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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： De Magistris, Giorgio Rodriguez, Juan David Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Italy

This paper addresses the challenge of differentiable rendering, focusing on a novel implementation designed to integrate 3D objects seamlessly into reconstructed 3D environments, thereby creating entirely new perspectives of the scene. Our methodology leverages Neural Radiance Field (NeRF) models to reconstruct the 3D environments with high fidelity, alongside monocular depth estimation algorithms for deriving the 3D characteristics of objects from single images. The main goal of our approach lies in harmonizing the depth map output from the NeRF model with the depth data of the inserted object. This synergy enables the accurate and space-coherent placement of the object within the scene, ensuring a natural integration that enhances the overall realism of the virtual environment. © 2023 Copyright for this paper by its authors.

关键词： Image reconstruction

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Giobergia at Multi-Task Transformer Tuning for Joint Conspiracy Theory Detection and Classification 8

Giobergia at Multi-Task Transformer Tuning for Joint Conspir...

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8th Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop, EVALITA 2023

作者： Giobergia, Flavio Department of Control and Computer Engineering Politecnico di Torino Turin Italy

Conspiracy theories, prevalent in contemporary society, often propagate misinformation and distrust, impacting public opinion and decision-making processes. In this paper, we present an automated approach to detect and classify conspiracy theories in Italian to address the Automatic Conspiracy Theory Identification (ACTI) task. Our methodology leverages a transformer-based architecture trained on a multi-task problem to tackle the challenging task of conspiracy theory identification. Through this multi-task learning framework, we aim to build a single model capable of addressing both the detection and the classification tasks simultaneously. We show that tackling both problems in a multi-task setting results in improved performance w.r.t. simple transformer-based solutions. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Decision making

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Research, Implementation and Analysis of Source Code Metrics in Rust-Code-Analysis 23

Research, Implementation and Analysis of Source Code Metrics...

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23rd IEEE International Conference on Software Quality, Reliability, and Security, QRS 2023

作者： Ardito, Luca Ballario, Marco Valsesia, Michele Politecnico di Torino Department of Control and Computer Engineering Torino Italy

ISBN: (纸本)9798350319583

The software industry is proliferating at an unprecedented pace, with a massive volume of software being released every day. Among the manifold challenges faced by software engineering researchers, one of the most significant is maintaining and enhancing software quality. Software metrics, designed to quantify various aspects of software, are essential in achieving this goal. They provide developers with a comprehensive snapshot of a codebase's status throughout its evolution, thereby facilitating timely intervention and continual improvement. Tools like Rust-Code-Analysis (RCA), developed and maintained by Mozilla, serve as crucial aids in this endeavour. RCA is a static code analyser that scrutinises a source code without executing it and computes a series of source code metrics, which quantitatively assess code characteristics such as complexity, maintainability, and robustness. The present article seeks to contribute to this area by undertaking a threefold task. Firstly, we intend to explore new source code Java metrics that can be integrated into RCA. We have chosen Java language due to its not yet declined pervasiveness in many industrial software and world of smartphones. The metrics will be selected based on their potential to provide valuable insights into codebase status and facilitate optimisation. Once the new metrics have been identified, the second part of our task involves implementing these metrics within RCA's library and also accessed through its CLI. This involves the coding and integration of the metrics using the modern Rust language, taking advantage of its unique features like memory safety without garbage collection, and data concurrency. Finally, to ascertain the effectiveness and reliability of metrics, we conduct an evaluation using diverse Java repositories. This involves studying the values generated by these metrics across repositories of varying sizes and levels of activity. From the smallest library to large-scale applications, our an

关键词： Static analysis

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GINN: Towards Gender InclusioNeural Network

GINN: Towards Gender InclusioNeural Network

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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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Relay Selection and Power control for Mobile Underwater Acoustic Communication Networks: A Dual-Thread Reinforcement Learning Approach

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2024年第2期9卷 1-1页

作者： Dai, Jun Li, Xinbin Han, Song Yu, Junzhi Liu, Zhixin Key Lab of Industrial Computer Control Engineering of Hebei Province China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China

This paper deals with a cooperation communication problem (relay selection and power control) for mobile underwater acoustic communication networks. To achieve satisfactory transmission capacity, we propose a reinforcement-learning-based cooperation communication scheme to efficiently resist the highly dynamic communication links and strongly unknown time-varying channel states caused by the mobility of Autonomous Underwater Vehicles (AUVs). Firstly, a particular Markov decision process is developed to model the dynamic relay selection process of mobile AUV in the unknown scenario. In the developed model, an experimental statistical-based partition mechanism is proposed to cope with the greatly increasing dimension of the state space caused by the mobility of AUV, reducing the search optimization difficulty. Secondly, a dual-thread reinforcement learning structure with actual and virtual learning threads is proposed to efficiently track the superior relay action. In the actual learning thread, the proposed improved probability greedy policy enables the AUV to strengthen the exploration for the reward information of potential superior relays on the current state. Meanwhile, in the virtual learning thread, the proposed upper-confidence-bound-index-based uncertainty estimation method can estimate the action-reward level of historical states. Consequently, the combination of actual and virtual learning threads can efficiently obtain satisfactory Q value information, thereby making superior relay decision-making in a short time. Thirdly, a power control mechanism is proposed to reuse the current observed action-reward information and transform the multiple unknown parameter nonlinear joint power optimization problem into a convex optimization problem, thereby enhancing network transmission capacity. Finally, simulation results verify the effectiveness of the proposed scheme. IEEE

关键词： Convex optimization

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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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