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FIFA: A Forest-based Sliding Window Aggregation Scheme for Out-of-Order Data Streams

IEEE Transactions on Big Data 2025年

作者： Huang, Jiande Deng, Yuhui Li, Jianjun Lu, Lijuan Yang, Qifen Min, Geyong Jinan University Department of Computer Science Guangzhou510632 China South China University of Technology School of Business Administration Guangzhou510641 China University of Exeter Department of Computer Science College of Engineering Mathematics and Physical Sciences ExeterEX44QF United Kingdom

Sliding window aggregation is a core operation in data stream analysis that extracts summaries from the most recent data stream. An evict or insert of the window can be handled in $O(1)$ for in-order data streams. However, real-world data streams are typically disordered due to network delays. To process out-of-order data streams, existing methods primarily use a tree to maintain the sliding windows. Since the complexity of the tree is related to the window size, the performance of these methods will drop sharply or become unavailable when facing a big window. To overcome the limitation of existing methods, this paper presents Finger B-Trees Forest Aggregation (FIFA). This novel forest-based sliding window aggregation scheme optimally handles out-of-order data streams. At its heart, FIFA uses aggregation forest to extend Finger B-Trees. Specifically, FIFA evenly divides a window into several chunks and constructs a separate tree to maintain each chunk. When an out-of-order item arrives, FIFA first locates the corresponding chunk of the item and then uses Finger B-Trees to insert it into the window efficiently. Chunking reduces the complexity of the tree and the coupling of aggregation results by isolating items within windows. Thus, an insert or evict takes amortized $O(c)$ in the worst case, where $c$ is the size of each chunk. Finally, extensive experiments based on real-world data demonstrate that FIFA achieves an average 2-fold throughput improvement on out-of-order data streams compared with the state-of-the-art (SOTA) aggregation schemes. © 2015 IEEE.

关键词： Data aggregation

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Recent Advances in Soft Biological Tissue Manipulating Technologies

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Chinese Journal of Mechanical engineering 2022年第3期35卷 3-36页

作者： Zhihua Liu Zhirong Liao Dong Wang Chengyong Wang Chengli Song Haonan Li Yao Liu Guangdong University of Technology GuangzhouChina Guangdong Provincial Key Laboratory of Minimally Invasive Surgical Instruments and Manufacturing Technology Guangdong University of TechnologyGuangzhouChina Faculty of Engineering University of NottinghamNottinghamUK Faculty of Engineering University of ExeterExeterUK School of Medical Instrument and Food Engineering University of Shanghai for Science and TechnologyShanghaiChina Shanxi Key Laboratory of Advanced Manufacturing Technology North University of ChinaTaiyuanChina

Biological soft tissues manipulation,including conventional(mechanical)and nonconventional(laser,waterjet and ultrasonic)processes,is critically required in most surgical ***,the soft tissues,with their nature of anisotropic and viscoelastic mechanical properties,and high biological and heat sensitivities,are difficult to ***,the mechanical and thermal induced damage on the surface and surrounding tissue during the surgery can impair the proliferative phase of ***,understanding the manipulation mechanism and the resulted surface damage is of importance to the *** recent years,more and more scholars carried out researches on soft biological tissue cutting in order to improve the cutting performance of surgical instruments and reduce the surgery induced tissue ***,there is a lack of compressive review that focused on the recent advances in soft biological tissue manipulating ***,this review paper attempts to provide an informative literature survey of the state-of-the-art of soft tissue manipulation processes in *** is achieved by exploring and recollecting the different soft tissue manipulation techniques currently used,including mechanical,laser,waterjet and ultrasonic cutting and advanced anastomosis and reconstruction processes,with highlighting their governing removal mechanisms as well as the surface and subsurface damages.

