The proceedings contain 21 papers. The topics discussed include: a review of unsupervised anomaly detection techniques for health insurance fraud;vehicular traffic flow prediction via decentralized federated meta-lear...
ISBN:
(纸本)9798350366389
The proceedings contain 21 papers. The topics discussed include: a review of unsupervised anomaly detection techniques for health insurance fraud;vehicular traffic flow prediction via decentralized federated meta-learning;fault detection in transmission production lines based on imbalanced multivariate time series;preserving cross-image relationship privacy;selecting attractive images from 3D captures of Buddhist statues using Grad-CAM++;research on named entity recognition method based on BERT model;from perception to action: leveraging LLMs and scene graphs for intuitive robotic task execution;an accurate classification and recommendation method of competitive math problems;and stock market prediction based on time series data and multimodal sentiments.
The proceedings contain 45 papers. The topics discussed include: bridging the European earth-observation and AI communities for data-intensive innovation;ECG classification using deep CNN and Gramian angular field;dig...
ISBN:
(纸本)9798350333794
The proceedings contain 45 papers. The topics discussed include: bridging the European earth-observation and AI communities for data-intensive innovation;ECG classification using deep CNN and Gramian angular field;digital twin-driven degradation modeling method for control moment gyroscope health management;safe route recommendation based on crime risk prediction with urban and crime data;illicit item detection in X-ray images for security applications;hybrid deep learning for efficient detection of recalled products;attribute-based access control rules supported by biclique patterns;innovative and successful real-time automatization for production in oil and gas industry detection of anomalies in multivariate time series using ensemble techniques;and data-driven analysis of EV energy prediction and planning of EV charging infrastructure.
The proceedings contain 33 papers. The topics discussed include: a domain generalization approach for out-of-distribution 12-lead ecg classification with convolutional neural networks;allocating resource capacities fo...
ISBN:
(纸本)9781665458900
The proceedings contain 33 papers. The topics discussed include: a domain generalization approach for out-of-distribution 12-lead ecg classification with convolutional neural networks;allocating resource capacities for an offload-enabled mobile edge cloud system;a novel method for enhancing the accuracy of box detection under noise effect of tags and complex arrangement of pile with cycle-GAN and mask-RCNN;reversible data hiding scheme based on image partitioning and histogram shifting;an expansion on prioritized experience replay with round robin scheduling;a big data approach for fuel oil consumption estimation in the maritime industry;assessing the impact of movie plot summaries on box office sales;SMARTREC: a conversational recommendation system using semantic knowledge graphs;a parallel community detection algorithm based on spanning trees;an artificial intelligence outlook for colorectal cancer screening;real-time detection of objects on roads for autonomous vehicles using deep learning;and on building real time intelligent agricultural commodity trading models.
The proceedings contain 29 papers. The topics discussed include: a deep learning approach for short term prediction of industrial plant working status;towards platform-agnostic and autonomous orchestration of big data...
ISBN:
(纸本)9781665434836
The proceedings contain 29 papers. The topics discussed include: a deep learning approach for short term prediction of industrial plant working status;towards platform-agnostic and autonomous orchestration of big data services;an encoder-decoder deep learning approach for multistep service traffic prediction;incremental community detection in distributed dynamic graph;fake news analysis and graph classification on a covid-19 twitter dataset;cyberbullying classification based on social network analysis;camera-based security check for face mask detection using deep learning;federated learning for object detection in autonomous vehicles;and crop identification based on remote sensing data using machine learning approaches for Fresno County, California.
The proceedings contain 32 papers. The topics discussed include: An architecture for dynamic context recognition in an autonomous driving testing environment;a privacy enhanced crowdsourcing architecture for road info...
ISBN:
(纸本)9781538691335
The proceedings contain 32 papers. The topics discussed include: An architecture for dynamic context recognition in an autonomous driving testing environment;a privacy enhanced crowdsourcing architecture for road information mining using smartphones;reducing marketplace response time by scoring workers;a unified framework for 5G network management tools;a resource usage prediction system using functional-link and genetic algorithm neural network for multivariate cloud metrics;data-aware web service recommender system for energy-efficient data mining services;data replication based on common interests in P2P social networks;kubernetes or openshift? which technology best suits eclipse hono IoT deployments;an expert interview study on areas of microservice design;and using osmotic services composition for dynamic load balancing of smart city applications.
The proceedings contain 38 papers. The topics discussed include: crack detection with multi-task enhanced faster R-CNN model;data structure for packet de-duplication in distributed environments;ScholarFinder: knowledg...
ISBN:
(纸本)9781728170220
The proceedings contain 38 papers. The topics discussed include: crack detection with multi-task enhanced faster R-CNN model;data structure for packet de-duplication in distributed environments;ScholarFinder: knowledge embedding based recommendations using a deep generative model;intracranial hemorrhage detection in CT scans using deep learning;classifying cognitive load for a proactive in-car voice assistant;distributed fog computing architecture for real-time anomaly detection in smart meter data;classifying cognitive load for a proactive in-car voice assistant;an ensemble of multiple boosting methods based on classifier-specific soft voting for intelligent vehicle crash injury severity prediction;and the delta big data architecture for mobility analytics.
