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IEEE International Conference on Automation science and Engineering (CASE)

作者： Aditya Parandekar Brady Moon Nayana Suvarna Sebastian Scherer Birla Institute of Technology and Science Sancoale Goa India Robotics Institute School of Computer Science at Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Uncrewed Aerial Vehicles (UAVs) are a leading choice of platforms for a variety of information-gathering applications. Sensor planning can enhance the efficiency and success of these types of missions when coupled with a higher-level informative path-planning algorithm. This paper aims to address these data acquisition challenges by developing an informative non-myopic sensor planning framework for a single-axis gimbal coupled with an informative path planner to maximize information gain over a prior information map. This is done by finding reduced sensor sweep bounds over a planning horizon such that regions of higher confidence are prioritized. This novel sensor planning framework is evaluated against a predefined sensor sweep and no sensor planning baselines as well as validated in two simulation environments. In our results, we observe an improvement in the performance by 21.88% and 13.34% for the no sensor planning and predefined sensor sweep baselines respectively.

关键词： computer aided software engineering Automation Data acquisition Autonomous aerial vehicles Cameras Planning

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Clustering-Based Artificial Rabbit Optimization: Approach for Enhanced Data Clustering

Clustering-Based Artificial Rabbit Optimization: Approach fo...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Balqees Ayal-Awwad Jamil Al-Sawwa Mohammad Almseidin Maen Alzubi Computer Science Department Tafila Technical University Tafila Jordan Department of Robotics and Artificial Intelligence Jadara University Irbid Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Data clustering is a data mining task used to group data points based on their similarity. The goal of clustering is to divide $N$ data points into $K$ clusters while minimizing the intra-cluster distance and maximizing the inter-cluster distance. Data clustering can be viewed as a combinatorial optimization problem, which is known as NP-hard. Thus, nature-inspired optimization algorithms have been applied to find better solutions for clustering problems. In this paper, the clustering-based artificial rabbit optimization (CARO) method is proposed. The main idea is to find the optimal centroids for the $K$ clusters by minimizing the sum of squared errors. Using seven benchmark datasets, the experimental results showed that the CARO achieved outstanding results compared to six nature-inspired optimization algorithms in terms of purity and entropy.

关键词： Rabbits Clustering algorithms Machine learning Benchmark testing Feature extraction Entropy Reliability Data mining Optimization Convergence

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Debiased Distillation for Consistency Regularization 39

Debiased Distillation for Consistency Regularization

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Wang, Lu Xu, Liuchi Yang, Xiong Huang, Zhenhua Cheng, Jun School of Computer Science and Engineering Northeastern University Shenyang China Institute of Applied Artificial Intelligence of the Guangdong-Hong Kong-Macao Greater Bay Area Shenzhen Polytechnic University Shenzhen China School of Computer Science South China Normal University Guangzhou China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology CAS China The Chinese University of Hong Kong Hong Kong

ISBN: (纸本)157735897X

Knowledge distillation transfers "dark knowledge" from a large teacher model to a smaller student model, yielding a highly efficient network. To improve the network's generalization ability, existing works use a larger temperature coefficient for knowledge distillation. Nevertheless, these methods may reduce the confidence of the target category and lead to ambiguous recognition of similar samples. To mitigate this issue, some studies introduce intra-batch distillation to reduce prediction discrepancy. However, these methods overlook the inconsistency between background information and the target category, which may increase prediction bias due to noise disturbance. Additionally, label imbalance from random sampling and batch size can undermine network generalization reliability. To tackle these challenges, we propose a simple yet effective Intra-class Knowledge Distillation (IKD) method that facilitates knowledge sharing within the same class to ensure consistent predictions. First, we initialize the matrix and the vector to store logits and class counts provided by the teacher, respectively. Then, in the first epoch, we calculate the sum of logits and sample counts per class and perform KD to prevent knowledge omission. Finally, in subsequent training, we update the matrix to obtain the average logits and compute the KL divergence between the student's output and the updated matrix according to the label index. This process ensures intra-class consistency and improves the student's performance. Furthermore, this method theoretically reduces prediction bias by ensuring intra-class consistency. Extensive experiments on the CIFAR-100, ImageNet-1K, and Tiny-ImageNet datasets validate the superiority of IKD. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Students

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D3GU: Multi-target Active Domain Adaptation via Enhancing Domain Alignment

arXiv

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arXiv 2024年

作者： Zhang, Lin Xu, Linghan Motamed, Saman Chakraborty, Shayok De la Torre, Fernando Robotics Institute Carnegie Mellon University United States Department of Computer Science Florida State University United States

