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2023 IEEE International Conference on Big Data, BigData 2023

作者： Berta, Matteo Vacchetti, Bartolomeo Cerquitelli, Tania Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798350324457

Today's data-driven systems and official statistics often oversimplify the concept of gender, reducing it to binary data, with far-reaching implications for policy development and equitable access to services. This simplification can lead to misclassification and discrimination against individuals who identify as *** are working to advance our research in this area to develop new, more equitable approaches that can avoid discrimination based on gender identity. Within this research framework, our primary focus is on mitigating the problem of underrepresentation and, in some cases, the complete absence of non-binary individuals in data *** this goal in mind, we present the GINN Gender InclusioNeural Network. This is our first attempt to develop an equitable neural network that accurately identifies gender in a multiclass context and includes individuals whose gender identity does not fall on the binary spectrum. To achieve this goal, we conducted a comprehensive comparative analysis of several fine-tuned neural network models. Our goal was to gain a deep understanding of the crucial distinguishing features in gender identify classification and to highlight the limitation of current methods using explainable AI *** initial results are promising and demonstrate the effectiveness of a fine-tuned EfficientNetB0 model in accurately categorizing images of individuals into their self-reported gender, but we are skeptical about the application in a real-world scenario because of the amount of data available about non-binary people at the moment. © 2023 IEEE.

关键词： Automatic Gender Recognition Gender Equality Neural Network Non-Binary

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Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach 4

Quantum Kernel Estimation With Neutral Atoms For Supervised ...

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4th IEEE International Conference on Quantum Computing and engineering, QCE 2023

作者： Russo, Marco Giusto, Edoardo Montrucchio, Bartolomeo Politecnico di Torino Department of Computer and Control Engineering Turin Italy

ISBN: (纸本)9798350343236

Quantum Kernel Estimation (QKE) is a technique based on leveraging a quantum computer to estimate a kernel function that is classically difficult to calculate, which is then used by a classical computer for training a Support Vector Machine (SVM). Given the high number of 2-local operators necessary for realizing a feature mapping hard to simulate classically, a high qubit connectivity is needed, which is not currently possible on superconducting devices. For this reason, neutral atom quantum computers can be used, since they allow to arrange the atoms with more freedom. Examples of neutral-atom-based QKE can be found in the literature, but they are focused on graph learning and use the analogue approach. In this paper, a general method based on the gate model is presented. After deriving 1-qubit and 2-qubit gates starting from laser pulses, a parameterized sequence for feature mapping on 3 qubits is realized. This sequence is then used to empirically compute the kernel matrix starting from a dataset, which is finally used to train the SVM. It is also shown that this process can be generalized up to N qubits taking advantage of the more flexible arrangement of atoms that this technology allows. The accuracy is shown to be high despite the small dataset and the low separation. This is the first paper that not only proposes an algorithm for explicitly deriving a universal set of gates but also presents a method of estimating quantum kernels on neutral atom devices for general problems using the gate model. © 2023 IEEE.

关键词： Support vector machines

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Investigation on Wireless Communication for Sensors in IoT Cold Chain 1

Investigation on Wireless Communication for Sensors in IoT C...

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1st IEEE Conference on AgriFood Electronics, CAFE 2023

作者： Dilillo, Nicola Ferrero, Renato Gandino, Filippo Rebaudengo, Maurizio Politecnico di Torino Department of Control and Computer Engineering Torino Italy

ISBN: (纸本)9798350327113

Thermal monitoring is a key requirement for cold chain management. In this context, the Internet of Things (IoT) offers new opportunities for dense and/or large-scale deployment of sensors, which need to collect data to effectively control the cooling system. Various technologies are used for data transmission. Although Bluetooth is widely exploited for transmitting data in IoT applications, its use in the cold chain management is rare. In this paper, the architecture of an IoT temperature monitoring system is studied and the technological choices of its components are analyzed and compared. In particular, the paper focuses on IoT node boards with Bluetooth, in order to highlight the opportunities of a currently undervalued technology. A theoretical analysis highlights its benefits for the application context and evaluates its suitability for monitoring systems suitable for cold rooms. The theoretical results are supported by an experimental analysis based on the implementation of different systems. © 2023 IEEE.

关键词： Bluetooth

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A Quantum Adaptation to Roll Truly Random Dice in Role Playing Games 5

A Quantum Adaptation to Roll Truly Random Dice in Role Playi...

