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检索条件"任意字段=2024 IEEE International Conference on Cognitive Computing and Complex Data"

共 1578 条记录，以下是921-930 订阅

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Research on Damage Effect Classification in Wargame Replay data Based on Deep Neural Network

Research on Damage Effect Classification in Wargame Replay D...

引用

ieee international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Yang Shi Yong Yang Longliang Chen Lei Xu Officers College of PAP Chengdu China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

In recent years, wargame competitions have become more and more important. However, there is no targeted tools to analyze the massive wargame data, which can be analyzed to increase the probability of winning. In this paper we focus on the analysis of direct aiming attack data in the wargame replay data, which is very important in the wargame confrontation, but there are many factors affecting the damage effect, so the calculation rules are complicated. In this paper we propose a model of deep neural network can obtain a good training effect, which can accurately predict the damage effect in various complex battlefield situations.

关键词： Training Cloud computing Computational modeling Artificial neural networks Predictive models Big data

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Welcome Message from REX-IO Workshop: Cluster 2024

Welcome Message from REX-IO Workshop: Cluster 2024

引用

ieee international conference on Cluster computing Workshops and Posters (CLUSTER WORKSHOPS)

ISBN: (数字)9798350383454

ISBN: (纸本)9798350383461

Welcome to the Fourth Annual Workshop on Re-envisioning Extreme-Scale I/O for Emerging Hybrid HPC Workloads (REX-IO 2024)! HPC applications are undergoing a transformation. Traditional scale-up modeling and simulation workloads are being complemented by scale-out workloads, including artificial intelligence, big data analytics, and complex multi-step workflows. Exascale systems intensify this trend, demanding flexible I/O systems to handle diverse workloads. Moreover, the growing complexity of storage systems and the widening performance gap between compute and storage necessitate innovative I/O management. Traditional approaches are insufficient for the challenges posed by extreme-scale hybrid workloads. Novel techniques, such as intelligent load balancing and I/O pattern prediction, are crucial to manage data and storage hierarchies efficiently. Furthermore, user-friendly tools are also essential for adapting to the diverse needs of HPC I/O workloads and optimizing resource utilization.

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Big data Control and Optimization of Project Management

Big Data Control and Optimization of Project Management

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ACIS international conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed computing (SNPD)

作者： Mengyu Liu University of Sydney Zhengzhou China

ISBN: (数字)9798350391954

ISBN: (纸本)9798350391961

With the advancement of society and economy, projects are becoming larger and more complex, requiring project managers to have a higher level of expertise. The emergence of big data technology presents both opportunities and challenges for project management. By effectively managing a variety of information, project delivery times can be reduced, risks can be mitigated in a scientific manner, and project schedules can be optimized. This article explores the use of big data in project management, focusing on schedule control and optimization analysis, offering fresh perspectives for innovation in project management. In today’s complex project environment, it is essential to fully leverage technology to improve project management skills and overcome the challenges posed by the abundance of data.

关键词： Schedules Technological innovation Project management Focusing Big data Artificial intelligence Optimization

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Target Detection Algorithm for Unmanned Vehicles in complex Environments

Target Detection Algorithm for Unmanned Vehicles in Complex ...

引用

ieee international conference on Cloud computing and Big data Analysis (ICCCBDA)

作者： Zhang Pengfei Liu Jian Department of Arms and Equipment Naval University of Engineering Wuhan China

ISBN: (数字)9798350373554

ISBN: (纸本)9798350373561

In the realm of unmanned vehicles, challenges persist regarding their limited perception and real-time obstacle detection capabilities. This paper introduces the YOLOv5-SA object recognition algorithm, built upon YOLOv5, which addresses these issues. Leveraging Laplace transformation and SSR image enhancement on the training dataset, coupled with the integration of the SiAM attention mechanism at the network's output layer, and the replacement of the SPPF module with the AIFI module, the proposed model significantly enhances detection speed and accuracy. Experimental results demonstrate lower target omission rates and faster detection in various complex environments.

关键词： Training YOLO Measurement Analytical models Accuracy Computational modeling Real-time systems

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Ecosystem-Based data Products and DaaS: complexity Analysis and Design Considerations

Ecosystem-Based Data Products and DaaS: Complexity Analysis ...

