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People use social media for both good and distasteful purposes. When used with malicious intent, it raises significant concerns as it involves the use of offensive language and hate speech that promote terrorism and other negative behaviors. To create a safe, secure and pleasant environment, these communications must be closely monitored to prevent severe problems, associated risks and other pertinent issues. With the help of AI, specifically Large Language Models (LLM), we can quickly analyze text and speech to determine whether the communications promote the dangers identified here above not to mention other toxic elements. For this research, the LLM used is the DistilRoBERTa model from the Transformers library using Hugging Face. The DistilRoBERTa model was trained on datasets consisting of terrorism-related conversations, offensive-related conversations, and neutral conversations. These datasets were obtained from publicly available sources. The results of the experimentation show that the model achieved 99% accuracy, precision, recall, F1 score, and ROC curve. To improve the robustness of the model, it must be continuously fine-tuned to predict dynamic communication behavior since real conversations are inaccessible due to restrictions. A drag-and-drop interface is used to upload the files and get the categorical output, ensuring seamless and easy interaction.

In an era characterized by the ubiquity of the internet, the proliferation of online services, and the increasing frequency of cyber threats, the detection of look-alike domains has become a critical component of cybersecurity. The current paper presents an approach for the detection of look-alike domains, leveraging the power of open-source intelligence (OSINT) tools. It included gathering and analyzing a wide range of publicly available data sources, including permutations, WHOIS records, IP information, website content, Geo IP, similarity percentage, name server, and mail server records, and building a comprehensive profile of domains under investigation. Through the application of online search engines, patterns and features that distinguish legitimate domains from their deceptive counterparts were established. The analysis demonstrated that OSINT tools provided significant information about the sample domains and successfully detected 1598 registered look-alike domains among 10 sample domains using dnstwist, while OpenSquat identified 103 squatting domains, 960 active phishing websites, and 53 domains with suspicious certificates across five sample websites. The research contributes to the enhancement of cybersecurity practices by providing a cost-effective and scalable solution for identifying look-alike domains, which can serve as precursors to various online threats, including phishing attacks, malware distribution, and fraud.

Modern society is heavily reliant upon the internet. Accordingly, it is vital to ensure that the data transmitted over the internet is safe. Several tools have been created for cybersecurity experts and organizations to test the security levels of organizational networks and websites. However, due to financial constraints, small organizations need to pay closer attention to managing data with limited resources. This study explores the role of automated penetration testing tools in providing small organizations with an effective and affordable data security system. This study employs a case-study approach using multiple data-gathering methods in a charitable organization. More specifically, data was collected using interviews and experiments evaluating penetration testing tools. The results revealed that cost-effective automated penetration testing tools could safeguard small organizations from cybersecurity threats. The penetration testing tools determined that the organization’s website had various vulnerabilities. The Nessus tool discovered no fewer than 37 vulnerabilities on the website application. The ZAP testing tool showed that the website application was critically failing, leading to the accumulation of vulnerabilities. The system had 3 medium-, 12 low-, and 4-informational-risk vulnerabilities. Through the evaluation of open ports, the NMAP tool identified various vulnerabilities. These findings have important implications for small organizations. First, automated penetration testing tools can be easily conducted by small organizations to safeguard their cybersecurity without obtaining costly expert help. Second, it is recommended in the light of the findings that automated penetration testing tools be used in multiple combinations as different tools have unique contributions to cybersecurity.

In response to the growing cyber-attack threat, incident response teams have become a critical component of an organization's cybersecurity strategy. These teams are responsible for detecting, analyzing, and responding to security incidents promptly and effectively. However, detecting code injection attacks can be particularly challenging, as they can be difficult to detect and often go unnoticed until it is too late. Cybersecurity professionals use detection tools to detect and respond to DLL injection attacks that monitor system activity and detect unusual behavior. A large portion of the related literature focuses on the use of commercial DLL injection tools. In contrast, little attention has been paid to the effectiveness of using open-source DLL injection detection tools. Thus, this research project aims to evaluate the effectiveness of three widely used open-source tools, VirusTotal, Sysinternals, and Yara, in detecting DLL injection incidents. This study's findings highlight each tool's strengths and limitations, which in turn enables cybersecurity professionals to make informed decisions when selecting the most suitable tool for DLL injection detection. Furthermore, the study emphasizes the importance of continuous tool development and updates to keep pace with evolving malware techniques and emerging threats. By highlighting the effectiveness of the tools, this research enhances the overall security posture of organizations and individuals, empowering them to mitigate the risks associated with DLL injection attacks proactively. The outcomes of this research project also underscore the significance of leveraging advanced tools to fortify cybersecurity defenses and safeguard critical systems and data.

In this work, two categories of deep learning and conventional machine learning were used to classify malware using a dataset of all possible API call sequences. Specifically, the objective was to determine the best strategy to tackle the ever-rising menace as malware becomes more complex. A new dataset was created employing Cuckoo Sandbox, where API call sequences originating from both benign and malware samples were recorded. The performance of these algorithms was benchmarked and tested using this dataset, which includes SVM, RF, KNN, XGB, GBC, CNN, and RNN. The study established that both deep learning and conventional machine learning algorithms provided high accuracy above 90%. Specifically, the recurrent neural networks (RNNs) demonstrated high accuracy rates ranging from 95% to 99%. These results are highly indicative of deep learning, especially RNN, as a promising approach to improving the effectiveness of malware detection. The data obtained from dynamic analysis, when integrated into a database, serves as a more reliable source for training and testing of such models, and can improve the model’s ability to identify new threats posed by malware. Thus, this work is salient in enhancing the development of new approaches to fight malware that constantly evolve in the modern world.

