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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Ryu, Jegyeong Nam, Dahyun Son, Junghyun John Han, Amy Kyungwon Lee, Deukhee Center for Healthcare Robotics Korea Institute of Science and Technology Seoul Korea Republic of Yonsei University Division of Mechanical Engineering Seoul Korea Republic of Doping Control Center Korea Institute of Science and Technology Seoul Korea Republic of Seoul National University Department of Mechanical Engineering Seoul Korea Republic of Kist School University of Science and Technology Division of AI-Robotics Seoul Korea Republic of Yonsei-KIST Convergence Research Institute Yonsei University Seoul13391 Korea Republic of

ISBN: (纸本)9788993215274

Blood is an analysis serves as a valuable diagnostic tool that can easily and simply diagnose health, and in particular, red blood cells (RBC) or hematocrit (Hct) and mean corpuscular volume (MCV) can diagnose various diseases. Electrochemical methods have been proposed to find out RBC, Hct, and MCV, but it is difficult to use them for infants or the elderly because they require a large amount of blood. As an alternative, method like blood smear has been suggested to make a diagnosis with a small amount of blood, but it takes a long time to dry the sample. In this study, we present a methodology to make the simple and quick blood analysis. This method uses a microscope image of a small amount of blood, without drying the blood sample. From the image, in order to detect all the blood cells, we adopt a loop algorithm consist of concave point detection, ellipse fitting, and blob detection. To assess the validity and accuracy of the proposed method, an investigation is conducted to examine its correlation with the Sysmex XN-1000, a widely utilized medical reference standard;we show that the correlation coefficients are higher than 0.88, showing more correlation than the method in previous studies by comparing the estimation results. By developing this method, it will be possible to establish a simple quick blood analysis method using microscope images, decreasing a time consumed for the health checkup, saving more lives. © 2023 ICROS.

关键词： Diagnosis

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A Continuous Off-Policy Reinforcement Learning Scheme for Optimal Motion Planning in Simply-Connected Workspaces

A Continuous Off-Policy Reinforcement Learning Scheme for Op...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Greece Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

In this work, an Integral Reinforcement Learning (RL) framework is employed to provide provably safe, convergent and almost globally optimal policies in a novel Off-Policy Iterative method for simply-connected workspaces. This restriction stems from the impossibility of strictly global navigation in multiply connected manifolds, and is necessary for formulating continuous solutions. The current method generalizes and improves upon previous results, where parametrized controllers hindered the method in scope and results. Through enhancing the traditional reactive paradigm with RL, the proposed scheme is demonstrated to outperform both previous reactive methods as well as an RRT* method in path length, cost function values and execution times, indicating almost global optimality.

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Contactless Interaction System Based on Facial Expression Recognition for Humanoid Piano Robot

Contactless Interaction System Based on Facial Expression Re...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Zhong, Ling Wang, Wen Zhu, Shiqiang Ji, Shanshan Zha, Changhai Wan, Minhong Gu, Jason Research Center for Intelligent Robotics Zhejiang Engineering Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Lab Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

With the wide application of service robots in people's daily life, people are not only satisfied with robots accomplishing tasks independently but also hope that robots can maintain sustainable interaction with human beings. Especially in the field of music, robots need to be equipped with intelligent cognitive skills in decision-making, so that the audience can enjoy immersive appreciation and emotional resonance in the robots performance. Visual perception and natural language understanding are essential for robots to establish human-robot friendly relationships. To enrich the interactive ability of music robots, we design a contactless interaction system based on facial expression recognition, which consists of four modules: voice wake-up, face detection, facial expression recognition, and music mapping. When people wake up the music robot with specific words, the robot will customize appropriate music to perform according to the recognition results of the FER module. As a final step, the interaction system has been successfully applied to the piano humanoid robot independently developed by our team. © 2022 IEEE.

关键词： Decision making

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Vision-Based Object Sorting in Dynamic Environments using YOLO for RoboCup ARM Challenge 2023

Vision-Based Object Sorting in Dynamic Environments using YO...

