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Latin American Test Workshop, LATW

作者： Juan-David Guerrero-Balaguera Josie E. Rodriguez Condia Robert Limas Sierra Matteo Sonza Reorda Politecnico di Torino - Department of Control and Computer Engineering (DAUIN)

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

1 1 Work supported by the National Resilience and Recovery Plan (PNRR) through the National Center for HPC, Big Data and Quantum *** Learning Accelerators (DLA) are pervasive hardware units in modern applications, including safety critical systems such as Automotive, Aerospace, Robotics, and health monitoring systems. Therefore, it is crucial to guarantee their reliability during the mission operation of any of these applications, as a failure can produce catastrophic results (e.g., loss of human lives). In fact, modern semiconductor technologies used to implement DLAs can be affected by faults due to several phenomena, such as aging, process variation, or manufacturing defects. Periodic testing and functional testing strategies have demonstrated their utility and effectiveness on DLA accelerators in spotting faults arising during the in-field operation of the system. Nonetheless, these approaches can have significant testing times and memory footprint overheads, making them hard to apply during the online operation of large-size accelerators. This work proposes an effective strategy for generating efficient functional test patterns for computational units in DLA accelerators by reducing the required testing time (43×) and memory footprints (3.3×) compared with literature solutions. Moreover, our strategy provides diagnostic capabilities for identifying defective units (e.g., multipliers).

关键词： Quantum computing Memory management AI accelerators Built-in self-test Hardware Manufacturing Reliability Robots Monitoring Resilience

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Optimizing the LSTM Model for Electricity Price Forecasting Using the MSADBO Algorithm

Optimizing the LSTM Model for Electricity Price Forecasting ...

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2025 IEEE Texas Power and Energy Conference, TPEC 2025

作者： Ahmadvand, Fatemeh Imani, Mahmood Hosseini Andani, Hamid Taheri Andani, Mansour Taheri Politecnico di Torino Department of Control and Computer Engineering Torino Italy Torino Italy Isfahan University of Technology Department of Electronic Engineering Isfahan Iran School of Manufacturing Systems and Networks Arizona State University TempeAZ United States

ISBN: (纸本)9798331541125

Achieving accurate electricity price prediction is essential for market participants aiming to maximize their profits. In this respect, forecasting wholesale electricity market price plays a pivotal role. The advanced nonlinear modeling capabilities of deep learning networks have shown notable success in forecasting electricity prices. However, designing such networks, particularly selecting their optimal hyperparameters, still remains a big challenge. This paper presents a Modified Sine Algorithm Dung Beetle Optimization algorithm (MSADBO) as an improved Dung Beetle Optimization (DBO) algorithm to optimize the hyperparameters of the Long Short-Term Memory (LSTM) neural network to handle the electricity price forecasting in the Italian wholesale market. After optimizing the three most important hyperparameters of the LSTM using MSADBO, the efficiency of the model has shown a drastic improvement. Comparisons with conventional and machine learning-based approaches show that the LSTM-MSADBO model is highly precise in predictions with increased robustness and effectively captures volatility in the electricity market price. © 2025 IEEE.

关键词： Long short-term memory

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A Cooperative Feature Removal Mechanism for Cell Outage Detection in Wireless Telecommunication Networks 19th

A Cooperative Feature Removal Mechanism for Cell Outage Det...

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19th International Conference on Critical Information Infrastructures Security, CRITIS 2024

作者： Wrona, Andrea Gentile, Simone De Santis, Emanuele Giuseppi, Alessandro Pietrabissa, Antonio Delli Priscoli, Francesco Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome via Ariosto 25 Lazio Rome00185 Italy Department of Electrical and Information Engineering Polytechnic of Bari via Re David 200 Puglia Bari70125 Italy

ISBN: (纸本)9783031842597

In the domain of crisis management for telecommunications infrastructures, the autonomous detection of cell outages within cellular networks is of paramount importance for prompt identification and resolution in ensuring uninterrupted connectivity to users. Traditional methods usually involve data aggregation at the core network, which is responsible for identifying cell failures. Proposing a novel approach, we leverage a Machine Learning-based distributed and cooperative feature removal mechanism in order to preserve the privacy of data and avoid any degradation in classification performance. Simulations carried out on a dataset retrieved from a real 4G-LTE rollout demonstrate that the proposed approach, through a cooperation among agents, maintains or even slightly improves accuracy, precision, recall, and F1-score in outage prediction compared to other conventional methods, showcasing its efficacy for cell outage detection purposes while maintaining data privacy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Federated learning

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Transforming Training with New Enabling Technologies: A Proposal to Verify the Efficacy of Virtual Reality Tools in the Occupational Health and Safety Sector 8th

Transforming Training with New Enabling Technologies: A Prop...