In smart city infrastructure, IP cameras play a pivotal role in crime prevention and detection. However, not much research has been conducted on IP cameras from a cybersecurity and forensics perspective. In this study, we investigate vulnerability assessment and forensic artifacts for Hanwha and Mobotix IP cameras, which are widely used in Saudi Arabia. Saudi Arabia is using IP cameras which are essential for its smart cities. In this paper, we examine IP cameras in two directions. The first is to assess the vulnerability of IP cameras through various attack scenarios such as denial of service (DoS), brute force, and unauthorized access, and we suggest countermeasures. The second shows how analysis for IP cameras can be used to investigate logs for cyberattacks. Through this study, we expect to contribute to research on cyber-attack and forensic perspectives on IP cameras to be used in smart cities.

La pervasività dei sistemi di intelligenza artificiale e il relativo impatto in termini di protezione dei dati personali impongono una profonda riflessione sulle recenti scelte normative del legislatore europeo e, in prospettiva, di quello nazionale. In questo senso, il contributo delinea, in primo luogo, l’attuale quadro legislativo europeo in materia di dati e IA, sullo scenario della complessa dimensione geopolitica in materia. Muovendo da queste premesse, il lavoro traccia un’analisi in chiave sistemica del Regolamento europeo sull’intelligenza artificiale (c.d. “AI Act”), evidenziando luci ed ombre del complesso rapporto con il Reg. (UE) n. 2016/679 (“GDPR”) e identificando analogie e differenze tra i due atti normativi. Il contributo analizza, quindi, in ottica comparata, il sistema sanzionatorio e l’apparato di governance definiti dall’AI Act, focalizzandosi sulla delicata scelta di un’autorità di controllo nazionale per regolare l’intelligenza artificiale in Italia. Infine, gli autori formulano alcune riflessioni conclusive sui principali nodi tematici emersi nel corso della trattazione e propongono una prospettiva de iure condendo alla luce dell’analisi svolta.

The paper analyses the efficacy of digital platforms for deliberative and participatory democracy in enhancing democracy. After examining the concepts of deliberative and participatory democracy (§IA), particularly their meaning from a legal standpoint and their relation to representative and direct democracy (§IB), the study introduces the broader concept of ‘digital democratic innovations’ (§IC) and adopts a classification framework (§II). Then, the current legal framework of e-participation in Italy, Germany, France, and the EU is analysed (§III). This comparative analysis sets the stage for a detailed examination of five digital platforms operating at both local and national levels, which serve as case studies. Through a systematic evaluation of the key features of these platforms, the paper classifies those platforms according to the classification framework (§IVA-E). It also assesses the quality of citizen participation facilitated by each digital democratic innovation exploring two indicators: the meaningfulness of participation and the extent of digital deliberation (§IVF). In drawing conclusions, the paper also suggests potential avenues for further legal research into digitised civic participation (§V).

Il contributo analizza la normativa italiana sul diritto di accesso civico generalizzato (c.d. FOIA) nel quadro di alcuni modelli ed esperienze maturate a livello europeo ed extraeuropeo. Oltre alla disciplina sulla libertà di informazione prevista dall’ordinamento giuridico italiano, lo scritto prende in esame la normativa sulla “libertà di informazione” adottata in Svezia, Francia, Spagna e infine negli Stati Uniti d’America. Dalla matrice comune delle normative prese in considerazione risaltano i tratti differenziali, legati al core di ogni ordinamento giuridico, alla sua storia costituzionale e al naturale processo evolutivo di valori e criteri direttivi.

L’articolo analizza le sentenze della Corte di giustizia dell’Unione europea del 7 dicembre 2023 (cause riunite C-26/22 e C-64/22 e C-634/21). Le decisioni offrono infatti l’opportunità di riflettere su due temi principali: l’ampiezza del sindacato giurisdizionale esercitato su una decisione di reclamo adottata da un’autorità di controllo e la liceità della raccolta e del trattamento, anche automatizzato, dei dati personali. Le forme di tutela connesse al trattamento dei dati personali, sviluppandosi in più livelli e con modalità differenti, delineano nel loro complesso un sistema volto a garantire la massima protezione dei dati, che, anche in un’ottica di bilanciamento degli interessi, appare – almeno alla luce delle sentenze in esame – prevalere sugli interessi commerciali connessi all’utilizzo degli stessi.

L’articolo svolge un’analisi critica della regolamentazione degli influencer quali fornitori di servizi di media audiovisivi di cui al Testo unico dei servizi di media audiovisivi (TUSMA), compiuta dall’Autorità per le garanzie nelle comunicazioni (Agcom) nelle Linee-guida del 2024. Per poter svolgere una siffatta analisi, nell’articolo ci si muove in una prospettiva di diritto costituzionale: accogliendosi, da un lato, la chiave di lettura teorica del costituzionalismo digitale; guardandosi, dall’altro, all’attività degli influencer nell’ottica dell’attività di informazione e della libertà di espressione di cui all’art. 21 Cost.

This work presents an improved parallel biobjective hybrid real-coded genetic algorithm (MORCGA-MOPSO-II). The approach is based on the combined use of the parallel Multi-Objective Real-Coded Genetic Algorithm (MORCGA) and the Multi-Objective Particle Swarm Optimization (MOPSO). At the same time, clustering-based selection techniques are used to form subpopulations of parent individuals. Using well-known clustering algorithms (e.g., k-Means, hierarchical clustering, c-means, and DBSCAN) in combination with the proposed clustering-based mutation (the CL-mutation) directed toward the obtained cluster centers allows for improving the quality of the Pareto fronts’ approximations. The results of the MORCGA-MOPSO-II were compared with other well-known multi-objective evolutionary algorithms (e.g., SPEA2, NSGA-II, FCGA, MOSPO, etc.). Moreover, the MORCGA-MOPSO-II was integrated with the previously developed agent-based model of a goods exchange through the objective functions. As a result, the Pareto fronts have been obtained for the agent-based model of a goods exchange in different configurations of the initial distribution of agents.

