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Maritime Metaverse: A Historical Graph-based NGSI-LD Framework for Digital Twin Integration

Maritime Metaverse: A Historical Graph-based NGSI-LD Framewo...

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Metaverse computing, Networking and Applications (MetaCom), ieee international conference on

作者： Jieun Lee JaeSeung Song Sejong University Seoul Republic Of Korea

ISBN: (数字)9798331515997

ISBN: (纸本)9798331516000

Leveraging the innovative Historical Graph-based NGSI-LD Framework, this research proposes a novel approach to enriching the metaverse by seamlessly integrating real-world data and digital twin platforms. The framework is meticulously designed to address the existing limitations of NGSI-LD API in handling complex queries and relationships, introducing a sophisticated integration with labeled-property graph technology for enhanced data retrieval and analysis. The core advancement lies in the framework's capability to reconstruct and analyze Auto Identification System (AIS) data, enabling the dynamic simulation of maritime activities within the metaverse. This not only enhances the realism and accuracy of digital twins but also facilitates real-time predictive modeling based on historical data analysis. Moreover, the framework introduces a robust method for complex query resolution through graph traversal, significantly improving user interaction within the metaverse. This is complemented by a comprehensive approach to historical data management with versioning, ensuring the metaverse is enriched with both current and past data for a more thorough understanding and interaction with digital twins. The integration of IoT and digital twin platforms via this framework stands to revolutionize metaverse applications across various sectors, including maritime, logistics, environmental monitoring, and beyond. It not only bridges the gap between the physical and digital worlds but also ensures a secure, intelligent, and dynamically updatable metaverse environment.

关键词： Analytical models data analysis Metaverse Predictive models Real-time systems data models Digital twins Environmental monitoring Artificial intelligence Logistics

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Swarm Intelligence-Based Dynamic Virtual Machine Placement Optimization in Cloud data Centers

Swarm Intelligence-Based Dynamic Virtual Machine Placement O...

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Advances in computing, Communication and Networking (ICAC2N), international conference on

作者： Sanjay Mishra Monisha Awasthi Uttaranchal School of Computing Science Uttaranchal University Dehradun India

ISBN: (数字)9798350356816

ISBN: (纸本)9798350356823

Virtual machine (VM) deployment is a critical aspect of cloud data center resource management that directly impacts performance, energy efficiency, and quality of service. Efficient VM deployment ensures optimal resource utilization, reduces power consumption, and lowers operating costs. However, the dynamic nature of cloud environments poses significant challenges to achieving optimal VM deployment. This study presents various intelligences, especially partial swarm optimization (PSO), as a powerful optimization technique for dynamic VM deployment. Many cognitive algorithms mimic the collective behavior of social organisms and provide an efficient approach to solving complex optimization problems. This study addresses the issue of deploying virtual machines to maximize resource utilization while reducing power consumption and ensuring quality of service. Extensive simulation shows that the proposed PSO-based method significantly outperforms conventional static and heuristic approaches regarding energy efficiency, resource utilization, and SLA compliance. The results show the potential of multiple intelligences in improving the operational efficiency of cloud data centers, leading to future research and practical implementation in dynamic cloud environments.

关键词： Cloud computing data centers Power demand Quality of service Virtual machines Energy efficiency Resource management Servers Particle swarm optimization Optimization

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Utilizing Causal Learning for cognitive Management of 6G Networks

Utilizing Causal Learning for Cognitive Management of 6G Net...

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Machine Learning for Communication and Networking (ICMLCN), ieee international conference on

作者： Mehmet Karaca Jishnu Sadasivan Ahmet Cihat Baktir Alexandros Palaios András Zahemszky Ericsson Research Turkey Ericsson Research India Ericsson Research Germany Ericsson Research Sweden

ISBN: (数字)9798350343199

ISBN: (纸本)9798350343205

In this paper, we study the use of causal reasoning for a cognitive network where we investigate causal discovery and its use in the context of 6G. The fundamental requirement in causal inference is to discover the causal relation among variables in a complex system (such as among a number of network KPIs). Once such causal relations are correctly captured, the cognitive capability of a network can significantly be increased by having the ability of causal and counterfactual reasoning. We envision that this can additionally bolster the trust and transparency to such cognitive system. First, we attempt to discover a causal graph that represents the causal relation in a networking setup. By applying the state-of-the-art causal discovery algorithms that include both constraint and score-based methods, we aim at uncovering which network KPIs and actions are causally impacting each other. It is observed that the causal discovery algorithms are able to capture the causal relations between the network KPIs and actions, reasonably well. Then, we give a use-case example within an autonomous network context, and evaluate the estimated causal graph with our realistic network emulator to predict the impact of an intervention on a network KPI. The preliminary results are promising and pave the way for more focused future studies that can support complex commercial networks in a scalable way.