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8th World Conference on Smart Trends in Systems Security and Sustainability, WorldS4 2024

作者： Bentivenga, Rosina Bernabei, Margherita Carli, Marco Colabianchi, Silvia Costantino, Francesco Ferrarotti, Anna Neri, Michael Pietrafesa, Emma Sorrentino, Edvige Stabile, Sara Department of Occupational and Environmental Medicine Epidemiology and Hygiene INAIL Rome Italy Department of Mechanical and Aerospace Engineering Sapienza University of Rome Rome Italy Department of Industrial Electronic and Mechanical Engineering University of Roma Tre Rome Italy Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Rome Italy

ISBN: (纸本)9789819793266

In the context of digital transformation in the occupational health and safety sector, this research focuses on the efficacy of virtual reality training tools in ensuring workplace safety during maintenance activities. A comprehensive methodology, including focus groups and workshops, is utilized to define a study protocol and evaluate the proposed virtual reality solution. The study suggests that immersive virtual reality training, which integrates the Skill-Rule-Knowledge framework, has the potential to enhance workers’ ability to manage unforeseen situations. It emphasizes the importance of practical, scenario-based training and outlines a detailed evaluation process. The conclusion highlights the need for ongoing validation and future steps to extend the application to supervisors, fostering improved health and safety management in the workplace. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Virtual environments

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Application of a digital intelligent assistant to support industrial processes: the case of adaptive allocation in the face of cyber attacks

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Procedia computer Science 2025年 253卷 1555-1564页

作者： Silvia Colabianchi Department of Computer control and management engineering “Antonio Ruberti” Via Ariosto 25 Rome 00185 Italy

The research proposes the application of Digital Intelligent Assistants (DIAs) as proactive agents that can support employees in dealing with cybersecurity issues in sustainable industrial processes underlying the importance of a fruitful Human-Artificial Intelligence collaboration. Cyber-attacks around the world are constantly increasing. DIAs are becoming more effective, also thanks to the use of Large Language Models. Users are required to recall security procedures and rules. Moreover, attacks are constantly evolving and following different patterns. The study presents how a DIA can be a backup agent during and after an attack. The application of digital intelligent assistance technology helps to reduce the cognitive load and pressure that users feel during downtime. In addition, the solution enhances attack reporting by decreasing the shame experienced by the victims. The research proposes a methodological design defining the agent’s technical and functional characteristics and its adaptive relationship with human characteristics. The solution is developed using the RASA framework and evaluated through a case study based on a phishing attack scenario.

关键词： chatbot adaptive automation industry 4.0 cybersecurity

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MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN 2

MRI Brain Tumor Classification Using Optimized APSO-LLRBFNN

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Krishna, Tiruveedula Gopi Sreenivas, Nune Ajay Kumar, M. Reddy, Chandra Sekhar Rathee, Davinder Sing Urgessa, Teklu College of Electrical Engineering and Computing Adama Science and Technology University Department of Computer Science and Engineering Adama Ethiopia Andhra Loyala institute of Engineering and Technology Department of Electrical and Electronics Engineering Vijayawada India College of Electrical Engineering and Computing Adama Science and Technology University Department of Electrical Power and Control Engineering Adama Ethiopia College of Electrical Engineering and Computing Adama Science and Technology University Department of ECE Adama Ethiopia

ISBN: (纸本)9798331505745

The research presents a new efficient machine learning method to classify brain tumors because this task remains vital in fighting the high incidence of brain cancers. The proposed approach unites all its operations including image segmentation with feature extraction and classification into a single processing system. This study uses Particle Swarm Optimization (PSO) and Adaptive PSO (APSO) methodologies to optimize Locally Linear Radial Basis Function Neural Network (LLRBFNN) resulting in PSO-LLRBFNN and APSO-LLRBFNN model implementations. The research examined the optimized APSO-LLRBFNN models against Support Vector Machines (SVM) and Least Mean Squares (LMS) based LLRBFNN classifier methods. Results from experiments show that APSO-LLRBFNN gives superior outcomes with 98.93% accuracy and takes 12.23 seconds for execution, which surpasses currently available methods. © 2025 IEEE.