引用

ieee international conference on Web Services (ICWS)

作者： Vincent Chen Perry Chen Amin Beheshti Westpac Bank Corporation Sydney NSW Australia DataEco Lab Sydney NSW Australia Macquarie University Sydney NSW Australia

ISBN: (数字)9798350368550

ISBN: (纸本)9798350368567

data activities and technologies in modern organisations are becoming more diverse and complex, and continuously operate, change, and evolve as an enterprise data ecosystem (EDE). This fast-changing environment presents both challenges and opportunities for many computing and design concepts and approaches and raises the questions whether and how they can be continuously and effectively applied. This paper analyses operation features and complexity of EDE and delves into design considerations for data products and data-as-a-Service (DaaS) to play their roles as solutions for data practice management improvement, integration, interoperability and design of services in data space.

关键词： Web services Ecosystems Software complexity theory Interoperability

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emsReACT: A Real-Time Interactive cognitive Assistant for Cardiac Arrest Training in Emergency Medical Services 19

emsReACT: A Real-Time Interactive Cognitive Assistant for Ca...

引用

19th Annual international conference on Distributed computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Rahman, M Arif Jia, Leon Mirza, Eimara Preum, Sarah Masud Alemzadeh, Homa Williams, Ronald D. Stankovic, John A. University of Virginia Dept. of Computer Science Charlottesville United States School of Medicine University of Virginia Charlottesville United States University of Virginia Dept. of Electrical and Computer Engineering Charlottesville United States Dartmouth College Dept. of Computer Science Hanover United States

ISBN: (纸本)9798350346497

EMS (emergency medical services) deals with cardiac arrest cases more frequently than any other fatal health conditions all over the world. In this situation, a sequence of complex and time sensitive interventions is performed to ensure the safe recovery of the patient. We have developed emsReACT, a real-time interactive cognitive assistant, to train EMS providers for cardiac arrest cases in an emergency situation. This customized tool interacts in real-time with the first-responder and collects critical information. Using the conversational audio data available at EMS training sessions, emsReACT provides responder-specific decision support during the training based on domain specific information extraction, context-aware tracking of cardiac arrest protocols, and the dynamically changing condition of the patient. emsReACT leverages a dynamic behavioral model and a task-graph of two frequently used cardiac arrest EMS protocols. We have developed an intelligent abstraction mechanism with a critical risk-rating that drives an anytime algorithm to meet time requirements for regular and critical situations. Our thorough experimentation reveals an average end-to-end time of 2.7 seconds and 1.8 seconds for regular and critical interventions, thereby meeting the time requirements of 7 and 3 seconds, respectively. A qualitative study also reflects that over 70% of the 31 surveyed EMS providers rate the system as helpful to properly train the first-responders for executing cardiac arrest protocols. © 2023 ieee.

关键词： Intelligent systems

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Multi-Level Augmentation Consistency Learning and Sample Selection for Semi-Supervised Domain Generalization

Multi-Level Augmentation Consistency Learning and Sample Sel...

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Mei Yu Yujian Zhang Xuewei Li Ruixuan Zhang Han Jiang Jie Gao Zhiqiang Liu College of Intelligence and Computing Tianjin University Tianjin China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin China Tianjin Key Laboratory of Advanced Networking Tianjin China School of Future Technology Tianjin University Tianjin China OpenBayes (Tianjin) IT Co. Ltd. Tianjin China

Semi-supervised domain generalization (SSDG) aims to build a domain-generalized model using partially labeled data from source domains. Mainstream SSDG methods follow the augmentation consistency in FixMatch. However, the extraction of domain-invariant features may be challenging due to the absence of feature-based operations, further leading to overfitting of the classifier. To this end, we propose Multi-level Augmentation Consistency Learning (MACMatch), which improves the generalization of feature extractor and classifier through feature-based augmentation consistency. On the other hand, existing methods assume labeled data are class-balanced and domain-balanced, which is easily violated in practice. Based on this, we introduce Representativity and Diversity-based Sample Selection (RDSS), which models data as graphs to evaluate reasonable samples for labeling, relaxing the assumption for labeled data. Experiments on PACS and OfficeHome demonstrate that MACMatch outperforms state-of-the-art SSDG methods. Furthermore, MAC-Match with RDSS achieves competitive results without domain and class priori assumptions. Code is available at https://***/Y-J-Zhang/MACMatch-RDSS.

