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AccTEF: A Transparency and Accountability Evaluation Framework for Ontology-Based systems

international JOURNAL OF SEMANTIC computing 2022年第1期16卷 5-27页

作者： Basereh, Maryam Caputo, Annalina Brennan, Rob Dublin City Univ Sch Comp Glasnevin Campus Dublin Ireland Dublin City Univ Sch Comp ADAPT Ctr Glasnevin Campus Dublin Ireland Dublin City Univ Dublin Sch Comp ADAPT Ctr Dublin 9 Ireland

This paper proposes a new accountability and transparency evaluation framework (AccTEF) for ontology-based systems (OSysts). AccTEF is based on an analysis of the relation between a set of widely accepted data governance principles, i.e. findable, accessible, interoperable, reusable (FAIR) and accountability and transparency concepts. The evaluation of accountability and transparency of input ontologies and vocabularies of OSysts are addressed by analyzing the relation between vocabulary and ontology quality evaluation metrics, FAIR and accountability and transparency concepts. An ontology-based knowledge extraction pipeline is used as a use case in this study. Discovering the relation between FAIR and accountability and transparency helps in identifying and mitigating risks associated with deploying OSysts. This also allows providing design guidelines that help accountability and transparency to be embedded in OSysts. We found that FAIR can be used as a transparency indicator. We also found that the studied vocabulary and ontology quality evaluation metrics do not cover FAIR, accountability and transparency. Accordingly, we suggest these concepts should be considered as vocabulary and ontology quality evaluation aspects. To the best of our knowledge, it is the first time that the relation between FAIR and accountability and transparency concepts has been found and used for evaluation.

关键词： FAIR data semantics AI governance

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Poster: Towards Constraint-Based Model Repair to Ensure Multiple Mission Objectives in UAV-Enabled MEC systems for Disaster Response and Rescue 2

Poster: Towards Constraint-Based Model Repair to Ensure Mult...

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2nd ieee international conference on Mobility, Operations, Services and Technologies, MOST 2024

作者： Wang, Yuxuan Chen, Jingshu Oakland University Dept. of Computer Science RochesterMI United States

ISBN: (纸本)9798350307733

In recent years, the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, combined with their aerial capabilities and versatility, has led to an increased deployment of UAVs in Multi-Access Edge computing (MEC) environments. This is particularly evident in critical applications such as emergency disaster response, where the reliable operation of UAVs can significantly impact outcomes of safety-critical impacts. Given the high-stakes nature of these applications, ensuring the correctness and reliability of system design is very crucial. However, ensuring such reliability presents substantial challenges, particularly when a single task involves multiple concurrent objectives, or when new objectives emerge to address unforeseen issues within a system designed for specific *** by this, our work proposes a formal approach which provides a constraints-based repair for system models of UAV-assisted MEC applications. Our approach first presents a general approach for modeling the system of UAV-assisted MEC applications using Markov decision process or continuous-time Markov chains. Then, our approach aims at developing a set of model repair algorithms for assuring the system model satisfying multiple mission objectives simultaneously. We demonstrated our approach through a case study focused on a UAV-assisted MEC system designed for disaster emergency rescue. © 2024 ieee.

关键词： Antennas

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Contextual Autonomy Evaluation of Unmanned Aerial Vehicles in Subterranean Environments 9

Contextual Autonomy Evaluation of Unmanned Aerial Vehicles i...

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9th international conference on Automation, Robotics and applications (ICARA)

作者： Donald, Ryan Gavriel, Peter Norton, Adam Ahmadzadeh, S. Reza Univ Massachusetts Lowell PeARL Lab Lowell MA 01854 USA Univ Massachusetts Lowell NERVE Ctr Lowell MA 01854 USA

ISBN: (纸本)9781665489218

In this paper we focus on the evaluation of contextual autonomy for robots. More specifically, we propose a fuzzy framework for calculating the autonomy score for a small Unmanned Aerial systems (sUAS) for performing a task while considering task complexity and environmental factors. Our framework is a cascaded Fuzzy Inference System (cFIS) composed of combination of three FIS which represent different contextual autonomy capabilities. We performed several experiments to test our framework in various contexts, such as endurance time, navigation, take off/land, and room clearing, with seven different sUAS. We introduce a predictive measure which improves upon previous predictive measures, allowing for previous real-world task performance to be used in predicting future mission performance.

关键词： Contextual Autonomy Unmanned Aerial Vehicles Fuzzy systems

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Deep Reinforcement Learning Based Efficient and Robust Navigation Method For Autonomous applications 35

Deep Reinforcement Learning Based Efficient and Robust Navig...

