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A Unified Framework for Average Reward Criterion and Risk 34th

A Unified Framework for Average Reward Criterion and Risk

引用

34th Brazilian Conference on Intelligent Systems

作者： Silva Reis, Willy Arthur Delgado, Karina Valdivia Freire, Valdinei Univ Sao Paulo Cidade Univ BR-05508220 Sao Paulo SP Brazil

ISBN: (纸本)9783031790287;9783031790294

The average reward criterion is used to solve infinite-horizon MDPs. This risk-neutral criterion depends on the stochastic process in the limit and can use (i) the accumulated reward at infinity, which considers sequences of states of size h = infinity, or (ii) the steady state distribution of the MDP (i.e., the probability that the system is in each state in the long term), which considers sequences of states of size h = 1. In many situations, it is desirable to consider risk during the process at each stage, which can be achieved with the average reward criterion using a utility function or a risk measure such as VaR and CVaR. The objective of this work is to propose a mathematical framework that allows a unified treatment of the existing literature using average reward and risk, including works that use exponential utility functions and CVaR, as well as to include interpretations with 1 <= h <= infinity not present in the literature. These new interpretations allow differentiating policies that may not be distinguished from existing criteria. A numerical example shows the behaviors of the criteria considering this new framework.

关键词： Markov decision process Average reward Risk-sensitive

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A Forward Kinematics Solution Method for Cable-Driven Hyper-Redundant Manipulators Based on Self-attention Mechanism 17th

A Forward Kinematics Solution Method for Cable-Driven Hyper-...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Wang, Tianao Leong, Zhenghao Nigel Wang, Guolei Tsinghua Univ Dept Mech Engn Beijing 100084 Peoples R China Nanyang Technol Univ Coll Comp & Data Sci 50 Nanyang Ave Singapore 639798 Singapore

ISBN: (纸本)9789819607884;9789819607891

Compared to traditional manipulators, cable-driven hyper-redundant manipulators (CDHRMs) have advantages such as remote drive, compact size, and multiple degrees of freedom, enabling flexible operation in narrow and complex environments. They are widely used in hazardous industrial settings. These manipulators use motors to drive steel cables, creating a mapping not only between the operational space (OS) and joint space (JS) but also between the cable-driven space (CDS) and JS. Due to the high coupling between the drive cables, solving this relationship analytically becomes overly complex. This paper proposes a forward kinematics (FK) solution method for CDHRMs based on the self-attention mechanism (SAM). The method leverages the SAM to identify relationships between cables across different joints as well as among different cables within the same joint. It allows for direct calculation of the manipulator's end position from cable lengths, achieving end-to-end output and improving convenience and computational efficiency. Finally, an 8-joint CDHRM was used as the experimental object, and simulation data were collected as experimental validation inputs into the network. The results show that the average end position error of the manipulator's final joint is 1.514 cm, verifying the effectiveness of the method.

关键词： Self-Attention mechanism Cable-Driven Hyper-Redundant Forward kinematics

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What's Real News Today? A Multimodal, Continual-Learning Approach for Detecting Fake News Over Time 27th

What's Real News Today? A Multimodal, Continual-Learning App...

引用

27th International Conference on Discovery Science

作者： Maiano, Luca Evangelisti, Martina Bianchini, Silvia Anagnostopoulos, Aris Sapienza Univ Dept Comp Control & Management Engn I-00185 Rome Italy Ubiquitous I-00185 Rome Italy

ISBN: (纸本)9783031789793;9783031789809

Multimodal fake news detectors are typically trained to work on fixed distributions, making them hardly applicable to ever-changing events. Although it is possible to apply transfer learning to retrain a model on the most recent facts, it will tend to lose its ability to recognize old contents. We mitigate this problem by considering news as a stream of data that becomes available over time and by introducing a continual-learning solution that learns from new events as they become available. Our solution maintains good performance on previously known tasks without limiting the applicability of this solution to older news, leading to a substantial gain of +9.22% accuracy on average compared to transfer learning and a +3.65% increase in F1 score over the ideal scenario where you train the model on all data in one session. Besides this, we introduce the Tri-Encoder, a state-of-the-art multimodal model that allows the cross-attention mechanism between images and texts to be applied.

关键词： Fake news Continual learning Multimodal learning

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A Fast Online Adapting Algorithm for SEMG-Based Gesture Recognition in Non-Ideal Conditions 17th

A Fast Online Adapting Algorithm for SEMG-Based Gesture Reco...