In the present work, the maximal generalized network flow, often referred to as the profit-loss flow, is examined, considering the motivation in the decision-making systems built on this flow. The general description of the features of motivation as a psychological process actively involved in decision-making systems through the generalized network flow is given. A method is proposed to embed motivation in a generalized network flow through the motivation coefficients on different network sections (arcs). It is shown that the proposed method offers more possibilities than the classical network flow. It is proven that the initial resource in the source does not match the final resource in the consumer. This has been suggested to be due to the influence of motivation. The theoretical and experimental results convincingly prove the possibilities of considering motivation in decision-making systems when managing the transportation of resources in an arbitrary transport network.

Knowledge-based recommender systems have always had their privileged place among all Decision Support Systems (DSS), given their advantage on several points over other techniques. Our paper presents a framework implementing a hybrid form of Rule-Based Reasoning and Case-Based Reasoning (RBR-CBR), to address the rarely discussed domain of educational planning. The system has been tested and presented outstanding results with a high accuracy, which will benefit educational planners’ decision support. We have also developed a dedicated application for this project to visualize the results obtained.

Radio Frequency IDentification (RFID) Network Planning (RNP) is the problem of placing RFID readers in a working area where a tag is interrogated by at least one reader and at the same time satisfies some constraints such as minimum number of placed readers, minimal interference, and minimal outside coverage. The RNP optimization has been proven NP-hard; thus, natural-inspired approaches are often used to find the optimal solution. The paper proposes an energy-efficient RNP approach in which the positions of placed readers are optimized by a Hopfield network, and the energy efficiency is achieved through a placement area restriction technique. A mechanism of redundant reader elimination is also added to minimize the number of placed readers. Simulation results show that the Hopfield network-based energy-efficient RNP approach achieves the maximum tag coverage and energy efficiency by reducing interference, outside coverage, and the number of placed readers.

An efficient face recognition system is essential for security and authentication-based applications. However, real-time face recognition systems have a few significant concerns, including face pose orientations. In the last decade, numerous solutions have been introduced to estimate distinct face pose orientations. Nevertheless, these solutions must be adequately addressed for the three main face pose orientations: Yaw, Pitch, and Roll. This paper proposed a novel deep transfer learning-based multitasking approach for solving three integrated tasks, i.e., face detection, landmarks detection, and face pose estimation. The face pose variation vulnerability has been intensely investigated here underlying three modules: image preprocessing, feature extraction module through deep transfer learning, and regression module for estimating the face poses. The experiments are performed on the well-known benchmark dataset Annotated Faces in the Wild (AFW). We evaluate the outcomes of the experiments to reveal that our proposed approach is superior to other recently available solutions.

The evolution of decentralized storage, propelled by blockchain advancements, has revolutionized data management. This paper focuses on content security in the InterPlanetary File System (IPFS), a leading decentralized storage network lacking inherent content encryption. To address this vulnerability, we propose a novel hybrid cryptographic algorithm, merging AES 128-bit encryption with Elliptic Curve Cryptography (ECC) key generation. The algorithm includes ECC key pairs, random IV generation, and content/AES key encryption using ECC public keys. Benchmarking against standard AES 256-bit methods shows a significant 20% acceleration in encryption speed and a 16% increase in decryption efficiency, affirming practicality for enhancing IPFS content security. This research contributes to securing decentralized storage and provides a performance-driven solution. The promising results highlight the viability of the proposed approach, advancing understanding and mitigating security concerns in IPFS and similar systems.

Despite the wide acceptance of blended learning in Higher Education Institutions (HEIs) worldwide, the issue of monitoring its implementation has been little addressed in the literature. The paper presents the results of the first stage of the study for the development and implementation of tools for monitoring the degree of use of blended learning courses within the learning process in HEIs. The tool introduced here extracts data from the database of the e-learning environment and visualizes the results of the data analysis in dashboards that provide valuable insights to decision-making for improving the quality of blended learning implementation. The tool allows governing bodies to track trends in the user registration, development, and updating of blended learning courses, the number of learners, and the usability of the courses by users for a selected period. Based on the results of tool experimental testing, goals for its further development are set.

This paper focuses on the development and deployment of a dApp (decentralized Application) for Smart Crop Production Data exchange (SCPDx) that runs on Antelope blockchain/IPFS infrastructure. The paper emphasizes practical approaches to dApp design and deployment, analyses architectural patterns of dApps, and underlines the role of smart contracts in implementing complex functionality. The paper’s contribution is the detailed description of the main smart contracts and the practical knowledge provided on the architecture and implementation of dApps, emphasizing the challenges and solutions in the development process, especially in the context of smart contract implementation. Future developments of the application towards additional data types processing, and design of an interface for leveraging, testing, and evaluating the performance of open source Large Language Models (LLMs) on specific datasets are commented on.

Malware remains a big threat to cyber security, calling for machine learning-based malware detection. Malware variations exhibit common behavioral patterns indicative of their source and intended use to enhance the existing framework’s usefulness. Here we present a novel model, i.e., African Vulture Optimization-based Decision Tree (AVO-DT) to increase the overall optimization.

In the present study, Krill Herd (KH) is proposed as a Feature Selection tool to detect spam email problems. This works by assessing the accuracy and performance of classifiers and minimizing the number of features. Krill Herd is a relatively new technique based on the herding behavior of small crustaceans called krill. This technique has been combined with a local search algorithm called Tabu Search (TS) and has been successfully employed to identify spam emails. This method has also generated much better results than other hybrid algorithm optimization systems such as the hybrid Water Cycle Algorithm with Simulated Annealing (WCASA). To assess the effectiveness of KH algorithms, SVM classifiers, and seven benchmark email datasets were used. The findings indicate that KHTS is much more accurate in detecting spam mail (97.8%) than WCASA.

