Open Cloud (O-Cloud) is the Open Radio Access Network (O-RAN) computing infrastructure spanning edge to cloud sites defined by the O-RAN Alliance to support and coordinate RAN infrastructure management shared among multiple Mobile Network Operators (MNO). In the standard definition, O-Cloud ensures reliable connectivity, active coordination, and efficient distribution of the Radio Access Network (RAN) deployments. Thanks to these properties, O-Cloud can support Mobile Network and Infrastructure operators to achieve fully automated infrastructure management, self-service MNO portals, enhanced security measures, and O-Cloud infrastructure-agnostic management. In this paper, we present TORNADO: TOSCA-enabled Orchestration for RAN in Next-Generation Networks Automating DevOps in O-Cloud, an O-Cloud automation infrastructure designed to ease DevOps deployment and operation phases of RAN network functions for multiple MNOs. Our solution introduces infrastructure-agnostic automation for multi-site, multi-MNO RAN components, offering high-level, secure self-service MNO portals for defining RAN deployments. Performance evaluation of our solution for various RAN network functions demonstrated the capability of TORNADO to automate the deployment in a multi-site, multi-MNO heterogeneous infrastructure.
The increasing reliance on the judiciary to secure access to healthcare in Brazil has created a vast corpus of legal rulings that contain insights into healthcare delivery. However, extracting actionable information from these unstructured texts remains a significant challenge. This study evaluates the performance of state-of-the-art large language models (LLMs) for healthcare-related named entity recognition (NER) in Brazilian judicial decisions. Using our release dataset, LexCare.BR, a manually annotated gold-standard dataset of 1,200 legal rulings, we assessed 10 predefined healthcare entities across multiple LLMs, including both open-source and closedsource models. Our results show that larger models, such as GPT-4o and Llama 3.1, achieved the highest overall F1-scores (0.739 and 0.694, respectively), demonstrating robust capabilities in extracting clinically and policy-relevant entities. However, we observed significant variations in performance across entity types, with higher precision for standardized codes, such as ICD-10, but lower recall for context-dependent categories like dietary supplements. These findings highlight the potential of LLMs to automate the extraction of structured healthcare data from judicial texts, thereby enabling real-time monitoring of healthcare judicialization and informing targeted policy interventions. Data is available at https://github.com/eliasjacob/lexcare.br
This study presents a fine-tuning of the BERTimbau model for Named Entity Recognition (NER) in Electronic Health Records (EHR), focusing on oncology data in Brazilian Portuguese. The proposed approach employs an annotated dataset formatted in the Inside-Outside-Beginning (IOB) scheme, enabling precise extraction of clinical entities. The model was trained using the BERTimbau Base Tokenizer, with hyperparameters optimized through the Hugging Face library. Experimental results demonstrate robust performance, achieving F1scores between 0.83 and 0.85 across disease, procedure, and medication categories. When applied to a dataset of 125,825 new clinical records, the model achieved a correct identification rate exceeding 92%, underscoring its potential for medical decision support. Key challenges include enhancing classification accuracy for less frequent entities and adapting the model to diverse clinical contexts. Future directions involve dataset expansion, data augmentation techniques, exploration of more advanced transformer-based models, and integration into hospital decision support systems to further improve healthcare analytics.
Public key X. 509 certificates play a powerful role in promoting effective electronic identification, but some significant practical issues still affect their scalability. Every time a public key certificate is used, it must be validated by the system or application relying on it for security services, generically called also relying party. The validation involves several processing steps and checks, and it has been measured that many applications (still) perform it incompletely. Furthermore, privacy issues may occur when validating certificates, for example a website visited by a user could be revealed to external parties. We propose TPValCert, an architecture tailoring a trusted proxy to provide privacy-preserving certificate validation service to the relying parties. By exploiting TPValCert, a desktop, IoT, or mobile system that needs to validate a public-key certificate may execute a transaction with a trusted proxy, which performs validation by considering certificate policy parameters, privacy, and validation options received from the client and returns the validation status. Besides reducing complexity on the client, exploiting such trusted validation parties may also bring privacy benefits. To communicate with the clients, we consider the SCVP (Server-based Certificate Validation Protocol) or DVCS (Data Validation and Certification Server) protocols, even though, depending on the context, lighter formats could be considered. Our implementation efforts emphasize the possibility of pursuing a tradeoff between timeliness, privacy, and computational resource usage, via dynamic selection of several configurable options.
