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pro vyhledávání: '"BATISTA, GUSTAVO"'
As the adoption of Internet of Things (IoT) devices continues to rise in enterprise environments, the need for effective and efficient security measures becomes increasingly critical. This paper presents a cost-efficient platform to facilitate the pr
Externí odkaz:
http://arxiv.org/abs/2408.13172
Internet of Things (IoT) devices have grown in popularity since they can directly interact with the real world. Home automation systems automate these interactions. IoT events are crucial to these systems' decision-making but are often unreliable. Se
Externí odkaz:
http://arxiv.org/abs/2407.19662
Autor:
Batista, Gustavo Silvano
Publikováno v:
Veritas, Vol 65, Iss 2, p ID37243 (2020)
Na abordagem hermenêutica das obras de arte, Gadamer discute a arquitetura como uma das expressões artísticas na qual se pode reconsiderar a relação ontológico-interpretativa com as coisas em geral. Repensando a estrutura representativa própri
Externí odkaz:
https://doaj.org/article/18917a826e454952a76581d16b47bf62
Stochastic gradient descent (SGD) and its many variants are the widespread optimization algorithms for training deep neural networks. However, SGD suffers from inevitable drawbacks, including vanishing gradients, lack of theoretical guarantees, and s
Externí odkaz:
http://arxiv.org/abs/2401.03619
Pedestrian trajectory prediction plays an important role in autonomous driving systems and robotics. Recent work utilizing prominent deep learning models for pedestrian motion prediction makes limited a priori assumptions about human movements, resul
Externí odkaz:
http://arxiv.org/abs/2309.09021
Autor:
Hamza, Ayyoob, Gharakheili, Hassan Habibi, Benson, Theophilus A., Batista, Gustavo, Sivaraman, Vijay
IoT networks are increasingly becoming target of sophisticated new cyber-attacks. Anomaly-based detection methods are promising in finding new attacks, but there are certain practical challenges like false-positive alarms, hard to explain, and diffic
Externí odkaz:
http://arxiv.org/abs/2304.04987
Autor:
Pashamokhtari, Arman, Okui, Norihiro, Nakahara, Masataka, Kubota, Ayumu, Batista, Gustavo, Gharakheili, Hassan Habibi
Millions of vulnerable consumer IoT devices in home networks are the enabler for cyber crimes putting user privacy and Internet security at risk. Internet service providers (ISPs) are best poised to play key roles in mitigating risks by automatically
Externí odkaz:
http://arxiv.org/abs/2301.06695
Machine Learning-based techniques have shown success in cyber intelligence. However, they are increasingly becoming targets of sophisticated data-driven adversarial attacks resulting in misprediction, eroding their ability to detect threats on networ
Externí odkaz:
http://arxiv.org/abs/2203.09792
An increasing number of artificial intelligence (AI) applications involve the execution of deep neural networks (DNNs) on edge devices. Many practical reasons motivate the need to update the DNN model on the edge device post-deployment, such as refin
Externí odkaz:
http://arxiv.org/abs/2203.04516