Incorporating Internet of Things (IoT) technology into indoor kale cultivation holds significant promise for revolutionizing organic farming methodologies. While numerous studies have investigated the impact of environmental factors on kale growth in IoT-based smart agricultural systems, such as temperature, humidity, and nutrient levels, indoor ultraviolet (UV) LED light’s operational efficiencies and advantages in organic farming still need to be explored. This study assessed the efficacy of 15 UV light-controlling indoor experiments in three distinct lighting groups: kale cultivated using conventional household LED lights, kale cultivated using specialized indoor UV lights designed for plant cultivation, and kale cultivated using hybrid household and LED grow lights. The real-time IoT-based monitoring of light, soil, humidity, and air conditions, as well as automated irrigation using a water droplet system, was employed throughout the experiment. The experimental setup for air conditioning maintained temperatures at a constant 26 degrees Celsius over the 45-day study period. The results revealed that a combination of daylight household lights and indoor 4000 K grow lights scored the highest, indicating optimal growth conditions. The second group exposed to warm white household and indoor grow red light exhibited slightly lower scores but larger leaf size than the third group grown under indoor grow red light, likely attributable to reduced light intensity or suboptimal nutrient levels. This study highlights the potential of indoor UV LED light farming to address challenges posed by urbanization and climate change, thereby contributing to efforts to mitigate agricultural carbon emissions and enhance food security in urban environments. This research contributes to positioning kale as a sustainable organic superfood by optimizing kale cultivation.

With the increase in the adoption of Internet of Things (IoT) devices, the security threat they face has become more pervasive. Recent research has demonstrated that most IoT devices are insecure and vulnerable to a range of cyber attacks. The impact of such attacks can vary significantly, from affecting the service of the device itself to putting their owners and their personal information at risk. As a response to improving their security, the focus has been on attacks, specifically on the network layer. However, the importance and impact of other vulnerabilities, such as low-level Radio Frequency (RF) attacks, have been neglected. Such attacks are challenging to detect, and they can be deployed using non-expensive equipment and can cause significant damage. This paper explores security vulnerabilities that target RF communications on popular commercial IoT devices such as Wi-Fi, Zigbee, and 433 Mz. Using software-defined radio, a range of attacks were deployed against the devices, including jamming, replay attacks, packet manipulation, protocol reverse engineering, and harmonic frequency attacks. The results demonstrated that all devices used were susceptible to jamming attacks, and in some cases, they were rendered inoperable and required a hard reset to function correctly again. This finding highlights the lack of protection against both intentional and unintentional jamming. In addition, all devices demonstrated that they were susceptible to replay attacks, which highlights the need for more hardened security measures. Finally, this paper discusses proposals for defence mechanisms for enhancing the security of IoT devices against the aforementioned attacks.

Smart cities can improve the quality of life of citizens by optimizing the utilization of resources. In an IoT-connected environment, people’s health can be constantly monitored, which can help identify medical problems before they become serious. However, overcrowded hospitals can lead to long waiting times for patients to receive treatment. The literature presents alternatives to address this problem by adjusting care capacity to demand. However, there is still a need for a solution that can adjust human resources in multiple healthcare settings, which is the reality of cities. This work introduces HealCity, a smart-city-focused model that can monitor patients’ use of healthcare settings and adapt the allocation of health professionals to meet their needs. HealCity uses vital signs (IoT) data in prediction techniques to anticipate when the demand for a given environment will exceed its capacity and suggests actions to allocate health professionals accordingly. Additionally, we introduce the concept of multilevel proactive human resources elasticity in smart cities, thus managing human resources at different levels of a smart city. An algorithm is also devised to automatically manage and identify the appropriate hospital for a possible future patient. Furthermore, some IoT deployment considerations are presented based on a hardware implementation for the proposed model. HealCity was evaluated with four hospital settings and obtained promising results: Compared to hospitals with rigid professional allocations, it reduced waiting time for care by up to 87.62%.

Rapid advancements in the development of smart terminals and infrastructure, coupled with a wide range of applications with complex requirements, are creating traffic demands that current networks may not be able to fully handle. Accordingly, the study of 6G networks deserves attention from both industry and academia. Artificial intelligence (AI) has emerged for application in the optimization and design process of new 6G networks. The developmental trend of 6G is towards effective resource management, along with the architectural improvement of the current network and hardware specifications. Cloud RAN (CRAN) is considered one of the major concepts in sixth- and fifth-generation wireless networks, being able to improve latency, capacity, and connectivity to huge numbers of devices. Besides bettering the current set-up in terms of setting the carriers’ network architecture and hardware specifications, among other potential enablers, the developmental trend of 6G also means that there must be effective resource management. As a result, this study covers a thorough analysis of resource management plans in CRAN, optimization, and AI taxonomy, and how AI integration might enhance existing resource management.

Introduction. The problem of approximation can be considered as the basis of computational methods, namely, the approximation of individual functions or classes of functions by functions that are in some sense simpler than the functions being approximated. Most often, the role of an approximant is played by a set of algebraic polynomials or (in the case of a periodic function) a set of trigonometric polynomials of a given order. The ideas and methods of approximation theory are used in various fields of science, especially applied areas, since tasks related to the need to replace one object with another, close in one sense or another to the first, but easier to study, arise very often. The purpose of the paper is consider the problems of approximation of a function, which is given by its values in a certain set of nodal points on a certain interval and belongs to a certain class of functions by trigonometric Fourier series, using the quadrature formulas for calculating integrals of fast oscillating functions on this class of functions, which are optimal in accuracy and close to them. The main attention is paid to the study of the sources of error of the proposed approach to function approximation. Results. Effective approximation algorithms from classes of differentiable functions with the help of Fourier series are proposed, using the Fourier coefficients optimal in accuracy and close to them on the given classes of quadrature formulas for calculating integrals of fast-oscillating functions to determine the Fourier coefficients. The error estimates of the proposed approximation algorithms using the quadrature formulas for calculating the Fourier coefficients of the optimal accuracy and close to them for calculating integrals of fast-oscillating functions from classes of differential functions with given values at the nodes of a fixed grid are presented. The corresponding quadrature formulas and constructive estimates of the error of the method of approximation of functions of these classes are given. Conclusions. Efficient by precision algorithms for approximating functions from classes of differentiable functions by means of Fourier series are constructed using the optimal accuracy and close to them quadrature formulas for calculating integrals of fast-oscillating functions from the above classes of functions to calculate the Fourier coefficients. A comprehensive analysis of the quality of the constructed algorithms for approximating functions by finite sums of the Fourier series is carried out.

