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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Wang, Yanzhou Al-Zogbi, Lidia Liu, Jiawei Shepard, Lauren Ghazi, Ahmed Tokuda, Junichi Leonard, Simon Krieger, Axel Iordachita, Iulian Johns Hopkins University Department of Mechanical Engineering The Laboratory for Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Laboratory of Computational Sensing and Robotics BaltimoreMD United States Johns Hopkins University Brady Urological Institute BaltimoreMD United States Harvard Medical School Department of Radiology Brigham and Women's Hospital BostonMA United States Johns Hopkins University Laboratory for Computational Sensing and Robotics Department of Computer Science BaltimoreMD United States

ISBN: (纸本)9798350371499

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation. © 2024 IEEE.

关键词： Biopsy

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PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration

arXiv

引用

arXiv 2025年

作者： Baek, Seungjae Moon, Brady Kim, Seungchan Cao, Muqing Ho, Cherie Scherer, Sebastian Jeon, Jeong hwan Ulsan National Institute of Science and Technology Ulsan Korea Republic of Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

Autonomous exploration in unknown environments requires estimating the information gain of an action to guide planning decisions. While prior approaches often compute information gain at discrete waypoints, pathwise integration offers a more comprehensive estimation but is often computationally challenging or infeasible and prone to overestimation. In this work, we propose the Pathwise Information Gain with Map Prediction for Exploration (PIPE) planner, which integrates cumulative sensor coverage along planned trajectories while leveraging map prediction to mitigate overestimation. To enable efficient pathwise coverage computation, we introduce a method to efficiently calculate the expected observation mask along the planned path, significantly reducing computational overhead. We validate PIPE on real-world floorplan datasets, demonstrating its superior performance over state-of-the-art baselines. Our results highlight the benefits of integrating predictive mapping with pathwise information gain for efficient and informed exploration. Copyright © 2025, The Authors. All rights reserved.

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EfficientNet-SAM: A Novel EffecientNet with Spatial Attention Mechanism for COVID-19 Detection in Pulmonary CT Scans

EfficientNet-SAM: A Novel EffecientNet with Spatial Attentio...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Ramy Farag Parth Upadhay Jacket Demby’s Yixiang Gao Katherin Garces Montoya Seyed Mohamad Ali Tousi Gbenga Omotara Guilherme DeSouza Department of Electrical Engineering and Computer Science Vision-Guided and Intelligent Robotics Lab - ViGIR Lab University of Missouri-Columbia

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Manual analysis and diagnosis of COVID-19 through the examination of Computed Tomography (CT) images of the lungs can be time-consuming and result in errors, especially given high volume of patients and numerous images per patient. So, we address the need for automation of this task by developing a new deep learning-based pipeline. Our motivation was sparked by the CVPR Workshop on "Domain Adaptation, Explainability and Fairness in AI for Medical Image Analysis", more specifically, the "COVID-19 Diagnosis Competition (DEF-AI-MIA COV19D)" under the same Workshop. This challenge provides an opportunity to assess our proposed pipeline for COVID-19 detection from CT scan images. The same pipeline incorporates one of the architectures in the EfficientNet "family", but with an added Spatial Attention Mechanism: EfficientNet-SAM. Also, unlike the traditional/past pipelines, which relied on a preprocessing step, our pipeline takes the raw selected input images without any such step, except for an image-selection step to simply reduce the number of CT images required for training and/or testing. Moreover, our pipeline is computationally efficient, as, for example, it does not incorporate a decoder for segmenting the lungs. It also does not combine different models nor combine RNN with a backbone, as other pipelines in the past did. Nevertheless, our pipeline outperformed all approaches presented by other teams in last year’s instance of the same challenge using the validation subset. It also placed 5th in this year’s competition, ranking less than 1.3% below the 1st place and close to 3.5% above the 6th place based on the macro-F1 score.

关键词： COVID-19 Training Visualization Attention mechanisms Computed tomography Conferences Pipelines

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Integrating Function and Connection: Two Perspectives to Telepresence Robot Design for Classroom 25

Integrating Function and Connection: Two Perspectives to Tel...

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2025 ACM International Conference on Supporting Group Work, GROUP Companion 2025

作者： Berhane, Roda Akiode, Deo Adejoke Joshi, Swapna Elmimouni, Houda School of Informatics Computing and Engineering Indiana University BloomingtonIN United States Field Robotics Lab Northeastern University BostonMA United States Department of Computer Science University of Manitoba WinnipegMB Canada Information Systems and New Media University of Siegen Siegen Germany

ISBN: (纸本)9798400711879

This study explores robotic telepresence in classrooms, from the perspectives of both the remote students (operators) and their in-person classmates. Analyzing a subset of field study data from 35 participants (22 operators, 13 classmates), we identified differing priorities: operators of the robot emphasize the need for improved functional aspects like camera control, while co-present classmates value social presence, interaction and identification, advocating for features like personalization and effective communication channels. These insights underline the need for telepresence robots that balance functional efficacy with social integration to provide a holistic educational experience. This work, though incomplete, enriches Group and CSCW literature on telepresence robots to facilitate collaborative interactions by highlighting the distinct needs of remote and in-person students, proposing design recommendations for telepresence robots that are technically proficient and socially responsive. © 2025 Copyright held by the owner/author(s).

