Search Results (98)

Preliminary activities of searching and selecting relevant articles are crucial in scientific research to determine the state of the art (SOTA) and enhance overall outcomes. While there are automatic tools for keyword extraction, these algorithms are often computationally expensive, storage-intensive, and reliant on institutional subscriptions for metadata retrieval. Most importantly, they still require manual selection of literature. This paper introduces a framework that automates keyword searching in article abstracts to help select relevant literature for the SOTA by identifying key terms matching that we, hereafter, call source words. A case study in the food and beverage industry is provided to demonstrate the algorithm’s application. In the study, five relevant knowledge areas were defined to guide literature selection. The database from scientific repositories was categorized using six classification rules based on impact factor (IF), Open Access (OA) status, and JCR journal ranking. This classification revealed the knowledge area with the highest presence and highlighted the effectiveness of the selection rules in identifying articles for the SOTA. The approach included a panel of experts who confirmed the algorithm’s effectiveness in identifying source words in high-quality articles. The algorithm’s performance was evaluated using the $F_1$ Score, which reached 0.83 after filtering out non-relevant articles. This result validates the algorithm’s ability to extract significant source words and demonstrates its usefulness in building the SOTA by focusing on the most scientifically impactful articles. Full article

The accelerating digitalization of the public and private sectors has made information technologies (IT) indispensable in modern life. As services shift to digital platforms and technologies expand across industries, the complexity of legal, regulatory, and technical requirement documentation is growing rapidly. This increase presents significant challenges in managing, gathering, and analyzing documents, as their dispersion across various repositories and formats hinders accessibility and efficient processing. This paper presents the development of an automated repository designed to streamline the collection, classification, and analysis of cybersecurity-related documents. By harnessing the capabilities of natural language processing (NLP) models—specifically Generative Pre-Trained Transformer (GPT) technologies—the system automates text ingestion, extraction, and summarization, providing users with visual tools and organized insights into large volumes of data. The repository facilitates the efficient management of evolving cybersecurity documentation, addressing issues of accessibility, complexity, and time constraints. This paper explores the potential applications of NLP in cybersecurity documentation management and highlights the advantages of integrating automated repositories equipped with visualization and search tools. By focusing on legal documents and technical guidelines from Portugal and the European Union (EU), this applied research seeks to enhance cybersecurity governance, streamline document retrieval, and deliver actionable insights to professionals. Ultimately, the goal is to develop a scalable, adaptable platform capable of extending beyond cybersecurity to serve other industries that rely on the effective management of complex documentation. Full article

As robotic systems become more integrated into our daily lives, there is growing concern about cybersecurity. Robots used in areas such as autonomous driving, surveillance, surgery, home assistance, and industrial automation can be vulnerable to cyber-attacks, which could have serious real-world consequences. Modern robotic systems face a unique set of threats due to their evolving characteristics. This paper outlines the SESAME project’s methodology for the automated security analysis of multi-robot systems (MRS) and the production of Executable Digital Dependability Identities (EDDIs). Addressing security challenges in MRS involves overcoming complex factors such as increased connectivity, human–robot interactions, and a lack of risk awareness. The proposed methodology encompasses a detailed process, starting from system description and vulnerability identification and moving to the generation of attack trees and security EDDIs. The SESAME security methodology leverages structured repositories like Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE), and Common Attack Pattern Enumeration and Classification (CAPEC) to identify potential vulnerabilities and associated attacks. The introduction of Template Attack Trees facilitates modeling potential attacks, helping security experts develop effective mitigation strategies. This approach not only identifies, but also connects, specific vulnerabilities to possible exploits, thereby generating comprehensive security assessments. By merging safety and security assessments, this methodology ensures the overall dependability of MRS, providing a robust framework to mitigate cyber–physical threats. Full article

Large language models’ domain-specific capabilities can be enhanced through specialized datasets, yet constructing comprehensive cybersecurity datasets remains challenging due to the field’s multidisciplinary nature. We present PenQA, a novel instructional dataset for penetration testing that integrates theoretical and practical knowledge. Leveraging authoritative sources like MITRE ATT&CK™ and Metasploit, we employ online large language models to generate approximately 50,000 question–answer pairs.We demonstrate PenQA’s efficacy by fine-tuning language models with fewer than 10 billion parameters. Evaluation metrics, including the BLEU, ROUGE, and BERTScore, show significant improvements in the models’ penetration testing capabilities. PenQA is designed to be compatible with various model architectures and updatable as new techniques emerge. This work has implications for automated penetration testing tools, cybersecurity education, and decision support systems. The PenQA dataset is available in our GitHub repository. Full article