Multiomics is an emerging biological analysis approach in which the datasets come from multiple “omics”, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. Nowadays, the convergence of Deep Learning and multiomics sciences presents an unprecedented opportunity to dissect the intricate interplay of biological processes. Specifically, multiomics data integration, propelled by Deep Learning methodologies, has revolutionised biological research, enabling a more holistic understanding of complex biological systems and disease mechanisms. This paper explores the current landscape of Deep Learning applications in multiomics, highlighting state-of-the-art techniques, emerging research areas, and the challenges that lie ahead. In particular, we delve into the application areas and computational methods that have been considered so far, offering guidance to researchers navigating this intricate field.
Network Function Virtualization (NFV) provides a flexible mechanism for deploying Virtual Network Functions (VNFs) within Service Function Chains (SFCs), thereby streamlining data transfers between end-users and edge/cloud resources. The distinct requirements for forward and backward traffic—each carrying different types of content—give rise to Hybrid SFCs (HSFCs), which must be carefully designed to address unique deployment and performance concerns. However, achieving robust disaster resilience in HSFC-based NFV environments poses significant challenges, as natural or hardware-induced disruptions within Disaster Zones (DZs) can degrade service quality or even cause outages. This paper describes the Resilient Hybrid Service Function Chain Resource Optimization (R-HSFC-RO) approach to ensure both efficient resource utilization and sustained service delivery under disaster conditions. Our model considers bandwidth consumption, computational resource allocation for VNF execution, VNF instantiation overheads, and end-to-end latency requirements. For resolution, we propose a Mixed-Integer Linear Programming (MILP) model and Constraint Programming (CP), thereby enabling optimal solutions. Simulation results demonstrate that R-HSFC-RO reduces total costs by up to 50%, enhances disaster resilience, and maintains high operational efficiency.
Federated Learning (FL) is increasingly used in healthcare to enable collaborative model training across decentralized medical institutions while preserving patient privacy. Despite its promise, FL faces significant challenges in medical image processing, such as class imbalance and client data heterogeneity, which can lead to biased models and reduced accuracy in detecting rare diseases. To address these issues, we propose a novel approach, called Syndicated Federated Learning (SyndFL), comprising a multi-layer client selection algorithm that emphasizes fair client representation based on dataset size, learning performance, label distribution, and domain-specific features. SyndFL not only prioritizes clients from minority data clusters but also includes adaptive weighting to ensure that rare conditions receive adequate representation, reducing the risk of bias in model aggregation. Experiments conducted on healthcare image datasets demonstrate that SyndFL achieves a $\mathbf{1 0}-\mathbf{2 0 \%}$ improvement in detecting rare conditions compared to standard FL methods, significantly enhancing both model robustness and fairness in clinical decision-making and diagnostics.
IoT botnets have been adopted as the prime infrastructure for a plethora of cybercrime and modern cyberwarfare. Evidently, conventional defence approaches fail to capture the full spectrum of IoT botnet activity by virtue of attackers evading schemes and limited Internet visibility. In this work, we develop a novel macroscopic analysis framework that profiles malware strains through payload signatures gathered from malicious traffic, revealing distinct botnet variants and their infrastructure. Through payload clustering distilled by information retrieval properties and DNS-based infrastructure mapping, we systematically group botnet families, identifying distinct exploitation trends and infrastructure reuse patterns. Our longitudinal study over real pre-captured datasets for a $\mathbf{4}$-year period reveals widespread lack of blacklist coverage with $65.94 \%$ of discovered malicious IPs and $\mathbf{9 8. 9 7 \%}$ of associated domains not yet blacklisted. We pinpoint a growing trend of botnet operators leveraging cloud services such as AWS, OVH, and Linode, hosting their command-and-control (C2) servers on reputable domains to bypass security filters and extend operational longevity. Through demonstrating practical metrics to assess botnet scan volume, vulnerability trends, and infection rates, we stress the need to refine existing defence mechanisms. In parallel, we set solid ground for practical threat hunting and risk profiling for next-generation cybersecurity schemes.
Stroke significantly impacts both survivors and their caregivers, who face numerous challenges in providing care, including emotional distress, physical strain, and a lack of adequate training. While mobile health applications offer some support, they often focus on knowledge dissemination rather than practical skill development, and fail to address the unique, culturally specific needs of caregivers. Furthermore, technical limitations, such as poor internet connectivity and unclear interfaces, hinder the effectiveness of these tools. This research explores the use of gamification as an innovative solution to improve caregiver training and support. Through a systematic literature review following the PRISMA guidelines, studies related to caregiver challenges and existing mobile applications for stroke management were examined. The review highlights the need for comprehensive, tailored training programs, and suggests that gamified interventions could enhance caregiver engagement, motivation, and knowledge retention, ultimately improving both caregiver preparedness and patient outcomes. The findings emphasize the potential of gamification to bridge gaps in current caregiver training solutions, addressing both practical caregiving skills and emotional support needs.
