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15th International Conference on Social Robotics, ICSR 2023

作者： Sabbella, Sandeep Reddy Kaszuba, Sara Leotta, Francesco Nardi, Daniele Department of Computer Control and Management Engineering DIAG Sapienza Universitá di Roma Via Ariosto 25 Rome Italy

ISBN: (纸本)9789819987177

Human-Robot Interaction (HRI) has grown increasingly important with the integration of various types of robots into industrial and daily life aspects. Non-verbal communication plays a vital role in HRI and heavily relies on gesture recognition. The ability of robot’s interpretation of human gestures is essential for enhancing the overall user experience. To achieve precision and effectiveness of gesture recognition in HRI, the incorporation of Virtual Reality (VR) technology offers promising avenues. In this article, we describe the development of a gesture recognition pipeline tailored to HRI in Precision Agriculture scenarios. Its key feature is the use of Virtual characters and Machine learning techniques to allow agricultural robots to recognize gestures performed by both digital agents and humans. We address challenges by presenting a set of gesture definitions, data generation, and system evaluation using varies combinations of real and virtual simulated data. Our results demonstrate how the adoption of virtual simulations can significantly increase the system’s accuracy and efficiency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Human robot interaction

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Characterization of Low Frequency Oscillations in Simple Hand Movements 46

Characterization of Low Frequency Oscillations in Simple Han...

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46th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2024

作者： Mongiardini, Elena Colamarino, Emma Toppi, J. Pichiorri, F. Mattia, D. Cincotti, F. Department of Computer Control and Management Engineering Sapienza University of Rome Italy Irccs Fondazione Santa Lucia Rome Italy

ISBN: (纸本)9798350371499

Recent studies, based on Local Field Potentials and source activity reconstruction from electroencephalographic (EEG) signals, conducted in animals and healthy individuals reported a power increase in the frequency range of 3-5 Hz over the cortical motor areas that accompanies the preparatory phase of movement. Such low frequency oscillations (LFOs) detected in the EEG of post-stroke patients under the same condition have been reported to show lower magnitude peak. In this work we investigate the LFOs activity in a sample of healthy participants performing the grasping and finger extension tasks with both hands separately. To characterize the LFOs in terms of temporal occurrence, we introduce a single-trial alignment around the LFOs peak. We also compared the tasks to determine if there are differences related to hand dominance and/or the type of task proposed. Our results suggest that there is a jitter in the occurrence of the LFOs power peak and that the side and the type of the experimental task have no influence on the LFOs activity. © 2024 IEEE.

关键词： Electroencephalography

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Comparative analysis of permissioned blockchain frameworks for industrial applications

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Blockchain(Research and Applications) 2023年第1期4卷 58-70页

作者： Vittorio Capocasale Danilo Gotta Guido Perboli Department of Control and Computer Engineering Politecnico di Torino10129 TurinItaly Service Innovation TIMTurinItaly Department of Management and Production Engineering Politecnico di Torino10129 TurinItaly

Blockchain is a technology that creates trust among non-trusting parties without relying on any ***,it has attracted the interest of companies operating in a multitude of ***,due to the number of different blockchain solutions that have emerged in the last few years and their rapid changes,it is challenging for such companies to orient their technological *** paper presents a comparative analysis of the key dimensions—namely,governance,maturity,support,latency,privacy,interoperability,flexibility,efficiency,resiliency,and scalability—of some of the most-used permissioned blockchain ***,we present the results of a performance evaluation considering the following frameworks:Hyperledger Fabric 2.2,Hyperledger Sawtooth 1.2,and ConsenSys Quorum 21.1(with both the GoQuorum client and the Hyperledger Besu client).The platforms were tested under similar conditions,and official releases were used,such that our findings provide a reference for companies establishing their technological orientation.

关键词： Blockchain performance evaluation Hyperledger Besu Hyperledger Fabric Hyperledger Sawtooth Quorum

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End-user Algorithmic Auditing for Music Discoverability: A Research Roadmap 2

End-user Algorithmic Auditing for Music Discoverability: A R...