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Conference on Information and Communication Technology (CICT)

作者： Yash Kumar Sahu Radhika Mittal Deep Patel Chayan Maiti M Sreekumar Department of Computer Science Centre for AI IoT and Robotics Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai India Department of Mechanical Engineering Centre for AI IoT and Robotics Indian Institute of Information Technology Design and Manufacturing Kancheepuram Chennai India

In the intersection of Artificial Intelligence (ai) and robotics, autonomous object manipulation has emerged as a pivotal research domain, finding applications in sectors as diverse as manufacturing and healthcare. While significant strides have been made in object identification, trajectory planning, and manipulation tasks, these advancements often fall short in real-world, dynamic settings, revealing a critical research gap in algorithmic adaptability and computational efficiency. This paper addresses the gap by applying YOLOv4, a state-of-the-art object detection ai algorithm, within the context of the RoboCup Autonomous Robot Manipulation (ARM) Challenge—a competitive platform designed to simulate real-world complexities in autonomous systems. The research objectives are twofold: firstly, the performance of YOLOv4 in object detection and classification under dynamically changing conditions is rigorously evaluated, employing varied training strategies in both simulated and real-world scenarios. Secondly, the robustness and adaptability of the algorithm in object manipulation tasks are scrutinized and facilitated by the real-world dynamics inherent to the competition. Empirical results demonstrate the algorithm’s high accuracy, efficiency, and robustness in dynamic environments, substantiating its broader applicability in the development of autonomous systems.

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MorphGrower: A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal Morphology Generation 41

MorphGrower: A Synchronized Layer-by-layer Growing Approach ...

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41st International Conference on Machine Learning, ICML 2024

作者： Yang, Nianzu Zeng, Kaipeng Lu, Haotian Wu, Yexin Yuan, Zexin Chen, Danni Jiang, Shengdian Wu, Jiaxiang Wang, Yimin Yan, Junchi School of Artificial Intelligence Department of Computer Science and Engineering MoE Lab of AI Shanghai Jiao Tong University China Guangdong Institute of Intelligence Science and Technology China SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University China XVERSE Technology China

Neuronal morphology is essential for studying brain functioning and understanding neurodegenerative disorders. As acquiring real-world morphology data is expensive, computational approaches for morphology generation have been studied. Traditional methods heavily rely on expert-set rules and parameter tuning, making it difficult to generalize across different types of morphologies. Recently, MorphVAE was introduced as the sole learning-based method, but its generated morphologies lack plausibility, i.e., they do not appear realistic enough and most of the generated samples are topologically invalid. To fill this gap, this paper proposes MorphGrower, which mimicks the neuron natural growth mechanism for generation. Specifically, MorphGrower generates morphologies layer by layer, with each subsequent layer conditioned on the previously generated structure. During each layer generation, MorphGrower utilizes a pair of sibling branches as the basic generation block and generates branch pairs synchronously. This approach ensures topological validity and allows for fine-grained generation, thereby enhancing the realism of the final generated morphologies. Results on four real-world datasets demonstrate that MorphGrower outperforms MorphVAE by a notable margin. Importantly, the electrophysiological response simulation demonstrates the plausibility of our generated samples from a neuroscience perspective. Our code is available at https://***/Thinklab-SJTU/MorphGrower. Copyright 2024 by the author(s)

关键词： Neurons

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How to Detect ai-Generated Texts? 14

How to Detect AI-Generated Texts?

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14th IEEE Annual Ubiquitous Computing, Electronics and Mobile Communication Conference, UEMCON 2023

作者： Nguyen, Trung T. Hatua, Amartya Sung, Andrew H. Winona State University Department of Computer Science WinonaMN55987 United States AI Center of Excellence Fidelity Investments BostonMA02210 United States The University of Southern Mississippi School of Computing Sciences and Computer Engineering HattiesburgMS39401 United States

ISBN: (纸本)9798350304138

Recent advances in large language models (LLMs) have significantly improved the quality of synthetic text data. LLMs imitate human writing patterns to produce highly natural text, raising serious ethical, moral, legal, social, and economic concerns in various industries. To address these issues, we present two methods to distinguish Synthetically Generated Text (SGT) from Human-Written Text (HWT): machine learning and text similarity. Our methods include procedures for dataset creation, feature engineering, model training, and result analysis with feature importance analysis and explanation models. As part of our research, we created two datasets - a Wikipedia-based and a US Election 2024 news article-based dataset using ChatGPT, from which we obtained promising results of up to ***% accuracy. These datasets can be used as open-source datasets in future studies. We also identified a set of handcrafted features that can serve as the baseline feature set for future research. © 2023 IEEE.

关键词： ChatGPT document similarity explanation models feature engineering feature importance LLM

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A Comparison of Approximate Analytic and Neural Network Solutions for Effective Mass Schrodinger Equation With Yukawa's Potential 2

A Comparison of Approximate Analytic and Neural Network Solu...