Delay Tolerant Networks (DTNs) play a vital role in disaster response to address intermittent connectivity, in particular when Unmanned Aerial Vehicles (UAVs) are deployed to relay critical information. However, most existing DTN protocols are highly susceptible to Denial-of-Service (DoS) attacks because their flooding-based or simplistic routing decisions can be exploited by malicious nodes to quickly saturate buffers, exhausting network resources and disrupting legitimate traffic. This paper presents an Epidemic Oracle (EO) implementation, to mitigate DoS threats by intelligently removing delivered messages from all buffers, thus reducing overhead and freeing network resources. Through extensive simulation in the ONE environment, EO is evaluated against three established encounter-based DTN protocols—Epidemic, Spray and Wait, and Spray and Wait Binary—under varying buffer sizes, transmission speeds, and both aggressive and stealthy DoS attacks. The findings indicate that EO substantially increases delivery ratios while curtailing buffer congestion, even under severe adversarial conditions. These improvements highlight the potential of oracle-based interventions to bolster performance in UAV-assisted disaster scenarios, paving the way for more resilient and efficient DTNs in emergency communications.
In the contemporary digital landscape, individuals are often overwhelmed by the plethora of choices available, including articles, the focus of this article. This paper explores recommender systems aimed at enhancing diversity and mitigating the homogeneity often found in traditional systems to help reduce the risk of filter bubbles and stereotypes, ensuring fair and inclusive user experiences and with little cold start problem as an item-based system. Compared to a Maximal Marginal Relevance (MMR) baseline, the GA-based system is adept at providing relevant and diverse recommendations, particularly when generating a large set of options based on limited input. In addition, the operational speed of the genetic algorithm (GA) due to its constant-time response to varying recommendation sizes underscores its practical advantages. For customizability, the system’s linear and predictable response to adjustments in the trade-off parameters enhances its adaptability to different user needs. These advancements make the GA-based recommender system a potent tool for addressing the complexity and diversity of user preferences in large-scale applications, presenting a significant step forward in the development of recommender systems.
Cloud-Edge Computing Continuum (CECC) systems drive digital transformation by linking cloud services to decentralized edge devices, supporting critical applications such as eHealth. However, trust management in such dynamic and distributed federated systems remains a challenge. This paper proposes the Trust Manager, an advanced framework that integrates the IEEE Federation Hosting Service (FHS) model with blockchain technology. It enhances trust through comprehensive trust profiles for infrastructure providers, incorporating performance metrics, Service Level Agreement (SLA) compliance, and user feedback, thus improving scalability, performance, and reliability in federated systems.
With the advancement of industrialization, air pollution has become a significant concern. This study presents a predictive analysis of PM2.5 in New York City using Long ShortTerm Memory (LSTM) neural networks to forecast pollution concentration levels. The key challenge is that the LSTM model lacks the spatial awareness needed for inference on a hyperlocal street level. To overcome this limitation, we enrich the dataset with additional geospatial features using geohash referencing and a geospatial join operation to capture PM2.5 patterns using features with varying spatial granularity. This enables the model to incorporate spatial context, shifting the problem from purely temporal to spatial temporal. We evaluated the models using several statistical metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), for the LSTM model, other machine learning (ML) models and networks, such as RNN, MLP, SVR, and LR. A comparison of the results indicates that the LSTM model with a geohash precision of 9 outperformed other models in predicting pollution levels.
Spirometry is used for evaluating lung function, playing a crucial role in assessing lung health and monitoring treatment effectiveness. Numerous studies demonstrate the potential of Machine Learning algorithms to match human experts in spirometry classification, although most approaches depend on custom data pre-processing and complex model architectures. Therefore, we apply efficient Time Series (TS) classifiers to quickly and computationally assure spirometry signal quality, enabling realtime deployment. Seven classifiers were implemented to classify spirometry curves as ‘Acceptable’ or ‘Not Acceptable’, with performance referenced against results from similar studies. The bestperforming classifier was FreshPRINCE, a TS method combining TSFresh feature extraction with Rotation Forest classifier. The FreshPRINCE model achieved an accuracy of 0.9449, precision, recall and $F 1$ Score of $0.9745,0.9586$ and 0.9665, consistently matching and sometimes outperforming more complex models. These findings suggest models, such as FreshPRINCE, could streamline spirometry analysis, reducing computational burden, whilst maintaining classification performance.
Live video streaming is becoming increasingly prevalent in today’s Internet landscape. Current streaming solutions struggle to maintain low latency while delivering high-quality content, particularly under varying network conditions. In this paper, we investigate the effectiveness of different adaptive streaming algorithms over TCP and QUIC protocols as potential solutions for low-latency live streaming. Our results reveal a fundamental trade-off between quality-focused and stabilityfocused algorithms. These findings provide practical guidelines for algorithm selection in live streaming applications and identify the need for new algorithms that better balance quality and stability.
