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International Symposium on Performance Evaluation of computer & Telecommunication systems SPECTS

作者： Paul Davern Noor Nashid Ahmed Zahran Cormac J Sreenan Mobile and Internet Systems Laboratory Department of Computer Science University College Cork Cork Ireland

Many different existing technologies and techniques have been developed to accelerate Hyper Text Transfer Protocol (HTTP) with the aim of improving the end-user's web browsing experience. However, there is no standard methodology for evaluating HTTP performance or for the evaluation of a given HTTP acceleration technology in a multi-user scenario. In this paper we present a framework HTTP-AE (HTTP Automated Evaluation) for measuring the end-user's web browsing quality of experience. We describe how HTTP-AE can be used to automate the evaluation of HTTP performance and for the evaluation of HTTP acceleration technologies in multi-user environments. We present three case studies in which we evaluate three different HTTP acceleration technologies for satellite systems using HTTP-AE. In our first two case studies we show that typical browser HTTP acceleration techniques operate sub-optimally on satellite systems. In our third case study we show that in multi-user scenarios, HTTP acceleration technologies, which break the end-to-end semantics of HTTP, can distribute network resources unfairly to the end-users.

关键词： Acceleration Satellites Browsers Delay Fires Bandwidth

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A memory-layout oriented run-time technique for locality optimization on SMPs

A memory-layout oriented run-time technique for locality opt...

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International Conference on Parallel Processing (ICPP)

作者： Yong Yan Xiaodong Zhang Zhao Zhang HAL Computer Systems Inc. Campbell CA USA Computer Science Department College of William and Mary Williamsburg VA USA

ISBN: (纸本)0818686502

Exploiting locality at run-time is a complementary approach to a compiler approach for those applications with dynamic memory access patterns. This paper proposes a memory-layout oriented approach to exploit cache locality for parallel loops at run-time on symmetric multi-processor (SMP) systems. Guided by application-dependent hints and the targeted cache architecture, it reorganizes and partitions a parallel loop through shrinking and partitioning the memory-access space of the loop at run-time. In the generated task partitions, the data sharing among partitions is minimized and the data reuse in a partition is maximized. The execution of tasks in partitions is scheduled in an adaptive and locality-preserved way to achieve balanced execution, for minimizing the execution time of applications by trading off load balance and locality. Based on simulation and measurement, we show our run-time approach can achieve comparable performance with the compiler optimizations for two applications, whose load balance and cache locality can be well optimized by the tiling and other program transformations. However our experimental results also show that our approach is able to significantly improve the memory performance for the applications with dynamic memory access patterns. This type of programs are usually hard to be optimized by compilers.

关键词： Runtime Delay Hardware computer science Educational institutions Application software Optimizing compilers Program processors Multiprocessing systems Sun

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Diagnostics as a reasoning process: From logic structure to software design

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Journal of Computing and Information Technology 2019年第Special Issue期27卷 43-57页

作者： Cristani, Matteo Olivieri, Francesco Tomazzoli, Claudio Viganò, Luca Zorzi, Margherita Department of Computer Science University of Verona Italy Data61 CSIRO Australia Department of Informatics King's College London United Kingdom

Diagnostic tests are used to determine anomalies in complex systems such as organisms or built structures. Once a set of tests is performed, the experts interpret their results and make decisions based on them. This process is named diagnostic reasoning. In diagnostic reasoning a decision is established by using both rules and general knowledge on the tests and the domain. The artificial intelligence community has focused on devising and automating different methods of diagnosis for medicine and engineering, but, to the best of our knowledge, the decision process in logical terms has not yet been investigated thoroughly. The automation of the diagnostic process would be helpful in a number of contexts, in particular when the number of test sets to make decision is too wide to be dealt with manually. To tackle such challenges, we shall study logical frameworks for diagnostic reasoning, automation methods and their computational properties and technologies implementing these methods. In this paper, we present the formalization of a hybrid reasoning framework TL that hosts tests and deduction rules on tests, and an algorithm that transforms a TL theory into defeasible logic, for which an implemented automated deduction technology (called Spindle) exists. We evaluate the methodology by means of a real-world example related to the Open Web Application Security Project requisites. The full diagnostic process is driven from the definition of the issue to the decision. © 2019 Journal of Computing and Information Technology.

关键词： Automation

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Partial sensing coverage and deployment efficiency in wireless directional sensor networks

Partial sensing coverage and deployment efficiency in wirele...

