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American Control Conference (ACC)

作者： Eugen Ernst Florian Pfaff Uwe D. Hanebeck Marcus Baum Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology (KIT) Germany Institute of Computer Science Georg- August-University Göttingen Germany

Different objectives and paradigms exist for tracking multiple targets when measurements do not contain information about the target identities (IDs). The Symmetric Measurement Equation (SME) filter can be used when one is agnostic to the labels and does not attempt to assign different IDs to the different targets. We present an extension of the Kernel-SME filter that, unlike the original variant, uses adaptive kernel widths that depend on the respective uncertainty. In our evaluation, it outperformed existing SMEbased approaches, while it is only second to a more complex global nearest neighbor tracker.

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Initial achievements in relation extraction from RNA-focused scientific papers 32

Initial achievements in relation extraction from RNA-focused...

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32nd Italian Symposium on Advanced Database systems, SEBD 2024

作者： Cavalleri, Emanuele Soto-Gomez, Mauricio Pashaeibarough, Ali Malchiodi, Dario Caufield, Harry Reese, Justin Mungall, Christopher J. Robinson, Peter N. Casiraghi, Elena Valentini, Giorgio Mesiti, Marco Department of Computer Science Università di Milano Via Celoria 18 Milano20133 Italy Environmental Genomics and Systems Biology Division Lawrence Berkeley National Laboratory BerkeleyCA United States ELLIS European Laboratory for Learning and Intelligent Systems Milan Unit Italy Berlin Institute of Health Charité Universitätsmedizin Berlin13353 Germany

Relation extraction from the scientific literature to comply with a domain ontology is a well-known problem in natural language processing and is particularly critical in precision medicine. The advent of large language models (LLMs) has paved the way for the development of new effective approaches to this problem, but the extracted relations can be affected by issues such as hallucination, which must be minimized. In this paper, we present the initial design and preliminary experimental validation of SPIREX, an extension of the SPIRES-based system for the extraction of RDF triples from scientific literature involving RNA molecules. Our system exploits schema constraints in the formulations of LLM prompts along with our RNA-based KG, RNA-KG, for evaluating the plausibility of the extracted triples. RNA-KG contains more than 9M edges representing different kinds of relationships in which RNA molecules can be involved. Initial experimental results on a controlled data set are quite encouraging. © 2024 Copyright for this paper by its authors.

关键词： Knowledge graph

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NUSense: Robust Soft Optical Tactile Sensor

arXiv

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arXiv 2024年

作者： Yergibay, Madina Mussin, Tleukhan Seitzhan, Saltanat Kenzhebek, Daryn Kappassov, Zhanat Soh, Harold Taunyazov, Tasbolat Robotics Department Institute of Smart Systems and Artificial Intelligence Nazarbayev University Astana Kazakhstan Dept. of Computer Science National University of Singapore Singapore

While most tactile sensors rely on measuring pressure, insights from continuum mechanics suggest that measuring shear strain provides critical information for tactile sensing. In this work, we introduce an optical tactile sensing principle based on shear strain detection. A silicone rubber layer, dyed with color inks, is used to quantify the shear magnitude of the sensing layer. This principle was validated using the NUSense camera-based tactile sensor. The wide-angle camera captures the elongation of the soft pad under mechanical load, a phenomenon attributed to the Poisson effect. The physical and optical properties of the inked pad are essential and should ideally remain stable over time. We tested the robustness of the sensor by subjecting the outermost layer to multiple load cycles using a robot arm. Additionally, we discussed potential applications of this sensor in force sensing and contact localization. Copyright © 2024, The Authors. All rights reserved.

关键词： Silicones

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Leveraging Transfer Learning for Improved Diagnostics in Dermatology 5

Leveraging Transfer Learning for Improved Diagnostics in Der...