关键词： Soft biological tissue Surgical processing Tissue cutting Tissue welding

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An Energy-aware Virtual Machine Scheduling Approach for Cloud Data Centers

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable Computing 2025年

作者： Li, Jie Deng, Yuhui Zhong, Zijie Wu, Zhaorui Pang, Shujie Cui, Lin Min, Geyong South China Normal University School of Artificial Intelligence Foshan528225 China Jinan University Department of Computer Science Guangzhou510632 China University of Exeter Department of Computer Science College of Engineering Mathematics and Physical Sciences ExeterEX44QF United Kingdom

The reduction of energy consumption will be even more urgent in cloud data centers due to the explosive increase of application data. Virtual machine (VM) integration is a relatively standard technology currently applied for computing facilities of data centers. However, excessive VM consolidation can easily lead to local hot spots that lower the energy efficiency and reliability of data centers. In addition, on account of the impact of heat recirculation in data centers, the traditional VM scheduling strategy cannot comprehensively ponder optimizing the holistic data center energy, which encompasses both server energy and cooling energy. To handle these issues, we proposed EAVMS- an Energy-Aware VM Scheduling approach for minimizing the holistic energy consumption of data centers. EAVMS adopts a two-phase approach to gain energy efficiency while guaranteeing QoS. First, EAVMS leverages a Blended Genetic algorithm and Simulated Annealing algorithm (BGSA) to optimize the initial placement of VMs. Second, EAVMS utilizes a dynamic migration algorithm to achieve effective migration by setting a maximum server temperature threshold without violating the service level agreement (SLA) that cuts down energy consumption by moderating the hot spots of servers. We conducted extensive experiments using two real-world traces (i.e., PlanetLab and Google Cluster datasets) to evaluate the effectiveness of EAVMS. The experimental results unveil that our approach is capable of saving 3.23 \%-43.07 \% in the holistic energy consumption of cloud data centers with only a tiny service performance degradation compared to other state-of-the-art alternatives (e.g., MJPM, GRANITE, TAS, XINT-GA, and Random). © 2016 IEEE.

关键词： Simulated annealing

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Contrastive Learning at the Relation and Event Level for Rumor Detection 48

Contrastive Learning at the Relation and Event Level for Rum...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Xu, Yingrui Hu, Jingyuan Ge, Jingguo Wu, Yulei Li, Tong Li, Hui Chinese Academy of Sciences Institute of Information Engineering Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China University of Exeter Faculty of Environment Science and Economy Department of Computer Science United Kingdom

ISBN: (纸本)9781728163277

Existing studies for rumor detection rely heavily on a large number of labeled data to operate in a fully-supervised manner. However, manual data annotation in realistic cases is very expensive and time-consuming. In this paper, we propose a novel self-supervised Relation-Event based Contrastive Learning (RECL) framework for rumor detection to address the above issue. Specifically, we present both the relation-level and event-level augmentation strategies to generate contrastive samples, which capture both the semantics revealed by repost relations and the structural features of rumor events. Moreover, contrastive learning tasks are devised to generate informative graph representations by utilizing self-supervision signals of unlabeled data. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model, especially with limited labeled data. © 2023 IEEE.

关键词： Graph neural networks

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Impact Assessment of Memetic Operators on Communication Topologies for the PSO

Impact Assessment of Memetic Operators on Communication Topo...

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Latin America Congress on Computational Intelligence (LA-CCI)

作者： Carlos Silva Clodomir Santana Carmelo Bastos-Filho Department of Computer Engineering University of Pernambuco Recife Brazil Department of Computer Science University of Exeter Exeter UK

Swarm intelligence has emerged as a promising research area due to its potential to solve complex problems. The Particle Swarm Optimisation (PSO) algorithm is widely used in this area due to its efficiency and easy implementation. However, PSO performance can be affected by the choice of the communication topology, which defines the interaction between particles. In this work, we propose a comparative analysis of the impact of memetic operators on three distinct PSO topologies. We explore Interaction Networks’ use to analyse individuals’ social interaction in PSO-based algorithms with memetic operators. Furthermore, we employ the Network Portrait Divergence metric to compare the structural similarity between networks of the algorithms and the Interaction Diversity to measure the diversity of information flow within the swarm. The results obtained in this study provide insights into the topology selection for the PSO and the effect of memetic operators on them.

