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TechRxiv 2022年

作者： Tanwar, Vishesh Kumar Sharma, Gaurav Raman, Balasubramanian Bhargava, Rama Center for Research in Computer Vision University of Central Florida United States Department of Mathematics Indian Institute of Technology Roorkee India The Indian Institute of Technology Roorkee Roorkee India The Department of Electrical and Computer Engineering University of Rochester RochesterNY14627-0231 United States The Department of Computer Science and Engineering Indian Institute of Technology Uttarakhand Roorkee247667 India

Cloud-based services have revolutionized data storage and processing tasks. However, these services raise security concerns as service providers may misuse the user’s stored data. Privacy loss is particularly problematic for hearing and speech impaired individuals that may need to use cloud infrastructure for sign language recognition (SLR). Addressing these challenges, this paper presents a privacy-preserving sign language recognition (P2SLR) as a cloud-service that operates over cloud infrastructure without revealing the individual’s visual information to the cloud service provider (CSP). The proposed P2SLR system is realized through two innovations: (a) block-based probabilistic image encryption scheme that combines fractional-order chaotic system (FOCS) and singular value decomposition (SVD) to obfuscate the visual information in video frames, and (b) a cloud-residing deep convolutional neural network (DCNN) based recognition architecture with a modified classifier to recognize gestures from encrypted video. The proposed scheme is validated for American, Argentinian, and German sign languages. The proposed scheme achieved recognition accuracy in the range 90.76 − 98.09%, comparable to existing state-of-the-art SLR techniques in the plain domain (PD). The proposed image encryption scheme is secure under standard cryptographic image attacks, protecting the individual’s identity. P2SLR is the first move towards developing a secure SLR system over the cloud. © 2022, CC BY.

关键词： Singular value decomposition

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THE BLESSINGS OF MULTIPLE TREATMENTS AND OUTCOMES IN TREATMENT EFFECT ESTIMATION

arXiv

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arXiv 2023年

作者： Wu, Yong Liu, Mingzhou Yan, Jing Fu, Yanwei Wang, Shouyan Wang, Yizhou Sun, Xinwei Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China Computer Science Department Peking University China School of Data Science Fudan University China Key Laboratory of Computational Neuroscience Brain-Inspired Intelligence China MOE Frontiers Center for Brain Science Fudan University China Zhangjiang Fudan International Innovation Center China Engineering Research Center of AI & Robotics Ministry of Education Fudan University China

Assessing causal effects in the presence of unobserved confounding is a challenging problem. Existing studies leveraged proxy variables or multiple treatments to adjust for the confounding bias. In particular, the latter approach attributes the impact on a single outcome to multiple treatments, allowing estimating latent variables for confounding control. Nevertheless, these methods primarily focus on a single outcome, whereas in many real-world scenarios, there is greater interest in studying the effects on multiple outcomes. Besides, these outcomes are often coupled with multiple treatments. Examples include the intensive care unit (ICU), where health providers evaluate the effectiveness of therapies on multiple health indicators. To accommodate these scenarios, we consider a new setting dubbed as multiple treatments and multiple outcomes. We then show that parallel studies of multiple outcomes involved in this setting can assist each other in causal identification, in the sense that we can exploit other treatments and outcomes as proxies for each treatment effect under study. We proceed with a causal discovery method that can effectively identify such proxies for causal estimation. The utility of our method is demonstrated in synthetic data and sepsis disease. Copyright © 2023, The Authors. All rights reserved.

关键词： Intensive care units

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Visual Perception of Robot Appearance Attributes in the Peripheral Field of View Depends on Human Observer Eye-Movement Behaviors

Visual Perception of Robot Appearance Attributes in the Peri...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Kolja Kü hnlenz Barbara Kü hnlenz Department of Electrical Engineering and Computer Science Robotics Research Lab Coburg University of Applied Sciences and Art Coburg Germany Academic Center of Sciences and Humanities Coburg University of Applied Sciences and Arts Coburg Germany