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5th International Conference on Information Management and Machine Intelligence, ICIMMI 2023

作者： Singh, Kiran Deep Saini, G.L. Singh, Prabh Deep Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India School of Computing and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India

ISBN: (纸本)9798400709418

The significance of leveraging transfer learning in dermatology lies in its potential to enhance early disease detection and diagnosis, ultimately leading to improved patient outcomes and more efficient healthcare delivery. This innovative approach offers a promising avenue for dermatologists to harness the power of AI in their practice, augmenting their expertise and expanding the horizons of skin disease diagnostics. This paper applies transfer learning to healthcare for Improved Diagnostics in Dermatology. It explores the core principles and methodologies of transfer learning, unveiling its applications across various domains, from computer vision to natural language processing. The discussion emphasizes the advantages of transfer learning over conventional methods, shedding light on its ability to mitigate data scarcity, expedite training, and enhance model generalization. Moreover, the dialogue delves into the steps involved in implementing transfer learning, offering practical guidance on data preparation, model adaptation, and rigorous evaluation, specifically referencing utilizing the Deep Learning Toolbox in MATLAB. Transfer learning transforms medical diagnoses sustainably, which finishes the discourse with a captivating dermatological case study that shows how skillfully implemented may revolutionize early illness detection, diagnosis, and patient care, demonstrating its enormous influence on healthcare and smart infrastructure. © 2023 ACM.

关键词： Deep learning

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Entropy Maximization in High Dimensional Multiagent State Spaces

Entropy Maximization in High Dimensional Multiagent State Sp...

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International Symposium on Multi-Robot and Multi-Agent systems (MRS)

作者： Ayhan Alp Aydeniz Enrico Marchesini Robert Loftin Kagan Tumer Collaborative Robotics and Intelligent Systems Institute Oregon State University Corvallis Oregon USA Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge Massachusetts USA Department of Computer Science University of Sheffield Sheffield United Kingdom

Underwater or planetary exploration are prime examples of missions that can benefit from autonomous agents working together. However, discovering effective team-level behaviors (i.e., coordinated joint actions) is challenging in these domains as agents typically receive a sparse reward (zero-or constant-for the majority of the interactions). To address this issue, intrinsic rewards encourage agents to explore diverse policies to visit the state space more effectively. Unfortunately, as the agents’ state space grows, intrinsic reward-based (i.e., curiosity) approaches become less effective as they cannot effectively distinguish a diverse set of states. In this direction, we introduce state entropy maximization for multiagent learning where agents explore using local (dense) rewards and learn to solve the coordination task by leveraging global (sparse) rewards. Because of the intrinsic ability to balance local and global rewards, our approach enables the state entropy function to remain effective in high dimensional state spaces. Experiments in tightly coupled tasks requiring complex joint actions, show that local entropy-based rewards enable agents to discover successful team behaviors in high dimensional spaces where previous hand-tuned count-based rewards fail.

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Dynamic Subgoal Based Path Formation and Task Allocation: A NeuroFleets Approach to Scalable Swarm robotics

Dynamic Subgoal Based Path Formation and Task Allocation: A ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Robinroy Peter Lavanya Ratnabala Eugene Yugarajah Andrew Charles Dzmitry Tsetserukou NeuroFleets (PVT) LTD Jaffna Sri Lanka Intelligent Space Robotics Laboratory Skolkovo Institute of Science and Technology Moscow Russia Department of Computer Science University of Jaffna Jaffna Sri Lanka

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

This paper addresses the challenges of exploration and navigation in unknown environments from the perspective of evolutionary swarm robotics. A key focus is on path formation, which is essential for enabling cooperative swarm robots to navigate effectively. We designed the task allocation and path formation process based on a finite state machine, ensuring systematic decision-making and efficient state transitions. The approach is decentralized, allowing each robot to make decisions independently based on local information, which enhances scalability and robustness. We present a novel subgoal-based path formation method that establishes paths between locations by leveraging visually connected subgoals. Simulation experiments conducted in the Argos simulator show that this method successfully forms paths in the majority of trials. However, inter-collision (traffic) among numerous robots during path formation can negatively impact performance. To address this issue, we propose a task allocation strategy that uses local communication protocols and light signal-based communication to manage robot deployment. This strategy assesses the distance between points and determines the optimal number of robots needed for the path formation task, thereby reducing unnecessary exploration and traffic congestion. The performance of both the subgoal-based path formation method and the task allocation strategy is evaluated by comparing the path length, time, and resource usage against the A * algorithm. Simulation results demonstrate the effectiveness of our approach, highlighting its scalability, robustness, and fault tolerance.

关键词： Systematics Protocols Navigation Scalability Simulation Heuristic algorithms Swarm robotics Robustness Resource management Traffic congestion

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Design and Development of a Fixed-Wing Tail-Sitter Tethered VTOL Drone with Custom Autopilot and Robust Control Techniques 16th

Design and Development of a Fixed-Wing Tail-Sitter Tether...