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35th ieee international conference on Tools with Artificial Intelligence (ICTAI)

作者： Hemming, Nathan Menon, Vineetha Univ Alabama Huntsville Huntsville AL 35899 USA

ISBN: (纸本)9798350342734

Robust and efficient navigation methods are needed to allow a robot to work independent of human control. Many methods leverage models of the world and robot, maps of the world, and graphs to generate navigation plans. Each of these tasks takes additional time away form the limited computational budget of real time systems present on robots. The methodology presented is called declarative re-planning (DRP) and is graph free, model free, and map free. The proposed method is able to achieve orders of magnitude increase in learning efficiency and to navigate and reach its assigned destination location 100% of the time in an experiment set. DRP leverages an hierarchical interface between a traditional path planner, a deep reinforcement learning based policy, and a classical model controller to achieve its performance. The top level create waypoints that serve as sub goals. The middle level generates a parametric path to reach the subgoal. The lowest level uses a model controller to follow the path. Each operates on a different time scale and is concerned with different aspects of the navigation task. All three levels of the hierarchy work together to obtain robust navigation capabilities.

关键词： Navigation Deep Reinforcement Learning Graph Free Map Free Model Free Autonomous systems Robotics AI Sample Efficiency

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Analysing Paralinguistic Information from Human Speech and its applications in Medicine 1

Analysing Paralinguistic Information from Human Speech and i...

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1st ieee international conference on Advances in Electronics, Communication, computing and Intelligent Information systems, ICAECIS 2023

作者： Kaushik, Palak Sharma, Ashish Chandigarh University Department of Computer Science Engineering Punjab Mohali India Department of Electrical Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9798350348057

The recent advances in Speech Emotion Recognition (SER) technology are explored in this work. Two of these include emotion detection using excitation features of speech and emotion recognition using Zero Shot Learning (ZSL). Both methods are speaker specific in nature. The recent research is evaluated on the basis on Geneva Minimalistic Acoustic Parameter Set, which is a proposed standard set of acoustic parameters. SER is heavily based on kernelization for classification. The acoustic parameters are, however, not optimized for computing in a high dimensional form. Dimensionality reduction methods are applied to overcome this. This work also explores how SER can enhance the existing technologies in medicine to detect various vocal pathologies and other disorders that may affect speech production and analyze their progression over time. © 2023 ieee.

关键词： Emotion Recognition

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A real- time Prognosis of State of Charge and State of Health of Remote Lithium-ion Battery on IoT Cloud 9

A Real- Time Prognosis of State of Charge and State of Healt...

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9th ieee India international conference on Power Electronics, IICPE 2023

作者： Malik, Gaurav Saini, Manish Kumar DCR University Science &tech. Department of Electrical Engineering Haryana India

ISBN: (纸本)9798350307252

this study concentrates on realtime estimation of state of charge (SoC) and state of health (SoH) on cloud platforms. The cloud offers the storage vast storage and computational facilities that can be utilized for realtime applications. Initially, a hardware loop is used to obtain the experimental data set to find the initial parameter of the I-RC model and form a regression model of the battery. A joint estimator is used for SoC and SoH estimation at different time scales. SoC is the critical parameter for a battery management system (BMS). Therefore it must be estimated at a shorter duration of micro intervals. SoH of the battery is calculated at the end of each life cycle of the battery. A strong calibration of time scaling is done while computing SoC and SoH at different durations of time. The forgetting factor recursive least square (FFRLS) method estimated the battery model parameters which is used by the extended Kalman filter EKF for SoC estimation at micro time scale. The important realtime estimation of SoC and SoH are stored in DBMS and they can be shown on the user interface graphically with the help of API (application programming interface). © 2023 ieee.

关键词： Battery management systems

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Design and Fabrication of High Sensitivity MEMS Pressure Sensors for Aerospace applications

Design and Fabrication of High Sensitivity MEMS Pressure Sen...

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2024 international conference on Optimization computing and Wireless Communication, ICOCWC 2024

作者： Yadav, Dhyan Chandra Faujdar, Pramod Kumar Mandal, Sanjeev Kumar Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Vivekananda Global University Department of Mechanical Engineering Jaipur India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350383348

This technical summary discusses the layout and fabrication of high-sensitivity MEMS strain sensors for aerospace applications. There may be a need for fairly particular and dependable pressure sensors that can screen the stress in the plane cabin, gas tanks, and different systems. MEMS pressure sensors are appropriate for such programs because they provide improved accuracy, flexibility, and strength consumption. The design of high-sensitivity MEMS strain sensors for aerospace programs needs to remember some of the necessities that are unique to such programs. As an example, the sensor needs to be capable of resisting the excessive temperatures and pressures associated with operations at high altitudes, as well as the potentially corrosive and extraordinarily electrically conductive environment of the cabin. The sensors need to additionally provide excessive sensitivity and speedy reaction times at the same time as keeping excessive accuracy and stability. A number of fabrication and design techniques may be applied. As an example, using lasers, photolithography, thin movie deposition, etching, and different microfabrication techniques can permit the fabrication of excessive decision MEMS systems with extraordinarily small characteristic sizes. © 2024 ieee.

