the proceedings contain 48 papers. the special focus in this conference is on Body Sensor Networks and Wearable Devices. the topics include: Continuous gesture recognition based on hidden Markov model;a new modeling m...
ISBN:
(纸本)9783319459394
the proceedings contain 48 papers. the special focus in this conference is on Body Sensor Networks and Wearable Devices. the topics include: Continuous gesture recognition based on hidden Markov model;a new modeling method of photoplethysmography signal based on lognormal basis;a neuro-fuzzy system for classifying fatigue degree of wheelchair user;detecting novel class for sensor-based activity recognition using reject rule;monitoring swimming motion using body sensor networks;service model design and application of product design and component procurement for small and medium sized concrete mixer manufacturers based on cloud manufacturing;a novel access control model for cloud computing;agreement in epidemic information dissemination;cloud-based wheelchair assist system for mobility impaired individuals;energy management policies in distributed residential energy systems;a dynamic heuristic application mapping algorithm based on lookup tables;distributed real-time database for the intelligent community;predicting telecommunication customer churn using data mining techniques;self labeling online sequential extreme learning machine;a modified genetic algorithm for agricultural by-products logistics delivery route planning problem;multi-objective optimization of warehouse system based on the genetic algorithm;a constraint programming based method for stockyard management problem;business process reengineering of road passenger transport based on unified modeling language method;design of distributed logistics vehicle monitoring system with high load;logistics vehicle travel preference of interest points based on speed and accessory state;a partition berth allocation scheduler based on resource utilization and load balancing and a high-precise matching algorithm.
the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical Image computing, Embodied AI and Robotics for Healthcare Wo...
ISBN:
(纸本)9783031776090
the proceedings contain 23 papers. the special focus in this conference is on Skin Imaging Collaboration, Interpretability of Machine Intelligence in Medical Image computing, Embodied AI and Robotics for Healthcare Workshop and MICCAI Workshop on distributed, Collaborative and Federated Learning. the topics include: DeCaF 2024 Preface;i2M2Net: Inter/Intra-modal Feature Masking Self-distillation for Incomplete Multimodal Skin Lesion Diagnosis;from Majority to Minority: A Diffusion-Based Augmentation for Underrepresented Groups in Skin Lesion Analysis;segmentation Style Discovery: Application to Skin Lesion Images;a Vision Transformer with Adaptive Cross-Image and Cross-Resolution Attention;lesion Elevation Prediction from Skin Images Improves Diagnosis;DWARF: Disease-Weighted Network for Attention Map Refinement;PIPNet3D: Interpretable Detection of Alzheimer in MRI Scans;Detecting Unforeseen Data Properties with Diffusion Autoencoder Embeddings Using Spine MRI Data;interpretability of Uncertainty: Exploring Cortical Lesion Segmentation in Multiple Sclerosis;TextCAVs: Debugging Vision Models Using Text;evaluating Visual Explanations of Attention Maps for Transformer-Based Medical Imaging;Exploiting XAI Maps to Improve MS Lesion Segmentation and Detection in MRI;EndoGS: Deformable Endoscopic Tissues Reconstruction with Gaussian Splatting;VISAGE: Video Synthesis Using Action Graphs for Surgery;a Review of 3D Reconstruction Techniques for Deformable Tissues in Robotic Surgery;SurgTrack: CAD-Free 3D Tracking of Real-World Surgical Instruments;MUTUAL: Towards Holistic Sensing and Inference in the Operating Room;Complex-Valued Federated Learning with Differential Privacy and MRI Applications;enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications;federated Impression for Learning withdistributed Heterogeneous Data;A Federated Learning-Friendly Approach for Parameter-Efficient Fine-Tuning of SAM in 3D Segmentation;probing the Effic
the proceedings contain 114 papers. the topics discussed include: accounting and monitoring infrastructure for distributedcomputing in the atlas experiment;the atlas EVENTINDEX using the HBase/phoenix storage solutio...
the proceedings contain 114 papers. the topics discussed include: accounting and monitoring infrastructure for distributedcomputing in the atlas experiment;the atlas EVENTINDEX using the HBase/phoenix storage solution;offline software and computing for the SPD experiment;the grid-characteristic method for applied dynamic problems of fractured and anisotropic media;fractal thermodynamics, big data and its 3D visualization;the technology and tools for the building of information exchange package based on semantic domain model;participation of Russian institutes in the processing and storage of ALICE data;a virtual testbed for optimizing the performance of a new type of accelerators;multithreaded event simulation in the BMNROOT package;and resource management in private multi-service cloud environments.
the proceedings contain 101 papers. the topics discussed include: a GNN-based routing and scheduling mechanism for multi-domain computing first network;travel time estimation neural differential equation model based o...
ISBN:
(纸本)9798350304428
the proceedings contain 101 papers. the topics discussed include: a GNN-based routing and scheduling mechanism for multi-domain computing first network;travel time estimation neural differential equation model based on self-attention mechanism;pritehyper2vec: a SCSPRITE data completion method based on hypergraph random walk;research and implementation of code similarity detection technology based on deep learning;research on calibration method of fisheye cameras based on VTD;path planning method based on extended random artificial potential field;Mongolian medicine named entity recognition via dictionary-based synonym generalization;manipulating multi-agent navigation task via emergent communications;image aesthetics assessment for virtual cinematography of cloud-based performing arts scenes;and a two-stage clustering undersampling for class-overlapped imbalanced classification.
the proceedings contain 467 papers. the topics discussed include: gesture based virtual assistant for deaf-mutes using deep learning approach;vehicle to vehicle communication using cognitive radio technique;applicatio...