This paper presents results from a study on the perception of robot attributes by human observers in their peripheral field of view depending on types of eye-movements of the latter. A between-subjects design is used, where a picture of a robot head is presented on a screen in the peripheral field of view of the participants with two conditions of eye-movements: static and pursuit. The two conditions are realized by a dot to be tracked by the participants, which is placed either in the center of the screen or moving linearly between left and right screen margins. As a subjective measure for appearance attributes, anthropomorphism is used using the multi-component Human-Robot Interaction Evaluation Scale (HRIES). Significant differences are revealed in the sociability and agency sub-scales of HRIES with respect to eye-movement behaviors. The results show, that humans perceive robot appearance attributes differently depending on whether or not pursuit eye-movements are conducted supporting the main hypothesis. Further, for pursuit movements, the scale center is rated for these sub-scales, which may indicate, that the particular robot characteristic is perceived less distinctively. No significant interactions are found for animacy and disturbance, which may be due to less applicability of these sub-scales to static images. The findings may have an impact on close interaction between humans and robots and potential anthropomorphism-related influences on task accomplishment to be considered in interaction design.

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Value Stream Mapping for Smart Pharmaceutical Management in a Thai Hospital

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Procedia computer science 2025年 253卷 495-504页

作者： Ganda Boonsothonsatit Rardchawadee Silapunt Supachai Vongbunyong Kamol Kaemarungsi Wirachchaya Chanpuypetch Nilubon Chonsawat Graduate School of Management and Innovation King Mongkut’s University of technology Thonburi Thailand Electronic and Telecommunication Engineering King Mongkut’s University of Technology Thonburi Thailand Hospital Automation Research Center Institute of Field Robotics King Mongkut’s University of Technology Thonburi Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Thailand Faculty of Agro-Industry Chiang Mai University Thailand Department of Agricultural Economy and Developement Faculty of Agriculture Chiang Mai University Thailand

The evolution of healthcare systems worldwide necessitates continual improvement in hospital management practices, particularly pharmaceutical management. This paper explores transforming traditional pharmaceutical management systems in large and medium-sized hospitals in Thailand into smart systems using Value Stream Mapping (VSM) as a guiding tool. Traditional systems often face challenges such as inefficiencies and medication errors. VSM, rooted in lean management principles, offers a structured approach to visualize and optimize processes. Additionally, smart technologies like automated inventory systems and electronic health records enhance pharmaceutical management effectiveness. Through a comprehensive research methodology encompassing case studies, data collection, and VSM analysis, this study identifies inefficiencies and proposes evidence-based strategies for improvement. Results from a case study highlight the benefits of VSM implementation in enhancing pharmacy operations, emphasizing improved patient care and operational efficiency. The implementation of a smart medication dispensing system shows promising results in reducing workload and accommodating increased patient arrivals. Continued evaluation and refinement are crucial for maximizing benefits and addressing emerging challenges effectively.

关键词： lean management smart medication dispensing smart pharmaceutical smart technologies Value Stream Mapping

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On Motion Artifacts Arising when Integrating Inertial Sensors into Loose Clothing Such as a Working Jacket

On Motion Artifacts Arising when Integrating Inertial Sensor...

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International Conference on Control, Decision and Information Technologies (CoDIT)

作者： Michael Lorenz Rebecca Keilhauer Takayuki Akiyama Takehiro Niikura Didier Stricker Bertram Taetz Gabriele Bleser-Taetz Department of Computer Science Augmented Vision Group German Research Center for Artificial Intelligence (DFKI) Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU) Kaiserslautern Germany Department of Mathematics Augmented Vision Group German Research Center for Artificial Intelligence (DFKI) Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU) Kaiserslautern Germany Research and Development Group Hitachi Ltd. Tokyo Japan IT & Engineering International University of Applied Sciences (IU) Erfurt Germany

Inertial human motion capture (IHMC) has become a robust tool to estimate human kinematics in the wild such as industrial facilities. In contrast to optical motion capture, where occlusions might take place, the kinematics of a worker can be continuously provided. This is for instance a prerequisite for an ergonomic assessments of the workers. State-of-the-art IHMC solutions require inertial sensors to be tightly attached to body segments. This requires an additional setup time and lowers the practicability and ease of use when it comes to an industrial application. In contrast, sensors integrated into loose clothing such as a working jacket, may yield corrupted kinematics estimates due to the additional motion of loose clothing. In this work we present a study of orientations deviations obtained from kinematics estimates using tightly attached inertial sensors and into a working jacket integrated ones. We performed a quantitative analysis using data from the two hardware setups worn by 19 subjects performing different industry related tasks and measures of their body shapes. Using this data we approximated probability distributions of the deviation angles for each person and body segment. Applying different statistical measures we could gain insights to questions like, how severe orientation deviations are, if there is an influence of body shapes on the distribution and how probability distributions of the deviation angles can indicate physical motion limitations of a sensor attached to a segment.

