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IEEE International Conference on Image Processing

作者： Axel Vierling Ajay Chawda Mahesh Kashyap Belakavadi Manjunath Karsten Berns Robotics Research Lab Department of Computer Science University of Kaiserslautern-Landau Germany

Interpreting the results of Convolutional Neural Networks remains a challenging task. Quantitative evaluations apart from precision, recall, and their extensions are rare and usually do not cover the necessary aspects of specific applications. In this work, a methodology based on the intrinsic dimensionality of the image space and latent space in multiple layers is presented. This methodology has been used in other literature for classification but is leveraged to object detection where the interpretation of the results is more complex. The suitability of the intrinsic dimensionality is evaluated first for general augmentation techniques in multiple datasets and with multiple networks and later on a specific use case with multiple disturbances included. With the help of the intrinsic dimensionality, conclusions about the robustness can be drawn which are not apparent from the precision and the suitability of the methodology as an auxiliary quantifiable metric therefore shown.

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Methodological Insights in Detecting Subtle Semantic Shifts with Contextualized and Static Language Models

Methodological Insights in Detecting Subtle Semantic Shifts ...

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2023 Findings of the Association for computational Linguistics: EMNLP 2023

作者： Hoeken, Sanne Alaçam, Özge Fokkens, Antske Sommerauer, Pia Computational Linguistics Dept. of Linguistics Bielefeld University Germany Center for Information and Language Processing LMU Munich Germany Computational Linguistics & Text Mining Lab Vrije Universiteit Amsterdam Netherlands Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands

ISBN: (纸本)9798891760615

In this paper, we investigate automatic detection of subtle semantic shifts between social communities of different political convictions in Dutch and English. We perform a methodological study comparing methods using static and contextualized language models. We investigate the impact of specializing contextualized models through fine-tuning on target corpora, word sense disambiguation and sentiment. We furthermore propose a new approach using masked token prediction which relies on behavioral information, specifically the most probable substitutions, instead of geometrical comparison of representations. Our results show that methods using static models and our masked token prediction method can detect differences in connotation of politically loaded terms, whereas methods that rely on measuring the distance between contextualized representations are not providing clear signals, even in synthetic scenarios of extreme shifts. © 2023 Association for computational Linguistics.

关键词： Semantics

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Revolutionizing Marine Stability: A Review of Cutting-Edge Gyro Stabilizer Designs and Techniques for Vessels

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IEEE Access 2025年 13卷 89542-89555页

作者： Rahiman, Wan Ghazali, Mohamad Hazwan Mohd Mahmood, Iskandar Al-Thani Universiti Sains Malaysia Engineering Campus School of Electrical and Electronic Engineering Penang Nibong Tebal14300 Malaysia Daffodil International University Daffodil Robotics Lab Department of Computer Science and Engineering Dhaka1216 Bangladesh Universiti Kebangsaan Malaysia Faculty of Engineering and Built Environment Department of Mechanical and Manufacturing Engineering Selangor Bangi43600 Malaysia PETRONAS Research Sdn Bhd Selangor Kajang43000 Malaysia

Advancement in marine vessel stabilization technologies has greatly improved the safety and comfort for crews, cargo, and passengers, while also lowering the risk of capsizing. Marine vessels operating in open waters experience disturbances from unpredictable waves and strong winds, leading to instability and potential accidents. To mitigate these risks, stabilizers are integrated into ships to maintain stability, safety, and seaworthiness. As modern vessels evolve, stabilizers play an increasingly vital role, with gyro stabilizers emerging as a highly effective solution for mitigating roll motion without external appendages. This paper provides a comprehensive review of gyro stabilizer designs and techniques, covering their historical development, mathematical modeling, advantages, limitations, and case studies. A review of current research highlights a strong emphasis on simulating gyro stabilizer control systems, demonstrating their potential in enhancing vessel stability. However, key challenges persist, including accurate wave moment prediction during the design phase and gyroscopic drift, which can impact long-term performance and efficiency. Despite these challenges, recent advancements in control algorithms and optimization techniques have improved gyro stabilizer adaptability under varying sea conditions. By exploring the latest developments, this review serves as a valuable reference for researchers, engineers, and maritime professionals seeking to advance marine stabilization technologies. The findings underscore the potential of next-generation gyro stabilizers, including fiber optic gyros, in achieving higher precision, stability, and operational efficiency. © 2013 IEEE.

关键词： Warships

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Crop Type Classification using Multi-temporal Sentinel-2 Satellite Imagery: A Deep Semantic Segmentation Approach

Crop Type Classification using Multi-temporal Sentinel-2 Sat...