关键词： Students

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ICE-SEARCH: A LANGUAGE MODEL-DRIVEN FEATURE SELECTION APPROACH

arXiv

引用

arXiv 2024年

作者： Yang, Tianze Yang, Tianyi Lyu, Fuyuan Liu, Shaoshan Liu, Xue School of Computer Science McGill University Canada MILA Canada Shenzhen Institute of Artificial Intelligence and Robotics for Society China

This study unveils the In-Context Evolutionary Search (ICE-SEARCH) method, which is among the first works that melds large language models (LLMs) with evolutionary algorithms for feature selection (FS) tasks and demonstrates its effectiveness in Medical Predictive Analytics (MPA) applications. ICE-SEARCH harnesses the crossover and mutation capabilities inherent in LLMs within an evolutionary framework, significantly improving FS through the model's comprehensive world knowledge and its adaptability to a variety of roles. Our evaluation of this methodology spans three crucial MPA tasks: stroke, cardiovascular disease, and diabetes, where ICE-SEARCH outperforms traditional FS methods in pinpointing essential features for medical applications. ICE-SEARCH achieves State-of-the-Art (SOTA) performance in stroke prediction and diabetes prediction;the Decision-Randomized ICE-SEARCH ranks as SOTA in cardiovascular disease prediction. The study emphasizes the critical role of incorporating domain-specific insights, illustrating ICE-SEARCH's robustness, generalizability, and convergence. This opens avenues for further research into comprehensive and intricate FS landscapes, marking a significant stride in the application of artificial intelligence in medical predictive analytics. Copyright © 2024, The Authors. All rights reserved.

关键词： Predictive analytics

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Fisherman Location Store, Updating, and Alert System using Cloud AWS with Satellite

Fisherman Location Store, Updating, and Alert System using C...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Kiruthiga Devi, M. Parthasarathy, R. Unnamalai, K. Perinba Athiban, S. Manikandan, K. Ramya, N. Dr. MGR Educational and Research Institute Chennai Department of Information Technology India Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Adarsh College for Women Department of Computer Science Anna India VIT Artificial Intelligence and Robotics Chennai India Sona College of Technology Department of Biomedical Engineering Salem India Sri Sai Ram Engineering College Department of CSE Chennai India

ISBN: (纸本)9798331522988

he successful demonstration of their location tracking alert system for fishermen, Satellites will employ Amazon Web Services (AWS). Using cloud-based computing technology, the activities and whereabouts of fishermen are monitored in real time through this system. The ACS sat will send a vast amount of information in short always-shrinking cycle and store data very efficiently, only the location data for live updates on every moves by the fishermen. Here using this project we are stressing on the need to analyze data and push alerts according protocols, for timely raise alarm which definitely saves Fishermen life. The aim is to develop a Real Time Location Tracking and Monitoring GIS Application, which can trace the fishermen real-time location in marine activities. Analyze location information with the best edge of the sword such as cloud computing and AI /ML. The GIS App will have a simple user interface along with extensive monitoring abilities. Building a GIS App to aid marine safety by improving the tracking and monitoring of fishing operations using cloud technology. In the end, with these benchmarks in tow this could mark a step change and benchmark for maritime operations;as we finally get, safety and productivity combined. © 2024 IEEE.

关键词： Benchmarking

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Implementing SLAM for Robot Navigation with Lidar in ROS Platform 4

Implementing SLAM for Robot Navigation with Lidar in ROS Pla...

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4th IEEE International Conference on Emerging Frontiers in Electrical and Electronic Technologies, ICEFEET 2024

作者： Verma, Gaurav Kumar Ray, Ranjit Swar, Surya Narayan Meena, Gagan Deep Dept. of Electrical Engineering National Institute of Technology Patna India Underwater Robotics & Autonomous Group CSIR-Central Mechanical Engineering Research Institute Durgapur India Department of Computer Science & Engineering Gandhi Institute of Engineering and Technology University Gunupur India

ISBN: (纸本)9798331528614

This research covers the implementation of SLAM (Simultaneous Localization and Mapping) with Ouster OS0 LiDAR (Light Detection and Ranging) sensor in the Robot Operating System (ROS1) Noetic. Using the high-frequency capabilities of the ouster, this method works towards mapping and localization in structured and unstructured environments. The paper explains the Ouster sensor integration in the ROS1 Noetic environment, providing information on the sensor calibration, data acquisition, and pre-processing. The ROS ecosystem in Ubuntu 20.04 is set to process the data streams coming from the ouster LiDAR sensor which is further utilised to create map and localize the robot. The Ouster LiDAR sensor scans every object in its field of view (in 3D), the data is then used for the SLAM. SLAM is executed using the Direct Lidar Odometry (DLO) algorithm for mapping and simultaneously helping the robot with localization and enabling it to navigate through the environment smoothly and precisely. © 2024 IEEE.

