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arXiv 2024年

作者： Rahman, Md Abdur Barek, Md Abdul Riad, A.B.M. Kamrul Islam Rahman, Md Mostafizur Rashid, Md Bajlur Ambedkar, Smita Miaa, Md Raihan Wu, Fan Cuzzocrea, Alfredo Ahamed, Sheikh Iqbal Dept. of Intelligent Systems and Robotics University of West Florida FL United States University of West Florida FL United States Department of Computer Science Tuskegee University TuskegeeAL United States IDEA Lab University of Calabria Rende Italy Department of Computer Science Marquette University WI United States

Although software developers of mHealth apps are responsible for protecting patient data and adhering to strict privacy and security requirements, many of them lack awareness of HIPAA regulations and struggle to distinguish between HIPAA rules categories. Therefore, providing guidance of HIPAA rules patterns classification is essential for developing secured applications for Google Play Store. In this work, we identified the limitations of traditional Word2Vec embeddings in processing code patterns. To address this, we adopt multilingual BERT (Bidirectional Encoder Representations from Transformers) which offers contextualized embeddings to the attributes of dataset to overcome the issues. Therefore, we applied this BERT to our dataset for embedding code patterns and then uses these embedded code to varioud machine learning approaches. Our results demonstrate that the models significantly enhances classification performance, with Logistic Regression achieving a remarkable accuracy of 99.95%. Additionally, we obtained high accuracy from Support Vector Machine (99.79%), Random Forest (99.73%), and Naive Bayes (95.93%), outperforming existing approaches. This work underscores the effectiveness and showcases its potential for secure application development. © 2024, CC BY.

关键词： Embeddings

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Early Prediction of Cryptocurrency Price Decline: A Deep Learning Approach

Early Prediction of Cryptocurrency Price Decline: A Deep Lea...

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International Conference on Computer and Information Technology (ICCIT)

作者： Mst Shapna Akter Hossain Shahriar Md Abdur Rahman Muhammad Rahman Alfredo Cuzzocrea Department of Intelligent Systems and Robotics University of West Florida USA Center for Cybersecurity University of West Florida USA Department of Mathematics Jahangirnagar University Savar Dhaka Bangladesh Department of Computer Science Clayton State University USA iDEA Lab University of Calabria Rende Italy

In recent times, the cryptocurrency market has emerged as one of the fastest-growing financial markets worldwide. It is, however, known for its high volatility and illiquidity compared to traditional markets such as equities, foreign exchange, and commodities. This inherent risk creates uncertainty among investors. The aim of this research is to forecast the level of risk in the cryptocurrency market. To assist cryptocurrency investors in navigating these challenges, we propose an approach that involves calculating the risk factor based on existing parameters. We employed various machine learning algorithms, including CNN, LSTM, BiLSTM, and GRU, to predict the risk factor in twenty elements of the cryptocurrency market. Through extensive experimentation, we developed a new model that outperformed existing models, achieving the highest Root Mean Square Error (RMSE) value of 1.3229 and the lowest RMSE value of 0.0089. Furthermore, we tested the generalization ability of our proposed model on a new dataset, different from the one used for training. Even with this new dataset, our model displayed robust performance. In contrast, the other existing models achieved higher RMSE values, with the highest being 14.5092 and the lowest 0.02769. By adopting our approach, investors can trade more confidently in complex and challenging financial assets such as Bitcoin, Ethereum, and Dogecoin. Our proposed model demonstrates superior performance and generalization capabilities, providing valuable insights for participants in the cryptocurrency market.

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A retrospective on the Robot Air Hockey Challenge: benchmarking robust, reliable, and safe learning techniques for real-world robotics 24

A retrospective on the Robot Air Hockey Challenge: benchmark...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Puze Liu Jonas Günster Niklas Funk Simon Gröger Dong Chen Haitham Bou-Ammar Julius Jankowski Ante Marić Sylvain Calinon Andrej Orsula Miguel Olivares-Mendez Hongyi Zhou Rudolf Lioutikov Gerhard Neumann Amarildo Likmeta Amirhossein Zhalehmehrabi Thomas Bonenfant Marcello Restelli Davide Tateo Ziyuan Liu Jan Peters Intelligent Autonomous Systems TU Darmstadt Germany and German Research Center for AI (DFKI) Intelligent Autonomous Systems TU Darmstadt Germany Huawei German research center Huawei Noah's Ark Lab London and University College London Idiap Research Institute Martigny Switzerland and EPFL Lausanne Switzerland Space Robotics Research Group (SpaceR) University of Luxembourg Luxembourg Karlsruher Institut für Technologie Karlsruhe Germany Universita di Bologna Bologna Italy and Politecnico di Milano Milano Italy Politecnico di Milano Milano Italy Intelligent Autonomous Systems TU Darmstadt Germany and German Research Center for AI (DFKI) and Centre for Cognitive Science Hessian.AI

