Deepfakes are a form of synthetic media that uses deep-learning technology to create fake images, video, and audio. The emergence of this technology has inspired much commentary and speculation from academics across a...
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Sarcasm is essential in humaninteraction, particularly on social networking, where individuals share their feelings through criticism, humour, and satire. Sarcasm detection is vital in understanding the context of co...
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Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. ...
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ISBN:
(纸本)9798400712456
Spatial-temporal data, fundamental to many intelligent applications, reveals dependencies indicating causal links between present measurements at specific locations and historical data at the same or other locations. Within this context, adaptive spatial-temporal graph neural networks (ASTGNNs) have emerged as valuable tools for modelling these dependencies, especially through a data-driven approach rather than pre-defined spatial graphs. While this approach offers higher accuracy, it presents increased computational demands. Addressing this challenge, this paper delves into the concept of localisation within ASTGNNs, introducing an innovative perspective that spatial dependencies should be dynamically evolving over time. We introduce DynAGS, a localised ASTGNN framework aimed at maximising efficiency and accuracy in distributed deployment. This framework integrates dynamic localisation, time-evolving spatial graphs, and personalised localisation, all orchestrated around the Dynamic Graph Generator, a light-weighted central module leveraging cross attention. The central module can integrate historical information in a node-independent manner to enhance the feature representation of nodes at the current moment. This improved feature representation is then used to generate a dynamic sparse graph without the need for costly data exchanges, and it supports personalised localisation. Performance assessments across two core ASTGNN architectures and nine real-world datasets from various applications reveal that DynAGS outshines current benchmarks, underscoring that the dynamic modelling of spatial dependencies can drastically improve model expressibility, flexibility, and system efficiency, especially in distributed settings.
In the domain of Cyber-Physical Systems (CPS) and the Internet of Things (IoT), this research presents a novel approach to Neoteric Threat Intelligence ensuring Digital Sovereignty and Trust through ML-Infused Proacti...
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In the domain of Cyber-Physical Systems (CPS) and the Internet of Things (IoT), this research presents a novel approach to Neoteric Threat Intelligence ensuring Digital Sovereignty and Trust through ML-Infused Proactive Defense Analytics for NEXT-G and Beyond Ecosystems. As Sixth Generation and Beyond (6G and B) wireless networks undergo rapid evolution, our framework is designed to proactively anticipate and counter security incidents by utilizing advanced machine learning algorithms. This approach effectively addresses the shortcomings of conventional models, ensuring that digital assets and communications remain secure, trustworthy, and under rightful control. The study delves into the theoretical integration of this paradigm within the NextG network architecture, reinforcing digital sovereignty through a dynamic and adaptable defense mechanism. In-depth technical examinations include advanced machine learning algorithms, adaptive defenses, and scalability considerations. By critically analyzing and comparing existing security approaches, this study significantly advances technical knowledge and practical applications for wireless network security, supporting defenses against the evolving and complex threats characteristic of the 6G and Beyond era.
This study investigated the differences between human and robot gaze in influencing preference formation, and examined the role of Theory of Mind (ToM) abilities in this process. human eye gaze is one of the most impo...
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With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is *** main purpose of this study is to develo...
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With the advancement of computer vision techniques in surveillance systems,the need for more proficient,intelligent,and sustainable facial expressions and age recognition is *** main purpose of this study is to develop accurate facial expressions and an age recognition system that is capable of error-free recognition of human expression and age in both indoor and outdoor *** proposed system first takes an input image pre-process it and then detects faces in the entire *** that landmarks localization helps in the formation of synthetic face mask prediction.A novel set of features are extracted and passed to a classifier for the accurate classification of expressions and age *** proposed system is tested over two benchmark datasets,namely,the Gallagher collection person dataset and the Images of Groups *** system achieved remarkable results over these benchmark datasets about recognition accuracy and computational *** proposed system would also be applicable in different consumer application domains such as online business negotiations,consumer behavior analysis,E-learning environments,and emotion robotics.
PCL is a good example of a combinatory innovation: Most components are already in place, but they have not yet been combined and tailored to fit the field of education. We think a system that directly assists users in...
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PCL is a good example of a combinatory innovation: Most components are already in place, but they have not yet been combined and tailored to fit the field of education. We think a system that directly assists users in practical learning tasks will help increase the overall quality of education. Additionally, it will reduce the stress for trainers and educators who must teach large groups with limited time resources. A motivating learning experience that incorporates the emotional cues of the student will help to raise motivation for selflearning and contribute to practice and skill acquisition. On the path toward PCL, there already is a first large-scale research project on the way: KoBeLU (contextaware learning environment). We are happy to collaborate with researchers from the areas of education, affective computing, pattern recognition, and machine learning. Therefore, if playful coached learning is something that might interest you or your students, do not hesitate to contact us.
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