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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zheng Chen Deepak Duggirala David Crandall Lei Jiang Lantao Liu Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Prediction beyond partial observations is crucial for robots to navigate in unknown environments because it can provide extra information regarding the surroundings beyond the current sensing range or resolution. In this work, we consider the inpainting of semantic Bird’s-Eye-View maps. We propose SePaint, an inpainting model for semantic data based on generative multinomial diffusion. To maintain semantic consistency, we need to condition the prediction for the missing regions on the known regions. We propose a novel and efficient condition strategy, Look-Back Condition (LB-Con), which performs one-step look-back operations during the reverse diffusion process. By doing so, we are able to strengthen the harmonization between unknown and known parts, leading to better completion performance. We have conducted extensive experiments on different datasets, showing our proposed model outperforms commonly used interpolation methods in various robotic applications.

关键词： Interpolation Navigation Semantics Diffusion processes Robot sensing systems Data models Sensors Intelligent robots

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Utilizing ETL Processes to Enhance Healthcare Education: Migrating MIMIC-IV to OpenEMR

Utilizing ETL Processes to Enhance Healthcare Education: Mig...

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Annual IEEE Symposium on Computer-Based Medical Systems

作者： Mohith Surya Kiran Kasula Sri Harsha Sudalagunta Keerthika Sunchu Saptarshi Purkayastha Luddy School of Informatics Computing and Engineering Indiana University Indianapolis Indianapolis IN

ISBN: (数字)9798350384727

ISBN: (纸本)9798350384734

The integration of real-world clinical data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database into the Open Electronic Medical Records (OpenEMR) system presents a unique opportunity to develop and implement innovative Extract, Transform, and Load (ETL) methodologies. This study focuses on the complex process of migrating 40,000 patient records from 33 tables in MIMIC-IV to the intricate schema of OpenEMR, which comprises 264 tables. The primary objective is to outline the novel approaches employed in the ETL process to ensure data fidelity, integrity, and usability within OpenEMR’s frontend interface. The methodology involves a three-stage process: extraction, transformation, and loading. During the extraction phase, relevant data is carefully selected from MIMIC-IV, taking into account data privacy considerations. The transformation phase involves intricate data mapping and manipulation to align the extracted data with OpenEMR’s schema, addressing challenges such as schema mismatches and data format inconsistencies. Finally, the loading phase ensures the accurate and efficient population of OpenEMR with the transformed data. The ETL process is designed to maintain data quality and integrity throughout the migration, employing robust validation and error-handling mechanisms. This study contributes to the advancement of ETL methodologies in the context of integrating real-world clinical data into electronic medical record systems. The detailed description of the innovative approaches employed in this project might serve as a valuable resource for researchers and practitioners working on similar data migration tasks.

关键词： Data privacy Databases MIMICs Education Loading Medical services Transforms

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Visual-Geometry GP-based Navigable Space for Autonomous Navigation

Visual-Geometry GP-based Navigable Space for Autonomous Navi...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Mahmoud Ali Durgkant Pushp Zheng Chen Lantao Liu Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Autonomous navigation in unknown environments is challenging and requires the consideration of both geometric and semantic information to assess the navigability of the environment. In this work, we propose a novel space modeling framework, Visual-Geometry Sparse Gaussian Process (VG-SGP), that simultaneously considers the semantics and geometry of the scene. Our proposed approach can overcome the limitation of visual planners that fail to recognize geometry associated with the semantic and the geometric planners that completely overlook the semantic information which is very critical in real-world navigation. The proposed method leverages dual Sparse Gaussian Processes in an integrated manner; the first is trained to forecast geometrically navigable spaces while the second predicts the semantically navigable areas. This integrated model is able to pinpoint the overlapping (geometric and semantic) navigable space. The simulation and real-world experiments demonstrate that the proposed VG-SGP model, coupled with our innovative navigation strategy, outperforms models solely reliant on visual or geometric navigation algorithms, highlighting a superior adaptive behavior. We provided a demonstration video 1 and open-sourced our code 2 .

关键词： Geometry Visualization Adaptation models Navigation Semantics Gaussian processes Prediction algorithms Reliability Intelligent robots Autonomous robots

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Autonomous Mapless Navigation on Uneven Terrains

Autonomous Mapless Navigation on Uneven Terrains

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Hassan Jardali Mahmoud Ali Lantao Liu Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

We propose a new method for autonomous navigation in uneven terrains by utilizing a sparse Gaussian Process (SGP) based local perception model. The SGP local perception model is trained on local ranging observation (pointcloud) to learn the terrain elevation profile and extract the feasible navigation subgoals around the robot. Subsequently, a cost function, which prioritizes the safety of the robot in terms of keeping the robot’s roll and pitch angles bounded within a specified range, is used to select a safety-aware subgoal that leads the robot to its final destination. The algorithm is designed to run in real-time and is intensively evaluated in simulation and real-world experiments. The results compellingly demonstrate that our proposed algorithm consistently navigates uneven terrains with high efficiency and surpasses the performance of other planners. The implementation of our method, including the supplementary video showing the experimental and real-world results, is available at https://***/3ov2r8.