Smart cities are Hyper-Connected Digital Environments (HCDEs) that transcend the boundaries of natural, human-made, social, virtual, and artificial environments. Human activities are no longer confined to a single environment as our presence and interactions are represented and interconnected across HCDEs. The data streams and repositories of HCDEs provide opportunities for the responsible application of Artificial Intelligence (AI) that generates unique insights into the constituent environments and the interplay across constituents. The translation of data into insights poses several complex challenges originating in data generation and then propagating through the computational layers to decision outcomes. To address these challenges, this article presents the design and development of a Hyper-Automated AI framework with Generative AI agents for sustainable smart cities. The framework is empirically evaluated in the living lab setting of a ‘University City of the Future’. The developed AI framework is grounded on the core capabilities of acquisition, preparation, orchestration, dissemination, and retrospection, with an independent cognitive engine for hyper-automation of these AI capabilities using Generative AI. Hyper-automation output feeds into a human-in-the-loop process prior to decision-making outcomes. More broadly, this framework aims to provide a validated pathway for university cities of the future to take up the role of prototypes that deliver evidence-based guidelines for the development and management of sustainable smart cities. Full article

Common challenges in the area of robotics include issues such as sensor modeling, dynamic operating environments, and limited on-broad computational resources. To improve decision making, robots need a dependable framework to facilitate communication between different modules and the optimal action for real-world applications. The Robotics Operating System (ROS) and Reinforcement Learning (RL) are two promising approaches that help accomplish precise control, seamless integration of sensors-actuators, and exhibit learned behavior. The ROS enables seamless communication between heterogeneous components, while RL focuses on learning optimal behaviors through trial-and-error scenarios. Combining the ROS and RL offers superior decision making, improved perception, enhanced automation, and reliability. This work focuses on investigating ROS-based RL applications across various domains, aiming to enhance understanding through comprehensive discussion, analysis, and summarization. We base our evaluation on the application area, type of RL algorithm used, and degree of ROS–RL integration. Additionally, we provide summary of seminal works that define the current state of the art, along with GitHub repositories and resources for research purposes. Based on the review of successfully implemented projects, we make recommendations highlighting the advantages and limitations of RL techniques for specific applications and environments. The ultimate goal of this work is to advance the robotics field by providing a comprehensive overview of the recent important works that incorporate both the ROS and RL, thereby improving the adaptability of these emerging techniques. Full article

The rapid expansion of software applications has led to an increase in the frequency of bugs, which are typically reported through user-submitted bug reports. Developers prioritize these reports based on severity and project schedules. However, the manual process of assigning bug priorities is time-consuming and prone to inconsistencies. To address these limitations, this study presents a Priority-Sensitive LSTM–Attention mechanism for automating bug priority prediction. The proposed approach extracts features such as product and component details from bug repositories and preprocesses the data to ensure consistency. Priority-based feature selection is applied to align the input data with the task of bug prioritization. These features are processed through a Long Short-Term Memory (LSTM) network to capture sequential dependencies, and the outputs are further refined using an Attention mechanism to focus on the most relevant information for prediction. The effectiveness of the proposed model was evaluated using datasets from the Eclipse and Mozilla open-source projects. Compared to baseline models such as Naïve Bayes, Random Forest, Decision Tree, SVM, CNN, LSTM, and CNN-LSTM, the proposed model achieved a superior performance. It recorded an accuracy of 93.00% for Eclipse and 84.11% for Mozilla, representing improvements of 31.11% and 40.39%, respectively, over the baseline models. Statistical verification confirmed that these performance gains were significant. This study distinguishes itself by integrating priority-based feature selection with a hybrid LSTM–Attention architecture, which enhances prediction accuracy and robustness compared to existing methods. The results demonstrate the potential of this approach to streamline bug prioritization, improve project management efficiency, and assist developers in resolving high-priority issues. Full article

