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CausalAgents: A Robustness Benchmark for Motion Forecasting

CausalAgents: A Robustness Benchmark for Motion Forecasting

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IEEE international conference on robotics and automation (ICRA)

作者： Liting Sun Rebecca Roelofs Ben Caine Khaled S. Refaat Ben Sapp Scott Ettinger Wei Chai Waymo Google Research

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

As machine learning models become increasingly prevalent in motion forecasting for autonomous vehicles (AVs), it is critical to ensure that model predictions are safe and reliable. In this paper, we examine the robustness of motion forecasting to non-causal perturbations. We construct a new benchmark for evaluating and improving model robustness by applying perturbations to existing data. Specifically, we conduct an extensive labeling effort to identify causal agents, or agents whose presence influences human drivers’ behavior, in the Waymo Open Motion Dataset (WOMD), and we use these labels to perturb the data by deleting non-causal agents from the scene. We evaluate a diverse set of state-of-the-art deep-learning models on our proposed benchmark and find that all evaluated models exhibit large shifts under non-causal perturbation: we observe a surprising 25-38% relative change in minADE as compared to the original. In addition, we investigate techniques to improve model robustness, including increasing the training dataset size and using targeted data augmentations that randomly drop non-causal agents throughout training. Finally, we release the causal agent labels as an extension to WOMD and the robustness benchmarks to aid the community in building more reliable and safe deep-learning models for motion forecasting 1 .

关键词： Training Sensitivity Perturbation methods Machine learning Predictive models Benchmark testing Robustness

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Enhancing Electricity Consumption Forecasting with Artificial Intelligence on Small Datasets: A Comparative Study

Enhancing Electricity Consumption Forecasting with Artificia...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Alexandru-George Berciu Dan Doru Micu Eva-Henrietta Dulf Automation Department EnTReC Technical University of Cluj-Napoca Cluj-Napoca Romania Department of Electrotechnics and Measurement EnTReC Technical University of Cluj-Napoca Cluj-Napoca Romania

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Accurate electricity consumption forecasting is essential for efficient resource allocation and grid management, particularly with limited data. the present paper investigates how artificial intelligence techniques can enhance forecasting in small datasets and addresses data quality concerns in the educational sector. It reviews challenges in traditional forecasting, explores artificial intelligence methods such as support vector machines, autoregressive integrated moving average, and long short-term memory networks, and evaluates their performance using real-world data from four educational buildings belonging to the Technical University of Cluj-Napoca. By combining theoretical insights with empirical results, the present study advances artificial intelligence-driven forecasting for electricity consumption in small datasets, offering insights for future research and industry applications in energy management and policy formulation.

关键词： Support vector machines Accuracy Electricity Buildings Transfer learning Predictive models Ensemble learning Forecasting Artificial intelligence Long short term memory

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SATYA: Smart AI-driven Talent Yield Analyzer for Resume Screening and Recommendation Evaluation

SATYA: Smart AI-driven Talent Yield Analyzer for Resume Scre...

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robotics, automation and Artificial Intelligence (RAAI), IEEE international conference on

作者： Nisarg Bhavsar Sachish Singla Divyansh Sharma Rwik Dey Subarno Maji Indian Institute of Technology Kharagpur India

ISBN: (数字)9798331520038

ISBN: (纸本)9798331520045

the hiring process is becoming increasingly complex, as HR departments are overwhelmed with large volumes of resumes and recommendations that require in-depth analysis. this paper presents SATYA (Smart AI-driven Talent Yield Analyzer), a comprehensive AI system that automates the resume screening process, validates recommendations, and enhances the accuracy of talent acquisition. By leveraging Natural Language Processing (NLP), Machine Learning (ML), and graph-based algorithms, SATYA assesses resumes, verifies skill claims, evaluates recommendation credibility, and generates final candidate scores. Trained on a dataset of 1000 resumes manually scored by HR experts, SATYA achieved an accuracy of 91.4%, significantly outperforming traditional resume screening methods. this paper describes the architecture of SATYA, detailing the resume scoring model, recommendation network analysis, skill validation process, and multi-objective candidate evaluation. the system's scalable design ensures that it can be integrated into existing Applicant Tracking Systems (ATS) to improve talent acquisition efficiency.