关键词： Navigation Gaussian processes Streaming media Cost function Real-time systems Distance measurement Safety

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Context-Generative Default Policy for Bounded Rational Agent

Context-Generative Default Policy for Bounded Rational Agent

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Durgakant Pushp Junhong Xu Zheng Chen Lantao Liu Luddy School of Informatics Computing and Engineering Indiana University Bloomington IN USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Bounded rational agents often make decisions by evaluating a finite selection of choices, typically derived from a reference point termed the ‘default policy,’ based on previous experience. However, the inherent rigidity of the static default policy presents significant challenges for agents when operating in unknown environment, that are not included in agent’s prior knowledge. In this work, we introduce a context-generative default policy that leverages the region observed by the robot to predict unobserved part of the environment, thereby enabling the robot to adaptively adjust its default policy based on both the actual observed map and the imagined unobserved map. Furthermore, the adaptive nature of the bounded rationality framework enables the robot to manage unreliable or incorrect imaginations by selectively sampling a few trajectories in the vicinity of the default policy. Our approach utilizes a diffusion model for map prediction and a sampling-based planning with B-spline trajectory optimization to generate the default policy. Extensive evaluations reveal that the context-generative policy outperforms the baseline methods in identifying and avoiding unseen obstacles. Additionally, real-world experiments conducted with the Crazyflie drones demonstrate the adaptability of our proposed method, even when acting in environments outside the domain of the training distribution.

关键词： Training Visualization Systematics Diffusion models Planning Rigidity Splines (mathematics) Trajectory optimization Intelligent robots Drones

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Gradual Guarantee via Step-Indexed Logical Relations in Agda

arXiv

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arXiv 2024年

作者： Siek, Jeremy G. School of Informatics Computing and Engineering Indiana University BloomingtonIN United States

The gradual guarantee is an important litmus test for gradually typed languages, that is, languages that enable a mixture of static and dynamic typing. The gradual guarantee states that changing the precision of a type annotation does not change the behavior of the program, except perhaps to trigger an error if the type annotation is incorrect. Siek et al. (2015) proved that the Gradually Typed Lambda Calculus (GTLC) satisfies the gradual guarantee using a simulation-based proof and mechanized their proof in Isabelle. In the following decade, researchers have proved the gradual guarantee for more sophisticated calculi, using step-indexed logical relations. However, given the complexity of that style of proof, there has not yet been a mechanized proof of the gradual guarantee using step-indexed logical relations. This paper reports on a mechanized proof of the gradual guarantee for the GTLC carried out in the Agda proof assistant. © 2024, CC BY.

关键词： Differentiation (calculus)

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IoT-Based Real-Time Water Quality Monitoring and Sensor Calibration for Enhanced Accuracy and Reliability

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第1期19卷 155-170页

作者： Bakar, Azinoor Azida Abu Bakar, Zahari Abu Yusoff, Zakiah Mohd Ibrahim, Mohd Johan Mohamed Mokhtar, Nurkhairany Amyra Zaiton, Siti Nurul’Ain School of Civil Engineering College of Engineering Universiti Teknologi MARA Selangor Malaysia Electrical Engineering Studies College of Engineering Universiti Teknologi MARA Johor Branch Johor Malaysia Civil Engineering Studies College of Engineering Universiti Teknologi MARA Johor Branch Johor Malaysia Mathematical Sciences Studies College of Computing Informatics and Mathematics Universiti Teknologi MARA Johor Branch Johor Malaysia Faculty of Applied Sciences Universiti Teknologi MARA Johor Branch Johor Malaysia

The increasing water pollution levels and the growing demand for clean water necessitate advanced monitoring solutions. This study aims to develop an Internet of Things (IoT)-based real-time water quality monitoring system that addresses the limitations of traditional methods, which are often time-consuming and lack continuous monitoring capabilities. The system integrates pH, temperature, and turbidity sensors with an Arduino microcontroller and NodeMCU for Wi-Fi connectivity, transmitting data to a cloud platform for real-time access and analysis. The methodology involved interfacing these sensors with the microcontroller, displaying LCD data, and providing LED alerts. The findings indicate that the prototype’s readings closely align with laboratory measurements, with minor deviations in pH and temperature values and slightly more significant differences in turbidity readings. This demonstrates the prototype’s reliability and accuracy in real-time environmental monitoring. The conclusion emphasises the system’s potential to enhance water resource management through continuous and accurate tracking, enabling timely interventions. Future recommendations include improving sensor calibration, expanding the range of monitored parameters, and integrating advanced data analytics for predictive capabilities. The system’s real-time data logging capabilities significantly enhance water management by providing timely alerts and enabling proactive responses to water quality issues. The impact of this system is substantial, offering a scalable and cost-effective solution for continuous monitoring, thus contributing to improved water resource management and public health protection. © 2025 by the authors of this article.