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Simulation of Optical Tactile Sensors Supporting Slip and Rotation using Path Tracing and IMPM

arXiv

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arXiv 2024年

作者： Shen, Zirong Sun, Yuhao Zhang, Shixin Chen, Zixi Sun, Heyi Sun, Fuchun Fang, Bin The Zhili College Tsinghua University Beijing100084 China The school of Artificial Intelligence Beijing University of Posts and Telecommunications 100876 China The Institute for Artificial Intelligence Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Beijing100083 China The BioRobotics Institute The Department of Excellence in Robotics and AI Scuola Superiore Sant’Anna Pisa56127 Italy

Optical tactile sensors are extensively utilized in intelligent robot manipulation due to their ability to acquire high-resolution tactile information at a lower cost. However, achieving adequate reality and versatility in simulating optical tactile sensors is challenging. In this paper, we propose a simulation method and validate its effectiveness through experiments. We utilize path tracing for image rendering, achieving higher similarity to real data than the baseline method in simulating pressing scenarios. Additionally, we apply the improved Material Point Method(IMPM) algorithm to simulate the relative rest between the object and the elastomer surface when the object is in motion, enabling more accurate simulation of complex manipulations such as slip and rotation. Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent robots

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Enhancing Motor Imagery EEG Signal Classification with Simplified GoogLeNet

Enhancing Motor Imagery EEG Signal Classification with Simpl...

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International Winter Workshop on Brain-computer Interface (BCI)

作者： Lu Wang Junkongshuai Wang Bo Wen Wei Mu Lusheng Liu Jiaguan Han Lihua Zhang Jie Jia Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical NeurobiologyAcademy for Engineering & Technology Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of AI & Robotics Institute of Meta-MedicalFudan University Shanghai China Ji Hua Laboratory Guangdong Province Foshan China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Department of Rehabilitation Medicine Huashan Hospital Fudan University Shanghai China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China

The brain-computer interface is a novel tool for interaction between the brain and external devices, and is an important tool at the forefront of international brain science research. Motor imagery does not require external stimulation and is one of the most widely used BCI paradigms, and is now widely used to address the treatment of neurological impairments such as paralysis, stroke, and acromegaly. Electrophysiological source imaging is a technique based on the observation of electromagnetic signals from the cerebral cortex, reflecting the electrophysiological activity of brain nerve cells on the scalp surface of the brain. The wavelet transform, on the other hand, is commonly used for the processing of non-smooth signals and aims to extract excellent signal features. This paper combines these two algorithms as feature extraction methods. In recent years, deep learning has been increasingly combined with MI-EEG-based BCI. GoogLeNet is one of the most classical convolutional neural networks and has made a splash in ImageNet image recognition. However, using GoogLeNet to train the BCI public dataset BCI IV IIa and dataset HGD has shown problems such as long training time, overfitting, and gradient disappearance. In response, this paper proposes an improved and streamlined version of GoogLeNet for training based on this dataset to achieve an overall improvement in accuracy, training time, number of parameters, and other indicators. It is useful for the realistic application of MI-based BCI systems.

关键词： Training Wavelet transforms Surface waves Scalp Pattern classification Electrophysiology Streaming media

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Fully Decentralized Design of Initialization-free Distributed Network Size Estimation

arXiv

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arXiv 2024年

作者： Lee, Donggil Kim, Taekyoo Lee, Seungjoon Shim, Hyungbo Center for Intelligent and Interactive Robotics Korea Institute of Science and Technology Seoul02792 Korea Republic of The Train Control and Communication Team Korea Railroad Research Institute Uiwang16105 Korea Republic of NAVER Labs Seongnam13161 Korea Republic of ASRI Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

In this paper, we propose a distributed scheme for estimating the network size, which refers to the total number of agents in a network. By leveraging a synchronization technique for multi-agent systems, we devise an agent dynamics that ensures convergence to an equilibrium point located near the network size regardless of its initial condition. Our approach is based on an assumption that each agent has a unique identifier, and an estimation algorithm for obtaining the largest identifier value. By adopting this approach, we successfully implement the agent dynamics in a fully decentralized manner, ensuring accurate network size estimation even when some agents join or leave the network. © 2024, CC BY-NC-ND.

