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International Conference on computers, Communications, and Systems (ICCCS)

作者： Hong Qian Wei Wu Qi Zhu Fei Wang Jinwei Zhao Yan Zheng School of Computer Science and Technology University of Science and Technology of China Hefei China System Software Research Department Jiangnan Institute of Computing Technology Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China

The Sunway family supercomputers have achieved a series of remarkable achievements. However, the toolchains provided by them are not perfect, which has brought great challenges to the development of high-performance application software. In this paper, a profiling and optimizing tool is proposed to assist people to analyze and optimize the performance of their programs. SWPFOPLD is independent of the application program and gathers the runtime performance data through the PMUs, an automatically hot functions rearrange optimization based on the performance data is furtherly accomplished. The evaluation shows that SWPFOPLD can be easily and effectively used to analyze and optimize the performance of the application programs.

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Energy-Efficient Clustering Algorithm Analysis for Intelligent Marine Ecosystem Monitoring

Energy-Efficient Clustering Algorithm Analysis for Intellige...

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Renewable Energies and Smart Technologies (REST), International Conference on

作者： Qamil Kabashi Isak Shabani Almas Abbasi Aaqif Afzaal Abbasi Department of Electronics Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosova Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Prishtina Prishtina Kosova Department of Computer Science Faculty of Computing and Information Technology International Islamic University Islamabad Pakistan Department of Earth and Marine Sciences National Biodiversity Future Center University of Palermo Palermo Italy

ISBN: (数字)9798350358902

ISBN: (纸本)9798350358919

Marine ecosystems are complex and dynamic environments. Monitoring these ecosystems is essential for understanding and identifying potential threats and conservation needs. Different sensory devices such as wireless sensor networks (WSNs) are used to monitor these environments. WSN have been in industrial use for the last two decades. However, energy efficiency in WSNs remains a topic that requires attention. With the rise of artificial intelligence and machine learning, numerous techniques have been proposed for developing energy-efficient clustering algorithms. These algorithms aim to improve the efficiency of battery- driven sensors to prolong the network's lifetime by balancing the energy consumption among all nodes in the network. In this paper, we present a comprehensive evaluation and literature review of state-of-the-art energy-efficient clustering algorithms that can be used for intelligent marine ecosystem monitoring. The review aims to dive into the theoretical aspects of clustering in sensor networks and its application to energy optimization, offering insights into the various clustering schemes proposed for wireless sensors, specifically focusing on energy efficiency.

关键词： Wireless communication Wireless sensor networks Renewable energy sources Machine learning algorithms Reviews Clustering algorithms Marine ecosystems

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Real-time Holistic Robot Pose Estimation with Unknown States

arXiv

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arXiv 2024年

作者： Ban, Shikun Fan, Juling Ma, Xiaoxuan Zhu, Wentao Qiao, Yu Wang, Yizhou Center on Frontiers of Computing Studies School of Computer Science Peking University China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Nat’l Key Lab of General Artificial Intelligence Department of Automation School of Electronics Information and Electrical Engineering Shanghai Jiao Tong University China

Estimating robot pose from RGB images is a crucial problem in computer vision and robotics. While previous methods have achieved promising performance, most of them presume full knowledge of robot internal states, e.g. ground-truth robot joint angles. However, this assumption is not always valid in practical situations. In real-world applications such as multi-robot collaboration or human-robot interaction, the robot joint states might not be shared or could be unreliable. On the other hand, existing approaches that estimate robot pose without joint state priors suffer from heavy computation burdens and thus cannot support real-time applications. This work introduces an efficient framework for real-time robot pose estimation from RGB images without requiring known robot states. Our method estimates camera-to-robot rotation, robot state parameters, keypoint locations, and root depth, employing a neural network module for each task to facilitate learning and sim-to-real transfer. Notably, it achieves inference in a single feed-forward pass without iterative optimization. Our approach offers a 12× speed increase with state-of-the-art accuracy, enabling real-time holistic robot pose estimation for the first time. Code and models are available at https: //***/Oliverbansk/Holistic-Robot-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

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A Deep Reinforcement Learning Deep Q Network-Based Approach for Systematic Detection of Distributed Denial of Service Attacks in Software-Defined Networking