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2023 International Conference on robotics and Automation in Industry, ICRAI 2023

作者： Khan, Asim Hameed Zafar, Zuhair Shahzad, Muhammad Berns, Karsten Fraz, Muhammad Moazam Islamabad Pakistan Data Science in Earth Observation Munich Germany University of Reading Department of Computer Science BerkshireRG6 6AH United Kingdom Technical University of Kaiserslautern Robotics Research Lab Germany

ISBN: (纸本)9781665464727

Crop Type classification using Semantic Segmentation and remote sensing data is an important tool for decision-making related to precision agriculture. Such classification remains an unsolved challenge due to the choice of landscape, processing methodology and selected satellite imagery and its optical features, and most importantly the availability and usage of such datasets in a developing country like Pakistan. State-of-the-art semantic segmentation models lack in processing the temporal dimension of time series imagery and evident solution to process multi-spectral bands available in the satellite imagery. We propose a methodology to overcome these shortcomings by selecting appropriate band combinations for crop type classification and treating time series visual data as a single image. The proposed methodology is evaluated on the data set of six different crops collected from National Agriculture Research Center (NARC) Islamabad. The experimental results yield 85% accuracy for classifying various crop types based on the evaluation of five different semantic segmentation models. The code and the trained models are available at https://***/asimniazi63/crop-type-narc for other researchers working in the same domain. © 2023 IEEE.

关键词： Satellite imagery

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Improve Discourse Dependency Parsing with Contextualized Representations

Improve Discourse Dependency Parsing with Contextualized Rep...

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2022 Findings of the Association for computational Linguistics: NAACL 2022

作者： Zhou, Yifei Feng, Yansong Cornell University Department of Computer Science United States WICT Peking University MOE Key Lab. of Computational Linguistics China

ISBN: (纸本)9781955917766

Recent works show that discourse analysis benefits from modeling intra- and inter-sentential levels separately, where proper representations for text units of different granularities are desired to capture both the meaning of text units and their relations to the context. In this paper, we propose to take advantage of transformers to encode contextualized representations of units of different levels to dynamically capture the information required for discourse dependency analysis on intra- and inter-sentential levels. Motivated by the observation of writing patterns commonly shared across articles, we propose a novel method that treats discourse relation identification as a sequence labelling task, which takes advantage of structural information from the context of extracted discourse trees, and substantially outperforms traditional directclassification methods. Experiments show that our model achieves state-of-the-art results on both English and Chinese datasets. Our code is publicly available. © Findings of the Association for computational Linguistics: NAACL 2022 - Findings.

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Mixed Reality Based Teleoperation of Surgical robotics

Mixed Reality Based Teleoperation of Surgical Robotics

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International Symposium on Medical robotics (ISMR)

作者： An Chi Chen Muhammad Hadi Peter Kazanzides Ehsan Azimi Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Computer Science Department Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Computer Science Department Malone Center for Engineering in Healthcare Johns Hopkins University Baltimore MD USA

Many surgical robotic systems are controlled by mechanical based devices that require the operator to remain at a fixed location away from the robot. This restriction in mobility and physical barrier between the surgeon and the robot may reduce procedural efficiency. Thus, we propose an alternative teleoperation approach and mixed reality based system that uses the surgeon's tracked hand poses to control the robot through the use of an untethered head mounted display. We conducted a controlled user study to assess the efficacy of our system. Our experimental results indicate that, for the ring-wire task we tested, there is not a considerable difference in the performance of users compared to existing mechanical based teleoperation devices.

关键词： Performance evaluation Medical robotics Head Surgery Mixed reality Control systems Task analysis

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Cooperative vs. Teleoperation Control of the Steady Hand Eye Robot with Adaptive Sclera Force Control: A Comparative Study

Cooperative vs. Teleoperation Control of the Steady Hand Eye...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mojtaba Esfandiari Ji Woong Kim Botao Zhao Golchehr Amirkhani Muhammad Hadi Peter Gehlbach Russell H. Taylor Iulian Iordachita Department of Mechanical Engineering Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Johns Hopkins Hospital Wilmer Eye Institute Baltimore MD USA Department of Computer Science Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

A surgeon’s physiological hand tremor can significantly impact the outcome of delicate and precise retinal surgery, such as retinal vein cannulation (RVC) and epiretinal membrane peeling. Robot-assisted eye surgery technology provides ophthalmologists with advanced capabilities such as hand tremor cancellation, hand motion scaling, and safety constraints that enable them to perform these otherwise challenging and high-risk surgeries with high precision and safety. Steady-Hand Eye Robot (SHER) with cooperative control mode can filter out surgeon’s hand tremor, yet another important safety feature, that is, minimizing the contact force between the surgical instrument and sclera surface for avoiding tissue damage cannot be met in this control mode. Also, other capabilities, such as hand motion scaling and haptic feedback, require a teleoperation control framework. In this work, for the first time, we implemented a teleoperation control mode incorporated with an adaptive sclera force control algorithm using a PHANTOM Omni haptic device and a force-sensing surgical instrument equipped with Fiber Bragg Grating (FBG) sensors attached to the SHER 2.1 end-effector. This adaptive sclera force control algorithm allows the robot to dynamically minimize the toolsclera contact force. Moreover, for the first time, we compared the performance of the proposed adaptive teleoperation mode with the cooperative mode by conducting a vessel-following experiment inside an eye phantom under a microscope.