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16th APCA International Conference on Automatic Control and Soft Computing, CONTROLO 2024

作者： Safaee, Ahmad Aguiar, A. Pedro Santos, Filipe Neves Moreira, Antonio Paulo SYSTEC-ARISE Research Center for Systems and Technologies Faculty of Engineering University of Porto Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering Technology and Science CRIIS-Centre for Robotics in Industry and Intelligent Systems Porto4200-465 Portugal INESC-TEC-Institute for Systems and Computer Engineering and DEEC/FEUP - Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto4200-465 Portugal

ISBN: (纸本)9783031817236

The need for aerial platforms capable of sustained operation is critical in fields such as surveillance, agricultural monitoring, disaster response, and temporary telecommunication systems. Traditional unmanned aerial vehicles (UAVs) are hampered by limited battery capacity and inefficient energy utilization. This paper introduces an autonomous vertical flight drone tethered to a ground-based platform, which overcomes these limitations. The drone is designed to optimize its position and energy usage by harnessing wind currents, functioning similarly to a kite when conditions permit. This is enabled by a custom-designed autopilot system comprising both hardware and software tailored for this application. We detail the model identification process for three vertical take-off and landing (VTOL) drone systems, employing authentic data that measures lateral positional errors from the tether. Robust mixed sensitivity controllers are developed and implemented. The effectiveness of these controllers is validated through simulations showing their capability to handle uncertainties. Furthermore, the practical application and robust performance of the system are confirmed by a real-world case study, highlighting its potential in real operational environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： VTOL/STOL aircraft

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Source Camera Device Identification from Videos

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SN computer science 2022年第4期3卷 316页

作者： Bennabhaktula, Guru Swaroop Timmerman, Derrick Alegre, Enrique Azzopardi, George Information Systems Group at the Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Group for Vision and Intelligent Systems at the Department of Electrical Systems and Automation University of León León Spain Researcher at INCIBE (Spanish National Cybersecurity Institute) León Spain

Source camera identification is an important and challenging problem in digital image forensics. The clues of the device used to capture the digital media are very useful for Law Enforcement Agencies (LEAs), especially to help them collect more intelligence in digital forensics. In our work, we focus on identifying the source camera device based on digital videos using deep learning methods. In particular, we evaluate deep learning models with increasing levels of complexity for source camera identification and show that with such sophistication the scene-suppression techniques do not aid in model performance. In addition, we mention several common machine learning strategies that are counter-productive in achieving a high accuracy for camera identification. We conduct systematic experiments using 28 devices from the VISION data set and evaluate the model performance on various video scenarios—flat (i.e., homogeneous), indoor, and outdoor and evaluate the impact on classification accuracy when the videos are shared via social media platforms such as YouTube and WhatsApp. Unlike traditional PRNU-noise (Photo Response Non-Uniform)-based methods which require flat frames to estimate camera reference pattern noise, the proposed method has no such constraint and we achieve an accuracy of 72.75 ± 1.1 % on the benchmark VISION data set. Furthermore, we also achieve state-of-the-art accuracy of 71.75 % on the QUFVD data set in identifying 20 camera devices. These two results are the best ever reported on the VISION and QUFVD data sets. Finally, we demonstrate the runtime efficiency of the proposed approach and its advantages to LEAs. © 2022, The Author(s).

关键词： Constrained ConvNet Digital video forensics Sensor pattern noise Source camera identification

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A Structure-Aware Argument Encoder for Literature Discourse Analysis 29

A Structure-Aware Argument Encoder for Literature Discourse ...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Yinzi Chen, Wei Wei, Zhongyu Huang, Yujun Wang, Chujun Wang, Siyuan Zhang, Qi Huang, Xuanjing Wu, Libo School of Data Science Fudan University China Research Institute of Intelligent Complex Systems Fudan University China Department of Environmental Science and Engineering Fudan University China School of Computer Science Fudan University China

Existing research for argument representation learning mainly treats tokens in the sentence equally and ignores the implied structure information of argumentative context. In this paper, we propose to separate tokens into two groups, namely framing tokens and topic ones, to capture structural information of arguments. In addition, we consider high-level structure by incorporating paragraph-level position information. A novel structure-aware argument encoder is proposed for literature discourse analysis. Experimental results on both a self-constructed corpus and a public corpus show the effectiveness of our model. Resources are available at https://***/lemuria-wchen/SAE. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Signal encoding

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