关键词： Analytical models Accuracy Machine learning algorithms automation Network analyzers Machine learning Natural language processing Intelligent systems Robots

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State and Disturbance Estimations of Typical Nonlinear Systems in Electromechanical Engineering

State and Disturbance Estimations of Typical Nonlinear Syste...

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robotics, Control and automation Engineering (RCAE), international conference on

作者： Tang Ye Wang Dan Zhao Xuliang Dong Sitong Xiang Yue Shenghui Guo Marketing Service Center State Grid Liaoning Electric Power Co. Ltd. Shenyang China School of Electronics and Information Engineering Suzhou University of Science and Technology Suzhou China

ISBN: (数字)9798350355642

ISBN: (纸本)9798350355659

Taking a single-link flexible system driven by a DC motor as an example, the Takagi-Sugeno (T-S) fuzzy model is used to study the state and disturbance estimation methods for nonlinear systems, which are commonly present in the field of electromechanical engineering. By analyzing the working mechanism of the system, a nonlinear model of the system is established and then expressed as a T-S fuzzy model. By utilizing system state extension, the original system is transformed into a formal generalized system to achieve both system state estimation and estimation of system output disturbances. A robust proportional-integral observer is designed to achieve system state and disturbance estimation. the solution for the observer gain matrix is obtained by solving linear matrix inequalities. the system is studied separately with and without disturbance, and the correctness and effectiveness of the proposed method are verified through simulation results.

关键词： Analytical models Motor drives automation Simulation Observers DC motors Linear matrix inequalities Takagi-Sugeno model Nonlinear systems Robots

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A Space Information-Enhanced Dense Video Caption for Indoor Human Action Recognition

A Space Information-Enhanced Dense Video Caption for Indoor ...

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robotics, Control and automation (ICRCA), international conference on

作者： Bin CHEN Yugo NAKAMURA Shogo FUKUSHIMA Yutaka ARAKAWA ISEE. Kyushu University Kyushu University Fukuoka Japan

ISBN: (数字)9798350344721

ISBN: (纸本)9798350344738

Dense video captioning tasks are used to detect interesting events and provide descriptive text for these events from untrimmed videos. this technology has the potential to be used in security surveillance and human care applications. However, current methods often overlook the relationships between objects in the video, which limits their applicability and makes it challenging to adapt them to specific domains, such as video summarization for indoor human activities. In these scenarios, human activities are closely intertwined with the objects in the scene. In this paper, we propose a plug-and-play module designed to enhance existing dense video captioning methods with spatial information. Specifically, we extract spatial information about the interesting objects using Red-Green-Blue-Depth (RGB-D) images and the results of image segmentation. We then integrate this information into the captions generated by the Dense Video Captioning (DVC) method using a fine-tuned Large Language Model (LLM). We evaluate the performance of our model on a custom dataset and demonstrate that our system provides a convenient and effective approach for obtaining space-enhanced captions.

关键词： Image segmentation Surveillance Large language models Pipelines Information retrieval Security Human activity recognition

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Study on the Psychological Acceptance of Level 3 Autonomous Driving

Study on the Psychological Acceptance of Level 3 Autonomous ...