引用

17th International Conference on Intelligent Robotics and Applications

作者： Zhou, Shengli Liu, Chuan Lv, Meibo Yu, Ruixing Yin, Kuiying Northwestern Polytech Univ Xian 710072 Peoples R China Nanjing Res Inst Elect Technol Nanjing 211100 Peoples R China

ISBN: (纸本)9789819607945;9789819607952

Gesture recognition based on sEMG signals has been studied for many years, but its practical applications are still limited due to the gap between laboratory study and practical applications. Among the many factors that influence the sEMG signals, the time factor and environmental noise was focused in this study. The data acquisition experiments were conducted for three consecutive days when seven electrodes were placed on the subject's forearm and hand. For each session, the subject was seated in a non-vibration environment and in a chair that vibrates in the vertical direction with vibration frequencies ranging from 0 Hz to 200 Hz randomly. Results of signal recognition with k-Nearest Neighbors (kNN) classifier show that the vibration will reduce the recognition accuracy, but the influence is limited when the test samples are from the same environment as the training samples. And the results of inter-session tests show that history sEMG signals is unreliable for new session sample recognition. When only one sample per movement was used as the training sample, the recognition accuracy was much higher than that with historical information. To solve such a problem, a fast online adapting algorithm based on training sample management and kd-tree was proposed. The results show that a much higher accuracy can be obtained when classifier is updated by using the samples that incorrectly recognized during calibration stage. The accuracies can be enhanced by 9.9% +/- 8.2%, and 21.8% +/- 10.7% respectively for the samples from non-vibration environment and vibration environment.

关键词： sEMG Vibration Non-ideal Inter-session

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Speeding up the Multi-objective NAS Through Incremental Learning 23rd

Speeding up the Multi-objective NAS Through Incremental Lear...

引用

23rd Mexican International Conference on artificial intelligence

作者： Garcia-Garcia, Cosijopii Derbel, Bilel Morales-Reyes, Alicia Jair Escalante, Hugo Inst Nacl Astrofis Opt & Electr Luis Enrique Erro 1 Cholula 72840 Puebla Mexico Univ Lille CNRS Inria Cent LilleUMR 9189 CRIStAL F-59000 Lille France

ISBN: (纸本)9783031755422;9783031755439

Deep neural networks (DNNs), particularly convolutional neural networks (CNNs), have garnered significant attention in recent years for addressing a wide range of challenges in image processing and computer vision. Neural architecture search (NAS) has emerged as a crucial field aiming to automate the design and configuration of CNN models. In this paper, we propose a novel strategy to speed up the performance estimation of neural architectures by gradually increasing the size of the training set used for evaluation as the search progresses. We evaluate this approach using the CGP-NASV2 model, a multi-objective NAS method, on the CIFAR-100 dataset. Experimental results demonstrate a notable acceleration in the search process, achieving a speedup of 4.6 times compared to the baseline. Despite using limited data in the early stages, our proposed method effectively guides the search towards competitive architectures. This study highlights the efficacy of leveraging lower-fidelity estimates in NAS and paves the way for further research into accelerating the design of efficient CNN architectures.

关键词： Evolutionary neural architecture search AutoML Lower fidelity estimates

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Enhancing Reptile Search Algorithm Performance for the Knapsack Problem with Integration of Chaotic Map 23rd

Enhancing Reptile Search Algorithm Performance for the Knaps...

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23rd Mexican International Conference on artificial intelligence

作者： Barrera-Garcia, Jose Cisternas-Caneo, Felipe Crawford, Broderick Soto, Ricardo Becerra-Rozas, Marcelo Giachetti, Giovanni Monfroy, Eric Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ Andres Bello Santiago Chile Univ Angers LERIA Angers France

ISBN: (纸本)9783031755422;9783031755439

This study investigates the binarization process of the Reptile Search Algorithm (RSA) using chaotic maps to solve the Knapsack Problem. We evaluate RSA, Particle Swarm Optimization (PSO), and Grey Wolf Optimizer (GWO) using the S4 transfer function with four binarization strategies: standard, standard with chaotic maps, elitist, and elitist with chaotic maps. Experimental results show that standard binarization strategies, particularly RSA with standard binarization rule (STD) and RSA with standard binarization rule with a chaotic map (STD SINE), consistently outperform elitist strategies across various Knapsack problem instances. Including chaotic maps, especially the sine chaotic map, slightly improves performance. Convergence analysis reveals that standard binarization ensures steady and strong convergence, while elitist binarization accelerates convergence but may risk settling on local optima early. This research highlights the importance of selecting appropriate binarization strategies and suggests further exploration of chaotic maps to enhance the performance of metaheuristic algorithms in solving binary combinatorial optimization problems.

关键词： Reptile Search Algorithm Combinatorial Problems Binarization Schemes Chaotic Maps Metaheuristics

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Revisiting Silhouette Aggregation 27th

Revisiting Silhouette Aggregation

引用

27th International Conference on Discovery Science

作者： Pavlopoulos, John Vardakas, Georgios Likas, Aristidis Athens Univ Econ & Business Dept Informat Patission 76 Athens 10434 Greece Archimedes Athena RC Athens Greece Univ Ioannina Dept Comp Sci & Engn Ioannina 45110 Greece

ISBN: (纸本)9783031789762;9783031789779

Silhouette coefficient is an established internal clustering evaluation measure that produces a score per data point, assessing the quality of its clustering assignment. To assess the quality of the clustering of the whole dataset, the scores of all the points in the dataset are typically (micro) averaged into a single value. An alternative path, however, that is rarely employed, is to average first at the cluster level and then (macro) average across clusters. As we illustrate in this work with a synthetic example, the typical micro-averaging strategy is sensitive to cluster imbalance while the overlooked macro-averaging strategy is far more robust. By investigating macro-Silhouette further, we find that uniform sub-sampling, the only available strategy in existing libraries, harms the measure's robustness against imbalance. We address this issue by proposing a per-cluster sampling method. An empirical analysis on eight real-world datasets in two clustering tasks reveals the disagreement between the two coefficients for imbalanced datasets.