ISBN: (纸本)9798331314385

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deploying learning-based approaches on real robots, extra effort is required to address the challenges posed by various real-world factors. To investigate the key factors influencing real-world deployment and to encourage original solutions from different researchers, we organized the Robot Air Hockey Challenge at the NeurIPS 2023 conference. We selected the air hockey task as a benchmark, encompassing low-level robotics problems and high-level tactics. Different from other machine learning-centric benchmarks, participants need to tackle practical challenges in robotics, such as the sim-to-real gap, low-level control issues, safety problems, real-time requirements, and the limited availability of real-world data. Furthermore, we focus on a dynamic environment, removing the typical assumption of quasi-static motions of other real-world benchmarks. The competition's results show that solutions combining learning-based approaches with prior knowledge outperform those relying solely on data when real-world deployment is challenging. Our ablation study reveals which real-world factors may be overlooked when building a learning-based solution. The successful real-world air hockey deployment of best-performing agents sets the foundation for future competitions and follow-up research directions.

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PandaNet : Anchor-Based Single-Shot Multi-Person 3D Pose Estimation

arXiv

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arXiv 2021年

作者： Benzine, Abdallah Chabot, Florian Luvison, Bertrand Pham, Quoc Cuong Achard, Catherine CEA LIST Vision and Learning Lab for Scene Analysis Sorbonne University CNRS Institute for Intelligent Systems and Robotics France

Recently, several deep learning models have been proposed for 3D human pose estimation. Nevertheless, most of these approaches only focus on the single-person case or estimate 3D pose of a few people at high resolution. Furthermore, many applications such as autonomous driving or crowd analysis require pose estimation of a large number of people possibly at low-resolution. In this work, we present PandaNet (Pose estimAtioN and Dectection Anchor-based Network), a new single-shot, anchor-based and multi-person 3D pose estimation approach. The proposed model performs bounding box detection and, for each detected person, 2D and 3D pose regression into a single forward pass. It does not need any post-processing to regroup joints since the network predicts a full 3D pose for each bounding box and allows the pose estimation of a possibly large number of people at low resolution. To manage people overlapping, we introduce a Pose-Aware Anchor Selection strategy. Moreover, as imbalance exists between different people sizes in the image, and joints coordinates have different uncertainties depending on these sizes, we propose a method to automatically optimize weights associated to different people scales and joints for efficient training. PandaNet surpasses previous single-shot methods on several challenging datasets: a multi-person urban virtual but very realistic dataset (JTA Dataset), and two real world 3D multi-person datasets (CMU Panoptic and MuPoTS-3D). Copyright © 2021, The Authors. All rights reserved.

关键词： Deep learning

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Efficient Robust Principal Component Analysis via Block Krylov Iteration and CUR Decomposition

Efficient Robust Principal Component Analysis via Block Kryl...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Shun Fang Zhengqin Xu Shiqian Wu Shoulie Xie School of Information Science and Engineering Wuhan University of Science and Technology China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Signal Processing RF & Optical Dept. Institute for Infocomm Research A*STAR Singapore

Robust principal component analysis (RPCA) is widely studied in computer vision. Recently an adaptive rank estimate based RPCA has achieved top performance in low-level vision tasks without the prior rank, but both the rank estimate and RPCA optimization algorithm involve singular value decomposition, which requires extremely huge computational resource for large-scale matrices. To address these issues, an efficient RPCA (eRPCA) algorithm is proposed based on block Krylov iteration and CUR decomposition in this paper. Specifically, the Krylov iteration method is employed to approximate the eigenvalue decomposition in the rank estimation, which requires $O(ndrq+n(rq)^{2})$ for an $(n\times d)$ input matrix, in which $q$ is a parameter with a small value, $r$ is the target rank. Based on the estimated rank, CUR decomposition is adopted to replace SVD in updating low-rank matrix component, whose complexity reduces from $O(rnd)$ to $O(r^{2}n)$ per iteration. Experimental results verify the efficiency and effectiveness of the proposed eRPCA over the state-of-the-art methods in various low-level vision applications.

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PediatricsGPT: large language models as Chinese medical assistants for pediatric applications 24

PediatricsGPT: large language models as Chinese medical assi...

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Dingkang Yang Jinjie Wei Dongling Xiao Shunli Wang Tong Wu Gang Li Mingcheng Li Shuaibing Wang Jiawei Chen Yue Jiang Qingyao Xu Ke Li Peng Zhai Lihua Zhang Academy for Engineering and Technology Fudan University Shanghai China and Cognition and Intelligent Technology Laboratory Shanghai China Tencent Youtu Lab Shanghai China Academy for Engineering and Technology Fudan University Shanghai China Academy for Engineering and Technology Fudan University Shanghai China and Cognition and Intelligent Technology Laboratory Shanghai China and Engineering Research Center of AI and Robotics Ministry of Education Shanghai China and AI and Unmanned Systems Engineering Research Center of Jilin Province Changchun China

ISBN: (纸本)9798331314385

Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures. To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline. In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase, a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. The project and data will be released at https://***/ydk122024/PediatricsGPT.

