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检索条件"任意字段=International Symposium on Software Engineering for Parallel and Distributed Systems"

共 10740 条记录，以下是21-30 订阅

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YFLM: An Improved Levenberg-Marquardt Algorithm for Global Bundle Adjustment 41st

YFLM: An Improved Levenberg-Marquardt Algorithm for Global ...

引用

41st Computer Graphics international Conference, CGI 2024

作者： Peng, Jiaxin Li, Tao Jiang, Qin Liu, Jie Wang, Ruibo Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Hunan Changsha410073 China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Hunan Changsha410073 China

ISBN: (纸本)9783031820205

The conventional Levenberg-Marquardt (LM) algorithm is a state-of-the-art trust-region optimization method for solving bundle adjustment problems in the Structure-from-Motion community, which not only takes advantage of the fast convergence of the Gauss-Newton method, but also the stability of the gradient descent method when approaching optimal solutions. However, the damping ratio of LM is simply provided by trial-and-error, which causes slow convergence rate for large-scale problems. This paper proposes the Yamashita-Fukushima LM (YFLM) algorithm to reduce the time complexity for global bundle adjustment, where the damping factor is determined by Yamashita and Fukushima’s method. YFLM dynamically calculates a more reasonable and optimal damping ratio according to the newest reprojection error. The experimental results show that the YFLM algorithm outperforms the conventional LM algorithm for most public bundle adjustment datasets. Besides this, the convergence of the YFLM algorithm is also evaluated with different σ∈(0,2]. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Damping

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Using Deep Learning for Deadlock Detection in Intelligent software systems

Using Deep Learning for Deadlock Detection in Intelligent So...

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iNnovative Informatics of Biskra (ISNIB), international symposium on

作者： RAOUDHA ROMDHANI OLFA MOSBAHI MOHAMED KHALGUI LISI SERCOM laboratory EPT University of Carthage Tunis Tunisia LISI laboratory INSAT University of Carthage Tunis Tunisia Faculty of Engineering German International University GIU-Berlin Berlin Germany

ISBN: (数字)9798331509576

ISBN: (纸本)9798331509583

This paper addresses deadlocks in concurrent software systems, presenting an AI-driven testing framework for detecting deadlocks in task interactions. Using advanced machine learning techniques, the system monitors task interactions, learns from historical data, and refines its detection ability. The approach formalizes task properties, such as states, resource demands, and dependencies, to build a robust interaction model. A supervised neural network algorithm provides real-time predictions, with evaluation metrics like detection accuracy and false positive rates for comparison with conventional methods. Results show significant improvements in deadlock detection accuracy, addressing scalability and manageability challenges faced by traditional solutions, offering a scalable AI alternative for modern distributed environments.

关键词： Deep learning software testing Accuracy Scalability Neural networks System recovery software systems Real-time systems software reliability Predictive analytics

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Adaptive Cyber Defence: Leveraging GANs for Simulating and Mitigating Advanced Network Attacks in IoT Environments 11th

Adaptive Cyber Defence: Leveraging GANs for Simulating and ...

引用

11th international symposium on Applied Computing for software and Smart systems, ACSS 2024

作者： Rao, P. Kiran Chatterjee, Subarna Prakash, P. Suman Ramana, K. Seshadri Rajeev Gandhi Memorial College of Engineering and Technology Andra Pradesh Nandyal518501 India Department of CSE MS Ramaiah University of Applied Science Karnataka Bengaluru India Department of CAI G Pullaiah College of Engineering and Technology Andra Pradesh Kurnool India Department of CAI Ravindra College of Engineering for Women Andra Pradesh Kurnoo518001 India

ISBN: (纸本)9789819797615

The progressive dissemination of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs) has ushered in a new era of connectivity, with vast applications spanning from medicare to smart city infrastructure. However, this expansion has been paralleled by a corresponding increase in the sophistication and variety of cyber threats targeting these networks. Traditional cyber security measures, designed for a less dynamic threat landscape, are proving increasingly insufficient in protecting against the innovative and varied attack methods now in commonplace. This study introduces an innovative application of Generative Adversarial Networks (GANs) to address this challenge, presenting a novel framework for the simulation and mitigation of advanced network attacks, particularly focusing on distributed Denial of Service (DDoS) and spoofing attacks which pose significant threats in IoT environments. Generative Adversarial Networks (GANs), comprising two neural networks-the generator and the discriminator compete in a game-theoretic scenario, facilitating a deep understanding of attack patterns through the generation of realistic, synthetic cyber-attack scenarios. This research exploits GANs to bridge the gap between the static nature of traditional security protocols and the dynamic, evolving landscape of cyber threats. By training on a comprehensive dataset of known attacks and normal network activities, our proposed model, the Dynamic Adaptive Threat Simulation GAN (DATS-GAN), is capable of producing varied and realistic attack scenarios. These simulations serve a dual purpose: they not only enhance the detection capabilities and responsiveness of current security systems but also provide a basis for the development of new, adaptive security mechanisms capable of dynamically responding to the ever-changing cyber threat landscape. The effectiveness of DATS-GAN is demonstrated through extensive empirical analysis, highlighting significant improvements in the detecti

关键词： Generative adversarial networks

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Communication-reduced Conjugate Gradient Variants for GPU-accelerated Clusters

Communication-reduced Conjugate Gradient Variants for GPU-ac...

