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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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Methods for Pitch Analysis in Contemporary Popular Music: Deviations From 12-Tone Equal Temperament in Vitalic’s Work

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AES: Journal of the Audio Engineering Society 2025年第5期73卷 256-281页

作者： Deruty, Emmanuel Arbez-Nicolas, Pascal Meredith, David Sony Computer Science Laboratories Paris France Citizen Records Dijon France Department of Architecture Design and Media Technology Aalborg University Denmark

Aims: Twelve-tone equal temperament (12-TET) is recognized as the modern standard tuning for music. However, contemporary popular music may exhibit significant deviations from this framework. This study investigates such deviations in the music of the electronic musician Vitalic and others. Methods: The study examines relations between signal features and perceived pitches. The artist’s involvement ensures that the analysis focuses on aspects relevant to the music. Results: Deviations from 12-TET can be observed as a result of 1) tuning of quasiharmonic tones outside of 12-TET, 2) audible mistuned partials within an otherwise quasiharmonic context, and 3) subsets of partials perceived as distinct pitches outside 12-TET. Examples from other artists suggest that the use of pitches outside 12-TET is not limited to Vitalic’s music. Conclusions: The deviations from 12-TET are deliberate and pervasive. They are often linked to the acoustic properties of the tones, suggesting 1) a continuum between timbre and pitch and 2) the concept of a "resulting pitch" analogous to the idea of "resulting harmony" in Renaissance polyphonic music. Such conclusions challenge the traditional view of musical pitch. © 2025 Audio Engineering Society. All rights reserved.

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Enhancing LLM Code Generation Using Natural Language Processing in the Context of Machine Learning 28th

Enhancing LLM Code Generation Using Natural Language Process...

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28th International Symposium on Database Engineered Applications, IDEAS 2024

作者： Nelson, Jordan Pavlidis, Michalis Fish, Andrew Polatidis, Nikolaos School of Architecture Technology and Engineering University of Brighton BrightonBN2 4GJ United Kingdom Department of Computer Science University of Liverpool LiverpoolL69 3BX United Kingdom

ISBN: (纸本)9783031834714

The rapid rise in popularity of Generative AI and Large Language Models (LLMs) has brought both innovation and controversy, particularly regarding plagiarism and IP law infringements. However, one underexplored concern is the generation of code by these models, which, despite their potential, often includes errors and promotes poor programming practices. This paper explores new methods to address these issues by integrating LLMs with Automated Machine Learning (AutoML). By leveraging AutoML’s capabilities in hyperparameter tuning and model selection, we propose a novel approach for generating robust machine learning algorithms. This integration aims to enhance the accuracy and reliability of code generation while mitigating legal risks. Our findings include the application of Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to interpret chatbot prompts, thereby improving the generation and customization of machine learning models. The proposed methodology demonstrates practical implementation and high prediction accuracy, offering a promising solution to the current challenges faced by LLM-based code generation. In summary the findings of the paper are as follows: A new implementation of natural language processing for natural language understanding in the context of chatbot prompts aims to serve as an initial step for feature extraction, which will be utilised by an AutoML system to generate machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Deep Learning-Based Cellular Nuclei Segmentation Using Transformer Model 5th

Deep Learning-Based Cellular Nuclei Segmentation Using Trans...

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5th Workshop on Intelligent Data - From Data to Knowledge, DOING 2024, 3rd Workshop on Knowledge Graphs Analysis on a Large Scale, K-GALS 2024, 6th Workshop on Modern Approaches in Data Engineering and Information System Design, MADEISD 2024 and 3rd Workshop on Personalization and Recommender Systems, PERS 2024 held in conjunction with 28th European Conference on Advances in Databases and Information Systems, ADBIS 2024

作者： Erezman, Mateusz Dziubich, Tomasz Computer Vision and Artificial Intelligence Laboratory Department of Computer Architecture Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Gdańsk Poland