Agriculture is a cornerstone of economic prosperity, but plant diseases can severely impact crop yield and quality. Identifying these diseases accurately is often difficult due to limited expert availability and ambiguous information. Early detection and automated diagnosis systems are crucial to mitigate these challenges. To address this, we propose a lightweight convolutional neural network (CNN) designed for resource-constrained devices termed as LeafNet. LeafNet draws inspiration from the block-wise VGG19 architecture but incorporates several optimizations, including a reduced number of parameters, smaller input size, and faster inference time while maintaining competitive accuracy. The proposed LeafNet leverages small, uniform convolutional filters to capture fine-grained details of plant disease features, with an increasing number of channels to enhance feature extraction. Additionally, it integrates channel attention mechanisms to prioritize disease-related features effectively. We evaluated the proposed method on four datasets: the benchmark plant village (PV), the data repository of leaf images (DRLIs), the newly curated plant composite (PC) dataset, and the BARI Sunflower (BARI-Sun) dataset, which includes diverse and challenging real-world images. The results show that the proposed performs comparably to state-of-the-art methods in terms of accuracy, false positive rate (FPR), model size, and runtime, highlighting its potential for real-world applications. Full article

The ability of AI to process vast datasets can enhance creativity, but its rigid knowledge base and lack of reflective thinking limit sustainable design. Generative Design Thinking (GDT) integrates human cognition and machine learning to enhance design automation. This study aims to explore the cognitive mechanisms underlying GDT and their impact on design efficiency. Using behavioral coding and quantitative analysis, we developed a three-tier cognitive model comprising a macro-cycle (knowledge acquisition and expression), meso-cycle (creative generation, intelligent evaluation, and feedback adjustment), and micro-cycle (knowledge base and model optimization). The findings reveal that increased task complexity elevates cognitive load, supporting the hypothesis that designers need to allocate more cognitive resources for complex problems. Knowledge base optimization significantly impacts design efficiency more than generative model refinement. Moreover, creative generation, evaluation, and feedback adjustment are interdependent, highlighting the importance of a dynamic knowledge base for creativity. This study challenges traditional design automation approaches by advocating for an adaptive framework that balances cognitive processes and machine capabilities. The results suggest that improving knowledge management and reducing cognitive load can enhance design outcomes. Future research should focus on developing flexible, real-time knowledge repositories and optimizing generative models for interdisciplinary and sustainable design contexts. Full article

Introduction: Chronic ulcers significantly burden healthcare systems, requiring precise measurement and assessment for effective treatment. Traditional methods, such as manual segmentation, are time-consuming and error-prone. This review evaluates the potential of artificial intelligence AI-powered mobile apps for automated ulcer segmentation and their application in clinical settings. Methods: A comprehensive literature search was conducted across PubMed, CINAHL, Cochrane, and Google Scholar databases. The review focused on mobile apps that use fully automatic AI algorithms for wound segmentation. Apps requiring additional hardware or needing more technical documentation were excluded. Vital technological features, clinical validation, and usability were analysed. Results: Ten mobile apps were identified, showing varying levels of segmentation accuracy and clinical validation. However, many apps did not publish sufficient information on the segmentation methods or algorithms used, and most lacked details on the databases employed for training their AI models. Additionally, several apps were unavailable in public repositories, limiting their accessibility and independent evaluation. These factors challenge their integration into clinical practice despite promising preliminary results. Discussion: AI-powered mobile apps offer significant potential for improving wound care by enhancing diagnostic accuracy and reducing the burden on healthcare professionals. Nonetheless, the lack of transparency regarding segmentation techniques, unpublished databases, and the limited availability of many apps in public repositories remain substantial barriers to widespread clinical adoption. Conclusions: AI-driven mobile apps for ulcer segmentation could revolutionise chronic wound management. However, overcoming limitations related to transparency, data availability, and accessibility is essential for their successful integration into healthcare systems. Full article