关键词： 6G mobile communication Machine learning algorithms Sensitivity analysis data integrity Noise Machine learning Prediction algorithms

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The Parallel DCNN-LSTM Model for Cloud computing Prediction Based on an Improved SG Filter

The Parallel DCNN-LSTM Model for Cloud Computing Prediction ...

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Image Processing and Computer Applications (ICIPCA), ieee international conference on

作者： Bowen Yang Kaiming Jin Longhua Zou School of Computer Science and Engineering Beihang University Beijing China School of Economics Xiamen University Xiamen China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

It is crucial to accurately predict the workload of large-scale cloud computing resources in order to better allocate and utilize them, but this has always been seen as a challenging task. Recently, LSTM has been widely used in the field of time series prediction, but it is not sufficient to learn complex and noisy patterns. This work aims to solve the accuracy problem of cloud data prediction and proposes a parallel dilated convolutional network. Firstly, in order to smooth and remove noise from the data, an improved SG filter that avoids future data leakage is proposed. Afterwards, this work combines LSTM with dilated convolution and adopts a parallel architecture. The purpose is to enable the model to learn patterns and potential representations of data from multiple scales, and enhance model's generalization and robustness. Finally, this work validated the performance of the proposed model on real datasets, Google Cluster Trace. Experimental results have shown that the proposed model performs better than LSTM and some widely used variants.

关键词： Cloud computing Computational modeling Time series analysis Predictive models Feature extraction data models Robustness Parallel architectures Convolutional neural networks Long short term memory

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Explainable Anomaly Detection: Counterfactual driven What-If Analysis

Explainable Anomaly Detection: Counterfactual driven What-If...

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ieee international conference on cognitive Machine Intelligence (CogMI)

作者： Logan Cummins Alexander Sommers Sudip Mittal Shahram Rahimi Maria Seale Joseph Jabour Thomas Arnold Computer Science & Engineering Mississippi State University US Department of Defense Engineer Research and Development Center

ISBN: (数字)9798350386721

ISBN: (纸本)9798350386738

There exists three main areas of study inside of the field of predictive maintenance: anomaly detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly detection alerts the stakeholder that an anomaly is occurring. This raises two fundamental questions: what is causing the fault and how can we fix it? Inside of the field of explainable artificial intelligence, counterfactual explanations can give that information in the form of what changes to make to put the data point into the opposing class, in this case "healthy." The suggestions are not always actionable which may raise the interest in asking "what if we do this instead?" In this work, we provide a proof of concept for utilizing counterfactual explanations as what-if analysis. We perform this on the PRONOSTIA dataset with a temporal convolutional network as the anomaly detector. Our method presents the counterfactuals in the form of a what-if analysis for this base problem to inspire future work for more complex systems and scenarios.

关键词： Fault diagnosis Detectors Convolutional neural networks Stakeholders complex systems Artificial intelligence Anomaly detection Machine intelligence Predictive maintenance

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Forecasting Time Series through Motif Structure Analysis within Visibility Graph

Forecasting Time Series through Motif Structure Analysis wit...

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international conference on Automation and computing (ICAC)

作者： Tao Wen Yu-Wang Chen Decision and Cognitive Sciences Research Centre The University of Manchester UK

ISBN: (数字)9798350360882

ISBN: (纸本)9798350360899

Time series forecasting is a fundamental and crucial problem in both academic research and industrial production. Various approaches have been developed to address this issue from different perspectives, each with its own limitations. In this work, we propose a motif-based approach for forecasting time series within the framework of visibility graph analysis. By representing time series interactions through a complex network, the most similar historical data point of the current one is identified by the motif distribution, which captures higher-order structural information beyond pairwise interactions. The fore-casted tendency of the time series is determined based on adjacent information and linear approximation, guided by proximity-based weighting. Through comparisons with typical network-based approaches using the real-world dataset, the effectiveness and applicability of our developed method are demonstrated, showcasing lower error indices.

关键词： Social networking (online) Time series analysis Linear approximation Transportation complex networks Production Market research Probability distribution Indexes Forecasting

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Understanding the Challenges of data Management in the AI Application Development

Understanding the Challenges of Data Management in the AI Ap...

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ieee international conference on Joint Cloud computing (JCC)

作者： Junchen Li Yang Zhang Kele Xu Tao Wang Huaimin Wang National University of Defense Technology China State Key Laboratory of Complex & Critical Software Environment China

ISBN: (数字)9798350387339

ISBN: (纸本)9798350387346

With the development of AI model ecology pioneered by the large language models, data plays a more important and diversified role than the traditional one. In recent years, data problems have been increasingly noticed and voiced by developers, and it is crucial to understand the existing challenges that they are facing in reality, as well as the potential features of these problems. Unfortunately, we currently have relatively little knowledge in this area. To fill this gap, we conduct an empirical study based on 2,154 posts and 785 issues by collecting model data problems from three online communities (i.e., GitHub, Stack Overflow, and Hugging Face). We present the first taxonomy of data problems around model production encountered by developers, covering 11 topics in 4 categories. Then, we analyze the popularity and the difficulty of the posts and issues in the derived topics. We distill many findings from the study and present the practical implications from the perspectives of different audiences.

