In the context of modern technology, Internet of Things (IoT) has garnered significant academic interest as a crucial tool for enhancing the efficiency of daily life management. IoT is vital for environmental monitori...
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This paper proposes an innovative user authentication system tailored for high-value asset transactions, leveraging advancements in brainwave analysis and emotional state detection. Traditional authentication methods ...
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Recently, deep learning-based super-resolution (SR) models have been used to improve SR performance by equipping preprocessing networks with baseline SR networks. In particular, in video SR, which creates a high-resol...
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Adversarial implementations of cryptographic primitives called kleptographic attacks cause the leakage of secret information. Subliminal channel attacks are one of the kleptographic attacks. In such attacks, backdoors...
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Adversarial implementations of cryptographic primitives called kleptographic attacks cause the leakage of secret information. Subliminal channel attacks are one of the kleptographic attacks. In such attacks, backdoors are embedded in implementations of randomized algorithms to elaborately control randomness generation, such that the secrets will be leaked from biased outputs. To thwart subliminal channel attacks, double-splitting is a feasible solution, which splits the randomness generator of a randomized algorithm into two independent generators. In this paper, we instantiate double-splitting to propose a secure randomness generation algorithm dubbed SRG using two physically independent generators: ordinary and public randomness generators. Based on public blockchains, we construct the public randomness generator,which can be verified publicly. Hashes of a sufficient number of consecutive blocks that are newly confirmed on a blockchain are used to produce public randomness. In SRG, outputs from the two generators are taken as inputs of an immunization function. SRG accomplishes immunization against subliminal channel ***, we discuss the application strategies of SRG for symmetric and public-key encryption.
In the modern era, prevalence of the Internet of Things (IoT) devices that have de facto protocol as IPv6 routing protocol for low power and lossy networks (RPL). Yet, RPL protocol is vulnerable to many attacks such a...
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This research study focuses on video categorization, which is a crucial area of computer vision with uses in entertainment, education, and surveillance. Convolutional Neural Networks (CNNs) are used in a two-stage app...
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The development of Deepfakes has become more prevalent with the rise of Generative Adversarial Networks (GANs). Deepfakes are synthetically created, modified images that have been made to look real;they pose severe so...
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T- Data classification and extraction are fundamental tasks in the field of computer vision and data analysis. This abstract presents an overview of these concepts along with the utilization of Python and OpenCV, a po...
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The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do **...
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The growing computing power,easy acquisition of large-scale data,and constantly improved algorithms have led to a new wave of artificial intelligence(AI)applications,which change the ways we live,manufacture,and do *** with this development,a rising concern is the relationship between AI and human intelligence,namely,whether AI systems may one day overtake,manipulate,or replace *** this paper,we introduce a novel concept named hybrid human-artificial intelligence(H-AI),which fuses human abilities and AI capabilities into a unified *** presents a challenging yet promising research direction that prompts secure and trusted AI innovations while keeping humans in the loop for effective *** scientifically define the concept of H-AI and propose an evolution road map for the development of AI toward *** then examine the key underpinning techniques of H-AI,such as user profile modeling,cognitive computing,and human-in-the-loop machine ***,we discuss H-AI’s potential applications in the area of smart homes,intelligent medicine,smart transportation,and smart ***,we conduct a critical analysis of current challenges and open gaps in H-AI,upon which we elaborate on future research issues and directions.
Spatiotemporal attention learning has always been a challenging research task in video question answering (VideoQA). It needs to consider not only the modelling of local neighbourhood dependencies between the adjacent...
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Spatiotemporal attention learning has always been a challenging research task in video question answering (VideoQA). It needs to consider not only the modelling of local neighbourhood dependencies between the adjacent frames in a video but also the modelling of long-term dependencies between nonadjacent frames. Although the existing methods are usually good at modelling temporal dependencies in one aspect, they cannot simultaneously and effectively model the temporal dependencies between adjacent and nonadjacent frames. To address this issue, we first derive a novel statistic-driven difference-aware generation function, which can efficiently calculate the difference between a sequence feature value and the whole mean value to identify the significance of the feature. Subsequently, we design a novel parameter-free spatiotemporal attention mechanism (PSAM), which captures the most relevant cues scattered in the context of a spatiotemporal video by generating functions and utilizes a gating mechanism to adaptively integrate and filter relevant and irrelevant information. Finally, we use the PSAM and hierarchical modelling to construct a lightweight multiscale context fusion- and reasoning-based VideoQA model. Extensive experimental research results obtained on five benchmark datasets for the VideoQA task show that our VideoQA model has high Q&A performance and lightweight characteristics. Simultaneously, comprehensive ablation experimental results show that the PSAM can not only improve the performance of the model but also significantly reduce the number of model parameters. In addition, extensive experimental findings obtained on the benchmark dataset of joint tasks (video moment retrieval and video highlight detection) further demonstrate that the PSAM is a general and effective spatiotemporal attention mechanism. IEEE
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