ISBN: (纸本)9783031704208

Accurate segmentation of cellular nuclei is imperative for various biological and medical applications, such as cancer diagnosis and drug discovery. Histopathology, a discipline employing microscopic examination of bodily tissues, serves as a cornerstone for cancer diagnosis. Nonetheless, the conventional histopathological diagnosis process is frequently marred by time constraints and potential inaccuracies. Consequently, there arises a pressing need for automated image analysis tools to augment medical practitioners’ efforts. In this paper, we present a novel approach utilising Transformer model, originally designed for natural language processing tasks, for automated cellular nuclei segmentation in whole-slide microscopic images. Specifically targeting cell nuclei, this methodology holds significance as the initial phase in diagnosing various illnesses, streamlining the analysis and quantification process. The study introduces a novel model that combines a U-Net architecture with a Transformer-based network functioning as a parallel encoder. This model was compared against three other popular architectures in the literature: U-Net, ResU-Net, and LinkNet-34. The impact of augmentation and colour normalisation techniques was investigated. The average Dice similarity coefficient for the considered images was found to be 0.8041. The obtained results seem to be clinically relevant. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Image segmentation

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Exploring 2D representation and transfer learning techniques for indoor localization

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Multimedia Tools and Applications 2025年 1-13页

作者： Kerdjidj, O. Himeur, Y. Atalla, S. Copiaco, A. Sohail, S.S. Amira, A. Fadli, F. Mansoor, W. Gawanmeh, A. Centre of Development of Advanced Techniques Algiers Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom School of Computing Science and Engineering VIT Bhopal University Kothri Kalan India Department of Architecture & Urban Planning Qatar University Doha2713 Qatar

Indoor localization (IL) plays a critical role in various applications, including asset tracking, emergency response, and indoor navigation. Traditional methods relying on one-dimensional (1D) representations of received signal strength indicator (RSSI) data often suffer from limitations such as low accuracy, environmental sensitivity, and susceptibility to interference. To overcome these challenges, this paper proposes Scalogram-Based Transfer Learning for Indoor Localization (STL-IL), a novel framework that significantly enhances localization performance. The proposed approach transforms 1D RSSI signals into two-dimensional (2D) representations using scalogram-based time-frequency analysis, coupled with advanced transfer learning (TL) techniques. By leveraging the scalogram method to generate image representations of the data and employing diverse wavelet types. Furthermore, pre-trained deep learning models, including GoogleNet and SqueezeNet, are utilized to achieve state-of-the-art performance. Experimental results demonstrate that the STL-IL system achieves remarkable accuracy levels of 95% and 96% for SqueezeNet and GoogleNet, respectively. The proposed framework not only improves localization accuracy but also enhances robustness in dynamic and challenging environments, making it a promising solution for real-world indoor localization applications. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Deep learning

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FUCOM-Kendall Model for Evaluating E-Scooter Adoption: A Case Study in Catania, Italy

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Transportation Research Procedia 2025年 86卷 1-8页

作者： Vincenza Torrisi Rosalia Camporeale Sarbast Moslem Tiziana Campisi Department of Electric Electronic and Computer Engineering Catania Italy Department of Technology and Society Division of Transport and Roads Lund Sweden School of Architecture Planning and Environmental Policy University College Dublin Ireland Faculty of Engineering and Architecture University of Enna KORE Enna Italy

Electric scooters (e-scooters) have emerged as a fundamental component of urban micro-mobility, particularly in European cities during the Covid-19 pandemic. This study employs the Full Consistency Method (FUCOM) and Kendall rank correlation method to assess the preferences and priorities of university students in Catania, Italy, regarding e-scooter adoption. By analyzing influencing factors, this research offers a comprehensive understanding of student attitudes towards e-scooters and their adoption, contributing to strategies for enhancing sustainable urban mobility. The findings highlight significant preferences for factors such as parking ease, portability, and intermodality, while also identifying safety concerns and the need for better integration with public transport. These insights underscore the necessity for targeted improvements in e-scooter infrastructure and policies, aiming to optimize urban mobility and meet the specific needs of potential users. Further research will focus on comparative analysis across different urban settings, investigating addition user-centric factors to capture a more comprehensive view of e-scooter adoption.

