In this article, we present an evolution language for graph databases and a method to realize evolution operations on graph databases and their schema. Graph database management systems like Neo4j can be used for diff...
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This paper presents a tool relying on data service architecture, where technical details of all VBS datasets are completely hidden behind an abstract stateless data layer. The data services allow independent developme...
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In recent years, the field of autonomous vehicles and driverless technology has seen remarkable advancements, driven by contributions from mainstream automotive manufacturers and open-source projects. This research ai...
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Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious...
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Cybersecurity threats are increasing rapidly as hackers use advanced *** a result,cybersecurity has now a significant factor in protecting organizational *** detection systems(IDSs)are used in networks to flag serious issues during network management,including identifying malicious traffic,which is a *** remains an open contest over how to learn features in IDS since current approaches use deep learning *** learning,which combines swarm intelligence and evolution,is gaining attention for further improvement against cyber *** this study,we employed a PSO-GA(fusion of particle swarm optimization(PSO)and genetic algorithm(GA))for feature selection on the CICIDS-2017 *** achieve better accuracy,we proposed a hybrid model called LSTM-GRU of deep learning that fused the GRU(gated recurrent unit)and LSTM(long short-term memory).The results show considerable improvement,detecting several network attacks with 98.86%accuracy.A comparative study with other current methods confirms the efficacy of our proposed IDS scheme.
The increasing complexity of designing, deploying, and maintaining Cyber-Physical Systems (CPS), particularly those incorporating multiple interacting robots, presents significant challenges regarding programming and ...
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ISBN:
(数字)9798350373011
ISBN:
(纸本)9798350373028
The increasing complexity of designing, deploying, and maintaining Cyber-Physical Systems (CPS), particularly those incorporating multiple interacting robots, presents significant challenges regarding programming and system integration. Existing methods and tools that aim to decrease the complexity of setting up such systems through modeling-based approaches often focus on single-robot interaction and rely on manual data entries, thus limiting their scalability and applicability to more intricate multi-robot environments. This contribution introduces the Robot ion Method (RobAM), a conceptual modeling method developed to decrease the design complexity of CPS through additional layers of abstraction. By utilizing conceptual modeling, capabilities of humanoid robots can be leveraged for the design of multi-robot interactions. Thereby, our approach enhances the modeling of complex CPS as well as the subsequent generation of code to enable efficient deployments. Through an experimental scenario, we demonstrate how RobAM simplifies the development process of CPS, paving the way for advancements regarding the integration of AI-supported technologies, such as Large Language Models, to ultimately generate contextually relevant semantics for future system design.
Design thinking has gained popularity in recent years as a toolbox to support the development of innovative business models, product offerings and services. Applying a user-centric paradigm during the co-creation phas...
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Multi-Modal Relation Extraction (MMRE) plays a key role in various multimedia applications including, recommendation and information retrieval systems. MMRE aims to extract the semantic relation between entities by le...
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Multi-Modal Relation Extraction (MMRE) plays a key role in various multimedia applications including, recommendation and information retrieval systems. MMRE aims to extract the semantic relation between entities by leveraging context from a text-image pair. By utilizing context from images, the challenge of learning from noisy images in MMRE emerges as a research problem by itself. For instance, subtle variations in similar images can act as noise and potentially impact the predictions made by MMRE models. To tackle this problem, current work utilizes attention mechanisms to fuse relevant text and image features or devise data augmentation techniques (e.g., via generative models) to improve generalization. However, the current performance still remains unsatisfactory. In an effort to improve upon the performance, we propose a Dual-Aspect Noise-based Regularization framework that encompasses two techniques: 1) noise removal through an adaptive gating mechanism, 2) fighting noise with noise to improve feature stability in the learning process. We find that combining these techniques encourages the model to focus on more relevant image features for MMRE. We carry out extensive experiments and demonstrate that our proposed model is further enhanced by exploring data augmentation techniques. This additional improvement leads the model to achieve state-of-the-art performance on the widely-used Multi-modal Neural Relation Extraction (MNRE) dataset, and show its effectiveness and generalizability on the Multi-Modal Named Entity Recognition task.
Learning models used for prediction are mostly developed without taking into account the size of datasets that can produce models of high accuracy and better performance. Although, the general believe is that, large d...
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The implementation of Artificial Intelligence in the aerospace field is fairly new and various domains of the aerospace discipline are to be explored. This paper provides a general review on the upbringing and improve...
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The search for exotic particles is one of the most challenging topics for physicists. This work aims to resolve the Big data problem in the exotic particle area using the Apache Spark environment with the MLlib librar...
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