关键词： SLAM robotics

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A Research on Scattering Removal Technology Using Compact GPU Machine for Real-Time Visualization 23

A Research on Scattering Removal Technology Using Compact GP...

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

作者： Ono, Seiya Kim, Hyun-Woo Cho, Myungjin Lee, Min-Chul Kyushu Institute of Technology Department of Computer Science and Networks Fukuoka Iizuka820-8502 Japan School of ICT Robotics and Mechanical Engineering Iitc Hankyong National University Kyonggi-do Anseong17579 Korea Republic of

ISBN: (纸本)9788993215274

In this paper, we propose a system that enables visualization under the situation of scattering media such as fog and smoke by Peplography which is scattering media removal system, on a small GPU machine. Compared to conventional haze removal techniques, peplography technology is capable of removing more dense scattering media and can handle cases where the density of fog varies in the image. On the other hand, the conventional peplography process is performed on a desktop PC, and no system has yet been developed to perform peplography in an outside environment. Thus, we have developed a peplography system using the Jetson AGX Xavier, a single-board computer equipped with a GPU, which is superior to conventional methods in terms of portability, execution speed, and power consumption. We also demonstrate the performance of this system by conducting experiments using this system under a simulated scattering media scene and evaluating the obtained results in terms of FPS and the image evaluation metric. © 2023 ICROS.

关键词： Graphics processing unit

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The Influence of Human Factors in Ensuring Cybersecurity in Organizations: Effectiveness of PDPA and GDPR Implementation 3

The Influence of Human Factors in Ensuring Cybersecurity in ...

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3rd IEEE Delhi Section Flagship Conference, DELCON 2024

作者： Sharifi, Ahmad Zia Walid, Md. Abul Ala Momand, Mohammad Dawood Safi, Ihsan Ul Haq Safi, Hamidullah Khanzai Hossain, Sadia Nangarhar University Faculty of Computer Science Department of Networking and Communication Afghanistan Bangabandhu Sheikh Mujibur Rahman Digital University Department of Data Science and Engineering Gazipur Bangladesh Lautsprecher Teufel GMbH Berlin Germany Bangabandhu Sheikh Mujibur Rahman Digital University Department of Internet of Things and Robotics Engineering Gazipur Bangladesh

ISBN: (纸本)9798331518592

Modern organizations face rising levels of cyber risks, making cybersecurity a top priority for safeguarding sensitive data and maintaining operational continuity. Recent studies find human factors as the most critical contributors to cyber risks. These human-related risks include user misuse, user mistakes, and user malice. A large proportion of cyber risks arise when hackers use social engineering or phishing techniques to manipulate employees into disclosing confidential information. While some employees knowingly engage in these actions, some incidents occur accidentally due to a lack of awareness about cybersecurity protocols. As a result, governments around the world are enacting data protection legislation aimed at enhancing employees' cyber risk. This study explores the perception of US employees on the effectiveness of data protection legislation in countering cyber threats. It surveyed 30 randomly selected employees from various industries. The results showed that employees perceive data protection legislation as effective in mitigating employeerelated cyber risks. © 2024 IEEE.

关键词： Phishing

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RL-VLM-F: Reinforcement Learning from Vision Language Foundation Model Feedback

arXiv

引用

arXiv 2024年

作者： Wang, Yufei Sun, Zhanyi Zhang, Jesse Xian, Zhou Bıyık, Erdem Held, David Erickson, Zackory Robotics Institute Carnegie Mellon University United States Department of Computer Science University of Southern California United States

Reward engineering has long been a challenge in Reinforcement Learning (RL) research, as it often requires extensive human effort and iterative processes of trial-and-error to design effective reward functions. In this paper, we propose RL-VLM-F, a method that automatically generates reward functions for agents to learn new tasks, using only a text description of the task goal and the agent’s visual observations, by leveraging feedbacks from vision language foundation models (VLMs). The key to our approach is to query these models to give preferences over pairs of the agent’s image observations based on the text description of the task goal, and then learn a reward function from the preference labels, rather than directly prompting these models to output a raw reward score, which can be noisy and inconsistent. We demonstrate that RL-VLM-F successfully produces effective rewards and policies across various domains — including classic control, as well as manipulation of rigid, articulated, and deformable objects — without the need for human supervision, outperforming prior methods that use large pretrained models for reward generation under the same assumptions. Videos can be found on our project website: https://***/. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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