Introduction. Wireless sensor networks are a part of information and communication technologies and the basis of the Internet of Things technologies. Data are collected, transmitted and processed in real time with the wireless sensor networks. The typical WSN consists of the large number wireless sensor nodes and the coordinator. The wireless network is based on wireless communication standards. Today, WSNs are used in the variety of industries such as medicine, military and digital agriculture. The purpose is to provide a comprehensive analysis of a wireless sensor node for use in digital agriculture. Results. The author analysed wireless communication standards for use in digital agriculture. The typical wireless node architecture is analysed. The parameters of the wireless node are identified. The types of Energy Harvesting Systems (EHS) and their functions are reviewed and analysed. Based on the requires of the wireless node for digital agriculture, the list of photovoltaic energy converters is proposed. In addition, the list of power management integrated circuits is suggested for the Energy Harvesting System. A Classification of Power Supplies for Wireless Sensor Nodes and Networks are developed. The types of sensors for use in digital agriculture are analysed. Conclusions. The main wireless communication standards for digital agriculture are Wi-Fi, IEEE 802.15.4, ZigBee, Bluetooth 4.0, GPRS, 4G, LoRaWAN and NB-IoT. The parameters of the wireless node are identified for further modelling. The list of photovoltaic energy converters is given. The types of power management integrated circuits are proposed. The classification enable to simplify the selection of WSN power supplies for network development. Based on the analysis of sensor parameters for digital agriculture, it is concluded that practically there are no wireless sensors on the world market for the express estimation of plants state.

Introduction. In studies concerning the problems of approaching moving objects, the authors, as a rule, use continuous dynamic models under integral constrains on controls. However, only discrete models under quadratic or resource constraints are suitable for practical applications. The purpose of the paper is to develop a discrete analog of the method of time dilation for solving the problem of guaranteed approaching a terminal set by a discrete conflict-controlled system trajectory. Results. We introduce the concept of integer function of time dilation. Its using, in the frames of the discrete analog of the Pontryagin First Direct method, makes it possible to deduce sufficient conditions for bringing the trajectory of the discrete conflict-controlled process to the terminal set. We outline the way of constructing current pursuer’s control, which brings the object trajectory to the terminal set under arbitrary admissible counteraction of the evader. It differs from the pursuer control choice in the continuous case, when the pursuer chooses his current control in view of the evader’s control at a certain moment of time in the past. In the discrete case, the pursuer constructs his control on the basis of information about the evader’s controls on a whole discrete interval of time in the past. We prove that such control satisfies original quadratic constraints. Conclusions. We derive conditions for approaching the trajectory of conflict-controlled discrete process a terminal set. In so doing, quadratic constraints on controls are fulfilled. The terminal set is supposed to be a subset that corresponds to the catching the evader by the pursuer.

Introduction. The article discusses mathematical models of problems of constructing circular routes of vehicles in a multicommodity hierarchical network. As a rule, such networks consist of a decentralized backbone network and networks in the internal service areas of the backbone nodes (internal networks). In multicommodity networks, each node can exchange products (goods, cargo) with other nodes. In contrast to the distribution problems of a homogeneous interchangeable product, in multicommodity problems the flows of products are not interchangeable, the flow of each product must be delivered from a specific primary object to a specific customer. It is assumed that the multi-level structure of the transport network is defined and the geographical location of the main hubs and its internal service areas with a set of nodes for the delivery and collection of goods (customers) are known. Therefore, the problems of determining the main routes of vehicles and constructing circular routes of internal vehicles are considered independently of each other. The types of costs of real transport processes, which should be taken into account in the formation of the objective function of routing problems, are discussed and mathematical models of problems for constructing circular delivery routes with a heterogeneous fleet of vehicles are proposed. The possibility of solving the formulated problems with the help of well-known packages of mixed and integer linear programming is noted. Purpose. The aim of the article is to formulate new mathematical models of the problem of constructing circular routes of vehicles in the internal networks of servicing the main nodes, which take into account the real costs of transport processes and the geographical features of internal networks. The technique. The research methodology is based on the system analysis of many modern models, methods and algorithms for solving the problems of constructing circular routes for customer service in the internal zones of the main nodes of the hierarchical network. Results. On the basis of the review and analysis of known mathematical models, several new variants of mathematical formulation of problems of designing routes of vehicles for the transportation of discrete cargo in the internal zones of the central nodes of the network have been developed. To solve the problems, precise, heuristic and metaheuristic methods and algorithms can be used, implemented in many commercial and non-commercial packages of mixed and integer programming programs, for example, IBM ILOG CPLEX, GAMS, AIMMS, Gurobi Optimizer, ABACUS, COIN-OR, GLPK, lp_solve. Many of them are available for free on the NEOS server (https://neos-server.org/neos/). Scientific novelty and practical significance. The novelty of the work lies in the formulation of mathematical models of the problem of constructing circular routes of vehicles, which take into account the real costs of transport processes and geographical features of internal networks. The materials of the article form the methodological basis for the development of modern mathematical support for solving the problems of long-term, current and operational planning and management in the internal zones of the trunk nodes of the global hierarchical network.