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Machine Learning Based Prediction of Energy Density for Additively Manufactured AlSi10Mg Samples by Laser Powder Bed Fusion Process 2

Machine Learning Based Prediction of Energy Density for Addi...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Bhut, Shubham Kumar, Krishan Chugh, Tinkle Sahlot, Pankaj National Institute of Technology Kurukshetra Department of Mechanical Engineering Kurukshetra India National Institute of Technology Kurukshetra Department of Computer Engineering Kurukshetra India University of Exeter Department of Computer Science United Kingdom

ISBN: (纸本)9798350369960

The laser Powder Bed Fusion (LPBF) process has recently become popular due to its ability to manufacture complex shapes with high precision. Among the various alloys for LPBF, AlSi10Mg stands out differently due to its lightweight nature, corrosion resistance, and design flexibility. The two important quality indicators for LPBF manufactured parts are surface roughness and Energy density, which should be within optimal range to ensure high-quality production. This study aims to improve this optimization process through a new approach - using a machine-learning model to predict the requisite laser energy density to manufacture AlSi10Mg. The study utilized machine learning models that were trained using four input parameters: Laser Power (W) Scan Speed (mm/s) Hatch Spacing (microns) and Layer Thickness (microns). Four different machine learning models were employed to predict the required Energy Density: Linear regression, Poisson regression XGBoost and ExtraTrees regression. The Poisson regression model yielded the results with an RMSE value of 11.13 and an R square value of 0.99. The XGBoost gradient boosting model also achieved outcomes with an RMSE of 52.18 and R values of 0.94. Overall, the trained machine learning models proved effective, in predicting the optimal energy density necessary for LPBF thereby enhancing manufacturing process accuracy and quality. © 2023 IEEE.

关键词： Additive manufacturing AlSi10Mg Laser Powder Bed Fusion Machine learning Optimization

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Distributed Nonlinear Conic Optimisation with partially separable Structure

arXiv

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arXiv 2024年

作者： Heusdens, Richard Zhang, Guoqiang Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands University of Exeter Exeter United Kingdom

In this paper we consider the problem of distributed nonlinear optimisation of a separable convex cost function over a graph subject to cone constraints. We show how to generalise, using convex analysis, monotone operator theory and fixed-point theory, the primal-dual method of multipliers (PDMM), originally designed for equality constraint optimisation and recently extended to include linear inequality constraints, to accommodate for cone constraints. The resulting algorithm can be used to implement a variety of optimisation problems, including the important class of semidefinite programs with partially separable structure, in a fully distributed fashion. We derive update equations by applying the Peaceman-Rachford splitting algorithm to the monotonic inclusion related to the lifted dual problem. The cone constraints are implemented by a reflection method in the lifted dual domain where auxiliary variables are reflected with respect to the intersection of the polar cone and a subspace relating the dual and lifted dual domain. Convergence results for both synchronous and stochastic update schemes are provided and an application of the proposed algorithm is demonstrated to implement an approximate algorithm for maximum cut problems based on semidefinite programming in a fully distributed fashion. Copyright © 2024, The Authors. All rights reserved.

关键词： Constrained optimization

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Saei-DT: Semi-asynchronous Edge Intelligence for Industrial Digital Twin Networks in 6G

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IEEE Network 2024年

作者： Tao, Ming Liao, Lingling Xie, Renping Zhang, Yin Min, Geyong Zhang, Yan Dongguan University of Technology School of Computer Science and Technology China University of Electronic Science and Technology of China School of Information and Communication Engineering China University of Exeter Department of Computer Science United Kingdom University of Oslo School of Information Engineering Norway

