Computers, Volume 12, Issue 12 (December 2023) – 23 articles

This paper presents the implementation of an intelligent real-time single-channel electromyography (EMG) signal classifier based on open-source hardware. The article shows the experimental design, analysis, and implementation of a solution to identify four muscle movements from the forearm (extension, pronation, supination, and flexion), for future applications in transradial active prostheses. An EMG signal acquisition instrument was developed, with a 20–450 Hz bandwidth and 2 kHz sampling rate. The signals were stored in a Database, as a multidimensional array, using a desktop application. Numerical and graphic analysis approaches for discriminative capacity were proposed for feature analysis and four feature sets were used to feed the classifier. Artificial Neural Networks (ANN) were implemented for time-domain EMG pattern recognition (PR). The system obtained a classification accuracy of 98.44% and response times per signal of 8.522 ms. Results suggest these methods allow us to understand, intuitively, the behavior of user information. Full article

In recent years, the combination of wireless body sensor networks (WBSNs) and the Internet ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This combination allowed for the smooth communication between medical devices that enabled the real-time monitoring of patient’s vital signs and health parameters. However, the increased connectivity also introduced security challenges, particularly as they related to the presence of attack nodes. This paper proposed a unique solution, an enhanced random forest classifier with a K-means clustering (ERF-KMC) algorithm, in response to these challenges. The proposed ERF-KMC algorithm combined the accuracy of the enhanced random forest classifier for achieving the best execution time (ERF-ABE) with the clustering capabilities of K-means. This model played a dual role. Initially, the security in IoMT networks was enhanced through the detection of attack messages using ERF-ABE, followed by the classification of attack types, specifically distinguishing between man-in-the-middle (MITM) and distributed denial of service (DDoS) using K-means. This approach facilitated the precise categorization of attacks, enabling the ERF-KMC algorithm to employ appropriate methods for blocking these attack messages effectively. Subsequently, this approach contributed to the improvement of network performance metrics that significantly deteriorated during the attack, including the packet loss rate (PLR), end-to-end delay (E2ED), and throughput. This was achieved through the detection of attack nodes and the subsequent prevention of their entry into the IoMT networks, thereby mitigating potential disruptions and enhancing the overall network efficiency. This study conducted simulations using the Python programming language to assess the performance of the ERF-KMC algorithm in the realm of IoMT, specifically focusing on network performance metrics. In comparison with other algorithms, the ERF-KMC algorithm demonstrated superior efficacy, showcasing its heightened capability in terms of optimizing IoMT network performance as compared to other common algorithms in network security, such as AdaBoost, CatBoost, and random forest. The importance of the ERF-KMC algorithm lies in its security for IoMT networks, as it provides a high-security approach for identifying and preventing MITM and DDoS attacks. Furthermore, improving the network performance metrics to ensure transmitted medical data are accurate and efficient is vital for real-time patient monitoring. This study takes the next step towards enhancing the reliability and security of IoMT systems and advancing the future of connected healthcare technologies. Full article

Injuries are an unfortunate part of professional sports. This study aims to explore the multi-dimensional impact of injuries in professional basketball, focusing on player performance, team dynamics, and economic outcomes. Employing advanced machine learning and text mining techniques on suitably preprocessed NBA data, we examined the intricate interplay between injury and performance metrics. Our findings reveal that specific anatomical sub-areas, notably knees, ankles, and thighs, are crucial for athletic performance and injury prevention. The analysis revealed the significant economic burden that certain injuries impose on teams, necessitating comprehensive long-term strategies for injury management. The results provide valuable insights into the distribution of injuries and their varied effects, which are essential for developing effective prevention and economic strategies in basketball. By illuminating how injuries influence performance and recovery dynamics, this research offers comprehensive insights that are beneficial for NBA teams, healthcare professionals, medical staff, and trainers, paving the way for enhanced player care and optimized performance strategies. Full article

