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Towards Trustworthy and Explainable Socially Assistive Robots: A Cognitive Architecture for Dietary Guidance

Towards Trustworthy and Explainable Socially Assistive Robot...

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2025 IEEE international conference on simulation, modeling, and Programming for Autonomous Robots, SIMPAR 2025

作者： D'Arco, Luigi Raggioli, Luca Randazzo, Gianni De Gasperis, Giovanni Chella, Antonio Costantini, Stefania Rossi, Silvia University of Naples Federico Ii Department of Electrical Engineering and Information Technologies Naples Italy University of Palermo Department of Engineering Palermo Italy Università Degli Studi Dell'Aquila Department of Information Engineering Computer Science and Mathematics L'Aquila Italy

ISBN: (纸本)9798331516857

Socially Assistive Robots (SARs) are rising as promising tools for promoting healthy lifestyle habits. To achieve such a goal, it is necessary that they are able to perform trustworthy and legible behaviors. In this work, we propose a cognitive architecture that integrates multimodal perception, symbolic reasoning, memory-enhanced decision-making, and adaptive interaction strategies to create an explainable and engaging dietary assistant. The key idea is to provide the robot with the capability to iteratively interact with a user and adapt the dietary plan based on their current state, preferences, and food restrictions, while conveying explicitly the inner decision and thought process. To achieve this, we employ a graph-enhanced Large Language Model (LLM), which queries contextual, semantic, and episodic acquired knowledge to generate personalized meal recommendations. These must subsequently be refined through a verification process that enforces constraints such as caloric limits and ingredient intolerances, ensuring dietary adherence. To have a transparent decision-verification process, the robot has to progressively verbalize the reasoning process while providing justifications for the recommendations to also enhance the user's trust. Non-verbal context-relevant movements are also generated to allow the robot to express empathy. We expect our framework to increase user trust, engagement, and adherence to healthy behaviors, allowing SARs to function as credible and effective health assistants. © 2025 IEEE.

关键词： Nutrition

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Lightweight design and deployment of object detection model for low-light scenes 4

Lightweight design and deployment of object detection model ...

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4th international conference on computer Vision, Application, and Algorithm, CVAA 2024

作者： Sun, Kangjian Huo, Ju Jiang, Xiaona Gao, Yichao Cai, Chen School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China National Key Laboratory of Modeling and Simulation for Complex Systems Harbin China China FAW Corporation Limited Changchun China College of Design and Engineering National University of Singapore Singapore Signal and Communication Research Institute China Academy of Railway Sciences Beijing China

ISBN: (数字)9781510687622

ISBN: (纸本)9781510687615

Low-light scenes are common in fields such as autonomous driving, which tests the robustness of intelligent systems. This paper tests the performance of object detection in low-light scenes based on the YOLOv8n model. In order to solve the constraint of limited computing power of embedded devices, the YOLOv8n model is adjusted through model pruning and knowledge distillation. The complexity of the model is reduced through these lightweight operations. While ensuring the detection performance as much as possible, the multiply-accumulate operations (MACs) and parameters of the model decreased by about 40% and 27% respectively. The proposed model is evaluated on the Exclusively Dark (ExDARK) dataset and achieves a mean average precision (mAP) value of 0.672. In addition, the proposed model is migrated to the embedded platform NVIDIA Jetson Xavier NX. The deployment process is optimized by multi-process scheduling and multi-thread scheduling. Experiments are also conducted on real data, and the results show that the proposed model can provide a feasible solution for vision-based nighttime autonomous driving. © 2025 SPIE.

关键词： Object detection Deep learning Image processing

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The Impact of Vectorization on the Efficiency of a Parallel PIC Code for Numerical simulation of Plasma Dynamics in Open Trap 4th

The Impact of Vectorization on the Efficiency of a Para...

