Recent years have witnessed the expeditious evolution of intelligentsmart devices and autonomous software technologies with the expandeddomains of computing from workplaces to smart computing in everydayroutine life a...
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Recent years have witnessed the expeditious evolution of intelligentsmart devices and autonomous software technologies with the expandeddomains of computing from workplaces to smart computing in everydayroutine life activities. This trend has been rapidly advancing towards the newgeneration of systems where smart devices play vital roles in acting intelligently on behalf of the users. Context-awareness has emerged from the pervasive computing paradigm. Context-aware systems have the ability to acquirecontextual information from the surrounding environment autonomously,perform reasoning on it, and then adapt their behaviors accordingly. With theproliferation of context-aware systems and smart sensors, real-time monitoring of environmental situations (context) has become quite trivial. However,it is often challenging because the imperfect nature of context can cause theinconsistent behavior of the system. In this paper, we propose a contextaware intelligent decision support formalism to assist cognitively impairedpeople in managing their routine life activities. For this, we present a semanticknowledge-based framework to contextualize the information from the environment using the protégé ontology editor and Semantic Web Rule Language(SWRL) rules. The set of contextualized information and the set of rulesacquired from the ontology can be used to model Context-aware Multi-AgentSystems (CMAS) in order to autonomously plan all activities of the users andnotify users to act accordingly. To illustrate the use of the proposed formalism,we model a case study of Mild Cognitive Impaired (MCI) patients usingColored Petri Nets (CPN) to show the reasoning process on how the contextaware agents collaboratively plan activities on the user’s behalf and validatethe correctness properties of the system.
Federated Learning (FL) has significant potential to protect data privacy and mitigate network burden in mobile edge computing (MEC) networks. However, due to the system and data heterogeneity of mobile clients (MCs),...
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Recent studies have shown remarkable success in face image generation ***,existing approaches have limited diversity,quality and controllability in generating *** address these issues,we propose a novel end-to-end lea...
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Recent studies have shown remarkable success in face image generation ***,existing approaches have limited diversity,quality and controllability in generating *** address these issues,we propose a novel end-to-end learning framework to generate diverse,realistic and controllable face images guided by face *** face mask provides a good geometric constraint for a face by specifying the size and location of different components of the face,such as eyes,nose and *** framework consists of four components:style encoder,style decoder,generator and *** style encoder generates a style code which represents the style of the result face;the generator translate the input face mask into a real face based on the style code;the style decoder learns to reconstruct the style code from the generated face image;and the discriminator classifies an input face image as real or *** the style code,the proposed model can generate different face images matching the input face mask,and by manipulating the face mask,we can finely control the generated face *** empirically demonstrate the effectiveness of our approach on mask guided face image synthesis task.
With the gradual development in industrialization, the advent of computer and information technology has evolved in the field of engineering and construction industry. BIM technology has become a valuable part of pref...
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Source code is an intermediary through which humans communicate with computer systems. It contains a large amount of domain knowledge which can be learned by statistical models. Furthermore, this knowledge can be used...
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Recently, Multimodal Learning (MML) has gained significant interest as it compensates for single-modality limitations through comprehensive complementary information within multimodal data. However, traditional MML me...
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Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on man...
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Artificial Intelligence has been playing a profound role in the global economy,social progress,and people’s daily *** the increasing capabilities and accuracy of AI,the application of AI will have more impacts on manufacturing and service areas in the era of industry *** study conducts a systematic literature review to study the state-of-the-art on AI in industry *** paper describes the development of industries and the evolution of *** paper also identifies that the development and application of AI will bring not only opportunities but also challenges to industry *** findings provide a valuable reference for researchers and practitioners through a multi-angle systematic analysis of *** the era of industry 4.0,AI system will become an innovative and revolutionary assistance to the whole industry.
Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control uni...
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The concept of risk management is habitually left unnoticed in agile software organizations, thus negatively af-fecting the project and disappointing the customer. Thus, it is mandatory to discern and rank the risks f...
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Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel...
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Glaucoma,a leading cause of blindness,demands early detection for effective *** AI-based diagnostic systems are gaining traction,their performance is often limited by challenges such as varying image backgrounds,pixel intensity inconsistencies,and object size *** address these limitations,we introduce an innovative,nature-inspired machine learning framework combining feature excitation-based dense segmentation networks(FEDS-Net)and an enhanced gray wolf optimization-supported support vectormachine(IGWO-SVM).This dual-stage approach begins with FEDS-Net,which utilizes a fuzzy integral(FI)technique to accurately segment the optic cup(OC)and optic disk(OD)from retinal images,even in the presence of uncertainty and *** the second stage,the IGWO-SVM model optimizes the SVM classification process,leveraging a gray wolf-inspired optimization strategy to fine-tune the kernel function for superior *** testing on three benchmark glaucoma image databases DRIONS-DB,Drishti-GS,and Rim-One-r3 demonstrates the efficacy of our method,achieving classification accuracies of 97.65%,94.88%,and 93.2%,*** results surpass existing state-of-the-art techniques,offering a promising solution for reliable and early glaucoma detection.
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