The proceedings contain 34 papers. The topics discussed include: secure virtual machine placement in infrastructure cloud services;distributed PathGraph: a cluster centric framework for distributed processing graph;ex...
ISBN:
(纸本)9781538613269
The proceedings contain 34 papers. The topics discussed include: secure virtual machine placement in infrastructure cloud services;distributed PathGraph: a cluster centric framework for distributed processing graph;exploring the search space between active and passive workflow replication;puffin: graph processing system on multi-GPUs;secure virtual machine placement in infrastructure cloud services;distributed PathGraph: a cluster centric framework for distributed processing graph;exploring the search space between active and passive workflow replication;cloud ready applications composed via HTN planning;a method to assess the economic feasibility of new commercial services in the smart grid;and a type-aware workload prediction strategy for non-stationary cloud service.
Distributed computing capabilities at the network's edge enable new use cases, e.g., smart factories, industrial internet of things, or autonomous mobility systems. While new applications evolve, managing the reso...
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ISBN:
(纸本)9798350361360;9798350361353
Distributed computing capabilities at the network's edge enable new use cases, e.g., smart factories, industrial internet of things, or autonomous mobility systems. While new applications evolve, managing the resources and being capable of integrating and adjusting the execution of tasks in a distributed edge infrastructure is of great importance. For this, applications need to be migrated between different nodes. These migrations must happen without interruption, allowing the system to meet service requirements while fully utilizing all available hardware resources. Therefore, applications should also be executable on all different compute nodes in a heterogeneous edge system without interfering with each other. To this end, applications should be granted only necessary permission, especially when un-trusted applications are integrated into the system. We, therefore, propose a migration method for isolated applications across heterogeneous compute nodes in service-oriented edge architectures. A service-oriented architecture is used to decouple applications, allowing for flexible scheduling. The migration method enables the fast migration of sandboxed applications based on WebAssembly by utilizing a two-stage migration approach. The concept can utilize multiple communication protocols for management and service communication. We have implemented a proof of concept based on the Zenoh(1) communication protocol. While the time required depends on the communication protocol and the memory size, we achieved migration delays of under 51 milliseconds for smaller applications. By providing a method for fast migration for applications across heterogeneous compute nodes, it is possible for distributed edge infrastructures to run applications independently from each other and adjust the execution node based on changes in the system's environment. By integrating applications into our framework, they are executed isolated and with strict access control, allowing for easier reuse
Edge computing emerges as a promising paradigm for managing and processing the immense data volumes generated by Internet of Things (IoT) devices. By moving data and computation closer to the client/devices, edge comp...
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ISBN:
(纸本)9798331539580
Edge computing emerges as a promising paradigm for managing and processing the immense data volumes generated by Internet of Things (IoT) devices. By moving data and computation closer to the client/devices, edge computing facilitates latency- and bandwidth-sensitive applications that would not be feasible through cloud and remote processing alone. Despite recent advancements, concerns persist regarding the requirements of cloud/edge-based applications. A distributed edge storage solution becomes indispensable to ensure data proximity, mitigate network congestion, and accommodate changing demands. Nevertheless, implementing an efficient edge-enabled storage system poses numerous challenges due to the distributed and heterogeneous nature of the edge, as well as its constrained resource capabilities. To this end, this paper presents an efficient cloud/edge storage framework with consideration on performance (QoS), latency reduction, and energy efficiency. The evaluations demonstrate significant improvements by reducing data operation execution time, enhancing performance, alleviating network infrastructure strain, and optimizing energy efficiency.
The modern computing scenario of the computing Continuum exhibits large and complex applications with heterogeneous requirements running on distributed infrastructure. Still, when it comes to coordinating and controll...
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ISBN:
(纸本)9798331539580
The modern computing scenario of the computing Continuum exhibits large and complex applications with heterogeneous requirements running on distributed infrastructure. Still, when it comes to coordinating and controlling such applications and infrastructures, it is common to rely on centralized or ad-hoc solutions. While these approaches are robust, scaling management solutions, managing local changes, and having a holistic perspective can be challenging. Additionally, they could be better suited for addressing new problems in dynamic environments. Therefore, new approaches are needed. In this paper, we present DICT, a novel method for managing the computing Continuum, i.e., the infrastructure and the applications. The proposed approach encompasses a series of modules for automatic management. The core idea is to develop a method for applying the intents coming from the infrastructure and application managers in an autonomic and dynamic way. The modules can communicate through coordinators that take observable inputs and send them back predictions on the next actions to take. These coordinators have the role of summarizing the sensed observation and extracting high-level information in light of the AI advancement that shows how discrete space representation of inputs improves generalization. Thus, they can have models that build their own semantics and "language." We envision that, through DICT, both the application and the infrastructure management will only have to specify high-level intents and not focus on defining encoded and difficult-to-change strategies.
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