引用

4th international conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Chernykh, Igor Kulikov, Igor Vshivkov, Vitaly Efimova, Anna Weins, Dmitry Chernoshtanov, Ivan Boronina, Marina Institute of Computational Mathematics and Mathematical Geophysics Siberian Branch of the RAS 6 Ac. Lavrentieva ave. Novosibirsk Russia

ISBN: (纸本)9783031812460

Last decades, we have seen a growing interest in thermonuclear fusion. There are a lot of installation prototypes created for controlled thermonuclear fusion. Our group developing software for numerical simulation of plasma physics processes in an open magnetic trap, which can be used for controlled thermonuclear fusion. This code is Fortran-based code. Parallel implementation using MPI for parallel computations. This parallel implementation is optimized for using CPUs with advanced vectorization instructions such as AVX2/AVX512. In this paper, we will show some optimization techniques for maximizing the performance of our parallel Particle-in-Cell (PIC) code using data alignment. This feature helps the compiler to optimize data and cycles in code during the compilation and building of the program. Also, we will show the performance comparison between optimized and nonoptimized programs and the effect of vector instructions utilization on the total code performance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： FORTRAN (programming language)

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A Machine Learning-Based Trading Strategy Integrating Technical Analysis and Multi-agent simulation 22nd

A Machine Learning-Based Trading Strategy Integrating Techni...

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22nd international conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2024

作者： Zouaghia, Zakia Kodia, Zahra Ben Said, Lamjed ISG Tunis SMART Lab University of Tunis Tunis Tunisia

ISBN: (纸本)9783031704147

This paper introduces TradeStrat-ML, a novel framework for stock market trading. It integrates various techniques: technical analysis, hybrid machine learning models, multi-agent-based simulations (MABS), and financial modeling for stock market analysis and future predictions. The process involves using a Convolutional Neural Network (CNN) to extract features from preprocessed financial data. The output of this model is then combined with three machine learning models (Gated Recurrent Unit (GRU), Extreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR)) to predict future stock price indices. Subsequently, the models are evaluated, compared, and the most accurate model is selected for stock market prediction. In the final stage, the selected model, along with the Simple Moving Average (SMA) indicator, is used to develop an optimized trading strategy. The TradeStrat-ML system is organized into four main layers and validated using MABS simulations. Comparative analysis and simulation experiments collectively indicate that this new combination prediction model is a potent and practical tool for informed investment decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Temperature Effects on Microalgae Cell Growth in an Outdoor HLTP 2nd

Temperature Effects on Microalgae Cell Growth in an Outdo...

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2nd international conference on Mathematical Analysis and Applications in modeling, ICMAAM 2023

作者： Nahar, Nurun Monir, Mohammad Iftekhar Islam, Md. Rashedul Deb, Ujjwal Kumar Department of Mathematics Chittagong University of Engineering and Technology Chattogram Bangladesh Department of Mathematics Premier University Chattogram Bangladesh Jamal Nazrul Islam Research Center for Mathematical and Physical Sciences University of Chittagong Chittagong Bangladesh Department of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh

ISBN: (纸本)9789819791934

Biofuel from microalgae is a promising alternative that can substitute fossil fuels because they are renewable and eco-friendly. The growth of microalgae is influenced by numerous culture parameters, and temperature is considered one of the vital growth factors among them. In this study, we developed a computational growth model related to irradiance related temperature for an outdoor operated Horizontal Loop Tubular Photobioreactor (HLTP) for a specific geographic location. The effects of direct and diffuse solar radiation on the Photobioreactor (PBR) temperature are considered in this model. The meteorological data associated with the geographic location has been collected for the simulation. We simulated the model by COMSOL Multiphysics and ob-served a temperature fluctuation between 24.85 ∘C and 38.45 ∘C throughout the PBR domain. The significant issue of temperature control is also suggested from this study. Over the simulated period, a uniform cell growth has been observed. The hydrodynamic behaviors of the suspension flow inside the reactor, radial and axial temperature distribution are also analyzed in this study. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Microalgae