With urban areas facing rapid population growth, public transport plays a key role to provide efficient and economic accessibility to the residents. It reduces the use of personal vehicles leading to reduced traffic congestion on roads and reduced pollution. To assess the performance of these transport systems, prior studies have taken into consideration the blank spot areas, population density, and stop access density; however, very little research has been performed to compare the accessibility between cities using a GIS-based approach. This paper compares the access and performance of public transport across Melbourne and Sydney, two cities with a similar size, population, and economy. The methodology uses spatial PostGIS queries to focus on accessibility-based approach for each residential mesh block and aggregates the blank spots, and the number of services offered by time of day and the frequency of services at the local government area (LGA) level. The results of the study reveal an interesting trend: that with increase in distance of LGA from city centre, the blank spot percentage increases while the frequency of services and stops offering weekend/night services declines. The results conclude that while Sydney exhibits a lower percentage of blank spots and has better coverage, performance in terms of accessibility by service time and frequency is better for Melbourne’s LGAs, even as the distance increases from the city centre. Full article

This paper focuses on enhancing the performance of the Nth-degree truncated-polynomial ring units key encapsulation mechanism (NTRU-KEM) algorithm, which ensures post-quantum resistance in the field of key establishment cryptography. The NTRU-KEM, while robust, suffers from increased storage and computational demands compared to classical cryptography, leading to significant memory and performance overheads. In environments with limited resources, the negative impacts of these overheads are more noticeable, leading researchers to investigate ways to speed up processes while also ensuring they are efficient in terms of area utilization. To address this, our research carefully examines the detailed functions of the NTRU-KEM algorithm, adopting a software/hardware co-design approach. This approach allows for customized computation, adapting to the varying requirements of operational timings and iterations. The key contribution is the development of a novel hardware acceleration technique focused on optimizing bus utilization. This technique enables parallel processing of multiple sub-functions, enhancing the overall efficiency of the system. Furthermore, we introduce a unique integrated register array that significantly reduces the spatial footprint of the design by merging multiple registers within the accelerator. In experiments conducted, the results of our work were found to be remarkable, with a time-area efficiency achieved that surpasses previous work by an average of 25.37 times. This achievement underscores the effectiveness of our optimization in accelerating the NTRU-KEM algorithm. Full article

In big data analysis, various zero-inflated problems are occurring. In particular, the problem of inflated zeros has a great influence on text big data analysis. In general, the preprocessed data from text documents are a matrix consisting of the documents and terms for row and column, respectively. Each element of this matrix is an occurred frequency of term in a document. Most elements of the matrix are zeros, because the number of columns is much larger than the rows. This problem is a cause of decreasing model performance in text data analysis. To overcome this problem, we propose a method of zero-inflated text data analysis using generative adversarial networks (GAN) and statistical modeling. In this paper, we solve the zero-inflated problem using synthetic data generated from the original data with zero inflation. The main finding of our study is how to change zero values to the very small numeric values with random noise through the GAN. The generator and discriminator of the GAN learned the zero-inflated text data together and built a model that generates synthetic data that can replace the zero-inflated data. We conducted experiments and showed the results, using real and simulation data sets to verify the improved performance of our proposed method. In our experiments, we used five quantitative measures, prediction sum of squares, R-squared, log-likelihood, Akaike information criterion and Bayesian information criterion to evaluate the model’s performance between original and synthetic data sets. We found that all performances of our proposed method are better than the traditional methods. Full article

Since the introduction of the first cryptocurrency, Bitcoin, in 2008, the gain in popularity of distributed ledger technologies (DLTs) has led to an increasing demand and, consequently, a larger number of network participants in general. Scaling blockchain-based solutions to cope with several thousand transactions per second or with a growing number of nodes has always been a desirable goal for most developers. Enabling these performance metrics can lead to further acceptance of DLTs and even faster systems in general. With the introduction of directed acyclic graphs (DAGs) as the underlying data structure to store the transactions within the distributed ledger, major performance gains have been achieved. In this article, we review the most prominent directed acyclic graph platforms and evaluate their key performance indicators in terms of transaction throughput and network latency. The evaluation aims to show whether the theoretically improved scalability of DAGs also applies in practice. For this, we set up multiple test networks for each DAG and blockchain framework and conducted broad performance measurements to have a mutual basis for comparison between the different solutions. Using the transactions per second numbers of each technology, we created a side-by-side evaluation that allows for a direct scalability estimation of the systems. Our findings support the fact that, due to their internal, more parallelly oriented data structure, DAG-based solutions offer significantly higher transaction throughput in comparison to blockchain-based platforms. Although, due to their relatively early maturity state, fully DAG-based platforms need to further evolve in their feature set to reach the same level of programmability and spread as modern blockchain platforms. With our findings at hand, developers of modern digital storage systems are able to reasonably determine whether to use a DAG-based distributed ledger technology solution in their production environment, i.e., replacing a database system with a DAG platform. Furthermore, we provide two real-world application scenarios, one being smart grid communication and the other originating from trusted supply chain management, that benefit from the introduction of DAG-based technologies. Full article

