This study presents a multi-agent approach to modeling deci-sion-making processes for cooperative-competitive game scenarios generated by long-tailed distributions. We introduce a novel simulation environment that aim...
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ISBN:
(纸本)9783031777301;9783031777318
This study presents a multi-agent approach to modeling deci-sion-making processes for cooperative-competitive game scenarios generated by long-tailed distributions. We introduce a novel simulation environment that aims to replicate real-world resource allocation challenges, particularly in healthcare settings. The environment incorporates dynamic changes in agents' perceptions and utilizes heavy-tailed distributions to model unpredictable events. We adapt the Multi-Agent Deep Deterministic Policy Gradient algorithm to this environment, demonstrating its effectiveness in learning and strategy development. Our approach includes a role-based system and a two-component reward function that guides agents through the learning process. The results, evaluated using metrics such as cumulative reward, average episodic reward, and approximate Kullback-Leibler divergence, indicate successful learning and sufficent agent strategies. This research contributes to the field of multi-agent systems and resource management by providing a framework that can generalize learned policies to varying numbers of agents and resources, making it applicable to real-world scenarios with changing conditions.
The proceedings contain 21 papers. The special focus in this conference is on Distributed Computing and intelligent Technology. The topics include: Invited Paper: Application of Physics-Based and data-Driven...
ISBN:
(纸本)9783031814037
The proceedings contain 21 papers. The special focus in this conference is on Distributed Computing and intelligent Technology. The topics include: Invited Paper: Application of Physics-Based and data-Driven Approaches for Drug-Like Property Prediction;invited Paper: Distributed Computability: A Few Results Masters students Should Know;ioT-Based Service Allocation in Edge Computing Using Game Theory;Exploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments;a "Symbolic" Representation of Object-Nets;optimal Dispersion in Triangular Grids: Achieving Efficiency Without Prior Knowledge;mobile Agents on Chordal Graphs: Maximum Independent Set and Beyond;faster Leader Election and Its Applications for Mobile Agents with Parameter Advice;a Novel Protocol for Mitigating Wormhole Attack in Multi-hop Payment Channels;unleashing Multicore strength for Efficient Execution of Blockchain Transactions;Enhancing QR Decomposition: A GPU-Based Approach to Parallelizing the Householder Algorithm with CUDA streams;source Sets in Temporal Graphs;team Formation Based on the Degree Distribution of the Social Networks;inVideo Search: Scene Description Clustering and Integrating Image and Audio Captioning for Enhanced Video Search;does data Balancing Impact stutter Detection and Classification?;deep learning-Driven Person Re-identification: Leveraging Color Space Transformations;an Ensembled Parking Space Classifier Across Diverse Weather Conditions;classification of Impacted Teeth from Panoramic Radiography Using Deep learning;HJCFL: Hashcash and Jaya-Based Communication Efficient Federated learning.
Climate change is one of the biggest challenges of the 21st century, with profound environmental, economic, and social impacts. Machine learning (ML) has emerged as a transformative tool to address climate challenges ...
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Handwriting recognition technologies have evolved significantly, with recent advancements incorporating wearable devices such as smartpens, smartwatches, and smartphones to capture motion and gesture data. This paper ...
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ISBN:
(纸本)9783031814037;9783031814044
Handwriting recognition technologies have evolved significantly, with recent advancements incorporating wearable devices such as smartpens, smartwatches, and smartphones to capture motion and gesture data. This paper reviews various efficient handwriting recognition models that utilize inertial sensors, visual sensors, EMG sensors, and acoustic and Wi-Fi signals. Inertial sensor-based approaches often use accelerometers and gyroscopes to collect three-dimensional data during handwriting, with models achieving high accuracy in recognizing digits and alphabets. Visual sensor-based techniques, leveraging cameras and image processing, have been fundamental in developing Optical Character Recognition (OCR), further enhanced by deep learning algorithms. EMG sensors have been explored for their potential to capture muscle activity for gesture recognition. At the same time, acoustic and WiFi signal-based methods offer innovative ways to recognize handwriting through sound and signal variation. Despite the progress, challenges such as sensor drift, data generalization, and computational requirements persist. Integrating multi-modal sensors and advanced machine learning techniques holds promise for overcoming these limitations and improving the accuracy and usability of handwriting recognition systems across diverse applications.
Drug discovery projects involve the identification of hits or lead compounds having favorable drug-like properties. In other words, it is a multivariate optimization problem and the properties to be optimized are bind...