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2nd Music Recommender Systems Workshop, MuRS 2024

作者： Porcaro, Lorenzo Gómez, Emilia Catarci, Tiziana Department of Computer Control and Management Engineering Sapienza University of Rome Italy Joint Research Centre European Commission Spain

The evolution of online platforms over the past decades has radically transformed the way people discover music, and thanks to social media and music streaming services nowadays listeners have access to an ever-increasing amount of tracks and artists. Within these platforms, one of the goals of recommender systems is to help users discover music without making them feel overwhelmed while exploring the huge catalogues available. However, these systems have come under scrutiny from the scientific community, policy-makers, and civil society due to their potential negative societal impact, notably with regard to issues of fairness, non-discrimination, inclusion and diversity. Algorithmic auditing has emerged as a tool to analyse the problematic behaviours exhibited by recommenders, and to offer remedies that can limit their negative impact. In this position paper, we advocate for the involvement of end-users in the auditing process, which can contribute to the recognition, analysis, and mitigation of problematic behaviours which may arise while discovering music. Highlighting how recommenders, by influencing the discoverability of music, may impact listeners’ exposure to culturally diverse content, we seek to address the challenges posed by music recommender systems problematic behaviours, ultimately with the goal of fostering a more inclusive and diverse environment for music discovery within the digital landscape. © 2024 Copyright for this paper by its authors.

关键词： Recommender systems

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Consistent Counterfactual Explanations via Anomaly control and Data Coherence

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第4期6卷 794-804页

作者： Movin, Maria Siciliano, Federico Ferreira, Rui Silvestri, Fabrizio Tolomei, Gabriele Spotify StockholmSE-111 Sweden Sapienza University of Rome Department of Computer Control and Management Engineering Rome00185 Italy Spotify LondonWC2N 6AT United Kingdom Sapienza University of Rome Department of Computer Science Rome00161 Italy

Algorithmic recourses are popular methods to provide individuals impacted by machine learning models with recommendations on feasible actions for a more favorable prediction. Most of the previous algorithmic recourse methods work under the assumption that the predictive model does not change over time. However, in reality, models in deployment may both be periodically retrained and have their architecture changed. Therefore, it is desirable that the recourse should remain valid when such a model update occurs, unless new evidence arises. We call this feature consistency. This article presents anomaly control and data coherence (ACDC), a novel model-agnostic recourse method that generates counterfactual explanations, i.e., instance-level recourses. ACDC is inspired by anomaly detection methods and uses a one-class classifier to aid the search for valid, consistent, and feasible counterfactual explanations. The one-class classifier asserts that the generated counterfactual explanations lie on the data manifold and are not outliers of the target class. We compare ACDC against several state-of-the-art recourse methods across four datasets. Our experiments show that ACDC outperforms baselines both in generating consistent counterfactual explanations, and in generating feasible and plausible counterfactual explanations, while still having proximity measures similar to the baseline methods targeting the data manifold. © 2024 IEEE.

关键词： Machine learning

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Hybrid Optimization Algorithm for Handwritten Document Enhancement

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computers, Materials & Continua 2024年第3期78卷 3763-3786页

作者： Shu-Chuan Chu Xiaomeng Yang Li Zhang Václav Snášel Jeng-Shyang Pan College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Information and Control Engineering Qingdao University of TechnologyQingdao266520China Faculty of Electrical Engineering and Computer Science VŠB-TU OstravaOstrava70080Czech Republic Department of Information Management Chaoyang University of TechnologyTaichung41349Taiwan

The Gannet Optimization Algorithm (GOA) and the Whale Optimization Algorithm (WOA) demonstrate strong performance;however, there remains room for improvement in convergence and practical applications. This study introduces a hybrid optimization algorithm, named the adaptive inertia weight whale optimization algorithm and gannet optimization algorithm (AIWGOA), which addresses challenges in enhancing handwritten documents. The hybrid strategy integrates the strengths of both algorithms, significantly enhancing their capabilities, whereas the adaptive parameter strategy mitigates the need for manual parameter setting. By amalgamating the hybrid strategy and parameter-adaptive approach, the Gannet Optimization Algorithm was refined to yield the AIWGOA. Through a performance analysis of the CEC2013 benchmark, the AIWGOA demonstrates notable advantages across various metrics. Subsequently, an evaluation index was employed to assess the enhanced handwritten documents and images, affirming the superior practical application of the AIWGOA compared with other algorithms.

关键词： Metaheuristic algorithm gannet optimization algorithm hybrid algorithm handwritten document enhancement

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On the Blockchain Selection in Agri-Food Tracking Systems: Student Paper 5

On the Blockchain Selection in Agri-Food Tracking Systems: S...