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Learning Human Objectives from Sequences of Physical Corrections

Learning Human Objectives from Sequences of Physical Correct...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mengxi Li Alper Canberk Dylan P. Losey Dorsa Sadigh Intelligent and Interactive Autonomous Systems Group (ILIAD) Stanford University Stanford CA Collaborative Robotics Lab (Collab) Virginia Tech

When personal, assistive, and interactive robots make mistakes, humans naturally and intuitively correct those mistakes through physical interaction. In simple situations, one correction is sufficient to convey what the human wants. But when humans are working with multiple robots or the robot is performing an intricate task often the human must make several corrections to fix the robot’s behavior. Prior research assumes each of these physical corrections are independent events, and learns from them one-at-a-time. However, this misses out on crucial information: each of these interactions are interconnected, and may only make sense if viewed together. Alternatively, other work reasons over the final trajectory produced by all of the human’s corrections. But this method must wait until the end of the task to learn from corrections, as opposed to inferring from the corrections in an online fashion. In this paper we formalize an approach for learning from sequences of physical corrections during the current task. To do this we introduce an auxiliary reward that captures the human’s trade-off between making corrections which improve the robot’s immediate reward and long-term performance. We evaluate the resulting algorithm in remote and in-person human-robot experiments, and compare to both independent and final baselines. Our results indicate that users are best able to convey their objective when the robot reasons over their sequence of corrections.

关键词： Automation Conferences Human-robot interaction Programming Robot sensing systems Trajectory Task analysis

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An Evaluation of Direct Image Based Visual Tracking System for Autonomous Manipulation* 48

An Evaluation of Direct Image Based Visual Tracking System f...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Islam, S. Saleh, A. Dias, J. Sunda-Meya, A. Xavier University of Louisiana Dual Degree Engineering Program Intelligent Robotics Mechatronics & Autonomous Systems Lab 1 Drexel Drive New OrleansLA70125 United States Khalifa University United Arab Emirates Coimbra3030-260 Portugal Khalifa University Khalifa University Autonomous Vehicles Lab United Arab Emirates Xavier University of Louisiana College of Arts & Sciences 1 Drexel Drive New OrleansLA70125 United States

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

In this paper, we implement and evaluate a direct image-based visual tracking system for autonomous manipulation applications. The direct image-based visual tracking method is developed to relax complex image processing tasks from traditional image and position-based visual tracking methods. The visual signals are constructed by using multiresolution coefficients. The method compares the multiresolution image transformation of the current and desired images and uses the mismatch between them to drive the system. The method needs to design a multiresolution interaction matrix with half and details images. The matrix connects the multiresolution image transformation coefficients with the velocity of the manipulator and controller. To illustrate the effectiveness, the direct image-based tracking system design is implemented and evaluated to locate the position of the valve stem and set of wrenches for autonomous grasping and manipulation for fire fighting applications. © 2022 IEEE.

关键词： Linear transformations

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

arXiv

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Transformer-Based Fusion of RGB and Depth Images for Terrain Recognition

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Procedia Computer Science 2024年 250卷 22-29页

作者： Hui Chen Chunjie Chen Zhuo Wang Fangliang Yang Heng Pan Xinyu Wu Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen 518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Guangdong-Hong Kong-Macao Joint Laboratory of Human–Machine Intelligence-Synergy Systems Shenzhen 518055 China

This research aims to explore key issues in the field of walking robots and exoskeletons. By using a depth camera to record human walking data in different terrains and employing RGB and depth information for image classification, our approach utilizes both early fusion and late fusion methods to explore their impact on terrain classification. This approach achieved fast and accurate performance in recognizing terrain. We captured data for walking upstairs, downstairs, upward ramps, downward ramps and level ground and used the vision transformer for data fusion. This work compares the performance of unimodal RGB model, unimodal deep model, Pre-Fusion model and Post-Fusion model in terms of accuracy, recall and processing frequency, with accuracies of 0.954, 0.934, 0.976, and 0.976, respectively. The results show that the Post-Fusion model slightly outperforms the Pre-Fusion model in terms of accuracy and recall with more reliable classification performance. To understand the attention mechanism better, we've been visualizing the weights of the model, observing how different attention heads may capture different features of the data from various dimensional perspectives. The visualization helps in interpreting the model's focus and how it distinguishes between features of different nature, such as color texture and spatial depth. These findings can inform the ongoing refinement of terrain classification systems for applications in robotics and autonomous navigation.

关键词： Multimodal fusion terrain recognition vision transformer depth camera

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