Reinforcement learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing sca...
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Reinforcement learning(RL)is gaining importance in automating penetration testing as it reduces human effort and increases ***,given the rapidly expanding scale of modern network infrastructure,the limited testing scale and monotonousstrategies of existing RLbased automated penetration testing methods make them less effective in practical *** this paper,we present CLAP(Coverage-based Reinforcement learning to Automate Penetration Testing),an RL penetration testing agent that provides comprehensive network security assessments with diverse adversary testing behaviours on a massive *** employs a novel neural network,namely the coverage mechanism,to address the enormous and growing action spaces in large *** also utilizes a Chebyshev decomposition critic to identify various adversary strategies and strike a balance between *** results across variousscenarios demonstrate that CLAP outperformsstate-of-the-art methods,by further reducing attack operations by nearly 35%.CLAP also provides enhanced training efficiency and stability and can effectively perform pen-testing over large-scale networks with up to 500 ***,the proposed agent is also able to discover pareto-dominant strategies that are both diverse and effective in achieving multiple objectives.
Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and tran...
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Disaster-resilient dams require accurate crack detection,but machine learning methods cannot capture dam structural reaction temporal patterns and *** research uses deep learning,convolutional neural networks,and transfer learning to improve dam crack *** deep-learning models are trained on 192 crack *** research aims to provide up-to-date detecting techniques to solve dam crack *** finding shows that the EfficientNetB0 model performed better than others in classifying borehole concrete crack surface tiles and normal(undamaged)surface tiles with 91%*** study’s pre-trained designs help to identify and to determine the specific locations of cracks.
Quality assurance and maintenance play a crucial role in engineering construction,as they have a significant impact on project *** common issue in concrete structures is the presence of *** enhance the automation leve...
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Quality assurance and maintenance play a crucial role in engineering construction,as they have a significant impact on project *** common issue in concrete structures is the presence of *** enhance the automation level of concrete defect repairs,thisstudy proposes a computer vision-based robotic system,which isbased on three-dimensional(3D)printing technology to repair *** system integrates multiple sensorssuch as light detection and ranging(LiDAR)and *** is utilized to model concrete pipelines and obtain geometric parameters regarding their ***,a convolutional neural network(CNN)is employed with a depth camera to locate defects in concrete ***,a method for coordinate transformation is presented to convert the obtained coordinates into executable ones for a robotic ***,the feasibility of this concrete defect repair method is validated through simulation and experiments.
Accurate and timely access to the spatial distribution of crops is crucial for sustainable agricultural development and food security. However, extracting multi-crop areasbased on high-resolution time-series data and...
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The pandemic has tested the resilience of organizations and consequently hastened the digital acceleration in many organizations. With the Philippine council for Agriculture, Forestry and Natural Resourcesresearch an...
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Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network t...
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Changing a person’s posture and low resolution are the key challenges for person re-identification(ReID)in various deep learning *** this paper,we introduce an innovative architecture using a dual attention network that includes an attentionmodule and a joint measurement module of spatial-temporal *** proposed approach can be classified into two main ***,the spatial attention feature map is formed by aggregating features in the spatial ***,the same operation is carried out on the channel dimension to formchannel attention ***,the receptive field size is adjusted adaptively tomitigate the changing person posture ***,we use a joint measurement method for the spatial-temporal information to fully harness the data,and it can also naturally integrate the information into the visual features of supervised ReID and hence overcome the low resolution *** experimental results indicate that our proposed algorithm markedly improves the accuracy in addressing changing human postures and low-resolution issues compared with contemporary leading *** proposed method showssuperior outcomes on widely recognized benchmarks,which are the Market-1501,MsMT17,and DukeMTMC-reID ***,the proposed algorithmattains a Rank-1 accuracy of 97.4% and 94.9% mAP(mean Average Precision)on the Market-1501 ***,it achieves a 94.2% Rank-1 accuracy and 91.8% mAP on the DukeMTMC-reID dataset.
Currently, the search history in search engines is presented in a list view of some combination of enumerated results by title, URL, or search query. However, this classical list view is not ideal in collaborative sea...
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Currently, the search history in search engines is presented in a list view of some combination of enumerated results by title, URL, or search query. However, this classical list view is not ideal in collaborative search environments as it does not always assist users in understanding collaborators' search history results and the project'sstatus. We present CollabGraph, a system for graph-basedsummary visualization in collaborative search learning environments. Our system differentiates from existing solutions by visualizing the summary of the collaboration results in a graph and having its core personal knowledge graphs (PKGs) for each user. Our research questions concentrate around the CollabGraph's usefulness, preference, and enhancement of participation of student's and teacher's feedback compared to the list view of search history results. We evaluate our approach with an online questionnaire in six different project-basedsearching aslearning (saL) scenarios (Lss). The evaluation of users' experience indicates that the CollabGraph is useful, highly likeable, and could benefit users' participation and teacher's feedback by providing more precise insights into the projectstatus. Our approach helps users better perceive about everyone's work, and it is a highly preferable feature alongside the list view. In addition, the results demonstrate that graph summary visualizations, such as the CollabGraph, are more suitable for closed-end scenarios and collaborative projects with many participants.
Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private ...
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Federated learning(FL)is an emerging privacy-preserving distributed computing paradigm,enabling numerous clients to collaboratively train machine learning models without the necessity of transmitting clients’private datasets to the central *** most existing research where the local datasets of clients are assumed to be unchanged over time throughout the whole FL process,our study addressessuch scenarios in this paper where clients’datasets need to be updated periodically,and the server can incentivize clients to employ as fresh as possible datasets for local model *** primary objective is to design a client selection strategy to minimize the loss of the global model for FL loss within a constrained *** this end,we introduce the concept of“Age of Information”(AoI)to quantitatively assess the freshness of local datasets and conduct a theoretical analysis of the convergence bound in our AoI-aware FL *** on the convergence bound,we further formulate our problem as a restless multi-armed bandit(RMAB)***,we relax the RMAB problem and apply the Lagrangian Dual approach to decouple it into multiple ***,we propose a Whittle’s Index based Client selection(WICs)algorithm to determine the set of selected *** addition,comprehensive simulationssubstantiate that the proposed algorithm can effectively reduce training loss and enhance the learning accuracy compared with some state-of-the-art methods.
The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training *** address this i...
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The current deep learning models for braced excavation cannot predict deformation from the beginning of excavation due to the need for a substantial corpus of sufficient historical data for training *** address this issue,thisstudy proposes a transfer learning model based on a sequence-to-sequence twodimensional(2D)convolutional long short-term memory neural network(s2sCL2D).The model can use the existing data from other adjacent similar excavations to achieve wall deflection prediction once a limited amount of monitoring data from the target excavation has been *** the absence of adjacent excavation data,numerical simulation data from the target project can be employed instead.A weight update strategy is proposed to improve the prediction accuracy by integrating the stochastic gradient masking with an early stopping *** illustrate the proposed methodology,an excavation project in Hangzhou,China is *** proposed deep transfer learning model,which uses either adjacent excavation data or numerical simulation data as the source domain,shows a significant improvement in performance when compared to the non-transfer learning *** the simulation data from the target project even leads to better prediction performance than using the actual monitoring data from other adjacent *** results demonstrate that the proposed model can reasonably predict the deformation with limited data from the target project.
The PROsPETTIVA project aims to improve secondary education in sicily by integrating AI technologies to promote active learning and AI literacy among students and teachers. This paper provides an overview of the PROsP...
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