Satellite edge cloud technology combines edge computing and satellite communications to improve data processing and reduce latency. However, traditional satellite systems have fixed hardware configurations and functions after launch, lacking the ability for dynamic adjustment. Due to the high-speed motion of low earth orbit (LEO) satellites and limited communication windows, the software update processes can take tens of thousands of seconds, posing significant challenges to meeting real-time requirements. To address these challenges, this paper proposes Astraeus, an efficient containerized deployment strategy designed to tackle the difficulties of heterogeneous software deployment and high startup latency. We propose a cluster partitioning strategy tailored for large-scale satellite nodes and design a cluster collaboration mechanism based on data partitioning and bandwidth resource optimization, which enables fast distribution of large image files. Furthermore, we innovatively integrate lazy pulling technology into the satellite edge cloud. This approach significantly reduces the dependency on downloading complete images for container startup by fetching only the image index files and dynamically loading the required content during runtime. Experimental results show that our proposed approach reduces container deployment time by $99 \%$ and accelerates file distribution speed by $98 \%$.
Evaluating chatbot adaptability after deployment remains critical for ensuring ongoing relevance and user satisfaction. While previous research compared federated vs. traditional architectures for intent classification, the post-deployment adaptation capabilities of chatbots-particularly through self-feeding mechanisms-remain relatively unexplored. This paper evaluates self-feeding mechanisms in federated and centralized chatbot architectures, specifically investigating the impact of explicit and implicit user feedback on chatbot adaptability post-deployment. We empirically assess the effectiveness of these feedback loops in addressing data drift and improving intent classification accuracy over time. Through a comparative analysis, the study highlights distinct strengths and limitations in each approach, providing new insights into how chatbots can continuously enhance user experience and learning performance. Our findings emphasize the critical role of self-feeding mechanisms for sustainable chatbot operations, extending beyond initial training toward robust, ongoing performance improvements, complementing the literature on privacy-centric federated chatbot systems.
E-health systems have revolutionized healthcare by enabling efficient data sharing and management. However, they face significant security and privacy challenges, including unauthorized access, data breaches, identity fraud, and insurance fraud. Existing solutions attempt to address these issues but suffer from single points of failure, lack of patient-defined access control, and inadequate privacy-preserving mechanisms. This paper proposes a dual-blockchain architecture integrated with Self-Sovereign Identity and Zero-Knowledge Proofs to enhance security, privacy, and fraud resilience. The framework employs Decentralized Identifiers and Verifiable Credentials for secure authentication while leveraging the InterPlanetary File System for decentralized Electronic Health Records storage. By addressing the limitations of current systems, the proposed solution ensures a more secure, scalable, and privacy-preserving e-health environment.
Food and nutrition insecurity is a growing global challenge, exacerbated by crises like the COVID-19 pandemic and disruptions in supply chains caused by geopolitical events. This study introduces a hybrid Web3-Web2 system to enhance food security through monitoring the transaction-based food system at a national level. The system comprises elements based on blockchain technology and business intelligence (BI) in the state of Israel. By combining the decentralization, transparency, and immutability of blockchain with some functionalities of Web2 applications, the system enables real-time monitoring and optimization of Israel’s food supply chain. The blockchain component, a natural candidate for storing transaction-based data, ensures data integrity, traceability, and trust among stakeholders, while the BI dashboards facilitate data-driven decision-making and efficient resource allocation. The system implements smart contracts to automate compliance verification while maintaining transaction privacy through strategic data partitioning between public and private storage. The system also leverages graph-based network analysis to identify inefficiencies, minimize food waste, and enhance supply chain sustainability. We discuss how technology can address food security challenges and outline pathways for implementation, offering a model for other nations facing similar issues.
The advancement of smart city services requires innovative approaches to manage urban transportation and enhance tourist mobility through sensor-based IoT data models for efficient processing. This work proposes an integrated tourist recommendation system within a multimodal travel orchestration platform, named Multipres. Designed to support sustainable tourism, Multipres optimizes urban infrastructure, including buses, bicycles, and pedestrian paths, while reducing resource strain. The platform leverages AI-based recommendation engines, integrates IoT mobility sensors, bike-sharing stations and vehicles’ parking platforms, and external applications, such as Waze. Results demonstrate that Multipres provides recommendations according to the users’ needs with updated information through the GenAI integration, in a multimodal approach with real-time data from the city sensing and external platforms, both for mobility and traffic events, and taking into account environmentally indicators.