ISBN:
(纸本)9798350397376
the proceedings contain 467 papers. the topics discussed include: gesture based virtual assistant for deaf-mutes using deep learning approach;vehicle to vehicle communication using cognitive radio technique;application of convolution neural network to detect the stages of Alzheimer disease for magnetic resonance imaging;multimodal wearable sensors-based stress and affective states prediction model;a build and deploy Ethereum smart contract for food supply chain management in truffle - ganache framework;hybrid blockchain and IPFS for secure industry 4.0 framework of IoT-based skin monitoring system;design and implementation of solar based day and night battery charger;fault analysis and security of direct current deficiency of modular multilevel converter system;analyzing data compression techniques for biomedical signals and images using downsampling and upsampling;a study on blood-cell segmentation method for the identification of hematological disorders;a review on IoT-based defensive devices for women security;artificial synthesis of single person videos through motion transfer using cycle generative adversarial networks and machine learning;and weeds and crop image classification using deep learning technique.
the scarcity of energy resources and computational capabilities poses significant challenges for Unmanned Aerial Vehicles (UAVs) when tasked with executing time-sensitive and complex demands, such as those necessary f...
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ISBN:
(纸本)9798350350227;9798350350210
the scarcity of energy resources and computational capabilities poses significant challenges for Unmanned Aerial Vehicles (UAVs) when tasked with executing time-sensitive and complex demands, such as those necessary for Artificial Intelligence (AI)-enabled applications. Integrating Unmanned Ground Vehicles (UGVs) in Ground-Air Cooperative systems offers a promising solution by providing additional computational and storage capacities near the point of need. However, these systems' distributed, dynamic, and resource-constrained nature presents a critical challenge for efficient task scheduling. this study introduces a novel two-tiered distributed online task scheduling approach aimed at optimizing the resource utilization of UGVs in edge computing environments. Our methodology encompasses two algorithms: a task dispatching algorithm designed to identify a UGV server that meets the deadline requirements while minimizing energy consumption and a computing scheduling algorithm that employs the Earliest Deadline First (EDF) strategy to refine the sequence in which tasks are processed, thereby reducing the average task completion time. We thoroughly assess the performance of the suggested algorithm with state-of-the-art methods using the OMNeT++ network simulator. the outcomes indicate that the proposed method significantly reduces the average task latency and energy consumption while meeting task deadline requirements.
Locally Repairable codes are used in the distributed storage system to minimize the I/O overhead. In this paper, we design topology-aware locally repairable codes based on the network topology of distributed storage s...
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High speed internet and advanced networking technology contribute to having large number of various edge devices in heterogeneous edge-cloud systems. In conventional cloud computingsystems, all device data is process...
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ISBN:
(纸本)9798350366495;9798350366488
High speed internet and advanced networking technology contribute to having large number of various edge devices in heterogeneous edge-cloud systems. In conventional cloud computingsystems, all device data is processed in the centralized cloud servers. the growing number of devices, i.e., increasing amount of device data, poses a challenge to the cloud servers to process data in a time- and energy-efficient manner. Studies show promise to reduce execution time and energy consumption by introducing collaborative edge-cloud computing paradigm. In this work, we study collaborative edge-cloud computing by introducing a framework of pairing the computations at edge and cloud resources to minimize execution time and energy consumption. First, the cloud servers (CSs) are made about 90% utilized by adjusting the device data i. e., computed data. then, each edge server (ES) is optimized using 50% or less of the previously generated device data i.e., cloud computed data. Finally, computations (i.e., device data) are distributed among the ESs and CSs, and performance is assessed to obtain the optimal pairing of computations. A heterogeneous system with one CS, two ESs, 10 edges, and 30 devices of five different types is modeled and simulated using VisualSim. Experimental results show that the proposed method helps reduce execution time and energy consumption by 90% and 56%, respectively. the proposed framework holds a promise for enhancing the scalability of heterogeneous systems, an avenue we intend to explore in our upcoming venture.
Deep learning has become promising across numerous fields in transforming conventional paradigms into smart eras in distributed applications. Large neural networks in recent years have been popular in solving massive ...
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ISBN:
(纸本)9798331511425;9798331511432
Deep learning has become promising across numerous fields in transforming conventional paradigms into smart eras in distributed applications. Large neural networks in recent years have been popular in solving massive real-world problems. However, the challenge behind the increasing complexity of deep neural networks impacts the training time. Appropriate resource provisioning and rightsizing is the requirement in all standard platforms like the cloud to handle this performance degradation. this research explores distributed CPU clusters as a scalable and cost-effective alternative for training large neural networks. the experiments on two different multi-processing machines with workers' distributions demonstrated the change in maximum accuracies is in a range of 92.96% to 96.74%. As our approach can be adopted and experiments can be extended to serverful and serverless computing training workloads, deep learning researchers and practitioners will benefit from our solution.
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