Promoted by integrating sensing, communication, and computing for edge intelligence in 6G networks, the Digital Twin (DT), as a typical 6G application, has been widely employed for minimizing the disruption of Industrial Internet-of-Things (IIoT) operations and facilitating the cost-effective execution through conducting equivalent model training. Nevertheless, in complex and changeable industrial scenarios, the existing approaches of DT model construction and update within the Digital Twin Edge Network (DITEN), do not fully consider the heterogeneity in computing and communication capabilities, resulting in unbalanced model training cost and loss, which also cannot respond quickly to the environmental changes and ensure the data consistency of virtual-real space. To realize the collaborative and efficient utilization of heterogeneous computing and communication resources, and strengthen the balance of training cost and loss in the whole life cycle of DT model, an approach of using semi-asynchronous federated learning (FL) for DT construction and update, named Saei-DT, is proposed, which includes the three significant operations, e.g., terminal selection for model training, self-adaption of local training iterations, and semi-asynchronous global aggregation. Through conducting extensive experiments on the dataset of Industrial-Equipment contained in Kaggle, the experimental results demonstrate the efficiency of Saei-DT in both training accuracy and loss. © 2024 IEEE.

关键词： Federated learning

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A Novel Graph-Representation-Based Multi-Scale Feature Fusion Approach for Road Segmentation

A Novel Graph-Representation-Based Multi-Scale Feature Fusio...

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IEEE International Conference on Networking, Sensing and Control

作者： Zhenyang Li Siyang Song Fuxiang Chen Lu Liu School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Computer Science University of Exeter Exeter United Kingdom

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Exploiting contextual information is important for road segmentation tasks. In contrast to existing work primarily focusing on expanding the receptive field and stacking network layers, we propose a novel Graph-representation-based Multi-scale Feature Fusion U-net model (GMFFUnet) to address this problem. Our method enhances global context extraction by establishing connections between features of varying types and scales. Specifically, our model projects the entire feature pyramid into multiple interaction spaces and models the complex relationships among them to construct a graph and facilitate information exchange. The enhanced features are then fused back into the original feature pyramid. Our method provides substantial benefits on road segmentation tasks with complex backgrounds. Comparative experiments were carried out to evaluate the performance of the proposed method, and the results demonstrated that our proposed method outperforms all SOTA methods on the Massachusetts road dataset.

关键词： Adaptation models Roads Stacking Neural networks Focusing Feature extraction Solids Sensors Information exchange

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Faster Configuration Performance Bug Testing with Neural Dual-Level Prioritization

Faster Configuration Performance Bug Testing with Neural Dua...

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International Conference on Software engineering (ICSE)

作者： Youpeng Ma Tao Chen Ke Li School of Computer Science and Engineering University of Electronic Science and Technology of China China IDEAS Lab School of Computer Science University of Birmingham United Kingdom Department of Computer Science University of Exeter United Kingdom

ISBN: (数字)9798331505691

ISBN: (纸本)9798331505707

As software systems become more complex and configurable, more performance problems tend to arise from the configuration designs. This has caused some configuration options to unexpectedly degrade performance which deviates from their original expectations designed by the developers. Such discrepancies, namely configuration performance bugs (CPBugs), are devastating and can be deeply hidden in the source code. Yet, efficiently testing CPBugs is difficult, not only due to the test oracle is hard to set, but also because the configuration measurement is expensive and there are simply too many possible configurations to test. As such, existing testing tools suffer from lengthy runtime or have been ineffective in detecting CPBugs when the budget is limited, compounded by inaccurate test oracle. In this paper, we seek to achieve significantly faster CPBug testing by neurally prioritizing the testing at both the configuration option and value range levels with automated oracle estimation. Our proposed tool, dubbed NDP, is a general framework that works with different heuristic generators. The idea is to leverage two neural language models: one to estimate the CPBug types that serve as the oracle while, more vitally, the other to infer the probabilities of an option being CPBug-related, based on which the options and the value ranges to be searched can be prioritized. Experiments on several widely-used systems of different versions reveal that NDP can, in general, better predict CPBug type in 87 % cases and find more CPBugs with up to $88.88 \times$ testing efficiency speedup over the state-of-the-art tools.

关键词： Runtime Source coding computer bugs Estimation Software systems Generators Numerical models Reliability Software debugging Testing

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