The Industrial Internet of Things (IIoT), where numerous smart devices associated with sensors, actuators, computers, and people communicate with shared networks, has gained advantages in many fields, such as smart manufacturing, intelligent transportation, and smart grids. However, security is becoming increasingly challenging due to the vulnerability of the IIoT to various malicious attacks. In this paper, the security issues of the IIoT are reviewed from the following three aspects: (1) security threats and their attack mechanisms are presented to illustrate the vulnerability of the IIoT; (2) the intrusion detection methods are listed from the attack identification perspectives; and (3) some defense strategies are comprehensively summarized. Several concluding remarks and promising future directions are provided at the end of this paper. Full article

Smart policing refers to the use of advanced technologies such as artificial intelligence to enhance policing activities in terms of crime prevention or crime reduction. Artificial intelligence tools, including machine learning and natural language processing, have widespread applications across various fields, such as healthcare, business, and law enforcement. By means of these technologies, smart policing enables organizations to efficiently process and analyze large volumes of data. Some examples of smart policing applications are fingerprint detection, DNA matching, CCTV surveillance, and crime prediction. While artificial intelligence offers the potential to reduce human errors and biases, it is still essential to acknowledge that the algorithms reflect the data on which they are trained, which are inherently collected by human inputs. Considering the critical role of the police in ensuring public safety, the adoption of these algorithms demands careful and thoughtful implementation. This paper presents a systematic literature review focused on exploring the machine learning techniques employed by law enforcement agencies. It aims to shed light on the benefits and limitations of utilizing these techniques in smart policing and provide insights into the effectiveness and challenges associated with the integration of machine learning in law enforcement practices. Full article

DevOps methodology and tools, which provide standardized ways for continuous integration (CI) and continuous deployment (CD), are invaluable for efficient software development. Current DevOps solutions, however, lack a useful functionality: they do not support simultaneous project developments and deployment on the same operating infrastructure (e.g., a cluster of Docker containers). In this paper, we propose a novel approach to address this shortcoming by defining a multi-project, multi-environment (MPME) approach. With this approach, a large company can organize many microservice-based projects operating simultaneously on a common code base, using self-hosted Kubernetes clusters, which helps developers and businesses to better focus on the product they are developing, and to reduce efforts on the management of their DevOps infrastructure. Full article

Within the integration and development of data-driven process models, the underlying process is digitally mapped in a model through sensory data acquisition and subsequent modelling. In this process, challenges of different types and degrees of severity arise in each modelling step, according to the Cross-Industry Standard Process for Data Mining (CRISP-DM). Particularly in the context of data acquisition and integration into the process model, it can be assumed with a sufficiently high degree of probability that the acquired data contain anomalies of various kinds. The outliers must be detected in the data preparation and processing phase and dealt with accordingly. If this is sufficiently implemented, it will positively impact the subsequent modelling in terms of accuracy and precision. Therefore, this paper shows how outliers can be identified using the unsupervised machine learning methods autoencoder, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Isolation Forest (iForest), and One-Class Support Vector Machine (OCSVM). Following implementing these methods, we compared them by applying the Numenta Anomaly Benchmark (NAB) and sufficiently presented the individual strengths and disadvantages. Evaluating the correctness, distinctiveness and robustness criteria described in the paper showed that the One-Class Support Vector Machine was outstanding among the methods considered. This is because the OCSVM achieved acceptable anomaly detections on the available process datasets with comparatively little effort. Full article

Due to increased traffic pressure, traditional urban rail vehicle–ground communication systems are no longer able to meet the increasing communication requirements. In this paper, ad hoc networks are applied to urban rail transit vehicle–ground communication systems to improve link reliability and reduce transmission delay. In the proposed network, a service-driven routing algorithm is proposed, which considers the distance factor for cluster head selection and optimizes the routing transmission delay by service priority and congestion level. An auxiliary node-based routing maintenance mechanism is also proposed to avoid the problem of frequent breakage of communication links due to the high-speed movement of trains. Through the simulation, the proposed algorithm can effectively reduce the packet loss rate, end-to-end delay, and routing overhead of vehicle–ground communication compared with the traditional routing algorithm, which is more conducive to meeting the next generation of urban rail transit vehicle–ground communication requirements. Full article