SSRN

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SSRN 2024年

作者： Eke, Roberts Ndukwe Onyejegbu, Laeticia Nneka Information and Communication Engineering Center of Information & Communication Technology Faculty of Engineering University of Port Harcourt Rivers State Nigeria Computer Science Department Faculty of Computing University of Port Harcourt Nigeria

Network security is now seriously threatened by cyberattacks, particularly in contexts where software-defined networking (SDN) is used. Deep Reinforcement Learning (DRL) has become a potent method for handling challenging decision-making situations in recent years. In this work, a machine learning model was developed using Deep Q-Network (DQN), which is a kind of DRL algorithm known as DRL-DQN, to forecast cyberattacks in SDN. In comparison to the conventional DQN, which had 0.9900 accuracy, assessment time of 5.40 s, and training time of 290.5 s, the proposed DQN model produced an accuracy of 0.99935 with evaluation time of 1.126 seconds (s) and training time of 100 s at 20 epochs. In order to validate the improved DQN model, the result was compared with the traditional machine learning (ML) algorithms. The proposed DQN has a higher accuracy and lower training time. Additionally, it is clear that the proposed DQN performed better than the other ML algorithms in terms of recall measures and training times, which is the performance metric used in this work. This methodology is proactive for detecting and mitigating the impact of Distributed Denial of Service (DDoS) attacks in SDN. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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ML-based Modeling to Predict I/O Performance on Different Storage Sub-systems

ML-based Modeling to Predict I/O Performance on Different St...

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International Conference on High Performance computing

作者： Yiheng Xu Pranav Sivaraman Hariharan Devarajan Kathryn Mohror Abhinav Bhatele Palantir Technologies Inc. Denver USA Department of Computer Science University of Maryland College Park USA Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore USA

ISBN: (数字)9798331509095

ISBN: (纸本)9798331509101

Parallel applications can spend a significant amount of time performing I/O on large-scale supercomputers. Fast near-compute storage accelerators called burst buffers can reduce the time a processor spends performing I/O and mitigate I/O bottlenecks. However, determining if a given application could be accelerated using burst buffers is not straightforward even for storage experts. The relationship between an application's I/O characteristics (such as I/O volume, processes involved, etc.) and the best storage sub-system for it can be complicated. As a result, adapting parallel applications to use burst buffers efficiently is a trial-and-error process. In this work, we present a Python-based tool called PrismIO that enables programmatic analysis of I/O traces. Using PrismIO, we identify performance bottlenecks when using burst buffers and parallel file systems, and explain why certain I/O patterns perform poorly. Further, we use machine learning to model the relationship between I/O characteristics and file system selections. We use IOR, an I/O benchmark to gather performance data for training the machine learning model. Our model can predict the better performing storage system for unseen IOR scenarios with an accuracy of 94.47% and for four real applications with an accuracy of 95.86%.

关键词： Training Accuracy File systems Computational modeling High performance computing Machine learning Predictive models Benchmark testing Data models Supercomputers

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Extremal Problems on Forest Cuts and Acyclic Neighborhoods in Sparse Graphs

arXiv

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arXiv 2024年

作者： Botler, F. Couto, Y.S. Fernandes, C.G. de Figueiredo, E.F. Gómez, R. dos Santos, V.F. Sato, C.M. Institute of Mathematics and Statistics University of São Paulo Brazil Department of Computer Science Federal University of Minas Gerais Brazil Center for Mathematics Computing and Cognition Federal University of ABC Brazil

Chernyshev, Rauch, and Rautenbach proved that every connected graph on n vertices with less than 11 5 n − 185 edges has a vertex cut that induces a forest, and conjectured that the same remains true if the graph has less than 3n − 6 edges. We improve their result by proving that every connected graph on n vertices with less than 94 n edges has a vertex cut that induces a forest. We also study weaker versions of the problem that might lead to an improvement on the bound obtained. Copyright © 2024, The Authors. All rights reserved.