Introduction. The main attention is paid to the need to take into account estimates of rounding errors when solving problems of transcomputational complexity. Among such tasks, one can highlight the tasks of calculating systems of linear algebraic equations with the number of unknowns in the tens of millions, digital signal processing, calculating nuclear reactors, modeling physical and chemical processes, aerodynamics, information protection, etc. Ignoring the rounding error when solving them leads to the fact that sometimes we obtain computer solutions that do not correspond to the physical content of the problem. The purpose of the article. It is shown how, using estimates of rounding errors, to build computational algorithms resistant to rounding errors. At the same time, the following are taken into account: the rounding rule, the calculation mode, the quality of rounding error estimates (non-improving estimate, asymptotic estimate, imputed estimate). If computing resources are available, it is advisable to use asymptotic and probabilistic estimates as they are more accurate compared to majorant estimates. The results. It is shown how the estimates of rounding errors are used in modern computer technologies to obtain ε-solution of the following problems of applied mathematics: – calculation of integrals from fast oscillating functions; – solving problems of digital signal processing; – calculating the discrete Fourier transform; – multi-bit arithmetic; – computer steganography. The greatest attention is paid to T-effective algorithms for calculating the discrete Fourier transform and solving the problems of spectral and correlation analysis of random processes. These classes of problems are included as components in solving problems of two-key cryptography and computer steganography. Conclusions. The importance of taking into account estimates of rounding errors in modern computer technologies for solving a number of classes of computational and applied mathematics problems is shown.

Introduction. The relevance of the traveling salesman problem is associated with the need to develop computational schemes for use in situations that require the analysis of information of a sufficiently large volume. The versatility of research on the traveling salesman problem makes it possible to reveal many properties and identify the necessary conditions for the optimality of the solution. One of the approaches to theoretical and practical research is based on computer technology of the modified distribution method Purpose. The goal of the work is to develop conditions for the optimality of the solution and the concept of three-element replacement operations to construct the corresponding algorithm and obtain an acceptable solution for a small number of operations used. Results. The concept of a three-element replacement operation is introduced. A necessary condition for the optimality of this solution and a special method for constructing an admissible cyclic solution to the transport problem corresponding to the traveling salesman problem are given. The search for an optimal solution can be represented as a transition from one cyclic solution to another cyclic solution using three-element replacement operations. Such replacement operations are performed for zero elements of an admissible solution with a positive value of the evaluation matrix. Conclusions. Due to the emergence of discrete optimization problems with the need to analyze a sufficiently large amount of information, the development of new computational schemes that give consistently good results is required. Research in this direction is carried out on the example of the traveling salesman problem, for which, with the use of three-element replacement operations, it is possible to obtain an acceptable solution to the problems.

Introduction. Chlorophyll fluorescence induction (CFI) is a method for receiving additional information about state of plants without its injuring. CFI occurs when the plant is illuminated in the blue spectrum of light. The so-called Kautsky curve in the red spectrum of light is measuring. Currently, there are scientific papers about determining water deficit using CFI and neural networks. Support Vector Machin (SVM) – the model of machine learning capable of performing linear or non-linear classification, regression and even finding outliers in data. SVM can be an alternative to neural networks for analysis of CFI measurements. The purpose of the paper is to assess the possibility of determining the need for artificial watering of soybean plants based on the SVM and compare the results with the results obtained by the author in earlier experiments of analyzing the CFI of zinnia plants. Results. SVM research was conducted using different SVM kernels, different methods of normalizing CFI measurements, different methods of the dimensionality reduction of CFI data. The SVM implementation from the Scikit-learn Python library, the SVR (Support Vector Regression) class was used. The best kernel of SVM, the best normalization method, and the best method of forming the input vector for the support vector machine were experimentally revealed for determining the need of artificial watering. Conclusions. The research of Support Vector Machine for the purpose of determining the need for artificial watering based on CFI curves were conducted. The research showed that the best result is obtained (1) using a polynomial kernel of the fourth degree, (2) using 10 points of CFI curve taken unevenly on an exponentiation scale (power of 1/8), (3) using minimax normalization of CFI measurements. SVM showed a worse result in the analysis of CFI curves of soybean plants than in the analysis of CFI curves of the zinnia plants. It can be explained by the fact that the soybean is a drought resistant plant and therefore CFI demonstrated worse a moisture deficit.

Introduction. A digital system is a collection of combinational and sequential blocks. Sequential blocks can be divided into library and non-standard classes. The first class includes, for example, counters or shift registers. To implement the circuits of such blocks, standard CAD programs are used. And for the second class, which is the control unit (CU), there are no standard library solutions. This explains the relevance of methods for synthesis and optimization of circuits of non-standard sequential blocks, such as CU. When synthesizing a finite state machine (FSM) circuit, a number of optimization problems arise that are aimed at improving CU characteristics. Methods for solving these problems depend on elemental base characteristics. This paper discusses the implementation of the FSM circuit on a FPGA (field-programmable logic array) basis. The main FPGA blocks that are used for FSM circuit implementation are LUT (look-up table) elements and EMB (embedded memory blocks) elements. Therefore, to solve optimization problems while developing an FSM circuit, it is necessary to reduce the number of these elements. The purpose of the article. This work presents an approach to lower hardware costs in the FSM Mile technique, which uses FPGA for implementation. The method is based on the extended coding of micro-operation sets, in which the set code also includes the transition state code. The state code is partial since it is determined for a set of states upon transition, from which this set is formed. To implement part of the FSM circuit, the built-in memory block EMB is used. If EMB capabilities are not enough to implement the circuit, then part of the circuit is implemented on LUT elements. It is proposed to implement part of the output signals (micro-operations) on EMB. An example of the synthesis of an FSM circuit using the proposed method is given. Results. To study the effectiveness of the proposed method, a comparison was made between the control unit of the known structure (U5) and the control unit obtained using the proposed method (U7). In this case, standard benchmarks from a well-known library were used. Research has shown that U7 can reduce the number of LUTs by 28 % of all benchmarks, and U5 only by 9 %. It is important to note that when implementing the entire 64 % standard MPA circuit, one EMB block is sufficient. Conclusions. The proposed method allows for a reduction in hardware costs (the number of LUT elements). The article shows the conditions for applying the proposed method. Results of experiments examining the effectiveness of the suggested approach to automata implementation with Virtex-7 family chips and the Vivado industrial package are given