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Machine Learning and simulation modeling Large Offshore and Production Plants to improve Engineering and Construction 6

Machine Learning and Simulation Modeling Large Offshore and ...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Bruzzone, Agostino G. Sinelshchikov, Kirill Gotelli, Marco Monaci, Filippo Sina, Xhulia Ghisi, Filippo Cirillo, Luca Giovannetti, Antonio DIME Genoa University Via Opera Pia 15a Genova16145 Italy Simulation Team Via Magliotto 2 Savona17100 Italy Sim4Future Via Trento 43 Genova16145 Italy

Floating Production Storage and Offloading (FPSO) units are essential yet complex assets in offshore oil and gas production. Due to their significant scale and the intricate nature of their construction and conversion processes, FPSO projects frequently face delays and cost overruns. This paper presents a strategic engineering approach integrating risk management frameworks with machine learning to enhance project execution for FPSO units. By focusing on early Material Take Off (MTO) estimations, Critical Path analysis, and Local Content requirements, this approach addresses major challenges in FPSO projects, such as supply chain constraints and fluctuating market conditions for exotic materials. Our hybrid model combines historical data analysis with neural network-based process-specific correlations to improve MTO accuracy and procurement planning. Results demonstrate that this approach not only mitigates project risks but also enhances efficiency, enabling smoother workflows and minimizing delays. The findings offer actionable insights and tools for project managers aiming to optimize FPSO project delivery within the smart industry framework. © 2024 The Authors. Published by Elsevier B.V.

关键词： Floating production storage and offloading

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modeling the economic value of rail timetable adjustments in agent-based transport simulation 16

Modeling the economic value of rail timetable adjustments in...

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16th international conference on Ambient Systems, Networks and Technologies Networks, ANT 2025 / 8th international conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Kim, Chansung Oh, Dongik Park, Jongchul Park, Jiyoung Korea Transport Institute Rail Research Division 370 Sicheong-daero Sejong City30147 Korea Republic of Sadang Dong Seoul07019 Korea Republic of Kongju National University Department of Geography 56 KongjuDaehak-ro Chungcheongnamnam-do 32588 Korea Republic of

Reducing train headways not only minimizes unnecessary schedule delay costs for travelers, but also induces mode shifts from private cars or generates new demand. Schedule delay costs occur when passengers depart too early from their origin or arrive late at their destination due to long train headways, and shorter headways alleviate these costs. Agent-based transport simulation models, such as MATSim, can estimate the economic benefits of reduced headways under the assumption of fixed demand. However, when reduced headways lead to induced demand, estimating the resulting benefits becomes more challenging. At the same time, traditional four-step travel demand models and benefit estimation methods can incorporate induced demand through destination choice models, although they may struggle to quantify the schedule delay cost reductions associated with shortened headways in public transit services, including rail. As an alternative to these options, this paper proposes a framework that integrates agent-based modeling using MATSim with traditional destination choice models. This combined approach enables the estimation of passenger benefits from both schedule adjustments and increased demand due to train timetable improvements. © 2025 Elsevier B.V.. All rights reserved.

关键词： Mass transportation

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modeling, simulation and control of the nutritional solution in the hydroponic cultivation of blueberries 6

Modeling, simulation and control of the nutritional solution...

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6th international conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Pacco, Honorato Ccalli Franco Medina, Jorge Lázaro Universidad Nacional de Moquegua Ancash s/n Moquegua18001 Peru

Much importance is being given worldwide to the cultivation of blueberries (Vaccinium corymbosum) for its nutritional qualities, probiotic properties, antioxidant and many others, so productivity and quality in its production is sought using new technologies for its cultivation, and that are efficient and profitable to obtain the good productivity and corresponding quality. Reasons why the fuzzy logic of the LabVIEW software has been used for the control of important parameters in the nutrient solution for the crop, and that allows us a non-linear graphical programming and very easy to use without the need of a programming specialist. The electrical conductivity (EC) in dS/m units and the concentration of hydrogen ions in the nutrient solution (pH) were used as measurement parameters, for this it was instrumented virtually and the fuzzy logic of the Mamdani method was programmed in the LabVIEW block panel. With which the expected results could be obtained in the control of the process since by varying the input variables to the need for nutrients in the nutrient solution system, productivity is achieved in said hydroponic cultivation of blueberries. © 2024 The Authors. Published by Elsevier B.V.