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international conference on Control, automation and robotics (ICCAR)

作者： Yilian Li Wenyu Wu Chang Gao Chenhao Li School of Mechanical Engineering Southeast University Nanjing China

ISBN: (数字)9798350373172

ISBN: (纸本)9798350373189

the emergence of autonomous vehicles (AVs) has brought numerous conveniences and development opportunities to human society, but it has also raised safety concerns. In recent years, AVs have been involved in multiple traffic accidents for various reasons, resulting in a continuous decline in user acceptance. In response, we need to propose some hypotheses to investigate the relationship between traffic accidents and the decline in user acceptance. this paper aims to summarize and generalize the objective problems observed during a real vehicle observation experiment and conduct in-depth interviews with participants regarding their usage scenarios, objectives, interactive behaviors, and operational methods related to AVs. By analyzing the factors that influence user acceptance, we will further develop, improve, and integrate the psychological factors affecting drivers' behaviors based on AVs, ultimately creating a model for the influencing factors of user acceptance in AVs.

关键词： Analytical models Correlation Loading Psychology Safety Autonomous vehicles Robots

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A Dual-Graph Learning Framework with Sparse Adaptive Embedding for EEG Emotion Recognition

A Dual-Graph Learning Framework with Sparse Adaptive Embeddi...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Wenkang Liu Fangkun Li Yang Li Department of Automation Science and Electrical Engineering Beihang University Beijing China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

EEG brain networks capture the complex relationships between multiple brain regions, providing rich information for emotion recognition. However, most existing methods rely on dynamically random initialized adjacent matrix, which limits their ability to effectively model emotional features across different brain regions. Additionally, these methods typically ignore the importance of synchronously modeling both adjacent and long-distance channels, resulting to suboptimal emotion recognition performance. In this paper, we propose a novel dual-graph learning framework with sparse adaptive embedding (DGLF-SAE) for EEG-brain-network emotion recognition, which includes a global hypergraph sparse adaptive embedding (GHG-SAE) module and a local adjacency graph sparse adaptive embedding (LAG-SAE) module. Specifically, the GHG-SAE module is designed to capture global relationships by associating long-distance channels, while the LAG-SAE module focuses on learning local relationships by modeling adjacent channels. We validate the effectiveness of our proposed DGLF-SAE on the SEED and SEED-IV datasets, with experimental results (98.05% and 92.83% on SEED and SEED-IV datasets, respectively) demonstrating that our approach outperforms state-of-the-art methods.

关键词： Emotion recognition Adaptation models Technological innovation Control engineering Service robots Graph convolutional networks Benchmark testing Brain modeling Electroencephalography Sparse matrices

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Push Recovery methods Based on Admittance Control Strategies for a NAO-H25 Humanoid

Push Recovery Methods Based on Admittance Control Strategies...

引用

RSI/ISM international conference on robotics and Mechatronics (ICROM)

作者： Fatemeh Sadat Mousavi Mehdi Tale Masouleh Ahmad Kalhor Payam Ghassemi Human and Robot Interaction Laboratory School of Electrical nad Computer Engineering University of Tehran Tehran Iran Faculty of New Sciences and Technologies University of Tehran Tehran Iran

ISBN: (纸本)9781728101286;9781728101279

this paper discusses two main investigations regarding push recovery of a NAO humanoid robot. the first part indicates the simulation results of different strategies retrieved from human behaviors, namely: Ankle, Hip, and Hip-ankle strategies. these methods are separately assessed and their efficiencies are discussed. To the end of converting torque parameter to robot's movement with a reasonable approximation in the aforementioned strategies' obtained models, a new approach, the so-called Virtual leg model, is introduced. the second discussion assesses the admittance control strategy, which relies on disturbances' energy absorption. Individual control algorithms are introduced for each disturbances' categories based on NAO's real conditions. this paper proposes a new strategy, which investigates the influence of admittance control coefficients against external disturbances, by considering a virtual interaction between robot's feet and the ground, and introducing a Kalman-filter-based estimator. All proposed methods are simulated in NAO-H25's simulation environment.

关键词： Legged locomotion Hip Admittance Robot kinematics Measurement uncertainty Robot sensing systems

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AI-Based Finite-Element Simulation automation Workflow Applied to Automotive Electronics Field

AI-Based Finite-Element Simulation Automation Workflow Appli...