Introduction. The considered implementation of the Viterbi algorithm provides a reduction in hardware and time costs for decoding convoluted code sequences, and can be used for semi-realistic modeling of existing means of data transmission (for example, in satellite communication). The purpose of the article. Show how when modeling the processes of encoding and decoding convolutional codes according to the improved Viterbi algorithm, as well as its implementation based on programmable logic devices of the FPGA type, it was possible to reduce the number of clocks of reading metrics and tracks from RAM by 2 times. The results. A two-fold decrease in the number of reading cycles of metrics and tracks (input sequences or reverse pointers) from RAM is achieved by joint processing of two receiver nodes that share two source nodes. Relatively small costs for a hardware calculator of edge metrics allow you to organize parallel calculation, comparison and multiplexing of metrics and tracks of two sources at the inputs of block RAM. Two-port block memory makes it possible to significantly (up to two times) speed up the decoding process, to abandon metric and track buffer registers. Conclusions: The Viterbi decoder is widely used in communication systems and is a practical method of error correction at high signal transmission speed in modern telecommunication systems. The Viterbi decoder is designed for decoding convolutional codes and is optimal in the sense of minimizing the probability of an error. The advantage of the Viterbi decoder is that its complexity is a linear function of the number of symbols in the codeword sequence. In addition, the Viterbi algorithm is widely used in pattern recognition systems using hidden Markov models.

L’attuazione della Direttiva NIS 2 comporterà moltissimi problemi di coordinamento, in grado di minare l’efficacia e il risultato atteso dal legislatore europeo e nazionale. Uno dei punti più critici, che sarà affrontato nel presente contributo, è costituito dal contributo richiesto alle regioni e agli enti locali, senza il quale sarà molto difficile raggiungere gli obiettivi di resilienza della pubblica amministrazione.

Il presente contributo analizza, a prima lettura, le previsioni del DDL 1717 (nella versione proposta all'esito dell'esame in commissione in sede referente) in materia di appalti pubblici. In particolare, l'attenzione è rivolta all'art. 10 del DDL (divenuto art. 13 dopo l'esame in commissione) e al difficile raccordo con le previsioni normative, già vigenti, introdotte dal d.lgs. 36/2023.

L'articolo analizza il disegno di legge cyber di prossima adozione soffermandosi sulla necessità di regolamentare le pratiche di vulnerability disclosure, discutendone le principali implicazioni legali e le possibili soluzioni.

Promuovendo la premessa che l’accesso a Internet costituisce una prerogativa per lo sviluppo della persona umana, questo contributo focalizza l’attenzione su una particolare tipologia di (potenziali) utenti di Internet, e cioè le persone private della libertà, in alcuni degli ordinamenti che vedono nella risocializzazione un elemento essenziale della fase esecutiva della pena. Si considererà, tuttavia, che, laddove si riconosca una compatibilità tra la possibilità di fruire di accedere a Internet e lo stato di detenzione, si deve operare un necessario bilanciamento tra lo sviluppo della personalità umana e la sicurezza. Muovendo, quindi, dallo studio del caso italiano, sarà, poi esplorata, in primo luogo, la giurisprudenza della Corte EDU e, successivamente, la configurazione che l’accesso a Internet in carcere assume in quelle realtà statuali che declinano tale accesso come una libertà (quali il Belgio, la Spagna e la Francia) e in quelle che lo elevano a diritto sociale (come in Finlandia e Norvegia).

Il presente contributo mira a far luce su un particolare aspetto critico sollevato dal cd. 'DDL Cybersicurezza', vale a dire, la risposta del legislatore nazionale in materia di cybersicurezza imperniata principalmente sull’aumento ed inasprimento della repressione penale alla luce di un modello, quello della c.d. direttiva NIS2, che, di converso, prevede inter alia un complesso quadro di strumenti di prevenzione e gestione del rischio di cybersicurezza di stampo più marcatamente privatistico-amministrativistico. Una lettura che è possibile avanzare è che tale ‘salto in avanti’ del legislatore italiano, nelle more del recepimento della direttiva NIS2, sia legato ad un più generale problema che caratterizza la regolamentazione della cybersicurezza nell’Unione: la tendenza a declinare questa in termini di sicurezza nazionale o di funzionamento del mercato interno e di cooperazione, a seconda del fatto che l’iniziativa legislativa venga presa rispettivamente dagli Stati membri o dalla Commissione europea.

Il contributo analizza il DDL Cybersicurezza, nella parte in cui propone l’estensione dello statuto penale “tipico” della criminalità organizzata di stampo mafioso alle forme più gravi di cybercriminalità organizzata diretta contro sistemi informatici e telematici "pubblici".

Questo articolo esamina criticamente il disegno di legge 1717, concentrandosi sul Capo II e sull'inasprimento delle sanzioni per i reati informatici previsti dalla legge 547 del 1993, discutendo le difficoltà pratiche nell'individuazione e perseguimento dei responsabili di tali reati. Viene inoltre evidenziata la necessità di misure preventive e di una pedagogia digitale per migliorare la consapevolezza e le competenze in materia di sicurezza informatica tra la popolazione. Infine, si affrontano alcune perplessità sistematiche riguardanti specifiche proposte del ddl, suggerendo possibili modifiche per migliorare l'efficacia della normativa.

Il contributo analizza la fattispecie di “estorsione informatica” che il DDL Cybersicurezza propone di inserire nel codice penale, valutandone la coerenza con la realtà criminologica degli attacchi ransomware e con le rilevanti categorie dogmatiche in diritto penale; in conclusione, si accenna anche al particolare coordinamento tra indagini penali e incident response previsto dal DDL.