关键词： e-scooter multi-criteria decision making FUCOM method Kendall rank correlation urban mobility

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Prediction of sentiment polarity in restaurant reviews using an ordinal regression approach based on evolutionary XGBoost

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PeerJ computer Science 2025年 11卷 e2370-e2370页

作者： Al-Qudah, Dana A. Al-Zoubi, Ala M. Cristea, Alexandra I. Merelo-Guervós, Juan J. Castillo, Pedro A. Faris, Hossam King Abdullah II School for Information Technology The University of Jordan Amman Jordan Faculty of Information Technology Applied Science Private University Amman Jordan Department of Computer Science Durham University Durham United Kingdom Department of Computer Architecture and Technology Universidad de Granada Granada Spain Granada Spain

As the business world shifts to the web and tremendous amounts of data become available on multilingual mobile applications, new business and research challenges and opportunities have been explored. This research aims to intensify the usage of data analytics, machine learning, and sentiment analysis of textual data to classify customers’ reviews, feedback, and ratings of businesses in Jordan’s food and restaurant industry. The main methods used in this research were sentiment polarity (to address the challenges posed by businesses to automatically apply text analysis) and bio-metric techniques (to systematically identify users’ emotional states, so reviews can be thoroughly understood). The research was extended to deal with reviews in Arabic, dialectic Arabic, and English, with the main focus on the Arabic language, as the application examined (Talabat) is based in Jordan. Arabic and English reviews were collected from the application, and a new model was proposed to sentimentally analyze reviews. The proposed model has four main stages: data collection, data preparation, model building, and model evaluation. The main purpose of this research is to study the problem expressed above using a model of ordinal regression to overcome issues related to misclassification. Additionally, an automatic multi-language prediction approach for online restaurant reviews was proposed by combining the eXtreme gradient boosting (XGBoost) and particle swarm optimization (PSO) techniques for the ordinal regression of these reviews. The proposed PSO-XGB algorithm showed superior results when compared to support vector machine (SVM) and other optimization methods in terms of root mean square error (RMSE) for the English and Arabic datasets. Specifically, for the Arabic dataset, PSO-XGB achieved an RMSE value of 0.7722, whereas PSO-SVM achieved an RSME value of 0.9988. Copyright 2025 Al-Qudah et al.

关键词： Particle swarm optimization (PSO)

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EarLock: Personal Authentication System for Hearables Using Sound Leakage Signals

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IEEE Transactions on Mobile Computing 2025年

作者： Amesaka, Takashi Watanabe, Hiroki Sugiura, Yuta Sugimoto, Masanori Shizuki, Buntarou Keio University Department of Information and Computer Science Kanagawa Japan Future University Hakodate Department of Media Architecture Hokkaido Japan Hokkaido University Graduate School of Information Science and Technology Hokkaido Japan the University of Tsukuba Department of Computer Science Ibaraki Japan

Earphone-type wearable devices, also known as "hearables,"will have many functions in the future. Some of those functions will require authentication of the wearer for access to the user's privacy information or settlement of payments. In this study, we propose a new personal authentication system for hearables called EarLock. EarLock authenticates the wearer by acquiring and analyzing ear canal and auricle shape information using sound leakage from the device. The system can be implemented using a speaker and external microphone that are highly compatible with hearables. We implemented three prototype devices and investigated EarLock's authentication performance under various practical scenarios, including walking conditions, noisy environments, and situations with object interference. Experimental results showed that the in-ear, open-ear, and bone-conduction devices achieved balanced accuracy (BAC) scores of 87.2-93.7%, 83.4-94.7%, and 85.9-90.0%. © 2025 IEEE.