Visual biosignals can be used to analyze human behavioral activities and serve as a primary resource for Facial Expression Recognition (FER). FER computational systems face significant challenges, arising from both spatial and temporal effects. Spatial challenges include deformations or occlusions of facial geometry, while temporal challenges involve discontinuities in motion observation due to high variability in poses and dynamic conditions such as rotation and translation. To enhance the analytical precision and validation reliability of FER systems, several datasets have been proposed. However, most of these datasets focus primarily on spatial characteristics, rely on static images, or consist of short videos captured in highly controlled environments. These constraints significantly reduce the applicability of such systems in real-world scenarios. This paper proposes the Facial Biosignals Time–Series Dataset (FBioT), a novel dataset providing temporal descriptors and features extracted from common videos recorded in uncontrolled environments. To automate dataset construction, we propose Visual–Temporal Facial Expression Recognition (VT-FER), a method that stabilizes temporal effects using normalized measurements based on the principles of the Facial Action Coding System (FACS) and generates signature patterns of expression movements for correlation with real-world temporal events. To demonstrate feasibility, we applied the method to create a pilot version of the FBioT dataset. This pilot resulted in approximately 10,000 s of public videos captured under real-world facial motion conditions, from which we extracted 22 direct and virtual metrics representing facial muscle deformations. During this process, we preliminarily labeled and qualified 3046 temporal events representing two emotion classes. As a proof of concept, these emotion classes were used as input for training neural networks, with results summarized in this paper and available in an open-source online repository. Full article

The precise monitoring of insect pest populations is the foundation of Integrated Pest Management (IPM) for pests of plants, humans, and animals. Digital technologies can be employed to address one of the main challenges, such as reducing the IPM workload and enhancing decision-making accuracy. In this study, digital technologies are used to deploy an automated trap for capturing images of insects and generating centralized repositories on a server. Subsequently, advanced computational models can be applied to analyze the collected data. The study provides a detailed description of the prototype, designed with a particular focus on its remote reconfigurability to optimize repository quality; and the server, accessible via an API interface to enhance system interoperability and scalability. Quality metrics are presented through an experimental study conducted on the constructed demonstrator, emphasizing trap reliability, stability, performance, and energy consumption, along with an objective analysis of image quality using metrics such as RMS contrast, Image Entropy, Image sharpness metric, Natural Image Quality Evaluator (NIQE), and Modulation Transfer Function (MFT). This study contributes to the optimization of the current knowledge regarding automated insect pest monitoring techniques and offers advanced solutions for the current systems. Full article

Network management and troubleshooting require not only a grasp of advanced concepts but also the development of analytical and problem-solving skills. To bridge this gap, this paper introduces a novel network administration system, DRACSC (Spanish acronym for device for automatic recovery and configuration of communication systems), designed for the automatic configuration and disaster recovery of communication equipment. This system transcends the limitations of current hardware and software solutions by combining their advantages, boasting portability, automated functions, and a cloud-based repository as its main features. The DRACSC system, undergoing a comprehensive large-scale evaluation involving diverse user groups across multiple institutions, was tested with 89 users, including students and teachers at educational centers and ICT (Information and Communication Technology) professionals. The benefits of the system were evaluated through a training program based on simulated real-world ICT environments, focusing on both quantitative results on the reduction in time to complete user tasks, as well as qualitative results on the interface and usability of the system. Statistical analysis, including Welch’s t-test on opinion surveys, indicated a significant increase in knowledge and understanding, demonstrating the system’s potential to enhance education and practice. Moreover, the evaluation shed light on the user experience, with positive impacts observed for learning and teaching implications. As a result, the study has verified that the system has the potential to significantly influence network management practices, enhancing both learning and professional application through improved efficiency and usability. Full article

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data. Full article

Controlled environment agriculture is a promising alternative to conventional production methods, as it is less affected by climate change and is often more sustainable, especially in circular and recycling frameworks such as aquaponics. A major cost factor in such facilities, however, is the need for skilled labor. Depending on available resources, there are endless possibilities on how to choose ingredients to realize a desired nutrient solution. At the same time, the composition of the desired solution is subject to fluctuations in fish water quality, fertilizer availability, weather, and plant development. In high-evaporation scenarios, e.g., summer, nutrient solutions might be mixed multiple times per day. This results in a complex, multi-variable task that is time-consuming to solve manually, yet requires frequent resolution. This work aims to help solve this challenge by providing methods to automate the nutrient mixing procedure. A simple mass-balance-based model of a nutrient mixing tank with connections to different water sources, drains, and fertilizers is provided. Using methods of static optimization, a program was developed which, in consideration of various process constraints and optimization variables, is able to calculate the necessary steps to mix the desired solution. The program code is provided in an open-source repository. The flexibility of the method is demonstrated in simulation scenarios. The program is easy to use and to adapt, and all necessary steps are explained in this paper. This work contributes to a higher automation level in CEA. Full article