关键词： Particle swarm optimization (PSO)

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Z-SASLM: Zero-Shot Style-Aligned SLI Blending Latent Manipulation

arXiv

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arXiv 2025年

作者： Borgi, Alessio Maiano, Luca Amerini, Irene Sapienza University of Rome Department of Computer Control and Management Engineering Italy

We introduce Z-SASLM, a Zero-Shot Style-Aligned SLI (Spherical Linear Interpolation) Blending Latent Manipulation pipeline that overcomes the limitations of current multi-style blending methods. Conventional approaches rely on linear blending, assuming a flat latent space leading to suboptimal results when integrating multiple reference styles. In contrast, our framework leverages the nonlinear geometry of the latent space by using SLI Blending to combine weighted style representations. By interpolating along the geodesic on the hypersphere, Z-SASLM preserves the intrinsic structure of the latent space, ensuring high-fidelity and coherent blending of diverse styles—all without the need for fine-tuning. We further propose a new metric, Weighted Multi-Style DINO VIT-B/8, designed to quantitatively evaluate the consistency of the blended styles. While our primary focus is on the theoretical and practical advantages of SLI Blending for style manipulation, we also demonstrate its effectiveness in a multi-modal content fusion setting through comprehensive experimental studies. Experimental results show that Z-SASLM achieves enhanced and robust style alignment. The implementation code can be found at: https://***/alessioborgi/ZSASLM. © 2025, CC BY.

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GenSUMO: GenAI-Creation of Critical Scenarios for Autonomous Vehicle Testing 22

GenSUMO: GenAI-Creation of Critical Scenarios for Autonomous...

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22nd IEEE Consumer Communications and Networking Conference, CCNC 2025

作者： Angelini, Sergio Augusto Gasco, Diego Casetti, Claudio Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798331508050

Recent advancements in autonomous vehicle research highlight the importance of Machine Learning (ML) models in tasks like motion planning, trajectory prediction, and emergency management. To support AI development, we propose a novel approach for generating on-demand datasets using the Simulator of Urban Mobility (SUMO) and a Generative Adversarial Network (GAN). Our method focuses on capturing critical events such as sudden pedestrian crossings, near-misses, and collisions, providing essential data to improve vehicle models' responses to emergency situations. © 2025 IEEE.

关键词： AI Autonomous vehicles Critical environments GAN SUMO Vehicular simulation

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Late Contribution: VeriSide: A Modified Verilator for Leakage Assessment at the RTL Level

Late Contribution: VeriSide: A Modified Verilator for Leakag...

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Latin American Test Workshop, LATW

作者： Behnam Farnaghinejad Antonio Porsia Annachiara Ruospo Alessandro Savino Stefano Di Carlo Ernesto Sanchez Department of Control and Computer Engineering Politecnico di Torino Turin Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

Leakage assessment at the Register Transfer Level (RTL) is essential for identifying vulnerabilities in various designs, including cryptographic systems, AI models, and other applications handling sensitive data during the design phase. This paper introduces VeriSide, an innovative framework built as a modified version of Verilator to generate compact format files that directly capture side-oriented information, such as Hamming Distance (HD) or Hamming Weight (HW) of the signals. VeriSide streamlines the power side-channel (PSC) analysis process by providing efficient and scalable solutions for large-scale designs. Traditional methods relying on verbose Value Change Dump (VCD) or Switching Activity Interchange Format (SAIF) files face significant scalability and resource challenges, especially for complex systems-on-chip (SoCs). These methods incur substantial storage and processing overheads. VeriSide overcomes these limitations by drastically reducing file size and eliminating post-simulation memory usage, while maintaining analysis accuracy.

关键词： Accuracy Scalability Switches Register transfer level Data models Hamming distances Cryptography Hamming weight Artificial intelligence Faces

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Energy-Free Sensing and Context Recognition Using Photovoltaic Cells

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IEEE Sensors Journal 2025年第9期25卷 15598-15611页

作者： Shintani, Kaede Rizk, Hamada Yamaguchi, Hirozumi Osaka University Graduate School of Information Science and Technology Department of Information Networking Suita565-0871 Japan Tanta University Tanta31733 Egypt RIKEN Center for Computational Science Kobe650-0047 Japan RIKEN Center for Computational Science Department of Computer and Control Engineering Kobe650-0047 Japan

The field of energy-free sensing and context recognition has recently gained significant attention as it allows operating systems without external power sources. Photovoltaic cells can convert light energy into electrical energy to power sensing devices, but their power may not be sufficient to ensure energy-free sensing due to the varying power needs of sensors and high computational demands. In this article, we propose the use of photovoltaic cells as a standalone sensor for the recognition of different contexts, including user identification, step counting, and location tracking. The system utilizes the photocurrent readings generated by the photovoltaic cells to capture the unique mobility patterns of different users. By analyzing these patterns, the system can accurately identify the user, count the number of steps taken, and track the user’s location. We propose a computationally efficient dynamic time warping (DTW) to match the variable length sequences of photocurrent readings to a database of known patterns and identify the closest subject and location matches. The system was rigorously evaluated in a realistic environment, and the results indicate that it can accurately estimate step count, identify subjects, and localize them with an accuracy of 88%, 90%, and 43 cm, respectively. This is achieved while the proposed system is nonintrusive and can operate without external power sources, making it a promising technology for energy-free sensing and context recognition. © 2001-2012 IEEE.

关键词： Photovoltaics

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