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5th Annual IEEE Conference on Games, CoG 2023

作者： Marceddu, Antonio Costantino Dilillo, Nicola Russo, Marco Ferrero, Renato Montrucchio, Bartolomeo Politecnico di Torino Department of Control and Computer Engineering Turin Italy

ISBN: (纸本)9798350322774

In role-playing games (RPGs), players are called upon to assume the role of a character moving in an imaginary environment and facing several challenges. Their success or failure often depends on randomizers like cards or dice. Regarding the latter, the most commonly used in RPGs are the Platonic solids with the addition of the ten-sided die. They are commonly simulated through classical computers, however, since true randomness is not in their nature, they can only generate pseudorandom numbers. On the contrary, quantum computers exploit the nondeterministic nature of quantum mechanics, so they are perfect candidates for truly random simulations in games of chance. For this reason, this paper proposes and tests various quantum circuits for sampling uniformly distributed discrete values within a fixed range, corresponding to the number of faces of the dice. The simulations reveal the pure randomness of the output of the implemented circuits. They were then used to generate random numbers within a three-dimensional dice-rolling game. © 2023 IEEE.

关键词： Random number generation

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Special Session: D-MATE: A Design Methodology for Connecting Automatic Test Equipment in Industry 4.0 26

Special Session: D-MATE: A Design Methodology for Connecting...

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26th IEEE Latin American Test Symposium, LATS 2025

作者： Biondani, Francesco Tosoni, Francesco Dall’Ora, Nicola Fraccaroli, Enrico Vinco, Sara Cheng, Dong Seon Fummi, Franco Department of Engineering for Innovation Medicine Section of Engineering and Physics University of Verona Italy Department of Engineering Sciences Guglielmo Marconi University Italy Department of Control and Computer Engineering Politecnico di Torino Italy

ISBN: (纸本)9781665477635

With the growing presence of semiconductor devices in healthcare, automotive, and consumer electronics, Automatic Test Equipment (ATE) systems play an increasingly vital role in ensuring quality and reliability during validation. Despite their importance, ATE systems often operate in isolation from other manufacturing processes, limiting interoperability and integration potential. Consequently, fully incorporating ATE systems within the Industry 4.0 framework remains a largely unaddressed challenge. To bridge this gap, we propose adopting Open Platform Communications Unified Architecture (OPC UA), the industry de-facto standard communication protocol for machines, with an accompanying specification tailored to ATE systems. We developed and validated our information model on an advanced ATE system, demonstrating its practical application. The results showcase the successful integration of the ATE system into a fully-fledged Industrial computer engineering (ICE) laboratory demonstrator. This study validates the effectiveness of our model in a real-world scenario and highlights the significance of our integration approach within the context of Industry 4.0. © 2025 IEEE.

关键词： Automatic testing

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Fauno: The Italian Large Language Model that will leave you senza parole! 13

Fauno: The Italian Large Language Model that will leave you ...

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13th Italian Information Retrieval Workshop, IIR 2023

作者： Bacciu, Andrea Trappolini, Giovanni Santilli, Andrea Rodolà, Emanuele Silvestri, Fabrizio Sapienza University Department of Computer Control and Management Engineering Italy Sapienza University Computer Science Department Italy

This paper presents Fauno, the first and largest open-source Italian conversational Large Language Model (LLM). Our goal with Fauno is to democratize the study of LLMs in Italian, demonstrating that obtaining a fine-tuned conversational bot with a single GPU is possible. In addition, we release a collection of datasets for conversational AI in Italian. The datasets on which we fine-tuned Fauno include various topics such as general question answering, computer science, and medical questions. We release our code and datasets on https://***/RSTLess-research/Fauno-Italian-LLM. © 2023 Copyright for this paper by its authors.

关键词： Computational linguistics

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SightAid: empowering the visually impaired in the Kingdom of Saudi Arabia (KSA) with deep learning-based intelligent wearable vision system

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Neural Computing and Applications 2024年第19期36卷 11075-11095页

作者： Talaat, Fatma M. Farsi, Mohammed Badawy, Mahmoud Elhosseini, Mostafa Machine Learning Department Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Faculty of Computer Science & amp Engineering New Mansoura University Gamasa35712 Egypt College of Computer Science and Engineering Taibah University Yanbu46421 Saudi Arabia Computers and Control Systems Engineering Department Faculty of Engineering Mansoura University Mansoura35516 Egypt Computer Science and Information Department Applied College Taibah University Madinah46537 Saudi Arabia