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Euromicro Conference on parallel, distributed and Network-Based Processing

作者： Massimo Bernaschi Mauro G. Carrozzo Alessandro Celestini Giacomo Piperno Pasqua D’Ambra Institute for Applied Computing (IAC) Via dei Taurini 19 Rome Institute for Applied Computing (IAC) Via P. Castellino 111 Naples

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Linear solvers are key components in any software platform for scientific and engineering computing. The solution of large and sparse linear systems lies at the core of physics-driven numerical simulations relying on partial differential equations (PDEs) and often represents a significant bottleneck in data-driven procedures, such as scientific machine learning. In this paper, we present an efficient implementation of the preconditioned s-step Conjugate Gradient (CG) method, originally proposed by Chronopoulos and Gear in 1989, for large clusters of Nvidia GPU-accelerated computing nodes. The method, often referred to as communication-reduced or communication-avoiding CG, reduces global synchronizations and data communication steps compared to the standard approach, enhancing strong and weak scalability on parallel computers. Our main contribution is the design of a parallel solver that fully exploits the aggregation of low-granularity operations inherent to the s-step CG method to leverage the high throughput of GPU accelerators. Additionally, it applies overlap between data communication and computation in the multi-GPU sparse matrix-vector product. Experiments on classic benchmark datasets, derived from the discretization of the Poisson PDE, demonstrate the potential of the method.

关键词： Linear systems Scalability Partial differential equations Machine learning Throughput Numerical simulation software Data communication Sparse matrices Standards

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Proceedings of the 1997 2nd international Workshop on software engineering for parallel and distributed systems

Proceedings of the 1997 2nd International Workshop on Softwa...

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Proceedings of the 1997 2nd international Workshop on software engineering for parallel and distributed systems

The proceedings contains 30 papers from the 2nd international Workshop on software engineering for parallel Processing and distributed systems. Topics discussed include: Ada-95 tasks modeling;Petri net semantics;algorithm for finding maximal conflict sets;asynchronous message-passing program reachability testing;a Tuple Channel coordination model;parallel implementation of the combination of broadcasting, reduction and scan;an automated algorithmic recognition technique that supports parallel software development;lift case study of specifying parallel and distributed systems in Object-Z;Web computing skeleton;message passing programs graphical programming environment;and designing concurrent systems using performance prototyping.

关键词： parallel processing systems

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Unknown Input Observer for Takagi-Sugeno Fuzzy Bilinear System with Input and Output Disturbances

Unknown Input Observer for Takagi-Sugeno Fuzzy Bilinear Syst...

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IEEE/SICE international symposium on System Integration

作者： Jun Yoneyama Department of Electrical Engineering and Electronics Aoyama Gakuin University Sagamihara Japan

ISBN: (数字)9798331531614

ISBN: (纸本)9798331531621

An estimation of the state variables for systems with disturbances is an important problem. In a practical situation, not all the state variables are measurable, and disturbance noises come into the system. Especially, it is difficult to estimate the state variables of complicated nonlinear systems with input and output disturbances. In this paper, observer design methods for a discrete-time Takagi-Sugeno fuzzy bilinear system with unknown inputs and input/output disturbances are proposed. Our observer filters out unknown input and estimates the state and input/output disturbances of the system. Since Takagi-Sugeno fuzzy bilinear system represents a quite large class of nonlinear systems, an unknown input observer that estimates the state and input/output disturbance of Takagi-Sugeno fuzzy bilinear system with unknown inputs and disturbances is essential in many engineering fields. Observer design has started with a parallel distributed observer(PDO), which is constructed with local linear observers and the appropriate grade of the membership functions. However, design conditions for PDO are very conservative. To overcome such disadvantage, nonPDO with multiple Lyapunov matrices technique is proposed to design our observer in this paper. Such a design method is based on a multiple Lyapunov function with a sum of the membership functions. This method drastically reduces the conservatism. To demonstrate the validity of our proposed observer design approach, an illustrative numerical example is provided. Lastly, we end with concluding remarks.

关键词： Filters Design methodology Noise Estimation System integration Observers Takagi-Sugeno model Noise measurement Nonlinear systems Lyapunov methods

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Recent Researches in software engineering, parallel and distributed systems - 10th WSEAS international Conference on software engineering, parallel and distributed systems, SEPADS'11

Recent Researches in Software Engineering, Parallel and Dist...