Unsupervised domain adaptation (UDA) for image classification has made remarkable progress in transferring classification knowledge from a labeled source domain to an unlabeled target domain, thanks to effective domain alignment techniques. Recently, in order to further improve performance on a target domain, many Single-Target Active Domain Adaptation (ST-ADA) methods have been proposed to identify and annotate the salient and exemplar target samples. However, it requires one model to be trained and deployed for each target domain and the domain label associated with each test sample. This largely restricts its application in the ubiquitous scenarios with multiple target domains. Therefore, we propose a Multi-Target Active Domain Adaptation (MT-ADA) framework for image classification, named D3GU, to simultaneously align different domains and actively select samples from them for annotation. This is the first research effort in this field to our best knowledge. D3GU applies Decomposed Domain Discrimination (D3) during training to achieve both source-target and target-target domain alignments. Then during active sampling, a Gradient Utility (GU) score is designed to weight every unlabeled target image by its contribution towards classification and domain alignment tasks, and is further combined with KMeans clustering to form GU-KMeans for diverse image sampling. Extensive experiments on three benchmark datasets, Office31, OfficeHome, and DomainNet, have been conducted to validate consistently superior performance of D3GU for MT-ADA 1 © 2024, CC BY.

关键词： Image classification

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Accessing Gender Bias in Speech Processing Using Machine Learning and Deep Learning with Gender Balanced Audio Deepfake Dataset 9

Accessing Gender Bias in Speech Processing Using Machine Lea...

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9th International Conference on Informatics and Computing, ICIC 2024

作者： Estella, Tricia Zahra, Amalia Fung, Wai-Keung Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta Indonesia Cardiff Metropolitan University Eureka Robotics Centre Department of Engineering Cardiff School of Technologies United Kingdom

ISBN: (纸本)9798331517601

The rapid advancement of AI has led to the rise of Audio Deepfakes (AD), which pose serious ethical and security concerns by accurately mimicking human speech. This research addresses the urgent need for effective AD detection, with a focus on gender bias that can reduce the effectiveness of detection models. We examined how gender affects the performance of both Machine Learning (Support Vector Machine, Random Forest, Logistic Regression, XGBoost) and Deep Learning (Deep Neural Networks, Convolutional Neural Networks) models using the GBAD dataset. Our findings show that models trained on female audio outperform those trained on male audio, likely due to the expressive nature of female voice features and high-pitched artifacts in FAKE audio. This highlights the need for more robust, gender-sensitive detection systems. Future work should focus on developing adaptive models to reduce gender bias, improving security, and creating lightweight models for wider public use. © 2024 IEEE.

关键词： Audio Deepfake Deep Learning Gender Bias Machine Learning Speech Processing

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Fundus Image Classification via an Integrated Deep Learning Model and Random Forest for Glaucoma Diagnostics 2nd

Fundus Image Classification via an Integrated Deep Learnin...

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2nd International Conference on Intelligent Manufacturing and robotics, ICIMR 2024

作者： Zeng, Haotian Cong, Jiheng Hong, Hantong Yu, Zihan Zhang, Fan Li, Guojie School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou China School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China Hebei University of Environmental Engineering Qinhuangdao China School of Electrical Engineering Electronics and Computer Science University of Liverpool Liverpool United Kingdom

ISBN: (纸本)9789819639489

Glaucoma is a leading cause of irreversible blindness, often worsened by delayed diagnosis. Traditional diagnostic methods have limitations in early detection and require significant expertise. Recent advancements in machine learning and deep learning have shown promise in enhancing the diagnostic accuracy and efficiency of glaucoma detection using fundus imagery. This study presents a four-stage method for glaucoma classification. Fundus images are processed using VGG16 and ViT for feature extraction, capturing both local and global features. These features are then fused and reduced in dimensionality using Principal Component Analysis. Finally, the reduced features are classified using a Random Forest classifier. The integrated feature fusion model demonstrates significant improvements in diagnostic performance, achieving higher accuracy, specificity, and sensitivity in distinguishing between non-referable and referable glaucoma classes compared to traditional methods. Specifically, our model achieved an accuracy of 94.2%, an F1 score of 94.2%, a sensitivity of 94.44%, and a specificity of 94.0%. The use of both CNNs and ViTs for feature extraction leverages their strengths, resulting in a more effective diagnostic tool. The combination of CNNs, ViTs, and Random Forest classifiers, along with advanced data augmentation techniques, shows substantial potential for early glaucoma detection and ongoing monitoring. This approach addresses the limitations of current machine learning models and enhances diagnostic accuracy and efficiency, making it a promising tool for clinical settings. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Ophthalmology

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A Survey of Stroke Image Analysis Techniques 9th

A Survey of Stroke Image Analysis Techniques

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9th EAI International Conference on Advancement of science and Technology, ICAST 2021

作者： Agizew, Henok Yared Beyene, Asrat Mulatu AI & Robotics CoE Addis Ababa Science and Technology University Addis Ababa Ethiopia Department of Electrical and Computer Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783030937089