关键词： Information leakage

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NSGA-III and MOACO-based decision-making framework for optimizing time, cost, quality, and carbon footprint in bridge construction: a hybrid approach

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Asian Journal of Civil Engineering 2025年第6期26卷 2331-2347页

作者： Behera, Amir Prasad Chauhan, Trupti Ravindra Manjunath, T.C. Chandini, Shaik Chandrakar, Vinay Kumar Singh Verma, Swapnil School of Management Centurion University of Technology and Management Odisha Paralakhemundi India Department of Design Manipal School of Architecture and Planning Manipal Academic of Higher Education Manipal Karnataka Udupi India Department of Computer Science and Engineering Rajarajeswari College of Engineering Bengaluru India Gulf American University Fajairah United Arab Emirates School of Civil Engineering Faculty of Engineering and Technology Madhyanchal Professional University Bhopal India Synviro Solutions Pvt. Ltd. Indore India

This study presents a hybrid optimization framework combining Non-dominated Sorting Genetic Algorithm III (NSGA-III) and Multi-Objective Ant Colony Optimization (MOACO) to optimize time, cost, quality, and carbon footprint in bridge construction projects. The construction industry faces growing demands for sustainable practices while maintaining efficiency and cost-effectiveness. The proposed framework addresses these challenges by leveraging NSGA-III’s capability to maintain diversity in Pareto-optimal solutions and MOACO’s strength in refining local solutions through pheromone-based exploration. The framework models construction activities with multiple execution modes, each characterized by specific costs, durations, quality levels, and carbon emissions. Four objective functions are developed to minimize time, cost, and carbon footprint while maximizing quality. Constraints such as precedence relationships and resource availability ensure realistic project planning. The framework is validated through a case study of a 300-m reinforced concrete girder bridge, using input data derived from practical project scenarios. Results demonstrate the hybrid framework’s superior performance compared to standalone NSGA-III, MOACO, Multi-Objective Teaching-Learning-Based Optimization, and Multi-Objective Particle Swarm Optimization. Trade-off and correlation analyses provide actionable insights, revealing relationships between objectives and enabling informed decision-making. This study contributes to sustainable project management by integrating advanced optimization techniques and practical decision-support tools. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Hybrid optimization MOACO Multi-objective decision-making NSGA-III Sustainable bridge construction

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Efficient clustering for NOMA with THz mMIMO D2D inspired 6G system model

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Telecommunication Systems 2025年第2期88卷 1-16页

作者： Jain, Preksha Gupta, Akhil Rakesh, Nitin Tanwar, Sudeep Pau, Giovanni Alqahtani, Fayez Tolba, Amr Indian Institute of Technology Bombay India Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Faculty of Engineering and Architecture Kore University of Enna Enna Italy Software Engineering Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Computer Science Department Community College King Saud University Riyadh Saudi Arabia

Terahertz (THz) communication, offering unprecedented bandwidth, is a key enabler for the high data rates envisioned in 6G wireless networks. To further enhance capacity and spectral efficiency, this paper proposes a novel 6G hybrid system model integrating THz communication with massive multiple-input multiple-output (mMIMO), cooperative non-orthogonal multiple access (NOMA), and device-to-device (D2D) communication within a heterogeneous network architecture comprising macrocells and small cells. This integrated approach aims to address the escalating demand for connectivity. A crucial aspect of our proposed system is the development of an Antenna Angle Bound (AAB) clustering technique. This innovative method leverages antenna parameters, specifically beam angle, and width, to intelligently group users. By forming clusters based on these spatial characteristics, the AAB technique minimizes inter-user interference, a critical challenge in mMIMO systems. This interference reduction directly contributes to significantly improving the overall system sum rate. Furthermore, we formulate an energy efficiency maximization problem to optimize power allocation coefficients across the various technologies employed. This optimization ensures a balance between achieving high data rates and minimizing energy consumption, a critical consideration for sustainable 6G deployments. The complex optimization problem is tackled using the alternating direction method of multipliers algorithm, which efficiently decomposes the problem into manageable sub-problems. The combination of THz communication, mMIMO, cooperative NOMA, D2D, the AAB clustering technique, and the optimized power allocation promises to deliver a substantial leap in performance for future 6G wireless communication systems, addressing the growing need for higher data rates and improved energy efficiency. The simulation shows that the proposed 6G hybrid system schemes perform better than the traditional models by attaini

关键词： 6G Energy efficiency optimization Hybrid system Interference mitigation mMIMO mmWave NOMA Power allocation THz

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