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Wireless Telecommunications Symposium, WTS

作者： Yun Wang Zhifeng Xiao Yanwei Wu Anthony G Stephan Jacob M Siegers Department of Computer Science and Information Systems Bradley University Peoria IL USA Department of Computer Science and Software Engineering The Behrend College Erie PA USA Division of Computer Science Western Oregon University Monmouth OR USA

Unlike most existing works that focus on a conventional omni-directional sensor network, we investigate the sensing coverage problem in a directional sensor network through mathematically modeling, analysis, and computer-based simulation evaluation. Research results show: 1) A factor of (2π/θ) more sensors will be required to provide the same sensing coverage in a θ(θ α = ln(1-α)-ln(1-f)/ln(1-f), is solely determined by the sensing coverage requirement in an application and is independent of sensor features. Simulation results validate the modeling, derivation, and analysis.

关键词： Sensors Wireless sensor networks Mathematical model Equations Analytical models Numerical models computer science

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Exploiting optimum acoustic features in COVID-19 individual's breathing sounds

Exploiting optimum acoustic features in COVID-19 individual'...

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2021 International Research Conference on Smart Computing and systems Engineering, SCSE 2021

作者： Milani, M. G. Manisha Ramashini, Murugaiya Murugiah, Krishani Chamal, Lanka Geeganage Shamaan Universiti Brunei Darussalam Faculty of Integrated Technologies Brunei Brunei Uva Wellassa University Department of Computer Science and Informatics Sri Lanka Pavendar Bharathidasan College of Engineering and Technology Department of Biotechnology India School of Engineering Sri Lanka Technological Campus Sri Lanka

ISBN: (纸本)9781665409650

The world is facing an extreme crisis due to the COVID-19 pandemic. The COVID-19 virus interrupts the world's economy and social factors;thus, many countries fall into poverty. Also, they lack expertise in this field and could not make an effort to perform the necessary polymerase chain reaction (PCR) or other expensive laboratory tests. Therefore, it is important to find an alternative solution to the early prediction of COVID-19 infected persons with a low-cost method. The objective of this study is to detect COVID-19 infected individuals through their breathing sounds. To perform this task, twenty-two (22) acoustic features are extracted. The optimum features in each COVID-19 infected breathing sound is identified among these features through a feature engineering method. This proposed feature engineering method is a hybrid model that includes;statistical feature evaluation, PCA, and k-mean clustering techniques. The final results of this proposed Optimum Acoustic Feature Engineering (OAFE) model show that breathing sound signals' Kurtosis feature is more effective in distinguishing COVID-19 infected individuals from healthy individuals. © 2021 IEEE.

关键词： COVID-19

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Enhancing Proximal Policy Optimization in the Pendulum-v1 Environment Through Clustering-Based State Space Simplification 1

Enhancing Proximal Policy Optimization in the Pendulum-v1 En...

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1st International Conference on Innovative and Intelligent Information Technologies, IC3IT 2024

作者： Abid, Mohamed Nadhir Beggas, Mounir Laouid, Abdelkader Kara, Mostefa Bounceur, Ahcene Computer Science University of El Oued University of El Oued El Oued Algeria King Fahd University of Petroleum and Minerals Interdisciplinary Research Center for Intelligent Secure Systems Dhahran31261 Saudi Arabia College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798331541064

This study aims to enhance the performance of the Proximal Policy Optimization (PPO) algorithm in continuous control tasks, particularly in the Pendulum-v1 environment. We introduce a clustering-based approach to simplify the state-action space, improving learning efficiency and maximizing cumulative rewards. The state-action pairs are clustered using k-means, emphasizing regions with higher rewards to guide the agent's policy optimization. Experimental results demonstrate that clustering significantly improves PPO's performance, with the 10 -cluster configuration yielding the best outcomes in terms of reward consistency and efficiency. After tuning, the clustering-enhanced PPO achieves near-optimal performance, outperforming the baseline PPO by a wide margin. © 2024 IEEE.