关键词： Water well logging

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Predicting Protein-Protein Interactions Using Self-Attention-Based Deep Neural Networks and FastText Embeddings 15

Predicting Protein-Protein Interactions Using Self-Attention...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Oviya, I.R. Shanmukha Sravya, N. Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School Of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350370249

Predicting Protein-protein interactions are central to understanding the intricate mechanisms governing cellular functions, as proteins seldom act in isolation. While current machine learning and deep learning models focus on encoding protein sequences based on residue composition or physicochemical properties, they often overlook the complex relationships between amino acid segments within lengthy sequences. To address these limitations, we propose a novel hybrid model that combines a self-attention to enhance Deep neural network feature extraction with an with FastText Protein embeddings for predicting PPI. Utilizing transfer learning via the unsupervised FastText embedding technique to derive statistical representations of protein sequences, offers advantages over traditional protein encoding techniques in the context of analysing k-Mer sequences. Our model's performance is assessed through 5-fold cross-validation on datasets of *** and Human-Bacillus Anthracis protein interactions, resulting in accuracies of 92.28% and 94.08%, respectively. Compared to existing machine learning models, our model demonstrates superior performance on both datasets. © 2024 IEEE.

关键词： Adversarial machine learning

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R2V-PPI: Enhancing Prediction of Protein-Protein Interactions Using Word2Vec Embeddings and Deep Neural Networks 4

R2V-PPI: Enhancing Prediction of Protein-Protein Interaction...

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4th International Conference on Advances in Electrical, computing, Communication and Sustainable Technologies, ICAECT 2024

作者： Oviya, I.R. Sravya N, Shanmukha Raja, Kalpana Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science Engineering Chennai601103 India Yale University Section for Biomedical Informatics and Data Science School of Medicine New HavenCT United States

ISBN: (纸本)9798350343670

Proteins play a crucial role in living organisms, and understanding protein-protein interactions is vital for comprehending their functions and aiding drug discovery. In recent years, advanced deep learning models have been developed to predict protein-protein interactions from sequences. However, these large-scale approaches often suffer from deficiencies, resulting in both false positive and false negative predictions. Traditional encoding methods struggle to effectively represent the semantic meaning of individual residues in protein sequences and their impact on neighbouring residues. To overcome these deficiencies, we propose a novel method named R2V-PPI (Residue-to-Vector embeddings based Protein-Protein Interaction Framework) that effectively represents protein sequences and predicts protein-protein interactions. Inspired by Word2Vec, R2V-PPI learns informative representations of individual amino acid residues, capturing the context and relationships between them in raw protein sequences. These representations provide valuable input features for a downstream deep learning model. Our methodology offers a robust and versatile approach for predicting protein-protein interactions, especially when structural information is limited or unavailable. Evaluation on the S. cerevisiae dataset demonstrates that R2V-PPI outperforms existing state-of-the-art machine learning models. © 2024 IEEE.

关键词： Deep Learning Machine Learning Protein Feature Encoding Protein Protein interaction Protein sequence

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Object on Use Case Description: Sequence Diagram Conformance based on Step Performed using Text Pre-Processing on Sipranta Application SRS

Object on Use Case Description: Sequence Diagram Conformance...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Abibi, Muhammad Alfath Priyadi, Yudi Dian Martha, Ati Suci Telkom University Department of Informatics Bandung Indonesia Telkom University Department of Software Engineering Bandung Indonesia Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9798350337617

In the Use Case Diagram, a Step Performed in Tabulation contains an object's explanation flow. A sequence diagram attempts to depict in detail the interaction of objects in a system, the messages or commands sent, and the implementation time. In other words, the Sequence Diagram, like the Use Case Diagram, encapsulates an object's explanation flow in a graphical diagram. The Step Performed and the Sequence Diagram should be related, yet the Step Performed and Sequence Diagram diverge while creating the Sipranta Application SRS. By adopting Text Pre-processing, this research intends to quantify the value of conformance between the Sequence Diagram and the Step Performed in Use Case Description. This study identifies the Step Performed and the Sequence Diagram using 22 documents, 11 of which are Step Performed and 11 of which are Sequence Diagrams. In addition, the Text Analysis process produces two documents with the highest conformity: d1 with d12 of 0.933461. Cohen Cappa Score produces a validation value of 0.29195, which is included in the "Fair Agreement"category. While the reliability results from 2 groups of experts, which is 0.8275, so it is included in the "Substantial agreement"category. The results differ between the two methods, which is 0.536. © 2023 IEEE.

关键词： Sequence Diagram Step Performed Text Pre-Processing

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