关键词： cluster analysis Silhouette cluster validity index

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Positioning Error Compensation for Fully-Gear-Driven Robotic Manipulator Based on Visual Calculation 17th

Positioning Error Compensation for Fully-Gear-Driven Robotic...

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17th International Conference on Intelligent Robotics and Applications

作者： Liu, Zhi Li, Jituo Chen, Wentang Xue, Tingxi Yuan, Pei Jin, Dingcan Jin, Jiefeng Zhang, Yifeng Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China Hangzhou Boomy Intelligent Technol Co Ltd Hangzhou 310052 Peoples R China

ISBN: (纸本)9789819607822;9789819607839

In response to the problem of excessive positioning errors in Fully-Gear-Driven Robotic Manipulator(FGDRM) that hinders automated operations, this paper proposes a positioning error compensation technique for FGDRM based on visual calculation. A visual measurement system is established, in which methods of multi-cameras arrangement optimization and FGDRM pose calculation are proposed. Additionally, an online positioning error compensation approach based on a fuzzy neural network PID algorithm is proposed. Experimental results demonstrate that the positioning accuracy for point-to-point, straight-line, and arc trajectories is improved by over 93%, with the mean positioning error reduced from 73.39mm to 2.93 mm. This technique provides a support for the automation of FGDRM operations.

关键词： Fully-gear-driven robotic manipulator visual pose calculation multi-cameras arrangement fuzzy neural network PID

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Kalman Filter-Based Acoustic Guidance Docking System for Autonomous Underwater Vehicle 1

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17th International Conference on Intelligent Robotics and Applications

作者： Wang, Zhao Xiang, Xianbo Xiong, Xinyang Yang, Shaolong Huazhong Univ Sci & Technol Sch Naval Architecture & Ocean Engn Wuhan 430074 Peoples R China Int Sci & Technol Cooperat Offshore Ctr Ship & Ma Wuhan Hubei Peoples R China

ISBN: (数字)9789819607921

ISBN: (纸本)9789819607914;9789819607921

In this paper, an ultra-short baseline positioning system-guided (USBL-guided) docking system is proposed to achieve smooth docking guidance for autonomous underwater vehicle (AUV). First, the main challenges of USBL-guided AUV docking are illustrated. Second, to address the issue of discrete USBL positioning samples due to the low positioning frequency and drifting outliers, Least Square Method is applied to select an appropriate initialize sample for USBL filter to prevent the wrong tendency of state prediction caused by the outliers of USBL. Subsequently, we introduce an adaptive exponential weight-improved Kalman Filter that dynamically adjusts the Kalman gain in update process based on the distance between prediction and measurement of state. Furthermore, the derivation process and proof of algorithm are also introduced. The feasibility and efficiency of the improved filter are validated by the comparative experiment carried out in lake trials with the ground truth of dead-reckoning of inertial navigation system (INS). The comparative result shows that the improved KF can efficiently reduce the drifting samples and get a smoother trajectory of AUV.

关键词： Autonomous underwater vehicle (AUV) Kalman Filter Underwater docking Ultra-short Baseline positioning system (USBL)

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Predicting Engagement of Brazilian Politicians on TikTok: A Machine Learning Approach 34th

Predicting Engagement of Brazilian Politicians on TikTok: A ...

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34th Brazilian Conference on Intelligent Systems

作者： Santana, Maria Santana, Jose Sampaio, Pablo Brito, Kellyton Univ Fed Rural Pernambuco Dept Computa Recife Brazil Univ Fed Rural Pernambuco DEINFO Recife Brazil

ISBN: (纸本)9783031790348;9783031790355

While established social media platforms like Facebook, Twitter, and Instagram have become staples in political campaigns, the 2022 Brazilian elections witnessed the rise of a new contender: TikTok. Despite its recent emergence in 2016, TikTok has already become the fourth most used social network in Brazil. This study investigates the potential of machine learning to predict engagement on the TikTok profiles of the two leading presidential candidates: Lula and Bolsonaro. Utilizing a dataset from previous studies, we implemented various machine learning models and found that the Support Vector Machine achieved the highest performance based on the F1-score metric for both candidates. Despite the results being better with Bolsonaro than with Lula, further analysis of metrics like recall and precision suggests valuable insights for social and political domains. These findings can aid both candidates and society in understanding what factors are most related to engagement on this emerging social media platform. Additionally, marketing and advertising teams can use this information to create content tailored to reach and engage with a politician's target electorate.

关键词： TikTok Political Campaigns Social Media Engagement Prediction Machine Learning Brazilian Election

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