As digitalisation sweeps through industries, companies are having to deal with the resultant changes in business models, core processes and organisational structures. This includes the reassessment of the role of the IT department, traditionally the guardians of technology standards and providers of corporate systems and infrastructure, and their ongoing maintenance. This article investigates this dynamic in two research studies. Study 1 focuses on the German automotive industry and adopts a qualitative inductive approach based on interviews with IT practitioners to ascertain the key aspects of digitalisation impacting the industry and to chart the emergence of a new model for the management of IT. Study 2 then reviews the deployment of digital technologies in other industry sectors via questionnaire responses from senior IT professionals in eight organisations. The results suggest that the transfer of IT roles and responsibilities to business functions, evident in the German automotive industry, is being replicated in other organisations in which digital technologies are now embedded in an organisation’s products or services. This article concludes with a model for cross-referencing the role of the IT function with the impact of digital technologies, representing a contribution to the growing literature on digital technology deployment in organisations. Full article

The automation industry faces the challenge of avoiding interference with obstacles, estimating the next move of a robot, and optimizing its path in various environments. Although researchers have predicted the next move of a robot in linear and non-linear environments, there is a lack of precise estimation of sectorial error probability while moving a robot on a curvy path. Additionally, existing approaches use visual sensors, incur high costs for robot design, and ineffective in achieving motion stability on various surfaces. To address these issues, the authors in this manuscript propose a low-cost and multisensory robot capable of moving on an optimized path in diverse environments with eight degrees of freedom. The authors use the extended Kalman filter and unscented Kalman filter for localization and position estimation of the robot. They also compare the sectorial path prediction error at different angles from 0° to 180° and demonstrate the mathematical modeling of various operations involved in navigating the robot. The minimum deviation of 1.125 cm between the actual and predicted path proves the effectiveness of the robot in a real-life environment. Full article

The digitization of information and technological advancements have enabled us to gather vast amounts of data from various domains, including but not limited to medicine, commerce, and mining. Machine learning techniques use this information to improve decision-making, but they have a big problem: they are very sensitive to data variation, so it is necessary to clean them to remove irrelevant and redundant information. This removal of information is known as the Feature Selection Problem. This work presents the Pendulum Search Algorithm applied to solve the Feature Selection Problem. As the Pendulum Search Algorithm is a metaheuristic designed for continuous optimization problems, a binarization process is performed using the Two-Step Technique. Preliminary results indicate that our proposal obtains competitive results when compared to other metaheuristics extracted from the literature, solving well-known benchmarks. Full article

In today’s financial landscape, traditional banking institutions rely extensively on customers’ historical financial data to evaluate their eligibility for loan approvals. While these decision support systems offer predictive accuracy for established customers, they overlook a crucial demographic: individuals without a financial history. To address this gap, our study presents a methodology for a decision support system that is intended to assist in determining credit risk. Rather than solely focusing on past financial records, our methodology assesses customer credibility by generating credit risk scores derived from psychometric test results. Utilizing machine learning algorithms, we model customer credibility through multidimensional metrics such as character traits and attitudes toward money management. Preliminary results from our prototype testing indicate that this innovative approach holds promise for accurate risk assessment. Full article

Static analysis is a software testing technique that analyzes the code without executing it. It is widely used to detect vulnerabilities, errors, and other issues during software development. Many tools are available for static analysis of Java code, including SpotBugs. Methods that perform a security check must be declared private or final; otherwise, they can be compromised when a malicious subclass overrides the methods and omits the checks. In Java, security checks can be performed using the SecurityManager class. This paper addresses the aforementioned problem by building a new automated checker that raises an issue when this rule is violated. The checker is built under the SpotBugs static analysis tool. We evaluated our approach on both custom test cases and real-world software, and the results revealed that the checker successfully detected related bugs in both with optimal metrics values. Full article