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A Multi-Modal Bio-Medical data Analysis Framework: A case study for Pandemic Preparedness 17

A Multi-Modal Bio-Medical Data Analysis Framework: A case st...

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17th ieee/ACM international conference on Utility and Cloud computing, UCC 2024

作者： Makkar, Aaisha Conway, Myra Meziane, Farid Mishra, Vinaytosh Singh, Ashutosh Kumar Department of Computing University of Derby United Kingdom Department of Bio-Medical Sciences University of Derby United Kingdom Gulf Medical University United Arab Emirates Indian Institute of Information Technology Bhopal India

ISBN: (纸本)9798350367201

Recently, the world has witnessed the emergence of various infectious diseases that have led to public health emergencies. These outbreaks have highlighted the need for effective pandemic preparedness strategies. One of the key components of such strategies is the efficient handling of large amounts of medical data by healthcare professionals and organizations. The scientific community is actively contributing in the introduction of innovative approaches such as artificial intelligence in medicine and healthcare systems. However, effectively managing such a large volume of data remains a considerable challenge for the World Health Organisation (WHO) and medical practitioners. In this work, the authors present a deep learning and explainable Artificial Intelligence (XAI)-empowered scheme for multi-modal bio-medical data analysis applications. The proposed scheme addresses these challenges using data garbling, class balancing, feature selection, and harnessing the power of these techniques. The proposed framework is evaluated using the case study of COVID-19 based on the NCBI COVID-19 dataset. Pre-processing medical data using the proposed scheme, resulted in a significant understanding of data, demonstrating the effectiveness of the proposed scheme for complex medical data. © 2024 ieee.

关键词： COVID-19

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Securing the Cloud “Advanced Techniques in data Encryption”

Securing the Cloud “Advanced Techniques in Data Encryption”

引用

ieee UP Section conference on Electrical Computer and Electronics (UPCON)

作者： Ambika Gupta Suyel Namasudra Prabhat Kumar Computer Science & Engineering NIT Patna Patna India Computer Science & Engineering NIT Agartala Agartala India

ISBN: (数字)9798350378726

ISBN: (纸本)9798350378733

As cloud computing becomes increasingly integrated into modern IT infrastructure, cloud-based data processing and storage security are pressing concerns. This paper delves into the complex issues surrounding cloud data security, identifying and analyzing potential solutions. It thoroughly examines the challenges inherent in cloud environments, including insider threats, data breaches, and infrastructure-sharing vulnerabilities. The paper evaluates various encryption methods used to safeguard data, assessing their efficiency and performance impact. Furthermore, it explores key management strategies, access control mechanisms, and regulatory compliance. This paper concludes by exploring cutting-edge developments in cloud data security, including the impact of quantum computing on cryptographic algorithms, the emergence of postquantum cryptography, and the application of $A I$ and machine learning to enhance cloud security measures.

关键词： Cloud computing Quantum computing Machine learning algorithms Cloud computing security Machine learning Pressing data breach data processing Encryption Secure storage

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PiAGI: The Concept, Mechanism and Framework

PiAGI: The Concept, Mechanism and Framework

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Digital Twins and Parallel Intelligence (DTPI), ieee international conference on

作者： Qiang Li Zhuoya Ju Lili Fan Xin Liu Wenhua Liu Feiyue Wang Scientific and Technological Innovation Center Beijing China The School of Automation Beijing Institute of Technology Beijing China School of Big Data & Cyber Security China University of Petroleum Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349252

ISBN: (纸本)9798350349269

As social production and living spaces gradually expand from the physical and information domains to the cognitive and social domains, this article addresses the new changes brought about by these expansions. Starting from the new impact of era of intelligent connectivity on social cognitive management, it analyzes the urgent need to enhance social cognitive management capabilities centered on cognitive decision-making, identifying the core key as Adversarial Generated Intelligence (AGI). It delves into the conceptual characteristics, operating mechanisms, and winning strategies of AGI, identifies the new challenges it faces, and proposes a Parallel Intelligence empowering AGI (PiAGI) system architecture through the empowerment of parallel intelligence. This provides theoretical support for improving social cognitive management capabilities centered on cognitive decision-making.

关键词： Decision making Systems architecture Production Digital twins Faces

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