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Environment-aware dynamic graph learning for out-of-distribution generalization 23

Environment-aware dynamic graph learning for out-of-distribu...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Haonan Yuan Qingyun Sun Xingcheng Fu Ziwei Zhang Cheng Ji Hao Peng Jianxin Li Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University and School of Computer Science and Engineering Beihang University Key Lab of Education Blockchain and Intelligent Technology Guangxi Normal University Department of Computer Science and Technology Tsinghua University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatiotemporal invariant patterns. Specifically, we first design the environment-aware EADGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

arXiv

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arXiv 2024年

作者： Zhong, Luying Pi, Yueyang Chen, Zheyi Yu, Zhengxin Miao, Wang Chen, Xing Min, Geyong College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster United Kingdom School of Engineering Computing and Mathematics University of Plymouth United Kingdom Department of Computer Science University of Exeter United Kingdom

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph algorithms

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Graceful Performance Degradation in Apache Storm 21st

Graceful Performance Degradation in Apache Storm

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21st International Conference on Parallel and Distributed computing, Applications, and Technologies, PDCAT 2020

作者： HoseinyFarahabady, Mohammad Reza Taheri, Javid Zomaya, Albert Y. Tari, Zahir School of Computer Science Center for Distributed and High Performance Computing The University of Sydney SydneyNSW Australia Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Science RMIT University MelbourneVIC Australia

ISBN: (纸本)9783030692438

The concept of stream data processing is becoming challenging in most business sectors where try to improve their operational efficiency by deriving valuable information from unstructured, yet, contentiously generated high volume raw data in an expected time spans. A modern streamlined data processing platform is required to execute analytical pipelines over a continues flow of data-items that might arrive in a high rate. In most cases, the platform is also expected to dynamically adapt to dynamic characteristics of the incoming traffic rates and the ever-changing condition of underlying computational resources while fulfill the tight latency constraints imposed by the end-users. Apache Storm has emerged as an important open source technology for performing stream processing with very tight latency constraints over a cluster of computing nodes. To increase the overall resource utilization, however, the service provider might be tempted to use a consolidation strategy to pack as many applications as possible in a (cloud-centric) cluster with limited number of working nodes. However, collocated applications can negatively compete with each other, for obtaining the resource capacity in a shared platform that, in turn, the result may lead to a severe performance degradation among all running applications. The main objective of this work is to develop an elastic solution in a modern stream processing ecosystem, for addressing the shared resource contention problem among collocated applications. We propose a mechanism, based on design principles of Model Predictive Control theory, for coping with the extreme conditions in which the collocated analytical applications have different quality of service (QoS) levels while the shared-resource interference is considered as a key performance limiting parameter. Experimental results confirm that the proposed controller can successfully enhance the p -99 latency of high priority applications by 67%, compared to the default round r

关键词： Storms

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Local Periodicity-Based Beat Tracking for Expressive Classical Piano Music

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IEEE/ACM Transactions on Audio Speech and Language Processing 2023年 31卷 2824-2835页

作者： Chiu, Ching-Yu Muller, Meinard Davies, Matthew E. P. Su, Alvin Wen-Yu Yang, Yi-Hsuan National Cheng Kung University Academia Sinica Graduate Program of Multimedia Systems and Intelligent Computing Tainan701401 Taiwan International Audio Laboratories Erlangen Erlangen91058 Germany University of Coimbra Department of Informatics Engineering Centre for Informatics and Systems Coimbra3004-531 Portugal National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan Taiwan AI Labs Yating Music Team Taipei103622 Taiwan Academia Sinica Research Center for IT Innovation Taipei11529 Taiwan

To model the periodicity of beats, state-of-the-art beat tracking systems use 'post-processing trackers' (PPTs) that rely on several empirically determined global assumptions for tempo transition, which work well for music with a steady tempo. For expressive classical music, however, these assumptions can be too rigid. With two large datasets of Western classical piano music, namely the Aligned Scores and Performances (ASAP) dataset and a dataset of Chopin's Mazurkas (Maz-5), we report on experiments showing the failure of existing PPTs to cope with local tempo changes, thus calling for new methods. In this paper, we propose a new local periodicity-based PPT, called predominant local pulse-based dynamic programming (PLPDP) tracking, that allows for more flexible tempo transitions. Specifically, the new PPT incorporates a method called 'predominant local pulses' (PLP) in combination with a dynamic programming (DP) component to jointly consider the locally detected periodicity and beat activation strength at each time instant. Accordingly, PLPDP accounts for the local periodicity, rather than relying on a global tempo assumption. Compared to existing PPTs, PLPDP particularly enhances the recall values at the cost of a lower precision, resulting in an overall improvement of F1-score for beat tracking in ASAP (from 0.473 to 0.493) and Maz-5 (from 0.595 to 0.838). © 2014 IEEE.

关键词： Hidden Markov models

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