关键词： Fruits

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Reinforcement Learning for Discretized Euclidean MDPs 2nd

Reinforcement Learning for Discretized Euclidean MDPs

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2nd international conference on Bridging the Gap Between AI and Reality, AISoLA 2024

作者： Jaeger, Manfred Larsen, Kim Guldstrand Department of Computer Science Aalborg University Aalborg Denmark

ISBN: (纸本)9783031754333

Modern model checking tools like UPPAAL Stratego provide a rich framework for modeling cyber-physical systems involving non-determinism as well as time, stochastic and continuous state descriptors. A key objective is to design controllers for such systems that optimize a given objective, e.g., minimizing energy consumption. At an abstract level, the controller design problem can be cast as optimizing a strategy in a continuous (Euclidean) Markov decision process. Partitioning the continuous state space is a simple yet effective strategy to solve this optimization problem in a flexible, non-parametric manner. In previous work we have introduced a reinforcement learning strategy under an undiscounted cost objective on dynamically refined partitions, and we have analyzed at the semantic level approximations of Euclidean MDPs by Imprecise MDPs. In this paper we are extending the approximation analysis to discounted and average cost objectives, and we are moving to close the gap between the theoretical analysis and the practical reinforcement learning approach. We introduce several alternative simulation strategies that on the one hand maintain approximation guarantees as the granularity of the partitioning increases, and on the other hand turns our learning scenario into a standard Q-learning procedure. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Markov processes

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19th international conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

19th International Conference on Artificial Intelligence: Me...

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19th international conference on Artificial Intelligence: Methodology, Systems, and Applications, AIMSA 2024

ISBN: (纸本)9783031815416

The proceedings contain 18 papers. The special focus in this conference is on Artificial Intelligence: Methodology, Systems, and Applications. The topics include: Cross-Lingual Style Transfer TTS for High-Quality Machine Dubbing;an Approach to Discovering, Tracking Over Time, and Summarizing Publicly Available Information on a Given Topic;reinforcement Learning Control of Cart Pole System with Spike Timing Neural Network Actor-Critic Architecture;Predictive and Explainable Modelling in Economics on the Case Study of Remittance Prediction Using the NeuDen AI Computational Architecture;deep Learning for Multi-class Diagnosis of Thyroid Disorders Using Selective Features;Advanced CNN-SVM Machine Learning Techniques for Facial Skin Ultrasound Image Analysis;Testing the NEAT Algorithm on a PSPACE-Complete Problem;investigating the Regularization of Deep Neural Networks for Affect Recognition with Relevance-Guided Local Explanations;Layered Data-Centric AI to Streamline Data Quality Practices for Enhanced Automation;Combining Graph NN and LLM for Improved Text-Based Emotion Recognition;a Novel Study on Modelling and Adaptive Optimal Control of a Tubular Reactor Based on Gaussian Processes;converging Dimensions: Information Extraction and Summarization Through Multisource, Multimodal, and Multilingual Fusion;enhancing Question Answering in Lecture Videos with a Multimodal Retrieval-Augmented Generation Framework;agent-Based simulation Leveraging Declarative modeling for Efficient Resource Allocation in Emergency Scenarios;enhancing Security in Federated Learning: Detection of Synchronized Data Poisoning Attacks;Clinical and Acquisition Data for Optimizing MGMT Methylation Status Prediction: A Comprehensive Ensemble Strategy Emphasizing Non-invasive Approaches.

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