In the Kingdom of Saudi Arabia, visual impairment poses significant challenges for approximately 17.5% of school-aged children, mainly due to refractive errors. These challenges extend to everyday navigation, environmental interaction, and overall life quality. Motivated by the desire to empower visually impaired individuals, who face navigational limitations, difficulties in object recognition, and inadequate assistance from traditional technologies, we propose SightAid. This innovative wearable vision system utilizes a deep learning-based framework, addressing the gaps left by current assistive solutions. Traditional methods, such as canes and GPS devices, often fail to meet the nuanced and dynamic needs of the visually impaired, especially in accurately identifying objects, understanding complex environments, and providing essential real-time feedback for independent navigation. SightAid comprises a seven-phase framework involving data collection, preprocessing, and training of a sophisticated deep neural network with multiple convolutional and fully connected layers. This system is integrated into smart glasses with augmented reality displays, enabling real-time object detection and recognition. Interaction with users is facilitated through audio or haptic feedback, informing them about the location and type of objects detected. A continuous learning mechanism, incorporating user feedback and new data, ensures the system's ongoing refinement and adaptability. For performance assessment, we utilized the MNIST dataset, and an Indoor Objects Detection dataset tailored for the visually impaired, featuring images of everyday objects crucial for safe indoor navigation. SightAid demonstrates remarkable performance with accuracy up to 0.9874, recall values between 0.98 and 0.99, F1-scores ranging from 0.98 to 0.99, and AUC-ROC values reaching as high as 0.9999. These metrics significantly surpass those of traditional methods, highlighting SightAid's potential to substan

关键词： Object recognition

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Design and control of Swerve Drive Robot Using Kinematic Model 14

Design and Control of Swerve Drive Robot Using Kinematic Mod...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Khairnar, Kunal Gavani, Madhukar Nalawade, Satyajeet Department of Automobile Engineering Pune India Department of Computer Engineering Pune India Department of Instrumentation and Control Engineering Pune India

ISBN: (纸本)9798350335095

Nowadays, wheeled mobile robots is an expanding field of scientific research and growing applications in both industrial and non-industrial fields. They are extensively used in surveillance, industrial automation, and transportation task. This paper describes the design and control of the Swerve drive robot with a kinematic model. In this swerve drive, all four swerve modules are operated independently, which provides greater agility and minimal steering hysteresis over the conventional holonomic drive. In Swerve drive, each swerve module is driven by a gear-train and steered by belt drive with the help of a BLDC motor. Each motor has an in-built hall sensor and acts as a feedback element for the PID controller. © 2023 IEEE.

关键词： controllers

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RISC-V Extension for Optimized PWM control 27

RISC-V Extension for Optimized PWM Control

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27th International Conference on System Theory, control and Computing, ICSTCC 2023

作者： Popovici, Cosmin-Andrei Stan, Andrei Manta, Vasile-Ion Department of Computer Engineering Faculty of Automatic Control and Computer Engineering Technical University "Gheorghe Asachi" Iași Romania

ISBN: (纸本)9798350337983

This paper proposes a RISC-V extension, named SigWavy, meant to optimize the PWM control for general purpose or application specific designs. The RISC-V extension named above is a PWM control Unit with a dedicated ISA extension set designed for configuring and driving up to 32 PWM signals. The extension is integrated into RiscPwm, an updated version of our previous work, the RisCanFd SoC, for taking advantage of CAN-FD, a massively used protocol in the areas of automation and mobility. Being configured with the dedicated ISA extension or with parameters directly extracted from CAN-FD commands, the proposed solution manages to configure/reconfigure PWM channels between 4.79x and 9.18x times faster than an ARM Cortex-M7 processor, although our SoC operates with a 6x lower frequency. © 2023 IEEE.

关键词： Pulse width modulation

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Exploiting Machine Learning and LSTM for Human Activity Recognition: Using Physiological and Biological Sensor Data from Actigraph 25

Exploiting Machine Learning and LSTM for Human Activity Reco...

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Oyeleye, Matthew Chen, Tianhua Su, Pan Antoniou, Grigoris School Of Computing And Engineering University Of Huddersfield Department Of Computer Science United Kingdom School Of Control And Computer Engineering North China Electric Power University China

ISBN: (纸本)9798350340266

Human activity recognition involves identifying the daily living activities of an individual through the utilization of sensor attributes and intelligent learning algorithms. The identification of intricate human activities proves to be a labo-rious task, given the inherent difficulty of capturing long-term dependencies and extracting efficient features from unprocessed sensor data. For this purpose, this study aims at recognizing and classifying human activities using physiological and biological sensor data generated by Actigraph, as they can accurately measure moderate-to-vigorous intensity physical which is mostly affected by body composition and also better suited for self-monitoring. We examined the effectiveness of these features by applying prevalent machine learning classifiers and long short-term memory (LSTM) networks on recently publicly available data, which includes accelerometer and heart rate recordings. The results from our experiments showed that LSTM models performed better than conventional ML classifiers with the best result achieving an accuracy of 86.5%. The findings also confirms the significance of the heart rate in accurately classifying and identification of human activity more. © 2024 IEEE.

关键词： Long short-term memory

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