关键词： Multi agent systems

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Maximum Probability Theorem: A Framework for Probabilistic Machine Learning

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第3期2卷 214-227页

作者： Amir Emad Marvasti Ehsan Emad Marvasti Ulas Bagci Hassan Foroosh Computational Imaging Lab University of Central Florida Orlando FL USA Department of Computer Science University of Central Florida Orlando FL USA Center for Research in Computer Vision University of Central Florida Orlando FL USA

We present a theoretical framework of probabilistic learning derived from the maximum probability (MP) theorem shown in this article. In this probabilistic framework, a model is defined as an event in the probability space, and a model or the associated event—either the true underlying model or the parameterized model—has a quantified probability measure. This quantification of a model's probability measure is derived by the MP theorem, in which we have shown that an event's probability measure has an upper bound given its conditional distribution on an arbitrary random variable. Through this alternative framework, the notion of model parameters is encompassed in the definition of the model or the associated event. Therefore, this framework deviates from the conventional approach of assuming a prior on the model parameters. Instead, the regularizing effects of assuming prior over parameters are imposed through maximizing probabilities of models or, according to information theory, minimizing the information content of a model. The probability of a model in our framework is invariant to reparameterization and is solely dependent on the model's likelihood function. In addition, rather than maximizing the posterior in a conventional Bayesian setting, the objective function in our alternative framework is defined as the probability of set operations (e.g., intersection) on the event of the true underlying model and the event of the model at hand. Our theoretical framework adds clarity to probabilistic learning through solidifying the definition of probabilistic models, quantifying their probabilities, and providing a visual understanding of objective functions. Impact Statement —The choice of prior distribution over the parameters of probabilistic machine learning models determines the regularization of learning algorithms in the Bayesian perspective. The complexity in choice of prior over the parameters and the form of regularization is relative to the complexi

关键词： Random variables Bayes methods Probabilistic logic Machine learning Linear programming Entropy Complexity theory

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CertainOdom: Uncertainty Weighted Multi-task Learning Model for LiDAR Odometry Estimation

CertainOdom: Uncertainty Weighted Multi-task Learning Model ...

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IEEE International Conference on robotics and Biomimetics

作者： Leyuan Sun Guanqun Ding Yusuke Yoshiyasu Fumio Kanehiro Department of Intelligent and Mechanical Interaction Systems Graduate School of Science and Technology University of Tsukuba Tsukuba Ibaraki Japan CNRS-AIST Joint Robotics Laboratory (JRL) IRL National Institute of Advanced Industrial Science and Technology (AIST). Digital Architecture Research Center (DARC) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo Japan Computer Vision Research Team Artificial Intelligence Research Center (AIRC) National Institute of Advanced Industrial Science and Technology (AIST) Japan

ISBN: (纸本)9781665481106

As a basic and indispensable module, LiDAR odom-etry estimation is widely used in robotics. In recent years, learning-based modeling approaches for odometry estimation have been validated to be feasible. However, it is necessary to consider security factors as the highest priorities when we apply the learning-based model to certain high-risk real-world scenarios, such as autonomous driving. The odometry uncertainty estimation provides more valuable information for downstream tasks, such as route planning and navigation. In this paper, we propose an end-to-end neural network (namely CertainOdom) to solve odometry and uncertainty estimation tasks by applying multi-task learning. Instead of using the manually-tuned hyper-parameters, we employ the learnable uncertainties to weigh the balance between the error of translation and orientation in the loss function. We evaluate the estimated trajectory and uncertainty on KITTI dataset. We also compare the robustness against the traditional geometry-based methods on our artificially degraded KITTI LiDAR dataset. Extensive experimental results show that our model with uncertainty weighted loss achieves competitive performance in LiDAR odometry estimation. We also explain our uncertainties qualitatively and quantitatively.

关键词： Uncertainty Laser radar Biological system modeling Estimation Multitasking Robustness Trajectory

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