关键词： Reinforcement learning

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Modular Neural Network Policies for Learning In-flight Object Catching with a Robot Hand-Arm System

arXiv

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arXiv 2023年

作者： Hu, Wenbin Acero, Fernando Triantafyllidis, Eleftherios Liu, Zhaocheng Li, Zhibin School of Informatics University of Edinburgh United Kingdom Department of Computer Science University College London United Kingdom

We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects, a task that requires fast, reactive, and accurately-timed robot motions. Our framework consists of five core modules: (i) an object state estimator that learns object trajectory prediction, (ii) a catching pose quality network that learns to score and rank object poses for catching, (iii) a reaching control policy trained to move the robot hand to pre-catch poses, (iv) a grasping control policy trained to perform soft catching motions for safe and robust grasping, and (v) a gating network trained to synthesize the actions given by the reaching and grasping policy. The former two modules are trained via supervised learning and the latter three use deep reinforcement learning in a simulated environment. We conduct extensive evaluations of our framework in simulation for each module and the integrated system, to demonstrate high success rates of in-flight catching and robustness to perturbations and sensory noise. Whilst only simple cylindrical and spherical objects are used for training, the integrated system shows successful generalization to a variety of household objects that are not used in training. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Intrinsic Language-Guided Exploration for Complex Long-Horizon Robotic Manipulation Tasks

arXiv

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arXiv 2023年

作者： Triantafyllidis, Eleftherios Christianos, Filippos Li, Zhibin The School of Informatics The University of Edinburgh United Kingdom The Department of Computer Science University College London United Kingdom

Current reinforcement learning algorithms struggle in sparse and complex environments, most notably in long-horizon manipulation tasks entailing a plethora of different sequences. In this work, we propose the Intrinsically Guided Exploration from Large Language Models (IGE-LLMs) framework. By leveraging LLMs as an assistive intrinsic reward, IGE-LLMs guides the exploratory process in reinforcement learning to address intricate long-horizon with sparse rewards robotic manipulation tasks. We evaluate our framework and related intrinsic learning methods in an environment challenged with exploration, and a complex robotic manipulation task challenged by both exploration and long-horizons. Results show IGE-LLMs (i) exhibit notably higher performance over related intrinsic methods and the direct use of LLMs in decision-making, (ii) can be combined and complement existing learning methods highlighting its modularity, (iii) are fairly insensitive to different intrinsic scaling parameters, and (iv) maintain robustness against increased levels of uncertainty and horizons. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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computer Aided Detection of Prostate Cancer using Fused Information from Dynamic Contrast Enhanced and Morphological Magnetic Resonance Images

Computer Aided Detection of Prostate Cancer using Fused Info...

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IEEE International Conference on Signal Processing and Communications, ICSPC

作者： D. Ampeliotis A. Antonakoudi K. Berberidis E. Z. Psarakis Computer Engineering and Informatics Department University of Patras Rio-Patras Greece Computing and Information Systems Department The Philips College Nicosia Cyprus

This paper presents a computer-aided diagnosis scheme for the detection of prostate cancer. The pattern recognition scheme proposed, utilizes fused dynamic and morphological features extracted from magnetic resonance images (MRIs). The performance of the proposed scheme has been evaluated through extensive training and testing on several patient cases, where the staging of their condition has been previously evaluated by both ultrasoundguided biopsy and radiological assessment. The classification scheme is based on Probabilistic Neural Networks (PNNs), whose parameters are estimated using the Expectation-Maximization (EM) algorithm during a training phase. Fusion of the image characteristics is performed by properly aligning the respective T1-weighted dynamic and T2-weighted morphological images, allowing accurate feature selection from both images. The proposed classification scheme as well as the effect of fusion on the extracted features is tested, with respect to the correct classification rate (CCR) of each case.

关键词： Cancer detection Prostate cancer Magnetic resonance Feature extraction Testing computer aided diagnosis Pattern recognition Data mining Magnetic resonance imaging Ultrasonic imaging

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Higher-order functions in aesthetic EC encodings

Higher-order functions in aesthetic EC encodings

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Congress on Evolutionary Computation

作者： James McDermott Jonathan Byrne John Mark Swafford Michael O'Neill Anthony Brabazon Department of Computer Science and Informatics The Natural Computing Research & Applications Group University College Dublin Dublin Ireland

The use of higher-order functions, as a method of abstraction and re-use in EC encodings, has been the subject of relatively little research. In this paper we introduce and give motivation for the ideas of higher-order functions, and describe their general advantages in EC encodings. We implement grammars using higher-order ideas for two problem domains, music and 3D architectural design, and use these grammars in the grammatical evolution paradigm. We demonstrate four advantages of higher-order functions (patterning of phenotypes, non-entropic mutations, compression of genotypes, and natural expression of artistic knowledge) which lead to beneficial results on our problems.

关键词： Grammar Context Encoding Compounds Automatic programming Three dimensional displays

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