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2nd IEEE International Conference on Artificial Intelligence in Everything, aiE 2022

作者： Fotso Kamsu, Brigitte Vubangsi, Mercel Tchoffo, M. Al-Turjman, Fadi University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Faculty of Science Cameroon Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Htttc Bambili Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

We model a compositionally graded heterostructure using the effective mass approach with a screened Coulomb potential. Approximate analytical solutions to the model are compared with a Neural Network output and good agreement is achieved, paving the way for Artificial Intelligence modeling of systems with graded composition. © 2022 IEEE.

关键词： Grading

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A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models

arXiv

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arXiv 2024年

作者： Dang, Cuong Le, Dung D. Le, Thai FPT Software AI Center Viet Nam College of Engineering and Computer Science VinUniversity Viet Nam Department of Computer Science Indiana University United States

Existing works have shown that fine-tuned textual transformer models achieve state-of-the-art prediction performances but are also vulnerable to adversarial text perturbations. Traditional adversarial evaluation is often done only after fine-tuning the models and ignoring the training data. In this paper, we want to prove that there is also a strong correlation between training data and model robustness. To this end, we extract 13 different features representing a wide range of input fine-tuning corpora properties and use them to predict the adversarial robustness of the fine-tuned models. Focusing mostly on encoder-only transformer models BERT and RoBERTa with additional results for BART, ELECTRA, and GPT2, we provide diverse evidence to support our argument. First, empirical analyses show that (a) extracted features can be used with a lightweight classifier such as Random Forest to predict the attack success rate effectively, and (b) features with the most influence on the model robustness have a clear correlation with the robustness. Second, our framework can be used as a fast and effective additional tool for robustness evaluation since it (a) saves 30x-193x runtime compared to the traditional technique, (b) is transferable across models, (c) can be used under adversarial training, and (d) robust to statistical randomness. Our code is publicly available at https://***/CaptainCuong/RobustText_ACL2024. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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A Survey on Control Design Approaches for Remotely Operated UAVs

A Survey on Control Design Approaches for Remotely Operated ...

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Mediterranean Conference on Control and Automation (MED)

作者： Viswa Narayanan Sankaranarayanan Sumeet Gajanan Satpute George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Team Lule&#x00E5 University of Technology Sweden

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Quadrotors find their roles in various sectors ranging from remote surveillance to autonomous delivery due to their capabilities of hovering, Vertical Take Off and Landing (VTOL) and rapid manoeuvring. They are a viable asset to humans in safety-critical and hazardous operations such as remote inspection and manipulation of tunnels and windmills. These applications induce external disturbances and noises along with the modelling uncertainties in the dynamics. Applications such as aerial manipulation require control from a ground station autonomously or semi-autonomously, which leads to unpredictable delays and lags. In this context, the quadrotor has to perform its goals of following the desired trajectory with minimal deviation and holding its position without any deviation while operating in the environment. So, this article analyses the existing control techniques for the quadrotor tracking problem, which also tackle parametric uncertainties, unknown time-varying delays and ensure safety.

关键词： Uncertainty Surveillance Optimal control Manipulators Stability analysis Delays Safety

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Publicly auditable privacy-preserving electoral rolls

arXiv

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arXiv 2024年

作者： Agrawal, Prashant Jhanwar, Mahabir Prasad Sharma, Subodh Vishnu Banerjee, Subhashis Department of Computer Science and Engineering Indian Institute of Technology Delhi New Delhi India Department of Computer Science Center for Digitalisation AI and Society Ashoka University Sonipat India

While existing literature on electronic voting has extensively addressed verifiability of voting protocols, the vulnerability of electoral rolls in large public elections remains a critical concern. To ensure integrity of electoral rolls, the current practice is to either make electoral rolls public or share them with the political parties. However, this enables construction of detailed voter profiles and selective targeting and manipulation of voters, thereby undermining the fundamental principle of free and fair elections. In this paper, we study the problem of designing publicly auditable yet privacy-preserving electoral rolls. We first formulate a threat model and provide formal security definitions. We then present a protocol for creation, maintenance and usage of electoral rolls that mitigates the threats. Eligible voters can verify their inclusion, whereas political parties and auditors can statistically audit the electoral roll. Further, the audit can also detect polling-day ballot stuffing and denials to eligible voters by malicious polling officers. The entire electoral roll is never revealed, which prevents any large-scale systematic voter targeting and manipulation. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic voting

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