La digitalizzazione del procedimento amministrativo è da considerarsi qualcosa di ormai ineludibile che si concretizzerà pienamente, con più o meno celerità nei prossimi anni, con la naturale evoluzione delle tecnologie e delle scienze informatiche, tuttavia l’automazione completa e/o parziale di processi e procedimenti amministrativi basata sull’impiego dell’Intelligenza Artificiale (IA) non è per nulla scontata. Si consideri infatti che già solo l’adozione di algoritmi “tradizionali”, ovvero non necessariamente di IA, per supportare la Pubblica Amministrazione in fasi procedimentali più o meno complesse, anche non decisorie, rappresenta ancora oggi quasi una sfida, culturale e di legittimità amministrativa e giuridica. Nel mentre, la percezione diffusa è che invece qualcosa di rivoluzionario si sia ormai messo in moto, che riguarda anche l’impiego dell’algoritmica (e dell’IA) per l’incremento della produttività e dell’efficacia dell’azione amministrativa. Una trasformazione epocale e in itinere quella della nostra PA che tuttavia, nell’avanzare, deve necessariamente coniugare la tutela dei diritti dei cittadini con i vantaggi offerti dalla tecnica, fabbisogno che questo lavoro si propone di affrontare, in termini di analisi e di approfondimenti, con una visione ibrida, possibilmente in simbiosi tra il diritto e l’informatica.

Il DDL Cybersicurezza introduce nuovi obblighi di notifica per i soggetti della Pubblica Amministrazione e si inserisce nel quadro degli interventi già predisposti con la normativa relativa al Perimetro di Sicurezza Nazionale. Tuttavia, in vista dell’implementazione della Direttiva NIS 2 entro ottobre 2024, appare necessario un ripensamento del legislatore al fine di evitare la frammentazione negli obblighi di segnalazione degli incidenti e di conseguenza incrementare i livelli di sicurezza attraverso un maggiore scambio di informazioni.

Note a prima lettura del disegno di legge governativo AC1717 (Governo Meloni) sulla cybersicurezza.

Rassegna delle novità in materia di Intelligenza artificiale e diritto con particolare riferimento al procedimento di formazione degli atti dell'Unione europea, alla dottrina sul tema e a recenti applicazioni informatiche.

The purpose of this study is to discuss epidemic communication for drones to share information in flight and to develop a wireless system for implementation. Various theoretical studies have been conducted on epidemic communication, but their applications are not clear, so a system that assumes practical use is developed. As the main evaluation items, we analyzed the effect of communication interference between drones on the amount of data transmission, and furthermore, proposed an optimal transmission method depending on the flight speed. In these analysis results, we designed functions to be implemented in drones, developed wireless devices, and confirmed their operation through demonstration tests using actual drones. Based on the results of this research, we succeeded in identifying issues to be addressed in order to implement the system on drones and in developing an epidemic communication system based on the results of demonstration experiments, thereby contributing to the realization of inter-drone communication in the future.

In the face of our rapidly expanding global population, the necessity of meeting the fundamental needs of every individual is more pressing than ever. Human survival depends upon access to water, making it a vital resource that demands novel solutions to ensure universal availability. Although our planet is abundant in water, 97.5% of it is saltwater, compelling nations to investigate ways to make it suitable for consumption. Seawater desalination is becoming increasingly vital for water sustainability. While seawater desalination offers a solution, existing methods often grapple with high energy consumption and maintaining consistent water quality. This paper proposes a novel hybrid water desalination system that addresses these limitations. Our system leverages solar energy, a readily available renewable resource, to power the desalination process, significantly improving its environmental footprint and operational efficiency. Additionally, we integrated a network of sensors and the Internet of Things (IoT) to enable the real-time monitoring of system performance and water quality. This allows for the immediate detection and improvement in any potential issues, ensuring the consistent production of clean drinking water. By combining solar energy with robust quality control via IoT, our hybrid desalination system offers a sustainable and reliable approach to meet the growing demand for freshwater.

Smart agricultural drones for crop spraying are becoming popular worldwide. Research institutions, commercial companies, and government agencies are investigating and promoting the use of technologies in the agricultural industry. This study presents a smart agriculture drone integrated with Internet of Things technologies that use machine learning techniques such as TensorFlow Lite with an EfficientDetLite1 model to identify objects from a custom dataset trained on three crop classes, namely, pineapple, papaya, and cabbage species, achieving an inference time of 91 ms. The system’s operation is characterised by its adaptability, offering two spray modes, with spray modes A and B corresponding to a 100% spray capacity and a 50% spray capacity based on real-time data, embodying the potential of Internet of Things for real-time monitoring and autonomous decision-making. The drone is operated with an X500 development kit and has a payload of 1.5 kg with a flight time of 25 min, travelling at a velocity of 7.5 m/s at a height of 2.5 m. The drone system aims to improve sustainable farming practices by optimising pesticide application and improving crop health monitoring.

The rapid advancement of technology, alongside state-of-the-art techniques is at an all-time high. However, this unprecedented growth of technological prowess also brings forth potential threats, as oftentimes the security encompassing these technologies is imperfect. Particularly within the automobile industry, the recent strides in technology have brought about increased complexity. A notable flaw lies in the CAN-FD protocol, which lacks robust security measures, making it vulnerable to data theft, injection, replay, and flood data attacks. With the rising complexity of in-vehicular networks and the widespread adoption of CAN-FD, the imperative to safeguard the protocol has never been more crucial. This paper aims to provide a comprehensive review of the existing in-vehicle communication protocol, CAN-FD. It explores existing security approaches designed to fortify CAN-FD, demonstrating multiple multi-layer solutions that leverage modern techniques including Physical Unclonable Function (PUF), Elliptical Curve Cryptography (ECC), Ethereum Blockchain, and Smart contracts. The paper highlights existing multi-layer security measures that offer minimal overhead, optimal performance, and robust security. Moreover, it identifies areas where these security measures fall short and discusses ongoing research along with suggestions for implementing software and hardware-level modifications. These proposed changes aim to streamline complexity, reduce overhead while ensuring forward compatibility. In essence, the methods outlined in this study are poised to excel in real-world applications, offering robust protection for the evolving landscape of in-vehicular communication systems.