引用

10th WSEAS international Conference on software engineering, parallel and distributed systems, SEPADS'11

ISBN: (纸本)9789604742776

The proceedings contain 31 papers. The topics discussed include: simplified bi-directional transformation of UML activities into Petri nets;representing Petri net structures as directed graphs;consideration on new theoretical solutions of special electric machines using specialized soft of electromagnetic field numerical analysis;apply e-learning platform in teaching electronic communication course;a probabilistic model for risk forecasting in medical informatics;challenges of a project-based learning approach towards requirement engineering;prolonging the lifetime of wireless sensor networks using multi-level clustering and heterogeneity;cooperative caching in mobile ad hoc networks through clustering;parallel shape transformation using slices;range free localization schemes for wireless sensor networks;and classifications and feature analysis method for sort algorithm programs based on program structure formalized method.

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A Methodical Approach to parallel IO Analysis in distributed Deep Learning Applications 30th

A Methodical Approach to Parallel IO Analysis in Distribut...

引用

30th international Conference on parallel and distributed Processing Techniques, PDPTA 2024, held as part of the World Congress in Computer Science, Computer engineering and Applied Computing, CSCE 2024

作者： Parraga, Edixon Leon, Betzabeth Mendez, Sandra Rexachs, Dolores Suppi, Remo Luque, Emilio Bellaterra Barcelona Spain Barcelona Spain

ISBN: (纸本)9783031856372

Deep learning applications have become crucially important for the analysis and prediction of massive volumes of data. However, these applications impose substantial input/output (I/O) loads on computing systems. Specifically, when running on distributed memory systems, they manage large amounts of data that must be accessed from parallel file systems during the training stage using the available I/O software stack. These accesses are inherently intensive and highly concurrent, which can saturate systems and adversely impact application performance. Consequently, the challenge lies in efficiently utilizing the I/O system to allow these applications to scale. When the volume of data increases, access can generate high training latency and add overhead significantly when data exceeds the main memory capacity. Therefore, it is essential to analyze the behavior of the I/O patterns generated during the training stage by reading the data set to analyze the behavior when the application scales and what amount of resources it will need. The paper presents a methodology to analyze parallel I/O patterns in Deep Learning applications in this context. Our methodological approach mainly aims at providing users with complete and accurate information. This involves a thorough understanding of how the application, the dataset, and the system parameters can significantly influence the parallel I/O of their deep learning application. We seek to empower users to make informed decisions through a structured methodology that allows them to identify and modify configurable elements effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Scheduling Tasks in the Cloud environment by using evolutionary optimization technique

Scheduling Tasks in the Cloud environment by using evolution...

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Emerging systems and Intelligent Computing (ESIC), international Conference on

作者： Liza Mahapatra Minakhi Rout Sarita Tripathy School of Computer Engineering Kalinga Institute Of Industrial Technology(KIIT)Deemed to be University Bhubaneswar Odisha

ISBN: (数字)9798331522100

ISBN: (纸本)9798331522117

Among the many software technologies around the world,there is no parallel to cloud computing(CC) .Businesses use cloud computing for hosting *** cloud computing, tasks are distributed across a group of resources which when executed, help reduce time needed to finish them *** optimization refers to the process of minimizing the time needed to accomplish tasks. These group of tasks are present in a domain related to computers. In CC, the goal of this optimization is reducing the total time needed to execute the group of tasks when distributing and controlling virtual machines or resources. This paper aims to obtain optimization in scheduling tasks in CC by utilizing genetic *** methods whose basis are natural selection and genetics are called Genetic algorithms. They combine the idea of "survival of the fittest" in string-based representations with a mix of structured and random information exchange, forming a search approach that includes a level of human-like creativity. .After the application of genetic algorithms to task scheduling problem, the minimum fitness per generation is seen in the graph after two generation of stability.

关键词： Cloud computing Processor scheduling Heuristic algorithms Search problems Virtual machines Stability analysis software Optimization Information exchange Genetic algorithms

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2008 international symposium on software Testing and Analysis - Proceedings of the 6th Workshop on parallel and distributed systems: Testing, Analysis, and Debugging 2008, PADTAD'08

2008 International Symposium on Software Testing and Analysi...

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6th Workshop on parallel and distributed systems: Testing, Analysis, and Debugging 2008, PADTAD'08

ISBN: (纸本)9781605580524

The proceedings contain 11 papers. The topics discussed include: reproducible testing of distributed software with middleware virtualization and simulation;dynamic testing of flow graph based parallel applications;scheduling considerations for building dynamic verification tools for MPI;memory tagging in Charm++;support for symmetric shadow memory in multiprocessors;on-the-fly race detection in multi-threaded programs;AtomRace: data race and atomicity violation detector and healer;towards a better collaboration of static and dynamic analyses for testing concurrent programs;software development tools for multi-core/parallel programming;systematic concurrency testing using CHESS;and pitfalls in teaching development and testing of concurrent programs and how to overcome them.

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