementation of security protocols in the Internet of Things. An overview of proposed standards is provided, including ISO/IEC JTC 1/SC 27 group and ISO/IEC 29192 standard, the latest standardization project. The hardware and software characteristics of systems that condition the implementation of lightweight cryptography, such as chip architecture, RAM size, algorithm implementation size, and energy consumption, are also discussed. On the other hand, the thesis presents several different lightweight cryptography methods, which include different approaches and have attracted the most attention from the professional community. The operation and application of each method are explained. Then, the required resources and performance of the methods are analyzed, using different microcontrollers that simulate the operation of microprocessors in the Internet of Things technology. The results are presented and compared in tables. Finally, the drawbacks and potential attacks on these methods are discussed, as well as the future application and further development of lightweight cryptography within the Internet of Things technology.