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international conference on (ICEPT) Electronic Packaging Technology

作者： Yinlong Wang Pei Xie Engineering Technology China & Digitalization Bosch Automotive Products (Suzhou) Co. Ltd. City Suzhou China

ISBN: (数字)9798350353808

ISBN: (纸本)9798350353815

Electronic component is one of the key design elements considered during the development of automotive ECU (Electronic Control Unit). As the current state-of-the-art method, FEM (Finite-Element Method) simulation is widely used for thermal design optimization and solder joint reliability assessment of electronics at early stage of ECU development. To address the challenges of modeling fidelity and efficiency, the AI (Artificial Intelligence) integrated automated simulation workflow platform is developed. Leveraging standard and Bosch in-house developed Python libraries called “pyact”, an Ansys-based automation toolset has been developed which accelerates the simulation workflow. With the assistance of AI methodologies, more precise electronic component models could be expected during calibration process. this paper presents 3 different industrial practices and examples focusing on the board level thermal management and solder joint reliability assessment. the first industrial example presents the standardization and automatization of the reliability simulation workflow of chip-resistor components. Utilizing Ansys ACT (Application Customization Toolkit), basic APIs (Application Programming Interface) of ACT are encapsulated into the Python library as pyact, enabling a fully automated simulation workflow to predicted solder joint lifetime. To further realize the fast assessment of reliability and component virtual release, an AI metamodel is derived from automated simulation-based DoE (Design of Experiments). For pursuit of accuracy for simulation models, the second industrial example presents the combination of Bayesian Optimization with BGA (Ball Grid Array) warpage calibration process to outperform the traditional manual methods. the third industrial example outlines another application of Bayesian optimization into the Zth (Transient thermal Impedance) calibration process for component thermal models. Utilizing the multi-objective optimization algorithm, the Zth c

关键词： thermal management of electronics automation Reliability engineering Calibration Finite element analysis Bayes methods Artificial intelligence

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Dynamically Mitigating Data Discrepancy with Balanced Focal Loss for Replay Attack Detection

Dynamically Mitigating Data Discrepancy with Balanced Focal ...

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international conference on Pattern Recognition

作者： Yongqiang Dou Haocheng Yang Maolin Yang Yanyan Xu Dengfeng Ke Engineering Research Center for Forestry-oriented Intelligent Information Processing National Forestry and Grassland Administration National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing China

It becomes urgent to design effective anti-spoofing algorithms for vulnerable automatic speaker verification systems due to the advancement of high-quality playback devices. Current studies mainly treat anti-spoofing as a binary classification problem between bonafide and spoofed utterances, while lack of indistinguishable samples makes it difficult to train a robust spoofing detector. In this paper, we argue that for anti-spoofing, it needs more attention for indistinguishable samples over easily-classified ones in the modeling process, to make correct discrimination a top priority. therefore, to mitigate the data discrepancy between training and inference, we propose to leverage a balanced focal loss function as the training objective to dynamically scale the loss based on the traits of the sample itself. Besides, in the experiments, we select three kinds of features that contain both magnitude-based and phase-based information to form complementary and informative features. Experimental results on the ASVspoof2019 dataset demonstrate the superiority of the proposed methods by comparison between our systems and top-performing ones. Systems trained with the balanced focal loss perform significantly better than conventional cross-entropy loss. With complementary features, our fusion system with only three kinds of features outperforms other systems containing five or more complex single models by 22.5% for min-tDCF and 7% for EER, achieving a min-tDCF and an EER of 0.0124 and 0.55% respectively. Furthermore, we present and discuss the evaluation results on real replay data apart from the simulated ASVspoof2019 data, indicating that research for anti-spoofing still has a long way to go.

关键词： Training Learning systems Deep learning Bridges Time-frequency analysis Neural networks Detectors

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