关键词： Tissue damage Heuristic algorithms Veins Force Surgery Phantoms Retina

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Can Automated Metadata Extraction Make Scientific Data More Navigable? 19

Can Automated Metadata Extraction Make Scientific Data More ...

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19th IEEE International Conference on e-science, e-science 2023

作者： Skluzacek, Tyler J. Chard, Kyle Foster, Ian National Center for Computational Sciences Oak Ridge National Lab Oak RidgeTN United States University of Chicago Department of Computer Science ChicagoIL United States Data Science and Learning Division Argonne National Lab LemontIL United States

ISBN: (纸本)9798350322231

FAIR principles require that scientific data be findable, discoverable, and reusable by users. To enable FAIRness, practioners of a science repository will often construct a rich, searchable index of metadata derived from the data. Unfortunately, manual metadata annotation methods do not scale to the many data files generated by many projects;and instead automated extraction systems are needed to scalably parse these files - often with nonstandard schema requiring specialized parsing strategies - and deposit representative metadata into a search index. In this work, we evaluate whether, and the extent to which, automatically extracted metadata make research repositories more navigable. We present a two-part user study conducted with scientists at two U.S. national laboratories from projects spanning spectroscopy and battery modeling. We constructed research indexes automatically by using the Xtract metadata extraction system. In the first part of our study, we learned about each user's role and identified key navigation concerns for scientists. We found that participants wished to navigate for purposes of discovery, retrieval, and organization. In the second part, participants completed simulated research data navigation tasks crafted to reflect real-world navigability concerns. We found that regardless of the interface used, participants consistently solved navigation tasks with high degrees of confidence and correctness, and significantly (1.2X-50×) faster than via their alternative methods (e.g., manual directory scans or designing a customized navigational tool). © 2023 IEEE.

关键词： Metadata

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Suturing Tasks Automation Based on Skills Learned from Demonstrations: A Simulation Study

Suturing Tasks Automation Based on Skills Learned from Demon...

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International Symposium on Medical robotics (ISMR)

作者： Haoying Zhou Yiwei Jiang Shang Gao Shiyue Wang Peter Kazanzides Gregory S. Fischer Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350377118

ISBN: (纸本)9798350377125

In this work, we develop an open-source surgical simulation environment that includes a realistic model obtained by MRI-scanning a physical phantom, for the purpose of training and evaluating a Learning from Demonstration (LfD) algorithm for autonomous suturing. The LfD algorithm utilizes Dynamic Movement Primitives (DMP) and Locally Weighted Regression (LWR), but focuses on the needle trajectory, rather than the instruments, to obtain better generality with respect to needle grasps. We conduct a user study to collect multiple suturing demonstrations and perform a comprehensive analysis of the ability of the LfD algorithm to generalize from a demonstration at one location in one phantom to different locations in the same phantom and to a different phantom. Our results indicate good generalization, on the order of 91.5%, when learning from more experienced subjects, indicating the need to integrate skill assessment in the future.

关键词： Training Medical robotics Automation Heuristic algorithms Instruments Phantoms Needles

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AI Based Solution to Optimize the Fertilizer Composition in Hydroponics Agriculture 23

AI Based Solution to Optimize the Fertilizer Composition in ...

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15th International Conference on Machine Learning and Computing, ICMLC 2023

作者： Ekanayake, Pubudu Fernando, Subha Rodrigo, Ranga Jayasena, Sanath CODEGEN QBITS Lab University of Moratuwa Sri Lanka Department of Computational Mathematics University of Moratuwa Sri Lanka Department of Electronics and Telecommunication University of Moratuwa Sri Lanka Computer Science and Engineering University of Moratuwa Sri Lanka

ISBN: (纸本)9781450398411

Precision agriculture is one of the trending research areas in the world. AI technologies have been applied to greenhouse hydroponics agriculture (which comes under precision agriculture) to control the requirements of the plants in greenhouse agriculture. Apart from the environmental conditions such as temperature, humidity, and light intensity, the optimum combination of the fertilizer components, EC, and pH value provided during the plants' growth phase is vital to obtaining a higher yield during the harvesting period. However, finding the optimal fertilizer composition is challenging due to the fact that the required fertilizer composition is different for different plant types and the experimental cost is higher. Sparse data samples will be received in data-driven experiments because of the large search space (number of permutations) of the fertilizer combinations. This paper presents an experimental design and AI based solution to find the optimal fertilizer composition for the lettuce plants in hydroponics agriculture. Further, this paper presents preliminary ANOVA analysis results and the designed AI algorithm which is based on Bayesian Neural Network, kNN algorithm, and Particle Swarm Optimization algorithm. © 2023 ACM.

关键词： Hydroponics

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