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5th Conference on Blockchain Research and Applications for Innovative Networks and Services, BRAINS 2023

作者： Scanu, Fabio Control and Management Engineering Antonio Ruberti Sapienza University of Rome DIAG-Department of Computer Italy

ISBN: (纸本)9798350317824

In recent years, blockchain technology has spread widely in the agri-food sector. In particular, several blockchain-based tracking systems have been proposed. Most of these projects use mature technologies, such as Ethereum and Hyper-ledger. However, other blockchains were proposed recently (some particularly suited to IoT, commonly used in agri-food tracking systems). In the literature, several methodologies are proposed to guide the choice of the type of blockchain. These methodologies are generic, and a gap remains between the criteria used to guide the selection of the type of blockchain and how concrete use cases influence them. Building upon existing methodologies, this paper presents a specific approach for agri-food tracking. Once the parameters of the use case that impact the blockchain's performance have been identified, the methodology allows the association of these parameters with the criteria for identifying the types of blockchain to use (e.g., public permissionless) and the most suitable consensus algorithm. The output tuple \ [types], [algorithms], [functions]), therefore, allows the choice of the blockchain that best suits the usage scenario. © 2023 IEEE.

关键词： Blockchain

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CMDD: A novel multimodal two-stream CNN deepfakes detector 4

CMDD: A novel multimodal two-stream CNN deepfakes detector

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4th Workshop on Reducing Online Misinformation through Credible Information Retrieval, ROMCIR 2024

作者： Mongelli, Leonardo Maiano, Luca Amerini, Irene Sapienza University of Rome Department of Computer Control and Management Engineering A. Ruberti Via Ariosto 25 Rome00185 Italy

Researchers commonly model deepfake detection as a binary classification problem, using an unimodal network for each type of manipulated modality (such as auditory and visual) and a final ensemble of their predictions. In this paper, we focus our attention on the simultaneous detection of relationships between audio and visual cues, leading to the extraction of more comprehensive information to expose deepfakes. We propose the Convolutional Multimodal deepfake detection model (CMDD), a novel multimodal model that relies on the power of two Convolution Neural Networks (CNNs) to concurrently extract and process spatial and temporal features. We compare it with two baseline models: DeepFakeCVT, which uses two CNNs and a final Vision Transformer, and DeepMerge, which employs a score fusion of each unimodal CNN model. The multimodal FakeAVCeleb dataset was used to train and test our model, resulting in a model accuracy of 98.9% that places our model in the top 3 ranking of models evaluated on FakeAVCeleb. © 2024 Copyright for this paper by its authors.

关键词： Convolution

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Semi-mind controlled robots based on Reinforcement Learning for Indoor Application 8

Semi-mind controlled robots based on Reinforcement Learning ...

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

作者： Naidji, Ilyes Tibermacine, Ahmed Guettala, Walid Tibermacine, Imad Eddine Department of Computer Science University Mohamed Khider of Biskra Algeria Department of Computer Control and Management Engineering Sapienza University of Rome Italy

This paper presents a solution to this challenge by introducing interactive feedback derived from brain signals to train robots using deep reinforcement learning, particularly in the context of indoor maze navigation. Our objective is to enhance the learning process in a human-robot interaction scenario by incorporating human emotion or attention feedback. To accomplish this, we empowered the robot to learn new tasks through a dynamic policy network based on human feedback, and we augmented this input with other sensor data, including LIDAR. Various experiments are conducted to compare the efficacy of manual feedback, brain signal feedback, and no brain signal feedback, employing diverse Reinforcement Learning models. Additionally, we explore different models for emotion classification, employing Graph Neural Network models and traditional deep learning models, and subsequently compare the outcomes. © 2023 Copyright for this paper by its authors.

关键词： Feedback

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EverydayFantasy - A Mobile Computing Application in Mental Healthcare 18

EverydayFantasy - A Mobile Computing Application in Mental H...

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18th Iberian Conference on Information Systems and Technologies, CISTI 2023

作者： Triohin, Victor Leba, Monica Ionica, Andreea Cristina University of Petrosani Department of System Control and Computer Engineering Petrosani Romania University of Petrosani Department of Management and Industrial Engineering Petrosani Romania

ISBN: (纸本)9789893347928

The paper proposes a game, EverydayFantasy, based on the principles of serious games, game mechanics and gamification and intended for young people with cognitive behavior issues. The game is a mobile computing application in mental healthcare, in the architecture of which is integrated the connection to a database through a server and the use of dedicated APIs such as the one for locating on the map. The game creates a fantasy environment superimposed on the real environment in which the player is motivated to move, socialize and solve different challenges, all to improve his estimated state. © 2023 ITMA.

关键词： Serious games

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