Reinforcement learning (RL)-based web GUI testing techniques have attracted significant attention in both academia and industry due to their ability to facilitate automatic and intelligent exploration of websites unde...
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ISBN:
(数字)9798400712487
ISBN:
(纸本)9798400712487
Reinforcement learning (RL)-based web GUI testing techniques have attracted significant attention in both academia and industry due to their ability to facilitate automatic and intelligent exploration of websites under test. Yet, the existing approaches that leverage a single RL agent often struggle to comprehensively explore the vast state space of large-scale websites with complex structures and dynamic content. Observing this phenomenon and recognizing the benefit of multiple agents, we explore the use of Multi-Agent RL (MARL) algorithms for automatic web GUI testing, aiming to improve test efficiency and coverage. However, how to share information among different agents to avoid redundant actions and achieve effective cooperation is a non-trivial problem. To address the challenge, we propose the first MARL-based web GUI testing system, MARG, which coordinates multiple testing agents to efficiently explore a website under test. To share testing experience among different agents, we have designed two data sharing schemes: one centralized scheme with a shared Q-table to facilitate efficient communication, and another distributed scheme withdata exchange to decrease the overhead of maintaining Q-tables. We have evaluated MARG on nine popular real-world websites. When configuring with five agents, MARG achieves an average increase of 4.34 and 3.89 times in the number of explored states, as well as a corresponding increase of 4.03 and 3.76 times in the number of detected failures, respectively, when compared to two state-of-the-art approaches. Additionally, compared to independently running the same number of agents, MARG can explore 36.42% more unique web states. these results demonstrate the usefulness of MARL in enhancing the efficiency and performance of web GUI testing tasks.
Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a smal...
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ISBN:
(纸本)9798350315684
Bridging the gap between physics-based modeling and data-driven machine learning promises to reduce the amount of training data required and to improve explainability in predictive maintenance applications. For a small fleet of industrial forklift trucks, we develop a physically inspired framework for predicting remaining useful life (RUL) for selected components by integrating physically motivated feature extraction, degradation modelling and machine learning. the discussed approach is promising for situations of limited data availability or large data heterogeneity, which often occurs in fleets of customized vehicles optimized for particular tasks.
this work presents DiverSim, a highly customizable simulation tool designed for the generation of diverse synthetic datasets of vulnerable road users to address key challenges in pedestrian detection for Advanced Driv...
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Brain-Computer Interfaces (BCIs) have great potential for improving the lives of people with disabilities. the success of a BCI system is largely driven by the accuracy of the BCI decoder. this accuracy, in turn, may ...
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ISBN:
(纸本)9781665462921
Brain-Computer Interfaces (BCIs) have great potential for improving the lives of people with disabilities. the success of a BCI system is largely driven by the accuracy of the BCI decoder. this accuracy, in turn, may be limited by the amount of labelled training data available for supervised machine learning algorithms. the success of deep learning algorithms in other computer science areas has not reached the field of BCI decoding due to this lack of abundant labelled data. We use a novel deep learning architecture trained in a selfsupervised manner to learn a common vector representation (embedding) of EEG signals that can be used in different BCI tasks. the vector representation is trained using EEG recordings without using any task labels. We validate our embedder using two separate BCI tasks: seizure detection and motor imagery, and assess its usefulness through distance similarity metrics in a clustering approach. the derived embeddings were successful in distinguishing binary classes in both tasks.
Maintenance is a critical aspect of modern industrial operations, as it ensures the reliability and longevity of equipment while minimising unplanned downtime. Traditional, schedule-based maintenance approaches are of...
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Withthe advent of smart transportation, technology plays a key role in improving the safety of people on roads. Pedestrian detection techniques have various applications in driving assistance systems, intelligent veh...
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this research introduces the Photonics-Enhanced Embedded Robotic Intelligence Model (PEERIM), an innovative approach that integrates fiber Bragg grating (FBG) sensors with photonics and deep reinforcement learning (DR...
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this research explores the therapeutic potential of music, specifically Indian classical ragas, for individuals suffering from diabetes, hypertension, and thyroid disorders. the proposed system uses a LGBM classifier ...
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Amid the transformative advancements of Generative Adversarial Networks (GANs) in machine learning, a pertinent challenge arises: discerning real instances from synthetic ones. this research introduces a novel neural ...
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this note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. Withthe aid of examples and recent work, this article attempts to gi...
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ISBN:
(纸本)9783031585012;9783031585029
this note surveys developments in particle physics due to advances made in the fields of statistics, machine learning, and artificial intelligence. Withthe aid of examples and recent work, this article attempts to give a flavor of the effect of these advances on particle physics, including brief mention of cloud computing, classic machine learning techniques, statistics applications, new ML/AI techniques, reinforcement learning, and other advances. Suggestions are made regarding the future.
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