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ISBN:
(纸本)9783031814037;9783031814044
Drug discovery projects involve the identification of hits or lead compounds having favorable drug-like properties. In other words, it is a multivariate optimization problem and the properties to be optimized are binding affinity, binding specificity, ADMET properties, solubility, and bioavailability. Computationally these properties can be estimated using physics-based and data-driven models. In the physics based models, the drug-like properties are computed as free energy differences. In data-driven models, the molecules are described numerically using various descriptors and finger-prints and machine learning and deep learning models can be built using dataset of descriptors/fingerprints and corresponding drug-like properties available from experiments. The physics-based models are usually computationally demanding and so are limited to smaller chemical space. However ML/DL models, can be applied to compute the drug-like properties of huge chemical libraries such as GDB17, Real Enamine DB, ZINC having more than billion compounds. The implementation, merits and demerits of these two approaches will be discussed in some detail in this paper.
An essential diagnostic technique for brain tumors, Magnetic Resonance Imaging (MRI) enables early detection and improved patient outcomes. However, manual interpretation of MRI scans can be time-consuming and subject...
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Histopathological analysis of biopsy sections is crucial for the detection of cancer and the distinction between different tumor subtypes. To this end, pathologists identify certain key regions of the biopsy from whic...
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ISBN:
(纸本)9783031777301;9783031777318
Histopathological analysis of biopsy sections is crucial for the detection of cancer and the distinction between different tumor subtypes. To this end, pathologists identify certain key regions of the biopsy from which a diagnosis is derived. The classification of whole slide images (WSIs) can be addressed as a multiple instance learning (MIL) problem where only slide-level labels are available. In order to model the relevance scores of the different WSI patches, attention mechanisms are implemented within the MIL framework. However, excessive flexibility in the attention mechanisms for computing attention scores to patches may lead to nearly uniform attention distributions, potentially deteriorating the model's performance. In this paper, we introduce an unsupervised auxiliary loss function to recalibrate the attention mechanism enhancing emphasis on crucial patches and downscaling the influence of less relevant ones. The proposed MIL framework has been evaluated on invasive breast carcinoma (TCGA-BRCA) and renal cell carcinoma (TCGA-RCC) subtyping. The results obtained show that attention regularization not only improves the predictive capacity of the model but also significantly increases its interpretability by identifying regions and patterns of high diagnostic value.
In recent days, the growing demand for automated language processing had brought Cross-Lingual Text Classification (CLTC) into focus as powerful approach for categorizing text across multiple languages which enabled u...
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The proceedings contain 15 papers. The special focus in this conference is on EuroXR. The topics include: Embodied Acts for Testing Mixed Reality Low-Fidelity Prototypes that Convey Intangible Cultural Heritage: Metho...
ISBN:
(纸本)9783031785924
The proceedings contain 15 papers. The special focus in this conference is on EuroXR. The topics include: Embodied Acts for Testing Mixed Reality Low-Fidelity Prototypes that Convey Intangible Cultural Heritage: Method and Case study;impact of Acceleration and Angle in the Real Environment on Cybersickness;study on the Influence of the Total Front HMI Size in intelligent Cabins on the Drivers’ Eye Movement Behavior;contrast and Hue in Depth Perception for Virtual Reality: An Experimental study;context-Based Annotation Visualisation in Virtual Reality: A Use Case in Archaeological data Exploration;3-2-3 Multi-AI Segmentation Framework: LoD-Based, Incremental Segmentation of 3D Scan data Using Any 2D AI;Enhancing Materiality in Adaptive BRDF Display with Light Ray Diffusion;a Tangible Interface for Creating Virtual Cutaways in Mixed Reality;MarkAR: Exploring the Benefits of Combining Microgestures and Mid-Air Marks to Trigger Commands in Augmented Reality;visual Search in People with Macular Degeneration: A Virtual Reality Eye-Tracking study;Engagement and Attention in XR for learning: Literature Review;exploring students’ Acceptance of Augmented Reality Technologies in Education: An Extended Technology Acceptance Model Approach;a Hybrid Collaboration Design for a Large Scale Virtual Reality Training Environment to Fulfil the Belongingness Needs of Maslow’s Theory.
Change is complex, uncomfortable, frustrating but importantly, worth the investment - to teach better and to make positive and long-lasting improvements within our educational system. However, navigating the complexit...
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