In this article, we present the first proposal for contract signing based on blockchain that meets the requirements of fairness, hard-timeliness, and bc-optimism. The proposal, thanks to the use of blockchain, does not require the use of trusted third parties (TTPs), thus avoiding a point of failure and the problem of signatories having to agree on a TTP that is trusted by both. The presented protocol is fair because it is designed such that no honest signatory can be placed at a disadvantage. It meets the hard-timeliness requirement because both signatories can end the execution of the protocol at any time they wish. Finally, the proposal is bc-optimistic because blockchain functions are only executed in case of exception (and not in each execution of the protocol), with consequent savings when working with public blockchains. No previous proposal simultaneously met these three requirements. In addition to the above, this article clarifies the concept of timeliness, which previously has been defined in a confusing way (starting with the authors who used the term for the first time). We conducted a security review that allowed us to verify that our proposal meets the desired requirements. Furthermore, we provide the specifications of a smart contract designed for the Ethereum blockchain family and verified the economic feasibility of the proposal, ensuring it can be aligned with the financial requirements of different scenarios. Full article

This article examines intrusion detection systems in depth using the CSE-CIC-IDS-2018 dataset. The investigation is divided into three stages: to begin, data cleaning, exploratory data analysis, and data normalization procedures (min-max and Z-score) are used to prepare data for use with various classifiers; second, in order to improve processing speed and reduce model complexity, a combination of principal component analysis (PCA) and random forest (RF) is used to reduce non-significant features by comparing them to the full dataset; finally, machine learning methods (XGBoost, CART, DT, KNN, MLP, RF, LR, and Bayes) are applied to specific features and preprocessing procedures, with the XGBoost, DT, and RF models outperforming the others in terms of both ROC values and CPU runtime. The evaluation concludes with the discovery of an optimal set, which includes PCA and RF feature selection. Full article

Specification and Description Language (SDL) is a language that can represent the behavior and structure of a model completely and unambiguously. It allows the creation of frameworks that can run a model without the need to code it in a specific programming language. This automatic process simplifies the key phases of model building: validation and verification. SDLPS is a simulator that enables the definition and execution of models using SDL. In this paper, we present a new library that enables the execution of SDL models defined on SDLPS infrastructure on a HPC platform, such as a supercomputer, thus significantly speeding up simulation runtime. Moreover, we apply the SDL language to a social science use case, thus opening a new avenue for facilitating the use of HPC power to new groups of users. The tools presented here have the potential to increase the robustness of modeling software by improving the documentation, verification, and validation of the models. Full article

Interpolating and approximating scattered scalar and vector data is fundamental in resolving numerous engineering challenges. These methodologies predominantly rely on establishing a triangulated structure within the data domain, typically constrained to the dimensions of 2D or 3D. Subsequently, an interpolation or approximation technique is employed to yield a smooth and coherent outcome. This contribution introduces a meshless methodology founded upon radial basis functions (RBFs). This approach exhibits a nearly dimensionless character, facilitating the interpolation of data evolving over time. Specifically, it enables the interpolation of dispersed spatio-temporally varying data, allowing for interpolation within the space-time domain devoid of the conventional “time-frames”. Meshless methodologies tailored for scattered spatio-temporal data hold applicability across a spectrum of domains, encompassing the interpolation, approximation, and assessment of data originating from various sources, such as buoys, sensor networks, tsunami monitoring instruments, chemical and radiation detectors, vessel and submarine detection systems, weather forecasting models, as well as the compression and visualization of 3D vector fields, among others. Full article

Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were utilized to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection. Full article

Social network systems are constantly fed with text messages. While this enables rapid communication and global awareness, some messages could be aptly made to hurt or mislead. Automatically identifying meaningful parts of a sentence, such as, e.g., positive or negative sentiments in a phrase, would give valuable support for automatically flagging hateful messages, propaganda, etc. Many existing approaches concerned with the study of people’s opinions, attitudes and emotions and based on machine learning require an extensive labelled dataset and provide results that are not very decisive in many circumstances due to the complexity of the language structure and the fuzziness inherent in most of the techniques adopted. This paper proposes a deterministic approach that automatically identifies people’s sentiments at the sentence level. The approach is based on text analysis rules that are manually derived from the way Italian grammar works. Such rules are embedded in finite-state automata and then expressed in a way that facilitates checking unstructured Italian text. A few grammar rules suffice to analyse an ample amount of correctly formed text. We have developed a tool that has validated the proposed approach by analysing several hundreds of sentences gathered from social media: hence, they are actual comments given by users. Such a tool exploits parallel execution to make it ready to process many thousands of sentences in a fraction of a second. Our approach outperforms a well-known previous approach in terms of precision. Full article