Stroke is one of the instantaneously shocking and spiking cerebrovascular diseases having substantial residual effects. Image analysis techniques have the ability to diagnosing and providing proper treatment for stroke patients. To lighten the problem, various techniques of image analysis have been proposed. Thus, this survey intended to analyze these proposed image analysis approaches intending to thoroughly examine the state-of-the-art image analysis techniques. To prepare this survey, the systematic literature review method was employed. Based on the reviewed literature, several clinical and biological image datasets are found to be used in the process of stroke diagnosis. However, there are very few publicly accessible datasets are available currently. In this survey, each image analysis technique used for stroke image analysis processes is briefly discussed. Finally, open research challenges are identified that could be addressed in the future. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Image analysis

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MDAF: Enhancing Temporal Dynamics in CNNs for Action Recognition

MDAF: Enhancing Temporal Dynamics in CNNs for Action Recogni...

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Chinese Automation Congress (CAC)

作者： Dousang Wu Hui Zhang Yihong Cao Rui Du Baheti Biekezat School of Robotics Hunan University Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Understanding temporal information in video sequences is crucial for various computer vision tasks, such as action recognition. Transformer-based methods and GCNs can effectively handle temporal information, but they suffer from high computational costs or poor generalization capabilities. Some research has shown promising results using 3D CNNs to process skeleton data instead of RGB data, but 3D CNNs may treat temporal dimension as a spatial one, limiting their ability to capture complex temporal dynamics. To address these issues, we propose the Multi-Dimensional Attention Fusion module (MDAF), which includes separate channel, spatial, and temporal attention computation modules, integrated through an attention fusion module. This design ensures the effective utilization of attention information across all dimensions. Our method enhances temporal dynamics, improving the recognition of complex actions. Experiments conducted on four benchmark datasets demonstrate that our approach outperforms state-of-the-art methods, achieving significant performance improvements.

关键词： computer vision Three-dimensional displays Limiting Video sequences computer architecture Benchmark testing Transformers Skeleton Robustness Computational efficiency

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NOISE-CONDITIONED ENERGY-BASED ANNEALED REWARDS (NEAR): A GENERATIVE FRAMEWORK FOR IMITATION LEARNING FROM OBSERVATION

arXiv

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arXiv 2025年

作者： Diwan, Anish Abhijit Urain, Julen Kober, Jens Peters, Jan Department of Cognitive Robotics TU Delft Netherlands Department of Computer Science TU Darmstadt Germany Germany Center for Cognitive Science TU Darmstadt Germany Germany

This paper introduces a new imitation learning framework based on energy-based generative models capable of learning complex, physics-dependent, robot motion policies through state-only expert motion trajectories. Our algorithm, called Noise-conditioned Energy-based Annealed Rewards (NEAR), constructs several perturbed versions of the expert’s motion data distribution and learns smooth, and well-defined representations of the data distribution’s energy function using denoising score matching. We propose to use these learnt energy functions as reward functions to learn imitation policies via reinforcement learning. We also present a strategy to gradually switch between the learnt energy functions, ensuring that the learnt rewards are always well-defined in the manifold of policy-generated samples. We evaluate our algorithm on complex humanoid tasks such as locomotion and martial arts and compare it with state-only adversarial imitation learning algorithms like Adversarial Motion Priors (AMP). Our framework sidesteps the optimisation challenges of adversarial imitation learning techniques and produces results comparable to AMP in several quantitative metrics across multiple imitation settings. Code and videos available at ***/noise-conditionedenergy-based-annealed-rewards/ © 2025, CC BY.

关键词： Adversarial machine learning

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FlowPlan: Zero-Shot Task Planning with LLM Flow Engineering for Robotic Instruction Following

arXiv

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arXiv 2025年

作者： Lin, Zijun Tang, Chao Ye, Hanjing Zhang, Hong Southern University of Science and Technology China Shenzhen Key Laboratory of Robotics and Computer Vision China National University of Singapore Singapore

Robotic instruction following tasks require seamless integration of visual perception, task planning, target localization, and motion execution. However, existing task planning methods for instruction following are either data-driven or underperform in zero-shot scenarios due to difficulties in grounding lengthy instructions into actionable plans under operational constraints. To address this, we propose FlowPlan, a structured multi-stage LLM workflow that elevates zero-shot pipeline and bridges the performance gap between zero-shot and data-driven in-context learning methods. By decomposing the planning process into modular stages—task information retrieval, language-level reasoning, symbolic-level planning, and logical evaluation—FlowPlan generates logically coherent action sequences while adhering to operational constraints and further extracts contextual guidance for precise instance-level target localization. Benchmarked on ALFRED and validated in real-world applications, our method achieves competitive performance relative to data-driven in-context learning methods and demonstrates adaptability across diverse environments. This work advances zero-shot task planning in robotic systems without reliance on labeled data. Project website: https://***/. Copyright © 2025, The Authors. All rights reserved.

关键词： Zero-shot learning

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