关键词： Altitudes Capable Characteristic Improved Potentially Sensors systems Technical

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2022 8th international conference on Contemporary Information Technology and Mathematics, ICCITM 2022

2022 8th International Conference on Contemporary Informatio...

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8th international conference on Contemporary Information Technology and Mathematics, ICCITM 2022

ISBN: (纸本)9798350334869

The proceedings contain 85 papers. The topics discussed include: prospects and challenges of the cloud of things in telemedicine;a survey on real time cloud-based robotics systems;scraping google scholar data using cloud computing techniques;on the use of virtual reality in visualizing interactive network models for big data;behavioral features of users as a security solution in cloud computing;design and implementation of smart greenhouse and experiment in strawberry cultivation;exploring attitudes concerning the applying of mobile learning in technical education with responsibility and generativity;selecting embedded feature modeling with software product line for smart home applications;the data stream principles, tools and applications: a review;and based complex key cryptography: new secure image transmission method utilizing confusion and diffusion.

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Comparing the Consistency of User Studies Conducted in Simulations and Laboratory Settings 8

Comparing the Consistency of User Studies Conducted in Simul...

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8th international conference on Robotic computing

作者： Hummer, Jonathan Riedelbauch, Dominik Henrich, Dominik Univ Bayreuth Chair Appl Comp Sci 3 Robot & Embedded Syst Bayreuth Germany

ISBN: (纸本)9798331521561;9798331521554

Human-robot collaboration enables highly adaptive co-working. The variety of resulting workflows makes it difficult to measure metrics as, e.g. makespans or idle times for multiple systems and tasks in a comparable manner. This issue can be addressed with virtual commissioning, where arbitrary numbers of non-deterministic human-robot workflows in assembly tasks can be simulated. To this end, data-driven models of human decisions are needed. Gathering the required large corpus of data with on-site user studies is quite time-consuming. In comparison, simulation-based studies (e.g., by crowdsourcing) would allow us to access a large pool of study participants with less effort. To rely on respective study results, human action sequences observed in a browser-based simulation environment must be shown to match those gathered in a laboratory setting. To this end, this work aims to understand to what extent cooperative assembly work in a simulated environment differs from that in an on-site laboratory setting. We show how a simulation environment can be aligned with a laboratory setting in which a robot and a human perform pick-and-place tasks together. A user study (N=29) indicates that participants' assembly decisions and perception of the situation are consistent across these different environments.

关键词： virtual commissioning digital human modeling human-robot collaboration crowdsourcing

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AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing Pipelines 23

AssemAI: Interpretable Image-Based Anomaly Detection for Man...

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23rd ieee international conference on Machine Learning and applications, ICMLA 2024

作者： Prasad, Renjith Shyalika, Chathurangi El Kalach, Fadi Venkataramanan, Revathy Zand, Ramtin Harik, Ramy Sheth, Amit Artificial Intelligence Institute College of Engineering and Computing University of South Carolina ColumbiaSC29208 United States College of Engineering and Computing University of South Carolina McNair Center for Aerospace Innovation and Research Department of Mechanical Engineering ColumbiaSC29208 United States College of Engineering and Computing University of South Carolina Intelligent Circuits Architectures and Systems Lab ColumbiaSC29208 United States CU-ICAR Clemson University College of Engineering and Computing GreenvilleSC29607 United States

ISBN: (纸本)9798350374889

Anomaly detection in manufacturing pipelines remains a critical challenge, intensified by the complexity and variability of industrial environments. This paper introduces AssemAI, an interpretable image-based anomaly detection system tailored for smart manufacturing pipelines. Utilizing a curated image dataset from an industry-focused rocket assembly pipeline, we address the challenge of imbalanced image data and demonstrate the importance of image-based methods in anomaly detection. Our primary contributions include deriving an image dataset, fine-tuning an object detection model YOLO-FF, and implementing a custom anomaly detection model for assembly pipelines. The proposed approach leverages domain knowledge in data preparation, model development and reasoning. We implement several anomaly detection models on the derived image dataset, including a Convolutional Neural Network, Vision Transformer (ViT), and pretrained versions of these models. Additionally, we incorporate explainability techniques at both user and model levels, utilizing ontology for user-level explanations and SCORE-CAM for indepth feature and model analysis. Finally, the best-performing anomaly detection model and YOLO-FF are deployed in a real-time setting. Our results include ablation studies on the baselines and a comprehensive evaluation of the proposed system. This work highlights the broader impact of advanced image-based anomaly detection in enhancing the reliability and efficiency of smart manufacturing processes. The image dataset, codes to reproduce the results and additional experiments are available at https:/***/renjithk4/AssemAI. © 2024 ieee.

关键词： Smart manufacturing

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