关键词： Biological system modeling Large language models Taxonomy Production Manuals data models Ecology

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Enhancing data-Free Robustness Stealing Attack via Boundary data Generation

Enhancing Data-Free Robustness Stealing Attack via Boundary ...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Xiaoji Ma Weihao Guo Pingyuan Ge Ying Chen Qiuling Yue Yuqing Zhang School of Cyberspace Security Hainan University Haikou China National Computer Network Intrusion Protection Center University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

With the continuous development of Machine Learning as a Service (MLaaS), model stealing has become an emerging problem in machine learning security in recent years. In model stealing, one typically obtains the soft labels of model queries and a proxy dataset as prior knowledge, but this scenario is highly idealised. How to steal models without data and hard labels is a pressing problem that needs to be solved. The current mainstream of model stealing attack methods mainly focus on stealing the accuracy of the model and overlook the robustness of the model. However, robustness is essential in security applications such as facial recognition and secure payment scenarios. Moreover, building robust models usually requires costly adversarial training and fine-tuning, making these models the primary targets for theft. To address these issues, in this paper, we propose a new data-free robustness stealing method under data-free conditions from the perspective of data generation, thereby better shaping the classification boundary data to optimise the accuracy and robustness of the models. Through testing, our method achieved clean accuracy and robust accuracy of 53.69% and 21.0%, respectively, under the more complex CIFAR-100 dataset classification. These results are only 3.06% and 3.94% different from the target model, respectively, showing a significant improvement over recent research.

关键词： Training Social computing Accuracy Machine learning Pressing data collection Robustness data models Security Testing

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PentraFormer: Learning Agents for Automated Penetration Testing via Sequence Modeling

PentraFormer: Learning Agents for Automated Penetration Test...

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ieee/ACM Int'l conference on & Int'l conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Yunfei Wang Shixuan Liu Wenhao Wang Cheng Zhu Changjun Fan Kuihua Huang Chao Chen National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China Laboratory for Big Data and Decision National University of Defense Technology Changsha China National University of Defense Technology Changsha China

ISBN: (数字)9798350351637

ISBN: (纸本)9798350351644

The exponential growth of computer networks has intensified the requirement for robust security measures, rendering penetration testing—an essential practice that assesses vulnerabilities by simulating attacks—critically important. Automated penetration testing (APT) has evolved from rule-based approaches to intelligent decision-making, such as reinforcement learning (RL), better emulating the adaptability and decision-making process of human penetration testers. However, using RL for APT is challenged by the high cost of real-network interactions, the scarcity of security datasets, and the unique complexities of APT scenarios, which include long decision sequences and delayed rewards, complicating RL efficacy and convergence. Inspired by the Decision Transformer’s proficiency in predicting action sequences for offline RL, coupled with its effectiveness in dealing with data efficiency and adaptability, we introduce a novel sequence-based methodology for designing APT agents. This approach is tailored to effectively manage the intricate aspects of data-scarce and complex APT scenarios. Our proposed model, PentraFormer, adeptly addresses these challenges prevalent in APT scenarios. The robustness of our model is attested to through extensive empirical evaluations conducted on APT-dedicated tasks.

关键词： Adaptation models Social computing Computational modeling Decision making Transformers Robustness data models complexity theory Security Penetration testing

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Security Analysis of Accounting Computerized Information System based on data Mining and Neural Networks

Security Analysis of Accounting Computerized Information Sys...

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Distributed computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： Yan Wang Xi 'an Mingde Institute of Technology Xi 'an Shaanxi China

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

The security issue of accounting by electronic data processing information systems is a complex system engineering. How to extract useful data from the massive amount of accounting by electronic data processing information and provide decision-making basis for enterprise decision-makers is a key issue. The article is based on data mining and neural network technology, and studies a security analysis method for accounting by electronic data processing information systems based on data mining and neural networks. This method extracts useful information from data and establishes corresponding models through neural networks to identify key factors that affect the security of accounting by electronic data processing information systems. The results show that the average reliability of the accounting computerization information system based on data mining is 84.38%. The average security of the accounting computerization information system based on neural networks is 94.86%. This article combines the two to create a better accounting by electronic data processing information system.

关键词： Neural networks Decision making Electric variables measurement Distributed databases data processing data models Security

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